Integrating Information

about

Aging Surveys

ELSA User DayLondon

November 17, 2008

Arie Kapteyn

Jinkook Lee

Bas Weerman

Agenda:

• Introduction

• Specific aims

• Current progress

• Next steps

Agenda:

• Introduction

• Specific aims

• Current progress

• Next steps

IntroductionComparable Data Collection Around

the World

• Aim is to have significant comparable content so that cross-national studies can be conducted

• But also allow for scientific innovation at the country level

• Content also has to reflect reality and policies of each country

• Initial meeting in Chiang Mai Thailand in February 2007 to discuss issues of comparability across countries – most PIs were able to attend, follow-up meeting in New Delhi, India in February 2009

The International Landscape in Comparable Data Collection

• The USA Health and Retirement Survey – HRS

– Nationally representative longitudinal survey of ~20,000 people age 51+ designed to produce public use data

– Began in 1992 with the birth cohorts of 1931-41

– Two year periodicity

– Administrative link to pension and health data

The International Landscape in Comparable Data Collection

• SHARE (Survey of Health, Ageing and Retirement in Europe)

– 14 countries in Europe- more on the horizon

– completed first wave 2004, approved for EU funding of second wave now in the field

– Similar instruments to HRS and ELSA

– Big innovation is very strict comparability of survey instruments across countries

The International Landscape in Comparable HRS Data Collection

on to Asia

• HRS, ELSA, SHARE

• South Korea- finished first wave and data are now available- second wave in field- KLoSA-

• Japan- internally funded- first wave completed-- JSTAR

• China- large pilot underway now- full survey next year- CHARLS

• India- LASI

Comparable Data CollectionDATA Distribution & Analysis

• All participating countries have committed to widespread and quick release of data into the public domain both within their country and to the international community

Great resources for cross-country comparative studies

Facilitating cross-country comparative studies, we propose to develop “Megameta data”

Developing Megameta Data

• What do we want to share?– Meta data (everything you ever wanted to know about a variable), Para data (e.g. time stamps, date/time of interview) and other (possibly extraneous) Information

• What are our goals?– Facilitate the use of different datasets in comparative studies, – Create a repository of information, knowledge and experience, and – Serve as a library of survey questions for aging surveys.

Agenda:

• Introduction

• Specific aims

• Current progress

• Next steps

Specific aims:

1. Create a digital library of survey questions

2. Develop a Google-like search facility

3. Create a “Wikipedia-like” system

4. Systematically compare surveys to establish comparability across surveys

5. Enrich datasets with contextual variables

1. Create a digital library of survey questions

• Storing all available metadata in a central database creates the foundation.

• The metadata for the digital library is filled automatically.

• A researcher can create a data set by searching for and joining variables together using the metadata library.

1. Create a digital library of survey questions

Input: what to store?

• General– Variable labels– Version– Survey– Wave– Question description

• Specific– Question text– Fills used in question text

– Answer (categories)– Position– Routing information– Remarks– Sample Information– Additional answer options

– Keywords– Data source– Restrictions

Dissemination database

INPUT OUTPUT

Survey systems

Researchers

1. Create a digital library of survey questions

Output: what can be retrieved?

• Browse for questions in all surveys for all waves

For selected questions display:

• All possible fills

• All routing that led to questions

• All routing for possible fills

• Comments made by researchers

AND….

2. Develop a Google-like search facility

Output: what can be retrieved?

• To the researcher it will appear as if he/she is accessing one big database, when the search facility will be looking across disparate databases.

• A Google-like search facility will allow users to enter queries like “smoking questions in ELSA and HRS in the period 2003-2005”.

Create datasets when databases are present on the users local machine or network.

Information Flow between the Meta Data and Local Data

INT

ER

NE

T

SERVER LOCAL

meta data

data

dataset

3. Create a “Wikipedia-like” system

3. Create a “Wikipedia-like” system

Wikipedia is a free encyclopedia that anyone can edit.

Our system would allow researchers to:

• Add comments and remarks to questions and variables

• Link and cross-reference questions

• Allow researchers to add variables to the system based on computations on the existing (cleaned) data. The system stores the algorithms that lead to the new variables and not the values. These new variables can be accessed by other users.

3. Create a “Wikipedia-like” system

– Collaboration among users will improve the system over time.

– Registered users may edit content, create new articles and papers and have their changes instantly displayed.

4. Systematically compare surveys to establish comparability across surveys

• Even when underlying theoretical concepts are comparably defined, at an empirical level, the cross-national comparability of the surveys is not self-evident.

4. Systematically compare surveys to establish comparability across surveys

• In certain domains data inherently exhibit substantial country-specific heterogeneity. – E.g., welfare programs, public and private pension,

educational systems, financial products and institutions, etc.

only ex post comparability can be established.

• For most demographic characteristics, health events, and expectations, – ex ante comparability can be established if the survey

instruments are equivalent.

5. Enrich datasets with contextual variables

• To conduct cross-national comparative studies, a researcher needs to understand the contextual characteristics of multiple countries.

• We will provide some key contextual information that

would facilitate cross-national studies of aging.

5. Enrich datasets with contextual variables

Population Information:

• Total population • Total population growth rates• Population composition by

age• Dependency ratios• Fertility rate

Health-related information:

• Life expectancy at birth• Healthy life expectancy at birth• Mortality rate• Causes of death

5. Enrich datasets with contextual variables

Economic Information:

• Gross Domestic Product (GDP)

• Real GDP growth rates• Consumer Price Index (CPI)• Long-term interest rates• Exchange rates• Purchasing power parities

(PPPs) • Standardized unemployment

rates• Employment rates by age• Employment rates by sector• Part-time employment rates

• Social benefits• Poverty rates• Poverty thresholds• Gini coefficients • Government revenue as %

of GDP• Tax revenue as % of GDP• Tax rates: highest income

taxes for individuals and firms

5. Enrich datasets with contextual variables

Health Care Resources:

• Human health resources– Number of physicians per

1,000 population– Number of nurses per

1,000 population – Number of dentists per

1,000 population– Number of pharmacists per

1,000 population

• Medical technology– MRI units– CT scanners – Radiation therapy

equipment– Mammography per million population

• Acute care beds– Beds per 1 000

population

5. Enrich datasets with contextual variables

Healthcare Expenditure

• Total health expenditure– Percentage of GDP – Per capita US dollar PPPs

• Government health expenditure– Percentage of total

expenditure on health– Percentage of total

government expenditure– Per capita US dollar PPPs

• Private health expenditure– Percentage of total

expenditure on health– Household out-of-pocket

expenditure as a percentage of private expenditure on health

– Per capita US dollar PPPs

Agenda:

• Introduction

• Specific aims

• Current progress

• Next steps

Next steps:

• Set up the database/website

• Add metadata of aging studies

• As soon as all the metadata are in, we allow researchers

to access the web pages

• Add derived variables from education, employment, income, assets, health etc

• Add papers using data from these aging studies

Your inputs:

• Contribute to survey comparability, both at the conceptual

and question levels.

• Review and revise contextual data

• Submit both an electronic version of CAPI instrument

and a print version of questionnaire for the inclusion of mega meta data base