WORKING PAPER SERIES

Explaining the Life Cycle of 

Bank-Sponsored Money Market Funds: 

An Application of the Regulatory Dialectic

Stefan Jacewitz

Federal Reserve Bank of Kansas City

Jonathan Pogach

Federal Deposit Insurance Corporation

Haluk Unal

University of Maryland and Federal Deposit 

Insurance Corporation

Chengjun Wu

University of Maryland

January 2024

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revision.
1

Explaining the Life Cycle of Bank-Sponsored Money Market 

Funds: An Application of the Regulatory Dialectic*

Stefan Jacewitz† Jonathan Pogach⸸ Haluk Unal‡ Chengjun Wu⸹

Abstract

In this paper, we present empirical evidence of the regulatory dialectic in the prime institutional 

money market fund (PI-MMF) industry. The “regulatory dialectic”, developed by Kane (1977, 

1981), describes how banks and regulators react to each other. For decades, a cap on commercial 

deposit interest rates fueled dramatic growth in bank-sponsored PI-MMFs as a form of shadow 

banking. During the growth period, banks with more commercial deposits were more likely to 

enter the PI-MMF industry in an effort to keep their commercial customers in affiliated 

subsidiaries. However, the 2008 crisis and subsequent regulatory changes halted the rapid growth 

of PI-MMFs. In the post-crisis regulatory regime, bank-sponsored funds were more likely to exit 

the industry than nonbank-sponsored funds. Simultaneously, the industry shifted from PI-MMFs 

to government institutional MMFs as substitute products. We conjecture that the collapse of the 

PI-MMF can lead further to the emergence of substitute products, such as stablecoins as part of 

the continuing dialectical process. 

JEL Classification: G2, G21, G23, G28, H12, H81.

Keywords: bank, bank holding company, bank run, financial crisis, liquidity risk, money market 

fund, systemic risk, too big to fail.

 * The views and opinions expressed in this paper reflect those of the authors and do not necessarily reflect the views 

of the Federal Deposit Insurance Corporation, the Federal Reserve Bank of Kansas City, the Federal Reserve 

System, or the United States. Comments from Rosalind Bennett, Mark Kutzbach, and George Shoukry are highly 

appreciated.

† Federal Reserve Bank of Kansas City

⸸ Federal Deposit Insurance Corporation, jpogach@fdic.gov

‡ Robert H. School of Business, University of Maryland and Federal Deposit Insurance Corporation

⸹ Robert H. School of Business, University of Maryland
2

1. Introduction

Bank-sponsored prime institutional money market funds (PI-MMFs)1 grew dramatically in the 

1990s, followed by a precipitous decline in the 2010s. In August 1987, bank-sponsored PI-MMFs

accounted for only 0.7 percent of the market share in the PI-MMFs industry. Over the next decade, 

however, bank-sponsored PI-MMFs reached a 50 percent market share, peaking in 2009 with more 

than $600 billion aggregate total net assets (TNA). In the first quarter of 2009, 25 bank holding 

companies (BHCs) sponsored 153 share classes of PI-MMFs.2 By the fourth quarter of 2016,

however, only 14 BHCs, sponsoring just 59 share classes with a TNA of $51 billion, remained in 

the industry.

In this paper, we show how the rise and the decline of the bank-sponsored PI-MMF industry 

provide the first empirical evidence of the regulatory dialectic framework proposed by Kane 

(1977) and developed by Kane (1981). Kane (1981, p. 355) describes the interplay between banks 

and their regulators as a balance between the “political processes of regulation and the economic 

process of regulatee avoidance [that], like riders on a seesaw, adapt continually to each other.” He 

argues that if banks are prohibited from performing a profitable activity, they have a strong 

incentive to circumvent the prohibition by developing a less regulated substitute product or service 

that closely mimics the regulated activity. After recognizing this behavior, regulators re-regulate 

the novel ways of performing the activity. Banks then seek to avoid the new regulation, and the 

process starts once again (see Eisenbeis [2023] for a retrospective).

3

 1 Also referred to as money market mutual funds. 2 Shareholders of a given fund are separated into “share classes” based on their characteristics, for instance 

institutional or retail investors. Though all share classes of a fund are tied to the same portfolio of assets, the share 

classes can have different expense ratios and minimum initial investment requirements.

3 The regulatory dialectic is closely related to, but distinct from regulatory arbitrage. The regulatory dialectic describes 

the continual process of regulate-circumvent-reregulate (the thesis, antithesis, and synthesis) driven by the opposing 

forces of regulator and regulatee (Kane, 1988). Regulatory arbitrage, on the other hand, describes “financial 

transactions designed specifically to reduce costs or capture profit opportunities created by differential laws or 

regulations” (Partnoy, 1996). Thus, regulatory arbitrage can be the circumvention stage of the greater dialectic process.
3

The evolution of the bank-sponsored PI-MMF industry between 1988 and 2016 exemplifies 

how banks and regulators react to one another. We show that the growth of bank-sponsored PIMMFs can be traced to the regulatory restrictions that prevented banks from paying interest on 

demand deposits. Banks faced disintermediation by commercial depositors who kept their funds 

in noninterest-bearing demand deposits. Because these deposits offered only implicit returns in the 

form of account services, commercial depositors had an incentive to move funds to accounts 

offering higher net (implicit and explicit) returns at money market funds. Bank sponsorship of PIMMFs emerged as an alternative to keep bank commercial customers under a bank’s corporate 

umbrella, countering the threat of disintermediation. In this way, the bank-sponsored MMF could 

pay interest on a product similar to commercial deposit, while keeping the revenue generated

within the corporate parent, thus increasing the size of the so-called “shadow banking system.” In 

aggregate, between 1986 and 2001, the banking industry experienced a massive reduction in 

noninterest-bearing deposits(our proxy for commercial deposits), while bank-sponsored PI-MMFs

showed rapid growth. 4

 Fund-level analysis shows that BHCs that relied relatively more on

commercial deposits and were thus more exposed to the threat of disintermediation posed by 

interest-bearing MMFs, were more likely to start and sponsor a PI-MMF.

The 2008 crisis had a profound impact on the PI-MMF industry, as government actions 

corroborated implicit safety-net coverage for “shadow banks” such as MMFs. For example, banksponsored shadow banks had no reserve or capital requirements and paid no fees to the FDIC for 

the risks their MMF affiliates created for the sponsoring banks (Acharya, Schnabl, and Suarez, 

2013; Jacewitz, Unal, and Wu, 2022). Consequently, federal regulation was then extended to 

 4 In regulatory filings, commercial deposits are not reported as a separate item. We use noninterest-bearing deposits, 

which includes commercial and individual accounts, as a proxy for commercial accounts. See Section 2 for more 

detail.
4

control the expansion of systemic risk inherent in these “loophole” deposit substitutes—again 

consistent with the regulatory dialectic hypothesis.

Regulatory changes during the crisis and post-crisis reforms made sponsorship of PI-MMFs 

less attractive to banks. Basel III required BHCs to hold additional liquidity against lines of credit 

to sponsored nonbank entities. In addition, the Dodd-Frank Act of 2010 repealed the statutory 

prohibition of interest rate payments on demand deposits and banks were allowed to pay market 

rates to their commercial depositors in 2011, decreasing the attractiveness of MMFs to investors. 

