Sick of Inequality?

Sick of Inequality?

An Introduction to the Relationship between Inequality and Health

Andreas Bergh, Therese Nilsson and Daniel Waldenström

There is a clear trend in rich countries that despite rising incomes and living standards, the gap between rich and poor is widening. What does this mean for our health? Does increasing income inequality affect outcomes such as obesity, life expectancy and subjective well-being? Are rich and poor groups affected in the same ways? This book reviews the latest research on the relationship between inequality and health. It provides the reader with a pedagogical introduction to the tools and knowledge required to understand and assess the issue. Main conclusions from the literature are then summarized and discussed critically.

Chapter 6: The ecological fallacy: what conclusions can be drawn from group averages?

Andreas Bergh, Therese Nilsson and Daniel Waldenström

Subjects: economics and finance, health policy and economics, welfare economics, politics and public policy, public administration and management, social policy and sociology, health policy and economics, sociology and sociological theory


In the preceding chapter, we discussed various methods of statistical analysis that can be used to analyse the correlation between life expectancy and inequality. Our empirical examples showed correlations between variables in levels and changes, both with and without additional control variables. However, all the regressions had in common that they were based on aggregate data, with observations being population averages or sums. These regressions related inequality in a particular country to the average life expectancy in that same country. However, it is not necessarily the case that a correlation at the aggregate level implies a correlation at the individual level. Conclusions about individuals based on aggregate data are vulnerable to the so-called ecological fallacy. An ecological fallacy manifests when the correlations produced by aggregate observations are different from the correlations in the underlying individual observations (Robinson, 1950). To attribute a group trait to individuals within that group can thus be a mistake. In this chapter, we discuss the importance of moving from aggregate to individual data and how this shift influences the analysis of the inequality effect and (possibly) the results. We devote a chapter to this issue because hundreds of previous studies have used aggregate variables exclusively. In contrast, recent research has benefitted from the creation of new individual-level databases, opening up the possibility of examining whether a statistically significant correlation between inequality and health at the aggregate (that is, country or state) level reflects a similar correlation between individual health and inequality at some level of society.

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