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Summary

This paper explores how central bank digital currencies (CBDCs) could enhance the delivery of social safety nets (SSNs). It assesses CBDC design features and their implications for payment administration and delivery. Findings suggest that using CBDCs solely as payment delivery solutions offers limited advantages over existing systems such as faster payment systems. However, leveraging CBDCs as payment administration platforms—with peer-to-peer transfers, decentralized ledger access, and advanced programmability—could transform SSN delivery by enabling agencies to automate transfers, operate independently from private financial intermediaries, and monitor transactions directly. These benefits come with significant challenges, including privacy concerns, compliance risks, and infrastructure requirements. The paper emphasizes that realizing CBDCs’ full potential for SSNs will depend on thoughtful integration with existing systems and a clear understanding of their comparative advantages. Aimed at social protection policymakers and finance specialists, it highlights the need for collaboration between CBDC developers and SSN administrators to ensure that digital currencies effectively support inclusive and efficient benefit delivery.

Subject:

Blockchain and DLT,
Central Bank digital currencies,
Smart contracts,
Technology

Keywords:

Blockchain and DLT,
Central Bank digital currencies,
Central Bank Digital Currencies,
Financial Inclusion,
Fintech,
Government Transfers,
Payment Systems,
Smart contracts,
Social Safety Nets

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Summary

This paper evaluates three approaches to address parameter proliferation issue in nowcasting: (i) variable selection using adjusted stepwise autoregressive integrated moving average with exogenous variables (AS-ARIMAX); (ii) regularization in machine learning (ML); and (iii) dimensionality reduction via principal component analysis (PCA). Utilizing 166 variables, we estimate our models from 2007Q2 to 2019Q4 using rolling-window regression, while applying these three approaches. We then conduct a pseudo out-of-sample performance comparison of various nowcasting models—including Bridge, MIDAS, U-MIDAS, dynamic factor model (DFM), and machine learning techniques including Ridge Regression, LASSO, and Elastic Net to predict China’s annualized real GDP growth rate from 2020Q1 to 2023Q1. Our findings suggest that the LASSO method outperform all other models, but only when guided by economic judgment and sign restrictions in variable selection. Notably, simpler models like Bridge with AS-ARIMAX variable selection yield reliable estimates nearly comparable to those from LASSO, underscoring the importance of effective variable selection in capturing strong signals.