Fiscal resilience of Brazilian states: an applied methodological proposal to support the preventive action of the Courts of Accounts

Abstract

This article develops and applies a methodology to operationalize the construct of fiscal resilience in the context of Brazilian governmental entities, aiming to provide an additional analytical tool to support the external control exercised by the Courts of Accounts. Grounded in the Governmental Financial Condition (GFC) literature and resilience approaches applied to the public sector, the study utilizes Principal Component Analysis (PCA) to process fiscal indicators for all Brazilian states between 2015 and 2024. This technique enabled the extraction of three structural dimensions that comprise a global resilience rating. The findings reveal that economic size, alone, does not ensure greater fiscal resilience. While states such as Goiás (GO), Mato Grosso (MT), and Rondônia (RO) were classified in higher rating tiers, economically larger entities such as Rio de Janeiro (RJ), Minas Gerais (MG), and São Paulo (SP) appear at lower levels due to structural fiscal constraints that reduce their fiscal response capacity. As a contribution, the article offers a methodological tool to support the comparative identification of fiscal vulnerabilities, with potential complementary application in fiscal analyses within the scope of the Courts of Accounts.

Keywords:
fiscal resilience; Courts of Accounts; ex-ante control; governmental financial condition; Brazilian federalism

Resumo

Este artigo desenvolve e aplica uma metodologia para operacionalizar o constructo de resiliência fiscal no contexto de entes governamentais brasileiros, com vistas a oferecer uma ferramenta analítica adicional de apoio ao controle externo exercido pelos Tribunais de Contas. Ancorado na literatura de Condição Financeira Governamental (CFG) e em abordagens de resiliência aplicadas ao setor público, o estudo utiliza Análise de Componentes Principais (ACP) para processar indicadores fiscais de todos os estados brasileiros entre 2015 e 2024. A técnica permitiu a extração de três dimensões estruturais que compõem um rating global de resiliência. Os achados revelam que o porte econômico, isoladamente, não assegura uma maior resiliência fiscal. Enquanto estados como Goiás (GO), Mato Grosso (MT) e Rondônia (RO) foram classificados em faixas superiores de rating, entes de maior porte econômico, como Rio de Janeiro (RJ), Minas Gerais (MG) e São Paulo (SP), figuram em níveis inferiores, em razão de restrições fiscais estruturais que reduzem sua margem de resposta fiscal. Como contribuição, o artigo oferece uma ferramenta metodológica de apoio à identificação comparativa de vulnerabilidades fiscais, com potencial de aplicação complementar em análises fiscais no âmbito dos Tribunais de Contas.

Palavras-chave:
resiliência fiscal; Tribunais de Contas; controle prévio; condição financeira governamental; federalismo brasileiro

Resumen

Este artículo desarrolla y aplica una metodología para operacionalizar el constructo de resiliencia fiscal en el contexto de las entidades gubernamentales brasileñas, con el propósito de ofrecer una herramienta analítica adicional de apoyo al control externo ejercido por los Tribunales de Cuentas. Basado en la literatura sobre Condición Financiera Gubernamental (CFG) y en enfoques de resiliencia aplicados al sector público, el estudio utiliza el Análisis de Componentes Principales (ACP) para procesar indicadores fiscales de todos los estados brasileños entre 2015 y 2024. La técnica permitió extraer tres dimensiones estructurales que componen una calificación global de resiliencia. Los hallazgos revelan que el tamaño económico, por sí solo, no garantiza una mayor resiliencia fiscal. Mientras estados como Goiás (GO), Mato Grosso (MT) y Rondônia (RO) fueron clasificados en categorías superiores de calificación, entidades de mayor tamaño económico, como Río de Janeiro (RJ), Minas Gerais (MG) y São Paulo (SP), figuran en niveles inferiores debido a restricciones fiscales estructurales que reducen su margen de respuesta fiscal. Como contribución, el artículo ofrece una herramienta modificada y metodológica de apoyo a la identificación comparativa de vulnerabilidades fiscales, con potencial de aplicación complementaria en análisis fiscales en el ámbito de los Tribunales de Cuentas.

Palabras clave:
resiliencia fiscal; Tribunales de Cuentas; control previo; condición financiera gubernamental; federalismo brasileño

1. INTRODUCTION

Economic crises and fiscal instability are recurrent events in the trajectory of governments, imposing ongoing challenges to the preservation of budgetary balance and the continued provision of essential public services (Batista et al., 2024). Episodes such as the COVID-19 pandemic exposed the structural vulnerability of public finances to exogenous shocks and the need for prompt responses in contexts of uncertainty (Chernova & Gridnev, 2023; Desdiani et al., 2022; Dzigbede et al., 2023). Events of this magnitude have fostered investigations aimed at understanding government strategies for institutional adaptation and fiscal risk mitigation (Klimanov et al., 2021; Lee & Chen, 2022; Munawar & Manurung, 2021).

In this context, the concept of resilience, within public administration, has consolidated itself as an important analytical framework for examining governments’ capacity to absorb impacts, reorganize decision-making processes, and preserve minimum standards of institutional functioning in the face of disruptions (Coyle & Ferry, 2022; Wójtowicz & Hodžić, 2022). The construct of fiscal resilience thus emerges, defined as the government’s capacity to face economic shocks while continuing to fulfill its fiscal functions and to ensure the sustainability of its public finances (Klimanov et al., 2021; Munawar & Manurung, 2021). Unlike approaches narrowly focused on budgetary balance, this construct incorporates dimensions such as expenditure flexibility, revenue diversification, and systematic risk management, which are associated with processes of organizational learning and adaptation (Rao et al., 2023).

In parallel, the literature on government financial condition, developed primarily in the United States, has consolidated a multidimensional approach to assessing fiscal health based on the integration of financial indicators, an approach widely used for fiscal diagnostics and the preventive monitoring of imbalances (Hendrick, 2004; Wang et al., 2007). However, the replication of these models in countries with distinct federative arrangements faces limitations, given that they are instruments designed under their own assumptions, characterized by greater municipal autonomy and particular accounting structures (Gomez et al., 2024; McDonald, 2018; Zhang et al., 2026). Furthermore, no consensus has emerged regarding the set of indicators or the ideal methodological structure for assessing government financial condition, resulting in a diversity of metrics and aggregation techniques tailored to specific institutional contexts (Iacuzzi, 2022).

In Brazil, although the Capacidade de Pagamento (Payment Capacity [Capag]) methodology, developed by the Secretaria do Tesouro Nacional (National Treasury Secretariat [STN]), measures the solvency and liquidity of states and municipalities, its scope is limited to the purpose of granting federal guarantees. Consequently, there remains scope for the development of standardized analytical instruments that integrate multiple dimensions of resilience and are oriented toward fiscal governance, without being limited to a credit perspective. This gap highlights the relevance of methodologies tailored to the specificities of Brazilian federalism and capable of supporting external control institutions in performing consistent assessments of the sustainability of public finances (Cordery & Hay, 2019; Gomez et al., 2024; Lino & Aquino, 2018).

In this scenario, the Brazilian Courts of Accounts (Tribunais de Contas, [TCs]) play a central role in fiscal governance by performing audit functions and fostering good public management practices (Rocha et al., 2020). However, their activities remain predominantly reactive, focused on legal compliance in completed fiscal years, which may limit the systematic incorporation of approaches aimed at the comparative identification of structural fiscal vulnerabilities (Lino & Aquino, 2020; Ribeiro et al., 2020). Additionally, the institutional diversity of these Courts results in asymmetries in the way fiscal risks are addressed, causing diagnoses and alerts to depend largely on discretionary choices and on the institutional profile of each Court (Lino & Aquino, 2018). These methodological divergences and the heterogeneity in the interpretation of fiscal rules (Nunes et al., 2019; Teixeira, 2020) may compromise the transparency of audit processes and limit the technical standardization required for the audit activities performed by these institutions (Lino & Aquino, 2020).

Against this background, this article presents the development and application of a rating model that operationalizes fiscal resilience for Brazilian states, combining the literature on government financial condition with statistical techniques for indicator aggregation. The proposal seeks to offer an additional analytical tool for the comparative identification of fiscal vulnerabilities, with potential for complementary use within the scope of external control. By demonstrating that fiscal resilience is not automatically associated with economic size or current revenue performance, the study aims to broaden the methodological repertoire available to TCs for the comparative assessment of fiscal risks.

