The Higher Education Act specifies a formula, known as the federal need analysis methodology, that is used to determine students' eligibility for federal student aid. A variety of federal grants and loans are available to assist students pay postsecondary expenses. While some federal aid is allocated based on a student’s need for financial aid that is determined by the formula, other federal aid is allocated regardless of need. Many states and institutions have their own student aid programs, providing students an additional source of aid to help pay for postsecondary expenses. The federal need analysis methodology is used to determine a student’s need for financial aid by comparing a student’s and/or family’s expected family contribution to the student’s cost of attendance (COA). The EFC is defined as the household financial resources that are considered available to help pay for the student’s postsecondary education expenses and is calculated by reducing the household financial resources reported by aid applicants on the Free Application for Federal Student Aid by certain expenses and allowances, including a state and other tax allowance. A student is classified as either financially dependent on his or her parents or independent in the financial aid process. This classification is important because it affects the factors used to determine a student’s EFC. For dependent students, the EFC is based on both the students and parents’ income and assets, as well as whether the family has other children enrolled in college. For independent students, the EFC is based on the student’s and, if married, spouse’s income and assets and whether the student has any dependents other than a spouse, and the number of family members enrolled in college. To capture and reflect changes in students’ and families’ state and other tax liabilities, Education is responsible for annually updating the state and other tax allowance tables that were established in the HEA. Education determines the state and other tax allowance based on state and local tax information from federal tax returns filed with the Internal Revenue Service and compiled by its Statistics of Income Division. For dependent students and independent students without children, the allowance is composed of state and local income taxes. For parents of dependent students and independent students with children, personal property taxes and real estate taxes are added to the allowance. The costs of attending a postsecondary institution that a student faces include tuition, fees, books, and living expenses. The student may be able to receive financial aid to help cover costs of attendance depending on where the student wants to enroll as well as the student’s and family’s financial resources. If the price of attendance is greater than the expected family contribution, the difference between the two represents the student’s financial need. If the EFC is greater than the price of attendance, the student is not eligible for federal need-based aid but may still qualify for aid that is not based on need. (See fig. 1.) Postsecondary institutions are responsible for determining individual student’s eligibility for specific sources of financial aid and compiling these sources to meet each student’s need—a process known as packaging. Part of this process involves deciding which types or sources of aid should be awarded first—for example, grants or loans, federal or nonfederal aid, need-based or non-need-based aid. In awarding aid, institutions typically first package any grants for which the student is eligible and then offer loans. Another factor considered in packaging aid is whether to reduce aid from any source in a student’s package to offset an aid award from another source. Title IV of the HEA, as amended, authorizes the following federal aid programs: Pell Grants—Pell Grants are grants to low- and middle-income undergraduate students who have federally defined financial need and who are enrolled in a degree or certificate program. In general, a student’s Pell Grant award is determined by subtracting a student and family’s EFC from either the maximum allowable Pell Grant award, currently $4,050, or the COA, whichever is less. Stafford and PLUS Loans—These loans may be made by private lenders and guaranteed by the federal government (guaranteed loans) or made directly by the federal government through a student’s school (direct loans). Subsidized Stafford Loans—Subsidized loans are made to students enrolled at least half-time in an eligible program of study that have federally defined financial need. The federal government pays the interest costs on the loan while the student is in school. Subsidized loans are subject to certain maximum loan limits and are awarded based on the difference between a student’s COA less EFC and other awards of student aid including Pell Grants, state or institutional grants, etc. A change in a student’s EFC may—or may not—affect the amount of a subsidized loan award depending on its effect on other components of the student’s financial aid package. Unsubsidized Stafford Loans—Unsubsidized Stafford loans are non- need-based loans made to students enrolled at least half-time in an eligible program of study. Although the terms and conditions of the loan (e.g., interest rates) are the same as those for subsidized loans, students are responsible for paying all interest costs on the loan. While Stafford unsubsidized loans are not need-based aid, a change in a student’s or family’s EFC may nonetheless affect the amount a student may borrow. Unsubsidized loans are awarded based on the difference between a student’s COA less other awards of student aid—including Pell Grants, state and institutional grants, and subsidized loans. These loans are subject to the combined maximum loan limits for subsidized and unsubsidized loan awards. A change in a student’s EFC that affects Pell Grant, subsidized loan, or state or institutional grant awards may therefore affect the amount of an unsubsidized loan award. PLUS Loans—PLUS loans are non-need-based loans made to creditworthy parents of dependent undergraduate students enrolled at least half-time in an eligible program of study. Borrowers are responsible for paying all interest on the loan. Like unsubsidized loans, PLUS loans are generally awarded based on the difference between a student’s COA less other awards of student aid including unsubsidized loan awards. As is the case with unsubsidized loans, a change in a student’s or family’s EFC can affect the amount of a PLUS loan that a parent may borrow. Dependent students may borrow combined subsidized and unsubsidized Stafford loans up to $2,625 in their first year of college, $3,500 in their second year, and $5,500 in their third year and beyond. Independent students and dependent students without access to PLUS loans can borrow combined subsidized and unsubsidized Stafford loans up to $6,625 in their first year, $7,500 in their second year, and $10,500 in their third year and beyond. There are aggregate limits for an entire undergraduate education of $23,000 for dependent students and $46,000 for independent students and dependent students without access to PLUS loans. Campus-Based Aid—Participating institutions receive separate allocations for three programs from Education. Funds are distributed to institutions in part on the basis of the institution’s previous allocation levels and in part on the basis of the aggregate financial need of eligible students in attendance. The institutions then award the following aid to students: Supplemental Educational Opportunity Grants (SEOG)—SEOG grants are grants for undergraduate students with federally defined financial need. Priority for this aid is given to Pell Grant recipients. In general, an annual SEOG award may not be less than $100 and may not exceed $4,000. Perkins Loans—Perkins loans are low-interest (5 percent) loans to undergraduate and graduate students. Interest does not accrue while the students are enrolled at least half-time in an eligible program. Priority is given to students who have exceptional federally defined financial need. Students can borrow up to $4,000 for any year of undergraduate education with an aggregate limit of $20,000. Work-Study—Work-Study is employment in on- or off-campus jobs for which students who have federally defined need earn at least the current federal minimum wage. The institution or off-campus employer pays a portion of their wages, while the federal government pays the remainder. Work-study is awarded based on the difference between a student’s need less other aid awarded. Students received an estimated $98 billion in financial aid in award year 2003–2004 from the Title IV federal aid programs as well as state and institutional grants, of which the federal government provided more than two-thirds. Federal assistance is composed of both loans and grants, and most federal grant aid is need-based. States distributed about $6 billion in student aid. Institutions provided about $23 billion in the forms of need- based grant and merit-based aid. (See fig. 2.) The current state and other tax allowance is based on 1988 tax data due in part to Education’s limited efforts in updating the