To some extent these figures may reflect the position of males and females in broader society. For example, it is more acceptable for males and females in their 20s to remain living in their parents' home, so they are apt to have a stable housing situation at that stage in their lives. As they move into their 30s and 40s, it becomes less acceptable to remain living in their parents' home (particularly for males) so their housing situations are more likely to be unstable. Also, many females in their 20s, 30s and 40s, are likely to be responsible for taking care of children on a daily basis which makes stable housing more important. A stable housing situation is associated with a higher likelihood of being counted in the Census (Martin 2007).

#### **6.3 Undercount by Sex and Race**

The overall Census coverage figures for males and females mask big differences by race. These differences are first examined using DA data then using data from DSE. Recall that in DA, the only race group that can be identified for all ages is Black and the residual category is called Non-Black.

Table 6.2 shows that based on data from DA, there was a net undercount of 4.6% for Black Alone males compared to a small net undercount (0.1%) for Black Alone females resulting in a 4.5 percentage point gap. For the Non-Black Alone males there was a net undercount of 0.3% compared to a net overcount of 1.1 for females in this population, so the gap is 1.4 percentage points. In other words, males have lower coverage rates than females in both groups, but the differential is much larger for Blacks Alone than for Non-Blacks Alone.

**Table 6.3** 2010 Census net undercount rates by sex, race, and age

*Source* U.S. Census Bureau (2012a), Table C

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different than zero

In the Black Alone population, the biggest difference in coverage rates between males and females is for the population in their 30s and 40s. The biggest single year difference is age 29 where there is an 8.6% net undercount for males and a 0.7% overcount for females (data not shown here).

Table 6.3 shows Census coverage for males and females by age in six different race/Hispanic groups based on DSE. In almost every comparison in Table 6.3, females have better coverage rates than males. In a few cases the differences are extreme. For example, for Blacks Alone or in Combination age 30–49, the net undercount rate for males was 10% compared to a slight net overcount (0.2%) for females in this age range. This comparison highlights the very precarious position of Black males in their 30s and 40s in terms of being missed in the Census.

Table 6.3 also highlights the difference sex makes in the 18–49-year-old category compared to age 50 and over. For the population age 50 and over most groups experience a net overcount and for those groups that did experience a net undercount it is lower than that for age 18–49.

**Table 6.4** 2010 Census net undercount rates for males and females by age, sex, and tenure

*Source* U.S. Census Bureau (2012a), Table A

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

**Table 6.5** 2010 census omission rates for male and females by age, sex, and tenure

*Source* U.S. Census Bureau (2012a), Table A

#### **6.4 Net Undercount and Omissions Rates for Males and Females by Age and Tenure**

Table 6.4 provides net undercount rates for males and females by age and tenure. For the population living in owner-occupied housing units, females are covered better than males and the same is true for the population living in rental occupied housing units. The gap between males and females is smaller for the population living in owner-occupied housing than the population living in rental housing. The group with the highest net undercount rate is male renters age 30–49 where there is a net undercount rate of 5.5%. This group combines three characteristics (male, renter, and age 30–49) that are associated with poor coverage in the Census.

As stated earlier, DSE is the only method that provides omissions rates. Table 6.5 shows omissions rates by age, sex, and tenure from the 2010 Census. The age/sex patterns for omissions are like previously seen patterns for net undercounts. Males have higher omissions rates than females in every age group. The omissions rates for male renters age 18–29 are the highest at 12.5%.

**Fig. 6.2** Census net undecount rates for males and females: 1940–2010. *Source* 2010 Data from May 2012 DA Release. 1940–2000 from U.S. Census Bureau (2001), Table 4

#### **6.5 Differential Census Coverage by Sex Over Time**

Figure 6.2 shows net undercount rates for males and females from 1940 to 2010 based on Demographic Analysis. There are three key points shown in Fig. 6.2.

First, the coverage of females has been better than the coverage of males in every Census since 1940. Second, the coverage of both males and females has improved consistently since the 1940 Census. Both males and females experienced high net undercounts in the 1940 Census (5% or more), but in the 2010 Census there was 1.1% overcount for females compared to a 0.8% undercount for males.

Third, the difference in Census coverage between males and females has widened somewhat since 1940. In 1940, the difference between the coverage rate of males and females was less than one percentage point but in 2010 the difference was nearly two percentage points.

#### **6.6 Sex and Sexual Orientation**

I would be remiss if I did not say a word about sexual identity and sexual orientation in the context of the Census. The "sex" question was introduced in the first Census in 1790 and has been used ever since without generating much controversy.  The U.S. General Accountability Office (2018, p. 5) identified "Lesbian gay bisexual transgender queer/questioning persons" as being hard-to-count.

In 1990, advocates ran the first national campaign to press the Bureau to report data on same-sex couples (National LGBTQ Task Force 2017). In 2005, the Census Bureau responded by beginning to test and report on data on same-sex couples in response to the "relationship" question (U.S. Census Bureau 2018). Starting in 2013, all Census tables that have a line for "married couples" include same-sex couples.

As we approach the 2020 Census, including measures of sexual orientation and gender identity on the 2020 Census questionnaire has become a widely discussed topic (Wang 2017). Modifications to the "relationship" question on the 2020 Census questionnaire will allow respondents to explicitly identify themselves as the married or unmarried same-sex partner of the householder for the first time. Other questions on gender identity and sexual orientation remain in the testing phase for possible inclusion on future Censuses or American Community Survey questionnaires. For more information about his topic see www.queertheCensus.org.

#### **6.7 Summary**

Generally, males have higher net undercount rates and higher omissions rates than females. There is virtually no gap between males and females for children (under age 18) or for the population over age 70. For adults in the 20s, 30s and 40s, males typically have a much a higher net undercount rates and higher omissions rates that females.

The male-female gap in coverage is larger for the population living in rental housing units than those living in owner-occupied units. The coverage gap between males and females is particularly large in the Black population.

The net undercount rates of males and females have both improved since the 1940 Census, but the gap between males and females has grown a little wider in recent decades.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 7 Census Coverage of the Hispanic Population**

**Abstract** The net undercount for Hispanics in the 2010 Census was relatively high at 1.5% compared to a net overcount of 0.8% for the Non-Hispanic White Alone population. The omissions rate for Hispanics (7.7%) was about twice the rate for Non-Hispanic White Alone (3.8%). Hispanic men age 18–49 had net undercount rates over 5% and omissions rates over 10% in the 2010 Census.

#### **7.1 Introduction**

Studying the recent history of Census coverage of Hispanics is important because they are the fastest growing major race/ethnic group in the U.S. and they have relatively high net undercount and omissions rates. The number of Hispanics in the U.S. grew from 50.5 million in 2010 to 57.5 million in 2016 (U.S. Census Bureau 2017c). Consequently, the Census coverage of Hispanics increasingly drives the overall Census coverage figures.

The Dual-Systems Estimates (DSE) method provides net undercount estimates and omissions rates for Hispanic by age-sex groups and tenure in the 2010 Census. Demographic Analysis (DA) only provides data by single year of age for Hispanics under age 20 because Hispanic were not recorded on birth and deaths certificates consistently until 1990. The term Hispanic is used here rather than Latino, because that is the term used most often in Census Bureau publications.

#### **7.2 Net Undercount Rates of Hispanic Adults**

Overall, there was a net undercount for Hispanics (1.5%) and a net overcount of Non-Hispanic White Alone (0.8%) resulting in a difference of 2.3 percentage points. This pattern is generally consistent across age and sex groups.

Table 7.1 shows net undercount rates from DSE for Hispanics and the Non-Hispanic White Alone population ages 10 and over from the 2010 Census broken down by age and sex. Data for the youngest age group (0–9) are not presented in

**Table 7.1** 2010 Census coverage of Hispanics compared to Non-Hispanic White Alone by age and sex

*Source* U.S. Census Bureau (2012c), Table C

A negative sign reflects an undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

Table 7.1 because there is strong evidence that the coverage estimates of young children from DSE in 2010 are problematic (O'Hare et al. 2016). For information on the Census coverage of young Hispanic children in the 2010 Census see the analysis of DA data later in this Chapter.

Every age/sex group of Hispanics except those over age 50, had a net undercount while only one group of Non-Hispanic White Alone population (males age 30–49) had a net undercount. The net undercount rate for Hispanic males age 18–29 was 5.2% and the net undercount rate for Hispanic males 30–49 was 5.1% and both rates are statistically significantly different than zero.

To focus on differential coverage, the net undercounts for Hispanics are compared to the Census coverage rates for Non-Hispanic White Alone population in the same age/sex category. The biggest coverage differential in Table 7.1 is among males age 18–29. The net undercount rate for Hispanic males age 18–29 is 5.2% compared to a net overcount of 1.5% for Non-Hispanic White Alone. This results in a difference of 6.7 percentage points. This percentage point differential is more than twice as high as any other age/sex group examined in Table 7.1 and underscores the special problems associated with trying to enumerate Hispanic males in their 20s.

The differential Census coverage for males age 18–29 may reflect differences in socio-economic status. Overcounts in this age group are often attributed to young people being counted in the college dormitory (or off campus residence) as well as their parents' home (see Chap. 5). Census undercounts are often attributed to unstable living arrangements or lack of attachment to a housing unit (Martin 1999, 2007). The undercount differential reported above suggests that young adult Hispanic males may be more likely to be experiencing unstable living situations while young adult Non-Hispanic White Alone males may be more likely to be in college.

#### 7.2 Net Undercount Rates of Hispanic Adults 73

The net undercount rates for Hispanic males age 30–49 was also relatively high (5.1%) but the differential between Hispanics and Non-Hispanic Whites Alone is not as high as for males age 18–29 because there is a statistically significant net undercount for Non-Hispanic White Alone males in this age group (2.1%) as well, leaving a net undercount differential of only 3.0 percentage points.

It is possible that the net undercount rate for Hispanic males age 18–49 is even higher than what was reported by the Census Bureau because correlation bias among Hispanic men is not accounted for in the DSE analysis. Correlation bias means the types of people missed in the Census are also likely to be missed in the Post-Enumeration Survey that is the basis for the DSE estimates (U.S. Census Bureau 2012a). Correlation bias leads to an underestimate of net undercount rates. The Census Bureau made an adjustment to the DSE estimates for Black adult males, based on the sex ratio shown in DA (U.S. Census Bureau 2012a). No adjustment was made for Hispanic adult males because the data needed for such an adjustment (DA sex ratios) were not available for Hispanics.

Another group that is noteworthy in Table 7.1 is Hispanic females age 50 or older. There is a large statistically significant net overcount of this group (3.6%). This is the only age/sex group of Hispanics to experience a statistically significant net overcount. There is also a large statistically significant overcount for Non-Hispanic White Alone females in this age group (2.2%) so the gap between Hispanics and Non-Hispanic White Alone is relatively small. It is possible the high net overcount of Hispanic females age 50 or older is due to an under-reporting of births more than a half-century ago and/or undetected in-migration.

#### **7.3 Omissions Rates for Hispanics**

Recall that the Census net undercount rate is a balance between people omitted and those included erroneously (mostly double counted). The omissions rate captures the share of a group missed in the Census. DSE is the only method that shows omissions rates.

In many ways the omissions rate is a more meaningful statistic than the net undercount rate because in the net undercount calculation omissions can be cancelled out by erroneous inclusions or double counting. A net undercount of zero could be the result of no one being missed and no one double counted, or for example, 10% missed, and 10% double counted.

Table 7.2 shows omissions rates for Hispanics and Non-Hispanic White Alone by age and sex. The overall omissions rate for Hispanics (7.7%) is twice that of the Non-Hispanic White Alone population (3.8%). To a large extent the omissions rates reflect the same age/sex pattern as the net undercount rates. In every age/sex group examined here, the omissions rate for Hispanics is higher than that of Non-Hispanic White Alone. Like the results for net undercount rates, Hispanic males age 18–49 had the highest omissions rates. The omissions rate for Hispanic males age 18–29 was 12.4% and the omissions rate for Hispanic males age 30–49 was 10.9%.

