Social Insurance, Income and Subjective Well-Being of Rural Migrants in China—An Application of Unconditional Quantile Regression

Fang, Zheng; Sakellariou, Christos

doi:10.1007/s10902-015-9663-3

Cited by 43 publications

(27 citation statements)

References 50 publications

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“…As expected, the less happy group of people tends to be more affected by materialistic considerations and pecuniary variables. It is found that 1% increase in income would increase the SWB by 0.007 points at the 10th percentile of the distribution, which gradually decreases to 0.003 points at the 90th percentile of the (Fang and Sakellariou 2015). As regards to the status of being unemployed, it brings down the SWB of people at the 25th percentile of the happiness distribution by 1.1 points and people at the median by 0.9 points.…”

Section: Two-step Estimatormentioning

confidence: 92%

“…Going beyond the average, heterogeneous effects of determinants on the happiness distribution have been studied by Coad (2011), Binder andFreytag (2013), Yuan and Golpelwar (2013) using quantile regressions developed by Koenker and Bassett (1978), and Fang and Sakellariou (2015) using unconditional quantile regressions developed by Firpo et al (2009).…”

Section: Econometric Methods Used In the Happiness Literaturementioning

confidence: 99%

“…5 Each question has four possible responses, with 1 indicating the best status and 4 indicating the worst. Following Fang and Sakellariou (2015), we sum up the responses of the 12 questions, and subsequently deduct it from 48. This gives us an index ranging between 0 and 36.…”

Section: Datamentioning

confidence: 99%

“…Alternatives include constructing a seven categorical measure (Akay et al 2012), or applying the Caseness GHQ score which counts the number of questions whose responses are one of the two low well-beings (Clark 2003). To ensure enough variation in the quantile regression, we choose to follow Fang and Sakellariou (2015)'s measure.…”

Section: Datamentioning

confidence: 99%

“…For example, using RUMiCFang and Sakellariou (2015) investigated how social insurance coverage and relative income could affect the subjective well-being of rural migrants in China.Panel Quantile Regressions and the Subjective Well-Being in……”

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confidence: 99%

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Panel Quantile Regressions and the Subjective Well-Being in Urban China: Evidence from RUMiC Data

Fang

2015

Soc Indic Res

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Using RUMiC data and a simple panel quantile regression method, this paper accounts for the time-invariant individual specific characteristics and investigates the heterogeneous effects of factors on the distribution of subjective well-being (SWB, measured by GHQ-12) in urban China. Comparing results from the pooled regression and fixed effect regression, we find that most results from pooled regressions are likely overestimated. Panel quantile regression results show that income affects the least happy 10 % group twice as much as the happiest 10 % people. Being unemployed brings down the happiness level by 0.97 points on average, but individuals with high SWB seem to be not significantly affected. The complete picture of the relationship between SWB and various factors would have been veiled without using quantile techniques. This paper therefore not only contributes to the happiness literature in China, but also adds evidence to stylized findings going beyond the average.

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Section: Two-step Estimatormentioning

confidence: 92%