Interjurisdictional Tax Performance within a Country: Cluster-Specific Panel Estimation of Provincial Tax Capacity, Effort, and Collection Effectiveness in Türkiye (2007-2022)

Interjurisdictional Tax Performance within a Country: Cluster-Specific Panel Estimation of Provincial Tax Capacity, Effort, and Collection Effectiveness in Türkiye (2007-2022) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Interjurisdictional Tax Performance within a Country: Cluster-Specific Panel Estimation of Provincial Tax Capacity, Effort, and Collection Effectiveness in Türkiye (2007-2022) Metin Allahverdi, Ferdi Çelikay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8378380/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Domestic revenue mobilisation depends not only on the size of the tax base but also on how effectively jurisdictions convert taxable capacity into realised collections. This paper develops a subnational diagnostic framework for Türkiye by jointly measuring tax capacity, tax effort, and tax collection effectiveness for 81 provinces over 2007–2022. To address structural heterogeneity across jurisdictions, the analysis proceeds in two integrated stages. First, we apply hierarchical clustering to group provinces into relatively homogeneous clusters based on economic structure, demographic pressure, and fiscal characteristics, producing an interpretable typology for differentiated policy targeting. Second, we estimate cluster-specific tax-capacity functions using fixed-effects panel regressions. Hausman tests reject random effects, indicating that time-invariant provincial heterogeneity is correlated with regressors. Diagnostics further suggest heteroskedasticity, serial correlation, and cross-sectional dependence, so inference relies on Driscoll-Kraay standard errors. Tax effort is computed as the ratio of actual tax burden to predicted capacity, enabling consistent comparisons of over- and under-performance across structurally different provinces. Results show that (i) tax capacity is highly uneven and shaped by industrialisation, openness, and urbanisation; (ii) determinants of capacity are cluster-specific, cautioning against a single national capacity function; and (iii) provinces with similar capacity can display markedly different effort and collection outcomes, highlighting the role of administrative capacity and compliance. The proposed three-dimensional taxonomy provides a practical basis for fiscal equalisation and transfer design by separating structural constraints from effort and collection gaps, offering a replicable template for countries with pronounced regional disparities. Tax capacity Tax effort Revenue mobilization Subnational public finance Fixed-effects panel Hierarchical clustering Figures Figure 1 Figure 2 Figure 3 Introduction Domestic revenue mobilisation sits at the centre of modern fiscal governance because it determines the state’s ability to finance public services, stabilise economic cycles, and sustain development investments. A large and growing body of work has therefore sought to explain why some jurisdictions raise more revenue than others, and whether observed tax outcomes reflect genuine economic constraints or weaknesses in policy and implementation. As earlier studies pointed out, headline revenue ratios are difficult to interpret without a benchmark, since the same tax burden can arise from very different underlying bases and enforcement realities (Lotz & Morss, 1967 ). This insight remains highly relevant today, particularly for economies experiencing rapid structural change and administrative modernisation, where the distance between “potential” and “performance” can widen or narrow over time. In the tax performance literature, the distinction between tax capacity (tax potential) and tax effort has become the standard analytic starting point. Put simply, tax capacity reflects the revenue a jurisdiction could plausibly raise given its structural “tax handles” (income level, sectoral composition, base visibility), while tax effort captures how closely actual outcomes approach that benchmark (Lotz & Morss, 1970 ; Chelliah et al., 1975 ). Subsequent research has suggested that effort is not merely a discretionary choice; rather, it is shaped by institutions, compliance conditions, and administrative capability, implying that two jurisdictions with similar structural capacity may nonetheless display very different realised revenues (Bird et al., 2006 ; Bird et al., 2008 ). In other words, capacity is partly “where you start,” but effort reflects how effectively policy and administration convert that starting point into collections. Türkiye provides a timely case for revisiting these concepts with a diagnostic lens. Recent OECD evidence shows that Türkiye’s tax-to-GDP ratio remains well below the OECD average: the OECD’s Revenue Statistics country note reports an increase from 23.2% in 2023 to 24.0% in 2024, compared to an OECD average of 34.1% (OECD, 2025a ). The same OECD series documents that Türkiye’s tax-to-GDP ratio rose from 20.9% in 2022 to 23.5% in 2023, highlighting both recovery dynamics and the policy importance of understanding what drives shortfalls and rebounds (OECD, 2024 ). Yet these aggregates cannot tell us whether gaps reflect limited taxable capacity, low effort, or weaknesses at the collection stage. As the OECD Economic Survey notes, Türkiye’s VAT system is “particularly complex” due to special rates and its VAT revenue is low relative to potential an observation that points to policy design and compliance/administration dimensions as well as structural constraints (OECD, 2025b ). Recent international research reinforces why separating these channels matters. For example, IMF evidence quantifies the revenue yields associated with improvements in tax administration capabilities, arguing that stronger administration is systematically associated with higher revenue performance (Adan et al., 2023 ). Related IMF work emphasises that “building tax capacity” requires a holistic, institution-based approach spanning policy, administration, and legal implementation, and that progress depends on sustained capability-building rather than isolated reforms (Benitez et al., 2023 ). In parallel, research on digitalisation suggests that technology can raise revenue performance when corporate-sector digitalisation is matched by sufficiently capable, digitised tax administrations; in cross-country estimates, stronger firm digitalisation is associated with higher tax revenues-to-GDP conditional on the state of tax-administration digitalization (Nose et al., 2025 ). Taken together, these studies suggest that tax effort and collection outcomes are potentially responsive to institutional and technological change precisely the kind of change Türkiye experienced across 2007–2022. These considerations become more acute once the unit of analysis shifts from the national economy to the province. Previous research has frequently argued that subnational units can diverge in fiscal performance even under a shared national legal framework because tax bases, informality, administrative reach, and compliance cultures vary by region. Türkiye’s own literature supports this view. For example, Turkish studies have estimated tax capacity for earlier periods (Dursun, 2008 ), examined determinants of tax capacity and tax effort for Türkiye (Atsan, 2017 ), and analysed tax effort dynamics in Türkiye (Saruç et al., 2018 ). Regional work also discusses how tax effort varies across regions and how it can be measured empirically in Turkish settings (Çelikay, 2020 , Allahverdi & Alagöz, 2023 ). Collectively, these studies make a strong case that provincial heterogeneity is real and policy relevant. However, as several strands of the literature imply, subnational evidence is often fragmented across time horizons, methods, and performance indicators, which complicates translation into targeted policy action. This is where a clearer problem emerges. Although the capacity effort framework is well established, much of the applied work has tended to either (i) focus on estimating capacity and inferring effort, or (ii) examine revenue outcomes without explicitly distinguishing the collection stage that is, the conversion of assessed liabilities into cash collections. Yet collection effectiveness is a distinct and policy-relevant dimension of performance: a province can display moderate effort relative to capacity but still underperform in realised revenues if collection processes are weak. Moreover, VAT performance debates illustrate how “gaps” can arise from both policy design and compliance/administration, motivating a separate look at realised-collection effectiveness rather than treating all shortfalls as a single residual. The European Commission’s VAT Gap framework explicitly separates compliance gaps from policy gaps, providing a widely used benchmark logic for thinking about how legal liabilities translate into receipts. While Türkiye’s provincial VAT gaps are not directly observed in standard datasets, the conceptual lesson is transferable: performance shortfalls can originate at multiple points in the revenue chain. Against this background, the present study develops a new approach to assessing provincial tax performance in Türkiye over the period 2007–2022. The study aims to determine provincial tax potential (capacity ) and tax effort, and then to classify provinces based on tax capacity, tax effort, and tax collection rates to generate an interpretable typology that can inform differentiated tax policy and administrative prioritisation. This design is motivated by two linked needs. First, as earlier research has noted, a benchmark is needed to interpret observed revenue ratios meaningfully (Lotz & Morss, 1967 ; Chelliah et al., 1975 ). Second, more recent work indicates that administration reforms and digital capability can shift outcomes, implying that “effort” and “collection effectiveness” should be analysed in a way that is sensitive to time variation and institutional change. Methodologically, the study combines panel-data benchmarking with cluster analysis to move beyond single rankings toward actionable categories. Cluster-based classification is not presented as a substitute for econometric identification; rather, it is used to translate multidimensional performance metrics into profiles that are easier to interpret for policy. In practical terms, a typology can distinguish provinces where the binding constraint is structural capacity (base development and formalisation), from those where the constraint is low effort (compliance and enforcement), and from those where collection rates signal an implementation bottleneck. By doing so over a long panel that spans crises, reform episodes, and accelerating digitalisation, the study speaks to both the foundational logic of tax effort and the contemporary policy agenda of strengthening revenue mobilisation in a targeted and evidence-based manner. Literature review and conceptual framework 2.1. Tax potential, tax capacity, and tax effort: concepts and measurement logic Tax performance metrics such as the tax-to-income or tax-to-GDP ratio are inherently ambiguous without a benchmark that reflects underlying economic structure and the feasibility of collection. The early comparative literature therefore distinguishes tax capacity (tax potential) from tax effort: capacity approximates the level of revenue a jurisdiction could raise given its structural “tax handles,” while effort reflects how closely observed revenue approaches that benchmark (Lotz & Morss, 1967 ; Lotz & Morss, 1970 ; Bahl, 1971 ). This distinction remains central because it separates low revenue caused by constrained bases from low revenue caused by weak compliance, administration, or incentives. The conceptual foundation was extended by the IMF tradition on tax ratios and effort, where capacity is estimated using structural determinants and effort is inferred from deviations (Chelliah, 1971 ; Chelliah et al., 1975 ). Subsequent work emphasized that capacity and effort are not purely “economic”; they are shaped by political and institutional constraints that define state capability and the enforceability of tax rules (Fauvelle-Aymar, 1999 ; Besley & Persson, 2014 ). This broadening is crucial for subnational settings: provinces operating under a uniform national legal framework can still diverge markedly in compliance environments, base visibility, and administrative reach. In the more recent empirical literature, “tax potential” is increasingly treated as a frontier rather than an average benchmark reflecting the best attainable performance conditional on fundamentals while effort is interpreted as distance from that frontier (Leuthold, 1991 ; Stotsky & WoldeMariam, 1997 ; Piancastelli, 2001 ; Pessino & Ricardo, 2010 ; Fenochietto & Pessino, 2013 ). In parallel, applied policy research has introduced widely used comparative norms and performance metrics for revenue potential especially for benchmarking and diagnostics arguing that a small set of core indicators can serve as credible reference points in cross-jurisdiction comparisons (Le et al., 2012 ). The implication for a province-level study is direct: benchmarking requires both a defensible estimate of potential and complementary performance signals that capture implementation and collection effectiveness. 2.2. Determinants of tax capacity and effort: structural, institutional, and administrative channels A broad consensus exists that structural variables shape the feasible tax base, and therefore tax capacity. Classic and subsequent comparative studies repeatedly identify per-capita income, sectoral composition (especially agriculture), openness and trade intensity, and macroeconomic stability as core “tax handles” that predict revenue capacity (Tanzi, 1968 ; Shin, 1969 ; Ansari, 1982 ; Tanzi, 1992 ). Later work refined these determinants by emphasizing that the tax system itself and not only the base responds to development pathways: the composition of revenue (direct versus indirect) and the design of exemptions and preferential regimes matter for the realised yield (Tanzi & Zee, 2000 ; Teera & Hudson, 2004 ; Dioda, 2012 ; Castro & Camarillo, 2014 ). Institutional and political economy perspectives add that the conversion of capacity into actual revenue is mediated by state capacity, credibility, and the broader fiscal contract. Governance constraints can depress tax effort even when structural capacity is non-trivial (Fauvelle-Aymar, 1999 ; Bird et al., 2006 ; Bird et al., 2008 ; Besley & Persson, 2014 ). Similarly, cross-country evidence for developing regions shows that corruption, policy choices, and the quality of public administration alter revenue mobilisation outcomes independent of the base (Ghura, 1998 ; Eltony, 2002 ; Gupta, 2007 ; Drummond et al., 2012 ). The historical and comparative literature on “tax sacrifice” and tax burden measurement reinforces the same point: performance comparisons must adjust for structural and institutional heterogeneity (Frank, 1959 ; Williamson, 1961 ; Bird, 1964 ). The last five years have further strengthened the emphasis on tax administration performance and digitalisation as channels that shape effort and collection outcomes. Recent IMF work quantifies revenue yields from tax administration reforms and shows that strengthening administrative capability can generate persistent gains (Adan et al., 2023 ; Atsebi et al., 2025 ). Parallel IMF contributions link administration performance to compliance outcomes and argue that compliance gaps are systematically shaped by administrative capacity (Baer et al., 2025 ). In addition, digital adoption within revenue administrations is framed as a catalyst for stronger compliance management (Nose & Mengistu, 2023 ), and newer evidence suggests that digital technologies can materially improve tax collection when administrative digital capability is sufficiently developed (Nose et al., 2025 ; International Monetary Fund, 2025 ). These mechanisms are relevant for Türkiye because the 2007–2022 period encompasses major shifts in e-services, e-invoicing ecosystems, and enforcement analytics, making administrative modernisation a plausible driver of effort dynamics. 2.3. Empirical approaches: benchmarks, panels, and frontier estimation The benchmark regression approach remains the most widely used method to estimate capacity and infer effort (Bahl, 1971 ; Chelliah et al., 1975 ; Leuthold, 1991 ). Its strength is transparency: capacity is the predicted tax ratio given fundamentals, and effort is the deviation. However, a major methodological critique is that “effort” can be contaminated by unobserved heterogeneity, measurement error, and structural breaks. This critique has been reinforced by recent re-estimations showing that effort rankings can shift under alternative specifications and datasets (McNabb et al., 2021 ). Frontier approaches respond to this critique by estimating an attainable frontier and treating deviations as inefficiency (effort shortfall), typically separating noise from persistent underperformance (Piancastelli, 2001 ; Fenochietto & Pessino, 2013 ). Yet frontier methods require strong assumptions and can be sensitive to outliers and distributional choices. For that reason, many studies advocate triangulation using alternative estimators and robustness checks rather than presenting a single “true” effort measure (Stotsky & WoldeMariam, 1997 ; Teera & Hudson, 2004 ; McNabb et al., 2021 ). Panel data design is particularly important in subnational settings because persistent provincial characteristics (economic geography, administrative traditions, firm structure) can bias cross-sectional inference. Methodological studies emphasise that serial correlation, error structures, and cross-sectional dependence can distort inference if ignored (Bhargava et al., 1982 ; Baltagi & Wu, 1999 ). In a 2007–2022 provincial panel, these issues are not peripheral: they shape how confidently differences in “effort” can be interpreted as behavioural/administrative rather than as artifacts of correlated shocks and persistent omitted characteristics. Recent work also proposes refined effort estimation using new data and updated econometric design. Hayruni et al. ( 2025 ), for example, frames effort estimation as a dataset-driven exercise and provides updated evidence on effort distributions supporting the general conclusion that effort is sensitive to modelling choices and therefore requires careful benchmarking and validation. The broader lesson is that capacity/effort estimation is now a methodological field in its own right rather than a mechanical residual computation. 2.4. Evidence from regional/subnational studies: reforms, convergence, and heterogeneous performance Subnational studies consistently show that tax effort varies across localities and responds to reforms, administrative restructuring, and local economic structure. In China, Wang, Shen, and Zou ( 2009 ) demonstrate that reforms and institutional arrangements can affect local government tax effort, implying that effort is not fixed even under a shared national framework. In India, work on state tax performance argues that tax effort can fall short of capacity due to structural and administrative constraints (Garg et al., 2017 ), while evidence from the post-reform period suggests that restructuring policies can move local tax effort toward convergence though heterogeneity remains substantial (Sen & Tulasidhar, 1988 ; Mahawar et al., 2015). Comparable frameworks have been applied to Mexico (Sobarzo, 2004 ) and other regions (Shang, 2017 ), reinforcing that capacity and effort are meaningfully separable in subnational panels and that local fiscal performance cannot be inferred from national averages. These studies support two implications that motivate the present design. First, capacity and effort must be treated as distinct constructs in regional analysis, since provinces can have high potential but underperform due to compliance and administration. Second, classification of jurisdictions into meaningful types can be more policy-relevant than a single ranking because it distinguishes structurally constrained areas from administratively underperforming ones. 2.5. Türkiye and regional tax effort: what is known and what remains under-specified Türkiye has a substantial domestic literature on tax capacity and effort, including both national and regional analyses. Early work focused on links between social structure and tax structure and on modelling Turkish tax performance historically (Heper, 1978 ), while subsequent Turkish scholarship developed the capacity/effort vocabulary and applied it to developing-country contexts (Berksoy, 1984 ). More applied national studies estimate Türkiye’s capacity and discuss structural determinants and reforms across different periods (Saraçoğlu, 2004 ; Günay, 2007 ; Dursun, 2008 ). In the regional domain, several contributions examine local or regional tax effort and its determinants, often emphasizing heterogeneity across provinces and regions (Çelik, 2006 ; Çelikay, 2016 ; Şimşek, 2013 ; Sağdıç, 2015 zıltan, 2018 ; Allahverdi, 2023 ). More recent work explicitly models regional effort and discusses measurement strategies for the Turkish case (Atsan, 2017 ; Saruç et al. 2018 ; Çelikay, 2020 ; Yıldırım, 2020 ). Two patterns stand out in this Türkiye-focused literature. First, regional studies repeatedly highlight that structural determinants alone do not explain provincial differences administrative effectiveness, compliance, and incentives matter. Second, measurement approaches vary considerably: some studies rely on regression benchmarks, others discuss elasticity/ buoyancy, while some adopt frontier logic. This variation creates scope for a design that (i) estimates provincial tax potential and effort consistently over a long period and (ii) offers an interpretable classification of provinces rather than only presenting rankings. Cross-country comparisons including Türkiye provide further context. Serin and Demir ( 2023 ) estimates tax capacity and effort for Türkiye alongside EU countries, illustrating that Türkiye’s relative position depends on benchmark choice and on how structural constraints are encoded in the capacity model. Such cross-country evidence is valuable for framing, but it cannot resolve within-Türkiye heterogeneity, which is precisely the motivation for a province panel. Finally, Türkiye’s contemporary policy debate underscores the relevance of consumption-tax performance and compliance. OECD assessments emphasise VAT design complexity and reduced rates as constraints on VAT yields relative to potential (OECD, 2025a ) and provide standardised comparative evidence on Türkiye’s tax mix (OECD, 2025b ). EU VAT gap work further formalises the distinction between policy and compliance components of underperformance (European Commission, 2025 ). Together, these sources justify interpreting provincial outcomes through a capacity-effort-collection lens, rather than relying on aggregate tax ratios. 