Social science research converses with itself. For example, you might’ve heard money doesn't increase happiness past a certain point, or you might’ve heard the opposite that money continues to increase happiness. In a recent paper, Mellers negotiates these seemingly contradictory findings: money continues to increase happiness for some people.1 Revolutionary.

Reading the paper more deeply, the log(income) part of its conclusion caught my attention. In computer science, we think of a O(log(n)) complexity algorithm as growing extremely slowly. For instance, binary search on 256 sorted items takes 8 comparisons, on 65,536 sorted items takes 16 comparisons, and on 4,294,967,296 sorted items takes 32 comparisons. Intuitively, to add happiness you would need to multiply your income.

As that description implies, once you earn past a comfortable income, you almost certainly have more efficient pursuits to increase emotional well-being, such as strengthening your relationships with others.


  1. “We discovered in a joint reanalysis of the experience sampling data that the flattening pattern exists but is restricted to the least happy 20% of the population, and that complementary nonlinearities contribute to the overall linear-log relationship between happiness and income.” ↩︎