SHADAC Staff

p 612.624.4802
e shadac@umn.edu

New Subsidized Marketplace Data and Other Data Tables Now Available from the 2019 American Community Survey (ACS)

The U.S. Census Bureau recently released 2019 estimates of income, poverty, and health insurance coverage from both the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) and the American Community Survey (ACS).

Along with the new estimates, several new data sets and features from the surveys are also now available for this year, including new estimates of subsidized marketplace insurance coverage, which is the main subject of this post.

New Data
In 2019, for the first time, the American Community Survey (ACS) asked respondents if they or a family member received a “tax credit or subsidy based on family income” to help pay for their coverage.¹ These subsidies are only available through the Affordable Care Act (ACA) marketplaces for individuals who are eligible based on their family income. By adding this question, researchers at the Census Bureau and other data users are now able to create estimates for the number and percent of the population who receive subsidized ACA marketplace coverage.

As part of a high-level analysis, SHADAC researchers found that at the national level, approximately 1.6% of the civilian noninstitutionalized population reported having subsidized marketplace coverage—representing nearly 5.3 million individuals.

Across the states, rates of subsidized marketplace coverage ranged significantly from a low of 0.7% in West Virginia and D.C. to a high of 3.4% in Florida and Utah. 

The five states with the largest populations of individuals with subsidized marketplace coverage were California, Florida New York, North Carolina, and Texas. More than 40% of total marketplace enrollees lived in one of these five states, and of that subsection of enrollees, nearly 3 in 5 lived in either California or Florida.

Eleven states (Florida, Idaho, Maine, Montana, Nebraska, North Carolina, South Carolina, South Dakota, Utah, Wisconsin, and Wyoming) had rates of subsidized marketplace coverage that were significantly higher than the national rate in 2019. Of these states, only Montana and Maine had implemented Medicaid expansion for the majority of 2019, which expands the portion of the population eligible for ACA subsidies. (Montana implemented Medicaid expansion as of January 1, 2019, and Maine implemented expansion on January 10, 2019.)

Twenty states (Alaska, Arizona, Arkansas, Connecticut, Delaware, Hawaii, Iowa, Illinois, Indiana, Kentucky, Louisiana, Massachusetts, Maryland, Minnesota, Mississippi, New Mexico, New York, Ohio, Washington, and West Virginia) and D.C. had rates of subsidized marketplace coverage that were significantly lower than the national rate in 2019. Of these 20 states and D.C., only Mississippi had chosen not to expand Medicaid as of January 1, 2019.

Nineteen states had rates of subsidized marketplace coverage that were not statistically different from the national rate.

New Data Tables and Geographic Breakdowns
Along with the new question and corresponding data table on subsidized coverage discussed above, other new data tables available from the ACS this year include:

• Population: a new table on place of birth shows the year of entry among the foreign-born population for the nine largest country of birth groups. Estimates are divided between year of entry before 2010 and year of entry beginning 2010 and later.
• Households and Families: two new tables provide information regarding (1) couples who live together with biological children, stepchildren, or adopted children of the main householder who are under 18 and have not been married; and (2) married couples, cohabiting couples, and single householders (male or female) with no spouse or partner present who also live with either relatives or their own children under 18.
• Quality Measure: this new table provides the unweighted total population sample for the nation, states, counties, and places.

Extensive modifications have also been made to existing ACS data tables, a full listing of which can be found here.

In addition to data table changes, the Census Bureau has also created an updated posting regarding geographic entities of varying sizes and designations (cities, towns, townships, school districts, Native American reservations, etc.) that have either come into existence, been absorbed into other entities, or have been dissolved in 2019. A full listing of all new, modified, or removed geographic breakdowns used for the 2019 ACS estimates can be found here.

Revised CPS Estimates Show less High Burden Medical Spending

March 2020:

Last year, the U.S. Census Bureau enacted a second and final round of changes in a planned, two-stage redesign process for the agency’s Current Population Survey Annual Social and Economic Supplement (CPS ASEC). The first revision, made in 2014, was a redesign of the CPS ASEC questions regarding health insurance, income, medical expenditure, and poverty data; the second, an implementation of a new data processing system that specifically takes into account the 2014 changes across each category in the questionnaire.[1]

Using the new processing system, the Census Bureau released 2018 estimates on health insurance, income, poverty, and medical expenditures, as well as re-processing collected data for 2017 in a bridge file meant to serve as the transition between the legacy and the new processing systems.

