The impact of bank failures on local bank pricing decisions

The Quarterly Review of Economics and Finance 40 (2000) 401– 416

The impact of bank failures on local bank pricing decisions Jill M. Hendrickson1,* Department of Economics, Saint Michael’s College, Winooski Park, Colchester, VT 05439, USA

Abstract This paper empirically tests for the existence of bank contagion at the local level. More specifically, bank-specific data for thirteen counties in Colorado and Kansas is used to perform regression analysis to test the hypothesis that the uninsured CD pricing behavior of a failing bank affects the prefailure uninsured CD pricing behavior of solvent banks within the same county as the failing institution. Quarterly data from the second quarter of 1987 through the first quarter of 1994 produce regression results that support the hypothesis. Thus, this study finds evidence of firm-specific bank contagion at a local level and extends existing contagion literature beyond an investigation of large failures during periods of crisis. © 2000 Bureau of Economic and Business Research, University of Illinois. All rights reserved.

1. Introduction Bank contagion, the possibility that the failure of one bank may weaken or cause the failure of another bank, is an important issue from the perspective of industry performance. For example, if solvent banks are forced to pay higher deposit interest rates because a failing bank is bidding up rates to attract deposits, the cost of funds increases thereby reducing profitability. Widespread contagion could then adversely impact the performance of the banking industry. For this reason, it is important to understand if bank contagion exists and, if so, the exact nature of the contagion. In other words does contagion exist within the entire banking industry or only surrounding larger failures? This paper contributes to existing contagion literature by examining how the failure of a

* Tel.: ⫹212-462-1954; fax: ⫹212-462-1955. E-mail address: [email protected] (J.M. Hendrickson). 1 Present address: University of the South, Department of Economics, Sewanee, TN 37383, USA. 1062-9769/00/$ – see front matter © 2000 Bureau of Economic and Business Research, University of Illinois. All rights reserved. PII: S 1 0 6 2 - 9 7 6 9 ( 0 0 ) 0 0 0 4 8 - X

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unit commercial bank impacts the uninsured certificate of deposit (CD) prefailure pricing decisions of other commercial banks in a local market. In doing so, this paper considers the deposit market contagion issue at a local level. At the same time, it focuses on regions of the United States that experienced relative financial sector stability in the late 1980s and early 1990s. By considering the pricing strategies of small, unit banks during periods of relative stability, this paper extends the existing contagion literature that previously focused on large failures during crisis.

2. Models of bank failures Within the bank failure literature, scholars often focus on explaining either why failures occur or how the failure impacts local markets or the behavior of other banks. A plethora of scholarly articles attempt to explain why banks fail (see, e.g., O’Driscoll, 1988; Wheelock and Wilson, 1995; Avery and Hanweck, 1984). Other literature considers the consequences of a failure for a community. For example, Gilbert and Kochin (1989) empirically investigate the impact of a bank failure on the local economy and find that local sales fall and, in some cases, unemployment also falls as a result of the failure. The third type of bank failure literature, known as bank contagion, concerns the effects of a failure on the performance and behavior of other banks, both solvent and insolvent. Because this paper addresses a specific aspect of bank contagion, emphasis is placed on this literature. Bank contagion refers to the spillover effects of a bank failure on other banks. The spillover effects may range from changes in interest rates or the return on equity to the failure or near failure of other institutions.1 Banking literature recognizes two types of bank failure contagion (see Kaufman, 1994). Industry-specific contagion develops when information about a failing bank affects all firms in the banking industry. The second type, firm-specific contagion, occurs when information about a failing bank affects other banks that share one or more characteristics (for example, size or location) with the failing institution. Both types of banking contagion are hypothesized to develop primarily because of information asymmetries that exist between banking institutions and their customers.2 Scholars have utilized several different approaches for testing the existence of both industry-specific and firm-specific contagion. One approach searches for evidence of contagion by examining abnormal stock returns near the failure event (see, e.g., Aharony and Swary, 1983; Wall and Peterson, 1989; Flannery, 1998 for a review of this literature). Most of this literature finds evidence of firm-specific contagion for banks within the same region. A second approach tests for contagion by considering changes in the currency to deposit ratio. If failure contagion exists, the failure of one bank may set off panic among depositors, who may then run on other banks to exchange their deposits for currency in full. Though the creation of federal deposit insurance in the Banking Act of 1933 has essentially ended currency runs, studies that examine predepression times find mixed results regarding the presence of industry-specific contagion. Changes in deposit interest rates also may signal contagion according to a third approach within the literature.3 To date, much of this literature has focused on the contagion of large bank failures or on contagion during periods of financial shock. Wall and Peterson (1989)