**Table 7.2** 2010 Census omissions rates for Hispanics and Non-Hispanic Whites Alone by age and sex

*Source* U.S. Census Bureau (2012c), Table C

Hispanic females age 18–29, also had a relatively high omissions rate of 9.6% but the omissions rates for Hispanic females age 30–49 (5.8%) was lower than the overall omissions rate for Hispanics.

#### **7.4 Differences in Census Coverage by Tenure**

Table 7.3 shows net undercount rates and omissions rates from the 2010 Census DSE analysis for the populations living in owner-occupied housing units and renteroccupied housing units among Hispanics and the Non-Hispanic White Alone population.

Two patterns are clear. First, Non-Hispanic White Alone population was covered better than Hispanics for the populations living in both owner-occupied housing units and rental housing units. Among the population living in owner-occupied units, there was a larger net overcount for Non-Hispanic Whites Alone (0.8%) than for Hispanics (0.3%) and the rate for Non-Hispanic Whites Alone was statistically significantly different than zero, while the rate for Hispanics was not. Among those living in renter-occupied housing units there was a statistically significant net undercount for Hispanics (3.3%) compared to a net overcount (0.9%) for Non-Hispanic White Alone. Omissions rates for Hispanics are higher than for Non-Hispanic White Alone for renters and owners.

Second, the gap between census coverage of Hispanics and Non-Hispanic Whites Alone is bigger for the population living in renter-occupied housing units than for the population living in owner-occupied housing units.

For some categories of Hispanic renters, the omissions rates are particularly high. The compound impact of Hispanic Origin Status, age, and tenure can be seen by looking at the omissions rates of Hispanic male renters age 18–29 where about one-

**Table 7.3** 2010 Census net undercount rates and omissions rates for Hispanics and Non-Hispanic White Alone by tenure

*Source* U.S. Census Bureau (2012c), Table B

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

sixth (16.1%) were missed in the 2010 Census and nearly one-seventh (14.1%) of Hispanic males age 30–49 living in rental housing units were missed in the 2010 Census (U.S. Census Bureau 2012b, Table C).

#### **7.5 Census Coverage of Hispanic Children Age 0–19**

In the 2010 Census, DA undercount estimates for Hispanics are limited to those under age 20 because it has only been since 1990 that Hispanics were systematically identified in birth and death certificates across all the states. In the 2010 Census cycle, the Census Bureau first produced a series of five DA estimates based on differing assumptions about births, deaths, and net international migration. The Census Bureau produced five different series to reflect some uncertainty in the DA estimates. The estimates were released in early December of 2010, to mitigate any perception that the actual Census count (released in late December) might influence the DA estimates. Subsequently, the Census Bureau updated the middle series DA estimate in 2012 but did not include Hispanics in the update because the updated data were primarily for use to develop sex ratios for Black adults to be used in the DSE estimates.

Figure 7.1 shows net undercount rates for Hispanic and Non-Hispanics by single year of age from 0 to 19, based on data from the Middle Series December 2010 DA release. Results for Hispanics are compared to those for Non-Hispanics because there are no data for Whites or Non-Hispanic Whites in the DA results for 2010. It

**Fig. 7.1** 2010 Census net undercount rates by single year of age for Hispanics and Non-Hispanics: Age 0–19. *Source* U.S. Census Bureau, December 2010 DA Release

should be noted that Non-Hispanics include Blacks and American Indians and these population have above average net undercount rates (see Chap. 4).

When the population ages 0–19 are examined collectively, Hispanics and Non-Hispanics show very similar net undercount rates. The net undercount rate for Hispanics age 0–19 is 1.2% and for Non-Hispanics it is 1.3%. However, these averages mask some important age differences among Hispanics and Non-Hispanics. At the youngest ages, the net undercount rate for Hispanics is much higher than that for Non-Hispanics. Based on the DA data released in 2010, there was a net undercount of 7.5% for Hispanics age 0–4 compared to a 3.6% net undercount for Non-Hispanics age 0–4 (O'Hare 2015). O'Hare (2015) also reports the net undercount rate for Hispanic children age 0–4 was 7.7% in the 2000 Census. But for ages 10–19, Hispanics had a higher net overcount than Non-Hispanics. For age 10–19, there was a net overcount of 4.3% for Hispanics and 0.1% for Non-Hispanics.

There are a couple of methodological notes on this topic that readers should be aware of. First, there is reason to believe that the DA estimates for the teenage Hispanics may be problematic because of net immigration assumptions for teenagers that did not take into consideration the economic downtown during the 2008 to 2010 period, which may have dampened in-migration of young Hispanics. But this probably had a small impact on the estimates.

Second, the data for age zero (those less than one year old) are problematic for both Hispanics and Non-Hispanics because Hogan and Griffin (2017) found many infants born after April 1, 2010, were included in the 2010 Census count erroneously. So, the net undercount rates for age 0 is actually higher than what is shown here.

Third, it is possible the high net undercount for the youngest Hispanics is partly due to assumptions about births to Hispanics in the 2008–2010 period that were too high. When the DA estimates were first issued in December 2010, the Census Bureau had to make assumptions about births and deaths in 2008, 2009 and the first quarter of 2010 because data from National Center for Health Statistics were not yet available. Recently, Jensen et al. (2016) provided updated estimates for young Hispanics in 2010 based on observed births and deaths in 2008–2010. The update suggests that the initial estimated net undercount for young Hispanics was a little high. The original net undercount estimate was 7.5% and the revised estimate was 6.5%.

Fourth, recently, the U.S. Census Bureau discovered data from the 2010 Mexican Census that shed light on net immigration estimates for young U.S.-born Hispanic children in 2010 (Jensen et al. 2018). The new data suggests earlier undercount estimates for young Hispanics were too high.

Collectively the new data generally show a lower net undercount rate for young Hispanics children than the original DA estimates, but the new net undercount estimates for young Hispanics are still very high compared to other demographic groups.

#### **7.6 Bilingual Questionnaires**

In one respect, Hispanics are different from other groups because bilingual (Spanish/English) Census questionnaires were sent to households in many neighborhoods with large Spanish-speaking populations in the 2010 Census (U.S. Census Bureau 2011). The 2010 Census was the first one to use a bilingual English/Spanish questionnaire. Almost half (46%) of all Hispanics in the U.S. resided in areas that received bilingual questionnaires in the 2010 Census.

Table 7.4 shows the net undercount and omissions rates for Hispanics and Non-Hispanics in Bilingual Mailing Areas compared to other areas. In both types of areas, Hispanics had a statistically significant net undercount. The Hispanic net undercount in Bilingual Mailing Areas (1.3%) is lower than that in other areas (1.7%), but the difference is small. The omissions rate for Hispanics in both types of areas are almost identical (7.9% in bilingual areas compared to 7.6% in other areas).

After evaluating the 2010 Census experience with bilingual questionnaires, the Census Bureau (2011, p. v) concluded, "Further results suggest that the bilingual questionnaire provides substantial benefit to the areas that were targeted…"

#### **7.7 Hispanic Trend Data from 1990 to 2010**

Table 7.5 shows estimated net undercount rates for 1990, 2000 and 2010 Censuses for Hispanics and Non-Hispanic Whites Alone based on the Census Bureau's DSE

**Table 7.4** 2010 Census undercount and omissions rates for Hispanics and Non-Hispanics inside and outside bilingual mailing areas

*Source* U.S. Census Bureau (2012b), Table 16

A negative sign reflects an undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Percent undercount figures in **BOLD** are statistically significantly different from zero

**Table 7.5** Estimates of net undercount rates of Hispanics and Non-Hispanic White Alone populations: 1990, 2000 and 2010

*Source* U.S. Census Bureau (2012b), Table 7

A negative sign reflects an undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

method. In terms of trends over time, the coverage of Non-Hispanic White Alone is consistently better than that of Hispanics, but the magnitude of the difference varies over time.

For Hispanics, there was a relatively high net undercount rate in 1990 (5.0%) but in the 2000 Census the net undercount rate for Hispanics (0.7%) was so low that it was not statistically significantly different than zero. In the 2010 Census, however, there was an estimated net undercount of 1.5% for Hispanics which was statistically significant. The undercount rate for Non-Hispanic Whites Alone went from a net undercount of 0.7% in 1990, to a net overcount of 1.1% in 2000, to a net overcount of 0.8% in 2010.

Looking at net undercount rates for both Hispanics and Non-Hispanic Whites from 1990 to 2010 there doesn't seem to be any consistent trend over the 1990–2010 period. The difference between the Census coverage rates of Hispanics and Non-Hispanic White Alone decreased from 1990 to 2000 but increased from 2000 to 2010.

#### **7.8 Census Coverage of Hispanic Subgroups**

All the Census coverage estimates on Hispanics produced by the Census Bureau treat Hispanics as a single group. But the Hispanic population is far from homogeneous. There are several distinctions that should to be made in the Hispanic population with respect to Census coverage.

First, there are several subgroups of Hispanics such as Mexicans, Puerto Ricans, Central and South Americans, and Cubans. The 2016 American Community Survey indicates there are 36.3 million Mexicans, 5.5 million Puerto Ricans, 5.3 million Central Americans, 3.5 million South Americans, and 2.2 million Cubans in the U.S. Hispanic population. The social, economic, and cultural differences among these subgroups of Hispanics suggest that they are likely to have different levels of Census coverage (National Research Council 2006; O'Hare 2017). There are no direct estimates of Census coverage rates for subgroups of Hispanics produced by the Census Bureau, but some researchers have produced estimates for some subgroups. Using multiple methods, one group of researchers, (Van Hook et al. 2014, p. 699) concluded, "Additionally, we find evidence that U.S. Census and ACS data miss substantial numbers of children of Mexican immigrants, as well as people who are most likely to be unauthorized: namely working-aged Mexican immigrants (ages 15–64) especially males."

Second, many Hispanics speak English well, but some do not. For those who do not speak English well, participating in the Census may be more difficult. About 73% of Hispanics speak a language other than English at home and 31% speak English less than "very well" which is typically the threshold used to determine limited English proficiency (The Leadership Conference Education Fund 2017).

Third, a large share of the Hispanic population are recent immigrants. Kissam (2017) provides ample evidence that recent Mexican immigrants in California had a high likelihood of being missed in the Census. Jensen et al. (2015) show that recent immigrants are more likely to be missed in the Census Bureau's American Community Survey. Many of the recent Hispanic immigrants fall into the category of cultural and linguistic minorities which Harkness et al. (2014) argue is a hard-tocount group in the Census.

Fourth, a substantial fraction of Hispanic immigrants is undocumented. Evidence suggests the undocumented population have high net undercount rates (Warren and Warren 2013; Van Hook et al. 2014). For example, Warren and Warren (2013, p. 307) estimate a net undercount rate of 10% in the 2000 Census for undocumented immigrants who entered the U.S in the 1990s. Also, Van Hook et al. (2014, p. 720) state, "Age and sex patterns further suggest that coverage error among unauthorized Mexican immigrants is probably higher than that for the entire Mexican-born population." It should also be noted that census coverage of some Hispanic subgroups has been addressed in qualitative studies (Romero 1992; Dominguez and Mahler 1993; Mahler 1993).

It is also important to note that Censuses in some other countries are not always viewed positively and immigrants may bring their negative views about census-taking with them when they move to the U.S.

The last-minute addition of a question on citizenship to the 2020 Census is likely to have big implications for the count of Hispanics in the 2020 Census (Ross 2018; Barabba and Flynn 2018; Meyers and Goerman 2018; U.S. Census Bureau 2017a, b). See Chap. 15 for more information on this issue.

#### **7.9 Summary**

The net undercount of Hispanics in the 2010 Census was 1.5% compared to a net overcount of 0.8% for the Non-Hispanic White Alone population. Other findings include:

#### **References**

Barabba, V., & Flynn, K. H. (2018). Ensure everyone is counted. *U.S. News*, January 30, 2018.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 8 Census Coverage of the Black Population**

**Abstract** In the 2010 Census, the Black population had the highest net undercount rate of any major race/Hispanic group. Based on the Demographic Analysis method there was a net undercount of 2.5% for the Black Alone population compared to a net overcount of 0.5% for Non-Blacks. Black males in their 20s, 30s, ands 40s had exceptionally high net undercount rates and high omissions rates. Historically, the Black population, and Black men in particular, experienced high net undercount rates in the Census. While the net undercount rates of Blacks have decreased over time, the differential net undercount between Blacks and Non-Blacks has improved little since 1940.

