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

Tadahiko Murata, Sho Sugiura, Takuya Harada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538627259
DOIs
Publication statusPublished - 2018 Feb 2
Externally publishedYes
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: 2017 Nov 272017 Dec 1

Publication series

Name2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume2018-January

Conference

Conference2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Country/TerritoryUnited States
CityHonolulu
Period17/11/2717/12/1

Keywords

  • income estimation
  • simulated annealing based approach
  • synthetic population

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

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