Policy Research Working Paper 11124 Navigating the Competition-Stability Nexus in Financial Services A Dynamic Extension of the Tinbergen Rule Pietro Calice Finance, Competitiveness and Investment Global Department May 2025 Policy Research Working Paper 11124 Abstract This paper extends the classic Tinbergen rule within the constraints of prudential instruments. The paper introduces context of financial regulation, explicitly accounting for the a dynamic optimization approach that calibrates policy inverted U-shaped relationship between market competi- instruments according to the financial system’s position tion and financial stability. Conventional policy frameworks, along the competition-stability curve. Additionally, it pro- premised on independent relationships between policy tar- vides a comprehensive taxonomy of regulatory instruments, gets and instruments, inadequately address the complex categorizing them based on their primary targets and sec- interactions inherent in the competition-stability nexus. ondary (cross-) effects, thereby facilitating state-dependent The proposed framework addresses this gap by incorpo- policy formulation. The paper also outlines practical institu- rating four critical dimensions overlooked in traditional tional arrangements and coordination mechanisms that are applications of the Tinbergen rule: (i) nonlinear inter- crucial for effective implementation. Overall, the approach actions among policy objectives, (ii) conflicts arising at may help to equip regulators with strategies for dynamically extremes of competition, (iii) a hierarchical prioritization managing competition and stability, ultimately enhancing of financial stability objectives, and (iv) inherent structural the efficiency and robustness of financial systems. This paper is a product of the Finance, Competitiveness and Investment Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at pcalice@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Navigating the Competition-Stability Nexus in Financial Services: A Dynamic Extension of the Tinbergen Rule Pietro Calice† Keywords: Tinbergen rule, �inancial regulation, �inancial stability, market competition, non-linear policy relationships, regulatory coordination JEL Classi�ication: E58, G18, G21, G28, L51 † Senior Financial Economist, World Bank. The author is indebted to Graciela Miralles Murciego, Cecile Niang, Ilias Skamnelos, and Vahe Vardanyan for their comments on an earlier version of this paper. The views expressed in this paper are those of the author and do not necessarily represent the views of the World Bank and its af�iliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. 1 Introduction “Without competition, in the long run there cannot be ef�iciency; without ef�iciency, in the long run there cannot be stability in the banking and �inancial industry.” − Pierluigi Ciocca (1998) The classic Tinbergen rule, established by Jan Tinbergen in 1952, provides a foundational principle for economic policy design by stipulating that achieving n independent policy targets requires at least n independent policy instruments (Tinbergen 1952). For decades, this principle has guided policy makers across various economic domains, offering a seemingly straightforward approach to policy formulation (Mundell 1962; Theil 1964; Sargent and Wallace 1975). However, as regulatory challenges grow increasingly complex, particularly in �inancial regulation, the traditional application of the Tinbergen rule reveals signi�icant limitations (Goodhart 2013; Haldane 2015). In particular, �inancial regulation stands at a crossroads between maintaining market competition to promote ef�iciency and safeguarding �inancial stability to ensure systemic resilience (Claessens and Laeven 2004; Bikker and Spierdijk 2010). Unlike the simpli�ied assumptions in Tinbergen's original framework, these objectives do not function as independent variables that can be neatly aligned with separate policy instruments. Instead, they display complex, interdependent relationships that conventional policy approaches struggle to address effectively (De Nicoló et al. 2004; Barth et al. 2013). This interdependence fundamentally challenges the basic premise of the Tinbergen rule and necessitates a more sophisticated framework for policy design and implementation. The relationship between market competition and �inancial stability exhibits four critical dimensions that traditional policy frameworks may fail to fully incorporate: • Non-linear relationship: Theoretical and empirical evidence reveals an inverted U- shaped relationship between competition and stability, re�lecting the synthesis of two competing hypotheses. On one hand, the franchise value hypothesis posits that excessive competition erodes banks’ franchise value, incentivizing greater risk-taking behavior (Saunders and Wilson 1996; Keeley 1990). On the other hand, the risk-shifting 2 hypothesis highlights that insuf�icient competition enables banks to exert market power, leading borrowers to pursue riskier projects due to higher loan rates (Boyd and De Nicoló 2005; Dell'Ariccia and Marquez 2006). These competing mechanisms can be reconciled in models implying that moderate competition levels optimizes stability outcomes by balancing these opposing forces (Martinez-Miera and Repullo 2010; Hakenes and Schnabel 2011; Ijtsma et al. 2017). • Potential con�lict at extremes: At extreme points on the competition spectrum, competition and stability objectives can directly con�lict, creating policy dilemmas that cannot be resolved by simple instrument-target matching. Excessively competitive markets, characterized by aggressive price-cutting and thin margins, may foster instability through heightened risk-taking incentives and contagion risks (Allen and Gale, 2004). Conversely, markets characterized by inadequate competition may foster inef�iciencies, lead to excessive concentration of risk, and embed vulnerabilities such as systemic banks that are too big to fail, thereby undermining long-term stability (Beck et al. 2013; Calice et al. 2021). • Hierarchical weighting: Financial stability carries a higher priority than market competition in the regulator’s objective function. Prudential regulators give primacy to stability concerns, effectively imposing a constraint that competition policies must not breach minimum stability requirements (Tucker 2016; Vives 2016). This hierarchy is re�lected in frameworks like the Basel “Core Principles for effective banking supervision”, which explicitly prioritize banking system stability over other goals (BCBS 2024). It becomes particularly binding during periods of stress or crisis. For example, during the global �inancial crisis, authorities tolerated reductions in competition (through bank mergers or rescue packages) to shore up stability. This hierarchical weighting deviates from Tinbergen’s original equal-weighting assumption. • Structural constraints: Many policy instruments aimed at �inancial stability inherently affect market competition, effectively constraining the feasible competitive landscape. For example, stringent capital requirements, liquidity ratios, or restrictions on activities (like separating investment from commercial banking) can limit entry or increase costs 3 for smaller �irms, thus dampening competition (De Nicolò et al. 2007; Kashyap et al. 2010; Schaeck and Cihák 2012). In essence, traditional stability tools may curtail the degree of competition as a byproduct of risk reduction. This structural interplay means the policy optimization problem is more complex: instruments cannot be assigned to targets independently because they have cross-cutting effects. This paper proposes a uni�ied framework that integrates these four dimensions to apply the Tinbergen rule in a dynamic, nuanced way to �inancial regulation. By explicitly accounting for the non-linear relationship between competition and stability, we transform the Tinbergen’s rule from a static accounting exercise into a dynamic optimization problem. This approach acknowledges that policy effectiveness is state-contingent—the impact of a given tool depends on the �inancial system’s current position on the competition-stability curve (Agénor and Pereira da Silva 2014; Cecchetti and Schoenholtz 2020). The framework thus aligns with the idea that multiple policy instruments must be coordinated and calibrated dynamically to achieve multiple interdependent targets. Our contribution is fourfold. First, we develop a uni�ied formalization of the competition- stability nexus by integrating the franchise value and risk-shifting paradigms, providing a coherent theoretical foundation for regulatory analysis. Second, we develop a taxonomy of regulatory instruments based on their primary target (competition or stability) and their cross-effects on the other objective. This taxonomy extends prior classi�ications (e.g., Schoenmaker and Wierts 2016; Cerutti et al. 2017) by explicitly considering how instrument effectiveness varies across the competition–stability spectrum. Third, we propose a state- contingent optimization approach that guides policy makers in selecting and calibrating instruments according to a �inancial system’s state relative to the competition-stability curve. This approach builds on recent work on state-dependent macroprudential policy design (Peek et al. 2020; Adrian et al. 2018) and demonstrates how regulators can dynamically balance multiple objectives over time. Finally, we outline the necessary institutional arrangements, including integrated assessment capabilities and coordination mechanisms, required to implement this extended Tinbergen approach to �inancial regulation. 