In addition, the Temporary Liquidity Guarantee Program (TLGP) also fundamentally changed 

MMF’s competitive environment and hampered the attractiveness of MMFs by extending 

unlimited deposit insurance coverage from October 2008 through 2010 to transaction accounts, a 

close MMF substitute, among other changes. Following TLGP, noninterest bearing transaction 

accounts remained temporarily covered under a provision of the Dodd-Frank Act starting in 2010 

until the end of 2012. 

The regulatory changes made the bank-sponsorship of MMFs less lucrative and opened 

avenues for substitute products. Consistent with the regulatory dialectic, our empirical tests show 

that, from 2010 to 2013, when the regulations took form, bank sponsors were more likely to close

their PI-MMF affiliates than nonbank sponsors. Importantly, institutional investors did not switch 

from bank-sponsored to nonbank-sponsored funds; we do not observe any increase in the number 

or asset size of nonbank-sponsored funds. Instead, as further evidence of the regulatory dialectic, 

our analysis on asset flows suggests that bank sponsors resorted to substitute products such as 

government institutional MMFs (GI-MMFs) in response to regulatory changes.
5 

In addition to these regulatory reforms affecting bank-sponsorship, the Securities and 

Exchange Commission (SEC) brought “structural and operational reforms”5

 in 2014, and new 

mechanisms that could restrict investors’ access to non-government funds. However, in contrast to 

the earlier bank regulatory changes, these SEC reforms affected primarily the MMF investors and 

not the bank sponsors of MMFs. The new rules discouraged investor redemptions, while granting 

fund sponsors new tools, like liquidity fees and redemption gates that allowed them to limit their 

run exposure. Taken together, these measures shifted the run risk from the sponsor, who may have 

needed to cover asset losses, to the investor. Having much more restrictive portfolios, GI-MMFs 

were exempt from the regulatory changes that applied to PI-MMFs. Therefore, while the SEC 

reforms may have contributed to the decline in PI-MMFs, we would not expect any differential 

response between bank and nonbank sponsors. Consistent with this expectation, we observe no 

differences in the exit decisions between bank and nonbank sponsors from 2014 to 2016. 

While the evolution of the PI-MMF market provides an illustrative historical example of the 

regulatory dialectic in action, Kane’s logic around the interplay between the financial industry and 

financial regulators continues to hold. Demand for less regulated, but close substitute, financial 

products remains in the economy. For example, many have noted that there are close structural 

similarities between the established MMF investment product and a relatively new creation: 

stablecoins (for instance, see Fed Chair Powell, 20216

; Gorton and Zhang, 2022; and Anadu, et. 

al, 2023). Both products provide a money-like investment opportunity, designed to maintain a 

steady nominal value, while performing liquidity transformation (see Anadu, et. al, 2023). Both 

 

5 See “SEC Adopts Money Market Fund Reform Rules”, Press Release (July 23, 2014), 

https://www.sec.gov/news/press-release/2014-143. 6

 “Stablecoins are like money market funds [...], but they are, to some extent, outside the regulatory perimeter, and it 

is appropriate that they be regulated—same activity, same regulation.” [emphasis added]; Congressional testimony by 

Jerome Powell; Serial No. 117-50 (House Hearing) - Oversight of the Treasury Department's and Federal Reserve's 

Pandemic Response, September 2021 [2] Source: Investment Company Institution via Haver Analytics.
6

PI-MMFs and stablecoins are also demonstrably susceptible to runs (for instance, the runs on 

IRON in 2021, terraUSD in 2022, and USDC in 2023). As regulatory tightening and other reforms 

have reduced demand for PI-MMFs, stablecoins – a less-regulated, close substitute – have grown 

dramatically. Since the end of our analysis sample in 2016, the total assets held in PI-MMFs has 

been no larger than a few hundred billion, down from more than $1.25 trillion at its peak.7

 Over

the same time period, the assets in just the current top three largest stablecoins (Tether, USDC, and 

Dai) increased from less than $10 million, to peak at close to $150 billion in 2022.8 Most recently, 

these two asset classes are of a similar magnitude. Of course, given the paucity of data, we make 

no conclusions, other than to state that these observations are consistent with a modern, active 

regulatory dialectic.

We organize the paper as follows. In Section 2, we present the data used in the analysis. In 

Section 3, we examine the rise of the bank-sponsored PI-MMFs. Section 4 discusses the regulatory

changes that contributed to the decline of these funds. Section 5 empirically examines the eventual 

decline of bank-sponsored PI-MMFs and asset flows into substitute products. Section 6 concludes.

2. Data 

A money market fund (MMF) is organized as a portfolio of assets managed by an investment 

advisor. A management company (fund sponsor) can establish a new money market fund as a 

separate legal entity and simultaneously operate many individual and distinct funds. A large group 

of investors, or shareholders, own the portfolio assets. Each shareholder owns shares of the 

portfolio and can withdraw funds by redeeming shares. The money market fund’s portfolio 

 7 Source: Investment Company Institution via Haver Analytics.

8 Source: Coin Gecko via Haver Analytics.
7

generates a daily yield (return). The advisor collects a percentage fee, called an expense ratio, and 

shareholders receive the net yield. 

Although the MMFs are considered default-free, shareholders still face the risk of their share 

values falling below par, also known as breaking the buck. Until the increased regulatory oversight 

in 2014, a money market fund could break the buck if its assets lost value such that the fund’s pershare market value dropped much below its historical cost of $1 (below $0.995, to be exact). MMF 

sponsors play a critical role in preventing this from occurring. Sponsors are not generally legally

obligated to provide support to their money market affiliates. However, they can implicitly 

guarantee to purchase at par assets that decline in value so that the MMF can meet redemption 

requests by the investors. Other forms of sponsor support include capital contributions, capital 

support agreements, letters of credit, and performance guarantees.9

We use data from iMoneyNet, which maintains detailed historical information on individual 

U.S. money market mutual funds since July 31, 1985. iMoneyNet collects available data across all 

U.S. money market funds and contains information on over 90 percent of all U.S. PI-MMFs.10

The dataset consists of share class-level observations. Each share class-level observation 

belongs to one specific fund, and each fund belongs to one specific fund complex. All share classes 

are claims on a common portfolio of assets, and thus share portfolio characteristics, such as total 

assets, gross yield, expense ratios, net yields, and weighted average maturity, and contain 

additional information on the asset composition of funds’ portfolios. We aggregate share class data 

 9 Moody’s (2010) provides examples of major sponsorship interventions and demonstrates that extensive instances of 

support were extended in 1994 and 2008. Indeed, sponsor support played a pivotal role during the crisis of September 

2008. The SEC estimates that from August 2007 to December 2008, almost 20 percent of all PI-MMFs received 

sponsor support (Securities and Exchange Commission (2009), page 20).

10 Schmidt, Timmermann, and Wermers (2016) compare iMoneyNet data to statistics from the Investment Company 

Institute and find that iMoneyNet data cover about 93.5 percent of the dollar value of the entire U.S. Prime PI-MMF 

universe.
8

to the fund level by each fund’s master class name 11 (following Duygan-Bump, Parkinson, 

Rosengren, Suarez, and Willen, 2013; Strahan and Tanyeri, 2015; and Jacewitz, Unal, and Wu, 

2022).

