A Macro-Level Analysis of Public Fiscal Effort and Its Regional Disparities in U.S. Education Policy (2006-2022) ()
1. Introduction
In both critical scholarship and public policy discourse, educational equity continues to be a primary goal. Concerns about the sustainability, equity, and sufficiency of education funding grow as the socioeconomic climate in the US becomes more divided. Fiscal effort, a measure of public investment in education relative to state economic capacity, is one of the most telling indicators of a state’s commitment to equitable education. With an emphasis on regional differences and underlying causes, this article conducts a macro-level analysis of fiscal effort across U.S. states from 2006 to 2022. Information from the State Indicators Database (2025 edition) was examined, which contains state-level indicators of structural and financial aspects of education. This investigation aims to determine not only how financial effort differs by region and over time, but why these variations occur.
Research Questions:
1) How has fiscal effort changed across U.S. states between 2006 and 2022?
2) How are differences in fiscal effort characterized by regional patterns?
3) What are the key structural factors that predict a state’s fiscal effort in education?
4) How can these findings be interpreted through a critical, structural lens?
This study is relevant in two ways. First, it adds empirical support to discussions on states’ roles in funding public education during a period of increasing fiscal austerity and privatization. Second, it highlights some of the political-economic structures that impact educational inequality and challenges the reductionist views of funding as purely technical or neutral by drawing on critical education and policy studies.
This article seeks to integrate fiscal metrics with structural, critical interpretations of state education policy by fusing quantitative analysis with a theoretically informed framework. The results show a wider trend of fiscal retrenchment in addition to regional differences in fiscal commitment, which should serve as a warning to proponents of educational equity.
2. Theoretical Framework and Literature Review
2.1. Fiscal Effort and Educational Equity: Concepts and Indicators
Nominal (or per-student) spending is not the only indicator to assess educational equity. Instead, an alternative critical metric is fiscal effort, defined as the proportion of a state’s economic capacity (typically measured by personal income or gross state product) allocated to public education. Unlike expenditure per pupil, which reflects wealth disparities, fiscal effort reveals the degree of prioritization that state structures assign to public education relative to available resources (Augenblick, Myers, & Anderson, 1997).
From a planning perspective, fiscal effort also reflects policy intentionality: it captures not only whether a state has resources, but whether it chooses to use them for education. As one of the indicators of policy intentions, fiscal effort functions as a window into institutional commitment, political will, and the ideological position of state actors regarding public education (Berne & Stiefel, 1999) and hence serves as a moral and redistributive signal beyond raw funding levels. Recent studies also reaffirm the policy relevance of fiscal effort. In a study by Jackson and Mackevicius (2021), it was shown that sustained increases in school funding improve student outcomes, especially in low-income districts.
2.2. Regional Inequality and the Structuring Role of the State
The U.S. federalist system devolves substantial responsibility for education funding to state governments. Consequently, regional disparities are not merely residual differences; they are one of the outcomes of structured, institutionalized funding regimes (Baker, Farrie, & Sciarra, 2016; Baker & Weber, 2016). Although some studies show that reforms initiated by the state are effective in mitigating inequalities (see Lafortune et al., 2018), there are other studies showing that Southern states, for example, have long demonstrated lower fiscal effort and higher reliance on local funding, reflecting histories of racial exclusion, weak public investment, neoliberal tax regimes, market competition and electoral dynamics (Orfield & Frankenberg, 2014; Wong & Shen, 2002). Studies in this second category imply that historical economic, racial and regional stratifications may have still been shaping fiscal policy. These regional disparities may reflect broader systemic structures, policies and ideologies—what critical theorists like Poulantzas (1978) and Apple (2004) identify as the state’s function in reproducing class and racial inequalities through selective policy.
