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Financements intermédiés et informalité en Afrique

Gregory Mvogo, Mohamed Coulibaly, Modeste Abate, Novice Bakehe

Cet article analyse l'impact des financements intermédiés des institutions financières internationales sur le niveau d'informalité des économies africaines. En s'appuyant sur un panel de 39 pays africains couvrant la période 2000-2018, l'étude évalue dans quelle mesure ces financements, canalisés via des intermédiaires financiers locaux, constituent un levier de formalisation des activités économiques, en particulier des petites et moyennes entreprises. L'analyse mobilise une approche quasi-expérimentale combinant l'équilibrage entropique comme méthode principale et l'appariement par score de propension pour les tests de robustesse. Les résultats empiriques montrent que les financements intermédiés exercent un effet négatif et statistiquement significatif sur l'informalité, avec une réduction comprise entre 8 % et 13 % selon les spécifications. Ces résultats suggèrent que l'accès à des ressources longues et formalisées renforce l'inclusion financière, améliore la transparence et incite les entreprises à se conformer aux exigences institutionnelles. L'étude comble ainsi un vide dans la littérature en mettant en évidence le rôle spécifique des financements intermédiés comme instrument de soutien à la formalisation et au développement économique en Afrique.

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Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring

Sullivan Hué, Christophe Hurlin, Christophe Pérignon, Sébastien Saurin

Because they play an increasingly important role in determining access to credit, credit scoring models are under growing scrutiny from banking supervisors and internal model validators. These authorities need to monitor the model performance and identify its key drivers. To facilitate this, we introduce the explainable performance (XPER) methodology to decompose a performance metric (e.g., area under the curve (AUC), [Formula: see text]) into specific contributions associated with the various features of a forecasting model. XPER is theoretically grounded on Shapley values and is both model-agnostic and performance metric-agnostic. Furthermore, it can be implemented either at the model level or at the individual level. Using a novel data set of car loans, we decompose the AUC of a machine-learning model trained to forecast the default probability of loan applicants. We show that a small number of features can explain a surprisingly large part of the model performance. Notably, the features that contribute the most to the predictive performance of the model may not be the ones that contribute the most to individual forecasts (Shapley additive explanation). Finally, we show how XPER can be used to deal with heterogeneity issues and improve performance. This paper was accepted by Kay Giesecke, finance. Funding: The authors thank the Institut Universitaire de France, the Autorité de contrôle prudentiel et de résolution Chair in Regulation and Systemic Risk, the HEC-Deloitte Chair on Artificial Intelligence for Business Innovation, the Excellence Initiative of Aix-Marseille University [A*MIDEX], and the French National Research Agency [Grants AMSE ANR-17-EURE-0020, Ecodec ANR-11-LABX-0047, and MLEforRisk ANR-21-CE26-0007] for supporting our research. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.02025 .

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Prediction of bubbles in presence of α-stable aggregates moving averages

Gilles de Truchis, Sébastien Fries, Arthur Thomas

Financial markets frequently exhibit dramatic episodes where asset prices undergo rapid growth followed by abrupt collapses, that are incompatible with standard linear time series models. While anticipative heavytailed linear processes offer a promising alternative for modeling such phenomena, they impose uniform bubble patterns across different episodes, contradicting empirical evidence. This paper introduces a new model, based on α-stable moving average aggregates, that accommodates heterogeneous bubble dynamics.

We establish the theoretical properties of this model, demonstrating that it admits a semi-norm representation on a unit cylinder, thereby enabling the prediction of extreme trajectories with varying growth dynamics. We develop a minimum distance estimation procedure based on the joint characteristic function and establish its asymptotic properties. Monte Carlo simulations confirm the estimator's good finite-sample performance across various specifications, and we implement a subsampling methodology to empirically verify the convergence to asymptotic normality. Our empirical application to the CBOE Crude Oil ETF Volatility Index successfully decomposes observed volatility dynamics into distinct components with different persistence properties, revealing that what appears as a single bubble episode actually consists of multiple superimposed processes with heterogeneous growth rates and crash probabilities.

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Tight belts, different cuts: How political preferences shape the effects of fiscal rules

Dorian Balvir

While fiscal rules are often viewed as an effective way to curb the deficit bias arising from, inter alia, partisan pressures in common-pool budget settings, much less is known about how their effects vary with partisan preferences. This paper fills that gap by estimating local projections for a panel of EU-27 countries over 1995-2019. We innovatively link COFOG expenditure categories with the Manifesto Project Database to study how political preferences condition the impact of tighter fiscal rules across spending functions. Our first result is that more stringent national fiscal rules are associated with lower public spending in the short-and medium-run. Digging deeper, we show that the recomposition of expenditure under tighter rules depends on governments' preferences: adjustment falls disproportionately on categories that are less favoured by the incumbent. These results are robust to alternative estimators, different definitions of the dependent variable, and placebo tests. Lastly, the cuts associated with stricter fiscal rules in low-preference government contexts are amplified when sovereign debt yields are higher.

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Heat and Hurdles: Unpacking the Impact of Climate Risks on Women's Empowerment

City Eldeep, Fida Karam, Chahir Zaki

Climate change poses complex risks that extend beyond environmental impacts, shaping social and economic inequalities. This paper examines how physical risks, such as extreme weather, and transition risks, linked to decarbonization and green policies, affect women's empowerment in firms across developing and developed contexts. Using firm-level data from the World Bank Enterprise Surveys and World Development Indicators, we measure empowerment through female employment, management, and ownership. Our results reveal contrasting effects: physical risks reduce female ownership, while transition risks boost female management, particularly in smaller, younger, non-exporting, and non-privately owned firms. The negative impact of physical risks is largely uniform, except in foreign-owned firms, and operates mainly through constraints on finance and land. These findings highlight the need for integrated climate and gender policies that recognize women not only as vulnerable to climate change but also as key agents of sustainable economic transformation.

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Public Health as a Buffer for FDI: The Role of Healthcare Services in Economic Stability

Zahra Khalilzadeh Silabi

This study examines how epidemic outbreaks influence foreign direct investment (FDI) inflows in developing countries, with a particular focus on whether healthcare systems can act as buffers during such shocks. Using a panel dataset of 98 countries from 2000 to 2022, the analysis combines two-way fixed effects (FE) models and the Local Projection Method (LPM). The analysis is structured in three parts: First, fixed-effects regressions assess the average effect of 20 major epidemics on FDI, revealing that diseases such as Ebola, MERS, Lassa Fever, and Leptospirosis significantly reduce investment inflows. Second, local projection methods trace the short-and medium-term responses of FDI to health shocks by transmission type. The results show varying recovery patterns: while FDI tends to rebound after direct contact or mosquito-borne outbreaks, airborne diseases cause more persistent declines. Third, the study explores whether stronger healthcare systems can mitigate these negative effects. Results suggest that countries with a higher density of nurses experience less severe FDI losses during outbreaks, particularly for diseases transmitted through direct contact or bodily fluids. These findings underline the importance of healthcare investment not only for public health but also for economic resilience. By distinguishing effects across disease types and highlighting the moderating role of health infrastructure, this study offers practical insights for policymakers seeking to safeguard investment flows during times of crisis.

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Nombre total de publications : 2776