Agent-based Modeling of Rural-urban Migration in China We propose to develop a large-scale agent-based model of rural-to-urban migration in China. Large-scale migrations, both within countries and across countries, are transforming the world's population. Agent-based modeling (ABM) is a promising and underutilized means for understanding the implications of these complex migrations for population distribution and growth because it can include feedback loops, allow actor-place interactions, and reveal unexpected, emergent properties of the complex system of migration. Recent developments in ABM, notably algorithms for the efficient use of powerful computing clusters to estimate models with billions of actors (Parker and Epstein 2011), make this technique especially promising for modeling large, complex spatial processes. Thus, we would argue that the development of ABM to study large-scale migrations is an innovation that is of high scientific priority for population research. China is an excellent choice for the development of the first such model because it has, since the mid-1980s, undergone the largest single migration in the world's history, transferring some 230 million rural people by 2011, accounting for 17.4% of China's total population (Chinese National Bureau of Statistics 2012). About half of China's rural migration is from the rural inner provinces to the urbanized coast. Rural migrants are drawn to coastal areas primarily because of the high employment prospects in export-oriented industries and migrant networks (Hao et al. 2013). This monumental population shift has global economic, demographic, health, and political ramifications. While its importance is well documented, little has been done to identify and test the basic underlying causal mechanisms. The scale and duration of China's mass migration present a unique opportunity to gain valuable insights into this important historical phenomenon. The project will (1) explore, develop, and validate the conceptual model by reformulating and improving existing theories to apply to China's rural-urban migration;(2) develop a large-scale agent-based model of rural-urban migration in China, using empirically calibrated initial conditions, ex ante structural parameters, and "open" parameters to be experimented ex post, and validate the simulated stylized patterns against the reality;(3) test the explanatory power of each of the component theories integrated in our base-case model;(4) explore the dynamic outcomes of the base-case model under varying policies, economic conditions, individual preferences, and initial population conditions, and (5) find general principles of mass migration and to produce and publish our methods and results so that ABM can be applied to other large, complex migration flows.

Public Health Relevance

We propose a large-scale agent-based model of rural migration in China with both conceptual and methodological innovations. China has undergone the largest single migration in the world's history. This monumental population shift has profound demographic and health ramifications. Not only the well-being of migrant family members but also the population-wide consequences of the resulted urbanization imbalance, stalled rural development, and rising inequality are pressing public health challenges. This study will reveal the causal mechanisms underlying rural migration, based on which it will forecast outcomes under varying policy (e.g., change in the household registration system) and financial environments (e.g., change in foreign direct investment). Findings from this research will provide empirical-data-based generative evidence for effective policy interventions to reduce the public health costs of rural migration.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21HD078808-01A1
Application #
8820409
Study Section
Special Emphasis Panel (SSPB)
Program Officer
Bures, Regina M
Project Start
2014-09-26
Project End
2016-08-31
Budget Start
2014-09-26
Budget End
2015-08-31
Support Year
1
Fiscal Year
2014
Total Cost
$240,100
Indirect Cost
$90,100
Name
Johns Hopkins University
Department
Social Sciences
Type
Schools of Arts and Sciences
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218