Vocational Rehabilitation Funding Formula: Options for Improving Equity in State Grants and Considerations for Performance Incentives
Published: 2009-09-30T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: State vocational rehabilitation (VR) agencies play a crucial role in helping individuals with disabilities obtain employment. In fiscal year 2008, the Department of Education (Education) distributed over $2.8 billion in grants to state agencies, using a funding formula that was last revised in 1978. Questions have been raised about whether this formula is outdated, allocates funds equitably, and adequately accounts for state agencies' performance. GAO was asked to: (1) examine the extent to which the current formula meets generally accepted equity standards, (2) present options for revising the formula, and (3) identify issues to consider with incorporating performance incentives into the formula. To address these objectives, GAO relied upon two equity standards commonly used to design and evaluate funding formulas: beneficiary equity, which stipulates that funds should be distributed so that each state can provide the same level of services to each person in need; and taxpayer equity, which stipulates that states should contribute about the same proportion of their resources to a given program. GAO analyzed data from Education, Department of the Treasury, Census Bureau, and other agencies; surveyed state VR agencies; interviewed agency officials and disability experts; and reviewed literature on performance incentives.
section_title: What GAO Found
paragraphs: The VR funding formula falls short of meeting equity standards because it uses imprecise measures of state needs and resources. The formula does not account for differences among states in the proportion of people with a disability or the costs of providing services. As a result, the amount of services that states can purchase per person with a disability varies, from $83 to $277 (see figure). In addition, the formula uses only per capita income to measure a state's ability to contribute to the program, excluding other taxable resources. GAO presents three options for revising the formula to illustrate a range of possibilities: the first distributes funds based on states' disability populations, the second also accounts for costs of providing services, and the third further accounts for state resources beyond per capita income. Because any formula change would redistribute funds among states, potentially disrupting services to individuals, GAO also presents options for establishing a transition period. Including performance incentives in the funding formula has potential for improving performance but can also pose challenges. These include: effectively balancing the VR program's multiple goals, rewarding agencies for meeting individuals' specific needs, and basing awards on an agency's performance rather than influences outside its control. GAO identified ways to mitigate these risks, such as using multiple performance measures to address different goals, and adjusting the performance level required for an agency to receive an incentive award. However, these approaches would still require careful consideration of several issues, such as how to account for clients' varying disability levels and needs and provide appropriate incentives for achieving desired outcomes.

section_title: Letter
paragraphs: State vocational rehabilitation (VR) agencies, under the auspices of the  Department of Education (Education), play a crucial role in helping  individuals with disabilities prepare for and obtain employment. In fiscal  year 2008, Education distributed over $2.8 billion in grants to state VR  agencies, using a funding formula that was last updated in 1978.  Questions have been raised about whether this funding formula is  outdated and allocates funds equitably. In addition, growing interes t in  program accountability has prompted some to ask whether the formula should incorporate performance measures to reward high performan currently constructed, the VR formula distributes funds according to the  size of a state’s population, its per capita income, and the amount of VR  funds the state received in 1978. States must match their federal grant  allocations by contributing about $1 to their VR programs for each $4 they  receive.  ce. As  There are two standards concerning the concept of equity that have  commonly been used to design and evaluate funding formulas. The first— known as beneficiary equity—stipulates that funds should be distributed  to states according to the needs of their respective populations so that  each state, with their federal allocation, can provide the same level of  services to each person in need. The second standard—known as taxpayer  equity—applies to programs, such as VR, in which states contribute funds.  This standard also seeks to provide individuals in need with the same level  of services, but in addition, considers a state’s ability to finance a program  from its own resources. It stipulates that states should be able to provide  comparable services to individuals, with each state contributing about the  same proportion of their resources to the program. To achieve this  standard, taxpayer equity formulas may allocate more funds to states with  fewer taxable resources, set the contribution they are required to make at  a lower level, or do both, so that poorer states do not contribute a larger  share of their resources than wealthier states. To meet the taxpayer equity  standard, a formula requires a reliable measure of a state’s ability to  finance a program from its own resources. To meet both equity standards,  a formula should use reliable and appropriate measures of the need  population in each state and the cost of providing services in each state.
In this context, you asked us to (1) examine the extent to which the  current formula meets generally accepted equity standards, (2) present  options for revising the formula to better meet these standards, and (3)  identify issues to consider with incorporating performance incentives into  the formula.
To determine the extent to which the VR funding formula meets equity  standards and to develop options for revising the formula, we examined  data from a number of sources, namely Education’s data on state VR  grants, Census Bureau data on state disability populations, Department of  the Treasury (Treasury) data on state taxable resources, Bureau of Labor  Statistics (BLS) data on wages, and Department of Housing and Urban  Development (HUD) data on rents. In addition, we interviewed experts in  the disability field and had key portions of our analyses formally reviewed  by three disability experts. We also surveyed all 80 VR agencies in the  states, territories, and District of Columbia to obtain their perspectives on  the current formula, options for changing it, and views on the use of  performance incentives in the VR program. We received responses from  74 (93 percent) of the agencies. We also conducted in-depth interviews  with state VR officials at 11 agencies in 9 states, as well as with members  of advisory councils to the VR agencies in 5 of these states. To further  consider potential benefits and challenges of using performance incentives  in the VR funding formula, we reviewed academic literature on the subject  and spoke with officials in three federal programs that currently provide  incentive awards. We conducted this performance audit from September  2008 to September 2009, 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. See  appendix I for a detailed discussion of our scope and methodology.
subsections: section_title: Background
paragraphs: The Rehabilitation Act, as amended, sets out a formula for distributing VR  grants to states and territories. Through this formula, a portion of the  funds appropriated for the VR program are distributed to states based  upon the grant allotment they received for fiscal year 1978. States’ 1978  allotments served to ensure that no state experienced a funding decrease  when the formula was revised through a 1978 amendment to the  Rehabilitation Act. Of the remainder of the funds, one-half is distributed  based upon states’ general population and a factor that compares their per  capita income to the national per capita income, and the other one-half,  according to their population and the square of the per capita income  factor. The larger a state’s population, the more funds it will receive.  Conversely, the higher a state’s per capita income compared to the  national level, the lower its allotment will be. The squaring of per capita  income increases its influence on a state’s allotment. However, the  formula mitigates the effect of per capita income for states with very high  or very low per capita income levels by setting upper and lower limits.   Ultimately, the final allotment for a state cannot be less than 1/3 of 1  percent of the total amount appropriated, or $3 million dollars, whichever  is greater. In fiscal year 2008, the minimum allotment was $9.5 million,  and 6 states were allotted this amount. See appendix II for further  information on the funding formula.
The Act requires states to share in funding the costs of the VR program.  Specifically, the Act sets the federal share for the funding of a state’s VR  program at 78.7 percent. As a result, in order to receive their full federal  allotment, each state must contribute at least 21.3 percent of the funds for  their VR programs. In cases where states do not meet this matching  requirement, the unmatched federal funds are redistributed to other states  near the end of the fiscal year. The Act also calls for annual funding  increases to the VR program, overall, to be minimally pegged to the  increase in the Consumer Price Index for All Urban Consumers (CPI-U).  However, funding changes for an individual state may differ from the  change to the CPI-U in any given year because state allocations are,  ultimately, determined by the funding formula. In redistributing the funds,  Education currently gives priority to those states that did not receive an  inflation-adjusted increase over their prior year’s allotment.
Under the VR program, state VR agencies are to provide vocational  rehabilitation services for individuals with disabilities—consistent with  the individual’s strengths, resources, priorities, abilities, interests, and  informed choice—so that they may prepare for and engage in gainful  employment. To do so, state agencies provide a variety of services to  individuals such as job placement assistance, medical treatment,  postsecondary education, occupational training, and assistive  technologies. Individuals may be eligible for VR services if they have a  physical or mental impairment that constitutes or results in a substantial  impediment to employment, and if they need VR services to prepare for,  secure, retain, or regain employment. According to the Rehabilitation Act,  if state VR agencies determine that they will not have enough funding to  serve all eligible individuals who apply for services, they may state the  order in which they will select individuals for services. Agencies using  such an “order of selection” process must develop criteria for ensuring  that individuals with the most significant disabilities will be selected first  for services.
The current VR funding formula does not include factors for rewarding  agency performance; however, pursuant to the Rehabilitation Act,  Education evaluates state VR agencies’ performance using a set of  performance indicators. These indicators are designed to assess how  well the agencies are helping individuals obtain, maintain, or regain high- quality employment and, also, how well they are ensuring that individuals  from minority backgrounds have equal access to VR services. The  Rehabilitation Act gives Education the authority to reduce or suspend  payments to a state agency whose performance falls below a certain level  and fails to enter into a program improvement plan or to substantially  comply with the terms and conditions of such a plan.
section_title: The Current Formula Falls Short of Meeting Equity Standards for Beneficiaries and Taxpayers
subsections: section_title: The Formula Does Not Account for Varying Disability Rates and Service Costs
paragraphs: The VR funding formula does not achieve equity for beneficiaries—the  individuals likely to be served by the VR program—for two reasons. First,  it does not recognize differences among states in the size of their  populations potentially needing VR services. Second, it does not account  for state differences in the costs of providing those services.