allowance. While Education has been required to revise the allowance tables annually since 1993, prior to 2004 it had attempted to update the allowance twice—once in 1993 and once in 2003—but the latter update was suspended. As a result, the 1988 tax data used for the 1993 update are still in effect. The lack of updates is primarily because Education did not annually seek data needed to update the allowance and did not establish effective internal control to guide the updating process. In addition, Education did not consider alternatives when data were not readily available. While Education has published the allowance tables used to award Title IV aid in the Federal Register annually since 1993, prior to 2003 these tables had been based on 1988 tax return information compiled by SOI. Congress incorporated the state and other tax allowance into the HEA in 1986 on the basis of 1983 SOI data but did not establish a mechanism to update the basis of the allowance until 1992. Amending the Higher Education Act in that year, Congress directed Education to “publish in the Federal Register…revised table of State and other tax allowances” annually, and to “develop such revised table after review of the Department of the Treasury’s Statistics of Income file and determination of the percentage of income that each State’s taxes represent” for those residents. Education published the first updated tables of the allowance in 1993 after reviewing SOI’s 1988 tax data, the most recent data available at that time. Tables 1 and 2 present these revised tax allowances, by dependency status and state. Although Education has published allowance tables annually since 1993, the published allowances continued to be based on 1988 SOI data until 2003, when new tables based on 2000 SOI tax data were published. Education intended to use the new tables to award student aid in 2004– 2005 but did not do so in light of legislation that prohibited it from doing so. As a result, the state and other tax allowance used to award financial aid continued to be based on 1988 tax data. Prior to 2003, Education’s efforts to update the allowance were limited: It neither annually sought data to update the allowance nor pursued alternatives when SOI data it had used previously were not readily available. According to Education’s records, the department only sought data to update the allowance for 6 of the 11 years since it was first directed to annually update the tax allowance. SOI records also document that Education did not routinely request data. Even when Education did request data, it is difficult to determine exactly what data were requested because such requests were not made in writing. Rather, Education’s documentation consists of informal file notes of telephone contacts with SOI officials that are minimal and do not describe the substance of what was discussed. Furthermore, as of the end of our audit work, Education could not provide us with written procedures guiding staff on the routine steps necessary to update the tax allowance or to identify what data would be needed to update the allowance. After Education published the 1993 update to the allowance, on the basis of 1988 SOI tax data, Education sporadically sought data from SOI to develop subsequent updates. According to both Education and SOI officials, however, SOI would not have provided these data on a cost-free basis. According to SOI officials, the 1988 tax data was produced to illustrate the type of information SOI could develop that clients, such as states, might find useful and be willing to purchase in the future. SOI never intended to produce the data as a regular series, and the fact that it was useful for Education’s purposes was coincidental. Education’s records do not indicate what actions the agency undertook when it first learned that SOI would not provide data cost-free, including the extent to which it considered paying for such data. Education officials told us, however, that they never sought a cost estimate from SOI because they did not wish to pay for the data. Moreover, Education officials told us that they did not consider using data other than SOI data because they believed Education did not have the discretion to do so under the law. Beginning in 2000, about 1 year after Education last contacted SOI, SOI began to annually publish on its Web site data that Education could have used to update the allowance. However, Education was unaware of these data because it did not contact SOI again until 2003 for the purpose of making its proposed update that year. As we have pointed out in numerous reports, weak internal control can be a contributing factor to, or cause of, insufficient execution of agency responsibilities. Collectively, internal controls are an integral component of an organization’s management intended to provide reasonable assurance that, among other things, operations are effective and efficient. Education’s failure to fully document its attempts to update the allowance over the past several years and its lack of written procedures to guide staff efforts to ensure that they take the steps necessary to update the allowance, such as a checklist, are indicative of an ineffective system of internal control. Our Standards for Internal Control in the Federal Government provides guidance to agencies to help them assess, evaluate, and implement effective internal controls that can be helpful in improving their operational processes. Under the proposed update, the state and other tax allowance would have decreased for most states; the change in the allowance, in turn, would have increased the amount that families are expected to contribute by about $500 on average for a majority of student aid applicants. Of those aid applicants with an increase in their EFC, some would have received lower Pell Grant awards or would have become ineligible for Pell Grants. Increases in EFCs would not only have affected Pell Grants but possibly other forms of aid, and these effects in turn would have affected Stafford and PLUS loan awards. The extent to which the proposed update would have affected federal Campus-Based, state, and institutional aid would likely have varied according to factors such as aid awarding policies and changes in a state’s allowance. Education’s tax allowance update would generally have increased the dollar amount that families would be expected to contribute to a student’s education, but the percentage of student aid applicants affected would have varied by state and household income, and the size of the increase would have varied by state. EFCs would increase by about $500 on average for those with an increase (from an average of about $9,620 to about $10,115), but aid applicants from states with larger decreases in their tax allowance rates would have had a larger increase in their EFCs. Table 3 shows the proposed changes to the allowance and the estimated EFC impacts, by state. For example, Delaware would have had a 4 percentage point decrease in its tax allowance for families earning $15,000 or more—from 7 percent to 3 percent—and a 2 percentage point decrease for individuals, resulting in an EFC increase of $834 on average, among applicants in Delaware with an increase. In contrast, Nevada would have had a 1 percentage point decrease in the allowance for families (and a 1 percentage point increase for individuals), and its residents would have had an expected contribution increase of $186 on average. Similarly, the percentage of applicants affected would have varied from state to state. In Wisconsin, for example, the percentage of student aid applicants affected would have been slightly over 80 percent, in contrast to Connecticut, where just under 1 percent of applicants would have been affected. With regard to household income, over 90 percent of families earning more than $25,000 would have been expected to contribute more under the update, while only about 20 percent of families earning $25,000 or less would have been expected to contribute more. Across all states, we estimate that the update would have affected more than 60 percent of aid applicants and would have resulted in an EFC increase of $3.5 billion collectively in award year 2004–2005. Had Education’s proposed update been adopted, thus raising the expected family contribution for aid applicants, 38 percent of recipients would have either seen a decrease in their Pell Grant award or would have become ineligible for the grant altogether; taken together, the average reduction among those with a decrease in their amount would have been $144. In particular, 36 percent of recipients would have seen a decrease of $133 on average in their Pell Grant award but would have remained eligible for the awards in award year 2004–2005. Another 92,000 recipients, or 2 percent of those receiving Pell Grants, would no longer have been eligible and typically would no longer have received the minimum Pell Grant award of $400. As a result, the proposed update would have decreased overall federal Pell Grant expenditures by $290 million. Students residing in states with larger decreases in their allowances would have faced larger decreases in Pell Grant amounts and are more likely to have become ineligible for them. Table 4 