IPUMS PMA is the harmonized version of the multinational survey Performance Monitoring for Action (formerly known as Performance Monitoring and Accountability 2020 - PMA2020). IPUMS PMA lets researchers easily browse the contents of the survey series and craft customized microdata files they download for analysis.
How to access the data
Register an Account
Upon approval, you will be granted access to download data for the countries you specified in your application. You may subsequently request access to additional countries, if your research question requires expanded geographic scope. Apply for access to additional countries' data here.
Browse the contents of the PMA survey
IPUMS is an integrated data discovery and extraction system. Video tutorials describe how to use the dissemination system. IPUMS PMA differs from most other IPUMS projects (except IPUMS DHS) by requiring you to select the unit of analysis before browsing and selecting variables. In IPUMS PMA, you choose between downloading records of persons (and their household characteristics) or records of health facilities for either family planning surveys or nutrition surveys. You may also choose infant-level data from a panel study on maternal and newborn health. See our video tutorial on IPUMS PMA for more detail about the units of analysis.
Design a customized data file
You can create a customized dataset by selecting only the samples and variables you need for your analysis. IPUMS PMA microdata are accessed through our online data extract system. See our video tutorial on how to make a data extract.
How to use the data
Understanding the data
IPUMS PMA includes survey data from women of childbearing age, health facilities, newborn children, and more. See our Sample Notes page for descriptions of each survey type included in IPUMS PMA.
Documentation regarding sample design and methodology is available on the source documents page.
Extensive documentation for each variable is available in the data extract system. Review the online information about variables that interest you. Clicking on a variable name brings you to a variable description, which shows unweighted codes and frequencies, text about the meaning of the variable, comparability issues, and a universe statement (who was included in the variable).
IPUMS PMA uses consistent codes to specify different types of missing data: NIU (not in universe), missing, and other non-valid response codes. Note that "not in universe" means that the question intentionally excludes someone; for example, never-married women would not be asked their age at first marriage. The following is an example of the various types of missing data codes for a 4-digit variable.
- Logical edit - missing
- Not interviewed (service delivery provider [SDP] questionnaire)
- Not interviewed (female questionnaire)
- Not interviewed (household questionnaire)
- Don't know
- No response or missing
- NIU (not in universe)
If a sampled respondent for the service delivery point questionnaire, the selected respondent for the household questionnaire, or an eligible respondent for the female questionnaire did not take the survey, they are included in the data file, but their responses are all coded as
Calculating representative estimates (weights)
Using the Service Delivery Point (SDP) data
Service delivery point data are designed to describe the service delivery environment that sampled females experience. See our user note on how to use the SDP and female data together.
Using the Contraceptive Calendar data
For the PMA survey redesign implemented in 2019 and onward surveys, the baseline female survey includes a 2 or 3 year retrospective contraceptive calendar. See our user note on how to use these data.
Creating pointer variables to a child's mother in Nutrition Round 2
PMA nutrition samples include separate records for mothers and children living in the same household. These records may be linked with a user-generate pointer variable, enabling researchers to connect antenatal healthcare interventions with infant health outcomes. This guide demonstrates how to construct such a variable with functional programming tools in R.
Refer to the FAQ or to other user resources at the support page. If you have a specific question, you can post to the IPUMS User Forum or email detailed information about your problem to the IPUMS User Support team at email@example.com.