2.3. Critical Approaches to Educational Finance
While technical models of education finance focus on efficiency, adequacy, or equity formulas (Odden & Picus, 2013), critical education scholars interrogate why certain populations and regions are consistently underfunded. They argue that education finance is not neutral but ideological—it is shaped by logics of capital accumulation, privatization, and political exclusion (Lipman, 2013); and finance metrics that are used to evaluate achievements, outcomes and performance in diverse fields of capitalist society should be analyzed as tools and outputs of capitalist competition, governance and class control (Davies, 2017; Fine, 2010; Soss et al., 2011). Similarly, others emphasize how markets see people (Fourcade & Healy, 2017) and shape them accordingly; how austerity discourses embed fiscal norms within state ideology (Blyth, 2013); how political institutions and outcomes are increasingly dominated by corporate capital and finance (Brown, 2015); how public administration is increasingly being transformed into a type of management that looks like the management of a corporation (Clarke & Newman, 1997); and how neoliberalism and free-market thinking dominate politics (Peck, 2010) particularly in policymaking that suppress dissent and valorize efficiency over equity.
In this framework, fiscal effort becomes more than a policy variable—it becomes an indicator of the class character and structural orientation of state policies. High fiscal effort signals a redistributive, social-democratic tendency; low fiscal effort is symptomatic of neoliberal austerity, weak labor influence, and a shift toward privatized education markets.
This article adopts such a critical-materialist lens, viewing state fiscal effort as a proxy for the broader political economy of education in the U.S. It asks not only how much states invest, but why certain states and regions choose systematically higher or lower effort levels—and what these reveal about the evolving structure of state power (Bowles & Gintis, 1976).
Despite the rich literature on school finance, adequacy models, and education inequality, there are relatively few studies that systematically examine long-term trends in fiscal effort across all U.S. states and regions using recent data. Even fewer combine empirical analysis with a critical theory framework to interrogate why these disparities persist and deepen. This study contributes to filling that gap by: Providing a longitudinal, regional analysis of fiscal effort (2006-2022); identifying key predictors of fiscal effort using multivariate modeling; and framing fiscal effort as a structural indicator of state ideology and political-economic orientation. This study aligns with calls for integrated models combining empirical finance and critical frameworks. In doing so, the article builds a bridge between empirical finance research and critical studies of the state, educational inequality, and public investment regimes.
3. Data and Methodology
3.1. Data Source & Variable Definitions
Data source used in this study is titled State Indicators Database (2025 edition, downloaded from: https://www.schoolfinancedata.org/download-data/) which contains annual data from all 50 U.S. states and the District of Columbia spanning the years 2006 to 2022. It includes variables related to education finance (e.g., fiscal effort, spending per pupil), economic context (e.g., state personal income), equity indicators (e.g., funding gaps by poverty quartile), and teacher compensation (e.g., salary parity by experience bands).
For this study, the central dependent variable is fiscal effort (effort) which is defined as “the ratio of state and local education revenue to gross state product”, representing the proportion of state income devoted to education.
Other key variables include income-adjusted fiscal effort (inc_effort), teacher salary parity at various experience levels (sal_parity25, sal_parity35, sal_parity45, sal_parity55), predicted teacher-to-pupil ratios at varying poverty thresholds (predicted_tchph0_, predicted_tchph10_, predicted_tchph20_, predicted_tchph30_), and equity gap (e.g., necm_fundinggap_q1 for high-poverty district shortfalls). Regional classifications are based on the U.S. Census Bureau’s four-region model: Northeast, Midwest, South, and West.
Below are the definitions of variables:
Fiscal effort (effort) is defined in the State Indicators Database as fiscal effort as a percent of gross state product (GSP): effort_it = (State + Local K–12 revenue_it) / (GSP_it)
Income-adjusted fiscal effort (inc_effort) is defined as fiscal effort as a percent of aggregate personal income: inc_effort_it = (State + Local K–12 revenue_it) / (Aggregate personal income_it)
Units: both are dimensionless shares; report as percentages (×100) if desired. Conceptually, effort uses production-side capacity (GSP) whereas inc_effort uses income-side capacity (personal income). They share a numerator but differ in the denominator, so inc_effort is not mechanically identical to effort, though correlation is expected.
salary parity: The ratio of the annual salary of teachers to the annual salary of people who aren’t teachers but have the same education and experience when they first start working. If the variable’s value is close to 1, it means that teachers and people who work in other fields make about the same amount of money. If the number is less than one, it means that teachers get paid less.
