# Financial Innovation And Local Governments Investment

Maxime Fajeau, Samuel Ligonnière, Alexandre Mayol

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Maxime Fajeau, Samuel Ligonnière, Alexandre Mayol. Financial Innovation And Local Governments Investment. 2025. hal-05482123

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FINANCIAL INNOVATION AND LOCAL GOVERNMENTS
INVESTMENT

Maxime Fajeau<sup>a</sup> ☐

Samuel Ligonnière<sup>b</sup>

Alexandre Mayol<sup>a</sup>

December 13, 2025

**Abstract**

Local public investment is a key driver of infrastructure provision and long-term growth. Hence, understanding investment financing choice is paramount. Unlike central governments, most local authorities face a narrow choice between debt and tax funding, subject to fiscal rules and financial market access constraints. To analyze this choice, we transpose Williamson (1988)'s corporate finance framework, which views financing as a governance decision aimed at minimizing transaction costs rather than as market neutral or an agency conflict. In this adaptation to public finance, debt corresponds to rules-based, market governance, while tax funding represents hierarchical, discretionary governance. In stable environments, debt dominates; under instability, the ranking reverses. We test these propositions using France's local public debt crisis that began in 2010, triggered by municipalities' exposure to toxic structured loans. Using a panel of 35,000 municipalities (2000–2018) and a distance-based instrumental-variable strategy, we find that before the crisis, structured loans did not alter investment behavior, but after the crisis, exposed municipalities sharply curtailed investment and re-ranked their financing choices away from borrowing toward higher self-financing. These results highlight the governance nature of local financing decisions and extend Williamson's corporate finance framework to public finance.

**JEL Classification** G2 · H12 · H7

**Keywords** Transaction Cost · Local Government · Financial Innovation · Public Finance

**ACKNOWLEDGEMENT** – The authors thank Matéo Dib for excellent research assistance. We are particularly grateful to Rémi Generoso for his invaluable guidance on the construction and interpretation of the geographic data, as well as for numerous insightful discussions throughout the project. We owe a great deal to an anonymous referee, whose insightful and constructive comments substantially shaped the final version of the paper. We also thank Murat Mungan, the editor, for his guidance throughout the review process. Finally, we thank participants at the INSP Workshop celebrating the 15th anniversary of Oliver Williamson's Nobel legacy and at the RENEL Chair for helpful comments.

<footnote>Corresponding author: maxime.fajeau@univ-lille.fr</footnote>

<footnote><sup>a</sup>Univ. Lille, CNRS, IESEG School of Management, UMR 9221-LEM-Lille Économie Management, F-59000 Lille, France</footnote>

<footnote><sup>b</sup>Université Paris-Saclay, Center for Economics at Paris-Saclay (CEPS), France</footnote>
# 1 Introduction

Local public investment is a key driver of infrastructure provision and long-term growth. A large literature documents sizable local multipliers, with early evidence that capital outlays stimulate economic activity (Hulten and Peterson, 1984; Oxley and Martin, 1991). Yet financing these investments is structurally more difficult for local than for central governments. Subnational entities operate under binding fiscal rules designed to enforce budget discipline (Buchanan and Tullock, 1965; Drazen and Eslava, 2010). Most lack direct access to sovereign bond markets, so the financing choice is limited to two costly options—borrowing or taxation (Benston and Smith, 1976; Clemens and Miran, 2012; Delatte et al., 2019). As a result, local governments face tighter and more distortion-prone financing constraints precisely where investment needs are persistent and long-term. Financial innovation emerged to relax these constraints, scaling to meet heterogeneous demand and improve risk sharing (Allen et al., 1994), but it also heightened exposure to tail risk and speculative behavior (Simsek, 2013). This paper examines one such episode—the structured-loan crisis that struck French municipalities in the early 2010s—which provides an archetypal case of high-risk financial innovation (Pérignon and Vallée, 2017).

Because local governments face financing trade-offs akin, to some extent, to those of private firms, corporate finance frameworks provide a useful lens through which to frame the analysis. In perfect markets, a firm's investment decision is independent of whether it relies on debt or equity to finance it (Modigliani and Miller, 1958). Once frictions are introduced, agency theory links financing choices to asymmetric information and incentive alignment (Jensen and Meckling, 1976). In a different vein, Williamson (1988) reconceptualizes investment financing as a governance choice. Debt represents market-based financing enforced through relatively rigid contracts—covenants, repayment schedules, and priority rights—whereas equity embodies internal financing governed by discretionary oversight from shareholders and boards. This framework posits debt as the efficient baseline choice for low-specificity assets in stable conditions, as rule-like contracts minimize transaction costs and discipline behavior without heavy monitoring. By contrast, crises and “unanticipated disturbances” interact with incomplete contracts and opportunism, rendering rule-bound debt “maladapted” and making equity-like, discretionary governance comparatively efficient. While agency frameworks have been applied to public settings (e.g. Pérignon and Vallée, 2017), Williamson (1988)'s governance lens has yet to be transposed to local public finance.

While this conceptual perspective clarifies how financing choices can operate as a form of governance, the empirical literature on local public investment has largely focused on other determinants. Existing research highlights structural, fiscal, and political drivers but has not systematically examined how financing modes shape investment behavior. Residents “vote with their feet,” so amenities influence local demand and fiscal effort (Tiebout, 1956; Saltz and Capener, 2016; Giesecke and Mateen, 2022). Fiscal capacity and demographic dynamics remain first-order determinants of investment (Fisher and Wassmer, 2015; Haraldsvik et al., 2023), and city size correlates with both the level and composition of capital outlays (Holtz-Eakin and Rosen, 1993). Municipal capital structures also depend on tax-base mobility and the legal environment governing fiscal distress, including creditor-leaning regimes (Carlson et al., 2024). Another strand of research shows that fiscal stress and macroeconomic shocks re-rank local budget priorities. Italy's Internal Stability Pact sharply curtailed municipal investment (Grembi et al., 2016), Spain's subnational rules shifted fiscal adjustment onto capital spending (Foremny, 2014), and U.S. balanced-budget constraints amplified procyclicality during the Great Recession (Clemens and Miran, 2012). Political incentives interact with these fiscal pressures, as the electoral returns to long-lived projects tend to decline in downturns, leading municipalities to postpone maintenance and new investment (Borge and Hopland, 2023). In this broader context, France's structured-loan crisis provides a particularly revealing case for studying how financial innovation, political incentives, and fiscal constraints jointly shaped local investment. Structured loan take-up reflected electoral timing and budgetary pressures (Pérignon and Vallée, 2017), toxic exposure later influenced electoral outcomes (Sartre and Daniele, 2022), and central-government backstops mediated fiscal adjustment (Fajeau et al., 2022).

We argue that existing research remains rooted in agency and public-choice frameworks and has not fully
explored how financial shocks reshape local governments' financing choices—and, ultimately, public investment. This paper contributes both conceptually and empirically. Conceptually, we reinterpret local financing decisions as a governance problem grounded in Williamson (1988)'s framework. Public debt corresponds to a rules-based, market regime, while tax funding reflects a discretionary, hierarchical regime. This mapping yields testable predictions within a transaction-cost framework. A simple sorting rule follows: debt is the efficient choice for low-specificity assets in stable times, but when "unanticipated disturbances" (e.g., crises) raise the cost of rule-bound contracting, efficiency shifts toward tax funding—or, where fiscal space is limited, toward investment deferral or reduction.

To test this framework, we examine France's local public debt crisis—also known as the structured-loan crisis. During the 2000s, structured loans marketed primarily by Dexia, the dominant municipal lender, offered initially low fixed rates that later reset to market indexes, providing municipalities with attractive short-term financing and temporarily relaxing budget constraints (Pérignon and Vallée, 2017; Fajeau et al., 2022). When variable rates surged after 2010, many of these contracts became unsustainable, triggering a wave of costly renegotiations and litigation. We argue that the relationship between financing choices and investment behavior—before and after the crisis—cannot be understood solely in terms of financial costs such as interest rates. It also reflects the transaction costs inherent in the governance of public investment. The structured-loan crisis thus provides a natural setting to examine how financial shocks re-rank financing choices and reshape local investment behavior through changes in governance costs and institutional rigidity.

We construct a panel of roughly 35,000 French municipalities spanning 2000–2018, based on a rich dataset combining administrative finances, political characteristics, and socio-demographic information, and observing exposure to Dexia structured loans at the contract level. Identification rests on the unanticipated toxicity shock and departs from selective adoption by implementing an instrumental-variable strategy based on a two-step nonlinear procedure (Wooldridge, 2010). We focus on three dependent variables—the investment ratio, saving ratio, and self-financing rate—which efficiently capture different dimensions of municipal financing choices. These outcomes provide a detailed view of how French municipalities adjusted their investment strategies in response to the structured-loan crisis and the rising governance costs of debt.

Using the structured-loan crisis as a natural experiment, we uncover a sharp before-after contrast. Before the crisis, structured loans did not alter municipal investment behavior relative to standard debt—consistent with comparable transaction costs across financing choices and a baseline preference for borrowing. After the crisis, however, municipalities exposed to toxic loans sharply curtailed borrowing and investment while increasing self-financing. This pattern aligns with the predicted re-ranking from our conceptual framework: under "unanticipated disturbances," contractual rigidity raises the governance cost of debt through tighter covenants, litigation risks, and heightened uncertainty. In such conditions, discretion via tax funding—or temporary deferral—minimizes expected transaction costs. These results are robust across specifications and hold under extensive heterogeneity checks by municipality size, political context, and degree of exposure.

This study provides the first systematic transposition of Williamson (1988)'s transaction-cost framework to public investment finance, mapping public debt to rules/market governance and tax funding to discretion/hierarchy. The findings show that structured loans left investment unchanged in tranquil times but depressed it once financial stress emerged—evidence that local public investment responds not only to credit supply or interest-rate differentials but also to governance frictions. This interpretation is reinforced by the design of the French local-government system, which precludes municipal bankruptcy and provides an implicit state guarantee, thereby limiting the plausibility of a pure supply-side credit-rationing channel. Overall, this perspective complements agency and public-choice approaches by explaining financing choices beyond financial costs or electoral incentives, and reframes public-finance trade-offs around how contracts and institutions jointly shape fiscal adjustment under stress.

The remainder of the paper is organized as follows. Section 2 develops the conceptual framework. Section 3 presents the institutional background, data, and key variables. Section 4 details the identification strategy. Section 5 reports the main results and robustness analyses, and Section 6 concludes.
## 2 Conceptual Framework and Hypotheses

We propose to extend the framework developed by Williamson (1988) in corporate finance to the field of public finance. In corporate theory, a central issue is how firms decide between debt and equity when financing investment projects. The standard view of Modigliani and Miller (1958) holds that financing structure is irrelevant in perfect markets. In contrast, agency theory (Jensen and Meckling, 1976) analyzes the issue through the lenses of asymmetric information and the separation between ownership and control. Williamson (1988) applies the transaction-cost framework to corporate finance, treating investment finance as a transaction like any other, where transaction costs refer to the expenses of searching, negotiating, and enforcing agreements. Accordingly, the debt-equity decision can be viewed as a governance choice (Coase, 1937). Debt is rules-based and market-governed, disciplined by contracts, covenants, and repayment schedules. Equity, by contrast, relies on internal governance—boards and shareholders—with greater discretion and adaptability; *residual control*, the right to decide when contracts are silent, rests with equity holders.

Williamson (1988) further argues that the debt-equity trade-off depends on asset specificity—the redeployability of the investment<fnref n="1" />—which is a key determinant of transaction costs. Debt is generally more efficient when specificity is low. As a result, the author posits that financing is a governance choice where “the TCE [Transaction Cost Economics] approach postulates that debt (the market form) is the natural financial instrument. Equity (the administrative form) appears as the financial instrument of last resort” (Williamson, 1988, p. 581).

