U.S. flag

An official website of the United States government

Skip Header

How We Complete the Census When Households or Group Quarters Don’t Respond

Written by:

As we continue to process 2020 Census responses, people have asked what happens when we don’t get a response from an address. In this blog, I’ll describe the statistical techniques we turn to when we do not get a response.

To get a complete and accurate census, we need to know certain information about every household address in the census:

  • Whether it is a real and livable residence.
  • If so, whether it is vacant or occupied.
  • If it's occupied, how many people live there.
  • Demographic characteristics about the people who live there, such as age, sex and race.

At the end of data collection, we had the information required to address the first three bullets on almost every address in the country. For the remaining small fraction of addresses, we apply a technique called count imputation to resolve whether they were occupied and how many people lived there.

During data processing, we also use another type of imputation — characteristic imputation — to fill in missing characteristics for people in the census.

In what follows, we distinguish between addresses for housing units and group quarters. We must answer similar questions about group quarters, but we treat the two differently in our data collection and in how we handle missing responses.

Below, I'll describe how we:

  • Collect census data when we can't reach someone in the household.
  • Use count imputation for the remaining housing units.
  • Apply a different type of count imputation for group quarters.
  • Apply characteristic imputation to handle missing demographic characteristics for housing units and group quarters. 

Filling in the Gaps for Housing Units

Our research has shown that the best information about a household comes from the household itself. However, some households didn’t respond even after the outreach efforts of nearly 400,000 partners across the country, a multilingual communications campaign, as well as a number of mailings and visits from census takers.

When we can’t get a response directly from an address, we look to three widely used statistical methods to fill the gaps:

  • Administrative records. First, if a census taker can’t get a response from a household, administrative records enable us to count the household with information they have already provided to the government, such as through a response to a previous census or survey, to the IRS through a tax return, or to one of several other government programs. We only use records when we have confidence in their quality for the household. We talked more about how we use this existing information in the recent Administrative Records and the 2020 Census blog.
  • Proxy responses. If high-quality administrative records aren’t available for an address after an initial visit, census takers continue to visit the address. If they still can’t get a response after three visits, they try to get information about the address from a neighbor, landlord or building manager. We refer to these as “proxy responses.” We also rely on these types of proxies to help us verify housing units that appear vacant or cannot be located.
  • Imputation. As a last resort, we use a statistical technique called “imputation” to fill the gaps in a dataset. It makes the overall dataset — or census in this case — more accurate than leaving the gaps blank. By using imputation, we fill in what we don’t know, using information we do know.

We will provide metrics related to our use of each of these three techniques on the same day we release the first census results later this month. More information about these upcoming metrics is available in the recent Introduction to Quality Indicators: Operational Metrics blog.

Count Imputation for Households

One type of imputation we use is called “count imputation.” This type helps us fill in the information that leads to the count of people living in a household — the first three items in the list I mentioned above.

For example, we use imputation to fill in whether an address is occupied and the number of people who live there by copying information from the nearest, similar neighbor. Here’s how that works:

  • To fill in missing information, we start by grouping every address based on what we’ve learned about it throughout the census process — information from our address list, data our census takers have gathered in the field, and other sources.
  • For addresses that are missing information, we copy data from its nearest neighbor in the same group.

We use this approach because it works well where vacant housing and houses of a similar size are clustered geographically. As a result, we can insert missing information about an address’s occupancy status and population size more accurately.

If we were to leave the count for an address blank after all attempts to obtain a response failed, it would be like assigning a count of 0. This would be less accurate overall than imputing a number statistically, since we often have information that people are living there.

It is important to note that count imputation for households has been used to complete the census count going back to the 1960 Census. Using this statistical method was challenged in the courts following the censuses of 1980 and 2000. Each time the courts upheld the use of count imputation.  

After we finish processing 2020 Census responses, we will report what percentage of the count comes from count imputation.

  • In each of the last five censuses, count imputation made up less than half of 1 percent of the total population count.
  • In 2010, 0.4 percent of the population count was imputed.
  • This decade, we expect a similarly small percentage of addresses will need count imputation because they were not accounted for during our data collection operations.
  • However, in the 2020 Census, the overall rate of count imputation will increase relative to 2010 for a different reason.

During our recent post-processing activities, we incorporated a new operation that removed a large number of people duplicated in the 2020 Census. (We’ll discuss this more in an upcoming blog.) This operation left a small set of household addresses unresolved and in need of count imputation.

As a result, when we report later this month on the percentage of addresses and people imputed, we will also report its two components:

  • The percentage unresolved after data collection.
  • The percentage unresolved because of unduplication.