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consider how other large banks performed in the wake of the 1984 Continental Illinois failure, which at the time of failure, was the eleventh largest bank holding company in the United States. The authors determined that the Continental failure increased the perception of bank risk, as measured by the spread between average CD rates and Treasury bill rates, even at solvent institutions. Also concerned with the effect of large failures, Cramer and Rogowski (1985) addressed the 1982 Penn Square and the Continental Illinois failure and found that interest rates on uninsured, negotiable CDs increased after the failures to reflect higher risk perceptions. Thus, both of these studies found evidence of spillover pricing (firm-specific contagion) after large bank failures. Other bank contagion literature focuses on spillover interest rate effects during periods of financial shock or crisis and finds evidence of firm-specific contagion. For example, Short and Robinson (1991) consider whether the deteriorating performance of one institution impacts solvent thrifts and banks within the state of Texas during the severe thrift crisis of the 1980s. Using deposit expenses on CDs over $100 000 as well as the average expense on interest bearing deposits, they find evidence of contagion as even solvent thrifts and banks must pay deposit premiums in Texas during the late 1980s. Focusing on that same crisis period, Short and Gunther (1988) also find evidence of spillover deposit pricing by both thrifts and commercial banks. More specifically, the authors indicate that Texas thrifts and banks, both solvent and insolvent, paid an interest rate premium on the average of all interest bearing deposits and on large CDs. This premium, according to Short and Gunther, is largely due to troubled institutions’ willingness to pay more to attract funds and, hopefully, use the funds to turn around performance. Thus, the risk taking by insolvent banks adversely affects the deposit costs at solvent institutions during the Texas thrift crisis. In another unstable episode, Cooperman, Lee, and Wolfe (1992) use weekly bank specific data from a sample of Ohio thrifts and banks to test for deposit rate contagion, as measured by six-month CD rates, during the 1985 Ohio Deposit Insurance crisis. The crisis came about when the failure of one savings bank exceeded the bank’s capital and the reserves of its insurer, the Ohio Deposit Guaranty Fund, (ODGF) a private deposit insurance company. As the news of the depletion of the deposit fund was dispersed runs on other ODGF institutions followed. In addition to finding rate contagion, Cooperman et al. finds the rate premiums on retail CDs to be risk based. Though the studies discussed above find evidence of spillover pricing (firm-specific contagion) during periods of financial distress, other rate contagion studies do not find evidence of contagion during crisis. For example, Calomiris and Mason (1997) investigate bank failures in Chicago during the Great Depression and fail to find evidence of contagion. Further, Cook and Spellman (1991) focus on the thrift crisis to determine how an insolvent federal guarantor impacts the pricing of firm debt. Their empirical findings suggests that when a federal guarantor is deemed insolvent by the market, the market responds by increasing insured CD rates relative to Treasury rates. Cook and Spellman (1996) again consider the thrift crisis and determine that interest premiums were the result not of firm contagion but rather from an increased risk perception of the deposit insurer. Thus, most previous studies of firm-specific contagion as measured by deposit rates tend to suggest that either during times of financial distress or in the event of a large bank failure, rate premiums on different types of CDs surface for both solvent and insolvent institutions. The evidence

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is mixed, however, with regards to the cause of the rate premium. Many found failure contagion to be the culprit whereas others found insurer insolvency. This paper contributes to the third approach within the contagion literature by focusing on changes in the large CD pricing behavior (firm-specific contagion) of banks within a local community when another bank within the community fails. Kaufman (1994) hypothesizes that the failure of one bank may benefit surviving banks once a resolution is complete. Remaining banks benefit either by reduced competition or through a correction in deposit interest rates. More specifically, deposit interest rates are hypothesized to fall after the resolution because solvent banks return to pricing practices employed before the failure. The theory is that a troubled bank will, in desperation, pay out higher interest rates on deposits to attract funds. Further, marginal banks may pay more for deposits to compensate for real or perceived increases in risk.4 In either case, the moral hazard problems of risk taking surface. This pricing strategy of the troubled bank is hypothesized to spillover to competing, solvent banks that follow the troubled bank by temporarily paying above market rates on deposits. In related literature regarding the thrift crisis, Kane (1989) hypothesizes that pricing becomes risk-based during failure episodes. More specifically, in the prefailure period risk taking increases and so interest rates are expected to rise to compensate depositors for the greater risk. This paper attempts to empirically test prefailure theories that suggest that both solvent and nonsolvent banks pay increasing rates on deposits before the failure of one of the institutions. In other words, this paper attempts to answer the question: are bank solvency problems contagious prefailure? At the same time, this paper breaks from previous deposit interest rate contagion studies in two ways. First, it focuses on the pricing behavior of unit, rather than large, commercial banks to determine if firm-specific contagion exists with smaller bank failures. Second, it considers a region of relative financial stability rather than instability to determine if firm-specific contagion is only a function of a larger crisis or if it may develop from small, local disturbances.

