This report is part of a series, Understanding and Improving Title I of ESEA. Nora Gordon is Professor at Georgetown University’s McCourt School of Public Policy, and Sarah Reber is the Joseph A. Pechman Senior Fellow in Economic Studies at the Brookings Institution.
Even without changing the four Title I grant formulas in a reauthorization of the Elementary and Secondary Education Act (ESEA), Congress can shape how funds are allocated to school districts by choosing how to divide the total amount of Title I funds appropriated across the four formulas (for a detailed discussion of each formula, see Title I of ESEA: How the Formulas Work). In this report, we demonstrate how different types of school districts benefit from the different Title I grant formulas by simulating a $10 billion increase in Title I funding.
Specifically, we simulate how much additional Title I funding per formula child each district would receive for a $10 billion increase in Title I funding if that increase were allocated solely through one of the four formulas. For each set of district characteristics—poverty rates, racial composition, state education spending, region, and enrollment—we ran the simulation for each of the four formulas, in turn. In this way, the simulation exercise shows how the division of new funding among the four formulas would affect different types of districts as characterized. We also explore alternative metrics that could be used in the Title I formulas to adjust for differences in state-level prices and whether a different approach than adjusted state per-pupil expenditure (adjusted SPPE) would significantly change the redistributive impact of Title I.
Need a refresher on all things Title I? Check out All4Ed’s explainer on the Title I program and why it matters for students and families.
¿Necesita un repaso de todo relacionado con el Título I? Consulte el explicador de All4Ed sobre el programa de Título I y por qué es importante para los estudiantes y las familias.
The simulations rely on two simplifications to make the distributional impacts of the four Title I grant formulas as clear as possible. First, we simulate what would happen were new funds to be funneled solely through one formula (in four simulations, we cover all four formulas). In practice, Congress historically has chosen a mix of formulas; all Title I funding increases since 2002 have been distributed equally through the Targeted Grant and the Education Finance Incentive Grant (EFIG) formulas. Second, we simulate the allocation of new money rather than the reallocation of existing money. We view this simplification as relatively costless given political constraints. Further, if Congress were to (for the first time in Title I’s history) reallocate standing appropriations from one formula to another, the effects would be muted in the near term because of Title I’s hold harmless provisions.1We do not account for hold harmless provisions or for the small state minimum (SSM) provision of the law in these simulations. SSM states comprise a small share of total enrollment; therefore, the SSM provision would not substantially influence the overall picture, weighted by enrollment. In practice, districts in small states have higher grants per formula child, all else equal, for this reason.
This report discusses how the simulated grant per formula child varies across school districts with different characteristics resulting from a $10 billion increase in Title I funding.
We focus on the simulated allocation per formula child in the discussion throughout this report; interested readers can refer to the appendix tables for these alternatives.
Our discussion draws on the Title I grant formulas themselves and key components, including formula children, weighted and unweighted counts and shares of eligible children, adjusted SPPE, and hold-harmless and small state minimum requirements. For detailed definitions of these terms and the allocation process, see Title I of ESEA: How the Formulas Work.
Figure 1 shows how the simulated $10 billion increase in Title I funding per formula child varies with school districts’ child poverty rates.2The child poverty rate is very close to the formula child share (formula child count divided by school-aged population) with a few notable exceptions: the formula child share used in the simulations differs from the child poverty rate because the poverty rate, and all of the other district characteristics we examined here, come from the 2018–19 school year, which is the most recent year for which we have all of the relevant data, whereas we use data from FY 2021 for the simulations. For the simulation, the districts are divided into deciles of child poverty, weighted by enrollment—the first decile includes the lowest-poverty districts (child poverty rates less than 5.6%) and the 10th decile includes the highest-poverty districts (poverty rates of 29% or more). Because the districts are weighted by enrollment, each decile contains 10% of national enrollment, not 10% of districts.
In Figure 1, the dark purple bars show the results simulating a $10 billion increase in Title I funding dedicated entirely to Basic Grants.3In practice, Congress has not increased funding for Basic or Concentration Grants since the No Child Left Behind Act of 2001, instead dividing funding increases equally between Targeted Grants and EFIG. Districts across the child poverty deciles have similar simulated Basic Grant allocations of around $1,200 per eligible child.4The Title I grant formula child count is largely comprised of children living in poverty, but it also includes other children whose families participate in Temporary Assistance for Needy Families, neglected and delinquent children, foster children, and children enrolled in Bureau of Indian Affairs schools. As described in Title I of ESEA: How the Formulas Work, nearly all formula children reside in districts eligible for Basic Grants; eligibility varies across the four formulas. The lowest-poverty districts would receive slightly more ($1,280 per eligible child in the first decile) than the highest-poverty districts ($1,150 in the top decile). These differences are due to differences in spending from state and local revenue (the adjusted SPPE portion of the Basic Grant formula): the lowest-poverty districts are disproportionately in states with higher adjusted SPPE, which explains the higher simulated Basic Grant per eligible child for this group.5While hold harmless can contribute to differences in actual allocations (as described in Title I of ESEA: How the Formulas Work), it does not affect these simulations because the simulation is of the allocation of new funding.
Figure 1. Average Simulated Increase in Title I Allocations per Formula Child, by Formula, by District Decile of Child Poverty
The green bars in Figure 1 show how $10 billion in new Title I funding allocated solely through the Concentration Grant formula would be distributed. School districts must have at least 6,500 or 15% formula children to receive Concentration Grants, and once a district is eligible, the per-formula child allocation does not depend on the share of children who are eligible. The 15% cutoff is around the median district’s percentage of formula children, so most districts in the 6th or higher deciles of child poverty in Figure 1 would receive funding, which then would not vary much across the higher deciles.6The 6th decile has a lower average simulated grant per formula child than the 7th and above deciles, despite being above the median child poverty rate because we are using the child poverty rate from 2018 (to be consistent with other variables such as racial composition and per-pupil expenditure), and some districts near the cutoff to receive Concentration Grants may have dropped below the 15% cutoff between 2018 and 2021, making them ineligible to receive Concentration Grants, though they likely receive some because of hold harmless provisions.
