Data Mapping on Ageing in Asia and the Pacific

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There have been increasing endeavors at international, national, and local levels in the Asia-Pacific region to collect information specifically related to older persons through reasonably representative surveys. These surveys tend to be either broadly comprehensive covering a wide range of life domains or at least comprehensive of particularly important domains, especially ones related to health. However, there is no systematic accounting of what the resulting datasets have addressed; to what extent different sources overlap or complement one another; how comparable they are within the same country and cross-nationally; and what data gaps remain. In addition, there are other sources that are not specifically directed towards the older population but can be useful in providing information about it.

Commissioned by HelpAge International with funding from UNFPA, this study documents the existence of data related to ageing issues as provided by surveys of older persons, censuses, and Demographic and Health Surveys (DHS) for 25 low- and middle-income Asia-Pacific countries, namely Bangladesh, Bhutan, Cambodia, China, Cook Islands, DPR Korea, Fiji, India, Indonesia, Iran, Lao PDR, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Pakistan, Palau, Philippines, Solomon Islands, Sri Lanka, Tonga, Thailand, Tuvalu, and Vietnam. The study was conducted by Bussarawan Teerawichitchainan of the School of Social Sciences, Singapore Management University; and John Knodel of the Population Studies Center, University of Michigan.

Using a systematic review approach, the authors evaluate the content of data sources and assess their comparability. The study also showcases best practices in data collection for various life domains at older ages (e.g., health status in later life and intergenerational exchanges) and identifies gaps in survey data collection related to ageing issues in the Asia-Pacific region.

Accompanying the main report are three Excel files providing data mapping details for three main categories of data sources:

 

Related publications:

 

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2017-02-14T10:11:19+00:00 Tags: , , |