We analyze a unique episode in the history of monetary economics, the 2016 Indian “demonetization.” This policy made 86% of cash in circulation illegal tender overnight, with new notes gradually introduced over the next several months. We present a model of demonetization where agents hold cash both to satisfy a cash-in-advance constraint and for tax evasion purposes. We test the predictions of the model in the cross-section of Indian districts using several novel data sets including: the geographic distribution of demonetized and new notes for causal inference; night light activity and employment surveys to measure economic activity including in the informal sector; debit/credit cards and e-wallet transactions data; and banking data on deposit and credit growth. Districts experiencing more severe demonetization had relative reductions in economic activity, faster adoption of alternative payment technologies, and lower bank credit growth. The cross-sectional responses cumulate to a contraction in aggregate employment and night lights–based output due to the the cash shortage of at least 2 percentage points and of bank credit of 2 percentage points in 2016Q4 relative to their counterfactual paths, effects that dissipate over the next few months. Our analysis rejects monetary neutrality using a large-scale natural experiment, something that is still rare in the vast literature on the effects of monetary policy.

This paper studies the political influence of individual firms on congressional decisions to suspend tariffs on U.S. imports of intermediate goods. We develop a legislative bargaining model in which firms influence legislators by transmitting information about the value of protection, using verbal messages and lobbying expenditures. Model estimation using firmlevel data on tariff suspension bills and lobbying expenditures reveals that the probability a suspension is granted decreases with each additional firm that expresses opposition. This effect is significantly larger than that of either opponent or proponent lobbying due to the greater information content of verbal opposition and legislative bargaining costs.

Does partisanship influence loan allocation through the Paycheck Protection Program (PPP)? We examine the 2020 Presidential campaign contributions made by lenders’ employees as a partisanship measure and leverage the PPP’s phased implementation under both the Trump and the Biden administrations. We find that partisan misalignment increases lending, particularly to small and first-time PPP borrowers, as well as those in Republican areas. Misalignment is also associated with higher payroll coverage for small businesses. Our findings are consistent with Republican-leaning lenders viewing the PPP’s 2021 phase as a legacy policy of the prior administration, shedding new light on the partisan-alignment phenomenon in finance.

India introduced credit scoring technology in 2007. We study its adoption by the two main types of banks operating there: new private banks (NPBs) and state-owned public sector banks (PSBs). Soon after the technology is introduced, NPBs start checking the credit scores of most borrowers before lending. PSBs do so equally quickly for new borrowers but very slowly for prior clients, although lending without checking scores is reliably associated with more delinquencies. We show that an important factor explaining the difference in adoption rates is the stickiness of past bank structures and managerial practices. Past practices inhibit better practices today.