Social status and the relationship between income rank and well-being in 109 nations

Domain: chemistry
Published: 2026-02-20
Editorial outcome: minor_revision

Abstract
Well-being is linked to income. However, lower well-being among lower-income individuals may reflect either economic relative deprivation or the lower social status associated with a lower income rank. Here, using Gallup World Poll data from 109 countries and over 90,000 individuals, we test a general model that includes both relative income deprivation and income rank as special cases. In 80% of countries, subjective well-being is more strongly associated with within-nation rank of income than with absolute income or relative income deprivation. Income rank coefficients are over three times larger in the most materialistic countries, but smaller in countries with higher social capital: In countries with the highest civic engagement, the association between income rank and well-being is about 80% smaller. Results replicated in multiple survey years and are consistent with a link between income-related social status and subjective well-being that is stronger when social capital is low.

Introduction
Are people with higher incomes happier? And, if they are, why are they happier? Many studies have examined the role of absolute income (e.g., refs. 1–3). However, other research has converged on the idea that what matters is relative income and that people’s subjective well-being (SWB) is linked to how their incomes compare to the incomes of other people. But relative income can itself be conceptualized in different ways. One possibility is that people compare their income to the average of a set of comparison incomes (e.g., ref. 4); a second is that income-related relative deprivation matters 5, and a third is that people care about the relative ranked position of their income within a social comparison set 6. These possibilities carry different psychological interpretations, implicate different mechanisms of social comparison, and differ in their implications for public policies that target SWB.

Several studies support a rank-based approach to the income-SWB link. It is widely suggested that people’s SWB is associated with the relative ranked position of their income (e.g., refs. 6–9). Income rank is typically operationalized as the ordinal position of an individual’s income within the relevant national income distribution, scaled between 0 and 1. Links between income rank and SWB have been found in single-nation studies in at least 8 different countries and in one study of 24 countries (ref. 10, see Supplementary Note 1).

The income rank hypothesis of SWB explains why country-level inequality has little or no association with aggregate SWB, at least in high-income countries (e.g., ref. 11) and why a given increment in income leads to a greater improvement in SWB in more equal countries 12. Moreover, many approaches to subjective socioeconomic status and its relation to health assign a central role to an individual’s rank within society 13. If income rank is a status indicator, the income rank hypothesis is consistent with evidence that people engage in social comparison and care about their social status (e.g., ref. 14). The income-rank account also fits well with rank-based cognitive process models of the psychology of judgment 15.

However, most research has failed to distinguish between relative income deprivation and social status accounts of income’s links to SWB. Social status accounts of income and well-being assume that what matters is the relative ranked position of an individual’s income within a social comparison set and hence that the number of higher (and lower) earners will be all that matters, independently of how much more the higher earners receive. For example, if a person earns $20,000, the existence of other people earning $30,000 and $40,000 will have the same effect on that person’s SWB as if the other people were earning $40,000 and $50,000. Measures of economic relative deprivation, in contrast, typically take into account the entire weight of income above that person’s own income — the deprivation of a person earning $20,000 will be increased more by a comparator earning $50,000 than by one earning $30,000. Although different measures of relative economic deprivation have been proposed (e.g., refs. 16,17), they (unlike rank approaches) typically assume that a person’s relative deprivation will increase as the sum of higher incomes earned by other people increases, even if the number of higher incomes is held constant. Economic measures of relative deprivation differ from measures developed within social psychology and sociology, stemming from Stouffer et al. 18,19, which incorporate subjective components (see, e.g., ref. 20).

While socioeconomic gradients in health and well-being have long been reported 21, to our knowledge, no studies have directly contrasted rank-based and deprivation-based models of the association of economic status with either SWB or health across multiple countries, although some single-country comparisons have recently been reported 5,17,22,23. An additional issue concerns SWB’s association with relative, rather than absolute, income. Almost all existing evidence linking SWB to ranked, rather than absolute, income comes from single-country studies, where the two measures are highly correlated (mean correlation of 0.97, across the 109 countries in the present study). This collinearity impedes interpretation (see ref. 24). Cross-country studies that have compared absolute income either with the within-country rank of income 10 or with the within-country mean-relative income (e.g., ref. 25) typically rely on country-level income measures as predictors (e.g., fixed effects), effectively reintroducing high collinearity and removing the variation needed to distinguish between the two. Finally, concern with income-based social rank is plausibly higher in more marketized/neoliberal economies and lower where social capital is higher 26,27, yet these potential moderating effects—which may constrain psychological interpretation—have received almost no attention.

Here, we outline and calibrate a general model to compare deprivation and relative rank accounts of the income-SWB relationship. The model includes absolute income, income rank, and several relative income deprivation models as specific cases, allowing for direct statistical comparisons within a unified framework (see also refs. 5,17,22,23).

Figure 1 illustrates several ways in which an individual’s perception of their income could be influenced by other incomes within a social comparison set. In “Methods,” we formalize the general model and show that all the possibilities illustrated in the figure can be seen as special cases.

The model has three key parameters. Parameters δ and α determine the weight given to upward and downward income comparisons, respectively, with one typically fixed at 1. A parameter γ governs sensitivity to distance: when γ < 0, individuals prioritize incomes closer to their own; conversely, when γ > 0, more distant incomes carry more weight. When γ = 0, all incomes above or below the individual’s are equally weighted (i.e., only income rank matters). This approach enables nested model comparison. For instance, setting γ to 0 yields a rank-only model whose fit can be compared to one with a free γ. A significantly better fit for the latter would suggest that income distances matter. Similar tests can be conducted to assess asymmetries in sensitivity to upward versus downward comparisons (i.e., loss aversion).

We therefore structure our model comparisons as follows. First, we test the ubiquity of income rank’s association with SWB as compared to absolute income’s association (see “Methods” for definitions). We find that income rank consistently outperforms absolute income in predicting four different measures of SWB in almost all countries we examined. The second set of analyses test the purity of income rank’s link to SWB. Specifically, using the general model described in Eq. (3) (Methods), we focus on distinguishing the income rank hypothesis from the relative deprivation income hypothesis. To foreshadow: we find strong evidence that social status (operationalized as relative income rank), not relative income deprivation, is independently associated with SWB. The third and fourth sets of analyses test the robustness of income rank associations by exploring moderators of the associations at both the country-level and individual-level.

We apply the model to data from 109 countries and more than 90,000 individuals, and examine both country-level and individual-level moderating variables, with a particular emphasis on social capital, market orientation, and materialistic values. Our outcome variable is SWB, which is correlated with objective measures of both health and economic well-being 28 and is increasingly seen as a target of public policy 29. We focus on purely income-related measures of both social status and relative deprivation, as only by doing so can we compare social rank and relative deprivation accounts using nested model-based analysis.

Results
Analysis 1: income vs income rank
Our first analysis investigates, across many countries, whether income’s relative rank or its absolute value is independently associated with life evaluation. To distinguish between the two, our empirical approach leverages cross-sectional variation between countries within survey years, using data from countries that vary widely in wealth. This method avoids the multicollinearity problem inherent in within-country analyses (see “Methods”), but raises concerns about unobserved heterogeneity across countries. To mitigate these concerns, we include a rich set of country-level variables capturing variation in economic development, social structures, and institutional context. We also extend the analysis to affective components of SWB, including positive affect (combining enjoyment and laughter) and negative affect (combining sadness, anger, worry, and stress).

The analysis uses the latest available GWP data collection round (2023–2024), which we replicate across earlier survey years in Supplementary Tables 3–6. Summary statistics (by GDP per capita quartiles) are shown in Supplementary Table 2. Life evaluation tends to be higher in countries with higher GDP per capita, with a two-point gap between the highest and lowest income quartiles on a 0–10 scale. Supplementary Fig. 1 shows the relationship between income measures and life evaluation, suggesting a linear relationship for income rank and a slightly concave one for log income.

First, we confirm that our approach successfully mitigates collinearity between absolute income and income rank. The correlation between absolute income and income rank is only r = 0.50 (95% CI [0.50, 0.51]) in the present study, as compared with within-country correlations averaging r = 0.97 (SD = 0.04, range [0.72, 1.00]). Thus, our results are unlikely to reflect multicollinearity.

Table 1 presents results from OLS regression models. Columns 1–4 show the effects of absolute income (measured as log income) and income rank separately, while column 5 combines them. Baseline specifications (columns 1 and 3) reveal significant positive associations for both income measures. In Column 1, the coefficient of 0.66 implies that moving from the 25th to the 75th percentile of income (approximately $4413 to $26,188) is associated with a 1.18-point increase in life evaluation on a 1–10 scale. Column 3 reports a coefficient of 1.39 for income rank, corresponding to the estimated difference in well-being between individuals at the very bottom and top of the national income distribution. This effect exceeds that of a substantial increase in absolute income. With controls, the absolute income coefficient drops by 50%, while the income rank coefficient decreases by 20%. In Column 5, absolute income becomes non-significant. The income rank coefficient in this fully specified model indicates that moving from the bottom to the top of the distribution is associated with a 0.97-point increase in life evaluation. A consistent pattern is found in earlier survey years (Supplementary Table 3), with the exception of 2017–2018.

Table 2 examines the association of income rank and absolute income with positive affect, negative affect, and predicted future life evaluation. In all three cases, income rank was the only significant predictor in the combined analyses.

To assess robustness, we tested the consistency of results across alternative specifications, including (i) alternative approaches to addressing missing covariate data, (ii) use of country fixed effects, (iii) gradual inclusion of individual- and country-level controls, and (iv) use of household-size equivalized incomes (see Supplementary Note 4). We also ran separate analyses for high- and low-income countries. Most results closely mirrored those of the main specification, showing associations of income rank with SWB but not of absolute income. However, when country-level controls were excluded, the coefficient for absolute income became statistically significant, likely because absolute income partially captures country-level differences in infrastructure, services, and general development—factors that extend beyond individual purchasing power, but which are captured by our inclusion of macro-level covariates. The robustness analyses also found that when income rank is based on equivalized income, it is no longer significantly associated with SWB. This result is consistent with the idea that social comparisons are based on visible, culturally salient indicators, such as unadjusted (gross) income, that align with how income is commonly discussed, displayed, and institutionalized, rather than on less visible metrics like household equivalence. Finally, the coefficient on income rank was larger for lower-income than for higher-income countries. However, consistent with the main analysis, only income rank, not absolute income, was independently associated with SWB in both country groups.

Overall, results show that income rank is more strongly and consistently associated with SWB than absolute income. When both measures are included, absolute income is not statistically significantly associated with SWB in most analyses. Other multi-country studies (e.g., ref. 25) have found only small effects of relative income, typically defined as income relative to the national mean. However, these estimates are difficult to interpret, as increases in mean-relative income, holding absolute income constant, may reflect not only shifts in relative standing but also broader changes in national income, potentially capturing other socioeconomic disparities.

Our coefficient on income rank for life evaluation is substantial. For example, it is twice as large as the coefficient on having a college degree and four times as large as the effect of being single rather than separated. It is also roughly equivalent to twice the difference in life evaluation between being unemployed and being employed full-time. Given these findings, we proceed to compare different specifications of relative income without further consideration of absolute income.

Analysis 2: income rank vs relative deprivation
In our second analysis, we distinguish between the income rank and relative deprivation hypotheses and examine whether individuals place greater weight on upward income comparisons, consistent with loss aversion. We define income rank as an individual’s ordinal position in the national income distribution, normalized between 0 and 1. A higher income rank indicates a better standing relative to others. Relative deprivation, in contrast, captures how much worse off someone is compared to those earning more. It takes into account not just whether others earn more, but how much more they earn.

We estimate life evaluation as a linear function of the generalized rank measure Ri (see Eq. (4) in “Methods”), using maximum likelihood estimation (MLE) to recover the marginal effect of Ri (i.e., β) and rank parameters (δ and γ), with α set to 1.

Simultaneously estimating δ and γ can cause parameter instability. Therefore, to test for asymmetry in upward and downward comparisons, we fix γ at 0 and vary δ. When δ > 1, upward comparisons weigh more, reflecting loss aversion. To assess sensitivity to income distance, we set δ to 1 and vary γ. When γ < 0, closer incomes carry more weight, but as γ exceeds 1, more distant incomes are weighed more heavily, consistent with relative income deprivation.

Unlike Analysis 1, which pools countries, Analysis 2 estimates country-specific parameters to capture potentially heterogeneous social comparison processes. We begin with the test of comparison asymmetry. Supplementary Fig. 2 presents point estimates of parameters δ (loss aversion) and β (marginal effect of income rank) using Round 18 of the GWP survey (2023–2024). Despite an average δ coefficient of 1.55, most δ estimates are not significantly different from 1.0 (55 out of 88 countries). Robustness checks using round 17 (2022–2023) revealed little consistency in δ estimates across years, suggesting limited evidence for asymmetric weighting (Supplementary Fig. 5).

Next, we report the key test of the purity of income rank associations, asking whether people compare their incomes primarily to those marginally better or worse off than themselves. The distribution of γ and β coefficients in Fig. 2 shows that γ estimates for most countries are not significantly different from zero, consistent with the social-status income rank hypothesis rather than the relative income deprivation hypothesis. Estimates from adjacent survey years (Supplementary Figs. 6 and 7) show no consistency over time. Despite this, we further examined whether upward and downward comparisons reflect different reference groups by estimating separate distance parameters (γu and γd), but the resulting estimates yielded similar null results (Supplementary Figs. 8 to 10).

Given the null results for asymmetry and distance sensitivity in income comparisons, we turn to our simplified income rank formulation (δ = 1 and γ = 0) with β as the only parameter. The distribution of β coefficients (Supplementary Fig. 11) consistently shows positive and precise estimates of the income rank coefficient. The estimates also remain stable within countries over time, with a correlation of 0.75 across adjacent survey years and 0.39 over a decade (Fig. 3).

Finally, we present BIC scores to determine which income rank specification best fits each country’s data. Supplementary Table 12 shows that the simple rank-based model, where each higher and lower income contributes equally to SWB, provides the best fit for 81 of 88 countries in Round 17 (2022–2023) and 79 of 88 countries in Round 18 (2023–2024) of the GWP. These results are consistent with the claim that people are sensitive to the relative ranked position of their income rather than to their relative income deprivation. Despite this, we observe considerable heterogeneity in the income rank coefficients across countries, raising the question of whether such country-level differences can be predicted. We examine this question in the next section.