Count Imputation for Group Quarters

Group quarters are residences where typically a number of unrelated people live, such as a college dormitory, a nursing home or a state prison.

As described in more detail in the 2020 Census Group Quarters blog, we had to adapt and delay some of the ways we counted group quarters because of the COVID-19 pandemic.

After the end of data collection, when we began processing census data from group quarters, we realized that many of them were occupied on April 1, 2020 (the reference day for the census), but didn't provide a population count. 

For example, when we enumerated them in midsummer, some group quarters said they were vacant but they were actually occupied on April 1. If not corrected, such cases could lead to an undercount. If the corrections were not properly coordinated with our procedures to remove duplicated people, they could contribute to an overcount. 

To address these issues, the Census Bureau reached out in a special telephone operation to contact many of the group quarters that provided us incomplete data. Simultaneously, we assembled a team to correct responses when possible and to apply a new count imputation procedure when more specific information was not available.

For some group quarters, we still didn't have response data to determine precisely how many people should be assigned to an occupied facility. For these cases, we devised business rules to determine which group quarters needed count imputation. We then developed, tested and applied imputation procedures to the appropriate cases. 

Unlike the count imputation procedure for housing unit addresses, the procedure for group quarters doesn't draw information from the nearest, similar neighbor.

Instead, we use imputation procedures that are more like those we use in economic surveys and censuses. To impute a count, this approach generally uses information already available on the group quarters under consideration — such as the expected count or the maximum capacity the group quarters reported during the advance contact. 

The Census Bureau had never before conducted count imputation on unresolved group quarters. In the coming months, we will provide metrics on the use of imputation for the group quarters population. For those individuals, we will also impute all their characteristics, as I describe below.

Handling Missing Characteristics

For housing units and group quarters, we use another type of imputation, “characteristic imputation,” to help us fill in a household’s missing characteristics, such as age or race. (Although the concept is similar for housing units and group quarters, our application differs slightly between the two.)

With this type of imputation, we look at a combination of sources to fill in the missing information — other information from the individual’s or household’s 2020 Census response, their responses from another census or survey, other existing records, or information from similar nearby neighbors.

For example:

  • If a person reported their date of birth, we can fill in their missing age. If date of birth is missing too, we can often use what they reported on another census or survey.
  • If race is reported for a parent, we could use that information to fill in their child’s missing race. If no information is available within the household, we would impute the information using data from similar nearby households.
  • If all of the characteristics are missing for every person in a household, we would look to prior survey or census responses and other existing records. If those are unusable, we would impute the information by copying data from similar nearby households.

As with count imputation, we plan to provide rates on characteristic imputation after we finish processing 2020 Census responses. (We apply this type of imputation during the next phase of data processing — creating the Census Edited File — so these rates will not be available yet when we release the count imputation rates toward the end of April.)

We emphasize that count imputation and characteristic imputation are only implemented long after all data collection has ended. They follow after all attempts to obtain a response — a self-response from the household or group quarters, an interview with a census taker, information from a proxy, or administrative records of high quality — have been exhausted.

We recognize that using information from these three techniques — imputation, using administrative records, and proxies — may not always match the reality of an address’s occupancy status or the characteristics of the people who live there. However, these techniques are widely used in statistics because they have been proven to be more accurate than leaving the information blank.

Ultimately, we know the best information about a household comes directly from the household or the people at the group quarters. When they don’t respond, these techniques help us deliver a more complete and accurate count.

Related blogs

Random Samplings Blog
Upcoming 2020 Census Coverage Estimates 
The U.S. Census Bureau released coverage estimates for the 2020 Census.

Random Samplings Blog
The Post-Enumeration Survey: Measuring Coverage Error
Although we undertake extensive efforts to accurately count everyone in the decennial census, sometimes people are missed or duplicated.

Random Samplings Blog
Using Demographic Benchmarks to Help Evaluate 2020 Census Results
One of the primary methods of evaluating the quality of a census is comparing the results to other population benchmarks.

Random Samplings Blog
Programa de Evaluaciones y Experimentos del Censo del 2020
Este blog describe la serie de evaluaciones formales que miden diferentes aspectos de las operaciones del censo y los desafíos.

Random Samplings Blog
2020 Census Program for Evaluations, Experiments, and Assessments
This blog describes the series of formal evaluations and assessments that measure different aspects of census operations and specific challenges.

Random Samplings Blog
Improvements to the 2020 Census Race and Hispanic Origin Question Designs, Data Processing, and Coding Procedures
This blog discusses how we improved the census questions on race and Hispanic origin, also known as ethnicity, between 2010 and 2020.