2.6. From estimation to classification: why clustering provinces is analytically useful The study’s objective is not only to estimate tax potential and effort but also to classify provinces jointly by tax capacity, tax effort, and tax collection rates. Classification is motivated by the recognition that fiscal underperformance is multidimensional and that different profiles imply different policy responses. Recent research already uses clustering to identify meaningful groups in tax-related contexts, including OECD tax indicator typologies (Tüzüntürk, 2024 ) and performance grouping of tax administrations (Pirvu et al., 2025 ). Efficiency-oriented work likewise supports performance assessment relative to potential and comparable peers (Afonso et al., 2024 ). Methodologically, clustering is most defensible when it is treated as a complement to econometric benchmarking and when cluster validity is assessed rather than assumed (Kaufman & Rousseeuw, 1990 ; Tibshirani et al., 2001 ; Jain, 2010 ; Strauss & von Maltitz, 2017 ; Özdamar, 2018 ). For a provincial tax study, clustering adds two concrete benefits. First, it produces an interpretable typology (e.g., high potential-low effort; low potential-high effort; strong effort-weak collection) that aligns with policy targeting. Second, it offers a structured way to represent heterogeneity that would otherwise be absorbed into residuals and misinterpreted as effort differences. 2.7. Positioning of the study and research contribution Taken together, the literature establishes (i) the conceptual necessity of distinguishing tax potential from realised performance, (ii) the importance of institutional and administrative mechanisms particularly in the recent work on reform yields and digitalisation and (iii) the empirical reality of subnational heterogeneity in capacity and effort. Building on these insights, the present study estimates provincial tax potential and tax effort for Türkiye over 2007–2022 and classifies provinces jointly by tax capacity, tax effort, and tax collection rates. This combined design is intended to support differentiated tax policy and administrative prioritisation across provinces by separating structurally constrained jurisdictions from those where the main constraints are effort and collection-related. Research Approach The research approach adopted in this study is fundamentally quantitative, explanatory, and model-driven, reflecting the objective of estimating provincial tax capacity and tax effort using measurable structural variables across Türkiye’s 81 provinces between 2007 and 2022. According to Saunders et al.’s ( 2019 ) research onion, a quantitative deductive approach is appropriate when the study aims to test theoretically informed relationships using statistical modelling and large-scale secondary data. In this context, the present study develops and empirically evaluates tax capacity functions derived from established public finance theory, thereby following a deductive logic in which hypotheses regarding structural determinants of tax burden are tested using econometric methods. The approach proceeds in two integrated stages. First, hierarchical cluster analysis is used inductively to identify homogeneous groups of provinces based on economic structure, demographic composition, and fiscal indicators. This step allows the study to reduce structural heterogeneity a major limitation in previous work and to construct internally consistent subpanels for subsequent estimation. While this stage contains inductive elements because it uncovers patterns emerging from the data, it remains embedded within a broader deductive framework, as the clustering serves to improve the validity of theory-driven tax capacity models. The second stage involves the estimation of fixed-effects panel data models to determine provincial tax capacity and tax effort. This modelling strategy is consistent with a deductive approach, as it uses theoretically supported determinants such as income, industrial composition, openness, public expenditure intensity, and demographic pressure to estimate predicted tax burdens. The use of panel methods allows the study to control for unobserved provincial characteristics and temporal shocks, while Driscoll-Kraay standard errors address heteroskedasticity, serial correlation, and cross-sectional dependence, ensuring robust inference. This econometric stage directly supports the study’s objective of quantifying tax potential and evaluating the extent to which provinces utilize this potential over time. Together, these two stages form a hybrid analytical strategy that combines inductive pattern identification with deductive model estimation. This blended approach enhances both empirical accuracy and theoretical coherence, making it well suited for developing a three-dimensional provincial classification that integrates tax capacity, tax effort, and tax collection performance. The next subsection describes the methodological procedures for data selection and variable construction, which constitute the foundation upon which the analytical approach is implemented. 3.1 Data and Variables The empirical analysis was based on a balanced panel dataset covering all 81 Turkish provinces over the period 2007–2022. The dataset was constructed from multiple official sources to ensure reliability and comparability. Provincial tax and fiscal indicators were obtained from the Revenue Administration of Turkey (GİB, 2025) and the Ministry of Treasury and Finance (Republic of Turkey, Ministry of Treasury and Finance, 2025 ), while socioeconomic and demographic indicators, including income, sectoral structure, trade, population, and dependency ratios, were retrieved from the Turkish Statistical Institute (TURKSTAT, 2025). Following the modern tax capacity literature (Gupta, 2007 ; Fenochietto & Pessino, 2013 ; Castro & Camarillo, 2014 ), the analysis included variables that captured both the economic potential to generate revenue and the institutional-demographic pressures that may influence actual tax collection. The main variables were defined as follows: Tax Burden (TaxBurden₍ i ,ₜ₎) : Total tax revenues divided by provincial GDP. This variable served as the dependent variable in the tax capacity model. Manufacturing Share (ManfGDP₍ i ,ₜ₎) : The share of manufacturing output in GDP, representing industrial capacity and formal-sector activity. Agricultural Share (AgrGDP₍ i ,ₜ₎) : The share of agricultural output in GDP; higher agricultural dependence typically indicated weaker tax bases. Per Capita Income (lnGDPpc₍ i ,ₜ₎) : Logged per capita GDP, representing provincial economic development. Trade Openness (Openness₍ i ,ₜ₎) : The ratio of total exports plus imports to GDP. Public Expenditure Intensity (PubExp₍ i ,ₜ₎) : Provincial public expenditure as a share of GDP. Population Size (lnPopulation₍ i ,ₜ₎) : Logged provincial population. Urbanization Rate (Urban₍ i ,ₜ₎) : Share of urban population. Age Dependency Ratio (AgeDep₍ i ,ₜ₎) : The ratio of dependents to the working-age population. Table 1 presents descriptive statistics for all variables, revealing substantial cross-provincial variation as well as meaningful within-province dynamics over time both of which justify a modeling framework capable of correcting for unobserved heterogeneity and dynamic interactions. Table 1 Summary of Data Set Variables Level Mean Std. Dev. Min. Max. Obs. Tax Burden: Tax Revenue / GDP Overall 0.069 0.077 0.016 0.723 N = 1296 Between 0.075 0.027 0.579 n = 81 Within 0.017 -0.153 0.212 T = 16 ManfGDP: Manufactor Income / GDP Overall 0.139 0.106 0.005 0.524 N = 1296 Between 0.102 0.008 0.427 n = 81 Within 0.031 0.025 0.295 T = 16 AgrGDP: Agriculture Income / GDP Overall 0.148 0.074 0.001 0.415 N = 1296 Between 0.071 0.001 0.338 n = 81 Within 0.022 0.071 0.245 T = 16 lnGDPpc Overall 10.017 0.785 8.205 12.621 N = 1296 Between 0.343 9.194 10.875 n = 81 Within 0.707 8.872 11.969 T = 16 Openness Overall 0.171 0.228 0 1.693 N = 1296 Between 0.198 0.001 0.842 n = 81 Within 0.115 -0.200 1.147 T = 16 Intensity of Public Expenditure Public Expenditure / GDP Overall 0.157 0.085 0.026 0.631 N = 1296 Between 0.083 0.033 0.492 n = 81 Within 0.019 0.023 0.297 T = 16 lnPopulation Overall 13.227 0.945 11.217 16.582 N = 1296 Between 0.949 11.286 16.477 n = 81 Within 0.057 12.977 13.466 T = 16 Urbanization Overall 0.726 0.182 0.317 1 N = 1296 Between 0.151 0.367 0.989 n = 81 Within 0.010 0.348 0.942 T = 16 Age Dependency Overall 0.142 0.049 0.042 0.315 N = 1296 Between 0.046 0.048 0.271 n = 81 Within 0.017 0.098 0.199 T = 16 For the cluster analysis, all continuous variables were standardized using z-scores to ensure comparability across provinces. Additionally, natural logarithmic transformations were applied to variables exhibiting skewed distributions, improving normality and reducing the influence of outliers. These preprocessing steps ensured that the dataset was well suited for the multi-stage empirical framework, which combined structural clustering, robust panel estimation, and endogeneity-corrected procedures. The analytical process proceeds in three steps. First, hierarchical cluster analysis is applied to classify provinces into relatively homogeneous subgroups based on their economic and demographic characteristics. This step reduces structural heterogeneity and enhances the interpretability of subsequent tax capacity estimates. Second, tax capacity is estimated for each subgroup using fixed-effects panel regression models with Driscoll-Kraay standard errors, which address heteroscedasticity, autocorrelation, and cross-sectional dependence. Finally, provincial tax effort is calculated by comparing actual tax burden with the estimated tax capacity, providing a measure of the extent to which each province utilizes its revenue-raising potential. 3.2 Cluster Analysis To address the substantial socioeconomic heterogeneity across Turkish provinces, the first stage of the empirical strategy relied on hierarchical cluster analysis. This procedure grouped provinces with similar structural characteristics, thereby improving the homogeneity of subgroups and enhancing the reliability of the subsequent tax capacity estimates. Cluster analysis has been widely recognized as an effective tool for simplifying large datasets into analytically meaningful groups, particularly in fiscal federalism and regional tax capacity research where multicollinearity and structural divergence present major challenges. In this study, provinces were classified using agglomerative hierarchical clustering based on the Manhattan distance metric and Ward’s minimum-variance linkage method. Ward’s method minimized the increase in within-cluster variance at each step of aggregation, yielding compact and internally coherent clusters. The Manhattan distance measure, defined as the sum of absolute deviations, has been shown to perform effectively in high-dimensional socioeconomic datasets (Strauss & von Maltitz, 2017 ; Özdamar, 2018 ). The dissimilarity between any two provinces i and j across p standardized variables was computed as: $$\:\begin{array}{cccc}&\:d({x}_{i},{x}_{j})=\sum\:_{k=1}^{p}\mid\:{x}_{ik}-{x}_{jk}\mid\:&\:&\:\text{(1)}\end{array}$$ where \(\:n\) denotes the number of provinces ( \(\:n=81\) ), and \(\:p\) denotes the number of clustering variables. Consistent with modern tax capacity literature, seven structural variables were used as inputs for clustering: AgrGDP - share of agriculture in provincial GDP, ManfGDP - share of manufacturing in GDP, PerCap - per capita income, Openness - ratio of total trade to GDP, PubExp - public expenditure as a share of GDP, Population - logged total population, Urbanization Rate - share of urban population. All variables were standardized using z-scores prior to clustering to ensure comparability and to remove scale effects. 3.3 Regression Models To estimate provincial tax capacity, the study first constructed a panel regression framework designed to predict the feasible tax burden of each province conditional on its structural characteristics. Tax capacity was defined as the maximum level of revenue that a province could generate given its economic composition, demographic structure, and administrative environment, whereas tax effort was later computed as the ratio of actual tax burden to this estimated capacity. Because Türkiye’s provinces exhibit substantial heterogeneity, the regression analysis was not estimated on the full sample directly. Instead, the provinces were first classified into relatively homogeneous groups using hierarchical cluster analysis. This clustering step served as a preprocessing procedure rather than an inferential model: its purpose was to reduce structural heterogeneity and allow the estimation of tax capacity functions that better reflect the economic realities of similar provinces. Importantly, the number of clusters was not imposed ex ante but was determined empirically based on the statistical properties of the dendrogram and cluster validity criteria. After the clustering stage, the tax capacity model was estimated separately for each cluster, allowing parameter estimates to vary across economically and demographically distinct groups of provinces. This cluster-specific estimation strategy is consistent with contemporary fiscal capacity research and avoids imposing a single uniform tax capacity function across structurally diverse regions. The baseline empirical specification was: where i denotes the province and t the period from 2007 to 2022. The dependent variable, the tax burden, was measured as total provincial tax revenue divided by provincial GDP. Explanatory variables manufacturing share, per capita income, openness ratio, public expenditure intensity, and age dependency reflect widely recognized determinants of tax capacity in the literature. All variables were transformed using natural logarithms to improve distributional properties and interpretation. The models were estimated using fixed effects to control for time-invariant unobserved provincial characteristics, while Driscoll-Kraay standard errors corrected for heteroscedasticity, serial correlation, and cross-sectional dependence features strongly detected in the diagnostic tests. For each empirically determined cluster, Eq. (2) was estimated independently. The resulting predicted tax burden values represent the estimated tax capacity for provinces within that cluster. Provincial tax effort was then calculated as the ratio of actual tax burden to predicted tax burden, forming the basis for the multidimensional evaluation of tax performance in subsequent sections. Findings 4.1 Results of the Cluster Analysis The hierarchical clustering procedure identified nine statistically distinct provincial groups, reflecting the pronounced structural heterogeneity across Türkiye’s 81 provinces. Clustering was performed using seven standardized indicators capturing economic structure, demographic pressure, and fiscal characteristics. The Ward linkage method combined with the Manhattan distance metric generated clusters with high between-group and low within-group variability, indicating a well-defined segmentation of provinces. Figure 1 visualizes the spatial and structural distribution of provinces across the nine clusters, illustrating clear distinctions between industrialized western provinces and the predominantly agricultural or demographically constrained eastern regions. The hierarchical clustering procedure identified nine statistically distinct provincial groups (Table 2), characterized by significant differences in economic structure, demographic composition, and fiscal indicators. Descriptive statistics (Table 2) show clear structural differences across clusters. For example, clusters with high manufacturing shares and strong urbanization (Clusters 1, 3, and 8) exhibit characteristics associated with higher taxable capacity, whereas clusters dominated by agriculture or low-income provinces (Clusters 4, 6, and 7) present structural constraints consistent with lower tax capacity. Table 2 Means of Clusters According to Variables (2007–2022) Cluster N (Provinces) Means AgrGDP ManfGDP PerCap (TRY) Openess PubExp Pop Urban Cluster 1 7 13.7% 12.2% 36.159 16.3% 9.8% 1.629.197 87.5% Cluster 2 16 17.9% 7.9% 27.969 5.1% 17.0% 350.973 62.2% Cluster 3 12 20.3% 16.0% 30.825 16.0% 12.1% 446.916 67.1% Cluster 4 6 28.0% 2.3% 20.816 4.0% 23.8% 269.409 48.1% Cluster 5 5 2.4% 24.4% 58.652 57.2% 8.1% 5.650.408 97.0% Cluster 6 8 9.1% 29.1% 43.749 12.3% 8.0% 414.124 71.7% Cluster 7 6 13.3% 1.7% 22.197 9.0% 34.5% 294.383 60.6% Cluster 8 10 8.6% 26.2% 35.535 44.3% 10.1% 1.000.604 84.9% Cluster 9 11 13.9% 8.1% 21.985 9.2% 21.4% 987.582 83.3% To assess the robustness of the classification, Kruskal-Wallis tests were conducted for each clustering variable. All seven indicators differ significantly across clusters at the 1% level, confirming that the groups capture meaningful economic and demographic divergence (Table 3). The internal validation metrics further support the quality of the clustering solution: the overall classification accuracy is 0.87, and Cohen’s Kappa equals 0.82, indicating strong agreement beyond chance (Table 4). Taken together, these results demonstrate that the clustering structure is statistically sound and substantively meaningful, providing a reliable foundation for estimating tax capacity functions within relatively homogeneous provincial groups. The use of cluster-specific tax capacity models is therefore empirically justified, as it avoids imposing a single nationwide relationship on provinces with markedly different structural characteristics. 4.2 Model Diagnostics and Estimation Strategy Before estimating the tax capacity functions, a comprehensive set of diagnostic tests was conducted to determine the appropriate panel data specification. The Hausman tests reported in Table 5 uniformly reject the random-effects model at the 1% significance level across all clusters, indicating the presence of time-invariant unobserved heterogeneity that is correlated with the regressors. This provides strong econometric justification for employing fixed-effects (FE) estimators. Tablo 5. Preliminary Tests Model for Group Hausman Test Heteroskedasticty Autocorrelation a Cross-Sectional Dependency Clusters F Test Chi2 Wald Testi Durbin Watson Baltagi-Wu Pesaran-CD Group 1 128.3*** 153.3*** 6838.3*** 0.791 0.543 63.3*** Group 2 149.2*** 181.2*** 7611.4*** 0.582 0.497 57.3*** Group 3 129.1*** 142.1*** 8442.9*** 0.524 0.586 48.8*** Group 4 39.3*** 57.5*** 5271.0*** 0.538 0.414 78.6*** Group 5 52.1*** 22.6*** 5145.7*** 0.839 0.485 88.1*** Group 6 43.6*** 29.3*** 6149.3*** 0.941 0.863 76.5*** Group 7 28.2*** 84.7*** 7236.1*** 0.756 0.358 65.6*** Group 8 113.4*** 34.8*** 4725.7*** 0.722 0.220 58.1*** Group 9 103.7*** 32.5*** 8513.9*** 0.881 0.214 98.2*** a The threshold value relied upon for the Durbin-Watson and Baltagi-Wu tests is "2". *** is the p-value, statistically significant at the 1% level. The remaining diagnostics confirm that the error structure of the data deviates from classical assumptions. Wald tests for heteroskedasticity, Durbin-Watson and Baltagi-Wu tests for serial correlation, and Pesaran’s CD test for cross-sectional dependence all indicate significant violations of homoskedasticity, independence, and cross-sectional orthogonality (Table 5). These patterns are consistent with the characteristics of subnational fiscal datasets, where economic shocks, institutional features, and spatial linkages often generate correlated disturbances across regions. Given these results, all tax-capacity models were estimated using fixed-effects regressions with Driscoll-Kraay standard errors, which are robust to heteroskedasticity, autocorrelation, and cross-sectional dependence. This estimator provides consistent coefficient estimates under weak assumptions and has become standard in empirical public finance applications involving subnational units. The diagnostic evidence therefore validates the empirical strategy adopted in this study and ensures that the subsequent tax capacity estimates are not biased by specification errors or misspecified error structures. 4.3 Tax Capacity Estimation Results The fixed-effects models estimated for each cluster reveal substantial heterogeneity in the structural determinants of provincial tax capacity (Table 6). As expected, the share of manufacturing in provincial GDP emerges as a strong and positive determinant in the more industrialized clusters, reflecting the role of formal-sector production in widening the effective tax base. In contrast, the coefficient is weak or insignificant in clusters dominated by agriculture or services, underscoring the limited contribution of informal or low-value-added activities to taxable capacity. Tablo 6. Tax Capacity Models Model 1 2 3 4 5 6 7 8 9 Manfgdp 0.051 (0.236) 0.231*** (0.052) -0.171** (0.041) 0.173*** (0.022) -0.149 (0.129) -0.143** (0.038) 0.002*** (0.037) 0.817*** (0.043) -0.097** (0.048) Openess -0.009* (0.478) -0.006 (0.257) 0.001** (0.125) 0.009* (0.165) 0.019** (0.381) 0.191 (0.228) 0.0001** (0.289) -0.095* (0.124) 0.093** (0.219) Publicexpgdp -0.151** (0.247) 0.380*** (0.262) 0.252 (0.273) 0.969** (0.171) 0.304** (0.111) -0.122* (0.149) 0.001 (0.118) 0.324** (0.131) 0.510* (0.320) Percap -0.121*** (0.021) -0.068* (0.291) -0.082** (0.094) -0.128*** (0.016) -0.027* (0.291) -0.112** (0.082) -0.002*** (0.001) -0.015** (0.079) -0.092*** (0.009) agedependencyratio 0.408 (0.339) -0.156 (0.221) 0.561*** (0.088) 0.622* (0.040) -0.072* (0.022) -0.576*** (0.061) -0.007** (0.019) -0.912* (0.094) 0.484*** (0.057) Constant 2.411*** (0.758) 1.108*** (0.851) 0.650*** (0.717) -2.180*** (0.189) 3.463*** (0.339) 4.632*** (0.587) 0.058*** (0.475) 1.384*** (0.229) -0.119*** (0.382) R2 0.302 0.311 0.326 0.347 0.349 0.412 0.401 0.291 0.309 Observation 112 256 192 96 80 128 96 160 176 Group 7 16 12 6 5 8 6 10 11 F Stats 71.44*** 72.92*** 94.88*** 89.14*** 91.33*** 85.48*** 91.62*** 83.42*** 77.16*** Max. lag 2 2 2 2 2 2 2 2 2 Method Fixed Effect Fixed Effect Fixed Effect Fixed Effect Fixed Effect Fixed Effect Fixed Effect Fixed Effect Fixed Effect Note. In the table, standard errors obtained with the Driscoll-Kraay estimator are shown in parentheses. Additionally, the stars are the p-value of the coefficient. * is statistically significant at the 10% level, ** at the 5% level, and *** at the 1% level. A noteworthy result is the predominantly negative coefficient on per capita income (lnGDPpc) across several clusters. This counterintuitive pattern, observed in recent subnational fiscal capacity studies, may indicate that high-income provinces benefit from sectoral exemptions, tax incentives, or base erosion mechanisms that suppress their effective tax burden. The finding suggests that income alone is not a reliable proxy for structural fiscal potential at the provincial scale. The effects of openness and public expenditure intensity vary considerably across clusters. In trade-oriented regions, openness positively influences tax burden, consistent with broader taxable activity and stronger administrative presence. However, in other clusters, the coefficient is insignificant or negative, possibly reflecting tax preferences for export-oriented production. Public expenditure intensity is positive and significant in several clusters, indicating that government spending particularly on administrative functions may enhance revenue mobilization capabilities. Demographic factors also play a differentiated role. Where significant, the age dependency ratio exerts a negative effect on tax burden, suggesting that provinces with higher dependency pressures face a structurally narrower tax base. In many clusters, however, demographic variables are statistically insignificant, reflecting diverse local labor market dynamics and migration patterns. Overall, the results confirm that tax capacity in Türkiye is highly context dependent. The determinants of revenue potential differ sharply across provincial groups, validating the decision to estimate tax-capacity functions within homogeneous clusters rather than nationally. A single pooled model would obscure these structural differences and lead to biased assessments of provincial tax effort. 