sal_parity25 (Teacher Salary Parity, ~2 - 5 years of experience): Salary parity for teachers who have been working for about 2 - 5 years.
sal_parity35 (Teacher Salary Parity, ~3 - 5 years of experience): Salary parity for teachers who have been working for about 3 - 5 years. It may be an indicator of whether early retention and fair compensation are supported.
sal_parity45 (Teacher Salary Parity, ~4 - 5 years of experience): Salary parity at the later part of early-to-mid career. Used to capture variations in salary compression or progression.
sal_parity55 (Teacher Salary Parity, ~5 - 5 years of experience): Salary parity for teachers with longer professional experience (5+ years). Indicates if states keep salaries competitive for teachers with a lot of experience.
predicted_tchph0_, predicted_tchph10_, predicted_tchph20_, predicted_ tchph30_: Predicted teacher-to-student ratios (teachers per 100 students) at different district poverty levels (0%, 10%, 20%, 30% child poverty). Expected teacher-to-student ratios (teachers per 100 students) at various district poverty levels (0%, 10%, 20%, 30% child poverty). These staffing-based indicators figure out how many teachers there should be for every 100 students at different levels of child poverty. This shows how fair educational inputs are across different socio-economic groups. In other words, these are modeled estimates of how many teachers a state would employ per pupil at varying poverty levels, based on cost models.
equity gap: A positive gap means high-poverty districts receive less funding than estimated need; a negative gap would mean they receive more.
4 regions (region4): U.S. Census Bureau’s 4-region classification (Northeast, Midwest, South, West). Used for regional comparison.
year: Calendar year (2006-2022). Used to analyze trends over time.
3.2. Data Cleaning and Preprocessing
There are 1190 observations in the original dataset. Rows with missing values were removed for the “effort” variable, which yields 862 complete observations. Categorical variables such as state abbreviations (stabbr) and region labels (region4) were preserved for grouping and comparative purposes. Numerical columns are normalized and outliers in the data set are checked. Multicollinearity among predictor variables in the regression model was also checked by calculating Variance Inflation Factors (VIFs). Moderate level multicollinearity was observed among salary parity indicators; however, none exceeded the standard threshold (VIF < 5). Year (year) as a continuous variable for trend analysis was retained.
In the 2006-2022 panel, effort is missing in 5 state-year cells, all for Vermont (2018-2022). Excluding these yields 862 observations. Additional covariates have higher missingness (e.g., necm_fundinggap_q1), which reduces the estimation sample in extended models. A complete-vs-dropped comparison shows that rows dropped due to funding-gap missingness have higher “mean” inc_effort and salary parity, suggesting that listwise deletion can change composition. As a sensitivity check, the extended model was re-estimated using inverse-probability weights for inclusion (predicted from year, region, inc_effort, and salary parity); substantive conclusions were unchanged.
3.3. Descriptive and Comparative Analysis
To assess how fiscal effort varies by state, region, and year, line plots were used to display temporal trends by state and by region; boxplots to compare fiscal effort distributions across regions; and scatterplots to visualize relations. These visual tools made it possible to detect both structural disparities and temporal dynamics.
3.4. Panel Regression Specification
Because the dataset consists of repeated state-year observations (2006-2022), pooled cross-sectional regression may yield biased estimates due to unobserved state-specific characteristics and common national shocks. To address this, a two-way fixed effects (FE) panel model was estimated:
effort_{it} = α_i + λ_t + β1 inc_effort_{it} + β2 sal_parity35_{it} + ε_{it}
With slightly different notation, the same model can be re-specified as follows:
effortit = αi + λt + β1 inc_effortit + β2 sal_parity35it + εit
where:
i indexes states (50 states + DC),
t indexes year (2006-2022),
α_irepresents state fixed effects capturing time-invariant characteristics (e.g., tax culture, institutional legacy),
λ_trepresents year fixed effects capturing common macroeconomic and policy shocks (e.g., the 2008 financial crisis, COVID-19),
X_itincludes theoretically motivated covariates,
ε_itis the idiosyncratic error term.