4 Additionally, we emphasize that while certain prudential instruments may be optimally suited for speci�ic competition zones, many retain relevance across multiple states of the competition-stability curve. The framework thus supports differential calibration rather than binary deployment of instruments—recognizing that a given policy tool (e.g., capital requirements, resolution regimes, or stress testing) may contribute to stability under diverse competitive conditions, provided its intensity and con�iguration are state-contingent. This nuance strengthens the practical applicability of the framework and responds to the real- world complexity of instrument targeting. The remainder of the paper is organized as follows. Section 2 presents a formal model capturing this non-linearity, including a dynamic extension of Tinbergen’s rule to allow state- dependent effects. Section 3 introduces a new classi�ication of policy instruments by their effects on competition and stability. Section 4 outlines a state-contingent policy optimization process, illustrating how the optimal policy mix shifts depending on whether a market has suboptimal, optimal, or excessive competition. Section 5 discusses the institutional and coordination mechanisms required to implement this framework, such as integrated assessment and inter-agency cooperation. Section 6 concludes with a summary and directions for future research. 2 Formal framework This section develops a formal mathematical framework that captures the non-linear relationship between market competition and �inancial stability, incorporating the hierarchical prioritization of policy objectives and the structural constraints imposed by stability instruments. 2.1 Target de�inition and instrument relationships We de�ine our policy targets as ∈ [0,1] representing �inancial stability and ∈ [0,1] representing market competition. Unlike the conventional Tinbergen framework, which treats targets as independent variables, our model explicitly acknowledges their interdependence through a non-linear functional relationship: 5 = ( ) (1) where (·) exhibits properties consistent with an inverted U-shaped curve. There exists an optimal competition level ∗ ∈ (0,1) such that ′( ) > 0 for all < ∗ (stability increases with competition in the low competition region) and ′ ( ) < 0 for all > ∗ (stability decreases with competition in the high competition region). For analytical tractability, we can express (·) as a quadratic function: = ( ) = − ², where and are positive parameters that determine the shape of the curve, with /2 = ∗ representing the optimal competition level that maximizes stability. The regulatory authority deploys two classes of policy instruments: ∈ ℝ representing stability-oriented instruments and ∈ ℝ representing competition-oriented instruments. The direct effects follow expected patterns (/ > 0 and / > 0), but the cross- effects reveal important complexities. Stability instruments typically constrain competition ( / < 0 ), capturing the structural constraint identi�ied by Hellmann et al. (2000). More critically, the impact of competition instruments on stability depends on the system's position: / > 0 if < ∗ and / < 0 if > ∗ . This state-contingent cross-effect constitutes a major departure from the traditional Tinbergen framework, as it implies that the sign of the cross-effect varies depending on the system's current state. 2.2 Hierarchical constraints and “Regulatory Frontier” Financial regulation typically assigns hierarchical priority to stability over competition objectives, which becomes particularly binding during periods of stress or crisis (Tucker 2016; Vives 2016). Moreover, this principle is enshrined in the Basel “Core principles for effective banking supervision”, which emphasize the primacy of �inancial system stability as a regulatory objective (BCBS 2024). This prioritization can be formalized as a constrained optimization problem: (, ) = + (1 − ) (2) subject to: 6 ≥ (3) where (, ) represents the policy maker's welfare function, ∈ [0,1] represents the relative weight assigned to stability, and denotes the minimum acceptable stability threshold. During normal periods, might be set at intermediate values, whereas during �inancial stress, approaches 1, re�lecting the heightened priority assigned to stability considerations. This formulation captures the idea of “do no harm to stability” as a baseline for any competition-enhancing policy. Given these relationships and constraints, we de�ine the Regulatory Frontier (RF) as the set of achievable combinations of competition and stability outcomes (Figure 1): = {(, )| = ( ), ≥ , ∈ [0,1]} (4) The RF is bounded by the non-linear relationship = ( ), the minimum stability threshold , and the natural boundaries of the competition measures. Figure 1: Regulatory Frontier Source: Own elaboration The Tinbergen problem becomes choosing a point in the optimal region that maximizes . In normal times, might not bind, and one would pick near the top of the curve. In crisis 7 times, might effectively force policy to the left side of the curve (reducing to boost ). This shows formally that when stability is paramount (high or binding ), competition may have to be sacri�iced (moving leftward in the region). Conversely, when stability is comfortably above minimum, regulators can afford to choose a point with more competition. In essence, the conventional Tinbergen rule (one instrument per target) is expanded to ensure there are enough instruments and calibrations to navigate within the optimal region and reach the highest indifference curve (level of ) possible without crossing the stability boundary or going beyond the trade-off frontier. 2.3 State-contingent optimization and dynamic considerations The optimal regulatory strategy depends critically on the �inancial system's current position on the competition-stability curve. In the low competition region ( < ∗ ), increasing competition enhances both stability and welfare (/ > 0 and / > 0), suggesting a focus on competition-enhancing policies. At the optimal competition region ( ~ ∗ ), the marginal effect of competition on stability approaches zero (/ ~ 0), indicating a focus on maintaining current competition levels while ensuring stability thresholds are met. In the high competition region ( > ∗ ), reducing competition can enhance stability / < 0), potentially justifying policies that moderate excessive competition when stability concerns dominate. To capture the dynamic nature of �inancial systems, we can extend our framework to a dynamic setting: / = (, , , , ) (5) / = (, , , , ) (6) where and represent exogenous factors affecting stability and competition, respectively. This speci�ication allows analysis of transition paths between different states on the curve and incorporation of hysteresis effects and institutional learning. Optimal instrument selection depends on three factors: the system's current state, the relative effectiveness of instruments, and the magnitude of cross-effects. For a system in the low competition region, optimal regulation favors stability instruments with minimal 8 negative cross-effects on competition and competition instruments that enhance both competition and stability. Conversely, in the high competition region, optimal regulation may leverage the negative cross-effects of stability instruments on competition to simultaneously enhance stability through both direct and indirect channels. In summary, our formal framework shows that the Tinbergen rule in a non-linear context is about dynamic optimization. It is not enough to have two instruments for two targets; one must also decide how to use those instruments differently when the system is in different states. This lays the foundation for a new taxonomy of instruments (Section 3) and a state- contingent policy approach (Section 4). 3 Instrument classi�ication Traditional regulatory discussions classify instruments by their objectives (monetary, prudential, competition, etc.) without explicitly considering how an instrument aimed at one objective might affect another. In our framework, understanding these cross-effects is crucial. We propose a comprehensive classi�ication of policy instruments based on: (a) whether their primary goal is stability or competition, and (b) how they affect the other dimension (enhancing, neutral, or reducing it). This taxonomy will guide policy makers in choosing the right tools for a given situation. 