4.4 Estimated vs. Actual Tax Capacity The comparison between estimated tax capacity and actual provincial tax burden reveals substantial spatial and structural disparities across Türkiye (Fig. 2 ). Provinces with diversified industrial structures most notably Kocaeli, İstanbul, İzmir, and Ankara display the highest predicted capacity levels, consistent with their large formal sectors, higher value-added activities, and stronger administrative infrastructures. These provinces also exhibit relatively stable capacity estimates over time, indicating persistent structural advantages. In contrast, provinces in the eastern and southeastern regions show systematically lower predicted tax capacity, reflecting limited industrialization, high informality, demographic pressures, and weaker administrative reach. The magnitude of the capacity gap between western and eastern clusters highlights the deep regional asymmetries that characterize Türkiye’s fiscal landscape. The comparison also reveals meaningful mismatches between actual tax burden and predicted capacity. Several provinces with strong structural potential such as Ankara, Bursa, Gaziantep, and Sakarya collect noticeably less revenue than their estimated capacity would suggest. This underperformance signals constraints in administrative effectiveness, compliance levels, or tax policy design. Conversely, some structurally constrained provinces (e.g., Ağrı, Aksaray, Artvin, Rize) exhibit actual tax burdens that exceed predicted levels, indicating compensatory administrative effort or above-expected compliance behavior. Overall, these results demonstrate that structural capacity alone does not fully determine observed revenue outcomes. The divergence between actual and estimated values underscores the importance of analyzing tax effort, which is derived precisely from this comparison. The next section formalizes these differences by quantifying provincial tax effort over the 2007–2022 period. 4.5 Tax Effort Results The tax effort estimates, derived as the ratio of actual tax burden to predicted tax capacity, reveal substantial variation across Turkish provinces over the 2007–2022 period (Table 7 ). The distribution of tax effort indicates a clear divergence between provinces that consistently mobilize revenues above their structural potential and those that underperform despite favorable economic conditions. Table 7 Ranking of Provinces by Tax Effort Index, Averages 2007–2022 Provinces High Tax Effort Provinces Medium Tax Effort Provinces Low Tax Effort Zonguldak 1.57 İstanbul 1.05 Erzincan 0.90 Mersin 1.54 Adana 1.04 Kırşehir 0.89 Aksaray 1.48 Antalya 1.02 Kayseri 0.88 Hatay 1.47 Bartın 1.01 Konya 0.88 Rize 1.36 Siirt 1.01 Çorum 0.87 Kocaeli 1.32 Bolu 1.01 Yozgat 0.87 Edirne 1.32 Kırklareli 1.00 Bayburt 0.86 Tekirdağ 1.28 Denizli 1.00 Ankara 0.85 Tunceli 1.27 Malatya 0.99 Burdur 0.85 Ağrı 1.26 Van 0.99 Ardahan 0.85 Samsun 1.24 Muş 0.99 Uşak 0.83 Artvin 1.23 Kütahya 0.99 Balıkesir 0.83 Iğdır 1.21 Manisa 0.99 Karaman 0.82 Yalova 1.20 Bingöl 0.98 Niğde 0.82 Kahramanmaraş 1.19 Giresun 0.98 Aydın 0.82 Çanakkale 1.16 Ordu 0.98 Kırıkkale 0.81 Karabük 1.13 Isparta 0.96 Gümüşhane 0.81 Batman 1.10 Sinop 0.96 Adıyaman 0.81 Osmaniye 1.10 Erzurum 0.95 Kilis 0.80 Trabzon 1.09 Tokat 0.94 Hakkari 0.80 Şanlıurfa 1.09 Afyonkarahisar 0.93 Şırnak 0.78 Bitlis 1.08 Muğla 0.93 Sakarya 0.76 Sivas 1.08 Kars 0.93 Bursa 0.73 İzmir 1.07 Elazığ 0.91 Bilecik 0.72 Nevşehir 1.06 Düzce 0.91 Gaziantep 0.69 Kastamonu 1.06 Eskişehir 0.90 Çankırı 0.67 Diyarbakır 106 Amasya 0.90 Mardin 0.62 Mean 1.22 Mean 0.97 Mean 0.81 SD 0.1539 SD 0.0413 SD 0.696 Min 1.06 Min 0.90 Min 0.62 Max 1.57 Max 1.05 Max 0.90 Provinces such as Zonguldak, Mersin, Hatay, Aksaray, and Rize exhibit high tax effort, often exceeding unity, suggesting that administrative capacity, compliance incentives, or local enforcement practices enable these regions to achieve revenue levels above what their structural characteristics would predict. These outcomes mirror findings from subnational contexts in other emerging economies, where strong local administration can partially compensate for weaker structural capacity. In contrast, several economically advanced provinces including Ankara, Bursa, Gaziantep, Sakarya, and Kayseri demonstrate persistent underperformance, with effort values below unity. This pattern indicates that high structural capacity does not automatically translate into strong revenue mobilization. Possible drivers include sector-specific exemptions, administrative fragmentation, or taxpayer optimization behavior that reduces effective revenue collection relative to potential. These mismatches between capacity and effort underscore the importance of evaluating performance beyond structural indicators. The national trajectory of tax effort also reflects sensitivity to macroeconomic shocks. Effort levels declined during the 2008–2009 global recession and again during the 2018 domestic currency crisis, consistent with contractions in tax bases, rising informality, and reduced compliance during downturns. A modest recovery appears in 2021–2022, driven partly by inflation-induced increases in nominal revenues. Taken together, the tax effort results highlight the regional heterogeneity in fiscal performance and demonstrate that structural capacity is only one component of revenue mobilization. The divergence between high-capacity/low-effort and low-capacity/high-effort provinces provides a policy-relevant framework for identifying jurisdictions requiring administrative strengthening, compliance interventions, or differentiated fiscal strategies. 4.6 Temporal Dynamics of Tax Effort (2007–2022) An examination of the temporal evolution of provincial tax effort across four subperiods 2007–2010, 2011–2014, 2015–2018, and 2019–2022 reveals that fiscal performance in Türkiye is highly sensitive to macroeconomic conditions and cyclical fluctuations (Table 8 ; Fig. 3 ). The period 2007–2010, which includes the global financial crisis, is marked by a broad decline in tax effort across most provinces. This pattern aligns with international evidence showing that economic contractions reduce tax bases, increase informality, and weaken compliance incentives. Table 8 Changes in Tax Efforts Segmented by Categories Over a 4-Year Period (2007–2022) Low Tax Effort Years Mean SD Min Max 2007–2010 0.80 0.0672 0.68 0.89 2011–2014 0.82 0.0745 0.65 0.93 2015–2018 0.80 0.0948 0.51 0.90 2019–2022 0.78 0.1026 0.49 0.86 Medium Tax Effort Years Mean SD Min Max 2007–2010 0.97 0.0389 0.89 1.03 2011–2014 1.00 0.0450 0.94 1.06 2015–2018 0.97 0.0489 0.90 1.05 2019–2022 0.94 0.0564 0.87 1.06 High Tax Effort Years Mean SD Min Max 2007–2010 1.20 0.1506 1.03 1.53 2011–2014 1.23 0.1452 1.06 1.61 2015–2018 1.24 0.1394 1.05 1.52 2019–2022 1.27 0.1938 1.07 1.85 Tax Collection Rate 2007–2010 2011–2014 2015–2018 2019–2022 Low 72.8% 68.2% 59.8% 60.4% Medium 76.2% 73.2% 64.7% 64.3% High 80.9% 77.5% 72.8% 72.3% A moderate recovery emerges during 2011–2014, a phase characterized by relative macroeconomic stability and stronger domestic demand. During this period, tax effort converges toward unity in many provinces, suggesting improved alignment between structural capacity and revenue mobilization. The 2015–2018 period displays renewed deterioration in tax effort, coinciding with heightened currency volatility, rising inflation, and declining real incomes. These shocks appear to have disproportionately affected provinces with narrower economic bases and higher informality, resulting in sharper drops in fiscal performance. Several high-capacity provinces also experience notable declines in effort, indicating that cyclical and structural factors jointly constrain revenue mobilization. The most recent period, 2019–2022, captures both the economic effects of the COVID-19 pandemic and the subsequent inflationary surge. During 2020, tax effort declines in almost all regions, reflecting mobility restrictions, temporary tax deferrals, and reduced economic activity. However, 2021–2022 show a partial rebound in tax effort, driven largely by increases in nominal tax collections and the recovery of certain service-sector activities. Despite this improvement, significant interprovincial disparities remain, indicating that the resilience of tax effort is conditioned by each province’s structural characteristics and administrative capacity. Overall, the temporal analysis demonstrates that tax effort in Türkiye is shaped by both short-term macroeconomic shocks and l ong-run structural conditions. These dynamics reinforce the need for province-specific policy responses that consider the differing vulnerabilities and fiscal capacities of regional economies. 4.7 Multidimensional Classification: Tax Capacity, Tax Effort, Tax Collection Rate To provide a comprehensive assessment of provincial fiscal performance, the study constructs a three-dimensional classification system based on tax capacity, tax effort, and tax collection rates (Table 9 ). Using the median values of each indicator as thresholds, provinces are categorized into eight distinct groups that capture the interaction between structural capacity, administrative performance, and actual revenue outcomes. Table 9 Classification of Provinces Based on Tax Capacity, Tax Effort and Tax Collection Rate, Averages of 2007–2022 Category Provinces Tax Capacity Tax Effort Tax Collection Rate 1 Hatay, İstanbul, İzmir, Kocaeli, Manisa, Mersin, Samsun, Tekirdağ, Trabzon, Zonguldak High (11.2%) High (1.26) High (82.8%) 2 Adana, Antalya, Denizli, Karabük, Malatya, Ordu, Van, Yalova High (5.7%) High (1.04) Low (67.2%) 3 Ankara, Balıkesir, Bursa, Çankırı, Çorum, Elazığ, Erzurum, Eskişehir, Isparta, Kayseri High (11.1%) Low (0.86) High (74.6%) 4 Adıyaman, Aydın, Burdur, Düzce, Gaziantep, Kırıkkale, Kırşehir, Kilis, Konya, Mardin, Muğla, Sakarya, Uşak High (6.7%) Low (0.81) Low (61.9%) 5 Ağrı, Aksaray, Artvin, Bartın, Bingöl, Bitlis, Çanakkale, Edirne, Giresun, Kahramanmaraş, Kastamonu, Muş, Rize, Tunceli Low (3.9%) High (1.17) High (76.8%) 6 Batman, Bolu, Diyarbakır, Iğdır, Kırklareli, Kütahya, Nevşehir, Osmaniye, Siirt, Sivas, Şanlıurfa Low (4.0%) High (1.06) Low (64.7%) 7 Ardahan, Bayburt, Erzincan, Gümüşhane Low (3.8%) Low (0.85) High (77.2%) 8 Afyonkarahisar, Amasya, Bilecik, Hakkari, Karaman, Kars, Niğde, Sinop, Şırnak, Tokat, Yozgat Low (4.3%) Low (0.86) Low (61.8%) The classification reveals important asymmetries. Provinces with high tax capacity and high effort such as İstanbul, İzmir, Kocaeli, and Hatay also record high collection rates, indicating that strong structural bases and effective administrative practices reinforce each other. These provinces represent the benchmark group where both economic potential and revenue mobilization are fully aligned. In contrast, a subset of high-capacity provinces with low tax effort, including Ankara, Bursa, and Gaziantep, show below-average collection rates, signalling underutilization of structural potential. This mismatch suggests that administrative constraints, compliance gaps, or sectoral tax privileges may impede revenue performance despite favorable economic conditions. Similar patterns have been documented in other emerging economies, where structural capacity does not guarantee effective extraction without supportive governance and enforcement mechanisms. Conversely, several low-capacity but high-effort provinces such as Aksaray, Artvin, and Rize achieve above-expected collection rates, demonstrating strong administrative commitment or comparatively higher compliance among taxpayers. These cases underscore that effective local tax administration can partially compensate for structural disadvantages, a finding consistent with recent evidence from subnational fiscal studies. The remaining group, consisting of provinces with low capacity, low effort, and low collection rates, represents regions facing the most severe fiscal challenges. These provinces, many of which are located in Türkiye’s eastern and southeastern regions, appear constrained by both economic structure and limited administrative capability. For these areas, structural reforms such as investment incentives, formalization policies, and administrative strengthening are likely prerequisites for improving revenue performance. Overall, the multidimensional taxonomy highlights substantial heterogeneity in provincial fiscal behavior and underscores that structural capacity, administrative effort, and realized collections do not necessarily move together. This framework allows policymakers to identify province-specific bottlenecks and tailor interventions accordingly, whether through strengthening tax administration, revising local incentives, or enhancing compliance strategies. The approach also offers a replicable template for assessing subnational tax performance in other countries with similar regional disparities. Discussion and Conclusions 5.1 Discussion This study set out to estimate provincial tax capacity and tax effort for 81 Turkish provinces over 2007–2022 and to classify them according to their tax capacity, tax effort, and tax collection performance. The empirical results broadly confirm, but also nuance, the predictions of the tax capacity literature that emphasizes structural determinants such as per capita income, sectoral composition, and openness as primary drivers of tax performance (Chelliah et al., 1975 ; Gupta, 2007 ; Castro & Camarillo, 2014 ; Le et al., 2012 ). First, the cluster analysis reveals strong structural heterogeneity across provinces. Industrialized and highly urbanized clusters, particularly those including İstanbul, Kocaeli, and İzmir, display characteristics associated with high tax capacity higher manufacturing shares, higher per capita income, and deeper integration into international trade. This pattern is consistent with cross-country evidence showing that industrialization and economic development expand the taxable base and increase the feasible tax-to-GDP ratio (Castro & Camarillo, 2014 ; Le et al., 2012 ; Serin & Demir, 2023 ). In contrast, clusters dominated by agriculture, low income, and lower urbanization levels exhibit structurally weaker tax capacity, in line with classic findings that agricultural dependence depresses taxable potential (Lotz & Morss, 1967 ; Chelliah et al., 1975 ). Second, the fixed-effects tax capacity models show that the impact of structural variables is highly context-dependent. Manufacturing share is strongly and positively associated with tax burden in industrial clusters, but it is weak or even negative in some mixed or less formal clusters. This heterogeneity resonates with recent regional and subnational studies where the contribution of manufacturing to tax capacity depends on the degree of formality, the sectoral tax regime, and local institutional capacity (Chigome & Robinson, 2021 ; Serin & Demir, 2023 ). A particularly striking result is the predominantly negative coefficient on per capita income in several clusters. Standard capacity models usually find a positive relationship between income and the tax-to-GDP ratio (Gupta, 2007 ; Le et al., 2012 ; Fenochietto & Pessino, 2013 ). The negative sign observed here suggests that, at the provincial level, high-income provinces may benefit from extensive exemptions, investment incentives, or base erosion mechanisms that reduce their effective tax burden an outcome closer to the “tax capacity not fully exploited” interpretation emphasized in recent work on structural tax gaps (Fenochietto & Pessino, 2013 ; Benítez et al., 2023). The mixed effects of trade openness and public expenditure intensity also merit discussion. In trade-oriented clusters, openness tends to raise tax burden, echoing evidence that international integration can expand taxable activities and encourage modernization of tax administration (Castro & Camarillo, 2014 ; Chigome & Robinson, 2021 ). In other clusters, however, openness is insignificant or negative, possibly reflecting preferential regimes for exporters or the dominance of low-taxable export sectors. Similarly, public expenditure intensity is positively related to tax burden in several clusters, consistent with the notion that higher government presence and administrative spending can enhance compliance and enforcement (Bird et al., 2008 ; Benítez et al., 2023), but it is negative or insignificant in others, pointing to inefficiencies or weak expenditure–revenue linkages. The comparison between estimated tax capacity and actual tax burden shows that structural factors alone cannot explain provincial revenue performance. Some provinces with strong structural capacity (e.g., Ankara, Bursa, Gaziantep, Sakarya) underperform relative to their estimated capacity, whereas several structurally constrained provinces (e.g., Ağrı, Aksaray, Artvin, Rize) exhibit tax burdens above predicted levels. This combination of high-capacity/low-effort and low-capacity/high-effort provinces closely parallels cross-country findings where institutional quality, governance, and administrative effort play a decisive role in closing or widening the gap between potential and actual revenue (Bird et al., 2008 ; Fenochietto & Pessino, 2013 ; Benítez et al., 2023; Saruç et al., 2018 ). Finally, the temporal analysis shows that tax effort is highly sensitive to macroeconomic conditions. Declines in tax effort during the 2008–2009 global crisis and the 2018 currency shock, followed by partial recovery, are consistent with international evidence that recessions compress tax bases, increase informality, and weaken compliance (Tanzi, 1977 ; Morrissey et al., 2016 ; OECD, 2020 ). The three-dimensional classification system highlights that these cyclical dynamics interact with structural and administrative factors, leading to persistent differences across provincial groups. 5.2 Theoretical Contribution Theoretically, the study speaks to the long-standing debate on how best to conceptualize and measure tax capacity and tax effort. Classical approaches, starting from Lotz and Morss ( 1967 ) and Chelliah et al. ( 1975 ), treated tax effort as the ratio of actual to predicted tax revenues, where predictions were based primarily on national-level structural variables. Later contributions refined the specification by incorporating a broader set of economic, social, and institutional determinants and by introducing more sophisticated econometric techniques (Gupta, 2007 ; Le et al., 2012 ; Fenochietto & Pessino, 2013 ). This study extends that tradition in three main ways. First, it relocates the tax capacity discourse to the subnational (provincial) level in a large emerging economy. Existing work on Türkiye has either focused on national aggregates or used relatively coarse regional groupings (Saruç et al., 2018 ; Çelikay, 2020 ; Allahverdi & Alagöz, 2023 ; Serin & Demir, 2023 ). By contrast, this study employs NUTS-3 data for 81 provinces and shows that the determinants of tax capacity vary sharply across structurally distinct provincial clusters. This reinforces the view, also found in subnational studies for other countries, that a single national tax capacity function is too coarse to capture region-specific dynamics (Arlashkin, 2020 ; Kawadia & Suryawanshi, 2021 ). Second, the study integrates cluster analysis with panel estimation to handle structural heterogeneity. While cluster analysis has been widely used in regional and evolutionary economic geography to identify groups of regions with similar development paths (Gültekin et al., 2023 ), its use as a pre-estimation step in tax capacity analysis is still rare. The finding that coefficients differ markedly across clusters supports the argument that tax capacity is context-dependent and that structural benchmarking should be performed within relatively homogeneous groups rather than at the national aggregate level (Le et al., 2012 ; Chigome & Robinson, 2021 ; Serin & Demir, 2023 ). Third, the study proposes a three-dimensional framework that jointly considers tax capacity, tax effort, and tax collection rate. Earlier tax effort indices generally used a two-dimensional view actual versus potential revenue (Lotz & Morss, 1967 ; Le et al., 2012 ; Fenochietto & Pessino, 2013 ). By adding tax collection efficiency as a third dimension, the analysis is able to distinguish between high-capacity/low-effort provinces with weak collections, low-capacity/high-effort provinces with relatively strong collections, and provinces that perform poorly on all three fronts. This typology aligns with recent calls in the literature for more nuanced performance measures that go beyond simple tax-to-GDP ratios (Benítez et al., 2023; Serin & Demir, 2023 ). Overall, the theoretical contribution lies in demonstrating that tax capacity and effort are multi-layered, spatially embedded constructs. The results support a more granular, regionally sensitive interpretation of the structural benchmarking paradigm developed in the international tax capacity literature. 