Dynamic Depositor Discipline in U.S. Banks 

Andrea M. Maechler and Kathleen M. McDill• 

Working Paper 2003-7 

November 2003 

•

 Kathleen McDill is a Senior Financial Economist in the Division of Insurance and Research 

of the Federal Deposit Insurance Corporation. Andrea Maechler is an Economist in the 

Monetary and Financial Systems Department of the International Monetary Fund. 

We are very grateful to Philip Bartholomew and Lynn Shibut for their insightful comments and 

helpful discussions. The paper also benefited from valuable comments by Urs W. Birchler, 

Jürg Blum, Hali Edison, Clemens Kool, Tonny Lybeck, and Philip Schellekens as well as by 

participants in the 2003 Workshop on Banking and Finance organized jointly by the 

Nederlandsche Bank and the Utrecht School of Economics. All errors are our own. The findings, 

interpretations, and conclusions expressed in this paper are entirely those of the authors and do 

not necessarily represent the views or policy of the International Monetary Fund (IMF) or the 

Federal Deposit Insurance Corporation (FDIC). 
 

Abstract 

This paper investigates the presence of depositor discipline in the U.S. banking sector. We 

test whether depositors penalize (discipline) banks for poor performance by withdrawing 

their uninsured deposits. While focusing on the movements in uninsured deposits, we also 

account for the possibility that banks may be forced to pay a risk premium in the form of 

higher interest rates to induce depositors not to withdraw their uninsured deposits. Our results 

support the existence of depositor discipline: a weak bank may not necessarily be able to stop a 

deposit drain by raising its uninsured deposit interest rates. 

2 
 

 

I. INTRODUCTION 

A. Motivation 

Most countries deal with bank insolvencies on a relatively regular basis.1

 Generally speaking, 

most countries have their share of bad banks. If widespread, bank failures are typically very 

costly, ranging anywhere from a few percentage points of GDP to as high as 50 percent of 

GDP.2

 In an effort to minimize such costly occurrences, policymakers and regulators have 

turned their attention increasingly to market discipline as a tool to induce banks to conduct 

their business in a transparent, effective, and sound manner.3

 In the banking sector, one form 

of market discipline is depositor discipline—the ability of depositors to penalize (discipline) 

banks for poor performance by withdrawing deposits. Investors in bank liabilities, such as 

uninsured deposits or subordinated debt, actively reward or punish banks for their relative 

performance. In the case of excessive risk taking, depositors can demand higher yields on 

their liabilities or withdraw their funds. By making risk taking more costly for banks, 

depositor discipline should curb banks’ incentives to take excessive risk and hence should 

contribute to the stability of the financial system. 

Depositor discipline strengthens the banking system by making bank managers accountable 

for their performance and depositors responsible for their investment choices. It helps reduce 

the likelihood that depositors (or their insurers) will implicitly subsidize the risks taken by 

their banks and hence mitigates moral hazard. But an extreme form of depositor discipline 

can lead to bank runs, which, if widespread, carry costly consequences for the economy as a 

whole.4

 As a result, few countries have been willing to rely fully on depositor discipline as an 

instrument to curb excessive risk-taking behavior on the part of banks, at least not without 

1

 In the United States., the 1986–95 savings and loan (S&L) crisis resulted in the closure of approximately 50 

percent of all federally insured thrift institutions (Curry and Shibut, 2002). Outside the United States, since 

1980 a number of systemically important countries, such as Canada, Sweden, Switzerland, and Japan, have been 

confronted with major problems involving significant numbers of bank failures (Bartholomew and Gup, 1999). 

2

 The budgetary cost of the 1997 Thai and Korean banking crises are estimated to have reached 30 percent of 

GDP, while the cost of the Indonesian banking crisis in the same year is believed to have reached up to 50 

percent of GDP (Caprio, 2001). 

3

 In the third consultative paper of the New Basel Accord, the Basel Committee on Banking Supervision (2003) 

has designated market discipline as part of the third pillar of a sound financial architecture, after minimum 

capital requirements and supervisory review process. 

4

 The U.S. banking crisis of the early 1930s showed the magnitude and degree of danger posed by the 

phenomenon of widespread bank runs. 

3 
 

 

offering some form of deposit protection.5 In the United States, the Federal Deposit 

Insurance Corporation (FDIC) guarantees all deposits up to $100,000 per depositor. Deposit 

insurance, however, weakens depositors’ monitoring incentives and undermines depositor 

discipline.6 Demirgürç-Kunt and Detragiache (2002), for example, provide evidence that 

explicit deposit insurance tends to increase the likelihood of banking crises in a sample of 61 

countries over the years 1980–1997.7 

In this paper, we investigate the presence of depositor discipline in the U.S. banking sector. The 

main hypothesis is that depositors withdraw uninsured deposits when bank performance 

deteriorates. Using panel data on bank-specific information, we test whether depositor discipline 

(or withdrawal of deposits) is linked to bank performance. We also account for the dynamic 

relationship between the price and quantity of uninsured deposits by controlling for banks’ ability 

to increase interest rates when facing a possible deposit drain. Our goal is to capture the 

dynamic interactions between price (interest rates) and quantity movements in uninsured 

deposits. 

When the quality of a bank’s fundamentals changes, both the demand and supply of 

uninsured deposits may be shifting simultaneously, obscuring the effect of depositor 

discipline. When bank fundamentals deteriorate, depositors withdraw their uninsured 

deposits, shifting the supply curve of uninsured deposits upward. As a result, the equilibrium 

moves upward along the bank’s demand curve, raising the price of deposits and lowering 

their quantity. In response, banks may offer a higher deposit interest rate to induce depositors 

not to withdraw their uninsured deposits. This would shift the demand curve upward, raising 

both the price and quantity of uninsured deposits. In the new equilibrium, the price of 

uninsured deposits is higher, with an ambiguous effect on the quantity of uninsured deposits. 

Clearly, depositors’ reaction and banks’ response is a jointly determined process. 

To account for these dynamic processes, we use the generalized-method-of-moments (GMM) 

estimators developed by Arellano and Bond (1991) for dynamic panel data. This generation 

of GMM models has the marked advantage that it is specifically designed to handle 

autoregressive properties in the dependent variable (uninsured deposits) and endogeneity 

issues between the dependent variable and an explanatory variable (the interest rate of 

uninsured deposits). 

5

 Demirgüç-Kunt and Sobaci (2001) provide a data set on the existence and extent of deposit insurance schemes 

across countries. For general reviews of deposit insurance systems around the world see also Demigürç-Kunt 

and Kane (2002) and Garcia (2000). 

6

 Some people have argued that the U.S. deposit insurance plan is responsible for the relatively small number of 

bank failures that has occurred since the creation of the FDIC (see discussion in FDIC, 1998). See also U.S. 

Department of the Treasury (1991) and U.S. Congressional Budget Office (1991). Kane (1992) provides 

evidence of the role of deposit insurance in the S & L crisis, and Kaufman (1995) discusses the impact of 

deposit insurance on bank capital. 

7

 In a related paper, Demirgüç-Kunt and Huizinga (1999) find that banks’ funding costs tend to be lower and 

less sensitive to fluctuations in bank fundamentals in countries with explicit deposit insurance. 

4 
 

 

Overall, our results support our choice of modeling interest rates as an endogenous 

mechanism. We find that banks can raise their level of uninsured deposits (as a share of total 

deposits) by raising their interest rates, although they can do so only at a relatively high price. 

We also find that, for a given increase in the interest rate of uninsured deposits, good banks 

attract relatively more uninsured deposits than the average bank, whereas bad banks may not 

necessarily be able to raise uninsured deposits. 

Note that while our empirical evidence supports the existence of depositor discipline 

(depositors recognize and penalize a bank’s bad performance), our results do not allow us to 

say anything about the effectiveness of depositor discipline (that is, the degree to which 

depositor discipline manages to reduce bank managers’ risk tolerance). This limitation, 

however, is a common feature of research in this area. We will return to this issue in Section 

I.C. 

B. Deposit Structure in the U.S. Banking Sector 

Deposits, including uninsured deposits, play an important role as a funding source for banks. 

In 2002, total deposits represented over 65 percent of U.S. banks’ total assets.8 This is shown 

in Figure 1. Although the share of total deposits to total assets has been falling progressively 

since 1992, this trend is primarily due to the substitution of insured deposits for non-deposit 

liabilities. The share of uninsured deposits to total assets, on the other hand, has been 

increasing sharply since 1995, rising from 11 percent in 1995 to 16 percent in 2002. 

Figure 1. Composition of Deposits (as Share of Total Assets) 

75.4 77.0 78.5 78.3 75.8 72.0 70.4 69.9 68.9 67.6 66.4 65.5 65.8 65.4 

13.4 13.2 12.7 12.7 11.8 11.3 12.1 13.4 14.2 14.3 14.4 15.5 16.3 12.7 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

1989

1990

1991

1992

19931994

1995

1996

1997

1998

1999

2000

2001

2002 

Percentage of Total Assets 

Total Deposits Uninsured Deposits 

8

 Total deposits include domestic deposits (insured and uninsured) and foreign deposits if applicable. These data 

are published in the “Quarterly Condition and Income Reports,” generally knows as “Call Reports.” The 

estimate for uninsured deposits is taken from the banks’ self-reported data published in the same source. 