#### **8.1 Introduction**

Studying the U.S. Census coverage of the Black population is important because they have been undercounted in the Census for many decades and they are a large part of the U.S. population. Consequently, coverage of the Black population has a big impact on the overall accuracy of the Census.

In reporting Census data on the Black population, it is very important to be clear about how the group is defined. Starting in the 2000 Census, people were allowed to select more than one race in the Census questionnaire (U.S. Office of Management and Budget 1997) and race is often shown two different ways in Census Bureau reports. One category is the number of people who only select Black (referred to as Black Alone) and second category is all those in the first category plus those who select Black along with at least one other race (referred to as Black Alone or in Combination). In the 2016 American Community Survey conducted by the Census Bureau there were 40.9 million people who marked Black Alone and 45.1 million who marked Black Alone or Black and some additional race. The Black Alone or in Combination population comprised about 14% of the U.S. total population in 2016. Black Alone or in Combination is the most inclusive definition and its use here is consistent with advice of the U.S. Office of Management and Budget (2001) about using race classifications. Most of the data presented in this Chapter are based on the Black Alone or in Combination definition. The term Black is used instead of African-American because it is the label used most often in Census Bureau publications.

#### **8.2 Census Coverage of the Black Population by Age and Sex**

Both methods for measuring Census accuracy (Demographic Analysis—DA and Dual-Systems Estimates—DSE) show high net undercounts for the Black population. In the 2010 Census, the net undercount rate for the Black Alone population was 2.5% based on Demographic Analysis (DA) while for Non-Blacks there was a 0.5% net overcount (Velkoff 2011). Based on Dual Systems Estimates (DSE) results, the 2010 Census shows a net undercount for the Black Alone or in Combination population of 2.1% compared to a net overcount of 0.8% for the Non-Hispanic White Alone population (U.S. Census Bureau 2012).

The overall net undercount rate for the Black population masks large differences by age and sex. For example, there was a net undercount of 4.6% for Black males compared to a 0.1% net undercount for Black females in the 2010 Census based on DA.

The Black adult male population is particularly vulnerable to net undercounts in the Census. Figure 8.1 shows net undercount rates in the 2010 Census for Black Alone males, Black Alone females, Non-Black Alone males, and Non-Black Alone females by five-year age groups based on DA. Figure 8.1 shows that Black Alone males stand out from the other groups in terms of having high net undercounts from their 20s to their 70s.

Early in life and late in life there are not big differences in the coverage rates of Black males and the other three race/sex groups shown in Fig. 8.1. However, starting in their early 20s into their 70s, the undercount differentials between Black Alone males and the other three groups are substantial. For example, at age 30 the difference in Census coverage rates between Black Alone males and Black Alone females is about 8 percentage points (net undercount rate of 8.3% for 30-year-old Black Alone males and 0% for 30-year-old Black Alone females).

This pattern is not new. High net undercount rates for Black men have been a persistent problem in the U.S. Census (Fein 1989; Hill 1975; Passel 1991; Robinson et al. 1990; Robinson 1997; Fay et al. 1988; West et al. 2014). The report on DA following the 1990 Census (U.S. Census Bureau 1991, p. 1) states,

To summarize, two groups stand out as having relatively high levels of net undercount nationally after considering the possible range of uncertainty in the estimates: 1) Black children aged 0-9 and 2) Black men aged 20-64.

Much of the public and political interest regarding Census undercounts in general and Census undercounts of Black men in particularly can be traced back to publications and events in the late 1960s and early 1970s (Valentine and Valentine 1971; Heer 1968). One of the first events to publicize the high net undercount of the black

**Fig. 8.1** 2010 census net undercount rates by sex and Black/Non-Black by five-year age groups. *Source* US Census Bureau, May 2013

population was a 1968 conference organized by David Heer at the behest of Daniel Patrick Moynihan. According to Heer (1968, p. 2) "Moynihan asked me if I would plan a conference that would (1) publicize the fact that many Negroes had not been counted in the 1960 census, and (2) attempt to rouse national concern about the matter."

One crucial study was that of Hill (1975) who used a synthetic estimation technique and age/sex/race net undercount estimates from the 1970 Census to produce net undercount estimates for large cities. These estimates showed that many large cities had relatively high net undercounts largely because they housed a disproportionately large share of Black males. Hill's study documented something many big-city mayors suspected but had been unable to quantify or demonstrate previously.

To some extent the studies cited above, and other similar studies, launched a series of efforts by the Census Bureau to address differential undercounts (Choldin 1994). Those efforts involved advocating for adjustment of Census results based on net undercounts and better Census outreach in the Black community (see Chap. 13 for more details on this topic).

Table 8.1 shows net undercount rates by age and sex for Black Alone or in Combination and Non-Hispanic White Alone from DSE and they underscore the pattern shown in Fig. 8.1. Data for the youngest ages (0–9) are not presented in Table 8.1 because there is strong evidence that the coverage estimates of young children from DSE in 2010 are problematic (O'Hare et al. 2016).

There are three main points that can be drawn from the data in Table 8.1. First, the Black Alone or in Combination population were generally undercounted while the Non-Hispanic White Alone population was over counted. Second, Black Alone

**Table 8.1** Net undercount rates for the Black Alone or in combination and Non-Hispanic White Alone populations in the 2010 census by age and sex

*Source* US Census Bureau (2012). Table C

A negative sign reflects an undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

or in Combination males were undercounted at a higher rate than Black Alone or in Combination females. Third, both Black Alone or in Combination and White Alone or in Combination females over age 50 have relatively high net overcount rates.

Data in Table 8.1 show net undercount rates for Black Alone or in Combination males in their 30s and 40 were among the highest recorded in the 2010 Census. Table 8.1 shows the net undercount rate for Blacks Alone or in Combination males age 18–29 was 5.9% and for Black Alone males age 30–49 was 10.0%. The results of the DA methodology are consistent with the DSE results. DA shows a net undercount rate for Black Alone males age 18–49 was 7.6%.

#### **8.3 Census Coverage of Black Children**

As noted in Chap. 5, the population age 0–4 had a higher net undercount rate than any other age group in the 2010 Census. There was a high net undercount of young Black children but the net undercount for Black Alone differs strikingly from Black Alone or in Combination. For the population age 0–4 the net undercount rate in the 2010 Census for Black Alone was 4.4% compared to 6.3% for Black Alone or in Combination (O'Hare 2015). Both rates are very high compared to other groups.

Much of the difference between Black Alone and Black Alone or in Combination net undercount rates probably stems from difficulties trying to make the race classifications based on birth certificates consistent with the race classifications based on self-reporting in the Census. Recall that race of a newborn on the birth certificate is assigned based on the race or the parents. Reconciling race from the Census and birth certificates has become more challenging since 2003 when the new birth certificates were introduced which allowed parents to mark more than one race. Complications with making race categories consistent between the Census and vital events were discussed in Chap. 3.

#### **8.4 Census Omissions Rates for the Black Population**

Recall that the net Census undercount rate is a balance between people omitted and those included erroneously (mostly double counted). The omissions rate captures the share of a group missed in the Census. DSE is the only method that shows omissions rates.

In many ways the omissions rate is a more meaningful statistic because in the net undercount calculation, omissions can be cancelled out by erroneous inclusions or double counting. A net undercount of zero could be the result of no one missed and no one double counted, or for example, ten% missed, and ten% double counted.

Table 8.2 shows the omissions rates for the Black Alone or in Combination population and the Non-Hispanic White Alone population by age and sex based on the DSE method. The omissions rate for the Black Alone or in Combination population in the 2010 Census was 9.3% compared to 3.8% for the Non-Hispanic White Alone population. But the omissions rate for the entire population masks substantial variations within age/sex subgroups of the population. Data for young children are not provided here because undercount estimates for young children from the DSE method are problematic (O'Hare et al. 2016).

The omissions rates shown in Table 8.2 follow a familiar pattern. In every demographic category examined in Table 8.2 the omissions rate for Blacks Alone or in Combination was higher than that of Non-Hispanic White Alone. In several cases the omissions rates for Blacks Alone or in Combination were more than twice as high as the rate for Non-Hispanic Whites Alone in the same category. The omissions

**Table 8.2** Omissions rates for Black Alone or in combination population and Non-Hispanic White Alone in the 2010 census by age and sex

*Source* US Census Bureau (2012). Table C

rate for Blacks Alone or in Combination males age 18–29 was 15.6% and for age 30–49 it was 16.7%. Data in Table 8.2 reinforces the very precarious situation of Black men age 18–49 in terms of the probability of being missed in the Census.

#### **8.5 Net Coverage by Tenure**

Table 8.3 shows net undercount rates and omissions rates for Black Alone or in Combination and Non-Hispanic White Alone from the 2010 Census by tenure. For both the population living in owner-occupied housing units and the population living in renter-occupied housing units, Blacks Alone or in Combination have net undercounts while Non-Hispanic Whites Alone have net over counts. The difference in Census coverage between Black Alone or in Combination and Non-Hispanic Whites Alone is bigger among renters than among owners.

The omissions rates for Black Alone or in Combination renters is particularly high at 11%. For some categories of Black Alone or in Combination renters the omissions rates are extremely high. The compound impact of race, age, and tenure is reflected in omissions rates for Black males age 30–49 living in rental housing units where the omissions rate is nearly one-fifth (19.7%) and about one-sixth (16.9%) of Black male renters age 18–29 were missed in the 2010 Census (U.S. Census Bureau 2012, Table C).

**Table 8.3** 2010 Census net undercount rates and omissions rates for Black Alone or in combination and Non-Hispanic White Alone by tenure

*Source* US Census Bureau (2012). Table B

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

#### **8.6 Census Coverage of the Black Population Over Time**

Historically, DA estimates have only been available for two groups: Black and Non-Black. This restriction is due to the lack of race specificity and consistency for data collected on the birth and death certificates historically. The only group that has been identified relatively consistently over time is Blacks and the residual category has been Non-Black. It is important to recognize that the Black population estimates from DA have not been completely consistent over the past several decades (see Robinson 2010) but these inconsistencies are unlikely to have a major impact on trends over time.

In some ways the changes in the Census coverage rates for the Black population over the past 70 years is a "good news/bad news" story. The good news is that the net undercount rate for the Black population has decreased dramatically since 1940. The bad news is that the difference between Census coverage rates for the Black population and Non-Black population has changed very little over that time period.

Figure 8.2 shows the net undercount rate for the Black population has gone from 8.4% in 1940 to 2.5% in 2010. While this is a substantial improvement in Census coverage since 1940, the Black net undercount rate in 2010 is still the highest of any major racial/ethnic group. Moreover, despite this substantial improvement in the Census coverage of the Black population since 1940, the differential undercount between Blacks and Non-Blacks has not changed much. Figure 8.2 shows the differential undercount (Non-Black undercount rate minus the Black undercount rate) was 3.4% age points in 1940 and 3.0% age points in 2010.

However, the short-term story (1990–2010) is somewhat different than the longterm story (1940–2010). The net undercount rate of 5.5% for Blacks in the 1990 Census fell to 2.5% in the 2010 Census. The differential undercount fell from 4.4 points in 1990 to 3.0 in 2010 driven largely by improvements in the coverage of the Black population.

**Fig. 8.2** Differential undercount rates for Blacks and Non-Blacks: 1940–2010. *Sources* Data for 1940–2000 from envisioning the 2020 Census, National Research Council, (2010) Table 2-1; Data for 2010 from Velkoff (2011) Table 2-1, p. 30

I am not aware of any commonly accepted explanation for the dramatic improvement in the Census coverage of the Black population between 1990 and 2010. It is possible the improvement is related to expanded outreach activities such as the Census Partnership Program and Paid Advertising started in the 2000 Census. It is also possible the large-scale incarceration of Black men resulted in a lower net undercount. Finally, changes between 1990 and 2010 in the way data on race have been collected may be related to the changes in net undercount of the Black population since 1990.

It should be noted that the composition of the Non-Black population has changed over time, particularly in the last few decades. Hispanics, another group with a relatively high net undercount, have become a larger part of the Non-Black population which confounds comparisons of Blacks and Non-Blacks over time.

