Income growth, redistribution, and subjective well-being in Taiwan – a simulation study

Abstract

This study simulates different income growth and income distribution scenarios in Taiwan in 2001, and examines how social happiness and people's happiness at different income levels change. Without taking downward comparison into consideration, the simulation supports income redistribution in favour of the poor. When the downward comparison is taken into consideration, the simulation does not support any kind of income redistribution. The present study investigates the relationship between income inequality and subjective well-being, and shows that a change in the Gini index can be interpreted in terms of a shift in revealed subjective well-being.

Keywords:

• happiness
• relative income
• subjective well-being
• growth
• income distribution

JEL Classification:

• D63
• A14

Acknowledgements

The data analysed in this article were collected as a result of the research project entitled ‘2001 Taiwan Social Change Survey’ sponsored by Taiwan's National Science Council, and compiled by the Institute for Social Sciences and the Office of Survey Research, Academia Sinica. The authors appreciate the assistance of the institutes in providing the data.

Notes

¹ In the survey by Benabou (1996, Table 2), before 1996, none of the studies shows a positive relationship between inequality and growth, but 10 studies show a negative relationship.

² The of the highest-income respondent is specified as being the same as his/her income, while the of the lowest-income respondent is specified as 0.

³ Educational attainment is represented by years of education. The years of education for ‘without education’, elementary school, junior high school, senior high school, 3-year college, 5-year college, college and graduate school are 0, 6, 9, 12, 14, 15, 16 and 18, respectively.

⁴ Business Executives and Managers (O₁= 1), Professionals (O₂= 1), Technicians and Associate Professionals (O₃= 1), Clerks and other staff at a similar technological level (O₄= 1), and Elementary Occupations (nontechnological workers). Industries are divided into five categories: (1) Traditional Manufacturing (I₁= 1); (2) Advanced Manufacturing (I₂= 1); (3) Construction and Utilities (I₃= 1); (4) Services (I₄= 1); and (5) Farming, Fishing and Mining. The manufacturing industry is classified into two categories. The Advanced Manufacturing category includes Machinery equipment (29), Electrical and Electronic equipment (31), and Precision equipment (33). The numbers in the brackets are the two-digit industry classifications. Other manufacturing industries are classified as part of the Traditional Manufacturing industry.

⁵ The formulae for the truncated means are E[y*|y* > a] = μ* + σ · [φ(α)/(1 − Φ(α))], E[y*|y* < a] = μ* − σ · [φ(α)/Φ(α)], and E[y*|b > y* > a] = μ* + σ[φ(α _a) − φ(α _b)]/[(Φ(α _a) − Φ(α _b)), where α _a= (a − μ*)/σ and α _a= (b − μ*)/σ. See chapter 24 in Greene (2008). We take as an example. E[y*|0.94 > y* > 0] = μ* + σ[φ(α _a) − φ(α _b)]/[(Φ(α _a) − Φ(α _b)) = 2.129 + 1.02[φ(−1.607) − φ(0.962)]/[(Φ(−1.607) − Φ(0.962)) = 2.059.

⁶ The threshold between ‘very unhappy’ and ‘unhappy’ is 0. All y* shift to the right by 0.0124. In other words, y* between −0.0124 and 0 will shift to the right-hand side of 0. That is, after Gini is reduced by 0.01, people in this distribution area will change their revealed happiness from ‘very unhappy’ to ‘happy’. The area is also the probability between a normal distribution with a mean equal to 2.129 and a SD equal to 1.02. 0.054 is the area between −0.0124 and 0 conditional upon y* ∼N(2.129,1.02). The other values are calculated in a similar way.