Analysis 3: country-level moderators
Next, we explore potential country-level moderators of the income rank effect by incorporating interaction terms with each potential moderator into our models, while controlling for demographics and general country-level characteristics. We examine economic and institutional indicators (e.g., GDP per capita, competitiveness, openness, inequality, redistribution, unemployment, urbanization), aggregate population sentiment (e.g., personal security, confidence in institutions, community engagement, acceptance of migrants), and societal values and economic preferences (e.g., individualism, materialism, risk/time preferences, altruism, reciprocity; see “Methods”).

Figure 4 shows how the association between income rank and life evaluation varies with these contextual factors. The left plot shows marginal effects estimated at representative values (e.g., ±1 SD) of each moderator; the right plot shows differences relative to a baseline category (the first category within each group). Across all specifications, marginal effects are positive and significant, indicating a robust association between income rank and life evaluation across varying economic conditions, levels of institutional confidence, societal values, and economic preferences.

We first examine economic and labor market indicators. Income rank coefficients are smaller in countries where unemployment is low, possibly because income position is less salient in the absence of widespread economic insecurity. This pattern complements previous findings that SWB is more strongly associated with material income in poorer countries 25,30–33. However, we find no consistent interactions with competitiveness or openness, and inequality does not moderate the association between income rank and SWB, consistent with Tan et al. 34 but differing from Macchia et al. 10. We resolve this apparent discrepancy in Supplementary Note 5, where we reproduce Macchia et al.’s findings in their original 24-country sample. However, when we expand the analysis to 113 countries and extend the time frame to 15 years (2009–2024), the interaction between income rank and inequality becomes inconsistent across years, consistent with a limited overall role for inequality.

Turning to social capital, we find negative interactions with community involvement and willingness to volunteer and help others (Civic Engagement Index), and attitudes toward migrants (Migrant Acceptance Index). The magnitudes are significant; for example, when the Civic Engagement Index is increased from its lowest to highest value, the coefficient on income rank decreases by 80% (from 1.69 to 0.34).

Finally, we examine interaction effects involving societal values and economic preferences elicited under monetary incentives. We find larger income rank coefficients in societies with higher scores on the Materialist Index, which captures the extent to which respondents prioritize economic and physical security over self-expression and autonomy (reverse-coded Inglehart scale). The coefficients are also substantial. For example, when the Materialist Index increases from its minimum to maximum, the coefficient increases by 205% (from 0.61 to 1.86), more than tripling in magnitude. We do not find moderating effects of societal measures of risk-taking, patience, or reciprocity.

Detailed regression estimates supporting these marginal effects are provided in Supplementary Tables 13–19, along with raw data patterns in Supplementary Figs. 15 and 16.

Analysis 4: individual-level moderators
Finally, we explore individual-level moderators of the income rank effect, focusing on demographics, subjective sentiments such as personal security, institutional confidence, community life satisfaction, and economic preferences like risk and trust. These results parallel those from our earlier country-level analysis.

Figure 5 shows the marginal effects of income rank on well-being at representative values of individual-level indicators, revealing a consistent positive and significant association between income rank and SWB across all demographics, levels of confidence in society, and economic preferences. This pattern mirrors our country-level analyses.

Turning to specific moderators, we find smaller income rank coefficients for younger individuals (ages 15–28), single people, and part-time workers who would prefer full-time employment. With respect to social capital, the coefficient decreases as individuals report higher community life satisfaction, social support, community commitment, or trust in institutions (Community Index, Social Life Index, Civic Engagement Index, National Institutions Index). For example, the income-rank coefficient is 46% smaller at the maximum value of the Civic Engagement Index relative to its minimum (1.34 vs. 0.72). Among economic preferences, only risk-taking behavior interacts with income rank, with smaller coefficients for risk-takers. Specifically, the coefficient is 49% lower for individuals at the highest level of risk-taking compared to those at the lowest (1.42 vs. 0.73).

Overall, these results indicate that the income rank coefficient is smaller for individuals who have higher levels of civic engagement, positive perceptions of social support from friends and relatives, and greater confidence in their government and key institutions. These variables mirror the values present in more communitarian societies with higher levels of social capital, where the importance of one’s individual status within a social group can be superseded by the status of the social groups to which one belongs.

Methods
This research was confirmed as being exempt from ethical approval by the Leeds University Cross-Faculty Research Ethics Committee, Business, Environment, and Social Sciences.

We formalize a general model that encompasses all possibilities illustrated in Figure 1 as special cases 17,59.

The relative rank of x is defined as:

Ri(x) = (i - 1) / (N - 1)

where i is the ordinal position of x’s income in a comparison set of N incomes.

Equation (1) can be rewritten as:

Ri(x) = 0.5 + (i - 1) / (N - 1) - (N - i) / (N - 1)

This form separates the number of incomes lower than i (= i - 1) and the number higher than i (= N - i). To account for both the size and direction of income differences, not just their number, we extend this formulation to include weighted comparisons:

Ri(x) = 0.5 + [α * Σ_{j=1}^{i-1} (xi - xj)^γ * δ + Σ_{j=i+1}^{N} (xj - xi)^γ] / [α * Σ_{j=1}^{i-1} (xi - xj)^γ + Σ_{j=i+1}^{N} (xj - xi)^γ]

In Eq. (3), parameters δ and α determine the weight given to upward and downward income comparisons, respectively, with one typically fixed at 1. The parameter γ governs sensitivity to distance: when γ < 0, individuals prioritize incomes closer to their own; conversely, when γ > 0, farther incomes carry more weight. When γ = 0, all incomes above or below the individual’s are equally weighted (i.e., only income rank matters). Equation (3) simplifies to Eq. (1) when δ = α = 1 and γ = 0.

Equation (3) allows nested model comparison. For instance, setting γ to 0 yields a rank-only model whose fit can be compared to one with a free γ. A significantly better fit for the latter would suggest that income distances matter.

To guide our empirical tests of income rank associations with SWB, we focus on three criteria: ubiquity, purity, and robustness. Ubiquity refers to the consistency of an association across contexts (e.g., countries and years); purity refers to the unique contribution of income rank beyond other income-related measures; and robustness refers to the need to deconfound absolute and relative income in cross-national studies as well as to avoid the incorrect assumption that relationships observed at the group level also hold at the individual level (i.e., the ecological fallacy).

Our analysis has four parts. We first test the ubiquity of the income rank hypothesis as an alternative to the absolute income hypothesis by analyzing its associations with four different measures of SWB. Next, we assess the purity of income rank associations with SWB. Using the general model described above, we distinguish the income rank hypothesis from the relative deprivation hypothesis by determining whether social status (relative income rank) or relative deprivation better accounts for SWB. In the third and fourth parts, we test the robustness of income rank associations by exploring potential moderators and by estimating country-level and individual-level moderation models separately, interpreting each within its appropriate scope. To avoid the ecological fallacy, we clearly distinguish between country-level and individual-level effects.

Throughout the paper, we use the term “subjective well-being” as a generic term for self-reported mental state well-being, rather than any specific measure.

Data
We use data from the GWP, analyzing six survey rounds covering a decade from 2013 to 2024: Rounds 8 (2013), 9 (2014–2015), 12 (2017–2018), 13 (2018–2019), 17 (2022–2023), and 18 (2023–2024). These rounds were selected to create three evenly-spaced pairs across the decade, allowing us to assess both short-term (consecutive rounds) and medium-term consistency of income ranks’ associations with well-being. Although the GWP includes data from a larger number of countries, complete data across all six rounds, necessary for creating a balanced panel, were only available for these 109 countries. This approach helps rule out the possibility that our findings are driven by shifts in country coverage across survey years.

The original dataset includes 703,965 records from 109 countries. After removing entries missing well-being (life evaluation) or income data, the number reduced to 695,922 records. Further exclusions were made based on missing age information, bringing the count down to 694,290. Finally, we eliminated outliers in income (outside 5th and 95th percentiles), as per standard practice to avoid regression artefacts (see ref. 24), resulting in 608,226 observations for analysis. The GWP collects data through standardized interviews with informed consent from all participants. Respondents self-report their gender (male/female) and age, and gender was included as a covariate in all models. No statistical method was used to predetermine sample size. Randomization and blinding are not applicable to this observational study.

Our life evaluation metric is based on the Cantril Self-Anchoring Striving Scale. Additionally, we examined how income rank is associated with positive and negative emotions. For Analyses 3 and 4, individual-level moderators were sourced from the GWP, while country-level cultural, institutional, and economic moderators were derived from various other sources as described below.

• Subjective Sentiment. Using GWP indices as proxies, we assess societal attitudes towards various political, social, and economic topics.

• Individualism and Materialism. Drawing from the World Values Survey 60, we constructed measures of societal emphasis on autonomy, friendship, wealth, success, work, leisure, and post-materialist values.

• Social Preferences. The Global Preferences Survey 61 includes individual-level measures of time preference, risk preference, trust, altruism, and reciprocity, which we aggregated to form country-level indicators.

• Economic Competitiveness and Openness. We used indicators from the Global Competitiveness Report 62 and the Legatum Institute’s Prosperity Index Report 63. These metrics measure market competition, higher education, goods and labor market conditions, business sophistication, innovation, market access, investment environment, enterprise conditions, and governance.

• General Country Characteristics. We used the World Bank and Standardized World Income Inequality Database for data on GDP per capita, health expenditure, unemployment, urban population, and income inequality.

Our moderation analyses use data from GWP Round 18 (2023–2024), which includes both the SWB outcomes and access to the widest and most recent set of relevant moderators. This round also minimizes the risk of potential reverse causality, as all moderators are measured prior to the well-being outcomes. Supplementary Table 1 presents a correlation matrix for the country-level variables used in the paper. Further details on the country and individual-level variables are available in Supplementary Notes 2 and 3.

Analysis 1: income vs income rank
Our first analysis investigates whether income’s relative rank or its absolute value is more closely associated with life evaluation, extending previous research across a broad set of countries. As noted in the introduction, there is typically a very high correlation between an individual’s income and their relative income rank within a single country, making it difficult to separate their associations statistically. When analyzing multiple countries, prior research has typically employed country fixed effects to control for country-level differences and exploit within-country variation. However, this approach reintroduces collinearity. Country fixed effects remove all between-country variation in income, effectively centering income within each country. As a result, the coefficient on income no longer reflects individual-level material standards across countries but rather income relative to the national average. In this setup, absolute income becomes a within-country relative measure, conceptually similar to income rank. This conflation makes it difficult or impossible to meaningfully compare their independent associations. Controlling for GDP per capita has a similar consequence: even without fixed effects, GDP per capita absorbs cross-country income differences, reducing absolute income to a residualized measure of relative standing (i.e., income relative to others within the same country). To address these limitations, we exploit cross-sectional variation across countries within each survey round, allowing us to retain both within- and between-country variation in income. This design avoids the collinearity issues inherent in fixed-effects models and enables a valid comparison between absolute and relative income coefficients. However, it raises concerns about unobserved country-level heterogeneity. To mitigate this, we include a rich set of macro-level controls: health expenditure per capita, the Gini index, the unemployment rate, and the percentage of the population living in rural areas. These variables help account for structural differences across countries that could otherwise confound the income-well-being relationship. We also cluster standard errors at the country level to account for within-country correlation.

To evaluate the association of SWB with income rank and absolute income, we estimate a utility function of the form shown in Eq. (4), where Uict is the utility of individual i in country c at year t (proxied by their life evaluation); xict is the natural log of individual i’s income, and X is a vector of control variables. These include demographic characteristics (a four-degree polynomial of age and its interaction with gender, as well as education, employment status, health problems, marital status, and urban/rural residence), and country-level characteristics (health expenditure, unemployment, urban population, Gini index, and absolute redistribution). Absolute redistribution is measured as the difference between market-income inequality (income before taxes and transfers) and net-income inequality (income after taxes and transfers).

Uict = U(xict, R(xict)) = λct + βRict + ωXict + ϵict

Income rank Ri is calculated as the scaled position of xi in the ordered set of N incomes (as in Eq. 2). For example, an individual with the 10th (20th) highest income out of 101 would have an index Ri of 0.9 (0.8). Like most previous studies, we assume individual i is sensitive to how their income ranks within the whole population of country c in year t. Use of sub-national geographically or demographically defined comparison groups is not feasible given our cross-national approach and data availability. The GWP is designed to be nationally representative, but not necessarily representative within finer-grained subgroups. Constructing income rank within small subpopulations, such as by region, age, or education, would introduce considerable statistical noise and arbitrariness in group definitions. However, we note that the generalized model estimated in Analysis 2 allows for the differential influence of others who are more similar in the sense of having more similar incomes.

We estimate multivariate regressions with reported life evaluation as the primary dependent variable. All regressions were estimated treating life evaluation (0–10) as approximately continuous. Formal tests of linear model assumptions (normality and homoscedasticity) were not conducted, as estimates are robust in large samples. To mitigate concerns about reverse causality, we use country-level controls from the year prior to the survey, or from the most recent year available within the 5 years preceding the survey. We included dummies for missing controls, hence retaining the maximum number of observations.

As a robustness check, we repeated the analysis restricting the sample to countries with complete country- and individual-level covariate data. These results are reported in Supplementary Table 8. Income is measured as annual household income in international dollars, calculated by the GWP using the World Bank’s PPP estimates and expressed in 2017 US dollars. We focus on Round 18 (2023–2024) of the GWP for our main results, but replicate findings across survey rounds for robustness.

In addition, we extend our analysis to measures of affective SWB, examining both positive affect (a composite of enjoyment and laughter) and negative affect (a composite of anger, worry, sadness, and stress).

Finally, to further assess robustness, we conduct a series of supplementary analyses (see Supplementary Note 4) testing the consistency of results across alternative specifications. These include models with country fixed effects (Supplementary Table 7); incremental inclusion of individual- and country-level controls (Supplementary Table 9); use of household-size equivalized incomes (Supplementary Table 10); and separate analyses for high- and low-income countries (Supplementary Table 11).

