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Publications

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Do tariff reductions alleviate energy poverty? Evidence for Sub-Saharan African countries

Windbeneti Arnaud Zahonogo


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Wealth inequality and carbon inequality

Ablam Estel Apeti, Bao We Wal Bambe, Eyah Denise Edoh, Alpha Ly


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When Trade Agreements Are Gender-Friendly: Impact on Women Empowerment Using Firm Data

Fida Karam, Chahir Zaki


This paper studies the effect of gender-related provisions in Regional Trade Agreements on under-explored aspects of gender inequality at the plant level, namely the difference in the managerial and ownership status between the two genders. This topic is timely and critical as gender equality considerations are becoming more prominent in new trade agreements, recognizing to different extents that trade policies developed with a gender perspective can help overcome gender inequalities by creating new opportunities for women's equal representation in export jobs and entrepreneurship. We use the World Bank Enterprise Surveys that encompasses data on 195,000 firms in 155 countries in 56 manufacturing and services sectors, and the dataset on gender-related provisions in trade agreements from the World Trade Organization. Our results show that the total number of gender-related provisions in trade agreements, as well their location in articles and in annexes are positively associated with a higher probability of the firm to be owned or managed by a female. This result is robust whether the firm is an exporter, importer, or involved in the global value chain of the sector of interest. When differentiating countries by income level, we find that the positive effect of gender-related provisions in trade agreements on women empowerment holds only for developed countries, pointing out the importance of making South-South agreements more gender friendly.

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Economic sanctions and sovereign debt default

Ablam Estel Apeti, Eyah Denise Edoh


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The Fairness of Credit Scoring Models

Christophe Hurlin, Christophe PĂ©rignon, SĂ©bastien Saurin


In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g., gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training data set or from the model itself. We show how to formally test the algorithmic fairness of scoring models and how to identify the variables responsible for any lack of fairness. We then use these variables to optimize the fairness-performance tradeoff. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, improved for the benefit of protected groups, while still maintaining a high level of forecasting accuracy. This paper was accepted by Will Cong, finance. Funding: This work was supported by the Autorité de ContrÎle Prudentiel et de Résolution (ACPR) Chair in Regulation and Systemic Risk, the Fintech Chair at Dauphine-PSL University, and the French National Research Agency (ANR) [MLEforRisk ANR-21-CE26-0007, Ecodec ANR-11-LABX-0047, and F-STAR ANR-17-CE26-0007-01]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03888 .

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Assessing volatility persistence in fractional Heston models with self-exciting jumps

Gilles de Truchis, Bernard Desgraupes, Elena-Ivona Dumitrescu


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