By targeting funds based on a state’s general population, the formula  assumes that the proportion of people needing services is largely the same  from state to state. In fact, as shown in figure 1, the proportion of the  general population that is working-aged and has a disability varies across  states, from 5.6 percent (in New Jersey) to 12.8 percent (in West Virginia)  in 2007. See appendix III for information on disability rates in each state.  In effect, the formula treats alike any two states with similar population  sizes, irrespective of the size of their working-aged disability population.  For example, New Mexico has a slightly greater population than West  Virginia (2.0 million and 1.8 million, respectively), and, therefore, would  receive more funding under the current formula (all other things being  equal) than West Virginia. However, working-aged people with disabilities  comprise nearly 13 percent of West Virginia’s population, compared to 8.7  percent in New Mexico. By not factoring in state disability populations, the  formula does not account for West Virginia having over 60,000 more  working-aged people with disabilities than New Mexico.
Education officials and one expert we spoke with speculated that the  formula’s use of per capita income might serve to target funds to states  with higher rates of disability since people with disabilities have, on  average, lower incomes. We found some correlation between states’  disability rates and their per capita income. As such, per capita income  is, at best, an imprecise measure of states’ disability rates.
The funding formula also fails to account for differences among states in  the cost of providing VR services. Focusing on average wages and rents in  each state, we estimated that the cost is 13 percent below the national  average in Idaho, for example, while it is 13 percent above average in  Massachusetts. This means that Massachusetts would need to pay $1.13  for the same set of services that Idaho could purchase for $0.87. By not  taking into account cost differences, VR allocations purchase fewer  services in states that have higher costs. See appendix IV for a table of our  estimates of state cost differences. Also, see appendix I for information on  how we estimated state service costs.
Not accounting for state differences in both disability populations and cost  of services results in a substantial variation in the amount of services that  states are able to purchase per person with a disability, from a low of $83  in Connecticut, to a high of over three times as much—$277 in North  Dakota. Figure 2 shows estimated state VR allotments, per working-aged  person with a disability, based on fiscal year 2008 funding, adjusted for  differences in costs of wages and rents between states. See appendix V  for a state-by-state listing of VR grant allocations and cost-adjusted  allotments per person with a disability.
section_title: The Formula Does Not Capture Certain Taxable Resources
paragraphs: While proper measures of need and cost are important for both beneficiary  and taxpayer equity, the VR funding formula lacks equity for state  taxpayers, in particular, because its measure of a state’s ability to  contribute to the VR program is limited to per capita income and does not  include all potentially taxable resources. Per capita income is based on the  personal income in a state, including income received by state residents in  the form of wages, rents, and interest income. However, using only this  measure excludes certain categories of corporate income that are not  received as income by state residents. For example, the formula does not  factor in corporate income that is retained by corporations for investment  purposes, which could theoretically be subject to state taxation through  corporate income taxes. The formula also excludes business income  received by out-of-state residents, such as dividends, that are potentially  taxable by the state. Although states may differ in their decisions about  whether to tax these resources, the measure used in a funding formula to  compare states’ ability to finance a program should capture all possible  revenue resources and should not be affected by an individual state’s fiscal  decisions. Treasury’s Total Taxable Resources provides more  comprehensive data on the amount of resources that are potentially  taxable in each state. Comparing states’ per capita income with their total  taxable resources shows that, for most states, the two measures are  similar. However, the formula’s use of per capita income particularly  understates the taxable resources in certain states and overstates it in  others (see figure 3). For example, the ratio of per capita income to total  taxable resources per capita is 0.80 in Alaska, which suggests that the use  of per capita income in the formula understates Alaska’s taxable resources  by 20 percent. The formula’s use of per capita income especially  understates the taxable resources in energy-exporting states, such as  Alaska and Wyoming, and in states with numerous corporate  headquarters, such as Delaware. The lack of precision in using per capita  income is accentuated by the squaring of the per capita income factor in  the formula. See appendix VI for a comparison of per capita income and  total taxable resources in each state. Also, see appendix I for a more  detailed explanation of our analyses of per capita income and total taxable  resources data and appendix II for a detailed explanation of the current  formula.
section_title: Over One-Quarter of Funds are Distributed Based on States’ 1978 Allotments
paragraphs: In fiscal year 2008, 27 percent of federal VR funds were distributed to  states based upon the amount of funds they received for fiscal year 1978.  This provision of the formula served a purpose when the formula was last  revised, in 1978, to ensure that no state experienced a funding decrease.  However, most disability experts we spoke with considered this provision  outdated and no longer an appropriate factor for distributing VR funds.  The Congressional Research Service also reported that due to the 1978  allotment, VR funding allotments do not fully reflect population changes  since the mid-1970s.
section_title: Most State Agencies Indicated Satisfaction with the Formula, though Some Believe It Results in Inadequate Funding for their States
paragraphs: Most state agencies that responded to our survey indicated satisfaction  with the current formula. Of respondents, 62 percent (46 of 74) expressed  the view that the current formula is appropriate, while 31 percent (23 of  74) viewed it as inappropriate. When asked about specific parts of the  formula, opinions varied. For example, 86 percent (64 of 74) considered  the use of general population to be appropriate, while only 27 percent (20  of 74) considered the 1978 allotment provision to be appropriate. See  appendix IX for responses to our survey questions. However, in their  comments to the survey and in interviews, some state agency officials  asserted that the formula does not provide them with adequate funds. For  instance, VR officials we spoke with in Massachusetts and Maryland said  that due to current funding allotments, their agencies are on an “order of  selection,” in which they give priority to individuals with significant  disabilities and place other individuals on waiting lists.
When we compared allotments per person with a disability against order  of selection status, we found that states that receive less funding per  person with a disability were somewhat more likely to report being under  an order of selection than those states that receive relatively more  funding. Specifically, we found in fiscal year 2008 that among states with  lower than median allotments per person with a disability (adjusting for  costs), 72 percent reported being under an order of selection, compared to  52 percent of states above the median. However, the data do not explain  whether or the extent to which the VR funding formula is causing states to  be under an order of selection. For example, many states above the  median allotment are also under orders of selection. Further, in interviews  state VR officials indicated that factors other than allotment levels could  also influence a state’s decision to be under an order of selection, such as  the level of state resources provided to the VR program, the effectiveness  of the agency’s management of program costs, and the agency’s decisions  about how to use existing funding.
section_title: Greater Equity in VR Funding Could Be Accomplished in Several Ways
subsections: section_title: Options for Introducing Greater Equity for Taxpayers or Beneficiaries
paragraphs: There are a number of ways to redesign the VR funding formula to achieve  greater equity for beneficiaries, taxpayers, or to balance equity for both.  We present three options, or prototypes, to illustrate the range of options.  See appendix VII for a more detailed description of each formula option.  For each of these options, we have retained the minimum allotment that  the current formula provides to ensure that each state would receive at  least a certain level of funds for its VR program.
A partial beneficiary equity formula: This option bases allocations solely  on the size of a state’s population potentially needing VR services. To  measure the need population, this option would use data on the states’  civilian working-aged disability populations from the Census Bureau’s  ACS.
A full beneficiary equity formula: This option also allocates funds based  on states’ working-aged disability populations using Census data, but also  includes estimates of the cost of providing VR services in each state. These  cost estimates reflect differences among states with respect to two basic  costs (i.e., wages and rents), which underlay the provision of many VR  services. We developed estimates of state costs using data on wages from  BLS and on rents from HUD. This option does not reflect differences in  other types of basic costs for which reliable data may not be readily  available. See appendix I for further information on the development of  our cost estimates.
A taxpayer equity formula: This option also distributes funds based on  states’ working-aged disability populations and the cost of providing VR  services, but it adds a third factor to the formula—a measure of each  state’s ability to contribute to the VR program. More funds would be  allocated to states with fewer taxable resources. To measure a state’s  ability to finance the VR program, we utilized data from Treasury on a  state’s total taxable resources, which includes per capita income as well as  other sources of potentially taxable state income, such as corporate  income produced within the state but not received by state residents.