shows the average decrease in Pell Grant awards for those who would have seen a decrease in their Pell Grant award or who would have become ineligible for them, by state. Students with relatively higher household incomes would have been more likely to face a decrease and would have faced substantially greater decreases in their Pell Grant awards than those with lower household incomes. On the other hand, the impact of the proposed update would not seem to have varied much by whether students are financially independent of their families. Figures 3 and 4 show the proportion of those facing a decrease in Pell awards and the median amount of such decreases, by income group. Stafford and PLUS loans could have been affected due to EFC changes as well. Those applicants for whom a change in EFC would have resulted in a change in other aid received—including Pell, state, and institutional grants—would likely have seen a change in their federal loans. This is because federal loan amounts depend, in part, on the amount of other aid received. However, even if a change in EFC would not have changed other aid received, some students may still have seen a change in their subsidized Stafford loan amount. Among those currently receiving federal loans, we estimate that over 20 percent of subsidized and unsubsidized Stafford loan holders and about 85 percent of PLUS loan holders could have seen a change in their loan amount due to their EFC increase. (See app. I for an explanation of our estimation methodology.) Figure 5 shows the proportion of undergraduate Stafford loan recipients who could have had a change in their subsidized loan amounts, by income and dependency status. Our case studies of students at selected schools for whom a change in EFC would have resulted in a change in their federal loans show that as the EFC would have increased, subsidized loans would have decreased, and unsubsidized loans would have increased in response to the decreases in subsidized loans and other forms of financial aid. In addition, PLUS loans could have made up for these decreases as well. While changes in the EFC could have affected Campus-Based awards, institutions have some discretion in allocating federal Campus-Based aid; as a result, the effects of the proposed update would have likely varied across institutions. The effect of the update on state and institutional need- based aid would also have varied based on differences in state and institutional aid awarding policies and on how much the tax allowance would have changed for each state. Our case studies of students at the four selected schools show that even though the EFC for the majority of students would have changed, Campus- Based awards would tend not to have been affected by the proposed update. However, some students would have been affected, and the effect would have varied across schools, due in part to differences in award policies. Specifically, had the proposed allowance been implemented for 2004–2005, we estimate that less than 15 percent on average of case study students receiving Supplemental Educational Opportunity Grants and Work-Study aid would have faced a lower award. The effects would have varied significantly by school because of differences in schools’ eligibility criteria for Campus-Based awards and the maximum awards provided. For example, eligibility for SEOG at one case study school is capped at an EFC of $3,850, whereas eligibility at another is at an EFC of $2,800, so a student whose EFC increased from $2,700 to $2,900 would have become ineligible for an SEOG at one school but not the other. As another example, one school offers $3,000 in Work-Study, and another limits the amount to $1,000, thereby demonstrating the different amounts involved. With regard to Perkins loans, we estimate that about 20 percent of students at the case study schools on average would have seen a decrease in their loan amount due to the proposed update. For students who see decreases in their Perkins loans, the decrease would have been about $1,200 on average but would have varied significantly by school due to differences in eligibility criteria. Table 5 shows the case study results of changes in these three school-administered federal programs. The majority of states use the federal need analysis methodology to allocate state need-based aid; as a result, the proposed update could have affected the amount of and the extent to which students receive state grants. The effect would have varied by state due to, among other factors, differences in changes to the tax allowance by state and differences in state award policies. In Wisconsin, for example, we estimate that over 50 percent of state aid award recipients in our case study would have seen a decrease in their state award. In contrast, in Tennessee, just over 10 percent of recipients in our case study would have seen a decrease in their state award. The average reduction for Wisconsin students would have been less than that for Tennessee students due to the differences in how the states compute awards: Wisconsin's computation decreases aid for each dollar increase in EFC by less than Tennessee's computation. (See table 6.) As with state aid, the effect of the proposed update on the need-based aid provided by schools themselves would have varied significantly across schools due to, among other factors, differences in institutional award policies and changes in the EFC of students attending the institutions. However, the impact would be limited to schools that use the federal methodology to award aid. Since institutional aid may change as a result of both changes in the EFC and changes in other aid awarded, the effect of the increased EFC on institutional aid cannot be easily determined. For example, a school that bases its award solely on EFC might decrease its award as a result of an EFC increase, while a school that bases institutional aid on other aid awarded might increase the institutional award for some students. At the two private nonprofit schools included in our case studies, our results show that while more than 20 percent of students at each school would have faced a decrease in institutional need- based aid under the proposed allowance, more than 10 percent of students at these same schools would have received more institutional aid. Overall, case study students attending one private nonprofit school would have seen a decrease in institutional aid of almost $800 on average, whereas students at the other school would have seen a decrease of over $425 on average. As a result of certain limitations of the SOI dataset for the purpose of calculating the allowance and problems with how Education uses this dataset, the current state and other tax allowance may not reflect the amount of taxes paid by students and families. The dataset is limited for this purpose because the taxpayers included in it are generally not representative of financial aid applicants, the tax data it provides do not include all state and other taxes paid by students and families, and the tax data are several years older than the income information reported by students and families on the FAFSA. In addition to the limitations of the SOI dataset, Education does not make full use of the dataset to account for the varying tax rates paid by taxpayers in different income groups. The tax allowance calculated by Education may not reflect the taxes paid by most financial aid applicants because it is drawn only from those who itemize deductions on federal income tax returns—filers who may be taxed at a different rate than those who do not itemize. Because many FAFSA applicants have lower income—and taxpayers in lower income groups tend not to itemize—many applicants may not itemize. Specifically, we estimate that about 63 percent of FAFSA applicants do not itemize. Further, itemizers and nonitemizers within the same gross income group may have different state and other tax rates. On the one hand, for example, itemizers may be more likely to own a home than nonitemizers and thus would have a higher state and local tax liability due to real estate taxes. Conversely, those who itemize on their federal tax return may be more likely to itemize on their state return—and therefore have larger deductions, a lower state taxable income, and thus a lower state income tax than those who do not itemize on their federal return. Although sales taxes were included in SOI data when Congress formally provided for a tax allowance in the 1986 HEA amendments, tax reform legislation subsequently disallowed the deduction of state and local sales taxes, effectively eliminating them from this dataset. Therefore, the data collected by SOI for tax year 1987 and beyond have not reflected all state and other taxes. Excluding sales taxes may cause the allowance to be lower than it otherwise would be, especially for students and families who reside in states where sales taxes compose a significant portion of state and local revenue. In October 2004, Congress passed and the President signed the American Jobs Creation Act of 2004, which provides taxpayers who itemize deductions the choice of claiming a state and local tax deduction for either sales or income taxes, but only for tax years 2004 and 