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FPG vs. FPL: What's the Difference?
May 2022:The terms "FPG" and "FPL" are often used interchangeably, but they are not actually the same thing; there are, in fact, important functional differences between the two concepts.
The federal poverty level (FPL) is the income threshold below which a “family,” and every individual in it, is considered to be in poverty.1 The poverty definition is based on money income before taxes and does not include capital gains or non-cash benefits. The official FPL is calculated annually in order to reflect inflation by the Census Bureau and is used primarily for statistical purposes; for example, to estimate the number of Americans in poverty each year. The Census Bureau assigns each person or family a singular threshold out of a possible 48, which can vary by family size (designated up to a nine-person family unit or more), number of children, and—in the case of one-person and two-person households—elderly status. The FPL is the same, however, for all 50 states and the District of Columbia (D.C.).
Table 1. Poverty thresholds for 2021 by size of family and number of related children under 18 years
| Size of family unit | Related children under 18 years | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| None | One | Two | Three | Four | Five | Six | Seven | Eight or more | |
| One person (unrelated individual): | |||||||||
| Under age 65 | $14,097 | ||||||||
| Aged 65 and older | $12,996 | ||||||||
| Two people (Householder): | |||||||||
| Under age 65 | $18,145 | $18,677 | |||||||
| Aged 65 and older | $16,379 | $18,606 | |||||||
| Three people | $21,196 | $21,811 | $21,831 | ||||||
| Four people | $27,949 | $28,406 | $27,479 | $27,575 | |||||
| Five people | $33,705 | $34,195 | $33,148 | $32,338 | $31,843 | ||||
| Six people | $38,767 | $38,921 | $38,119 | $37,350 | $36,207 | $35,529 | |||
| Seven people | $44,606 | $44,885 | $43,925 | $43,255 | $42,009 | $40,554 | $38,958 | ||
| Eight people | $49,888 | $50,329 | $49,423 | $48,629 | $47,503 | $46,073 | $44,585 | $44,207 | |
| Nine people or more | $60,012 | $60,303 | $59,501 | $58,828 | $57,722 | $56,201 | $54,826 | $54,485 | $52,386 |
Source: U.S. Census Bureau. (2022, April 21). Poverty thresholds.
https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html
The federal poverty guideline (FPG) is a poverty threshold issued by the Department of Health and Human Services (HHS) for administrative purposes; for example, determining financial eligibility for federal programs. FPG, like FPL, varies by family size. However, elderly status is not considered in FPG calculations. Additionally, FPG is not uniform nationally: The 48 contiguous states and D.C. use the same FPG, while Alaska and Hawaii each have their own FPG. Reflective of new administrative practices for the Office of Economic Opportunity (OEO) during the 1966-1970 period, separate guidelines were established for Alaska and Hawaii. Other U.S. territories, such as Puerto Rico and the U.S. Virgin Islands, for instance, do not have separate guidelines, and FPG determinations use either the rate for the 48 contiguous states or some other calculation made by local program officials.
Table 2. Poverty guidelines (FPG) for 2022
| Number of People in Family/Household |
48 contiguous states and D.C. |
Alaska | Hawaii |
|---|---|---|---|
| One person | $13,590 | $16,900 | $15,630 |
| Two people | $18,310 | $22,890 | $21,060 |
| Three people | $23,030 | $28,790 | $26,490 |
| Four people | $47,750 | $34,690 | $31,920 |
| Five people | $32,470 | $40,590 | $37,350 |
| Six people | $37,190 | $46,490 | $42,780 |
| Seven people | $41,910 | $52,390 | $48,210 |
| Eight people | $46,630 | $58,290 | $53,640 |
| Nine people or more | Add $4,720 for each additional person. |
Add $5,900 for each additional person. |
Add $5,430 for each additional person. |
Source: Office of the Assistant Secretary for Planning and Evaluation (ASPE). Poverty Guidelines. Department of Health and Human Services (HHS). https://aspe.hhs.gov/topics/poverty-economic-mobility/poverty-guidelines
Ultimately, FPL and FPG identify different numbers of people below the same poverty threshold, with FPG generally placing more people in lower poverty categories than FPL. Additionally, the two measures are released at different times relative to the year to which they apply: The Census Bureau issues its final FPL calculations in the year after the year for which poverty is being measured (e.g., the 2021 FPL, which reflects the calendar year 2021, was issued in April 2022). FPG, on the other hand, is issued by HHS in late January after the year for which poverty is being measured but is named for the year in which it is released (e.g., the 2022 FPG was issued in January 2022, but reflects price changes through calendar year 2021 only). Although the naming conventions for the FPL and the FPG seem to reflect different years, they do, in fact, provide measures for the same year and are therefore comparable.
