Central Bank Digital Currency, Flight-to-Quality, and Bank-Runs in an Agent-Based Model

TL;DR

The paper investigates the welfare and financial-stability implications of introducing a Central Bank Digital Currency (CBDC) in an agent-based macroprudential framework. It endogenizes bank risk, depositor flight-to-quality decisions, and potential bank-runs, and evaluates five CBDC adoption rules ranging from a fixed 10% transfer to risk-based, bounded schemes with optional deposit insurance. The results show that unconstrained CBDC adoption can trigger significant macro- and financial-instability effects, including bank defaults and large output volatility, whereas bounded adoption (notably a 30–40% cap) with deposit insurance largely mitigates these risks and can improve welfare under realistic inequality aversion. The findings imply that CBDC design—especially adoption caps and safety nets—crucially shapes disintermediation, interbank dynamics, and overall welfare, offering concrete guidance for safe CBDC deployment in real economies.

Abstract

We analyse financial stability and welfare impacts associated with the introduction of a Central Bank Digital Currency (CBDC) in a macroeconomic agent-based model. The model considers firms, banks, and households interacting on labour, goods, credit, and interbank markets. Households move their liquidity from deposits to CBDC based on the perceived riskiness of their banks. We find that the introduction of CBDC exacerbates bank-runs and may lead to financial instability phenomena. The effect can be changed by introducing a limit on CBDC holdings. The adoption of CBDC has little effect on macroeconomic variables but the interest rate on loans to firms goes up and credit goes down in a limited way. CBDC leads to a redistribution of wealth from firms and banks to households with a higher bank default rate. CBDC may have negative welfare effects, but a bound on holding enables a welfare improvement.

Figures (8)

• Figure 1: Share of households' liquidity converted into CBDC by adoption rules.
• Figure 2: Cross-correlation between main variables for the baseline model.
• Figure 3: Median values for the time series.
• Figure 4: Complementary cumulative distribution functions (CCDFs) of selected variables, CCDF is defined as $1-F(x)$, where $F(x)$ is the cumulative distribution function of $x$.
• Figure 5: Average bank-run CDP of banks, log scale. The CDP represents the average probability (across banks and simulations) that a bank defaults by time $t$.