Community Development Block Grants: Sources of Data on Community Income Are Limited
Published: 2016-09-06T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: Administered by HUD, the CDBG program provides funding for housing, community, and economic development programs. After set asides, HUD must allocate 70 percent of funds to cities and urban counties, known as entitlement communities, and 30 percent to states for distribution to eligible nonentitlement communities. In fiscal year 2015, Congress appropriated $3 billion for the CDBG program, of which HUD allocated $900 million to states. Seventy percent of CDBG funds must principally benefit low- and moderate-income persons, and Census Bureau data are used for this determination. In 2014, HUD transitioned from using decennial census long form income data (which are no longer collected) to ACS income data, which HUD uses to update its income data every 5 years.
GAO was asked to review HUD's policies related to communities that disagree with their CDBG eligibility determination based on HUD's use of ACS data. This report examines (1) HUD's and states' eligibility policies and (2) potential alternative data sources. GAO interviewed CDBG administrators from 8 states and from nonentitlement communities in each of these states, all of which were selected based on available data and CDBG stakeholders' recommendations; spoke with other CDBG stakeholders; and reviewed CDBG guidance from 49 states and Puerto Rico. GAO also analyzed how use of ACS data can affect community eligibility.
GAO makes no recommendations in this report. HUD and the Department of Commerce provided technical comments.
section_title: What GAO Found
paragraphs: The Department of Housing and Urban Development's (HUD) and states' primary method for communities to demonstrate eligibility when they disagree with HUD's eligibility determination is to allow communities to conduct their own local income surveys to show that they meet the Community Development Block Grant (CDBG) income threshold. HUD instructs small communities, known as nonentitlement communities, to use data based on the Census Bureau's American Community Survey (ACS) to determine whether at least 51 percent of residents in their proposed project service areas are low- and moderate-income persons and are therefore eligible for CDBG funds. However, communities may disagree with their eligibility determination based on ACS data, or they may be unable to use this method because the project's service area is larger or smaller than the census boundaries. In these cases, HUD and states allow communities to conduct their own local income surveys to demonstrate eligibility. State officials GAO interviewed said it is common for nonentitlement communities to use local income surveys as an alternative to HUD's ACS-based data, and HUD and states provide guidance on conducting these surveys. However, stakeholders cited costs and other challenges nonentitlement communities face in conducting local income surveys, including resource constraints, administrative burdens, and difficulty in obtaining a sufficient number of survey responses. Other than local income surveys, alternative methods for showing eligibility for CDBG funds are limited. For example, communities may qualify by funding activities that serve populations HUD presumes to be low- and-moderate income, such as the elderly or homeless.
Stakeholders GAO interviewed cited challenges associated with measuring income and limitations associated with alternative data sources that might be used to demonstrate that communities meet the low- and moderate-income requirements. For example, stakeholders cited general challenges associated with measuring income and poverty, such as fluctuations in an individual's or community's income over a year. Some stakeholders cited alternative sources of income information that have not been used by communities that disagree with census data to determine CDBG eligibility. However, they noted that these sources would likely have one or more of the following limitations:
does not fully measure a community's income;
is not easily accessible by communities;
is not available at small geographic levels; or
is not more precise than ACS.
For example, some sources of income data, such as income tax data, may have limited public availability—limiting their accessibility by communities—and income tax data would not include low-income earners who are not required to file tax returns. Other sources, such as the Supplemental Nutrition Assistance Program and other income-based programs, would not provide data at a small enough geographic level to be useful for this purpose. The Census Bureau is in the process of exploring ways to use external data sources, such as Social Security Administration and Internal Revenue Service data, to supplement ACS to improve the data and expects to make recommendations by March 2017.

section_title: Letter
paragraphs: The Community Development Block Grant (CDBG) program is the federal  government’s principal community development program. The primary  objective of the program is the development of viable urban communities  by providing decent housing and a suitable living environment and  expanding economic opportunities, principally for persons of low and  moderate income. In fiscal year 2015, Congress provided approximately  $3 billion for the program to fund housing, economic development,  neighborhood revitalization, and other community development activities.  Administered by the Department of Housing and Urban Development  (HUD), the CDBG program provides funding to metropolitan cities and  urban counties—known as entitlement communities—and to states for  distribution to other, generally smaller communities—known as  nonentitlement communities. Activities undertaken with program funds,  with the exception of funds allowed for administrative and planning  activities, must (1) benefit low- and moderate-income (LMI) persons, (2)  aid in the prevention or elimination of slums or blight, or (3) meet urgent  community development needs.
By statute, 70 percent of CDBG funds must be used toward the objective  of benefitting LMI persons. Communities demonstrate that their CDBG  projects would meet this objective primarily by using HUD’s Low- and  Moderate-Income Summary Data (LMI summary data), which provide  information on the percentage of the population that is considered LMI for  different geographic areas. Historically, HUD has based its LMI summary  data on the decennial census long form, which asked a sample of  households detailed questions, including those related to income. After  the 2000 decennial census, the U.S. Census Bureau discontinued the  long form and replaced it with the American Community Survey (ACS),  and in July 2014, HUD began using ACS as the basis for its LMI  summary data.
You asked us to review options for nonentitlement communities that  disagree with their ACS-based CDBG eligibility determination. This report  examines (1) HUD’s and states’ policies for communities that disagree  with their eligibility determination based on HUD’s use of ACS data or that  are not able to use HUD’s LMI summary data and the challenges, if any,  communities face in using available options, and (2) stakeholders’ views  on whether there are possible alternative data sources for determining  eligibility under CDBG’s LMI objective.
