Household Aging in China and Housing Demand Projections in Hebei Province: ProFamy Extended Cohort-Component Method and its Applications

In their presentation entitled “Household and Living Arrangement Projections: The Extended Cohort-Component Method and Applications to the U.S. and China”, Zeng Yi and Zhenglian Wang will introduce an innovative demographic toolkit for the projection of household structures and living arrangements.

Wenzhao Shi will present new forms of data processing in his talk headed “Demographers May Play an Important Role in Big Data Analysis Revolution”.

About the presenters:

Zeng Yi is a Professor at the Center for Study of Aging and Human Development, Medical School of Duke University, Center for Healthy Aging and Development Studies, National School of Development at Peking University; Distinguished Research Scholar of the Max Planck Institute for Demographic Research; and a foreign member of the Royal Netherlands Academy of Arts and Sciences.

Dr. Zhenglian Wang is President of Households and Consumption Forecasting Inc., NC, U.S.A., and a Senior Research Scientist at the Center for Study of Population and Health at Duke University.

Wenzhao Shi is a Senior Research Fellow and Technical Director of China National Population Administration Decision-making Information System (PADIS), Executive Director of Data Technology Center, and General Manager of Public Business Division of Digital China Group.

Abstracts:

Household and Living Arrangement Projections: The Extended Cohort-Component Method and Applications to the U.S. and China
We present in this seminar an innovative demographic toolkit known as the ProFamy extended cohort-component method for the projection of household structures and living arrangements with empirical applications to the United States, the largest developed country, and China, the largest developing country. The ProFamy method uses demographic rates as inputs to project detailed distributions of household types and sizes, living arrangements of all household members, and population by age, sex, race/ethnicity, and urban/rural residence at national, sub-national, or small area levels.  It can also project elderly care needs and costs, pension deficits, and household consumption. Our presentation consists of four parts. The first part presents the methodology, data, estimation issues, and empirical assessments. The next parts highlight applications in the United States (part two) and China (part three), including brief summaries of forecasting future trends of household type/size, elderly living arrangements, disability, and home-based care costs, and household consumption including housing and vehicles. The fourth part briefly demonstrates the ProFamy software to project households, living arrangements, and home-based consumptions.

Demographers May Play an Important Role in Big Data Analysis Revolution
Big data usually includes data sets with sizes too large to be captured, curated, managed, or processed by commonly used software tools. New forms of processing are necessary to handle big data, enabling enhanced decision making and facilitating insightful discoveries. As the volume, velocity, and variety of data increases, human judgment becomes a more vital aspect of the analytical process. Demographic analysis, as one of the key approaches for social analysis, may also be evolving in the era of big data. In this seminar, I will present our research linking demographic variables with social-economic factors, education expenses, healthcare costs, energy consumption, and environmental data within a closed loop system. Finally, I will outline how to properly analyze these effects within the context of a big data.

Last edited: 25 July 2013