Analysis 2: income rank vs relative deprivation
In our second analysis, we distinguish between the income rank and relative deprivation hypotheses, and examine whether individuals place greater weight on upward income comparisons, consistent with loss aversion. As described earlier, income rank refers to an individual’s ordinal position within the national income distribution. Relative deprivation, by contrast, captures how much worse off an individual is compared to higher earners, incorporating not just the number of people earning more but also the magnitude of those income gaps.

Our generalized income rank measure (Ri, ranging from 0 to 1), defined in Eq. (3), includes parameters α, δ, and γ that enable flexible modeling of social comparisons. Parameters α and δ weight downward and upward income comparisons, respectively, enabling tests of loss aversion and income deprivation effects.

The parameter γ captures distance sensitivity—whether individuals compare more strongly to those closer (negative γ) or further (positive γ) from their own income. When α = δ = 1 and γ = 0, Eq. (3) simplifies to Eq. (2), describing a standard relative rank measure where equal weights are given to higher and lower earners in the social comparison.

To identify country differences in income comparison asymmetry, we use a simple univariate model, modeling life evaluations as linear functions of Ri for each country, without individual controls. Unlike in Analysis 1, which pools countries, in Analysis 2, country-specific parameters are estimated to better capture potentially heterogeneous social comparison processes. Pooling could obscure these differences. We use MLE to estimate the intercept (λ), parameter β (marginal effect of Ri, as in Eq. 4), and rank parameters (δ and γ), with α set to 1. This estimation does not rely on OLS assumptions such as normality or homoscedasticity.

Because estimating δ and γ simultaneously can lead to instability, we examine each separately. To test upward and downward income comparison asymmetry, we fix γ at 0 and allow δ to vary. When δ > 1, upward comparisons weigh more, reflecting loss aversion. To evaluate distance sensitivity, we fix δ = 1 and vary γ. When γ < 0, closer incomes carry more weight, but as γ exceeds 1, further incomes are weighed more heavily, as suggested by the relative income deprivation hypothesis.

For computational efficiency and to avoid local maxima of the log-likelihood function, we bound our parameter space: 0 ≤ δ ≤ 10; -10 ≤ γ ≤ 10; 0 ≤ λ ≤ 10; 0 ≤ β ≤ 10. We discard out-of-bounds estimates, leaving 88 countries. Confidence intervals are computed from the Hessian matrix at the maximum likelihood estimates.

We first report results on asymmetry in upward and downward comparisons, followed by analyses of income distance sensitivity. Finally, we compare these generalized specifications with the simplified income rank formulation from Eq. (2).

Analysis 3: country-level moderators
Analysis 3 explores potential country-level moderators of the income rank associations. We begin by investigating various measures of economic development and performance, including GDP per capita, inequality, economic competitiveness, and openness. Subsequently, we consider general socioeconomic factors such as unemployment and urbanization. We then shift our focus to aggregate population sentiments, examining aspects like personal security, confidence in key institutions, perceptions of corruption, social support, community commitment, community life satisfaction, and acceptance of migrants. Finally, we investigate societal values (e.g., individualism, materialism) and economic preferences, including risk and time preferences, trust, altruism, and reciprocity.

Moderation effects are assessed by interacting income rank with each moderator in Eq. (4). We analyze each moderator separately to prevent multicollinearity and potential bias in our estimates, as some of these candidate moderators (such as perceptions about personal security or confidence in institutions) could shape social preferences like altruism or reciprocity, or societal values like individualism or materialism. As in Analysis 1, we use linear regression, treating life evaluation (0–10) as approximately continuous and control for demographics and general country-level characteristics, including health expenditure per capita, unemployment rate, urban population rate, Gini index, and absolute redistribution level. All country-level variables, except the general characteristics, are standardized for comparability.

Due to data limitations, this analysis focuses exclusively on Round 18 (2023–2024) of the GWP. Many of the moderators of interest—such as economic competitiveness, openness, societal values, and incentivized economic preferences—are only available for recent years or are not consistently collected across survey rounds. Conducting moderation analyses across the full dataset is therefore infeasible. Using earlier SWB data would also risk reverse causality effects, as it would require pairing SWB outcomes with moderator variables measured in later years (e.g., explaining 2014 well-being using 2017 competitiveness data). By restricting the analysis to Round 18, we ensure that moderators precede the well-being outcomes, reducing ambiguity about directionality.

To visualize moderation effects, we plot marginal effects of income rank at representative values of each moderator (e.g., ±1 SD). Detailed regression estimates used in calculating these marginal effects are provided in Supplementary Tables 13–19. For completeness, we also present (unconditional) raw data patterns in Supplementary Fig. 13, accompanied by fitted values on solid lines. Fitted values correspond to percentiles 25 and 75 of the variable under analysis while holding the remaining covariates at their mean values, or at their mode values for discrete covariates.

Analysis 4: individual-level moderators
Analysis 4 explores individual-level moderators of the income rank coefficient. We begin by examining demographic differences. Next, we analyze individual subjective sentiment, covering the same aspects as in our previous country-level analysis: personal security, institutional confidence, corruption perceptions, social support, community commitment, and community life satisfaction. These social capital and subjective sentiment variables, calculated by the GWP, range from 0 to 100 and are not standardized, offering a clearer view of the income rank coefficients for individuals with extreme judgments. Finally, we investigate links between income rank coefficients and various economic preferences, such as risk, time, trust, altruism, and reciprocity, which are standardized across countries, as calculated by Falk et al. 61.

Following the same estimation procedure as for our country-level analysis, we analyze each potential moderator separately. We test the moderating role of each individual-level variable by adding an income rank interaction term to Eq. (4), while retaining the full set of control variables. We use data from Round 18 of the GWP for most analyses, except for the evaluation of economic preferences, whose individual scores are only available for Round 7 (2012–2013).