To determine the policies HUD and states have in place for communities  that disagree with their CDBG eligibility determination based on ACS  data, we reviewed HUD’s CDBG regulations and guidance in addition to  CDBG guidance from 49 states and Puerto Rico. We interviewed HUD  officials, as well as CDBG administrators from a nongeneralizable sample  of eight states and from selected nonentitlement communities within each  of these states. We chose states and communities based on whether they  had available data on their use of surveys and a high number of rural  communities, and on recommendations from other state officials. Findings  from the interviews with states and communities cannot be generalized to  those with which we did not speak. We also interviewed other CDBG  stakeholders, such as representatives of community development  associations and private companies that assist communities with CDBG  applications.
To obtain stakeholders’ views on whether there are possible alternative  data sources for determining eligibility under CDBG’s LMI objective, we  interviewed the stakeholders identified above, as well as officials from the  Census Bureau. Additional stakeholders we consulted included  researchers from the Urban Institute, George Washington University, and  the National Opinion Research Center at the University of Chicago. We  selected a purposive subset of such researchers based on our knowledge  of organizations active in conducting research on topics relevant to our  inquiry, a review of relevant studies, and a recommendation we received  during an interview. We also reviewed prior GAO work to help identify  potential alternative sources of income information. More information on  our scope and methodology is contained in appendix I. In addition,  appendix II presents analysis of how margins of error around LMI  estimates based on ACS data show the degree to which there is  uncertainty about CDBG eligibility determination for nonentitlement  communities. Appendix III presents analysis of changes in the eligibility  status of nonentitlement communities in eight states for CDBG funds  between the 2000 LMI summary data and the LMI summary data used by  HUD beginning in 2014 and based on 2006–2010 ACS data.
We conducted this performance audit from May 2015 to September 2016  in accordance with generally accepted government auditing standards.  Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on our audit objectives.
subsections: section_title: Background
subsections: section_title: CDBG Program
paragraphs: The Housing and Community Development Act of 1974 created the  CDBG program to develop viable urban communities by providing decent  housing and a suitable living environment and by expanding economic  opportunities, principally for LMI persons. Program funds can be used for  housing, economic development, neighborhood revitalization, and other  community development activities. After funds are set aside for special  statutory purposes—the Indian Community Development Block Grant  program and allocated insular areas—the annual CDBG appropriation is  allocated to entitlement communities and states. Entitlement communities  generally are principal cities of metropolitan statistical areas, other  metropolitan cities with populations of at least 50,000, and qualified urban  counties with populations of 200,000 or more (excluding the populations  of entitlement cities). States distribute their allocated CDBG funds to  nonentitlement communities. After the set-asides, 70 percent of CDBG  funds are allocated to entitlement communities, and the remaining 30  percent are allocated to states to distribute to nonentitlement  communities. In fiscal year 2015, Congress appropriated about $3.1  billion for the Community Development Fund, $66 million of which was set  aside for Native American tribes. Of the remaining amount, roughly $2.1  billion was allocated to entitlement communities, roughly $900 million was  allocated to states to distribute to nonentitlement communities, and  roughly $7 million was set aside for insular areas.
Both entitlement and nonentitlement communities must use CDBG funds  toward one of three national objectives: (1) benefitting LMI persons, (2)  aiding in the prevention or elimination of slums or blight, or (3) meeting  urgent community development needs. At least 70 percent of all funds  allocated to entitlement communities and states must be used toward the  first objective over a period of 1, 2, or 3 years (as specified by the  grantee). Figure 1 illustrates the distribution of CDBG funds.
For the LMI national objective, HUD defines an activity to be principally  benefitting LMI persons if at least 51 percent of the population of the  community or 51 percent of project beneficiaries meet HUD’s LMI  definition, which we discuss in detail later. There are several ways a  community can qualify for CDBG funds under the objective of principally  benefitting LMI persons:    If a project is designed to serve the entire community or a smaller  area within a community—for example, a waste water project—the  community or project area would need to qualify by showing that the  entire community or project area is majority LMI persons. HUD refers  to these projects as area-benefit activities.
If a project is designed to serve a smaller area within a community— for example, a sidewalk for a neighborhood—the community may  need to conduct a local income survey to demonstrate that the  majority of residents to be served by that project are LMI persons. We  refer to this type of survey as a local income survey since it is used to  collect income data from a community or individuals that reside in a  project service area.
If a project is designed to serve a specific clientele—for example, a  senior center or homeless shelter—the community may be able to  qualify for funding under the LMI objective by showing that the  beneficiaries of the project fall under one of the population categories  that HUD presumes to be LMI, such as the elderly or homeless.
If a project directly benefits LMI persons—for example, rehabilitation  of single-family housing that will be occupied by a LMI household or a  job creation activity that will create jobs the majority of which will be  held by LMI persons—the community can qualify for funding under the  LMI objective by showing that the direct beneficiaries are LMI.