2. THEORETICAL AND CONCEPTUAL FRAMEWORK

2.1. From the Concept of Resilience to the Operationalization of Fiscal Resilience in the Public Sector

Studies on resilience emerged in response to the need to understand how individuals and organizations cope with adverse events (Chernova & Gridnev, 2023; Dzigbede et al., 2023). The diversity of approaches associated with the concept endows it with a multifaceted character, indicating that its analytical application depends on the institutional context and on the dimensions considered in each study (Barbera et al., 2017).

Within public administration, resilience has been mobilized to understand how governments cope with crises while preserving the continuity of essential policies and services, particularly in contexts of fiscal constraints (Desdiani et al., 2022). Literature emphasizes that this capacity is not limited to reactive responses but also involves anticipatory and adaptive dimensions associated with planning, institutional learning, and risk management, which may facilitate the absorption of shocks in a less disruptive manner (Barbera et al., 2020; Rao et al., 2023). Such approaches tend to enhance the response capacity of government entities, whereas purely absorptive strategies may deplete institutional resources over the long term (Dougherty & de Biase, 2021; Duit, 2016).

In this regard, recurrent patterns have been identified in government responses to crises, ranging from short-term strategies, often reactive and oriented toward expenditure containment, to more structural approaches based on intertemporal planning and the strengthening of institutional capacities (Agyemang et al., 2023). In contexts marked by limited anticipatory capacity, short-termist responses predominate, such as payment deferrals and abrupt spending cuts, which tend to amplify future vulnerabilities (Wójtowicz & Hodžić, 2022).

In the Brazilian context, studies on local governments indicate limitations in planning capacity and in the preventive management of fiscal risks, with reactive strategies prevailing in response to revenue declines and budgetary pressures (Aquino & Cardoso, 2017; Lima & Aquino, 2019). This logic, often associated with short-term political cycles, conceals structural weaknesses in resilience, thereby reinforcing the relevance of analytical instruments capable of identifying vulnerabilities before more severe fiscal deterioration occurs (Batista et al., 2024; Soares & Gonçalves, 2022).

From this perspective, fiscal resilience has emerged as a central concept in public management, reflecting the capacity of public entities to sustain essential functions and respond to economic shocks (Klimanov et al., 2021; Wójtowicz & Hodžić, 2022). The construct goes beyond the notion of financial robustness by incorporating dimensions of anticipation, absorption, and adaptation to disruptions, thereby shifting the analysis toward dynamic and intertemporal fiscal decisions (Munawar & Manurung, 2021; Rao et al., 2023).

Nevertheless, few approaches operationalize fiscal resilience in a systematic and comparable manner, particularly in the context of local governments, where fiscal vulnerabilities tend to take on specific configurations (Coyle & Ferry, 2022; Soares & Gonçalves, 2022). Most approaches focus on ex post analyses or on the use of isolated indicators, which limits an integrated understanding of the structural dimensions that sustain fiscal adaptive capacity (Costa et al., 2025; Iacuzzi, 2022).

This analytical fragmentation tends to hinder the preventive function of external control by limiting the integrated identification of signs of fiscal deterioration before they evolve into more severe imbalances. This limitation reinforces the relevance of linking the topic to government financial condition, whose multidimensional framework provides an appropriate conceptual basis for the integrated and comparable operationalization of fiscal resilience.

2.2. Government Financial Condition: From Solvency Measurement to Fiscal Risk Management

The debate on government financial condition emerged in the international literature, associated with the fiscal crises faced by U.S. local governments from the 1970s onward (Maher et al., 2020). These episodes revealed the limitations of analyses centered on annual budgetary balance and spurred the development of approaches aimed at a broader assessment of government fiscal sustainability (Hendrick, 2004).

The seminal literature on government financial condition converges on the understanding that the fiscal health of a government entity is not limited to budgetary balance but rather reflects its capacity to meet financial obligations and sustainably maintain public services (Brown, 1993; Groves et al., 1981; Hendrick, 2004). While Groves et al. (1981) introduced a multidimensional perspective for analyzing warning signals, Brown (1993) and Hendrick (2004) advanced the systematization of indicators that capture dimensions of solvency across different time horizons. This theoretical foundation is relevant for distinguishing legal compliance from fiscal resilience, supporting a financial analysis that goes beyond a narrow focus on cash flow and incorporates institutional flexibility as a dimension of federative stability.

In addition, Wang et al. (2007) reinforced government financial condition as a systemic construct, in which different dimensions interconnect to sustain stability over time. However, there is no consensus on an ideal model, given the dependence on specific institutional contexts and federative arrangements (McDonald, 2018). Other studies reinforce this conceptual and methodological heterogeneity, indicating that government financial condition can be analyzed as a multidimensional phenomenon sensitive to the specific characteristics of each governmental system (Gomez et al., 2024; Poudel et al., 2025; Zhang et al., 2026).

In Brazil, growing efforts have been made to adapt international models of government financial condition to the analysis of local governments (Araújo et al., 2023; Ferreira et al., 2024; Sacramento et al., 2024), although the literature recognizes the absence of consensus regarding the ideal set of indicators to capture the fiscal health of Brazilian government entities (Gomez et al., 2024; Iacuzzi, 2022). At the institutional level, Capag stands out as an instrument aimed at assessing solvency for regulatory purposes and the granting of federal guarantees. Although it represents progress in standardization, its scope is limited to specific dimensions and does not integrate other facets of government financial condition (Barbera et al., 2017; Rao et al., 2023). Due to its normative nature and credit-oriented purpose, Capag provides a circumscribed view of fiscal health, failing to capture structural aspects that underpin the fiscal resilience of federative entities.

In view of this debate, interest has emerged in approaches that link government financial condition to the construct of fiscal resilience, in order to capture the capacity to cope with crises and preserve the sustainability of public finances from a broader perspective. This linkage takes on relevance within the scope of external control, insofar as systematic and comparable instruments can provide additional support for more timely and comparative fiscal assessments.

2.3. Courts of Accounts and Fiscal Governance

The administrative reforms initiated in the 1990s, in a context of economic liberalization, redemocratization, and recurrent fiscal crises, expanded demands for transparency, fiscal responsibility, and efficiency in the management of public resources, thereby strengthening the role of TCs in monitoring government action (Lino & Aquino, 2018; Loureiro et al., 2009).

The Brazilian model, characterized by the coexistence of TCs across the three levels of the federation, gives external control a broad institutional reach (Lino & Aquino, 2018). However, the literature suggests that this decentralized structure has been more associated with the formal verification of processes than with the assessment of long-term fiscal sustainability (Lino & Aquino, 2020). It is also worth noting that TCs have consolidated themselves as Supreme Audit Institutions (SAIs) in contexts of increasing complexity, in which institutional autonomy and technical capacity are relevant elements for ensuring accountability (Cordery & Hay, 2019).

In Brazil, the Fiscal Responsibility Law (Lei de Responsabilidade Fiscal, [LRF]) strengthened the activities of TCs by expanding their responsibilities in monitoring fiscal management (Costa et al., 2018; Loureiro et al., 2009). However, the emphasis on legal compliance may favor predominantly retrospective auditing, with more limited incorporation of early warnings regarding the deterioration of fiscal resilience (Lino & Aquino, 2020; Ribeiro et al., 2020).

Despite these advances, the LRF also introduced significant challenges. As Lino and Aquino (2018) and Loureiro et al. (2009) point out, one of the main challenges concerns the lack of uniformity in the interpretation of the law. In some cases, this absence of harmonization has undermined the application of the law, making auditing heterogeneous and susceptible to divergent interpretations (Nunes et al., 2019; Teixeira, 2020). This pattern of interpretive variation, in which criteria may differ across TCs to accommodate local specificities, tends to limit the pedagogical and preventive function of external control (Lino & Aquino, 2020; Rocha et al., 2020).

Another consequence concerns the relaxation of fiscal limits, with a loss of effectiveness of existing rules (Aquino & Azevedo, 2017; Lino & Aquino, 2018). Teixeira (2020) demonstrates that TCs adopt different criteria for calculating these limits, contributing to the artificial maintenance of compliance. Against this backdrop, it becomes relevant to discuss the incorporation of systematized quantitative instruments, such as the proposed resilience rating, which may help mitigate margins of interpretive discretion and enhance the comparative assessment of fiscal vulnerabilities, thereby providing additional analytical support for external control.