The iMoneyNet dataset contains a binary bank sponsorship identifier and a variable listing a 

sponsoring bank. The two sponsorship variables do not vary over time; the variables are fixed at 

values corresponding to a single date—the last date in each iMoneyNet dataset. To identify the 

sponsorship status of each fund complex, we manually match the name of each fund complex in 

our data to a bank or nonbank sponsor and build a sponsorship chronology for each fund complex

using SEC filings and the business press. Next, we match each sponsoring bank to its Bank Holding 

Company Call Reports (FR Y-9C Reports) and Commercial Bank Call Reports (FFIEC 

031/041/051) using the Federal Reserve’s National Information Center Database (NIC). We define 

a fund complex as being bank-sponsored if it is owned (or held) by a bank holding company 

(BHC), a financial holding company (FHC) with a subsidiary commercial bank, or a commercial 

bank that is not a member of a holding company.12

Figure 1 shows the total net assets of PI-MMFs by sponsorship type from 1988 through 

December 2013. 13 Each graph depicted in Figure 1 displays a distinct period with distinct 

behaviors in the PI-MMF industry during our sample period. The striking observation in the first 

graph is the relative growth of the bank-sponsored MMFs compared to the growth of the nonbanksponsored MMFs between 1988 and 2000. While the total TNA for bank-sponsored PI-MMFs in 

1988 is negligible, within 10 years they reached a 50 percent market share. During this period, and 

 11 In the data, this is identified using the Master Class Fund Name. When this value is missing, the fund’s unique 

identifier (Fund Code) is used instead, thereby assuming that it is a single share class fund. This occurs in 

approximately 8 percent of the cases over the entire data span. The frequency of the missing data is generally higher 

earlier in the sample, peaks at 41 percent in 1993, and falls to nothing in 2003 and thereafter. 12 The definition of commercial bank excludes the following types of institution: industrial loan company, federal 

savings bank, savings and loan institution, savings and loan holding company, and credit union. 13 Our data source iMoneyNet has data on bank-sponsored PI-MMFs starting in 1988.
9 

particularly in the first half, the growth rate in the bank-sponsored funds exceeded that of the 

nonbank-sponsored funds. This observation implies that the bank-sponsored funds attracted new 

institutional investment at a much faster rate than the nonbank MMFs. 

The second graph shows that both sponsorship type funds progress at relatively similar levels 

from 2000 until the 2008 crisis, when the nonbank-sponsored funds experience a sharp decline 

relative to the bank-sponsored funds. Much of this steep decline can be attributed to the conversion 

of Goldman, Merrill Lynch, and Morgan Stanley to BHC charters. These institutions converted to 

BHCs in late 2008, ostensibly to gain access to the government’s financial safety net, including 

insured deposits, but thereby accepting more rigorous regulatory requirements.14 In unreported 

analysis, excluding these three institutions show that both bank- and nonbank-sponsored funds 

experience similar declines. Nevertheless, even including the converted institutions, the decline in 

nonbank-sponsored funds is higher. Following the September 2008 shock, the TNA of banksponsored funds catches up to that of nonbank-sponsored funds. The trends diverge after 2009, as 

can be observed in the third graph: bank-sponsored funds’ TNA decline after 2010 while the 

nonbank-sponsored PI-MMFs maintain their TNA levels.

Our empirical analysis explores the behavior depicted in these three graphs in the context of 

regulatory dialectic: why did bank-sponsored funds grow in the early 1980s, and why did banksponsored funds decline after the Global Financial Crisis (GFC) in 2008? These questions naturally 

divide our analysis into two periods of interest, namely, the “rise” period in the 1980s and 1990s 

and the “decline” period from 2010 to the implementation of Money Market Fund Reform in 2016.

Table 1 summarizes the key variables in the two periods. Our analysis on the “rise” of banksponsored PI-MMFs focuses on the BHCs’ decision to sponsor PI-MMF business. Panel A of Table 

 

14 See “Shift for Goldman and Morgan Marks the End of an Era”, New York Times, (2008); 

https://www.nytimes.com/2008/09/22/business/22bank.html.
10

1 examines the bank characteristics that determine the entry decision. We observe that BHCs that 

enter the PI-MMF industry, on average, possess higher levels of assets and commercial deposits 

but lower equity ratio than the non-sponsoring BHCs. On the other hand, in the “decline” period, 

we examine the fund characteristics that determine the exit decision. Panel B of Table 1 shows that

BHC-sponsored funds have higher levels of TNA and are less risky in terms of weighted average 

maturity and risky holdings. The following sections examine these observations in a multi-variate 

setting. 

3. The Rise of the Bank-Sponsored PI-MMFs

Money market funds (MMFs), first introduced in 1971, are structured as registered 

investment companies under the Investment Act of 1940. They were not subject to deposit-rate 

ceilings (i.e., they were not subject to Regulation Q) and therefore could offer market rates. 

Between 1970 and 1980, banks experienced serious disintermediation due to competition from 

MMFs (Gilbert, 1986).

Bank sponsorship of PI-MMFs emerged during this period, asinterest payments forsavings

accounts had ceilings and were prohibited on transaction accounts. Rate ceilings remained below 

market rates during much of 1966 to 1979, which caused large outflows of small denomination 

deposits from depository institutions to money market mutual funds (Gilbert, 1986). In response,

regulators lifted ceilings on several deposit categories. Money market certificates (MMCs) and 

small-saver certificates, with variable interest rates and automatic transfer service accounts were 

authorized in 1978 and 1979. Congress eventually phased out Regulation Q for most types of 

deposits by passing the Depository Institutions Deregulation and Monetary Control Act 

(DIDMCA) in 1980. The Depository Institution Deregulation Committee (DIDC) established by 
11

DIDMCA allowed banks to offer Negotiable Order of Withdrawal (NOW) accounts nationwide in

1981, which were checkable and earned interest.

15 Despite these regulatory changes, the banking 

system was still unable to compete with the MMF industry. To enhance competition, the Garn-St 

Germain Act of 1982 introduced Money Market Deposit Accounts (MMDAs), which were 

required to be “directly equivalent to and competitive with money market mutual funds” (§327).16

Despite regulatory changes that allowed depository institutions to compete with the MMF 

industry for individual, nonprofit, and government accounts, commercial deposits were kept under 

Regulation Q and banks were prohibited from paying interest on commercial demand deposits. In 

this paper, we argue that the prohibition of paying interest on commercial deposits catalyzed

disintermediation and incentivized the BHCs to organize and sponsor PI-MMFs outside of the 

traditional bank regulatory system (contributing to the creation of “shadow banks”). 

Sponsorship of a PI-MMF allowed banks to offer commercial customers an opportunity to earn 

market rates under the same corporate umbrella as the bank subsidiary and implicitly enjoy the 

same access to the safety net that banks uniquely hold. Hence, consistent with the regulatory 

dialectic, we argue that the growth of bank-sponsored PI-MMFs allowed BHCs to circumvent the 

regulatory restrictions and offer a substitute product to their commercial customers. 

To quantify the effect of commercial deposits, we use the Call Report item “noninterest-bearing 

deposits” as a proxy for all types of commercial (business) deposits. Some consumers could also 

hold noninterest-bearing deposit (NIBD) accounts. However, given that regulations allowed

consumers to hold interest-bearing near-exact substitutes (MMDA and NOW accounts), we find it 

 15 NOW accounts emerged in Massachusetts in the 1970s and had a seven-day notice period. Although they were 

functionally demand deposits, because of the notice requirement NOW accounts were not considered demand deposits 

for regulatory purposes and thus were exempted from the interest rate ceilings.