5.3 Practical Implications The findings carry several implications for tax policy, fiscal equalization, and public administration in Türkiye. For provinces with high tax capacity and high effort and high collection rates (e.g., İstanbul, İzmir, Kocaeli, Hatay), the results suggest that both structural conditions and administrative frameworks are aligned. These provinces can serve as benchmark cases for best practice in tax administration and compliance management, similar to the “front-runners” identified in comparative studies of tax capacity and effort in Europe and Southern Africa (Chigome & Robinson, 2021 ; Serin & Demir, 2023 ). Policy in these regions may focus on maintaining administrative quality, preventing base erosion, and managing the distributional consequences of high tax burdens. Provinces with high capacity but low effort and moderate collection rates such as Ankara, Bursa, Gaziantep, and Sakarya constitute a critical policy concern. The structural potential exists, but the underperformance suggests gaps in enforcement, compliance culture, or the design of local tax incentives. International evidence shows that improving tax administration, simplifying tax systems, and strengthening institutional trust can narrow such effort gaps (Saruç & Sağbaş, 2003 ; Bird et al., 2008 ; Benítez et al., 2023). For these provinces, targeted interventions could include risk-based audits, digitalization of tax processes, and more transparent links between tax revenue and local public services. By contrast, low-capacity but high-effort provinces including Aksaray, Artvin, and Rize demonstrate that administrative commitment and taxpayer compliance can partially compensate for structural disadvantages. Similar patterns have been observed in Indonesian local governments, where institutional reforms and changes in tax assignment rules significantly shaped local tax effort (Noviyanti & Zen, 2022 ). For such provinces, policies to broaden the tax base through formalization, infrastructure investment, and sectoral diversification are likely to yield high marginal returns, especially if combined with continued administrative strengthening (OECD, 2023 ). Finally, provinces with low capacity, low effort, and low collection rates are structurally and administratively constrained. Many are located in Türkiye’s eastern and southeastern regions, where high informality, weaker administrative presence, and demographic pressures limit revenue mobilization. For these areas, the results support the case for a dual strategy: (i) long-term structural policies that promote productive diversification and regional development, consistent with the related variety and smart specialization perspective (Gültekin et al., 2023 ), and (ii) targeted capacity-building programs in line with the IMF’s guidance on building tax capacity in low-income and developing contexts (Benítez et al., 2023). At the national level, the three-dimensional taxonomy offers a practical tool for fiscal equalization and intergovernmental transfers. Instead of relying solely on per capita income or simple revenue indicators, central authorities can incorporate information on tax capacity, effort, and collection rates when designing transfer formulas. This is consistent with international practice, where representative tax system approaches and fiscal capacity equalization mechanisms seek to balance fiscal autonomy with horizontal equity (Arlashkin, 2020 ; OECD, 2023 ). 5.4 Conclusion The study provides a comprehensive, province-level assessment of tax capacity and tax effort in Türkiye over 2007–2022 by integrating hierarchical cluster analysis with fixed-effects panel estimation and robust standard errors. The main conclusions can be summarized as follows. Tax capacity is highly uneven across provinces and is strongly shaped by industrialization, trade openness, and urbanization. Western and coastal provinces enjoy structurally higher tax capacity, while many eastern and southeastern provinces face persistent structural constraints. The determinants of tax capacity are cluster-specific. Manufacturing share, openness, public expenditure, and demographic structure do not influence tax burden uniformly. This confirms the importance of accounting for structural heterogeneity and cautions against using a single national tax capacity function. Tax effort varies widely, with some provinces collecting significantly more than their structural characteristics would predict and others underperforming despite high capacity. This underscores the central role of administrative capacity, compliance behaviour, and local policy choices in shaping realized revenues. Tax effort is sensitive to macroeconomic conditions. Crisis periods are associated with declines in effort and collection rates, while recoveries are uneven across provincial groups. The three-dimensional classification of provinces by capacity, effort, and collection rate provides a rich diagnostic framework for designing differentiated, region-specific fiscal and administrative strategies. Taken together, these findings contribute to the broader literature on tax capacity and effort by demonstrating the importance of subnational heterogeneity and by offering a replicable framework for other countries experiencing significant regional disparities. 5.5 Limitations and Future Research Despite its contributions, the study has several limitations that should be acknowledged and that point to avenues for future research. First, the analysis relies on aggregate provincial data, which inevitably mask intra-provincial disparities and micro-level behavioural responses. Firm and household-level data could allow future research to disentangle how different taxpayer groups respond to economic shocks, policy changes, or administrative reforms, as emphasized in recent work on tax capacity and inclusive development (Abdel-Kader & de Mooij 2020 ; Benítez et al., 2023). Second, while the study corrects for heteroscedasticity, serial correlation, and cross-sectional dependence via Driscoll-Kraay standard errors, it does not explicitly estimate spatial econometric models. Given the strong evidence of spatial interactions in tax performance (LeSage & Pace, 2009 ; Chigome & Robinson, 2021 ; Adeleke, 2022 ; Ha et al, 2022 ), future work could employ spatial lag or spatial error models to distinguish between own-province effects and spillovers from neighbouring provinces. Third, the measure of tax capacity is based on observable structural variables only. Important institutional and behavioural factors such as informality, tax morale, quality of local governance, and enforcement practices are not directly observed and instead enter the model via fixed effects. Incorporating explicit measures of institutional quality and governance, building on the literature that stresses their importance for tax effort (Bird et al., 2008 ; Fenochietto & Pessino, 2013 ; Saruç et al., 2018 ), would strengthen the explanatory power of future models. Fourth, the three-dimensional classification system is static in the sense that it uses average values over 2007–2022. Although the temporal analysis separately examines changes in tax effort, future research could construct dynamic trajectories of provinces across categories, identifying movers and stayers and relating these transitions to policy changes, shocks, or institutional reforms. Finally, the study focuses on Türkiye as a single case. While this in-depth perspective is valuable, comparative research that applies the same methodology to other countries particularly those with pronounced regional disparities, such as India, Indonesia, or South Africa would help assess the generality of the findings and refine the proposed classification framework (Chigome & Robinson, 2021 ; Kawadia & Suryawanshi, 2021 ; Noviyanti & Zen, 2022 ). Addressing these limitations would deepen our understanding of the interplay between structural conditions, institutions, and tax administration, and would further strengthen the contribution of provincial-level tax capacity and effort analysis to both theory and policy. Declarations Funding The author(s) received no financial support for the research, authorship, and/or publication of this article. Data availability statement The data that support the findings of this study are available on request from the corresponding author. 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Allahverdi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIie3RMUvDQBTA8XccnMuzXRsO61d4EiiESj6LIeBa3TJGDjL1AzRU/BjOkTdkKbgKnaSrQyAgggU9U3QpF+rW4f5w8JYf9+AB+HzH2InMZTewrABuAEawe+6k+CXqCoD+CB1AKqTDyNCIu/a2uJwNamybhuKkzOXTGmE7c5ERC6PL4joq+fQxWFCaLEGlUwSKcpdhkWtcMZElEqlKHgAn2hLnZuf2l09cfVmCm3bbkeF7LyEWhcas+iGgwZIloOolF5ZMMUspYDUJ5pSGpVFhdE+hk4xrw2ukmAbPvGk+svhsUZvXl7ds3HOYvboz/Qf4fD6fb69vRFdNEAbgbbkAAAAASUVORK5CYII=","orcid":"","institution":"Selçuk University","correspondingAuthor":true,"prefix":"","firstName":"Metin","middleName":"","lastName":"Allahverdi","suffix":""},{"id":561976647,"identity":"41ae905e-bab6-4d57-8ffc-91cf9ecce2da","order_by":1,"name":"Ferdi Çelikay","email":"","orcid":"","institution":"Eskisehir Osmangazi University","correspondingAuthor":false,"prefix":"","firstName":"Ferdi","middleName":"","lastName":"Çelikay","suffix":""}],"badges":[],"createdAt":"2025-12-16 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1","display":"","copyAsset":false,"role":"figure","size":550505,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8378380/v1/2ad92dcb29d564cacbd85689.png"},{"id":98624684,"identity":"475c8222-9083-4c0e-986e-206b20614fb6","added_by":"auto","created_at":"2025-12-19 17:08:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":190954,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8378380/v1/eeb9d69d4248f542b37e557d.png"},{"id":98490328,"identity":"9e7e3808-5a19-4c02-a331-667d9375f9eb","added_by":"auto","created_at":"2025-12-18 07:46:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":168159,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8378380/v1/b6e027bb7b4cd572eb719cae.png"},{"id":99308412,"identity":"97a9f027-ee29-4d5a-80a0-1c7595c897c4","added_by":"auto","created_at":"2025-12-31 16:08:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2908700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8378380/v1/4fd525b8-a689-4894-ae3a-5ad45f763aee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interjurisdictional Tax Performance within a Country: Cluster-Specific Panel Estimation of Provincial Tax Capacity, Effort, and Collection Effectiveness in Türkiye (2007-2022)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDomestic revenue mobilisation sits at the centre of modern fiscal governance because it determines the state\u0026rsquo;s ability to finance public services, stabilise economic cycles, and sustain development investments. A large and growing body of work has therefore sought to explain why some jurisdictions raise more revenue than others, and whether observed tax outcomes reflect genuine economic constraints or weaknesses in policy and implementation. As earlier studies pointed out, headline revenue ratios are difficult to interpret without a benchmark, since the same tax burden can arise from very different underlying bases and enforcement realities (Lotz \u0026amp; Morss, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). This insight remains highly relevant today, particularly for economies experiencing rapid structural change and administrative modernisation, where the distance between \u0026ldquo;potential\u0026rdquo; and \u0026ldquo;performance\u0026rdquo; can widen or narrow over time.\u003c/p\u003e \u003cp\u003eIn the tax performance literature, the distinction between tax capacity (tax potential) and tax effort has become the standard analytic starting point. Put simply, tax capacity reflects the revenue a jurisdiction could plausibly raise given its structural \u0026ldquo;tax handles\u0026rdquo; (income level, sectoral composition, base visibility), while tax effort captures how closely actual outcomes approach that benchmark (Lotz \u0026amp; Morss, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Chelliah et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). Subsequent research has suggested that effort is not merely a discretionary choice; rather, it is shaped by institutions, compliance conditions, and administrative capability, implying that two jurisdictions with similar structural capacity may nonetheless display very different realised revenues (Bird et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bird et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In other words, capacity is partly \u0026ldquo;where you start,\u0026rdquo; but effort reflects how effectively policy and administration convert that starting point into collections.\u003c/p\u003e \u003cp\u003eT\u0026uuml;rkiye provides a timely case for revisiting these concepts with a diagnostic lens. Recent OECD evidence shows that T\u0026uuml;rkiye\u0026rsquo;s tax-to-GDP ratio remains well below the OECD average: the OECD\u0026rsquo;s Revenue Statistics country note reports an increase from 23.2% in 2023 to 24.0% in 2024, compared to an OECD average of 34.1% (OECD, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). The same OECD series documents that T\u0026uuml;rkiye\u0026rsquo;s tax-to-GDP ratio rose from 20.9% in 2022 to 23.5% in 2023, highlighting both recovery dynamics and the policy importance of understanding what drives shortfalls and rebounds (OECD, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Yet these aggregates cannot tell us whether gaps reflect limited taxable capacity, low effort, or weaknesses at the collection stage. As the OECD Economic Survey notes, T\u0026uuml;rkiye\u0026rsquo;s VAT system is \u0026ldquo;particularly complex\u0026rdquo; due to special rates and its VAT revenue is low relative to potential an observation that points to policy design and compliance/administration dimensions as well as structural constraints (OECD, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent international research reinforces why separating these channels matters. For example, IMF evidence quantifies the revenue yields associated with improvements in tax administration capabilities, arguing that stronger administration is systematically associated with higher revenue performance (Adan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Related IMF work emphasises that \u0026ldquo;building tax capacity\u0026rdquo; requires a holistic, institution-based approach spanning policy, administration, and legal implementation, and that progress depends on sustained capability-building rather than isolated reforms (Benitez et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In parallel, research on digitalisation suggests that technology can raise revenue performance when corporate-sector digitalisation is matched by sufficiently capable, digitised tax administrations; in cross-country estimates, stronger firm digitalisation is associated with higher tax revenues-to-GDP conditional on the state of tax-administration digitalization (Nose et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Taken together, these studies suggest that tax effort and collection outcomes are potentially responsive to institutional and technological change precisely the kind of change T\u0026uuml;rkiye experienced across 2007\u0026ndash;2022.\u003c/p\u003e \u003cp\u003eThese considerations become more acute once the unit of analysis shifts from the national economy to the province. Previous research has frequently argued that subnational units can diverge in fiscal performance even under a shared national legal framework because tax bases, informality, administrative reach, and compliance cultures vary by region. T\u0026uuml;rkiye\u0026rsquo;s own literature supports this view. For example, Turkish studies have estimated tax capacity for earlier periods (Dursun, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), examined determinants of tax capacity and tax effort for T\u0026uuml;rkiye (Atsan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and analysed tax effort dynamics in T\u0026uuml;rkiye (Saru\u0026ccedil; et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Regional work also discusses how tax effort varies across regions and how it can be measured empirically in Turkish settings (\u0026Ccedil;elikay, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Allahverdi \u0026amp; Alag\u0026ouml;z, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Collectively, these studies make a strong case that provincial heterogeneity is real and policy relevant. However, as several strands of the literature imply, subnational evidence is often fragmented across time horizons, methods, and performance indicators, which complicates translation into targeted policy action.\u003c/p\u003e \u003cp\u003eThis is where a clearer problem emerges. Although the capacity effort framework is well established, much of the applied work has tended to either (i) focus on estimating capacity and inferring effort, or (ii) examine revenue outcomes without explicitly distinguishing the collection stage that is, the conversion of assessed liabilities into cash collections. Yet collection effectiveness is a distinct and policy-relevant dimension of performance: a province can display moderate effort relative to capacity but still underperform in realised revenues if collection processes are weak. Moreover, VAT performance debates illustrate how \u0026ldquo;gaps\u0026rdquo; can arise from both policy design and compliance/administration, motivating a separate look at realised-collection effectiveness rather than treating all shortfalls as a single residual. The European Commission\u0026rsquo;s VAT Gap framework explicitly separates compliance gaps from policy gaps, providing a widely used benchmark logic for thinking about how legal liabilities translate into receipts. While T\u0026uuml;rkiye\u0026rsquo;s provincial VAT gaps are not directly observed in standard datasets, the conceptual lesson is transferable: performance shortfalls can originate at multiple points in the revenue chain.\u003c/p\u003e \u003cp\u003eAgainst this background, the present study develops a new approach to assessing provincial tax performance in T\u0026uuml;rkiye over the period 2007\u0026ndash;2022. The study aims to determine provincial tax potential (capacity\u003cb\u003e)\u003c/b\u003e and tax effort, and then to classify provinces based on tax capacity, tax effort, and tax collection rates to generate an interpretable typology that can inform differentiated tax policy and administrative prioritisation. This design is motivated by two linked needs. First, as earlier research has noted, a benchmark is needed to interpret observed revenue ratios meaningfully (Lotz \u0026amp; Morss, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Chelliah et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). Second, more recent work indicates that administration reforms and digital capability can shift outcomes, implying that \u0026ldquo;effort\u0026rdquo; and \u0026ldquo;collection effectiveness\u0026rdquo; should be analysed in a way that is sensitive to time variation and institutional change.\u003c/p\u003e \u003cp\u003eMethodologically, the study combines panel-data benchmarking with cluster analysis to move beyond single rankings toward actionable categories. Cluster-based classification is not presented as a substitute for econometric identification; rather, it is used to translate multidimensional performance metrics into profiles that are easier to interpret for policy. In practical terms, a typology can distinguish provinces where the binding constraint is structural capacity (base development and formalisation), from those where the constraint is low effort (compliance and enforcement), and from those where collection rates signal an implementation bottleneck. By doing so over a long panel that spans crises, reform episodes, and accelerating digitalisation, the study speaks to both the foundational logic of tax effort and the contemporary policy agenda of strengthening revenue mobilisation in a targeted and evidence-based manner.\u003c/p\u003e"},{"header":"Literature review and conceptual framework","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Tax potential, tax capacity, and tax effort: concepts and measurement logic\u003c/h2\u003e \u003cp\u003eTax performance metrics such as the tax-to-income or tax-to-GDP ratio are inherently ambiguous without a benchmark that reflects underlying economic structure and the feasibility of collection. The early comparative literature therefore distinguishes tax capacity (tax potential) from tax effort: capacity approximates the level of revenue a jurisdiction could raise given its structural \u0026ldquo;tax handles,\u0026rdquo; while effort reflects how closely observed revenue approaches that benchmark (Lotz \u0026amp; Morss, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Lotz \u0026amp; Morss, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Bahl, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). This distinction remains central because it separates low revenue caused by constrained bases from low revenue caused by weak compliance, administration, or incentives.\u003c/p\u003e \u003cp\u003eThe conceptual foundation was extended by the IMF tradition on tax ratios and effort, where capacity is estimated using structural determinants and effort is inferred from deviations (Chelliah, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Chelliah et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). Subsequent work emphasized that capacity and effort are not purely \u0026ldquo;economic\u0026rdquo;; they are shaped by political and institutional constraints that define state capability and the enforceability of tax rules (Fauvelle-Aymar, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Besley \u0026amp; Persson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This broadening is crucial for subnational settings: provinces operating under a uniform national legal framework can still diverge markedly in compliance environments, base visibility, and administrative reach.