For the portion of funds that are allocated to states to distribute to  nonentitlement communities, HUD provides states with flexibility in  determining specific requirements, including those related to program  oversight and selecting activities to fund. Specifically, HUD’s CDBG  regulations state that HUD will give “maximum feasible deference” to the  state's interpretation of the statutory and regulatory requirements,  provided that these interpretations are not plainly inconsistent with the  statute. States formulate community development objectives for their  state and determine how to distribute funds among nonentitlement  communities, which submit applications for funding to their respective  state. States can use a formula, competition, open application, or a  combination of methods to distribute funds to nonentitlement  communities.
section_title: HUD’s Low- and Moderate-Income Summary Data
paragraphs: For projects designed to serve either the entire community or a smaller  area within a community, HUD produces the LMI summary data to help  states and communities determine whether at least 51 percent of a  proposed project’s service area is comprised of LMI persons. The LMI  summary data draw income data from ACS and provide estimates of the  number and percentage of persons in a proposed project’s service area  who can be considered low or moderate income. Specifically, a person is  considered to be of low income if he or she is a member of a family  whose income is at or below 50 percent of area median income.  Similarly, CDBG moderate income level is generally tied to 80 percent of  area median income. Unrelated individuals in a household are considered  one-person families for CDBG purposes.
Historically, HUD used data collected by the decennial census long form  as the basis for the LMI summary data. However, after the 2000  decennial census, the Census Bureau discontinued the long form and  began collecting detailed demographic and income information using  ACS. As a result, the current LMI summary data, which were released in  July 2014, are based on aggregated 2006–2010 ACS data. In contrast to  the decennial census, which was conducted once every 10 years, ACS is  an ongoing survey, and the Census Bureau updates its publicly available  data annually. In addition, ACS sample sizes (which changed in 2011)  and the geographic level at which data are available differ from those of  the decennial census long form. See table 1 for a comparison of selected  features of the decennial census long form, ACS before 2011, and ACS  after 2011.
Differences between the two surveys have resulted in differences  between the LMI summary data values that are based on each survey,  including the following examples:
Smaller sample size and larger error rates: ACS has a smaller  sample size than the decennial census long form, so the LMI  estimates are based on a sample of fewer households. The 2000  decennial census long form was mailed to approximately 20.9 million  housing unit addresses, all completed within several months of April  2000. The 2006–2010 5-year ACS estimates included 2.9 million  housing unit addresses per year, for a total of 14.5 million addresses  over the 2006–2010 5-year period, and surveys were mailed to  250,000 addresses each month. Because the ACS sample size is  smaller, HUD uses a 5-year ACS average for its LMI summary data,  with the current LMI summary data based on 2006–2010 ACS  estimates. Even using 5 years of data, the ACS sample currently used  by HUD is still smaller, reaching about 12.5 percent of all addresses,  compared with the roughly 17.1 percent of addresses that received  the long form of the 2000 decennial census.
As a result of smaller sample sizes, ACS generally has higher  sampling errors, and its estimates are less precise (see app. II for  more details on ACS confidence intervals and their potential effects).  HUD and Census Bureau officials noted that unlike the decennial  census long form, ACS publishes margins of error with its public data  for greater transparency.
In 2011, the Census Bureau increased ACS’s overall sample sizes, as  well as the sample sizes of small areas. These improvements will be  reflected in HUD’s next update of the LMI summary data, which will be  based on 2011–2015 data. According to Census Bureau analysis, for  a poverty estimate of 10 percent in an average-sized tract and at the  90 percent confidence level, increasing the sample size from 2.9  million to 3.54 million would result in a 9.2 percent improvement in  margins of error.
More frequent LMI summary data updates: Another difference  between the decennial census long form and ACS is that the long- form survey was conducted every 10 years; therefore, HUD updated  its LMI summary data every 10 years as well. In contrast, the Census  Bureau updates ACS’s 5-year estimates annually. Since switching to  ACS data, HUD has chosen to update its LMI summary data once  every 5 years. HUD officials said this approach allows communities to  develop long-term plans and limit uncertainty related to gaining or  losing LMI status from year to year, but still allows for more timely  information than the decennial census long form did.
Different geographic areas: HUD was able to produce its LMI  summary data at a smaller geographic area with the decennial census  long form than with ACS. Specifically, the LMI summary data were  available at the smaller split-block group-level with the decennial  census long form and are only available at the block group-level with  ACS.
section_title: HUD and State Policies Allow Local Income Surveys as an Alternative to Census Data
paragraphs: HUD’s and states’ primary policy for nonentitlement communities that  disagree with their CDBG eligibility determination based on census data  is to allow communities to conduct local income surveys. In addition,  some state and local officials said that because the ACS-based LMI  summary data are not available at as small a geographic level as the prior  LMI summary data, projects’ service areas more often do not align with  HUD’s data. As a result, more communities have had to conduct local  income surveys to demonstrate eligibility. State and local officials we  interviewed said that conducting a local income survey can be a  challenge for small communities due to resource constraints and high  costs. Other options include applying for funding under one of the other  national objectives or for a project targeted to beneficiaries that are  presumed to be low income. However, state and community officials said  these options can be difficult to use because they may not allow for  projects that meet the needs of their communities.
subsections: section_title: Communities Have the Option to Conduct a Local Income Survey When They Disagree with Census Data
paragraphs: For nonentitlement communities that disagree with their CDBG eligibility  determination based on HUD’s use of ACS data, HUD’s and states’  primary policy is to allow these communities to conduct local income  surveys. HUD instructs communities submitting CDBG applications for  area-benefit activities—that is, projects designed to serve an entire  community or area within a community—to use the LMI summary data to  the fullest extent feasible to show that the project area meets the 51  percent LMI threshold. However, if a community disagrees with its  eligibility determination based on LMI data, it has the option to conduct a  local income survey. According to HUD guidance, in order to conduct a  local income survey, a community must develop a set of questions to  determine household size and household income, identify the survey  population or a random sample of households that would benefit from the  activity, then select the type of survey to use (e.g., in-person, telephone,  mail). The survey results must then be tabulated, and if they show that  the project’s service area meets the 51 percent LMI threshold, the  community may submit the survey results to the state with its CDBG  application.
