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What Affects Happiness Inequality? Evidence from Japan

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Abstract

This paper examines the determinants of happiness inequality in Japan using unique data from the “Preference Parameters Study” of Osaka University, a nationally representative survey conducted in Japan. By estimating Recentered Influence Function regressions, we find that household income has a negative and significant effect on happiness inequality, as found for other advanced economies, though people’s perception of their relative standing in the income spectrum also matters for the dispersion of happiness. Moreover, the regression results show that the insecurity faced by people about their jobs and life after retirement is also significantly associated with the widening of happiness inequality. Such findings are cause for grave concern given that the share of irregular jobs, which tend to be low paid and insecure, in total employment significantly increased in Japan during the Lost Two Decades and that this increasing trend has not yet been reversed.

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Fig. 1

Source: Happiness inequality: Calculations based on the 2003–2013 Preference Parameters Study data. Real GDP per capita: Calculations based on data on real GDP from the National Accounts of Japan (Cabinet Office, http://www.esri.cao.go.jp/jp/sna/menu.html, accessed on April 14, 2015) and data on population from the Population Estimates (Statistics Bureau, Ministry of Internal Affairs and Communications, http://www.stat.go.jp/data/jinsui/2.htm, accessed on April 14, 2015)

Fig. 2

Source: Calculations based on the 2013 Preference Parameters Study data

Fig. 3

Source: The Labor Force Survey (Historical data: Tables 9 and 10), Statistics Bureau, Ministry of Internal Affairs and Communications (http://www.stat.go.jp/data/roudou/longtime/03roudou.htm, accessed on May 14, 2015)

Fig. 4

Source: Calculations based on the 2013 Preference Parameters Study data

Fig. 5

Source: Calculations based on the 2013 Preference Parameters Study data

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Notes

  1. For instance, under the initiative of the former French President Sarkozy, the Commission on the Measurement of Economic Performance and Social Progress was created in 2008 to identify the limitations of GDP as an indicator of economic performance and social progress and to propose alternative measures. In the case of the United Kingdom, the Measuring National Well-being Programme was launched in 2010 to construct measures of the nation’s well-being beyond GDP. A set of objective and subjective indicators has been selected and national well-being has been monitored through these indicators since then. As for the Organisation for Economic Co-operation and Development (OECD), it launched the Better Life Index Initiative in 2011, which has developed statistics to capture aspects of life that matter to people in terms of material living conditions and that shape the quality of people’s lives.

  2. See, for example, Fleurbaey (2009) and Stiglitz et al. (2009) for a summary of various approaches suggested for measuring people’s well-being or quality of life.

  3. As noted by Diener et al. (2002), subjective well-being can be defined as an individual’s affective and cognitive evaluations of his/her life, which include emotional reactions to events (both positive and negative affect) as well as cognitive judgements of life satisfaction. As such, subjective well-being can be considered as “a general area of scientific interest rather than a single specific construct” (Diener et al. 1999, p. 277).

  4. According to the Helliwell et al. (2015), Japan’s rank of happiness (for the period 2012–2014) was 25th among the 34 OECD member countries.

  5. Japan’s average annual unemployment rate was about 4.2% between 2000 and 2015 according to data from the Statistics Bureau, Ministry of Internal Affairs and Communications (http://www.stat.go.jp/data/roudou/longtime/03roudou.htm#hyo_1, accessed on March 1, 2016).

  6. Irregular employees include those who are working as a part-time worker, temporary worker, fixed-term worker, or dispatched worker from a temporary agency.

  7. While employees in the private sector participate in the Employees’ Pension Plan, government employees participate in the Mutual Aid Pension Plan instead.

  8. On average, only about 52% of irregular employees were found to have participated in the Employees’ Pension Plan in 2014 according to data from the Ministry of Health, Labour and Welfare (http://www.mhlw.go.jp/toukei/itiran/roudou/koyou/keitai/14/index.html, accessed on March 7, 2016).