3.1 Stability instruments ( ) Stability instruments are regulatory tools primarily designed to bolster the resilience of the �inancial system. Examples include capital and liquidity requirements, stress testing, supervisory oversight, deposit insurance schemes, resolution frameworks, etc. However, not all stability instruments have the same effect on competition – some may inadvertently promote competition, some are neutral, and others constrain competition. We identify three subcategories. Competition-enhancing stability instruments These are “win-win” stability tools that improve resilience while also fostering or preserving competition. They are particularly useful in low-competition environments, where the goal is to increase competition without sacri�icing stability. Examples include: 9 • Robust bank resolution frameworks for dealing with failing institutions can enhance stability by reducing systemic risk (ending “too big to fail”) and simultaneously level the playing �ield. By credibly allowing even large banks to fail without catastrophic fallout, resolution regimes remove the implicit subsidy enjoyed by too-big-to-fail banks, thereby encouraging more competition from smaller banks. Berger et al. (2019) �ind that such policies can reduce systemic risk and also improve competitive neutrality in the long run. • Systemic risk surcharges or capital buffers for systemically important banks similarly force big players to internalize their externalities, which can curb their competitive advantage and make room for others, all while improving stability by strengthening buffers for those key �irms. Competition-neutral stability instruments These instruments improve stability with minimal effect on competitive dynamics. They can be applied broadly without worrying about unintended competitive side-effects, making them versatile tools in many situations (low, medium, or high competition environments). Examples include: • Governance and risk management standards (e.g., requirements for banks to have strong risk committees, transparent reporting, and robust internal controls) fall in this category. Laeven and Levine (2009) show that better governance reduces risk-taking; importantly, such standards generally impose similar proportional costs on all banks and do not advantage one competitor over another, thus having negligible impact on market structure. • Transparency and disclosure requirements (forcing �irms to reveal more information about their risk exposures) can enhance market discipline and stability without altering the number of competitors or their ability to compete – all �irms simply play with more disclosure. • Stress-testing exercises mandated by regulators can reveal vulnerabilities and prompt corrective action, improving stability, but every bank undergoes them, so competitive dynamics remain largely unchanged. 10 Competition-constraining stability instruments These instruments enhance stability partly by limiting or moderating competitive dynamics. Their deployment is most appropriate in highly competitive environments where excessive competition may undermine stability. Examples include: • Minimum capital requirements represent the archetypal example of this category. By forcing banks to hold a higher equity cushion, they reduce default risk. However, higher capital standards can raise the cost of entry and expansion for banks (especially smaller ones that might �ind it hard to raise capital), thus potentially reducing competitive pressure. Hellmann et al. (2000) theoretically show that capital requirements can tame excessive risk-taking incentivized by competition, effectively acting as a brake on cut- throat competition for deposits that could otherwise lead to gambling behavior. Empirically, Corbae and D’Erasmo (2021) �ind that higher capital requirements in a calibrated banking model lead to a slightly less fragmented banking industry but a more stable one – indicating a competition-constraining effect. • Restrictive licensing and entry regulations (limiting the number of new banks or �intech �irms entering the market) or activity restrictions (like Glass-Steagall-type separation of commercial and investment banking). These improve stability by preventing certain risky activities or unchecked expansion, but they also directly limit competition by keeping some players or activities out. 3.2 Competition instruments ( ) Competition instruments are policies primarily aimed at in�luencing the level of competition in the �inancial sector. These include antitrust enforcement (merger approvals or denials, breaking up cartels), entry policies (chartering new banks, allowing foreign banks, encouraging �intech entrants), consumer protection measures that affect competition (reducing switching costs, mandating data portability), and so on. As with stability tools, competition instruments can have varying effects on stability. 11 Stability-enhancing competition instruments These are pro-competition measures that also tend to improve �inancial stability. Such instruments are most effective when deployed in low-competition environments, where increasing competition can directly reduce some systemic risks. Examples include: • Reducing barriers to entry in banking or insurance sectors that are overly concentrated can yield stability bene�its. For instance, Carletti and Hartmann (2003) argue that allowing new entrants in a market dominated by a few big banks can reduce systemic risk by cutting down the concentration of exposure and mitigating the “too-big-to-fail” problem. Empirical evidence by Schaeck et al. (2009) suggests that banking systems with more competition (often a result of liberal entry policies) were less prone to crisis, all else equal, supporting the idea that at least up to a point, more competition increased stability. A policy example might be easing the requirements to obtain a banking license in a country where a few banks hold a monopoly, or encouraging entry of well-capitalized foreign banks to increase competition. This can improve ef�iciency and also introduce more diversi�ied behaviors in the system (as new banks might bring better risk management or different portfolios, reducing correlation in exposures). Stability-neutral competition instruments These instruments increase (or regulate) competition with minimal direct impact on �inancial stability. They can be pursued for their competition bene�its without worrying much about stability trade-offs, as long as stability is being handled by other tools. Examples include: • Consumer protection measures that lower switching costs (like account number portability, standardized information disclosure, or open banking APIs that let customers easily migrate their data and history) can intensify competition by empowering customers to shop around, without directly making banks or insurers take on more risk. Vives (2016) notes that well-designed consumer �inance regulations can increase competition by reducing information asymmetry and locking-in effects. 12 • Transparent pricing requirements – forcing banks to clearly disclose fees and rates – which can lead to more price competition but does not inherently encourage riskier behavior by banks. • Basic antitrust enforcement (e.g., preventing mergers or approving mergers with remedies) also typically just maintains the status quo level of competition and does not, in itself, affect how risky banks are. Stability-reducing competition instruments These are policies that intensify competition but could undermine stability, especially if the market is already quite competitive. They need careful evaluation and possibly complementary measures to counteract their negative stability impacts. In some cases, these might still be pursued for broader economic reasons (innovation, consumer bene�it), but regulators must be cognizant of the stability risks introduced. Examples include: • Aggressive market deregulation or fragmentation in an already competitive system can exemplify this. Horvath and Wagner (2017) point out that forcing too much fragmentation (for instance, breaking banks into many smaller entities or encouraging too many new entrants in a saturated market) might increase competition but also interconnectedness and the likelihood of simultaneous failures, thereby raising systemic risk. For example, if regulators were to eliminate all barriers in a market with many players, the ensuing �ierce price wars could erode margins industry-wide to dangerously low levels. Another example is overly generous licensing of new lenders or �intechs without adequate oversight – competition increases, but if these new players are not stable or properly supervised, it can introduce fragility (as seen in some peer-to-peer lending booms that later busted). 