We interviewed officials from eight states and representatives of local  governments within those states, and most expressed concerns with the  use of ACS data for making CDBG eligibility determinations. Several state  officials said that nonentitlement communities have needed to conduct  local income surveys due to ACS’s small sample sizes and large margins  of error. For example, officials from one state attributed 20 nonentitlement  communities’ loss of LMI status to a lack of precision in ACS estimates.  Similarly, officials from several states and communities we interviewed  noted instances where communities did not meet HUD’s LMI threshold  based on ACS estimates but were able to demonstrate LMI status with  local income surveys. For example, according to officials from one state,  nine nonentitlement communities successfully showed that their project  service areas met the LMI threshold with local income surveys when the  LMI summary data indicated that they did not meet the threshold. State  officials may not learn of nonentitlement communities that conducted local  income surveys but were unable to show their project service areas met  the LMI threshold. Officials from two of the states we interviewed told us  that they were not aware of nonentitlement communities that were unable  to show that their project service areas met the LMI threshold using a  local income survey because the state officials only received local income  surveys from communities that successfully showed their project service  areas met the LMI threshold. See appendix II for our analysis of the ACS  confidence intervals and resulting uncertainty over nonentitlement  communities’ LMI status.
section_title: HUD and States Allow Local Income Surveys When Service Areas Do Not Align with Census Geographic Boundaries
paragraphs: HUD’s CDBG regulations and local income survey guidance also discuss  the use of local income surveys in place of the LMI summary data when  the service area benefitting from an activity is larger or smaller than the  census boundaries. Specifically, HUD’s local income survey guidance  states that a local income survey may be the most appropriate way to  determine eligibility when (1) a service area comprises only a small  portion of a block group, (2) a service area includes all or part of several  nonentitlement communities and may also include both incorporated and  unincorporated places, or (3) a service area is sparsely populated.
HUD’s guidance explains that when a service area for an activity only  comprises a small portion of a block group, the LMI summary data may  not reflect the characteristics of the households being served. In addition,  when a service area covers multiple nonentitlement communities, a local  income survey may be necessary to supplement the LMI summary data.
HUD does not specify when a service area would be considered too small  or too large to use the LMI summary data. In their role as administrators  of the state CDBG program, states may specify when a service area  should be considered too small or too large and, therefore, when a  nonentitlement community would be required to conduct a local income  survey. One state official we interviewed had specific guidelines on when  a service area is considered too small to use the LMI summary data.  Specifically, in this state, the LMI summary data may be used only when  at least 60 percent of the census geographic area is benefitting from the  proposed activity. Other state officials we interviewed said they did not  have specific requirements and determined whether project service areas  were too large or too small to use the LMI summary data on a case-by- case basis. Based on our review of CDBG guidance from 49 states and  Puerto Rico, 1 state does not allow nonentitlement communities to use  the LMI summary data to show they meet the LMI threshold; instead, this  state requires all communities applying for funding using the area-benefit  criteria to conduct local income surveys. Officials from this state  explained that they require surveys because project service areas in their  state rarely align with the block groups. In addition, they said that the  state has a geographically dispersed population and that it is more  efficient to interview a small number of households than it is to determine  the proper census area to use.
Since HUD’s issuance of state CDBG regulations in 1988, communities  have been allowed to conduct local income surveys when project service  areas do not align with census areas. However some state and  community officials we interviewed said that the loss of the smaller split- block group-level data with the transition from the decennial census long  form to ACS has resulted in challenges. For example, officials from one  state told us that the larger geographic areas reported in the ACS-based  LMI summary data have made it difficult for communities to show income  data for more targeted service areas. According to Census Bureau officials,  concerns  about confidentiality and the lack of precision around the data at  these small geographic levels led  the Census Bureau to discontinue  publishing the data at the split-block group level. Census Bureau officials  noted that margins of error at these small geographic levels would be  very high, as they were with the decennial census long form. However,  they said that the high margins of error with the decennial census long  form would not have been evident to grantees because margins of error  were not published. In addition, a few state and local officials attributed  the need to conduct local income surveys to changes in the available  geographic level of the data. For example, officials from one state and  one nonentitlement community explained that the LMI summary data  being reported at the larger geographic level has resulted in two cases  where wealthier neighborhoods were included in the area. They said that  this resulted in two nonentitlement communities not being able to use the  LMI summary data to demonstrate eligibility.
HUD does not collect data on the extent to which nonentitlement  communities have conducted local income surveys instead of using the  LMI summary data either because they disagreed with eligibility  determinations based on ACS data or because a project service area did  not align with census boundaries. However, officials from most of the  eight states we interviewed told us that it is common for local income  surveys to be used by nonentitlement communities to show that their  project service areas meet the LMI threshold. For example, one state  noted that out of 567 applications the state received during its last funding  cycle, around 200 of those applications based eligibility on local income  surveys. An official from another state said that out of 184 projects that  the state funded in 2014, 75 of the eligible projects were supported by  local income surveys. Several of the state officials we interviewed  discussed nonentitlement communities’ reasons for conducting surveys.  For example, one state official said that out of 36 income surveys  conducted, 26 were conducted because the activities’ service areas did  not align with census geographic areas; the remaining 10 were conducted  because the communities’ did not agree with their LMI percentages based  on ACS data. Similarly, officials from another state said that most local  income surveys conducted in their state were due to project areas not  aligning with census areas. In addition, another state official said that  about half of all surveys conducted in her state were related to  geographic areas not aligning and the other half were related to  disagreement with ACS-based eligibility determinations. Officials from  one state said that they could not determine why nonentitlement  communities conducted surveys in their state.