5 
 

 

Despite the relatively large deposit insurance coverage, only 12.2 percent of all U.S. banks 

(1,152 banks) have less than 5 percent of their deposits that are uninsured; 22.3 percent of all 

banks (or 2,105 banks) hold more than 20 percent of uninsured deposits (as a share of total 

deposits); and 4 percent of all banks (or 403 banks) have more than 40 percent of their 

deposits in the form of uninsured deposits. At the far end of the spectrum, 25 banks hold 

more than 90 percent of their deposits in the form of uninsured deposits.9 The distribution of 

uninsured deposits (as a share of total deposits) across the U.S. banking sector is illustrated in 

Figure 2. 

Figure 2. Histogram of Uninsured Deposits, 2002 

1,465 

1,165 

736 

484 

312 

170 114 65 38 32 25 16 49 26 15 9 14 

1,152 

1,725 1,828 

0 

200 

400 

600 

800 

1000 

1200 

1400 

1600 

1800 

2000 

5% 

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

More 

Number of US Banks

Uninsured Deposits / Total 

Deposits 

Most uninsured deposits are held by the largest banks in the United States. Over 73 percent 

of uninsured deposits are held by the 172 banks that hold more than $5 billion in assets. The 

smallest banks (4,893 banks with less than $100 million assets) hold only 3 percent of total 

uninsured deposits. The remaining 24 percent of uninsured deposits are held in 4,386 banks 

with assets between $100 million and $5 billion. The distribution of uninsured deposits by 

bank size is shown in Figure 3, while the frequency of banks by bank size is illustrated in 

Figure 4. 

9

 These banks tend to be primarily commercial banks and, to a lesser extent, consumer banks and trust 

companies. 

6 
 

Figure 3. Uninsured Deposits (in U.S. dollars) by Bank Size 

3% 

9% 

15% 

28% 

45% 

<=100 Mil 

100-500 Mill 

500 Mill - 5 B 

5 -50 Bill 

>50 Bill 

Figure 4. Number of U.S. Banks by Size Category 

22 

3,517 

150 

859 

4,893 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

<=100 Mil 100-500 Mill 500 Mill - 5 B 5 -50 Bill >50 Bill 

Numbers of Banks and Savings Institutions 

It is interesting to note that the size of the bank does not significantly alter its reliance on 

uninsured deposits for funding. According to Figure 5, small banks (with total assets up to 

$100 million) fund on average 13 percent of assets with uninsured deposits; banks with assets 

between $5 billion and $50 billion fund 16 percent of assets with uninsured deposits. 

However, the difference between small and large banks is slightly larger if we look at 

uninsured deposits as a share of total deposits because total deposits represent a small 

funding source for larger banks. Small banks hold 16 percent of uninsured deposits (as a 

share of total deposits), whereas large banks (with assets between $5 billion and $50 billion) 

hold as much as 26 percent of uninsured deposits (as a share of total deposits). Again, these 

figures support the view that uninsured deposits represent an important funding source for 

banks, regardless of size. 

7 
 

 

Figure 5. Balance Sheet (in U.S. dollars) 

Percentage of Total Assets 100% 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

<=100 Mil 100-500 Mill 

0.11 

0.05 

0.71 

0.13 

0.10 

0.08 

0.65 

0.16 

0.10 

0.17 

0.55 

0.18 

0.10 

0.29 

0.44 

0.16 

0.08 

0.30 

0.45 

0.16 

500 Mill - 5 B 5 -50 Bill >50 Bill 

Uninsured Deposits Other Deposits Other Liabilities Equity 

C. Related Literature 

Researchers have expended considerable effort to measure depositor discipline. The most 

widely used approach is the price-based approach, which uses yield spreads (the difference 

between a market yield on bank debt and a market yield on a risk-free asset such as 

government debt) as a proxy for the market’s perception of bank risk. This strand of the 

literature investigates whether depositors punish bad banks by demanding a higher yield 

spread for holding uninsured bank liabilities. For example, Flannery and Sorescu (1996) 

investigated the nonlinear relationship between yield spreads on large uninsured bank 

liabilities and various measures of risks and found that spreads were closely related to 

balance-sheet and market measure of bank risk. Since then, the ability of financial markets to 

discipline bank behavior by pricing banks’ uninsured debt according to their risk has 

gathered significant attention (Evanoff and Wall, 2001, Morgan and Stiroh, 2001, Hancock 

and Kwast, 2001, and Sironi, 2003).10 

Another approach, the quantity-based approach, examines whether depositors discipline their 

banks by withdrawing their uninsured deposits whenever the performance of their banks is 

no longer satisfactory. For example, Goldberg and Hudgins (1996, 2002) find that failed 

thrift institutions exhibit declining proportions of uninsured debt before failure. Jordan 

(2000) finds a similar result for New England banks in the 1990s.11 Park and Peristiani 

10 A good overview of this strand of literature is given in Board of Governors of the Federal Reserve System 

(1999) and Board of Governors of the Federal Reserve System and the U.S. Treasury (2000), and U.S. empirical 

evidence on private investors’ abilities to assess the financial condition of banks can be found in Flannery 

(1998). 

11 Typically, in the literature on depositor discipline, risk measures are based on CAMEL indicators derived 

from balance sheets and income statements, such as non-accrual loans, past-due loans, other real estate 

ownership, equity ratios, leverage ratios, measure of profits, and loss ratios. 

8 
 

 

(1998) examine the effect of bank health on the quantity of uninsured deposits and the 

interest rate on these deposits. They find that riskier thrifts pay higher interest and attract 

smaller amounts of uninsured deposits. More recently, McDill and Maechler (2003) note that 

uninsured depositors are more responsive to movements in bank fundamentals in banks that 

already have low levels of equity. Similar evidence of depositor discipline has been found in 

countries other than the United States.12 

From a slightly different perspective, Covitz, Hancock and Kwast (2000) find that relatively 

weak banks are unwilling (or unable) to issue subordinated debt in bad times. While Gilbert 

and Vaughan (2001) discern no effect on the behavior of uninsured savings deposits 

following a public enforcement announcement, Billet, Garfinkel, and O’Neal (1998) notice 

that a downgrade from Moody’s increases a bank’s reliance on insured deposits. 

This paper is based on the tradition of the quantity approach. The purpose of the paper, 

however, is not to focus solely on how the quality of bank fundamentals influences the 

behavior of uninsured deposits, a phenomenon that is already well documented in the 

literature. Its objective is to add a new dimension to the analysis and account for the fact that 

both prices and quantities of uninsured deposits may change in response to deteriorating 

fundamentals. When bank fundamentals deteriorate, depositors discipline their banks by 

withdrawing their uninsured deposits. This raises the price of uninsured deposits. Banks, 

however, can mitigate this drain on deposits by further raising the deposit interest rates. 

Depending on depositors’ interest sensitivity, troubled banks may continue to attract funds by 

offering higher interest rates to compensate uninsured depositors for the increasing risk they 

bear. To our knowledge, no paper has attempted to capture explicitly the possible 

endogeneity between the quantity and price mechanisms of uninsured deposits. 

In a general equilibrium framework, Boyd, Chang, and Smith (2002) study the role of the 

elasticity of the deposit supply curve on banks’ behavior. In contrast to our model, their 

model focuses on the cost of funding as banks’ binding constraint. In their model, when the 

supply of savings is allowed to be interest sensitive, banks cannot costlessly adjust their 

deposit interest rates in the face of higher deposit insurance risk premia and must adjust their 

risk profile to reduce their funding costs. We are interested in a similar phenomenon, except 

that our attention is on the quantity of uninsured deposits as banks’ binding constraint. If 

deposits are an important source of funding, and assuming that the supply of uninsured 

deposits is interest sensitive, weak banks may not be able maintain their deposit base through 

higher prices and presumably will need to reduce their risk profile. 

12 Using data for Argentina, Chile, and Mexico, Martinez Peria and Schmukler (2001) find that even small 

insured depositors exert depositor discipline by withdrawing deposits from weak banks. Barajas and Steiner 

(2000) notice that banks with stronger fundamentals benefit from lower interest costs and higher lending rates in 

Colombia. Meanwhile, Birchler and Maechler (2002) discover that in Switzerland depositors respond to 

institutional changes in the Swiss depositor protection system and behave differently across different types of 

banks with different implicit insurance levels. 