16 See https://www.congress.gov/97/statute/STATUTE-96/STATUTE-96-Pg1469.pdf.
12

unlikely that the NIBD account is populated with a material number of consumer accounts or that 

such funds represent a material fraction of noninterest-bearing deposits, especially after the mid1980s.

To understand the determinants of PI-MMF sponsorship and the role of the disintermediation

threat at the BHC level, we undertake two empirical tests. We first examine the entry decision and 

investigate whether BHCs that had a higher level of commercial deposits were more or less likely 

to organize a bank-sponsored PI-MMF business. In the second test, we examine the BHCs’ entry 

and expansion in the PI-MMF industry with a longer panel. Under our hypothesis, BHCs with a 

heavier reliance on commercial deposits would be most affected by the disintermediation in 

commercial deposits, and hence have a higher incentive to enter the MMF industry and grow their 

MMF businesses to keep the commercial customers within the banking group. 

To construct the data for the first test, we identify the entry date of each bank-sponsored fund 

during the PI-MMF growth period (1988 to 1999) from iMoneyNet. We start in 1988 because of 

data availability. We end in 1999 because the growth of bank-sponsored PI-MMFs levels off and 

becomes equal to the nonbank-sponsored PI-MMFs. There are 44 distinct BHC sponsors entering 

during the PI-MMF growth period first quarter 1988 to fourth quarter 1999, while 1,456 BHCs did 

not sponsor a PI-MMF. These BHCs sponsored 139 funds at the end of 1999. We define entry as

an occurrence of a fund-sponsor pairing that was not observed in the PI-MMF market in the 

previous quarter. We obtain data on BHC financial characteristics from bank holding companies’ 

mandatory consolidated reports (FR Y-9Cs), which are filed quarterly by all U.S. regulatory highholder BHCs. 

The entry decision into the PI-MMF industry can be seen as a duration process in which each 

period a BHC chooses to enter or not conditional on having not entered previously. As a result, we 
13

use a Cox proportional hazards model (Therneau, Crowson and Atkinson (2023)) to study the 

BHCs’ entry into the PI-MMF industry from first quarter 1988 to fourth quarter 1999. Although 

hazard models are more frequently used to model exit and survival, they have previously been 

used in the literature to model adoption (Saloner and Shepard (1995), Kerr and Newell (2003)). In 

addition to reflecting the economic intuition of a duration model in which banks make entry 

decisions each period, a duration model also allows us to estimate the entry decision as a function 

of time-varying characteristics that could plausibly affect a banks entry decision. The hazard model 

is specified as:

λλ(tt) = λλ0(tt)exp(ββ1CCDDiiii + ββ2SSSSSSeeiiii + ββ3EEEEEE yyiiii + ββ43_MMMMMMMMMM MMMM MMyy ) (1)

In equation (1), λλ(tt) is the hazard function of BHC entry determined by a set of covariates. 

λλ0(tt) is the baseline hazard corresponding to the value of the hazard if the covariates are zero, 

representing the underlying hazard of BHC entry over time without accounting for covariates. The 

main variable of interest, CCDDiiii , is the bank ’s relative reliance on commercial deposits 

(normalized by BHC assets) in quarter tt. SSSSSSeeiiii is the log of BHC’s total assets and EEEEEE yyiiii is 

the ratio of equity to total assets in quarter tt. We also control for the interest rates measured by 

three-month Treasury rates. 

Table 2 presents the results from the Cox proportional hazards model. In column (1), we 

use the full sample of all BHCs and find that larger BHCs and BHCs that had more commercial 

deposits are significantly more likely to enter the PI-MMF industry. However, commercial deposits

can also serve as an indicator of the bank positioning itself as an acquisition target. Indeed, 

Akhigbe, Madura, and Whyte (2004) find that banks with a higher commercial deposit-to-assets 

ratio are more likely to be acquired. To control this possibility, we remove those banks that are 

acquired during our sample period. Given the disparities between characteristics of entrants and 
14

non-entrants in Table 1, we use the non-parametric Coarsened Exact Matching (CEM) based on 

the pre-entry period average of BHCs’ size and equity (Iacus, King, and Porro, 2012). The CEM 

methodology first temporarily coarsens the variables used for matching and then selects the exact 

matches on these coarsened data, effectively filtering out outliers that are distant from the size and 

equity ratio of the entering BHCs. CEM ensures a common support for all calculated measures 

and has also been shown to have better statistical properties than other standard matching methods. 

Columns (2) and (3) present regression results from equation (1) on the non-acquired sample of 

banks, without and with CEM, respectively. In both cases, we find that a larger relative size of 

commercial deposits is significantly related to the BHCs’ decision to enter the MMF business. The 

coefficient in column (3) indicates that a 1 percent increase in the commercial deposit ratio is 

associated with a 5 percent (=exp(4.662*0.01) - 1) higher likelihood of entry. The result is 

consistent with the regulatory dialectic argument that banks that could be more affected by the 

regulatory restrictions, such as the interest rate prohibition of Regulation Q, are more likely to

enter a less regulated substitute product and service, such as sponsoring a MMF, that mimics the 

regulated activity. 

In each of the specifications reported in Table 2, the three-month Treasury rate is not 

statistically related to the entry decision.17 The absence of a relationship is surprising given the 

variation of Treasury rates over the time period, ranging from over 9 percent early in the sample 

period to 2.75 percent in late 1992, and rising and remaining around 5 percent for the remainder 

of the sample period. Despite the variation over time, the difference between the zero rates imposed 

by Regulation Q and the three-year Treasury rate were still substantial (2.75 percent) even at their 

lows during the sample period. 

 17 This is also the case when using changes in interest rates rather than levels.
15

An alternative way to model the hazard of BHC entry is the discrete-time hazard model 

proposed by Shumway (2001), which is essentially a pooled logit model. The Cox proportional 

hazard model above is modeling the (log) hazard rate, the rate of entry at a given time, interested 

in how long it takes for BHC to enter, using time-to-event data. In contrast, a pooled logit models

the (log) odds ratio of BHC entry, without accounting for the within period time structure of exit.

In an unreported analysis, we find that the results from a pooled logit model are qualitatively 

similar to those from the Cox model, suggesting that a higher commercial deposits ratio is 

associated with a higher probability of entry.

One limitation of the Cox model in our setting is that it only models the entry decision of BHCs 

and ignores the subsequent expansion behaviors of BHCs in the PI-MMF industry. To take both 

entry and expansion of BHCs into account, we employ a Poisson regression model on the full 

panel where the dependent variable is the number of funds sponsored by the BHC and the 

covariates and the sample period are the same as the Cox model above. The results from the 

Poisson regression model are presented in Table 3. The consistently positive coefficients suggest 

that larger BHCs and BHCs with higher levels of commercial deposits expand their presence in 

the PI-MMF industry and sponsor more PI-MMFs during our sample period. Holding the other 

covariates constant, a 5 percent (one standard deviation) increase in the commercial deposit ratio 

contributes to a 35 to 40 percent increase in the number of funds sponsored. 

