Abstract
Population aging necessitates new approaches to understanding and explaining inequality in a society. If so, what approaches should be taken to deal with social disparity issues in a society with an increasing number of elderly people? What kinds of information and research data are needed for this purpose? To address these issues, this chapter endeavors to estimate male elderly people’s income history, that is, income at all age points in the past, based on their job history data. Furthermore, this chapter analyzes how the elderly people’s estimated income history affects their current socioeconomic status. Through these analyses, this chapter proposes a new approach to understanding inequality among elderly people based on the perspective that employment positions, from the past to the present, determine one’s socioeconomic status.
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Notes
- 1.
The amount of retirement allowance and pension also largely vary according to their type of employment and firm size. This is another reason that we should pay special attention to the effects of these employment conditions in the job history on the socioeconomic status of elderly people.
- 2.
Some countries allow government information on income and taxation to be used for academic purposes. For example, Guvenen et al. (2017) used sample data extracted from an income list belonging to the US Social Security Administration to directly calculate people's actual lifetime incomes. However, as Japan does not yet permit such data usage, the method described in this chapter is the only way to analyze an individual's income history. In this chapter, the attempt to estimate income history based on a person's past employment at various ages is similar to that by Sato & Yoshida (2007) and Lefranc, Ojima, & Yoshida (2014), with which they estimated fathers' incomes, based on their employment information, to analyze intergenerational income mobility in Japan.
- 3.
To compile person-year data, the SPSS syntax for person-year-data conversion (ver. 2.0 for SSM 2015 v070 data) by Professor Tokio Yasuda was modified and used. I would like to express my gratitude to Professor Yasuda.
- 4.
However, it has been demonstrated that the effects of various job conditions, as well as age and education, on income are somewhat different at different points in time (Kanomata, 2001, 2008; Yoshida, 2005). The differences in the shape of the income function at different times should be considered in the future research.
- 5.
Years of experience were calculated as the number of employment years, starting from a person's first job, minus the number of unemployment years, while tenure was calculated as the number of employment years at the current workplace. Experience of unemployment was ascertained based on job history data. The timing of the survey was controlled for by incorporating a 2005 dummy variable.
- 6.
The following interaction terms and squared terms are incorporated: educational attainment × years of experience, educational attainment × the square of years of experience, status in employment × tenure, occupation × tenure, industry × tenure, firm size × tenure, the square of years of experience, the square of tenure.
- 7.
- 8.
Given the hierarchy of variables in the model, in the initial addition, the interaction terms for years of experience/tenure are not included and only the main effect terms are added to the base model, which only includes the 2005 dummy variable. In the final addition, interaction terms for years of experience/tenure are added simultaneously with the main effect terms.
- 9.
The large effect of managerial title on income in Japan was also indicated by Kanomata (2001). Few surveys, other than the SSM surveys, have asked for a detailed history of the managerial titles held by an individual; therefore, SSM survey data is extremely useful for estimating people’s income histories in Japan.
- 10.
Because income is estimated based on information concerning jobs held at specific ages in the past, this chapter essentially estimates earned income. The analyses estimate the income earned in jobs as the value in 2015.
- 11.
Average personal income was 4.86 million yen and 3.31 million yen for the 50–64 age group and 65–79 age group, respectively, and the respective standard deviations for the groups were 3.58 million yen and 4.12 million yen.
- 12.
Compared to Model 3, Model 2 has a smaller AIC and BIC, indicating better model fitness.
- 13.
The eight categories of SSM Comprehensive Job Classifications are as follows: professionals, white-collars in large firms, white-collars in middle to small firms, self-employed white-collars, blue-collars in large firms, blue-collars in middle to small firms, self-employed blue-collars, and farmers. The unemployment dummy variable in the models also applies to cases in which occupation is unknown.
- 14.
Unemployment was assigned a value of 0. However, the effect of being unemployed is estimated by the unemployment dummy variable that is also incorporated into the model.
- 15.
Additionally, because the SSM Comprehensive Job Classifications are categorical variables, applying them to respondents’ entire job histories is not practically useful.
- 16.
The coefficients of determinant are shown only for the period from age 23 to age 65, when the estimated income at the age has a positive coefficient in the model.
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Arita, S. (2022). Explanation of Socioeconomic Inequality Among the Male Elderly: An Approach Based on Estimated Income History. In: Shirahase, S. (eds) Social Stratification in an Aging Society with Low Fertility. Economy and Social Inclusion. Springer, Singapore. https://doi.org/10.1007/978-981-19-3647-0_9
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