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Subjective Well-Being and Adaptation. The Case of Uruguay

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Abstract

We assess the recent evolution of the quality of life in Uruguay, analysing whether current subjective well-being levels are conditioned by the objective well-being trajectory of each individual. We explore subjective well-being in 3 domains: life, economic situation and housing satisfaction. Although adaptation has been addressed in the empirical literature for developed countries, there is scarce evidence for developing countries due to the lack of suitable panel datasets. In this article, we provide an econometric test of the adaptation hypothesis based on longitudinal data from Uruguay for the years 2004, 2006 and 2011/12 (Estudio Longitudinal de Bienestar en Uruguay). Our main findings show that present levels of life, economic and housing satisfaction are each positively correlated with the corresponding contemporary and lagged objective variable of interest. Thus, we reject the adaptation hypothesis in all the dimensions considered. We also explore the role of social interactions in the 3 subjective well-being dimensions, finding out that average objective well-being of the reference group (either income or crowding) is not associated with individual subjective well-being levels.

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Notes

  1. Differently to Elster, in Nussbam and Sen’s view, reduced aspirations result from being exposed to long-term deprivation, but this does not necessarily entail the existence of a previous period in which individual desires were not downsized.

  2. However, Elster argues that adaptive preferences formation is not the only possible adaptive response. An alternative strategy is what he defines as “character planning”, which is an adaptive process generated by people consciously adjusting wishes to real possibilities (Elster 1988; Pereira 2009).

  3. In recent decades, a significant strand of the empirical literature has been examining reported happiness and life satisfaction as an approximation to measure cardinal utility and its determinants (Clark and Oswald 1994; Blanchflower and Oswald 2000; Easterlin 1995; Frey and Stutzer 2007). Lelkes (2006) offers a complete systematization of the main findings of this literature.

  4. Redistributive policies included the introduction of the personal income tax, a health reform, the restoration of centralized wage-setting mechanisms and a substantial expansion of non-contributory cash transfers schemes.

  5. As in most Latin American countries, national poverty thresholds are based on absolute lines, following the ECLAC methodology, but with a higher Orshansky coefficient. Montevideo’s per capita official poverty line is equivalent to 100 U$S a month. Poverty and inequality figures were calculated by the authors based on micro-data from official household surveys (Encuestas Continuas de Hogares) carried out by Instituto Nacional de Estadística (INE).

  6. He also remarks that these relations vary depending on the specific wording of the question used to capture subjective well-being: life satisfaction evaluations and experiencing happiness the day before.

  7. Whereas in this paper we use school groups as the basis of social comparisons, Leites and Ramos define broad categories in terms of education, sex and region and obtain average income from the national household survey (Encuesta Continua de Hogares).

  8. Information on the dataset, survey questionnaires and micro-data can be found at http://www.fcea.edu.uy/estudio-del-bienestar-multidimensional-en-uruguay.html

  9. Details on ECH can be found at http://www.ine.gub.uy/web/guest/encuesta-continua-de-hogares3

  10. Survey instruments can be found at http://www.fcea.edu.uy/estudio-longitudinal-del-bienestar-en-uruguay/datos/cuestionarios.html

  11. A potential source of endogeneity might be given by the fact that improvements in life satisfaction levels could have an impact on income. Di Tella and MacCulloch (2008) conducted empirical tests that suggest that such shocks do not significantly bias the coefficients.

  12. Full regression outputs can be requested from the authors.

  13. Fixed effects estimations yield to different results in the three domains, but divergences are larger in the case of economic satisfaction.

  14. Extreme poverty was measured using the official national threshold (INE 2018). In Uruguay the per capita extreme poverty line corresponds to the monthly monetary value of the food basket needed to meet the daily nutritional requirements of an individual. As in the case of the poverty line mentioned before in this article, is calculated by the National Statistical Office (INE 2006) following ECLAC’s guidelines.

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Acknowledgements

We are grateful to Sabina Alkire, Veronica Amarante, Rodrigo Arim, Andrew Clark, Flavio Comim, Ana Fascioli, Peter Fitermann, Gustavo Pereira, Ambra Poggi, Agustín Reyes, participants at the meetings of the Human Development and Capabilities Association in Lima and ECINEQ in Catania and two anonymous referees for many comments and suggestions on earlier versions of this article. Financial help from Fondo Clemente Estable and Comision Sectorial de Investigación Científica de la Universidad de la República is very gratefully acknowledged. All errors remain our own.

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Authors and Affiliations

  1. Instituto de Economia, Universidad de la Republica, Gonzalo Ramírez 1926, Montevideo, Uruguay

    Gonzalo Salas & Andrea Vigorito

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  1. Gonzalo Salas
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  2. Andrea Vigorito
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Correspondence to Andrea Vigorito.

Appendices

Annex 1

A comparative analysis of the ELBU data with that from the official household survey (Encuesta Continua de Hogares) run by the local statistical office (INE) in terms of income, employment situation and other socio-economic variables shows very similar results (see Failache et al. 2016) for details). However, as is the case of most household surveys, recent comparisons with information coming from income tax records show that labour earnings and pensions are well captured in the ECH, although top and capital incomes may be undercaptured (Burdín et al. 2015). Informal income is included in the ECH and the ELBU. However, in the period under analysis, 80% of the labour force had formal employment (INE 2018).

Table 6 depicts a comparison between data from ECH for 2004 and the first ELBU wave run the same year. In order to pick similar children at ECH, in column (1) we include all children attending first grade at primary schools in Montevideo and the Metropolitan area, and in column (2), we restrict the sample to the sub-group attending public schools, which be a closer comparison to ELBU. In regard to ELBU, the observations included in column (3) correspond to the balanced panel used in this study, whereas column (4) includes all cases for Montevideo and the Metropolitan gathered in the first wave. The comparison among the two columns allows to assess the effect of panel attrition on the indicators included in the Table.

A first set of indicators was computed at the household level (average years of education, crowding, per capita household income). A second group attempts to capture the characteristics of ELBU respondents. Although in ECH it was not possible to exactly identify a group of women similar to ELBU respondents (which basically correspond to the mother of the reference child), we restricted in both cases the sample to women aged 55 years or less, as in the baseline they had to be mothers of children aged 6–7 years old. ECH and ELBU show very similar results, in terms of average years of education among adults and crowding. Per capita household income is 30% lower in ELBU compared to the equivalent ECH population. However, it can also be noticed that average labour earnings results are very similar in the two surveys. At the same time, employment rates among respondents are very similar, and even higher at ELBU. Average age among potential respondents are very similar in the two cases.

Hence, differences in per capita household income among the two surveys cannot be attributed to labour earnings or employment rates. This suggests that non labour income explains the gap, and, probably, this is relate to the fact that ECH has a larger set of questions assessing capital income and contributory and non contributory transfers.

Finally, comparing the whole ELBU baseline indicators and those restricted to the balanced panel, it can be noticed that the indicators are almost the same, suggesting that panel attrition did not have a significant effect on observable variables.

Table 6 2004 baseline indicators: Official household survey (ECH) and ELBU first round. Montevideo and the metropolitan area
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Annex 2

Table 7 Estimation results-separate cross-section OLS regressions
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Salas, G., Vigorito, A. Subjective Well-Being and Adaptation. The Case of Uruguay. Applied Research Quality Life 14, 685–703 (2019). https://doi.org/10.1007/s11482-018-9616-1

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