Standard errors are clustered at the state level to account for serial correlation and heteroskedasticity within states over time.
Model specification follows the theoretical framework developed in Section 2 rather than correlation-based screening. Variables are grouped into three conceptual domains:
Macrofiscal Alignment: Income-adjusted fiscal effort (inc_effort)
Compensation Equity: Teacher salary parity (3 - 5 years of experience; sal_parity35)
Staffing and Funding Equity (Robustness Models): Predicted teacher-pupil ratio differentials across poverty levels; high-poverty funding gaps (necm_ fundinggap_q1)
Sequential models were estimated to assess robustness:
Model (1): Baseline two-way FE
Model (2): Excluding inc_effort (non-tautology check)
Model (3): Extended equity controls
Within R2 is reported for panel models.
3.5. Trend Analysis
In order to assess long-term trends, Pearson correlations between year and average fiscal effort by region were computed.
4. Findings
In this section, key findings reached (by using statistical analysis and data visualizations) related to fiscal effort across U.S. states between 2006 and 2022 are presented. Region-based comparisons, temporal trends, and regression modeling are among the findings that facilitate discussions about the structural determinants of public educational investment.
4.1. Fiscal Effort across States and over Time
Substantial state-level differences in fiscal effort, as well as observable trends over are revealed in the line chart in Figure 1: Fiscal effort by state and year (2006-2022). Figure 1 visualizes substantial between-state heterogeneity in fiscal effort from 2006 to 2022. While most state trajectories cluster in a relatively narrow band, a small number of states exhibit persistently higher effort and/or greater volatility, consistent with divergent fiscal-political regimes. The downward drift visible in many trajectories also foreshadows the region-level retrenchment documented later in Figure 3.
State-level fiscal effort from 2006 to 2022 is displayed in Figure 1. Fiscal efforts for most states fall in the interval “0.02 - 0.06”. There are outliers such as Vermont, New Jersey, and West Virginia, which exhibit higher-than-average effort. Trajectories of some states are stable over time, while those of others are volatile, which may reflect economic shocks or shifting political priorities.
4.2. Regional Disparities in Fiscal Effort
Based on the U.S. Census Bureau’s four-region model, Figure 2 presents a comparative visualization of fiscal effort distributions across regions. As shown in Figure 2, the regional distributions are systematically shifted: the Northeast’s median and upper quartile exceed those of the Midwest, South, and West. The wider spread in the South and West suggests greater within-region dispersion in state commitment to education spending relative to economic capacity.
Table 1 quantifies these distributional differences by reporting the regional means and variability. The Northeast has the highest average fiscal effort (mean = 0.0416), while the West and South exhibit lower averages, consistent with the ordering implied by the boxplot in Figure 2.
Figure 1. Fiscal Effort by State and Year (2006-2022). [Each thin line represents one state (50 states + DC); the vertical axis shows fiscal effort as a share of gross state product; the horizontal axis represents calendar year].
Figure 2. Fiscal effort by region.
Table 1. Fiscal effort by region.
|
mean |
std |
count |
Midwest |
0.036 |
0.0038 |
204 |
Northeast |
0.0416 |
0.0059 |
148 |
South |
0.0356 |
0.0068 |
289 |
West |
0.0347 |
0.0061 |
221 |
The box plot above shows the distribution of fiscal effort by Census region. The Northeast region has the highest average fiscal effort; next comes the Midwest, South, and West. Table 1 provides the mean, standard deviation, and count of observations for each region, and highlights that the Northeast not only leads in average effort but also has a relatively higher spread compared to other regions. This suggests regional differences in how states prioritize fiscal effort in education funding.
This box plot confirms systematic regional inequality. The Northeast exhibits the highest median and upper quartile fiscal effort, followed by the Midwest, South, and West. The South and West show greater internal variance, which may suggest inconsistency in commitment among states within those regions.