Detailed regression estimates used in calculating these marginal effects are provided in the Supplementary Tables 20–22. Here again, for completeness, we also present (unconditional) raw data patterns in Supplementary Figs. 15 and 16, accompanied by fitted values on solid lines. Fitted values correspond to percentiles 25 and 75 of the variable under analysis while holding the remaining covariates at their mean values, or at their modal values for discrete covariates.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a review of the paper “Social Status, Relative Deprivation, and Social Capital: The Ubiquity, Purity and Robustness of Income Rank Effects on Well-being in 90 Nations.” Using data from the Gallup World Poll, the article explores the role of income rank and income deprivation on wellbeing as well as several moderators and mechanisms. I will now proceed to comment on each section of the paper.\nPoint 1. Abstract\nIt would be good to know the dataset and methods used (e.g., what does ‘we calibrate a general model’ mean?). I would also be good to know how relative deprivation and income rank are calculated as readers who are not experts on these topics might not know what these two concepts are reflecting.\nPoint 2. Introduction\nPoint 2.1. SWB stands for ‘Subjective wellbeing’ not self-reported wellbeing as stated in the first paragraph of the introduction.\nPoint 2.2. The introduction is overall good with good detail about the studies.\nPoint 3. Results\nPoint 3.1. Study 1 should include sadness as another indicator of negative affect.\nPoint 3.2. Study 1. This result should be explained further “Although some panels indicate a negative association between absolute income and affect levels, this association is small and non-significant in earlier survey waves” why might this be the case?\nPoint 3.3. Study 1. It looks like the mediation results are based on adding positive and negative affect as additional covariates. Although this method can tell whether a variable can be a potential mediator, this cannot be called mediation analyses and some test like Structural Equation Modelling should be conducted.\nPoint 3.4. Study 2. Was the test in Study 2 also run with pooling countries as in Study 1?\nPoint 3.5. Study 3 and 4 are good and clearly explained although same concerns about waves still remain.\nPoint 4. Discussion\nPoint 4.1. Summary of results and limitations are well described. More on psychological mechanisms is needed.\nPoint 5. Methods\nThe article uses data from the Gallup World Poll (GWP) and there are a few points that need further clarification.\nPoint 5.1. It is not clear why the data used is 2009-2019 if the GWP goes from 2005 to 2024 already.\nPoint 5.2. A minor point is that the years might be better labelled as ‘survey years’ instead of ‘waves’ because ‘waves’ is usually used for longitudinal data and the GWP is cross-sectional.\nPoint 5.3. The methods section says “Social Status, Relative Deprivation, and Social Capital: The Ubiquity, Purity and Robustness of Income Rank Effects on Well-being in 90 Nations.” It is not clear how the authors came to this information because the GWP contains data from 165 countries and territories and more than 2 million respondents now.\nPoint 5.4. The methods section states “Each wave was analyzed independently and compared with the immediately preceding wave for robustness checks.” It is not clear why all survey years were not analysed together, and country and year fixed effects used in the regressions. If there is a clear rationale behind the choice of this method, this should be clearly explained.\nPoint 5.6. Why the moderation analyses were conducted only with 2018-2019 survey years should also be explained. Is it because the relevant variables are not available the previous years? In the methods section it says that this choice was made to ‘minimize the risk of potential reverse causality’, why is this the case?\nPoint 5.7. Study 1 replicated prior work with an alternative method. The authors challenged the method widely used in prior work by saying that this method introduced multicollinearity when also including GDP per capita as a control and country fixed effects. For example, the authors say that “When studying multiple countries, previous research has typically employed country fixed effects to control for country-level differences and exploit within-country variations. However, this method introduces multicollinearity due to the high correlation among income-related variables.” If the issue is the within country-variation, the analyses should be conducted by each country not by each survey year or wave (I see an attempt of this in Study 2). This is because when conducting the analyses by survey year or wave, all the countries are still pooled together which is what the authors challenge in the method used in prior work. The findings presented in this paper should be replicated with this widely used method with and without GDP as a control.\nPoint 5.8. It would be good to know how the mediation analyses were conducted. This is important because the dependent variable and the mediators are all self-reported and reverse causality, multicollinearity, and endogeneity might be present.\nPoint 5.9. In section 4.3 about Study 2, income rank and relative deprivation need to be defined (e.g., what do these mean). We know how these were calculated from the equation details, but we don’t know what they mean in plain words.\nPoint 5.10. Study 3 is well explained but the use of only ‘wave 13’ should be better justified and robustness checks with all the survey years together conducted.\nPoint 5.11. More detail is needed regarding each moderator used in Study 3 and Study 4.\nPoint 5.12. Ubiquity, purity, and ecological fallacy should be defined in the first part of the methods section.\n(Remarks on code availability)\nI didn't revise the code because I had to request access to do so.", "reviewer_2": "The paper contributes to our understanding of the relationship between income and wellbeing, which is an actively debated topic in wellbeing studies. The authors first conclude that it is relative income within a given country, rather than the absolute level of income, that matters for wellbeing. They then explore various approaches to measuring relative income, finding that a simple rank-based measure best fits the data. Finally, the authors demonstrate how the relationship between income rank and wellbeing varies across countries and among individuals with different characteristics.\nIt is a important contribution that the simple measure of rank performs as well, at least globally, as more complex measures accounting for asymmetries (Study 2). Your heterogeneity analyses (Studies 3 and 4) offer almost encyclopaedic detail — I find the results on civic engagement particularly interesting. However, I believe the main finding of the paper comes from Study 1, where you conclude that relative income rank within a country matters more for wellbeing than absolute income levels. I would want to better understand how one should interpret this result in the context of your modelling approach.\n1. The level-of-income vs. relative-income result is derived from a pooled cross-country regression without country fixed effects. The model controls for household income and income rank at the same time. This model specification is unusual. A standard cross-country wellbeing model would include household income and country fixed effects. The coefficient on household income from a standard model is then interpreted as the relations between wellbeing and higher or lower income in a given country, which is effectively relative income. The authors remove country fixed effect and include income both as a level (which is not significant) and as a percentile of income distribution (which is significant). The latter, effectively, captures similar variation to the coefficient on income from a model with county fixed effect. As a results, though the model formulation is unusual, I do not find the result surprising.\n2. In Table 1, R-squared in Model 1 (with income in levels) is three times higher than in Model 3 (with relative income), suggesting that a combination of income level and wellbeing fits the data better than a combination of income rank and wellbeing. It would be helpful to see a model that includes both income variables without additional controls. I would expect the coefficient on log income in this model to be positive, because having higher income conditional on a given income rank implies living in a wealthier country. If it is not, it would be helpful if you could provide some intuition on why that is the case.\n3. I assume that the coefficient on individual income in Model 5 in not significant, because the wellbeing benefits of leaving in a richer country are captured by controlling for health expenditures (correlation with GDP of 0.96) and absolute redistribution (correlation with GDP of 0.76). It is not clear to me how to select a set of country-level controls that allow controlling for heterogeneity without controlling away the effects of leaving in a wealthier country. It would be helpful to see the sensitivity of the results in Column 5 of Table 1 to including subsets of controls, e.g. individual level only, country-level with low correlation with GDP, etc.\n4. The authors state that the magnitude of the coefficient on the income rank is high. One can interpret the magnitude of this coefficient as a wellbeing difference between the poorest and the richest household in a country. Then, the coefficient of 1.5 on a 0 to 10 scale in Column 5 of Table 1, though still large, does not look enormous. It would be helpful to see some intuition on how to compare relative magnitudes of the coefficients on income level and income rank, e.g. the expected effect of moving from the 25th to the 75th percentile according to the estimates from Columns 1 and 3.\n5. I don’t believe that your findings corroborate the following statement in the conclusion: “The zero-sum property implies that, to the extent that a given domain of SWB is affected by income rank rather than absolute income, neither general increases in societal income not redistributive policies will, in themselves, increase SWB.” Firstly, given than your model effectively controls for GDP per capita, which can be interpreted as the level of societal income, I believe that non-significant coefficient on individual income does not prove that that general increases in societal income will not increase wellbeing. On redistributive policies, given that your analysis is cross-sectional, it is not informative about (potentially asymmetric) wellbeing benefits and loses from gaining or loosing rank for people in different parts of the distribution. Since your research does not speak to that, I don’t this that it can be used to support a statement about potential (non-)effectiveness of redistributive policies.\nMinor comments:\n1. Wellbeing literature that uses comparator group typically defines it as a group with similar characteristics (e.g. age, gender, education, region). Could you please discuss why you believe national comparison to be the best choice in your case. Especially given that using subgroups of the population might also help you to alleviate concerns of within-country multicollinearity between income level and income rank.\n2. I am concerned about negative coefficient on the level of income in models for positive affects will all controls, both in the main result (Table 2) and in wave 12 (Table S5). Could you please provide some intuition on why this might be the case?\n3. It would be helpful to see if your main results in Table 1 hold for the equivalence-scale adjusted household income, since it is more accurately represents the level of income individual enjoys.\nThank you for your work.\n(Remarks on code availability)\nI could not access the codes because the files linked above, https://osf.io/bj9cz/?view\nonly=8e0e7ff929e34cdab499695ef5843796, require submitting a request for permission to gain access.", "reviewer_3": "Comments on Social Status, Relative Deprivation, and Social Capital: The Ubiquity, Purity and Robustness of Income Rank\nEffects on Well-being in 90 Nations\nSubmitted to Nature Communications\nNCOMMS-24-42657-T\nThis paper studies the relationships between individuals’ absolute income and relative income and their subjective\nwellbeing. We know that wellbeing increases with income (and that the effect is in part causal) but it is on ongoing topic of\nresearch as to how much is due to absolute vs relative effects. The absolute component is the degree to which an individual\nhas more resources at their disposal. The relative component reflects the fact that a higher income might imply a greater\nposition within a social hierarchy. If relative effects are dominant, this has some troubling implications for society, as it\nimplies that economic growth (rising absolute incomes everywhere) may be limited in their ability to produce greater\nwellbeing.\nThe research here is an extremely comprehensive empirical exercise looking at the associations between income and\nincome position (and a couple of related/derived measures) with wellbeing across 90 countries. The main innovation comes\nfrom the breadth of data used, and the recognition that country fixed-effects (normally a positive feature of these models) is\nactually a liability within this context. The reason is that an individuals’ income and their income rank (which in turn capture\nthe absolute and relative components) are very highly correlated (the authors quote a correlation coefficient of 0.98). This\nmeans that it is immensely difficult to disentangle the effects of each, as there aren’t many examples in the data where an\nindividual’s absolute income is much higher or lower that what would be implied by their rank.\nThe pooling of many country-level datasets offers a partial solution as ranks get defined within countries while income gets a\nsingular definition across countries. So by comparing people with $X in poor countries and in rich countries (where the\nabsolute value is constant, but the relative position changes) the authors can resolve this problem. The note that this\npooling introduces a lot of heterogeneity, which they resolve with an extensive set of controls. The authors find considerable support\nfor income ranks as a dominant driver of wellbeing, and distinguish this from a related concept of relative deprivation. They\nalso find that income ranks are less important in countries with high levels of civic engagement, and uncover suggestive\nevidence that countries with more neoliberal politics might place more emphasis on relative effects.\nThis is a very strong paper. A high level of technical and conceptual expertise is shown throughout. The writing is careful,\nclear, and well-structured. I believe the paper is suitable for Nature Communications but have made some comments and\nsuggestions below. All should be taken seriously, but I don’t feel any are particularly critical.\n• The analysis hinges very strongly on how well the country-level controls remove heterogeneity that could be correlated\nwith income and may potentially explain wellbeing. While we can’t tell for sure whether or not the controls are sufficient,\nif the results are heavily dependent upon a particular control, this may imply that that underlying factor isn’t properly\nadjusted for. The authors could examine this further. It is a bit difficult to see what is happening as they just report “individual controls\n– yes/no” (and the same for countries). And sometimes the results bounce around and sometimes they don’t.\n• The authors don’t use the concepts of exogeneity/endogeneity at all in the paper. I appreciate that this is a general interest\njournal, and the readership may not be versed in econometric terminology, but these ideas are too useful to just gloss over.\nSince the pdf is 75 pages long it would be easy to briefly explain these concepts and make it clear to the reader that\nthe causal claims are fairly muted.\n• Following on from the above point, the authors should probably further mute their causal claims and tone down the causal\nlanguage.\n• I have my doubts about using the indicator approach used to handle missing data. This is a simple method where a\nmissing value for a covariate is imputed (usually with the mean, but it doesn’t matter), and an additional dummy variable is\nincluded in the regression to indicate that a value was imputed. This is only done for the country-level data, but it can\nbe a source of bias in non-experimental studies (e.g., Groenwold et al., 2012). Does this improve statistical power? Maybe,\nbecause you get more data, but maybe not, because you have to estimate more covariates. Why not use a standard multiple\nimputation technique? Or just report results for the data you have?\n• Out of interest, what happens to the estimates in Table 1 when the fixed-effects are included?\n• It is tempting to think that relative effects will become larger in richer countries, which the authors demonstrate. But do\nabsolute effects dominate in poorer countries? Surely a starving child cares much more about food than they do about social\nstatus. This would be simple to carry out and could be reported in an appendix. If true, it would also serve as a rough\nintuition check for the rest of the paper.\n• Some of the tables are too big and contain too much information to be easily interpretable.\n• The fact that income ranks seem less important in countries with more social capital, and for individuals with more social\ncapital, seems like a meaningful and under-discussed result. It is easy to see how the concept of endogeneity could be used\nto explain the latter (e.g., people with positive social characteristics may be autonomously happy and insensitive to income\nin general) but not the former.\n• The authors worry a little that their self-reported income data may not be reliable. If miserable people over-report their\nincome (which seems more likely than the reverse) then this would attenuate the results. So the real correlations might be\nstronger than the ones reported here.\n• The depressing conclusion that income rank is zero sum could be expanded upon. The stakes of this research are quite\nhigh, and a better discussion of the unpleasant result and its social implications would help.\n(Remarks on code availability)\nI had a quick look at the code. It looks very professional, but I didn't scrutinize it for errors. Since the data is public it should\nbe straightforward to reproduce all results.", "reviewer_4": "This is a very ambitious paper. I think the paper's most significant and interesting contribution is to tease apart the differing\neffects of income rank and relative deprivation--showing that income rank is by far a stronger measure (Study 2). The\nexploration of heterogenous effects of income rank in Studies 3 and 4 is interesting as well and complements Study 2 nicely.\nMy largest concern is with study 1, which examine absolute vs relative income. The authors note the problem that \"Within\nsingle countries, incomes and relative income ranks are highly correlated, making it difficult to separate their effects\nstatistically.\" (p. 6). It seems the problem is \"solved\" by pooling countries, so effectively our variation is by comparing two\npeople with the same income, but one in a poor country where they have a higher rank) to someone in a rich country (where\nthey have a lower rank).