3. METHODOLOGY

To develop a methodology aimed at operationalizing the concept of fiscal resilience, this study adopts a mixed-methods approach (Creswell & Creswell, 2017). The quantitative approach is based on the modeling of official fiscal indicators using multivariate statistical techniques, whereas the qualitative approach is reflected in the theoretical and normative grounding, the selection and interpretation of indicators, and the institutional articulation of the proposed model. This strategy is aligned with contemporary approaches that integrate fiscal performance metrics and institutional criteria in analyses of the sustainability of public finances (Munawar & Manurung, 2021).

For this purpose, secondary data were extracted from the Brazilian Public Sector Accounting and Fiscal Information System (Sistema de Informações Contábeis e Fiscais do Setor Público Brasileiro [Siconfi]), covering all 26 Brazilian states and the Federal District for the fiscal years from 2015 to 2024. This ten-year time frame is appropriate to the objectives of the study, as it makes it possible to capture the behavior of state public finances across multiple cycles of economic crisis and recovery. The initial selection of indicators resulted from the convergence of three analytical strands: (i) a review of the specialized literature on government financial condition and fiscal resilience; (ii) a normative analysis of reference frameworks issued by the STN, with emphasis on the Capag methodology; and (iii) an examination of the jurisprudence of TCs.

Initially, 15 indicators were collected (Table 1), expressed as ratios and encompassing variables related to revenue, expenditure, primary balance, Net Current Revenue (Receita Corrente Líquida, [RCL]), among others. Each indicator was classified according to the direction of fiscal risk, indicating whether higher values are associated with greater vulnerability (inverse relationship) or greater resilience (direct relationship). This step is relevant because it supports the conversion of raw data into structured risk signals, thereby facilitating diagnostic interpretation by external control institutions. After collection, the data were deflated using the Extended National Consumer Price Index (Índice Nacional de Preços ao Consumidor Amplo, [IPCA]), with 2024 as the base year, in order to ensure intertemporal comparability and neutralize inflationary effects in the analysis of the historical series.

TABLE 1
INDICATORS IDENTIFIED IN THE THEORETICAL AND NORMATIVE LITERATURE

Indicator	Formula (Ratio)	Relationship with Fiscal Resilience
IND1	Gross Consolidated Debt / Net Current Revenue	Inverse (−)
IND2	Current Expenditure / Current Revenue	Inverse (−)
IND3	(Gross Cash Availability − Short-Term Financial Obligations) / Net Current Revenue	Direct (+)
IND4	Primary Balance / Net Current Revenue	Direct (+)
IND5	Recovered Outstanding Tax Debt / Total Stock of Outstanding Tax Debt	Direct (+)
IND6	(Personnel Expenditures + Debt Service) / Total Expenditure	Inverse (−)
IND7	Intergovernmental Transfers / Total Revenue	Inverse (−)
IND8	(Prior-Year Expenditures + Carry-Over Commitments) / Net Current Revenue	Inverse (−)
IND9	Pension Revenue / Pension Expenditure	Direct (+)
IND10	Contingency Reserve / Net Current Revenue	Direct (+)
IND11	Recovered Outstanding Tax Debt / Total Revenue	Direct (+)
IND12	(Pension Revenue − Pension Expenditure) / Net Current Revenue	Direct (+)
IND13	Contingency Reserve / Total Expenditure	Direct (+)
IND14	(Prior-Year Expenditures + Carry-Over Commitments) / Total Expenditure	Inverse (−)
IND15	Recovered Outstanding Tax Debt / Net Current Revenue	Direct (+)

• Source: Elaborated by the authors.

The final dataset comprised 2,970 observations, which were subjected to a data-cleaning process to ensure the consistency of the subsequent analyses. Initially, descriptive statistical analysis was performed, followed by an assessment of distributional normality using the Shapiro-Wilk test. This diagnostic procedure made it possible to identify the asymmetric nature of the fiscal data, which are commonly affected by structural heterogeneities and extreme values (outliers). Subsequently, a correlation analysis was conducted among the indicators using Spearman’s coefficient (Hair et al., 2009), a nonparametric technique appropriate to the distribution of the data and useful for identifying redundancies that could compromise the validity of the final model.

The suitability of the dataset for Principal Component Analysis (PCA) was assessed using the Kaiser-Meyer-Olkin (KMO) test, Bartlett’s test of sphericity, and the Measure of Sampling Adequacy (MSA). These tests indicated the presence of sufficient underlying correlations for extracting dimensions representative of the facets of fiscal resilience. Once statistical adequacy was confirmed, PCA was conducted with the aim of reducing the dimensionality of the indicators while preserving the largest possible share of explained variance. Overall, the exclusion of redundant indicators did not entail a substantive loss of analytical scope but contributed to the internal validity of the model by avoiding informational overlaps.

Next, the definition of the number of retained components was based on the analysis of Kaiser’s eigenvalues (criterion > 1) and inspection of the scree plot, seeking to balance model simplification with the preservation of the information contained in the data. Subsequently, orthogonal varimax rotation was applied to facilitate the interpretation of the extracted components. The naming of the components resulted from the cross-referencing of theoretical and normative content with the factor loadings obtained, aiming to ensure consistency with the dimensions of fiscal resilience and government financial condition.

In the final stage, the overall fiscal resilience rating was constructed. For indicators without consolidated normative parameters, the classification ranges were established based on percentiles of the empirical distribution, so that the model could capture the observed and comparative behavior of Brazilian states. By contrast, for the variables covered by the Capag methodology, the official parameters were adopted, thereby favoring regulatory adherence and the integration of the model with already consolidated fiscal monitoring practices.

The component-level classification resulted from the aggregation of the latent dimensions, culminating in the assignment of an overall rating to each federative entity. This approach is supported by the literature, which recognizes fiscal resilience as a multidimensional phenomenon and highlights such instruments as relevant for the synthesis and communication of fiscal risks (Liu & Tan, 2009).

As an additional procedure, the overall fiscal resilience rating was compared with the Capag classification of the states for the 2024 base year, as disclosed by the STN. In addition to the descriptive comparison between the categories assigned by each metric, the correlation between the classifications was estimated based on a unified ordinal coding of the scales, while preserving the relative order of the categories in each instrument. Patterns of convergence between the metrics were also identified in order to examine the extent to which the proposed model reproduces, complements, or diverges from Capag’s classificatory logic.

Finally, to assess the robustness of the overall rating and mitigate potential specification biases, a leave-one-indicator-out sensitivity analysis was conducted on the set of indicators composing the fiscal resilience metric. The objective was to verify whether the overall classification of states preserved analytical stability when changes were introduced into the composition of the indicator set. The procedure consisted of successively reapplying PCA, excluding one indicator at a time, while preserving, for comparability with the baseline model, the same dimensional structure adopted in the final specification of the index. In each scenario, the following aspects were assessed: (i) the suitability of the dataset for PCA; (ii) the cumulative explained variance; and (iii) Spearman’s ordinal correlation between the baseline rating and the alternative rating.

4. RESULTS

The descriptive statistics revealed pronounced fiscal heterogeneity, with differences in measures of central tendency and dispersion (Appendix, Table A). Discrepancies between mean and median, wide ranges, and measures of skewness and kurtosis indicate non-normal distributions and the presence of outliers, which was confirmed by the Shapiro-Wilk test. These results point to the limitations of fiscal analyses based exclusively on averages and reinforce the need for multivariate techniques capable of capturing the complexity of fiscal disparities.

Spearman’s correlation matrix (Appendix, Table B) indicated significant associations among certain indicators. To avoid informational overlap and promote the independence of the rating dimensions, highly correlated indicators were excluded, while those with greater explanatory capacity were retained. As a result, four indicators were removed from the initial model (IND9, IND10, IND14, and IND15), reducing potential biases arising from informational redundancy.

Once the suitability of the dataset for PCA had been confirmed by Bartlett’s test of sphericity (p < .001) and the KMO test, the technique was applied to the remaining indicators, resulting in the retention of four components, which explained 67.4% of the total variance. To enhance the model’s explanatory capacity and refine the statistical specification criteria, additional procedures were conducted, including the analysis of individual MSA values and uniqueness. Based on these criteria, two additional indicators were excluded (IND4 and IND5), resulting in a final set of nine indicators.

The application of PCA to the refined set led to the retention of three components, whose cumulative variance reached 74.6% (Table 2), indicating reasonable robustness of the model and adequate representation of the structural determinants of fiscal resilience. Retention was supported by the Kaiser criterion (eigenvalues > 1), inspection of the scree plot, and parallel analysis, which indicated reduced marginal gains from the fourth component onward (Figure 1).