For the taxpayer equity option, an issue to consider is whether the  matching requirement would be the same across all states, as is the case  with the current formula, or would vary based upon a state’s ability to  finance the VR program. To fully achieve taxpayer equity, the matching  requirement would need to vary according to each state’s financing ability.  If the matching requirement were the same for all states, those with fewer  resources would receive more federal funds but would also need to  provide more state funds for the match. This could result in poorer states  having to contribute a greater share of their resources to the VR program  than wealthier states. See appendix VII for an explanation of how a  variable matching requirement could be incorporated into the taxpayer  equity option.
Table 1 shows the amount of funds redistributed among states, as well as  the number of states gaining and losing funds, for each of the three  formula options. For example, each of our three prototypes would  redistribute approximately 4 to 6 percent of the VR funds, with about 20  states receiving more funds and at least 20 states receiving less in funds  than they do under the current formula. Between 5 and 11 states would  experience a change in funding levels of 20 percent or more. See appendix  VIII for a state-by-state table of allocations under our three formula  options.
In our survey of state VR agencies, many respondents expressed  reservations about options for revising the current funding formula. Our  survey presented state agencies with three general approaches to revising  the formula, roughly based on our three formula options. Most  respondents expressed reservations about options that were generally  based on partial beneficiary and taxpayer equity, and they were divided on  the option generally based on full beneficiary equity. Specifically, 45  percent of respondents expressed support for an approach that would  distribute funds so that all states would receive funding to be able to  provide the same level of services to each individual potentially eligible for  VR services, taking into account certain differences in the cost of  providing services, while 47 percent expressed disapproval of this  approach, and the remainder expressed no opinion or preference.
section_title: Options for Phasing In Changes to Minimize Disruption to State Programs
paragraphs: When a new federal formula is implemented, Congress often provides a  transition period so that grant recipients have time to adjust, especially  those recipients whose grants will be reduced. An abrupt reduction in  funding level could disrupt a state agency’s ability to provide VR services.  Transition periods allow for greater predictability and stability in state  funding levels, which in turn, help avoid major disruptions to existing state  services and allow states to develop long-range plans and program  commitments.
One way to ease the change to a new formula is to phase it in gradually  over a number of years. During the phase-in period, the state allocations  would be a combination of the old and new formulas, with a gradual  increase in the portion of funding distributed through the new formula,  until the phase-in period is complete. By way of example, figure 4 depicts a  5-year transition period, under which the amount of money allocated  under the old formula would be reduced by 20 percent each year, and the  amount allocated under the new formula would be increased by 20 percent  each year, until all of the allocations are made using the new formula. To  further minimize the disruptive effects of a new formula, the phase-in  period could be longer, although this would, of course, postpone full use  of the new formula.
Another approach to minimize disruption to state VR programs is to  establish a hold harmless provision that limits the amount of funding that  states could lose under a new formula. One example of this approach  would be to hold states entirely harmless in the first year that the new  formula is implemented, but would allow minimal decreases during the  second and successive years, such as by 1 or 2 percent. Because state  agencies could also have difficulties adjusting to large and sudden funding  increases, limits could also be set on the increases that states would  receive from one year to the next. This graduated approach would allow  agencies to better plan for the additional funds and manage growth in their  VR programs. It should be noted that use of a hold harmless provision  would effectively reduce the amount of funds available for distribution  through the new formula in the early years of a change because most of  the funds would be allocated through the hold harmless provision.  However, over time, as the total amount of funds appropriated for the VR  program increases, more of the funds would be allocated through the new  formula.
section_title: Incorporating Financial Performance Incentives in the VR Formula Poses Challenges
paragraphs: Some research and experiences suggest that providing financial incentive  awards based on program performance has the potential to improve  government programs, and a slight majority of VR agencies surveyed are  open to using them in the VR program. Some federal programs currently  provide incentive awards and officials we spoke with from three of these  programs noted some benefits, such as motivating state and local agencies  to improve performance. Of the state VR agency officials who responded  to our survey, 59 percent were open to including some form of incentive  awards in the VR program. Some state officials noted that doing so could  reward high performing agencies, improve VR client success, or motivate  agencies to focus on continuous improvement.
Nevertheless, there are challenges to incorporating incentive awards into  the VR program, whether through its funding formula or outside it due, in  part, to the multiple and potentially competing facets of the VR program’s  mission. According to the Rehabilitation Act, state VR programs should  help clients achieve employment by providing individualized services,  while also prioritizing service to those with the most significant disabilities  when agencies cannot provide services to all eligible applicants. VR  stakeholders we spoke with, including state agency officials, state  advisory council officials, representatives from private sector VR  companies, and disability researchers, identified three main challenges to  incorporating incentive awards into the VR program. To some extent,  these challenges are already present in the VR program’s current  performance measurement system and could be accentuated by linking  program performance to incentive awards. These challenges are:    Challenge of balancing potentially competing program goals: Unless  carefully designed, a financial performance incentive system could run the  risk of encouraging state VR agencies to focus on achieving certain  program goals, at the expense of others. For example, 89 percent of state  VR officials who responded to our survey thought it likely that providing  state agencies with additional funds based on performance would result in  agencies focusing more heavily on clients who were expected to positively  impact agency performance. In interviews, many VR stakeholders  expressed particular concern that if incentive awards were focused on  achieving employment outcomes for clients, they could induce state  agencies to concentrate on serving those most likely to obtain  employment, at the expense of those with greater barriers, such as those  with the most significant disabilities. This would run counter to the VR  program requirement that state agencies serve individuals with the most  significant disabilities first when operating under an order of selection.  Similar concerns have arisen in other employment programs that have  used incentive awards. For example, we previously reported that local  agency officials in the Department of Labor’s Workforce Investment Act  (WIA) Title IB programs may be reluctant to provide services to job  seekers less likely to find and maintain a job.
Challenge of rewarding agencies for providing appropriate  individualized services to clients: The VR program provides both long- term services, such as supporting youth with disabilities as they transition  out of high school and pursue higher education, and short-term services,  such as identifying job opportunities for people who already have skills  and qualifications. Several VR stakeholders expressed concern that if  incentive awards did not take into account some clients’ specialized needs  for higher-cost or longer-term services, they could cause agencies to focus  on providing short-term services. Officials from one state advisory council  cautioned that incentive awards might encourage VR counselors to focus  on providing short-term services, even if it resulted in low-paying jobs,  instead of placing VR clients in higher education programs that could  ultimately yield long-term, higher paying career positions.
Challenge of basing incentive awards on an agency’s performance,  without accounting for factors outside its control: A variety of factors  outside an agency’s control may influence performance outcomes, such as  the state’s economy and the characteristics and needs of the individuals  who seek rehabilitation services. If an agency’s performance cannot be  distinguished from these factors, the provision or withholding of incentive  awards would not necessarily reflect agency actions. Of state VR agency  officials who responded to our survey, 77 percent stated that isolating an  agency’s performance from factors outside its control would be a great or  very great challenge to appropriately distributing incentive awards. For  example, officials in one VR agency said that some parts of their state have  20 percent unemployment, which decreases their ability to place clients in  jobs. Another state agency official noted that his agency has had very  high employment outcomes, in part because the state had one of the  lowest unemployment rates in the country. He added that agencies in  other states whose economies are weak have had poorer employment  outcomes through no fault of their own. In addition, some state officials  suggested that agencies operating under an order of selection would be at  a competitive disadvantage compared to those that are not, because the  caseload of the former would include a greater proportion of clients with  the most significant disabilities and barriers to employment.
Our research into the types of incentive awards used by other federal  programs, as well as the views of VR stakeholders, revealed several ways  to mitigate such challenges, but none are without potential pitfalls.  Specifically, research on designing incentive award systems suggests the  following options:    Using multiple measures of success: Using a range of performance  measures to determine incentive awards could help motivate state  agencies to focus attention on all aspects of the VR program’s mission.   For example, the performance of state VR agencies might be measured in  terms of both the proportion of people with the most significant  disabilities who find employment, and the proportion of all clients who  achieve this outcome. Another option that could encourage agencies to  provide long-term services, when appropriate, is to establish intermediate  measures of client achievement or the services provided that have  increased a client’s prospect for employment. Such an intermediate  measure could be the number of VR clients who successfully complete  training programs or college degrees. Nevertheless, there are challenges to  developing appropriate measures. For example, although Education  already uses a measure for the VR program focused on people with  significant disabilities, it may be difficult to develop a measure specifically  on people with the most significant disabilities because the Rehabilitation  Act allows states to individually define the term, and our past work found  that state agencies’ definitions vary. Although Education uses multiple  measures to evaluate state VR agencies’ performance, an issue to consider  is whether or not the current measures would be appropriate to use for  distributing incentive awards. Our prior work on the VR program found  that the current measures do not consider agencies’ success in assisting  individuals who have not yet exited the program and do not specifically  track outcomes for youth transitioning out of high school. As a result, we  recommended that Education reevaluate its performance measures to  determine whether they reflect the agency’s goals and values.