In contrast to Table 2, the equity to asset ratios in Table 3 are statistically significant in some 

specifications. We argue that this is reasonable as the expansion of funds relies upon investors 

perceiving more financial strength from a sponsor than is required from a sponsor upon entry, as 

expansion represents a more intense commitment from the fund sponsor. 
16

4. The Decline of the Bank-Sponsored PI-MMFs

During the GFC, the government safety net was extended to protect certain “shadow 

banks” that had exposed the financial system to systemic risk (for instance, see the blanket 

guarantee of money market funds provided by the U.S. Treasury Department’s Temporary 

Guarantee Program for Money Market Funds). In the aftermath of the GFC, regulators 

implemented major reforms in 2010, 2011, and 2016 designed to build resilience against systemic 

vulnerabilities and reform the risk exposure of the MMF industry. These reforms added new costs, 

especially on bank sponsors of MMFs and made MMF investors to share the default risk of the 

funds. 

a. 2010 and 2011 Regulations 

The 2010 and 2011 regulations that affected the MMF industry are the reforms associated 

with the Basel III agreements and the repeal of Regulation Q by the Dodd-Frank Act.

18 

Basel III required banks to hold liquid assets against their contingent commitments—

including sponsored PI-MMFs. Although implementation of Basel III through rulemaking did not 

occur coincident with the final version of the agreement, the final report was issued in June 2011, 

and earlier drafts informed expectations about future regulations. For example, Section B of the 

final report states that the rule ensures that banks have “sufficient high quality liquid resources to 

survive an acute stress scenario” and that they have sufficient resources “relative to the liquidity 

 

18 The final rule implementing this repeal was entirely effect on July 21, 2011. See 

https://www.govinfo.gov/content/pkg/FR-2011-07-18/pdf/2011-17886.pdf. 
17

profiles of the assets, as well as the potential for contingent liquidity needs arising from off-balance 

sheet commitments.”19 

Section 32 of the Liquidity Coverage Ratio (LCR) rule finalized in September 2014 to 

implement parts of the 2013 Basel III liquidity framework specifies that contractual commitments 

to sponsored funds such as MMFs must be backed one-for-one with high quality liquid assets.20

In contrast, commercial deposits required only a 25 percent backing with high quality liquid assets, 

assuming balances fall above the deposit insurance limit. Thus, under the new rules, having an 

MMF subsidiary brought new costs to the BHCs and changed the relative value between holding 

commercial deposits and MMF sponsorship. 

About one month after the release of the final version of Basel III’s global regulatory 

framework,

21 the Federal Reserve issued a final rule repealing Regulation Q,22 thereby allowing

interest payments on demand deposits. The final rule implemented the 2010 Dodd-Frank Act’s 

repeal of the statutory prohibition against payment of interest on demand deposits set forth in 

Section 19(i) of the Federal Reserve Act. This regulatory change implied that banks were allowed 

to pay interest on commercial transaction deposit accounts for the first time in nearly 80 years, 

since the 1929 Glass-Steagall Act.23 As explained in Section 3, restrictions on interest payments 

caused the commercial deposit disintermediation during the early 1980s. As of July 21, 2011, 

member banks and federal savings associations were able to pay interest on demand-deposit 

accounts. This development was important because it reduced the attractiveness of PI-MMFs for 

 

19 See https://www.bis.org/publ/bcbs189.htm. 20 Section 32 refers to 12 CFR 50.32 for OCC-supervised institutions, 12 CFR 249.32 for Federal Reservesupervised institutions, and 12 CFR 329.32 for FDIC-supervised institutions. 21 See https://www.bis.org/publ/bcbs189.htm. 22 See https://www.govinfo.gov/content/pkg/FR-2011-07-18/pdf/2011-17886.pdf. 23 The July 2011 change was a result of Section 627 of the Dodd-Frank Act, which removed the prohibition against 

interest payments on demand deposit accounts, also known as “the prohibition of interest on demand deposits.” See 

Federal Register, Vol. 76, No. 135, page 41392.
18

institutional investors in the sense that they could now earn explicit market interest rates from bank 

deposit accounts while maintaining a higher degree of safety of their deposits provided by the 

financial safety net. 

The regulatory dialectic hypothesis predicts that institutions are incentivized to find ways to

circumvent regulatory restrictions by developing a less regulated substitute product or service that 

mimics that activity. The Basel III framework required liquidity reserves for bank subsidiaries’ 

liquidity commitments to sponsored MMFs, increasing the cost of sponsorship. The elimination of 

Regulation Q in 2011 opened a new way for BHCs to directly offer their institutional investors 

substitute products, such as interest-bearing deposits. Extensions and expansions of insurance of 

transaction accounts through the Dodd-Frank Act may have also helped BHCs to attract 

institutional investors.

24 In addition, BHCs could promote government institutional MMFs as a 

substitute product from the same fund complex, which were not subject to Basel III requirements

after 2011. 

The regulatory dialectic suggests that BHCs respond to the re-regulation efforts by reducing 

activities in the newly regulated activities and circumvent these by producing substitute products. 

Basel III and many of the regulations required by the Dodd-Frank Act only targeted the banking 

industry. Therefore, we argue that nonbank-sponsored MMFs are a reasonable control group for

identifying the regulatory dialectic and we should see the substitution effect only in banksponsored funds. 

 24 The specific types and features of transaction accounts that were covered varied over time and across the TLGP 

and Dodd-Frank Act transaction account guarantee programs. 
19

5. Empirical Analysis

Figure 2 displays the chronological order of significant events between September 2008 and 

October 2013. In addition, we display the dates pertaining to the SEC reforms of the MMF, which 

we analyze in Section 5c. 

We analyze the regulation dialectic from 2010 to 2016 period in two parts. First, we focus on 

2010 to 2013 period, during which Dodd-Frank Act repealed Regulation Q 25 and reforms 

associated with the Basel III agreements in 2010 were publicized, though many of the Basel III 

reforms were implemented between 2013 and 2019.26 Our second analysis uses 2014 to 2016 

during which SEC proposed and passed the 2016 money market reform. We analyze these 

regulatory changes in two sub periods because the first set of regulations has an impact on bank 

sponsors whereas the second has an impact on both bank and nonbank sponsors. 

However, because the proposal and passage of these regulatory changes span 2010 to 2016, it 

is almost impossible to separate the effects of each regulatory change from each other in a clean 

way. Our approach to tackling this problem is twofold. First, we observe the general trends in the 

industry, and we assess whether these observations are held at the sponsor level. Second, we use 

nonbank sponsored MMFs as a control group and assess whether the bank sponsors behave in the 

direction of regulatory dialectic and differ from the behavior of the nonbank sponsored PI-MMFs.

 

25 The final rule implementing this repeal was entirely effect on July 21, 2011. See 

https://www.govinfo.gov/content/pkg/FR-2011-07-18/pdf/2011-17886.pdf. 26 See https://www.bis.org/bcbs/history.htm.
20

a. Impact on the Number of Funds

Panel C of Figure 1 shows the trend of assets under management of the bank- and nonbanksponsored PI-MMFs from 2010 to 2013.27 We observe that both types of funds had roughly the 

same amount of assets under management, around $600 billion in 2010, and both types started 

declining before the passage of the Dodd-Frank Act in July 2010. Moreover, starting June 2011,

both bank and nonbank PI-MMFs assets sharply declined. The decline stabilized by 2013, but 

bank-sponsored funds experience a three-times-larger asset loss than nonbank-sponsored funds 

relative to asset levels in 2010. Nonbank-sponsored funds declined 10 percent, whereas banksponsored funds declined 28 percent.

