Scenario forecast model of long term trends in rural labor transfer based on evolutionary games

Abstract

Chinese rural labor transfer is characterized by acquaintance introduction and is mainly composed of young people, consistent with the bounded rationality hypothesis in evolutionary games. The paper first applies evolutionary game theory combined with scenario analysis for the quantitative prediction of long term trends in rural labor transfer in China. First, it discusses the evolutionary mechanism of the general replicator dynamic model in rural labor transfer. In particular, we use evolutionary games to build a causality prediction model forecasting the long term trends in rural labor transfer, where the residuals of the forecasting model, after removing the economic payoff differences, are considered as the social factors, i.e., the employment barriers in the urban sectors, affecting the transfer decision-making. Based on the historical time series data of income and population distribution of rural employment in various sectors, the paper builds a time series forecasting model of employment barriers of different urban sectors. Given the future uncertainties in economic factors, i.e., the urban–rural income gaps which affect the rural labor transfer, we introduce scenario analysis into the evolutionary game forecasting model. We consider three scenarios of the urban–rural income differences: (1) the average situation – no change in the current urban–rural income differences, (2) the pessimistic situation – the income differences fall into the middle-income trap, and (3) the optimistic situation – the income gap approaches the level in developed countries, to forecast the long term trends in the rural labor transfer in China.