Academic journal article Economic Quarterly - Federal Reserve Bank of Richmond

Currency Quality and Changes in the Behavior of Depository Institutions

Academic journal article Economic Quarterly - Federal Reserve Bank of Richmond

Currency Quality and Changes in the Behavior of Depository Institutions

Article excerpt

(ProQuest: ... denotes formulae omitted.)

The Federal Reserve System distributes currency to and accepts deposits from Depository Institutions (DIs). In addition, the Federal Reserve maintains the quality level of currency in circulation by inspecting all deposited notes. Notes that meet minimum quality requirements (fit notes) are bundled to be reentered into circulation while old and damaged notes are destroyed (shredded) and replaced by newly printed notes.

Between July 2006 and July 2007, the Federal Reserve implemented a Currency Recirculation Policy for $10 and $20 notes. Under the new policy, Reserve Banks will generally charge DIs a fee on the value of deposits that are subsequently withdrawn by DIs within the same week. In addition, under certain conditions the policy allows DIs to treat currency in their own vaults as reserves with the Fed. It is reasonable to expect that the policy change will result in DIs depositing a smaller fraction of notes with the Fed. While the policy is aimed at decreasing the costs to society of currency provision, it may also lead to deterioration of the quality of notes in circulation since notes that are deposited less often are inspected less often.

This article analyzes the interaction between deposit behavior of DIs and the shred decision of the Fed in determining the quality distribution of currency. For a given decrease in the rate of DIs' note deposits with the Fed, absent any change in the Fed's shred decision, what effect would there be on the quality distribution of currency in circulation? What kind of changes in the shred criteria would restore the original quality distribution?

To answer these questions, we use the model developed by Lacker and Wolman (1997).1 In the model, the evolution of the currency quality distribution over time is governed by (i) a quality transition matrix that describes the probabilistic deterioration of notes from one period to the next, (ii) DIs' deposit probabilities for notes at each quality level, (iii) the Fed's shred decision for notes at each quality level, (iv) the quality distribution of new notes, and (v) the growth rate of currency.

We estimate three versions of the model for both $5 and $10 notes.We have not estimated the model for $20 notes because they were redesigned recently, and the new notes were introduced in October 2003. The transition from old to new notes makes our estimation procedure impractical; we discuss this further in the Conclusion.2 Although the policy affects $10 and $20 notes only, we also estimate the model for $5 notes because the policy change initially proposed in 2003 included $5 notes. (It is possible that at some point the recirculation policy might be expanded to cover that denomination.) Also, it is likely that the reduced deposits of $10 and $20 notes may induce DIs to change the frequency of transporting notes to the Fed and, hence, affect the deposit rate of other denominations. The model predicts roughly comparable results for both denominations.

In each version of our model, we choose parameters so that the model approximates the age and quality distributions of U.S. currency deposited at the Fed. For each estimated model, we describe the deterioration of currency quality following decreases in DI deposit rates of 20 and 40 percent, and we provide examples of Fed policy changes that would counteract that deterioration. As described in more detail below, we view a 40 percent decrease in deposit rates as an upper bound on the change induced by the recirculation policy.

According to the model(s), a 20 percent decrease in the DI deposit rate would eventually result in an increase in the number of poor quality (unfit) notes of between 0.8 and 2.5 percentage points. While this range corresponds to different specifications of the model, not to a statistical confidence interval, it should be interpreted as indicating the range of uncertainty about our results. …

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