TABLE 2
STRUCTURE OF EXPLAINED VARIANCE IN PCA

	Unrotated Solution			Rotated Solution
Component	Eigenvalue	Proportion of Variance	Cumulative Variance	Sum of Squared Loadings	Proportion of Variance	Cumulative Variance
1	3.78	0.42	0.42	2.718	0.302	0.302
2	1.575	0.175	0.595	2.484	0.276	0.578
3	1.359	0.151	0.746	1.512	0.168	0.746

• Source: Elaborated by the authors using JASP software.

FIGURE 1
SCREE PLOT OF PRINCIPAL COMPONENTS

The component matrix revealed consistent factor loadings and moderate levels of uniqueness, indicating that a relevant share of the indicators’ variance is captured by the identified factor structure (Table 3). The clarity of the loadings suggests that the model has analytical capacity to distinguish distinct fiscal dimensions in a synthetic and coherent manner, thereby reinforcing its statistical robustness and potential for synthesis.

TABLE 3
FACTOR LOADINGS AND UNIQUENESS OF INDICATORS IN PCA

Indicator	Component 1	Component 2	Component 3	Uniqueness
IND2 (PC)	-0.857			0.20
IND3 (LR)	0.751			0.33
IND12 (SP)	0.734			0.27
IND1 (DC)	-0.454	-0.581		0.45
IND8 (COA)	-0.451		0.502	0.42
IND7 (DT)		0.826		0.21
IND11 (RDA)		-0.784		0.38
IND6 (RO)		0.402	0.57	0.51
IND13 (MC)			-0.735	0.38

• Source: Elaborated by the authors using JASP software.

Based on the substantive interpretation of the latent dimensions identified through PCA, Component 1 was found to concentrate high loadings on the indicators of Current Savings (PC), Relative Liquidity (LR), and Pension Sustainability (SP), reflecting a dimension associated with Structural Solvency and Liquidity (SLE). In analytical terms, this dimension brings together aspects related to cash flow robustness and actuarial sustainability.

Component 2 is determined by Transfer Dependency (DT), Recoverability of Outstanding Tax Debt (RDA), and Consolidated Debt (DC), characterizing a dimension related to Financing Autonomy and Sustainability (ASF). This dimension synthesizes aspects associated with the capacity to mobilize own-source revenues, the degree of dependence on external revenues, and the sustainability of fiscal financing over time.

Component 3, in turn, shows relevant loadings for Budgetary Rigidity (RO), Commitment to Prior-Year Obligations (COA), and Contingency Margin (MC), indicating a dimension linked to Flexibility and Exposure to Fiscal Risks (FER). This dimension is associated with budgetary room for maneuvering and the capacity to absorb fiscal pressures without an immediate deterioration in the entity’s fiscal position.

The moderately low uniqueness values observed for most indicators suggest adequate representation in the factor space, reinforcing the statistical consistency of the dimensional structure adopted to measure fiscal resilience. The individual classification ranges for the indicators are summarized in Table 4.

TABLE 4
RATING SCALE FOR FISCAL RESILIENCE INDICATORS

Fiscal Resilience Dimension	Indicator	Value Ranges	Partial Rating
Structural Solvency and Liquidity (SLE)	Relative Liquidity (LR)	LR > 5%	A
		0 ≤ LR ≤ 5%	B
		LR < 0	C
	Current Savings (PC)	PC < 85%	A
		85% ≤ PC < 95%	B
		PC ≥ 95%	C
	Pension Sustainability (SP)	SP > -7.17%	A
		-12.17% ≤ SP ≤ -7.17%	B
		SP < -12.17%	C
Financing Autonomy and Sustainability (ASF)	Consolidated Debt (DC)	DC < 60%	A
		60% ≤ DC < 100%	B
		DC ≥ 100%	C
	Transfer Dependency (DT)	DT < 26.5%	A
		26.5% ≤ DT ≤ 43.3%	B
		DT > 43.3%	C
	Recoverability of Outstanding Tax Debt (RDA)	RDA > 0.93%	A
		0.41% ≤ RDA ≤ 0.93%	B
		RDA < 0.41%	C
Flexibility and Exposure to Fiscal Risks (FER)	Budgetary Rigidity (RO)	RO < 45.12%	A
		45.12% ≤ RO < 52.13%	B
		RO ≥ 52.13%	C
	Commitment to Previous-Year Obligations (COA)	COA < 6.08%	A
		6.08% ≤ COA < 9.91%	B
		COA ≥ 9.91%	C
	Contingency Margin (MC)	MC > 1.02%	A
		0.37% ≤ MC ≤ 1.02%	B
		MC < 0.37%	C

• Source: Elaborated by the authors.

After applying the formulas to the collected data, individual results were obtained for the indicators comprising the fiscal resilience dimensions (Appendix, Table C). These results show, for each state, the observed values for each indicator and the corresponding scores, assigned according to the previously defined reference ranges. This disaggregation provides a detailed overview of potentially relevant vulnerabilities for analyses conducted within the scope of TCs.

Consequently, the overall fiscal resilience rating results from the combination of the three analytical dimensions, synthesizing the systemic balance among solvency, autonomy, flexibility, and exposure to risks. Unlike isolated indicators, the proposed rating organizes evidence related to the adaptive capacity of the federative entity, that is, the fiscal conditions associated with coping with shocks without immediately compromising the provision of public services.

The most favorable combinations, characterized by the predominance of “A” classifications across the three dimensions, result in AAA and A+ levels, indicating upper ranges of fiscal resilience. Conversely, the predominance of “C” classifications across the three dimensions leads to C and D ratings, characterizing a situation of fiscal vulnerability (Appendix, Table D). Based on this systematic approach, the fiscal resilience ranking of Brazilian states was obtained for the 2024 base year (Table 5).

TABLE 5
PARTIAL AND OVERALL FISCAL RESILIENCE RATINGS BY STATE — BASE YEAR 2024

	Indicators			Dimension 1	Indicators			Dimension 2	Indicators			Dimension 3	Fiscal Resilience Rating
UF	LR	PC	SP	SLE	DC	DT	RDA	ASF	RO	COA	MC	FER
GO	A	C	B	B	B	A	A	A	B	A	A	A	A+
MT	A	A	A	A	A	A	A	A	A	C	C	C	A
RO	A	B	A	A	A	B	A	A	B	C	B	C	A
AM	B	B	A	B	A	B	C	B	A	B	A	A	B+
DF	B	B	A	B	A	A	A	A	A	C	A	B	B+
ES	A	A	A	A	A	B	B	B	B	B	A	B	B+
MA	B	B	A	B	A	C	A	B	A	B	A	A	B+
MS	A	B	B	B	A	A	B	A	B	B	B	B	B+
PA	B	B	A	B	A	B	B	B	A	A	B	A	B+
PR	A	B	B	B	A	A	A	A	B	B	B	B	B+
SC	A	B	B	B	A	A	A	A	B	A	C	B	B+
BA	A	B	A	A	A	B	B	B	B	B	C	C	B
CE	A	B	A	A	A	B	B	B	C	A	C	C	B
PB	A	B	B	B	A	C	B	B	C	A	A	B	B
PE	B	B	B	B	A	B	B	B	B	A	C	B	B
RR	A	B	A	A	A	C	C	C	C	B	A	B	B
RS	C	B	C	C	C	A	A	B	A	B	A	A	B
SE	A	B	B	B	A	C	B	B	B	B	B	B	B
TO	B	B	A	B	A	C	A	B	B	C	A	B	B
AP	B	A	A	A	B	C	C	C	C	B	B	C	C+
RJ	C	B	B	C	C	A	A	B	B	A	B	B	C+
SP	B	B	B	B	C	A	A	B	A	C	C	C	C+
AC	C	B	A	B	A	C	C	C	C	B	C	C	C
AL	B	B	A	B	B	C	C	C	B	C	B	C	C
MG	C	B	B	C	C	A	A	B	C	C	B	C	C
PI	B	B	A	B	B	C	C	C	B	C	C	C	C
RN	C	C	B	C	A	B	C	B	C	B	B	C	C

• Source: Elaborated by the authors.

The results reveal pronounced heterogeneity, reflecting differentiated trajectories across each dimension of fiscal resilience. There is a coexistence of Federative Units (UFs): some positioned in upper ranges, associated with greater structural robustness and adaptive capacity, and others classified at intermediate or lower levels, marked by fiscal weaknesses that restrict room for maneuver in times of crisis. This disparity suggests that resilience is not a static attribute but rather results from the combination of accumulated fiscal conditions, institutional arrangements, and management choices over time.