9 
 

 

 

Note that this paper does not shed light on the effectiveness of depositor discipline in 

reducing banks’ risk appetite. It examines the extent to which banks can (and do) raise their 

interest rates to retain their deposit base. In a theoretical framework, Blum (2002) 

demonstrates that higher prices may not necessarily ensure that a bank reduces its risk 

profile. Practical limitations have constrained empirical studies to investigate how depositors 

react to excessive bank risk taking, as opposed to how bank managers respond to or prevent 

higher funding costs. In a recent empirical study on U.S. bank holding companies, Bliss and 

Flannery (2002, page 361) conclude that the ability of changes in security prices to influence 

subsequent managerial actions “remains, for the moment, more a matter of faith than of 

empirical evidence.” In an attempt to overcome this difficulty, Nier and Baumann (2003) 

examine the relationship between bank asset risk and bank capital, and find that banks with a 

higher share of uninsured liabilities choose larger capital buffers for a given level of risk. 

II. MODELING APPROACH 

A. Empirical Methodology 

We use the generalized-method-of-moments (GMM) estimators developed for dynamic 

models of panel data by Arellano and Bond (1991).13 This methodology, which will be 

referred to hereafter as the Arellano and Bond (AB) model, is specifically designed to 

address three econometric issues relevant to the present paper, namely (i) the presence of 

unobserved individual effects (in the present case, bank-specific effects); (ii) the 

autoregressive process in the data regarding the behavior of uninsured deposits (i.e., the need 

to use a lagged-dependent-variable model); and (iii) the likely endogeneity between the 

dependent variable, the quantity of uninsured deposits, and one of the explanatory variables, 

the price (i.e., the interest rates) of uninsured deposits. 

In this paper, we are particularly interested in capturing the interaction between the 

movement in the quantity of uninsured deposits and a change in these same deposits’ interest 

rate margin. The panel estimator controls for this possible endogeneity by using internal 

instruments, that is, instruments based on lagged values of the explanatory variables. We use 

the following general reduced form: 

3 

(UDit −UDit −1 ) = α '∑(UDit −s −UDit −s−1 ) +β '(IM it − IM it −1 ) + s=1 (1) 

ρ'(X − X ) +θ '(M − M ) + (ε − ε ) it −1 it −2 it it −1 it it −1 

where UDit is the share of uninsured deposits to total deposits, Xit-1 is a vector of bankspecific variables, IMit, the interest rate margin between the interest rate on uninsured 

deposits and that on total deposits, and Mt is a vector of macroeconomic variables. 

13 For further readings, see also Arellano and Bover (1995) and Blundell and Bond (1998). 

10 
 

The vector of bank-specific variables, Xit-1, is included with a lag to account for the fact that 

balance-sheet and income-statement information is available to the public with a certain 

delay. In some regressions, we include an interest rate margin to control for movements in 

the price of uninsured deposits. According to equation 1, a bank’s ratio of uninsured deposits 

to total deposits, apart from bank-specific differences, is determined by four main factors: 

previous behavior of uninsured deposits, movements of the price variable, the evolution of 

the bank fundamentals, and general developments in the macroeconomy. 

B. Data 

To investigate the presence of depositor discipline in the United States, we use only public 

bank-specific information that is available to depositors. These data are derived from data 

submitted quarterly by the banks in the Quarterly Condition and Income Reports, generally 

known as the Call Reports. The Thrift Financial Report (TFR) supplied the data for thrift 

institutions. These data were supplemented with macroeconomic data to control for factors 

that might influence broad movements in the availability of uninsured deposits. The data for 

real GDP growth, GDP deflator, and inflation rate are derived from the Bureau of Economic 

Analysis, whereas interest rate information, such as the Treasury one-year constant maturities 

and the federal funds rate, came from the Federal Reserve Board. 

We initially collected data for all FDIC-insured institutions from 1987 to 2000. We used an 

annual frequency and selected the June quarter’s data, because June had the most complete 

reporting requirements for deposit information. The initial data contained 19,852 FDICinsured financial institutions (banks and thrifts). First, we eliminated merged institutions at 

the time of their merger. It was necessary to have a consistent approach to institutions that 

had merged during the sample period. If we had used the standard merger adjustment for 

banks, which involves creating a weighted average of the variables of merged banks, a great 

deal of useful information about risky banks would have been lost in the merger-adjusting 

process. Without any adjustments, the levels of the bank-specific variables would have 

jumped each time two institutions merged. Therefore, we decided to eliminate the merged 

institutions from the sample at the time of their merger. This reduced the data set to 19,551 

financial institutions. 

We also dropped those institutions for which we had fewer than five years of observations, 

which reduced the number of institutions to 12,921. For computational reasons, we removed 

the small banks, those that had assets of less than $100 million (in 1996 dollars). This 

restriction further decreased the sample to 3,095 institutions. The sample was further limited 

to 1,863 because of missing data for explanatory variables. 

In the regressions, all of the bank-specific variables are lagged by one year in order to reduce 

the possibility of endogeneity in one or more of the right-hand-side variables of the equation 

and because we think depositors will normally react to bank-specific information after some 

time has elapsed. This delay may stem from a delay in access to information or, in the case of 

11 
 

 

certificates of deposit (CDs), from a desire of the depositor to avoid penalties by waiting until 

maturity to withdraw deposits. 

C. Summary Statistics 

The summary statistics are presented in Table 1. The names of the variables are provided in 

column 1. The remaining three columns present the summary statistics for the variables over 

the entire 1987–2000 sample period (column 2), in 1989 (column 3), and in 2000 (column 4). 

For each period, the table indicates the total number of observations and the average value 

for each variable, with the standard deviation presented below in parentheses. The number of 

banks included in the sample declines between 1989 and 2000, both because the total number 

of U.S. banks fell over the sample period but also because merged banks were dropped from 

the sample at the time of their merger, as mentioned in the previous section. 

Dependent variable 

We define the dependent variable in the model as the quantity of uninsured deposits as a 

percentage of total deposits, or UninsDep. According to Table 1, uninsured deposits 

represented on average 15 percent of total deposits, rising from under 14 percent in 1989 to 

over 19 percent in 2000 for the average bank in our sample. 

Price variables 

To control for the dynamic process between the quantity of uninsured deposits and their 

price, we compute UninsIntMarg, the margin between a bank’s interest rate on uninsured 

deposits (Jumbo CDs) and its average deposit interest rate. This price variable captures the 

desired composition of deposits in a particular bank (i.e., whether a bank is willing to pay a 

higher price to attract more insured or uninsured deposits). The interest rate data are inferred 

indirectly from computing the ratio of interest expenditures on Jumbo CDs to the level of 

total Jumbo CDs. A rise in UninsIntMarg means that bank i pays a relatively higher price for 

its uninsured deposits than for its insured deposits; hence, the rise should be associated with a 

higher share of uninsured deposits to total deposits, the dependent variable UninsDep. 

To check the robustness of our results, we have computed two alternative measures of 

interest margins: JumboIntMarg, the margin between the interest rate of bank i on a jumbo 

CD and the industry average interest on jumbo CDs; and DepIntMarg, the margin between 

the deposit interest rate of bank i and the industry average deposit interest rate. By capturing 

the price of deposits offered by a particular bank relative to the price offered on average in 

other banks, these alternative price variables help explain the allocation of deposits among 

banks (i.e., whether a particular bank is willing to offer a higher interest rate relative to that 

offered by other banks). 

12 
 

Table 1: Summary Statistics in Level 

(as a percentage unless indicated otherwise) 

Variable Name 

(1) 

Full Set of Data 

(2) 

In 1989 

(3) 

In 2000 

(4) 

Obs Mean 

(Std. Dev.) 

Obs Mean 

(Std. Dev.) 

Obs Mean 

(Std. Dev.) 

Uninsured deposit 1/ 20630 94,747 1846 91,110 830 153,909 

(633,882) (655,872) (1,061,837) 

Total deposits 1/ 20630 478,418 1846 409,267 830 809,934 

(4,281,228) (2,728,150) (9,181,614) 

Dependent Variable 

UninsDep 20630 14.91 

(13.98) 

1846 13.75 

(13.74) 

830 19.16 

(15.17) 

Price Factors 

UninsIntMarg 20030 1.59 1761 2.21 826 1.74 

(1.55) (1.57) (0.93) 

JumboIntMarg 20030 -0.01 1761 -0.04 826 0.03 

(1.48) (1.41) (0.91) 

DepIntMarg 20601 -0.33 1843 -0.44 829 -0.19 

(0.83) (0.92) (0.77) 

Bank Specific Variables 

Assets 1/ 20630 725,633 1846 666,980 830 1,088,684 

(6496123) (4882174) (11,900,000) 

Size 20630 12.49 1846 12.44 830 12.69 

(0.86) (0.89) (0.86) 

AssetGrowth 18768 8.64 1839 6.34 830 7.59 

(54.54) (12.56) (12.70) 

Equity 20630 9.02 1846 8.20 830 10.46 

(3.31) (2.48) (4.37) 

RoA 20630 1.11 1846 1.08 830 1.30 

(1.06) (0.74) (0.82) 

Loans 20630 59.26 1846 60.81 830 62.67 

(15.13) (15.00) (14.97) 

ResidLoans 20621 34.70 1845 31.17 829 40.28 

(22.67) (21.25) (24.98) 

NonCLoans 20375 1.70 1763 1.87 829 0.76 

(2.21) (2.07) (1.08) 

Macroeconomic Variables 

GDP 20630 3.03 1846 3.55 830 5.09 

(1.46) (0.00) (0.00) 

Infl 20630 2.70 1846 3.96 830 2.23 

(0.82) (0.00) (0.00) 

Treas1 20630 6.11 1846 8.44 830 6.17 

(1.44) (0.00) (0.00) 

1/ In thousands of 1996 U.S. dollars. 