2005. As a result, the data collected by SOI for tax years 2004 and 2005 will likely include a mix of sales and income tax deductions reflecting the choices made by tax filers. Were these data used to update the allowance, the deductibility of sales taxes could increase the allowance for students and families, especially for those who reside in states where sales taxes compose a significant portion of state and local revenue. Regardless, the SOI data will not reflect both state and local sales and income taxes paid by individual taxpayers, as was the case prior to tax year 1987. SOI data available for any given award year are several years older than the income information reported by aid applicants on the FAFSA. For example, in its proposed update for award year 2004–2005 that was published in May 2003, Education used 2000 SOI data, the most recent available at the time of its data request. Because applicants would report 2003 income information for award year 2004–2005, had the allowance been implemented, there would have been a mismatch of 3 years between the tax data and the income data. Table 7 shows when SOI publishes the state and local tax data after the end of a tax year. Some time lag between the end of a tax year and when SOI publishes data for that year is expected because returns are collected after the end of the tax year and because of the time needed for processing those returns. This time lag could be extended when there are unexpected difficulties in processing the returns. For example, 2002 tax data were published after about 2 years, while 2000 and 2001 were published in 15 months. Further, SOI officials reported that the agency may be unable to publish the 2003 tax tables because it has been experiencing technical problems in processing returns from that year. Education’s method of calculating the state and other tax allowance does not accurately capture the amount in taxes paid by students and families. While Education calculates an allowance for each of the two income categories established by Congress—those earning less than $15,000 and those earning $15,000 or more—its methodology does not take into account the varying level of taxes paid by these two groups. To determine the allowance for families with income less than $15,000, Education uses the total of state and local taxes paid by all tax itemizers regardless of income, despite the fact that the SOI data provide separate information for 12 different income groups. Education’s methodology likely overestimates the taxes paid by the lower-income group for two reasons. First, higher-income individuals generally face higher tax rates than lower- income individuals. Our analysis of 2001 SOI tax data shows that those with an income below $20,000 have a state and other tax liability of about 3 percent on average, while those with an income of $20,000 or more have an average 5 percent tax liability. (See app. III.) Second, higher-income individuals are also more highly represented in the SOI data than lower- income individuals. For example, our analysis of the 2001 income distribution of financial aid applicants and of itemizers shows that about 35 percent of aid applicants have an income of less than $20,000, while less than 10 percent of itemizers have incomes in that range. (See table 8.) To calculate the allowance for the higher-income group, Education deducts a percentage point from the rate it calculates for the lower income group, a process that fails to account for the fact that higher-income individuals face higher tax rates than lower-income individuals. Since the estimate for the lower-income group reflects more of the taxes paid by those with higher income, this methodology likely underestimates the taxes paid by this higher-income group. We have identified four strategies for addressing the limitations of the tax allowance that range from modest to more substantial changes to the process: (1) continue to use SOI data but with a revised method for calculating the allowance, (2) substitute SOI data with one of several alternative data sources, (3) use the same allowance for all aid applicants without regard to state of residence, or (4) collect information directly from the aid applicants themselves. Except for the first option, use of these strategies would require legislative changes. Also, these four strategies differ in their impacts on federal costs and on aid applicants. The first strategy would be to make better use of SOI data to calculate the tax allowance, such as by modifying how the allowance is calculated and coordinating with SOI to ensure that the most recently available data are used. Education could use the SOI data on separate income bands to calculate the allowance for families rather than using the aggregate totals that SOI publishes. This would ensure that tax rates for different income bands are based on information more representative of those groups. With regard to coordinating with SOI, Education obtained SOI data for tax year 2000 for its update in 2003 about 3 months before SOI published data for tax year 2001. Thus, when Education published its proposed update in 2003, it was not based on the most recently available data. Coordinating with SOI could reduce the mismatch between the year of the income data collected from applicants and the tax data collected from SOI from 3 to 2 years. Education officials acknowledged that in the future, they may have the flexibility to wait for more recently available SOI data and still meet their schedule for publishing notice of a proposed update to the state and other tax allowance. Appendix IV shows what the tax allowance would be under this strategy for each state. The second strategy would be to discontinue use of SOI tax data and to replace it with publicly available data, such as the following: Bureau of Economic Analysis Personal Income and U.S. Census Bureau Description—The Bureau of Economic Analysis (BEA) annually publishes “Personal Income” tables, which cover aggregate household income, by state and are based on data from federal and state government programs, such as state unemployment insurance programs. The U.S. Census Bureau annually publishes “State Government Tax Collections” tables, which include overall state figures for individual income taxes, real estate taxes levied by states but not local governments, property taxes, and sales taxes for both individuals and businesses. This information is gathered by the U.S. Census Bureau through a mail canvass of appropriate state government offices that are directly involved with state-administered taxes; locally collected and retained tax amounts are not included in the survey. Both sets of tables and related documentation are available via the Internet. Use—Education could calculate the allowance by combining the information from both sets of tables. This approach has three potential advantages over using SOI data: The BEA data includes income from the entire population, including both filers and nonfilers, and the census data covers all tax filers instead of only itemizers, whereas SOI data only include itemizers, sales taxes are included in the tax collections tables—although they include taxes paid by businesses— and information is available 4 months after the end of a year. This allows income data reported by aid applicants and tax information corresponding to the prior year to be used to develop the allowance. A disadvantage of census data as compared with SOI data is that property tax information is more limited. Like the SOI data, the tables published by the BEA and the U.S. Census Bureau reflect aggregate measures of taxes and income and would not necessarily reflect the experiences of the typical family. Also, because the BEA and the U.S. Census Bureau report information in the aggregate—whereas SOI data are separated into different income bands—Education would need to make adjustments to differentiate tax rates by income. U.S. Census Bureau—Current Population Survey (CPS)—Annual Social and Economic Supplement (ASEC) Description—The Census Bureau annually publishes the Annual Social and Economic Supplement to the Current Population Survey, which includes income, estimated state income taxes, and estimated real estate taxes. The CPS household income information is gathered through a survey of 100,000 households. State income tax information is estimated by the U.S. Census Bureau based on reported income and filing status information and review of state income tax regulations. Real estate tax information is generated in a similar manner. Household characteristics are matched to the Census Bureau’s American Housing Survey to provide simulated real estate and property taxes. The CPS dataset and related documentation are available via the Internet. Use—Education could use CPS household-level data to generate tax allowances by income. An advantage of using the CPS is that it allows Education to estimate the taxes paid by the typical family rather than the taxes paid in aggregate, and CPS data also reflect the entire population—itemizers, nonitemizers, and nonfilers. Two disadvantages are that although we have assessed the information collected by the U.S. Census Bureau in generating the CPS to be reliable, the