A new brief from SHADAC researchers Brett Fried, MPP, and Lacey Hartman, MPP, takes a look at estimates of medical out-of-pocket spending in both the legacy and new production files, comparing changes in the data between both. In particular, the brief highlights how the number of individuals with high burden medical spending decreased between the two files as a result of the new processing system, rather than from any changes in policy.

The brief also includes a more detailed exploration of the changes made to medical expenditure questions in the CPS, just how the data processing system changed from the legacy system to the new, calculations of the differing estimates of high burden medical spending at the national and state levels, and concludes with a discussion of potential implications for policymakers as well as for researchers in attempting to produce accurate estimates of medical burden.

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[1] SHADAC has produced these resources regarding the two phases of CPS redesign:
Turner, J. (2016). Guide to using the 2014 and 2015 Current Population Survey public use files. Retrieved from https://www.shadac.org/publications/guide-using-2014-and-2015-current-population-survey-public-use-files
Fried, B. (2019, October 31). Understanding the new CPS processing system and new 2018 health insurance coverage estimates. Retrieved from https://www.shadac.org/news/understanding-new-cps-processing-system-and-new-2018-health-insurance-coverage-estimates

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

Understanding the New CPS Processing System and New 2018 Health Insurance Coverage Estimates

On Tuesday, September 10^th, the U.S. Census Bureau released estimates of income, poverty, and health insurance coverage for 2018 from two large-scale federal data surveys conducted annually and administered by the Bureau: the American Community Survey (ACS) and the Current Population Survey Annual Social and Economic Supplement (CPS ASEC). The CPS ASEC is a key source of data on poverty, health insurance coverage, health status, and medical out-of-pocket spending, in addition to a range of other economic and demographic characteristics for the United States population. For information on the ACS estimates see the recently published blog, “National Uninsurance Increased alongside Eight States; Three States Saw Decreases.”

The 2019 CPS ASEC (2018 calendar year estimates) is the first release of a “Production File” to feature a new data processing system that takes into account changes made to the health insurance questions in the 2014 CPS ASEC redesign. Changes include the addition of new variables related to types of coverage that a person held at the time of interview, Marketplace coverage, and sub-annual coverage.^[1] For a detailed discussion of these changes, please see several papers produced by the Census Bureau that are available here. This blog briefly discusses the implication of the new processing system on the ability to trend estimates of health insurance coverage.  

According to Census, health insurance estimates created using the new processing system should not be compared with estimates produced using the old processing system.¹ Currently, the Census Bureau has released three files that that were produced using the new processing system: the 2017 CPS ASEC Research File, the 2018 CPS ASEC Bridge File, and the 2019 CPS ASEC Production File. The Census Bureau publication released this past week—“Health Insurance Coverage in the United States, 2018”—compares the calendar year 2018 health insurance estimates from the 2019 CPS ASEC Production File with the calendar year 2017 estimates from the 2018 Bridge File because both of these files use the new processing system.

The table below illustrates the importance of distinguishing between estimates of health insurance coverage produced by the old versus the new processing system.

Sources: The uninsurance estimates are from “Health Insurance Coverage in the United States, 2018” and “Health Insurance Coverage in the United States, 2017”.

For example, when comparing estimates of the 2017 national uninsured rate, the old processing system (2018 Production File) produces an estimate of 8.8 percent, while the new processing system (2018 Bridge File) produces an estimate of 7.9 percent, nearly a whole percentage point lower. When examining changes in the uninsured rate between 2017 and 2018, the Census Bureau uses the 2017 estimate from the 2018 Bridge File (7.9 percent), created using the new processing system, to compare against the 2018 estimate from the 2019 Production File (8.5 percent), also created using the new processing system.

Using only estimates from data produced by the new processing system thus allows for a true apples-to-apples comparison.