Income Allocation to Workers in Synthetic Populations Using Basic Survey on Wage Structure

In this paper, we propose a synthetic reconstruction method (SR method) to allocate an income attribute to each worker in synthetic populations. A synthetic population is a population synthesized based on statistics. We assign an income attribute to each worker in individual households using statistics in Japan. In order to add that attribute, we first prepare a synthetic population of households with members synthesized by our previously proposed SR method. Then we assign job attributes such as a working status, a type of employment, a type of industry, and a size of enterprise according to four statistics using our SR method. After determining the working status, we assign job attributes besides the working status for each worker. We determine the monthly income of each worker based on his/her job attributes. To see the validity of allocated monthly income, we compare the average income of each type of industry in the synthesized population with the statistics of the average income of each type of industry that is not used in the synthesizing procedure. The result showed that the error over all industries becomes − 0.8% to 10.3% in allocating income to each worker in a synthesized population.

[1]  Tadahiko Murata,et al.  Comparing Transition Procedures in Modified Simulated-Annealing-Based Synthetic Reconstruction Method without Samples , 2017 .

[2]  Amir Reza Mamdoohi,et al.  Population Synthesis Using Iterative Proportional Fitting (IPF): A Review and Future Research , 2016 .

[3]  Tadahiko Murata,et al.  A Two-Fold Simulated Annealing to Reconstruct Household Composition from Statistics , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[4]  Lu Ma,et al.  Synthetic Population Generation with Multilevel Controls: A Fitness‐Based Synthesis Approach and Validations , 2015, Comput. Aided Civ. Infrastructure Eng..

[5]  Tadahiko Murata,et al.  Estimating agents' attributes using simulated annealing from statistics to realize social awareness , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[6]  Johan Barthelemy,et al.  Synthetic Population Generation Without a Sample , 2013, Transp. Sci..

[7]  Guillaume Deffuant,et al.  Generating a Synthetic Population of Individuals in Households: Sample-Free Vs Sample-Based Methods , 2012, J. Artif. Soc. Soc. Simul..

[8]  Alison J. Heppenstall,et al.  Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques , 2012, J. Artif. Soc. Soc. Simul..

[9]  Michel Bierlaire,et al.  Simulation based Population Synthesis , 2013 .

[10]  Guillaume Deffuant,et al.  An Iterative Approach for Generating Statistically Realistic Populations of Households , 2010, PloS one.

[11]  Graham Clarke,et al.  SimBritain: a spatial microsimulation approach to population dynamics , 2005 .

[12]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[13]  A. G. Wilson,et al.  A new representation of the urban system for modelling and for the study of micro-level interdependence , 1976 .

[14]  W. Deming,et al.  On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known , 1940 .