Solving the income-happiness paradox

Easterlin notes a contradiction in the data. While the cross-sectional data set shows that happiness is a positive monotonic function of income, the time-series data set of high income countries demonstrates that happiness does not rise with the rise of income. To solve the paradox, this paper proposes that each data set reveals a different facet of happiness. The cross-sectional data set asks people how they assess their current well-being in general. This question prompts people to contrast their current well-being with a well-being in the distant past. This explains why happiness tracks income. In comparison, the time-series data ask people how they feel at the moment. This question prompts people to contrast their current well-being with an aspired goal in the future. Their response is a function of the gap that exists between their current well-being and the aspired one. The gap is usually steady for high income countries and, hence, happiness is likewise steady, i.e., insensitive to the rise of income. The proposed solution highlights the operation of contextual assessment: we have two facets of happiness following the two kinds of context in operation.