4.3. Regional Trends over Time
Figure 3 presents a line plot of “regional average fiscal effort over time” that reveals long-term patterns of retrenchment. Figure 3 shows a broad pattern of retrenchment across all four Census regions over 2006-2022, with clear differences in baseline levels. The Northeast remains highest throughout most of the period, but the downward slope is evident across regions, indicating a shared long-run contraction in fiscal effort.
Figure 3. Fiscal effort overtime by region.
Table 2 contextualizes the trend lines by summarizing each region’s distribution (mean, dispersion, and range). Notably, the South shows the lowest minimum value, indicating that the floor of fiscal effort is substantially lower in some Southern state-years than in other regions. Fiscal efforts in all four regions decline over time. The steepest declines were observed in the West and Northeast. These declining trajectories are consistent with broader national policies of fiscal austerity and education privatization, particularly after the 2008 financial crisis.
Table 2. Summary statistics by region.
Region |
Mean Effort |
Std. Dev. |
Min |
Max |
Northeast |
0.0416 |
0.0059 |
0.0302 |
0.0552 |
Midwest |
0.0360 |
0.0038 |
0.0267 |
0.0481 |
South |
0.0356 |
0.0068 |
0.0158 |
0.0530 |
West |
0.0347 |
0.0061 |
0.0242 |
0.0544 |
Table 3 formalizes the visual decline in Figure 3 by reporting region-specific trend correlations and “percent” changes from 2006 to 2022. The West experiences the largest relative decline (−10.1%), consistent with the steepest downward trajectory in the time-series plot. Fiscal effort declines for all regions, with the steepest drop in the West and Northeast, which indicates widespread fiscal retrenchment, despite regional variation in baseline effort.
Table 3. Regional trend analysis.
Region |
Trend Correlation |
% Change (2006-2022) |
Northeast |
−0.89 |
−6.2% |
South |
−0.83 |
−8.2% |
Midwest |
−0.71 |
−8.0% |
West |
−0.71 |
−10.1% |
4.4. Key Predictors of Fiscal Effort
Table 4 reports bivariate correlations between fiscal effort and the main candidate predictors used in later panel specifications. The strongest correlation is between effort and income-adjusted effort (r ≈ 0.67), with teacher salary parity measures also showing moderately strong positive associations (r ≈ 0.52). These correlations are presented for descriptive purposes only. Variable inclusion in panel models follows the theoretical structure developed in Section 2 rather than correlation ranking. The above analysis indicates that income-adjusted effort has the strongest correlation with fiscal effort (0.67), and the correlations of teacher salary parity measures with fiscal effort are around 0.52. Teacher-pupil ratios, funding gaps for high-poverty districts, and predicted spending levels also show moderate positive correlations.
Consistent with Table 4, Figure 4 indicates a clear positive relationship between effort and income-adjusted effort and a weaker—but still positive—pattern for salary parity measures. The dispersion around these relationships also suggests that bivariate associations alone cannot substitute for the fixed-effects structure reported in Table 5. The pairplot shows positive linear relationships between fiscal effort and income-adjusted effort, and moderate associations with teacher salary parity at mid-career levels (3 - 5 and 4 - 5 years). This supports the idea that structural commitments to teacher equity drive higher fiscal effort.
Figure 4. Correlation pairplot of top factors.
Table 4. Correlation analysis.
|
effort |
inc_effort |
0.6693390576 |
sal_parity35 |
0.5181568205 |
sal_parity45 |
0.5172998399 |
sal_parity25 |
0.5169812447 |
sal_parity55 |
0.5163564999 |
predicted_tchph10_ |
0.3888476724 |
necm_fundinggap_q1 |
0.3777991478 |
predicted_tchph0_ |
0.3637078866 |
predicted_tchph20_ |
0.3585402726 |
4.5. Panel Regression Results
Table 5 reports results from two-way fixed effects panel models with state-clustered standard errors. Table 5 summarizes three sequential two-way fixed-effects models designed to test whether the association between fiscal effort and key structural predictors holds net of unobserved, time-invariant state characteristics and common year shocks. Reading across columns highlights how coefficient magnitudes and significance shift when macro-fiscal alignment (inc_effort) is included versus excluded.
Table 5. Two-way fixed effects regression of fiscal effort (2006-2022).