\u003c/p\u003e \u003cp\u003eIn the more recent empirical literature, \u0026ldquo;tax potential\u0026rdquo; is increasingly treated as a frontier rather than an average benchmark reflecting the best attainable performance conditional on fundamentals while effort is interpreted as distance from that frontier (Leuthold, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Stotsky \u0026amp; WoldeMariam, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Piancastelli, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Pessino \u0026amp; Ricardo, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fenochietto \u0026amp; Pessino, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In parallel, applied policy research has introduced widely used comparative norms and performance metrics for revenue potential especially for benchmarking and diagnostics arguing that a small set of core indicators can serve as credible reference points in cross-jurisdiction comparisons (Le et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The implication for a province-level study is direct: benchmarking requires both a defensible estimate of potential and complementary performance signals that capture implementation and collection effectiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Determinants of tax capacity and effort: structural, institutional, and administrative channels\u003c/h2\u003e \u003cp\u003eA broad consensus exists that structural variables shape the feasible tax base, and therefore tax capacity. Classic and subsequent comparative studies repeatedly identify per-capita income, sectoral composition (especially agriculture), openness and trade intensity, and macroeconomic stability as core \u0026ldquo;tax handles\u0026rdquo; that predict revenue capacity (Tanzi, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1968\u003c/span\u003e; Shin, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1969\u003c/span\u003e; Ansari, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Tanzi, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Later work refined these determinants by emphasizing that the tax system itself and not only the base responds to development pathways: the composition of revenue (direct versus indirect) and the design of exemptions and preferential regimes matter for the realised yield (Tanzi \u0026amp; Zee, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Teera \u0026amp; Hudson, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Dioda, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Castro \u0026amp; Camarillo, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInstitutional and political economy perspectives add that the conversion of capacity into actual revenue is mediated by state capacity, credibility, and the broader fiscal contract. Governance constraints can depress tax effort even when structural capacity is non-trivial (Fauvelle-Aymar, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Bird et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bird et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Besley \u0026amp; Persson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, cross-country evidence for developing regions shows that corruption, policy choices, and the quality of public administration alter revenue mobilisation outcomes independent of the base (Ghura, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Eltony, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gupta, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Drummond et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The historical and comparative literature on \u0026ldquo;tax sacrifice\u0026rdquo; and tax burden measurement reinforces the same point: performance comparisons must adjust for structural and institutional heterogeneity (Frank, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1959\u003c/span\u003e; Williamson, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1961\u003c/span\u003e; Bird, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1964\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe last five years have further strengthened the emphasis on tax administration performance and digitalisation as channels that shape effort and collection outcomes. Recent IMF work quantifies revenue yields from tax administration reforms and shows that strengthening administrative capability can generate persistent gains (Adan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Atsebi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Parallel IMF contributions link administration performance to compliance outcomes and argue that compliance gaps are systematically shaped by administrative capacity (Baer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, digital adoption within revenue administrations is framed as a catalyst for stronger compliance management (Nose \u0026amp; Mengistu, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and newer evidence suggests that digital technologies can materially improve tax collection when administrative digital capability is sufficiently developed (Nose et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; International Monetary Fund, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These mechanisms are relevant for T\u0026uuml;rkiye because the 2007\u0026ndash;2022 period encompasses major shifts in e-services, e-invoicing ecosystems, and enforcement analytics, making administrative modernisation a plausible driver of effort dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Empirical approaches: benchmarks, panels, and frontier estimation\u003c/h2\u003e \u003cp\u003eThe benchmark regression approach remains the most widely used method to estimate capacity and infer effort (Bahl, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Chelliah et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Leuthold, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Its strength is transparency: capacity is the predicted tax ratio given fundamentals, and effort is the deviation. However, a major methodological critique is that \u0026ldquo;effort\u0026rdquo; can be contaminated by unobserved heterogeneity, measurement error, and structural breaks. This critique has been reinforced by recent re-estimations showing that effort rankings can shift under alternative specifications and datasets (McNabb et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrontier approaches respond to this critique by estimating an attainable frontier and treating deviations as inefficiency (effort shortfall), typically separating noise from persistent underperformance (Piancastelli, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Fenochietto \u0026amp; Pessino, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Yet frontier methods require strong assumptions and can be sensitive to outliers and distributional choices. For that reason, many studies advocate triangulation using alternative estimators and robustness checks rather than presenting a single \u0026ldquo;true\u0026rdquo; effort measure (Stotsky \u0026amp; WoldeMariam, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Teera \u0026amp; Hudson, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; McNabb et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePanel data design is particularly important in subnational settings because persistent provincial characteristics (economic geography, administrative traditions, firm structure) can bias cross-sectional inference. Methodological studies emphasise that serial correlation, error structures, and cross-sectional dependence can distort inference if ignored (Bhargava et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Baltagi \u0026amp; Wu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In a 2007\u0026ndash;2022 provincial panel, these issues are not peripheral: they shape how confidently differences in \u0026ldquo;effort\u0026rdquo; can be interpreted as behavioural/administrative rather than as artifacts of correlated shocks and persistent omitted characteristics.\u003c/p\u003e \u003cp\u003eRecent work also proposes refined effort estimation using new data and updated econometric design. Hayruni et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), for example, frames effort estimation as a dataset-driven exercise and provides updated evidence on effort distributions supporting the general conclusion that effort is sensitive to modelling choices and therefore requires careful benchmarking and validation. The broader lesson is that capacity/effort estimation is now a methodological field in its own right rather than a mechanical residual computation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Evidence from regional/subnational studies: reforms, convergence, and heterogeneous performance\u003c/h2\u003e \u003cp\u003eSubnational studies consistently show that tax effort varies across localities and responds to reforms, administrative restructuring, and local economic structure. In China, Wang, Shen, and Zou (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) demonstrate that reforms and institutional arrangements can affect local government tax effort, implying that effort is not fixed even under a shared national framework. In India, work on state tax performance argues that tax effort can fall short of capacity due to structural and administrative constraints (Garg et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while evidence from the post-reform period suggests that restructuring policies can move local tax effort toward convergence though heterogeneity remains substantial (Sen \u0026amp; Tulasidhar, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Mahawar et al., 2015). Comparable frameworks have been applied to Mexico (Sobarzo, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and other regions (Shang, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), reinforcing that capacity and effort are meaningfully separable in subnational panels and that local fiscal performance cannot be inferred from national averages.\u003c/p\u003e \u003cp\u003eThese studies support two implications that motivate the present design. First, capacity and effort must be treated as distinct constructs in regional analysis, since provinces can have high potential but underperform due to compliance and administration. Second, classification of jurisdictions into meaningful types can be more policy-relevant than a single ranking because it distinguishes structurally constrained areas from administratively underperforming ones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. T\u0026uuml;rkiye and regional tax effort: what is known and what remains under-specified\u003c/h2\u003e \u003cp\u003eT\u0026uuml;rkiye has a substantial domestic literature on tax capacity and effort, including both national and regional analyses. Early work focused on links between social structure and tax structure and on modelling Turkish tax performance historically (Heper, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), while subsequent Turkish scholarship developed the capacity/effort vocabulary and applied it to developing-country contexts (Berksoy, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). More applied national studies estimate T\u0026uuml;rkiye\u0026rsquo;s capacity and discuss structural determinants and reforms across different periods (Sara\u0026ccedil;oğlu, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; G\u0026uuml;nay, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dursun, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the regional domain, several contributions examine local or regional tax effort and its determinants, often emphasizing heterogeneity across provinces and regions (\u0026Ccedil;elik, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; \u0026Ccedil;elikay, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Şimşek, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sağdı\u0026ccedil;, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003ezıltan, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Allahverdi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). More recent work explicitly models regional effort and discusses measurement strategies for the Turkish case (Atsan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saru\u0026ccedil; et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; \u0026Ccedil;elikay, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yıldırım, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo patterns stand out in this T\u0026uuml;rkiye-focused literature. First, regional studies repeatedly highlight that structural determinants alone do not explain provincial differences administrative effectiveness, compliance, and incentives matter. Second, measurement approaches vary considerably: some studies rely on regression benchmarks, others discuss elasticity/ buoyancy, while some adopt frontier logic. This variation creates scope for a design that (i) estimates provincial tax potential and effort consistently over a long period and (ii) offers an interpretable classification of provinces rather than only presenting rankings.\u003c/p\u003e \u003cp\u003eCross-country comparisons including T\u0026uuml;rkiye provide further context. Serin and Demir (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) estimates tax capacity and effort for T\u0026uuml;rkiye alongside EU countries, illustrating that T\u0026uuml;rkiye\u0026rsquo;s relative position depends on benchmark choice and on how structural constraints are encoded in the capacity model. Such cross-country evidence is valuable for framing, but it cannot resolve within-T\u0026uuml;rkiye heterogeneity, which is precisely the motivation for a province panel.\u003c/p\u003e \u003cp\u003eFinally, T\u0026uuml;rkiye\u0026rsquo;s contemporary policy debate underscores the relevance of consumption-tax performance and compliance. OECD assessments emphasise VAT design complexity and reduced rates as constraints on VAT yields relative to potential (OECD, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e) and provide standardised comparative evidence on T\u0026uuml;rkiye\u0026rsquo;s tax mix (OECD, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). EU VAT gap work further formalises the distinction between policy and compliance components of underperformance (European Commission, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Together, these sources justify interpreting provincial outcomes through a capacity-effort-collection lens, rather than relying on aggregate tax ratios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. From estimation to classification: why clustering provinces is analytically useful\u003c/h2\u003e \u003cp\u003eThe study\u0026rsquo;s objective is not only to estimate tax potential and effort but also to classify provinces jointly by tax capacity, tax effort, and tax collection rates. Classification is motivated by the recognition that fiscal underperformance is multidimensional and that different profiles imply different policy responses. Recent research already uses clustering to identify meaningful groups in tax-related contexts, including OECD tax indicator typologies (T\u0026uuml;z\u0026uuml;nt\u0026uuml;rk, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and performance grouping of tax administrations (Pirvu et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Efficiency-oriented work likewise supports performance assessment relative to potential and comparable peers (Afonso et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Methodologically, clustering is most defensible when it is treated as a complement to econometric benchmarking and when cluster validity is assessed rather than assumed (Kaufman \u0026amp; Rousseeuw, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Tibshirani et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jain, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Strauss \u0026amp; von Maltitz, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u0026Ouml;zdamar, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor a provincial tax study, clustering adds two concrete benefits. First, it produces an interpretable typology (e.g., high potential-low effort; low potential-high effort; strong effort-weak collection) that aligns with policy targeting. Second, it offers a structured way to represent heterogeneity that would otherwise be absorbed into residuals and misinterpreted as effort differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Positioning of the study and research contribution\u003c/h2\u003e \u003cp\u003eTaken together, the literature establishes (i) the conceptual necessity of distinguishing tax potential from realised performance, (ii) the importance of institutional and administrative mechanisms particularly in the recent work on reform yields and digitalisation and (iii) the empirical reality of subnational heterogeneity in capacity and effort. Building on these insights, the present study estimates provincial tax potential and tax effort for T\u0026uuml;rkiye over 2007\u0026ndash;2022 and classifies provinces jointly by tax capacity, tax effort, and tax collection rates. This combined design is intended to support differentiated tax policy and administrative prioritisation across provinces by separating structurally constrained jurisdictions from those where the main constraints are effort and collection-related.\u003c/p\u003e \u003c/div\u003e"},{"header":"Research Approach","content":"\u003cp\u003eThe research approach adopted in this study is fundamentally quantitative, explanatory, and model-driven, reflecting the objective of estimating provincial tax capacity and tax effort using measurable structural variables across T\u0026uuml;rkiye\u0026rsquo;s 81 provinces between 2007 and 2022. According to Saunders et al.\u0026rsquo;s (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) research onion, a quantitative deductive approach is appropriate when the study aims to test theoretically informed relationships using statistical modelling and large-scale secondary data. In this context, the present study develops and empirically evaluates tax capacity functions derived from established public finance theory, thereby following a deductive logic in which hypotheses regarding structural determinants of tax burden are tested using econometric methods.\u003c/p\u003e \u003cp\u003eThe approach proceeds in two integrated stages. First, hierarchical cluster analysis is used inductively to identify homogeneous groups of provinces based on economic structure, demographic composition, and fiscal indicators. This step allows the study to reduce structural heterogeneity a major limitation in previous work and to construct internally consistent subpanels for subsequent estimation. While this stage contains inductive elements because it uncovers patterns emerging from the data, it remains embedded within a broader deductive framework, as the clustering serves to improve the validity of theory-driven tax capacity models.\u003c/p\u003e \u003cp\u003eThe second stage involves the estimation of fixed-effects panel data models to determine provincial tax capacity and tax effort. This modelling strategy is consistent with a deductive approach, as it uses theoretically supported determinants such as income, industrial composition, openness, public expenditure intensity, and demographic pressure to estimate predicted tax burdens. The use of panel methods allows the study to control for unobserved provincial characteristics and temporal shocks, while Driscoll-Kraay standard errors address heteroskedasticity, serial correlation, and cross-sectional dependence, ensuring robust inference. This econometric stage directly supports the study\u0026rsquo;s objective of quantifying tax potential and evaluating the extent to which provinces utilize this potential over time.\u003c/p\u003e \u003cp\u003eTogether, these two stages form a hybrid analytical strategy that combines inductive pattern identification with deductive model estimation. This blended approach enhances both empirical accuracy and theoretical coherence, making it well suited for developing a three-dimensional provincial classification that integrates tax capacity, tax effort, and tax collection performance. The next subsection describes the methodological procedures for data selection and variable construction, which constitute the foundation upon which the analytical approach is implemented.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data and Variables\u003c/h2\u003e \u003cp\u003eThe empirical analysis was based on a balanced panel dataset covering all 81 Turkish provinces over the period 2007\u0026ndash;2022. The dataset was constructed from multiple official sources to ensure reliability and comparability. Provincial tax and fiscal indicators were obtained from the Revenue Administration of Turkey (GİB, 2025) and the Ministry of Treasury and Finance (Republic of Turkey, Ministry of Treasury and Finance, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while socioeconomic and demographic indicators, including income, sectoral structure, trade, population, and dependency ratios, were retrieved from the Turkish Statistical Institute (TURKSTAT, 2025).\u003c/p\u003e \u003cp\u003eFollowing the modern tax capacity literature (Gupta, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Fenochietto \u0026amp; Pessino, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Castro \u0026amp; Camarillo, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the analysis included variables that captured both the \u003cem\u003eeconomic potential\u003c/em\u003e to generate revenue and the \u003cem\u003einstitutional-demographic pressures\u003c/em\u003e that may influence actual tax collection. The main variables were defined as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eTax Burden (TaxBurden₍\u003csub\u003ei\u003c/sub\u003e,ₜ₎)\u003c/em\u003e: Total tax revenues divided by provincial GDP. This variable served as the dependent variable in the tax capacity model.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eManufacturing Share (ManfGDP₍\u003csub\u003ei\u003c/sub\u003e,ₜ₎)\u003c/em\u003e: The share of manufacturing output in GDP, representing industrial capacity and formal-sector activity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eAgricultural Share (AgrGDP₍\u003csub\u003ei\u003c/sub\u003e,ₜ₎)\u003c/em\u003e: The share of agricultural output in GDP; higher agricultural dependence typically indicated weaker tax bases.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePer Capita Income (lnGDPpc₍\u003csub\u003ei\u003c/sub\u003e,ₜ₎)\u003c/em\u003e: Logged per capita GDP, representing provincial economic development.