If you are looking for ways to measure income inequality, there are three approaches that are popular with scholars. The first two come from the political science literature. The third comes from the economics literature. In this post, we explore the idea that income inequality can be measured using macroeconomic variables and micro economic indicators.
Economic theorists usually measure income inequality using macroeconomic tools. These include national income accounts, national income estimates from private enterprise, and national health estimates from Statistics Canada. These examples support the view that it’s important to study the robustness of this income inequality concept using a range of macroeconomic indicators. Also, using a range of macroeconomic variables allows for more meaningful examination of the pathogenic potential impacts of gaps in the income spectrum across the economic horizon. Two such variables that are often used are the gross domestic product (GDP) and the personal productivity series.
The topic of income inequality can also be examined using more familiar economic indicators. Two such closely related concepts that have been used for decades are hazardous and non-covarian processes. The random process inequality describes the tendency for prices to fall in response to random shocks to the economy. Non-covarian prices reflects the tendency for prices to rise in response to unobserved demand shocks.
An alternative to measuring inequality by means of macroeconomic instruments is to study the health effects of differences in income distribution. One way to estimate the effect of changes in the income distribution is to use time-series data. For example, using hospital charges as a measure of health effects since the 1970s suggests that overall health and life expectancy have been relatively stable over that period. This is not the case, however, with the extreme rise in health spending seen in many countries over the last decade. Examining the health impact of changes in the income distribution via time-series analyses is therefore important in order to evaluate how changes in the distribution of income have affected public health.
To this end, a working paper by economists Bhargava, Zhu and Song (2021) has applied a novel statistical method called the logistic regression discontinuity analysis. Using data from the pooled British and United Nations surveys covering around forty-nine countries, they found that there is a large and significant relationship between income inequality and health spending. The analysis indicates that poor health can cause large increases in health spending, whereas good health leads to lower expenditure. This suggests that income inequality could be a major deterrent to investment in health. Further analysis by the researchers suggests that the slowdown in recent years in the growth of healthcare coverage is the likely reason for the negative correlation between income distribution and health spending.
Another branch of economics which looks at the measurement of inequality with an eye to its economic impact has come up with some interesting results. Michael Norton and Niels Ebenboe have developed what they call the Oxford Glycemic Index (OGI). Their index, based on the consumption of sugars, is thought to capture the persistence of health differences across income groups. The authors show that there is a very strong and significant link between income inequality and the levels of OGI. The strength of this link is especially strong when the effects of increases in income inequality are accounted for through changes in dietary choices. Other researchers have also suggested that the OGI may be an important factor behind obesity.
Unfortunately the present period is too short to examine the potential causal role of changes in dietary choices in the development of obesity. However, some studies have suggested that the stronger relationship between poor diet quality and obesity may be due to the fact that poor diet quality directly affects energy intake, which is then in turn correlated with the severity of obesity. Other researchers have argued that there is an important association between obesity and poor health measures such as low plasma vitamin D. Finally, others have pointed to a negative correlation between remark scoring and measures of socioeconomic class and obesity. This last suggestion is not a proven fact, but it may deserve a closer look given the potential confounders and oversimplification inherent in any health-related analysis.
The question of whether income inequality is correlated with obesity has been addressed using several different approaches. Using regression analysis, researchers have found a significant negative correlation between obesity and income inequality. However, when the study is restricted to people who are wealthy, there is no significant association between the two factors. This result is puzzling because those who are wealthy are not necessarily the most overweight or obese individuals. More likely than not, the poor are the obese ones. It is still an open question whether wealth is the cause of obesity or the effect of obesity on the poor, although most nutrition experts believe that the poor are the ones who are at the highest risk of heart disease and other chronic diseases.