#### **8.7 Summary**

The 2010 Census net undercount and omissions rates for the Black population were the highest of any major race or ethnic group. Black adult males have had particularly high net undercount and omissions rates. The relatively high net undercount of Blacks has been seen in every Census since 1940. Some demographic groups within the Black population were particularly vulnerable.

Other findings include:

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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## **Chapter 9 Census Coverage of the Asian Population**

**Abstract** In the 2010 Census, the net undercount for Asians Alone or in Combination was so small it rounds to zero compared to a net overcount of 0.8% for the Non-Hispanic White Alone population. Asians also had a relatively low omissions rate in the 2010 Census. The omissions rate for Asians Alone or in Combination (5.3%) was slightly higher than the rate for Non-Hispanic White Alone (3.8%). The Asian age/sex group with the highest net undercount rate was Asian Alone or in Combination males age 18–29 who had a net undercount rate of 2.2% in 2010.

#### **9.1 Introduction**

Studying the recent history of U.S. Census coverage of Asians is important because they are one of the fastest growing major race/ethnic groups in the country. Based on Census Bureau population estimates, the Asian Alone or in Combination population grew by 21% between 2010 and 2016 (from 17.3 million in 2010 to 20.9 million in 2016).

In reporting Census data on the Asian population, it is very important to be clear about how the group is defined. Starting in the 2000 Census, people were allowed to select more than one race in the Census questionnaire (U.S. Office of Management and Budget 1997) and race is often shown two different ways in Census Bureau reports. One category is the number of people who only select Asian (referred to as Asian Alone) and second a category is those in the first category plus those who select Asian along with at least one other race (referred to as Asian Alone or in Combination). In the 2016 American Community Survey conducted by the Census Bureau there were 17.6 million people who marked Asian Alone and 20.9 million who marked Asian Alone or Asian and some additional race. Asian Alone or in Combination is the primary population examined in this Chapter. This is the most inclusive definition and its use here is consistent with advice of the U.S. Office of Management and Budget (2001).

The Demographic Analysis method does not produce data on Census coverage for Asians, so all the data in this Chapter come from the Census Bureau's Dual-Systems Estimates (DSE) method.

**Table 9.1** Net coverage rates for Asian alone or in combination and Non-Hispanic White Alone populations in the 2010 census by age and sex

*Source* US Census Bureau (2012). 2010 Components of census coverage for Race groups and Hispanic origin by age, sex, and tenure in the United States. DSSD 2010 Census Coverage Measurement Memorandum Series #2010-E-51, US Census Bureau, Washington, DC. Table C

*Note* The directionality of undercounts and over counts have been reversed from the original publication in order to keep them consistent within this publication in other words, a negative sign implies an undercount

Figures in **BOLD** are statistically significantly different from zero

#### **9.2 2010 Census Coverage of Asians Alone or in Combination by Age and Sex**

Table 9.1 shows net undercount rates from DSE for Asians Alone or in Combination and Non-Hispanic Whites Alone broken down by age and sex. Data for the youngest age group (0–9) are not presented in Table 9.1 because there is strong evidence that the coverage estimates of young children in the DSE in 2010 are problematic (O'Hare et al. 2016).

In the 2010 Census, the net undercount rate for all Asians Alone or in Combination was near zero (Table 9.1). By comparison, there was a statistically significant net overcount of Non-Hispanic White Alone population of 0.8%.

As with most racial and ethnic groups examined here, the net undercount rate for the total Asian Alone or in Combination population masks some important differences by age and sex. However, within the Asian population, the differences by age and sex are not as pronounced as they are for Black or Hispanic populations.

The highest net undercount rate in Table 9.1 is for males age 30–49. The net undercount rate for Asians Alone or in Combination males in this age range is 2.2% and this is the only net undercount estimate for Asian Alone or in Combination that is statistically significantly different than zero. The relatively high net undercount rate of Asian Alone or in Combination males age 30–49 is consistent with most other race and Hispanic groups, where males in this age group have high net undercount rates.

**Table 9.2** Omissions rates for Asian Alone or in combination and Non-Hispanic White Alone populations in the 2010 census by age and sex

*Source* U.S. Census Bureau (2012). DSSD 2010 CENSUS COVERAGE MEASURMENT MEMORANDUM SERIES #2010-E-51, Table C

#### **9.3 Census 2010 Omissions Rates for Asians Alone or in Combination**

Recall that the net Census undercount rate is a balance between people omitted and those included erroneously (mostly double counted). The omissions rate captures the share of a group missed in the Census. DSE is the only method that shows omissions rates.

In many ways the omissions rate is a more meaningful statistic because in the net undercount calculation, omissions can be cancelled out by erroneous inclusions or double counting. A net undercount of zero could be the result of no one missed and no one double counted, or, for example, 10% missed, and 10% double counted.

Examination of omissions rates are particularly important for the Asian Alone or in Combination population because the net undercount of zero could leave people with the impression that no Asians were missed in the 2010 census. The data below show that is not the case.

Table 9.2 shows omissions rates for Asians Alone or in Combination and Non-Hispanic White Alone. The omissions rate for Asians Alone or in Combination (5.3%) is higher than the Non-Hispanic White Alone population (3.8%). Omissions rates vary by age and sex but in each age/sex group Asians Alone or in Combination have higher omissions rates than Non-Hispanic Whites Alone. To a large extent the omissions rates by age and sex among Asians Alone or in Combination reflect the same demographic pattern as the net undercount rates.

Like the results for net undercount rates, Asian Alone or in Combination males age 18–49 had relatively high omissions rates. The omissions rate for both Asians Alone or in Combination males and females age 18–29 was 8.4%. The omissions rate for Asian males age 30–49 (7.8%) is relatively high but the rate for Asian Alone or in Combination females in this age range is only 4.4%. The omissions rates for older Asians Alone or in Combination (age 50 and over) are relatively low.

**Table 9.3** 2010 census net undercount rates and omissions rates for Asian Alone or in combination and Non-Hispanic White Alone by tenure

*Source* US Census Bureau (2012). 2010 components of census coverage for Race Groups and Hispanic origin by age, sex, and tenure in the United States. DSSD 2010 Census Coverage Measurement Memorandum Series #2010-E-51, US Census Bureau, Washington, DC. Table B A negative sign reflects a net undercount. The signs here are reversed from the source report in

order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

#### **9.4 Differential Undercounts of Asians by Tenure**

Table 9.3 shows net undercount rates and omissions rates from the 2010 Census DSE analysis for the population in owner-occupied housing units and for the population in renter-occupied housing units among Asians Alone or in Combination and Non-Hispanic White Alone. The net undercount rates for Asians Alone or in Combination was virtually identical (zero) for both the population living in owner-occupied housing units and those living rental housing units. Interestingly, the net undercount rates for renters and owners are nearly identical for the Non-Hispanic White Alone population as well. This is one of the few places where tenure doesn't seem to make a difference in net undercount rates.

Omissions rates are somewhat higher for Asian Alone or in Combination renters than for homeowners. For both owners and renters, the omissions rates for Asians Alone or in Combination are about one percentage point higher than those of Non-Hispanic White Alone.

**Table 9.4** Net undercount rates for Non-Hispanic Asian Alone and Non-Hispanic White Alone; 1990, 2000, 2010

*Source* US Census Bureau (2012). 2010 census coverage measurement estimation report: summary of estimates of coverage for persons in the United States. DSSD 2010 Census Coverage Measurement Memorandum Series #2010-G-01, US Census Bureau, Washington, DC. Table 7

aA negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

*Note* in this table the race categories are race alone, not race alone or in combination. The data are presented this way to make the 2010 categories consistent with the 1990 categories Figures in **BOLD** are statistically significantly different than zero

#### **9.5 Trend Data from 1990 to 2010**

Table 9.4 shows net undercount rates for 1990, 2000 and 2010 Censuses for Asians Alone and Non-Hispanic Whites Alone based on the Census Bureau's DSE method. Asian Alone is the category used here because that is the only one available in 1990.

Trends in the net undercount rates for both Asians Alone and Non-HispanicWhites Alone have been inconsistent from 1990 to 2010. For Asians, there was a relatively high net undercount rate in 1990 (2.4%) but in the 2000 Census the net undercount rate for Asians (0.8%) was not statistically significantly different than zero. In 2010, there was a net undercount of 0.1% for Asians Alone compared to an overcount of 0.8 for Non-Asian White Alone population.

The undercount differential between Asians Alone and Non-Hispanic White Alone population fell a little between 1990 and 2000 but increased slightly between 2000 and 2010.

#### **9.6 Census Coverage of Asian Subgroups**

In the Census coverage calculations done by the Census Bureau, Asians are treated as a single population. But there are several distinctions that should be made in the Asian population with respect to the probability of being missed in the Census.

There are a great variety of languages, religions, and customs among Asian subgroups (U.S. Census Bureau 2012). Among the Asians counted in the Census Bureau's 2016 American Community Survey there were 4 million Chinese, 3.8 million Asian Indians, 2.8 million Filipinos, 1.8 million Vietnamese, 1.4 million Koreans, and millions of Asians from other countries or ethnic backgrounds. The social, economic, and cultural differences among these subgroups of Asians suggest that they may differ in the levels of Census coverage. O'Hare (2017) shows how undercount-related socioeconomic measures vary for Asian subgroups in California.

Part of the diversity among Asians is linked to the large number of immigrants in this population. The 2016 American Community Survey indicates 66% of the Asian population in the U.S. is foreign-born. In recent years, the number of immigrants from some Asian countries has been higher than any other nation. In 2016, the number of immigrants from India (175, 100) and China/Hong Kong (160, 400) outnumbered those from Mexico (150, 400) (Zong et al. 2018). Consequently, a large and growing segment of the Asian population in the U.S. have many of the characteristics that make recent immigrants difficult to enumerate in the Census (Massey 2014). According to Jensen et al. (2015, p. 1) "The foreign-born, especially recent immigrants, are believed to be a hard-to-count group which increases the likelihood of coverage error for this population." It is also important to note that censuses in some other countries are not always viewed positively and immigrants may bring their negative views about census-taking with them when they move to the U.S.

Given the fact that a substantial portion of the Asian population in the U.S. are recent immigrants from many different Asian counties, they fall into the category of cultural and linguistic minorities. Harkness et al. (2014, p. 245) state, "Cultural and linguistic minorities can be hard to survey, either as the target population or as a subpopulation of a general population survey." The U.S. General Accountability Office (2018, p. 5) also recognizes Cultural and Linguistic minorities as a hard-tocount group in the Census.

A substantial number of Asian immigrants are undocumented immigrants. One estimate indicates there are 1.5 million undocumented immigrants from Asian in the U.S. (Pew Research Center 2017). Evidence suggests the undocumented populations have higher net undercount rates than other immigrants (Warren and Warren 2013; Van Hook et al. 2014).

Given the large number of recent immigrants and undocumented immigrants in the Asian population, the last-minute addition of a question on citizenship to the 2020 Census is likely to have implications for the count of Asians in the 2020 Census (Ross 2018; Barabba and Flynn 2018; Meyers and Goerman 2018, U.S. Census Bureau 2017a, b). See Chap. 15 for more information on this issue.

There are no direct estimates of Census coverage rates for subgroups of Asians produced by the Census Bureau, but the heterogeneity noted above suggests there are likely to be substantial differences in census coverage among Asian subgroups. At least in part, this heterogeneity is why some observers fear Asians may be undercounted in the 2020 Census (Fuchs 2017; The Leadership Conference Education Fund 2107; Kang 1990; Horikoshi and Minnis 2018).

#### **9.7 Summary**

While the net undercount for Asian Alone or in Combination is very low in the 2010 Census, there was an omissions rate of 5.3% for Asians Alone or in Combination, some age/sex groups within the Asian population experienced high net undercounts and omissions rates. In addition, it is likely that the Census coverage rates for some Asian subgroups are different than the Census coverage for all Asians.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 10 Census Coverage of American Indians and Alaskan Natives**

**Abstract** Overall, the net undercount of American Indians and Alaskan Natives Alone or in Combination is relatively low (0.2%) but some subgroups within this population have high net undercount and omissions rates. The omissions rate for American Indians and Alaskan Natives Alone or in Combination was 7.6% which is double the rate for Non-Hispanic White Alone (3.8%). Also, Census coverage for American Indians and Alaskan Natives living on reservations was much worse than for this population living elsewhere. The net undercount rate for American Indians Alone and Alaskan Natives or in Combination living on reservations was 4.9% in the 2010 Census.