However, several forces may shift this trade-off away from debt financing and toward equity. Under bounded rationality and asymmetric information, “unanticipated disturbances” (as termed by Williamson, 1988) or high asset specificity increase transaction costs. Major financial environmental changes can create opportunities for contractual opportunism—via renegotiations or legal disputes—thus increasing ex-post maladaptation costs. Equity does not eliminate renegotiation altogether, but by housing residual control internally it makes adaptation less dependent on rigid financial contracts and is therefore comparatively more flexible under such conditions.

**A transaction-cost perspective on public finance.** We extend the analytical framework developed by Williamson (1988) to the field of public finance, and more specifically to local governments. When financing investment, the public sector effectively chooses between debt and tax funding. The former corresponds to Williamson’s rules-based regime, while the latter can be viewed as the public-sector analogue of equity. In corporate finance, equity implies self-financing or capital provided by shareholders; in the public sector, it takes the form of increased taxation or expenditure reallocation.

Within this transaction-cost framework, debt financing is governed by formal, externally enforced contracts—whether through banks or bond markets—whereas tax funding is discretionary, determined through political deliberation and votes, akin to internal governance decisions within firms. Flexibility, however, entails costs: administrative, political, and bureaucratic frictions that Bel and Warner (2015) describe as political transaction costs. As in private finance, asset specificity and unforeseen shocks that push debt contracts into maladaptation shift the balance between these two modes of financing, influencing whether governments rely more on borrowing or on fiscal adjustment.

To summarize, public debt corresponds to a rules-based, market form of governance—characterized by low organizational costs and strong pre-commitment in stable conditions, but prone to maladaptation when circumstances change, leading to renegotiations, delays, or contractual hold-ups. In contrast, tax funding reflects a discretionary, hierarchical mode of governance—costlier in political and administrative terms, yet more adaptable to shocks and less exposed to value-destroying rigidity.

**Hypotheses.** When public investment projects are simple and predictable, debt financing is typically preferred: contractual arrangements are unlikely to entail costly ex-post maladaptation. By contrast, tax funding—

<footnote n="1">¹In Williamson's framework, asset specificity refers to how specialized an asset is to a particular use or relationship; highly specific assets have low redeployability and lose value if used elsewhere.</footnote>
the public-sector analogue of equity, functioning as a self-financing mechanism—requires budgetary action through tax adjustments or expenditure reallocations, and therefore involves significant bureaucratic and political costs. As emphasized by Williamson (1988, p. 581), equity is “reserved for projects where the needs for nuanced governance are great.” Extending this logic to public finance leads to the following hypothesis:

*Hypothesis 1 (Debt dominance in normal times).* For low-specificity public investments in stable environments, debt financing dominates tax funding.

This baseline presumes low asset specificity and limited uncertainty. When conditions change—such as during a financial crisis that constrains credit—added uncertainty leaves governments more frequently “in a maladapted state,” and rule-based systems come under stress (Williamson, 1988, p. 584). Debt contracts tighten through stricter covenants and enhanced monitoring, raising rigidity and legal risk while shifting control toward lenders. Where fiscal space allows, governments adjust by tilting from debt to tax funding; where it does not, investment is deferred. Because financing represents a governance choice between rules and discretion, shocks affect not only the financing but also the invest-or-defer margin.

*Hypothesis 2 (Tax-funding dominance in crisis times).* In crisis conditions, policymakers shift away from debt to prefer tax funding; when discretionary fiscal capacity is insufficient, investment projects are postponed or canceled.

# 3 Institutional Setting and Data Description

## 3.1 Financing Framework of French Municipalities

At the beginning of the 1980s, the French government initiated a vast decentralization movement, strengthening local governments’ autonomy. Consequently, local governments witnessed an increase in their intervention scope and expenses over time while being forced to vote for a balanced budget. The decentralization process is a crucial issue regarding budgetary autonomy for local authorities and has brought about a specific fiscal framework. The fiscal rules governing local governments differ from those relating to the central government. When voted on, local budgets must be balanced and respect the *golden rule*. The French local public accounting system distinguishes between two budget sections: operating and investment accounts. Unlike the central government, the *golden rule* requires that each budget account be voted in balance and that loans are intended only to balance the investment account.

This fiscal architecture implies that any increase in local investment must be matched by either self-financing—excess operating revenue—or new borrowing.<fnref n="2" /> Because loans are exclusively dedicated to financing investments (capital expenditure and purchase of durables), French municipalities can choose between debt and tax-funding/saving, under the respect to this legal *golden rule* of local public finance.

## 3.2 The Structured Toxic Loan Phenomenon

In the 2000s, French municipalities faced tightening fiscal constraints. Central government transfers—long a cornerstone of local budgets—declined steadily in real terms (Cour des Comptes, 2024), while the 2003 decentralization wave expanded local competences without providing commensurate resources. With limited scope to raise local taxes, municipalities increasingly relied on debt as the main adjustable lever of their budget constraint. As a result, outstanding local debt reached €108 billion by 2014—a 40% increase since the decentralization wave initiated a decade earlier.<fnref n="3" />

<footnote n="2">²Table A1 in the Appendix presents the accounting framework of French municipalities. It distinguishes an operating section—recording current revenues and expenditures—from an investment section devoted to capital spending. Surpluses from the operating account (gross and net savings) are used to repay existing debt and finance new investments, while borrowing is strictly limited to financing investment. Interest payments are treated as operating expenses, reflecting intertemporal budget balance requirements.</footnote>

<footnote n="3">³See Figure A1 in the Appendix.</footnote>
<figure><img src="image_2.png" /><figcaption>**Figure 1:** Structured loans to French municipalities: annual subscriptions (Panel A) and geographical distribution (Panel B).</figcaption></figure>

Note: Panel A reports the yearly number of municipalities contracting at least one structured loan. Panel B maps the 1,586 municipalities holding Dexia structured loans in 2011, with color intensity indicating the overhead ratio—the excess cost from the variable-rate component—highlighting the spatial pattern and severity of exposure across France.

Amid this tightening environment, local governments sought to sustain investment without raising taxes or breaching borrowing limits. This tension fostered the rapid expansion of structured loans—financial instruments offering temporarily low interest rates in exchange for long-term exposure to market risk. Dexia, historically the dominant municipal lender and accounting for over 80% of the structured-loan market, played a pivotal role in their diffusion. These contracts typically combined an initial fixed rate for two to five years with a subsequent variable rate linked to exchange-rate or yield-curve movements. As documented by Pérignon and Vallée (2017), their adoption reflected both fiscal pressures and political incentives, particularly among highly indebted or electorally competitive municipalities.

Between 2000 and 2010, more than 1,500 municipalities signed over 3,000 structured-loan contracts nationwide (Panel A of Figure 1), illustrating the breadth of the phenomenon (Panel B of Figure 1). The 2008 financial crisis revealed the embedded risks: falling interest rates and sharp exchange-rate movements—especially the appreciation of the Swiss franc—triggered substantial increases in borrowing costs (see Figure 2). Some interest rates rose from 3–4% to more than 40%, with overhead ratios exceeding 80%. The resulting toxic loan crisis affected roughly €10 billion in outstanding debt (Bartolone and Gorges, 2011).

As documented in Fajeau et al. (2022), the episode led to widespread debt renegotiations, the failure of Dexia, and a large-scale public intervention to help municipalities unwind their contracts. The estimated fiscal cost of this resolution plan reached €6.6 billion. This central-government rescue underscores that, under French law, municipalities cannot ultimately default: the state will step in as lender of last resort, thereby making bank-driven credit rationing a less convincing account. This context justifies complementing a pure cost-of-funds view with transaction-cost framework proposed. Ex-ante, municipalities facing the debt–tax trade-off favored borrowing as a means to sustain investment under legal and fiscal constraints. Structured loans provided a temporary workaround, offering short-term flexibility within rigid budget rules. Ex-post, however, the same contractual governance mechanisms proved maladapted once financial conditions shifted, prompting widespread renegotiation and a costly national stabilization effort.

## 3.3 Data Description

**Local government-level data.** We use detailed annual balance-sheet data for all French municipalities provided by the *Directorate General of Public Finances*, covering the period 2000–2018 and including about
<figure><img src="image_3.png" /><figcaption>**Figure 2:** Exchange rate USD/CHF fluctuations and structured-loan interest rate dynamics.</figcaption></figure>

Note: Panel A displays the evolution of the USD/CHF exchange rate, while Panel B shows the corresponding variation in the interest rate on a Dexia structured loan indexed to this parity. The contract initially offered a preferential fixed rate of 3.45%, but once the USD/CHF exchange rate fell below 1 in 2011, the variable component was triggered, raising the effective rate faced by the municipality from 3.45% to over 40%. This example illustrates the extreme volatility embedded in such contracts.

35,000 municipalities. These records report the full structure of municipal budgets, distinguishing operating and investment accounts, and thereby allow a comprehensive analysis of local public finances. To capture political characteristics, we rely on information from the *National Register of Elected Representatives* maintained by the *Ministry of the Interior*, which identifies each mayor's political affiliation as well as personal characteristics of the mayor in office before the crisis, including gender, age, and socio-professional background. Political factors are an important dimension in understanding the subscription of structured loans, as highlighted by Pérignon and Vallée (2017). Finally, we complement these data with socio-demographic characteristics of municipalities from Piketty and Cagé (2023), capturing education levels, labor market conditions, and local income ratios.

**Structured Loan-level data.** Our analysis also relies on a loan-level dataset documenting the structured loans issued by Dexia to French municipalities. The file, obtained by the newspaper *Libération* in 2011 during Dexia's restructuring negotiations, reports 3,016 loans granted to 1,586 municipalities, including information on contract dates, maturities, and additional costs.

This dataset does not encompass the entire structured-loan market, as Dexia was not the sole issuer. Potential biases could arise from the exclusion of loans granted by smaller regional or specialized institutions, whose portfolios may differ from Dexia's in terms of size, risk structure, or client profile. Nonetheless, these omissions are unlikely to materially affect our results for at least three reasons.

First, the dataset provides a highly reliable representation of municipal exposure, as Dexia dominated the market—accounting for over 80% of structured loans to municipalities (Bartolone and Gorges, 2011; Cour des Comptes, 2014). Second, following Dexia's 2011 rescue, municipalities were barred from contracting new structured loans, meaning that the dataset effectively captures nearly all loans ever issued and the entire stock still active at the onset of the crisis. Finally, our empirical design compares Dexia-exposed municipalities with a much larger pool of non-exposed ones, while controlling for fiscal, political, and demographic characteristics.<fnref n="4" />

<footnote n="4">⁴The 1,586 municipalities in the dataset represent about 80% of all municipalities that contracted structured loans, implying—by simple proportion—that roughly 350 additional municipalities are unobserved, i.e., about 1% of France's nearly 35,000 municipalities.</footnote>
**Historical CLF shareholders data.** Following the identification strategy of Sartre and Daniele (2022), we construct an instrumental variable exploiting the geography of municipalities' historical participation in the capital of the former *Crédit Local de France* (CLF), Dexia's predecessor. The exhaustive list of shareholder municipalities is drawn from the *Journal Officiel* publication of December 16, 1994, which authorized local governments to acquire equity stakes in the bank. We match these municipalities with their geographic coordinates from the *Institut national de l'information géographique et forestière* (IGN) to compute the geodesic ("as-the-crow-flies") distance from each municipality to the nearest historical CLF shareholder. This distance forms the basis of our instrumental variable.

**Variables of interest.** The objective of this paper is to evaluate how the structured-loan crisis affected municipal investment behavior and financing choices—whether municipalities relied on borrowing or self-financing. We analyze these outcomes through three complementary dependent variables, each reflecting a distinct aspect of local public finance:

*Investment ratio* – the ratio of investment expenditures to operating revenues, captures a municipality's investment effort relative to its financial capacity. Normalizing by operating revenues rather than population provides a more consistent measure of investment intensity across municipalities of varying size and wealth.

*Saving ratio* – the ratio of gross savings to operating revenues, measures the share of current revenues not devoted to operating expenses and thus reflects the capacity to generate internal resources for future investment.

