1. Data access and development: The IPUMS perspective United Nations Commission on Population and Development The data revolution in action: National and international experiences with microdata dissemination and public use New York 11 April 2016

2.  World’s largest archive of population data  Individual-level microdata describing ~3 billion persons enumerated in 100 countries  IPUMS model:  Harmonize variables codes across data collection  Integrate documentation  Web dissemination  Free to the research and policy community IPUMS Data Integration Projects

3. IPUMS Data Dissemination, 1995-2015 Gigabytes per week

4. Outline  International IPUMS projects  IPUMS-International  Integrated DHS  Terra Populus  Spatial data integration  Implications for SDGs

5. IPUMS-International Coverage Over 100 Collaborating National Statistical Agencies

6.  82 countries  277 censuses  614 million person records  Two-thirds of samples from developing countries Microdata Currently Disseminated

7. IPUMS Samples per Country Argentina5Fiji5Malawi3Senegal2 Armenia1France7Malaysia4Sierra Leone1 Austria4Germany4Mali2Slovenia1 Bangladesh3Ghana2Mexico7South Africa3 Belarus1Greece4Mongolia2South Sudan1 Bolivia3Guinea2Morocco3Spain3 Brazil6Haiti3Nepal1Sudan1 Burkina Faso3Hungary4Netherlands3Switzerland4 Cambodia2India5Nicaragua3Tanzania2 Cameroon3Indonesia9Nigeria5Thailand4 Canada4Iran1Pakistan3Turkey3 Chile5Iraq1Palestine2Uganda2 China2Ireland9Panama6Ukraine1 Colombia4Israel1Peru2UK2 Costa Rica4Italy1Philippines3USA7 Cuba1Jamaica3Portugal3Uruguay6 Dominican Republic5Jordan1Puerto Rico5Venezuela4 Ecuador6Kenya5Romania3Vietnam3 Egypt2Kyrgyz Republic2Rwanda2Zambia3 El Salvador2Liberia2Saint Lucia2

8. IPUMS Samples per Country Argentina5Fiji5Malawi3Senegal2 Armenia1France7Malaysia4Sierra Leone1 Austria4Germany4Mali2Slovenia1 Bangladesh3Ghana2Mexico7South Africa3 Belarus1Greece4Mongolia2South Sudan1 Bolivia3Guinea2Morocco3Spain3 Brazil6Haiti3Nepal1Sudan1 Burkina Faso3Hungary4Netherlands3Switzerland4 Cambodia2India5Nicaragua3Tanzania2 Cameroon3Indonesia9Nigeria5Thailand4 Canada4Iran1Pakistan3Turkey3 Chile5Iraq1Palestine2Uganda2 China2Ireland9Panama6Ukraine1 Colombia4Israel1Peru2UK2 Costa Rica4Italy1Philippines3USA7 Cuba1Jamaica3Portugal3Uruguay6 Dominican Republic5Jordan1Puerto Rico5Venezuela4 Ecuador6Kenya5Romania3Vietnam3 Egypt2Kyrgyz Republic2Rwanda2Zambia3 El Salvador2Liberia2Saint Lucia2

9.  geographic location (places of 20,000+ persons in most samples)  assets and utilities: water supply, sewage, toilet, electricity, mobile telephones, Internet  building materials—floor, roof, etc.  educational attainment, literacy, school enrollment  economic activities, unemployment, disabilities  fertility history and child mortality Many IPUMS variables are relevant to SDGs Microdata: custom analyses tailored to local conditions

10. Dissemination

11. Select Samples

12. Select Variables

13.  12,000 approved users  70,000 custom data extracts IPUMS Usage

14. IPUMS Users – Region of Residence

15. IPUMS Samples Extracted, by Region

16. Web Portal: UNECA, Addis Ababa

17. IPUMS On-line Tabulator

18. 3.1 million cases in one second

19. Physical Data Recovery: Old Media

20. Darfur 1973

21. Bangladesh 1981

23. Metadata Preservation

24. Collaboration of IPUMS, DHS Program, USAID Integrated Demographic and Health Surveys

25. Motivation: DHS is incredibly valuable, but it’s hard to capitalize on its full potential. Problems:  Data discovery  Dispersed documentation  Data management  Variable changes over time and between countries Why an Integrated DHS?

26. Integrated DHS Coverage

28. Three Source Data Formats Microdata Characteristics of individuals and households Small-area data Characteristics of places defined by administrative boundaries Raster data Values tied to spatial coordinates

29. Location-Based Integration 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

30. Location-Based Integration Attach land cover and climate data to individuals Microdata Area-level data: Summarize rasters Rasters: Environmental data

31.  To analyze change over time within countries  To combine data sources at the subnational level Spatial Data Integration

32. Spatial Integration – Digitize Maps

33. Spatial data integration Buenos Aires province, Argentina

34. Spatial data integration Some units must be merged or split to make footprint consistent over time

35. Global harmonized boundaries Harmonized 1st-level boundaries for all countries released 2014

36. Global harmonized boundaries Harmonized 2nd-level boundaries, in process

37. Ratio of literate women to men, 15-24 years old Source: Cuesta and Lovatón (2014) 1990 Census round Millennium Development Goals

38. Census 1993 Census 2005 Colombia: Adolescent Birth Rate

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

40. Source: IPUMS-International, Minnesota Population Center Percentage of the urban population living in slums Mexico, 2000 Sustainable Development Goals

41. Source: IPUMS-International, Minnesota Population Center Percentage of the urban population living in slums Mexico, 2010

42. Sustainable Development Goals Men Percentage of the population that owns of mobile phone by sex, Ghana 2010 Women

43. Conclusions Data integration is expensive, but it saves money in the long run, reduces the potential for error, and simplifies replication Administrative and survey data to assess development goals should be centrally integrated at the individual level wherever possible We need consistent geographic units over time to make sub-national estimates of change Sub-national estimates of change are essential for identifying places where progress has stalled and more resources are needed

44. Thank You! Matt Sobek sobek@umn.edu

45. United States 1960