3.3 Calibration instruments ( ) Beyond static policy levers that directly push stability or competition, we identify a third class of instruments focused on calibrating and adjusting the system dynamically. These tools help ensure the system gravitates toward the optimal competition–stability balance and stays there through ongoing adjustments. 13 Market structure instruments These tools shape the structural competitive environment in a way that is conducive to stability. They are not about day-to-day regulation but about the broader market architecture. Examples include: • Differentiated licensing regimes. Regulators can create tiers of banks (community banks, regional banks, large complex banks) with different permitted activities and requirements, as advocated by Vives (2016). By doing so, they can encourage competition at the lower end (making it easier for small banks or �intech �irms to get licenses to foster competition in retail banking) while imposing stricter rules on systemically important institutions (ensuring stability at the top end). This tiering maintains appropriate levels of competition where it is needed and curtails it where it could be dangerous. • Ownership limits or separating banking and commerce, to prevent conglomerates (or �inancial ecosystems) from dominating �inance, thus keeping the market structure more open and stable (though such separation can also be seen as a competition constraint). Incentive alignment instruments These are mechanisms embedded in the regulatory framework that align the incentives of �inancial �irms with stability goals, particularly in how they compete. Examples include: • Risk-adjusted deposit insurance premiums: If banks have to pay higher insurance fees when they take on more risk, they have less incentive to engage in reckless competition (like underpricing loans to gain market share). Anginer et al. (2014) suggest that properly pricing the safety net can encourage stability without removing competition. Many countries have moved towards such differentiated premiums so that banks internalize the cost of their risk to the insurance fund (Demirgüç-Kunt and Detragiache 2002). • Structured compensation rules (limiting bonuses or tying them to long-term performance) to prevent banks from trying to out-compete each other via short-term risk-taking. 14 4 Policy optimization: A state-contingent approach Having laid out the targets, relationships, and toolkit, we now turn to how policy makers can systematically optimize policy in a world where competition and stability are jointly determined. We propose a structured approach to regulation that involves: (1) diagnosing the current position (state) of the �inancial system, (2) selecting and calibrating instruments targeted to that state, and (3) continuously adapting as conditions evolve. Figure 2 summarizes this approach. Figure 2: State-Contingent Policy Optimization Process Source: Own elaboration 4.1 Identifying the current market position The foundational step in our optimization process is accurately determining the �inancial system's current position (state) on the competition-stability curve: suboptimal competition, near-optimal, or excessive competition zone. Note these zones are a conceptual guide; in practice, a continuum exists, but having discrete categories helps in formulating clear policy regimes. 15 Determining which zone a �inancial system is in is non-trivial. It requires considering multiple indicators from both competition and stability perspectives, and how they interact. We propose an assessment that covers several dimensions: • Structural competition measures: e.g., market concentration indices like the Her�indahl- Hirschman Index (HHI) or concentration ratios (CR5, share of top 5 institutions), the number of competitors and ease of entry/exit, and persistence of market shares. If HHI is very high and new entry is rare, that may point to a suboptimal competition zone. Conversely, a very low HHI with many new entrants could indicate high competition (possibly excessive if stability issues are observed). Beck et al. (2013) document substantial cross-country variations in banking competition that correlate with stability outcomes, underscoring the role of structure. • Behavioral competition indicators: these look at outcomes and conduct. For instance, pro�it margins (net interest margins, spreads between loan and deposit rates) – very high margins may indicate lack of competition, while ultra-low margins could mean cutthroat competition. The pass-through of policy rates to lending rates can show how competitive pressure forces banks to adjust prices (less pass-through might indicate slack competition). Also consider the degree of product differentiation vs. commoditization: if banks compete mostly on price for identical products, competition is likely intense; if they each have captive markets, competition is low. Finally, indicators such as the Lerner index, the Panzar-Rosse H statistic and the Boone indicator are widely used metrics to measure competition in �inancial services, though they require good data and are sensitive to model speci�ications. • Ef�iciency and performance metrics: these include cost-to-income ratios, measures of X- ef�iciency (how close banks operate to the best practice cost frontier), and technology adoption. In a well-functioning competitive market, we expect high ef�iciency (low cost- to-income, rapid adoption of cost-saving tech). If ef�iciency is low and banks seem complacent, competition might be lacking. If ef�iciency gains are maxed out or banks struggle to cover costs, competition might be very intense. For example, a high cost-to- 16 income ratio alongside high pro�itability suggests limited competition, whereas a low cost-to-income but low pro�itability suggests hyper-competition. • Stability indicators: these are traditional risk measures – average and distribution of bank capital ratios, asset quality (non-performing loan ratios), liquidity ratios, reliance on volatile funding, etc., as well as system-wide metrics like the banking sector’s Z-score or measures of systemic risk (e.g., ΔCoVaR, SRISK). If stability indicators are �lashing red (like low capital, high NPLs) and we also see signs of stiff competition, that combination might place us in an excessive competition zone. If stability is strong but competition indicators show slack, likely suboptimal zone. One should also consider the volatility of stability metrics: a very volatile environment (booms and busts) could imply too much competitive pressure cyclically. • Empirical indicators of competition-stability dynamics: importantly, we also examine how competition and stability metrics move together over time. For example, has an increase in competition (entry of new banks, or interest rate liberalization) historically led to more instability (spike in bank non-performing loans)? Or vice versa? If we identify a threshold in competition metrics beyond which stability worsens, that gives an empirical estimate of ∗ or the boundary between zones (Ijtsma et al. 2017 use such techniques to �ind where concentration stops being bene�icial and becomes harmful). The above information can be synthesized in a scoring system for each category of indicators to get an initial “competition intensity” and “stability health” score. The scores or key indicator values could be plotted against an expected inverted-U curve or compared to benchmark thresholds gleaned from theory/empirics. For instance, if a system’s HHI and margins are in the top decile (indicating low competition) but its Z-scores are average, that suggests left side of the curve (suboptimal comp hurting stability via risk-shifting). If a system’s margins are extremely low and loan losses are rising, that suggests right side (excessive comp). One can visualize the system’s trajectory over time to see if it is moving toward more or less stability and competition. This quantitative assessment should be complemented and backed up by qualitative analyses (expert judgment on market conditions, business practices, etc.). 