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Climbing up the Social Ladders: Identity, Relative Income, and Subjective Well-being

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Abstract

This study incorporates the concept of identity to explain the relationship between relative income and subjective well-being. Based upon the theoretical framework of an identity model developed by Akerlof and Kranton Q J Econ 115:715–753, (2000), the empirical findings of this study suggest that an increase in relative income leads to a gain in positional identity and therefore raises the level of subjective well-being. This approach extends our understanding about the connection between positional concern and subjective well-being and explains the importance of social comparison in shaping individuals’ preferences and tastes. People are concerned about their relative income as it represents a positional identity of social status, dependent upon ideals and norms derived from the interactions between an individual and others within a society.

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Notes

  1. The ordered probit model is usually can be described as \( y^{*} = \beta^{'} x + \varepsilon \), where \( y^{*} \)is an unobservable latent variable. What can be observed is:

    $$ \begin{array}{*{20}c} {y = 0\;if\;y^{*} \le 0,} \hfill \\ {y = 1\;if\;0 < y^{*} \le \mu_{1} ,} \hfill \\ {y = 2\;if\;\mu_{1} < y^{*} \le \mu_{2} ,} \hfill \\ {y = 3\;if\;\mu_{2} < y^{*} \le \mu_{3} ,} \hfill \\ \cdot \hfill \\ \cdot \hfill \\ {y = J\;if\;\mu_{J - 1} \le y^{*} ,} \hfill \\ \end{array} $$

    where \( \mu \)’s are unknown parameters to be estimated with \( \beta \). In an ordered probit estimation, the marginal effects of the regressors x on the probabilities are not equal to the coefficients. Since there are four possible values (very satisfied = 3, satisfied = 2, somewhat satisfied or somewhat dissatisfied = 1, dissatisfied or very dissatisfied = 0 about the level of life satisfaction) for the dependent variable (y, Satisfaction) in our ordered probit estimation, the marginal effects of an explanatory variable (x) indicating the changes in the probabilities of each possible values of the dependent variable caused by a one-unit change of the explanatory variable can be calculated by \( \frac{\partial Prob(y = 3)}{\partial x},\frac{\partial Prob(y = 2)}{\partial x},\frac{\partial Prob(y = 1)}{\partial x} , {\text{and}}\;\frac{\partial Prob(y = 0)}{\partial x} \), (Greene 2000). The marginal effects of the regressors x (explanatory variables) from our ordered probit estimations are found to be very similar to the coefficients from the OLS regressions. To save space, they are not reported in the paper, but they are available from the author upon request.

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Authors and Affiliations

  1. Department of Public Finance, National Taipei University, 151 University Road, San-Shia, Taipei, Taiwan

    Wen-Chun Chang

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  1. Wen-Chun Chang
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Correspondence to Wen-Chun Chang.

Appendices

Appendix 1

See Table 5.

Table 5 Definitions of variables
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Appendix 2

See Table 6.

Table 6 Descriptive statistics for the full sample (N = 1,502)
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Appendix 3

See Table 7.

Table 7 Descriptive statistics for the subsamples, Position = 1 versus Position = 0
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Appendix 4

See Table 8.

Table 8 Income distribution
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Appendix 5

See Table 9.

Table 9 Estimation of life satisfaction with an interaction term
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Appendix 6

See Table 10.

Table 10 Estimation of life satisfaction with an interaction term reference income sorted by occupation
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Chang, WC. Climbing up the Social Ladders: Identity, Relative Income, and Subjective Well-being. Soc Indic Res 113, 513–535 (2013). https://doi.org/10.1007/s11205-012-0108-7

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