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eTrade Openness (Openness₍\u003csub\u003ei\u003c/sub\u003e,ₜ₎)\u003c/em\u003e: The ratio of total exports plus imports to GDP.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePublic Expenditure Intensity (PubExp₍\u003csub\u003ei\u003c/sub\u003e,ₜ₎)\u003c/em\u003e: Provincial public expenditure as a share of GDP.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePopulation Size (lnPopulation₍\u003csub\u003ei\u003c/sub\u003e,ₜ₎)\u003c/em\u003e: Logged provincial population.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eUrbanization Rate (Urban₍\u003csub\u003ei\u003c/sub\u003e,ₜ₎)\u003c/em\u003e: Share of urban population.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eAge Dependency Ratio (AgeDep₍\u003csub\u003ei\u003c/sub\u003e,ₜ₎)\u003c/em\u003e: The ratio of dependents to the working-age population.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics for all variables, revealing substantial cross-provincial variation as well as meaningful within-province dynamics over time both of which justify a modeling framework capable of correcting for unobserved heterogeneity and dynamic interactions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Data Set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTax Burden: Tax Revenue / GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eManfGDP: Manufactor Income / GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAgrGDP: Agriculture Income / GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003elnGDPpc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIntensity of Public Expenditure Public Expenditure / GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003elnPopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUrbanization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge Dependency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the cluster analysis, all continuous variables were standardized using z-scores to ensure comparability across provinces. Additionally, natural logarithmic transformations were applied to variables exhibiting skewed distributions, improving normality and reducing the influence of outliers. These preprocessing steps ensured that the dataset was well suited for the multi-stage empirical framework, which combined structural clustering, robust panel estimation, and endogeneity-corrected procedures.\u003c/p\u003e \u003cp\u003eThe analytical process proceeds in three steps. First, hierarchical cluster analysis is applied to classify provinces into relatively homogeneous subgroups based on their economic and demographic characteristics. This step reduces structural heterogeneity and enhances the interpretability of subsequent tax capacity estimates. Second, tax capacity is estimated for each subgroup using fixed-effects panel regression models with Driscoll-Kraay standard errors, which address heteroscedasticity, autocorrelation, and cross-sectional dependence. Finally, provincial tax effort is calculated by comparing actual tax burden with the estimated tax capacity, providing a measure of the extent to which each province utilizes its revenue-raising potential.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Cluster Analysis\u003c/h2\u003e \u003cp\u003eTo address the substantial socioeconomic heterogeneity across Turkish provinces, the first stage of the empirical strategy relied on hierarchical cluster analysis. This procedure grouped provinces with similar structural characteristics, thereby improving the homogeneity of subgroups and enhancing the reliability of the subsequent tax capacity estimates. Cluster analysis has been widely recognized as an effective tool for simplifying large datasets into analytically meaningful groups, particularly in fiscal federalism and regional tax capacity research where multicollinearity and structural divergence present major challenges.\u003c/p\u003e \u003cp\u003eIn this study, provinces were classified using agglomerative hierarchical clustering based on the Manhattan distance metric and Ward\u0026rsquo;s minimum-variance linkage method. Ward\u0026rsquo;s method minimized the increase in within-cluster variance at each step of aggregation, yielding compact and internally coherent clusters. The Manhattan distance measure, defined as the sum of absolute deviations, has been shown to perform effectively in high-dimensional socioeconomic datasets (Strauss \u0026amp; von Maltitz, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u0026Ouml;zdamar, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dissimilarity between any two provinces \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e across \u003cem\u003ep\u003c/em\u003e standardized variables was computed as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{cccc}\u0026amp;\\:d({x}_{i},{x}_{j})=\\sum\\:_{k=1}^{p}\\mid\\:{x}_{ik}-{x}_{jk}\\mid\\:\u0026amp;\\:\u0026amp;\\:\\text{(1)}\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003edenotes the number of provinces (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=81\\)\u003c/span\u003e\u003c/span\u003e), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003edenotes the number of clustering variables.\u003c/p\u003e \u003cp\u003eConsistent with modern tax capacity literature, seven structural variables were used as inputs for clustering:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eAgrGDP\u003c/em\u003e - share of agriculture in provincial GDP,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eManfGDP\u003c/em\u003e - share of manufacturing in GDP,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePerCap\u003c/em\u003e - per capita income,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eOpenness\u003c/em\u003e - ratio of total trade to GDP,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePubExp\u003c/em\u003e - public expenditure as a share of GDP,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePopulation\u003c/em\u003e - logged total population,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eUrbanization Rate\u003c/em\u003e - share of urban population.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAll variables were standardized using z-scores prior to clustering to ensure comparability and to remove scale effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Regression Models\u003c/h2\u003e \u003cp\u003eTo estimate provincial tax capacity, the study first constructed a panel regression framework designed to predict the feasible tax burden of each province conditional on its structural characteristics. Tax capacity was defined as the maximum level of revenue that a province could generate given its economic composition, demographic structure, and administrative environment, whereas tax effort was later computed as the ratio of actual tax burden to this estimated capacity.\u003c/p\u003e \u003cp\u003eBecause T\u0026uuml;rkiye\u0026rsquo;s provinces exhibit substantial heterogeneity, the regression analysis was not estimated on the full sample directly. Instead, the provinces were first classified into relatively homogeneous groups using hierarchical cluster analysis. This clustering step served as a preprocessing procedure rather than an inferential model: its purpose was to reduce structural heterogeneity and allow the estimation of tax capacity functions that better reflect the economic realities of similar provinces. Importantly, the number of clusters was not imposed ex ante but was determined empirically based on the statistical properties of the dendrogram and cluster validity criteria.\u003c/p\u003e \u003cp\u003eAfter the clustering stage, the tax capacity model was estimated separately for each cluster, allowing parameter estimates to vary across economically and demographically distinct groups of provinces. This cluster-specific estimation strategy is consistent with contemporary fiscal capacity research and avoids imposing a single uniform tax capacity function across structurally diverse regions.\u003c/p\u003e\u003cp\u003eThe baseline empirical specification was:\u003c/p\u003e\n\u003cdiv id=\"Equb\"\u003e\n \u003cdiv id=\"FileID_Equb\" name=\"EquationSource\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cem\u003ei\u003c/em\u003e denotes the province and \u003cem\u003et\u003c/em\u003e the period from 2007 to 2022. The dependent variable, the tax burden, was measured as total provincial tax revenue divided by provincial GDP. Explanatory variables manufacturing share, per capita income, openness ratio, public expenditure intensity, and age dependency reflect widely recognized determinants of tax capacity in the literature.\u003c/p\u003e\n\u003cp\u003eAll variables were transformed using natural logarithms to improve distributional properties and interpretation. The models were estimated using fixed effects to control for time-invariant unobserved provincial characteristics, while Driscoll-Kraay standard errors corrected for heteroscedasticity, serial correlation, and cross-sectional dependence features strongly detected in the diagnostic tests.\u003c/p\u003e\n\u003cp\u003eFor each empirically determined cluster, Eq.\u0026nbsp;(2) was estimated independently. The resulting predicted tax burden values represent the estimated tax capacity for provinces within that cluster. Provincial tax effort was then calculated as the ratio of actual tax burden to predicted tax burden, forming the basis for the multidimensional evaluation of tax performance in subsequent sections.\u003c/p\u003e"},{"header":"Findings","content":"\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e4.1 Results of the Cluster Analysis\u003c/h2\u003e\n \u003cp\u003eThe hierarchical clustering procedure identified nine statistically distinct provincial groups, reflecting the pronounced structural heterogeneity across T\u0026uuml;rkiye\u0026rsquo;s 81 provinces. Clustering was performed using seven standardized indicators capturing economic structure, demographic pressure, and fiscal characteristics. The Ward linkage method combined with the Manhattan distance metric generated clusters with high between-group and low within-group variability, indicating a well-defined segmentation of provinces.\u003c/p\u003e\n \u003cp\u003eFigure 1 visualizes the spatial and structural distribution of provinces across the nine clusters, illustrating clear distinctions between industrialized western provinces and the predominantly agricultural or demographically constrained eastern regions.\u003c/p\u003e\n \u003cp\u003eThe hierarchical clustering procedure identified nine statistically distinct provincial groups (Table 2), characterized by significant differences in economic structure, demographic composition, and fiscal indicators.\u003c/p\u003e\n \u003cp\u003eDescriptive statistics (Table 2) show clear structural differences across clusters. For example, clusters with high manufacturing shares and strong urbanization (Clusters 1, 3, and 8) exhibit characteristics associated with higher taxable capacity, whereas clusters dominated by agriculture or low-income provinces (Clusters 4, 6, and 7) present structural constraints consistent with lower tax capacity.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMeans of Clusters According to Variables (2007\u0026ndash;2022)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eN (Provinces)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eMeans\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgrGDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eManfGDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePerCap (TRY)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOpeness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePubExp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePop\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.629.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e446.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e269.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.650.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e414.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e294.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e987.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTo assess the robustness of the classification, Kruskal-Wallis tests were conducted for each clustering variable. All seven indicators differ significantly across clusters at the 1% level, confirming that the groups capture meaningful economic and demographic divergence (Table\u0026nbsp;3). The internal validation metrics further support the quality of the clustering solution: the overall classification accuracy is 0.87, and Cohen\u0026rsquo;s Kappa equals 0.82, indicating strong agreement beyond chance (Table\u0026nbsp;4).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\u003cp\u003eTaken together, these results demonstrate that the clustering structure is statistically sound and substantively meaningful, providing a reliable foundation for estimating tax capacity functions within relatively homogeneous provincial groups. The use of cluster-specific tax capacity models is therefore empirically justified, as it avoids imposing a single nationwide relationship on provinces with markedly different structural characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Model Diagnostics and Estimation Strategy\u003c/h2\u003e \u003cp\u003eBefore estimating the tax capacity functions, a comprehensive set of diagnostic tests was conducted to determine the appropriate panel data specification. The Hausman tests reported in Table\u0026nbsp;5 uniformly reject the random-effects model at the 1% significance level across all clusters, indicating the presence of time-invariant unobserved heterogeneity that is correlated with the regressors. This provides strong econometric justification for employing fixed-effects (FE) estimators.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTablo 5.\u003c/b\u003e Preliminary Tests\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel for Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHausman Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeteroskedasticty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAutocorrelation\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCross-Sectional Dependency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClusters\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eF Test\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChi2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWald Testi\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eDurbin Watson\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBaltagi-Wu\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePesaran-CD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128.3***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153.3***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6838.3***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.3***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149.2***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181.2***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7611.4***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.3***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129.1***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142.1***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8442.9***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.8***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.3***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.5***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5271.0***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e78.6***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.1***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.6***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5145.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88.1***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.6***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.3***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6149.3***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.5***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.2***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7236.1***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.6***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113.4***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.8***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4725.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.1***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.5***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8513.9***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.2***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e The threshold value relied upon for the Durbin-Watson and Baltagi-Wu tests is \"2\".\u003c/p\u003e \u003cp\u003e*** is the p-value, statistically significant at the 1% level.\u003c/p\u003e \u003cp\u003eThe remaining diagnostics confirm that the error structure of the data deviates from classical assumptions. Wald tests for heteroskedasticity, Durbin-Watson and Baltagi-Wu tests for serial correlation, and Pesaran\u0026rsquo;s CD test for cross-sectional dependence all indicate significant violations of homoskedasticity, independence, and cross-sectional orthogonality (Table\u0026nbsp;5). These patterns are consistent with the characteristics of subnational fiscal datasets, where economic shocks, institutional features, and spatial linkages often generate correlated disturbances across regions.\u003c/p\u003e \u003cp\u003eGiven these results, all tax-capacity models were estimated using fixed-effects regressions with Driscoll-Kraay standard errors, which are robust to heteroskedasticity, autocorrelation, and cross-sectional dependence. This estimator provides consistent coefficient estimates under weak assumptions and has become standard in empirical public finance applications involving subnational units.\u003c/p\u003e \u003cp\u003eThe diagnostic evidence therefore validates the empirical strategy adopted in this study and ensures that the subsequent tax capacity estimates are not biased by specification errors or misspecified error structures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Tax Capacity Estimation Results\u003c/h2\u003e \u003cp\u003eThe fixed-effects models estimated for each cluster reveal substantial heterogeneity in the structural determinants of provincial tax capacity (Table\u0026nbsp;6). As expected, the share of manufacturing in provincial GDP emerges as a strong and positive determinant in the more industrialized clusters, reflecting the role of formal-sector production in widening the effective tax base. In contrast, the coefficient is weak or insignificant in clusters dominated by agriculture or services, underscoring the limited contribution of informal or low-value-added activities to taxable capacity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTablo 6.\u003c/b\u003e Tax Capacity Models\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManfgdp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003cp\u003e(0.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.231***\u003c/p\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.171**\u003c/p\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.173***\u003c/p\u003e \u003cp\u003e(0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003cp\u003e(0.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.143**\u003c/p\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003cp\u003e(0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.817***\u003c/p\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.097**\u003c/p\u003e \u003cp\u003e(0.048)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.009*\u003c/p\u003e \u003cp\u003e(0.478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003cp\u003e(0.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003cp\u003e(0.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003cp\u003e(0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019**\u003c/p\u003e \u003cp\u003e(0.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003cp\u003e(0.228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0001**\u003c/p\u003e \u003cp\u003e(0.289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.095*\u003c/p\u003e \u003cp\u003e(0.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.093**\u003c/p\u003e \u003cp\u003e(0.219)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublicexpgdp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.151**\u003c/p\u003e \u003cp\u003e(0.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.380***\u003c/p\u003e \u003cp\u003e(0.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003cp\u003e(0.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.969**\u003c/p\u003e \u003cp\u003e(0.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.304**\u003c/p\u003e \u003cp\u003e(0.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.122*\u003c/p\u003e \u003cp\u003e(0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e(0.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.324**\u003c/p\u003e \u003cp\u003e(0.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.510*\u003c/p\u003e \u003cp\u003e(0.320)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.121***\u003c/p\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.068*\u003c/p\u003e \u003cp\u003e(0.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.082**\u003c/p\u003e \u003cp\u003e(0.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.128***\u003c/p\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.027*\u003c/p\u003e \u003cp\u003e(0.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.112**\u003c/p\u003e \u003cp\u003e(0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.002***\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.015**\u003c/p\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.092***\u003c/p\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eagedependencyratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003cp\u003e(0.339)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003cp\u003e(0.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.561***\u003c/p\u003e \u003cp\u003e(0.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.622*\u003c/p\u003e \u003cp\u003e(0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.072*\u003c/p\u003e \u003cp\u003e(0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.576***\u003c/p\u003e \u003cp\u003e(0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.007**\u003c/p\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.912*\u003c/p\u003e \u003cp\u003e(0.