3. Data and methodology Due to the accessibility of stock data, the abnormal stock return approach is the most prevalent in the bank contagion literature. Such an approach works well when considering large commercial bank failures and the contagiousness of those failures on other large banks. However, when considering contagion within a community of small banks the abnormal stock return approach is not feasible largely because many of these small banks are not publicly traded. Consequently, this paper measures contagion by considering changes in large CD rates paid by the small commercial banker. 3.1. Data Data on US commercial banks comes from quarterly FDIC Call Reports for the second quarter of 1987 through the first quarter of 1994 for all commercial banks headquartered in specific counties in the states of Colorado and Kansas. The data are a time series, crosssection panel of balance sheet and income statement data from bank call reports. The FDIC

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Call Reports contain only data on banks that are headquartered within the specified county. In other words, only independent banks and bank holding companies headquartered within the county are contained in the data used for this analysis. Though this may initially seem to pose a problem as it excludes branch units that may be located within the county, theoretically, independent banks and bank holding companies possess more pricing autonomy than branch units. For example, Rhoades and Savage (1981) argue that bank holding companies tend to have more autonomy than branch units because individual bank presidents and boards make the decisions whereas decision making at branch units tends to be centralized in the parent bank.5 Consequently, variability in pricing strategies, as a result of a local failure, may best be captured through independent banks headquartered within the county. The sample period 1987 through 1994 was chosen primarily for two reasons. First, complete quarterly data sets were available for the local markets of interest. Second, and more importantly, this time frame captures a period when banks were failing, but is not limited only to the period of excessively high failures in the mid 1980s. In this way, the sample captures a relatively more stable time in the history of bank performance. From within the sample period, states and then counties within states, were chosen. The FDIC’s Historical Statistics on Banking lists all failed insured commercial banks beginning in 1934. States with an excessive number of bank failures (e.g., Texas, California) were omitted as outliers as were states with very few failures (e.g., Washington, Virginia, Tennessee). Of the remaining states, two were chosen: Colorado and Kansas. From within each state, counties were determined according to several criteria. First, the county containing the identified bank failure must not also contain other failures. Second, the sample was to include varying bank failure sizes. The results of these sampling criteria are produced in Table 1 for Colorado and in Table 2 for Kansas. Colorado and Kansas lend themselves well to interstate comparison for two reasons. First, they both are members of the Tenth District of the Federal Reserve System. Second, both allow statewide branching and entry by out of state bank holding companies. Consequently, though this sample considers primarily independent banks, both states allow nonindependent bank entry and expansion so that, ceteris paribus, we may assume similar branch bank and bank holding company competition. 3.2. Model This analysis uses regression methods to determine if the variation in pricing strategies of local commercial banks is affected by the failure of another bank within the market. The local market is defined as the county in which the failed bank was located6. The sample period and counties specified allow for an analysis of pricing strategies for several years before the failure and several years after the failure. To test the hypothesis of spillover pricing in the deposit market, this study utilizes the model: CDRATE it ⫽ ␤ 0 ⫹ ␤ 1SIZE it ⫹ ␤ 2 FAILDATE it ⫹ ␤ 3CAPITAL it ⫹ ␤ 4UNEM it ⫹ ␤ 5 FAILDATE it • CAPITAL it ⫹ ␤ 6FAILDATE it • SIZE it ⫹ ␮ it i ⫽ 1, . . . , N; t ⫽ 1, . . . , T

(1)

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Table 1 Colorado failure data 1987–1994 County

Number of banks

Market structure

Failure date

Failure size (in thousands)

Charter type

Resolution method

Archuleta

2

rural

10/25/91

14 566

State nonmember

Purchase & assumption

Douglas

9

urban

12/6/90

5371

national

Purchase & assumption

Eagle

8

rural

10/5/89

6315

national

Purchase & assumption

Gunnison

3

rural

10/22/91

20 399

State member

Bank assistance

Larimer

18

urban

6/15/89

6914

Lincoln

3

rural

3/29/91

11 414

State nonmember National

Purchase & assumption Deposit transfer

Yuma

5

rural

8/24/89

26 604

State member

Purchase & assumption

Sources: FDIC Call Reports and FDIC’s Historical Statistics on Banking.

