Data Projects at the Minnesota Population Center Resources for Comparative Population and Health Research Seattle, Washington May 22, 2014 Elizabeth Boyle, Miriam King, Matthew Sobek Minnesota Population Center, University of Minnesota

Integrated Public Use Microdata Series

U.S. Labor Force Participation: Men Women

Steve Ruggles 1995: “King of Quant” President Population Association of America

New U.S. Data From Ancestry.com

 We build data infrastructure for research community. Specialize in data harmonization.  World’s largest collection of individual population and health data, across 9 projects.  50,000 registered users from over 100 countries.  Free Minnesota Population Center

MPC Data Dissemination, Gigabytes per week

MPC Data Projects

The Problem 1.Combining data from multiple sources is time consuming  Discovery  Data management 2.It’s error prone  Recoding data  Overlook documentation 3.Hard to replicate results 4.Discourages comparative research

Outline  Harmonization methods  Dissemination system  International projects  Integrated DHS  Terra Populus  IPUMS-International

Terminology Harmonization: Combining datasets collected at different times or places into a single, consistent data series. “Integration” Metadata: Data about data. Documentation in broadest sense.

Relation to head Marital status Education Occupation Microdata

Summary Data

Harmonization Methods  Metadata  Data  Dissemination

Systematize Metadata (record layout file, pdf)

MPC Data Dictionary

Water Access Convert Questionnaires to Metadata (Mexico 2000)

Metadata: Questionnaire Text

Water access Bedrooms Rooms XML-Tagged Questionnaire Text

Data: Variable Harmonization Marital Status: IPUMS-International Bangladesh = Unmarried 2 = Married 3 = Widowed 4 = Divorced/separated Mexico = Married, civil & relig 2 = Married, civil 3 = Married, religious 4 = Consensual union 5 = Widowed 6 = Divorced 7 = Separated 8 = Single Kenya = Never married 2 = Monogamous 3 = Polygamous 4 = Widowed 5 = Divorced 6 = Separated

Translation Table Input Bangladesh = Divrc or separated 1 = Unmarried 2 = Married 3 = Widowed Mexico = Married, civil & relig 2 = Married, civil 3 = Married, religious 4 = Consensual union 5 = Widowed 6 = Divorced 7 = Separated 8 = Single Kenya = Never married 2 = Monogamous 3 = Polygamous 4 = Widowed 5 = Divorced 6 = Separated

LabelCode Translation Table Harmonized 1 = Never married1 = Married, civil & relig 4 = Divrc or separated 1 = Unmarried 2 = Married 3 = Widowed 2 = Married, civil 3 = Married, religious 4 = Consensual union 5 = Widowed 6 = Divorced 7 = Separated 8 = Single Single Married or in union Married, formally Civil Religious Civil and religious Monogamous Polygamous Consensual union Separated Divorced 2 = Monogamous 3 = Polygamous 4 = Widowed 5 = Divorced 6 = Separated Mexico 1970 Input Bangladesh 2011 Kenya 1999 Divorced or separated 3 Widowed 4

LabelCode Translation Table Harmonized 1 = Never married 1 = Married, civil & relig 4 = Divrc or separated 1 = Unmarried 2 = Married 3 = Widowed 2 = Married, civil 3 = Married, religious 4 = Consensual union 5 = Widowed 6 = Divorced 7 = Separated 8 = Single Single Married or in union Married, formally Civil Religious Civil and religious Monogamous Polygamous Consensual union Separated Divorced 2 = Monogamous 3 = Polygamous 4 = Widowed 5 = Divorced 6 = Separated Mexico 1970 Input Bangladesh 2011 Kenya 1999 Divorced or separated 3 Widowed 4

Data Dissemination System

Variables Page

238 censuses

Sample Filtering

Variables Page – Filtered

Variable Page: Marital Status

Variable Codes (Marital status)

Variable Codes (Marital status)

Variable Codes (Marital status)

Variable Page: Marital Status

Variable Comparability Discussion (Marital status)

Variable Page: Documentation

Questionnaire Text

(Marital status, Cambodia)