However, we note that in mid-2011, a “slow-motion run” hit the PI-MMF industry due to the 

fears about the European sovereign debt crisis, leading to over 170 billion redemptions in two 

months (Cipriani and La Spada, 2018). The concurrence of the slow-motion run and the regulatory 

changes confounds the interpretation of the trends in Panel C of Figure 1. Thus, we test the 

hypothesis more rigorously on the difference in bank sponsors’ and nonbank sponsors’ behavior.

To analyze whether the differences in the observed trends between the bank- and nonbanksponsored PI-MMFs are significant, we first test whether the bank-sponsored funds are more likely 

to exit the industry than the nonbank-sponsored funds after 2010. Figure 3 plots the exit of banksponsored and nonbank-sponsored PI-MMFs from 2010 to 2013. We construct the graph by 

plotting the number of exits in each quarter as a percent of existing number of funds at the 

beginning of each quarter. Consistent with the staggered nature of the announcement, passage, and 

 27 The timing of the announcement of the liquidity coverage ratio (LCR) in December 2010 in the Basel III framework, 

finalization of the framework in June 2011, and implementation of the LCR in the United States in October 2014 

complicates the definition of the appropriate time horizon. Yankov (2020) demonstrates that the likely effects of LCR 

on affected banks’ HQLAs were largely realized by the time the U.S. issued the proposal for implementation of the 

LCR in October 2013, consistent with anticipation effects.
21

implementation of new regulations fund exits for both banks and nonbanks are spread throughout 

the time period, and bank-sponsored fund exit rates generally exceed nonbank-sponsored fund exit 

rates during the time period. 

To test more formally differences in exit rates between bank- and nonbank-sponsored funds, 

we model the exit decision as a Cox proportional hazard model, where sponsors choose each 

quarter whether a fund exits the PI-MMF industry conditional on survival until that quarter. The 

Cox model has the advantage over a cross-sectional probit model as it allows for time-variant 

covariates. 

We create a binary variable, Exit, which equals one if the fund exited the MMF industry in that 

quarter between January 2010 and July 2013 and zero otherwise. The data consist of 115 PI-MMFs

that existed in January 2010, of which 45 funds were bank-sponsored and 70 were nonbanksponsored. At the end of the sample period, the number of nonbank-sponsored funds declined by 

18.6 percent to 57, while the number of bank-sponsored funds dropped by 35.6 percent to 29. 

Similar to the entry decision, we model the exit decision as:

 λλ(tt) = λλ0(tt)exp(ιι + γγBB BBiiii + Υ′ Uiiii) (2) 

The variable BB iiii is an indicator of bank sponsorship for fund i at time t, and γγ is our coefficient 

of interest. Uiiii is a vector of controls, and Υ is a vector of coefficients. We control for the size of 

the prime institutional fund measured by the natural log of TNA (ln(Fund TNA)), and the 

performance of the fund portfolio measured by the average monthly annualized simple gross yield 

(Yield). We control for the riskiness of a fund portfolio by including the natural log of weighted 

average maturity (ln(WAM)) and holdings of risky assets as a percentage. Following Kacperczyk 

and Schnabl (2013), we define the risky assets as bank obligations, floating rate notes and 

commercial paper including asset-backed commercial paper.
22

We present the results of this analysis in Table 4. In all five specifications displayed in Table

4, the coefficient on the bank sponsorship indicator is positive and highly significant, indicating 

that bank-sponsored funds have higher hazard of exiting the PI-MMF business than the nonbanksponsored funds from 2010 to 2013.

28 The coefficient in Column (5) indicates that having a bank 

sponsor is associated with 11 percent (=exp(2.477*1) – 1) higher likelihood of exiting. In addition, 

we observe that small-size PI-MMFs are more likely to leave the industry, which increases the 

concentration of the industry around large-size funds. Interestingly, results show that the weighted 

average maturity of portfolio holdings, which we use as a proxy for portfolio risk, has a negative 

sign, indicating that high-risk-portfolio funds are less likely to exit the industry. In an unreported 

analysis, we find that the results are robust to an alternative pooled logit model.

We infer from the results that the restrictions had a differential impact on fund closures during 

2010 to 2013 for BHCs-sponsored PI-MMFs. Bank-sponsored funds closed operations at a higher 

rate than the nonbank-sponsored funds. This observation is consistent with predictions of 

regulatory dialectic that banks ceased activities (or redirected them) once they became more highly 

regulated. 

b. Analyzing Fund Inflows and Outflows

Consistent with regulatory dialectic predictions, the elimination of interest rate restrictions on 

commercial deposits could have accelerated the flow of dollars from PI-MMFs and into substitute 

products belonging to the sponsoring BHC. However, equation (2) does not establish the link 

between the decline in PI-MMF fund assets and the flows into alternative products within the 

sponsor complex. BHCs can offer two possible substitute products: government institutional 

 28 Columns (3)–(5) have a lower number of observations because WAM data is available for only a subset of funds.
23

MMFs (GI-MMFs) within their sponsored fund complex and uninsured deposits within their bank 

subsidiaries. 

Unfortunately, given data limitations, we cannot identify the flows from PI-MMFs to bank 

deposits. The repeal of Regulation Q induced secular inflows of deposits and thus the inflows from 

PI-MMF is diluted and difficult to isolate. In addition, The Temporary Liquidity Guarantee 

Program’s unlimited insurance coverage to transaction accounts confounds the flow from PI-MMF 

to bank deposits. Therefore, in this section, we focus only on the flows from PI-MMFs to GIMMFs.

Figure 4 shows the total net assets for bank sponsored and nonbank sponsored funds for PIMMF and GI-MMF from 2010Q1 to 2013Q3, similar to the time period in Figure 3. We observe 

that prior to 2011, there does not appear to be aggregate substitution from PI-MMF into GI-MMF; 

the dashed lines (nonbanks) and solid lines (bank) commove. In contrast, during the shaded region 

of 2011, the trends for PI-MMF and GI-MMF deviate for both bank and nonbank sponsored funds; 

In particular, aggregate decreases in PI-MMFs beginning 2011 are offset by increases in aggregate 

GI-MMFs for both sponsor types. Following the shaded period, beginning in 2012, aggregate PIMMF and GI-MMF remain relatively flat.

To test whether the observed aggregate substitution between PI-MMF and GI-MMF in Figure 

4 holds at the sponsor level, we estimate the following Equation 3 using panel data model for the 

sponsors during 2011. This is the year during which we observe aggregate substitution from PIMMF to GI-MMF. There were 35 institutions that sponsored both PI-MMFs and GI-MMFs in 

2011. Among the 35 sponsors, 16 are nonbank sponsors and the remaining are bank sponsors.

ΔPPrimeInstAssetit = β0 + β1ΔGG GG GG GG ttiiii + β2PP PP PP + αii + γ + εit (3)
24

In Equation 3, ΔPPrimeInstAssetit and ΔGG GG GG GG ttiiii denote the rate of changes in 

quarter tt for sponsor for their collective prime institutional and government institutions funds, 

respectively. Sponsor fixed effects are denoted by αii and time fixed effects are denoted by γ , 

which respectively capture time-invariant characteristics of sponsors and industry-wide shocks to 

assets growth. We control the size of prime institutional assets proxied by the ratio of prime 

institutional assets to complex assets. The key variable of interest is the change in government 

institutional assets. We estimate Equation 3 separately for bank and nonbank sponsors.