*New investment self-financing rate* – the ratio of net savings to effective capital expenditure (excluding loan repayments)—indicates the share of new investment financed internally rather than through borrowing. Higher values imply greater fiscal autonomy, while lower ones signal stronger debt dependence.

To isolate the causal effect of toxic-loan exposure and mitigate potential reverse causality, we include an extensive set of structural, financial, and socio-economic controls. Financial controls encompass both expenditure- and revenue-side variables: total operating expenses per capita, the structural rigidity ratio (personnel and interest expenses relative to total operating expenditure), the operating grant from the central government per capita, and the composition of operating revenues between government transfers and local taxes. Lagged financial debt per capita accounts for existing financial commitments. We further control for population size (log) and for the mayor's political affiliation, classified into five groups—"far left," "left," "independent or others," "right," and "far right". Additional controls capture the mayor's individual characteristics—age, gender, socio-professional category, and incumbency status—as well as local socio-economic conditions, including the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios measuring local wealth.

# 4 Identification Strategy

## 4.1 Addressing endogeneity concerns

This study examines how exposure to structured loans affected municipalities' investment and financing behavior over time. The goal is to identify the causal impact of financial exposure on fiscal choices while disentangling it from structural, financial, and political factors that may simultaneously shape borrowing and budgetary outcomes.

The sharp rise in interest rates that triggered the toxicity of structured loans was highly unlikely to have been anticipated by municipalities or lenders, and can thus be regarded as exogenous (Bartolone and Gorges, 2011; Cour des Comptes, 2014). By contrast, the initial decision to contract such products was not random. Evidence from Pérignon and Vallée (2017) and Sartre and Daniele (2022) shows that municipalities adopting structured loans systematically differed from others in both financial and strategic dimensions. These municipalities often faced tighter budget constraints, making the temporarily lower fixed rates offered by structured loans particularly appealing. Political considerations also influenced adoption. Indeed, structured
<figure><img src="image_4.png" /><figcaption>**Figure 3:** *Crédit Local de France* (CLF) shareholder municipalities and adoption of structured loans.
Note: Panel A shows the kernel distribution of the first year of structured-loan adoption across municipalities. Panel B maps the minimum geodesic ("as-the-crow-flies") distance to the nearest CLF shareholder municipality and indicates municipalities that contracted structured loans.</figcaption></figure>

loans became especially attractive ahead of the 2008 municipal elections, since they allowed incumbents to momentarily reduce debt-service costs and local tax rates.

Moreover, supply-side factors may have reinforced selection: Cori and Le Gall (2013) and Sartre and Daniele (2022) document that Dexia's internal incentive structure—linking staff bonuses to structured-loan sales—encouraged proactive marketing toward specific municipalities based on their size, financial situation, or historical relationship with the bank. These mechanisms jointly suggest that both fiscal stress and strategic targeting shaped the pattern of exposure, underscoring the need for an identification strategy capable of isolating causal effects from these underlying differences.

## 4.2 Distance-based IV

The potential endogeneity of structured-loan adoption prompts us to seek an instrumental variable that satisfies two key conditions: (i) it must predict the likelihood of contracting a structured loan with Dexia, and (ii) conditional on observable characteristics, it must not be directly correlated with municipal investment or financing decisions in the aftermath of the crisis. Geographic distance has long been identified as a major determinant of credit relationships (Degryse and Ongena, 2005; Bharath et al., 2011; Drexler and Schoar, 2014; Beck et al., 2018), and has been used in the literature as a source of exogenous variation in lending activity. Pérignon and Vallée (2017) instrumented the propensity to contract structured loans using the distance between each municipality and the nearest Dexia branch. While informative, this measure is limited by the fact that Dexia branches were largely located in regional capitals, making the instrument partially correlated with urban status and, by extension, with local economic conditions.

To overcome this limitation, we follow Sartre and Daniele (2022) and exploit the historical relationship between French municipalities and Dexia's predecessor, the *Crédit Local de France* (CLF). Dexia was created in 1996 from the merger of the CLF and the Belgian *Crédit Communal de Belgique*. In 1994, prior to this merger, 362 French municipalities became shareholders of the CLF. We construct an instrument based on the geographic distance of each municipality to the nearest 1994 CLF shareholder municipality. The underlying intuition is that municipalities geographically close to these historical shareholders were more likely to develop early lending relationships with Dexia, and consequently to adopt structured loans in the 2000s. Figure 3 (Panel A) displays kernel distributions of the earliest loan contracting years for shareholder
and non-shareholder municipalities, showing that shareholder municipalities adopted structured loans significantly earlier.

This spatial pattern is consistent with Dexia's business strategy in the late 1990s and early 2000s. As described by Cori and Le Gall (2013), Dexia's commercial policy aimed to expand its structured-loan portfolio by first targeting municipalities with historical ties to the bank, and then extending offers to neighboring municipalities. Figure 3 (Panel B) maps the distribution of toxic-loan-exposed municipalities alongside their geodesic distance ("as-the-crow-flies") to the nearest historical CLF shareholder across the French territory, highlighting that most treated municipalities were located within a short range of these early-linked cities.

Our exclusion restriction is plausible, as the instrument is based on mayoral decisions made in 1994—well before the diffusion of structured loans between 2001 and 2010—and is thus unlikely to correlate with subsequent investment choices or fiscal policies many years later. The historical shareholder municipalities are geographically dispersed across France and include many small and medium-sized municipalities, making it unlikely that the instrument merely captures proximity to economic centers or other spatial determinants of investment. Nevertheless, in all specifications, we include a rich set of structural and fiscal controls, encompassing financial variables, incumbent characteristics, and socio-demographic attributes, along with a fixed-effects framework that further absorbs potential unobserved heterogeneity.

## 4.3 Probit-Control Function Approach

Our identification strategy builds on an instrumental-variable framework. In a standard linear setting, endogeneity would typically be addressed using two-stage least squares (2SLS). However, this approach requires both stages to be linear, whereas our endogenous variable—the indicator of Dexia exposure—is binary. A tempting shortcut would be to replace the linear first stage with a probit and insert the fitted probabilities into the second-stage regression. As emphasized by Angrist and Pischke (2009), this constitutes a “forbidden regression”: not only does it fail to yield consistent estimates—since the predicted values remain correlated with the structural error term and violate the exclusion restriction (Wooldridge, 2010)—but it also fails to correct endogeneity and produces invalid standard errors by ignoring the estimation uncertainty introduced in the first stage.

To properly address this issue, we adopt an instrumental-variable strategy based on a two-step nonlinear procedure—commonly known as the Probit-Control Function or Probit-2SLS approach—following Rivers and Vuong (1988) and Wooldridge (2010). This method first estimates a probit model for the probability of exposure using the excluded instrument and, in the second stage, includes the generalized residuals from this probit as an additional regressor in the outcome equation. These residuals capture the endogenous component of exposure correlated with the structural error term. Their inclusion restores the orthogonality condition required for consistent estimation of the causal parameter, extending the logic of instrumental variables to the case of a binary endogenous treatment.

**First stage.** We begin by estimating a probit model for the probability of contracting a structured loan, using as an excluded instrument the log-distance to the nearest historical CLF shareholder along with a full set of controls:

$$P(1_{Toxic_i} = 1) = \Phi(\log(km_i^{CLF}), X_i), \quad (1)$$

where $\Phi(\cdot)$ denotes the standard normal cumulative distribution function. From this probit, we compute the generalized residuals $\hat{v}_i$ which accounts for the correlation between $1_{Toxic_i}$ and the structural error term. These residuals are then used in the second-stage regression to correct for endogeneity (Wooldridge, 2010).

Table 1 reports the first-stage results predicting municipalities' exposure to Dexia structured loans. The instrument—the logarithm of the distance to the nearest historical CLF shareholder—exhibits the expected negative and highly significant effect: municipalities located closer to former shareholders were substantially more likely to have contracted a structured loan. The estimated magnitudes closely match those reported by Sartre and Daniele (2022) in a comparable setting. As additional financial, incumbent, and socio-
<table><caption>Table 1: IV Regression (Probit First Stage)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Probit-MLE</th><th>LPM-OLS</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>1<sub>Toxic</sub></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Log of distance to closest CLF city</td><td>-0.403***<br>(0.016)</td><td>-0.385***<br>(0.016)</td><td>-0.322***<br>(0.017)</td><td>-0.287***<br>(0.018)</td><td>-0.045***<br>(0.003)</td></tr><tr><td>Marginal effect</td><td>-0.031***<br>(0.001)</td><td>-0.027***<br>(0.001)</td><td>-0.021***<br>(0.001)</td><td>-0.018***<br>(0.001)</td><td></td></tr><tr><td>Department FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Financial controls</td><td>no</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Incumbent controls</td><td>no</td><td>no</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Population controls</td><td>no</td><td>no</td><td>no</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>35,559</td><td>35,514</td><td>34,308</td><td>33,166</td><td>33,166</td></tr><tr><td>Chi<sup>2</sup></td><td>1708.09</td><td>2116.06</td><td>2653.33</td><td>2661.52</td><td>—</td></tr><tr><td>P>Chi<sup>2</sup></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>—</td></tr><tr><td>Kleibergen-Paap Wald F-stat</td><td>—</td><td>—</td><td>—</td><td>—</td><td>256.6</td></tr></tbody></table>

Notes: The dependent variable is Dexia exposure, an indicator variable equal to one if the local government has borrowed with a structured loan before 2010. The instrument is the logarithm of the distance to the nearest CLF. Financial controls capture the financial position of municipalities in 2001 and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe characteristics of the mayor in office before the crisis, such as age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the share of tertiary-educated residents, the share of foreign residents, and local income and capita ratios. Robust standard errors clustered at the municipality level are reported in parentheses. The first-stage Kleibergen-Paap Wald F-statistic equals 256.6, far exceeding the Stock and Yogo (2002) critical value of 16.4 for a 10% maximal IV bias, which rules out weak-instrument concerns. *** p < 0.01, ** p < 0.05, * p < 0.10.

demographic controls are introduced, the coefficient remains stable though slightly decreases in magnitude, alongside a minor loss of observations due to data availability in the extended specifications. Results are consistent across the probit and linear probability models. We retain Column (4) as our preferred specification, as it includes the full control set and provides the most conservative estimate. In the linear probability model reported in Column (5), the first-stage Kleibergen-Paap Wald F-statistic of 256.6 far exceeds the Stock and Yogo (2002) critical value of 16.4, confirming the instrument's strength and relevance.

**Second stage.** The second stage's empirical framework then relies on dynamic panel regressions with municipality and time fixed effects, controlling for persistence of investment and financing behavior through the lagged dependent variable (Dufrénot et al., 2011).<fnref n="5" /> Specifically, for each dependent variable $y_{it}$ (in log form), we estimate the following second-stage equation:

$$y_{it} = \lambda y_{it-1} + \beta 1_{Toxic, it \ge 2010} + X'_{it} \theta + \omega \hat{v}_{it \ge 2010} + \mu_i + \gamma_t + u_{it}, \quad (2)$$

where $\mu_i$ and $\gamma_t$ denote municipality and time fixed effects, respectively, and $\hat{v}_{it \ge 2010}$ are the generalized residuals from the first stage. Including these residuals purges the correlation between the exposure indicator and the structural error, ensuring consistent estimation of $\beta$ under the same exclusion restriction as a standard IV framework (Wooldridge, 2010). Intuitively, this two-step procedure retains the logic of instrumental variables—using exogenous variation from the instrument to correct endogeneity—while accommodating the discrete nature of the endogenous treatment.