  9. See Frey and Stutzer (2002) and Clark et al. (2008) for a comprehensive survey of the literature.

  10. The data used for their analysis come from the World Values Surveys, the German Socio-Economic Panel, the British Household Panel Survey, the American General Social Survey, and the Household, Income and Labour Dynamics in Australia Survey (Clark et al. 2014). While Becchetti et al. (2014) note a rise in happiness inequality in Germany between 1992 and 2007, Clark et al. (2014) look at a longer period and obtain a different picture, namely that happiness inequality fell sharply in the 1980s and then fluctuated around a flat trend in the 1990s.

  11. Note that this paper focuses on examining the determinants of the level of, rather than trends over time in, happiness inequality using data from the 2013 wave because this wave contains some useful variables that are not available in earlier waves. Since the number of studies that examine the determinants of happiness inequality remains limited, analyzing the determinants of the level of happiness inequality itself provides useful findings. The regressions were also estimated using pooled data from the 2012 and 2013 waves (the time period was too short to conduct a panel data analysis). The estimation results were very close to those presented in this paper, which were obtained from regressions estimated using only data from the 2013 wave.

  12. These criteria include: (1) single finite number as result, (2) interval level of measurement, (3) independence of scale range, (4) independence of sample size, (5) independence of the mean, (6) equal values for equally unequal distributions, (7) differentiation between more and less unequal distributions, and (8) sensitive to degree of inequality (Kalmijn and Veenhoven 2005).

  13. Kalmijn and Veenhoven (2005) computed happiness inequality for various countries using those four acceptable inequality metrics and find that those four statistics generate similar results in terms of the ranking of countries based on happiness inequality. These results therefore lead them to conclude that no advance in understanding is to be expected from switching from using the standard deviation to some other inequality statistic (Kalmijn and Veenhoven 2005, p. 389).

  14. See Frey and Stutzer (2002) and Clark et al. (2008) for a comprehensive survey of the findings on the determinants of the level of happiness.

  15. It should also be noted that, in the case of Japan, there is a relatively large number of people who participate only in the National Pension Plan and receive only the basic pension, which is not related to their income levels, as noted earlier.

  16. As part of the Comprehensive Reform of Social Security and Tax, the relevant law was enacted to expand the application of the Employees’ Pension to part-time workers in 2012, which became effective in October 2016.

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Author information

Authors and Affiliations

  1. Asian Growth Research Institute, 11-4 Ohtemachi, Kokurakita, Kitakyushu, Fukuoka, 803-0814, Japan

    Yoko Niimi

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  1. Yoko Niimi
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Correspondence to Yoko Niimi.

Additional information

The empirical work undertaken in this paper utilizes micro data from the Preference Parameters Study of Osaka University’s 21st Century COE Program ‘Behavioral Macrodynamics Based on Surveys and Experiments’ and its Global COE Project ‘Human Behavior and Socioeconomic Dynamics.’ I acknowledge the program/project’s contributors—Yoshiro Tsutsui, Fumio Ohtake and Shinsuke Ikeda. I am also grateful to Charles Yuji Horioka and participants of the SASE (Society for the Advancement of Socio-Economics) 27th Annual Conference, Singapore Economic Review Conference 2015, and the 2015 IAREP (International Association for Research in Economic Psychology)–SABE (Society for the Advancement of Behavioral Economics) Joint Conference for their invaluable comments. This work was supported by JSPS (Japan Society for the Promotion of Science) KAKENHI Grant Number 15H01950, a project research grant from the Asian Growth Research Institute, and a grant from the MEXT Joint Usage/Research Center at the Institute of Social and Economic Research, Osaka University.

Appendix

Appendix

Description of variables

Variables

Description

Age

Age expressed in years

Age squared

Age squared

Female

Dummy variable that equals one if respondents are female

Marital Status

 Never married

Dummy variable that equals one if respondents have never been married

 Married

Dummy variable that equals one if respondents have spouses or partners who are living with them

 No longer married

Dummy variable that equals one if respondents are divorced, widowed or separated

Education

 Junior high school

Dummy variable that equals one if respondents have completed junior high school education or lower

 High school

Dummy variable that equals one if respondents have completed high school education

 Junior college

Dummy variable that equals one if respondents have completed junior college education

