Data Projects at the Minnesota Population Center Resources for Comparative Population and Health Research Seattle, Washington May 22, 2014 Elizabeth Boyle,

Data Projects at the Minnesota Population Center

Resources for Comparative Population and Health Research

Seattle, WashingtonMay 22, 2014

Elizabeth Boyle, Miriam King, Matthew Sobek Minnesota Population Center, University of Minnesota

[email protected]

Integrated Public Use Microdata Series

U.S. Labor Force Participation: 1850-2012

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, 1993-2012

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 DictionaryVariable Start Width Value Var ValueLabel Frequency Universe

SMOKE100 57 1 Ever smoked 100 cigarettes All persons

1 Yes 54,189

2 No 59,501

7 Don't know/Not sure 205

9 Refused 39

SMOKENOW 58 1 Smoke cigarettes now Persons who ever smoked

1 Yes 25,644

2 No 28,535


9 Refused 10

Blank [no label] 59,745

SMOKE30 59 2 Number of days smoked in the last 30 Persons who currently smoke

1 to 30 Number of days 25,290


88 None 49

99 Refused 12


SMOKENUM 61 2 Number of cigarettes smoked per day Persons who currently smoke

0 to 76 Number of cigarettes 22,292


99 Refused 43


WaterAccess

Convert Questionnaires to Metadata(Mexico 2000)

5. Number of Rooms

How many rooms are used for sleeping without counting hallways? _____ Write the number

Without counting the hallways or bathrooms how many total rooms are in this dwelling? Count the kitchen

_____Write the number

6. Access to water

Read all of the options until you get an affirmative answer. Circle only one answer

1 Running water inside the dwelling 2 Running water outside the dwelling but on the land 3 Running water from a public faucet or hydrant 4 Running water that is carried from another dwelling 5 Tanked in by truck 6 Water from a well, river, lake, stream or other

Answers 3, 4, 5, 6 continue with number 8

7. Water supply

How many days of the week is water available? Circle only one answer

1 Daily 2 Every third day 3 Twice a week 4 Once a week 5 Occasionally

Metadata: Questionnaire Text

Water access

Bedrooms

Rooms

XML-Tagged Questionnaire Text

Data: Variable Harmonization

Marital Status: IPUMS-International

Bangladesh 2011

1 = Unmarried

2 = Married

3 = Widowed

4 = Divorced/separated

Mexico 1970

1 = Married, civil & relig

2 = Married, civil

3 = Married, religious

4 = Consensual union

5 = Widowed

6 = Divorced

7 = Separated

8 = Single

Kenya 1999

1 = Never married

2 = Monogamous

3 = Polygamous

4 = Widowed

5 = Divorced

6 = Separated

Translation TableInput

Bangladesh

2011

4 = Divrc or separated

1 = Unmarried

2 = Married

3 = Widowed

Mexico1970

1 = Married, civil & relig2 = Married, civil



5 = Widowed

6 = Divorced

7 = Separated

8 = Single

Kenya1999

1 = Never married

2 = Monogamous

3 = Polygamous

4 = Widowed

5 = Divorced

6 = Separated

LabelCode

Translation TableHarmonized

1 = Never married1 = Married, civil & relig


1 = Unmarried

2 = Married

3 = Widowed

2 = Married, civil



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

1 0 0

2 0 0

2 1 0

2 1 1

2 1 2

2 1 3

2 1 4

2 1 5

2 2 0

0 0

3 1 0

3 2 0

0 0

Mexico1970

Input

Bangladesh

2011Kenya1999

Divorced or separated3

Widowed4

LabelCode

Translation TableHarmonized

1 = Never married

1 = Married, civil & relig


1 = Unmarried

2 = Married

3 = Widowed

2 = Married, civil



5 = Widowed

6 = Divorced

7 = Separated

8 = SingleSingle

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

1 0 0

2 0 0

2 1 0

2 1 1

2 1 2

2 1 3

2 1 4

2 1 5

2 2 0

0 0

3 1 0

3 2 0

0 0

Mexico1970

Input

Bangladesh

2011Kenya1999

Divorced or separated3

Widowed4

Data Dissemination System

Data Dissemination System

Variables Page

Variables Page

238 censuses

Sample Filtering

Variables Page – Filtered

Variable Page: Marital Status

Variable Codes(Marital status)



Variable Page: Marital Status

Variable Comparability Discussion(Marital status)

Variable Page: Documentation

Questionnaire Text

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 alreadyharmonized, right?