CPS tax information is not based on actual taxes paid but rather on U.S. Census Bureau tax models and is therefore subject to error and that the CPS does not include sales taxes. In addition, CPS data are available only somewhat sooner than SOI data, and because of the size of its sample, a 3-year average must be taken to generate reliable state-level information. Institute on Taxation and Economic Policy (ITEP)—Who Pays? A Distributional Analysis of the Tax Systems in All 50 States Description—ITEP is a nonprofit research and education organization that has published two reports on state taxes, one in 1996 and one in 2003, both entitled Who Pays? A Distributional Analysis of the Tax Systems in All 50 States. According to an ITEP official, ITEP plans to publish future updates every 3 years. These reports present estimated state information on income, real estate, property, and sales tax rates. The ITEP state tax tables are based on the 1988 public-use SOI sample of 365,000 federal tax returns, stratified so that they are representative at the state level and aged to reflect the most recent statistics on general population and tax filer characteristics published by the IRS and the U.S. Census Bureau. These returns and state tax regulations are analyzed to estimate state, local, real estate, property, and sales taxes paid based on household characteristics. Adjustments are made to reflect potential nonfilers as well. These reports are available via the Internet. Use—Were Education to determine ITEP data reliable, Education could use the ITEP tax figures to generate tax allowances by income band. Two advantages of ITEP data are that they include sales taxes and that an adjustment is made to estimate what nonfilers pay in sales taxes, whereas SOI data do not reflect sales taxes and do not account for nonfilers. A disadvantage is that ITEP’s income bands are not consistent across states and do not match those established by Congress. While these publications are publicly available, Education could also contract with any of these organizations to customize a dataset for the purpose of developing the tax allowance. The third strategy would be to apply the same allowance to all aid applicants, regardless of their state of residence. This would involve creating a standard allowance based on the CPS that reflects the median taxes paid by all households. This strategy would have the advantage of simplifying the need analysis methodology, but a disadvantage is that it would not account for the variation in taxes paid across states or income bands. For example, using a standard allowance may on average underestimate the taxes paid by those from high-tax states but may overestimate the taxes paid by those from low-tax states. The fourth strategy would be to collect tax information directly from aid applicants by adding questions to the financial aid application form. Under this strategy, applicants would report state and other taxes along with their federal taxes paid, information that could be used to reduce available household financial resources directly, making an allowance unnecessary. While documentation would likely be available for aid applicants to use in reporting their state income and property taxes, documentation concerning sales taxes may not be as readily available. Independent of this report, the Advisory Committee on Student Financial Assistance is currently assessing this strategy in the context of simplifying the financial aid application process and is expected to release its report in early 2005. One of the options considered by the Advisory Committee on Student Financial Assistance is to have the FAFSA questions tailored to the applicant, where applicants from different states (and with different financial circumstances) would answer different questions, and questions not relevant to an applicant would not be asked. Education officials expressed concern with this strategy because it might add to the administrative burden of students, schools, and Education. For example, Education’s current guidance directs applicants to specific line items from their federal tax returns for their federal taxes paid, and it would be difficult to do the same with state taxes, given the variations among state tax forms. Because institutions are required, on a limited basis, to verify information reported by students and families on the FAFSA, Education officials noted that having students and families report additional information on the FAFSA could increase the burden on institutions of verifying such information. These various strategies would have differed in their impacts on federal expenditures and on financial aid applicants if they were applied to the 2004–2005 award year. First, each strategy would have changed federal expenditures for grant and loan programs, for acquiring data, and for other administrative activities. For example, we estimate that Pell Grant program expenditures could have increased by as much as $400 million or decreased by as much as $200 million were the different options adopted and used to allocate aid for 2004–2005. Second, each strategy would have affected the amount of federal, state, and institutional aid that financial aid applicants receive and the number of applicants receiving such aid. For Pell Grants, using a standard allowance of 4 percent would have caused about 83,000 recipients to become ineligible for the program, but the other options would have affected fewer recipients. Table 9 shows the potential merits of each option in terms of federal expenditures for the Pell Grant program and the impact on expected family contribution, and table 10 shows the extent to which the tax allowances calculated under each strategy would accurately reflect state and local taxes paid by students and families. Millions of students rely on federal, state, and institutional aid every year to help pay for their postsecondary education. These awards are distributed to students and their families based in part on estimates about what they can afford to pay out of their own pockets. Yet if these estimates are considerably incorrect, the awards may not be distributed as equitably as they could be. Because state and local tax rates may have changed over the past decade, and Education has updated the allowance only once and given the limited way in which Education uses SOI data, it is very likely that the federal government may have been making an allowance for more taxes than were actually paid, or in other cases, undercompensating for taxes that were paid. Although Education has taken some recent steps to update the allowance, these efforts have not been successful. An inaccurate allowance could yield adverse effects for the federal government and students and their families. On the one hand, students and families could erroneously gain eligibility, which would cause federal funds to be misdirected. On the other, students and families could inappropriately lose eligibility for aid. Because state and institutional aid programs also make use of the federal need analysis methodology, such losses may be compounded for students and families. To ensure that relevant tax data from the Statistics of Income are requested systematically and that the most recent data are obtained, we recommend, in the short run, that the Secretary of Education develop formalized updating procedures and document such procedures in writing. Such procedures could include (1) making annual written requests to the Internal Revenue Service for state and local tax information and documenting those requests and (2) coordinating with the IRS to make sure Education knows when SOI data will be publicly released and to ensure that the most currently available data are used. To better capture the amount of taxes paid by students and families, we also recommend, in the short run, that Education revise its methodology for calculating the state and other tax allowance. Revisions could include using tax figures reflective of the different income groups to calculate the allowance rather than figures based on all itemized tax returns. To determine whether alternative methodologies and data would better enable Education to annually update the allowance, we recommend, in the longer run, that Education assess the cost and reliability of available data, including the alternative data sources identified in this report. If Education determines that statutory changes are needed to implement more effective alternatives, it should seek such changes from Congress. In written comments on our draft report Education generally agreed with our reported findings and recommendations. In its letter, Education offered a number of suggestions and observations. Education requested that we refer to the state tax rates as “‘proposed state tax rates under the HEA’ in the final report rather than using the label ‘proposed state tax rates of the Department,’” because it believes it does not have the authority to “ignore the clear statutory requirement to perform the update.” Because we explain in our report that the Congress incorporated the state and other tax allowance in the HEA and required Education to annually revise the allowance, we do not believe our characterization