17 4.2 Targeted optimization process Once the current position is established, the next step is to select the appropriate policy mix to move the system toward the optimal zone (or maintain it there) while respecting stability constraints. We outline tailored strategies for each zone, consistent with Figure 1 in a step- by-step optimization logic. Optimization in the suboptimal competition zone ( < ∗ ) In �inancial systems characterized by insuf�icient competition, our framework prioritizes enhancing competition to improve both ef�iciency and stability, while ensuring stability does not fall below acceptable levels during the transition. The formal optimization problem can be expressed as: (, ) = + (1 − ) (7) subject to: ≥ (8) / > 0 (9) /, > 0 (10) /, > 0 (11) This optimization problem seeks to enhance welfare by increasing competition, taking advantage of the positive relationship between competition and stability in this region. Key elements of the policy approach in this zone are: • Primary competition strategy: Deploy stability-enhancing competition instruments vigorously. These are the ideal tools here because they achieve exactly what is needed: more competition and more stability. For example, if banking is too concentrated, regulators could encourage entry of new banks or nonbank lenders in sectors where it is safe to do so; reduce barriers like strict licensing if applicants meet prudential criteria; break down monopolistic structures like exclusive credit information sharing (so new entrants can assess credit risk effectively). Introducing quali�ied new competitors in 18 concentrated markets can enhance both competition and stability by reducing concentration risk and improving market discipline (Schaeck et al. 2009). • Supporting stability strategy: Use competition-enhancing or competition-neutral stability instruments to shore up resilience during this transition. For instance, as regulators open the market to new entrants, they could also improve supervision of incumbents’ risk management so they do not respond to new competition by imprudent lending or reaching for yield. If large banks lose some market share and pro�its due to new competition, regulators could ensure they do not double down on risk-taking— strong governance requirements and stress tests can check that tendency. Also, tools like enhanced resolution regimes reduce the chance that introducing new competition inadvertently triggers instability via a failing incumbent, because authorities can handle failures smoothly. • Constraint management: Calibrate the pace of reforms to ensure the stability constraint is never breached. This often means sequencing reforms. For example, regulators could start by granting licenses to a few well-supervised new banks and observe effects on incumbent behavior and system risk before granting many more. Or they could allow foreign bank entry gradually, while monitoring domestic banks’ health. If stability indicators start to deteriorate (e.g., incumbent banks’ capital ratios fall or risk measures rise), regulators could pause competition-enhancing measures and bolster stability (maybe requiring those incumbents to raise capital) before continuing. Essentially, the reform process should be managed with an eye on maintaining a safety margin above . Transparency and communication help: regulators should clearly signal that the goal is a more open market and a safe system, so that market participants adjust expectations accordingly (e.g., incumbents realize they must adapt rather than expect deregulation to be rolled back at the �irst sign of trouble). Many emerging markets in the 1990s had very concentrated banking sectors with associated inef�iciencies and occasional stability issues (due to high loan rates causing defaults). In the suboptimal competition zone, countries like Kenya and Malaysia undertook measures to increase banking competition—issuing new licenses, allowing foreign banks in, encouraging 19 the growth of micro�inance institutions—while simultaneously strengthening prudential oversight and modernizing regulation (like implementing Basel capital standards and better supervision). These combined efforts improved access and ef�iciency (competition) and maintained or improved stability by diversifying the banking system and forcing incumbents to become more ef�icient and prudent. Optimization in the optimal competition zone ( ~ ∗ ) For �inancial systems operating near the optimal competition level, our framework prioritizes maintaining the current balance of stability and competition, and if possible, improving stability further without upsetting the competitive equilibrium. The formal optimization problem becomes: (, ) = + (1 − ) (12) subject to: ≥ (13) / ~ 0 (14) |C − C ∗ | < (15) where represents an acceptable deviation from the optimum competition level. This optimization problem seeks to maintain the optimal competitive intensity while potentially enhancing stability through other channels. The recommended policy approach includes: • Stability enhancement: Focus on competition-neutral stability instruments to continue improving resilience without altering competitive dynamics. For instance, even if competition is healthy, banks can still bene�it from stronger risk management standards, more rigorous stress tests, better resolution planning, etc. These tools raise the stability bar while leaving market structure and contestability untouched. • Competition maintenance: Implement stability-neutral competition instruments to address any emerging competitive issues without undermining stability. For example, if 20 a few banks start gaining excessive market share through mergers or anti-competitive practices, antitrust authorities should step in to preserve the competitive environment. Likewise, if there are minor frictions (e.g., high switching costs for consumers) that prevent the market from being fully competitive, regulators should address them (e.g., by enforcing account number portability). These actions ensure the system does not slide into a suboptimal zone due to complacency or nascent monopolistic tendencies. • Enhanced monitoring: Develop sophisticated monitoring to detect incipient shifts away from the optimal zone. This means closely watching indicators like market entry/exit, pricing trends, innovation rates (for competition) and early-warning risk indicators (for stability). If signs of trouble appear—say a credit boom indicating banks might be starting a risk race, or conversely, widening margins indicating competition easing— regulators can act preemptively. Monitoring also includes scanning for external changes (e.g., technological disruptors or macroeconomic shifts) that could tip the balance. It is also worth emphasizing that instruments deployed in the optimal zone are not entirely distinct from those used elsewhere. Rather, it is their calibration, sequencing, and framing that differ. For example, resolution planning—while essential in low competition regions to neutralize too-big-to-fail advantages—can also enhance loss-absorbing capacity and crisis preparedness in highly competitive systems. Likewise, capital and liquidity requirements may remain active across all regions, but applied with differentiated thresholds or buffers to re�lect the prevailing balance between competitive intensity and systemic resilience. This reinforces the idea that the same regulatory tools can serve as versatile instruments, dynamically adjusted in line with the system's position on the competition–stability curve. Canada’s banking system in the 2000s could be seen as near optimal: a few strong banks (not too many to be unstable, not too few to be completely unchallenged) and generally stable performance. Canadian regulators maintained that balance by upholding stringent stability standards (high capital, etc.) and at the same time not allowing further concentration (the government disallowed major bank mergers on competition/stability grounds). They monitored developments—when �intech started rising, they cautiously allowed innovation (open banking initiatives) but ensured it did not compromise stability by gradually 21 integrating �intech into the regulatory perimeter. The result was a sustained period of stability and reasonably good banking services (though one could argue perhaps a bit more competition could bene�it consumers, regulators judged the trade-off was well-managed). Optimization in the excessive competition zone ( > ∗ ) In �inancial systems characterized by excessive competition, our framework prioritizes curbing the destabilizing effects of excessive competition to enhance stability, while preserving essential bene�its of competition and avoiding over-correction. The formal optimization problem is: (, ) = + (1 − ) (16) subject to: ≥ (17) / < 0 (18) /, < 0 (19) > (20) where represents a minimum acceptable competition level. This optimization problem seeks to enhance welfare by moderating excessive competition, leveraging the negative relationship between competition and stability in this region. The optimal policy approach involves: • Stabilization strategy: Deploy competition-constraining stability instruments. These include raising capital and liquidity requirements, tightening loan underwriting standards, increasing provisioning, etc. By doing so, banks are forced to be more prudent even if competition pressures them to take risks. Higher capital, for instance, both directly improves stability and indirectly can dampen some aggressive expansion (since equity is more expensive than debt, banks might ration themselves). Similarly, stricter lending 22 rules prevent the race-to-the-bottom in credit standards. These measures act as circuit breakers in a market where competition has led to thin margins and risk-taking. • Targeted competition moderation: Rather than broadly suppressing competition, focus on speci�ic destabilizing competitive behaviors and moderate them. For example, if price wars in mortgage lending are leading banks to issue loans to very risky borrowers, regulators could implement or increase