100 Muslim/Islam 4 = Muslim 7 = Moslem 1 = Muslim 2 = Muslim200 Christian 2 = Christian 3 = Christian201 Catholic 2 = Catholic 1 = Catholic202 Protestant 1 = Protestant203 Anglican 2 = Anglican204 Methodist 3 = Methodist205 Presbyterian 4 = Presbyterian206 Pentacostal 5 = Pentecostal208 Other Christian 3 = Other Christian 6 = Other Christian300 Other301 Hindu 0 = Hindu 1 = Hindu302 Sikh 3 = Sikh 4 = Sikh303 Buddhist 5 = Buddhist302 Jain 6 = Jain305 Jewish 7 = Jewish306 Parsi/Zoroastrian 8 = Parsi/Zoroastrian307 Doni-Polo 10 = Donyi polo400 Traditional/spiritual 8 = Trad/spiritualist401 Traditional 5 = Traditional402 Spiritual403 Animist500 No religion 0 = No religion 9 = No religion 9 = No religion600 Other 96 = Other 4 = Other 96 = Other

Ghana 1993V130

Ghana 2008V130

India 1992V130

India 2005V130

Harmonization: Religion

Egypt 1995 S802 Ever circumcisedEgypt 2005 S801 Respondent circumcisedEgypt 2008 G102 Respondent circumcisedEthiopia 2000 FG103 CircumcisedEthiopia 2005 FG103 CircumcisedGhana 2003 S821 CircumcisedKenya 1998 S1002 Respondent circumcisedKenya 2003 S821 CircumcisedKenya 2008 G102 Respondent circumcisedMali 1995 S551 CircumcisedMali 2001 FG103 Circumcised?Mali 2006 G102 Respondent circumcisedNigeria 1999 S521 Type of circumcisionNigeria 2003 FG103 CircumcisedNigeria 2008 G102 Respondent circumcised

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: 2015


Complete the woman files

Children & birth files

Timeline: 2017


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 usefrom satellites and government records (Global Landscapes Initiative)

Climate from weather stations (WorldClim)

Environmental DataRasters (Grid Cells)

Microdata

Area-level dataRasters

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



Summarized environmental and population

Microdata


County IDG17003100001G17003100002G17003100003G17003100004G17003100005G17003100006G17003100007

County IDMean Ann. Temp.

Max. Ann. Precip.

G17003100001 21.2 768G17003100002 23.4 589G17003100003 24.3 867G17003100004 21.5 943G17003100005 24.1 867G17003100006 24.4 697G17003100007 25.6 701

County IDMean Ann. Temp.

Max. Ann. Precip.

Rent, Rural

Rent, Urban

Own, Rural

Own, Urban

G17003100001 21.2 768 3129 1063 637 365G17003100002 23.4 589 2949 1075 1469 717G17003100003 24.3 867 3418 1589 1108 617G17003100004 21.5 943 1882 425 202 142G17003100005 24.1 867 2416 572 426 197G17003100006 24.4 697 2560 934 950 563G17003100007 25.6 701 2126 653 321 215

characteristics for administrative

districts


Rasters of population and environment data

Microdata



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

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

ParticipatingDisseminating

IPUMS Censuses Per Country

IPUMS Censuses Per Country

Variables Included in Extracts

Top Institutional UsersCountry Institution Country Institution

1 USA University of Minnesota 16 Brazil Universidade Federal de Minas Gerais

2 USA Harvard University 17 Mexico El Colegio de México

3 USA University of Michigan Ann Arbor 18 USA Yale University

4 USA Columbia University 19 China University of Hong Kong

5 Spain Autonomous University Barcelona 20 USA University of Washington

6 USA Arizona State University 21 UK London School Economics

7 Singapore National University of Singapore 22 UK University of Stirling

8 IADB Inter American Development Bank 23 France Université de Bordeaux 4

9 WB World Bank Group 24 Austria University of Vienna

10 USA University of California Berkeley 25 Malaysia National University of Malaysia

11 USA Vanderbilt University 26 Austria Vienna Institute of Demography

12 USA University of Chicago 27 USA Pew Research Center

13 Australia University of Queensland Australia 28 Colombia Universidad del Valle

14 USA University of California Los Angeles 29 USA University of Delaware

15 USA Dartmouth College 30 USA Brown University

Millennium Development Goals

Ratio of literate women to men, 15-24 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!

[email protected]

Documents

Data Projects at the Minnesota Population Center Resources for Comparative Population and Health Research Seattle, Washington May 22, 2014 Elizabeth Boyle,