Current and future eligibility for Medicaid is based on FPG, as are the exchange-based, cost-sharing, and premium subsidies that take place under the federal Affordable Care Act (ACA). Given that these programs affect a substantial, as well as growing, number of people, it is important to acknowledge that FPG is distinct from FPL in ways that have significant ramifications on a practical level.
A previous version of this blog from December 2019 can be downloaded here.
Sources on FPL and FPG
Office of the Assistant Secretary for Planning and Evaluation (ASPE). Poverty guidelines, research, and measurement. Department of Health and Human Services (HHS). https://aspe.hhs.gov/topics/poverty-economic-mobility/poverty-guidelines/prior-hhs-poverty-guidelines-federal-register-references
U.S. Census Bureau. How the Census Bureau measures poverty. https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html
1 A “family” for these purposes refers to a family unit, which can be a single person household but cannot be any singular or multiple individuals living in nontraditional housing such as in group quarters (e.g., institutions, college dorms, military barracks, shelters, etc.). Additionally, a family unit does not include unrelated children under the age of 15.
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SHADAC Advocates a Data-based Approach to Advancing Medicaid and CHIP Access Monitoring Plan (Response to CMS Request for Information)
April 29, 2021:On February 17, 2022, the Centers for Medicare & Medicaid Services (CMS) released a request for information (RFI) regarding access to coverage and care in Medicaid and the Children’s Health Insurance Program (CHIP).
SHADAC researchers focused our response on Objective 4: Question 1, which asked researchers to consider how CMS might develop a stronger Medicaid and CHIP access monitoring approach through data reporting and analysis, and is copied for reference below.
Portions of SHADAC’s response were also included as part of a larger commentary on each of the RFI’s five total objectives submitted to CMS by President and CEO Richard Besser, MD, of the Robert Wood Johnson Foundation (RWJF) on April 18, 2022.
|
Centers for Medicare & Medicaid Services (CMS) – Request for Information (2022) Objective 4: CMS has data available to measure, monitor, and support improvement efforts related to access to services (i.e., potential access; realized access; and beneficiary experience with care across states, delivery systems, and populations). CMS is interested in feedback about what new data sources, existing data sources (including Transformed Medicaid Statistical Information System [T-MSIS], Medicaid and CHIP Core Sets, and home and community based services (HCBS) measure set), and additional analyses could be used to meaningfully monitor and encourage equitable access within Medicaid and CHIP programs. 1. What should CMS consider when developing an access monitoring approach that is as similar as possible across Medicaid and CHIP delivery systems (e.g., fee-for-service and managed care programs) and programs (e.g., HCBS programs and dual eligibility in Medicaid and Medicare) and across services/benefits? Would including additional levels of data reporting and analyses (e.g., by delivery system or by managed care plan, etc.) make access monitoring more effective? What type of information from CMS would be useful in helping states identify and prioritize resources to address access issues for their beneficiaries? What are the most significant gaps where CMS can provide technical or other types of assistance to support states in standardized monitoring and reporting across delivery systems in areas related to access? |
Response from SHADAC
Thank you for the opportunity to help inform the development of an improved Medicaid and CHIP access monitoring plan. These comments are based on SHADAC’s experience providing data and evaluation technical assistance (TA) to states, which has given us a unique view into the day-to-day challenges and barriers that states encounter related to these issues. These comments mirror recent testimony to the Medicaid and CHIP Payment Access Commission (MACPAC) provided by SHADAC’s Deputy Director.
We present five points for your consideration, each of which are guided by the same principle, which is: That any monitoring plan should seek to minimize burden on state agencies.
First, consider an iterative approach that starts with a limited number of measures and expands over time. This will require difficult tradeoffs regarding priorities, but these tradeoffs will be worth it if the first iteration is achievable for a wide range of states. As the plan develops and more complicated measurement concepts are added, it should engage leading states by including them in additional measure selection and incentivizing them to participate in pilots that test the collection and analysis of data – sharing concrete implementation lessons with other states.