The observed pattern is consistent with the literature, according to which economic and institutional crises affect governments asymmetrically, depending on preexisting fiscal conditions and specific institutional arrangements (Chernova & Gridnev, 2023; Coyle & Ferry, 2022; Desdiani et al., 2022). From this perspective, the proposed rating contributes to enhancing the comparative assessment of federative entities by organizing distinct vulnerabilities into a synthetic analytical structure. It is, therefore, a methodological tool with potential for complementary use in risk-oriented external control approaches.

The aggregate reading of the ranking also shows that the position of the federative entities does not linearly follow economic size, the absolute level of revenue, or fiscal performance. This pattern becomes particularly visible in the cases of Minas Gerais (MG), Rio de Janeiro (RJ), and São Paulo (SP), which are among the UFs with the highest RCL and revenue collection in the country (Figures 2 and 3), but received lower classifications in the rating.

FIGURE 2
NET CURRENT REVENUE: SP, MG, RJ VS. NATIONAL AVERAGE

FIGURE 3
TOTAL COLLECTED REVENUE: SP, MG, RJ VS. NATIONAL AVERAGE

This mismatch indicates that, despite robust revenue collection, some UFs face structural constraints that, taken together, limit fiscal room for maneuver and increase dependence on adjustment mechanisms. In the case of MG, for example, high RO limits discretionary expenditure adjustment and tends to intensify the effects of fiscal shocks, consistent with the literature associating inflexible spending structures with greater vulnerability and lower adaptive capacity (Soares & Gonçalves, 2022; Zhang et al., 2026).

In addition, cases such as RJ and SP show performance in Dimensions 1 and 3 that reveals relevant constraints from a fiscal resilience perspective. In the case of RJ, high indebtedness (DC), combined with liquidity constraints (LR), undermines fiscal sustainability and reduces the capacity to absorb shocks without resorting to extraordinary financing measures (Lee & Chen, 2022). For SP, the low MC and the high intertemporal commitment of expenditures (COA) indicate lower availability of reserves and safety margins to withstand adverse scenarios (Aquino & Azevedo, 2017; Klimanov et al., 2021). Taken together, these factors describe a setting in which high resource availability does not automatically translate into greater effective capacity to cope with crises.

By contrast, federative entities positioned at the top of the ranking, although presenting lower absolute levels of revenue collection, display fiscal structures with greater adaptive and response capacity. States such as Goiás (GO), Mato Grosso (MT), and Rondônia showed consistent performance in Dimensions 1 and 2, suggesting that resilience depends less on the size of the revenue base and more on the preservation of room for maneuver, the sustainability of assumed commitments, and the quality of fiscal management. For the TCs, this evidence suggests the usefulness of analytical approaches that complement the observation of current fiscal flows and incorporate the assessment of structural vulnerabilities.

In this context, the Current Savings (PC) indicator stands out as a relevant variable for fiscal sustainability, since recurrent deficits signal a structural inability to finance expenditures with own-source revenues, thereby increasing future vulnerabilities (Rao et al., 2023; Wójtowicz & Hodžić, 2022). The maintenance of positive PC levels among more resilient states reveals the existence of structural fiscal margins, allowing for adjustment trajectories less dependent on extraordinary measures, frequently observed in large UFs with lower performance in the rating.

In Dimension 2, the level of Transfer Dependency (DT) emerges as a relevant factor of vulnerability, as it exposes federative entities to exogenous shocks and reduces their capacity for autonomous response in the face of fiscal constraints (Chernova & Gridnev, 2023; Klimanov et al., 2021). The relatively favorable performance of GO, MT, and RO on this indicator is consistent with structural fiscal balance and helps explain their greater capacity for fiscal absorption and recovery (Batista et al., 2024).

The results also reinforce the interpretation that fiscal resilience is a multidimensional attribute resulting from the interaction among different components and, therefore, cannot be adequately captured by isolated indicators (Iacuzzi, 2022; McDonald, 2018). Overall, the results highlight the limitations of analyses based exclusively on individual variables or partial readings of the fiscal situation, in line with the literature that emphasizes the integrated nature of public finance sustainability (Barbera et al., 2017; Rao et al., 2023). From this perspective, the combination of different dimensions provides a more comprehensive analytical framework for understanding situations of greater fiscal vulnerability.

As an external benchmark, the fiscal resilience rating was compared with the Capag classification of the states for the 2024 base year. The analysis (Table 6) revealed a positive and moderate ordinal association between the two metrics, with a Spearman’s coefficient of 0.526. This result suggests that the proposed model engages with an established fiscal benchmark in the Brazilian context, without, however, mechanically reproducing it. Therefore, there is sufficient convergence to rule out the hypothesis of entirely disconnected rankings, but also sufficient divergence to indicate that the instruments do not capture exactly the same phenomenon.

TABLE 6
SUMMARY OF SPEARMAN’S CORRELATION TEST BETWEEN THE CLASSIFICATIONS OF THE PROPOSED MODEL AND CAPAG

Indicator	Value	Interpretation	Rule
Number of UFs	27	Strong positive association	ρ ≥ 0,70
Spearman’s Coefficient (ρ)	0.526	Moderate positive association	0,40 ≤ ρ < 0,70
t-statistic (approx.)	3.095	Weak positive association	0 < ρ < 0,40
ρ-value (approx.)	0.0133	No positive association	ρ ≤ 0

• Source: Elaborated by the authors.

In addition, the classificatory comparison illustrates this relationship of proximity (Chart 1 and Table 7). Only four of the 27 UFs showed matching classifications between Capag and the proposed analytical model, whereas, in six cases, the proposed model assigned a relatively more favorable classification. This distribution suggests that the divergences between the metrics are not random but rather reflect a systematic pattern of classificatory asymmetry. In aggregate terms, the proposed model proved to be more restrictive than Capag for most of the federative entities analyzed, although it also produced relatively higher classifications in a non-negligible subset of cases.

CHART 1
COMPARISON BETWEEN THE PROPOSED MODEL CLASSIFICATION AND CAPAG FOR BRAZILIAN STATES (BASE YEAR 2024)

TABLE 7
SUMMARY OF COMPARISON PATTERNS BETWEEN CAPAG AND THE PROPOSED ANALYTICAL MODEL

Comparison Pattern	States (n)	States (%)	UFs
Model > Capag	6	22.2%	DF, GO, MA, PA, RJ, RS
Convergent	4	14.8%	AC, AM, MG, RN
Model < Capag	17	63.0%	AL, AP, BA, CE, ES, MS, MT, PB, PE, PI, PR, RO, RR, SC, SE, SP, TO

• Source: Elaborated by the authors.

This classificatory asymmetry can be interpreted considering the differences in scope between the two metrics. While Capag is anchored in indicators focused on payment capacity, the proposed model incorporates additional variables related to financing autonomy, flexibility, and exposure to fiscal risks. The predominance of relatively less favorable classifications in the proposed model suggests that some federative entities exhibit relevant structural constraints when observed from a broader fiscal resilience perspective. Thus, the comparison with Capag reinforces the complementary usefulness of the fiscal resilience rating as an instrument for the comparative assessment of vulnerabilities within the scope of external control.

As an additional robustness check, a leave-one-indicator-out sensitivity analysis was conducted on the final set of indicators. The results indicate that, in all scenarios, the suitability of the dataset for PCA was preserved, with KMO values ranging from 0.643 to 0.712 and Bartlett’s test of sphericity remaining statistically significant in all cases, as summarized in Table 8. The cumulative explained variance remained at an acceptable level, ranging from 64.6% to 69.7%, although below the 74.6% observed in the baseline model. Taken together, these findings suggest that the isolated exclusion of specific indicators does not eliminate the statistical viability of the adopted dimensional structure, preserving a sufficient margin of analytical stability to support the interpretation of the index.

TABLE 8
STRUCTURAL ROBUSTNESS AND ORDINAL STABILITY IN THE LEAVE-ONE-INDICATOR-OUT SENSITIVITY ANALYSIS

Excluded Indicator	KMO	Bartlett’s ρ	Number of Components	Cumulative Variance	Spearman’s Rank Correlation
None (baseline scenario)	0.700	< 0.001	3	74.6%	100.0%
PC	0.684	< 0.001	3	65.6%	79.9%
LR	0.657	< 0.001	3	66.0%	90.0%
SP	0.646	< 0.001	3	65.0%	88.2%
DC	0.643	< 0.001	3	67.5%	71.3%
COA	0.697	< 0.001	3	69.4%	85.4%
DT	0.705	< 0.001	3	64.6%	81.0%
RDA	0.695	< 0.001	3	67.9%	86.9%
RO	0.712	< 0.001	3	69.7%	87.5%
MC	0.687	< 0.001	3	68.8%	77.2%

• Source: Elaborated by the authors.