Adjusting performance standards to account for differences in local  economies and program clients: The level of performance required of  each state VR agency to receive an incentive award could be adjusted to  account for the challenges they face. For instance, the performance  standard required to receive an incentive award may be set lower for  agencies in states with poor economies than for states with better  economies. These adjustments could be made using a mathematical  model, negotiations between federal and state agencies, or a combination  of the two approaches. For example, the Job Training Partnership Act  program (JTPA) used mathematical models to quantify the relative effect  of participants and economies on agency outcomes. Some researchers of  the JTPA program found that this approach was perceived to “level the  playing field” for agencies and had lessened the perverse incentives to  focus more heavily on the most promising clients. However, the research  also suggests that it is difficult to identify and measure all the external  factors that can undermine or lessen agency performance. If key factors  are missing from mathematical models, the adjusted performance  standards may lead to inaccurate estimates. Taking a different approach,  WIA Title IB employment and training programs set performance  standards by negotiating with state agencies. Although advocates of this  approach say that it increases agency involvement and may better capture  qualitative factors that affect agency performance, we and others have  criticized the WIA negotiation approach as unsystematic and inconsistent  across states. Specifically, we have suggested using an approach that  combines negotiations with mathematical models. In a prior report, we  recommended that Labor develop an adjustment model or other  systematic method to account for different populations and local  economies when negotiating performance levels. Labor officials  generally agreed with our recommendation and told us recently that are starting to provide states with adjustment models to inform t negotiation process. Finally, some researchers and public officials  identified concerns about adjusting performance standards, regardless of  the method. For example, some researchers have expressed concern that  adjusting performance standards may be unfair to clients because it allows  agencies to settle for less desirable outcomes for harder-to-serve  populations.
Beyond these risks, there are a number of considerations involved in  deciding to incorporate incentive awards directly into the VR funding  formula itself. First, it would be important to consider whether or not  states should be required to match the additional funds allocated for  performance, and if not, whether those funds could be treated as part of  the state’s matching contribution. Another consideration is that rewarding  high performance through the funding formula would, in effect, penalize  other states insofar as it reduces the total funding available for distribution  to all agencies based on other formula factors. Some state VR agency  officials we spoke with suggested that incentive awards should not result  in any decrease to base funding allocations. HUD’s Public Housing Capital  Fund program, which provides incentive awards through its funding  formula, includes provisions that minimize the impact of the awards on  states’ funding. However, HUD officials we spoke with still expressed  concern that this system penalizes housing authorities that may need  additional funds if they are to improve their performance.
Alternatively, incentive awards could be distributed independently of the  VR funding formula to avoid these inherent penalties. Options for  distributing funds outside the main VR funding formula include providing  incentive awards as grants, as occurs in the WIA Title IB programs, or  through a separate incentive award formula, as occurs in the Child  Support Enforcement program. Of the state VR officials who responded  to our survey, 51 percent supported providing incentive awards distributed  independently of formula-determined funds, while only 22 percent  supported providing the incentive awards through the formula.
Finally, regardless of whether incentive awards are provided through the  VR funding formula or independent of it, there are still other  considerations involved in designing and carrying out an incentive award  system. For instance, it is important that incentive awards are based on  reliable data about agency performance. It is also important to consider  the extent to which an incentive award system will allow for future  modifications should there be changes in program priorities or available  data, or if perverse and unanticipated results ensue. Our earlier work  discusses these and other, related considerations.
section_title: Concluding Observations
paragraphs: Although the measures currently used to allocate VR funds may have been  the best available when the formula was last revised in 1978, better data  are now available for factoring in both the potential need for and ability to  support a program. Improved information offers policymakers the  opportunity to update the formula to more closely align funding with the  need for services, as well as with each state’s ability to contribute to the  program. In deciding whether a revision to the formula is warranted,  policymakers will likely want to consider how to strike a balance among  all important factors—need, the cost of providing services, and the extent  to which state resources are available. Certainly, revising the funding  formula poses challenges because any formula change will result in  funding decreases for some states, along with increases for others.  However, there are ways to ease the transition to a new formula, so as to  minimize disruption to VR programs and the people they serve. On the  other hand, incorporating performance incentives into the formula might  introduce more complexity and risk. While there are mechanisms to  mitigate these challenges, the potential benefits for the VR program would  need to be carefully weighed against the potential risks.
section_title: Agency Comments and Our Evaluation
paragraphs: We provided a draft of this report to Education for review and comment.  Education provided technical comments and we modified the report, as  appropriate, to address these comments.
We are sending copies of this report to the Secretary of Education,  relevant congressional committees, and other interested parties. The  report will also be available at no charge on GAO’s Web site at  http://www.gao.gov.
If you or your staffs have any questions about our report, please contact  me at (202) 512-7215 or bertonid@gao.gov. Contact points for our Offices  of Congressional Relations and Public Affairs may be found on the last  page of the report. Key contributors to this report are listed in appendix X.
section_title: Appendix I: Objectives, Scope, & Methodology
paragraphs: Our objectives were to: (1) assess the extent to which the funding formula  meets generally accepted equity standards, (2) develop options for revising  the formula to better meet these standards, and (3) identify issues to  consider with incorporating performance incentives into the formula. We  used two generally accepted formula design standards intended to achieve  equity for beneficiaries and taxpayers. To meet both equity standards, a  formula should use reliable and appropriate measures of the need  population in each state and the cost of providing services in each state. A  taxpayer equity formula additionally requires a reliable measure of a  state’s ability to finance a program from its own resources. In the  following sections, we describe how we measured the need population,  cost of providing services, and financing capacity in each state, and how  we analyzed the extent to which the current formula meets equity  standards and developed various formula options. To address all three  objectives, we also surveyed the 80 vocational rehabilitation (VR) agencies  in the states, territories, and District of Columbia and conducted in-depth  interviews with 11 VR agencies in 9 states. Finally, for the third objective,  we reviewed literature on performance incentives and obtained the  opinions of officials at 3 federal government programs that use incentive  awards, which we describe in more detail in the last section.
We conducted this performance audit from September 2008 to September  2009, 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: Measuring State Need Populations
paragraphs: Data that directly measure the number of people in a state who potentially  need VR services do not exist. Although state VR agencies have data on the  size of their own caseloads, these data are not appropriate for use in a  funding formula for two reasons. First, caseloads may be influenced by  state funding levels. For example, an agency’s caseload may be relatively  small because of limited funds, not because of limited demand for  services. Second, data that can be controlled by state agency officials  should not be used in a funding formula because they could introduce  some “undesirable incentives” into the program. For instance, if a state’s  allotment were determined by the size of its caseload, a state agency might  be rewarded for taking inappropriate actions, such as enrolling individuals  into the VR program who do not require VR services or, in the case of an  agency under an order of selection, enrolling individuals who do not meet  its criteria for receiving priority for services.
There are, however, several national surveys that provide estimates of the  number of people with disabilities by state. These surveys are conducted  by statistical agencies such as the Census Bureau and Bureau of Labor  Statistics (BLS). We reviewed several of these surveys: (1) the Census  Bureau’s American Community Survey (ACS), (2) the Decennial Census,  (3) the Current Population Survey’s Basic Monthly Survey, (4) the Current  Population Survey’s Annual Social and Economic Supplement, and (5) the  Center for Disease Control’s Behavioral Risk Factor Surveillance System.  We sought to identify data on the populations with all types of disabilities  in each state.
We ultimately selected the Census Bureau’s ACS to use as a measure of  state populations potentially in need of VR services for several reasons.  First, the ACS provides data on states’ disability populations on an annual  basis. Second, the ACS has a large sample size (with about 3 million  housing units surveyed across all 50 states, the District of Columbia, and  Puerto Rico), which would allow for more accurate estimates of the need  population in each state. Third, the ACS surveys people in more types of  group quarters than any of the other surveys, such as those living in  college dormitories, group homes, prisons, and nursing care facilities. This  is significant, since about 10 percent of VR clients exiting the program in  2006 and 2007 lived in group quarters such as group homes or  rehabilitation facilities when they applied to receive VR services,  according to Education’s data. Fourth, the ACS asks six questions that are  designed to capture a wide variety of disabilities (see table 2), and these  questions are asked consistently across all states. One limitation of the  ACS for purposes of allocating VR funds, however, is that the data are not  available for U.S. territories, with the exception of Puerto Rico.
We analyzed the data produced by only 5 of the 6 disability questions from  the 2006 and 2007 ACS. We did not use data from the sixth question  regarding the difficulty of working (question 6 in table 2) because the  Census Bureau had removed this question in 2008 at the recommendation  of an inter-agency task force. The 2008 ACS data is expected to be released  in the fall of 2009 and was not available to use for this study. Since this  question will not be included on future surveys, we sought to produce an  analysis that would more closely reflect future available data. However,  because changes were also made to each of the five other ACS questions  for the 2008 survey, we cannot say whether our analysis of 2006 and 2007  data will be predictive of conditions in 2008 or thereafter.
