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Social Capital Dimensions and Subjective Well-Being: A Quantile Approach

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Abstract

This paper investigates the effects of different dimensions of social capital (i.e., trust, social network and norms of civic engagement) on subjective well-being (SWB) at the individual level by attending to differences between the extremes of SWB distribution, that is, between the happiest and the unhappiest people. To this end, we use the 7th wave of the 2014 European Social Survey to run a quantile regression analysis to investigate whether any of these dimensions of social capital has a heterogeneous effect on the full distribution of well-being. We also perform a factor analysis to summarize the principal components of these three dimensions. Our results show that each dimension of social capital has a positive and significant correlation with SWB, but the different dimensions have a heterogeneous effect on the different quantiles of the well-being distribution. All of these dimensions of social capital have a stronger effect on the SWB of the least happy people in society than on the happiest.

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Notes

  1. The QR approach, developed by Koenker and Bassett (1978), is quite useful when the conditional distribution of the dependent variable does not meet the assumption of normality, as it improves the robustness of the estimates in the presence of outliers.

  2. For more information, see the Big Five Theory (Digman 1990; John and Srivastava 1999).

  3. Criticisms of the concept of social capital are reviewed by Fine (2010), Bjørnskov and Sønderskov (2013) and Andriani and Christoforou (2016).

  4. For detailed information on the ESS and the data collected, see http://www.europeansocialsurvey.org.

  5. Austria, Belgium, Switzerland, Czech Republic, Germany, Denmark, Estonia, Spain, Finland, France, United Kingdom, Hungary, Ireland, Lithuania, Netherlands, Norway, Poland, Portugal, Sweden and Slovenia.

  6. Prior to this analysis, we performed categorical principal component analysis—nonlinear principal component analysis (NLPCA)—using CATPCA in SPSS14, due to the ordinal and nominal nature of the variables (see Manisera et al. 2010).

  7. Some of the control variables that have been recoded or aggregated are the following: (1) Income level, which has been recoded into three new categories. From the original variable: “Household's total net income, all sources”, deciles 1–4 have been recoded as “Low”, deciles 5–7 as “Medium” and deciles 8–10 as “High”. (2) Political position. From the original EES question: “In politics people sometimes talk of “left” and “right”. Where would you place yourself on this scale, where 0 means the left and 10 means the right?”, it has been recoded into the following categories: “Left” (0–2), “Center” (3–7) and “Right” (8–10). (3) Religion. From the original question: “Regardless of whether you belong to a particular religion, how religious would you say you are?”, it has been recoded into the categories “Low” (0–2), “Medium” (3–7) and “High” (8–10).

  8. This package follows the estimation procedure explained by Geraci and Bottai (2014).

  9. E(Y) is equal to 2.7 in Q.10, 5 in Q.25, 6.67 in Q.50, 8 in Q.75 and 9.41 in Q.90.

  10. For the sake of space, we include the full results for the QR on life satisfaction in the Appendix. Table 7 shows that the signs of the coefficients of the variables of interest largely coincide, although there are differences in the size of the effects.

  11. See coefficients in column Q(0.10) of Table 2.

  12. See coefficients in column Q(0.90) of Table 2.

  13. See descriptive statistic by quantile of the 20 ESS questions explaining dimensions of social capital in Table 8, “Appendix 1”.

  14. See the last columns in Tables 8 and 9 (Appendix 1).

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Acknowledgements

We would like to thank the Editor of this journal, Dr. David Bartram, and two anonymous referees, for their valuable suggestions and helpful comments, which have greatly enhanced the quality of the paper.