Table 2 Kansas failure data 1987–1994 County

Number of banks

Market structure

Failure date

Failure size (in thousands)

Charter type

Resolution method

Brown

6

rural

6/13/91

142 020

State nonmember

Purchase & assumption

Crawford

10

rural

2/16/89

1357

State nonmember

Payoff

Elk

4

rural

6/4/92

3674

State nonmember

Payoff

Graham

4

rural

2/18/88

15 273

State member

Purchase & assumption

Lincoln

5

rural

8/3/89

5237

Miami

6

urban

2/3/88

20 381

State nonmember State nonmember

Purchase & assumption Purchase & assumption

Sources: FDIC Call Reports and FDIC’s Historical Statistics on Banking.

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with i denoting the county and t denoting time. The dependent variable captures county variation in uninsured CD interest rates relative to Treasury rates and the explanatory variables capture balance sheet and failure county variation. Before estimation, the model is checked for the presence of auto regressive conditional heteroskedasticity (ARCH) and for serial correlation. The ARCH procedure finds no evidence of this particular specification of heteroskedasticity. However, the Durbin–Watson statistic from the ordinary least squares estimation of Linear Model (1) reveals serial correlation. To correct for the serial correlation, the technique of first-order auto regressive correction is employed. The first-order auto regressive error specification transforms the linear model, y t ⫽ x t␤ ⫹ u t

(2)

u t ⫽ ␳ u t⫺1 ⫹ ␧ into the nonlinear model: y t ⫽ ␳ y t⫺1 ⫹ 共 x t ⫺ ␳ x t⫺1兲 ␤ ⫹ ␧ t

(3)

The serial correlation correction then estimates the coefficients ␳ and ␤ by applying a Marquardt nonlinear least squares algorithm to the transformed equation. The transformed equation is linearized around initial starting values and new values for the coefficients are determined by applying least squares to the linearized equation. The results of this study are all first-order auto regressive [AR(1)] correction estimates.7 Further, the correlation matrix is checked for multicollinearity and none of the independent variables are highly correlated. 3.2.1. Dependent variable The dependent variable, CDRATEt, captures the variation in the rate banks paid on uninsured certificates of deposit.8 More specifically, similar to Wall and Peterson (1989), the dependent variable is the spread between the six-month Treasury bill rate and the rate paid on uninsured CDs where the Treasury bill rate serves as a benchmark for interest rate movement outside of the local community. This interest rate spread measures the risk perception of individual banks in light of the presence of a troubled institution. The use of uninsured CD rates is consistent with previous contagion studies (see, e.g., Short and Gunther (1988); Cramer and Rogowski, 1985) and are appropriate rates given that certificate of deposits tend to be more rate sensitive and subject to contagion. 3.2.2. Explanatory variables SIZEt, the ratio of failed bank assets to total bank assets within a county, captures the impact of the size of the bank failure on spillover pricing. In theory, one would expect that a large bank failure would have a greater impact on the pricing at other banks because of the larger share of the CD market at the failed institution. From this perspective, it is expected that the larger the failure, the greater the spillover pricing. However, another school of thought considers the practice of the Federal Deposit Insurance Corporation (FDIC) and its historical actions to protect large bank failures as potential to offset the possibility of spillover pricing. For example, Short and Robinson (1991) argue that large bank failures may