We measured each state’s disability population by counting the number of  civilians of working age (16 to 64) who responded “yes” to any of the five  disability questions. By excluding the difficulty working question, our  measure excluded 12.5 percent of the population who responded “yes” to  the difficulty working question. The remaining 87.5 percent of those who  responded “yes” to this question were people who also responded “yes” to  one or more of the five other disability questions. As a result, they were  included in our measure.
Table 3 provides a breakdown of the total U.S. population into the  different components of our measure of the need population for the VR  program. As shown in the table, our measure—the civilian working-aged  population with a disability—comprises 7.6 percent of the total U.S.  population.
We assessed the reliability and validity of ACS data by interviewing Census  Bureau officials and disability experts, reviewing documentation and  literature, and conducting comparisons with other disability data.  Specifically, we compared the ACS data with data from the Social Security  Administration (SSA) on recipients of two types of disability benefits,  Social Security Disability Insurance (SSDI) and Supplemental Security  Income (SSI) benefits. For individuals to receive SSDI or SSI benefits, SSA  or a state agency must first determine that they have a disability that  prevents them from working. We compared the proportion of state  populations with disabilities, according to ACS data, with the proportion  of their populations receiving SSA disability benefits and found a high  correlation—0.872 for SSDI and 0.788 for SSI. This indicates that ACS data  and the SSA data showed similar trends; states with higher rates of  disability according to ACS data also tended to have higher proportions of  their population receiving SSA disability benefits. We had our work on  identifying a measure of need population reviewed by three disability  experts, and they concurred that ACS data provide a reasonable measure  of the size of a state’s population potentially needing VR services.
section_title: Measuring State Cost Differences
paragraphs: It is difficult to estimate differences among states in the costs of providing  VR services. Although one approach for estimating cost differences is to  estimate the costs for the same basket of goods in different states,  attempting to do this for VR services would be extremely costly and labor- intensive because of the wide array of services that VR agencies provide,  including assessment, counseling, higher education, occupational training,  medical diagnosis and treatment, and transportation. Another challenge is  finding a reliable data source on cost. State agencies’ data on  expenditures, in part, reflect cost of services, but using these data in a  formula runs the risk of allowing undesirable incentives to be introduced  into the program. For example, a state agency that efficiently manages its  program will be able to provide the same quality of services at a lower cost  than an agency with less efficient management. In this case, if the formula  provided higher allocations to states with higher reported costs, it could  reward agencies that are more inefficient. In addition, expenditures data  are of limited use in measuring state cost differences because they reflect  many other factors besides costs. For example, the amount of funds that  agencies spend per client reflects the level of funds they receive from state  and federal sources, as well as the types of clients they serve and the types  of services they provide. For example, some agencies may choose to  provide more intensive, higher-cost services to a smaller number of  clients, while other agencies may choose to serve a larger number of  clients with lower-cost services.
To address these challenges, we developed and used measures for the  costs of resources—or inputs—that go into providing services, which are  beyond the direct control of state agencies. Specifically, we focused on  two basic inputs—labor and office space—that are needed to provide the  different types of VR services. Where wages or rents are higher because  the general cost of living is high, state agencies must pay more for workers  or office space to provide services. In the following subsections, we  describe in detail how we developed a cost index to reflect differences in  costs for these two types of inputs, labor and office space. There are many  other resources used to provide VR services, such as equipment and  supplies, but data are not readily available on them. Obtaining such data  would be time-intensive and costly, requiring detailed surveys of the  specific services that each VR agency provides and the particular  resources that go into each service. As a result, our cost index may not  capture differences in the cost of some key inputs. For example, due to  lack of data, our index would not capture transportation costs, which  could be higher in states that have geographically dispersed populations.
While our index has some limitations, we believe it is a reasonable proxy  that can reflect general differences across states in the cost of providing  VR services. Because any cost index will only be an approximation of true  cost differences, the index we developed is based on what we believe are  reasonable assumptions that avoid overstating or exaggerating cost  differences among states. We believe our measure allows us to at least  partially recognize real cost differences among the states, while avoiding  inappropriate incentives. A similar cost of services index is used in the  funding formulas for the Community Mental Health Services and the  Substance Abuse Prevention and Treatment block grants.
In the following subsections, we describe: (1) our work to identify a data  source for estimating wages in each state, (2) the data source we used to  estimate rental costs in each state, (3) our methodology for estimating  how much to weight wages and rents in the cost index, and (4) how we  combined our weights with the data on wages and rents to develop a cost  of services index for each state. Since the purpose of our cost index is to  help distribute federal VR funds among states, we did not examine cost  differences between agencies for the blind and general VR agencies. In  states with two VR agencies, the state determines how to divide the  federal grant allocation among the agencies.
subsections: section_title: Identifying a Proxy for Wages in the VR Program
paragraphs: A measure of state labor costs should reflect the wages of all types of  workers potentially involved in the VR program, including those directly  employed by state VR agencies, as well as those employed by public or  private-sector organizations that VR agencies have contracted with to  provide VR services. To obtain an understanding of the types of workers  who may be involved in state VR programs, we first reviewed data from  Education’s Annual Vocational Rehabilitation Program/Cost Report (RSA- 2), which provides information on state agencies’ expenditures on various  types of services, both those provided by state agency employees and  those provided through contracts or purchases from other organizations.  The data indicate the proportion of expenditures state agencies spent on  the following types of services in fiscal year 2007:    7 percent on postsecondary education    14 percent on occupational and vocational, job readiness, and all other    13 percent on assessment, counseling, guidance, and placement    7 percent on diagnosis and treatment of physical and mental impairments    4 percent on rehabilitation technology    1 to 2 percent each on other types of services, such as income support,  transportation, and personal assistance services Once we obtained information on the types of services that the VR  program provides, we reviewed sources of data from BLS on average  annual wages in each state for various industries and occupations, as well  as wage data from the Centers for Medicare and Medicaid Services (CMS)  that are used to adjust payments to healthcare providers. Specifically, we  examined BLS data from the Quarterly Census of Employment and Wages  (QCEW) and Occupational Employment Statistics (OES). The QCEW data,  which come from employer filings for unemployment insurance, cover  nearly all civilian employment. It classifies wages and employment levels  by industry, using the North American Industry Classification System  (NAICS). We examined private sector wages, but also included public  sector wages for state government employees. In addition, we also  reviewed data on specific occupations related to the VR industry from the  OES. The OES classifies occupations according to the Standard  Occupational Classification (SOC) system. Finally, we examined wage  indices used to allocate funds in the Medicare program for skilled nursing  and inpatient rehabilitation facilities. The specific data series we reviewed  are listed below:    BLS, Quarterly Census of Employment and Wages    Vocational rehabilitation services industry, private sector (NAICS  6243)
Social assistance industries, private sector (NAICS 624)    Healthcare and social assistance industries, private sector (NAICS 62)    Education, healthcare, and social assistance industries, private sector  (QCEW industry code 1025)
Service-providing industries, private sector (QCEW industry code 102)    State government sector (QCEW industry code 10, state government)
BLS, Occupational Employment Statistics    Rehabilitation counselors (SOC 21-1015)    Substance abuse and behavioral disorder counselors (SOC 21-1011)    Educational, vocational, and school counselors (SOC 21-1012)    Community and social services occupations (SOC 21)
Wage indices for Medicare’s Prospective Payment System  We determined that the data for the most narrowly-defined industries and  occupations— the QCEW data for the vocational rehabilitation services  industry, and the OES data on rehabilitation counselors—were less  suitable for use in a cost index after examining the data and speaking with  officials from BLS. The QCEW data on the vocational rehabilitation  industry showed some peculiar values. For example, the wages in Vermont  were the highest in the nation in the vocational rehabilitation services  industry, but they were not among the highest in other data series we  examined. We contacted BLS officials to better understand these data.  They informed us that Vermont’s data for the vocational rehabilitation  services industry come from a small number of employers, and as a result,  could be affected by two factors: possible differences in the types of work  performed and in the number of hours worked per week. The average  annual wage in the QCEW is calculated by including both employees who  worked full-time and part-time. If the proportion of VR employees working  part-time varies substantially across states, this could cause state annual  wages to vary. At a broader industry level, this is less likely to be a  problem because the data cover more employers and employees. With  regard to the OES data on rehabilitation counselors, the data were  incomplete. There were no published wages for Alaska and Utah.