#### **10.1 Introduction**

In reporting Census data on the American Indian and Alaskan Native population it is very important to be clear about how the group is defined. Starting in the 2000 Census, people were allowed to select more than one race in the Census questionnaire (U.S. Office of Management and Budget 1997). Given this option, race is often shown two different ways in Census Bureau reports. One category is the number of people who only select American Indian and Alaskan Native (referred to as American Indians and Alaskan Natives Alone) and a second category includes everyone in the first category as well as those who select American Indian and Alaskan Native along with at least one other race (referred to as American Indians and Alaskan Native Alone or in Combination).

The Census Bureau population estimates for 2016 show 4.1 million people identified as American Indian and Alaskan Native Alone and 6.7 million selected American Indian and Alaskan Native Alone or in Combination. American Indian and Alaskan Natives Alone or in Combination is the primary definition for the population examined in this Chapter. This is the most inclusive definition and using it in this Chapter is consistent with advice of the U.S. Office of Management and Budget (2001).

The Demographic Analysis (DA) method does not produce data on Census coverage for American Indians and Alaskan Natives, because this category has not been used consistently in birth and death certificates over time or across states. All the data in this section come from the Census Bureau's Dual-Systems Estimates (DSE) method. Keep in mind the sample size of American Indians and Alaskan Natives in the Post-Enumeration Survey used in the DSE method is smaller than most other groups so small differences between rates may not be significant.

#### **10.2 Undercount of American Indians and Alaskan Natives**

Table 10.1 shows net undercount rates from DSE for American Indians and Alaskan Natives Alone or in Combination and Non-Hispanic Whites Alone broken down by age and sex. Data for the children under age 10 are not provided in Table 10.1 because there is strong evidence that the DSE coverage estimates of young children are problematic (O'Hare et al. 2016).

In the 2010 Census, there was a small net undercount (0.2%) for American Indians and Alaskan Natives Alone or in Combination which was not statistically significantly different from zero. By comparison, there was a statistically significant net overcount of Non-Hispanic White Alone population of 0.8%.

As with all the racial and ethnic groups examined in this publication, the net undercount rate for the total population masks some important differences among age/sex subgroups. However, within the American Indian and Alaskan Native Alone or in Combination population, the differences by age and sex are not as pronounced as they are in the Black and Hispanic populations.

**Table 10.1** Net undercount rates for American Indians and Alaskan Natives Alone or in Combination population and Non-Hispanic White Alone population in the 2010 census by age and sex

*Source* U.S. Census Bureau (2012a). "2010 Components of Census Coverage for Race Groups and Hispanic Origin by Age, Sex, and Tenure in the United States". DSSD 2010 CENSUS COVERAGE MEASUREMENT MEMORANDUM SERIES#2010-E-51, U.S. Census Bureau, Washington, DC A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

The highest net undercount rate in Table 10.1 is for American Indian and Alaskan Native Alone or in Combination males age 30–49 where the net undercount rate is 3.1% which is statistically significant. There is also a high statistically significant net undercount rate for females age 18–29 (2.6%). On the other hand, there is a large net overcount of American Indian and Alaskan Native Alone or in Combination females over age 50 (4.6%). It is not clear why older American Indians and Alaskan Natives Alone or in Combination females have such a high net overcount rate. These are the only net coverage estimates for American Indians and Alaskan Natives Alone or in Combination that are statistically significantly different than zero.

#### **10.3 Census Coverage on Reservations**

One important distinction among American Indians and Alaskan Natives Alone or in Combination with respect to the accuracy of Census counts is whether they live on an Indian Reservation. Data indicate American Indians and Alaskan Natives Alone or in Combination living on Indian reservations have much higher net undercounts than those living elsewhere in the country (U.S. Census Bureau 2012b, Table 7). In the 2010 Census, there were 571,000 American Indians or Alaskan Natives living on Indian Reservations (U.S. Census Bureau 2012b, Table 9).

The net undercount in the 2010 Census for American Indians and Alaskan Natives Alone or in Combination living on reservations was 4.9%. For American Indians Alone and Alaskan Natives or in Combination living in elsewhere there was a net overcount of 2.0%. The omissions rate for American Indians and Alaskan Natives Alone or in Combination living on reservations was 13.7% (U.S. Census 2012b, Table 9).

The high net undercount and omissions rates of American Indians and Alaskan Natives living on reservations may reflect the presence of some of the same hard-tocount (HTC) characteristics (high poverty and unemployment rates, unconventional housing, and low levels of literacy, for example) that exist in many HTC urban neighborhoods.

#### **10.4 Omissions Rates for American Indians and Alaskan Natives**

Recall that the net Census undercount rate is a balance between people omitted and those included erroneously (mostly double counted). The omissions rate captures the share of a group missed in the Census. DSE is the only method that shows omissions rates.

**Table 10.2** Omissions rates for American Indians and Alaskan Natives Alone or in Combination and Non-Hispanic White Alone populations in the 2010 census by age and sex

*Source* U.S. Census Bureau (2012a). "2010 Components of Census Coverage for Race Groups and Hispanic Origin by Age, Sex, and Tenure in the United States". DSSD 2010 CENSUS COVERAGE MEASUREMENT MEMORANDUM SERIES#2010-E-51, U.S. Census Bureau, Washington, DC. Table C

In many ways the omissions rate is a more meaningful statistic than the net undercount rate because in the net undercount calculation omissions can be cancelled out by erroneous inclusions or double counting. A net undercount of zero could be the result of no one missed and no one double counted, or for example, ten percent missed, and ten percent double counted.

Examination of omissions rates are particularly important for the American Indians and Alaskan Natives Alone or in Combination population because the very low net undercount could leave people with the impression that almost no American Indians and Alaskan Natives Alone or in Combination were missed in the 2010 Census. The data below show that is not the case.

Omissions rates show much bigger differences between American Indian and Native Alaskans Alone or in Combination and Non-Hispanic Whites Alone than were seen in comparisons of net undercount rates. Table 10.2 shows omissions rates by age and sex for American Indians and Alaskan Natives Alone or in Combination and the Non-Hispanic Whites Alone population. The omissions rate for American Indians and Alaskan Natives Alone or in Combination (7.6%) is double that of the Non-Hispanic White Alone population (3.8%).

Data in Table 10.2 show that the omissions rates for American Indians and Alaskan Natives Alone or in Combination are higher than the corresponding cell for Non-Hispanic Whites Alone in every case. The omissions rates for American Indians and Alaskan Natives Alone or in Combination varies by age and sex and to a large extent the omissions rates reflect the same pattern as the net undercount rates.

Like the results for net undercount rates, American Indian and Alaskan Native Alone or in Combination males age 18–49 had higher omissions rates than any other sex/age subgroup. For young adults (age 18–29) the omissions rates were high for both males and females. The omissions rate for American Indians and Alaskan

**Table 10.3** 2010 census net undercount rates and omissions rates for American Indians and Alaskan Natives Alone or in Combination and Non-Hispanic White Alone by Tenure

*Source* U.S. Census Bureau (2012a). "2010 Components of Census Coverage for Race Groups and Hispanic Origin by Age, Sex, and Tenure in the United States". DSSD 2010 CENSUS COVERAGE MEASUREMENT MEMORANDUM SERIES#2010-E-51, U.S. Census Bureau, Washington, DC. Table B

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

Natives Alone or in Combination males age 18–29 was 11.2% and for females age 18–29 it was 10.3%. The omissions rate for males age 30–49 was 9.9%. Older American Indians and Alaskan Natives Alone or in Combination (age 50 and over) omissions rates were lower.

#### **10.5 Coverage of American Indians and Alaskan Natives by Tenure**

Table 10.3 shows net coverage rates and omissions rates from the 2010 Census DSE analysis for the population living in owner-occupied housing units and the population living in renter-occupied housing units among American Indians and Alaskan Natives Alone or in Combination compared to the Non-Hispanic White Alone population.

In terms of net undercount rates, the population of American Indians and Alaskan Natives Alone or in Combination living in owner-occupied housing units had a net overcount of 1.3% which is slightly higher than that of Non-Hispanic Whites Alone (0.8%). On the other hand, American Indian and Alaskan Natives Alone or in Combination living in rental-occupied housing units had a 1.9% net undercount rate compared to a 0.9% net overcount for Non-Hispanic Whites Alone living in rental housing units.

**Table 10.4** Net undercount rates for American Indians Alone, Non-Hispanic White Alone in the U.S. Census, 1990, 2000, and 2010

*Source* U.S. Census Bureau (2012a). "2010 Census Coverage Measurement Estimation Report: Summary of Estimates of Coverage for Persons in the United States". DSSD 2010 CENSUS COV-ERAGE MEASUREMNET MEMORANDUM SERIES#2010-G-01, U.S. Census Bureau, Washington, DC. Table 7

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **Bold** are statistically significantly different than zero

In terms of omissions rates, American Indian and Alaskan Natives Alone or in Combination had higher rates than Non-Hispanic Whites Alone for both owners and renters, but the difference was a little larger for owner.

#### **10.6 Trend Data from 1990 to 2010**

Table 10.4 shows net undercount rates for 1990, 2000, and 2010 Censuses for American Indians Alone and Non-Hispanic Whites Alone based on the Census Bureau's DSE method. The race alone category is used in Table 10.4 because that is the only category available in 1990. The data for American Indians Alone has been broken out by whether or not a person was living on an Indian Reservation because there are big differences between people living on a reservation or somewhere else.

The net undercount rates for American Indians Alone on reservations have been wildly inconsistent from 1990 to 2010. For American Indians Alone living on reservations there was a very high net undercount rate in 1990 (12.2%) but in the 2000 Census the there was a small (0.9%) net overcount which is not statistically significantly different from zero. In the 2010 Census there was a statistically significant net undercount rate of 4.9%. It is difficult to discern any trends over time from this data and I am not aware of any explanation for the big swings in Census coverage for American Indians from 1990 to 2010. For American Indians Alone living somewhere other than a reservation there was a small net undercount in 1990 and 2000 but a net overcount in 2010.

#### **10.7 Potential Addition of a Question on Citizenship**

It may seem odd to being talking about American Indians and a question on citizenship, but I think it may be more relevant than it first appears. In March 2018, Commerce Secretary Ross (2018) announced that the 2020 Census questionnaire would have a question on citizenship. At the Spring 2018 Meeting of the Census Bureau's National Advisory Committee it was reported that some American Indians would not identify as being citizens of the United States, because they feel they are citizens of their tribal nation. This is consistent with the 2016 American Community Survey, which found 3.3% of American Indian and Alaskan Natives Alone or in Combination reported they were not a U.S. citizen and 6% did not provide a response to this question. The nonresponse rate for the citizenship question is much higher than the nonresponse rates for basic demographic characteristics such as age, sex, and race, which suggests in may be a more sensitive question (O'Hare 2018). This is likely to add another (unforeseen) twist to the last-minute addition of the citizenship question to the 2020 Census questionnaire (Ross 2018; Barraba and Flynn 2018).

#### **10.8 Summary**

Overall, the net undercount of American Indians and Alaskan Natives is relatively low (0.2%) but some subgroups within this population have high net undercount and omissions rates. Like most other groups examined in this book, males age 18–49 had the highest net undercount and omissions rates.