Large districts can qualify for Concentration Grants with lower formula child rates if they have 6,500 formula children. For example, a district with 50,000 children could qualify for a Concentration Grant with only 13% of formula children rather than 15%. This is one reason the average Concentration Grant allocations for lower-poverty districts are smaller, but not zero, in the simulation. In addition, some districts that had child poverty rates below the threshold for Concentration Grants based on 2018 data had formula child shares above the threshold for FY 2021, which is used in this simulation.
The second panel of Figure 1 shows the same results for simulated Targeted Grant (blue) and EFIG (magenta) funding increases. Two patterns are notable:
For analyzing which districts benefit from simulated Targeted Grants and EFIG, districts fall roughly into three groups: (1) the lowest-poverty decile, which would receive small per-formula-child grants on average (about $270); (2) districts in the 2nd to 7th poverty deciles, which receive between $850 and $1,100; and (3) districts in the top three poverty deciles, which receive the most per-formula-child grants (between $1,230 and $1,545) on average. Perhaps surprisingly, districts in the 8th poverty decile have the highest average simulated Targeted Grant and EFIG funding per formula child. This is because this decile contains many large districts that benefit from number weighting in calculating the weighted child eligibility count in the Targeted Grant and EFIG formulas; i.e., a large share of their eligible children receive the highest weight compared to smaller districts with higher poverty rates.7See Title I of ESEA: How the Formulas Work for more details about how number weighting influences the allocations of Title I aid. Simulated Concentration and Basic Grants per formula child are also slightly higher in the 8th decile compared to the 7th or 9th because a disproportionate share of districts in this decile are in states with a high adjusted SPPE.
In sum, the simulated Title I allocation per formula child is largely unrelated to district child poverty rates for the Basic Grants. Concentration Grants would produce the most progressive allocation of funding (concentrating more funding per eligible child in higher-poverty districts), and Targeted Grants and EFIG would fall somewhere in between.
Figure 2 shows the average simulated increase in districts’ Title I funding, by formula, by quartiles of the percentage of enrolled students who are White, Black, and Hispanic,8We follow the racial and ethnic naming conventions of the data source, the National Center for Education Statistics Common Core of Data. White and Black refer to non-Hispanic White and Black students, but we omit the “non-Hispanic” qualifier for ease of exposition. Race categories exclude persons of Hispanic ethnicity; Hispanic refers to students of any race who identify as Hispanic. respectively (we exclude EFIG because it is similar to Targeted Grant). We focus on these race/ethnicity groups because they are the three largest; results for all of the racial groups are in Appendix Table 2. For the Basic Grant formula (purple bars), the average simulated per-formula-child grant does not vary much across racial composition of districts, ranging from $1,126 to $1,218 per formula child. Both the Concentration Grant formula (green bars) and Targeted Grant formula (blue bars) distribute large simulated grants per formula child to school districts enrolling smaller shares (i.e., lower quartiles) of White students because those districts are more likely to have lower poverty rates.
The pattern across district quartiles based on percentage of Black students served and percentage of Hispanic students served tell a similar story. Basic Grants per formula child do not vary much, but Targeted and Concentrated Grants per formula child tend to be larger in districts serving larger shares of Black and Hispanic students because those districts tend to have higher poverty rates.
Figure 2. Average Increase in Simulated Title I Allocations per Formula Child, by Formula, by District Racial/Ethnic Composition
White Students
Black Students
Hispanic Students
Figure 3 shows how the simulated Title I grant increases vary with school districts’ per-pupil current expenditure net of federal funding, i.e., the amount of state and local funding that districts have.
The average simulated Basic Grant is about $1,000 per eligible child for the lowest-spending half of districts compared to more than $1,400 for the highest-spending quartile. This is because of the use of adjusted SPPE in all of the Title I grant formulas. Higher-spending districts tend to be in higher-spending states, so they receive larger grants per formula child regardless of which formula is used to distribute the additional $10 billion in Title I funding.
Increases in funding based solely on the Targeted Grant formula would benefit districts that have the most resources from state and local sources even more disproportionately than using the Basic or Concentration Grant formulas.9See Title I of ESEA: How the Formulas Work for more discussion of the use of adjusted SPPE in the formulas. Some of this comes from compensating districts that face higher labor costs, but grants are higher in high-spending districts even accounting for local wage differences.10Appendix Table 4 presents the full results, except that the simulated grants per formula child have been adjusted using the Comparable Wage Index for Teachers (CWIFT) to account for differences in prices across districts; that is, we calculate the simulated grants for each of the four formulas, scale them up or down based on the CWIFT, and renormalize so that the total equals $10 billion.
Figure 3. Average Increase in Simulated Title I Allocations per Formula Child, by Formula, by District Per-Pupil Current Expenditure Excluding Federal Funding
Figure 4 shows how the $10 billion in simulated district Title I grants, on average, vary by region. Again, differences in the average simulated Basic Grants districts would receive are due to differences in state spending. For example, southern states have the lowest SPPE, on average, followed by midwestern and western states and then northeastern states, which have average simulated Basic Grants almost 50% higher than in the south. Northeastern districts receive more, on average, from all of the Title I grant formulas due to the high average SPPE in that region. For the Concentration and Targeted Grant formulas, lower spending on education in some regions is somewhat offset by higher poverty rates. Southern districts have higher simulated Concentration Grants, and western districts have higher simulated Targeted Grants (and EFIG) compared to the other non-northeastern regions (results by state are in Appendix Table 1).