13 
 

Bank-specific factors 

The size of a bank may influence depositors’ willingness to hold uninsured deposits at that 

institution. Larger banks may attract large businesses that require large transaction accounts 

and a variety of special lending and service arrangements. Larger banks may also be 

perceived as less likely to fail, either because their larger, more diversified customer base 

makes them appear more substantial (and safer) or because they may have a larger chance of 

being bailed out if they should run into financial difficulties. Against this background, we 

computed three proxies to control for the size of the bank. First, the table of summary 

statistics presents the level of assets (in thousands of 1996 U.S. dollars), or Assets. According 

to Table 1, the assets of an average bank in our sample increased by over 60 percent between 

1989 and 2000, increasing from $667 million in 1989 to $1,089 million in 2000. Because the 

constant (1996) dollar value of Assets had such a large relative variance when compared with 

the percentage of uninsured deposits to total deposits, we included in our regressions the 

natural log of Assets, which we defined as our size variable, Size. We also included asset 

growth (AssetGrowth), defined as the percentage growth rate of real assets between two 

years, to control for differences between higher-growth banks and lower-growth banks. Over 

the sample period, assets grew on average by 8.6 percent. Presumably banks with higherthan-average growth are flourishing and may exhibit a higher demand for uninsured deposits. 

On the other hand, high growth in a bank may indicate excessive risk taking and an 

aggressive asset acquisition policy, possibly to the detriment of asset quality, and thus be 

associated with lower uninsured deposits. 

The remaining explanatory variables include a set of four proxies for bank quality that 

uninsured depositors are most likely to monitor. A bank’s equity level is an important 

indicator of the bank’s health and ability to withstand adverse shocks. To examine the effect 

of equity on uninsured deposits, we included the variable Equity, measured as the ratio of 

total equity capital (the residual between liabilities and assets) to total assets, multiplied by 

100 to convert it into a percentage. According to Table 1, Equity over the sample averaged 9 

percent of assets and increased from 8.2 percent in 1989 to 10.5 percent in 2000. Another 

important indicator of bank health is return on assets, RoA, which averaged 1.1 percent 

return over the whole sample, increasing from slightly less than 1.1 percent in 1989 to 1.3 

percent in 2000. 

Interest income and fees generated by lending activities, which typically represent a major 

source of income for banks, affect overall bank profitability and hence influence depositors’ 

willingness to keep uninsured money in their banks. To capture this effect, we included loans 

and leases as a percentage of assets, or Loans. Loans represented slightly under 60 percent of 

assets for the banks in the sample overall, increasing from 59 percent in 1989 to 63 percent in 

2000. We also included loans made for the purchase of residential real estate as a percentage 

14 
 

 

of total loans, or Residential.

14 These loans, which include only loans made for properties 

that accommodate small numbers of families, are typically considered to be very safe. 

Therefore, a bank that invests heavily in this type of loan (rather than, for example, in 

commercial and industrial loans) would generally be considered a low-risk bank. On average, 

the loan portfolios of the banks in the sample consisted of 35 percent residential real estate 

loans; this percentage rose from 31 percent to 40 percent between 1989 and 2000. 

We also included a variable NonCLoans, which is defined as the percentage of loans that are 

non-current — 90 days or more past due — to capture the quality of banks’ loan portfolios. 

This variable is typically considered to be a very good indicator of bank health. Over the 

sample period, average NonCLoans fell 1.1 percentage points, from 1.9 percent to 0.8 

percent between 1989 and 2000, with a panel average of 1.7 percent across the sample. 

Macroeconomic factors 

To control for the general conditions of the banking system, we included three 

macroeconomic variables to capture the possibility that the behavior of uninsured deposits is 

linked to general economic conditions. Our regressions contained the GDP growth rate and 

its lagged variable (GDPt and GDPt-1) to control for the general strength of the economy, the 

change in the GDP deflator (Infl) to control for the falling value of insurance limits,15 and the 

one-year constant maturity U.S. Treasury bond interest rate (Treas1). This Treasury interest 

rate controls for the effect of changes in the return on alternative saving instruments on bank 

deposits. We expect GDP and Infl to have a positive relationship with the dependent variable 

(UninsDep), and Treas1 to have a negative relationship with the dependent variable. The 

latter expectation is based on the idea that bank deposits may lose their attractiveness when 

the return on other savings instruments, such as Treasury bonds, rises. 

Real GDP growth surged toward the end of the sample to 5.1 percent in 2000, whereas 

inflation remained fairly low throughout the period, with a sample average of 2.7 percent. 

The one-year Treasury interest rate fluctuated between 8.4 percent in 1989 and 6.2 percent in 

2000. 

D. Simple Correlations 

Table 2 provides the partial correlations between the dependent variable and bank-specific 

variables: in the top section, the partial correlation is in levels; in the bottom section it is in 

differences. 

14 The residential loans in this variable include only mortgages made on properties that house one to four 

families. 

15 In addition, controlling for both the inflation rate and a proxy for the nominal interest rate allows us to talk 

about movements in the real interest rate. 

15 
 

The most noteworthy result is the contrast between the positive partial correlation between 

the level of uninsured deposits and the interest margin (top section) and the negative partial 

correlation between changes in uninsured deposits and changes in the interest rate (bottom 

section). The banks that exhibit a relatively high share of uninsured deposits are also the 

banks that offer a relatively high interest rate on their uninsured deposits. However, this does 

not mean that banks can necessarily raise funds from uninsured depositors by raising their 

interest rates on uninsured deposits. 

Table 2. Partial Correlations 

In Levels UninsDep UninsDep_L1 UninsIntMarg Equity_L1 Size NonCL_L1 

UninsDep 

UninsDep_L1 

UninsIntMarg 

Equity_L1 

Size 

NonCurrLoans_L1 

ResidLoans_L1 

1 

0.9063 

0.0869 

0.0519 

0.4467 

0.0915 

-0.4021 

... 

1 

0.0954 

0.043 

0.4457 

0.1059 

-0.4092 

... 

... 

1 

-0.0419 

0.0123 

0.0193 

-0.1164 

... 

... 

... 

1 

-0.0971 

-0.071 

0.1458 

... 

... 

... 

... 

1 

0.0484 

-0.1586 

... 

... 

... 

... 

... 

1 

-0.1674 

In First Differences D.UninsDep D.UninsDep_L1 D.UninsIMarg D.Equity_L1 D.Size D.NonCL_L1 

D.UninsDep 

D.UninsDep_L1 

D.UninsIMarg 

D.Equity_L1 

D.Size 

D.NonCurrLoans_L1 

D.ResidLoans_L1 

1 

-0.2761 

-0.019 

0.0427 

0.1677 

-0.019 

0.0144 

1 

0.0377 

-0.0541 

0.0114 

-0.013 

-0.0207 

... 

... 

1 

0.0419 

-0.0231 

-0.0015 

0.0081 

... 

... 

... 

1 

-0.0224 

-0.0015 

0.0095 

... 

... 

... 

... 

1 

-0.0467 

-0.0367 

... 

... 

... 

... 

... 

1 

-0.0261 

1/ L1 = with one-period lag; D = in first difference 

The negative relationship between a change in the price and a change in the quantity of 

uninsured deposits suggests that some banks that raise their interest rates experience a fall in 

the level of uninsured deposits. This seems to indicate that some banks face an upward shift 

in their supply of uninsured deposits. Although the data are marred with noise both across 

banks and across economic cycles, some banks may have to offer a higher interest rate in 

order to attract the same, or even a lower, level of uninsured deposits. 

At this juncture in the model, a mechanism is needed that allows us to differentiate between 

an upward shift in the supply curve of uninsured deposits (where, for each given level of 

interest rate, depositors are willing to supply a lower volume of uninsured deposits) and an 

upward shift in the demand curve (where, for each given level of interest rate, banks are 

demanding a larger quantity of uninsured deposits). Assume a bank’s deteriorating 

fundamentals induce an outflow of uninsured deposits, which reduces the supply of deposits 

and endogenously raises their price. The relationship between price and quantity can be 

further obscured because a bank may respond to such a deposit drain by offering a higher 

interest rate on its uninsured deposits. Thus, it is important to use an econometric model that 

can control for the endogenous price effect of a supply shift and can focus on banks’ 

independent pricing decisions. Assume, for example, that a weak bank raises its interest rate 

to retain its deposit base. At this higher price, some depositors may be willing to leave their 

16 
 

 

uninsured deposits with this (weak) bank. Even though a simple reading of the quantity data 

may not necessarily suggest that depositors penalize weak bank performance, the behavior of 

depositors would be consistent with depositor discipline. These issues are examined 

empirically in the next section. 

