User Guide

Overview

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

To download data, users must register for a free account. Apply for access here. We also have a video tutorial on how to create an account. Your application will be reviewed within two working days.

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.

9000
Logical edit - missing
9994
Not interviewed (service delivery provider [SDP] questionnaire)
9995
Not interviewed (female questionnaire)
9996
Not interviewed (household questionnaire)
9997
Don't know
9998
No response or missing
9999
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 missing. IPUMS assigns these uniformly missing cases distinct codes (*94, *95, or *96), to distinguish such cases from other kinds of non-responses. Such cases are also identified in the variables CONSENTSQ, CONSENTHQ, and CONSENTFQ, for service delivery point questionnaires, household questionnaires, and female questionnaires, respectively. By default, the data extract system will filter out these non-interviewed cases, but users can opt to include them by choosing the "All Cases" option on the Sample Selection screen. Find more information on case selection in our video tutorial.

Calculating representative estimates (weights)

PMA data are designed to be used with weights to yield accurate estimates. For information about weights and weighting procedures, see our weighting guide, and the FAQ discussion.

IPUMS PMA produces a population count weight for female-level variables called POPWT. See our user note on how to use POPWT, or our video tutorial on how POPWT was created.

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.

Other questions?

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 ipums@umn.edu.