of the state tax rates leads to any confusion. Accordingly, we did not change how we refer to the state tax rates. Education also commented on the strategies we identified that address some of the limitations associated with the tax allowance and noted that it would review each of the alternative data sources discussed in our report that could be used to substitute for the SOI file data, as we recommended. Education noted that it believed all four strategies we identified in our report would likely require congressional action. We agree that those strategies that involve using alternative data sources to substitute for the SOI file data would require legislative changes, as we noted in our report. We also agree with Education’s comment that using income bands other than those specified by Congress would likely require legislative change. We disagree that congressional action is required for Education to continue to use SOI data but with a revised method for calculating the allowance—one of the strategies identified in our report. While the HEA directs Education to use the SOI file to revise the allowance, and establishes the income categories for which the allowance should be calculated for parents of dependent students and independent students with dependents other than a spouse (which we define as “families” in this report), the HEA does not specify a particular method to calculate the allowance. Therefore, we believe that Education could revise its methodology, as we recommended, without congressional action. Education also echoed some of the disadvantages we discussed in our report associated with applying the same allowance to all applicants and collecting tax information directly from aid applicants by adding questions to the application form. Education also stated that it generally agreed with our assessment of the impact of the revised allowance tables on Pell Grant recipients but noted that we could have provided additional information concerning applicants who would no longer have been eligible for Pell Grants. As we stated in the report, these applicants typically would no longer have received the minimum Pell Grant award, reflecting that such applicants typically have higher incomes than those who would have continued to receive Pell Grants. Additionally, we show that Pell Grant recipients with household income over $25,000 would have been significantly more likely to have either received less in Pell Grants or become ineligible for them. Education also suggested in its letter that it would be helpful to clarify that a change in EFC would not necessarily cause an identical change to a student’s award amount with respect to federal student loans, Campus- Based aid, and state and institutional financial aid programs: “in other words,” the department noted, “include a brief explanation of potential interactive effects.” As noted in our report, our case studies of students at selected schools showed that as the EFC would have increased, subsidized loans would have decreased, and unsubsidized loans would have increased in response to the decreases in other forms of financial aid. In response to Education’s comment, however, we added information concerning how EFC changes would have affected need-based aid overall with respect to our case study schools. Education also noted that it understood why we chose to analyze the effects of the proposed 2003 update had it been implemented in 2004–2005. (Soon after we had submitted our draft report to Education for comment, the department published, on December 23, 2004, an updated allowance for the 2005–2006 award year.) In its letter, Education includes the results from its preliminary analysis of the effects of the 2004 update for 2005–2006. Education’s results are generally consistent with the results from our analysis. We did not, however, verify the accuracy of Education’s estimates. Education also expressed concern that we misinterpreted the department’s intentions with respect to updating the allowance for the 2005–2006 award year. While we understood the department’s intentions, we made technical clarifications to the report to address Education’s concern. With respect to our recommendation that the department establish formal procedures to ensure that it annually requests and obtains the most current tax data from the IRS, Education stated that it had such procedures in place as “evidenced by the update published in the spring of 2003.” However, as noted in our report, Education could not provide us with written procedures guiding staff on the routine steps necessary to update the tax allowance or to identify what data would be needed to update the allowance. In response to the department’s comment, we clarified that our recommendation included documenting formalized procedures in writing. Lastly, Education provided technical comments, which we incorporated as appropriate. Education’s written comments appear in appendix V. As agreed with your offices, unless you publicly announce its contents earlier, we plan no further distribution of this report until 7 days from its date. At that time we will send copies of this report to the Secretary of Education, appropriate congressional committees, and other interested parties. In addition, the report will be available at no charge on GAO’s Web site at http://www.gao.gov. If you or your staff have any questions about this report, please call me on (202) 512–8403 or Jeff Appel, Assistant Director, on (202) 512–9915. You may also reach us by e-mail at AshbyC@gao.gov or AppelC@gao.gov. Other contacts and staff acknowledgments are listed in appendix VI. The objectives of this study were to determine (1) what tax data form the basis of the current tax allowance and what factors have affected regular updates, (2) the effect the Department of Education’s (Education) proposed update would have had in award year 2004–2005 on financial assistance for students and families, (3) the extent to which current methods for determining the allowance accurately measure how much students and families have paid in state and other taxes, and (4) the strategies available to address any problems in deriving the allowance. To carry out the objectives, we analyzed Education’s 2002–2003 aid applicant sample file and Education’s Cost Estimation and Analysis Division’s Statistical Abstract (CEAD STAB), the most current versions available at the time of our review. We worked closely with financial aid officials from two states—Tennessee and Wisconsin—and four colleges— one public and one private nonprofit school in each of the two states. We interviewed officials from the U.S. Department of Education, Advisory Committee on Student Financial Assistance (ACSFA), and U.S. Department of the Treasury’s Internal Revenue Service–Statistics of Income (SOI) Division—as well as officials from the states and schools we contacted. We also interviewed officials from associations representing institutions, including the American Association of State Colleges and Universities (AASCU), National Association of Independent Colleges and Universities (NAICU) and the College Board, as well as other experts. In addition, we reviewed and analyzed the statutory requirements and legislative history of the state and other tax allowance. Furthermore, we reviewed and analyzed state and other tax data from SOI, Bureau of the Census, Bureau of Economic Analysis, and the Institute on Taxation and Economic Policy. We performed our work in accordance with generally accepted government auditing standards between October 2003 and November 2004. In estimating how Education’s proposed update would affect students’ and their families’ eligibility for financial assistance, we analyzed two datasets. We used Education’s aid applicant sample file from the 2002–2003 award year to estimate changes in (a) expected family contribution and Pell awards nationally, (b) state need-based aid for Wisconsin and Tennessee, (c) Supplemental Educational Opportunity Grants (SEOG), Perkins loans and Work-Study, and (d) institutional need-based aid for the four institutions. This dataset is a randomly drawn, nationally representative sample of over 450,000 aid applicants. To estimate the percentage of Stafford subsidized and unsubsidized and Parent Loans for Undergraduate Students (PLUS) recipients that are likely to have their loan award changed, we used Education’s CEAD STAB. CEAD STAB is a randomly drawn, representative sample of 1.8 million borrowers (about 7 million loans) from the National Student Loan Data System (NSLDS), which is a comprehensive national database of Title IV loan and grant recipients. Our analysis of the CEAD STAB focused on Stafford subsidized and unsubsidized and PLUS borrowers who originated loans from July 2002 to June 2004. We assessed the reliability of both datasets by conducting electronic testing of key variables for obvious problems in accuracy and completeness, interviewing appropriate Education officials, and reviewing related documentation. Based on these tests and reviews, we determined that the samples were sufficiently reliable for our purposes. To estimate changes in expected family contribution (EFC) and Pell Grant awards nationally, our analysis followed