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.484***\u003c/p\u003e \u003cp\u003e(0.057)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.411***\u003c/p\u003e \u003cp\u003e(0.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.108***\u003c/p\u003e \u003cp\u003e(0.851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.650***\u003c/p\u003e \u003cp\u003e(0.717)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.180***\u003c/p\u003e \u003cp\u003e(0.189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.463***\u003c/p\u003e \u003cp\u003e(0.339)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.632***\u003c/p\u003e \u003cp\u003e(0.587)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.058***\u003c/p\u003e \u003cp\u003e(0.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.384***\u003c/p\u003e \u003cp\u003e(0.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.119***\u003c/p\u003e \u003cp\u003e(0.382)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF Stats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.92***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.88***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85.48***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91.62***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e83.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.16***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. lag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNote.\u003c/b\u003e In the table, standard errors obtained with the Driscoll-Kraay estimator are shown in parentheses. Additionally, the stars are the p-value of the coefficient. * is statistically significant at the 10% level, ** at the 5% level, and *** at the 1% level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA noteworthy result is the predominantly negative coefficient on per capita income (lnGDPpc) across several clusters. This counterintuitive pattern, observed in recent subnational fiscal capacity studies, may indicate that high-income provinces benefit from sectoral exemptions, tax incentives, or base erosion mechanisms that suppress their effective tax burden. The finding suggests that income alone is not a reliable proxy for structural fiscal potential at the provincial scale.\u003c/p\u003e \u003cp\u003eThe effects of openness and public expenditure intensity vary considerably across clusters. In trade-oriented regions, openness positively influences tax burden, consistent with broader taxable activity and stronger administrative presence. However, in other clusters, the coefficient is insignificant or negative, possibly reflecting tax preferences for export-oriented production. Public expenditure intensity is positive and significant in several clusters, indicating that government spending particularly on administrative functions may enhance revenue mobilization capabilities.\u003c/p\u003e \u003cp\u003eDemographic factors also play a differentiated role. Where significant, the age dependency ratio exerts a negative effect on tax burden, suggesting that provinces with higher dependency pressures face a structurally narrower tax base. In many clusters, however, demographic variables are statistically insignificant, reflecting diverse local labor market dynamics and migration patterns.\u003c/p\u003e \u003cp\u003eOverall, the results confirm that tax capacity in T\u0026uuml;rkiye is highly context dependent. The determinants of revenue potential differ sharply across provincial groups, validating the decision to estimate tax-capacity functions within homogeneous clusters rather than nationally. A single pooled model would obscure these structural differences and lead to biased assessments of provincial tax effort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Estimated vs. Actual Tax Capacity\u003c/h2\u003e \u003cp\u003eThe comparison between estimated tax capacity and actual provincial tax burden reveals substantial spatial and structural disparities across T\u0026uuml;rkiye (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Provinces with diversified industrial structures most notably Kocaeli, İstanbul, İzmir, and Ankara display the highest predicted capacity levels, consistent with their large formal sectors, higher value-added activities, and stronger administrative infrastructures. These provinces also exhibit relatively stable capacity estimates over time, indicating persistent structural advantages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, provinces in the eastern and southeastern regions show systematically lower predicted tax capacity, reflecting limited industrialization, high informality, demographic pressures, and weaker administrative reach. The magnitude of the capacity gap between western and eastern clusters highlights the deep regional asymmetries that characterize T\u0026uuml;rkiye\u0026rsquo;s fiscal landscape.\u003c/p\u003e \u003cp\u003eThe comparison also reveals meaningful mismatches between actual tax burden and predicted capacity. Several provinces with strong structural potential such as Ankara, Bursa, Gaziantep, and Sakarya collect noticeably less revenue than their estimated capacity would suggest. This underperformance signals constraints in administrative effectiveness, compliance levels, or tax policy design. Conversely, some structurally constrained provinces (e.g., Ağrı, Aksaray, Artvin, Rize) exhibit actual tax burdens that exceed predicted levels, indicating compensatory administrative effort or above-expected compliance behavior.\u003c/p\u003e \u003cp\u003eOverall, these results demonstrate that structural capacity alone does not fully determine observed revenue outcomes. The divergence between actual and estimated values underscores the importance of analyzing tax effort, which is derived precisely from this comparison. The next section formalizes these differences by quantifying provincial tax effort over the 2007\u0026ndash;2022 period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Tax Effort Results\u003c/h2\u003e \u003cp\u003eThe tax effort estimates, derived as the ratio of actual tax burden to predicted tax capacity, reveal substantial variation across Turkish provinces over the 2007\u0026ndash;2022 period (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The distribution of tax effort indicates a clear divergence between provinces that consistently mobilize revenues above their structural potential and those that underperform despite favorable economic conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRanking of Provinces by Tax Effort Index, Averages 2007\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Tax Effort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"31\" rowspan=\"32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedium Tax Effort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"31\" rowspan=\"32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow Tax Effort\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZonguldak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eİstanbul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eErzincan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMersin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKırşehir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAksaray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntalya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKayseri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHatay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBartın\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKonya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSiirt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eArdahan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamsun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMuş\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUşak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArtvin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003cp\u003eKarab\u0026uuml;k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIsparta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG\u0026uuml;m\u0026uuml;şhane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSinop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdıyaman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsmaniye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eErzurum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKilis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrabzon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTokat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHakkari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eŞanlıurfa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAfyonkarahisar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eŞırnak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBitlis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMuğla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSakarya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSivas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBursa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eİzmir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElazığ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBilecik\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNevşehir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD\u0026uuml;zce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGaziantep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKastamonu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEskişehir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026Ccedil;ankırı\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiyarbakır\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmasya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMardin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eProvinces such as Zonguldak, Mersin, Hatay, Aksaray, and Rize exhibit high tax effort, often exceeding unity, suggesting that administrative capacity, compliance incentives, or local enforcement practices enable these regions to achieve revenue levels above what their structural characteristics would predict. These outcomes mirror findings from subnational contexts in other emerging economies, where strong local administration can partially compensate for weaker structural capacity.\u003c/p\u003e \u003cp\u003eIn contrast, several economically advanced provinces including Ankara, Bursa, Gaziantep, Sakarya, and Kayseri demonstrate persistent underperformance, with effort values below unity. This pattern indicates that high structural capacity does not automatically translate into strong revenue mobilization. Possible drivers include sector-specific exemptions, administrative fragmentation, or taxpayer optimization behavior that reduces effective revenue collection relative to potential. These mismatches between capacity and effort underscore the importance of evaluating performance beyond structural indicators.\u003c/p\u003e \u003cp\u003eThe national trajectory of tax effort also reflects sensitivity to macroeconomic shocks. Effort levels declined during the 2008\u0026ndash;2009 global recession and again during the 2018 domestic currency crisis, consistent with contractions in tax bases, rising informality, and reduced compliance during downturns. A modest recovery appears in 2021\u0026ndash;2022, driven partly by inflation-induced increases in nominal revenues.\u003c/p\u003e \u003cp\u003eTaken together, the tax effort results highlight the regional heterogeneity in fiscal performance and demonstrate that structural capacity is only one component of revenue mobilization. The divergence between high-capacity/low-effort and low-capacity/high-effort provinces provides a policy-relevant framework for identifying jurisdictions requiring administrative strengthening, compliance interventions, or differentiated fiscal strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Temporal Dynamics of Tax Effort (2007\u0026ndash;2022)\u003c/h2\u003e \u003cp\u003eAn examination of the temporal evolution of provincial tax effort across four subperiods 2007\u0026ndash;2010, 2011\u0026ndash;2014, 2015\u0026ndash;2018, and 2019\u0026ndash;2022 reveals that fiscal performance in T\u0026uuml;rkiye is highly sensitive to macroeconomic conditions and cyclical fluctuations (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The period 2007\u0026ndash;2010, which includes the global financial crisis, is marked by a broad decline in tax effort across most provinces. This pattern aligns with international evidence showing that economic contractions reduce tax bases, increase informality, and weaken compliance incentives.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in Tax Efforts Segmented by Categories Over a 4-Year Period (2007\u0026ndash;2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLow Tax Effort\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedium Tax Effort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMax\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh Tax Effort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMax\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTax Collection Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2007\u0026ndash;2010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2011\u0026ndash;2014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2015\u0026ndash;2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2019\u0026ndash;2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA moderate recovery emerges during 2011\u0026ndash;2014, a phase characterized by relative macroeconomic stability and stronger domestic demand. During this period, tax effort converges toward unity in many provinces, suggesting improved alignment between structural capacity and revenue mobilization.\u003c/p\u003e \u003cp\u003eThe 2015\u0026ndash;2018 period displays renewed deterioration in tax effort, coinciding with heightened currency volatility, rising inflation, and declining real incomes. These shocks appear to have disproportionately affected provinces with narrower economic bases and higher informality, resulting in sharper drops in fiscal performance. Several high-capacity provinces also experience notable declines in effort, indicating that cyclical and structural factors jointly constrain revenue mobilization.\u003c/p\u003e \u003cp\u003eThe most recent period, 2019\u0026ndash;2022, captures both the economic effects of the COVID-19 pandemic and the subsequent inflationary surge. During 2020, tax effort declines in almost all regions, reflecting mobility restrictions, temporary tax deferrals, and reduced economic activity. However, 2021\u0026ndash;2022 show a partial rebound in tax effort, driven largely by increases in nominal tax collections and the recovery of certain service-sector activities. Despite this improvement, significant interprovincial disparities remain, indicating that the resilience of tax effort is conditioned by each province\u0026rsquo;s structural characteristics and administrative capacity.\u003c/p\u003e \u003cp\u003eOverall, the temporal analysis demonstrates that tax effort in T\u0026uuml;rkiye is shaped by both short-term macroeconomic shocks and \u003cb\u003el\u003c/b\u003eong-run structural conditions. These dynamics reinforce the need for province-specific policy responses that consider the differing vulnerabilities and fiscal capacities of regional economies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Multidimensional Classification: Tax Capacity, Tax Effort, Tax Collection Rate\u003c/h2\u003e \u003cp\u003eTo provide a comprehensive assessment of provincial fiscal performance, the study constructs a three-dimensional classification system based on tax capacity, tax effort, and tax collection rates (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Using the median values of each indicator as thresholds, provinces are categorized into eight distinct groups that capture the interaction between structural capacity, administrative performance, and actual revenue outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of Provinces Based on Tax Capacity, Tax Effort and Tax Collection Rate, Averages of 2007\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTax Capacity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTax Effort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTax Collection Rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHatay, İstanbul, İzmir, Kocaeli, Manisa, Mersin, Samsun, Tekirdağ, Trabzon, Zonguldak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(82.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdana, Antalya, Denizli, Karab\u0026uuml;k, Malatya, Ordu, Van, Yalova\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(67.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnkara, Balıkesir, Bursa, \u0026Ccedil;ankırı, \u0026Ccedil;orum, Elazığ, Erzurum, Eskişehir, Isparta, Kayseri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(74.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdıyaman, Aydın, Burdur, D\u0026uuml;zce, Gaziantep, Kırıkkale, Kırşehir, Kilis, Konya, Mardin, Muğla, Sakarya, Uşak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(61.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAğrı, Aksaray, Artvin, Bartın, Bing\u0026ouml;l, Bitlis, \u0026Ccedil;anakkale, Edirne, Giresun, Kahramanmaraş, Kastamonu, Muş, Rize, Tunceli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(76.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBatman, Bolu, Diyarbakır, Iğdır, Kırklareli, K\u0026uuml;tahya, Nevşehir, Osmaniye, Siirt, Sivas, Şanlıurfa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(64.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArdahan, Bayburt, Erzincan, G\u0026uuml;m\u0026uuml;şhane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(77.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfyonkarahisar, Amasya, Bilecik, Hakkari, Karaman, Kars, Niğde, Sinop, Şırnak, Tokat, Yozgat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(61.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe classification reveals important asymmetries. Provinces with high tax capacity and high effort such as İstanbul, İzmir, Kocaeli, and Hatay also record high collection rates, indicating that strong structural bases and effective administrative practices reinforce each other. These provinces represent the benchmark group where both economic potential and revenue mobilization are fully aligned.\u003c/p\u003e \u003cp\u003eIn contrast, a subset of high-capacity provinces with low tax effort, including Ankara, Bursa, and Gaziantep, show below-average collection rates, signalling underutilization of structural potential. This mismatch suggests that administrative constraints, compliance gaps, or sectoral tax privileges may impede revenue performance despite favorable economic conditions. Similar patterns have been documented in other emerging economies, where structural capacity does not guarantee effective extraction without supportive governance and enforcement mechanisms.\u003c/p\u003e \u003cp\u003eConversely, several low-capacity but high-effort provinces such as Aksaray, Artvin, and Rize achieve above-expected collection rates, demonstrating strong administrative commitment or comparatively higher compliance among taxpayers. These cases underscore that effective local tax administration can partially compensate for structural disadvantages, a finding consistent with recent evidence from subnational fiscal studies.\u003c/p\u003e \u003cp\u003eThe remaining group, consisting of provinces with low capacity, low effort, and low collection rates, represents regions facing the most severe fiscal challenges. These provinces, many of which are located in T\u0026uuml;rkiye\u0026rsquo;s eastern and southeastern regions, appear constrained by both economic structure and limited administrative capability. For these areas, structural reforms such as investment incentives, formalization policies, and administrative strengthening are likely prerequisites for improving revenue performance.\u003c/p\u003e \u003cp\u003eOverall, the multidimensional taxonomy highlights substantial heterogeneity in provincial fiscal behavior and underscores that structural capacity, administrative effort, and realized collections do not necessarily move together. This framework allows policymakers to identify province-specific bottlenecks and tailor interventions accordingly, whether through strengthening tax administration, revising local incentives, or enhancing compliance strategies. The approach also offers a replicable template for assessing subnational tax performance in other countries with similar regional disparities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion and Conclusions","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Discussion\u003c/h2\u003e \u003cp\u003eThis study set out to estimate provincial tax capacity and tax effort for 81 Turkish provinces over 2007\u0026ndash;2022 and to classify them according to their tax capacity, tax effort, and tax collection performance. The empirical results broadly confirm, but also nuance, the predictions of the tax capacity literature that emphasizes structural determinants such as per capita income, sectoral composition, and openness as primary drivers of tax performance (Chelliah et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Gupta, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Castro \u0026amp; Camarillo, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Le et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirst, the cluster analysis reveals strong structural heterogeneity across provinces. Industrialized and highly urbanized clusters, particularly those including İstanbul, Kocaeli, and İzmir, display characteristics associated with high tax capacity higher manufacturing shares, higher per capita income, and deeper integration into international trade. This pattern is consistent with cross-country evidence showing that industrialization and economic development expand the taxable base and increase the feasible tax-to-GDP ratio (Castro \u0026amp; Camarillo, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Le et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Serin \u0026amp; Demir, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, clusters dominated by agriculture, low income, and lower urbanization levels exhibit structurally weaker tax capacity, in line with classic findings that agricultural dependence depresses taxable potential (Lotz \u0026amp; Morss, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Chelliah et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1975\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, the fixed-effects tax capacity models show that the impact of structural variables is highly context-dependent. Manufacturing share is strongly and positively associated with tax burden in industrial clusters, but it is weak or even negative in some mixed or less formal clusters. This heterogeneity resonates with recent regional and subnational studies where the contribution of manufacturing to tax capacity depends on the degree of formality, the sectoral tax regime, and local institutional capacity (Chigome \u0026amp; Robinson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Serin \u0026amp; Demir, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA particularly striking result is the predominantly negative coefficient on per capita income in several clusters. Standard capacity models usually find a positive relationship between income and the tax-to-GDP ratio (Gupta, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Le et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fenochietto \u0026amp; Pessino, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The negative sign observed here suggests that, at the provincial level, high-income provinces may benefit from extensive exemptions, investment incentives, or base erosion mechanisms that reduce their effective tax burden an outcome closer to the \u0026ldquo;tax capacity not fully exploited\u0026rdquo; interpretation emphasized in recent work on structural tax gaps (Fenochietto \u0026amp; Pessino, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ben\u0026iacute;tez et al., 2023).