<footnote n="5">⁵The model can be consistently estimated by the fixed-effects OLS estimator as the time dimension $T$ increases (Nickell, 1981; Judson and Owen, 1999). Given our long panel (18 years), the Nickell bias is expected to be negligible.</footnote>
<table><caption>**Table 2: Baseline Probit-2SLS Specification**</caption><thead><tr><th rowspan="2"></th><th>First Stage<br>(Probit)</th><th colspan="3">Second Stage<br>(IV-OLS)</th></tr><tr><th>(1)<br>I<sub>Toxic</sub></th><th>(2)<br>Investment<br>Ratio</th><th>(3)<br>Saving<br>Ratio</th><th>(4)<br>Self-Financing<br>Rate</th></tr></thead><tbody><tr><td>Log of distance to closest CLF city</td><td>-0.287***<br>(0.018)</td><td></td><td></td><td></td></tr><tr><td>Marginal effect</td><td>-0.018***<br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub></td><td></td><td>-0.029**<br>(0.012)</td><td>0.690***<br>(0.103)</td><td>1.393***<br>(0.221)</td></tr><tr><td>Generalized residuals</td><td>—</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Controls</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>33,166</td><td>579,141</td><td>579,141</td><td>579,141</td></tr><tr><td>Municipalities</td><td>—</td><td>33,166</td><td>33,166</td><td>33,166</td></tr></tbody></table>

*Notes:* The first stage reports a Probit regression of the probability that a municipality held toxic structured loans before 2010 on the logarithm of the distance to the closest historical municipal shareholder of Dexia (formerly CLF). The second stage implements a control-function (Probit-2SLS) specification estimating the causal effect of Dexia exposure on municipal investment, saving, and self-financing ratios. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for pre-crisis financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, the structural rigidity ratio, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors clustered at the municipality level are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.

# 5 Results

## 5.1 Baseline Estimates: The Real Effects of Structured Loan Use

**Reduced investment effort.** Table 2, column (2), reports the estimates for the investment ratio. Municipalities exposed to Dexia structured loans experienced a marked contraction in investment after the crisis. Controlling for persistence and a rich set of financial and demographic covariates, the estimated effect implies an average annual decline of about 3% in investment effort relative to non-exposed municipalities over the post-2010 period. This result supports the view that the financial shock generated by structured loans tightened local public investment, consistent with debt-overhang and balance-sheet stress mechanisms.

To further assess the mechanism, Table 3, columns (2a) and (2b), introduce two measures of exposure intensity: the overhead ratio (the excess cost induced by the variable-rate component) and the share of structured loans in total debt. At the mean overhead ratio (12%), the decline in investment mirrors the baseline estimate, reinforcing internal consistency. At one standard deviation above the mean (21%), investment falls by an additional 7%, and similarly for municipalities where structured loans represent 59% of total debt (one standard deviation above the mean), the decline reaches roughly 11%. At extreme exposure levels—overhead ratios nearing 80%—the implied contraction approaches 20%, a magnitude that, given the scale of local public infrastructure, constitutes a substantial reduction in investment capacity.

**Higher precautionary savings.** Table 2, column (3), presents the results for the saving ratio, defined as the share of operating revenues not absorbed by current expenditures. Exposed municipalities significantly increased their savings after 2010, displaying on average a 70% higher saving ratio relative to unaffected ones. This finding indicates a shift from debt financing toward greater liquidity accumulation, reflecting precautionary fiscal behavior in response to financial distress.
<table><caption>**Table 3: Probit-2SLS Specification with Intensity and Exposure Measures**</caption><thead><tr><th rowspan="2"></th><th colspan="1">First Stage<br>(Probit)</th><th colspan="4">Second Stage<br>(IV-OLS)</th></tr><tr><th>I<sub>Toxic</sub><br>(1)</th><th colspan="2">Investment Ratio</th><th colspan="2">Saving Ratio</th><th colspan="2">Self-Financing Rate</th></tr><tr><th></th><th></th><th>(2a)</th><th>(2b)</th><th>(3a)</th><th>(3b)</th><th>(4a)</th><th>(4b)</th></tr></thead><tbody><tr><td>Log of distance to closest CLF city</td><td>-0.287***<br>(0.018)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Marginal effect</td><td>-0.018***<br>(0.001)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.233***<br>(0.079)</td><td></td><td>2.553***<br>(0.560)</td><td></td><td>8.773***<br>(1.042)</td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.182***<br>(0.028)</td><td></td><td>0.584***<br>(0.106)</td><td></td><td>2.382***<br>(0.257)</td></tr><tr><td>Generalized residuals</td><td>—</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Municipality FE</td><td>—</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>—</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Controls</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>33,166</td><td>579,141</td><td>579,141</td><td>579,141</td><td>579,141</td><td>579,141</td><td>579,141</td></tr><tr><td>Municipalities</td><td>—</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td></tr></tbody></table>

Notes: The first stage reports a Probit regression of the probability that a municipality held toxic structured loans before 2010 on the logarithm of the distance to the closest historical municipal shareholder of Dexia (formerly CLF). The second stage implements a control-function (Probit-2SLS) specification estimating the causal effect of Dexia exposure on municipal investment, saving, and self-financing ratios. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for pre-crisis financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors clustered at the municipality level are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.

The results in Table 3, columns (3a) and (3b), show that this reaction scales with exposure intensity. Municipalities facing more toxic contracts—those with overhead ratios one standard deviation above the mean (21%)—record an additional 50% increase in their saving ratio, while those with a toxic-debt share one standard deviation above the mean (59%) exhibit a rise of about 35%. Extrapolating to extreme exposure levels (overhead ratios near 80%) suggests that savings could more than double relative to pre-crisis levels. These magnitudes underscore how the structured-loan crisis reshaped fiscal policy, prompting municipalities to accumulate buffers and reduce financial dependence, consistent with the literature on liquidity hoarding under credit stress (Duchin et al., 2010; Mody et al., 2012; Sun and Wang, 2015; Guerrieri and Lorenzoni, 2017).

**Shift toward self-financing.** Finally, Table 2, column (4), reports results for the self-financing rate of new investments, which measures the extent to which municipalities finance new investment from internal resources rather than through new borrowing. Exposed municipalities display self-financing rates more than twice as high as those of non-exposed counterparts in the post-crisis years, reflecting a persistent reorientation of financing choices toward internal resources. Self-financing thus emerges as the key adjustment channel once external borrowing becomes costly or uncertain.

As shown in Table 3, columns (4a) and (4b), this effect once again intensifies with exposure. For municipalities facing more toxic contracts (overhead ratios one standard deviation above the mean), the self-financing rate is nearly three times higher than in unaffected municipalities, a pattern mirrored when exposure is measured by the share of structured loans in total debt. At extreme exposure levels—overhead ratios approaching 80%—the self-financing rate would be almost seven times higher than before the crisis, indicating a dramatic reliance on internal funds for investment. Whether this reflects fiscal prudence or binding credit constraints remains open (Ivashina and Scharfstein, 2010), yet the inclusion of detailed financial and demographic controls and the absence of evidence of widespread credit rationing suggest an endogenous
shift toward risk aversion and fiscal autonomy. Overall, these results show that the structured-loan crisis not only reduced investment but also permanently altered municipalities' financing structure, entrenching self-financing as the dominant mode of local capital financing.

As a result, the evidence is consistent with our two hypotheses. In tranquil times, structured loans allowed municipalities to finance investment without breaching fiscal *golden rules*, but the macro-financial shock re-priced risk and reset contractual terms. In transaction-cost terms, the crisis maladapted rule-bound debt contracts—raising renegotiation, monitoring, and litigation costs—and thereby worsened subsequent market access for exposed municipalities. Econometrically, we document a clear shift in behavior: affected jurisdictions reduced new borrowing, cut investment, and increased self-financing (savings). This pattern both corroborates the normal-times debt dominance ($H_1$) and the crisis-time re-ranking toward discretion or exit ($H_2$). Overall, the structured-loan crisis is consistent with the predictions of the transaction-cost framework: the shock increases governance (transaction) costs under debt, shifts the efficient financing margin toward tax funding or deferral.

## 5.2 Boom & Bust: Financial Innovation Without Real Investment Gains

We next examine the effects of structured loans over the full financial cycle. A financial shock in 2010 exposed the embedded risks of these contracts, turning many structured loans into highly costly liabilities and defining two distinct phases: a pre-crisis boom and a post-crisis bust.<fnref n="6" /> While prior studies have typically examined either the benefits or the risks in isolation, our approach adopts a longer-term, cyclical perspective—evaluating the gains realized during the boom against the costs incurred during the bust. We specifically assess whether municipalities that subscribed to structured loans expanded their investment capacity before the crisis, which could in turn explain the subsequent contraction observed after 2010. The objective is to capture municipalities' overall fiscal behavior across the cycle—how temporary financial relief during the expansion translated into post-crisis fiscal retrenchment once adverse conditions materialized. To formally test this cyclical dynamic, we estimate the following empirical specification:

$$y_{it} = \lambda y_{it-1} + \beta \mathbb{1}_{Toxic, it \ge 2010} + \delta \mathbb{1}_{Dexia, it < 2010} + X'_{it} \theta + \mu_i + \gamma_t + \varepsilon_{it} \quad (3)$$

where $y_{it}$ denotes one of the three dependent variables related to municipal investment and financing decisions. The specification includes the same set of controls and fixed effects as in the baseline model. The new indicator $\mathbb{1}_{Dexia, it < 2010}$ captures the municipality's commitment to structured loans during the expansionary phase, while $\mathbb{1}_{Toxic, it \ge 2010}$ measures exposure in the subsequent bust. Because municipalities adopted these instruments at different points in time, the model exploits staggered adoption rather than a single treatment date, improving identification.

Table 4 shows that structured loans significantly affected municipal spending only during the bust phase, consistent with our baseline causal estimates from Section 5.1. During the pre-crisis boom, municipalities that contracted Dexia's structured loans did not exhibit higher investment levels once we control for fiscal, demographic, and political covariates. Column (1c) even suggests a slightly negative—though statistically insignificant—association. These results support Hypothesis (H₁): the use of structured loans reflected a financing choice rather than an investment motive. There was thus no meaningful trade-off between innovative and conventional debt in terms of investment outcomes. Outside crisis periods, this finding implies that transaction costs were effectively equivalent across debt types, confirming that debt remained the preferred financing channel for local governments but without generating trade-off opportunities or real investment gains.

<footnote n="6">⁶Most Dexia contracts were tied to the USD/CHF exchange rate, which became highly volatile due to the Swiss National Bank's interventions. Following a sharp appreciation of the Swiss franc in 2010–2011, the SNB introduced a minimum exchange rate of 1.20 CHF per EUR in September 2011 before unexpectedly removing this floor in January 2015, triggering a 30% appreciation within hours. Other contracts indexed to LIBOR or similar rates were also affected by sharp increases during the Great Recession. Robustness checks later confirm that results remain consistent under alternative turning points (2007, 2008, 2011, or 2015) and contract types. See Section 5.3.</footnote>
<table><caption>**Table 4:** Distinction between the boom and bust phases</caption><thead><tr><th></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Ratio</th></tr><tr><th></th><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>Boom phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I<sub>Dexia,t&lt;2010</sub></td><td>0.000<br>(0.013)</td><td>0.002<br>(0.012)</td><td>-0.020*<br>(0.012)</td><td>-0.025<br>(0.027)</td><td>-0.099***<br>(0.029)</td><td>-0.133***<br>(0.025)</td><td>0.609***<br>(0.082)</td><td>0.186**<br>(0.076)</td><td>0.115<br>(0.070)</td></tr><tr><td>Bust phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub></td><td>-0.035**<br>(0.014)</td><td></td><td></td><td>0.320***<br>(0.044)</td><td></td><td></td><td>1.551***<br>(0.103)</td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.307***<br>(0.083)</td><td></td><td></td><td>2.006***<br>(0.405)</td><td></td><td></td><td>8.778***<br>(0.798)</td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.192***<br>(0.028)</td><td></td><td></td><td>0.489***<br>(0.066)</td><td></td><td></td><td>2.472***<br>(0.172)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>596,127</td><td>596,127</td><td>596,127</td><td>596,127</td><td>596,127</td><td>596,127</td><td>596,127</td><td>596,127</td><td>596,127</td></tr><tr><td>Municipalities</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td></tr><tr><td>Ad. R<sup>2</sup></td><td>0.020</td><td>0.020</td><td>0.020</td><td>0.118</td><td>0.118</td><td>0.118</td><td>0.099</td><td>0.098</td><td>0.098</td></tr></tbody></table>

*Notes:* The regressions estimate the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality’s wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.