limits on loan-to-value ratios or debt-to-income ratios (so banks simply cannot go below a certain standard, taming the competition on risk dimension). Or if deposit competition is driving banks to rely on very unstable funding sources, regulators could tighten liquidity coverage ratios to ensure they hold enough high-quality liquid assets, or impose a cap on deposit rates for weaker banks. These targeted moves address the ways excessive competition harms stability without killing competition in safer aspects. • Competitive safeguards: As stability-oriented interventions take effect, ensure that some level of competition remains so that the system does not swing to the other extreme. This might involve setting a �loor on competition such as maintaining entry openness (even if few will enter during tough times, signal that the market is not closed) and planning to roll back extraordinary measures once stability is restored. If, for example, regulators temporarily facilitated a merger of two struggling banks to save them, they could impose remedies, requiring that certain branches or portfolios be divested to other players later to avoid permanent concentration. In cases where competition is reduced, regulators can also play a role in preventing exploitation of market power (e.g., ensure banks do not signi�icantly hike fees in the wake of less competition, which could hurt consumers and slow economic recovery). Consider the U.S. banking sector in the late 1980s during the Savings & Loan (S&L) crisis. The S&L industry had engaged in �ierce competition for deposits and loans after deregulation, leading to risky bets and widespread insolvencies. The policy response included stability tools (government interventions, capital injections, eventually Basel I capital requirements) and also a contraction of the industry (many S&Ls closed or merged, reducing the number of competitors). This was effectively moving left on the curve to regain 23 stability. In the 1990s, once stability was largely restored, regulators and market forces allowed competition to pick up again gradually (e.g., new banks formed, interstate banking got liberalized by 1994) but now under a stricter supervisory regime, thus ideally moving to a more balanced point. While the taxonomy above links speci�ic instruments to particular competition regimes based on their predominant effects, it is important to recognize that many instruments retain policy relevance across the entire competition-stability spectrum. The key distinction lies in the magnitude and calibration of their application, rather than in their binary presence or absence. For example, strengthening the bank resolution framework is especially valuable in low competition settings to level the playing �ield and dismantle too-big-to-fail advantages. However, it is equally pertinent in highly competitive environments, where robust resolution tools—backed by meaningful MREL (Minimum Requirement for Own Funds and Eligible Liabilities) thresholds and Resolution Fund contributions—can build loss-absorbing capacity and mitigate risks from thin-margin, risk-driven competition. In such cases, the resolution framework serves a dual purpose: reinforcing stability and subtly dampening excess competition through increased compliance costs that induce more prudent behavior. Similarly, minimum capital requirements, traditionally viewed as instruments to temper excessive competition, may be retained in optimal or low competition regimes, albeit with more nuanced implementation (e.g., proportional capital buffers or differentiated surcharges). This continuity underscores the importance of viewing instruments as adjustable levers whose force and direction must be adapted to prevailing market conditions. 4.3 Dynamic adjustment mechanisms Financial systems are not static entities but evolve continuously in response to technological innovation, market developments, and regulatory changes. Our optimization framework must therefore incorporate dynamic adjustment mechanisms that allow for adaptation as conditions change. 24 Regular assessment protocols The position on the competition–stability curve may shift due to endogenous market evolution or exogenous shocks, requiring regular reassessment. We propose a multi-tiered assessment protocol: • Continuous monitoring: Track key indicators in real-time or high-frequency (e.g., quarterly) to identify signi�icant shifts or anomalies that might signal position changes. This includes both competition indicators (entry announcements, pricing trends, market share volatility) and stability indicators (asset quality trends, market risk spreads). If something noteworthy occurs (like a spike in credit growth combined with narrowing spreads), it triggers an alert that the system might be moving toward the excessive competition zone. • Periodic comprehensive reviews: Conduct thorough assessments at regular intervals (e.g., annually or biennially) to evaluate the system’s position and trajectory. These reviews could coincide with existing processes like annual Financial Stability Reports, but with an added focus on competition metrics. The outcome would be an of�icial diagnosis and an evaluation of whether policy adjustments are needed. • Trigger-based assessments: Initiate special reviews following signi�icant market events, regulatory changes, or technological innovations that could alter the competition– stability dynamics. For instance, if a large �intech company launches a new banking product that quickly gains market share, regulators should reassess whether competition has suddenly increased and if stability implications arise. Or if a crisis hits (stability shock), reassess how competition might be affected (often crises lead to consolidation, moving toward suboptimal competition zone). These on-demand assessments ensure that even between regular cycles, the framework stays responsive. Forward-looking analysis Effective policy optimization requires not only responding to current states but also anticipating future developments. We propose integrating forward-looking elements: 25 • Scenario analysis: Evaluate how different market developments or policy changes might shift the system’s position, allowing for preemptive adjustments. For example, scenario analysis might explore: What if a big tech �irm enters full-scale banking in our country? Will competition jump to excessive? What stability risks come with that (maybe big tech could cause rapid disintermediation of traditional banks)? Based on this, regulators might prepare speci�ic tools (like a framework for regulating big tech in �inance) in advance. Similarly, consider macro scenarios: What if interest rates remain very low for a long time? That could erode bank margins and intensify competition – how to handle stability then? By simulating such scenarios, regulators can have contingency plans ready. • Technology impact assessment: Systematically analyze how emerging technologies (e.g., digital platforms, AI in credit scoring, cryptocurrencies) might alter competitive dynamics and stability implications. For instance, AI might reduce information asymmetry, potentially making competition more stability-friendly (shifting ∗ right), whereas cryptocurrencies might bypass banks and create instability in a different way. Understanding these can help update the framework (maybe the inverted U �lattens or steepens with certain tech). Regulators could then adapt policies—like by incorporating �intechs into the regulatory perimeter quicker (to mitigate instability) or by facilitating bene�icial tech adoption (to reap stability bene�its from more ef�icient competition). • Structural change evaluation: Assess how deeper structural changes in �inancial intermediation models might shift the optimal competition level itself, potentially altering the shape of the competition–stability curve. Examples of structural changes: disintermediation (more �inancing via capital markets vs banks), fragmentation of �inance, or major shifts in risk appetite of investors. Such changes could mean that what used to be excessive competition is no longer excessive (if systemic risk is spread differently), or that concentration risks have grown more dangerous (if few institutions dominate globally). Regulators should periodically revisit estimates of ∗ and the trade- off shape in light of structural trends. For instance, if non-bank �inancial institutions take over a lot of credit provision from banks, the competition among banks might matter less for stability (since banks are smaller part of system risk). The framework might then need expanding to those non-banks, or ∗ for banks might effectively move. 26 4.4 An application to banking regulation To illustrate our optimization framework, we apply it to a stylized banking sector scenario across three distinct competition zones: low, optimal, and excessive. Each zone entails a different set of risks and opportunities, requiring regulators to adapt their policy mix accordingly. Figure 3 