Data – Use It for Good

Comparing Census Bureau and IPUMS USA Inflation Adjustments to American Community Survey Income Variables

August 18, 2026 by mpcblog

By Kari Williams & Isabel Pastoor

Researchers working with variables that report income must account for the changing value of a dollar across time. IPUMS USA, and other IPUMS data collections, include resources that streamline the process of adjusting monetary variables into constant dollars to facilitate comparison. These tools are very useful for researchers who are initiating research projects and exclusively using IPUMS data; however, these tools can make it difficult to directly compare, for example, IPUMS and the original U.S. Census Bureau versions of public use microdata sample (PUMS) data. This blog post provides an overview of how the Census Bureau reports income values in the American Community Survey (ACS) PUMS data, the adjustment factors provided by the Census Bureau, and how those map onto variables and ACS microdata extracts from IPUMS USA.

Want to skip the gory details? The key points are summarized in the next paragraph and in Table 1.

TL;DR

The Census Bureau does not automatically adjust for inflation in the PUMS files. They provide an adjustment factor, available as the variable ADJINC in the original Census Bureau PUMS files, that users can apply manually. This adjustment factor is intended to account for the differential reference period of respondents based on the rolling sample design of the ACS and, in multi-year files, to account for inflation across the five-year (or three-year) period. In contrast, IPUMS USA does automatically adjust for inflation across multi-year periods. However, IPUMS does not automatically apply or recommend using the within-year reference period adjustment because it does not adequately account for the differences in reference periods between respondents within a single year of the ACS. Users who wish to apply this adjustment can use the IPUMS USA variable ADJUST, which provides the within-year adjustment factor only (i.e., it does not include the multi-year inflation adjustment as that is automatically applied by IPUMS to multi-year ACS PUMS samples).

Table 1: Comparing U.S. Census Bureau and IPUMS USA Adjustment of Income Variables, 1-Year and 5-Year ACS PUMS

	Census Bureau	IPUMS
Inflation Across Years	1-Year: N/A	1-Year: N/A
Inflation Across Years	5-Year: Apply manually, included in ADJINC variable	5-Year: Applied automatically
Within-Year Reference Period Adjustment	1-Year: Apply manually, entirety of ADJINC variable	1-Year: Not recommended; apply manually using ADJUST variable
Within-Year Reference Period Adjustment	5-Year: Apply manually, included in ADJINC variable	5-Year: Not recommended; apply manually using ADJUST variable

Family Interrelationships Variables in IPUMS MEPS

July 31, 2026July 1, 2026 by mpcblog

By Etienne Breton

Health and family are inextricably tied. Their interplay is complex and dynamic, ranging from biological transmissions to the presence or absence of familial support over the life course. Elucidating these associations often requires vast datasets collected over multiple decades – to account for the ever-changing health and family circumstances of our lives. Researchers interested in investigating these questions at scale may now add a new tool to their toolkit: IPUMS family interrelationship variables are now available in IPUMS MEPS!

Also known as family pointers, these variables identify the location of a person’s probable co-resident spouse and/or parent(s) in the household. They increase reproducibility, flexibility and ease of use when analyzing family units and relationships within households. Whether interested in studying simple parent-child dyads or complex multigenerational arrangements, users may now seamlessly attach characteristics of in-household family members to a person’s records in MEPS.

IPUMS has pioneered the development of family pointers on nationally-representative samples of households and individuals, and these variables have since been added to most of our data collection projects. Their recent addition to IPUMS MEPS presents exciting opportunities owing to the unique richness of the MEPS data, which includes the possibility to eventually expand these pointers to a panel format.

How do the IPUMS MEPS family pointers compare to those in other IPUMS data collections?

The construction of these family interrelationship variables is comparable with other IPUMS microdata collections centered in the US: these are IPUMS USA¹, IPUMS CPS, IPUMS ATUS and IPUMS NHIS. The logic underpinning both common and project-specific codes is best described in the rule variables (as exemplified in the variables descriptions for MEPS: SPRULE and MOMRULE). These variables detail how pointers were attributed to certain individuals and not others, which further allows users to adjust the strictness of pointer attributions.