III. RESULTS 

A. Controlling for Bank Fundamentals 

As a first approximation, we examine the extent to which movements in bank-specific 

variables explain the behavior of uninsured depositors, controlling for general 

macroeconomic conditions. In this set of regressions, we do not include any price 

mechanism. The dependent variable and the explanatory variables are all expressed in first 

differences. The results are illustrated in Table 3. Column 1 presents the results of the firststep estimation, while column 2 presents the two-step estimates. Arellano and Bond (1991) 

recommend using the first-step estimates for inference on coefficients, since these are 

calculated using a robust estimator of the variance-covariance matrix. The two-step estimates 

are used primarily to compute robustness tests. 

The particular model specification (and variable choice) presented in columns 1 and 2 has 

been selected because (i) it follows closely the bank-specific variables used in earlier related 

papers, (ii) it replicates standard results found in the literature using a minimum number of 

bank-specific variables, and (iii) the results remain robust across a wide variety of variables 

and groupings of variables.16 Thus, we are confident that our regression results are not 

specific to the particular choice of bank-specific variables included in our model. 

The data seem to support our general hypothesis that the fraction of uninsured deposits to 

total deposits is related to the movements in a bank’s fundamentals. Except for non-current 

loans and leases, NonCLoans, which is not statistically significant, we find that all of our 

bank-specific variables are statistically significant and have the expected sign. For example, a 

bank with a rising equity rate will experience an upward movement in its share of uninsured 

deposits. This is shown by the statistically significant and positive coefficient of Equity. 

16 We have run the model specification of column 1 with a large number of alternative variables and sets of 

variables, all of which turned out either not to be statistically significant or not to add value to the interpretation 

of the results. 

17 
 

Table 3. Empirical Results 

Without 

Price Mechanism 

Exogenous 

Price Mechanism 

Endogenous 

Price Mechanism 

Variable Name Lags 1/ One-step Two-step 

Robust 

(1) (2) 

One-step Two-step 

Robust 

(3) (4) 

One-step Two-step 

Robust 

(5) (6) 

UninsDep L1D 

L2D 

L3D 

UninsIntMarg D 

Equity L1D 

Size D 

NonCLoans L1D 

ResidLoans L1D 

GDP D 

L1D 

Infl D 

Treas_1 D 

Cons 

Obs. 

Banks 

AB test of no AR(1)

 Pr > z = 

AB test of no AR(2)

 Pr > z = 

Sargan test of overidentification 

 Pr > chi2 = 

0.419 *** 0.387 *** 

0.022 0.026 *** 

-0.002 0.003 

... ... 

0.468 *** 0.425 *** 

9.705 *** 11.433 *** 

-0.013 0.006 

0.053 * 0.037 ** 

0.190 *** 0.151 *** 

0.040 0.072 *** 

0.496 *** 0.147 

-0.203 ** -0.173 *** 

-0.005 -0.066 

13,085 13,085 

1,863 1,863 

0.00 0.00 

0.93 0.95 

... 0.02 

0.392 *** 0.342 *** 

0.025 0.029 *** 

-0.004 -0.008 

0.098 -0.036 

0.468 *** 0.429 *** 

10.058 *** 12.839 *** 

-0.023 0.014 

0.054 * 0.048 *** 

0.207 *** 0.150 *** 

0.056 0.087 *** 

0.556 *** 0.106 

-0.280 *** -0.184 *** 

-0.024 -0.100 * 

12,826 12,826 

1,863 1,863 

0.00 0.00 

0.73 0.96 

... 0.08 

0.381 *** 0.339 *** 

0.026 0.022 ** 

-0.001 -0.007 

0.890 * 0.502 *** 

0.459 *** 0.445 *** 

9.889 *** 12.882 *** 

-0.052 0.006 

0.058 * 0.049 *** 

0.236 *** 0.181 *** 

0.080 * 0.095 *** 

0.599 *** 0.132 

-0.456 *** -0.288 *** 

-0.047 -0.122 ** 

12,826 12,826 

1,863 1,863 

0.00 0.00 

0.60 0.88 

... 0.12

1/ Li D = in first difference with i lag(s); and D = in first difference. 

The coefficient of Size is positive and particularly strong. Because the AB model uses all 

variables in first difference, the coefficient for Size can be thought of as the effect of asset 

growth. It may also reflect an increased demand for uninsured deposits on the part of the 

bank. This possible demand effect is discussed in more depth below. Similarly, the positive 

coefficient of ResidLoans is consistent with the relatively safe nature of residential loans and 

suggests that uninsured depositors have more confidence in a bank that exhibits a higher 

share of residential mortgage lending in its credit portfolio. 

The behavior of uninsured deposits seems to respond to business conditions. All three 

macroeconomic variables are strongly significant with the expected sign. The growth of 

18 
 

 

 

uninsured deposits tends to rise during booms (high GDP growth), during periods when 

inflation is increasing, and during periods when Treasury bond rates are falling.17 

It is interesting to note that our results suggest a strong persistence in the behavior of 

uninsured depositors. If an innovation raises uninsured deposits today, the effect of this 

innovation will persist over the following year(s), although at a decreasing rate. This is 

shown in column 1 of Table 3, where the first lag and in some cases the second lag of 

uninsured deposits (respectively, UninsDepL_D and UninsDep_L2D) are significant in 

predicting next year’s growth of uninsured deposits. This result is confirmed by the AB test 

of autocorrelation presented at the bottom of column 1.18 The persistence in the behavior of 

uninsured deposits reflects the relative stability of the U.S. banking system. Typically, if a 

bank is small today, it will remain small tomorrow and hence attract the same type of 

depositors. Similarly, if a bank has a good reputation today, it is likely to enjoy a good 

reputation tomorrow. But if something changes in the system — say, a bank’s fundamentals 

start deteriorating — the results suggest that, in line with the depositor discipline hypothesis, 

depositors will punish this bank by withdrawing their uninsured deposits. Thus, persistence 

in the behavior of deposits can be consistent with depositor discipline. 

Finally, consistency of the GMM estimator depends also on the validity of the instruments. 

To address this issue, we use the Sargan test of over-identifying restrictions, which tests the 

overall validity of the instruments. If the regression specification passes the test, we can 

safely discard the possibility that the relationship between the quantity of uninsured deposits 

(as a share of total deposits) and the price of uninsured deposits is due to a simultaneity bias 

or omitted variables.19 Unfortunately, the results for this regression do not satisfy the Sargan 

test, suggesting that the model specification presented in columns 1 and 2 is misspecified. A 

possible reason for this misspecification may be that we do not control properly for the 

dynamic interactions between the quantity and price of uninsured deposits. This problem 

may be best illustrated with an example. 

We have seen above that the coefficient of Size is positive and particularly strong, suggesting 

that depositors interpret strong asset growth as a signal of good health. Presumably, however, 

17 The last two results suggest that the share of uninsured deposits to total deposits falls when real interest rates 

rise. 

18 In the AB framework, by construction there should be first-order autocorrelation in the residuals, but there 

should be no second-order autocorrelation.. Our findings are consistent with this requirement. The null 

hypothesis of no first-order autocorrelation in the differenced residuals is rejected, but it is not possible to reject 

the null hypothesis of no second-order autocorrelation. 

19 Under the Sargan test, the null hypothesis assumes that the over-identifying restrictions are valid. If the 

Sargan test from the one-step homoskedastic estimator rejects the null hypothesis, this may be due to 

heteroskedasticity (the AB framework allows for the presence of first-order autocorrelation in the differenced 

residuals, as this type of autocorrelation does not imply inconsistent estimates). Using a two-step estimator can 

produce large efficiency gains. For this reason, the two-step Sargan test is typically used for inference on model 

specification. 

19 
 

strong asset growth may also reflect a bank’s greater need for deposits, which may or may 

not be a function of the bank’s good performance. For example, a bank that engages in an 

aggressive asset acquisition policy may also experience strong asset growth, although this 

would be the result of excessive risk taking rather than prudent management. Assuming that 

this bank is willing to offer higher interest rates to satisfy its growing funding needs, strong 

asset growth may be associated with a rise in uninsured deposits, even if depositors recognize 

the higher underlying risk profile of the bank. To properly conclude whether good 

fundamentals or a bank’s desire to retain its uninsured deposit base through higher interest 

rates causes a rise in uninsured deposits, we need to control for movements in the interest 

rates. This issue is addressed in the next section. 