Estimates for the saving ratio and the self-financing rate of new investment are less stable across specifications and lack robustness. Although the coefficients suggest a modest decline in savings and a slight increase in self-financing, their statistical significance and economic magnitude remain limited. Overall, financial innovation reshaped the financing side of local budgets without yielding tangible productive benefits.

The post-2010 results contrast sharply with these pre-crisis dynamics. Municipalities most exposed to Dexia's structured loans exhibited a pronounced fiscal adjustment, characterized by a substantial rise in the saving ratio and a marked increase in the self-financing rate of new investments. These adjustments point to precautionary behavior and tighter budget management in response to higher debt-related risks. Together, the coefficients depict a defensive fiscal stance centered on liquidity preservation and reduced reliance on external borrowing. These findings are consistent with Hypothesis (H₂) and with the results presented in Section 5.1, reinforcing the robustness of our conclusions. In the next section, we complement these results with further analyses and robustness checks.

## 5.3 Further Analysis and Robustness Checks

We conduct an extensive set of robustness checks covering both the boom ($H_1$) and bust ($H_2$) phases of the financial cycle. The purpose is to verify that our results are not driven by model specification, sample composition, or measurement choices, and to provide additional insight into the mechanisms at play.

**Parsimonious specification.** We first estimate a simplified version of the model with a reduced set of controls to address potential multicollinearity concerns (Table A2). The results confirm that the baseline effects are not driven by overfitting or excessive control saturation, as coefficient magnitudes and significance levels remain broadly consistent.

**Political controls and electoral competition.** We extend the set of political variables to include measures of electoral competition, such as the first-round electoral margin and the Herfindahl-Hirschman Index (Tables A3, A4, A10, and A11). These additional controls, following Pérignon and Vallée (2017), test whether
the results are sensitive to variations in political contestability across municipalities. The estimated effects remain stable, indicating that political competition does not materially affect our findings.

**Excluding large municipalities.** We exclude the ten largest municipalities with more than 200,000 inhabitants (Tables A5 and A12) to ensure that results are not driven by major urban centers with atypical financial structures or preferential access to credit markets. The exclusion leaves coefficients virtually identical in magnitude and sign to the baseline, confirming that large cities do not drive the observed effects.

**Heterogeneity by municipality size.** We then assess how the effects vary across population brackets (Tables A6 and A13). The estimated coefficients are stronger among small and medium-sized municipalities, which tend to be more financially constrained and less diversified, confirming that fiscal vulnerability amplifies the impact of toxic-loan exposure.

**Excluding the Global Financial Crisis.** We re-estimate the baseline models after removing the years 2007–2010 (Tables A7 and A14) to verify that the results are not mechanically driven by the macroeconomic environment of the Global Financial Crisis. The investment ratio estimates lose significance only in the simplest specification that does not account for exposure intensity; once the latter is included, results remain stable and precisely estimated. Notably, dropping 2007–2010 removes substantial pre-crisis information, explaining part of the precision loss.

**Political heterogeneity.** We test whether the fiscal response to the structured-loan crisis differs by political orientation (Tables A8 and A15). The effects remain consistent across ideological groups, except for the far-right subsample, where the coefficients become insignificant—likely due to the very small sample of only 84 municipalities in our sample. This robustness check indicates that the investment response was not systematically shaped by partisan preferences.

**Exposure heterogeneity.** We distinguish between municipalities below and above the median of structured-loan exposure, using both the overhead ratio and the share of toxic loans (Tables A9 and A16). The results reveal asymmetric responses: the decline in investment is markedly stronger among the top 50% of municipalities—those most exposed to structured loans—while the increase in saving ratios and self-financing rates is more pronounced among the bottom 50%. This pattern suggests that highly exposed municipalities sharply curtailed investment, whereas less exposed ones relied more on internal financing to sustain spending.

**Alternative turning points.** We test the robustness of the results to alternative definitions of the boom and bust phases, using turning points in 2007, 2008, 2011, and 2015 (Table A17). The estimated patterns remain robust across all specifications. Using 2015 as the break-point yields markedly larger bust-phase coefficients, consistent with the Swiss National Bank's 2015 policy shift, which exacerbated exchange-rate-linked loan burdens. This confirms that our baseline choice of 2010 as the crisis onset is conservative.

**Contract-type heterogeneity.** Finally, we differentiate across structured-loan contract types—money-market, flexibility, and exchange-rate indexed—and combine these categories with alternative turning-point definitions (Tables A18–A19). Among the contracts signed by municipalities, some are linked to money-market rates, others are indexed to exchange-rate movements, and a smaller group includes so-called “flexibility” clauses tied to financial-market indicators. The results hold consistently across contract types. Minor variations appear in the boom phase—where effects are slightly weaker or occasionally positive for “flexibility” contracts—likely reflecting the more forgiving design of such instruments.

Across all exercises, the direction, magnitude, and statistical significance of the estimated effects remain broadly consistent with the baseline specification, confirming the robustness and internal validity of the paper's main conclusions.
# 6 Conclusion

This paper makes two contributions, one conceptual and one empirical. Conceptually, it extends Williamson (1988)'s transaction-cost view of corporate finance to the public sector by mapping tax funding onto the equity (discretionary) margin and public debt onto the rules-based, market-governed margin. Empirically, we test these propositions by examining how financial innovation and crisis reshaped local public investment in France. Using the structured-loan crisis as a natural experiment, we find that before the crisis, structured loans did not alter investment behavior—consistent with similar effective transaction costs across financing and a prevailing preference for debt ($H_1$). After the crisis, municipalities exposed to toxic loans sharply reduced borrowing and investment while increasing self-financing, consistent with higher transaction costs of bank credit and a shift toward discretion or exit ($H_2$). These findings are robust to alternative specifications, endogeneity controls, and long-window tests.

This study bridges research on local public investment, financial crises, and transaction-cost economics. Local authorities, unlike central governments, operate within much tighter financial constraints. They rely heavily on bank intermediation and have limited contractual flexibility. These features magnify the transaction costs of debt when financial shocks occur. Even after large-scale restructuring programs, municipalities remain reluctant to borrow, revealing lasting fiscal scars and persistent underinvestment at the local level.

Taken together, the theory and evidence reinterpret local governments' financing choices—between debt and tax funding—as a problem of governance under transaction costs. This perspective opens a research agenda on how institutional design can balance rules and discretion in public financing. In the spirit of Williamson (1988), the concept of "dequity"—a contraction of *debt* and *equity*—captures this intermediate space of hybrid governance, blending market discipline with adaptive capacity. Future research could explore the emergence of such hybrid financing in the public sector, including mixed-capital special purpose vehicles and public-private partnerships that externalize part of the financing while retaining partial public funding. In France, these instruments expanded notably after the public debt crises of the early 2010s, as local governments sought flexibility within tighter fiscal constraints. Applying the proposed transaction-cost economic framework to these "dequity-like" arrangements would offer a promising avenue for understanding how hybrid governance forms evolve to manage uncertainty and balance financial discipline with discretion.

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# Appendices

## The Municipal Environment

<table><tr><td>A1</td><td>French local government budgetary accounting framework</td><td>21</td></tr></table>

## Additional Estimates on The Real Effect of Structured Loan Use

<table><tr><td>A2</td><td>Simple OLS-FE Specification with Parsimonious Set of Controls</td><td>22</td></tr><tr><td>A3</td><td>Including First-Round Margin</td><td>22</td></tr><tr><td>A4</td><td>Including First-Round Herfindahl-Hirschman Index</td><td>23</td></tr><tr><td>A5</td><td>Excluding Municipalities Above 200,000 Inhabitants</td><td>23</td></tr><tr><td>A6</td><td>Heterogeneity by Municipality Size</td><td>24</td></tr><tr><td>A7</td><td>Excluding the Global Financial Crisis Period (2007–2010)</td><td>24</td></tr><tr><td>A8</td><td>Heterogeneity by Political Affiliation</td><td>25</td></tr><tr><td>A9</td><td>Sensitivity Analysis, Median Split of Toxic Loan Exposure (Bottom 50% vs Top 50%)</td><td>26</td></tr></table>

## Additional Estimates on Boom & Bust

<table><tr><td>A10</td><td>Including First-Round Margin</td><td>27</td></tr><tr><td>A11</td><td>Including First-Round HHI</td><td>28</td></tr><tr><td>A12</td><td>Excluding Municipalities Above 200,000 Inhabitants</td><td>28</td></tr><tr><td>A13</td><td>Heterogeneity by Municipality Size</td><td>29</td></tr><tr><td>A14</td><td>Excluding the Global Financial Crisis (2007–2010)</td><td>30</td></tr><tr><td>A15</td><td>Heterogeneity by Political Affiliation</td><td>31</td></tr><tr><td>A16</td><td>Sensitivity Analysis by Median Split of Toxic Loan Exposure (Bottom 50% vs Top 50%)</td><td>32</td></tr><tr><td>A17</td><td>Turning points robustness</td><td>33</td></tr><tr><td>A18</td><td>Contract classes robustness</td><td>34</td></tr><tr><td>A19</td><td>Contracts and turning points robustness</td><td>35</td></tr></table>
## A1 The Municipal Environment

<figure><img src="image_5.png" /><figcaption>**Figure A1:** Evolution of local public debt from 1996 to 2015 (Source: INSEE)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Table A1:** French local government budgetary accounting framework</figcaption></figure>
## A2 Additional Estimates on The Real Effect of Structured Loan Use

<table><caption>Table A2: Simple OLS-FE Specification with Parsimonious Set of Controls</caption><thead><tr><th rowspan="2"></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Rate</th></tr><tr><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>Log(y<sub>t-1</sub>)</td><td>0.102***<br>(0.00)</td><td>0.102***<br>(0.00)</td><td>0.102***<br>(0.00)</td><td>0.090***<br>(0.00)</td><td>0.090***<br>(0.00)</td><td>0.090***<br>(0.00)</td><td>0.124***<br>(0.00)</td><td>0.124***<br>(0.00)</td><td>0.124***<br>(0.00)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub></td><td>-0.038***<br>(0.01)</td><td></td><td></td><td>0.288***<br>(0.04)</td><td></td><td></td><td>1.218***<br>(0.08)</td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.330***<br>(0.07)</td><td></td><td></td><td>1.903***<br>(0.32)</td><td></td><td></td><td>7.856***<br>(0.65)</td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.182***<br>(0.03)</td><td></td><td></td><td>0.507***<br>(0.06)</td><td></td><td></td><td>2.284***<br>(0.15)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Controls</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>R<sup>2</sup></td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.11</td><td>0.11</td><td>0.11</td><td>0.09</td><td>0.09</td><td>0.09</td></tr><tr><td>Municipalities</td><td>35,734</td><td>35,734</td><td>35,734</td><td>35,734</td><td>35,734</td><td>35,734</td><td>35,734</td><td>35,734</td><td>35,734</td></tr><tr><td>Observations</td><td>616,673</td><td>616,673</td><td>616,673</td><td>616,673</td><td>616,673</td><td>616,673</td><td>616,673</td><td>616,673</td><td>616,673</td></tr></tbody></table>

Notes: The regressions estimate the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the bust phase of the financial cycle, but with few number of controls to avoid multicollinearity risks. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for total operating expenses per capita, the structural rigidity ratio, the general operating grant per capita, the composition of operating revenues between government allocations and local taxes, and lagged debt. We also control for population size (log) and the mayor's political affiliation. Robust standard errors, clustered at the municipality level, are reported in parentheses.