\u003c/p\u003e \u003cp\u003eThe mixed effects of trade openness and public expenditure intensity also merit discussion. In trade-oriented clusters, openness tends to raise tax burden, echoing evidence that international integration can expand taxable activities and encourage modernization of tax administration (Castro \u0026amp; Camarillo, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chigome \u0026amp; Robinson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In other clusters, however, openness is insignificant or negative, possibly reflecting preferential regimes for exporters or the dominance of low-taxable export sectors. Similarly, public expenditure intensity is positively related to tax burden in several clusters, consistent with the notion that higher government presence and administrative spending can enhance compliance and enforcement (Bird et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ben\u0026iacute;tez et al., 2023), but it is negative or insignificant in others, pointing to inefficiencies or weak expenditure\u0026ndash;revenue linkages.\u003c/p\u003e \u003cp\u003eThe comparison between estimated tax capacity and actual tax burden shows that structural factors alone cannot explain provincial revenue performance. Some provinces with strong structural capacity (e.g., Ankara, Bursa, Gaziantep, Sakarya) underperform relative to their estimated capacity, whereas several structurally constrained provinces (e.g., Ağrı, Aksaray, Artvin, Rize) exhibit tax burdens above predicted levels. This combination of high-capacity/low-effort and low-capacity/high-effort provinces closely parallels cross-country findings where institutional quality, governance, and administrative effort play a decisive role in closing or widening the gap between potential and actual revenue (Bird et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fenochietto \u0026amp; Pessino, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ben\u0026iacute;tez et al., 2023; Saru\u0026ccedil; et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the temporal analysis shows that tax effort is highly sensitive to macroeconomic conditions. Declines in tax effort during the 2008\u0026ndash;2009 global crisis and the 2018 currency shock, followed by partial recovery, are consistent with international evidence that recessions compress tax bases, increase informality, and weaken compliance (Tanzi, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Morrissey et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; OECD, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The three-dimensional classification system highlights that these cyclical dynamics interact with structural and administrative factors, leading to persistent differences across provincial groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Theoretical Contribution\u003c/h2\u003e \u003cp\u003eTheoretically, the study speaks to the long-standing debate on how best to conceptualize and measure tax capacity and tax effort. Classical approaches, starting from Lotz and Morss (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) and Chelliah et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), treated tax effort as the ratio of actual to predicted tax revenues, where predictions were based primarily on national-level structural variables. Later contributions refined the specification by incorporating a broader set of economic, social, and institutional determinants and by introducing more sophisticated econometric techniques (Gupta, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Le et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fenochietto \u0026amp; Pessino, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This study extends that tradition in three main ways.\u003c/p\u003e \u003cp\u003eFirst, it relocates the tax capacity discourse to the subnational (provincial) level in a large emerging economy. Existing work on T\u0026uuml;rkiye has either focused on national aggregates or used relatively coarse regional groupings (Saru\u0026ccedil; et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; \u0026Ccedil;elikay, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Allahverdi \u0026amp; Alag\u0026ouml;z, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Serin \u0026amp; Demir, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By contrast, this study employs NUTS-3 data for 81 provinces and shows that the determinants of tax capacity vary sharply across structurally distinct provincial clusters. This reinforces the view, also found in subnational studies for other countries, that a single national tax capacity function is too coarse to capture region-specific dynamics (Arlashkin, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kawadia \u0026amp; Suryawanshi, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, the study integrates cluster analysis with panel estimation to handle structural heterogeneity. While cluster analysis has been widely used in regional and evolutionary economic geography to identify groups of regions with similar development paths (G\u0026uuml;ltekin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), its use as a pre-estimation step in tax capacity analysis is still rare. The finding that coefficients differ markedly across clusters supports the argument that tax capacity is context-dependent and that structural benchmarking should be performed within relatively homogeneous groups rather than at the national aggregate level (Le et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chigome \u0026amp; Robinson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Serin \u0026amp; Demir, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, the study proposes a three-dimensional framework that jointly considers tax capacity, tax effort, and tax collection rate. Earlier tax effort indices generally used a two-dimensional view actual versus potential revenue (Lotz \u0026amp; Morss, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Le et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fenochietto \u0026amp; Pessino, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). By adding tax collection efficiency as a third dimension, the analysis is able to distinguish between high-capacity/low-effort provinces with weak collections, low-capacity/high-effort provinces with relatively strong collections, and provinces that perform poorly on all three fronts. This typology aligns with recent calls in the literature for more nuanced performance measures that go beyond simple tax-to-GDP ratios (Ben\u0026iacute;tez et al., 2023; Serin \u0026amp; Demir, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the theoretical contribution lies in demonstrating that tax capacity and effort are multi-layered, spatially embedded constructs. The results support a more granular, regionally sensitive interpretation of the structural benchmarking paradigm developed in the international tax capacity literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Practical Implications\u003c/h2\u003e \u003cp\u003eThe findings carry several implications for tax policy, fiscal equalization, and public administration in T\u0026uuml;rkiye.\u003c/p\u003e \u003cp\u003eFor provinces with high tax capacity and high effort and high collection rates (e.g., İstanbul, İzmir, Kocaeli, Hatay), the results suggest that both structural conditions and administrative frameworks are aligned. These provinces can serve as benchmark cases for best practice in tax administration and compliance management, similar to the \u0026ldquo;front-runners\u0026rdquo; identified in comparative studies of tax capacity and effort in Europe and Southern Africa (Chigome \u0026amp; Robinson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Serin \u0026amp; Demir, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Policy in these regions may focus on maintaining administrative quality, preventing base erosion, and managing the distributional consequences of high tax burdens.\u003c/p\u003e \u003cp\u003eProvinces with high capacity but low effort and moderate collection rates such as Ankara, Bursa, Gaziantep, and Sakarya constitute a critical policy concern. The structural potential exists, but the underperformance suggests gaps in enforcement, compliance culture, or the design of local tax incentives. International evidence shows that improving tax administration, simplifying tax systems, and strengthening institutional trust can narrow such effort gaps (Saru\u0026ccedil; \u0026amp; Sağbaş, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Bird et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ben\u0026iacute;tez et al., 2023). For these provinces, targeted interventions could include risk-based audits, digitalization of tax processes, and more transparent links between tax revenue and local public services.\u003c/p\u003e \u003cp\u003eBy contrast, low-capacity but high-effort provinces including Aksaray, Artvin, and Rize demonstrate that administrative commitment and taxpayer compliance can partially compensate for structural disadvantages. Similar patterns have been observed in Indonesian local governments, where institutional reforms and changes in tax assignment rules significantly shaped local tax effort (Noviyanti \u0026amp; Zen, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For such provinces, policies to broaden the tax base through formalization, infrastructure investment, and sectoral diversification are likely to yield high marginal returns, especially if combined with continued administrative strengthening (OECD, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, provinces with low capacity, low effort, and low collection rates are structurally and administratively constrained. Many are located in T\u0026uuml;rkiye\u0026rsquo;s eastern and southeastern regions, where high informality, weaker administrative presence, and demographic pressures limit revenue mobilization. For these areas, the results support the case for a dual strategy: (i) long-term structural policies that promote productive diversification and regional development, consistent with the related variety and smart specialization perspective (G\u0026uuml;ltekin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and (ii) targeted capacity-building programs in line with the IMF\u0026rsquo;s guidance on building tax capacity in low-income and developing contexts (Ben\u0026iacute;tez et al., 2023).\u003c/p\u003e \u003cp\u003eAt the national level, the three-dimensional taxonomy offers a practical tool for fiscal equalization and intergovernmental transfers. Instead of relying solely on per capita income or simple revenue indicators, central authorities can incorporate information on tax capacity, effort, and collection rates when designing transfer formulas. This is consistent with international practice, where representative tax system approaches and fiscal capacity equalization mechanisms seek to balance fiscal autonomy with horizontal equity (Arlashkin, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; OECD, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Conclusion\u003c/h2\u003e \u003cp\u003eThe study provides a comprehensive, province-level assessment of tax capacity and tax effort in T\u0026uuml;rkiye over 2007\u0026ndash;2022 by integrating hierarchical cluster analysis with fixed-effects panel estimation and robust standard errors. The main conclusions can be summarized as follows.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTax capacity is highly uneven across provinces and is strongly shaped by industrialization, trade openness, and urbanization. Western and coastal provinces enjoy structurally higher tax capacity, while many eastern and southeastern provinces face persistent structural constraints.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe determinants of tax capacity are cluster-specific. Manufacturing share, openness, public expenditure, and demographic structure do not influence tax burden uniformly. This confirms the importance of accounting for structural heterogeneity and cautions against using a single national tax capacity function.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTax effort varies widely, with some provinces collecting significantly more than their structural characteristics would predict and others underperforming despite high capacity. This underscores the central role of administrative capacity, compliance behaviour, and local policy choices in shaping realized revenues.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTax effort is sensitive to macroeconomic conditions. Crisis periods are associated with declines in effort and collection rates, while recoveries are uneven across provincial groups.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe three-dimensional classification of provinces by capacity, effort, and collection rate provides a rich diagnostic framework for designing differentiated, region-specific fiscal and administrative strategies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTaken together, these findings contribute to the broader literature on tax capacity and effort by demonstrating the importance of subnational heterogeneity and by offering a replicable framework for other countries experiencing significant regional disparities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Limitations and Future Research\u003c/h2\u003e \u003cp\u003eDespite its contributions, the study has several limitations that should be acknowledged and that point to avenues for future research.\u003c/p\u003e \u003cp\u003eFirst, the analysis relies on aggregate provincial data, which inevitably mask intra-provincial disparities and micro-level behavioural responses. Firm and household-level data could allow future research to disentangle how different taxpayer groups respond to economic shocks, policy changes, or administrative reforms, as emphasized in recent work on tax capacity and inclusive development (Abdel-Kader \u0026amp; de Mooij \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ben\u0026iacute;tez et al., 2023).\u003c/p\u003e \u003cp\u003eSecond, while the study corrects for heteroscedasticity, serial correlation, and cross-sectional dependence via Driscoll-Kraay standard errors, it does not explicitly estimate spatial econometric models. Given the strong evidence of spatial interactions in tax performance (LeSage \u0026amp; Pace, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chigome \u0026amp; Robinson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adeleke, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ha et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), future work could employ spatial lag or spatial error models to distinguish between own-province effects and spillovers from neighbouring provinces.\u003c/p\u003e \u003cp\u003eThird, the measure of tax capacity is based on observable structural variables only. Important institutional and behavioural factors such as informality, tax morale, quality of local governance, and enforcement practices are not directly observed and instead enter the model via fixed effects. Incorporating explicit measures of institutional quality and governance, building on the literature that stresses their importance for tax effort (Bird et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fenochietto \u0026amp; Pessino, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Saru\u0026ccedil; et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), would strengthen the explanatory power of future models.\u003c/p\u003e \u003cp\u003eFourth, the three-dimensional classification system is static in the sense that it uses average values over 2007\u0026ndash;2022. Although the temporal analysis separately examines changes in tax effort, future research could construct dynamic trajectories of provinces across categories, identifying movers and stayers and relating these transitions to policy changes, shocks, or institutional reforms.\u003c/p\u003e \u003cp\u003eFinally, the study focuses on T\u0026uuml;rkiye as a single case. While this in-depth perspective is valuable, comparative research that applies the same methodology to other countries particularly those with pronounced regional disparities, such as India, Indonesia, or South Africa would help assess the generality of the findings and refine the proposed classification framework (Chigome \u0026amp; Robinson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kawadia \u0026amp; Suryawanshi, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Noviyanti \u0026amp; Zen, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAddressing these limitations would deepen our understanding of the interplay between structural conditions, institutions, and tax administration, and would further strengthen the contribution of provincial-level tax capacity and effort analysis to both theory and policy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Metin Allahverdi. The first draft of the manuscript was written by Ferdi \u0026Ccedil;elikay and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdel-Kader, K., \u0026amp; de Mooij, R. (2020). \u003cem\u003eTax Policy and Inclusive Growth\u003c/em\u003e. IMF Working Paper 20/271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020271-print-pdf.pdf\u003c/span\u003e\u003cspan address=\"https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020271-print-pdf.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdan, H., Atsebi, J. M. 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Verginin mali a\u0026ccedil;ısından T\u0026uuml;rkiye\u0026rsquo;de 1990\u0026ndash;2018 yılları arası d\u0026ouml;nemde vergi gayretinin incelenmesi. \u003cem\u003eVergi Raporu\u003c/em\u003e, \u003cem\u003e246\u003c/em\u003e, 196\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vergiraporu.com.tr/upImage/org/246.1280a7d6.PDF\u003c/span\u003e\u003cspan address=\"https://vergiraporu.com.tr/upImage/org/246.1280a7d6.PDF\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tax capacity, Tax effort, Revenue mobilization, Subnational public finance, Fixed-effects panel, Hierarchical clustering","lastPublishedDoi":"10.21203/rs.3.rs-8378380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8378380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDomestic revenue mobilisation depends not only on the size of the tax base but also on how effectively jurisdictions convert taxable capacity into realised collections. This paper develops a subnational diagnostic framework for T\u0026uuml;rkiye by jointly measuring tax capacity, tax effort, and tax collection effectiveness for 81 provinces over 2007\u0026ndash;2022. To address structural heterogeneity across jurisdictions, the analysis proceeds in two integrated stages. First, we apply hierarchical clustering to group provinces into relatively homogeneous clusters based on economic structure, demographic pressure, and fiscal characteristics, producing an interpretable typology for differentiated policy targeting.\u003c/p\u003e \u003cp\u003eSecond, we estimate cluster-specific tax-capacity functions using fixed-effects panel regressions. Hausman tests reject random effects, indicating that time-invariant provincial heterogeneity is correlated with regressors. Diagnostics further suggest heteroskedasticity, serial correlation, and cross-sectional dependence, so inference relies on Driscoll-Kraay standard errors.\u003c/p\u003e \u003cp\u003eTax effort is computed as the ratio of actual tax burden to predicted capacity, enabling consistent comparisons of over- and under-performance across structurally different provinces. Results show that (i) tax capacity is highly uneven and shaped by industrialisation, openness, and urbanisation; (ii) determinants of capacity are cluster-specific, cautioning against a single national capacity function; and (iii) provinces with similar capacity can display markedly different effort and collection outcomes, highlighting the role of administrative capacity and compliance.\u003c/p\u003e \u003cp\u003eThe proposed three-dimensional taxonomy provides a practical basis for fiscal equalisation and transfer design by separating structural constraints from effort and collection gaps, offering a replicable template for countries with pronounced regional disparities.\u003c/p\u003e","manuscriptTitle":"Interjurisdictional Tax Performance within a Country: Cluster-Specific Panel Estimation of Provincial Tax Capacity, Effort, and Collection Effectiveness in Türkiye (2007-2022)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 07:46:07","doi":"10.21203/rs.3.rs-8378380/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8caf73a4-968f-4757-94ef-015dad8fba50","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T23:53:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 07:46:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8378380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8378380","identity":"rs-8378380","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}