Figure 4. Average Increase in Simulated Title I Allocations per Formula Child, by Formula, by Region
A district’s count of weighted eligible children for Targeted Grants is determined using the number weighted or the percentage weighted brackets, whichever yields the higher count. The same is true for within-state allocation of EFIG to districts. As the analysis in Title I of ESEA: How the Formulas Work shows, holding constant the formula child share, larger districts (measured by child population) generate a higher weighted count from number weighting in the Targeted Grant formula. Because the districts benefiting from number weighting are larger, the higher simulated grant per formula child that large districts receive because of number weighting translates to a large total (when multiplied by the number of formula children). This matters because the total appropriation each year for Targeted Grants and EFIG is fixed, so any additional funding going to larger districts because of number weighting reduces the allocations available for smaller districts.
Figure 5 shows how the average simulated allocations districts would receive per formula child (with a $10 billion increase in Title I funding allocated solely through each of the existing formulas) vary by district enrollment. The average simulated Basic Grant allocation is just under $1,200 per formula child across all categories. This suggests there is little correlation between district enrollment and adjusted SPPE (the main driver of differences in simulated allocations for the Basic Grant). The simulated Concentration Grant allocation is about 15% larger, on average, for districts in the largest enrollment category, presumably because they are more likely to meet the eligibility threshold for the Concentration Grant (which is far higher than for any of the other three formulas). The impact of number weighting can be seen for the simulated Targeted Grant allocations (blue bars). Districts with enrollments of more than 5,000 have simulated allocations per formula child that are about 45% larger than those for the smaller districts.
Figure 5. Average Increase in Simulated Title I Allocations per Formula Child, by Formula, by District Enrollment
State spending on K–12 education—through adjusted SPPE—plays a central role in all four Title I grant formulas. It introduces significant cross-state differences in funding per formula child for the Basic Grant, which is otherwise close to uniformly distributed with exceptions for the small state minimum and hold harmless provisions (for more, see Figure 2 of Title I of ESEA: How the Formulas Work). Figure 6 shows that adjusted SPPE is negatively correlated with a state’s formula child share. Poorer states, on average, have a lower adjusted SPPE. For example, Washington has a relatively low formula child share and relatively high spending, while the opposite is true for Louisiana.
While this relationship holds on average, there is considerable variation. Idaho has a similar formula child share to Washington but with much lower spending, and Colorado has a relatively low formula child share but spends similarly to Louisiana. The application of maximum and minimum bounds for adjusted SPPE in the Title I grant formulas further weakens that relationship.11 Adjusted SPPE is calculated as a ratio at the state level; both the numerator (expenditures, excluding major federal revenue sources) and denominator (student count) are constructed as averages over a three-year period, with a lag. For Basic, Concentration, and Targeted Grants, high-spending states have their SPPE adjusted downward to a maximum of 120% of the national average (40% of 120% of the national average is 48% of the national average, as described in the law), and low-spending states get their adjusted SPPE bumped up to a minimum of 80% of the national average (40% of 80% of the national average is 32% of the national average, as described in the law). For EFIG state-level allocations, the floor is 34% of the national average, and the ceiling is 46% of the national average.
Figure 6. Correlation Between Adjusted SPPE and Formula Child Share, by State, FY 2021
Because adjusted SPPE is used in all four Title I grant formulas—and advantages higher-spending states which tend to have lower formula child shares—it somewhat mutes the formula’s redistributive impact. Why, then, use adjusted SPPE to allocate Title I funding?
The inclusion of SPPE in the formulas has long been justified as a way to adjust for cross-state variation in the prices of educational inputs such as staff. SPPE measures how much is spent on education, which depends on both prices (e.g., teacher salaries, which are influenced by local cost of living) and on quantities (e.g., class size). States and districts that face similar prices often choose different levels of education spending, i.e., some districts spend more not only because they need to pay teachers and staff more (they face higher prices), but also because they hire more teachers and other staff (they choose higher quantities). Using adjusted SPPE in the Title I grant formulas means sending less money, all else equal, to places where spending is already lower and more money to higher-spending places. 12In this way, SPPE could be viewed not only as a proxy for cost of living, but also—at least in theory—as an incentive for states to spend more. The Effort Factor in the EFIG formula is related but different because it considers SPPE compared to the state’s per-capita income. In this section, we investigate how much that matters for the progressivity of Title I allocations.
At the time that adjusted SPPE was incorporated into the Title I grant formulas, other measures of differences in input prices were not available. Since then, the National Center for Education Statistics published an experimental index that could serve this purpose. The Comparable Wage Index for Teachers (CWIFT) uses American Community Survey data to derive measures of earnings for four-year-college graduates who are not teachers (Cornman et al., 2018). It is available at the local school district and state levels, so it could allow for more fine-grained adjustments across districts within states. And while SPPE (and adjusted SPPE) measures prices and quantities lumped together, the CWIFT isolates differences in prices, so it would not automatically reinforce existing cross-state spending disparities due to state and local support for education spending (i.e., quantities).
To see how alternative approaches to adjusting Title I allocations to account for differences in input prices, we simulate the distribution of an additional $10 billion of Title I funds under the status quo (adjusted SPPE) and three alternatives:
As above, we do not account for small state minimums in these simulations. SSMs are important for the states that benefit from them, but they do not affect the overall allocation of funding much.