<table><caption>Table A3: Including First-Round Margin</caption><thead><tr><th rowspan="2"></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Rate</th></tr><tr><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>First-round margin</td><td>0.063***<br>(0.024)</td><td>0.060**<br>(0.024)</td><td>0.060**<br>(0.024)</td><td>-0.068<br>(0.041)</td><td>-0.069*<br>(0.041)</td><td>-0.073*<br>(0.041)</td><td>-0.242*<br>(0.129)</td><td>-0.237*<br>(0.128)</td><td>-0.254**<br>(0.128)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub></td><td>-0.056***<br>(0.015)</td><td></td><td></td><td>0.144***<br>(0.026)</td><td></td><td></td><td>0.647***<br>(0.090)</td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.478***<br>(0.084)</td><td></td><td></td><td>0.678***<br>(0.175)</td><td></td><td></td><td>3.953***<br>(0.587)</td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.287***<br>(0.033)</td><td></td><td></td><td>0.179***<br>(0.052)</td><td></td><td></td><td>1.327***<br>(0.183)</td></tr><tr><td>Generalized residuals</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td></tr><tr><td>Municipalities</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td></tr><tr><td>R<sup>2</sup></td><td>0.022</td><td>0.022</td><td>0.023</td><td>0.086</td><td>0.086</td><td>0.086</td><td>0.081</td><td>0.081</td><td>0.081</td></tr></tbody></table>

Notes: The table reports the second-stage results estimating the causal effect of Dexia exposure on municipal investment, saving, and self-financing ratios. It further adds political competition, measured by the first-round electoral margin in the previous municipal election. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses.

<footnote>*** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.</footnote>

<footnote>*** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.</footnote>
<table><caption>Table A4: Including First-Round Herfindahl-Hirschman Index</caption><thead><tr><th rowspan="2"></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Rate</th></tr><tr><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>First-round HHI</td><td>0.062<br>(0.032)</td><td>0.058<br>(0.032)</td><td>0.056<br>(0.032)</td><td>-0.107<br>(0.061)</td><td>-0.099<br>(0.059)</td><td>-0.104<br>(0.061)</td><td>-0.173<br>(0.190)</td><td>-0.130<br>(0.187)</td><td>-0.137<br>(0.191)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub></td><td>-0.046**<br>(0.016)</td><td></td><td></td><td>0.270***<br>(0.065)</td><td></td><td></td><td>1.356***<br>(0.197)</td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.345***<br>(0.091)</td><td></td><td></td><td>0.832<br>(0.430)</td><td></td><td></td><td>4.529***<br>(1.071)</td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.267***<br>(0.034)</td><td></td><td></td><td>0.085<br>(0.075)</td><td></td><td></td><td>1.414***<br>(0.237)</td></tr><tr><td>Generalized residuals</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>96,427</td><td>96,427</td><td>96,427</td><td>96,428</td><td>96,428</td><td>96,428</td><td>96,428</td><td>96,428</td><td>96,428</td></tr><tr><td>Municipalities</td><td>13,300</td><td>13,300</td><td>13,300</td><td>13,300</td><td>13,300</td><td>13,300</td><td>13,300</td><td>13,300</td><td>13,300</td></tr><tr><td>R<sup>2</sup></td><td>0.022</td><td>0.022</td><td>0.023</td><td>0.086</td><td>0.086</td><td>0.086</td><td>0.083</td><td>0.083</td><td>0.082</td></tr></tbody></table>

Notes: The table reports the second-stage results estimating the causal effect of Dexia exposure on municipal investment, saving, and self-financing ratios. It further adds political competition, measured by the first-round Herfindahl-Hirschman index (HHI) in the previous municipal election. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.

<table><caption>Table A5: Excluding Municipalities Above 200,000 Inhabitants</caption><thead><tr><th rowspan="2"></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Rate</th></tr><tr><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>1<sub>Toxic,t≥2010</sub></td><td>-0.030*<br>(0.012)</td><td></td><td></td><td>0.695***<br>(0.104)</td><td></td><td></td><td>2.396***<br>(0.223)</td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.234**<br>(0.080)</td><td></td><td></td><td>2.556***<br>(0.561)</td><td></td><td></td><td>8.760***<br>(1.043)</td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.182***<br>(0.029)</td><td></td><td></td><td>0.586***<br>(0.106)</td><td></td><td></td><td>2.384***<br>(0.257)</td></tr><tr><td>Generalized residuals</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>579,027</td><td>579,027</td><td>579,027</td><td>579,028</td><td>579,028</td><td>579,028</td><td>579,028</td><td>579,028</td><td>579,028</td></tr><tr><td>Municipalities</td><td>33,159</td><td>33,159</td><td>33,159</td><td>33,159</td><td>33,159</td><td>33,159</td><td>33,159</td><td>33,159</td><td>33,159</td></tr><tr><td>R<sup>2</sup></td><td>0.020</td><td>0.020</td><td>0.020</td><td>0.117</td><td>0.117</td><td>0.117</td><td>0.099</td><td>0.099</td><td>0.099</td></tr></tbody></table>

Notes: The table reports the second-stage results estimating the causal effect of Dexia exposure on municipal investment, saving, and self-financing ratios. It further excludes the ten largest municipalities with more than 200,000 inhabitants. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.
<table><caption>Table A6: Heterogeneity by Municipality Size</caption><thead><tr><th>Municipality size (population)</th><th>&lt;1.5k</th><th>1.5k-3k</th><th>3k-10k</th><th>10k-100k</th><th>&gt;100k</th></tr></thead><tbody><tr><td colspan="6"><strong>Part A: Investment Ratio</strong></td></tr><tr><td>1<sub>Toxic,t≥2010</sub>×Overhead Ratio</td><td>-1.301**<br>(0.614)</td><td>-1.006***<br>(0.340)</td><td>-0.332***<br>(0.0981)</td><td>-0.155*<br>(0.0855)</td><td>0.376<br>(0.263)</td></tr><tr><td colspan="6"><strong>Part B: Saving Ratio</strong></td></tr><tr><td>1<sub>Toxic,t≥2010</sub>×Overhead Ratio</td><td>7.153*<br>(3.880)</td><td>1.611***<br>(0.468)</td><td>1.187***<br>(0.186)</td><td>0.277<br>(0.172)</td><td>0.931**<br>(0.379)</td></tr><tr><td colspan="6"><strong>Part C: Self-financing Ratio</strong></td></tr><tr><td>1<sub>Toxic,t≥2010</sub>×Overhead Ratio</td><td>19.21**<br>(0.614)</td><td>5.311***<br>(0.340)</td><td>5.457***<br>(0.0981)</td><td>2.264***<br>(0.0855)</td><td>-3.194<br>(0.263)</td></tr><tr><td>Generalized residuals</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>486,038</td><td>53,852</td><td>41,036</td><td>14,507</td><td>593</td></tr><tr><td>Municipalities</td><td>28,667</td><td>4,010</td><td>2,830</td><td>946</td><td>39</td></tr></tbody></table>

Notes: The table reports the second-stage results estimating the causal effect of Dexia exposure on municipal investment, saving, and self-financing ratios. It assesses the robustness of our results by splitting the sample according to municipal population size, using thresholds of 1,500, 3,000, 10,000, and 100,000 inhabitants to preserve a sufficient number of observations in each group. For readability, we report only the models including the intensity of Dexia's crisis exposure. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.

<table><caption>Table A7: Excluding the Global Financial Crisis Period (2007–2010)</caption><thead><tr><th></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Rate</th></tr><tr><th></th><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>1<sub>Toxic,t≥2010</sub></td><td>-0.009<br>(0.015)</td><td></td><td></td><td>0.761***<br>(0.137)</td><td></td><td></td><td>2.718***<br>(0.288)</td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.208*<br>(0.095)</td><td></td><td></td><td>2.890***<br>(0.721)</td><td></td><td></td><td>10.137***<br>(1.336)</td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.189***<br>(0.032)</td><td></td><td></td><td>0.633***<br>(0.135)</td><td></td><td></td><td>2.726***<br>(0.321)</td></tr><tr><td>Generalized residuals</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>413,393</td><td>413,393</td><td>413,393</td><td>413,393</td><td>413,393</td><td>413,393</td><td>413,393</td><td>413,393</td><td>413,393</td></tr><tr><td>Municipalities</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td></tr><tr><td>R<sup>2</sup></td><td>0.018</td><td>0.018</td><td>0.018</td><td>0.112</td><td>0.112</td><td>0.112</td><td>0.095</td><td>0.095</td><td>0.094</td></tr></tbody></table>

Notes: The table reports the second-stage results estimating the causal effect of Dexia exposure on municipal investment, saving, and self-financing ratios. It further excludes the Global Financial Crisis period (2007–2010). All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.
<table><caption>Table A8: Heterogeneity by Political Affiliation</caption><thead><tr><th>Political affiliation</th><th>Far left</th><th>Left</th><th>Unlabeled</th><th>Right</th><th>Far right</th></tr></thead><tbody><tr><td><strong>Part A: Investment Ratio</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>0.201<br>(0.267)</td><td>-0.341***<br>(0.129)</td><td>-1.875**<br>(0.951)</td><td>-0.245**<br>(0.112)</td><td>-11.381<br>(7.456)</td></tr><tr><td><strong>Part B: Saving Ratio</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Intensity</td><td>2.255***<br>(0.659)</td><td>1.548***<br>(0.262)</td><td>4.258<br>(4.646)</td><td>2.933***<br>(0.709)</td><td>2.423<br>(9.023)</td></tr><tr><td><strong>Part C: Self-financing Ratio</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>7.251***<br>(1.460)</td><td>6.916***<br>(0.735)</td><td>11.007<br>(6.822)</td><td>9.938***<br>(1.223)</td><td>-82.335***<br>(23.535)</td></tr><tr><td>Generalized residuals</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>10,846</td><td>182,377</td><td>56,562</td><td>345,850</td><td>492</td></tr><tr><td>Municipalities</td><td>985</td><td>15,100</td><td>9,321</td><td>26,096</td><td>84</td></tr></tbody></table>

Notes: The table reports the second-stage results estimating the causal effect of Dexia exposure on municipal investment, saving, and self-financing ratios. It tests the robustness of our results by splitting the sample according to political affiliation. For readability, only the models including the intensity of Dexia's crisis exposure are reported. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.
<table><caption>**Table A9:** Sensitivity Analysis, Median Split of Toxic Loan Exposure (Bottom 50% vs Top 50%)</caption><thead><tr><th></th><th colspan="2">Investment Ratio</th><th colspan="2">Saving Ratio</th><th colspan="2">Self-financing Rate</th></tr><tr><th></th><th>(1a)</th><th>(1b)</th><th>(2a)</th><th>(2b)</th><th>(3a)</th><th>(3b)</th></tr></thead><tbody><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Top 50%</td><td>-0.376***<br>(0.080)</td><td></td><td>2.685***<br>(0.571)</td><td></td><td>9.183***<br>(1.062)</td><td></td></tr><tr><td>Bottom 50%</td><td>-0.088<br>(0.225)</td><td></td><td>6.043***<br>(1.155)</td><td></td><td>19.539***<br>(2.599)</td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Top 50%</td><td></td><td>-0.217***<br>(0.027)</td><td></td><td>0.672***<br>(0.117)</td><td></td><td>2.670***<br>(0.275)</td></tr><tr><td>Bottom 50%</td><td></td><td>0.008<br>(0.074)</td><td></td><td>1.870***<br>(0.329)</td><td></td><td>6.608***<br>(0.779)</td></tr><tr><td>Generalized residuals</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>579,141</td><td>579,141</td><td>579,142</td><td>579,142</td><td>579,142</td><td>579,142</td></tr><tr><td>Municipalities</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td><td>33,166</td></tr><tr><td>R<sup>2</sup></td><td>0.020</td><td>0.020</td><td>0.117</td><td>0.117</td><td>0.099</td><td>0.099</td></tr></tbody></table>

Notes: The table reports the second-stage results estimating the causal effect of Dexia exposure on municipal investment, saving, and self-financing ratios. It distinguishes municipalities below and above the median of Dexia exposure, based on either the toxic debt share or the overhead ratio. Each variable retains its original continuous value within the corresponding half of the distribution and is set to zero otherwise. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses.