section_title: States and Communities Cited Costs and Challenges in Conducting Local Income Surveys, but Some Assistance Is Available
paragraphs: All of the state and local stakeholders we interviewed described a number  of challenges nonentitlement communities face when conducting local  income surveys. For example, officials from three states we interviewed  stated that the administrative burdens associated with conducting local  income surveys can be difficult for nonentitlement communities with small  staffs and small budgets. Local stakeholders also told us that conducting  a local income survey can be time-consuming, with one survey generally  taking a few months to complete depending on the resources available to  the community. Some stakeholders also noted that conducting a  methodologically sound local income survey can be demanding to these  small communities. For example, officials from four states said it is  challenging for communities to obtain a sufficient number of survey  responses to be considered representative of the nonentitlement  community’s income. Also, two of these states and two local stakeholders  told us that survey respondents are generally unwilling to share  information on their income.
State and local stakeholders cited cost as a challenge associated with  conducting a local income survey. Several of these stakeholders  estimated the cost of a survey to be from $5,000 to $10,000. However,  one community official said his community only paid the price of postage  and the time of two employees for its survey. Officials from five states and  four nonentitlement communities told us that in some cases, communities  can obtain free assistance from volunteers, grant preparation firms, and  regional development organizations. In addition, according to HUD  officials, states could choose to allow the cost of conducting surveys to be  considered an administrative or program delivery cost, and reimburse  communities for it, but they noted that states have limited funds for  administrative expenses. Officials from most of the states we interviewed  did not provide financial assistance to nonentitlement communities  conducting surveys; however, officials from one state told us that they  allowed application preparation as an eligible administrative expense and  that some communities in the state may have used some of these funds  for conducting surveys.
HUD and most states provide guidance that nonentitlement communities  can use to develop and conduct their local income surveys. HUD’s  guidance states that if a community follows HUD’s recommended survey  methodologies, the survey will yield acceptable levels of accuracy. The  guidance covers how to select the type of survey to use (e.g., in-person  interviews versus interviews conducted by phone), how to develop a  questionnaire, how to select a sample, and how to handle nonresponses.  In general, state officials we interviewed felt HUD’s guidance was  sufficient and did not have any suggestions for additional guidance that  might be needed. In addition, almost all states have CDBG guidance that  includes discussions of conducting local income surveys. The guidance  often includes methodological requirements, such as sampling  requirements, and how long nonentitlement communities may use local  income survey results. In addition, in some cases, the guidance also  includes minimum response rates and sample survey questionnaires.  Further, state and local stakeholders told us that some nonentitlement  communities can receive technical assistance for conducting local income  surveys from nonprofit organizations and consultants that offer grant  administration services.
section_title: Other Options for Communities That Disagree with ACS Are Limited
paragraphs: If a community disagrees with its LMI percentage based on ACS data or if  the LMI summary data do not align with a project service area for the  purpose of meeting the LMI threshold, options for qualifying for CDBG  funds for area benefit projects other than local income surveys are  limited. HUD officials noted that communities can still qualify for CDBG  funds under one of the other national objectives—reducing or eliminating  slums/blight and meeting an urgent need within the community. However,  state and local stakeholders we interviewed told us that because the  CDBG authorizing statute requires that 70 percent of CDBG funding be  used for LMI activities, shifting activities to one of the other national  objectives is a challenge. In addition, as noted previously, communities  have the option to qualify for the national objective of benefitting LMI  persons in other ways that do not require using the LMI summary data or  conducting a local income survey, such as funding activities that serve  populations presumed to be LMI or activities that directly benefit LMI  persons. However, some state and local stakeholders told us that  nonentitlement communities’ greatest needs are for area-benefit activities  such as infrastructure projects, and that they therefore must rely on the  LMI summary data or local income surveys for eligibility determination.
In addition, a provision in the Consolidated Appropriations Act, 2016,  states that a limited number of nonentitlement communities, tribal areas,  and counties may continue using the 2000 decennial census long form- based LMI summary data to demonstrate their LMI eligibility if they are  designated as a Promise Zone or a Distressed County as defined by the  Appalachian Regional Commission. HUD officials said that this  provision was included because these areas expressed concern to  Congress about losing LMI status with the transition to ACS. This  provision would only be applicable from fiscal years 2017 through 2020.  The number of nonentitlement communities eligible under this provision to  continue using the 2000 decennial census long form to demonstrate their  LMI eligibility is relatively small. Since 2014, 22 communities across the  country were designated as Promise Zones. These locations are  generally larger cities and, in some cases, Indian reservations, and  therefore generally do not participate in the nonentitlement CDBG  program. The Distressed Counties are all located in the Appalachia  region, covering 93 counties in 9 states. According to HUD officials, many  of these Distressed Counties would qualify as LMI communities using the  ACS-based LMI summary data.
section_title: Stakeholders Noted That Potential Alternative Data Sources Have Limitations and That Collecting Accurate Income Information Is Generally Challenging
paragraphs: Stakeholders we interviewed noted that alternative data sources that  might be used to demonstrate that communities meet the LMI  requirements of the CDBG program have limitations, and they cited  challenges associated with measuring income generally. With respect to  measuring income in general, some stakeholders noted that collecting  accurate household income information can be challenging. They noted  that income may be measured at a point in time, but household members  might move from one household to another during the year. These  stakeholders also noted that income information may also be inaccurately  reported because survey respondents may have difficulty recalling their  income over the past year, particularly if they had fluctuations in income  levels. One stakeholder also said that a community’s income level may be  difficult to determine if the community has many seasonal workers. In  these cases, the community’s income level may vary depending on the  time of year.