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result in less spillover pricing because of the pervasive use of the too-big-to-fail resolution policy of the FDIC (see also Crane, 1976; Gilbert, 1983). Often the term “too-big-to-fail” is used to mean the excessive use by the FDIC to resolve failures through the purchase and assumption (P&A) method. Because using the P&A protects all depositors, even those uninsured, there is little incentive for depositors to monitor the risk exposure of the banks. And because the FDIC has used the P&A method rather extensively (69% of the failures in this sample were resolved through P&A) there is reason to suspect the depositors may be less than rigorous in their scrutiny of bank risk taking. If this policy was used disproportionately at larger banks one would expect to see, ceteris paribus, larger banks escape paying prefailure interest rate increases in the same order as small banks. In sum, expectations for the impact of the size of the failure on spillover pricing is mixed as theory suggests more spillover pricing with large failures but given the practice of the FDIC’s resolution policy, some scholars expect less spillover pricing. FAILDATEt, a dummy variable equal to one for all quarters before the failure and zero after the failure, controls for the prefailure period. Because of increased risk taking by the troubled institution, other solvent banks in the local market are hypothesized to potentially be seen as more risky themselves, leading all banks to pay more for deposits before the failure. This definition of FAILDATEt is similar, though not identical, to the failure control variable used in Cooperman et al. (1992).9 An important balance sheet variable, CAPITALt, is the capital to asset ratio for each bank within the sample. As a standard measure of risk in the literature it indicates the ability of the bank to withstand shocks such as unanticipated deposit withdrawals, unexpected nonperforming loans and, potentially, other bank failures (see, e.g., Short and Robinson, 1991; Cooperman et al., 1992). Higher capitalized banks are less risky and hence may be less likely to follow fluctuations in deposit pricing resulting from local failures. Further, higher capitalized banks may be perceived by depositors as less risky and hence not need to follow more aggressive pricing strategies of less capitalized institutions. UNEMt captures the county unemployment rate and is a control for local economic performance.10 Finally, Eq. (1) contains two interaction variables. FAILDATEt•CAPITALt is the product of the failure dummy and the capital to asset ratio. This interaction variable captures the impact of individual bank characteristics on the CD rate spread. Following Cooperman et al. (1992) it is expected that the estimated coefficient on this interaction variable will be positive indicating that CD rate premiums are risk based. In other words, increased risk taking is hypothesized to occur before the failure, thus if capital is a risk proxy, the capital ratio should decline pre failure, leading to higher interest rates and hence a lower rate spread. The second interaction variable, FAILDATEt•SIZEt, is the product of the failure dummy and the ratio of failed bank assets to total bank assets. As was mentioned in the discussion of SIZEt, scholars are mixed regarding the impact of the size of the failure on spillover pricing. Larger bank failures, according to one group, should lead to less spillover pricing because of the tendency of the FDIC to resolve such failures using the purchase and assumption method. However, another school of thought argues that because larger failed institutions tend to have a larger share of the CD market, their tendency to increase interest rates before the failure will spillover to other solvent banks. Consequently, the impact of this interaction variable on the rate spread is uncertain.

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Table 3 AR(1) Estimation results for Colorado counties: six month treasury rate less uninsured CD rate as dependent variable County

SIZE

FAIL date

CAPITAL UNEM

FAIL• CAP

FAIL• SIZE

constant ␳

FAdjusted statistic R2

Archuleta 1.034* ⫺0.849* (8.542) (2.925)

0.112 (0.498)

⫺0.198 (0.485)

1.326 (0.385)

1.382 (0.586)

0.492 0.384* 403.37 0.983 (0.398) (3.035)

Douglas

1.984* ⫺0.392* (7.820) (4.304)

0.036 (1.392)

0.291* 2.572)

0.894 (0.499)

4.058* (3.105)

1.298* 0.557* 394.49 0.987 (3.325) (4.038)

Eagle

1.392* ⫺0.677* ⫺0.025 (9.602) (4.589) (1.037)

0.037* ⫺0.398* (3.893) (0.773)

Gunnison

1.392* ⫺0.398* (8.293) (2.941)

0.184 (0.853)

Larimer

1.023* ⫺0.999* ⫺0.198 (8.291) (5.119) (1.293)

0.045* ⫺0.097 (4.239) (0.879)

3.293* (2.941)

0.391* 0.693* 621.54 0.988 (3.902) (4.293)

Lincoln

0.932* ⫺0.142* (6.339) (2.927)

0.019* (2.781)

0.055 (0.895)

0.668 (0.785)

2.083 (1.119)

1.028 0.938* 711.34 0.983 (0.847) (3.378)

Yuma

1.493* ⫺0.873* (5.291) (3.984)

0.069 (0.893)

0.019 (1.561)

0.313 (0.378)

1.049* (3.067)

0.684* 0.839* 241.38 0.985 (4.392) (3.261)

0.027 (1.082)

2.493* ⫺0.872* 0.759* 347.61 0.991 (3.495) (4.094) (3.892)

0.982 ⫺0.745 (1.326) (0.653)

0.984 0.384* 503.45 0.983 (1.419) (5.034)

A* and ** denotes significance at the 5% and 10% level, respectively. Absolute values of the t statistic appear in parentheses. The coefficient ␳, rho, results from the serial correlation correction estimation process. Source: See Section 2 of text.