<footnote>*** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.</footnote>
A3 Additional Estimates on Boom & Bust

<table><caption>Table A10: Including First-Round Margin (Cycle Phases: Boom vs Bust)</caption><thead><tr><th rowspan="2"></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Rate</th></tr><tr><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>First round margin</td><td>0.062***<br>(0.024)</td><td>0.060**<br>(0.024)</td><td>0.060**<br>(0.024)</td><td>-0.068*<br>(0.041)</td><td>-0.069*<br>(0.041)</td><td>-0.073*<br>(0.041)</td><td>-0.234*<br>(0.128)</td><td>-0.235*<br>(0.128)</td><td>-0.255**<br>(0.128)</td></tr><tr><td>Boom phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub></td><td>-0.024<br>(0.016)</td><td>-0.015<br>(0.015)</td><td>-0.038**<br>(0.015)</td><td>-0.031<br>(0.028)</td><td>-0.074***<br>(0.027)</td><td>-0.087***<br>(0.027)</td><td>0.539***<br>(0.103)</td><td>0.271***<br>(0.090)</td><td>0.252***<br>(0.090)</td></tr><tr><td>Bust phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub></td><td>-0.068***<br>(0.018)</td><td></td><td></td><td>0.129***<br>(0.029)</td><td></td><td></td><td>0.904***<br>(0.109)</td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.504***<br>(0.089)</td><td></td><td></td><td>0.553***<br>(0.180)</td><td></td><td></td><td>4.408***<br>(0.628)</td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.309***<br>(0.035)</td><td></td><td></td><td>0.128**<br>(0.054)</td><td></td><td></td><td>1.474***<br>(0.196)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td><td>98,914</td></tr><tr><td>Municipalities</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td></tr><tr><td>R<sup>2</sup></td><td>0.021</td><td>0.022</td><td>0.022</td><td>0.085</td><td>0.085</td><td>0.085</td><td>0.081</td><td>0.081</td><td>0.081</td></tr></tbody></table>

Notes: The table builds on the baseline specification, which estimates the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. It further adds political competition, measured by the first-round electoral margin in the previous municipal election. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.10.
<table><caption>Table A11: Including First-Round HHI (Cycle Phases: Boom vs Bust)</caption><thead><tr><th rowspan="2"></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Rate</th></tr><tr><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>First-round HHI</td><td>0.067**<br>(0.032)</td><td>0.062**<br>(0.032)</td><td>0.060*<br>(0.031)</td><td>-0.097*<br>(0.059)</td><td>-0.090<br>(0.057)</td><td>-0.094<br>(0.059)</td><td>-0.191<br>(0.186)</td><td>-0.146<br>(0.183)</td><td>-0.153<br>(0.187)</td></tr><tr><td>Boom phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I<sub>Dexia,t&lt;2010</sub></td><td>-0.034**<br>(0.016)</td><td>-0.018<br>(0.014)</td><td>-0.041***<br>(0.014)</td><td>-0.024<br>(0.028)</td><td>-0.059**<br>(0.029)</td><td>-0.083***<br>(0.027)</td><td>0.573***<br>(0.099)</td><td>0.301***<br>(0.092)</td><td>0.264***<br>(0.088)</td></tr><tr><td>Bust phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub></td><td>-0.078***<br>(0.018)</td><td></td><td></td><td>0.139***<br>(0.029)</td><td></td><td></td><td>0.960***<br>(0.106)</td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.502***<br>(0.094)</td><td></td><td></td><td>0.753**<br>(0.294)</td><td></td><td></td><td>4.827***<br>(0.786)</td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.313***<br>(0.034)</td><td></td><td></td><td>0.134**<br>(0.055)</td><td></td><td></td><td>1.522***<br>(0.192)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>100,462</td><td>100,462</td><td>100,462</td><td>100,462</td><td>100,462</td><td>100,462</td><td>100,462</td><td>100,462</td><td>100,462</td></tr><tr><td>Municipalities</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td><td>13,879</td></tr><tr><td>R<sup>2</sup></td><td>0.022</td><td>0.022</td><td>0.023</td><td>0.083</td><td>0.083</td><td>0.083</td><td>0.080</td><td>0.080</td><td>0.079</td></tr></tbody></table>

Notes: The table builds on the baseline specification, which estimates the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. It further adds political competition, measured by the first-round Herfindahl-Hirschman index (HHI) in the previous municipal election. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.

<table><caption>Table A12: Excluding Municipalities Above 200,000 Inhabitants (Cycle Phases: Boom vs Bust)</caption><thead><tr><th rowspan="2"></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Rate</th></tr><tr><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>Boom phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I<sub>Dexia,t&lt;2010</sub></td><td>0.000<br>(0.013)</td><td>0.002<br>(0.012)</td><td>-0.020*<br>(0.012)</td><td>-0.025<br>(0.027)</td><td>-0.099***<br>(0.030)</td><td>-0.132***<br>(0.025)</td><td>0.608***<br>(0.082)</td><td>0.189**<br>(0.076)</td><td>0.119*<br>(0.070)</td></tr><tr><td>Bust phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub></td><td>-0.035**<br>(0.014)</td><td></td><td></td><td>0.321***<br>(0.044)</td><td></td><td></td><td>1.548***<br>(0.103)</td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.308***<br>(0.083)</td><td></td><td></td><td>2.007***<br>(0.405)</td><td></td><td></td><td>8.771***<br>(0.798)</td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.191***<br>(0.028)</td><td></td><td></td><td>0.490***<br>(0.066)</td><td></td><td></td><td>2.473***<br>(0.172)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>595,980</td><td>595,980</td><td>595,980</td><td>595,980</td><td>595,980</td><td>595,980</td><td>595,980</td><td>595,980</td><td>595,980</td></tr><tr><td>Municipalities</td><td>34,537</td><td>34,537</td><td>34,537</td><td>34,537</td><td>34,537</td><td>34,537</td><td>34,537</td><td>34,537</td><td>34,537</td></tr><tr><td>R<sup>2</sup></td><td>0.020</td><td>0.020</td><td>0.020</td><td>0.118</td><td>0.118</td><td>0.118</td><td>0.099</td><td>0.098</td><td>0.098</td></tr></tbody></table>

Notes: The table builds on the baseline specification, which estimates the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. It further excludes the ten largest municipalities with more than 200,000 inhabitants. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.
<table><caption>Table A13: Heterogeneity by Municipality Size (Cycle Phases: Boom vs Bust)</caption><thead><tr><th>Municipality size (population)</th><th>&lt;1.5k</th><th>1.5k-3k</th><th>3k-10k</th><th>10k-100k</th><th>&gt;100k</th></tr></thead><tbody><tr><td colspan="6"><strong>Part A: Investment Ratio</strong></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub> (boom phase)</td><td>0.0747<br>(0.0671)</td><td>-0.00561<br>(0.0345)</td><td>0.00992<br>(0.0158)</td><td>-0.0277*<br>(0.0142)</td><td>-0.0245<br>(0.0491)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>-1.131*<br>(0.659)</td><td>-1.021***<br>(0.372)</td><td>-0.315***<br>(0.103)</td><td>-0.190**<br>(0.0888)</td><td>0.354<br>(0.256)</td></tr><tr><td colspan="6"><strong>Part B: Saving Ratio</strong></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub> (boom phase)</td><td>0.332<br>(0.231)</td><td>-0.00447<br>(0.0557)</td><td>-0.0178<br>(0.0330)</td><td>-0.0445<br>(0.0284)</td><td>-0.0684<br>(0.0469)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>7.907**<br>(3.966)</td><td>1.600***<br>(0.503)</td><td>1.156***<br>(0.202)</td><td>0.222<br>(0.167)</td><td>0.871**<br>(0.365)</td></tr><tr><td colspan="6"><strong>Part C: Self-financing Ratio</strong></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub> (boom phase)</td><td>0.409<br>(0.544)</td><td>0.410**<br>(0.188)</td><td>0.368***<br>(0.0970)</td><td>0.221*<br>(0.122)</td><td>-0.715<br>(0.481)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>20.13**<br>(8.497)</td><td>6.375***<br>(1.418)</td><td>6.100***<br>(0.795)</td><td>2.541***<br>(0.713)</td><td>-3.818<br>(2.570)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>486,038</td><td>53,852</td><td>41,036</td><td>14,507</td><td>593</td></tr><tr><td>Municipalities</td><td>28,667</td><td>4,010</td><td>2,830</td><td>946</td><td>39</td></tr></tbody></table>

Notes: The table builds on the baseline specification, which estimates the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. It assesses the robustness of our results by splitting the sample according to municipal population size, using thresholds of 1,500, 3,000, 10,000, and 100,000 inhabitants to preserve a sufficient number of observations in each group. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.
<table><caption>Table A14: Excluding the Global Financial Crisis Period (2007–2010) (Cycle Phases: Boom vs Bust)</caption><thead><tr><th></th><th colspan="3">Investment Ratio</th><th colspan="3">Saving Ratio</th><th colspan="3">Self-financing Rate</th></tr><tr><th></th><th>(1a)</th><th>(1b)</th><th>(1c)</th><th>(2a)</th><th>(2b)</th><th>(2c)</th><th>(3a)</th><th>(3b)</th><th>(3c)</th></tr></thead><tbody><tr><td>Boom phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I<sub>Dexia,t&lt;2010</sub></td><td>-0.049**<br>(0.021)</td><td>-0.049**<br>(0.020)</td><td>-0.073***<br>(0.020)</td><td>0.009<br>(0.041)</td><td>-0.105**<br>(0.044)</td><td>-0.119***<br>(0.042)</td><td>0.598***<br>(0.138)</td><td>0.012<br>(0.133)</td><td>0.025<br>(0.134)</td></tr><tr><td>Bust phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub></td><td>-0.017<br>(0.015)</td><td></td><td></td><td>0.321***<br>(0.046)</td><td></td><td></td><td>1.544***<br>(0.108)</td><td></td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td></td><td>-0.218**<br>(0.093)</td><td></td><td></td><td>2.149***<br>(0.434)</td><td></td><td></td><td>9.600***<br>(0.874)</td><td></td></tr><tr><td>I<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td></td><td></td><td>-0.178***<br>(0.030)</td><td></td><td></td><td>0.530***<br>(0.073)</td><td></td><td></td><td>2.737***<br>(0.194)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>459,161</td><td>459,161</td><td>459,161</td><td>459,161</td><td>459,161</td><td>459,161</td><td>459,161</td><td>459,161</td><td>459,161</td></tr><tr><td>Municipalities</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td></tr><tr><td>R<sup>2</sup></td><td>0.019</td><td>0.019</td><td>0.019</td><td>0.114</td><td>0.114</td><td>0.114</td><td>0.096</td><td>0.096</td><td>0.096</td></tr></tbody></table>

Notes: The table builds on the baseline specification, which estimates the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. It further excludes the Global Financial Crisis period (2007–2010). All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality’s wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** * p < 0.01, ** * p < 0.05, * * p < 0.10.
<table><caption>Table A15: Heterogeneity by Political Affiliation (Cycle Phases: Boom vs Bust)</caption><thead><tr><th>Political affiliation</th><th>Far left</th><th>Left</th><th>Unlabeled</th><th>Right</th><th>Far right</th></tr></thead><tbody><tr><td colspan="6"><strong>Part A: Investment Ratio</strong></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub> (boom phase)</td><td>-0.055<br>(0.053)</td><td>0.021<br>(0.018)</td><td>-0.137<br>(0.118)</td><td>0.011<br>(0.017)</td><td>0.650<br>(0.426)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>0.109<br>(0.275)</td><td>-0.289**<br>(0.143)</td><td>-2.593***<br>(0.921)</td><td>-0.221*<br>(0.124)</td><td>0.000<br>(.)</td></tr><tr><td colspan="6"><strong>Part B: Saving Ratio</strong></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub> (boom phase)</td><td>-0.180*<br>(0.105)</td><td>-0.102***<br>(0.034)</td><td>0.392<br>(0.446)</td><td>-0.067<br>(0.055)</td><td>-0.138<br>(0.515)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>1.956***<br>(0.676)</td><td>1.293***<br>(0.281)</td><td>6.320<br>(4.459)</td><td>2.784***<br>(0.817)</td><td>0.000<br>(.)</td></tr><tr><td colspan="6"><strong>Part C: Self-financing Ratio</strong></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub> (boom phase)</td><td>0.246<br>(0.319)</td><td>0.089<br>(0.109)</td><td>0.743<br>(0.989)</td><td>0.256**<br>(0.121)</td><td>4.700***<br>(1.344)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>7.662***<br>(1.607)</td><td>7.137***<br>(0.839)</td><td>14.910**<br>(6.549)</td><td>10.508***<br>(1.402)</td><td>0.000<br>(.)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>10,846</td><td>182,377</td><td>56,562</td><td>345,850</td><td>492</td></tr><tr><td>Municipalities</td><td>985</td><td>15,100</td><td>9,321</td><td>26,096</td><td>84</td></tr></tbody></table>