We show the results in Table 5. Columns 1 and 2 correspond to the results from regressions 

using the full sample of both bank- and nonbank-sponsored funds, sample of nonbank-sponsored 

funds, and sample of bank-sponsored funds, respectively. In column 1, the regression using the 

full sample indicates that the assets flow from the PI-MMFs to GI-MMFs is not distinguishable 

from zero. The finding is consistent with the notion that 2011 regulatory changes did not target the 

operations of nonbank-sponsored funds. In a sharp contrast, however, we find from column 2 that 

in bank-sponsored fund complexes, the growth of government institutional assets is significantly 

associated with a decline in prime institutional assets, supporting our central hypothesis. Column 

3 reports that bank and nonbank sponsors are statistically different in their substitution from PIMMFs to GI-MMFs during 2011, consistent with the difference between the parameter estimates 

in columns 1 and 2.

In sum, both the exit decision and the substitution results provide support to the hypothesis that 

steps to re-regulate banking in the wake of the GFC had a profound impact on bank-sponsors 

activities in the PI-MMF industry. In contrast to non-bank sponsors, bank-sponsors reacted 

consistently with regulatory dialectic predictions and reduced activities in the regulated product 

and substituted these profitable activities with unregulated products. 
25

c. Response to the 2016 Money Market Reform

In 2014 the SEC adopted a structural and operational reform to mitigate the run-risk in MMFs. 

Effective October 14, 2016, nongovernment PI-MMFs were no longer able to maintain a fixed net 

asset value (NAV), the redemption price of the shares. Instead, they would use a floating asset 

value, marking to market the value of their securities. These rules also provided the boards of 

nongovernment MMFs with new tools—liquidity fees and redemption gates—which shifted the 

run risk from the sponsor to the investors. Thus, the sponsors’ safety net access provided a smaller

external benefit to fund investors. However, government and retail MMFs were not subject to these 

new restrictions. 

These new restrictions made prime institutional funds less attractive to investors relative to the 

pre-crisis period. Institutional investors in PI-MMFs bore additional risks of having their accounts 

unexpectedly frozen (potentially amid a more general financial crisis) or losing value due to 

fluctuations in NAV. However, one distinctive feature of these rules is that they were not bank or 

nonbank specific. All sponsors were affected in a similar fashion.

Therefore, the 2014 SEC rules act as a placebo test of our methodology. Unlike Basel III and 

repeal of Regulation Q, the SEC reforms affected the whole MMF industry regardless of the 

sponsor type. Thus, we expect no difference in the exit behavior of bank and nonbank sponsors

after the passage of the 2014 SEC rules.

Figure 5 plots the exit rates for bank and nonbank sponsors from 2014Q3 to 2016Q4. The 

figure shows that bank and nonbank sponsored PI-MMFs exit rates spiked in 2016Q3 to 

comparable levels as the rule took effect. The exit rates are consistent with the view that the 

reforms decreased the attractiveness of PI-MMFs similarly for bank and nonbank sponsors. 
26

We conduct an analysis of sponsor exit following the MMF reform parallel to the hazard model 

used to model exit from 2010 to 2013. Table 6 shows the results. The sample in this hazard model 

starts from July 2014 when SEC finalized MMF reform and ends in December 2016, three months 

after the reform took effect. The results in all five columns suggest that in response to the MMF 

SEC reform, bank and nonbank sponsors do not exhibit a difference in their exit decisions. 

6. Conclusion

Our findings provide an evidence-based explanation for the life cycle of bank-sponsored PIMMFs that supports the regulatory dialectic of Kane (1977). Our findings show that banksponsored MMFs emerged in the early 1980s as a reaction to the restrictions on interest rates for 

bank commercial deposits. BHCs with more commercial deposits were more likely to enter the PIMMF industry. By 2000, bank sponsors had accumulated roughly 50 percent of the MMF industry 

assets. Banks appeared to value MMF sponsorship because they kept commercial depositors in 

corporate-affiliated subsidiaries, extracted fee income from these funds, and avoided paying 

additional deposit insurance premiums or keeping higher liquidity buffers. Hence, MMFs provided 

a means to circumvent regulations for sponsoring BHCs. 

However, after the 2008 crisis, the regulatory response reduced the comparative benefits of 

MMFs and bank sponsors closed their MMF business at a faster pace than the nonbank sponsors.

From January 2010 to July 2013, the number of nonbank-sponsored funds declined by 18.6

percent, while the number of bank-sponsored funds dropped by 35.6 percent. Our result shows that 

bank sponsorship is associated with a higher likelihood of sponsored PI-MMFs exiting the 

industry. Further, we find that in reaction to the regulatory changes, the industry shifted from PIMMFs to substitute product GI-MMFs. 
27

Collectively, our findings point to the existence of the regulatory dialectic. Banking 

organizations find ways to circumvent regulation by developing products or services that mimic 

the regulated products but are less regulated. When the regulators discover that the regulations 

cause unregulated alternatives, they re-regulate, and banks then react to the new set of regulations. 

In the case of PI-MMFs, it took about 30 years to discover that regulations set in early 1980s fueled 

the growth of shadow banks and created significant risks to the U.S. financial system. 
28

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Figure 1. Total Net Assets of PI-MMFs, January 1986 to December 2013. This figure shows

the total net assets of prime institutional funds by sponsorship type from January 1988 to December

2013. The time period is separated to accentuate the relative dynamics over different periods. The 

top panel shows the total net assets of prime institutional funds by sponsorship type from January 

1988 to 2000 in log scale. The middle panel shows the evolution from 2000 to 2010 in log scale. 

The last panel shows the relative decline of bank-sponsored funds from January 2010 to December

2013.
31

Figure 2. Chronological Order of Significant Events.
32

Figure 3. Percentage of Prime Institutional Money Market Funds Exiting, by Sponsor Type, 

2010Q1 to 2013Q3. This figure shows the exit rate of bank-sponsored and nonbank-sponsored PIMMFs from 2010 to 2013. The exit rate is calculated as the number of PI-MMF exits in each 

quarter as a percentage of the number of existing funds at the beginning of each quarter.
33

Figure 4. Aggregate Total Net Assets, 2010Q1 to 2013Q3. This figure shows the evolution of 

total net assets of prime and government institutional assets held by bank-sponsored and nonbanksponsored funds from 2010 through the middle of 2013. 
34

Figure 5. Percentage of Prime Institutional Money Market Funds Exiting, by Sponsor Type, 

2014Q3 to 2016Q3. This figure shows the exit rate of bank-sponsored and nonbank-sponsored PIMMFs from 2014 to 2016. The exit rate is calculated as the number of PI-MMF exits in each 

quarter as a percentage of the number of existing funds at the beginning of each quarter.
35

Table 1. Descriptive Statistics. This table presents the summary statistics of the key variables used 

in the samples for the rise and the decline periods of the bank-sponsored MMF. The standard

deviations are in the parentheses below the mean.

