California HTC Index

Adapted from California Complete Count - Census 2020 ( and the OC Counts Orange County Census Playbook (

As of 2017, 72% of all Californians (29 million) belonged to a group that has historically been undercounted in the decennial census. To prepare for the 2020 Census, the California Department of Finance Demographic Research Unit created a California-focused “hard-to-count” metric modeled on the U.S. Census Bureau’s Hard-to-Count Score of past censuses.

The California Hard-to-Count (CA-HTC) Index was based on multiple demographic, housing and socioeconomic variables correlated with an area being difficult to enumerate. Census tracts with higher CA-HTC index values were more likely to pose significant challenges to enumerate in 2020, while tracts with lower index values were anticipated to be easier to count.

The California Census Office created this interactive map to view the HTC index of California census tracts and block groups. Outreach partners used the HTC index to prioritize and focus outreach efforts and funding to populations at highest risk of undercount.

In Orange County, it was estimated that 25% of the population was considered HTC, amounting to 750,000-800,000 individuals. Orange County is home to 8.1% of the State population.

The following section describes the 14 HTC characteristics and how the index was calculated.

CA-HTC Index Variables (with data source):

  1. Percent of households without broadband subscriptions (California Public Utilities Commission): More than 10 million California households were asked to complete the census online. Due to the COVID-19 pandemic, many outreach efforts were conducted online, as well. A household without a broadband subscription was anticipated to be less likely to know about the census and more likely to not self-respond.

  2. Percent of households that are non-family (Table B11001, U.S. Census Bureau 2013-2017 American Community Survey [ACS]): Nonfamily households generally involve multiple roommates. The household member who completed the census form may have forgotten to include some of these people.

  3. Percent of households that are rented (Table B25003, ACS): The percentage of renter households in a tract or block group was among the strongest hard-to-count indicators. Renters move more often and were anticipated to have a greater chance of being missed during the census-taking process.

  4. Percent of total housing units that are vacant (Table B25002, ACS): Vacant housing units change status quickly. Housing units considered vacant by census takers in reality could have been occupied on April 1, 2020 (Census Day).

  5. Percent of households that are overcrowded (i.e. the percent of occupied housing units with more than 1.5 persons per room. Table B25014, ACS): As with nonfamily households, people living in crowded households were anticipated to be more likely to be left off census forms. Also, the person who completed the form may have omitted occupants if the household exceeded landlord or government limits.

  6. Percent of the population that was born outside of the U.S. (Table B05001, ACS): People who are born in other countries were anticipated to be less likely to be familiar with the census. Some also were not U.S. citizens and may have feared the consequences of revealing their presence and legal status to the government.

  7. Percent of adults (25 or older) who are not high-school graduates (Table S1501, ACS): Non-high school graduates were anticipated to be less likely to be engaged in civic affairs and more likely to be working multiple low-wage jobs that left little spare time for completing census forms.

  8. Percent of the population with income below 150 percent of poverty level (Table C17002, ACS): Multiple issues were anticipated to increase the odds of an undercount among the poor. They tended to be renters. Administrative records to supplement the census, such as tax returns, may have been incomplete for this group. They also were less likely to have internet access.

  9. Percent of households receiving public assistance income (Table B19057, ACS): People may have been reluctant to share their true household size because the information could contradict government assistance records. Households receiving public assistance income were also likely living near or below the poverty line.

  10. Percent of the population (ages 16 or older) that is unemployed (Table B23025, ACS): Unemployed people spend much of their time looking for a job. They also may have been unhoused.

  11. Percent of households with limited-English speaking ability (i.e.the percent of households in which no person aged 14 years or older speaks English very well. Table S1602, ACS): People who don’t speak English well were anticipated to have trouble understanding census materials, including the rationale for the census.

  12. Percent of the population who moved to the County in the past year (Table B07003, ACS): Recent arrivals were anticipated to have little connection to local civic affairs. Proxy information and administrative records about this population may have also been more difficult to come by.

  13. Percent of the population that are children 5 and under (Table S0101, ACS): More children are living in complex family situations, such as shared parental custody or with a grandparent, increasing the chances they would have been left off the census form. Some new parents mistakenly believed the census incorporates birth records.

  14. Percent of total housing units in a multi-unit structure with 3 or more units (Table B25024, ACS): There could have been a fence or gate around these types of housing units, hampering census takers’ non-response follow-up operation. Individual units may not have had addresses, skewing non-response data.

To calculate the CA-HTC index for California’s 8,057 census tracts, each of the above variables was sorted from high to low (e.g. sort tracts from the highest percent unemployed to the lowest.) Each tract was given a score from 0-11 with 11 representing the highest values for each characteristic. The sum of the 14 scores equates to the tract’s CA-HTC Index value.