Education’s approach to estimating EFC and Pell awards in the 2004–2005 award year. To do this, the 2002–2003 aid applicant sample file was converted to better reflect aid applicants in the 2004–2005 award year by adjusting all income and asset amounts for inflation and changing the weights assigned to each sample applicant so that the sample takes into account projected changes in the number and type of applicants. We reviewed Education’s approach to converting the sample file to the 2004–2005 award year and calculating EFC and Pell awards for accuracy and interviewed Education officials about the approach’s reliability. We determined that Education’s approach was sufficiently reliable for our purposes. EFC and Pell awards in the 2004–2005 award year were estimated for each sample aid applicant using both the current 2004–2005 state and other tax allowance, which is based on 1988 SOI data, and the proposed 2004–2005 state and other tax allowance, which is based on 2000 SOI data. To assess the impact of the update on EFC and Pell Grant awards, these amounts were compared. We also examined how these impacts vary by family income, dependency status, and state of residence. We designated student state of residence as the state of residence of the parent(s) when they differed. Our methodology for obtaining national-level estimates on the percentage of loan recipients who could have had their loan award affected involved the steps listed below. 1. Using the aid applicant sample file, we estimated the percentage of applicants in award year 2004–2005 who would have had their EFC changed because of the proposed update for each combination of dependency status, state of residence, and specified income group. 2. We used the resulting percentages to estimate the likelihood that each CEAD-STAB sample borrower’s EFC would have been changed due to the update. 3. We estimated the likelihood that each individual borrower would have had his or her Stafford subsidized and unsubsidized loan award affected as equal to this percentage if the recipient borrowed less than the maximum allowed by law. We estimated as zero the likelihood that each individual borrower would have had his or her Stafford subsidized and unsubsidized loan award affected if the recipient borrowed the maximum allowed by law. 4. For PLUS recipients, the likelihood that each recipient would have had his or her aid award affected was estimated as equal to the percentage who would have had their EFC changed because of the proposed update that we estimated from our analysis of the aid applicant sample file. Estimating whether students would have seen a change in their loan amounts because of the proposed update is complex largely because these loan amounts depend on the extent to which all other financial aid awards—including Campus-Based, state, and institutional aid—would have been affected, and no complete information is available on the specific awarding policies of all states and institutions for these types of aid. To compensate for this lack of information, we made several assumptions regarding how Stafford and PLUS loans would have been affected, which may either over- or underestimate what the actual changes would have been. However, these assumptions may somewhat offset each other, and we believe our estimates are informative of the percentage of borrowers whose loan awards could have been affected. Stafford and PLUS loan estimates may be biased upward for the following reasons. Stafford loan estimates may be biased upward because we assumed that all borrowers who currently receive less than the maximum allowed and whose EFC would have changed under the proposed update would have had their loan award amount changed as well, yet this is not always the case. For example, because the subsidized loan award equals the cost of attendance less EFC and other financial aid awards, subject to the loan limits, subsidized Stafford loan amounts would not have been affected if the decrease in other financial aid awards exactly offset the EFC increase resulting from the proposed update. Because the unsubsidized Stafford loan award equals the cost of attendance less other financial aid awards (including subsidized loan awards), subject to the loan limits, unsubsidized loan amounts would not have been affected if other aid awards did not change because of the update. Furthermore, we assumed that all PLUS borrowers whose EFC would have changed because of the update would have had their loan award affected, but, similar to unsubsidized Stafford loans, this would not have occurred had other aid awards not changed because of the update. It is difficult to know the size of this upward bias because the dataset does not include information on the extent to which other financial aid awards would have been affected. Our estimates for Stafford loans may be biased downward because we assumed that all borrowers who receive the maximum allowed would not have had their loan award affected, which also is not always the case. For unsubsidized Stafford loans, this bias appears to be very small because unsubsidized loans would only decrease if students have a cumulative net increase in their other financial aid awards, which case studies and expert interviews show to be unlikely. For subsidized Stafford loans, this bias may be larger, yet we believe that it is still relatively small. Subsidized Stafford loan awards that are currently at the maximum would only decrease when the EFC plus other aid increase enough to cause the student to lose eligibility for the maximum loan amount, and analysis of the National Postsecondary Student Aid Survey (NPSAS) shows that most students are not likely to face this circumstance. We also assumed that the borrowers in award year 2004–2005, the year of the proposed update, are from the same states, have the same incomes, and have the same costs of attendance as the most recent CEAD-STAB borrowers, and the extent to which they differ would cause our estimates to be less accurate. For example, if we underestimate the number of students in likely high-impact states, our estimates would likely underestimate the overall proportion of students who could face a change in their loan award. To complement this national-level loan analysis, we determined the percentage of students at our case study schools who would have experienced a change in their subsidized and unsubsidized loans, along with the size and direction of the changes. We could not determine this information for PLUS loans for our case study schools since the schools did not package PLUS loans for the purpose of estimating potential impacts of the proposed update. To provide illustrative examples of how the proposed update can affect state and institutional need-based aid and Campus-Based aid, we worked closely with two states—Wisconsin and Tennessee—and four colleges, including one public and one private nonprofit institution in each of the two states. We chose these states because they use the Higher Education Act (HEA) federal methodology to disburse state aid, are geographically dispersed, and represent high- and low-impact states, based on an index we calculated for the following components: (1) the average EFC change resulting from Education’s update, (2) the percentage of full-time undergraduates receiving grant aid, and (3) the average state need-based grant per undergraduate. We averaged the three components to generate an index of the overall average impact. The 44 states and the District of Columbia that use the federal methodology were then sorted in descending index order and separated into three groups of 15, with the highest index group being designated “high-impact states,” the next group being designated “medium-impact states,” and the last group being designated “low-impact states.” For the components and index value we estimated for each state, see appendix II. The institutions we chose include the University of Wisconsin at Madison, Wisconsin’s Marian College, Middle Tennessee State University, and Tennessee’s Carson- Newman College. We chose schools within the two selected states based on the following criteria: (1) use of the HEA federal methodology to disburse aid, (2) participation in the federal Campus-Based aid program, (3) provision of institutional need-based aid, (4) number of students with household income between $25,000 and $75,000, and (5) willingness and capacity to calculate estimated impacts on need-based aid. For each of these two states and four institutions, we generated a subsample from Education’s aid applicant sample file that reflects the students who attended school in these two states and the students who attended school at the four institutions in the 2002–2003 award year. For each student, the dataset contained information on the student’s estimated EFC under the current and proposed allowance and the student’s dependency status. Using these subsamples, examples of the effect of the proposed update on state, institutional, and Campus-Based aid were calculated. While the aid applicant sample file is a nationally representative sample, it may not be representative of the aid applicants who attend school in these specific states or at these specific institutions. The