presents a stylized summary of the recommended instruments. The �igure maps instruments—stability instruments and competition instruments—according to their optimal use across the competition-stability spectrum. However, it is essential to recognize that these categories are not rigidly segmented. Many tools traverse regions, and their effectiveness lies in calibration, not exclusivity. We elaborate below on each region with real-world or stylized examples. Figure 3: State-Contingent Banking Regulation Optimization Source: Own elaboration 27 Low competition region: Enhance competition with stability safeguards In systems where banking markets are overly concentrated or entry is restricted, competition is insuf�icient to ensure ef�iciency or broad access to �inancial services. However, simply opening markets without guardrails can back�ire, leading to instability. In this zone, the regulatory objective is to stimulate competition while protecting the system against transition risks. Policy interventions in this zone typically include easing entry requirements for new and quali�ied institutions—such as digital banks or foreign lenders—and introducing anti- monopoly or conduct regulations to counteract incumbent dominance. Simultaneously, stability must be maintained through calibrated instruments. For example, robust bank resolution regimes help neutralize the advantages of institutions deemed too big to fail (TBTF), and systemic capital surcharges ensure that dominant banks internalize their risks. The United Kingdom's post-crisis reforms provide a useful illustration. Regulators encouraged the entry of challenger banks like Monzo and Starling by easing licensing procedures while enhancing the resolution regime via the Bank of England. India took a similar approach with the introduction of small �inance and payments banks, which opened competitive niches while maintaining strong prudential oversight. In South Africa, the licensing of new entrants such as TymeBank and Bank Zero has increased competition in the retail segment, while reforms to improve the resolution regime and enhance capital requirements have strengthened �inancial stability. Optimal competition region: Maintain balance and enhance resilience In markets where competitive intensity and �inancial stability are both healthy, the regulatory focus shifts from structural reform to preservation. The goal is to maintain the current balance while building buffers against emerging risks. Instruments used here are generally neutral with respect to competition. Stress testing, supervisory reviews, and improved risk governance frameworks help enhance resilience without altering the market structure. Resolution planning ensures readiness for institutional failure while preserving contestability. On the competition side, consumer 28 protection rules and transparency standards help ensure that bene�its �low to customers, and targeted antitrust enforcement prevents the gradual erosion of competition through creeping mergers or collusion. Forward-looking tools are particularly valuable in this zone. Scenario analysis and regulatory technology assessments help anticipate disruptive trends that might push the system toward instability or reduced competition. Australia offers a strong example of this zone. Its major banks operate in a competitive and stable environment, supported by robust supervision from APRA (Australian Prudential Regulation Authority), rigorous stress testing, and a clear resolution framework. Regulators have also worked to maintain competitive neutrality, including through open banking reforms that enable data portability while ensuring that new entrants meet consistent prudential standards. Similarly, Chile has maintained a competitive and stable banking sector through a mix of high capital standards, active supervision, and targeted consumer protection reforms. Excessive competition region: Moderate excessive competition for stability When competition becomes excessive, market participants may engage in aggressive pricing or risky behavior to maintain margins, undermining �inancial stability. Here, the objective shifts toward tempering harmful competition and restoring capital buffers without reversing the bene�its of market openness. Stability-oriented interventions include raising capital and liquidity requirements, and tightening underwriting standards. These measures directly improve the resilience of banks and discourage destabilizing strategies. Regulators may also impose macroprudential constraints on speci�ic products, such as loan-to-value (LTV) or debt-to-income (DTI) caps in mortgage lending, to prevent races to the bottom. In extreme situations, authorities might temporarily facilitate mergers in distressed sectors, albeit with conditions to prevent excessive concentration. Calibration tools play a signi�icant role in this region. Countercyclical capital buffers and trigger-based supervisory actions ensure that interventions are proportional and responsive to risk signals. The Republic of Korea provides a compelling example of managing excessive competition. Rapid expansion of household credit and intense competition among banks and non-bank 29 lenders prompted the government to impose DTI and LTV caps, along with enhanced capital buffers for mortgage exposures. These macroprudential actions helped contain systemic risks without signi�icantly impairing access to credit. In Kenya, the proliferation of digital lenders led to aggressive pricing and over-indebtedness risks, prompting regulatory interventions to enhance licensing requirements to restore prudential balance. As emphasized throughout this paper, many regulatory instruments are relevant across all competition zones. Their effectiveness hinges on how they are designed, calibrated, and sequenced. For instance, resolution regimes serve different purposes depending on context: in low competition regions, they remove TBTF distortions; in high competition settings, they help manage crisis fallout. Capital requirements may begin at a baseline in balanced systems and scale up into countercyclical surcharges when risk-taking accelerates in hyper- competitive environments. Transparency and disclosure standards, meanwhile, serve as constant safeguards—improving market discipline and acting as early warning systems. This dynamic use of instruments underscores our central thesis: regulatory effectiveness depends not merely on having multiple instruments, but on the capacity to adjust their deployment based on the system's state. The result is a dynamic, state-contingent implementation of the Tinbergen principle. 5 Institutional arrangements for implementation Having described how to choose and adjust instruments, this section discusses the institutional setup needed to implement this integrated, dynamic approach effectively. Effective implementation of a competition–stability framework requires a proper institutional setup. Regulators must coordinate closely, maintain independent oversight, and adapt policies as conditions change to manage the trade-off between market competition and �inancial stability. This section discusses how clear mandates, inter-agency coordination, independent but connected oversight, and adaptive policy making can be structured to balance competition and stability objectives in line with best-practice principles. 30 5.1 Integrated analytical and assessment frameworks A strong institutional foundation begins with shared analytical frameworks and data systems that allow prudential and competition authorities to jointly assess market conditions. International guidance emphasizes that competition and stability objectives should be analytically aligned from the start. In practice, this means developing common indicators and modeling approaches that capture both �inancial soundness and market competitiveness. For example, regulators can maintain integrated surveillance dashboards tracking key prudential indicators (capital adequacy, liquidity ratios, etc.) alongside competition metrics (market concentration, entry barriers, pricing spreads). By viewing the �inancial system through a common lens, authorities reduce the risk of contradictory assessments and ensure that emerging risks are evaluated in terms of both stability and competitive dynamics. Structured information-sharing arrangements support this joint analytical capacity. Many jurisdictions use formal agreements (such as Memoranda of Understanding) and secure data exchanges between the central bank or �inancial supervisor and the competition authority to enable timely access to relevant data. Regular inter-agency analytical meetings or working groups can further deepen the shared understanding of the �inancial system’s condition. These practices re�lect a broader principle: effective cooperation and intra-agency coordination are essential to align competition and stability goals (Vives, 2016). Importantly, best practices call for regulatory impact assessments (RIA) that explicitly evaluate competition effects alongside stability implications for any major policy change. Before implementing new prudential rules or interventions, authorities should analyze not only the direct stability impact but also how the rules might affect market structure or competitive behavior. Many jurisdictions have adopted such comprehensive impact analysis to design less distortive alternatives when possible. By institutionalizing competition analysis in rulemaking, agencies ensure that the competition-stability nexus is considered at every step of policy design. 