B. Introducing an Exogenous Price Mechanism 

In this section, we examine whether the price (the interest rate) of uninsured deposits 

influences the behavior of depositors. We include UninsIntMarg, the margin between the 

interest rate on uninsured deposits and that on total deposits, as an additional explanatory 

variable. In this specification, we focus on whether an increase in the interest margin is 

associated with a rise in the level of uninsured deposits (as a share of total deposits). 

We find that an increase in the interest margin, when treated as an exogenous change, has an 

effect on uninsured deposits that is very slightly positive but not statistically different from 

zero. This is shown in column 3, where the coefficient of UninsIntMarg is positive but not 

statistically significant. The positive relationship between changes in the price and quantity 

of uninsured deposits contrasts with the negative partial correlation shown in Table 2. By 

controlling for risk factors that shift the quantity of uninsured deposits, we are able to capture 

some of the mechanisms driving the negative partial correlation, although we are not yet able 

to say convincingly whether banks can raise their level of uninsured deposits by raising the 

price of uninsured deposits. The other bank-specific and macroeconomic coefficients remain 

consistent with those found under the previous specification presented in columns 1 and 2. 

As in the previous section, our results are consistent with the requirement of no second-order 

serial correlation and continue to fail the Sargan test of over-identification. This suggests that 

we are still not properly controlling for the dynamic process between the quantity and price 

of uninsured deposits. One problem may be that we are missing a dimension in the 

endogenous mechanism driving the behavior of uninsured deposits. 

A bank’s decision to raise its interest rates may not be independent of depositors’ desire to 

withdraw their funds, which in itself is a function of banks’ risk profile. To address this issue, 

we need to control for the impact of a change in bank fundamentals on the quantity and price 

of uninsured deposits so that we can examine separately the fundamental effect of a bank’s 

decision to raise its interest rates on the behavior of uninsured deposits. If a bank’s 

deteriorating fundamentals cause depositors to withdraw their uninsured deposits, the bank in 

response may decide to raise its interest rate in an effort to retain its uninsured deposits. The 

overall effect on uninsured deposits would be ambiguous, as suggested by our results. In 

practice, depositors may be disciplining (or penalizing) their banks by shifting the supply of 

20 
 

 

uninsured deposits upward and raising the cost of bank funding. The disciplining effect of 

depositors is obscured by the bank’s ability to raise its interest rates. Thus, we need to 

address the three-dimensional relationship between the price of uninsured deposits, the 

quantity of uninsured deposits, and a bank’s financial health. 

C. Addressing the Endogeneity of the Price Mechanism 

In this section, we account for the fact that movements in the price and quantity of uninsured 

deposits may be jointly determined with the financial soundness of a bank. For this, we need 

to control for the possibility that a weak bank, which is experiencing a drain of uninsured 

deposits due to deteriorating fundamentals, may raise its interest rate. The idea is to eliminate 

potential parameter inconsistency arising from simultaneity or reverse causality between 

quantity and price movements of uninsured deposits and examine whether a change in the 

interest rate can exogenously influence the quantity of uninsured deposits. 

According to the standard depositor discipline hypothesis, a deterioration in a bank’s 

fundamentals should induce depositors to withdraw their uninsured deposits from this weak 

bank. Graphically, this would represent an upward shift of the supply curve of uninsured 

deposits, where the equilibrium moves along the demand curve to a lower quantity and 

higher price of uninsured deposits. It is possible, however, that a weak bank may raise its 

interest rate on uninsured deposits further to contain the deposit drain, hence shifting upward 

the demand curve for uninsured deposits. To disentangle these two effects, we need to 

control for the possible price impact of deteriorating bank fundamentals (i.e., the supply shift 

due to deteriorating fundamentals) and examine whether a rise in the interest rates can exert a 

causal impact on the quantity of uninsured deposits (i.e., a shift in the bank’s demand for 

uninsured deposits). 

The way the AB model controls for the dynamic interactions between quantity and price 

movements of uninsured deposits is by using internal instruments. Appropriate instruments 

are highly correlated with the endogenous variables and not correlated with the error term. 

Hansen (1982) has shown that a first-difference transformation, like the one used in equation 

1, allows the use of suitably lagged levels of the endogenous variables as valid instruments.20 

More recently, AB have identified how these lagged levels can be combined with firstdifferences of the strictly exogenous variables to control for potential biases induced by 

simultaneity or reversal causality between endogenous variables.21 This technique allows us 

20 The use of lagged observations of the independent and explanatory variables as instruments was first 

introduced in the GMM framework pioneered by Hansen (1982). Efficient GMM estimators will typically 

exploit a different number of instruments in each time period. For more details, see Arellano and Bond (1991), 

Arellano and Bover (1995), and Blundell and Bond (1998). 

21 This methodology has been used widely in the growth literature, where it is important to disentangle the 

factors that contribute to economic growth (such as financial development or capital accumulation) from the 

reverse effect of how economic growth influences these factors. A good illustration is presented in Levine, 

Loayza and Beck (2000). 

21 
 

to determine whether interest rate movements have an exogenous impact on the quantity of 

uninsured depositors, independently of the endogenous impact of deteriorating fundamentals 

on the price and quantity of uninsured deposits. 

The first- and second-step estimates are presented in, respectively, columns 5 and 6 of Table 

3. The model specification passes the Sargan test, suggesting that the relationship between 

the quantity of uninsured deposits (as a share of total deposits) and the price of uninsured 

deposits is not flawed by a simultaneity bias or omitted variables. Similarly, the model 

satisfies the no second-order serial correlation requirement. Except for UninsIntMarg, the 

coefficients remain broadly consistent with those found in the previous regressions. In the 

case of UninsIntMarg, the coefficient is positive and statistically significant. This result 

suggests that once we control for the dynamic process determining the behavior of uninsured 

deposits, banks are able to borrow additional uninsured deposits by offering higher interest 

rates. 

If banks are able to raise their uninsured liabilities by offering higher interest rates, the 

question remains by how much should banks have to raise their interest rates to benefit from 

an inflow of uninsured deposits. Our results suggest that banks have to pay a relatively high 

price to receive additional uninsured funds. According to column 5, the coefficient of 

UninsIntMarg is 0.89. This means that if a bank were to increase its interest margin on 

uninsured deposits by 1 percentage point (from, say, 2.2 percent to 3.2 percent), uninsured 

deposits would rise by 0.89 percentage points (from, say, 10 percent to 10.89 percent). This 

seems to represent a very high funding cost to the banks. 

For illustration, assume a bank has $10 million uninsured deposits, which represent 10 

percent of the bank’s total deposits. For the sake of this exercise, assume that at the 

beginning the bank pays no interest rate on its deposits. Then, to raise more funds, it is 

willing to pay 1 percent interest rate on its uninsured deposits. For simplicity, further assume 

that the rise in interest rate induces an inflow of new uninsured deposits. In this case, it is 

easy to show that uninsured deposits would increase by $9.99 million to $109.99 million and 

the bank would have to pay $1.099 million in interest, or an 11 percent funding cost. The 

supply curve of uninsured deposits seems to be fairly inelastic. It is likely that this high 

average funding cost comes from large differences among banks of different qualities. In 

Section IV.A we examine the possibility of a nonlinear relationship between the interest 

elasticity and a bank’s quality. 

The results suggest that uninsured depositors discipline their banks. This is an interesting 

result, given that approximately 75 percent of all deposits are guaranteed under the U.S. 

deposit insurance scheme. Thus, despite a high level of deposit insurance, we find evidence 

that in the United States the depositors holding the remaining 25 percent of uninsured 

deposits discipline their banks. 

22 
 

 

IV. ROBUSTNESS TESTS 

A. Weak versus Healthy Banks 

In this section, we examine whether depositors in weak banks are more sensitive and require 

a higher price premium than depositors in healthy banks. We use the CAMEL ratings to 

subdivide our sample of banks into different quality categories. CAMEL ratings are the result 

of a supervisory examination and are confidential information. They assess the Capital 

adequacy, Asset quality, Management, Earnings, and Liquidity of banks and express an 

overall rating on a scale of 1 to 5 in ascending order, where 1 is the best rating and 5 is the 

worst rating.22 Although depositors are not generally aware of changes in the CAMEL rating 

of their bank, these ratings are a good proxy for the overall quality of a bank. 

Given the time series nature of the econometric model, we needed a system for categorizing 

the quality of a bank that was consistent for a given bank throughout the entire sample. Thus, 

using the CAMEL ratings, we have computed three broad types of banks; “good” banks (if 

the bank always had a CAMEL rating of 1 or 2), “bad” banks (if the bank ever earned a 

CAMEL rating of 3, 4, or 5 at any time during the sample), and “very bad” banks (if the bank 

ever earned a CAMEL rating of 4 or 5 at any time during the sample).23 The majority of 

banks included in our regression results are good banks (1,307 banks). There were also 556 

bad banks and 207 very bad banks. On average, the good banks included in our regressions 

held a lower percentage of uninsured deposits to total deposits than banks with worse 

CAMEL ratings. Good banks held 15.2 percent of uninsured deposits (as a share of total 

deposits), whereas the bad banks had an average uninsured deposits of 16.2 percent, and the 

very bad banks, those with CAMEL ratings of 4 or 5, had an average of 16.7 percent of 

uninsured deposits to total deposits.24 

22 In 1997, a fifth component, Sensitivity was added to the rating system to capture sensitivity to market risk. 

Since then, the acronym has been changed to CAMELS. 

