Working Paper: NBER ID: w19950
Authors: Timothy N. Bond; Kevin Lang
Abstract: We show that, without strong auxiliary assumptions, it is impossible to rank groups by average happiness using survey data with a few potential responses. The categories represent intervals along some continuous distribution. The implied CDFs of these distributions will (almost) always cross when estimated using large samples. Therefore some monotonic transformation of the utility function will reverse the ranking. We provide several examples and a formal proof. Whether Moving-to-Opportunity increases happiness, men have become happier relative to women, and an Easterlin paradox exists depends on whether happiness is distributed normally or log-normally. We discuss restrictions that may permit such comparisons.
Keywords: Happiness; Ordinal Scales; Utility; Cumulative Distribution Functions
JEL Codes: D60; I30; N30
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| moving to opportunity (J62) | happiness (I31) |
| happiness distribution (D39) | average happiness estimate (C13) |
| utility function transformation (D11) | happiness ranking (I31) |
| assumptions about happiness distribution (D39) | causal claims about happiness (I31) |