The remaining data series could serve as reasonable proxies for state  wages in the VR program, but we selected the QCEW data for the  education, healthcare, and social assistance industry sector for our proxy  because it covers the wide array of services that the VR program provides.  These include training, healthcare-related services, and social services. In  addition, this industry had the smallest range for wage differences across  states—the state with the lowest wage in this industry was 17 percent  below the average, and the state with the highest wage was 25 percent  above average. As a result, compared to the other industries, the  education, healthcare, and social assistance industry would produce the  more conservative results. Table 4 shows the median, minimum, and  maximum values for each of the data series we examined for which data  were available for all 50 states and the District of Columbia. To compare  wages across states and across data series, we used wage indices. A value  of 1 is equal to the national average. Values greater than 1 are above the  national average, and values less than 1 are below the national average.
Table 5 presents correlations of the various wage indices. It shows that the  various wage data we reviewed are correlated with each other, which  suggests that the different data series would generally produce similar  results in funding allocations. The vocational rehabilitation services  industry (NAICS 6243), in the first row, has the lowest level of correlation  with the other indices. Its highest correlation coefficient is 0.63 with the  social assistance industry (NAICS 624), while no two other wage indices  have a correlation coefficient less than 0.65.
The distribution of values for the education, healthcare, and social  assistance wage index is shown in figure 5. Values for more than half of  the states lie between 0.9 and 1.1, which suggests that most states have  similar wages in this industry sector.
section_title: Identifying a Proxy for Commercial Rents
paragraphs: State-by-state data on the cost of office space are not available. As a result,  we used as a proxy residential rental rates. The Department of Housing  and Urban Development (HUD) annually collects the rental cost of  housing for 530 metropolitan areas and 2,045 non-metropolitan counties  across the nation. These Fair Market Rents (FMR) data are used by several  programs to set housing subsidies. The FMR data are also used in  Medicare’s physician fee schedule as a measure of office rents.
Since the FMR provides data on a local level, we aggregated the data to  statewide averages and used an index to compare rental costs across  states. The distribution of rents is shown in figure 6. Similar to figure 5, a  value of 1 is equal to the national average. Values greater than 1 are above  the national average, and values less than 1 are below the national  average. Unlike the wage index, the rental cost index shows that over half  of the states have a rental index of 0.85 or less, while 11 states have a  rental index of 1.15 or higher. This suggests that rental costs vary  substantially among states.
section_title: Weighting of Inputs
paragraphs: To determine how much to weight the wage and rental costs, we first  surveyed state VR agencies to learn what proportion of their fiscal year  2007 and 2008 expenditures was spent on wages and rents. Because many  agencies contract out or purchase services, our survey asked them first to  report how much of their expenditures were for contractors or purchased  services, and how much was spent in-house. Then we asked them to  report how much of their in-house expenditures went to wages and rents.  We used these responses to compute average proportions, which we then  applied to categories of expenditures using RSA-2 data. In doing so, we  assumed that the proportions of expenditures on wages and rents that  state agencies reported for in-house expenditures was the same as the  proportions for contracts and purchased services. With regard to the RSA- 2 data, we examined state VR agencies’ spending in three broad categories:  administration, individual services, and group services.
Table 6 shows the results of our analyses of the survey responses and the  RSA-2 data, as well as how we combined these results to develop the  weights for wages, rents, and other inputs in the cost index. In table 6, the  first column shows the expenditure categories from the RSA-2 data. The  second column shows the average proportion of expenditures that went to  administration, individual, and group services, according to the RSA-2  data. The third through fifth columns show our survey results on the  average proportion of expenditures that went to labor costs, rents, and  other inputs. We assumed that for administration and individual services,  expenditures went to labor, rents, and other inputs in the precise  proportions that the survey results suggested. For example, we assumed  that 69 percent of expenditures for administration and individual services  were spent on wages, 5.6 percent on rents, and the remainder for “other,”  such as materials and supplies. However, group services can include  activities such as construction of a community rehabilitation program. It is  not clear what comprises these services. As a result, its input costs were  assigned entirely to the “other” category. Finally, the last three columns  multiply the prior columns, and the sum of the columns yields some  preliminary weights.
Once we obtained the results shown in table 6, we rounded the weights to  the nearest 5 percent. Ultimately, we estimated the weights to be 0.65 for  wages, 0.05 for rents, and 0.30 for all other inputs.
section_title: Constructing the Cost Index
paragraphs: We then constructed the index, using the following formula:  Cost index = 0.65 Wages + 0.05 Rent + 0.30 Other  With this formula, we calculated a cost index for each state, using each  state’s average wage from 2005 to 2007 in the education, healthcare, and  social assistance sector, according to QCEW data, and its average rental  cost from fiscal years 2007 to 2009, according to the FMR data. The cost  for “other” inputs besides wages and rents was assigned a weight of 0.30  and assumed to be constant for all states. We made this assumption to  simplify the construction of the cost index because we were unable to  readily or reliably capture differences in the costs of these inputs among  states. As noted earlier, estimating these costs would be difficult because  identifying the various materials and supplies used to provide the wide  variety of VR services would be costly and labor-intensive. In addition, it is  unlikely that there would be nationally available data on the costs of any  materials or supplies we could identify. The assumption that the cost of  “other” inputs is the same across states may be reasonable because some  materials and supplies are likely to be purchased by state VR agencies  from a national market and, therefore, the geographical variation in these  costs would be limited.
Figure 7 shows the distribution of the cost index. If a state’s cost index is  1, its costs are estimated to be the same as the national average. If a state’s  index is greater than 1, its costs are estimated to be above the national  average. Finally, if a state’s index is less than 1, its costs are estimated to  be below the national average. Values for 36 of the states lie between 0.9  and 1.1. See appendix IV for a listing of the cost index for each state.
We had our work on the cost of services index reviewed by three external  experts in the disability field. They generally concurred with our  methodology for developing the cost index using existing data. However,  each noted that our index does not capture certain key inputs or costs  underlying the provision of VR services, such as higher education,  transportation, contracted services, and tax rates. We generally agree that  it would be preferable to reflect all key cost differences that affect the  provision of VR services; however, to do so requires reliable data from VR  agencies (to determine average costs and develop weights for each input),  and from independent sources (to estimate cost differences for those  inputs among states). Either one or the other, or both, were not readily  available. For example, with respect to transportation costs, data were not  readily available from VR agencies that would allow us to develop average  costs and weights, and we are not aware of independent data on  transportation cost differences among states. Similarly, data were not  readily available from VR agencies to identify the specific services that  agencies contracted for and to determine the inputs that are used to  provide those services. Finally, one of the experts noted that high tax rates  in certain states may result in higher wages and rents, and suggested we  incorporate states’ tax rates into our cost index. We agree that tax rates  may influence wages and rents; however, research suggests that many  factors affect state differences in wages and rents, and determining the  relative significance of state tax rates would be a highly complex  analytical undertaking. In summary, although data were not readily  available to reliably reflect cost differences for additional inputs suggested  by our experts, these experts generally agreed that we appropriately  accounted for cost differences in two basic inputs, i.e., wages and rents.
section_title: Measuring State Financing Capacity
paragraphs: A taxpayer equity standard stipulates that funds are distributed so that  states can provide individuals comparable services using both state and  federal funds, while each state contributes about the same proportion of  their resources to a given federal program. This equity standard requires a  formula to include an indicator of each state’s ability to finance a given  program from its own sources. In a funding formula, a good indicator of a  state’s financing ability would measure all types of taxable resources and  would not be affected by an individual state’s actual fiscal decisions.
We used Total Taxable Resources (TTR), as reported by Treasury, to  measure state resources. The Treasury, as required by federal law,  provides annual estimates of TTR in order to estimate states’ financing  ability. The estimates are used in formulas to allocate federal funds among  states for the Community Mental Health Services and the Substance Abuse  Prevention and Treatment block grants. TTR is a more comprehensive  measure of financing ability than per capita income because it includes  personal income received by state residents, types of corporate income  and capital gains that per capita excludes, as well as income produced  within a state that is received by individuals who reside out-of-state.
section_title: Analysis of the Extent to which the Current Formula Meets Equity Standards
paragraphs: For our first objective, we reported state-level information on the  proportion of the population of working age with a disability, allotments  per person with a disability (adjusting for costs), and ratio of per capita  income to total taxable resources. In addition, we analyzed the extent to  which state per capita income is correlated with state disability rates in  order to determine whether the current use of per capita income in the  funding formula adequately accounts for a state’s need population. Finally,  we examined whether agencies in states with below median levels of  funding per person with a disability were more often on an order of  selection, compared to states above the median. For each of the above  analyses, we examined only the 50 states and the District of Columbia. We  did not analyze the U.S. territories because complete data were not  available on the territories from the various data series we used. This  section describes each of the analyses we conducted for our first  objective.