Figure 7 shows the mean simulated Basic Grant allocation per formula child for the status quo (dark purple bars) and these three alternative scenarios by the district’s decile of child poverty rate, weighted by enrollment. As in Figure 1, simulated allocations per formula child are somewhat larger, on average, in lower-poverty districts because adjusted SPPE is negatively correlated with child poverty. The green bars show average simulated Basic Grant allocations if the formula were to allocate funds based solely on formula children without using SPPE (i.e., using constant national SPPE). In this scenario, Basic Grants per formula child are the same for all districts, because we are simulating the allocation of new money (so hold harmless provisions do not apply), and because we are ignoring the small-state minimum provisions; SPPE is the only other factor that influences a district’s Basic Grant allocation per formula child. The difference between the dark purple and green bars shows the effect of the correlation between adjusted SPPE and child poverty on the progressivity of Title I Basic Grants.
The blue bars show the results when the state-level CWIFT is used, and the magenta bars show the results adjusting for differences in costs at the district level. These tend to fall somewhere between the dark purple and green bars, suggesting the adjusted SPPE is indeed “overcorrecting” for price differences. Results for other district characteristics and for simulated changes in the Targeted Grant are presented in Appendix Table 4.
Figure 7. How Alternative Approaches to Adjusting for Price Differences Would Affect Simulated Basic Grant Allocations, by District Poverty Decile
As Figure 7 shows, the differences across the approaches do not strongly alter the pattern of allocations per formula child by child poverty rate. While the current use of adjusted SPPE does direct more funds to the lowest-poverty districts than the three alternative approaches simulated here, it appears to be a relatively minor factor overall. This is because even though higher-poverty districts have lower unadjusted SPPE on average, the ceiling and floor in the adjusted SPPE used in the formula weaken the relationship between the state’s child poverty rate and Title I funding per formula child relative to what it would be if SPPE were not adjusted in this manner. In addition, there are high-poverty districts in both high- and low-spending states. Together, these facts mean that the correlation between district-level child poverty rate and adjusted SPPE is not strong.
However, because the figure shows average grants by district-level poverty rate, it masks some horizontal inequity. High-poverty districts in low-spending states (which have low adjusted SPPE) receive less Title I funding than high-poverty districts in high-spending states. If policymakers wish to provide larger Title I grants to districts that face higher labor costs, adjusting grants using a local cost index like the CWIFT, which was designed for this purpose, would be preferable to using adjusted SPPE. The latter varies not only because of differences in labor costs but also because districts in some states use more inputs (mostly staff) than others.
If instead policymakers wish to reward states that raise more revenue for schools from state and local sources, using something like EFIG’s Effort Factor, which considers state-level education spending compared to income relative to national averages, would make more sense than SPPE, which disregards state fiscal capacity. However, the incentives in EFIG for state policymakers to change state spending levels are weak (see Title I’s Education Finance Incentive Grant Program Is Unlikely to Increase Effort and Equity in State Policy for more discussion). This is likely to be true unless the amount of Title I funding available were increased dramatically.
Title I allocations per formula child vary widely across school districts and often in ways that do not obviously map to the program’s original anti-poverty intent. Because the Title I grant formulas and the process of allocating funding are complex, these patterns can be challenging to understand or anticipate. The common—and correct—understanding that some formulas (Targeted Grants and EFIG) allocate funds based on weighted formula children leads many to expect Title I funding to be targeted more to high-poverty districts than it is. The intuition is correct, but the specific numeric values of the brackets and weights used in weighting matter in ways that can be surprising. To shed light on which types of districts benefit more or less from each of the four Title I grants, this report simulates the effects of increased funding through each of the four formulas rather than relying on broad characterizations of the four formulas.
Districts with higher poverty rates generally receive more Title I funding per formula child. This relationship varies across the four formulas and is weaker than might be expected, particularly for Basic Grants. Why is this?
Nearly all districts are eligible for Title I funds through the Basic Grant, Targeted Grant, and EFIG formulas. This explains why Concentration Grants target more of the simulated increase in Title I funding to districts with higher poverty rates than any of the other Title I grant formulas. Although the Targeted Grant and EFIG formulas use a progressive “bracket” approach that weights more heavily formula children in districts with large numbers or high percentages of formula children, nearly all districts are eligible for Targeted Grants and EFIG, and even low-poverty districts receive substantial funding compared to what they would receive from the Concentration Grant program.13As described in Title I of ESEA: How the Formulas Work, EFIG originally was proposed as a replacement for Basic and Concentration Grants, so it is not surprising that it maintains funding for lower-poverty districts. In other words, formula children in low-poverty districts receive a weight of 1 in the EFIG and Targeted Grant formulas, but they effectively receive a weight of 0 in the Concentration Grant formula because of the stricter eligibility criteria that set it apart from the other three formulas.
Further, because of the positive correlation between district poverty rates and the percentage of students in the district who are Black and Hispanic, a similar story plays out with regard to districts benefiting from a simulated increase in Title I funding considering students’ race/ethnicity. Increasing funding through the Targeted Grant, EFIG, and Concentration Grant—instead of the Basic Grant—tends to benefit districts serving larger shares of Black and Hispanic students.
In large part due to the use of adjusted SPPE in all four formulas, higher-spending districts—which tend to be in higher-spending states (and regions)—receive somewhat larger grants per formula child in the simulation regardless of which formula is used. This explains how Basic Grants provide slightly more funding per formula child in the simulation to the lowest-poverty districts than the highest-poverty districts. Low-poverty districts also tend to be in states with a high adjusted SPPE.
Relatedly, higher-spending states tend to be clustered in certain regions of the country. Southern states have the lowest SPPE, on average; followed by midwestern and western states. The highest-spending states are northeastern states; northeastern districts would receive an average simulated Basic Grant almost 50% higher than districts in the south.
Even though SPPE is negatively correlated with poverty rates, the bounds on the adjusted SPPE in the Title I grant formulas somewhat limit its regressivity. Indeed, our simulations show that removing adjusted SPPE from the Title I grant formulas and replacing it with another measure—or nothing at all—would not significantly change the program’s progressivity. Using adjusted SPPE introduces horizontal inequities; high-poverty districts in low-spending states receive less Title I funding than high-poverty districts in high-spending states. Switching to a local, price-based adjustment metric (e.g., CWIFT) makes theoretical sense, would marginally increase the progressivity of Title I, and would be more horizontally equitable.
