Two Americas of Well-Being: Divergent Rural-Urban Patterns of Life Satisfaction and Happiness from 2.6 B Social Media Posts

TL;DR

The paper addresses the rural–urban divide in subjective well-being by separating evaluative life satisfaction from hedonic happiness. It introduces The Human Flourishing Geographic Index (HFGI) methods, leveraging about $2.6$ billion geolocated tweets and a fine-tuned LLM to generate county-year indicators for $lifesat$ and $happiness$, and uses precision-weighted logistic and OLS models with year fixed effects. Key findings show a two-sided geography: rural counties express higher $lifesat$, while urban counties exhibit higher $happiness$; partisan margins depress both outcomes but in context-dependent ways, with pandemic years producing sharp declines in $happiness$. The study demonstrates that language-based, large-scale indicators can complement traditional surveys, offering high-resolution, near real-time monitoring of population well-being and informing policy.

Abstract

Using 2.6 billion geolocated social-media posts (2014-2022) and a fine-tuned generative language model, we construct county-level indicators of life satisfaction and happiness for the United States. We document an apparent rural-urban paradox: rural counties express higher life satisfaction while urban counties exhibit greater happiness. We reconcile this by treating the two as distinct layers of subjective well-being, evaluative vs. hedonic, showing that each maps differently onto place, politics, and time. Republican-leaning areas appear more satisfied in evaluative terms, but partisan gaps in happiness largely flatten outside major metros, indicating context-dependent political effects. Temporal shocks dominate the hedonic layer: happiness falls sharply during 2020-2022, whereas life satisfaction moves more modestly. These patterns are robust across logistic and OLS specifications and align with well-being theory. Interpreted as associations for the population of social-media posts, the results show that large-scale, language-based indicators can resolve conflicting findings about the rural-urban divide by distinguishing the type of well-being expressed, offering a transparent, reproducible complement to traditional surveys.

Figures (6)

• Figure 1: Relationship between Twitter-based life satisfaction and happiness across U.S. counties in 2020. Each point represents a county, colored by its rurality code (1--9). The dashed line shows the fitted linear regression with 95% confidence band. The correlation is moderate and positive ($r = 0.55$, $p < 0.001$, $N = 3{,}143$), indicating that, while the two affective indicators are related, they capture distinct facets of expressed well-being.
• Figure 2: Relationship between Twitter-based life satisfaction and happiness by rurality code (1–9) in 2020. Each panel shows counties within the same rural classification, with a fitted linear regression (dashed line) and 95% confidence band. The positive association between life satisfaction and happiness is consistent across all levels of rurality, though the strength of the correlation varies slightly by group.
• Figure 3: Predicted probability of expressing positive life satisfaction ($P(\texttt{lifesat}>0)$) across the Democratic–Republican vote margin for U.S. counties in 2022, based on the full model in equation \ref{['eq:fullmodel']}. Panels correspond to counties at the 20th, 50th, and 80th percentiles of median household income (ACS 5-year estimates). Lines represent rurality codes (1–9), and shaded areas denote 95% confidence intervals. Life satisfaction decreases as the Democratic vote share increases, with a steeper decline in more-rural counties (higher rural codes), consistent with the negative margin:rural interaction in Table \ref{['tab:lifesat']}.
• Figure 4: Predicted probability of expressing positive life satisfaction ($P(\texttt{lifesat}>0)$) across median household income levels for U.S. counties in 2022, based on the full model in equation \ref{['eq:fullmodel']}. Panels correspond to Republican-leaning ($\leq -10$ pp), toss-up ($\pm10$ pp), and Democratic-leaning ($\geq +10$ pp) counties. Within each panel the partisan margin is held at the class mean. Lines represent rurality codes (1–9), and shaded areas denote 95% confidence intervals. Life satisfaction increases with household income across all partisan contexts, and the rural–urban gap widens at higher incomes. These results align with the positive income and rural coefficients and the negative margin:rural interaction in Table \ref{['tab:lifesat']}, also showing that economic prosperity strengthens the rural Republican advantage in evaluative well-being.
• Figure 5: Predicted probability of expressing positive happiness ($P(\texttt{happiness}>0)$) across the Democratic–Republican vote margin for U.S. counties in 2022, based on the full model in equation \ref{['eq:happiness_full']}. Panels correspond to the 20th, 50th, and 80th percentiles of median household income (ACS 5-year estimates). Lines represent rurality codes (1–9), with shaded 95 % confidence intervals. Happiness levels are uniformly high but decline slightly with rurality and show no meaningful partisan gradient, consistent with the regression results in Table \ref{['tab:happiness']}.