4. Estimated results Tables 3 and 4 contain the AR(1) estimation results of the deposit interest spread variable for the counties in Colorado and Kansas, respectively.11 Overall, each county model produces robust results as indicated by the F statistic and high adjusted coefficient of determination. In general terms, across counties in both Colorado and Kansas the results indicate that SIZEt and FAILDATEt are statistically significant in each county whereas UNEMt is statistically significant in six of the thirteen counties and FAILDATEt•SIZEt is significant in eight of the thirteen counties. However, these results indicate that the premiums paid on uninsured CDs are not risk based as CAPITALt is insignificant in ten of the thirteen counties whereas the other interaction variable, FAILDATEt•CAPITALt, is insignificant in all but one county. A positive SIZEt coefficient indicates that increases in the size of the bank failure increases the uninsured CD rate spread variable. In other words, larger bank failures result in a decrease in CD interest rates. This finding is consistent with Short and Robinson (1991) who hypothesize that large bank failures are less likely to result in firm-specific contagion, or spillover pricing, because the too-big-to-fail application of the FDIC removes the perceived riskiness of larger banks. It is also consistent with the findings of Cooperman et al. (1992) who indicate that depositors may have more confidence in larger banks which, in turn,

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Table 4 AR(1) Estimation results for Kansas counties: six month treasury rate less uninsured CD rate as dependent variable County

SIZE

FAIL date

CAPITAL UNEM

FAIL• CAP

FAIL• SIZE

constant ␳

FAdjusted statistic R2

Brown

2.392* ⫺1.384* (4.382) (2.749)

0.392 (0.172)

⫺0.076* 0.403 ⫺1.394* (4.382) (1.038) (4.439)

1.287 0.384* 293.40 0.986 (0.734) (3.283)

Crawford

1.825* ⫺0.884* (5.382) (4.573)

0.341 (0.838)

0.113* 0.388 ⫺4.398* 3.983) (0.934) (4.387)

0.467* 0.767* 501.39 0.994 (3.989) (9.182)

Elk

2.677* ⫺0.936** ⫺0.186* (10.271) (2.004) (2.895)

⫺0.349* 0.783* ⫺5.328* ⫺0.392* 0.313* 341.39 0.986 (2.893) (0.840) (3.767) (0.485) (5.893)

Graham

3.027* ⫺0.739* (3.384) (3.826)

⫺0.007 (0.894)

⫺0.382 (0.493)

0.530* ⫺2.355* (2.112) (4.293)

Lincoln

1.836* ⫺1.025* (5.398) (5.836)

0.663 (1.202)

⫺0.956 ⫺0.594 (1.648) (0.390)

3.293 (1.082)

0.747* 1.293* 293.45 0.990 (4.037) (4.293)

Miami

1.392* ⫺0.821* (5.117) (5.082)

0.198** ⫺0.294 ⫺0.738 (1.452) (1.394) (0.934)

2.387 (1.026)

0.489* 0.229* 382.37 0.987 (3.183) (8.583)

0.893* 0.867* 304.39 0.991 (3.144) (6.490)

A* and ** denotes significance at the 5% and 10% level, respectively. Absolute values of the t statistic appear in parentheses. The coefficient ␳, rho, results from the serial correlation correction estimation process. Source: See Section 2 of text.

means the larger institutions need not follow increases in CD rates offered by other banks within the community. In other words, less spillover pricing is evident with larger failures than with smaller failures because the failing institution may not need to raise deposit interest rates to retain deposits because depositors feel confident in repayment. Competing banks, in turn, then need not raise their interest rates to remain competitive. The estimated results for the dummy variable FAILDATEt, which controls for the prefailure period, were statistically significant and negative. The negative estimated coefficient is consistent with the hypothesis that before a bank failure, risk taking increases by the troubled bank creating a perception that other banks are also more risky forcing all banks in the market to pay higher deposit rates. Thus this finding suggests that firm-specific bank contagion exists among small failures during times of relative stability. The estimated coefficients to the failure size interaction variable, FAILDATEt•SIZEt, were statistically significant and larger coefficient values were found in those counties with smaller bank failures. For example, in Kansas the largest bank failure in the sample occurred in Brown county and the estimated results indicate that the CD spread changes much less as a result of the Brown failure than the other failures in Kansas. In other words, these results suggest that when small banks are in trouble there tends to be larger changes in the CD spread variable that is consistent with the findings of Short and Robinson (1991) and Cooperman et al. (1992). The control for local economic performance, UNEMt, was statistically significant in six of