Finally, the simulations make clear that the use of “number weighting” in the Targeted Grant and EFIG formulas benefits districts with large numbers of formula children. Districts with enrollments above 5,000 have simulated Targeted Grant allocations per formula child that are about 45% larger than those for the smaller districts. This pattern is not observed with Basic and Concentration Grants.
Our work put forth in this report shows that while it is tempting to deduce from the formulas themselves which districts will benefit most from Title I, the formulas are so complex that the best way to understand their distributional implications is by simulating formula changes—with or without hold harmless rules, depending on whether the funds will be “new” money. Based on the simulations in this analysis, when deciding future Title I appropriations and debating the underlying formulas themselves, policymakers would be wise to consider how eligibility criteria, state spending, and weighting schemes factor into each formula—and the trade-offs that result.
This report points to some areas where existing federal funding programs could be refined, and All4Ed discusses them and other potential changes further in Title I of ESEA: Considerations and Recommendations. Given differences in state-level capacity to fund schools, the federal government has a unique role to play in addressing cross-state differences in school funding.
| State | Basic Grant | Concentration Grant | Targeted Grant | EFIG |
|---|---|---|---|---|
| Alabama | $996 | $1,175 | $947 | $1,045 |
| Alaska | $1,490 | $573 | $1,318 | $1,563 |
| Arizona | $995 | $1,026 | $999 | $1,018 |
| Arkansas | $996 | $1,099 | $840 | $1,120 |
| California | $1,298 | $1,250 | $1,357 | $1,235 |
| Colorado | $1,075 | $732 | $968 | $1,007 |
| Connecticut | $1,493 | $1,240 | $1,175 | $1,399 |
| Delaware | $1,472 | $768 | $1,174 | $1,472 |
| District of Columbia | $1,493 | $1,987 | $1,938 | $1,700 |
| Florida | $996 | $1,252 | $1,292 | $1,073 |
| Georgia | $1,010 | $1,204 | $1,070 | $1,027 |
| Hawaii | $1,493 | $1,605 | $1,671 | $1,700 |
| Idaho | $995 | $342 | $719 | $963 |
| Illinois | $1,493 | $1,430 | $1,480 | $1,319 |
| Indiana | $995 | $811 | $804 | $1,013 |
| Iowa | $1,136 | $760 | $803 | $1,177 |
| Kansas | $1,075 | $945 | $867 | $1,033 |
| Kentucky | $1,122 | $1,317 | $1,051 | $1,194 |
| Louisiana | $1,097 | $1,348 | $1,199 | $1,161 |
| Maine | $1,479 | $1,081 | $948 | $1,402 |
| Maryland | $1,493 | $1,637 | $1,731 | $1,588 |
| Massachusetts | $1,491 | $1,211 | $1,215 | $1,337 |
| Michigan | $1,172 | $1,137 | $1,083 | $1,211 |
| Minnesota | $1,245 | $655 | $966 | $1,168 |
| Mississippi | $996 | $1,225 | $1,000 | $1,091 |
| MIssouri | $1,058 | $1,061 | $869 | $1,017 |
| Montana | $1,089 | $860 | $792 | $1,113 |
| Nebraska | $1,223 | $639 | $1,017 | $1,242 |
| Nevada | $996 | $1,282 | $1,448 | $1,030 |
| New Hampshire | $1,466 | $209 | $809 | $1,226 |
| New Jersey | $1,485 | $1,171 | $1,154 | $1,380 |
| New Mexico | $995 | $1,235 | $1,042 | $1,068 |
| New York | $1,491 | $1,640 | $1,804 | $1,446 |
| North Carolina | $996 | $1,238 | $1,047 | $1,061 |
| North Dakota | $1,242 | $415 | $860 | $1,285 |
| Ohio | $1,215 | $1,152 | $1,097 | $1,247 |
| Oklahoma | $995 | $1,087 | $897 | $1,018 |
| Oregon | $1,199 | $702 | $927 | $1,211 |
| Pennsylvania | $1,493 | $1,362 | $1,442 | $1,400 |
| Rhode Island | $1,493 | $1,223 | $1,259 | $1,519 |
| South Carolina | $1,052 | $1,171 | $1,033 | $1,134 |
| South Dakota | $995 | $584 | $825 | $995 |
| Tennessee | $996 | $1,130 | $995 | $1,030 |
| Texas | $995 | $1,145 | $1,066 | $1,046 |
| Utah | $996 | $305 | $911 | $1,012 |
| Vermont | $1,493 | $589 | $901 | $1,477 |
| Virginia | $1,173 | $1,052 | $1,061 | $1,057 |
| Washington | $1,398 | $705 | $1,067 | $1,348 |
| West Virginia | $1,178 | $1,365 | $972 | $1,294 |
| Wisconsin | $1,188 | $851 | $1,035 | $1,193 |
| Wyoming | $1,493 | $225 | $966 | $1,485 |
Simulated Allocations Per Formula Child
Percentage of Simulated Grant Allocated to These Districts
| District Characteristic | Number of Formula Children | Basic | Con-centration | Targeted | EFIG | Basic | Con-centration | Targeted | EFIG |
|---|---|---|---|---|---|---|---|---|---|
| Child Poverty Decile 1 | 207,744 | $1,283 | $1 | $270 | $264 | 3% | 0% | 1% | 1% |