Second, support states in efforts to improve existing data with a focus on the ability to disaggregate. There are existing data streams to draw on (as documented by Urban Institute in 2017 and discussed at a recent MACPAC meeting), and while none are perfect, a successful plan should first focus on improving those. One tangible way to improve states’ existing data is to support data disaggregation efforts. There is a renewed focus and energy to promote equity within Medicaid and calls from stakeholders to see data about important groups of interest. This includes an interest in better data by race, ethnicity, sexual orientation, gender identity, disability, and geography. Improving existing data streams to better support disaggregation is a good investment and one that will meet multiple demands.
For example, the Transformed Medicaid Statistical Information System (T-MSIS) will likely play a role in monitoring service use, but there are concerns regarding the existing quality and completeness of the race and ethnicity data. We have worked with several states who are trying to improve the collection of race and ethnicity data in Medicaid—modifying question wording and expanding response options to better represent the populations they serve, making technical changes to better capture the data, and modifying instructional language and scripts for enrollment assisters to make them stronger partners in data collection. And, most importantly, they are doing this with community input. But they continue to face challenges in this work. For example, current OMB standards for demographics are dated and do not align with the most current research. Additionally, rules for collecting race and ethnicity data are not uniform across federal programs. A new access monitoring effort could serve as further impetus for federal agencies to revise the guidance on race and ethnicity and for states to take action to improve existing data collection to address this critical data gap.
Third, states need both direct funding and hands-on technical assistance to support this work. We appreciate CMS’s understanding that states will need assistance to implement the access monitoring framework. However, based on our work with states, we think this needs to go beyond documentation and uniform measure specifications. To do this well, states should have access to experts who can provide practical, hands-on advice that is responsive to their specific needs. The type of assistance needed will vary and needs to be flexible, from states who are further along in their process and may seek help troubleshooting a particular coding challenge to other states whose request might require more fundamental support, such as walking through the requirements to assess staff and training needs, setting priorities, and help developing contract amendments or RFPs. We would also recommend that any TA effort include a forum for states to discuss implementation challenges with their peers.
The funding provided to states for this effort should also be flexible. It should support direct costs like system modifications, but also things like stakeholder engagement, which is critical to the iteration and improvement of the monitoring plan, and related data collection, but will also allow the results of this monitoring to be shared in a meaningful way.
Fourth, some areas of Medicaid access monitoring are best addressed through federal data collection. We believe that the access monitoring effort would benefit from periodic fielding of a 50-state Medicaid Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey aimed at both children and adults and administered by the federal government. The federal government has a long and successful track record of fielding high-quality surveys that produce estimates for all 50 states, including a one-time National Medicaid Adult CAHPS survey.
While this undertaking would be resource intensive, it seems to us to be the most efficient way to collect comparable information on important facets of access, like enrollee experience, and a starting point for comparing individuals enrolled in fee-for-service versus managed care.
Finally, it is critical to treat states as a full partner in this process, including the communication of results. It goes without saying that states should be consulted in the development of the monitoring plan, but they should also be consulted when the data are being released, preferably beforehand. Ideally, they should have access to analytic files so that they can do their own data runs and share customized findings with stakeholders. Finally, once the data are collected, there should be a commitment that the data be published or released in some format in a timely manner.
Blog & News
MPH , Senior Research Fellow
Expert Perspective and Issue Brief: Tracking the Data on Medicaid’s Continuous Coverage Unwinding (State Health & Value Strategies Cross-Post)
January 21, 2022:The following content is cross-posted from State Health and Value Strategies published on January 21, 2022.
Authors: Emily Zylla, Elizabeth Lukanen, and Lindsey Theis, SHADAC
Medicaid and Children’s Health Insurance Plan (CHIP) programs have played a key role in the response to the COVID-19 pandemic, providing a vital source of health coverage for millions of people. However, when the Families First Coronavirus Response Act (FFCRA) Medicaid “continuous coverage” requirement is discontinued states will restart eligibility redeterminations, and millions of Medicaid enrollees will be at risk of losing their coveragei.