From the perspective of the relative ordering of federative entities, a positive ordinal association was observed between the baseline ranking and the alternative rankings in all scenarios, with Spearman’s coefficients ranging from 0.713 to 0.900. The most sensitive scenarios corresponded to the exclusion of DC, PC, and MC, whereas the exclusion of RO, SP, and RDA produced relatively more stable results. This pattern suggests that, although the model does not depend disproportionately on any single indicator, certain variables exert greater influence on the comparative ordering of federative entities, especially those associated with the structural sustainability of financing and the existence of fiscal adjustment margins.

Table 9 complements this interpretation by showing that the classificatory stability of the overall rating was moderate, with exact classification being preserved for between 33.3% and 55.6% of the states, depending on the indicator excluded. Overall, most changes were concentrated in shifts of a single rating range, whereas changes of two or more ranges were less frequent. Even so, these more abrupt shifts proved relatively more relevant in the scenarios without PC (25.9%), DC (18.5%), and MC (14.8%), reinforcing the centrality of these variables in the composition of the final rating.

TABLE 9
STABILITY OF THE OVERALL RATING IN THE LEAVE-ONE-INDICATOR-OUT ANALYSIS

Excluded Indicator	Exact Rating Stability (n)	Exact Rating Stability (%)	1-Tier Shift (n)	1-Tier Shift (%)	≥ 2-Tier Shift (n)	≥ 2-Tier Shift (%)
PC	13	48.1%	7	25.9%	7	25.9%
LR	12	44.4%	12	44.4%	3	11.1%
SP	9	33.3%	16	59.3%	2	7.4%
DC	10	37.0%	12	44.4%	5	18.5%
COA	11	40.7%	13	48.1%	3	11.1%
DT	15	55.6%	9	33.3%	3	11.1%
RDA	15	55.6%	10	37.0%	2	7.4%
RO	15	55.6%	12	44.4%	0	0.0%
MC	15	55.6%	8	29.6%	4	14.8%

• Source: Elaborated by the authors.

The detailed analysis of state-level reclassifications (Appendix, Table E) confirms that the changes resulting from the exclusion of specific indicators tend to produce minor rearrangements in the relative positioning of federative entities, without undermining the overall coherence of the model. In these terms, the results of the sensitivity test suggest that the proposed index presents satisfactory structural robustness and reasonable classificatory stability, although with differentiated sensitivity among its components. This evidence reinforces the applied methodological nature of the proposal and helps support its use as a comparative tool to assist in the assessment of fiscal vulnerabilities.

5. CONCLUSION

This study was grounded in the premise that, although the TCs occupy a central position in fiscal governance, asymmetries persist in the comparative and structured analysis of fiscal vulnerabilities across government entities. The institutional diversity of these Courts and the methodological heterogeneity in the interpretation of fiscal indicators hinder the production of comparable diagnoses, thereby reinforcing the usefulness of additional analytical instruments.

For this purpose, the article described the processing and application of a fiscal resilience rating methodology for all Brazilian states, articulating the literature on government financial condition with the notion of resilience in the public sector. The use of PCA made it possible to reduce the dimensionality of the set of indicators and organize fiscal information into structural dimensions, allowing the construction of a synthetic metric for comparison among the federative entities analyzed.

The results revealed pronounced federative heterogeneity, showing the coexistence of federative entities classified in higher rating ranges, such as GO, MT, and RO, alongside others marked by constraints that limit their budgetary room for maneuver. This configuration corroborates the literature according to which macroeconomic shocks do not affect governments homogeneously, since their effects are conditioned by preexisting institutional arrangements (Chernova & Gridnev, 2023; Coyle & Ferry, 2022; Desdiani et al., 2022).

Moreover, the lower performance observed in SP, MG, and RJ suggests that fiscal resilience does not automatically derive from economic size or revenue volume, but rather depends on broader factors related to the structure of public finances and the quality of fiscal management. This finding reinforces the multidimensional nature of fiscal resilience and demonstrates that more robust economic structures may coexist with persistent fiscal vulnerabilities, especially when institutional adaptive capacity is limited.

As an additional procedure to assess analytical robustness, the comparison between the proposed model and Capag revealed partial convergence between the two metrics, with a moderate positive association. Although only four of the 27 states showed strict classificatory agreement, in 17 cases the analytical model assigned a relatively less favorable classification than Capag. This pattern suggests that the fiscal resilience rating does not mechanically reproduce the logic of payment capacity but rather captures complementary dimensions of the federative entities’ fiscal situation, reinforcing its usefulness as an additional analytical instrument for the comparative assessment of vulnerabilities within the scope of external control.

Finally, the leave-one-indicator-out sensitivity analysis indicated that the proposed model provides evidence of satisfactory structural robustness and reasonable classificatory stability, although with differentiated sensitivity across its components. In all scenarios, the suitability of the dataset for PCA was maintained, with acceptable KMO values, a statistically significant Bartlett’s test of sphericity, and cumulative explained variance sufficient to support the interpretation of the latent dimensional structure. From a classificatory perspective, a positive ordinal association was observed between the baseline ranking and the alternative rankings, while reclassifications were concentrated in shifts of limited magnitude, mainly involving a single rating range. Taken together, these results suggest that the model does not depend disproportionately on any single indicator, although some variables exert a relatively greater influence on the final ordering of the federative entities.

In these terms, the main contribution of the study lies in proposing a methodological tool that broadens the repertoire of fiscal analysis available to external control institutions. The aim is therefore not to replace existing metrics, but rather to offer a complementary analytical instrument for a multidimensional assessment of fiscal sustainability. Potential applications within the scope of the TCs are understood here as possibilities for institutional use to be tested and validated in future research, rather than as an empirically demonstrated effect of this study.

As limitations, the study is restricted to the empirical universe of Brazilian states, relies on secondary data, and, at this stage, does not include tests aimed at temporal, institutional, or predictive validation. For this reason, future research may further examine the stability of the model using longer time series, expand its application to municipalities, and more systematically assess its adherence to concrete institutional routines within control bodies.

ACKNOWLEDGMENTS

The authors thank the contributions received within the academic discussions held at the Graduate Program in Administration of the University of Brasília (UnB). They also thank the reviewers of the Revista de Administração Pública (RAP), whose contributions and suggestions improved the quality of the article, as well as the examining committee of the Professional Master’s Program in Public Administration of the University of Brasília (UnB), for the considerations that assisted in the refinement of the analyses presented.

APPENDIX

TABLE A
DESCRIPTIVE STATISTICS

Indicator	Valid (N)	Median	Average	Standard Derivation	Skewness	Kurtosis	Shapiro-Wilk	p-value	Minimum	Maximum	33rd Percentile	66th Percentile
IND1	270	0.601	0.798	0.596	1.719	2.209	0.768	3.017E-19	0.126	3.219	0.487	0.726
IND2	270	1.029	1.025	0.077	-0.239	0.203	0.988	0.02534	0.795	1.282	0.998	1.064
IND3	270	0.031	0.028	0.174	0.836	19.86	0.716	3.816E-21	-0.781	1.418	0.011	0.063
IND4	270	0.035	0.039	0.073	0.494	1.331	0.976	0.0001729	-0.153	0.296	0.009	0.058
IND5	270	0.009	0.013	0.015	5.51	42.39	0.545	6.257E-26	0.00076	0.159	0.007	0.014
IND6	270	0.487	0.489	0.073	0.023	1.134	0.984	0.003814	0.202	0.752	0.451	0.521
IND7	270	0.318	0.343	0.159	0.266	-0.803	0.968	0.00001065	0.051	0.709	0.265	0.433
IND8	270	0.081	0.087	0.038	0.774	0.083	0.943	1.031E-08	0.021	0.218	0.061	0.099
IND9	270	0.48	2.051	20.06	16.33	267.8	0.051	1.935E-34	0.178	329.8	0.409	0.584
IND10	270	0.008	0.017	0.031	4.316	23.41	0.525	2.139E-26	6E-06	0.26	0.004	0.013
IND11	270	0.006	0.008	0.006	1.94	8.265	0.865	1.248E-14	0.00011	0.049	0.004	0.009
IND12	270	-0.094	-0.095	0.09	-0.076	0.882	0.975	0.00009479	-0.336	0.184	-0.122	-0.072
IND13	270	0.006	0.015	0.029	4.102	19.61	0.512	1.102E-26	5E-06	0.213	0.004	0.01
IND14	270	0.067	0.072	0.031	0.63	-0.278	0.955	2.002E-07	0.012	0.167	0.052	0.085
IND15	270	0.007	0.01	0.008	2.511	12.33	0.815	3.174E-17	0.00012	0.067	0.005	0.011

• Source: Elaborated by the authors using JASP software.