| Child Poverty Decile 2 | 361,719 | $1,235 | $122 | $865 | $849 | 4% | 0% | 3% | 3% |
| Child Poverty Decile 3 | 476,643 | $1,230 | $227 | $959 | $947 | 6% | 1% | 5% | 5% |
| Child Poverty Decile 4 | 597,867 | $1,224 | $437 | $1,020 | $1,004 | 7% | 3% | 6% | 6% |
| Child Poverty Decile 5 | 730,127 | $1,174 | $639 | $961 | $943 | 9% | 5% | 7% | 7% |
| Child Poverty Decile 6 | 833,669 | $1,138 | $1,199 | $1,079 | $1,036 | 9% | 10% | 9% | 9% |
| Child Poverty Decile 7 | 996,678 | $1,132 | $1,454 | $1,086 | $1,020 | 11% | 14% | 11% | 10% |
| Child Poverty Decile 8 | 1,164,992 | $1,213 | $1,607 | $1,545 | $1,481 | 14% | 19% | 18% | 17% |
| Child Poverty Decile 9 | 1,284,825 | $1,163 | $1,547 | $1,231 | $1,247 | 15% | 20% | 16% | 16% |
| Child Poverty Decile 10 | 1,816,939 | $1,150 | $1,530 | $1,356 | $1,458 | 21% | 28% | 25% | 26% |
| Child Poverty <= 30% | 6,814,541 | $1,182 | $1,090 | $1,126 | $1,098 | 81% | 74% | 77% | 75% |
| Child Poverty > 30% | 1,657,066 | $1,151 | $1,532 | $1,384 | $1,493 | 19% | 25% | 23% | 25% |
| % American Indian or Alaska Native (AIAN) Quartile 1 | 2,250,122 | $1,180 | $1,205 | $1,085 | $1,095 | 27% | 27% | 24% | 25% |
| % AIAN Quartile 2 | 2,077,594 | $1,179 | $1,204 | $1,262 | $1,263 | 24% | 25% | 26% | 26% |
| % AIAN Quartile 3 | 1,877,034 | $1,168 | $1,095 | $1,153 | $1,134 | 22% | 21% | 22% | 21% |
| % AIAN Quartile 4 | 2,266,771 | $1,177 | $1,191 | $1,207 | $1,210 | 27% | 27% | 27% | 27% |
| % Asian and Pacific Islander (AAPI) Quartile 1 | 2,582,757 | $1,128 | $1,224 | $957 | $974 | 29% | 32% | 25% | 25% |
| % AAPI Quartile 2 | 2,275,020 | $1,135 | $1,166 | $1,126 | $1,135 | 26% | 27% | 26% | 26% |
| % AAPI Quartile 3 | 1,978,438 | $1,188 | $1,137 | $1,299 | $1,310 | 24% | 22% | 26% | 26% |
| % AAPI Quartile 4 | 1,635,306 | $1,295 | $1,165 | $1,446 | $1,389 | 21% | 19% | 24% | 23% |
| % Hispanic or Latino Quartile 1 | 1,984,083 | $1,163 | $1,049 | $922 | $955 | 23% | 21% | 18% | 19% |
| % Hispanic or Latino Quartile 2 | 1,821,659 | $1,158 | $1,012 | $1,064 | $1,120 | 21% | 18% | 19% | 20% |
| % Hispanic or Latino Quartile 3 | 2,100,586 | $1,205 | $1,218 | $1,331 | $1,294 | 25% | 26% | 28% | 27% |
| % Hispanic or Latino Quartile 4 | 2,565,193 | $1,175 | $1,358 | $1,326 | $1,289 | 30% | 35% | 34% | 33% |
| % Black Quartile 1 | 1,789,706 | $1,183 | $961 | $913 | $910 | 21% | 17% | 16% | 16% |
| % Black Quartile 2 | 1,583,983 | $1,187 | $846 | $951 | $916 | 19% | 13% | 15% | 15% |
| % Black Quartile 3 | 2,251,508 | $1,201 | $1,255 | $1,346 | $1,285 | 27% | 28% | 30% | 29% |
| % Black Quartile 4 | 2,846,324 | $1,147 | $1,434 | $1,334 | $1,401 | 33% | 41% | 38% | 40% |
| % White Quartile 1 | 3,220,411 | $1,218 | $1,527 | $1,498 | $1,504 | 39% | 49% | 48% | 48% |
| % White Quartile 2 | 2,111,948 | $1,126 | $1,165 | $1,157 | $1,139 | 24% | 25% | 24% | 24% |
| % White Quartile 3 | 1,622,386 | $1,139 | $854 | $918 | $915 | 18% | 14% | 15% | 15% |
| % White Quartile 4 | 1,516,776 | $1,199 | $795 | $796 | $809 | 18% | 12% | 12% | 12% |
| Enrollment <= 500 | 162,587 | $1,159 | $1,092 | $881 | $935 | 2% | 2% | 1% | 2% |
| Enrollment 501-1499 | 596,560 | $1,196 | $1,056 | $884 | $890 | 7% | 6% | 5% | 5% |
| Enrollment 1500-5000 | 1,604,461 | $1,194 | $1,058 | $878 | $864 | 19% | 17% | 14% | 14% |
| Enrollment > 5001 | 6,108,339 | $1,170 | $1,222 | $1,291 | $1,292 | 71% | 75% | 79% | 79% |
| Quartile 1 of Per-Pupil Expenditure (PPE), excluding Federal | 2,340,561 | $1,003 | $1,141 | $1,072 | $1,046 | 23% | 27% | 25% | 24% |
| Quartile 2 of PPE, excluding Federal | 2,071,021 | $1,065 | $1,067 | $985 | $1,010 | 22% | 22% | 20% | 21% |
| Quartile 3 of PPE, excluding Federal | 1,959,972 | $1,239 | $1,083 | $1,116 | $1,121 | 24% | 21% | 22% | 22% |
| Quartile 4 of PPE, excluding Federal | 2,099,742 | $1,420 | $1,412 | $1,538 | $1,536 | 30% | 30% | 32% | 32% |
| Northeast | 1,231,944 | $1,491 | $1,400 | $1,488 | $1,410 | 18% | 17% | 18% | 17% |
| Midwest | 1,629,683 | $1,211 | $1,057 | $1,073 | $1,185 | 20% | 17% | 17% | 19% |
| South | 3,759,658 | $1,040 | $1,207 | $1,101 | $1,093 | 39% | 45% | 41% | 41% |
| West | 1,879,202 | $1,213 | $1,073 | $1,212 | $1,184 | 23% | 20% | 23% | 22% |
*Each simulation shows what would happen with $10 billion of new appropriations.