\nBut, the authors explain in the methods that \"We measure income as annual household income in international dollars,\ncalculated by the GWP using World Bank’s PPP estimates, represented in 2011 US dollars.\" So, in my example above, two\npeople with the same income (as measured in international dollars) have the same purchasing power, regardless of which\ncountry they are in. Thus, it's little surprise that they their income rank would have more influence than absolute income.\nAlso related to Study, 1, in the conclusion of the paper, the authors finish by stating that \"...The zero-sum property implies\nthat, to the extent that a given domain of SWB is affected by income rank rather than absolute income, neither general\nincreases in societal income not redistributive policies will, in themselves, increase SWB.\" (p. 19). But, note that the models\nfrom Study 1 have included measures such as log health expenditures per capita (which has a large positive effect on SWB,\nTable 1), and which has a .96 correlation with log GPD (Table S1). If a general increase in societal income is part of an\nincrease in GDP or health expenditures (or whatever else these proxy for), then this likely does increase SWB.\n# Other (more minor) comments:\n* Social capital is in the title, but it plays a very minor role in the results (alongside with other potential mediators), and has\nno part in the background. There is no review of the extensive literature on how social capital is measured and how it may\naffect SWB. I think these are still fine analyses to have, but “social capital” certainly shouldn't be part of the title, as it’s not\nwhat the paper is about.\n* In study 1, the authors write: \"These results show that (a) the rank of a person’s income is a better predictor...\" As far as\nprediction accuracy, the R-squared of both rank and absolute income are similar (and both are low). The authors mean\nsomething like a larger effect (although the units are different...)--regardless of how they want to say it, more care is needed\nhere.\n* I like the moderation analyses in Studies 3 and 4, but the figures fall a bit short in conveying the key information. The\nevidence of an effect is the size of the gap between the two predicted marginal effects for each variable. This is hard to\neyeball. I wonder if the authors could find a way to include this information as well?\n* In the supplement, the authors write \"In our study design, we do not conduct a power analysis to determine our sample\nsize, as we are not powering for null hypothesis significance testing.\" But really, as soon as you are reporting statistical\nsignificance tests, that's exactly what you are doing. The statistical significance test is based on the logic of null hypothesis\ntesting. I don't necessarily feel that power analyses are needed, but this statement should be omitted at the very least.\n(Remarks on code availability)\nI looked at some of the code (including the README), but the data are not available, so I did not run anything."}, "author_response": "REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis is a review of the paper “Social Status, Relative Deprivation, and\nSocial Capital: The Ubiquity, Purity and Robustness of Income Rank\nEffects on Well-being in 90 Nations.” Using data from the Gallup World\nPoll, the article explores the role of income rank and income deprivation\non wellbeing as well as several moderators and mechanisms. I will now\nproceed to comment on each section of the paper.\nPoint 1. Abstract\nIt would be good to know the dataset and methods used (e.g., what does\n‘we calibrate a general model’ mean?). I would also be good to know how\nrelative deprivation and income rank are calculated as readers who are not\nexperts on these topics might not know what these two concepts are\nreflecting.\nWe agree that it is important to be clear about the\ndataset and methods, especially about how income rank\nand relative deprivation are calculated. However, we\nwere unable to do this fully within the constraints of\na 150-word abstract. We have, however, now specified in\nthe abstract that we use the Gallup dataset, made some\nother changes, and done our best to make sure that the\ncritical concepts are introduced clearly and as early\nas possible in the revised manuscript.\nBy “we calibrate a general model,” we mean that we\nestimate the parameters of a general model (Equation 3,\np. 5) using maximum likelihood estimation,\nwhich allows us to compare the effects of income rank\nand relative income deprivation on subjective well-\nbeing (SWB) within a single, unified framework.\nIncome Rank Calculation: This is defined in Equation 1\n(p. 3) and further detailed in Equation 2 and 3 (p. 5).\nRank is operationalized as the ordinal position of an\nindividual's income within their national distribution,\nscaled between 0 and 1.\nRelative Deprivation Calculation: We distinguish this\nconcept from rank-based models both theoretically and\nmathematically, especially in the section beginning on\np. 3 and visualised in Figure 1 (p. 4). We adopt and\nextend established formulations such as the Yitzhaki\nIndex, and estimate alternative models by adjusting\nweighting parameters (γ, δ) in Equation 3 to reflect\ndeprivation-based weighting (pp. 4–5).\nPoint 2. Introduction\nPoint 2.1. SWB stands for ‘Subjective wellbeing’ not self-reported\nwellbeing as stated in the first paragraph of the introduction.\nWe thank the reviewer for pointing this out and have\ncorrected it.\nPoint 2.2. The introduction is overall good with good detail about the\nstudies.\nWe thank the reviewer for the positive comments.\nPoint 3. Results\nPoint 3.1. Study 1 should include sadness as another indicator of negative\naffect.\nWe thank the reviewer for the suggestion to include\nsadness as an additional indicator of negative affect.\nWe have now incorporated sadness into the negative\naffect index in Study 1 and updated the manuscript\ntext, relevant analyses, and figure descriptions\naccordingly.\nThe results are shown in Table 2, and, consistent with\nother results find that only income rank (and not\nabsolute income) predicts negative affect.\nPoint 3.2. Study 1. This result should be explained further “Although some\npanels indicate a negative association between absolute income and\naffect levels, this association is small and non-significant in earlier survey\nwaves” why might this be the case?\nThe negative association is no longer present (in the\nupdated results using the latest-available survey).\nWhile the association appears in Round 12 (2017–2018)\nand a similar effect arises in Round 13 (2018–2019),\nas presented in the earlier version of our manuscript, it\nis not statistically significant in earlier or later\nrounds and so we do not consider the result robust. In\nline with the journal’s policy on null effects, and to\navoid overinterpreting unstable findings, we refrain\nfrom drawing conclusions based on this association.\nPoint 3.3. Study 1. It looks like the mediation results are based on adding\npositive and negative affect as additional covariates. Although this method\ncan tell whether a variable can be a potential mediator, this cannot be\ncalled mediation analyses and some test like Structural Equation\nModelling should be conducted.\nThank you for this helpful comment. We agree that the\nanalyses we included—adding affective and eudaimonic\nwell-being variables as covariates—do not constitute a\nformal mediation analysis. As noted, methods such as\nstructural equation modelling or causal mediation\napproaches would be required to make such claims.\nBecause mediation was not the focus of the study, and\nour intent was only to explore the relationships\nbetween variables descriptively, we have removed all\nreferences to mediation from the manuscript and\nsupplementary materials. This change helps maintain the\nclarity of our contribution, which is focused on the\nassociation between income, income rank, and SWB.\nPoint 3.4. Study 2. Was the test in Study 2 also run with pooling countries\nas in Study 1?\nThank you for the question. No, unlike Study 1, which\npools data across countries to estimate overall\neffects, Study 2 only estimated the generalised model\n(Equation 3) separately for each country. This was\nnecessary because the model includes country-specific\nincome distributions and parameters (e.g., δ, γ) that\ncapture potentially heterogeneous social comparison\nprocesses. Pooling would therefore obscure these\ndifferences. We have clarified this distinction more\nexplicitly in the manuscript to avoid confusion.\nPoint 3.5. Study 3 and 4 are good and clearly explained although same\nconcerns about waves still remain.\nWe thank the reviewer for the positive assessment of\nStudies 3 and 4. We understand and appreciate the\nconcern regarding our reliance on a single survey round\n(Round 13, 2017–2018, in the original manuscript) for\nthese analyses. This decision was driven by data\navailability: many of the moderators we examine—such as\neconomic competitiveness, openness indicators, social\ncapital measures, institutional trust, and economic\npreferences—are only available for a limited number of\nyears. These variables are not consistently collected\nacross all survey rounds, making it infeasible to\nconduct moderation analyses that span the full dataset.\nOur updated analysis (now focusing on Round 18, 2023–\n2024) includes both the SWB outcomes and access to the\nwidest and most recent set of relevant moderators.\nWe also wish to highlight the issue of temporal\nmismatch. Using earlier rounds of SWB data would\nrequire pairing them with moderator variables measured\nin later years—for example, explaining 2014 SWB using\n2017 competitiveness data. This would raise concerns\nabout reverse causality: earlier well-being levels\ncould plausibly influence later perceptions of\ninstitutional quality or economic conditions, rather\nthan the other way around. This would undermine the\ninterpretation of any moderation effect. By focusing\non Round 18, we ensure that all moderators are measured\nprior to the well-being outcomes, which reduces\nambiguity about the direction of influence.\nWe have clarified both the data availability\nconstraints and reverse causality rationale more\nexplicitly in the Methods section.\nPoint 4. Discussion\nPoint 4.1. Summary of results and limitations are well described. More on\npsychological mechanisms is needed.\nThank you for this suggestion. Although space\nlimitations prevent a lengthy treatment, we have added\n(in the Discussion section at the end of the\nmanuscript) some additional material about the relevant\nunderlying psychological mechanisms.\nPoint 5. Methods\nThe article uses data from the Gallup World Poll (GWP) and there are a few\npoints that need further clarification.\nPoint 5.1. It is not clear why the data used is 2009-2019 if the GWP goes\nfrom 2005 to 2024 already.\nWe have now updated the result throughout using the\nmost recent available survey years. This has the\nadvantage that a larger number of countries can be\nused.\nWe appreciate the reviewer’s question. In the original\nversion of the paper, we analysed data from six rounds\nof the GWP covering a decade from 2009 to 2019:\nspecifically, Rounds 4 (2009–2010), 5 (2010–2011), 8\n(2013), 9 (2014–2015), 12 (2017–2018), and 13 (2018–\n2019). These rounds were selected to create three\nevenly spaced pairs across the decade, allowing us to\nassess both short-term (consecutive rounds) and medium-\nterm (wider-interval) consistency of income rank\neffects on well-being.\nTo ensure cross-round comparability, we included only\ncountries with complete data across all six GWP rounds,\nthereby forming a balanced panel. This approach helps\nreduce the risk that findings are driven by changing\ncountry coverage or sample composition across years.\nIn the updated manuscript, we extended the analysis to\ncover the most recent decade available, now spanning\nfrom 2013 to 2024: Round 8 (2013), 9 (2014–2015), 12\n(2017–2018), 13 (2018–2019), 17 (2022–2023), and 18\n(2023–2024). As before, we restricted the sample to\ncountries with complete data across all included rounds\nto maintain longitudinal consistency. Our main results\nand conclusions remain both consistent and robust when\ntested against this updated dataset.\nPoint 5.2. A minor point is that the years might be better labelled as\n‘survey years’ instead of ‘waves’ because ‘waves’ is usually used for\nlongitudinal data and the GWP is cross-sectional.\nWe thank the reviewer for this helpful clarification.\nWe agree that “survey years” is a more accurate term\ngiven that the GWP consists of repeated cross-sectional\nsurveys rather than longitudinal panels. We have\nupdated the manuscript accordingly by replacing “waves”\nwith “survey years” throughout. When referring to\nGallup’s internal survey cycle numbering, we now use\nthe term “rounds” (e.g., “Round 13”), as a way to avoid\nthe potentially misleading term “waves,” which Gallup\nuses internally.\nPoint 5.3. The methods section says “Social Status, Relative Deprivation,\nand Social Capital: The Ubiquity, Purity and Robustness of Income Rank\nEffects on Well-being in 90 Nations.” It is not clear how the authors came\nto this information because the GWP contains data from 165 countries\nand territories and more than 2 million respondents now.\nThank you for your comment. We appreciate the\nopportunity to clarify this point.\nAs noted in our response to point 5.1, the original\nversion of the paper included data from 90 countries.\nThese were selected based on the availability of data\nacross six specific GWP survey rounds: rounds 4 (2009–\n2010), 5 (2010–2011), 8 (2013), 9 (2014–2015), 12\n(2017–2018), and 13 (2018–2019). We chose these rounds\nbecause they form three evenly spaced pairs over a ten-\nyear span, allowing us to examine the consistency of\nincome rank effects on well-being both in the short\nterm (between consecutive rounds) and in the medium\nterm (across wider intervals).\nTo ensure comparability over time, we restricted the\nsample to countries with complete data in all six\nrounds, creating a balanced panel. This approach helps\nrule out the possibility that our findings are driven\nby shifts in country coverage across survey years.\nIn the updated analysis, we extended the analysis to\ncover the most recent decade available, now spanning\nfrom 2013 to 2024, beginning from round 8 (2013) and\ncontinuing through rounds 9, 12, 13, 17 (2022–2023),\nand 18 (2023–2024). Using the same criteria, we\nincluded only countries with complete data across these\nsix rounds, resulting in a larger sample of 109\ncountries.\nWe hope this clarifies the rationale for the number of\ncountries included in the analysis and the logic behind\nthe panel construction.\nPoint 5.4. The methods section states “Each wave was analyzed\nindependently and compared with the immediately preceding wave for\nrobustness checks.” It is not clear why all survey years were not analysed\ntogether, and country and year fixed effects used in the regressions. If\nthere is a clear rationale behind the choice of this method, this should be\nclearly explained.\nWe have now clarified our methodology and the rationale\nunderlying it. Specifically, our aim is to exploit\noverall income differences between countries and hence\ndistinguish between absolute and relative income more\nclearly than previous (fixed effects, in the case of\nmulti-country) analyses have been able to. The use of\nfixed effects effectively re-introduces very high\ncollinearity between absolute and relative income\nmeasures, thereby limiting interpretability.\nTo mitigate potential confounding while preserving\nbetween-country variation, we have controlled for\ncountry-level differences far as possible.\nMore specifically: In Study 1, our primary goal is to\ndirectly compare the predictive power of absolute\nincome versus relative income rank for SWB. Including\ncountry fixed effects would, by construction, remove\nall between-country variation in income — effectively\ntransforming absolute income into a relative measure\n(i.e., income relative to others within the same\ncountry). In other words, absolute income would be\nturned into a within-country relative measure, much\nlike income rank, and the model would no longer capture\nthe independent effect of absolute income.\nBy analysing each survey round independently and\navoiding country fixed effects, we retain both within-\nand between-country variation in income. This enables a\nconceptually valid comparison between absolute and\nrelative income effects. In addition, comparing results\nacross survey cycles allows us to assess the robustness\nof findings over time, while maintaining a clear\ninterpretation of the income measures. We have revised\nthe text to explain this point more clearly.\nPoint 5.6. Why the moderation analyses were conducted only with 2018-\n2019 survey years should also be explained. Is it because the relevant\nvariables are not available the previous years? In the methods section it\nsays that this choice was made to ‘minimize the risk of potential reverse\ncausality’, why is this the case?\nThank you for raising this point. As we outline in our\nresponse to point 3.5, the moderation analyses in\nStudies 3 and 4 were conducted using the 2018–2019 GWP\nsurvey data (Round 13) for two main reasons: data\navailability and concerns about reverse causality. We\nelaborate on each of these below.\nData availability: Many of the moderators we examine—\nsuch as economic competitiveness, openness indicators,\nsocial capital measures, institutional trust, and\neconomic preferences—are only available for a limited\nnumber of years. These variables are not consistently\ncollected across all survey rounds, making it\ninfeasible to conduct moderation analyses that span the\nfull dataset. Our updated analysis (now focusing on\nRound 18, 2023–2024) includes both the SWB outcomes and\naccess to the widest and most recent set of relevant\nmoderators.\nReverse causality concern: Using earlier rounds of SWB\ndata would require pairing them with moderator\nvariables measured in later years—for example,\nexplaining 2014 SWB using 2017 competitiveness data.\nThis raises concerns about reverse causality: earlier\nwell-being levels could plausibly influence later\nperceptions of institutional quality or economic\nconditions, rather than the other way around. This\nwould undermine the interpretation of any moderation\neffect. By focusing on Round 18, we ensure that all\nmoderators are measured prior to the well-being\noutcomes, which reduces ambiguity about the direction\nof influence.\nPoint 5.7. Study 1 replicated prior work with an alternative method. The\nauthors challenged the method widely used in prior work by saying that\nthis method introduced multicollinearity when also including GDP per\ncapita as a control and country fixed effects. For example, the authors say\nthat “When studying multiple countries, previous research has typically\nemployed country fixed effects to control for country-level differences and\nexploit within-country variations. However, this method introduces\nmulticollinearity due to the high correlation among income-related\nvariables.” If the issue is the within country-variation, the analyses should\nbe conducted by each country not by each survey year or wave (I see an\nattempt of this in Study 2). This is because when conducting the analyses\nby survey year or wave, all the countries are still pooled together which is\nwhat the authors challenge in the method used in prior work. The findings\npresented in this paper should be replicated with this widely used method\nwith and without GDP as a control.