Leila Aghabarari

Economist at the World Bank | Expert in Analytical and Data Skills | Policy Work Specialist | Passionate Learner of AI and Neuroscience

Is there help indeed, if there is help in need? The case of credit unions during the global financial crisis(with Andre Guettler, Mahvish Naeem, and Bernardus Van Doornik), Economic Inquiry 59, no. 3 (2021): 1215-1233.

Credit unions (CUs) may respond to a financial shock differently than other types of banks because of their unique membership-based governance structure. We exploit the financial crisis of 2008/09 as a negative shock to Brazilian banks and analyze the lending behavior of CUs in comparison to non-CUs and the subsequent effects on the commercial clients' labor force. We find that during the financial crisis, CUs tightened their members' access to credit to a lesser extent (insurance effect) than did other bank types. Notably, the labor market impact of the insurance effect of CUs is positive for very small firms.

Conflict and the nature of precautionary wealth(with Rishabh Sinha and Ahmed Rostom), Oxford Economic Papers, Volume 74, Issue 2, April 2022, Pages 567–593.

This article examines the accumulation of precautionary wealth in Afghanistan using detailed data from a large household survey. A percent increase in income variance is associated with a 0.22 and 0.84% increase in broad and narrow wealth, respectively. This precautionary response is most pronounced at the extremes of wealth distribution. Wealthy households rely exclusively on livestock, while less affluent use jewelry for self-insurance. Conflict amplifies the precautionary motive, with households in high-conflict provinces reporting higher wealth at the same level of uncertainty. An increase in conflict intensity is also associated with an expansion of livestock weight in the asset portfolio.


Credit cycles in countries in the MENA region- Do they exist? Do they matter?(with Ahmed Rostom), Global Economy Journal, Vol. 2, No. 01, 2050001 (2020).

This paper estimates the private sector credit cycles for most of the oil-importing and oil-exporting countries in the Middle East and North Africa. Credit cycles are the medium-term component in spectral analysis of real private sector credit growth. Besides, the paper estimates the credit cycles for several developed countries. The analysis finds substantial differences and rare similarities between credit cycles in the Middle East and North Africa and advanced countries. During 1964–2017, credit cycles in the Middle East and North Africa do not appear to be associated with GDP growth. They only explained a fraction of the growth in private sector credit, and they do not seem to be synchronized across oil-exporters and oil-importers.

Working Papers

The Transmission of Real Effects of Monetary Policy: Evidence from Brazil (with Sophia Chen, Deniz Igan, Bernardus Van Doornik)

We examine the role of government credit in monetary policy transmission, using a comprehensive credit registry dataset from Brazil. We find that (i) the pass-through is stronger via government bank direct credit than private bank credit; (ii) a positive relationship between the exposure to earmarked credit and the pass-through to non-earmarked credit; (iii) the effects exhibit an asymmetry during periods of monetary policy tightening and loosening and heterogeneity across large and small firms. The findings suggest a nuanced role of government credit in monetary policy transmission. Government credit can reinforce monetary policy transmission. It may also have a spillover effect to non-earmarked private credit.

Smart Pricing: Using AI to Revolutionize Access to Essential Goods for Marginalized Communities.

This paper explores the potential of artificial intelligence (AI) to develop pricing models that enhance access to essential goods and services for marginalized communities. As the cost of living continues to rise, traditional pricing mechanisms often fail to account for the needs of underserved populations, exacerbating social inequalities. By leveraging AI technologies, this study aims to create dynamic pricing models that ensure affordability and accessibility while maintaining economic viability for providers. The paper examines the current state of AI in pricing strategies, identifies key factors that influence pricing decisions, and analyzes case studies where AI-driven pricing has successfully improved access to essential services. Additionally, it assesses the social and economic impacts of these models, providing policy recommendations to promote social inclusion and equity. Through a comprehensive analysis, this paper aims to contribute to the development of inclusive pricing policies that harness the power of AI to support vulnerable communities.

JEL Classification: C63, D12, I32
Keywords: Artificial Intelligence, Pricing Models, Social Inclusion, Essential Services, Marginalized Communities

The Impact of AI on Consumer Protection in the Financial Sector: Risks, Opportunities, and Regulatory Challenges

This paper explores the impact of artificial intelligence (AI) on consumer protection within the financial sector. As AI technologies are increasingly integrated into financial services, they offer numerous opportunities for enhancing consumer experiences and operational efficiencies. However, these advancements also bring significant risks and challenges that need to be addressed to protect consumers effectively. The paper examines the benefits of AI in financial services, such as improved fraud detection, personalized financial advice, and streamlined customer service. It also analyzes the risks, including data privacy concerns, algorithmic biases, and the potential for increased financial exclusion. Additionally, the study reviews current regulatory frameworks and proposes policy recommendations to ensure that AI-driven financial services are safe, fair, and inclusive. By providing a comprehensive analysis of the impact of AI on consumer protection, this paper aims to contribute to the development of effective regulatory strategies that balance innovation with consumer safeguards.

JEL Classification: C63, D12, I32
Keywords: Artificial Intelligence, Pricing Models, Social Inclusion, Essential Services, Marginalized Communities