Proportion of states’ population that is civilian, of working age, and  with a disability: To determine the proportion of each state’s general  population that is civilian, of working age, and has a disability, we  analyzed 2007 ACS data to obtain the number of civilians in each state  from age 16 to 64 who responded “yes” to at least one of five disability  questions. As discussed above, we did not include individuals who  responded “yes” to the question on difficulty working because this  question was eliminated from the ACS, starting in 2008. We then divided  the working-aged disability population numbers by the total population  (all ages) for each state, which we also obtained from the 2007 ACS. These  proportions are presented in appendix III.
Correlation between states’ disability populations and their per capita  income: To test whether the formula’s use of per capita income is a  reasonable proxy for states’ disability rates, we analyzed the correlation  between their disability rates and their “allotment percentage,” which is  the part of the formula that includes per capita income. As discussed  above, we determined a state’s disability rate as the proportion of a state’s  total population that is civilian, of working age, and has a disability. For  the per capita income factor, we calculated each state’s “allotment  percentage,” which according to the formula, is one minus one-half of a  state’s per capita income level divided by the national per capita income  level (see appendix II for a further explanation of the allotment  percentage). We then obtained the correlation coefficient between states’  disability rates and their allotment percentages.
Allotments per working-aged person with a disability (cost-adjusted):  To determine for each state the allotment per working-aged person with a  disability, as adjusted for the costs of providing services, we used  Education data on the VR grant allotments that states received in fiscal  year 2008, ACS data on state disability populations in 2007 (using the five  disability questions, as described earlier), and the cost index, as described  earlier. Specifically, we used the allotments that Education initially  calculated for each state for fiscal year 2008. To calculate the allotments  per working-aged person with a disability while adjusting for costs, we  divided each state’s grant allotment by the product of the cost index and  the state’s 2007 working-aged disability populations. See appendix V for a  state-by-state listing of allotments per working-aged person with a  disability, as adjusted for costs of services.
We tested the reliability of Education’s data on VR grant allotments by  replicating Education’s formula calculations and interviewing Education  officials knowledgeable about the data. Our replications of the formula  calculations produced results that were virtually identical to Education’s.  As a result, we determined that the data are sufficiently reliable for our  purposes.
Order of Selection Status: We examined whether agencies in those states  with below median allotments per working-aged person with a disability  (adjusting for costs) more often reported being under an order of selection  than those states whose allotments were above the median. To obtain  information on states’ order of selection status, we used Education’s RSA- 113 data, which are quarterly data that states submit on their caseloads.  For states with two VR agencies, we considered a state to be under an  order of selection if either of its agencies reported being under an order of  selection. Table 7 shows the number of states that we considered under an  order of selection, by type of VR agency.
We assessed the reliability of RSA-113 data by interviewing Education  officials knowledgeable about the data and conducting edit checks.  Education officials informed us that when a state agency reports being  under an order of selection, the Department verifies that the state agency  has documented in its state plan its intention to provide services on an  “order of selection” basis. However, Education officials also informed us  that the RSA-113 data on a state’s order of selection status do not  necessarily indicate whether state agencies are currently operating on this  basis by actively limiting services to individuals. For example, they noted,  and we subsequently confirmed through our own review of the data, that  some states reported being under an order of selection, but reported  having no individuals on a waiting list. As a result, we determined that the  RSA-113 data on order of selection was sufficiently reliable to provide  information on the number of states reporting they were on an order of  selection, but we cannot say whether these states were actually operating  under their order. Our analysis also did not allow us to conclude whether  there is any causal link between states’ funding levels and their order of  selection status.
Comparison of per capita income and total taxable resources: We  analyzed how per capita income compares with TTR in each state. To do  this, we obtained data on per capita income from the Department of  Commerce and TTR data from Treasury from 2004 to 2006, the latest years  for which data from both sources were available. We took 3-year averages  (2004, 2005, and 2006) of the per capita income and total taxable resources  levels for each state in order to limit the effects of any year-to-year  fluctuations. We then calculated the total taxable resources per capita for  each state by dividing the average total taxable resource amount by the  state’s average population from 2004 to 2006. Next, we created indices by  dividing each state’s per capita income and total taxable resources per  capita by the U.S. averages of per capita income and total taxable  resources per capita, respectively. To compare states’ per capita income  with their total taxable resources, we divided each state’s per capita  income index by its index of total taxable resources per person to obtain  ratios. See appendix VI for a state-by-state listing of indices of per capita  income and total taxable resources per capita, as well as their ratios.
section_title: Analysis of Formula Options
paragraphs: For our second objective, we developed three formula options based upon  equity standards commonly used to design and evaluate funding formulas.  See appendix VII for detailed descriptions of the formula options. We also  estimated the grant allotments that each state would receive under each  formula option, using data on states’ disability populations from the ACS,  our cost index, and TTR data from Treasury. See appendix VIII for a table  of our estimates of the grant allotments. Specifically, we used for states’  need populations, the average of their 2006 and 2007 populations of people  of working age with a disability in order to limit the effects of any year-to- year fluctuations. As described above, we used the five disability questions  from the ACS. As a measure of cost of services, we used the cost index  that we developed, also described above. As a measure of state resources,  we used the average of states’ total taxable resources from 2004 to 2006.
section_title: Survey of State VR Agencies
paragraphs: We conducted a Web-based survey of VR agencies in states, territories,  and the District of Columbia to gather information on agency officials’  opinions regarding the current formula, potential modifications to the  formula, and the incorporation of performance incentives into the formula.  In addition, we used the survey to obtain data on agency expenditures that  we needed to develop our cost index. The Web-based survey was  conducted using a self-administered electronic questionnaire posted on  the Web. This Web-based survey was compatible with computer software  that makes Web sites accessible to people with visual impairments. We  received completed surveys from 74 of 80 VR agencies, for a response rate  of 93 percent.
We took steps in the development of the questionnaire, the data collection,  and the data analysis to minimize nonsampling errors. The practical  difficulties of conducting any survey may introduce errors, commonly  referred to as nonsampling errors. For example, difficulties in how a  particular question is interpreted, in the sources of information that are  available to respondents, or in how the data are entered into a database or  were analyzed can all introduce unwanted variability into survey results.  To minimize such nonsampling errors, a social science survey specialist  designed the initial questionnaire, in collaboration with GAO staff who had  subject matter expertise. The draft questionnaire was pretested with  officials from 5 state VR agencies to ensure that the questions were  relevant, clearly stated, and easy to comprehend. When the data were  analyzed, an independent analyst checked all answers using a statistical  program. Since the survey was a Web-based survey, respondents entered  their answers directly into the electronic questionnaire, thereby  eliminating the need to have the data keyed into a database and avoiding  data entry errors. See appendix IX for selected responses to our survey.
section_title: Interviews with Experts and Agency Officials
paragraphs: To learn more about state agency officials’ opinions on all three of our  research objectives, we spoke with VR agency officials from 3 states when  we designed our methodology and we followed our survey work by  interviewing officials from 8 agencies in 6 states. In selecting these 8  agencies, we identified states with a diversity of characteristics in terms of  their: (1) disability population rates; (2) per capita income levels; (3)  geographic dispersion; and (4) order of selection status. In addition, we  sought to interview both state agencies that serve individuals with a wide  variety of disabilities and agencies that primarily serve blind individuals. In  5 states, we also spoke with representatives from the state rehabilitation  councils, which are advisory councils for state VR agencies.
We also spoke with officials from: (1) Education’s Rehabilitation Services  Administration to obtain relevant programmatic data and perspectives on  the VR program; (2) SSA regarding data on the population receiving Social  Security disability benefits; (3) the Census Bureau regarding ACS disability  data; (4) BLS regarding data on wages; and, (5) the Department of  Transportation regarding the use of Census data on disability that was  used in a formula to distribute funds for the Department’s New Freedom  program.
In addition, we conducted about a dozen interviews with researchers  having expertise in disability data and the VR program and with advocacy  groups. We also attended the fall 2008 conference of the Council of State  Administrators of Vocational Rehabilitation (CSAVR) to learn more about  matters of interest to VR stakeholders and to obtain the views of the  council’s executive committee members. Also, we spoke with  representatives from four private sector companies that provide  vocational rehabilitation services, in order to learn about their experiences  with performance incentives in the private sector. We chose these four  companies based on the recommendations of a trade organization official  familiar with the fields of disability insurance and private vocational  rehabilitation.
section_title: Review of Literature on Performance Incentives
paragraphs: To identify issues to consider when incorporating performance incentives  into the VR formula, we reviewed literature produced by academic  experts, think tanks, and government agencies such as the Congressional  Research Service. We also reviewed prior GAO studies dealing with  performance accountability in government programs. We identified  relevant literature by reviewing research databases, such as EconLit and  the Education Resources Information Center (ERIC). We were also  referred to literature through citations in other literature and by the  recommendations of GAO staff and the external experts we interviewed.  In conducting our search and review, we endeavored to collect a diverse  body of literature that offered different views about the use of incentive  awards.