subsamples had the following number of observations: Wisconsin: 7,469, Tennessee: 8,385, University of Wisconsin at Madison: 580, Wisconsin’s Marian College: 54, Middle Tennessee State University: 533, and Tennessee’s Carson-Newman College: 62. We estimated the impacts on Wisconsin’s state need-based aid using the Wisconsin subsample, along with the state aid award methodologies provided to us by the Wisconsin Higher Educational Aids Board. The state need-based aid programs that we analyzed were the Wisconsin Higher Education Grant and the Wisconsin Tuition Grant programs. The Tennessee Student Assistance Corporation, the agency responsible for determining state aid in Tennessee, performed its own analysis of the impact on state need-based aid using the Tennessee subsample and then shared the results with us. The aid program analyzed was the Tennessee Student Assistance Award. To verify the validity of the Tennessee aid determination, we verified that the aid awarded fell within the maximum award limit and that those within an EFC range received similar award amounts; that is, that those with a lower EFC received a higher award amount. To estimate the impacts on institutional need-based and Campus-Based aid, financial aid directors or financial aid specialists at each of the four selected schools calculated the impacts using their relevant subsample. While the focus was on need-based institutional aid and Campus-Based aid, three institutions also calculated the effect on Stafford loans. To check the validity of these simulations, we checked (1) the order in which different forms of aid were awarded to see if they were consistent with common aid packaging protocols, (2) the formulas used to calculate remaining need at each stage of the award packaging process to make sure they were accurate, (3) the range of aid levels awarded to make sure they fell within bounds defined by regulation, (4) the total aid awarded to make sure it did not exceed financial need, and (5) the relationship between aid awarded and EFC levels to make sure that those with lower EFCs were provided more aid than those with higher EFCs. For the purposes of this report, we define “families” to include parents of dependent children (students) and independent students with dependents other than a spouse, and we define “individuals” to include dependent students and independent students without dependents other than a spouse. The specific methodologies used for each source are described in the footnotes to table 9. In assessing the reliability of state personal income estimates from the U.S. Bureau of Economic Analysis (BEA), we reviewed information available online from the BEA Web site on its data quality assurance processes and interviewed relevant officials. On the basis of the results of our document review and discussions with relevant officials, we concluded that the BEA data we used were reliable for our purposes for this analysis. In assessing the reliability of state government tax collections estimates from the U.S. Census Bureau, we interviewed relevant officials, who stated that there was no published data reliability documentation. Thus, we were unable to determine if the Census data we used were reliable for our purposes for this analysis. In assessing the reliability of data from the U.S. Census Bureau’s Annual Social and Economic Supplement, we reviewed information available online from the U.S. Census Bureau Web site on its data quality assurance processes and interviewed relevant officials. On the basis of the results of our document review and discussions with relevant officials, we have determined that the information collected by the U.S. Census Bureau in generating the Current Population Survey (CPS) is reliable, but we were unable to determine whether the CPS tax data we used, which is not collected directly but rather generated from U.S. Census Bureau tax models, were reliable for our purposes for this analysis. In assessing the reliability of data from the Institute on Taxation and Economic Policy’s Who Pays publication, we interviewed a relevant official and reviewed available documentation. However, we were unable to reach a determination as to the reliability of the data, primarily because of a lack of sufficient documentation. Because our analysis relied on samples of aid applicants and borrowers, our estimates are subject to sampling errors. Sampling errors are often represented as a 95 percent confidence interval: an interval that 95 times out of 100 will contain the true population value. For the percentages and numbers presented in this report on the EFC, Pell award, Stafford loan, and PLUS loan impacts, we are 95 percent confident that the results we would have obtained had the entire population been studied are within plus or minus 5 percent of the results, unless otherwise noted. The results for state and institutional need-based aid and Campus-Based aid are not necessarily based on representative samples and therefore should be considered as case study findings, or illustrative examples. Thus, we did not calculate sampling errors for these three categories of aid. Report on State-Sponsored Student Financial Aid, 2001-02 Academic Year. Appendix III: Average Tax Rates on Adjusted Gross Income, by State and Income Level level (percentage) level (percentage) Appendix IV: Simulation of Tax Allowance Percentages under Various Options, by State—Families with Adjusted Gross Income of $15,000 or More allowance (4%) 4 allowance (4%) In addition to those named above, the following people made significant contributions to this report: Jeff Weinstein, Cynthia Decker, Bob Parker, Sue Bernstein, Amy Buck, James Wozny, and Melba Edwards.

Summary: In 2003, the Department of Education (Education) proposed an update to the state and other tax allowance, a part of the federal need analysis for student financial aid. Most federal aid as well as some state and institutional aid is awarded based on the student's cost of attendance less the student's and/or family's ability to pay these costs--known as the expected family contribution (EFC). The allowance, which accounts for the amount of state and other taxes paid by students and families, effectively reduces the EFC. Given the potential impact of the allowance on the awarding of aid, we determined what factors have affected the updating of the tax data on which it is based, the effects the proposed 2003 update would have had on financial assistance for aid applicants, any limitations in the method for deriving the allowance, and strategies available to address them. While Education has been required to revise the allowance annually since 1993, prior to 2004 it attempted to update the allowance only twice--in 1993 and again in 2003--but the latter update was suspended. As a result, the 1988 IRS tax data used for the 1993 update remained in effect. The lack of updates is primarily because Education did not annually seek data needed to update the allowance or establish effective internal control to guide the updating process. Also, Education did not consider alternatives when data were not readily available. Had the update been implemented in 2004-2005, the allowance would have decreased for most states; as a result, the EFC would have increased by about $500, on average, for a majority of aid applicants. Of those with an EFC increase, 38 percent would either have received less in Pell Grants ($144 less on average) or would have become ineligible for them; the percentage of recipients affected would have varied by income. Overall Pell Grant expenditures would have decreased by $290 million. Increases in EFCs could also have affected other forms of aid, including state aid; these effects in turn could have affected Stafford loans and Parent Loans for Undergraduate Students. The impact of the proposed update on Campus-Based, state, and institutional need-based aid would likely have varied based on state and institutional aid awarding policies and changes in state allowances. Due to certain limitations of the IRS dataset with respect to calculating the allowance, and problems with how Education uses this dataset, the current allowance may not reflect the amount of taxes paid by students and families. The dataset is limited because the taxpayers included in it are generally not representative of aid applicants, it does not include all state and other taxes paid by students and families, and the tax data are several years older than the income information reported by applicants on aid applications. In addition to these limitations, Education does not make full use of the dataset to better reflect the varying tax rates paid by taxpayers in different income groups. Strategies we identified for addressing the limitations of the tax allowance include (1) using IRS data with revisions to the method for calculating the allowance, (2) substituting IRS data with one of several alternative data sources, (3) using a standard allowance for all aid applicants irrespective of state of residence, or (4) collecting tax information directly from aid applicants. These could require modest to substantial changes, would differ in their impact on applicants and federal costs, and could require legislative changes