Rethinking Monetary Policy Transmission : Nonbank Finance, Central Bank Digital Currencies, and the Role of Bank Market Power
Bibliographic record
Abstract
Essay I I study the transmission of monetary policy through banks and nonbank financial intermediaries (NBFIs) in the United States. First, I construct a dataset on nonbank financial intermediation that accounts for the various linkages between financial intermediaries. I empirically demonstrate that following monetary policy tightenings, U.S. households substitute bank deposits with nonbank-created liquidity. Bank lending contracts while nonbank intermediation expands. Second, to explain these empirical findings, I develop a New-Keynesian model incorporating both banks and nonbanks. Banks face a liquidity constraint that limits their ability to issue debt, a restriction that nonbanks do not encounter. As the policy rate rises, banks keep their deposit rates relatively low while nonbanks increase the returns on their liabilities in tandem with the policy rate. This generates the household portfolio rebalancing and the shift toward nonbank finance observed in the data. Moreover, I show that in the absence of nonbank financial intermediation, the economic contraction following a monetary tightening is deeper. These findings suggest that the presence of NBFIs weakens the effect of monetary policy. Essay II (with Matthias Hänsel and Hiep Nguyen): Interest rates on new central bank digital currencies (CBDCs) can be expected to enter the monetary policy toolkit soon. Using an extended Sidrauski (1967) model featuring an oligopsonistic banking sector, we study the complex transmission of interest rates on CBDC, which generally involve both direct and indirecteffects. This is because a CBDC rate cut does not only affect the rate on the CBDC itself, but also induces the non-competitive deposit providers to adjust their spreads, as the new substitute for their products becomes relatively less attractive. A calibration exercise suggests that the indirect effects depend strongly on the sources of deposit market power: If driven by high concentration, they substantially amplify the aggregate effects of the CBDC policy rate, both in response to transitory shocks as well as regarding its long-run welfare effects. This contrasts them with policies directed at the banking sector which are weakened by a less competitive deposit market. Essay III (with Maria Elena Filippin): We examine the risks to bank intermediation following the introduction of a central bank digital currency (CBDC). In our framework, CBDC competes with commercial bank deposits as a household liquidity source, and commercial banks can secure central bank funding by posting collateral. First, we revisit the equivalence results of Brunnermeier and Niepelt (2019) and Niepelt (2022). We show that the central bank can, even in the presence of a collateral constraint, ensure the same equilibrium allocation and price system following the introduction of a CBDC by offering loans to banks. However, to access the loans, banks must hold collateral at the expense of extending credit to firms. Thus, while the government can ensure that the introduction of CBDC has no real effects on the economy, it does not guarantee "full neutrality" as the portfolio and policy changes affect banks' business models. Second, we study the dynamic responses of the economy to a near-permanent increase in CBDC, without imposing equivalence. In this case, we find that the introduction of CBDC need not disintermediate banks, but could in fact expand the banks' credit to firms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".