A lack of publicly available data on Medicaid enrollment, renewal, and disenrollment makes it difficult to understand exactly who is losing Medicaid coverage and for what reasons. Publishing timely data in an easy-to-digest, visually appealing way would help improve the transparency, accountability, and equity of the Medicaid program. It would inform key stakeholders, including state staff, policymakers, and advocates, allowing them to more fully understand the impacts of Medicaid policy changes on enrollees’ access, and give them an opportunity to modify or implement intervention strategies as needed. States already collect a significant amount of data that could inform their success in enrolling and retaining eligible individuals in Medicaid. Many advocates and researchers have been calling for increased transparency around this data in order to better understand the barriers and challenges individuals face when trying to enroll in or maintain coverage.
One effective way to monitor this dynamic issue is by creating and publishing a Medicaid enrollment and retention dashboard. A typical data dashboard is designed to organize complex data in an easy-to-digest visual format, thus allowing the audience to easily interpret key trends and patterns at a glance. A new issue brief examines the current status of Medicaid enrollment and retention data collection, summarizes potential forthcoming reporting requirements, and describes some of the best practices when developing a data dashboard to display this type of information.
The issue brief lays out a phased set of priority measures and provides a model enrollment and retention dashboard template that states can use to monitor both the short-term impacts of phasing out public health emergency (PHE) protections and continuous coverage requirements, as well as longer-term enrollment and retention trends.
State Medicaid Enrollment and Retention Dashboard – Measurement Priorities
Priority 1 – Use currently reported data: Start with the data that are already collected and submitted to the Centers for Medicare & Medicaid Services (CMS) under the 11 Medicaid performance topics.
Priority 2 – Track reasons for disenrollment: Include measures in the proposed Build Back Better Act (BBB) legislative language that address the reasons why people are being disenrolled.
Priority 3 – Monitor coverage transitions: Add measures to address issues of transitions between programs and churn—the moving in and out of coverage—that frequently occurs in Medicaid and CHIP.
Priority 4 – Explore reasons for and consequences of disenrollment: Field disenrollment surveys that could provide quantitative and qualitative data that could be used to understand both the enrollee’s experience navigating Medicaid processes as well as the consequences of disenrollment.
Regardless of the measures highlighted, an overarching goal of any Medicaid enrollment and retention dashboard should be a focus on displaying disaggregated data. Providing data broken down by various population characteristics (e.g., age, race/ethnicity, income, gender, language, or program type) or geographic areas (urban, rural) will make it easier to understand the potentially disproportionate impact of administrative enrollment and renewal policies on communities of color, persons with lower incomes, and other populations that face disparities. Access to this type of granular data provides stakeholders an opportunity to take action in order to minimize needless loss of coverage.
Designing an easy-to-understand dashboard that is accessible to all interested stakeholders—state or county program staff, navigators or enrollment assisters, and advocates—will highlight the early warning signs of large numbers of people losing Medicaid coverage. States should start small, using data dashboard best practices and as they gain experience publicly reporting this data, consider adding additional measures over time.
i Buettgens, M. & Green, A. (September 2021). What Will Happen to Unprecedented High Medicaid Enrollment after the Public Health Emergency? [Research report]. Urban Institute. https://www.urban.org/sites/default/files/publication/104785/what-will-happen-to-unprecedented-high-medicaid-enrollment-after-the-public-health-emergency_0.pd
Blog & News
Research Fellow
NHIS: National Rates of Health Insurance Coverage for Third Quarter of 2021 Statistically Unchanged from 2020
January 25, 2022:On Wednesday, January 19, the National Center for Health Statistics (NCHS) announced that health insurance coverage estimates from the National Health Interview Survey (NHIS) Early Release Program are now available for Quarter 3 of 2021 (July-September)
At a high level, the new estimates show no significant changes in coverage type (public, private) or uninsured rate across all ages and income groups when compared to the same time period in 2020, as discussed below.
Age
Among nonelderly adults (ages 18 to 64) surveyed between July and September of 2021, 13.0% were uninsured at the time of interview, 21.1% had public coverage, and 67.3% had private coverage. Comparatively, these rates measured at 14.1%, 20.3%, and 67.4% from July to September in 2020, as shown in Figure 1.
Figure 1. Type of Health Insurance Coverage Nonelderly Adults (18-64 years), Q3 2020 and Q3 2021
Poverty Level
Again among nonelderly adults at three differing thresholds of family income as a percentage of the federal poverty level (less than 100% FPL, 100%-199% FPL, and 200%+ FPL), rates of uninsurance as well as public and private coverage remained statistically unchanged in Q3 2021 from Q3 2020.