In the 2010 Census, the net undercount rate of American Indians living on reservations was very high (4.9%). Many American Indian reservations have some of the same hard-to-count characteristic (high poverty and unemployment rates, unconventional housing, and low levels of literacy, for example) that exist in many hard-tocount urban neighborhoods.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 11 Census Coverage of the Native Hawaiian or Pacific Islander Population**

**Abstract** In general, the count of Native Hawaiian or Pacific Islanders Alone or in Combination in the U.S. Census is relatively accurate. The net undercount rate for Native Hawaiian or Pacific Islanders Alone or in Combination in 2010 was 1.0% compared to a net overcount of 0.8% for the Non-Hispanic White Alone population. The omissions rate for Native Hawaiian or Pacific Islanders Alone or in Combination (7.9%) is about double the rate for Non-Hispanic White Alone (3.8%). The Native Hawaiian or Pacific Islanders Alone or in Combination demographic group with the highest net undercount rate was males age 18–29 who had a net undercount rate of 8.0% in 2010. This is the only net undercount rate among Native Hawaiian or Pacific Islanders that was statistically significantly different than zero.

#### **11.1 Introduction**

In reporting Census data on the Native Hawaiian or Pacific Island population it is very important to be clear about how the group is defined. Starting in the 2000 Census, people were allowed to select more than one race in the Census questionnaire (U.S. Office of Management and Budget 1997) and race is often reported two different ways in Census Bureau reports. One category is the number of people who only select Native Hawaiian or Pacific Islander (referred to as Native Hawaiian or Pacific Islander Alone) and the second category is those in the first group plus those who select Native Hawaiian or Pacific Islander along with at least one other race (referred to as Native Hawaiian or Pacific Islander Alone or in Combination).

There is a substantial difference between the number of people who are in the Native Hawaiian or Pacific Islander Alone category and those in the Native Hawaiian or Pacific Islander Alone or in Combination category. In the 2016 American Community Survey conducted by the Census Bureau there were about 600,000 people who marked Native Hawaiian or Pacific Islander Alone and 1.4 million who marked Native Hawaiian or Pacific Islander and some additional race. Except for data on trends over time, Native Hawaiians or Pacific Islanders Alone or in Combination is the population examined in this Chapter. This is the most inclusive definition and consistent with advice of the U.S. Office of Management and Budget (2001).

It is also noteworthy that the number of Native Hawaiians or Pacific Islanders Alone or in Combination increased by 40% between 2000 and 2010 (U.S. Census Bureau 2012). About half of the Native Hawaiian or Pacific Islander Alone or in Combination population resides in Hawaii or California.

This population is not only increasing rapidly but given the unique history and evolution of Native Hawaiians or Pacific Islanders in the U.S. population, they fall into the category of cultural and linguistic minorities discussed by Harkness et al. (2014). Cultural and linguistic minority are often considered a hard-to-count population which means they are more difficult to enumerate accurately in the census.

The Demographic Analysis method does not produce data on Census coverage for Native Hawaiian or Pacific Islanders, so all the data in this section comes from the Census Bureau's Dual-Systems Estimates (DSE) method. Keep in mind the sample size of Native Hawaiians and Alaskan Natives in the Post-Enumeration Survey used in the DSE method is smaller than most other groups so small differences between rates may not be significant.

#### **11.2 Census Coverage of Native Hawaiian or Pacific Islanders Alone or in Combination**

Table 11.1 shows net undercount rates from DSE for Native Hawaiian or Pacific Islanders Alone or in Combination broken down by age and sex. Data for the youngest age groups (0–9) are not presented in Table 11.1 because there is strong evidence that the coverage estimates of young children in the DSE in 2010 are problematic (O'Hare et al. 2016).

In the 2010 Census, the net undercount rate for all Native Hawaiian or Pacific Islanders Alone or in Combination was 1.0% (Table 11.1) and the rate is not statistically significant different from zero. By comparison, there was a statistically significant net overcount of Non-Hispanic White Alone population of 0.8%.

As with the other racial and Hispanic Origin groups examined in this book, the net undercount rate for the total Native Hawaiian or Pacific Islander Alone or in Combination population masks some important differences by age and sex. However, within the Native Hawaiian or Pacific Islander Alone or in Combination population the differences by age and sex are not as pronounced as they are in Black or Hispanic populations.

The highest net undercount in Table 11.1 is for males age 18–29. The net undercount rate for Native Hawaiian or Pacific Islander males in this age range is 8.0% and this is the only net undercount estimate for Native Hawaiians or Pacific Islanders Alone or in Combination that is statistically significantly different than zero. The relatively high net undercount of males age 18–29 is consistent with high net undercounts for this age/sex group in most other race and Hispanic groups. Young adult males are a demographic group that has been identified as difficult to enumerate regardless of race or ethnicity in many countries (Simpson and Middleton 1997).

**Table 11.1** Net undercount rates for Native Hawaiians or Pacific Islanders Alone or in Combination and Non-Hispanic White Alone populations in the 2010 census by age and sex

*Source* U.S. Census Bureau (2012). "2010 Components of Census Coverage for Race Groups and Hispanic Origin by Age, Sex, and Tenure in the United States". DSSD 2010 CENSUS COVERAGE MEASUREMENT MEMORANDUM SERIES#2010-E-51, U.S. Census Bureau, Washington, DC. Table C

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

#### **11.3 Census 2010 Omissions Rates for Native Hawaiian or Pacific Islanders Alone or in Combination and Non-Hispanic Whites Alone**

Recall that the net census undercount rate is a balance between people omitted and those included erroneously (mostly double counted). The omissions rate captures the share of a group missed in the Census. DSE is the only method that shows omissions rates.

In many ways the omissions rate is a more meaningful statistic because in the net undercount calculation, omissions can be cancelled out by erroneous inclusions or double counting. A net undercount of zero could be the result of no one missed and not one double counted, or for example, 10% missed, and 10% double counted.

The omissions rate for Native Hawaiian or Pacific Islanders Alone or in Combination (7.9%) is slightly more than double that of the Non-Hispanic White Alone population (3.8%) but omissions rates vary by age and sex. To a large extent the omissions rates among Native Hawaiian or Pacific Islanders Alone or in Combination reflect the same age/sex demographic pattern as the net undercount rates (Table 11.2).

Like the results for net undercount rates, Native Hawaiian or Pacific Islander Alone or in Combination males age 18–29 had a very high omissions rate. The omissions rate for Native Hawaiian or Pacific Islander Alone or in Combination males 18–29 was 15.7% which is almost twice as high as the overall omissions rate for the Native Hawaiian or Pacific Islanders Alone or in Combination population. The omissions

**Table 11.2** Omissions rates for Native Hawaiians and Pacific Islanders Alone or in Combination and Non-Hispanic White Alone populations in the 2010 census by age and sex

*Source* U.S. Census Bureau (2012). "2010 Components of Census Coverage for Race Groups and Hispanic Origin by Age, Sex, and Tenure in the United States". DSSD 2010 CENSUS COVERAGE MEASUREMENT MEMORANDUM SERIES#2010-E-51, U.S. Census Bureau, Washington, DC. Table C

rate for Native Hawaiian or Pacific Islanders Alone or in Combination males age 30–49 (6.4%) is relatively low compared to the overall omissions rate (7.9%).

#### **11.4 Coverage of Native Hawaiian or Pacific Islanders Alone or in Combination by Tenure**

Table 11.3 shows net undercount rates and omissions rates from the 2010 Census DSE analysis for the population living in owner-occupied housing units and renteroccupied housing units among Native Hawaiian or Pacific Islanders Alone or in Combination and Non-Hispanic White Alone.

Like most other demographic groups examined in this book, there is a big difference in census coverage between the population living in owner-occupied housing units and those living in rental units. The net undercount rate for the population of Native Hawaiians or Pacific Islanders Alone or in Combination living in renteroccupied housing units is 3.7% (which is statistically significantly different than zero) compared to a net overcount of 1.8% for Native Hawaiians or Pacific Islanders Alone or in Combination living in owner-occupied housing units. The net coverage gap between Native Hawaiian and Pacific Islanders Alone or in Combination and Non-Hispanic Whites is more than 4 percentage points for renters compared to only 1 percentage point for owners.

Native Hawaiian or Pacific Islanders alone or in Combination have higher omissions rates than Non-Hispanic White Alone in both owner and rental situations. Among the population living in owner-occupied housing units, the omissions rate was 5.6% for Native Hawaiian or Pacific Islanders Alone or in Combination and

**Table 11.3** 2010 census net undercount rates and omissions rates for Native Hawaiians and Pacific Islanders Alone or in Combination and Non-Hispanic White Alone by Tenure

*Source* U.S. Census Bureau (2012), DSSD 2010 CENSUS COVERAGE MEASUREMENT MEM-ORANDUM SERIES 2012, 2010-E-51, Table B

*Note* The directionality of undercounts and overcounts have been reversed from the original table in order to keep them consistent within this publication In order words, a negative sign implies an undercount

Figures in **BOLD** are statistically significantly different from zero

3.0% for Non-Hispanic White Alone. Among renters the omissions rate for Native Hawaiian or Pacific Islanders Alone or in Combination Native Hawaiian or Pacific Islanders Alone was 10.1% compared to 6.4% for Non-Hispanic White Alone. The omissions rate for Native Hawaiians or Pacific Islanders Alone or in Combination for renter (10.1%) is higher than that of owners (5.6%) which again show the impact of home-ownership on Census accuracy.

#### **11.5 Trend Data from 1990 to 2010**

Table 11.4 shows net undercount rates for 1990, 2000, and 2010 Censuses for Native Hawaiian or Pacific Islanders Alone and Non-Hispanic Whites Alone based on the Census Bureau's DSE method. Note that the Native Hawaiian or Pacific Islander "Alone" race definition is used in this table to make categories consistent over time.

The net undercount rates for Native Hawaiians or Pacific Islander Alone have decreased steadily over time from 2.4% in 1990, to 2.1% in 2000, to 1.3% in 2010. While the improvement since 1990 is positive it was very small, and it should be noted that Native Hawaiians and Pacific Islanders still have a net undercount while Non-Hispanic Whites have an overcount.

The difference between the census coverage rates of Native Hawaiian or Pacific Islanders has decreased slightly from 3.1 percentage points in 1990 to 2.1 percentage points in 2010. A small positive improvement.

**Table 11.4** Net undercount rates for Non-Hispanic Asian and Non-Hispanic White 1990, 2000, and 2010

*Source* U.S. Census Bureau (2012). "2010 Census Coverage Measurement Estimation Report: Summary of Estimates of Coverage for Persons in the United States". DSSD 2010 CENSUS COV-ERAGE MEASUREMNET MEMORANDUM SERIES#2010-G-01, U.S. Census Bureau, Washington, DC. Table 7

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

*Note* In this table the race categories are race alone, not race alone or in combination. The data are presented this way to make the 2010 categories consistent with the 1990 categories

Figures in **BOLD** are statistically significantly different than zero

#### **11.6 Summary**

Overall, the Native Hawaiian or Pacific Islander Alone or in Combination population had a small net undercount (1.0%) compared to a small net overcount for the Non-Hispanic White Alone population. Native Hawaiian or Pacific Islander Alone or in Combination males age 18–29 had the highest net undercount rate of any age/sex group in this population and they had the highest omissions rate of any age/sex group. The net undercount rates for Native Hawaiian or Pacific Islanders have decreased from 1990 to 2010 but it should be noted that Native Hawaiians and Pacific Islanders still have a net undercount while Non-Hispanic Whites have an overcount.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 12 Undercount Differentials by Tenure**

**Abstract** Over the past several decades the population living in rental housing units has consistently had net undercounts while the population living in owner-occupied housing units had net overcounts. In this Chapter the coverage differentials by tenure are analyzed and the impact of this differential on different sociodemographic groups is explored.

#### **12.1 Introduction**

Home ownership is associated with a set of characteristics that often impact the likelihood of being counted in the Census. A lot of the relationships between tenure and other demographic characteristics have already been covered in earlier Chapters of this book but some are repeated here.

The number and share of the population living in rental housing units has increased since 2010. In the 2010 Census, there were 99.5 million people (33% of the total population) living in rental housing units but the 2016 American Community Survey (U.S. Census Bureau 2018a) shows 111 million people living in rental units which represents 35.3% of the population. As noted above, the population living in rental housing units had higher net undercount and higher omissions rates than the population living in owner-occupied housing units. All other things being equal, this trend is one more factor that will make the 2020 Census more difficult that the 2010 Census (see Chap. 15 for more on this topic).

