Unmet Aspirations and Urban Malaise

Corroborating existing evidence, Table 1 demonstrates that aspirations in urban areas appear higher than in rural. The largest urban–rural difference in aspirations concerns the two metrics related to success and wealth respectively. The remaining two concerning ambition and authority are nevertheless also on average higher in the most urbanized areas.

However, not only aspirations but also the corresponding outcomes are for the most part higher in urban areas than in rural ones, thereupon diminishing the urban–rural differences vis-à-vis fulfilment of aspirations. The only exception to this pattern concerns authority, where rural residents on average state higher outcomes than do urban ones. Thus, on the whole, Table 1 substantiates the assumption of the first objective, i.e., that urban and rural dwellers aspire differently, and that the actual outcomes also differ.

The metrics in the table further indicate—for urban and rural residents alike—that the gap between aspiration and outcome would on average be negative concerning ambition and success, and positive concerning wealth and authority. The latter case can, however, not be interpreted so that the expectations of most respondents would on average actually have been exceeded. This numeric excess may simply be the effect of the normalization method used for the metrics, the original scale of the data, and the scale the data is now superimposed on. What can be said though is that, for an individual respondent, the more positive the measured distance of outcome minus aspiration is—or conversely the less negative it is—the better the aspirations of that respondent have been fulfilled.

The mere facts that urban and rural dwellers aspire differently and that the outcomes differ do not yet explain whether urban inhabitants are reporting lower SWB due to this. The next step of the analysis takes us a bit further on that inquiry, as Table 2 presents three multiple regression models with overall satisfaction with life as the dependent variable.

The first model (A) demonstrates that the better aspirations are met, the higher is the overall satisfaction with life, thereby corroborating the aspiration-level theory in general. In this data set, as much as 7.4% of the variation in life satisfaction can be accounted for by these four aspirational discrepancies. Among the four, the wealth gap is the strongest predictor for SWB. In practical terms, a one-unit positive increase in the gap between wealth aspiration and wealth outcome generates a 0.1 unit increase in life satisfaction (on a scale of 0 to 10). Taking into account that the total range of the wealth gap lies between ‒10 and + 8 units (i.e., altogether 19 units), the effect size of this association appears very strong. The effect of the ambition gap is also nearly as substantial.

The following model (B), from which the four aspiration-outcome gaps are removed, is included to demonstrate the effect that individual characteristics in this data set exert on SWB. It also introduces country-fixed effects, the urban–rural dichotomy together with subjective settlement, as well as controls for the four different levels of aspiration in the equation. Now the model accounts for 27.2% of the variation in SWB. As operationalized here, urban malaise is discernible both at the structural (urban–rural typology) as well as partly at the subjective geographic level (self-reported domicile). Not unexpectedly however, the effect size of these “geographies” is nonetheless modest when compared to personal characteristics such as age, health, cohabitation status, or unemployment.

The final model C once more includes the four aspiration-outcome gap metrics while keeping all remaining items equal to model B. The introduction of the four gaps raises the amount of variation explained to 27.6% in this full model C.

Aspiration-outcome gaps related to authority, and to a slightly lesser extent to success, are now the principal discrepancies associated with lower levels of life satisfaction. The strength of the former is nearly on a par with unemployment. Taking into account that involuntary unemployment has consistently been demonstrated to be one of the strongest predictors of lower SWB (cf. Dolan et al., 2008), the effect of this aspiration gap appears substantial. While the wealth in a technical sense is also still valid in this model, its practical effect, however, remains somewhat more modest. However, above all, age and subjective health still exert the strongest effect on SWB. Overall, this model nonetheless corroborates the assumptions of the second objective by demonstrating that—holding standard control variables constant—a negative discrepancy between expectations and their outcome affects SWB negatively.

As expected, the introduction of the four self-enhancement discrepancies depreciates the urban malaise aspect slightly. On average, holding all else constant, if living in any of the 32 major metropolitan areas, the SWB of these residents is lowered roughly by the same magnitude as the negative SWB effect of being a female or the positive effect of being retired does.

