Trends of subjective well-being (SWB) in transition countries are peculiar: they show substantial changes that are more strongly correlated with the trends of GDP than in other developed countries. This paper examines the role of the trends of GDP and of social trust in predicting the trends of well-being. We find that the strength of the relationship between social trust and SWB over the medium-term is comparable to that of GDP. We conclude that in the medium-term, even in countries where material concerns strongly affect well-being, social trust is a powerful predictor of the trends of SWB. However, in the short run the relationship between social trust and SWB does not hold and GDP stands out as the only significant correlate of SWB.
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Easterlin and Angelescu (2009) and Easterlin et al. (2010) criticized these results arguing that they fail to distinguish between the long and the short run. In particular, Easterlin and colleagues showed that the positive and significant relation estimated by Stevenson and Wolfers (2008) and Sacks et al. (2010) is generated by the inclusion in the sample of countries with short time-series. Easterlin et al. (2010) documented that GDP matters for SWB in the short run, but that this correlation vanishes in the long-term. The tendency of SWB and GDP to vary together during contractions and expansions has been documented also by Di Tella et al. (2001) and Bartolini and Sarracino (2014).
Opinions about social capital under communism differ. The “dictatorship theory of social capital” states that dictatorship destroys trust and cooperation among citizens (Fidrmuc and Gërxhani 2008; Paldam and Svendsen 2001). On the other hand, Paldam and Svendsen (2001) claims that an inefficient planned economy forced creation of “negative social capital”, i.e. informal networks (gray zones, corruption) that “fixed” the economic system and allowed its functioning despite inefficiencies.
In the WVS/EVS the distances between two consecutive waves are not regular, ranging from 1 to 8 years. It is therefore impossible to attribute the variations between contiguous surveys to the long, medium and short-term. Having data that are regularly surveyed in time is fundamental to define which time horizon is measured by such intervals.
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This result is also confirmed for models with non standardized variables, see “Appendix 3”.
Dfbetas measure how much a given coefficient changes after excluding a specific country from the sample (Coxe et al. 2013, p. 49). In the medium run the influential countries are: Hungary, Slovakia and Ukraine (model for happiness) and Estonia and Poland (model for life satisfaction). In the short run the influential countries are: Estonia, Hungary and Ukraine (model for happiness) and Hungary and Bulgaria (model for life satisfaction).
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Appendix 1: Data Missingness in the ESS Data-Set
The sixth column of Table 5 informs that the percentage of missing data is on average <1 %. Only in the case of the index of social trust the percentage of missingness raises to 1.4 %. However, such a small percentage does not raise any particular worry for the reliability of our estimates (Allison 2001). Data missingness is further analysed across waves in Table 6. Figures inform that also in this case percentages of missingness are negligible and, according to the literature on data missingness, they are not likely to affect estimates (Schafer 1997, 1999; Allison 2001).
Appendix 2: Factor Analysis for Trust Questions in the ESS
Table 7 informs that in the pooled sample, factor loadings range from 0.80 to 0.85 thus suggesting that the three variables contribute equally to the definition of a latent concept that we call “social trust”. When observing results across waves (see Table 8), we notice that discrepancies arise mainly in the first and third wave where factor loadings range from about 0.79 for the helpfulness variable to 0.84 for the fairness variable. The slight variability among factor loadings both in the pooled sample and within waves convinced us of the opportunity to build an aggregated index of social trust resulting from the standardized weighted average of the three items.
Appendix 3: Non-standardized Trivariate Regressions
Appendix 4: Long-Term Relationships, Controlling for Outliers
See Table 11.
Appendix 5: Short-Term Relationships, Controlling for Outliers
See Table 12.
Appendix 6: Country Acronyms in the ESS
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Bartolini, S., Mikucka, M. & Sarracino, F. Money, Trust and Happiness in Transition Countries: Evidence from Time Series. Soc Indic Res 130, 87–106 (2017). https://doi.org/10.1007/s11205-015-1130-3