Would a chain index provide a better guide to change in the quality of life?

A chain index is an index that is constructed from information about changes in a variable from year to year, rather than by measuring absolute levels of the variable. A chain index is likely to be more accurate if we can measure change from year to year more accurately than we can measure absolute levels.

I think there is reason to believe that survey information yields more accurate information on change in quality of life than on absolute quality of life. I have explained why in a previous post (here).

In the following chart I have used information published by the Pew Research Center to construct a chain index of the quality of life from survey data on respondents assessments of their current quality of life and their assessments of their quality of life five years earlier (here). This provided estimated of the change in quality of life over the previous five years. The data on change in quality of life was annualized and converted to percentage changes. After gaps in the data were filled in by interpolation the series was converted into the form of the chain index shown below.

The happiness index shown in the chart is constructed from the Pew Research Center’s survey data on current quality of life.




The quality of life index shown in the chart actually shows a larger increase than the increase in real GDP per capita over this period. I therefore find it difficult to accept that the increase in quality of life was actually as large as is shown. The main point of the exercise is to suggest that the quality of life in the U.S. probably increased over this period by a larger magnitude than indicated by the happiness index shown in the chart.

(Research presented on this blog – as on any other blog - should be viewed with more caution than peer-reviewed research presented in academic journals. For quality assurance purposes I am prepared to make detailed results of research available to anyone who wants them and the data available to anyone who wants to replicate studies.)