In considering challenges associated with using income as a measure of  economic well-being—such as whether members of a household are  classified as living in poverty—some stakeholders we interviewed said  that determining the appropriate threshold for defining and measuring  poverty can be difficult. A few stakeholders noted that a more complete  assessment of a household’s income level would include its expenses  and benefits, and that the Supplemental Poverty Measure produced by  the Census Bureau considers these aspects of a household’s financial  circumstances. However, according to the Census Bureau, this measure  was designed as an experimental poverty measure and is not used to  determine eligibility for government programs.
Stakeholders we interviewed cited several types of alternative sources of  income information that have not been used by communities that  disagree with census data to determine CDBG eligibility:
Other large-scale Census Bureau surveys that include  information on income. For example, the Survey of Income and  Program Participation and the Current Population Survey both include  questions on income.
Administrative data that can also provide information on income.
These could include federal and state tax data or data on enrollment  in income-based programs, such as the National School Lunch  Program for free and reduced-price meals, Medicaid, or the  Supplemental Nutrition Assistance Program (SNAP).
Sources of information that use a combination of survey and  administrative data. For example, the Census Bureau’s Small Area  Income and Poverty Estimates (SAIPE) supplements ACS income  data with administrative data such as federal income tax return data.  Sources such as the Department of Health and Human Service’s  Social Vulnerability Index and Medically Underserved Areas  designations can use survey information, administrative data, or a  combination of survey and administrative data to compile indices that  may provide an indication of a community’s income level.
However, while these potential sources could provide information on  income at the individual or community level, stakeholders also noted that  they would likely have one or more of the following limitations:
Does not fully allow communities to determine if they are LMI.
Some sources of income data may not fully measure a community’s  income level and therefore may not be a more accurate measure of  whether a community should be considered LMI than the LMI  summary data. For example, the Internal Revenue Service publishes  some information from federal tax returns, but while this is a direct  measure of income levels, it may exclude those low-income  households that do not file taxes. In addition, data on participation in  income-based programs such as the National School Lunch Program,  Medicaid, and SNAP may provide indications of a community’s  income level, but they also have limitations. For example, not  everyone who is eligible for the programs participates in them.  Therefore, participation rates may undercount the extent to which a  community's population is low income.
Proxies for income, such as measures of medically underserved areas,  the Department of Health and Human Service’s Social Vulnerability Index,  and the U.S. Department of Agriculture’s Food Access Research Atlas,  also have limitations. Specifically, the extent to which these indices  correlate with income and the level of income with which they correlate is  unclear. Finally, some sources of income information may use a different  measure of income, which may not correspond to HUD’s LMI measure.  For example, SAIPE estimates median household income, which would  not allow communities to determine the percentage of their population  that meets HUD’s LMI threshold.
Is not easily accessible by communities. To be used broadly, an  alternative data source may need to be easily accessible by  nonentitlement communities. Some data, such as tax data, may have  some public availability but would likely require states or other  agencies to either provide data to nonentitlement communities that  are not typically made available, or calculate LMI percentages for the  communities. However, sharing this information may raise  confidentiality concerns. It may also raise concerns about whether  communities would be able to access the information from states or  other agencies that may feel burdened by having to provide the data  or LMI calculations based on the data.
Is not available at small geographic levels. Because  nonentitlement communities are small communities, an alternative  data source would need to be available at a sufficiently small  geographic level. Specifically, the current LMI summary data are  available for census block groups and places. However, many other  sources of income data or related proxies do not have data available  at these levels. For example, Census Bureau officials said this would  be the case for Census surveys other than ACS. They noted that  SAIPE income data, for example, are available at the county level,  which would likely be too large for many nonentitlement CDBG  projects. Other data sources, including SNAP participation rates and  Bureau of Economic Analysis per capita income data, are also only  available at the county level.
Census Bureau officials noted that it may be possible to use survey and  administrative data to create estimates of income levels for small areas  using statistical models. They noted that SAIPE is based on such models,  which can estimate income levels for a specific geographic level based on  other characteristics, even if there is no specific income data point for that  area. However, they said that such models do not currently exist for  geographic areas small enough to be used by nonentitlement  communities in the CDBG program.
Is not more precise than ACS. To address the concern that ACS  data do not provide sufficiently precise estimates of LMI for many  nonentitlement communities, an alternative data source would need to  be considered reliable for small geographic areas. However, Census  Bureau officials said that ACS is the nation’s largest household survey  that includes income information, and other Census Bureau surveys  that include income information (e.g., Survey of Income and Program  Participation, Current Population Survey) have smaller sample sizes  than ACS, which would be associated with less certainty about the  reliability of the data. Administrative data, such as those on program  participation, would not have limitations related to sample size that  would affect their preciseness, but they would face at least one of the  other previously noted challenges, such as not allowing the  community to fully determine if it is LMI.
