Peiyi Jin

Abstract:
This paper investigates whether cryptocurrencies have become a new conduit for laundering diverted foreign aid. Using World Bank aid disbursement data from 2018 to 2024, linked with forensically tagged on-chain Bitcoin transactions and off-chain exchange activity, we document systematic surges in crypto transactions for anonymous wallets after disbursements, especially on exchanges located in tax-haven jurisdictions. A one-standard-deviation increase in lagged aid is associated with a 0.51 log-point rise in anonymous transactions on tax-haven exchanges—approximately a 66% increase—concentrated in newly created wallets and fading within two quarters. Network analysis reveals a real-time laundering pattern: funds flow through regulated platforms, then through mixers and tax-haven exchanges, mirroring the classic placement, layering, and integration stages. Off-chain data confirm spikes in transactions on suspect, lightly regulated platforms. To address endogeneity in aid allocation, we use an IV strategy based on historical aid shares interacted with governance quality. Overall, our findings suggest that cryptocurrencies are increasingly used for offshore banking in concealing aid diversion. Our study shows how blockchain forensics can trace hidden financial flows and offers new tools for anti-corruption and crypto regulation.

Conferences and talks: ABFER (scheduled, 2026), MFA (2026), 38th Australasian Finance and Banking Conference (2025)

Abstract:
Exploiting an exogenous tax reporting shock imposed on cryptocurrency gains and millions of DeFi transactions, we identify a borrow up, trade down strategy whereby U.S. traders reduce taxable trading and increase borrowing of the same tokens to monetize their needs and defer tax payment. This substitution effect is particularly pronounced among borrowers with higher loan-to-value ratios and higher returns. We show that when adopted at scale, tax considerations reduce trading activity and market depth, generating what we term tax-planning-induced illiquidity. We establish a causal link between this illiquidity and heightened credit risk. A one-standard-deviation increase in tax-induced illiquidity more than doubles the value of defaulted loans, with broader implications for financial stability in decentralized lending markets. Results are robust to multiple checks, including highly tax-sensitive borrowers, validation against tax-awareness periods, and alternative proxies for U.S. traders.

Conferences and talks: AFA PhD Session (2026); ABR-Fudan Conference (2025); IMF Workshop in Digital Money and Taxation (2025)*; Hawai’i Accounting Research Conference (2025)*; Tokenomics Conference (2024)*; Waseda University Workshop on the Economics of Technology and Decentralization*; NUS; Cornell–Tsinghua Summer Finance Institute*; IESE Barcelona Tax Conference*; IC3 Blockchain Camp at Cornell Tech*; Finance and Accounting Annual Research Symposium*; Research Symposium on Finance and Economics*; Bank of Finland; European Systemic Risk Board*; Swiss National Bank Conference on Cryptoassets and Financial Innovation*; Euroasia Conference*; Hong Kong University Summer Conference*; Bank of Japan*; FeAT International Conference on AI*; Tsinghua University (SEM and PBC, 2024); Singapore FinTech Festival*; 14th FMCG Conference*; AI Global Finance Research Conference (Ho Chi Minh City, 2023).

Abstract:
This paper studies the consequences of privacy regulation by exploiting Google’s 2019 restriction on CDR access for a major Indian FinTech lender. We show that this intervention reflects a key policy trade-off in digital credit markets: strengthened privacy protections raise loan applications, consistent with higher demand, yet simultaneously induce tighter screening, reflecting an overall contraction in credit supply. This credit contraction disproportionately excludes economically and socially marginalized applicants. Linking to economy-wide credit bureau records, we quantify the "FinTech ladder effect" whereby initial digital credit access serves as a gateway to broader formal credit. Privacy-induced rejection reduces the probability of obtaining any formal credit by 13.7 percentage points even four years later. Using a structural model, we decompose the welfare effects of privacy regulation and show that the regulation generates a 0.53% increase in consumer surplus and reduces lender profits by 15%.

Conferences and talks: MFA (2026)

Abstract:
This paper studies how digital technologies alter household responsiveness to income shocks by increasing the salience of consumption. I evaluate a government-led intervention in Singapore that provides real-time energy usage feedback through a mobile app. Using proprietary banking transaction data and a staggered rollout design, I find that treated households reduce utility payments by approximately $21 per month. This reduction is not driven by price changes or liquidity constraints but by increased cognitive attention to consumption. The intervention also produces behavioral spillovers: households reduce spending on paper goods, carbon-intensive foods, and taxi rides. Untreated residents living near treated areas exhibit similar changes, suggesting that salience effects diffuse spatially. These findings challenge the Permanent Income Hypothesis by showing that visibility increases short-run consumption elasticity. The results have implications for monetary and fiscal policy design. Digital salience can raise marginal propensities to consume, especially among liquidity-constrained households, and may strengthen policy transmission in heterogeneous-agent macroeconomic models.

Conferences and talks: AEA PhD Session (2026), 38th Australasian Finance and Banking Conference (2025)