Variables |
Model (1) |
Model (2) |
Model (3) |
inc_effort |
0.58*** |
— |
0.55*** |
|
(0.07) |
|
(0.08) |
sal_parity35 |
0.012 |
0.083** |
0.041 |
|
(0.021) |
(0.035) |
(0.028) |
Staffing equity differential |
— |
— |
0.004 |
|
|
|
(0.003) |
High-poverty funding gap |
— |
— |
−0.002 |
|
|
|
(0.001) |
State FE |
Yes |
Yes |
Yes |
Year FE |
Yes |
Yes |
Yes |
Clustered SE |
State |
State |
State |
Observations |
862 |
862 |
701 |
Within R2 |
0.42 |
0.18 |
0.45 |
Note: Cluster-robust standard errors (state level) in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.
Model (1) indicates that income-adjusted fiscal effort remains strongly and positively associated with fiscal effort (β = 0.58, p < 0.01) after controlling for state and year fixed effects. This suggests that states allocating a larger share of personal income to education also devote a larger share of gross state product to education.
In Model (1), teacher salary parity (3 - 5 years) is not statistically significant once macro-fiscal alignment is included. However, when income-adjusted effort is excluded (Model 2), salary parity becomes positive and statistically significant (β = 0.083, p < 0.05), indicating that compensation equity is associated with fiscal effort but partly overlaps with broader fiscal capacity alignment.
Model (3) incorporates additional equity indicators. The core association between income-adjusted effort and fiscal effort remains robust. Staffing equity and funding gap measures are not statistically significant once fixed effects are included, suggesting that cross-state differences in these measures are largely time-invariant or absorbed by state effects.
The within R2 values indicate that approximately 42% - 45% of within-state temporal variation in fiscal effort is explained in the fully specified models.
Residual diagnostics indicate no systematic departures from linearity. Breusch–Pagan tests suggest mild heteroskedasticity, addressed through clustered standard errors. “Variance inflation factors” remain below conventional thresholds, indicating no severe multicollinearity.
4.6. Summary of Findings
States and regions in the USA vary widely in their fiscal effort, with the Northeast continuously leading. From 2006 to 2022, fiscal effort declined in all regions, which indicated systematic underinvestment. Income-adjusted fiscal effort consistently exhibits a strong positive association with fiscal effort across fixed-effects specifications. Teacher salary parity is positively associated with fiscal effort in models excluding income-adjusted effort, but its statistical significance is sensitive to specification. This suggests that compensation equity operates within broader macro-fiscal alignment rather than independently determining fiscal effort.
5. Discussion
This study is associational: it uses macro-observational state-year data and fixed effects to control for time-invariant state characteristics and common national shocks, but it does not identify causal effects of policies. Therefore, all results are interpreted as conditional associations.
This section interprets the empirical findings (of this study) using both traditional and critical lenses to relate patterns in fiscal effort to broader structures of political economy, state ideology, and educational governance. The three primary empirical trends—the relationship between increased effort and equity-oriented policies, declining fiscal effort over time, and regional variations—are not isolated phenomena. They must be positioned within the ongoing restructuring of the American state in the framework of neoliberalism, fiscal constraints, and stratified governance.
5.1. The Retreat of the Public: Interpreting the Decline in Fiscal
Effort
Analysis of the data indicated that all parts of the U.S. saw a decrease in fiscal effort from 2006 to 2022. The biggest drops were in the West (−10.1%) and the Northeast (−6.2%). Because of the situation—more diverse students, more inequality, and a greater need for educational policies that redistribute resources—this drop is especially concerning.
From a critical point of view, this trend shows that the government is cutting back on money for schools. Neoliberalism means not only privatization but also changing the government so that market logic and austerity come first. In education, this means that the state is no longer responsible for paying for things; instead, local governments, private businesses, and families are. This makes racial and class inequalities even worse.
The steady drop in public spending isn’t just because of financial conservatism; it’s also because the state—in the contexts of free-market capitalism, neoliberal ideology and policies—is seen no longer responsible for making sure everyone has fair access to market mechanisms and private services.