It is, however, still too early to conclude that these aspiration gaps would play out differently across urban and rural regions. For that, we need to analyse inhabitants in predominantly urban and other regions separately. A so-called moderated multiple regression is presented in Table 3. In this, there are separate but content-wise identical regressions for inhabitants in predominantly urban regions and for those in intermediate or predominantly rural ones. Accordingly, the dichotomous urban–rural variable itself is omitted as an explanatory factor. In all other aspects, the two models in Table 3 are duplicates of the final model C of Table 2, although the lower parts of the tables are not displayed here due to space constraints. Four principal observations can be made from this moderated regression.

First, although the contents of the two models are identical, the included predictors are able to explain much more variation in the SWB of the inhabitants of the 32 major metropolitan regions than for the remaining inhabitants. The coefficient of determination (adjusted R²) for regions of the former type amounts to 0.304 in comparison to 0.270 for inhabitants of the latter type.

Thus, third, for inhabitants of predominantly urban regions, the success gap is the strongest predictor of SWB among the four, while the authority gap also exerts a substantial effect. Contrary to Stutzer’s (2004) findings, the wealth gap does not appear to exert any effect on SWB in these 32 metropolitan surroundings, whereas it does so moderately for inhabitants of other types of regions. However, for inhabitants of these intermediate or predominantly rural regions, the authority gap appears as the strongest predictor for life satisfaction. Additionally, the SWB of persons living in these less urbanized areas also correlates moderately with the ambition gap.

In essence this entails that discrepancies related to self-enhancement have, on average, a stronger impact on the overall satisfaction with life of inhabitants of the 32 major urban areas of Western Europe than they have on the SWB of inhabitants of less urbanized surroundings in the same ten countries. This strongly suggests that urban–rural differences in the magnitude of the gap between expectations and outcome related to self-enhancement, as well as the differences in the combination of these gaps, would be able to explain some of the urban–rural differences in SWB. As such, this ultimately is in accord with the basic premise expressed in the third objective, i.e., that urban–rural differences related to self-enhancement are—for their part—an additional explanation for urban malaise.

In line with aspiration-level theory, this analysis has demonstrated that the better a person’s aspirations are fulfilled, the higher is his or her overall satisfaction with life. The analysis has also demonstrated that inhabitants of 32 major metropolitan regions in ten western European countries have substantially higher aspirations when it comes to self-enhancement than do their more rural counterparts. However, the level of fulfilment of these aspirations is also generally higher among these urban residents. The analysis has ultimately demonstrated that the discrepancy between aspirations and outcome affects overall assessments of subjectively reported SWB more strongly in highly urbanized areas than it does in less urbanized surroundings. The primary scientific contribution of this paper is thus that it indicates that such urban–rural differences should be added to currently dominant explanations for the phenomenon of urban malaise. In a theoreticized manner, Fig. 1 illustrates the relationship between an aspiration-outcome gap on the one hand and resulting urban–rural differences in SWB on the other.

This simple analytic model however assumes that the outcome-aspiration-SWB vector would be linear. In reality, it appears plausible to assume that these gradients would be of a logarithmic nature adhering to the process of decreasing marginal utility. This issue thus calls for further research with emphasis on non-linear techniques.

Human aspirations are most likely policy amenable only to a very marginal degree. Fostering less ambition among urban inhabitants or migrants to cities by means of public campaigns, or nurturing more “rural” values in urban communities, appears to be an extremely implausible path of development. Amplified urban aspiration levels should thus probably be considered an inherent principle of nature guiding urban life. They could be viewed on a par with aspects such as age or gender, which have a strong impact on SWB, but are utterly difficult to directly address by public policy. Accordingly, it would probably be more feasible for public policy to try to pay attention to the outcome levels instead, thereby diminishing the gap. However, whereas public policy doubtlessly would be able to affect the wealth outcome of urban residents, it remains equally uncertain what it could do about the remaining three self-enhancement outcomes (ambition, success, authority). At the least, however, it would be important to acknowledge that although the city—travestying Glaeser (2011)—might make us richer, smarter, healthier, or even greener, it does not necessarily make us happier too and, apparently, not more satisfied with our lives either.