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the 13 county regressions indicating that the strength of the local economy also influences the degree to which firm-specific contagion is evident. Interestingly, neither of the capital variables were significant with any consistency. This suggests that the pricing of uninsured CDs in these counties was, largely, not risk based. This finding is at odds with Cooperman et al. (1992) who find CD rates during the Ohio crisis to be risk based and with Short and Gunther (1998) who find risk based deposit premiums during the Texas thrift crisis. Perhaps risk matters more during times of crisis. In other words, these previous rate contagion studies found that the capital to asset ratio, and in some cases a capital interaction variable, explain variations in deposit rates. Yet these same studies consider periods of financial crisis so they find that interest rate premiums tend to be risk based during periods of crisis. In contrast, according to the findings of this study, during periods of non crisis the pricing of uninsured CDs is not risk based but, rather, reflect the size of the bank failure, the failure date, and local economic conditions. In general, estimated results from the AR(1) correction to Eq. (1) statistically suggest that bank contagion exists at the local level. It is unclear if this contagion is a part of the market process or if it is made worse by regulation and policy.12 5. Conclusion This local examination of the spillover effects before a bank failure, as measured by fluctuations in uninsured CD rates, reveals evidence of firm-specific contagion. Though these results stem from a population of small, primarily unit banks during a period of relative financial stability, they largely confirm the findings of earlier contagion studies on large failures during crisis. This suggests that small bank failures impact the pricing behavior of other local banks just as large failures change deposit price strategies at solvent banks. Similarly, the spillover effect does not seem to be restricted to only periods of financial instability as even during more stable periods, in more stable regions, solvent banks tend to match price changes of the failing bank. However, unlike some previous rate contagion studies, this study does not find evidence that the contagion is risk based. Rather, it seems as though local contagion during more stable times is explained by the size of the failure and local economic conditions. Acknowledgments I am grateful to Mark W. Nichols, Maria Minniti, Tom Mondschean, and two anonymous referees for comments and discussions, to Steven Bruce for competent research assistance, and to the University of the South for the Development Grant that made this study possible. Notes 1.

A more serious potential outcome of contagion, known as systemic risk, develops when bank contagion sets in motion failures and disruption beyond the banking

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Table 5 AR(1) Estimation results for Colorado counties: first difference in six month treasury rate less uninsured CD rate as dependent variable County

SIZE

FAIL date

⌬CAPITAL ⌬UNEM FAIL• CAP

FAIL• SIZE

constant ␳

FAdjusted statistic R2

Archuleta

1.493* ⫺0.676* (7.394) (2.394)

0.009 (0.118)

⫺0.023* (3.492)

Douglas

2.293* ⫺0.749* (3.499) (4.930)

0.029 (1.037)

0.092* (2.697)

0.934 (0.392)

4.302* 0.943* 1.293* 322.39 0.985 (3.656) (3.313) (5.408)

Eagle

1.943* ⫺0.938* ⫺0.113 (9.394) (5.387) (1.384)

0.625* (4.293)

0.384 (0.559)

2.300* 0.403* 0.293* 401.20 0.984 (3.392) (4.004) (3.494)

0.193 (1.392)

1.293 (1.005)

3.293 1.293 1.292* 657.53 0.985 (0.394) (1.403) (3.405)

Gunnison

1.751* ⫺0.141* (10.382) (3.078)

0.023 (1.004)

1.293 ⫺0.137* 1.293 0.203* 405.39 0.984 (0.392) (3.029) (0.677) (4.049)

Larimer

1.902* ⫺0.930* ⫺0.094 (8.376) (8.492) (0.293)

0.948* ⫺0.832 (3.827) (0.917)

1.293* 0.506* 0.594* 704.22 0.983 (0.958) (5.301) (8.394)

Lincoln

2.390* ⫺0.192* (5.596) (2.384)

0.008* (2.224)

0.293 (1.432)

0.304 (0.038)

2.004 0.697 0.938* 343.39 0.984 (1.107) (1.495) (5.422)

Yuma

1.493* ⫺0.874* (8.131) (4.503)

0.193 (0.406)

0.029 (0.938)

0.377 (0.606)

0.118 0.896* 0.342* 191.24 0.982 (1.092) (4.417) (3.928)

A* and ** denotes significance at the 5% and 10% level, respectively. Absolute values of the t statistic appear in parentheses. The coefficient ␳, rho, results from the serial correlation correction estimation process. Source: See “Data and Methodology” section of text.