\nWe thank the reviewer for this observation. We\nappreciate the opportunity to clarify our approach and\nthe focus of our critique regarding prior methods.\nOur concern is not with pooling cross-country data or\nwith analysing within-country variation per se, but\nspecifically with the use of country fixed effects or\nGDP per capita controls when the goal is to estimate\nthe effect of absolute income. These approaches, each\nin their own way, undermine the interpretability of\nabsolute income and compromise the comparison with\nrelative income.\nCountry fixed effects remove all between-country\nvariation in income. As a result, the absolute income\nvariable becomes centered within each country, meaning\nits coefficient reflects income relative to the\nnational average rather than true individual-level\nmaterial standards, as measured by income, across\ncountries. In this context, absolute income effectively\nbecomes a within-country relative measure, conceptually\nsimilar to income rank. This conflation makes it\nimpossible to meaningfully compare the independent\neffects of absolute and relative income, and introduces\nmulticollinearity due to the high correlation among\nincome-related variables.\nControlling for GDP per capita has a similar effect:\neven without fixed effects, it partials out cross-\ncountry income differences, reducing absolute income to\na residualised measure of relative standing across\ncountries. This again compromises the core distinction\nwe aim to test.\nConducting separate analyses for each country, as\nsuggested, would yield a similar challenge: absolute\nincome and income rank would remain correlated within-\ncountry, making it difficult to isolate their distinct\neffects. That approach is better suited for examining\npurely relative income effects, which is precisely what\nwe do in Study 2, where absolute income is no longer\npart of the analysis and the focus is on testing models\nof social comparison.\nIn Study 1, we instead analyse data separately by\nsurvey year, without country fixed effects, and include\na rich set of country-level covariates. This approach\nallows us to preserve between-country variation in\nabsolute income and within-country variation in income\nrank, enabling a conceptually valid and statistically\nidentifiable comparison between the two. We have\nrevised the text (see Methods) to clarify this\nrationale and distinguish it more explicitly from prior\napproaches.\nPoint 5.8. It would be good to know how the mediation analyses were\nconducted. This is important because the dependent variable and the\nmediators are all self-reported and reverse causality, multicollinearity, and\nendogeneity might be present.\nWe thank the reviewer for this comment. The mediation\nanalyses in the original manuscript were intended as a\nbrief, exploratory extension to examine whether\naffective well-being or eudaimonic purpose might\nstatistically account for some of the association\nbetween income rank and life evaluation. We did not\ninterpret these results as causal and avoided making\nany such claims in the manuscript.\nHowever, we agree that introducing mediation models —\neven when cautiously framed — may shift attention\ntoward questions of mechanism that go beyond the core\nfocus of the paper. Since the main goal of the\nmanuscript is to test and validate the robustness and\ngenerality of income rank effects across contexts, we\nhave decided to remove the mediation analyses in the\nrevised version. We appreciate the reviewer’s comment,\nwhich helped us refine the manuscript accordingly.\nPoint 5.9. In section 4.3 about Study 2, income rank and relative\ndeprivation need to be defined (e.g., what do these mean). We know how\nthese were calculated from the equation details, but we don’t know what\nthey mean in plain words.\nWe thank the reviewer for this observation. While the\nformal definitions of income rank and relative\ndeprivation are provided in the equations and technical\nexposition, we agree that a plain-language explanation\nwould help clarify the distinction for readers who are\nless familiar with these constructs.\nWe have now added brief, intuitive definitions in\nSection 4.3 of the revised manuscript. Specifically, we\nexplain that:\nIncome rank refers to a person’s position in the\nnational income distribution — that is, how high or low\ntheir income stands compared to others in their\ncountry, scaled between 0 (lowest) and 1 (highest).\nRelative deprivation reflects the extent to which a\nperson falls short of those who are richer than them.\nIt captures how much better off others are, on average,\nand reflects upward comparisons.\nRelative deprivation takes into account not just the\nnumber of higher earners, but the size of the income\ngaps.\nWe hope these additions improve the accessibility of\nthe paper for a broader readership.\nPoint 5.10. Study 3 is well explained but the use of only ‘wave 13’ should\nbe better justified and robustness checks with all the survey years\ntogether conducted.\nThank you for raising this point again. As noted\npreviously (Point 5.6), we focused on the 2018–2019\nsurvey year (Round 13) in Study 3 because, at the time\nof writing the original manuscript, it included the\nmost comprehensive set of moderator variables. Many key\nmoderators—such as social capital indicators, economic\ncompetitiveness and openness measures, and\ninstitutional trust—were only available in or around\n2017–2018. This lack of consistent coverage makes it\ninfeasible to replicate the moderation analyses across\nall survey years. Moreover, attempting to pair early\nsurvey years with moderator data from later periods\nwould introduce serious concerns about reverse\ncausality, compromising the interpretation of\nmoderation effects.\nTo provide an updated validation of our findings, we\nre-ran the analysis using Round 18 (2023–2024), the\nmost recent available data. The manuscript now focuses\non this round, which includes both SWB outcomes and the\nmost comprehensive and current set of relevant\nmoderators. Our main results and conclusions remain\nconsistent and robust when tested against this updated\ndataset.\nIn particular, the moderation analyses, at both the\nindividual and country levels, continue to show that\nsocial capital variables reduce the effect of income\nrank on well-being. We observe negative interaction\neffects with measures of social support, civic\nengagement (e.g., community involvement and willingness\nto volunteer or help others), and attitudes toward\nmigrants. These findings replicate well, confirming\nthat the moderation effects observed in Round 13 are\nnot idiosyncratic to a single year, but are also\nevident in the most recent available data.\nOne exception is the interaction with market openness,\nwhich was significant in the original analysis but is\nno longer significant in the updated results. While\nmarket openness was not a primary focus of our original\nanalysis, we report the updated result here for\ncompleteness. In the original manuscript, we used the\nLegatum Global Index of Economic Openness (2019). In\nthe revised analysis, we draw on the 2023 Legatum\nProsperity Index, which covers the same thematic\npillars of economic openness but reflects a revised\nmethodology. These changes, as well as broader shifts\nin economic conditions following the COVID-19 pandemic,\nlikely account for the difference in findings and limit\nthe comparability of openness indicators across survey\nyears.\nWe have clarified our approach in the Methods section.\nPoint 5.11. More detail is needed regarding each moderator used in Study\n3 and Study 4.\nWe thank the reviewer for this suggestion. In response,\nwe have expanded the Supplementary Material to provide\nmore detailed descriptions of all moderators used in\nStudies 3 and 4. For each moderator, we now include the\noriginal data source (e.g., Gallup World Poll, World\nValues Survey, Global Preferences Survey), the year of\ndata collection, the exact question wording or a\nconcise description of item content, and any\nstandardization or transformations applied during\nanalysis.\nPoint 5.12. Ubiquity, purity, and ecological fallacy should be defined in the\nfirst part of the methods section.\nWe thank the reviewer for this suggestion. In the\nrevised manuscript, we have added brief definitions:\nUbiquity refers to the consistency of an effect across\ndifferent countries and time points, in our case,\nwhether income rank predicts SWB universally.\nPurity refers to the unique contribution of income rank\nto well-being, after accounting for other income-\nrelated variables such as absolute income or relative\ndeprivation.\nEcological fallacy refers to the mistaken inference\nthat relationships observed at the group or country\nlevel necessarily hold at the individual level. We\nexplicitly avoid this fallacy by analysing country-\nlevel and individual-level moderators separately and\ninterpreting effects only within their appropriate\nlevel of analysis.\nThese definitions have been added near the beginning of\nthe Methods section, where we introduce our analytic\nstrategy.\nReviewer #1 (Remarks on code availability):\nI didn't revise the code because I had to request access to do so.\nThis has been fixed.\nReviewer #2 (Remarks to the Author):\nThe paper contributes to our understanding of the relationship between\nincome and wellbeing, which is an actively debated topic in wellbeing\nstudies. The authors first conclude that it is relative income within a given\ncountry, rather than the absolute level of income, that matters for\nwellbeing. They then explore various approaches to measuring relative\nincome, finding that a simple rank-based measure best fits the data.\nFinally, the authors demonstrate how the relationship between income\nrank and wellbeing varies across countries and among individuals with\ndifferent characteristics.\nIt is a important contribution that the simple measure of rank performs as\nwell, at least globally, as more complex measures accounting for\nasymmetries (Study 2). Your heterogeneity analyses (Studies 3 and 4) offer\nalmost encyclopaedic detail — I find the results on civic engagement\nparticularly interesting. However, I believe the main finding of the paper\ncomes from Study 1, where you conclude that relative income rank within\na country matters more for wellbeing than absolute income levels. I would\nwant to better understand how one should interpret this result in the\ncontext of your modelling approach.\nWe thank the reviewer for the positive comments and\ndetail below how we have addressed the points regarding\ninterpretation.\n1. The level-of-income vs. relative-income result is derived from a pooled\ncross-country regression without country fixed effects. The model controls\nfor household income and income rank at the same time. This model\nspecification is unusual. A standard cross-country wellbeing model would\ninclude household income and country fixed effects. The coefficient on\nhousehold income from a standard model is then interpreted as the\nrelations between wellbeing and higher or lower income in a given country,\nwhich is effectively relative income. The authors remove country fixed\neffect and include income both as a level (which is not significant) and as a\npercentile of income distribution (which is significant). The latter,\neffectively, captures similar variation to the coefficient on income from a\nmodel with county fixed effect. As a results, though the model formulation\nis unusual, I do not find the result surprising.\nThank you for your observation regarding our model\nspecification. We would like to clarify the rationale\nbehind our approach. While including country fixed\neffects is standard practice in cross-country well-\nbeing models to capture within-country variation, it\nwas essential to exclude them for our specific research\nquestion: distinguishing between the effects of\nabsolute income and relative income rank.\nBy excluding country fixed effects, we ensured that the\ncoefficient on household income reflects changes in\nabsolute income across the sample, while the\ncoefficient on income rank captures the effect of\nrelative status within each country. Including country\nfixed effects would shift the interpretation of\nhousehold income to reflect income relative to the\nnational average within each country, making it\nimpossible to interpret it as an absolute measure. This\nwould have limited our ability to differentiate between\nthe impacts of absolute income and relative social\nstatus.\nPrevious studies that include country fixed effects\ntypically find significant associations between income\nand well-being. However, these findings primarily\nreflect the effects of relative income within\ncountries. In that light, it is perhaps not surprising\nthat our models—without fixed effects—also show a\nstrong association between income rank and well-being.\nWhat our results clarify, however, is that while income\nrank remains a robust predictor, absolute income plays\na comparatively weaker role when both are modelled\nsimultaneously.\nTo address potential confounding from broader country-\nlevel factors that influence well-being, such as access\nto public services, social infrastructure, and overall\nquality of life, we include a set of macro-level\ncontrols. These include health expenditure per capita,\nthe Gini index, unemployment rate, and the percentage\nof the population living in rural areas. These\nvariables help account for structural differences\nacross countries that might otherwise obscure the\ndistinct contributions of income to SWB. Associated\nlimitations are acknowledged explicitly in the revised\nmanuscript.\n2. In Table 1, R-squared in Model 1 (with income in levels) is three times\nhigher than in Model 3 (with relative income), suggesting that a\ncombination of income level and wellbeing fits the data better than a\ncombination of income rank and wellbeing. It would be helpful to see a\nmodel that includes both income variables without additional controls. I\nwould expect the coefficient on log income in this model to be positive,\nbecause having higher income conditional on a given income rank implies\nliving in a wealthier country. If it is not, it would be helpful if you could\nprovide some intuition on why that is the case.\nWe thank the reviewer for this comment. In response, we\nhave added a new set of models in Table S9\n(Supplementary Material), where both log income and\nincome rank are included as predictors without any\nadditional controls (Column 3). We also present three\nadditional models that sequentially introduce\ncovariates, allowing readers to observe how the\ninclusion of individual- and country-level controls\naffects the relative contributions of each income\nmeasure. Column 4 adds individual-level controls,\nincluding age, gender, and a four-degree polynomial of\nage interacted with gender. Column 5 introduces\ncountry-level controls that have low correlation with\nlog GDP per capita. Column 6 adds country-level\ncontrols that are highly correlated with GDP per capita\n(correlation > 0.7).\nIn line with the reviewer’s intuition, the estimates in\nColumn 3 (no controls) show that absolute income has a\nstatistically significant effect, while income rank\ndoes not. However, it is important to note that in\nmodels without controls, the coefficient on absolute\nincome may partly reflect broader country-level\ndifferences that also influence SWB, such as access to\npublic services, social infrastructure, and overall\nquality of life. Without adjusting for these contextual\nfactors, the apparent predictive power of absolute\nincome may be inflated, as it captures not only\nindividual purchasing power but also national-level\nadvantages that co-vary with income.\nBy introducing country-level controls in Columns 5 and\n6, we aim to more precisely isolate the role of income\nitself, rather than broader country-level advantages\ncorrelated with it.\nWe have added a discussion of this analysis to the\nRobustness Analyses subsection of the Supplementary\nMaterial and incorporated a summary into the Results\nsection of Study 1. We believe this addition clarifies\nthe statistical relationship between absolute and\nrelative income across different model specifications.\n3. I assume that the coefficient on individual income in Model 5 in not\nsignificant, because the wellbeing benefits of leaving in a richer country\nare captured by controlling for health expenditures (correlation with GDP\nof 0.96) and absolute redistribution (correlation with GDP of 0.76). It is not\nclear to me how to select a set of country-level controls that allow\ncontrolling for heterogeneity without controlling away the effects of leaving\nin a wealthier country. It would be helpful to see the sensitivity of the\nresults in Column 5 of Table 1 to including subsets of controls, e.g.\nindividual level only, country-level with low correlation with GDP, etc.\nWe thank the reviewer for this comment. We would like\nto clarify that our individual-level absolute income\nvariable (log of PPP-adjusted household income) is\nintended to capture a person’s own material living\nstandard, not the broader effects of living in a\nwealthier country.\nIn response to the reviewer’s suggestion, we have added\nthe requested sensitivity analyses in Table S9 of the\nSupplementary Material. Specifically, we observe that\nwhen macro-level variables are excluded, the\ncoefficient on absolute income becomes statistically\nsignificant. However, this likely reflects the fact\nthat, in the absence of such controls, absolute income\npartially captures national-level differences in\ninfrastructure, services, and overall development—\nfactors that go beyond an individual’s purchasing\npower. In other words, without contextual controls, the\nabsolute income coefficient conflates individual\nmaterial resources with broader national living\nstandards, making its interpretation ambiguous.\nAs shown in Table S9, once country-level controls are\nincluded, income rank consistently emerges as a\nstronger predictor of SWB than absolute income. This\npattern supports our interpretation that relative\nsocial position plays a more prominent role in shaping\nwell-being, once basic needs and broader contextual\nconditions are accounted for.\nAlthough some of the country-level controls we include\n(e.g., health expenditure and redistribution measures)\nare correlated with GDP per capita, we retain them\nbecause they capture specific, policy-relevant\ndimensions of national context (such as public service\nprovision and social safety nets) that may\nindependently affect SWB. Using these targeted\nvariables, rather than GDP per capita, allows us to\nretain meaningful between-country variation in absolute\nincome without transforming it into a fully relative\nmeasure.\nImportantly, after adjusting for both income rank and\nthese macro-level contextual factors, absolute income\nno longer significantly predicts SWB. This highlights\nthe stronger and more consistent role of social\ncomparison once structural conditions are taken into\naccount.