Notes: The table builds on the baseline specification, which estimates the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. It tests the robustness of our results by splitting the sample according to political affiliation. For readability, only the models including the intensity of Dexia's crisis exposure are reported. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.
<table><caption>**Table A16:** Sensitivity Analysis by Median Split of Toxic Loan Exposure (Bottom 50% vs Top 50%; Boom vs Bust Phases)</caption><thead><tr><th></th><th></th><th colspan="2">Investment Ratio</th><th colspan="2">Saving Ratio</th><th colspan="2">Self-financing Rate</th></tr><tr><th></th><th></th><th>(1a)</th><th>(1b)</th><th>(2a)</th><th>(2b)</th><th>(3a)</th><th>(3b)</th></tr></thead><tbody><tr><td>Boom phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub></td><td></td><td>0.009<br>(0.012)</td><td>-0.010<br>(0.012)</td><td>-0.062**<br>(0.029)</td><td>-0.104***<br>(0.023)</td><td>0.365***<br>(0.078)</td><td>0.270***<br>(0.072)</td></tr><tr><td>Bust phase</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>Top 50%</td><td>-0.332***<br>(0.084)</td><td></td><td>1.889***<br>(0.418)</td><td></td><td>8.213***<br>(0.813)</td><td></td></tr><tr><td></td><td>Bottom 50%</td><td>0.072<br>(0.242)</td><td></td><td>3.825***<br>(0.570)</td><td></td><td>17.602***<br>(1.567)</td><td></td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Toxic Debt Share</td><td>Top 50%</td><td></td><td>-0.198***<br>(0.028)</td><td></td><td>0.472***<br>(0.064)</td><td></td><td>2.382***<br>(0.169)</td></tr><tr><td></td><td>Bottom 50%</td><td></td><td>0.010<br>(0.083)</td><td></td><td>1.064***<br>(0.249)</td><td></td><td>5.549***<br>(0.614)</td></tr><tr><td>Municipality FE</td><td></td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td></td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td></td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td></td><td>596,127</td><td>596,127</td><td>596,127</td><td>596,127</td><td>596,127</td><td>596,127</td></tr><tr><td>Municipalities</td><td></td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td><td>34,547</td></tr><tr><td>R<sup>2</sup></td><td></td><td>0.020</td><td>0.020</td><td>0.118</td><td>0.118</td><td>0.098</td><td>0.098</td></tr></tbody></table>

Notes: The table builds on the baseline specification, which estimates the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. It distinguishes municipalities below and above the median of toxic loan exposure, based on either the toxic debt share or the overhead ratio. Each variable retains its original continuous value within the corresponding half of the distribution and is set to zero otherwise. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.10.
<table><caption>**Table A17: Distinction between the Boom and Bust Phases — Robustness to Turning Points**</caption><thead><tr><th>Turning point t*:</th><th>2007</th><th>2008</th><th>2011</th><th>2015</th></tr></thead><tbody><tr><td colspan="5"><strong>Part A: Investment Ratio</strong></td></tr><tr><td>1<sub>Dexia,t&lt;t*</sub> (boom phase)</td><td>-0.043**<br>(0.018)</td><td>0.020<br>(0.015)</td><td>-0.028***<br>(0.011)</td><td>-0.020*<br>(0.011)</td></tr><tr><td>1<sub>Toxic,t≥t*</sub> × Overhead Ratio</td><td>-0.149**<br>(0.075)</td><td>-0.267***<br>(0.075)</td><td>-0.280***<br>(0.085)</td><td>-0.406***<br>(0.095)</td></tr><tr><td colspan="5"><strong>Part B: Saving Ratio</strong></td></tr><tr><td>1<sub>Dexia,t&lt;t*</sub> (boom phase)</td><td>-0.081**<br>(0.040)</td><td>-0.133***<br>(0.034)</td><td>-0.041<br>(0.030)</td><td>0.019<br>(0.036)</td></tr><tr><td>1<sub>Toxic,t≥t*</sub> × Overhead Ratio</td><td>1.500***<br>(0.336)</td><td>1.694***<br>(0.351)</td><td>2.058***<br>(0.437)</td><td>3.111***<br>(0.608)</td></tr><tr><td colspan="5"><strong>Part C: Self-financing Ratio</strong></td></tr><tr><td>1<sub>Dexia,t&lt;t*</sub> (boom phase)</td><td>0.009<br>(0.127)</td><td>0.024<br>(0.094)</td><td>0.438***<br>(0.075)</td><td>0.692***<br>(0.083)</td></tr><tr><td>1<sub>Toxic,t≥t*</sub> × Overhead Ratio</td><td>7.969***<br>(0.716)</td><td>8.258***<br>(0.736)</td><td>8.616***<br>(0.834)</td><td>9.305***<br>(1.116)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr></tbody></table>

Notes: The table builds on the baseline specification, which estimates the effect of Dexia exposure on municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. It examines alternative breakpoints for the transition between boom and bust years—2007, 2008, 2011, and 2015—reported in separate columns. For readability, only the models including the intensity of Dexia's crisis exposure are reported. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality's wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.10.
<table><caption>**Table A18: Distinction between the Boom and Bust Phases — Robustness to Contract Classes**</caption><thead><tr><th>Contract class:</th><th>Money markets</th><th>Flexibility</th><th>Exchange-rate</th></tr></thead><tbody><tr><td colspan="4"><em>Part A: Investment Ratio</em></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub> (boom phase)</td><td>0.001<br>(0.013)</td><td>0.052*<br>(0.026)</td><td>-0.003<br>(0.013)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>-0.373***<br>(0.124)</td><td>-0.211**<br>(0.097)</td><td>-0.362***<br>(0.102)</td></tr><tr><td colspan="4"><em>Part B: Saving Ratio</em></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub> (boom phase)</td><td>-0.113***<br>(0.024)</td><td>-0.041<br>(0.071)</td><td>-0.099***<br>(0.033)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>1.529***<br>(0.325)</td><td>1.797***<br>(0.554)</td><td>2.002***<br>(0.498)</td></tr><tr><td colspan="4"><em>Part C: Self-financing Ratio</em></td></tr><tr><td>1<sub>Dexia,t&lt;2010</sub> (boom phase)</td><td>0.150*<br>(0.079)</td><td>0.625***<br>(0.186)</td><td>0.202**<br>(0.079)</td></tr><tr><td>1<sub>Toxic,t≥2010</sub> × Overhead Ratio</td><td>8.335***<br>(0.919)</td><td>7.454***<br>(1.053)</td><td>9.076***<br>(0.908)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td></tr></tbody></table>

*Notes:* The table builds on the baseline specification that estimates how Dexia exposure affected municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. Each column separates contracts according to the nature of their variable component: (i) contracts linked to money market rates, (ii) contracts labeled as “flexibility,” referring to instruments tied to financial market indicators such as the LIBOR, and (iii) contracts indexed to exchange rate movements. For readability, only the models including the intensity of Dexia’s crisis exposure are reported. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality’s wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.
<table><caption>**Table A19:** Distinction between the Boom and Bust Phases — Combined Robustness to Contract Classes and Turning Points</caption><thead><tr><th rowspan="2">Turning point t*</th><th colspan="2">Money markets</th><th colspan="2">Flexibility</th><th colspan="2">Exchange-rate</th></tr><tr><th>2007</th><th>2008</th><th>2007</th><th>2008</th><th>2008</th><th>2011</th></tr></thead><tbody><tr><td colspan="7"><strong>Part A: Investment Ratio</strong></td></tr><tr><td>I<sub>Dexia,t&lt;t*</sub> (boom phase)</td><td>-0.022<br>(0.018)</td><td>0.016<br>(0.016)</td><td>0.036<br>(0.054)</td><td>0.132***<br>(0.031)</td><td>0.014<br>(0.016)</td><td>-0.028**<br>(0.012)</td></tr><tr><td>I<sub>Toxic,t≥t*</sub> × Overhead Ratio</td><td>-0.181<br>(0.121)</td><td>-0.259**<br>(0.121)</td><td>-0.165*<br>(0.092)</td><td>-0.190**<br>(0.093)</td><td>-0.333***<br>(0.099)</td><td>-0.299***<br>(0.106)</td></tr><tr><td colspan="7"><strong>Part B: Saving Ratio</strong></td></tr><tr><td>I<sub>Dexia,t&lt;t*</sub> (boom phase)</td><td>-0.079***<br>(0.031)</td><td>-0.143***<br>(0.028)</td><td>-0.087<br>(0.159)</td><td>-0.049<br>(0.083)</td><td>-0.120***<br>(0.036)</td><td>-0.059<br>(0.037)</td></tr><tr><td>I<sub>Toxic,t≥t*</sub> × Overhead Ratio</td><td>1.044***<br>(0.293)</td><td>1.154***<br>(0.304)</td><td>1.444***<br>(0.439)</td><td>1.528***<br>(0.466)</td><td>1.593***<br>(0.437)</td><td>2.040***<br>(0.563)</td></tr><tr><td colspan="7"><strong>Part C: Self-financing Ratio</strong></td></tr><tr><td>I<sub>Dexia,t&lt;t*</sub> (boom phase)</td><td>-0.030<br>(0.118)</td><td>-0.064<br>(0.098)</td><td>0.508<br>(0.461)</td><td>0.591**<br>(0.254)</td><td>-0.012<br>(0.097)</td><td>0.369***<br>(0.082)</td></tr><tr><td>I<sub>Toxic,t≥t*</sub> × Overhead Ratio</td><td>7.446***<br>(0.882)</td><td>7.505***<br>(0.900)</td><td>6.957***<br>(0.927)</td><td>6.997***<br>(0.946)</td><td>8.197***<br>(0.839)</td><td>8.932***<br>(0.990)</td></tr><tr><td>Municipality FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Time FE</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Control variables</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr></tbody></table>

Notes: The table builds on the baseline specification that estimates how Dexia exposure affected municipal investment, saving, and self-financing ratios during the boom and bust phases of the financial cycle. It combines different types of contracts—those linked to money market rates, exchange rate movements, or labeled as “flexibility,” referring to instruments tied to financial market indicators such as the LIBOR—and explores two alternative turning points in the financial cycle, 2007 and 2008, compared with the 2010 baseline. For readability, only the models including the intensity of Dexia’s crisis exposure are reported. All specifications include municipality and year fixed effects and control for the lagged log of the dependent variable. The regressions further account for financial, demographic, socio-economic, and political characteristics. Financial controls reflect the fiscal position of municipalities and are defined per inhabitant: total operating expenses, the structural rigidity ratio, the operating grant from the central government, the composition of operating revenues between government allocations and local taxes, and lagged debt. Incumbent controls describe mayoral characteristics—age, gender, socio-professional category, and political affiliation. Socio-economic controls include the logarithm of population size, the unemployment rate, the shares of tertiary-educated and foreign residents, and relative income and capital ratios, capturing the municipality’s wealth compared to other areas. Robust standard errors, clustered at the municipality level, are reported in parentheses. *** $p < 0.01$, ** $p < 0.05$, * $p < 0.10$.