The issue of causality calls for attention. Based on the analysis herein, it is not possible to establish a clear causal direction between the level of aspiration fulfilment and urban environments. It may well be that cities act as magnets for persons with high aspirations in certain areas of life, thus aggravating the discrepancy. However, it may equally well be that the billboards, the neon lights, or the popular cultural image of cities in themselves are the cause of higher aspirations. Utilizing the vocabulary of Ballas and Tranmer (2012:p. 94), quality of life might be dependent not only on the contextual setting of the community, “something about the place”, but also on “the characteristics of its inhabitants”. As an indication of the latter—concentrating specifically on persons migrating from rural to urban areas—Cardoso et al. (2019), for example, argued that one reason for lower SWB among such migrants can be attributed to the effect that several cognitive biases exert on creating overoptimistic expectations concerning the outcome of their move.

However, some recent findings also support the notion that location has an impact on the SWB of its inhabitants. In the developed world, the prevailing neoliberal dogma based on preference fulfilment through consumption choices is more closely related to urban communities than more rural ones (Oliveira et al., 2019). Examining data for approximately a million European citizens across 27 nations and over three decades, Michel et al. (2019) found robust evidence of a negative correlation between the amount of advertising and the level of life satisfaction. Advertising creates a mental demand, which, according to the hedonic treadmill theory, in the long run is likely to remain unfulfilled.

Although there are few specific indications as to whether urban inhabitants are more exposed to advertising than rural ones, at least out-of-home advertising (i.e. printed or digital billboards) could be expected to be more of an urban concern. Also, online shopping still appears to be a predominantly urban phenomenon (Beckers et al., 2018; Farag et al., 2006), exposing urban residents more than their rural counterparts to digital advertising.

Recently, the well-established regulatory focus theory has also been used for establishing causality between settlement size and human behaviour. Stemming from psychology, the regulatory focus theory states that human goals are guided by two opposing motivating systems: promotion and prevention. Persons motivated by promotion goals focus on growth and accomplishment and tend to take risks. Persons motivated by prevention goals are risk aversive and focus on safety and security. Ross and Portugali (2018) applied this theory in a large vs. small city context and found that a large city affects a person’s behaviour so that it intensifies both motivating systems, depending on the individual’s personal regulatory focus. As they were, furthermore, able to demonstrate clear causality, i.e., that the size of the city affects the regulatory system and not the other way around, this might also constitute a tentative explanation for why urbanites have a tendency to aspire to more.

The issue of aggravated urban expectations would benefit from further interest from the scientific community. An examination of the degree of fulfilment of spheres of human aspirations other than mere self-enhancement, such as benevolence, security or conformity, might shed additional light on why rural residents tend to report higher SWB than do urban ones. In a technical sense, longitudinal analysis is needed so as to overcome the gap between present expectations and future outcome.

Perhaps even more importantly, there is a need to address inequality. The prevailing public policy discourse excessively stresses both the necessity and the societal benefits of urbanization (cf. Brenner & Schmid, 2015; Gleeson, 2014; Soininvaara, 2020). This discourse, however, generally departs from an aggregated point of view where average performances across different types of settlements are compared. This study also falls into that category. However, as pointed out above, the positional treadmill—where people tend to compare their lives against those of others in their vicinity—exerts a strong influence on SWB. Also, in terms of fulfilled aspirations, some people get what they desire, whereas others do not. Since socioeconomic differences in general tend to be larger in urban areas, it could also be expected that the amplitude of disparity among urban inhabitants in terms of aspirations and meeting them is much larger than is the case in rural surroundings. Hence, examining not only the levels of aspiration fulfilment across different types of settlements on average, but also considering intra-regional disparities of that very same nature, may additionally clarify reasons behind urban malaise.