Let us provide a very brief overview of these procedures. In IPUMS MEPS, as in other IPUMS data collections, the assignment of family pointers and the corresponding rule variables rely primarily on information provided by the variable RELATE (denoting relationship to the householder or household reference person), and additionally on information from variables AGE, SEX and MARSTAT (marital status). The vast majority of family pointers are assigned using direct links established by RELATE (i.e., when a respondent is listed as the child or spouse of the householder). In IPUMS MEPS, these direct attributions represent between 94.7% and 98.9% of all assigned pointers depending on the year and the family pointer variable under consideration.

There remains, therefore, cases that RELATE does not directly solve. For instance, RELATE identifies persons who are grandchildren of the householder but does not specify who are the parents of those grandchildren among all children of the householder. In such clear but indirect cases, our codes algorithmically assign parent-child and spouse-spouse links based on information from RELATE as well as respondents’ age and marital status. These assignments are not probabilistic but instead follow a predefined logic which relies on a small number of well-defined assumptions². Crucially, the values of the rules variables listed above correspond to how direct (first digit) and unambiguous (second digit) each case is, with lower numbers indicating more direct and/or unambiguous cases. This means that users can rely on these rule variables to tailor the levels of directness and clarity they prefer for assigning family pointers.

Note that MEPS data are collected in a panel format: they encompass five interview rounds carried out over two calendar years. Currently, we provide family pointers for person records reported at the annual-level (or full-year consolidated files); variables reported at this level may differ from individual round-level observations, for which we do not yet offer family pointers. These variables should, therefore, be interpreted as reflecting household membership and family interrelationships within households as of December 31 of the survey year under consideration. The vast majority of family pointers are assigned using direct links established by RELATE (i.e., when a respondent is listed as the child or spouse of the householder)³.

Does 1 + 2 = 8? Automating QA/QC for Tabular Data

May 21, 2026May 21, 2026 by mpcblog

By Tracy Kugler and Tsu Zhu

The problem with OCR and numbers

To extract data tables from census reports only available as print documents, IPUMS IHGIS uses optical character recognition (OCR) software to automate the conversion of scanned images into digital representations of letters and numbers. OCR software has made great strides in accuracy for textual information by using dictionaries of known words to interpret uncertain letters. However, dictionaries do not help in distinguishing uncertain numerical digits. While a dictionary can suggest that the third character in “wh_t” should be an ‘a’ and not an ‘o’, there is no simple way to tell whether the third digit in “45_” should be a 3 or an 8. To ensure that IHGIS data are accurate, we must have confidence that each number has been recognized correctly and matches the number in the source document.

To address this gap, we developed an R package that leverages IHGIS structured metadata to identify logical relationships between cell counts and row/column totals and determine where cells don’t add up as expected. Often, a given cell participates in multiple relationships, which allows the package to use patterns among discrepancies to pinpoint and correct errors. The package can automatically identify and correct up to 95% of error cells, depending on the structure of relationships.

Identifying relationships from structured metadata

The R package currently relies on structured metadata generated by earlier stages in the IHGIS data processing pipeline to identify sum and total relationships among rows and columns. After tables are OCR’ed from source documents, we use a customized markup framework to generate metadata. We then convert the marked up files into CSV files with a standard structure, which serve as input to the quality assurance/quality control (QA/QC) process. The CSV files include hierarchical labels for categories on the columns and geographic units on the rows. Within the labels, blanks are used to indicate totals. The package identifies a column/row with a blank header cell as the sum of other columns/rows that share the same non-blank label(s) and have sub-category labels corresponding to the blank.

IPUMS DHS Goes Global

May 4, 2026 by mpcblog

By Miriam L. King and Sula Sarkar

IPUMS DHS now includes integrated variables for 84 counties (up from 51) and nearly 350 samples (up from 233), including new data from Latin America, Eastern Europe, Oceania, the Caribbean, and Central and East Asia. Providing DHS data in a form that facilitates micro-analyses across countries is one of IPUMS’ greatest strengths, so researchers will be excited to learn that they can now do even more! Our latest data release expands the scope of IPUMS DHS beyond its initial coverage of Africa, the Middle East, and South Asia and adds the latest samples for 12 countries previously in the database. Figure 1 shows the full geographic scope of IPUMS DHS, as well as highlighting newly added countries and previously included countries with new samples.