#### **12.2 Census Coverage by Tenure**

Table 12.1 shows net undercount rates and omissions rates for the population living in owner-occupied housing units and the population living in renter-occupied housing units in the 2010 Census. Data in Table 12.1 show that people living in owneroccupied housing units had a net overcount (0.6%) and people living in rental units

**Table 12.1** 2010 census net undercount rates and omissions rates by tenure

*Source* U.S. Bureau of the Census, 2012, DSSD 2010 CENSUS COVERAGE MEASUREMENT MEMORANDUM SERIES 2012, 2010-E-51, Table B

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

had a net undercount (1.1%). Both figures are statistically significantly different than zero. The omissions rate for people living in rental units is 8.5% compared to 3.7% for those living in owner-occupied units.

#### **12.3 Differential Census Coverage by Tenure, Race, and Hispanic Origin**

Table 12.2 provides data for net undercount rates by Race, Hispanic Origin, and Tenure. In general, the population living in rental units has higher net undercount rates than those living in owner-occupied units. Only one race group of owners (Black Alone or in Combination) had a net undercount. Among renters, four of the six Race/Hispanic groups had a net undercount.

For Blacks Alone or in Combination, Native Hawaiian or Pacific Islanders Alone or in Combination, and for Hispanics, renters have a net undercount of 3% or more, signaling that these groups are particularly vulnerable to being missed in the Census.

It is worth noting that for Asians Alone or in Combination the net coverage rate for both renters and owners is zero and the net overcount rates for Non-Hispanic Whites Alone owners and renters are nearly identical (0.8% for owners compared to 0.9% for renters). The average income for Asians and Non-Hispanic Whites is above average, which suggests renters who have the means to secure better rental property and may not be disadvantaged in the Census.

#### **12.4 Differential Omissions Rates by Tenure, Race, and Hispanic Origin**

Recall that the net Census undercount rate is a balance between people omitted and those included erroneously (mostly double counted). The omissions rate captures the share of a group missed in the Census. DSE is the only method that shows omissions rates.

**Table 12.2** 2010 census net undercount rates by race, Hispanic Origin, and tenure

*Source* U.S. Bureau of the Census, 2012, DSSD 2010 CENSUS COVERAGE MEASUREMENT MEMORANDUM SERIES 2012, 2010-E-51, Tables A and B

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **BOLD** are statistically significantly different from zero

In many ways the omissions rate is a more meaningful statistic than the net undercount rate because in the net undercount calculation, omissions can be cancelled out by erroneous inclusions or double counting. A net undercount of zero could be the result of no one missed and no one double counted, or for example, 10% were missed, and 10% double counted.

Table 12.3 shows omissions rates by Race, Hispanic Origin and Tenure. For every group examined in Table 12.3 the omissions rates for the population living in rental housing units are higher than those for the population living in owner-occupied housing units. Like net undercounts, Black Alone or in Combination, Native Hawaiian or Pacific Islander Alone or in Combination, and Hispanic renters have the highest omissions rates among the groups examined here (10% or more), indicating that these groups are particularly vulnerable to being missed in the Census.

Some of the more detailed categories of the population living in rental housing units show extremely high omissions rates. Detailed data from the 2010 Census Coverage Measurement program (U.S. Census Bureau 2012, Table D), for example, found Black Alone or in Combination males age 30–49 living in rental housing units had an omissions rate of 19.7% and Black Alone or in Combination males age 18–29 living in rental housing units had an omissions rate of 16.9%. For Hispanic Origin males age 18–29 the omissions rate was 16.1% and for American Indian and Alaskan Native Alone or in Combination males age 30–39 the omissions rate was 12.5%. Word (1997) also shows the powerful effect of tenure on self-response rates in the 1990 Census.

**Table 12.3** 2010 census omissions rates by race, Hispanic Origin, and tenure

*Source* U.S. Bureau of the Census, 2012, DSSD 2010 CENSUS COVERAGE MEASUREMENT MEMORANDUM SERIES 2012, 2010-E-51, Tables A and B

*Note* The directionality of undercounts and overcounts have been reversed from the original publication in order to keep them consistent within this publication In other words, a negative sign implies an undercount

Figures in **bold** are statistically significantly different from zero

#### **12.5 Net Coverage Rates Over Time by Tenure**

Table 12.4 shows net undercount rates by tenure for the 1990, 2000 and 2010 Censuses. In every Census since 1990, there was a net undercount for the population living in rental housing units and a net overcount for the population living in owneroccupied housing units (there was a small net overcount for owners in 1990 but it rounds to zero). The net undercount rate for renters fell from 4.5% in 1990 to 1.1% in 2000, but it did not change between 2000 and 2010. The Census coverage gap between the population living in owner-occupied units and those living in renteroccupied units went from 4.5 percentage points in 1990 to 1.7 percentage points in 2010.

**Table 12.4** Net undercount rates by tenure: 1990, 2000 and 2010

*Source* U.S. Bureau of the Census, 2012, DSSD 2010 CENSUS COVERAGE MEASUREMENT MEMORANDUM SERIES 2012, 2010-G-1 Table 10

A negative sign reflects a net undercount. The signs here are reversed from the source report in order to keep directionality consistent within this publication

Figures in **bold** are statistically significantly different from zero

**Table 12.5** Poverty and median household income by tenure: 2015 ACS

*Source* Author's analysis of U.S. Census Bureau's 2016 American Community Survey PUMS file on IPUMS system at the University of Minnesota and ACS Table B25119 Downloaded from American Factfinder

There does not seem to be any linear temporal pattern to the net coverage rates of people living in owner-occupied housing units. The net undercount for the population living in owner-occupied housing units went from zero in 1990 to a net overcount of 1.3% in 2000 but then showed a net overcount rate of 0.6% in 2010.

#### **12.6 Tenure and Socioeconomic Status**

In some ways the coverage differentials reflected by tenure are symptomatic of socioeconomic differences and tenure is sometimes used as a proxy for differences by socioeconomic status. Table 12.5 shows the family poverty rates and the median household income for renters and homeowners. The family poverty rate for the those living in renter-occupied units (27%) is almost four times that of the population living in owner-occupied units (7%) and the median household income renters (\$37,264) is about half that of owners (\$73,127). The results here underscore the connection between socio-economic status and Census coverage.

Another reason renters tend to be covered less well than owners is that many rental units are in multi-unit structures and living in a multi-unit structure is associated with being difficult to enumerate. Among people in single family housing units (including mobile homes) only 20% are in rental units compared to 78% in multifamily structure being in rental units. Also, the population living in rental housing units are more difficult to enumerate because they are more likely to move. The 2016 ACS (U.S Census Bureau 2018b) shows 25% of people in rental units moved in the previous 12 months compared to 8% in owner-occupied units.

#### **12.7 Summary**

The population living in rental housing units has a net undercount while those living in owner-occupied housing units are overcounted. In addition, the population in rental housing units have higher omissions rates than the population living in owner-occupied housing units. For every racial group examined here renters tend to be covered less well than owners.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 13 Potential Explanations for Why People Are Missed in the U.S. Census**

**Abstract** Knowing the characteristic of people most likely to be missed in the Census is not the same as knowing why they are missed. In this Chapter information is provided on several of the leading ideas about why people are missed in the Census along with data related to many of the ideas. The topic is first approached from broad theoretical perspective then more detailed reasons are examined. The chapter draws heavily on the literature in survey research methodology.

#### **13.1 Introduction**

Most of this book has focused on the characteristics of people and groups that have high census net undercount and omissions rates. But knowing the characteristics of people who are missed is different from knowing why they are missed. This Chapter explores several possible reasons why people are missed in the U.S. Census and examines statistical data related to many of the ideas. Some of the ideas examined here reflect broad factors (like the characteristics of households and living arrangements) and some reflect narrow factors (like the imputation algorithm used by the Census Bureau).

It is widely believed that there is no single reason why people are missed in the Census. With respect to differential undercounts of racial and ethnic groups, de la Puente (1993, p. 2) captured the dominant perspective more than 20 years ago,

Empirically and in the aggregate, there is no single reason why a disproportionate number of ethnic and racial minorities were not counted by the 1990 census. Rather, there are a constellation of factors that interact and contribute to the differential undercount.

The quote above focuses on undercounts by race and ethnicity, but the lack of a single predominant reason for people being missed in the Census goes beyond race and ethnic groups. With respect to the high net undercount of young children, the Census Bureau Task Force on the Undercount of Young Children (U.S. Census Bureau 2014) concluded, "The task force is convinced that there is no single cause for this undercount".

Despite several reports that address the issue of why people are missed and/or barriers to enumeration in the Census (U.S. Census Bureau 1992; de la Puente 1993; Martin and de la Puente 1993; Simpson and Middleton 1997; Schwede and Terry 2013; West and Fein 1990) there is no consensus framework for examining this issue. Because the Decennial Census is a large and complex operation, there is widespread belief that people are missed in the Census for many different reasons, and researchers approach census omissions from many different disciplines and perspectives, it is not surprising that there is no consensus on the best framework to use for understanding this problem.

#### **13.2 What Is an Omission?**

Determining what is an omissions in the Census is not as easy as it may seem. For example, in the 2010 Census there were 6 million whole person imputations (U.S. Census Bureau 2012a, Table 6). People are imputed into the Census count if they don't self-respond and there is good evidence they exist (for example, a housing unit that looks occupied), but the Census enumerator can never find anyone at home. If a person does not respond to the Census but data are imputed to represent them in the census count is that an omission?

Also, some households and persons are included in the Census by proxy respondents. If a Census enumerator is unable to contact residents of a household after several attempts, the enumerator may seek data on the household from someone like a neighbor or a landlord. These are referred to as proxy respondents. Should people included in the census count by proxy respondents be called omissions?

It is also important to make a distinction between people who are totally missed in the Census and those who are included but misclassified. For example, if a Black person is included in the Census, but has their race coded as White (perhaps because that characteristic was imputed) that would not impact overall omissions, but it would impact the omissions for Blacks and Whites.

#### **13.3 Broad Ideas About Why People Are Missed in the Census**

Many of the ideas or theories about why people are missed in the Census are linked to broader questions of non-responsiveness in surveys (National Research Council 2013; Groves and Couper 1998). Several leading theories for understanding omissions in surveys and censuses from a theoretical perspective, including social capital theory, leverage-saliency theory, social exchange theory, are discussed in a report by the National Research Council (National Research Council 2013, pp. 33–36).

**Table 13.1** Why barriers leads to being missed in the census

*Note* This table draws heavily from Appendix 1, in Robinson et al. (2007)

A report by Robinson et al. (2007) lists several barriers to census enumeration and in Appendix 1 of the report they explain how each barrier is tied to the likelihood of being missed in the Census. Table 13.1 lists the barriers offered by Robinson et al. (2007) and a short explanation of how each barrier could results in someone not be counted in the Census.

Other researchers have provided similar ideas about why people are missed in the Census (West and Fein 1990; de la Punte 1993; Schwede et al. 2015).

#### **13.4 People Missed in the Census Due to Failure of Steps in the Data Collection Process**

Another approach to understanding why people are missed in the Census is offered by researchers who examine the steps in the data collection process and try to understand which step failed and why (Beimer et al. 1991). Many researchers start by decomposing Census coverage along the lines of Olson (2009) who posits that any omissions in the Census-taking process must come from a failure of one of three steps below;

To reconfigure these three reasons for omissions, the first means the household did not get enumerated, and the second and third, means the household was enumerated, but not everyone in the household who should have been included, was included in the Census count.

#### **13.5 Missing Households**

Table 13.2 shows 4.2 million housing units were missed in the 2010 Census. Of the 4.2 million housing units missed, 2.2 million were occupied. This estimate is based on comparing the housing units found in the Post-Enumeration Survey to those found in the Census (U.S. Census Bureau 2012a). It is easy to understand why someone living in an overlooked housing unit might be missed in the Census. It is unlikely that the omitted housing units received a census questionnaire and therefore it is unlikely that occupants of those housing units were included in the 2010 Census.

The number of persons in the missed households is not directly available from the Census Bureau, but if the 2.2 million occupied households that were missed had the average number of persons per household in the 2010 Census (2.58) (U.S. Census

**Table 13.2** Housing units omitted in the 2010 census

*Source* U.S. Census Bureau (2012a), Table 3

Bureau 2018a) the missed households would account for 5.7 million missed persons out of a total of almost 16 million omissions in the 2010 Census (U.S. Census Bureau 2012b, Table 2). Missed housing units appears to be a big reason for the high number of omissions in the 2010 Census.