Looking at the below than 100% FPL category for nonelderly adults from July-September 2021, 21.7% were uninsured, 52.3% had public coverage, and 27.2% had private coverage. These rates were 28.6%, 51.3%, and 22.3%, respectively in 2020 (Figure 2).
Figure 2. Type of Health Insurance Coverage (<100% FPL) Nonelderly Adults (18-64 years), Q3 2020 and Q3 2021
COVID-19 and Cautions for 2020 NHIS Estimates
As has been extensively documented in reports from both SHADAC and NCHS, COVID-19 caused numerous disruptions to federal survey data collection and production efforts. For the NHIS in particular, personal visits were suspended beginning on March 19, 2020, and data collection in late Q1 and for all of Q2 in 2020 switched to a telephone-only mode. Personal visits (with telephone attempts first) resumed in all areas in September 2020.
Data collection methodologies were not the only casualty of the pandemic, however. While the initial NCHS report examined preliminary nonresponse bias in Q1 and Q2 of 2020, the same team published a follow-up report in September 2021 looking at effects of the pandemic on estimates for the entire year, including July to December 2020. Though in-person operations resumed, lingering concerns about low response rates and possible loss of coverage caused survey conductors to replace approximately half of the usual sample for the last 5 months of 2020 with a longitudinal component where a subset of the 2019 sample adults were re-interviewed over the telephone using the 2020 NHIS questionnaire. This process change means that comparisons between estimates from July–December 2020 and other time periods may be impacted by these differences in survey mode and methodology.
About the Numbers
The above estimates provide a point-in-time measure of health insurance coverage, indicating the percent of persons with that type of coverage at the time of the interview. The 2021 estimates discussed in this blog are only from Q3 (July-September) as well as the same period in 2020.
Differences described in this post are statistically significant at the 95% confidence level unless otherwise specified.
Citations
Cohen, R.A. & Cha, A.E. (2022, January 19). Health Insurance Coverage: Early Release of Quarterly Estimates from the National Health Interview Survey, July 2020–September 2021. National Center for Health Statistics (NCHS). https://www.cdc.gov/nchs/data/nhis/earlyrelease/Insur201902.pdf.
Dahlhamer, J.M., Bramlett, M.D., Maitland, A., & Blumberg, S.J. (February 2021). Preliminary evaluation of nonresponse bias due to the COVID-19 pandemic on National Health Interview Survey estimates, April-June 2020. National Center for Health Statistics (NCHS). https://www.cdc.gov/nchs/data/nhis/earlyrelease/nonresponse202102-508.pdf
Bramlett, M.D., Dahlhamer, J.M., & Bose, J. (September 2021). Weighting procedures and bias assessment for the 2020 National Health Interview Survey. National Center for Health Statistics (NCHS). https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2020/nonresponse-report-508.pdf
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SHADAC’s New State Survey Research Resource Page
December 10, 2021:
Early this fall, SHADAC updated its State Survey Research Activity web page using an interactive infographic (preview to the right) to allow users to easily identify the type and frequency of state-level health insurance surveys (household and/or employer) available for all 50 states and the District of Columbia. The surveys accessible through the page were collected from 2010 through 2021, with surveys that predate 2010 housed in SHADAC’s archives and available upon request.
State Surveys in Summary
In all, 27 states field and report ongoing household and/or employer surveys, and 6 of these states have conducted surveys nearly every year since 2010. Ohio has the longest continuous record with 11 iterations of its employer survey fielded since 2010.
Available Information
The updated State Survey web page uses an infographic to enable quick navigation to PDF-format state survey materials, including survey questionnaires, findings, methodology reports, technical documentation, and summary briefs. The update includes surveys that are accessible online and free of charge. SHADAC will be adding additional surveys, including the 2021 Massachusetts Health Insurance Survey and 2021 Oregon Health Insurance Survey (both of which are currently underway), as they become available.
State Health Insurance Surveys Highlights
Since 2019, twenty states have conducted household or employer surveys. Eleven of these states have conducted their surveys multiple times over the course of at least a fifteen-year period, and even longer in some cases. The long-standing surveys conducted by states including California, Colorado, Idaho, Iowa, Louisiana, Massachusetts, Minnesota, Ohio, Vermont, Wisconsin, and Wyoming are highlighted below.