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Authors and Affiliations

  1. Quantitative Economics Department, Faculty of Economics and Business, Universidade de Santiago de Compostela, Avda. Xoan XXIII s/n, 15704, Santiago de Compostela, Spain

    Isabel Neira

  2. Economic Development Department, School of Economics and Bussiness, Universidad Autónoma de Madrid, Avda. Francisco Tomás y Valiente 5, 28049, Madrid, Spain

    Maricruz Lacalle-Calderon

  3. Finance and Accounting Department, Faculty of Economics and Business, Universidade de Santiago de Compostela, Avda. Xoan XXIII s/n, 15704, Santiago de Compostela, Spain

    Marta Portela

  4. Departamento de Economía, Universidad Católica del Norte, Avenida Angamos #0610, Pabellón X-10, Piso 4, 1270709, Antofagasta, Chile

    Manuel Perez-Trujillo

Authors
  1. Isabel Neira
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  2. Maricruz Lacalle-Calderon
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Correspondence to Isabel Neira.

Appendices

Appendix 1

See Tables 3, 4, 5, 6, 7, 8, 9, 10 and 11.

Table 3 Results of the PCA
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Table 4 Communalities of PCA
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Table 5 Description of control variables
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Table 6 Descriptive statistics of variables in our model
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Table 7 Results for life satisfaction (QR-estimator)
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Table 8 Trust by quantiles of happiness
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Table 9 Networks by quantiles of happiness
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Table 10 Results for happiness. LQMM method
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Table 11 Results for life satisfaction (LQMM method)
Full size table

Appendix 2

  • eststo clear

  • *Happiness estimations

  • *OLS

  • eststo, ti(“OLS”): qui reg HAPPY_i AGE_i AGESQR_i ib(first).GNDR_i ib(first).MARITALSTATUS_i ib(first).ISCED_i ib(first).INCOMELEVEL_i ib(last).SUBHEALTH_i ib(first).LEFTRIGHESC_i ib(first).RELIGIOUS_i ib(first).DOMICILE_i ib(11).COUNTRY INSTTRUST_i SOCTRUST_i CIVICNET_i CIVIC_i SOCNET_i

  • *QUANTILES

  • foreach q in 0.10 0.25 0.50 0.75 0.90 {eststo, ti(“Q(‘(`q’)”): qui sqreg HAPPY_i AGE_i AGESQR_i ib(first).GNDR_i ib(first).MARITALSTATUS_i ib(first).ISCED_i ib(first).INCOMELEVEL_i ib(last).SUBHEALTH_i ib(first).LEFTRIGHESC_i ib(first).RELIGIOUS_i ib(first).DOMICILE_i ib(11).COUNTRY INSTTRUST_i SOCTRUST_i CIVICNET_i CIVIC_i SOCNET_i, q(`q’) reps(1000)}

  • *INTERQUANTILES

  • eststo, ti(“Interquantile 0.10-0.90”): qui iqreg HAPPY_i AGE_i AGESQR_i ib(first).GNDR_i ib(first).MARITALSTATUS_i ib(first).ISCED_i ib(first).INCOMELEVEL_i ib(last).SUBHEALTH_i ib(first).LEFTRIGHESC_i ib(first).RELIGIOUS_i ib(first).DOMICILE_i ib(11).COUNTRY INSTTRUST_i SOCTRUST_i CIVICNET_i CIVIC_i SOCNET_i, q(0.10 0.90) reps(1000)

  • eststo, ti(“Interquantile 0.25-0.75”): qui iqreg HAPPY_i AGE_i AGESQR_i ib(first).GNDR_i ib(first).MARITALSTATUS_i ib(first).ISCED_i ib(first).INCOMELEVEL_i ib(last).SUBHEALTH_i ib(first).LEFTRIGHESC_i ib(first).RELIGIOUS_i ib(first).DOMICILE_i ib(11).COUNTRY INSTTRUST_i SOCTRUST_i CIVICNET_i CIVIC_i SOCNET_i, q(0.25 0.75) reps(1000)

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Neira, I., Lacalle-Calderon, M., Portela, M. et al. Social Capital Dimensions and Subjective Well-Being: A Quantile Approach. J Happiness Stud 20, 2551–2579 (2019). https://doi.org/10.1007/s10902-018-0028-6

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