23 Banks may switch from one rating category to another, and for the purpose of this exercise it is not relevant to 

restrict our analysis to banks that always remain within one particular category. Instead, we have computed 

broader categories that allow a bank’s performance to be followed across CAMEL ratings. 

24 This result is driven mostly by the large sample size differences across categories and the relatively flat 

distribution curves. Typically, the sample size problem (especially in the case of bad and very bad banks) is best 

addressed by looking at the median rather than the mean. Another important note is that a bank needs to have 

only a one-time CAMEL 3 or worse rating to be categorized as a “bad” bank. This factor helps explain the 

relatively high share of uninsured deposits for our category of bad and very bad banks. If we restrict our 

attention to the CAMEL rating categories, the median share of uninsured deposits to total deposits is highest for 

the best-rated banks (12.3 percent) and drops progressively to 8.5 percent for the worst-rated banks. 

23 
 

 

Table 4. Empirical Results across Different CAMEL-based Classifications 1/ 2/ 

Variable Name Lags 3/ 

Benchmark 

(1) 

Good Banks 

(always 1 or 2) 

(2) 

Bad Banks 

(if ever 3, 4 or 5) 

(3) 

Very Bad Banks 

(if ever 4 or 5) 

(4) 

Uninsured Deposits 

(% of total deposits) 

15.474 

(13.508) 

15.161 

(13.452) 

16.185 

(13.608) 

16.676 

(13.974) 

UninsDep L1D 0.382 *** 0.349 *** 0.376 *** 0.426 *** 

(0.064) (0.074) (0.093) (0.111) 

L2D 0.025 0.028 0.005 -0.065 

(0.036) (0.045) (0.040) (0.070) 

L3D -0.002 -0.027 0.036 0.041 

(0.030) (0.040) (0.028) (0.058) 

UninsIntMarg D 0.880 * 

(0.497) 

1.292 * 

(0.746) 

0.162 

(0.205) 

0.698 

(0.680) 

Size 9.721 *** 8.549 *** 10.581 *** 13.066 *** 

(2.340) (3.251) (2.849) (4.022) 

Loans&Leases 0.038 * 0.067 ** -0.008 -0.043 

(0.021) (0.030) (0.034) (0.054) 

ResidLoans D 0.061 ** 0.039 0.093 * 0.195 * 

(0.031) (0.039) (0.050) (0.106) 

Equity L1D 0.445 *** 0.439 ** 0.386 *** 0.626 *** 

(0.133) (0.180) (0.126) (0.205) 

NonCLoans L1D -0.046 -0.094 -0.038 -0.014 

(0.040) (0.061) (0.052) (0.069) 

GDP L1D 0.245 *** 0.257 *** 0.205 *** 0.132 

(0.047) (0.059) (0.061) (0.128) 

L1D 0.090 * 0.141 ** -0.051 -0.145 

(0.049) (0.061) (0.069) (0.164) 

Infl D 0.599 *** 0.908 *** 0.140 -0.352 

(0.187) (0.245) (0.217) (0.555) 

Treas_1 L1D -0.474 *** -0.716 *** 0.031 0.283 

(0.146) (0.200) (0.107) (0.313) 

Cons D -0.048 0.004 -0.010 -0.083 

(0.124) (0.174) (0.128) (0.209) 

Observations 12826 8912 3914 1380 

Banks 1863 1307 556 207 

Test of no AR(1) 4/ 0.000 0.000 0.000 0.007 

Test of no AR(2) 4/ 0.597 0.838 0.456 0.340 

Sargan test 5/ 0.146 0.032 0.095 0.318 

1/ Model with endogenous price mechanism. 

2/ Coefficients from first-step robust estimations, unless specified otherwise. 

3/ Li D = in first difference with i lag(s); and D = in first difference. 

4/ AB(i ) is the Arellano and Bond test of no autocorrelation of degree i. 

5/ Sargan test is based on two-step regression results. 

24 
 

Table 4 presents the regression results across different CAMEL-based categories. Column 1 

reprints the results of the benchmark model specification, that is, the first-step version of the 

model with the endogenous price mechanism presented in column 5 of Table 3. The next 

three columns in Table 4 present the results of the same model specification run under the 

various CAMEL-based bank categories. Column 2 presents the regression results for the 

good banks, column 3 for the bad banks, and column 4 for the very bad banks. 

According to our benchmark model presented in Section III.A, banks can raise the ratio of 

uninsured deposits to total deposits by increasing their uninsured deposit interest rates, but 

only by paying a relatively high price (a 1 percentage point increase in the interest margin 

raises the ratio of uninsured deposits to total deposits by 0.89 percentage point). By 

differentiating between different qualities of banks, we find that for a 1 percentage point 

increase in the interest rate, good banks can raise a relatively higher proportion of uninsured 

deposits than banks with a lower-quality standing. This is shown in column 2, where the 

coefficient of UninsIntMarg is higher (1.29) than that for the benchmark model (0.89) in 

column 1. We also find that weak banks cannot attract more uninsured deposits (as a share of 

total deposits) by raising their uninsured deposit interest rates. This is shown in columns 3–5, 

where the coefficients of UninsIntMarg are not statistically significant for banks with a 

CAMEL rating worse than 2. 

B. Alternative Interest Margins 

In this section, we check whether our results are robust to different interest margin 

specifications. So far, our price variable UninsIntMarg reflects the spread between the 

interest rate on jumbo CDs and the interest rate on total deposits in bank i. Because this 

variable tracks interest rate movements between two different types of deposits held in a 

particular bank, it can be interpreted as capturing the ability of a bank to change the 

composition of its deposits by changing its relative deposit interest rates. 

Next, we compute two further interest rate margins that capture differences in interest rates 

among banks, by comparing a bank’s interest rates with the industry average. First, we 

compute the margin between the interest rate of bank i on a jumbo CD and the annual 

industry average interest rate on jumbo CDs (JumboIntMarg). This price variable captures 

the extent to which a particular bank can attract new uninsured deposits by offering a higher 

interest rate on its uninsured deposits relative to the industry average. Presumably, a rise in 

JumboIntMarg is associated with a rise in uninsured deposits (as a share of total deposits) in 

bank i. 

We also compute the margin between the average deposit interest rate of bank i and the 

industry average deposit interest rate (DepIntMarg). We would expect an increase in 

DepIntMarg, which raises the average price of deposits in bank i, to augment total deposits. 

This effect may or may not be associated with a rise in uninsured deposits (as a share of total 

deposits), depending on whether the bank attracts mostly insured or uninsured deposits. 

25 
 

Table 5 provides the partial correlation between the alternative interest rate margins. The 

margin that a bank pays for uninsured deposits relative to its overall interest rate, 

UninsIntMarg, and the margin it pays for uninsured deposits relative to the industry, 

JumboIntMarg, are highly positively correlated both in levels and in first differences. The 

interest rate margin that a bank pays on total deposits above the industry average, 

DepIntMarg, is much less highly correlated to the other interest rate measures. This would 

tend to indicate that changes in the UninsIntMarg are being driven by changes in the interest 

rate on Jumbo CDs rather than by changes in the interest rate on deposits more generally. 

Table 5. Partial Correlations with Alternative Interest Margins 1/ 

In Levels UninsDep UninsIntMarg JumboIntMarg DepIntMarg 

UninsDep 1.000 ... ... ... 

UninsIntMarg 0.083 1.000 ... ... 

JumboIntMarg -0.006 0.837 1.000 ... 

DepIntMarg -0.160 -0.310 0.224 1.000 

In First difference D.UninsDep D.UninsIMarg D.JumboIMarg D.DepIMarg 

D.UninsDep 1.000 ... ... ... 

D.UninsIntMarg -0.032 1.000 ... ... 

D.JumboIntMarg -0.050 0.956 1.000 

D.DepIntMarg -0.100 -0.094 0.152 1.000 

1/ D = in first differences. 

The regression results are presented in Table 6. We find that if a bank raises its average 

interest rates, it attracts mostly insured deposits: the ratio of uninsured deposits to total 

deposits falls. This is shown in column 3, where the coefficient of DepIntMarg is negative 

and statistically significant. This result is consistent with our prior hypothesis that deposit 

insurance mitigates depositor discipline. It seems to be relatively easy for a bank to attract 

insured deposits by raising its interest rates. In the case of uninsured deposits, however, 

depositors seem to be more cautious before entrusting their savings to a particular bank, 

examining both the risk profile of a bank and the rate of return it offers. This result is robust 

across different specifications, including when the model controls for movements in other 

interest margins, such as UninsIntMarg and JumboIntMarg, as is shown in columns 5–7. 