\nWe have clarified this point in the Robustness Analyses\nsubsection of the Supplementary Material and in the\nMethods section of Study 1 (Section 4.2), and we\nappreciate the reviewer’s suggestion, which prompted a\nvaluable extension of our analysis.\n4. The authors state that the magnitude of the coefficient on the income\nrank is high. One can interpret the magnitude of this coefficient as a\nwellbeing difference between the poorest and the richest household in a\ncountry. Then, the coefficient of 1.5 on a 0 to 10 scale in Column 5 of\nTable 1, though still large, does not look enormous. It would be helpful to\nsee some intuition on how to compare relative magnitudes of the\ncoefficients on income level and income rank, e.g. the expected effect of\nmoving from the 25th to the 75th percentile according to the estimates\nfrom Columns 1 and 3.\nWe appreciate this helpful suggestion. To improve the\ninterpretability of the coefficients in Table 1, we\nhave added contextual comparisons for both absolute\nincome (Column 1) and income rank (Column 3). As now\ndescribed in the paper, absolute income is modeled as\nlog(income/1000). In Column 1, the coefficient of 0.66\nimplies that moving from the 25th to the 75th\npercentile of income (approximately $4,413 to $26,188)\nis associated with a 1.18-point increase in life\nevaluation on a 1–10 scale.\nIn contrast, Column 3 shows a coefficient of 1.39 for\nincome rank (ranging from 0 to 1), representing the\nestimated difference in well-being between individuals\nat individuals\nat\nat the very bottom and top of the national income\ndistribution. This rank-based effect is larger than the\neffect of a substantial change in absolute income.\nWhile the magnitude may not appear extreme on its own,\nwe note that it exceeds the effect sizes of several\nwell-established socioeconomic variables in Column 4.\nFor example, it is more than twice the size of the\ndifference between having a college degree versus\nelementary education, and more than four times the\ndifference between being single and separated. It is\nalso roughly equivalent to twice the difference in life\nevaluation between being unemployed and being employed\nfull-time. These comparisons help to place the rank\ncoefficient in context and show that its effect, while\nnot extreme in absolute terms, is substantively large\nrelative to other common predictors of SWB. We have\nadded these comparisons to the main Results section of\nStudy 1 to improve interpretability for readers.\n5. I don’t believe that your findings corroborate the following statement in\nthe conclusion: “The zero-sum property implies that, to the extent that a\ngiven domain of SWB is affected by income rank rather than absolute\nincome, neither general increases in societal income not redistributive\npolicies will, in themselves, increase SWB.” Firstly, given than your model\neffectively controls for GDP per capita, which can be interpreted as the\nlevel of societal income, I believe that non-significant coefficient on\nindividual income does not prove that that general increases in societal\nincome will not increase wellbeing. On redistributive policies, given that\nyour analysis is cross-sectional, it is not informative about (potentially\nasymmetric) wellbeing benefits and loses from gaining or loosing rank for\npeople in different parts of the distribution. Since your research does not\nspeak to that, I don’t this that it can be used to support a statement about\npotential (non-)effectiveness of redistributive policies.\nWe thank the reviewer for this well-justified critique.\nWe agree that the original statement in the conclusion\nwas too strong, given the limitations of our data and\ndesign.\nSpecifically, because our models include controls for\ncountry-level variables that partially capture\ndifferences in infrastructure, services, and overall\ndevelopment, our findings do not directly speak to the\nwell-being effects of increases in societal income.\nSimilarly, our cross-sectional design does not allow us\nto assess the potential asymmetric or dynamic effects\nof redistribution, such as gains or losses in rank\nacross different parts of the distribution.\nIn light of this, we have revised the conclusion to\nmore cautiously state that general income growth may\nhave limited well-being effects when income rank is the\ndominant predictor, and that redistribution may still\naffect well-being through mechanisms not tested here\n(e.g., social cohesion, fairness, inequality\nperceptions).\nWe thank the reviewer for prompting us to refine the\nscope and interpretation of our findings.\nMinor comments:\n1. Wellbeing literature that uses comparator group typically defines it as a\ngroup with similar characteristics (e.g. age, gender, education, region).\nCould you please discuss why you believe national comparison to be the\nbest choice in your case. Especially given that using subgroups of the\npopulation might also help you to alleviate concerns of within-country\nmulticollinearity between income level and income rank.\nWe thank the reviewer for this comment. We agree that\nsome studies of social comparison and SWB focus on\nreference groups defined by demographic or contextual\nsimilarity, such as age, gender, education, or local\nregion.\nIn our study, we define income rank at the national\nlevel for both practical and conceptual reasons. First,\nthe GWP is designed to be nationally representative,\nbut not necessarily representative within finer-grained\nsubgroups. Constructing income rank within small\nsubpopulations, such as by region, age, or education,\nwould introduce considerable statistical noise and\narbitrariness in group definitions. Rankings within\nsmall groups are less stable and potentially less\nmeaningful, particularly in cross-national data where\nsubgroup structures vary substantially. By contrast,\nnational-level income rank offers a consistent and\ncomparable reference frame across countries and years.\nSecond, while individuals may indeed make local\ncomparisons in everyday life, national-level reference\npoints are often more prominent in public discourse.\nNational benchmarks, such as poverty lines, income\ndeciles, and inequality indicators, are widely used in\nmedia, politics, and policy debates. These framings\nhelp make national income rank a psychologically and\nsocially salient comparator, even if it is not the only\none people use.\nWe also note that our modeling approach in Study 2\naddresses this concern more directly. There, we\nimplement flexible models of social comparison that\nallow the weight of others’ incomes to vary based on\ntheir distance from an individual’s own income. These\nmodels help capture whether individuals are more\nsensitive to close versus distant incomes, or whether\nthey respond differently to those who earn more versus\nless. This framework can accommodate both national and\nmore local comparison processes, and provides a\ncomplementary test of the underlying mechanisms.\nWe have added a short discussion of this rationale to\nthe revised Methods section (Section 4.2) to clarify\nour choice of national income rank in light of our data\nstructure and research design.\n2. I am concerned about negative coefficient on the level of income in\nmodels for positive affects will all controls, both in the main result (Table 2)\nand in wave 12 (Table S5). Could you please provide some intuition on\nwhy this might be the case?\nWe thank the reviewer for highlighting this unexpected\nresult. As noted, in fully specified models, which\nincludes covariates at both the individual and national\nlevels, the coefficient on log income becomes negative\nfor positive affect in Round 12 (2017–2018) and a\nsimilar effect arises in Round 13 (2018–2019), as\npresented in the earlier version of our manuscript.\nThis pattern is consistent across these two rounds but\nis not observed in earlier survey years or in more\nrecent rounds (e.g., Rounds 17 and 18), as shown in the\nupdated version of the manuscript. In line with the\njournal’s policy on null effects, and to avoid\noverinterpreting unstable findings, we refrain from\ndrawing conclusions based on this association.\n3. It would be helpful to see if your main results in Table 1 hold for the\nequivalence-scale adjusted household income, since it is more accurately\nrepresents the level of income individual enjoys.\nWe agree that equivalence-scale adjusted income\nprovides a more precise measure of the economic\nresources available to individuals within households.\nIn response to this comment, we conducted a robustness\ncheck using a per capita income variable provided by\nGallup. This measure is calculated by dividing total\nannual household income by the number of household\nmembers.\nIn Table S9, we report our main sensitivity analysis\nusing the standard household income variable (PPP-\nadjusted, but not adjusted for household size). In\nTable S10, we replicate the same model specifications\nusing per capita household income in place of the raw\nhousehold income.\nWhen we constructed income rank based on equivalised\nincome, we found that this adjusted rank no longer\nsignificantly predicted SWB. This contrast suggests\nthat individuals derive their sense of social status\nfrom their rank in the distribution of unadjusted\nincome. That is, based on how their raw income compares\nto others rather than from a rank that reflects\nhousehold-adjusted purchasing power.\nWhile equivalized income more accurately captures\nmaterial well-being, social status is shaped by\nperceived relative standing, which appears to be\nanchored in unadjusted income.\nThis likely reflects the fact that raw income is a more\nvisible and culturally salient metric: it is how income\nis typically discussed, compared, and institutionally\nencoded (through tax brackets, salary negotiations, job\ntitles, and public discourse). In contrast, equivalized\nincome requires knowledge of household composition,\nwhich is neither intuitive nor socially salient.\nThese findings support the view that the effects of\nincome rank on SWB are driven by social comparison and\nstatus-based mechanisms, rather than material\nconditions per se. Unadjusted income rank appears to\nbetter capture how individuals perceive their place in\nthe social hierarchy, and it is this perception, rather\nthan objective economic capacity, that predicts SWB. We\nhave added a discussion of this robustness check and\nits implications to the Robustness Analyses subsection\nof the Supplementary Material.\nThank you for your work.\nReviewer #2 (Remarks on code availability):\nI could not access the codes because the files linked above,\nhttps://osf.io/bj9cz/?view only=8e0e7ff929e34cdab499695ef5843796,\nrequire submitting a request for permission to gain access.\nThis is now fixed.\nReviewer #3 (Remarks to the Author):\nComments on Social Status, Relative Deprivation, and Social Capital: The\nUbiquity, Purity and Robustness of Income Rank Effects on Well-being in\nThis paper studies the relationships between individuals’ absolute income\nand relative income and their subjective wellbeing. We know that\nwellbeing increases with income (and that the effect is in part causal) but it\nis on ongoing topic of research as to how much is due to absolute vs\nrelative effects. The absolute component is the degree to which an\nindividual has more resources at their disposal. The relative component\nreflects the fact that a higher income might imply a greater position within\na social hierarchy. If relative effects are dominant, this has some troubling\nimplications for society, as it implies that economic growth (rising absolute\nincomes everywhere) may be limited in their ability to produce greater\nwellbeing.\nThe research here is an extremely comprehensive empirical exercise\nlooking at the associations between income and income position (and a\ncouple of related/derived measures) with wellbeing across 90 countries.\nThe main innovation comes from the breadth of data used, and the\nrecognition that country fixed-effects (normally a positive feature of these\nmodels) is actually a liability within this context. The reason is that an\nindividuals’ income and their income rank (which in turn capture the\nabsolute and relative components) are very highly correlated (the authors\nquote a correlation coefficient of 0.98). This means that it is immensely\ndifficult to disentangle the effects of each, as there aren’t many examples\nin the data where an individual’s absolute income is much higher or lower\nthat what would be implied by their rank.\nThe pooling of many country-level datasets offers a partial solution as\nranks get defined within countries while income gets a singular definition\nacross countries. So by comparing people with $X in poor countries and in\nrich countries (where the absolute value is constant, but the relative\nposition changes) the authors can resolve this problem. The note that this\npooling introduces a lot of heterogeneity, which they resolve with an\nextensive set of controls. The authors find considerable support for\nincome ranks as a dominant driver of wellbeing, and distinguish this from a\nrelated concept of relative deprivation. They also find that income ranks\nare less important in countries with high levels of civic engagement, and\nuncover suggestive evidence that countries with more neoliberal politics\nmight place more emphasis on relative effects.\nThis is a very strong paper. A high level of technical and conceptual\nexpertise is shown throughout. The writing is careful, clear, and well-\nstructured. I believe the paper is suitable for Nature Communications but\nhave made some comments and suggestions below. All should be taken\nseriously, but I don’t feel any are particularly critical.\nWe thank the reviewer for their positive evaluation and\ntheir helpful comments. We address the comments below.\n• The analysis hinges very strongly on how well the country-level controls\nremove heterogeneity that could be correlated with income and may\npotentially explain wellbeing. While we can’t tell for sure whether or not the\ncontrols are sufficient, if the results are heavily dependent upon a\nparticular control, this may imply that that underlying factor isn’t properly\nadjusted for. The authors could examine this further. It is a bit difficult to\nsee what is happening as they just report “individual controls – yes/no”\n(and the same for countries). And sometimes the results bounce around\nand sometimes they don’t.\nWe agree that it is important to assess how robust our\nfindings are to different sets of covariates,\nparticularly at the country level. In response to this\ncomment, which was also raised by Reviewer 2, we have\nexpanded our analysis to include a stepwise\nspecification approach. We now present models that\nsequentially add controls: starting with individual-\nlevel covariates (e.g., age, gender, education, marital\nstatus), followed by blocks of country-level controls\nthat have low correlation with GDP per capita, and\nfinally adding those with high correlation (e.g.,\nabsolute redistribution, urbanization levels).\nThis breakdown is now presented in a revised\nsupplementary table (Table S9), which shows how the\ncoefficients on income level and income rank change as\nadditional controls are introduced.\nUnlike the original version of the table, where control\nblocks were grouped using “yes/no” labels, the revised\ntable displays the individual coefficients for each\ncontrol variable. The only exception is the set of age-\nrelated controls: models in Columns 4 to 6 include a\nfour-degree polynomial of age interacted with gender.\nDue to the large number of resulting terms, we opted\nnot to report these coefficients in the table itself,\nbut we clearly note their inclusion in both the Methods\nsection and the table note.\nAs shown in Table S9, once country-level controls are\nincluded, income rank consistently emerges as a\nstronger predictor of SWB than absolute income. This\npattern supports our interpretation that relative\nsocial position plays a more prominent role in shaping\nwell-being, once basic needs and broader contextual\nconditions are accounted for.\nWe believe this revised table provides a clearer and\nmore detailed view of how specific controls influence\nthe relationship between income and well-being, and we\nthank the reviewer for this helpful suggestion.\n• The authors don’t use the concepts of exogeneity/endogeneity at all in\nthe paper. I appreciate that this is a general interest journal, and the\nreadership may not be versed in econometric terminology, but these ideas\nare too useful to just gloss over. Since the pdf is 75 pages long it would be\neasy to briefly explain these concepts and make it clear to the reader that\nthe causal claims are fairly muted.\nWe thank the reviewer for this important point. While\nwe aimed to maintain accessibility for a general\naudience, we fully agree that concepts such as\nexogeneity and endogeneity are critical for framing the\nlimits of causal interpretation in observational data.\nIn response, we have now added a short section in the\nDiscussion where we explicitly discuss the potential\nfor endogeneity in our models, including issues such as\nomitted variable bias, measurement error, and reverse\ncausality. We clarify that, while our models include\nextensive individual- and country-level controls,\nthey do not identify causal effects in the strict\neconometric sense.\nTo address this, we now describe our results as\nreflecting associations rather than effects unless\notherwise specified, and we explicitly state that\ncausal claims should be interpreted with caution. We\nalso provide a brief explanation of endogeneity and\nexogeneity for readers who may be unfamiliar with these\nterms, and note that future work using panel or\nexperimental designs would be needed to establish\ncausal pathways.\nThese revisions appear in the updated Discussion.\n• Following on from the above point, the authors should probably further\nmute their causal claims and tone down the causal language.\nThank you for the comment. We have revised the\nmanuscript to mute the causal claims and tone down the\ncausal language throughout the text.\n• I have my doubts about using the indicator approach used to handle\nmissing data. This is a simple method where a missing value for a\ncovariate is imputed (usually with the mean, but it doesn’t matter), and an\nadditional dummy variable is included in the regression to indicate that a\nvalue was imputed. This is only done for the country-level data, but it can\nbe a source of bias in non-experimental studies (e.g., Groenwold et al.,\n2012). Does this improve statistical power? Maybe, because you get more\ndata, but maybe not, because you have to estimate more covariates. Why\nnot use a standard multiple imputation technique? Or just report results for\nthe data you have?\nWe appreciate the reviewer’s concern regarding our use\nof the missing-indicator method to handle missing\nobservations in country-level covariates. For\ncovariates with missing values, we set the missing\nentries to zero and included a corresponding dummy\nvariable indicating that the value was originally\nmissing. This approach allowed us to retain countries\nwith incomplete data while accounting for the presence\nof missingness in the model. We adopted this strategy\nto preserve comparability across models and avoid\nfurther reducing the number of countries, which is\nalready limited in some parts of the analysis. However,\nwe fully acknowledge that this method has limitations\nand may introduce bias if the data are not missing at\nrandom.