There are several reasons why housing units may be missed in the Census. In some cases, homeowners turn a basement or a garage into a separate housing unit so what appears to be a single-family residence is a multi-unit structure and the new housing units are easy to miss. Mahler (1993) indicates landlords sometime create illegal units in multi-unit structures and they are reluctant to reveal the existence of these units.

In many rural areas housing units may be far off the road and hidden from view and so they are not included in the Master Address File. For example, de la Puente (1993, p. 15) states,

In rural areas, unlike in urban areas, unmarked and/or hidden roads and mismatches between mail delivery address of housing units and the actual physical location of the housing unit are conditions associated with the omissions of housing units from the Census.

Also, in both urban and rural settings Census Bureau canvassers must determine if a housing unit is inhabitable. If a building is deemed uninhabitable it will not receive a Census questionnaire. Making a judgment about the inhabitability of a structure is difficult, despite guidance provided by the Census Bureau; but just as important, some structures that are deemed uninhabitable may actually have people living there. Kissam (2017) and Fein (1989) provide detailed descriptions of some of the many ways housing units are likely to be missed in the Census.

#### **13.6 People Omitted on Census Questionnaires that Are Returned**

In addition to people who are not counted because their whole household was missed, some people may be missed because they were left off Census questionnaires. People may be omitted because they are left off a questionnaire that was mailed back, missed by an enumerator during the Non-Response Followup operation or possibly missed in another way such as an incorrect proxy response, a data processing error, or incorrect imputation.

Most omissions can be classified as the result of either confusion or concealment. Confusion can lead to a missed person if the respondent is confused about whether a given person should be thought of as part of the household or family and included in the census questionnaire. Confusion can also lead to a person being missed if the respondent is unsure if a certain type of person (like a young child or noncitizen) is supposed to be included in the Census.

Other people may be left off Census questionnaires because the respondent wants to conceal a person or whole household from the authorities. There are many reasons why a person might be concealed. For example, some people fear the federal government and they see the Census Bureau as just another federal government agency. Growing fear of the federal government in many communities is likely to be a big factor in the 2020 Census.

#### **13.7 People Omitted in the Census Because of Confusion**

The "usual place of residence" is a key concept used by the Census, but Martin (1999, 2007) argues that concept is not always clear to respondents and attachment to a single household may not apply to some people. Martin talks about the concept of "residential ambiguity" which reflects uncertainty about whether an individual belongs to a housing unit or household. Moreover, most of the rules respondents usually use to determine who they think lives in their household (economic contributions, doing household chores, receiving mail at the address) are not the rules used in the Census, so respondents may be confused about the concepts of families and households used in the Census. According to West and Robinson (1999, p. 10),

The Census rules of residence instruct that the person in whose name the house or apartment is owned, being bought or rented be listed as person 1 on the form. The respondent is then asked to identify members of the household in relation to person 1. This often contradicts the respondent's notion of family or household.

It is easy to understand how someone who is only marginally attached to a housing unit may be missed in the Census. Whoever is filling out the Census questionnaire for a household may think the marginally attached person does not really live in the housing unit, so they are not entered on the Census roster. It is also feasible that the person filling out the census questionnaire may think someone who does not live at a housing unit continuously, for example, children in joint custody or someone who travels on business regularly, is being counted elsewhere. In reporting on confusion on the part of respondents in the 2010 Census, Schwede and Terry (2013, p. 89) concluded, "Additionally, the situation of mobility of people cycling between housing units and trying to determine from time spent in each where they should be counted was a reason for inconsistencies".

The problems with residence rules have been noted before. According to The National Research Council (2004, p. 153),

The current definition of residence rules is confusing both to field enumerators and to residents. Difficulties arise for people with multiple residences, including those with movement patterns that are primarily within a week, or those that move seasonally. Such movement patterns are typically true of retirees, those involved in joint custody, arrangements, those with weekend homes in the country, students away at college during the school year, and people temporarily overseas.

One common situation related to confusion about the occupants of a housing unit is people living in complex households (Schwede 2018). For example, West and Robinson (1999, p. 9) describe one situation that may lead to a child being missed in the Census.

A child who resides in a diverse household structure and in a unique living arrangement among multiple nuclear families…Unusual living arrangements involving people that make it difficult for the respondent to roster the household correctly on the Census form, e.g. presence of multiple nuclear families, unrelated people or step people of the respondent.

Complex households often involve the presence of subfamilies in a household and that can make correct rostering of household members more complicated. One official at the Census Bureau "…noted that she was aware of instances with multiple families, for example, where the household respondent did not include people in the second family" (cited in U.S. Census Bureau 2014, p. 16). Martin (2007) contends people more remotely linked to the person filling out the census questionnaire, are more likely to be missed.

A report from the U.S. Census Bureau (2016) indicates that many young mothers were not included in the 2010 American Community Survey (ACS) and therefore they argue many were probably unreported in the Census. Many of the young mothers missed in the ACS were probably single mothers with their child(ren) living with the parent(s) of the mother or with some other householder. It is feasible that the respondent believes the young mother and her children are only living with them temporarily and so does not enter them on the Census questionnaire roster. It is also easy to imagine a grandmother filling out the Census questionnaire may think of the young grandchild in her household as part of her daughters' family rather than part of her family.

A rapidly growing type of living arrangement for people (percentage-wise) is cohabitating households. Since cohabiting couples reflect living arrangements that are relatively unstable (compared to married-couple families) and the relationships among adults and children are different from those in a nuclear family, it would not be surprising if a disproportionately high share of people in cohabiting households were not being reported in the Census.

Newborns may be particularly likely to be living in complex households A recent report from the Census Bureau (Monte and Ellis 2014, p. 2) found "more than one in five women with a birth in the past 12 months reported at the time of the survey that they were living in someone else's home". In another analysis (Gooding 2008) shows that 13% of mothers were not co-residing with their biological child under age 1 and rates are higher for Blacks and Hispanics where the net undercount of people is also higher. This may help explain the high omissions rate for young children (O'Hare 2015).

#### **13.8 Large and Complex Households**

People living in large households may be missed for many different reasons. First, large households are often complex households and people may be left off the questionnaire for reasons described in the previous section. Martin (1999, 2007) shows that being closely related to the respondent in a survey greatly decreases ambiguity related to residential attachment and complex households are more likely to have occupants who are not closely linked to the householder.

Examination of survey data taken prior to the 2010 Census shows that having more than two people in the household lowers the likelihood of respondents saying they will participate in the 2010 Census independent of other factors (Walejko et al. 2011). Data from the 2010 Census show that 84% of 2-person households mailed back their Census questionnaire compared to only 72% of 7-person households (Letourneau 2012).

Larger households are likely to contain children and child care demands may interfere with completing the Census questionnaire. This is a point made by Hillygus and colleagues (2006, p. 103) who note,

Respondents who are married with children have a lower mail-back rate (83 percent) than those who are married without children (90 percent), suggesting that the time demands of child care work against taking on this particular duty.

In a poll conducted just prior to the 2010 Census (Pew Research Center 2010) about a third of those who said there were not planning to participate in the 2010 Census cited "too busy or not enough time" as the reason.

#### **13.9 Confusion About What Types of People Should Be Included in the Census**

Another reason people are missed in the Census is because respondents may believe the Census Bureau does not want some categories of people included in the Census. In a series of short surveys by the Census Bureau (Nichols et al. 2014a, b, c) respondents were asked, "What information do you think the Census typically collects every 10 years?" and were offered several choices. The percentage who thought the Census Bureau collects "Names of children living at your address" was 7–9 percentage points lower than the percentage who thought the Census Bureau collects, "Names of adults living at your address". While this question asks about names rather than about information on individuals, it suggests that some people think the Census does not request information on children.

A recent report (Vargas 2018) based on a nationwide survey of Latinos conducted by the National Association of Latino Elected Officials found that 15% of respondents who had a child under age 5 in the home would not count them in the Census or do not know if they would count them. These attitudes help explain why young children have a higher omissions rate than any other age group in the 2010 Census (O'Hare 2015). Also, in their qualitative study of 2010 Census respondents Schwede and Terry (2013) indicated many respondents do not believe the Census Bureau (or the federal government) wants children included in the Census count.

Young children are not the only group some people think are not supposed to be included in the Census. Data from the Census Barriers Attitudes and Motivators Survey done in 2008, indicates only 76% of the people interviewed think the Census Bureau wants non-citizens included in the Census (U.S. Census Bureau 2009, p. 88). In a similar study in 2018 (U.S. Census Bureau 2018b) only 55% of respondents were sure that noncitizens were supposed to be counted in the Census.

#### **13.10 People Deliberately Concealed**

There is evidence to support the idea that some people may be left off Census questionnaires on purpose. In some situations, there may be an effort to conceal an entire household, and in other situations the effort may be focused on concealing selected individual(s) within a household.

Some people may also be purposively left off census questionnaires based on rules about the housing unit where they are living. People could be left off because there are too many people living in the housing unit relative to rules about maximum capacity. If a housing unit is only supposed to have four people, by rule, but there are five people who regularly live there, one may be left of the census questionnaire because the respondent fears reporting five people in the unit might jeopardize continued occupancy.

Some people may be left off a census questionnaire because of the types of people who are allowed to live in a housing unit. For example, the report of the Census Bureau Task Force on the Undercount of People (U.S. Census Bureau 2014, p. 16) states one of the reasons people are left off forms is, "Respondents deliberately not mentioning kids for fear of some reprisals or bad outcomes from landlords, immigration agencies, social service agencies, etc". West and Robinson (1999, p. 7) also conclude,

Listing some members of the households may have other negative consequences. For example, a respondent may fear that disclosure of certain members of the household will affect eligibility for social services, that people illegally in the country will be deported, or that the whereabouts of a child in hiding from a custodial parent will be detected.

Pitkin and Park (2005) also mention "systematic concealment" as a potential reason children are undercounted in the Census.

Concealment may be driven by lack of trust in the Census Bureau and/or the federal government. Despite assurances from the about the confidentiality of responses to the Census (Congressional Research Service 2018), many people believe that data given to the Census Bureau may be shared with other government agencies. A poll (Pew Research Center 2010, p. 5) conducted just prior to the 2010 Census indicated that among those who said they were not planning to participate in the 2010 Census, 18% cited distrust of government and 8% cited privacy concerns. In the 2010 Census Barriers Attitudes and Motivators study (U.S. Census Bureau 2011) researchers classified 10% of the population as cynical and 14% as suspicious of the Census Bureau.

Census Bureau staff report growing number of respondents are refusing to cooperate because of fear (Meyers and Goerman 2018; U.S. Census Bureau 2017a, b). There is growing distrust of the federal government and for most people the Census Bureau is seen as another branch of the federal government. Abowd (2018) indicates that fear among undocumented population is also likely to be have an impact on many of the people who are in the country legally because they live in a household where undocumented people are living. One recent report concludes (Alsan and Yang 2018, p. i). "Though not at personal risk of deportation, Hispanic citizens may fear their participation could expose non-citizens in their network to immigration authorities". This report was focused on participation in safety net programs, but I think it applies to the Census as well.

Such distrust affects certain populations more than others. Immigrants are one population where concealment may be common because of fear of the federal government. The 2016 ACS (Table S0501) shows there are 43.7 million foreign-born residents in the U.S. and 22 million noncitizens. The Pew Research Center (2018) reports that there were about 11 million unauthorized immigrants in the U.S. in 2015. The estimate from the Pew Research Center is similar to estimate for January 2014 from Homeland Security (2018) of between 10.8 million and 12.1 million. This impacts not only noncitizens and undocumented immigrants but the people living in a household with them. There are 6.4 million children living in a household with at least on undocumented immigrant. Abowd (2018, p. 6) states, "From the 2016 ACS we estimate that 9.8% of all households contain at least one noncitizen". Almost all children less than 5 years old are citizens but 20% of children in this age range live with at least one noncitizen (Population Reference Bureau 2018).

#### **13.11 Barriers Posed by Questionnaire Design**