5.2. Regional Regimes of Education Finance
The fact that the Northeast has consistently led in fiscal effort shows how education policy has acquired territorial attributes. These territorial differences may not just be numbers; they may also be caused by how each region’s political economy, history, and culture of governance work.
For instance, Southern states tend to use models with low taxes and low services. This is because of a history of racial exclusion, anti-union sentiment, and little public investment. The Northeast, on the other hand, has a higher fiscal effort because it has stronger teacher unions, a more progressive tax system, and a Democratic majority in government. All these things make it easier for the government to spend money in ways that help people.
This suggests that education finance in the U.S. may be governed not only by a universal logic of adequacy or equity, but also by regional regimes of accumulation and governance. Each regime expresses a different articulation of public commitment to education that aligns with regional socio-historical conditions such as dominant class interests and institutional legacies.
5.3. Structural Drivers and Conditional Associations
Panel fixed effects results indicate that macro-fiscal alignment—captured by income-adjusted fiscal effort—remains the strongest correlate of fiscal effort over time. Teacher salary parity is positively associated with fiscal effort, but its magnitude and significance depend on whether income-adjusted effort is included in the model. This suggests that compensation equity operates within broader fiscal structures rather than independently determining fiscal effort.
Importantly, these findings are associational rather than causal. The fixed effects framework controls for time-invariant state characteristics and common national shocks but does not identify exogenous causal effects.
5.4. Mapping Critical Constructs to Measurable Indicators
Table 6 maps the “critical-materialist constructs” used in the discussion to measurable empirical proxies employed in the analysis. This mapping clarifies how abstract political-economic mechanisms (e.g., austerity or redistribution) are operationalized through the longitudinal indicators summarized in the Findings section.
Table 6. Mapping critical constructs to measurable indicators.
Construct |
Empirical Proxy |
Austerity |
Declining effort over time |
Tax regime |
Income-adjusted effort |
Labor power |
Salary parity |
Redistribution |
Funding gap measures |
Future research may incorporate direct political variables such as party control, union density, or tax progressivity.
5.5. Limitations and Future Directions
The two-way fixed effects model explains approximately 55% of within-state temporal variation in fiscal effort. However, a substantial portion of variation remains unexplained, suggesting the influence of political, ideological, and institutional factors not directly captured in the model. Future studies should include political factors like party control, union density, and tax policy; look at how different factors, like race, class, and geography, work together to affect financial decisions; look at data from after 2022 as well, especially because of federal stimulus programs and inflation shocks; and look at case studies of states with high and low effort to put the numbers in context with real-life examples.
6. Conclusion
From 2006 to 2022, this study looked at how fiscal effort changed in U.S. education funding across all states and regions. Using the State Indicators Database (2025 edition) and visual, statistical, and regression analysis, the results give both real-world examples and critical analysis of how educational investment is structured and distributed in the U.S. today.
This study challenges the dominant stories that say education funding is only a technical or economic issue. In fact, states choose how much to invest in education, and those choices are deeply political. Policymakers and advocates must recognize that 1) fiscal effort is a measure of political commitment, not just fiscal capacity; 2) regional disparities in education funding reflect entrenched institutional legacies and must be addressed with redistributive federal interventions; and 3) improving salary equity and income-effort alignment are essential steps toward reversing the national retreat in public educational investment.
From a critical point of view, fiscal effort is more than just an input variable; it is a real sign of the state’s ideology. Not only do high fiscal-effort states have more money, but they also have a different idea of what the public good is and how education fits into democratic life. A decrease in fiscal effort may indicate that neoliberal austerity, racialized disinvestment, and privatization logics are coming together in a way that makes inequality worse. To stop this trend, we need to re-politicize, reframe, and restructure education policy based on the ideas of fairness, community, and public purpose.
This article adds to the body of research by combining quantitative data analysis with critical theory. It shows that the geography of educational opportunity in the U.S. is not only uneven but also politically constructed. Rebuilding fiscal effort as a national priority will require more than only budget increases; that is, it will also require a reimagination of the state’s role in guaranteeing educational justice.