\nTo address this concern directly, we now report results\nfrom a complete-case analysis, restricting the sample\nto countries with no missing values in country-level\ncovariates (as well as no missing data at the\nindividual level). These results are presented in\nSupplementary Table S8, which replicates our main\nanalysis from Table 1 (Study 1) using the reduced\ndataset.\nThe results are highly consistent with those in the\nmain specification. The effect of income rank remains\nstatistically significant and is slightly larger in\nmagnitude, and the relative magnitudes of other key\npredictors remain stable. We now reference this\nrobustness check in both the Methods section and\nResults to provide transparency and reassurance\nregarding our approach.\n• Out of interest, what happens to the estimates in Table 1 when the fixed-\neffects are included?\nWhile we believe that including country fixed effects\ncomplicates the interpretation of the absolute income\ncoefficient, since all between-country variation is\nabsorbed and the effect is estimated solely from\nwithin-country differences, we agree that presenting\nthese models is informative for comparison purposes.\nIn Supplementary Table S7, we therefore now replicate\nthe main results from Table 1 (Study 1) with country\nfixed effects included. As expected, all country-level\ncovariates are omitted due to multicollinearity. The\ncoefficient on income rank remains consistent and\nstatistically significant in the fully specified model\n(last column). However, the coefficient for absolute\nincome becomes negative and non-significant. This\nlikely reflects the fact that, in the presence of fixed\neffects, absolute income is effectively centered within\ncountries. As a result, its coefficient no longer\ncaptures variation in individual-level absolute income\nacross countries, but rather income differences\nrelative to each country's mean. This redefinition\nmakes it conceptually similar to income rank and\nintroduces substantial multicollinearity between the\ntwo variables (r ≈ 0.97), which inflates standard\nerrors and limits interpretability.\nWe reference these results in the revised manuscript\nand Supplementary Material and thank the reviewer for\nprompting this analysis.\n• It is tempting to think that relative effects will become larger in richer\ncountries, which the authors demonstrate. But do absolute effects\ndominate in poorer countries? Surely a starving child cares much more\nabout food than they do about social status. This would be simple to carry\nout and could be reported in an appendix. If true, it would also serve as a\nrough\nintuition check for the rest of the paper.\nThank you for this suggestion. We have re-estimated the\nmodels from Table 1, splitting the sample at the median\nGDP per capita to distinguish between poorer and richer\ncountries. The results are reported in Table S11 in the\nSupplementary Material. Columns 1–3 present results for\ncountries below the median GDP per capita, while\nColumns 4–6 present results for those above the median.\nAs anticipated, the effect of absolute income is larger\nin the poorer-country subsample, where income is more\ndirectly linked to quality of life through its role in\nmeeting basic needs. In contrast, the effect of income\nrank is stronger in the richer-country subsample, where\nincome tends to affect well-being more through\ndiscretionary spending and relative social standing.\nNotably, however, the income rank variable remains\nstatistically significant in the full model (including\ncontrols for demographics and country characteristics)\nin both groups. In contrast, the absolute income\neffect, although larger in magnitude for poorer\ncountries, becomes statistically non-significant once\ncontrols are included. This finding supports your\nintuition that income rank may play a relatively\ngreater role in shaping life evaluation in wealthier\ncontexts.\n• Some of the tables are too big and contain too much information to be\neasily interpretable.\nWe thank the reviewer for this helpful comment. We\nacknowledge that some tables, particularly those in\nStudy 1, are extensive and may be dense to read. This\nreflects our decision to display the full set of\ncontrol variables, rather than summarising them with\nbinary indicators (e.g., “YES/NO”), in order to enhance\ntransparency. We appreciate the reviewer’s earlier\nsuggestion on this point and have aimed to strike a\nbalance between clarity and completeness—especially in\nour stepwise robustness checks, where understanding the\nrole of specific covariates is important.\nTo improve interpretability in Studies 3 and 4, we have\nenhanced the visual presentation of results by\npresenting marginal effects using plots that now also\ninclude comparisons with reference categories (i.e.,\nthe first variable within each group of covariates),\nmaking within-category comparisons easier. The\ncorresponding regression tables are included in the\nSupplementary Material. We now clarify in the\nmanuscript that the figures are directly linked to\nthese tables, which should help guide readers while\nstill providing full access to the underlying\nestimates.\nWe hope this approach offers a clear and accessible\npresentation of the findings while maintaining\ntransparency in model specification.\n• The fact that income ranks seem less important in countries with more\nsocial capital, and for individuals with more social capital, seems like a\nmeaningful and under-discussed result. It is easy to see how the concept\nof endogeneity could be used to explain the latter (e.g., people with\npositive social characteristics may be autonomously happy and insensitive\nto income in general) but not the former.\nWe thank the reviewer for this observation. We agree\nthat the finding, that the association between income\nrank and SWB is weaker in contexts with higher social\ncapital, is both theoretically meaningful and deserves\ngreater emphasis. In the revised manuscript, we expand\nthe Discussion to highlight this point more clearly.\nWe now distinguish between individual- and country-\nlevel moderation effects. At the individual level, it\nis possible that people with higher trust or stronger\nsocial connections are less sensitive to income\ncomparisons because of stable personality traits or\nemotional dispositions that also enhance SWB. This\nraises the possibility of endogeneity, in which the\nobserved moderation effect may reflect underlying\ndifferences in individual temperament rather than the\nprotective role of social capital per se.\nHowever, the country-level moderation is less\nvulnerable to this concern. National levels of social\ncapital, such as trust in others, civic engagement, and\ncommunity support, reflect broader structural and\ncultural conditions that are unlikely to be influenced\nby individual SWB. This asymmetry strengthens the\ninterpretation that higher social capital at the\nnational level may buffer the psychological effects of\nincome inequality. We now emphasize this point in the\nrevised Discussion and we believe it highlights an\nimportant avenue for future work on the interaction\nbetween social context and the consequences of relative\nincome.\n• The authors worry a little that their self-reported income data may not be\nreliable. If miserable people over-report their income (which seems more\nlikely than the reverse) then this would attenuate the results. So the real\ncorrelations might be stronger than the ones reported here.\nWe thank the reviewer for this observation. We agree\nthat self-reported income may be affected by\nmeasurement error, which could attenuate associations\nwith well-being, particularly for absolute income.\nWe now acknowledge this as a limitation in the\nDiscussion section. While our findings consistently\nshow that income rank is a stronger predictor of well-\nbeing, we recognise that differential sensitivity to\nmeasurement error may partially contribute to this\npattern. At the same time, we note that a central aim\nof the paper is to test a theoretical claim about the\npsychological relevance of relative standing. This\ntheoretical contribution remains valid, even if the\nmagnitude of the absolute income effect may be\nunderestimated due to measurement error.\n• The depressing conclusion that income rank is zero sum could be\nexpanded upon. The stakes of this research are quite high, and a better\ndiscussion of the unpleasant result and its social implications would help.\nWe have revised the conclusion to more cautiously state\nthat general income growth may have limited well-being\neffects when income rank is the dominant predictor,\nand that redistribution may still affect well-being\nthrough mechanisms not tested here (e.g., social cohesion,\nfairness, inequality perceptions).\nReviewer #3 (Remarks on code availability):\nI had a quick look at the code. It looks very professional, but I didn't\nscrutinize it for errors. Since the data is public it should be straightforward\nto reproduce all results.\nReviewer #4 (Remarks to the Author):\nThis is a very ambitious paper. I think the paper's most significant and\ninteresting contribution is to tease apart the differing effects of income\nrank and relative deprivation--showing that income rank is by far a\nstronger measure (Study 2). The exploration of heterogenous effects of\nincome rank in Studies 3 and 4 is interesting as well and complements\nStudy 2 nicely.\nMy largest concern is with study 1, which examine absolute vs relative\nincome. The authors note the problem that \"Within single countries,\nincomes and relative income ranks are highly correlated, making it difficult\nto separate their effects statistically.\" (p. 6). It seems the problem is\n\"solved\" by pooling countries, so effectively our variation is by comparing\ntwo people with the same income, but one in a poor country where they\nhave a higher rank) to someone in a rich country (where they have a lower\nrank).\nBut, the authors explain in the methods that \"We measure income as\nannual household income in international dollars, calculated by the GWP\nusing World Bank’s PPP estimates, represented in 2011 US dollars.\" So,\nin my example above, two people with the same income (as measured in\ninternational dollars) have the same purchasing power, regardless of which\ncountry they are in. Thus, it's little surprise that they their income rank\nwould have more influence than absolute income.\nWe thank the reviewer for raising this important\nconceptual point. While it is true that PPP-adjusted\nincome equalises average purchasing power across\ncountries, this adjustment does not eliminate\nmeaningful variation in individual-level income, either\nwithin or across countries. PPP allows for cross-\ncountry comparisons in what a given income can buy, for\nexample, comparing the purchasing power of a dollar in\nKenya to that of a dollar in Canada, but it does not\ncompress national income distributions or remove\nbetween-country differences in individual income\nlevels.\nIn our dataset, PPP-adjusted income remains highly\nvariable. The comparatively weaker effect of absolute\nincome in our models is therefore not an artefact of\nPPP adjustment, but rather reflects the stronger\nexplanatory power of relative standing (income rank)\nwhen both variables are included in the same\nspecification. Additionally, the inclusion of country-\nlevel controls helps isolate the individual-level\neffect of income from broader contextual factors, such\nas infrastructure, public services, and inequality,\nthat might otherwise inflate the apparent role of\nabsolute income.\nWe appreciate the opportunity to further clarify the\ndistinction between PPP-adjusted income and the social\nstatus associated with income. While PPP ensures\ncomparable purchasing power (meaning what a given\nincome can buy), it does not account for the relative\neconomic rank or the social and psychological\nimplications tied to it. Two individuals with the same\nPPP-adjusted income may occupy very different positions\nin their national income distributions. For example,\none individual with such income might hold a high\nrelative rank and greater social standing in a lower-\nincome country, whereas another might have a modest\nrank and lower perceived wealth in a higher-income\ncountry.\nConversely, individuals at the same income rank in\ndifferent countries may have very different absolute\nincomes, reflecting variation in the underlying income\ndistributions across countries. This underscores that\nincome rank and PPP-adjusted income capture\nconceptually and empirically distinct aspects of\neconomic experience.\nAlso related to Study, 1, in the conclusion of the paper, the authors finish\nby stating that \"...The zero-sum property implies that, to the extent that a\ngiven domain of SWB is affected by income rank rather than absolute\nincome, neither general increases in societal income not redistributive\npolicies will, in themselves, increase SWB.\" (p. 19). But, note that the\nmodels from Study 1 have included measures such as log health\nexpenditures per capita (which has a large positive effect on SWB, Table\n1), and which has a .96 correlation with log GPD (Table S1). If a general\nincrease in societal income is part of an increase in GDP or health\nexpenditures (or whatever else these proxy for), then this likely does\nincrease SWB.\nWe thank the reviewer for this comment, which was also\nraised by Reviewer 2. We agree that the original\nstatement in the conclusion was too strong, given the\nlimitations of our data and study design.\nSpecifically, because our models include controls for\ncountry-level variables that partially capture\ndifferences in infrastructure, services, and overall\ndevelopment, our findings do not directly speak to the\nwell-being effects of increases in societal income.\nSimilarly, our cross-sectional design does not allow us\nto assess the potential asymmetric or dynamic effects\nof redistribution, such as gains or losses in rank\nacross different parts of the distribution.\nIn light of this, we have revised the conclusion to\nmore cautiously state that general income growth may\nhave limited well-being effects when income rank is the\ndominant predictor, and that redistribution may still\naffect well-being through mechanisms not tested here\n(e.g., social cohesion, fairness, inequality\nperceptions).\nWe appreciate the reviewer’s comment, which prompted us\nto refine the scope and interpretation of our findings\nin the revised manuscript.\n# Other (more minor) comments:\n* Social capital is in the title, but it plays a very minor role in the results\n(alongside with other potential mediators), and has no part in the\nbackground. There is no review of the extensive literature on how social\ncapital is measured and how it may affect SWB. I think these are still fine\nanalyses to have, but “social capital” certainly shouldn't be part of the title,\nas it’s not what the paper is about.\nWe acknowledge this point. However we also note that\nReviewer 3 highlighted this aspect as a meaningful and\nunder-discussed result:: “The fact that income ranks\nseem less important in countries with more social\ncapital, and for individuals with more social capital,\nseems like a meaningful and under-discussed result.”\nWe have therefore added in a small amount of additional\ndiscussion of the social capital results (see response\nto Reviewer 3) and have opted to retain the current\ntitle, which we believe reflects one of the key\ncontributions of the paper.\n* In study 1, the authors write: \"These results show that (a) the rank of a\nperson’s income is a better predictor...\" As far as prediction accuracy, the\nR-squared of both rank and absolute income are similar (and both are\nlow). The authors mean something like a larger effect (although the units\nare different...)--regardless of how they want to say it, more care is needed\nhere.\nWe thank the reviewer for pointing out this important\ndistinction. Our original phrasing was imprecise and\nmay have suggested that income rank explains\nsignificantly more variance in well-being outcomes,\nwhich is not supported by the R² values. What we\nintended to convey is that income rank shows a larger\nand more consistent association with well-being, and\nthat when income rank and absolute income are both\nincluded as predictors, only income rank significantly\npredicts the outcomes.\nTo avoid confusion, we have revised the relevant\nsentence in Study 1 (p.10).\nWe believe the revised wording more accurately reflects\nthe results and avoids overstatement.\n* I like the moderation analyses in Studies 3 and 4, but the figures fall a bit\nshort in conveying the key information. The evidence of an effect is the\nsize of the gap between the two predicted marginal effects for each\nvariable. This is hard to eyeball. I wonder if the authors could find a way to\ninclude this information as well?\nThank you for this suggestion. The revised plots for\nStudies 3 and 4 now display the moderation effects for\ndifferent groups of indicators—such as demographics,\nconfidence in institutions, and social preferences—on\nthe left side. The right panel shows the differences in\neffects relative to the reference category (the first\ncategory within each group). We believe this revised\npresentation facilitates clearer comparison of marginal\neffects within groups.\n* In the supplement, the authors write \"In our study design, we do not\nconduct a power analysis to determine our sample size, as we are not\npowering for null hypothesis significance testing.\" But really, as soon as\nyou are reporting statistical significance tests, that's exactly what you are\ndoing. The statistical significance test is based on the logic of null\nhypothesis\ntesting. I don't necessarily feel that power analyses are needed, but this\nstatement should be omitted at the very least.\nWe agree. We have removed this statement.\nReviewer #4 (Remarks on code availability):\nI looked at some of the code (including the README), but the data are not\navailable, so I did not run anything.\nAn updated version of the code incorporating the new\nrobustness tests is now available."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors provided comprehensive responses to the comments. The only issue is that I couldn't still review the code. As\nfar as I could see the file included in the OSF link contains a list of R scripts used but not the actual scripts. Actual scripts\nshould be readily accessible in the OSF project.\n(Remarks on code availability)\nThe file included in the OSF link contains a list of R scripts used but not the actual scripts. Actual scripts should be readily\naccessible in the OSF project.", "reviewer_2": "Dear Authors,\nThank you for addressing my comments from the previous round and for the additional analyses. I am satisfied with your\nresponses, and I have no further comments at this stage.\nCongratulations on your work.\n(Remarks on code availability)", "reviewer_3": "This is a solid revision. I have no further comments.\n(Remarks on code availability)"}, "author_response": ""}]

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