HUD officials said that in circumstances where a nonentitlement  community disagreed with what ACS showed for their LMI percentage,  HUD would consider allowing states to use an alternative data source  beyond an income survey. However, they noted that such a provision  would need to be fairly and consistently applied within the state. HUD  officials said that they have not received inquiries from states about using  alternative data sources to demonstrate LMI status. As a result, HUD has  not developed formal guidance on accepting alternative data sources and  is still determining how any such requests should be evaluated. In  addition, in July 2015, the Census Bureau announced a plan to evaluate  the availability and sustainability of using external data sources, such as  Social Security Administration and Internal Revenue Service data, to  supplement income information collected by ACS, which they said could  improve the data. According to the research plan, the Census Bureau  expects to make specific recommendations before March 2017.
section_title: Agency Comments
paragraphs: We provided a draft of this report to HUD and the Department of  Commerce for their review and comment. They provided technical  comments, which we incorporated as appropriate.
We are sending copies of this report to the Secretary of the Department  of Housing and Urban Development, the Secretary of the Department of  Commerce, and other interested parties. In addition, the report will be  available at no charge on the GAO website at http://www.gao.gov.
If you or your staff have any questions about this report, please contact  me at (202) 512-8678 or shearw@gao.gov. Contact points for our Offices  of Congressional Relations and Public Affairs may be found on the last  page of this report. GAO staff who made key contributions to this report  are listed in appendix IV.
section_title: Appendix I: Objectives, Scope, and Methodology
paragraphs: You asked us to review options for nonentitlement communities that  disagree with their Community Development Block Grant (CDBG)  eligibility determination based on the Department of Housing and Urban  Development’s (HUD) use of American Community Survey (ACS) data.  This report examines (1) HUD’s and states’ policies for communities that  disagree with their eligibility determination based on HUD’s use of ACS  data or are not able to use HUD’s Low- and Moderate-Income (LMI)  summary data and the challenges, if any, communities face in using  available options, and (2) stakeholders’ views on whether there are  possible alternative data sources for determining eligibility under CDBG’s  LMI objective. In addition, appendix II presents analysis of how margins of  error around ACS’s LMI estimates can affect whether nonentitlement  communities meet HUD’s LMI threshold. Appendix III presents analysis of  changes in the LMI status of nonentitlement communities between the  2000 and 2006–2010 LMI summary data.
subsections: section_title: HUD’s Policies
paragraphs: To identify HUD’s and states’ policies for nonentitlement communities that  disagree with their CDBG eligibility based on ACS data, we reviewed  relevant portions of the Housing and Community Development Act of  1974 and relevant HUD regulations, policies, and local income survey  guidance. We also reviewed state CDBG guidance for 49 states and  Puerto Rico. In addition, we interviewed officials from HUD and the U.S.  Census Bureau, as well as CDBG administrators from eight states:  California, Kansas, Nebraska, Ohio, Texas, Utah, Washington, and  Wisconsin.
We used multiple methodologies to select the nongeneralizable sample of  eight states. Specifically, we selected some of the states based on HUD- reported data that identified states with nonentitlement communities that  conducted local income surveys. However, these data could not be used  to definitively determine the total number of local income surveys  conducted in individual states. Therefore, we selected an additional two  states with high numbers of rural communities. We selected these  additional states based on the U.S. Department of Agriculture’s Rural  Continuum codes because rural communities were more likely to have  smaller sample sizes and therefore more likely to be affected by larger  margins of error around their LMI estimates. As such, communities in  these states may have been more likely to have conducted local income  surveys due to disagreements with their eligibility determinations based  on ACS data. Finally, we identified two additional states based on  recommendations from state officials we interviewed. We also interviewed  a range of state and local CDBG administrators. For example, we asked  officials from each of the states we selected for interviews to identify a  few nonentitlement communities in their state that had conducted or  attempted to conduct a local income survey or that disagreed with their  LMI percentage based on ACS data. Based on those recommendations,  we interviewed representatives from one or two of these communities in  each of six states. Findings from the interviews with states and  nonentitlement communities cannot be generalized to those with which  we did not speak. In addition, in two states, we interviewed  representatives of organizations that provide services to nonentitlement  communities. We also interviewed representatives of a CDBG advisory  council from California. Furthermore, we interviewed representatives from  community development groups, including the Council of State  Community Development Agencies, the Housing Assistance Council, and  the National Association of Housing and Redevelopment Officials.
section_title: Alternative Data Sources
paragraphs: To obtain information on stakeholders’ views on alternative data sources  that HUD could consider in addition to ACS for determining a  nonentitlement community’s LMI eligibility, we consulted knowledgeable  stakeholders, including researchers from the Urban Institute, George  Washington University, and the National Opinion Research Center at the  University of Chicago. We selected a purposive subset of such  researchers based on our knowledge of organizations active in  conducting research on topics relevant to our inquiry, a review of relevant  studies, and a recommendation we received during an interview. We also  interviewed HUD and Census Bureau officials, state and local  government officials, as well as representatives of the community  development associations listed previously, and asked whether they  could identify alternative sources of income information that could be  used for these purposes. We also reviewed past GAO reports to identify  any potential alternative sources of income information.
section_title: Analysis of ACS Data
paragraphs: To illustrate how margins of error around LMI estimates based on ACS  data can result in uncertainty about CDBG eligibility determination for  nonentitlement communities, we analyzed 2008–2012 ACS special  tabulations that the Census Bureau produced for HUD. Although HUD’s  most recent LMI summary data were based on 2006–2010 ACS data,  HUD officials said they no longer maintained margin of error information  for these data, and they instead provided us with the margins of error for  the 2008–2012 data. We also received a list of nonentitlement  communities from HUD and used this list to identify nonentitlement  communities in the 2008–2012 special tabulation dataset. We used the  information on margins of error to create confidence intervals around the  LMI estimates for these communities. We then selected a random,  representative sample of 100 nonentitlement communities and graphed  their LMI point estimates and the confidence intervals around the  estimates.
