Income allocation to each worker in synthetic populations using basic survey on wage structure

Tadahiko Murata, Sho Sugiura, Takuya Harada

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

In this paper, we propose a simulated-annealing based method to allocate an income attribute to each worker in synthetic populations. An income attribute is one of important attributes when microsimulations or agent-based simulations are conducted for making or examining some policy of government, enterprises or firms. We add an income attribute to workers in individual households using Basic Survey on Wage Structure in Japan. In order to add that attribute, we first prepare the synthetic populations of households with members where their sex, age, family type, role and kinship that are already determined by our previously proposed synthetic population generation method (SPGM). Then we add a working status (working or not working) and an industry type if the working status of a household member is working according to three statistics that show the relation between sex, family type, and age in a prefecture or a city using a simulated annealing based SPGM. After determining the working status and their industry, we add average monthly income to each worker in the synthesized population. To see the validity of allocated monthly income, we compare the average income of each industry in the synthesized population with the statistics of the average income of each industry that is not used in the synthesizing procedure.

本文言語English
ホスト出版物のタイトル2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-6
ページ数6
ISBN(電子版)9781538627259
DOI
出版ステータスPublished - 2018 2月 2
外部発表はい
イベント2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
継続期間: 2017 11月 272017 12月 1

出版物シリーズ

名前2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
2018-January

Conference

Conference2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
国/地域United States
CityHonolulu
Period17/11/2717/12/1

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • 制御と最適化

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