Simulated Allocations Per Formula Child
| District Characteristic | Basic Grant | Con- centration Grant | Targeted Grant | EFIG |
|---|---|---|---|---|
| Child Poverty Decile 1 | $1,195 | $1 | $263 | $259 |
| Child Poverty Decile 2 | $1,178 | $103 | $828 | $814 |
| Child Poverty Decile 3 | $1,195 | $206 | $933 | $923 |
| Child Poverty Decile 4 | $1,212 | $404 | $1,008 | $996 |
| Child Poverty Decile 5 | $1,190 | $658 | $981 | $963 |
| Child Poverty Decile 6 | $1,151 | $1,213 | $1,094 | $1,049 |
| Child Poverty Decile 7 | $1,158 | $1,478 | $1,109 | $1,045 |
| Child Poverty Decile 8 | $1,166 | $1,535 | $1,461 | $1,399 |
| Child Poverty Decile 9 | $1,174 | $1,553 | $1,233 | $1,245 |
| Child Poverty Decile 10 | $1,183 | $1,566 | $1,401 | $1,502 |
| Child Poverty <= 30% | $1,176 | $1,083 | $1,116 | $1,089 |
| Child Poverty > 30% | $1,180 | $1,562 | $1,425 | $1,534 |
| % American Indian or Alaska Native (AIAN) Quartile 1 | $1,221 | $1,257 | $1,138 | $1,149 |
| % AIAN Quartile 2 | $1,163 | $1,187 | $1,241 | $1,235 |
| % AIAN Quartile 3 | $1,149 | $1,076 | $1,136 | $1,115 |
| % AIAN Quartile 4 | $1,168 | $1,172 | $1,189 | $1,198 |
| % Asian and Pacific Islander (AAPI) Quartile 1 | $1,241 | $1,343 | $1,065 | $1,084 |
| % AAPI Quartile 2 | $1,155 | $1,179 | $1,155 | $1,163 |
| % AAPI Quartile 3 | $1,137 | $1,077 | $1,246 | $1,251 |
| % AAPI Quartile 4 | $1,151 | $1,031 | $1,299 | $1,248 |
| % Hispanic or Latino Quartile 1 | $1,286 | $1,178 | $1,040 | $1,075 |
| % Hispanic or Latino Quartile 2 | $1,193 | $1,054 | $1,109 | $1,164 |
| % Hispanic or Latino Quartile 3 | $1,157 | $1,172 | $1,279 | $1,244 |
| % Hispanic or Latino Quartile 4 | $1,096 | $1,268 | $1,246 | $1,207 |
| % Black Quartile 1 | $1,251 | $1,030 | $979 | $982 |
| % Black Quartile 2 | $1,188 | $869 | $964 | $932 |
| % Black Quartile 3 | $1,148 | $1,192 | $1,280 | $1,219 |
| % Black Quartile 4 | $1,146 | $1,429 | $1,337 | $1,399 |
| % White Quartile 1 | $1,133 | $1,422 | $1,407 | $1,413 |
| % White Quartile 2 | $1,123 | $1,186 | $1,171 | $1,152 |
| % White Quartile 3 | $1,191 | $925 | $982 | $976 |
| % White Quartile 4 | $1,327 | $915 | $904 | $919 |
| Enrollment <= 500 | $1,295 | $1,228 | $1,001 | $1,067 |
| Enrollment 501-1499 | $1,317 | $1,178 | $993 | $1,004 |
| Enrollment 1500-5000 | $1,272 | $1,151 | $957 | $945 |
| Enrollment > 5001 | $1,134 | $1,182 | $1,257 | $1,256 |
| Quartile 1 of Per-Pupil Expenditure (PPE), excluding Federal | $1,065 | $1,207 | $1,139 | $1,111 |
| Quartile 2 of PPE, excluding Federal | $1,140 | $1,147 | $1,061 | $1,089 |
| Quartile 3 of PPE, excluding Federal | $1,206 | $1,054 | $1,093 | $1,105 |
| Quartile 4 of PPE, excluding Federal | $1,310 | $1,287 | $1,410 | $1,401 |
| Northeast | $1,396 | $1,293 | $1,382 | $1,298 |
| Midwest | $1,262 | $1,093 | $1,116 | $1,229 |
| South | $1,094 | $1,269 | $1,161 | $1,150 |
| West | $1,123 | $988 | $1,126 | $1,104 |
*Each simulation shows what would happen with $10 billion of new appropriations.
