Income inequality in mental illness

In South Korea, rising inequality, mental illness — and inequality in mental illness.

South Korea has a high and rising suicide rate, which doubled from 1997 to 2008, becoming the worse of any country in the Organization for Economic Cooperation and Development (OECD). During that time, income inequality also increased.

A new paper in the journal World Psychiatry (the source of those figures) shows that the concentration of mental illness among poorer people in South Korea also increased during that period. That is, the income inequality in mental illness grew worse. Using a large survey of self-reported mental health and income, the authors, Jihyung Hong and colleagues, calculated he distribution of illness along the income distribution, like a Lorenz curve and its related Gini coefficient.

I have rescaled their numbers, so that zero equals equality (same rates of illness at all income levels) and 1 equals complete inequality (all illness among the poor), and plotted the trends here:

South Korea had a major economic crisis at the end of the 1990s, the shocks from which reverberated in many social aspects of the society. For example, that high rate of economic inequality in suicide attempts in 1998 took place in a year that saw a big jump in suicide attempts nationwide.

Inequality has not increase continuously for all three measures during this period, but they are all substantially higher in 2007 than they were 10 years earlier — and they all show considerable economic inequality in major mental illness.


Filed under Research reports

2 responses to “Income inequality in mental illness

  1. Pingback: Tweets that mention Income inequality in mental illness « Family Inequality --

  2. Ness Blackbird

    It strikes me that counting “mental illness” is a very difficult thing to do…the number of diagnoses made for different conditions would be more important than the overall figure — like depression, obviously.

    But it occurs to me to wonder whether various artifacts could substantially skew numbers like that: for example, higher rates of healthcare coverage for the poor leading to a greater count of diagnoses, or a push to help lower-income schools, say, with healthcare.


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