To describe changes in the number of nonentitlement communities  eligible for CDBG funds between the 2000 and 2006–2010 LMI summary  data in eight states, we used HUD’s list of nonentitlement communities to  identify these communities in the 2000 and 2006–2010 LMI summary  datasets. We limited our analysis to nonentitlement communities at the  place level and we determined the extent to which communities lost or  gained LMI status between these two LMI summary datasets. Based on  discussions with HUD and Census officials, review of HUD  documentation, and electronic testing of the data, we determined the data  to be sufficiently reliable for these purposes.
We conducted this performance audit from May 2015 to September 2016  in accordance with generally accepted government auditing standards.  Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on our audit objectives.
section_title: Appendix II: Effects of ACS Confidence Intervals on Nonentitlement Communities’ LMI Status
paragraphs: Both the decennial census long form and the American Community  Survey (ACS) are based on samples and therefore subject to sampling  error. While margins of error are available for ACS, the decennial census  long form did not  publish margins of error, so a direct comparison of the  two surveys’ sampling error is difficult.  Nonetheless, ACS sample sizes  are smaller than the decennial census long form and, all other things  equal, smaller sample sizes have larger sampling error. Larger sampling  error results in larger margins of error and wider confidence intervals.  The  confidence level (e.g., 90 percent or 95 percent) indicates the level of  certainty that the actual value lies within the confidence interval. For  example, if a random sample survey estimated, with a confidence level of  90 percent, that 45 percent of a community’s population were low- and  moderate-income (LMI) with a 10 percent margin of error, then one could  say with 90 percent confidence that the community’s actual LMI  percentage is somewhere between 35 percent and 55 percent. If the  margin of error were instead 5 percent, with the same confidence level,  then one could say with 90 percent certainty that the community’s actual  LMI population lies between 40 percent and 50 percent.
Figure 2 shows the ACS LMI estimates and confidence intervals around  the estimates, at a 90 percent confidence level, for a random sample of  100 nonentitlement communities. The circles in the figure show the LMI  point estimates for each of the 100 randomly selected nonentitlement  communities. The horizontal bars on either side of the point estimates  indicate the confidence intervals. The vertical line at the 0.51 mark  indicates the 51 percent LMI threshold. The circle is shaded if a  community’s confidence interval includes this threshold and therefore  there is uncertainty about whether the community’s actual LMI value lies  above or below the threshold.
Confidence intervals that span the 51 percent threshold result in  uncertainty over whether a community actually should be considered LMI.  Across all 30,823 nonentitlement communities, roughly 45 percent had  confidence intervals that spanned the 51 percent threshold. As shown in  figure 3, a higher proportion of communities above the 51 percent  threshold had confidence intervals that spanned the 51 percent threshold  than those below the 51 percent threshold (76 percent versus 36 percent,  respectively). Therefore, there were more nonentitlement communities  that were deemed eligible when they may not be than nonentitlement  communities that were deemed ineligible when they may be eligible.
A confidence interval could span the 51 percent threshold because of a  wide confidence interval, because the point estimate is close to the 51  percent line, or both. As can be seen in figure 2 above, all three cases  were present among nonentitlement communities. The median ACS  confidence intervals for all 30,823 nonentitlement communities’ LMI  estimates was roughly +/- 10 percent, meaning half of nonentitlement  communities had confidence intervals around their LMI estimates that  spanned more than 20 percentage points from the lower to the upper  bound.
section_title: Appendix III: Changes in the LMI Status of Nonentitlement Communities between 2000 and 2006–2010 Low- and Moderate-Income Summary Data
paragraphs: Officials from the Department of Housing and Urban Development (HUD)  and some states we interviewed noted that each time HUD updates its  Low- and Moderate-Income (LMI) summary data, whether from one  decennial census to another as in the past, or the more recent 2014  transition from the 2000 decennial census to the 2006–2010 American  Community Survey (ACS) data, some nonentitlement communities see  changes in their LMI status. We analyzed the extent to which  nonentitlement communities that are Census-designated places in the  eight states we interviewed saw changes in their LMI status during the  most recent update to HUD’s LMI summary data. We found the  following:
2,842 out of 3,803 communities (75 percent) did not see a change in  their LMI status. Specifically, 568 (15 percent) had LMI status with  both the 2000 LMI summary data and the 2006–2010 LMI summary  data, and 2,274 communities (60 percent) did not have LMI status  under either LMI summary data.
498 communities (13 percent) lost LMI status.
463 communities (12 percent) gained LMI status.
Communities that did see a change when HUD updated its LMI summary  data could have lost or gained LMI status for a number of reasons,  including economic or demographic changes from one survey period to the  the nature of random surveys—different samples taken from the same  geographic area may produce different outcomes; differences in the features of ACS and decennial long form; and differences in methodologies used for calculating LMI.
We could not determine the extent to which losses and gains in LMI  status between the 2000 LMI summary data and the 2006–2010 LMI  summary data could be attributed to any of these individual factors.
section_title: Appendix IV: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Staff Acknowledgments
paragraphs: In addition to the contact named above, Andrew Pauline (Assistant  Director), Winnie Tsen (Analyst-in-Charge), Rachel Batkins, Bethany  Benitez, Justin Fisher, Cindy Gilbert, Ty Mitchell, Marc Molino, and  Jennifer Schwartz made key contributions to this report.