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

Letter

Background

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.

The Current Formula Falls Short of Meeting Equity Standards for Beneficiaries and Taxpayers

The Formula Does Not Account for Varying Disability Rates and Service Costs

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.

The Formula Does Not Capture Certain Taxable Resources

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.

Over One-Quarter of Funds are Distributed Based on States’ 1978 Allotments

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.

Most State Agencies Indicated Satisfaction with the Formula, though Some Believe It Results in Inadequate Funding for their States

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.

Greater Equity in VR Funding Could Be Accomplished in Several Ways

Options for Introducing Greater Equity for Taxpayers or Beneficiaries

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.

Options for Phasing In Changes to Minimize Disruption to State Programs

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.

Incorporating Financial Performance Incentives in the VR Formula Poses Challenges

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.

Concluding Observations

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.

Agency Comments and Our Evaluation

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.

Appendix I: Objectives, Scope, & Methodology

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.

Measuring State Need Populations

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.

Measuring State Cost Differences

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.

Identifying a Proxy for Wages in the VR Program

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.

Identifying a Proxy for Commercial Rents

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.

Weighting of Inputs

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.

Constructing the Cost Index

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.

Measuring State Financing Capacity

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.

Analysis of the Extent to which the Current Formula Meets Equity Standards

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.

Analysis of Formula Options

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.

Survey of State VR Agencies

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.

Interviews with Experts and Agency Officials

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.

Review of Literature on Performance Incentives

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.

Appendix II: Description of the Current Vocational Rehabilitation (VR) Funding Formula

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.

Appendix III: Percentage of Population that Is Civilian Working Age with a Disability, 2006 and 2007

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.

Appendix IV: Estimates of Differences among States in the Cost of Providing Vocational Rehabilitation (VR) Services

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.

Appendix V: Vocational Rehabilitation (VR) Grant Allotments by State and Cost-Adjusted Allotments per Working-Aged Person with a Disability, Fiscal Year 2008

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.

Appendix VI: Comparison of Per Capita Income and Total Taxable Resources (TTR), by State, 2004 to 2006

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.

Appendix VII: Description of Formula Options

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.

Appendix VIII: Funding Allocations under Three Vocational Rehabilitation Formula Options

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.

Appendix IX: Responses to Selected Questions from GAO Survey of State Vocational Rehabilitation (VR) Agencies

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.

Appendix X: GAO Contacts and Staff Acknowledgments

Daniel Bertoni, Director, (202) 512-7215 or bertonid@gao.gov.

Acknowledgments

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.