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Would Socio-Economic Inequalities in Depression Fade Away with Income Transfers?

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Abstract

A number of recent studies have questioned the conventional view regarding the existence of income-related inequalities in depression and have suggested that other factors have a more marked impact, most notably those socio-environmental effects linked to professional status and educational attainment. This paper seeks to measure and decompose the degree of socio-economic inequality in the factors underlying reported depression by drawing on data from Spain (Spanish National Health Survey, 2003), a country in which mental care coverage is somewhat limited, but where a marked social transformation has been apparent in recent decades. Contrary to recent evidence, our findings point towards the existence of significant income-related inequalities in the prevalence of reported (diagnosed) depression. However, the results from our decomposition analysis are more mixed. While a modest proportion of overall inequalities (6–13%) is accounted for by income alone, labour status, demographics and education appear to be more relevant. However, when controlling for potential endogeneity between income and depression by using instrumental variables (IV), income is found to account for more than 50% of overall inequality in reported depression.

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Notes

  1. For instance, Smith and DeFelice (2004) argue that depression is a factor that shapes health production affecting the individual’s tastes and changes in mood, which might interact with other factors of health production such as the body mass production. Some studies find empirical evidence of a connection between obesity and depression (Istvan et al. 1992 or Costa-Font and Gil 2006). However, health production functions typically do not examine potential interactions between mental and physical health inputs.

  2. Several other studies reveal an association between income inequality (at a regional level) and the prevalence of depression among older Americans (Scheffler et al. 2001; Scheffler 1999).

  3. We concentrate on income-related inequalities rather than other measures of SES (i.e., education, occupation or social class) in line with previous studies such as Wagstaff and Van Doorslaer (2000).

  4. Notable exceptions are García-Gómez and López (2005) on mental health inequalities in Catalonia and Wildman’s study (2003) in Great Britain.

  5. Notice that, in fact, income correlates with a number of the factors affecting depression described here, most notably education and labour status.

  6. Indeed, ecological determinants seem to be an important underlying predictor of depression (La Gory and Fitzpatrick 1992). Some studies even claim that discrimination, and particularly that suffered by women (Belle and Doucet 2003) and racial minorities (Meltzer et al. 2004), is a common cause of inequalities in depression.

  7. An example of the complex relationship between depression and income is provided by Frank and McGuire (2000), who point out that “personal characteristics which make a positive contribution to earnings (e.g., creativity, energy or attention to detail) are (relatively) more common among persons who have mania or obsessive-compulsive disorders”.

  8. Cf. MHCA (2005).

  9. The survey follows a stratified multi-stage sampling procedure in which the primary strata are the Autonomous Communities, and sub-strata are then defined according to population size in particular areas. Within the sub-strata, municipalities and sections (primary and secondary sampling units respectively) are selected using a proportional random sampling scheme. Finally, individuals are randomly selected from the sections.

  10. Other covariates are also valid candidates for measuring the economic position of the household (e.g., education or occupation). However, the decomposition procedure employed in the analysis requires a continuous measure of SES.

  11. In other to avoid identification problems in the computation of the inequality index, we adjusted the (household) income equation by using characteristics of the head of the household and also information regarding the spouse and children.

  12. There is a potential problem of sample selection. It is plausible to conceive a respondent suffering from depression but not reporting it simply because there is not a GP/specialist visit.

  13. As is shown in Sect. 4, the results do not change substantially when the linear marginal effects of a probit estimation model are alternatively employed to deduce the inequality index of depression.

  14. Note that recent contributions suggest that linear models can be employed in the context of happiness measurement with little gain from imposing non-linearity (Ferrer-i-Carbonell and Frijters 2004).

  15. Actually, our sample slightly overestimates the prevalence of diagnosed depression: 5.9% vs. 5.4% in the original sample for adults aged 16–99. The latter suggests estimates that are closer to those in recent studies employing standardised survey techniques (World Mental Health Survey Consortium 2004).

  16. We applied the Hausman (1978) test to control for endogeneity by taking into account heteroskedastic-robust standard errors, and found only weak evidence supporting the inexistence of a reverse causality relationship between income and depression.

  17. We need to interpret these results with caution, since it has been demonstrated that poor instruments can actually create more bias in an estimator than least squares in the presence of endogeneity.

  18. Given that the components of Eq. 5 are non-linear functions of the data with complex sampling distributions, we opted to use bootstrapping methods to derive standard errors of the concentration indices. The number of replications was set to 100.

  19. These features have also been observed in the EU (cf. Van Doorslaer and Koolman 2004).

  20. This contrasts with findings by García-Gómez and López (2005). However, these authors restrict the analysis to the region of Catalonia and use a shortened GHQ instrument to measure mental health.

  21. There is, however, a debate regarding the extent to which inequalities are largely avoidable or unavoidable. Some authors argue that it depends on normative standpoints and that it is not possible to establish a distinction with any degree of certainty (Vallgårda 2006).

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Acknowledgements

We are grateful for the comments and suggestions received from three anonymous referees. These have enabled us to improve the clarity of the contribution. We also thank the Autonomous Government of Catalonia for research project 2005-SGR-460 and the Ministry of Science and Technology for projects SEJ2005-03196/ECON and SEJ2005-06270/ECON. Joan Costa-Font thanks the support from the Institut Ramon Llull, Generalitat de Catalunya.

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  1. European Institute & LSE Health, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, UK

    Joan Costa-Font

  2. Departament de Teoria Econòmica & CAEPS, Universitat de Barcelona, Catalonia, Spain

    Joan Costa-Font & Joan Gil

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  1. Joan Costa-Font
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Correspondence to Joan Costa-Font.

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Table 5 Interval regression for monthly net household income (Household head data)
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Costa-Font, J., Gil, J. Would Socio-Economic Inequalities in Depression Fade Away with Income Transfers?. J Happiness Stud 9, 539–558 (2008). https://doi.org/10.1007/s10902-008-9088-3

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