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Individual and Social Dimensions of Subjective Well-Being: Evidence Across Latin-American Countries

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Handbook of Happiness Research in Latin America

Part of the book series: International Handbooks of Quality-of-Life ((IHQL))

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Abstract

In this chapter, we explore the effect of individual and of interpersonal variables on the individual and social domains of subjective well-being. Using data derived from the Latinobarómetro 2007, we analyze if some of the variables affect differently individual or social dimensions. We estimate generalized probit models to allow the effects to be different also across the distribution of subjective well-being in each dimension. We further explore if there are some domains for which there is some possible degree of substitution between personal characteristics (resources) and interpersonal relationships as to improve well-being. We find that substitution is a possibility for individual dimensions, but not for social or institutional ones.

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Notes

  1. 1.

    Although we follow Lora’s classification, we are conscious that not all these “abilities” are of the same nature. For instance, education belongs to the category of abilities that can change over time and are susceptible of some public intervention, whereas personal characteristics as age, gender or ethnicity are of a different nature . However, since in our analysis we have cross-section data and this fact will determine the methods for the analysis, we can consider all of them abilities in a rough sense.

  2. 2.

    The interest of conducting subjective well-being research using panel data in order to better address unobserved heterogeneity and causality problems is to be further explained in the presentation of the estimation method. For a general discussion, see Van Praag and Ferrer-i-Carbonell 2004.

  3. 3.

    Eighteen years for all the countries considered, with the exceptions of Brazil and Nicaragua, where the legal age for adulthood is 16.

  4. 4.

    If we consider the index, the minimum level of observations is 9415 for the micro dimensions.

  5. 5.

    Principal components analysis is a statistical procedure using a set of variables to extract the few orthogonal linear combinations of the variables that capture the common information in the most satisfactory way. Consistently, the first principal component of a set of variables is the linear index of all the variables that capture the largest amount of information common to all the variables. Thus, our dependent variables that use information coming from different domains are additive and linear (Van Praag and Ferrer-i-Carbonell 2004; Rojas 2006).

  6. 6.

    In all cases of satisfaction variables, the scale is reversed.

  7. 7.

    Principal components analysis is a statistical procedure using a set of variables to extract the few orthogonal linear combinations of the variables that capture the common information in the most satisfactory way. Consistently, the first principal component of a set of variables is the linear index of all the variables that capture the largest amount of information common to all the variables. Thus, our dependent variables that use information coming from different domains are additive and linear (Van Praag and Ferrer-i-Carbonell 2004; Rojas 2006).

  8. 8.

    We differentiate between illiterate and incomplete primary education levels because being able to read and write can make a difference in low income countries in terms of capabilities.

  9. 9.

    The individuals classified themselves as a person of mixed race, particularly of indigenous and white parentage.

  10. 10.

    This category includes individuals of mixed black and white parentage.

  11. 11.

    As mentioned before, principal components analysis is a statistical procedure using a set of variables to extract the few orthogonal linear combinations of the variables that capture the common information in the most satisfactory way. Consistently, the first principal component of a set of variables is the linear index of all the variables that capture the largest amount of information common to all the variables.

  12. 12.

    The LAPOP index includes information about the ownership of a landline and microwave, and excludes owning a home and owning a second home.

  13. 13.

    The internal and external validity of this type of index and its robustness to the inclusion of different assets can be found in a paper by Filmer and Prichett (2001). Wealth indexes (showing high correlation with household consumption expenditure) are used to explain the relationship between households’ material conditions and children’s school enrollment in India.

  14. 14.

    The implications of the nature of the dependent variable and the estimation method are assessed in Ferrer-i-Carbonell and Frijters (2004). A brief though updated overview of estimation methods can be found in MacKerron (2012).

  15. 15.

    For a complete revision of assumptions and goodness of fit test, see Mora and Moro-Egido (2008).

  16. 16.

    An especially useful concept and magnitude for policy evaluation (van-Praag and Ferrer-i-Carbonell 2004).

  17. 17.

    Recall that this measure is an overall measure of evaluation of life, and it could be considered as a measure of higher hierarchy than the rest, and therefore the alternative to the micro-satisfaction index.

  18. 18.

    For the sake of simplicity to the reader, we have not included the estimation results concerning these variables, but they are available upon request from authors.

  19. 19.

    This corresponds to the second interpretation of the MPE that GOP allows to make.

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Authors and Affiliations

  1. BETS-Dpto. Fundamentos del Análisis Económico II, University of the Basque Country (UPV/EHU), Bilbao, Spain

    Victoria Ateca Amestoy

  2. Dpto. Métodos Cuantitativos para la Economía y la Empresa, Universidad de Granada, Granada, Spain

    Teresa García-Muñoz

  3. Dpto. Teoría e Historia Económica, Universidad de Granada, Granada, Spain

    Ana I. Moro Egido

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  1. FLACSO-Mexico & UPAEP, Mexico City, Mexico

    Mariano Rojas

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Ateca Amestoy, V., García-Muñoz, T., Moro Egido, A.I. (2016). Individual and Social Dimensions of Subjective Well-Being: Evidence Across Latin-American Countries. In: Rojas, M. (eds) Handbook of Happiness Research in Latin America. International Handbooks of Quality-of-Life. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7203-7_21

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