TABLE B
SPEARMAN’S CORRELATIONS BETWEEN SELECTED FISCAL INDICATORS

Variable	IND1	IND2	IND3	IND4	IND5	IND6	IND7	IND8	IND9	IND10	IND11	IND12	IND13	IND14	IND15
IND1	—
IND2	0.252***	—
IND3	-0.236***	-0.675***	—
IND4	0.077	-0.422***	0.341***	—
IND5	0.077	-0.036	-0.046	0.047	—
IND6	-0.105	-0.049	0.112	0.276***	-0.054	—
IND7	-0.389***	-0.018	0.167**	-0.081	-0.038	0.397***	—
IND8	0.160**	0.132*	-0.398***	-0.174**	-0.043	-0.107	-0.188**	—
IND9	-0.363***	-0.440***	0.261***	0.055	0.072	0.088	0.360***	0.116	—
IND10	-0.167**	-0.096	-0.090	0.058	0.032	0.168**	0.079	0.115	0.124*	—
IND11	0.119	-0.071	-0.091	0.013	0.475***	-0.154*	-0.592***	0.145*	-0.188**	-0.008	—
IND12	-0.404***	-0.572***	0.422***	0.123*	0.101	0.127*	0.446***	-0.018	0.897***	0.134*	-0.232***	—
IND13	-0.200***	-0.147*	-0.036	0.093	0.029	0.222***	0.121*	0.092	0.164**	0.993***	-0.014	0.182**	—
IND14	0.025	-0.026	-0.250***	-0.056	-0.062	0.104	-0.072	0.940***	0.247***	0.190**	0.138*	0.135*	0.194**	—
IND15	0.164**	-0.039	-0.123*	-0.003	0.489***	-0.228***	-0.629***	0.174**	-0.216***	-0.033	0.991***	-0.268***	-0.047	0.132*	—

• Source: Elaborated by the authors using JASP software.

TABLE C
INDIVIDUAL VALUES (%) AND SCORES OF FISCAL RESILIENCE INDICATORS BY STATE — BASE YEAR 2024

	Structural Solvency and Liquidity (SLE)						Financing Autonomy and Sustainability (ASF)						Flexibility and Exposure to Fiscal Risks (FER)
	LR		PC		SP		DC		DT		RDA		RO		COA		MC
UF	Value	Score	Value	Score	Value	Score	Value	Score	Value	Score	Value	Score	Value	Score	Value	Score	Value	Score
AC	-0.43	C	92.14	B	-5.77	A	38.63	A	65.49	C	0.22	C	52.62	C	6.51	B	0.02	C
AL	1.01	B	90.28	B	-3.90	A	90.77	B	47.53	C	0.14	C	48.96	B	13.41	C	0.39	B
AM	0.82	B	93.92	B	-3.43	A	40.72	A	29.28	B	0.30	C	41.73	A	8.49	B	2.78	A
AP	0.11	B	79.36	A	18.45	A	66.46	A	65.02	C	0.09	C	53.45	C	9.59	B	0.51	B
BA	5.39	A	86.97	B	-6.06	A	53.41	A	36.62	B	0.79	B	48.55	B	6.58	B	0.05	C
CE	9.51	A	90.06	B	-1.62	A	54.81	A	40.83	B	0.67	B	52.14	C	4.61	A	0.06	C
DF	3.54	B	92.23	B	0.73	A	46.62	A	17.61	A	1.51	A	41.84	A	11.33	C	3.07	A
ES	11.49	A	81.72	A	-6.54	A	33.75	A	26.65	B	0.57	B	46.31	B	6.93	B	1.91	A
GO	19.19	A	95.76	C	-7.52	B	65.09	B	21.51	A	2.16	A	51.79	B	4.32	A	3.27	A
MA	6.13	B	89.02	B	0.27	A	37.58	A	55.93	C	1.03	A	43.88	A	9.21	B	1.99	A
MG	-10.33	C	94.93	B	-10.44	B	178.05	C	19.37	A	1.20	A	55.80	C	12.18	C	0.90	B
MS	7.91	A	87.99	B	-9.04	B	45.03	A	24.16	A	0.48	B	47.84	B	7.70	B	0.85	B
MT	11.91	A	81.92	A	-0.81	A	12.61	A	19.93	A	1.06	A	43.75	A	11.82	C	0.01	C
PA	1.40	B	89.97	B	-2.66	A	19.55	A	35.46	B	0.78	B	43.77	A	5.30	A	0.45	B
PB	24.22	A	85.27	B	-9.05	B	41.65	A	46.04	C	0.60	B	56.50	C	3.88	A	1.47	A
PE	0.77	B	93.48	B	-7.88	B	44.54	A	39.57	B	0.44	B	48.76	B	4.71	A	0.10	C
PI	1.39	B	92.05	B	-3.34	A	68.56	B	43.48	C	0.04	C	46.12	B	12.28	C	0.24	C
PR	23.68	A	87.34	B	-7.80	B	49.07	A	22.27	A	1.33	A	51.26	B	7.85	B	0.42	B
RJ	-11.24	C	94.42	B	-11.34	B	203.65	C	9.47	A	1.28	A	51.07	B	4.03	A	0.78	B
RN	-13.91	C	95.23	C	-9.97	B	27.41	A	42,85	B	0,36	C	56,88	C	8,84	B	0.62	B
RO	10.29	A	86.88	B	-1.20	A	41.57	A	41.65	B	1.14	A	47.67	B	11.63	C	0.50	B
RR	9.23	A	91.35	B	5.16	A	27.83	A	64.06	C	0.12	C	54.77	C	7.30	B	3.39	A
RS	-9.77	C	95.57	B	-14.69	C	218.58	C	16.66	A	1.63	A	42.52	A	9.72	B	1.16	A
SC	8.58	A	89.44	B	-9.58	B	54.23	A	20.15	A	2.20	A	50.86	B	5.01	A	0.00	C
SE	6.47	A	90.95	B	-9.45	B	39.49	A	49.39	C	0.53	B	50.47	B	7.06	B	0.83	B
SP	3.59	B	89.64	B	-9.23	B	152.38	C	11.86	A	1.75	A	42.36	A	12.26	C	0.02	C
TO	4.66	B	90.15	B	2.96	A	25.87	A	46.25	C	0.97	A	50.23	B	11.23	C	1.74	A

• Note: LR = Relative Liquidity; PC = Current Savings; SP = Pension Sustainability; DC = Consolidated Debt; DT = Transfer Dependency; RDA = Recoverability of Outstanding Tax Debt; RO = Budgetary Rigidity; COA = Commitment to Previous-Year Obligations; MC = Contingency Margin.
• Source: Elaborated by the authors.

TABLE D
FISCAL RESILIENCE ASSESSMENT MATRIX BY DIMENSIONS

Dimension Rating			Overall Fiscal Resilience Rating	Description	Category
SLE	ASF	FER
A	A	A	AAA	Very high fiscal resilience: solid fiscal situation, with high solvency and liquidity, consistent financial autonomy, and low exposure to structural risks.	Resilient
A	A	B	A+	High fiscal resilience: favorable fiscal condition, with predominantly strong results and no relevant critical weaknesses.
A	B	A	A+
B	A	A	A+
A	A	C	A	Moderately high fiscal resilience: good overall performance, although with one point of vulnerability requiring preventive monitoring.
A	C	A	A
C	A	A	A
B	B	A	B+	Favorable intermediate fiscal resilience: relatively balanced intermediate situation, with limited but existing capacity for shock absorption.	Stable
B	A	B	B+
A	B	B	B+
A	B	C	B	Intermediate fiscal resilience: fiscal heterogeneity, with the coexistence of strengths and weaknesses that constrain the capacity to respond to shocks.
A	C	B	B
B	A	C	B
B	C	A	B
C	A	B	B
C	B	A	B
B	B	B	B
B	B	C	C+	Low fiscal resilience: relevant fiscal fragility, with reduced room for maneuver and greater exposure to risks in adverse scenarios.	Vulnerable
B	C	B	C+
C	B	B	C+
C	C	A	C+
A	C	C	C+
C	A	C	C+
C	C	B	C+
C	C	B	C	Very low fiscal resilience: dominant fragility, with high vulnerability and restricted fiscal adjustment capacity
C	B	C	C
B	C	C	C
C	C	C	D	Critical fiscal resilience: severe structural impairment, with systemic incapacity to absorb fiscal shocks	Critical

• Source: Elaborated by the authors.