5.2 Coordination mechanisms and governance structures Clear institutional governance mechanisms are needed to coordinate policy decisions across agencies and to manage potential con�licts between objectives. International experience 31 shows two broad models of organizing competition and stability oversight, each requiring speci�ic coordination frameworks: • Separate specialized agencies with formal cooperation: In many countries, an independent competition authority and a prudential regulator split the mandates. This separation can help avoid con�licts of interest and clarify missions. However, it demands robust coordination to align goals. Best practices include establishing joint committees or councils that bring together senior of�icials from the central bank/supervisor and the competition agency on a regular basis (Kirakul, Yonmg & Zamil, 2021). Such bodies provide a forum to discuss policy initiatives (e.g. new regulations, bank licensing or mergers) and ensure neither stability nor competition concerns are overlooked. For example, when a bank merger is proposed, concurrent approval by both prudential and competition authorities (“double greenlight”) may be required, or at least consultation must occur, so that stability risks and anti-competitive effects are weighed in parallel. Clear protocols should de�ine circumstances under which �inancial stability concerns can override competition (such as during a crisis or for systemically important institutions), and vice versa, to provide transparency and predictability. Formal information-sharing agreements, cross-representation on advisory committees, and coordinated decision processes (especially for critical areas like merger reviews or bank resolutions) are common tools to make this dual-agency model work effectively. • Integrated mandates within one agency: Some jurisdictions assign both prudential supervision and certain competition objectives to the same authority (for instance, a central bank or an integrated �inancial supervisor with a competition mandate). This model can streamline decision-making but risks internal trade-offs, so best practice is to build internal governance structures that maintain balanced oversight (Kirakul, Yonmg & Zamil, 2021). Agencies with multiple objectives often use separate departments or units for each function (e.g. a dedicated competition or market conduct division distinct from the prudential supervision division) and independent reporting lines up to senior management. This structural separation ensures that, for example, staff focused on promoting competition can operate without undue in�luence from those focused on bank safety. At the apex, high-level committees or the agency’s board oversee the reconciliation 32 of objectives: con�licts or policy trade-offs are escalated to these senior forums where decisions consider the institution’s mandate hierarchy. A notable example is the Prudential Regulation Authority (PRA) in the UK, which has a primary objective of safety and soundness of banks and of the banking system, and a secondary objective to facilitate effective competition. In practice, the PRA’s governance and processes are set such that the stability mandate prevails if there is a direct con�lict, but competition considerations are systematically factored into policy making and supervision wherever possible. The agency provides guidance and training to its staff on incorporating the secondary competition objective, ensuring that policies are scrutinized for unnecessary anti- competitive effects without compromising prudential standards. This kind of clear mandate structure – often codi�ied in law – and internal checks help aligned agencies remain focused on stability while still advancing competition when it is prudent to do so. Regardless of the model, clarity of mandates and priorities is a cornerstone of sound institutional design. Con�licts between competition and stability goals often arise when legislation does not specify the primacy of the stability mandate. Best practice is to explicitly prioritize �inancial stability in the legal framework or in formal strategy documents while allowing pursuit of competition and other objectives to the extent they do not jeopardize the primary goal (BCBS 2024). At the same time, making stability the priority does not mean competition is ignored; rather, it ensures that any measures to foster competition are designed in ways compatible with prudential soundness. 6 Concluding remarks This paper has developed a uni�ied framework for �inancial regulation that incorporates the non-linear relationship between market competition and �inancial stability. By extending the classic Tinbergen rule to accommodate the complexities of this relationship, we have transformed a static accounting principle into a dynamic optimization approach that provides a more robust foundation for regulatory policy design. Our framework attempts to make several contributions to the practical implementation of �inancial regulation: 33 • First, we have formalized the non-linear relationship between market competition and �inancial stability, synthesizing the theoretical perspectives of the franchise value hypothesis and the risk-shifting hypothesis into a uni�ied model. This formalization provides a more accurate representation of the complex interactions between these policy objectives than the linear assumptions implicit in traditional regulatory approaches. • Second, we have developed a comprehensive taxonomy of regulatory instruments based on their effects across different positions on the competition-stability curve. This classi�ication extends previous regulatory taxonomies by explicitly acknowledging the state-contingent nature of instrument effectiveness. • Third, we have established a state-contingent optimization approach that guides policy makers in selecting and calibrating instruments based on a �inancial system's current location on the non-linear curve. This approach transforms �inancial regulation from a one-size-�its-all exercise to a contextual process that adapts to the speci�ic characteristics of different �inancial systems. • Fourth, we have identi�ied the institutional arrangements necessary for implementing our extended Tinbergen approach. These arrangements include integrated assessment capabilities and policy coordination mechanisms. Crucially, our approach recognizes that the applicability of many regulatory instruments is not limited to a speci�ic point on the competition-stability curve. Rather, the same tool can be effectively deployed across different regions, provided its intensity, design features, and implementation logic are tailored to context. While our framework offers substantial advantages over traditional approaches, it also faces several limitations and implementation challenges that warrant acknowledgment. First, accurately determining a �inancial system's position on the competition-stability curve requires sophisticated analytical capabilities and comprehensive data that may not be readily available in all jurisdictions. Second, the optimal position on the competition-stability curve may vary across different �inancial market segments within the same jurisdiction, complicating the development of a coherent regulatory strategy. Third, the position- 34 dependent effectiveness of regulatory instruments introduces additional complexity into policy design and implementation. Despite these limitations, we believe that the bene�its of our extended Tinbergen approach outweigh its implementation challenges. By acknowledging the complexity of the competition-stability relationship and developing a structured approach for navigating this complexity, our framework provides a more realistic and effective foundation for �inancial regulation than traditional approaches based on simpli�ied assumptions. Our framework opens several promising avenues for future research, including empirical calibration of the competition-stability curve across different �inancial market segments, deeper investigation of the transmission mechanisms between competition and stability, development of enhanced regulatory impact assessment methodologies, research on optimal institutional arrangements for implementing state-contingent regulation, and exploration of how our framework might apply to other policy domains characterized by non-linear relationships between objectives. 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