Aside from conducting a general review of literature on performance  incentives, we also identified and reviewed literature specific to four  federal programs in order to understand their experiences and identify  issues related to providing incentive awards. These programs are the  Workforce Investment Act (WIA), Child Support Enforcement (CSE),  Public Housing Capital Fund, and the Job Training Partnership Act (JTPA)  programs. The latter was discontinued and replaced by WIA Title IB  programs, authorized in 1998. We also obtained the views of federal  officials in the Departments of Labor, Health and Human Services, and  HUD, which are responsible, respectively, for the WIA, CSE, and Capital  Fund programs.
section_title: Appendix II: Description of the Current Vocational Rehabilitation (VR) Funding Formula
paragraphs: The current VR funding formula allocates federal funds to states annually,  based on three factors: (1) the amount of federal funds they received for  their VR program for fiscal year 1978, (2) their population size, and (3)  their per capita income level, as compared with the national per capita  income.
States’ fiscal year 1978 allotments became part of the formula when it was  revised through a 1978 amendment to the Rehabilitation Act. This  provision ensured that no state experienced a funding decrease with the  formula change. As currently constructed, the formula first provides states  with the amount of federal funds that they were allotted for their VR  program in fiscal year 1978.
The allotment percentage is designed to be higher for poorer states. For  example, a state that has a per capita income level equal to the national  level will have an allotment percentage of 0.50. If a state’s per capita  income is lower than the national level, its allotment percentage will be  above 0.50. If a state’s per capita income is higher than the national level,  its allotment percentage will be lower than 0.50. However, to mitigate the  influence of per capita income for states with very high or very low per  capita income levels, the Rehabilitation Act sets both a floor and a ceiling  on the allotment percentage—it cannot be less than 33 1/3 percent or  greater than 75 percent. Further, the allotment percentage is set at 75  percent for U.S. territories and the District of Columbia. Federal law  requires the Department of Education (Education) to calculate the  allotment percentages in even-numbered years, using the average of the  three most recent years of available data on per capita income. Education  obtains the data from the Department of Commerce’s Bureau of Economic  Analysis.
If a state’s allotment is calculated to fall  below this amount, its allotment is increased to that level, and the  allotments of other states are decreased proportionately.
section_title: Appendix III: Percentage of Population that Is Civilian Working Age with a Disability, 2006 and 2007
paragraphs: Table 8 below shows the proportion of each state’s total population that is  civilian, of working age (16 to 64), and with a disability. See appendix I for  further information on our data source and methodology for determining  these proportions.
section_title: Appendix IV: Estimates of Differences among States in the Cost of Providing Vocational Rehabilitation (VR) Services
paragraphs: Table 9 provides a cost index for each state, designed to estimate the  differences among states in the cost of providing VR services. The index is  a weighted average of the costs of two primary resources needed to  provide VR services, labor and office space. See appendix I for further  information on the development of our cost index. The average cost  nationally is represented by an index of 1.00. A state with an index above  1.00 is estimated to have costs greater than average, while a state with an  index below 1.00 is estimated to have costs less than average. For  example, with a cost index of 0.95, Alabama is estimated to have costs 5  percent below the national average.
section_title: Appendix V: Vocational Rehabilitation (VR) Grant Allotments by State and Cost-Adjusted Allotments per Working-Aged Person with a Disability, Fiscal Year 2008
paragraphs: Table 10 shows the amount of federal funds each state received for fiscal  year 2008, and the amount of services each state would be able to  purchase per working-aged person with a disability with those funds. The  per person allotments were adjusted to take into account differences  among states in the cost of wages and rents, using the cost index shown in  appendix IV. See appendix I for more information about our data sources  and methodologies.
section_title: Appendix VI: Comparison of Per Capita Income and Total Taxable Resources (TTR), by State, 2004 to 2006
paragraphs: Table 11 illustrates the difference between each state’s financial resources  as measured by per capita income and TTR. The second column shows,  for each state, its per capita income indexed to national per capita income,  and the third column shows each state’s TTR per capita indexed to  national TTR per capita. These indices are based on averages of three  years of data from 2004 to 2006. They were created by dividing each state’s  three-year average by the national average. States with income or TTR per  capita levels that are higher than the national averages have indices that  are greater than 1, and states with levels that are below the national  averages have indices that are less than 1. For example, the TTR per capita  index for Alabama is 0.79, meaning that the state’s TTR per capita is 21  percent below the national average. The final column in the table shows  the ratio of each state’s per capita income index to its TTR per capita  index. States in which the formula’s use of per capita income understates  its potentially taxable resources have ratios that are less than 1. States in  which the use of per capita income overstates its potentially taxable  resources have ratios above 1. For example, the ratio is 0.80 for Alaska,  meaning that the formula’s use of per capita income understates Alaska’s  taxable resources by 20 percent. See appendix I for further information  regarding our analysis of per capita income and TTR.
section_title: Appendix VII: Description of Formula Options
paragraphs: This appendix provides detailed information on three formula options or  prototypes for revising the VR funding formula: (1) a partial beneficiary  equity formula that distributes funds based only on the size of a state’s  population potentially needing services, (2) a full beneficiary equity  formula with the addition of a cost of services factor, and (3) a taxpayer  equity formula with the addition of a measure of state resources. ∑ Need population = sum of the need population across states, or the total need          population nationally This formula would allocate funds based on each state’s share of the total  need population nationally. It would only partially achieve beneficiary  equity because it does not account for differences among states in the cost  of providing services.
Cost adjusted need population = Need population x Cost index ∑ Cost adjusted need population = Sum of the cost adjusted need population         across states This formula would achieve full beneficiary equity because it accounts for  both states’ need populations and costs of providing services. The cost  index in the formula estimates each state’s cost of providing services.
Cost adjusted need population x allotment percentage Cost adjusted need population x allotment percentage
Cost adjusted need population = Need population x Cost index
Allotment percentage =  1- 0.20  ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ This formula would achieve taxpayer equity by basing allotments on a  state’s need population, adjusted for the cost of providing services, and its  ability to fund program services. In this option, the formula includes an  “allotment percentage” to account for a state’s ability to contribute funding  to the VR program. A state with fewer taxable resources compared to  other states would have a larger allotment percentage and, therefore, a  larger final allotment (all else being equal). “TTR” is used to indicate a  measure of state’s financing ability, since we regard Treasury’s Total  Taxable Resources (TTR) data to be a comprehensive measure of a state’s  taxable resources. The 0.20 in the allotment percentage equation indicates  that, nationally, states’ required contribution to the VR program is  approximately 20 percent. If the matching requirement were to vary for  each state, then an individual state’s matching rate would simply be  determined by its allotment percentage.
section_title: Appendix VIII: Funding Allocations under Three Vocational Rehabilitation Formula Options
paragraphs: Table 12 shows the allocations for each state and the percentage change  from their fiscal year 2008 allocations under the three formula options  (which are described in detail in appendix VII). For each of these options,  we retained the minimum allotment that the current formula provides, 1/3  of 1 percent of the total federal funds appropriated to the VR program, or  $9,463,837 for fiscal year 2008.
section_title: Appendix IX: Responses to Selected Questions from GAO Survey of State Vocational Rehabilitation (VR) Agencies
paragraphs: As part of our study, we distributed a Web-based survey to all 80 VR  agencies in the states, territories, and District of Columbia to obtain  agency officials’ views regarding the current formula, potential  modifications to the formula, and the possibility of incorporating  performance incentives into the formula. In addition, we used the survey  to obtain data on agency expenditures that were needed to develop our  cost index. We received completed surveys from 74 of 80 VR agencies, for  a response rate of 93 percent. The following figures show responses to all  closed-ended questions, except for those questions concerning agency  expenditures, which are discussed in appendix I. For more information  about our methodology for designing and distributing the survey, see  appendix I.
section_title: Appendix X: GAO Contacts and Staff Acknowledgments
paragraphs: Daniel Bertoni, Director, (202) 512-7215 or bertonid@gao.gov.
subsections: section_title: Acknowledgments
paragraphs: Michele Grgich, Assistant Director; Yunsian Tai, Analyst-in-Charge;  Gregory Dybalski, Thanh Lu; Karine McClosky; and Barbara Steel-Lowney  made significant contributions to all aspects of this report. Susan  Bernstein provided writing assistance. Other advisers included Joanna  Chan, Robert Dinkelmeyer, Ronald Fecso, DuEwa Kamara, Stuart  Kaufman, Jacqueline Nowicki, Patricia Owens, Max Sawicky, and Roger  Thomas.