 University

Dummy variable that equals one if respondents have obtained a university degree or higher

Poor health

Dummy variable for visiting a doctor on a regular basis because of a chronic disease or injury

Child

Dummy variable that equals one if respondents have a child/children

Household size

Total number of household members

Household income

Since the choices of the answers to the question on annual household income were in brackets, we created a continuous variable by assigning the following values to each answer:

 (1) Less than 1 million yen: 800,000 yen

 (2) 1 million to less than 2 million yen: 1.5 million yen

 (3) 2 million to less than 4 million yen: 3 million yen

 (4) 4 million to less than 6 million yen: 5 million yen

 (5) 6 million to less than 8 million yen: 7 million yen

 (6) 8 million to less than 10 million yen: 9 million yen

 (7) 10 million to less than 12 million yen: 11 million yen

 (8) 12 million to less than 14 million yen: 13 million yen

 (9) 14 million to less than 16 million yen: 15 million yen

 (10) 16 million to less than 18 million yen: 17 million yen

 (11) 18 million to less than 20 million yen: 19 million yen

 (20) 20 million yen or over: 25 million yen

We have then divided the figure (in 10,000 yen) by the number of household members and taken its natural logarithm.

Homeownership

Dummy variable that equals one if respondents own a house or an apartment

Has loans

Dummy variable that equals one if respondents have loans

Employment

 Regular job

Dummy variable that equals one if respondents have a regular job

 Irregular job

Dummy variable equals one if respondents have an irregular job (i.e., working as a part-time worker, temporary worker, fixed-term worker, or dispatched worker from a temporary agency)

 Unemployed

Dummy variable that equals one if respondents are unemployed

 Not in labor force

Dummy variable that equals one if respondents are not in the labor force (i.e., housewives/husbands, students or retired)

Likely unemployed

Dummy variable that equals one if respondents are currently employed but perceive a high risk of being unemployed in the next 2 years

Public pensions

 (1) Between 0 and 9%: 5%

 (2) Between 10 and 19%: 15%

 (3) Between 20 and 29%: 25%

 (4) Between 30 and 39%: 35%

 (5) Between 40 and 49%: 45%

 (6) Between 50 and 59%: 55%

 (7) Between 60 and 60%: 65%

 (8) Between 70 and 79%: 75%

 (9) Between 80 and 80%: 85%

 (10) 90% or over: 95%

Altruistic

Dummy variable that equals one if respondents have donated any money in the previous year

Risk lover

Chance of rain (%) that will make respondents bring an umbrella with them when they go out

Low time preference

Dummy variable that equals one if respondents used to get homework done right away or fairly early during school vacations when they were a child

Relatively poor

Dummy variable that equals one if respondents think that the living standard of others is much higher or somewhat higher than their own

Relatively rich

Dummy variable that equals one if respondents think that the living standard of others is much lower or somewhat lower than their own

Regions

 Hokkaido

Dummy variable that equals one if respondents reside in Hokkaido

 Tohoku

Dummy variable that equals one if respondents reside in Tohoku

 Kanto

Dummy variable that equals one if respondents reside in Kanto

 Koshinetsu

Dummy variable that equals one if respondents reside in Koshinetsu

 Hokuriku

Dummy variable that equals one if respondents reside in Hokuriku

 Tokai

Dummy variable that equals one if respondents reside in Tokai

 Kinki

Dummy variable that equals one if respondents reside in Kinki

 Chugoku

Dummy variable that equals one if respondents reside in Chugoku

 Shikoku

Dummy variable that equals one if respondents reside in Shikoku

 Kyushu

Dummy variable that equals one if respondents reside in Kyushu

Major city

Dummy variable that equals one if respondents reside in a major (ordinance-designated) city

  1. Given that the question on respondents’ education was not included in the 2013 survey, we obtained the relevant information from the 2011 survey data using respondents’ unique ID numbers

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Niimi, Y. What Affects Happiness Inequality? Evidence from Japan. J Happiness Stud 19, 521–543 (2018). https://doi.org/10.1007/s10902-016-9835-9

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  • DOI: https://doi.org/10.1007/s10902-016-9835-9

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