Abbreviations: Comparable Wage Index for Teachers (CWIFT)
| District Characteristic | Status Quo | Constant SPPE | State-Level CWIFT | Local CWIFT |
|---|---|---|---|---|
| Child Poverty Decile 1 | $1,283 | $1,163 | $1,194 | $1,249 |
| Child Poverty Decile 2 | $1,235 | $1,176 | $1,195 | $1,238 |
| Child Poverty Decile 3 | $1,230 | $1,176 | $1,190 | $1,211 |
| Child Poverty Decile 4 | $1,224 | $1,176 | $1,185 | $1,190 |
| Child Poverty Decile 5 | $1,174 | $1,177 | $1,171 | $1,163 |
| Child Poverty Decile 6 | $1,138 | $1,177 | $1,160 | $1,167 |
| Child Poverty Decile 7 | $1,132 | $1,177 | $1,169 | $1,154 |
| Child Poverty Decile 8 | $1,213 | $1,177 | $1,217 | $1,221 |
| Child Poverty Decile 9 | $1,163 | $1,177 | $1,174 | $1,167 |
| Child Poverty Decile 10 | $1,150 | $1,177 | $1,155 | $1,143 |
| Child Poverty <= 30% | $1,182 | $1,176 | $1,182 | $1,184 |
| Child Poverty > 30% | $1,151 | $1,177 | $1,155 | $1,147 |
| % American Indian or Alaska Native (AIAN) Quartile 1 | $1,180 | $1,175 | $1,151 | $1,136 |
| % AIAN Quartile 2 | $1,179 | $1,177 | $1,181 | $1,194 |
| % AIAN Quartile 3 | $1,168 | $1,177 | $1,185 | $1,196 |
| % AIAN Quartile 4 | $1,177 | $1,177 | $1,191 | $1,185 |
| % Asian and Pacific Islander (AAPI) Quartile 1 | $1,128 | $1,176 | $1,125 | $1,068 |
| % AAPI Quartile 2 | $1,135 | $1,177 | $1,155 | $1,154 |
| % AAPI Quartile 3 | $1,188 | $1,177 | $1,194 | $1,227 |
| % AAPI Quartile 4 | $1,295 | $1,176 | $1,267 | $1,319 |
| % Hispanic or Latino Quartile 1 | $1,163 | $1,175 | $1,099 | $1,061 |
| % Hispanic or Latino Quartile 2 | $1,158 | $1,176 | $1,136 | $1,138 |
| % Hispanic or Latino Quartile 3 | $1,205 | $1,177 | $1,197 | $1,221 |
| % Hispanic or Latino Quartile 4 | $1,175 | $1,177 | $1,249 | $1,257 |
| % Black Quartile 1 | $1,183 | $1,175 | $1,176 | $1,114 |
| % Black Quartile 2 | $1,187 | $1,177 | $1,196 | $1,177 |
| % Black Quartile 3 | $1,201 | $1,177 | $1,212 | $1,227 |
| % Black Quartile 4 | $1,147 | $1,177 | $1,139 | $1,175 |
| % White Quartile 1 | $1,218 | $1,177 | $1,235 | $1,258 |
| % White Quartile 2 | $1,126 | $1,177 | $1,161 | $1,179 |
| % White Quartile 3 | $1,139 | $1,176 | $1,141 | $1,121 |
| % White Quartile 4 | $1,199 | $1,175 | $1,113 | $1,058 |
| Enrollment <= 500 | $1,159 | $1,158 | $1,117 | $1,034 |
| Enrollment 501-1499 | $1,196 | $1,176 | $1,142 | $1,065 |
| Enrollment 1500-5000 | $1,194 | $1,176 | $1,148 | $1,103 |
| Enrollment < 5000 | $1,170 | $1,177 | $1,189 | $1,210 |
| Quartile 1 of Per-Pupil Expenditure (PPE), excluding Federal | $1,003 | $1,177 | $1,113 | $1,115 |
| Quartile 2 of PPE, excluding Federal | $1,065 | $1,177 | $1,115 | $1,104 |
| Quartile 3 of PPE, excluding Federal | $1,239 | $1,177 | $1,224 | $1,214 |
| Quartile 4 of PPE, excluding Federal | $1,420 | $1,175 | $1,264 | $1,282 |
| Northeast | $1,491 | $1,175 | $1,269 | $1,262 |
| Midwest | $1,211 | $1,176 | $1,110 | $1,127 |
| South | $1,040 | $1,177 | $1,122 | $1,123 |
| West | $1,213 | $1,176 | $1,282 | $1,271 |
*Each simulation shows what would happen with $10 billion of new appropriations.
Abbreviations: Comparable Wage Index for Teachers (CWIFT)
S. Q. Cornman et al., Education Demographic and Geographic Estimates (EDGE) Program: American Community Survey Comparable Wage Index for Teachers (ACS-CWIFT) (NCES 2018-130) (Washington, DC: U.S. Department of Education National Center for Education Statistics, 2019), https://nces.ed.gov/programs/edge/docs/EDGE_ACS_CWIFT_FILEDOC.pdf (accessed October 6, 2022).
Nora Gordon is Professor at Georgetown University’s McCourt School of Public Policy and Research Associate of the National Bureau of Economic Research. Her research evaluates how federal and state policies and programs affect K-12 educational opportunities and outcomes. Nora has served on the Institute of Education Sciences Expert Panel on the Study of the Title I Formula and DC’s state Title I Committee of Practitioners. She currently serves on the Professional Advisory Board of the National Center for Learning Disabilities, on the FutureEd Advisory Board, and as an academic advisor to the DC Policy Center’s Education Policy Group. Nora and Carrie Conaway are the authors of Common-Sense Evidence: The Education Leader’s Guide to Using Data and Research.
Sarah Reber is the Joseph A. Pechman Senior Fellow in Economic Studies at the Brookings Institution. Her research focuses on college access, elementary and secondary education finance policy, and school desegregation. She is also a Research Associate at the National Bureau of Economic Research (NBER) and a California Policy Lab (CPL) affiliated expert. Previously, she was Associate Professor of Public Policy at the UCLA Luskin School of Public Affairs, a Robert Wood Johnson Foundation Scholar in Health Policy Research at UC Berkeley, and a Research Assistant and Staff Economist on the Council of Economic Advisers (CEA).
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