Random Samplings Blog
How We Complete the Census When Demographic and Housing Characteristics Are Missing
Although we strive to obtain all demographic and housing data from every individual in the census, missing data are part of every census process.

Random Samplings Blog
Censo del 2020: Métricas de calidad, Publicación 2
Este blog proporciona datos destacados del segundo grupo de métricas operacionales de calidad del Censo del 2020.

Random Samplings Blog
2020 Census Operational Quality Metrics: Release 2
Today we released the second round of 2020 Census operational quality metrics.

Random Samplings Blog
Examining Operational Quality Metrics
The Census Bureau is taking a multifaceted approach to studying the quality of the 2020 Census, so as to produce a more complete and informative picture.

Random Samplings Blog
Comparisons to Benchmarks as a Measure of Quality
Data quality is multidimensional and so approaching it from multiple angles produces a more insightful and holistic picture of a dataset.

Random Samplings Blog
Revisión de los datos del Censo del 2020
En este blog hablamos sobre cómo estamos realizando una de las revisiones de datos más completas en la historia reciente del censo, para el Censo del 2020.

Random Samplings Blog
2020 Census Data Review
For the 2020 Census, we are conducting one of the most comprehensive reviews in recent census history.

Random Samplings Blog
Cómo completamos el censo cuando los hogares o alojamientos de grupo no responden
Mientras continuamos procesando las respuestas al Censo del 2020, las personas han preguntado qué sucede cuando no obtenemos una respuesta de una dirección.

Random Samplings Blog
How We Complete the Census When Households or Group Quarters Don’t Respond
As we continue to process 2020 Census responses, people have asked what happens when we don’t get a response from an address.

Random Samplings Blog
Administrative Records and the 2020 Census
Each decade we are asked, “Why don’t you just use the information the government already has about me for the census? Why ask me again?”

Random Samplings Blog
Los registros administrativos y el Censo del 2020
Este blog describe cómo el Censo del 2020 usó los registros administrativos para contar a las personas que no respondieron.

Random Samplings Blog
Introduction to Quality Indicators: Operational Metrics
In the coming weeks, the U.S. Census Bureau will release the first set of results from the 2020 Census. Our goal for every census is to count everyone once, only once, and in the right place.

Random Samplings Blog
2020 Census Group Quarters
As we continue processing 2020 Census results, we’d like to provide more information on how we count people living in group quarters (GQs).

Random Samplings Blog
Encontrar ‘anomalías’ demuestra que los controles de calidad del Censo del 2020 funcionan
El 9 de marzo de 2021, la Oficina del Censo de los EE. UU. publicó un blog (en inglés) sobre las “anomalías” que encontramos al procesar los datos del Censo del 2020.

Random Samplings Blog
Finding ‘Anomalies’ Illustrates 2020 Census Quality Checks Are Working
We’re in the midst of data processing for the 2020 Census. As Acting Census Bureau Director Ron Jarmin acknowledged in a recent blog, we’ve discovered some “anomalies” along the way that we’re looking into and resolving.

Random Samplings Blog
Adaptación de las operaciones de campo para enfrentar desafíos sin precedentes
La oficina del Censo de los EE. UU. compartió información en una publicación de blog el 1 de marzo de 2021, acerca de cómo la realización de un censo es una tarea enorme, incluso en circunstancias ideales.

Random Samplings Blog
Adapting Field Operations to Meet Unprecedented Challenges
As we process census responses and analyze the quality of the 2020 Census, it’s helpful to look back at some of the unprecedented challenges we faced during this census.

Random Samplings Blog
Ensuring a Robust and Accurate Data Quality Analysis in the 2020 Census
Asking outside experts to review our work is standard operating procedure at the U.S. Census Bureau. It underscores our commitment to quality and transparency.

Random Samplings Blog
Timeline for Releasing Redistricting Data
We expect to deliver the redistricting data to the states and the public by Sept. 30, 2021.

Random Samplings Blog
Census Data Processing 101
Michael Thieme describes how census data processing works to ensure the census is accurate.

Directors Blog
2020 Census Processing Updates
I’m writing to provide an update on data processing for the 2020 Census.

Random Samplings Blog
Update on 2020 Census Data Processing and Quality
The Census Bureau has begun processing the data collected for the 2020 Census. Data collection for the decennial census is always a herculean task and 2020 was no exception.

Is this page helpful?
Thumbs Up Image Yes Thumbs Down Image No
255 characters maximum 255 characters maximum reached
Thank you for your feedback.
Comments or suggestions?


Back to Header