Figure 1: Countries included in IPUMS DHS

Linking children and adolescents to their mothers using IPUMS MICS

March 20, 2026 by mpcblog

By Anna Bolgrien

IPUMS MICS offers hundreds of harmonized variables related to children’s health and wellbeing that allow for rich and innovative research. From the IPUMS MICS website, users can browse variables and create custom data extracts within a selected unit of analysis. In order to conduct many analyses, however, users will want to combine and link datasets relating to different units of analysis available in MICS.

For example, to investigate how child characteristics are related to characteristics of their mother, users will need to download and link data between the Children (either 0-4 or 5-17) unit of analysis and the Women unit of analysis.

IPUMS MICS provides instructions for linking across units of analysis as a user note. This user note lists the variables available as linking keys for each unit of analysis, and is a general guide for linking across the units, such as linking household characteristics with individual person records.

In this blog post, we provide more detailed information on how to link children and adolescents to their mothers. Similar logic can be applied to link children to fathers or other caregivers in the household. As IPUMS MICS requires Stata to conduct harmonization, we provide example code in Stata syntax.

New Tool! ATUS-CPS Linking Counts

February 11, 2026 by mpcblog

By Sarah Flood

The team at IPUMS is excited to introduce something brand new! ATUS-CPS Linking Counts is an interactive tool for exploring the number of ATUS respondents who can be linked to specific CPS months. We know that linkages between ATUS and CPS have great potential for enabling exciting new research, but we also know firsthand how hard it can be to wrap your head around the panel component of the CPS, the relationship between ATUS and CPS, and the many possibilities for linking them. Even researchers who have deep knowledge of the ATUS and CPS may still wonder whether there is a sufficient number of cases to conduct an analysis of interest. This new tool helps address all of these challenges. It very quickly allows you to view the number of ATUS respondents who should appear in each CPS month and determine if there is sufficient sample size for a particular application of linked ATUS-CPS data.

Linking ATUS and CPS data enables an incredible wealth of research questions. This tool allows users to specify and view different linking scenarios to assess the feasibility of various ATUS to CPS linkages. For example, you may want to investigate the relationship between food security in the CPS with shopping or eating-related behavior in the ATUS. This interactive tool would allow you to select only years of ATUS data that contain, for example, the Eating and Health module and view the CPS months in which the Food Security supplement was fielded to assess the sample sizes for your desired analysis. Figure 1 shows how you would select ATUS years of interest and find information about which ATUS modules were fielded in each year.

Figure 1. Selecting ATUS Years of Interest

New Variables! IPUMS International Fall 2025 Data Release

January 27, 2026 by mpcblog

By Rodrigo Lovaton Davila

IPUMS International recently added twenty-one new harmonized variables that expand the thematic coverage of the data collection and enable new possibilities for research. Most notably, the data release introduces harmonized variables representing sample level information, including selected characteristics of the statistical operation and the sampling design (accessible in technical household). This information was previously available in the sample descriptions section, but is now also accessible through variables that can be included in data extracts. Read on for more details on these new sample-level variables and a few new work and household amenity variables!

New variables about the statistical operation describe whether the data correspond to a census or a survey; whether enumeration was de jure or de facto; the type of form received by respondents in the sample; and the month of data collection. The IPUMS International data collection currently includes 395 census samples, 233 labor force surveys, and 27 population surveys.

FORMTYPE allows users to identify whether the data for each sample consist of responses to a single, standard questionnaire applied to the entire population; responses to a short or long form, in a census that gathered more information from a sample of the population; or records derived from administrative registers (with no questionnaire used in data collection.) Most datasets in the collection correspond to one standard questionnaire (79% of 395 census samples). For censuses where a short and a long form were applied, the samples in IPUMS typically correspond to the long questionnaire (78% of 78 samples), which includes additional questions and is richer for research purposes.

ENUMTYPE indicates whether the enumeration was de jure or de facto, an important distinction for understanding how the population was counted in the census operation. Some censuses enumerate combining both de jure (usual residents) and de facto (those present on the census reference date whether resident or visitor), which is reflected in this new variable. Importantly, users can work with the existing variable RESIDENT to eliminate double-counting of persons who were enumerated both at their permanent residence and at the residence they were visiting on census night. ENUMMO complements the variable YEAR to provide a more accurate indicator of the timing of data collection.