arXiv 30 Dec 2022 · Econometrics
arXiv:2212.14622 · PDF · DOI · OpenAlex · Extracted main text
Survey questions often ask respondents to select from ordered scales where the meanings of the categories are subjective, leaving each individual free to apply their own definitions in answering. This paper studies the use of these responses as an outcome variable in causal inference, accounting for variation in interpretation of the categories across individuals. I find that when a continuous treatment variable is statistically independent of both i) potential outcomes; and ii) heterogeneity in reporting styles, a nonparametric regression of response category number on that treatment variable recovers a quantity proportional to an average causal effect among individuals who are on the margin between successive response categories. The magnitude of a given regression coefficient is not meaningful on its own, but the ratio of local regression derivatives with respect to two such treatment variables identifies the relative magnitudes of convex averages of their effects. These results can be seen as limiting cases of analogous results for binary treatment variables, though comparisons of magnitude involving discrete treatments are not as readily interpretable outside of the limit. I obtain a partial identification result for comparisons involving discrete treatments under further assumptions. An empirical application illustrates the results by revisiting the effects of income comparisons on subjective well-being, without assuming cardinality or interpersonal comparability of responses.
appendix boundary found by appendix_command · 37% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Luttmer, Erzo F. P (2005) Neighbors as Negatives: Relative Earnings and Well-Being* | 0.904 | 23 | 5 | 74% |
| 2 | Bond, Timothy N., Lang, Kevin (2019) The Sad Truth about Happiness Scales | 0.778 | 17 | 6 | 47% |
| 3 | Barrington-Leigh, C.P (2024) The econometrics of happiness: Are we underestimating the returns to education and income? | 0.737 | 3 | 3 | 67% |
| 4 | Goff, Leonard (2022) Treatment Effects in Bunching Designs: The Impact of the Mandatory Overtime Pay on Hours self | 0.737 | 3 | 2 | 100% |
| 5 | Hoderlein, Stefan, Mammen, Enno (2007) Identification of Marginal Effects in Nonseparable Models without Monotonicity | 0.693 | 6 | 3 | 33% |
| 6 | Kasy, Maximilian (2022) Who wins, who loses? Identification of conditional causal effects, and the welfare impact of changing wages | 0.585 | 5 | 5 | 20% |
| 7 | Sasaki, Yuya (2015) What do Quantile Regression Identify for General Structural Functions? | 0.585 | 3 | 3 | 33% |
| 8 | Matzkin, Rosa L (1994) Chapter 42 Restrictions of economic theory in nonparametric methods | 0.550 | 6 | 3 | 17% |
| 9 | Benjamin, Daniel J., Heffetz, Ori, Kimball, Miles S., Rees-Jones, Alex (2014) Can Marginal Rates of Substitution Be Inferred from Happiness Data? Evidence from Residency Choices | 0.511 | 2 | 2 | 50% |
| 10 | Montgomery, Mallory (2022) Reversing the gender gap in happiness | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 79 scored citations.