TABLE E
LEAVE-ONE-INDICATOR-OUT SENSITIVITY ANALYSIS

Baseline Model			Without PC			Without LR			Without SP			Without DC			Without COA			Without DT			Without RDA			Without RO			Without MC
UF	Rating	Ranking	Rating	Ranking	Shift Rating	Rating	Ranking	Shift Rating	Rating	Ranking	Shift Rating	Rating	Ranking	Shift Rating	Rating	Ranking	Shift Rating	Rating	Ranking	Shift Rating	Rating	Ranking	Shift Rating	Rating	Ranking	Shift Rating	Rating	Ranking	Shift Rating
AC	C	23	C	24	0	C	21	0	D	26	1	D	26	1	D	27	1	C	24	0	C	23	0	D	27	1	C	23	0
AL	C	24	C	25	0	C	22	0	C	21	0	C	23	0	C+	18	1	C+	18	1	C	24	0	C	24	0	C	24	0
AM	B+	4	B	11	1	B+	1	0	B	7	1	B	11	1	B+	4	0	B+	2	0	B+	1	0	B	9	1	B	9	1
AP	C+	20	C+	22	0	C	23	1	C	22	1	C	24	1	C	22	1	C+	19	0	C+	15	0	C+	17	0	C+	18	0
BA	B	12	B	12	0	C+	14	1	C	23	2	C+	15	1	C+	19	1	C+	20	1	C+	16	1	C+	18	1	B	10	0
CE	B	13	B	13	0	C+	15	1	C+	16	1	C+	16	1	C+	20	1	B	11	0	B	8	0	B	10	0	B	11	0
DF	B+	5	A+	1	2	B+	2	0	A	1	1	B+	3	0	A+	1	2	B+	3	0	B+	2	0	B+	4	0	B+	3	0
ES	B+	6	A+	2	2	B	5	1	B	8	1	B+	4	0	B+	5	0	B+	4	0	B+	3	0	B+	5	0	B+	4	0
GO	A+	1	A+	3	0	B	6	3	B+	2	2	B+	5	2	B+	6	2	B+	5	2	B	9	3	A+	1	0	B	12	3
MA	B+	7	A+	4	2	B+	3	0	B+	3	0	B+	6	0	B+	7	0	A+	1	2	B	10	1	B	11	1	B	13	1
MG	C	25	C+	23	1	C	24	0	C	24	0	C+	17	1	C	23	0	D	27	1	C	25	0	C	25	0	C	25	0
MS	B+	8	B	14	1	B	7	1	B	9	1	C+	18	2	B	9	1	C+	21	2	C+	17	2	B	12	1	C+	19	2
MT	A	2	B+	8	1	B	8	2	B+	4	1	A	1	0	A+	2	1	B+	6	1	B+	4	1	A	2	0	A+	1	1
PA	B+	9	A+	5	2	B+	4	0	B	10	1	B+	7	0	B	10	1	B+	7	0	B+	5	0	B	13	1	B+	5	0
PB	B	14	B	15	0	C+	16	1	B	11	0	C+	19	1	B	11	0	B+	8	1	B	11	0	B+	6	1	B	14	0
PE	B	15	B	16	0	C+	17	1	C+	17	1	C+	20	1	C	24	2	C+	22	1	C+	18	1	C+	19	1	B	15	0
PI	C	26	C	26	0	C	25	0	D	27	1	C	25	0	C	25	0	C	25	0	C	26	0	C	26	0	C	26	0
PR	B+	10	A+	6	2	B	9	1	B+	5	0	B+	8	0	B+	8	0	B+	9	0	B	12	1	B+	7	0	B+	6	0
RJ	C+	21	B+	9	2	C+	18	0	B	12	1	B+	9	2	B	12	1	B	12	1	B	13	1	B	14	1	B+	7	2
RN	C	27	C	27	0	C	26	0	C	25	0	D	27	1	C	26	0	C	26	0	C	27	0	C+	20	1	C	27	0
RO	A	3	B	17	2	B	10	2	B+	6	1	B	12	2	A+	3	1	B+	10	1	B+	6	1	A	3	0	B+	8	1
RR	B	16	B	18	0	C+	19	1	C+	18	1	C+	21	1	C+	21	1	B	13	0	C+	19	1	C+	21	1	C+	20	1
RS	B	17	B	19	0	B	11	0	B	13	0	A	2	2	B	13	0	B	14	0	C+	20	1	C+	22	1	C+	21	1
SC	B+	11	A	7	1	B	12	1	B	14	1	B+	10	0	B	14	1	B	15	1	B+	7	0	B+	8	0	A+	2	2
SE	B	18	B	20	0	C+	20	1	B	15	0	C+	22	1	B	15	0	B	16	0	C+	21	1	B	15	0	C+	22	1
SP	C+	22	B	21	1	C	27	1	C+	19	0	B	13	1	B	16	1	C+	23	0	C+	22	0	C+	23	0	B	16	1
TO	B	19	B+	10	1	B	13	0	C+	20	1	B	14	0	B	17	0	B	17	0	B	14	0	B	16	0	B	17	0

• Note: LR = Relative Liquidity; PC = Current Savings; SP = Pension Sustainability; DC = Consolidated Debt; DT = Transfer Dependency; RDA = Recoverability of Outstanding Tax Debt; RO = Budgetary Rigidity; COA = Commitment to Previous-Year Obligations; MC = Contingency Margin. The ordinal rating score corresponds to the conversion of qualitative classification categories into an ordered numerical scale, adopted exclusively for the purpose of comparing scenarios and defining the relative positioning of federative entities in the ranking. For this purpose, the following correspondence was used: AAA = 8; A+ = 7; A = 6; B+ = 5; B = 4; C+ = 3; C = 2; D = 1. The “Rating Shift” column indicates the number of levels by which the entity moved relative to the baseline model, following the individual exclusion of each indicator.
• Source: Elaborated by the authors.

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Reviewers:

The reviewers did not authorize the disclosure of their identities.

Peer review report:

The peer review report is available at https://periodicos.fgv.br/rap/article/view/97069/90457

RESEARCH DATA AVAILABILITY

The secondary data used in this study are of a public nature and were obtained from official sources, especially the Brazilian Public Sector Accounting and Fiscal Information System (Siconfi), developed by the National Treasury Secretariat (STN), available at https://siconfi.tesouro.gov.br/siconfi/index.jsf, as well as from transparency portals, annual accountability reports, general balance sheets, and accounting statements of the states. The consolidated and processed dataset by the authors, which underpins the results presented in this article, is available upon request to the corresponding author, Felipe Ramos Barbosa. The dataset was not deposited in a public repository due to the volume of processed information and the need for organization, standardization, and processing of data extracted from multiple official sources.

ARTIFICIAL INTELLIGENCE USAGE

The artificial intelligence tool ChatGPT was used to support technical and grammatical revision, the improvement of textual clarity, terminological standardization, and the normalization of references. The definition of the research problem, the selection of indicators, the methodological procedures, the data analysis, the interpretation of the results, and the conclusions of the study are the sole responsibility of the authors.

FUNDING

This research did not receive any specific grant from public, commercial, or not-for-profit funding agencies.

*

Corresponding Author

Editor-in-chief:

[Translated version] Note: All English quotes were translated by this article’s translator.

Editor-in-chief:

Gregory Michener, Fundação Getulio Vargas, Rio de Janeiro, RJ, Brazil

Associate editor:

Gabriela Spanghero Lotta, Fundação Getulio Vargas, São Paulo, SP, Brazil

CONFLICT OF INTEREST

The authors declare no conflict of interest.

Publication Dates

• Publication in this collection
  28 Aug 2026
• Date of issue
  2026

History

• Received
  09 Jan 2026
• Accepted
  18 May 2026