We also find that a bank cannot automatically increase its uninsured deposits (as a share of 

total deposits) by raising the interest rate on uninsured deposits above the industry average. 

This is shown in column 2, where the coefficient of JumboIntMarg is not statistically 

significant. Again, this result is robust across specifications as shown in columns 6 and 7. 

The only exception is found in column 4, where the coefficient of JumboIntMarg is strongly 

significant and negative, in conjunction with that of UninsIntMarg, which is strongly 

significant and positive. This result needs to be interpreted with a high degree of caution 

since it is severely tainted by the high degree of correlation between the two interest rates 

(see Table 5). 

26 
 

 

 

Table 6. Empirical Results across Different Interest Margins 1/ 2/ 

Variable Name Lags 3/ (1) (2) (3) (4) (5) (6) (7) 

UninsDep L1D 

L2D 

L3D 

0.382 *** 

(0.064) 

0.025 

(0.036) 

-0.002 

(0.030) 

0.394 *** 

(0.064) 

0.024 

(0.037) 

-0.005 

(0.030) 

0.368 *** 

(0.067) 

0.021 

(0.036) 

0.004 

(0.031) 

0.374 *** 

(0.065) 

0.029 

(0.036) 

0.008 

(0.030) 

0.348 *** 

(0.067) 

0.024 

(0.036) 

0.005 

(0.030) 

0.352 *** 

(0.066) 

0.026 

(0.036) 

0.006 

(0.030) 

0.353 *** 

(0.066) 

0.027 

(0.036) 

0.006 

(0.030) 

UninsIntMarg D 

JumboIntMarg 

DepIntMarg 

0.880 * 

(0.497) 

... 

... 

... 

0.061 

(0.312) 

... 

... 

... 

-2.298 ** 

(1.056) 

2.200 ** 

(0.904) 

-1.953 ** 

(0.942) 

... 

0.197 

(0.233) 

... 

-2.315 ** 

(0.989) 

... 

0.285 

(0.268) 

-2.505 *** 

(0.975) 

-0.496 

(0.558) 

0.818 

(0.709) 

-3.073 ** 

(1.355) 

Size D 9.721 *** 

(2.340) 

9.914 *** 

(2.321) 

9.574 *** 

(2.126) 

9.697 *** 

(2.245) 

9.829 *** 

(2.211) 

9.871 *** 

(2.223) 

9.881 *** 

(2.226) 

Equity L1D 0.445 *** 

(0.133) 

0.460 *** 

(0.128) 

0.373 *** 

(0.129) 

0.394 *** 

(0.127) 

0.379 *** 

(0.127) 

0.384 *** 

(0.127) 

0.387 *** 

(0.129) 

NonCLoans L1D -0.046 

(0.040) 

-0.012 

(0.041) 

-0.070 * 

(0.042) 

-0.073 * 

(0.040) 

-0.086 ** 

(0.041) 

-0.085 ** 

(0.041) 

-0.087 ** 

(0.043) 

ResidLoans L1D 0.061 ** 

(0.031) 

0.056 * 

(0.031) 

0.061 ** 

(0.031) 

0.067 ** 

(0.031) 

0.061 ** 

(0.031) 

0.059 * 

(0.031) 

0.059 * 

(0.031) 

LoansLeases L1D 0.038 * 

(0.021) 

0.033 * 

(0.018) 

0.040 ** 

(0.018) 

0.034 ** 

(0.018) 

0.036 ** 

(0.018) 

0.039 ** 

(0.018) 

0.040 ** 

(0.018) 

GDP D 0.245 *** 

(0.047) 

0.212 *** 

(0.043) 

0.196 *** 

(0.042) 

0.279 *** 

(0.052) 

0.226 *** 

(0.042) 

0.210 *** 

(0.042) 

0.185 *** 

(0.043) 

L1D 0.090 * 

(0.049) 

0.062 

(0.050) 

0.086 * 

(0.050) 

0.135 *** 

(0.052) 

0.114 ** 

(0.050) 

0.106 ** 

(0.050) 

0.095 ** 

(0.046) 

Infl D 0.599 *** 

(0.187) 

0.563 *** 

(0.170) 

0.456 *** 

(0.171) 

0.461 ** 

(0.189) 

0.520 *** 

(0.175) 

0.489 *** 

(0.167) 

0.438 ** 

(0.173) 

Treas_1 D -0.474 *** 

(0.146) 

-0.277 *** 

(0.094) 

-0.294 *** 

(0.090) 

-0.725 *** 

(0.215) 

-0.391 *** 

(0.098) 

-0.336 *** 

(0.090) 

-0.214 ** 

(0.100) 

Cons -0.048 

(0.124) 

-0.021 

(0.124) 

0.005 

(0.123) 

-0.111 

(0.124) 

-0.019 

(0.125) 

-0.008 

(0.123) 

0.018 

(0.125) 

Obs. 12,826 12,826 12,826 12,826 12,826 12,826 12,826 

Banks 

AB test of no AR(1)

1,863 1,863 1,863 1,863 1,863 1,863 1,863 

 Pr > z = 

AB test of no AR(2)

0.000 0.000 0.000 0.000 0.000 0.000 0.000 

 Pr > z = 

Sargan test of over0.597 0.730 0.941 0.742 0.782 0.758 0.755 

identification 4/ 

 Pr > chi2 = 

0.1463 0.0397 0.0006 0.0001 0.0002 0.0000 0.0000

1/ Model with endogenous price mechanism. 

2/ Coefficients from first-step robust estimations, unless specified otherwise. 

3/ Li D = in first difference with i lag(s); and D = in first difference. 

4/ Sargan test is based on two-step regression results. 

27 
 

 

Overall, our results suggest that a bank can raise its share of uninsured deposits to total 

deposits by raising the premium it pays on uninsured deposits relative to the interest rate on 

total deposits (the coefficient of UninsIntMarg is positive). However, the evidence that a 

bank can induce an inflow of (new) uninsured deposits (as a share of total deposits) by 

raising its uninsured deposit interest rate above the industry average is much weaker (the 

coefficient of JumboIntMarg is not statistically significant). 

Finally, if a bank raises its deposit interest rates above the industry average, it attracts mostly 

insured deposits (the coefficient of DepIntMarg is negative), raising total deposits (the 

denominator of the dependent variable). Note, however, that the very strong negative 

coefficient of DepIntMarg may suggest that a large part of the movement in the ratio of 

uninsured deposits to total deposits is related to movements in total deposits. In fact, it is 

possible to argue that weak banks may actually reduce the interest rate premium on uninsured 

deposits rather than raise it, as suggested earlier. This would still be consistent with the 

positive coefficient of UninsIntMarg but would suggest that banks in need of funding may try 

to substitute insured deposits for uninsured deposits.25 

V. CONCLUSIONS 

This paper attempts to capture banks’ dynamic response to depositors’ desire to withdraw 

their uninsured deposits if the performance of their bank is no longer satisfactory. In our 

view, depositors’ reaction and banks’ response must be modeled as a jointly determined 

process. When a bank’s fundamentals are deteriorating, both the price and quantity of 

uninsured deposits may be shifting simultaneously, obscuring the effect of depositor 

discipline. 

According to our results, the behavior of uninsured deposits is sensitive to bank 

fundamentals. This result, which is robust over a broad selection of banks and thrifts, shows 

evidence that the quality of a bank helps determine the quantity of uninsured deposits that a 

particular bank can attract for a given price. Furthermore, we find that the behavior of 

uninsured deposits is driven not only by changes in bank quality but also by price 

movements. Once we control for the endogeneity between the price and quantity mechanisms 

of uninsured deposits, our results suggest that banks are able to increase the quantity of 

uninsured deposits by raising the price (i.e., the interest rate) of uninsured deposits. We find, 

however, that they are able to do so only at a relatively high price. When differentiating 

among various qualities of banks, we find that good banks are in a better position to use 

prices to attract uninsured deposits than banks with a lower quality standing, suggesting that 

25 Further preliminary research suggests that a rise in the interest rate on uninsured deposits still has a 

substantial effect on the quantity of uninsured deposits as a share of total assets. These results, which eliminate 

the possible denominator effect, are consistent with those presented in this paper. 

28 
 

the price of raising uninsured deposits is related to a bank’s quality, which is consistent with 

our hypothesis of depositor discipline. 

This paper confirms the presence of depositor discipline in the U.S. banking system. 

Uninsured depositors monitor the health of their banks and discipline bad bank behavior by 

withdrawing their uninsured deposits and demanding a higher risk premium. Given the high 

level of U.S. deposit insurance coverage, this discipline for practical purposes affects only 

25 percent of all deposits. Nevertheless, our results suggest that regulators may be able to 

exploit the information contained in declining insured deposits or a rising premium for 

uninsured deposits. In addition, if regulators can encourage depositor discipline, our results 

suggest that depositors would generate a greater penalty to banks for excessive risk appetite. 

29 
 

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33