Variables Page

Extract Summary

Case Selection

Age of spouse Employment status of father Occupation of father Attached Characteristics

Extract Summary

Download or Revise Extract

On-line Analysis

The International Projects

Integrated DHS

 Foremost source of health information for the developing world  Funded by USAID  Since 1980s, over 300 surveys, 90 countries  Topics: fertility, nutrition, HIV, malaria, maternal and child health, etc Demographic and Health Surveys

 5-year NIH grant (end of year 2)  Focus on Africa, with India  Partnership with ICF-International and USAID IDHS Project

Motivation: DHS is incredibly valuable, but it’s hard to capitalize on its full potential. Problem:  Data discovery  Dispersed documentation  Data management  Variable changes over time Not unique to DHS: endemic to any survey that’s persisted over decades. Why an Integrated DHS?

DHS Research Process Example: Find data on female genital cutting Survey Search Tool

Recode notes Data dictionary Just the woman file – for one survey. 61 to go. Still need Report (377 page pdf) Contains questionnaire and sample design information Errata file

DHS “Recode Variables” make it more harmonized than most surveys  Consistent variable names  Each DHS phase has a shared model questionnaire But:  6 phases over 25+ years  Country control over final wording of surveys  Country-specific variables  The recode variables can be a two-edged sword At least the DHS variables are already harmonized, right?

Ghana 1993 V130 Ghana 2008 V130 India 1992 V130 India 2005 V130 Harmonization: Religion

Harmonization: Female Circumcision Ever Circumcised

Timeline: 2014 (current) 9 countries, 39 samples Much of woman files Women of child bearing age as unit of analysis

Timeline: countries, 69 samples Complete the woman files Children & birth files

Timeline: countries, 94 samples Men and couples files

Timeline: Next grant 41 African countries, 130+ samples 11 Asian countries, 32+ samples

Beta

Lower barriers to conducting research on population and the environment. Motivation: The data from different domains have incompatible formats, and few researchers have the skills to combine them Terra Populus Goal

5 year grant NSF  At mid-point: year 3 TerraPop

6 countries:  Argentina  Brazil  Malawi  Spain  United States  Vietnam Population Microdata

Tabulations of census data for administrative units Area-level Data

Land cover from satellite images (Global Land Cover 2000) Agricultural use from satellites and government records (Global Landscapes Initiative) Climate from weather stations (WorldClim) Environmental Data Rasters (Grid Cells)

Microdata Area-level data Rasters Mix and match variables originating in any of the data structures Obtain output in the data structure most useful to you Location-Based Integration

Individuals and households with their environmental and social context Microdata Area-level data Rasters Location-Based Integration

Summarized environmental and population Microdata Area-level data Rasters County ID G G G G G G G County ID Mean Ann. Temp. Max. Ann. Precip. G G G G G G G County ID Mean Ann. Temp. Max. Ann. Precip. Rent, Rural Rent, Urban Own, Rural Own, Urban G G G G G G G characteristics for administrative districts Location-Based Integration

Rasters of population and environment data Microdata Area-level data Rasters Location-Based Integration

Rasterization of Area-Level Data

Area-Level Summary of Raster Data

Linkages across data formats rely on administrative unit boundaries Particular needs  Lower level boundaries  Historical boundaries Boundaries are Key

Geographic Harmonization

Web interface will change significantly in fall 2014 Fast microdata tabulator needed Beta Version

IPUMS-International

Census microdata from around world Funded by NSF and NIH Motivation:  Provide data access  Preservation

Khartoum, CBS-Sudan

Dhaka, Bangladesh Bureau of Statistics

IPUMS-International Participating Disseminating

IPUMS Censuses Per Country

Variables Included in Extracts

Top Institutional Users

Millennium Development Goals Ratio of literate women to men, years old Source: Cuesta and Lovatón (2014) 1990 Census round

Millennium Development Goals Source: Cuesta and Lovatón (2014) Data Source: IPUMS-International, Minnesota Population Center Census 1993 Census 2005 Colombia: Adolescent Birth Rate

 Data acquisition  Outreach: developing countries  Virtual data enclave IPUMSI Future

Thank you!