Panel A: “Rise” period 1988 – 1999 

*** p<0.01, ** p<0.05, * p<0.1 

Panel B: “Decline” period 2010-2016 

*** p<0.01, ** p<0.05, * p<0.1 

Sponsoring BHCs Non-sponsoring BHCs Difference

Comm. Dep./Assets 0.147 0.128 0.019***

(0.046) (0.062)

Size (ln(BHC Assets)) 16.748 13.302 3.446***

(1.152) (1.338)

Equity/Assets 0.076 0.084 -0.008***

(0.016) (0.033)

3-Mon Treasury Rate 5.401

(1.611)

Number of BHCs 44 1,456

Bank-sponsored 

PI-MMFs

Nonbank-sponsored 

PI-MMFs Diff

ln(Fund TNA) 9.082 8.779 0.303***

(9.520) (9.368)

ln(WAM) 3.573 3.662 -0.089***

(2.536) (2.587)

Yield 0.267 0.288 -0.021***

(0.113) (0.114)

ABCP (%) 0.081 0.109 -0.028***

(0.085) (0.105)

Commercial Paper (%) 0.310 0.326 -0.016***

(0.160) (0.172)

Floating Rate Notes (%) 0.136 0.183 -0.047***

(0.113) (0.163)

Bank Obligations (%) 0.208 0.243 -0.035***

(0.132) (0.176)

Number of Funds 51 77
36

Table 2. Entry of Bank-Sponsored Funds into the PI-MMF Industry. This table presents the 

results from a Cox proportional hazard model of a BHC’s entry into the PI-MMF industry as a 

function of banks’ commercial deposit ratio and controls, from first quarter 1988 to fourth quarter 

1999. Robust z-statistics are in parentheses (clustered at the BHC level). Coarsened Exact 

Matching is used to build the sample used in Column 3.

VARIABLES

Indicator of BHC Entry

All BHCs Removing acquired BHCs

(1)

Full Matched 

(2) (3)

Comm. Dep./Assets 4.267** 5.020** 4.662**

(2.022) (2.441) (2.159)

Size 0.910*** 0.880*** 0.860***

(13.658) (11.605) (10.988)

Equity/Assets 1.505 -0.945 -3.659

(0.209) (-0.098) (-0.323)

3-Mon Treasury Rate 0.007 -0.132 -0.077

(0.058) (-0.764) (-0.457)

Observations 41,937 29,944 27,605

Wald Test 217*** 159*** 145.3***

*** p<0.01, ** p<0.05, * p<0.1
37

Table 3. Entry and Expansion of Bank-Sponsored Funds into the PI-MMF Industry. This 

table presents the results from a Poisson regression model of the number of funds sponsored by 

BHCs as a function of banks’ commercial deposit ratio and controls, from first quarter 1988 to 

fourth quarter 1999. Robust z-statistics are in parentheses (clustered at the BHC level). Coarsened 

Exact Matching is used to build the sample used in Column 3.

#Fund

All BHCs Removing acquired BHCs

(1) Full Matched

VARIABLES (2) (3)

Comm. Dep./Assets 5.89** 6.91*** 6.88***

(2.29) (2.60) (2.62)

Size 1.17*** 1.16*** 1.16***

(12.76) (12.56) (12.44)

Equity/Assets 7.90*** 6.25* 6.53

(4.55) (1.70) (1.43)

3-Mon Treasury Rate -0.08* -0.05 -0.05

(-1.85) (-0.86) (-0.80)

Constant -22.55*** -22.28*** -22.27***

(-12.85) (-11.44) (-11.21)

Observations 42,756 30,559 28,216

*** p<0.01, ** p<0.05, * p<0.1
38

Table 4. Exit Among Money Market Funds from 2010 to 2013. This table presents the results 

from a Cox proportional hazard model of fund closures between January 2010 and July 2013 as a 

function of PI-MMFs' bank sponsorship status. Robust z-statistics are in parentheses (clustered at 

the fund level).

(1) (2) (3) (4) (5)

VARIABLES Indicator of 

Fund Exit

Indicator of 

Fund Exit

Indicator of 

Fund Exit

Indicator of 

Fund Exit

Indicator of 

Fund Exit

Bank-sponsored 0.740** 1.378*** 1.755** 1.738** 2.477***

(1.996) (3.280) (2.251) (2.107) (2.795)

ln(Fund TNA) -0.455*** -0.248*** -0.238*** -0.281***

(-5.208) (-3.720) (-3.520) (-3.151)

ln(WAM) -1.110*** -1.082*** -1.738***

(-4.860) (-3.483) (-4.766)

Yield -0.974 -3.338

(-0.223) (-0.572)

ABCP (%) 0.020

(0.741)

Commercial Paper (%) 0.051***

(2.637)

Floating Rate Notes (%) -0.020

(-0.935)

Bank Obligations (%) 0.050***

(3.356)

Observations 4,245 4,245 2,871 2,871 2,871

*** p<0.01, ** p<0.05, * p<0.1
39

Table 5. Substitution Between PI-MMF and GI-MMF in 2011. This table presents results from 

estimating Equation 3 from the first to the last quarter of 2011. The outcome variable is the change 

in prime institutional assets at the sponsor level. Robust t-statistics are in parentheses (clustered at 

the sponsor level).

Prime Inst Asset Change

(1) (2) (3)

VARIABLES Nonbank Bank Nonbank/Bank

ΔΔ(Govt Assets) 0.022 -0.164*** 0.017

(1.052) (-5.913) (1.013)

Bank Dummy * ΔΔ(Govt Assets) -0.182***

(-3.848)

Prime Inst Asset Ratio (%) 0.016* 0.016*** 0.016**

(1.838) (3.443) (2.397)

Constant -0.664* -0.587*** -0.627**

(-1.827) (-3.542) (-2.417)

Observations 62 74 136

R-squared 0.387 0.597 0.417

Sponsor FE YES YES YES

Time FE YES YES YES

*** p<0.01, ** p<0.05, * p<0.1
40

Table 6. Exit Among Money Market Funds During 2014 to 16. This table presents the results 

from a Cox proportional hazard model that models fund closure between July 2014 and December 

2016 as a function of PI-MMFs' bank sponsorship status. Robust z-statistics are in parentheses

(clustered at the fund level).

(1) (2) (3) (4) (5)

VARIABLES Indicator of 

Fund Exit

Indicator of 

Fund Exit

Indicator of 

Fund Exit

Indicator of 

Fund Exit

Indicator of 

Fund Exit

Bank-sponsored -0.268 0.101 -0.163 -0.376 -0.499

(-1.047) (0.418) (-0.543) (-1.197) (-1.481)

ln(Fund TNA) -0.375*** -0.348*** -0.238*** -0.242**

(-6.565) (-5.522) (-2.751) (-2.524)

ln(WAM) -0.250 -0.031 -0.105

(-1.570) (-0.187) (-0.702)

Yield -7.157** -2.906

(-2.568) (-0.922)

ABCP (%) -0.031

(-1.114)

Commercial Paper (%) -0.017

(-1.312)

Floating Rate Notes (%) -0.025**

(-2.022)

Bank Obligations (%) -0.028*

(-1.906)

Observations 2,177 2,177 1,384 1,384 1,384

*** p<0.01, ** p<0.05, * p<0.1