2. 3.

4. 5.

sector and into the real economy. The fear of systemic risk has been the impetus behind much of the bank regulation and FDIC policy during the postdepression era. For a thorough discussion of the asymmetric information problems within banking see Mishkin (1991). In related literature, scholars consider the relationship between bank risk and interest rates paid on uninsured deposits as a measure of market discipline. See for example, Park and Peristiani (1998) who finds that higher risk thrifts must pay more on uninsured deposits and thus concludes that thrift depositors are able to discipline risk taking. For a review of the market discipline literature, see Gilbert (1990). More recently, Flannery (1998) reviews the empirical evidence about the existence of market discipline and, in doing so, reconsiders the appropriateness of banking policy formulated by governments rather than by private means. Shoven et al. (1991) advance similar reasons for interest rate premiums offered by troubled thrifts. See also Keeton (1995) who argues that different bank structures possess different degrees of decision making autonomy. Specifically, Keeton argues that multioffice structures (branch units) are constrained by centralized decision making whereas independent and single-office structures possess much more autonomy. Calem and Nakamura (1995) also address the issue of autonomous decision making within

J.M. Hendrickson / The Quarterly Review of Economics and Finance 40 (2000) 401– 416

413

Table 6 AR(1) Estimation results for Kansas counties: first difference in six month treasury rate less uninsured CD rate as dependent variable County

⌬SIZE FAIL date

Brown

1.392* ⫺0.893* (6.554) (2.433)

⌬CAPITAL ⌬UNEM FAIL• CAP

FAIL• SIZE

constant ␳

FAdjusted statistic R2

⫺0.392* ⫺0.038 (2.293) (0.521)

⫺1.773* 0.938* 0.293* 203.39 0.986 (4.023) (0.334) (4.983)

Crawford 1.388* ⫺0.203** 0.382 (5.218) (1.882) (0.394)

⫺0.998* 4.392)

0.493 (0.445)

⫺1.273* 1.293* 0.584* 476.58 0.991 (5.483) (3.293) (4.593)

Elk

1.928* ⫺1.029** ⫺0.028 (7.118) (3.773) (0.466)

⫺0.277* (2.327)

0.938* ⫺3.277* ⫺0.004* 0.885* 151.29 0.983 (0.329) (2.964) (0.392) (6.594)

Graham

2.198* ⫺0.836* ⫺0.283 (3.287) (3.281) (1.493)

⫺0.182 (1.364)

0.842** ⫺3.448* 0.738* 0.884* 154.36 0.993 (2.008) (4.286) (3.036) (6.382)

Lincoln

1.897* ⫺0.552* (4.373) (5.493)

0.738** (1.823)

⫺0.077** 0.727 (1.847) (0.342)

2.514 (0.144)

0.378* 0.583* 167.66 0.982 (4.039) (4.293)

Miami

1.829* ⫺0.594* (3.283) (4.844)

0.594** (1.074)

⫺0.523 (0.783)

1.447 (1.776)

0.047* 0.687* 250.40 0.985 (3.032) (4.392)

0.098 (0.483)

⫺0.634 (0.594)

A* and ** denotes significance at the 5% and 10% level, respectively. Absolute values of the t statistic appear in parentheses. The coefficient ␳, rho, results from the serial correlation correction estimation process. Source: See Section 2 of text.

6. 7.

8. 9.

10.

banking institutions and find that competition among branching units for traveling consumers limit their ability to increase prices at branch units, thereby supporting the hypothesis of Keeton (1995) and Rhoades and Savage (1981). See Gilbert and Kochin (1989) who argue that counties provide a good approximation to the primary area serviced by commercial banks. See also Eisenbeis (1970). Though Wallis (1972) suggests that the current disturbances of quarterly data in a regression model may be correlated with the disturbances of four quarters previous rather than the preceding quarter, this study found no evidence of fourth-order autocorrelation. See Appendix 1 for all variable definitions. Cooperman et al. (1992) define their control variable as equal to one during the crisis period and zero otherwise. In this sense, the crisis period in this paper may be thought of as the period preceding the failure when rising deposit interest rates are hypothesized to spillover onto solvent banks. It is not possible, given the data constraints, to identify a crises episode as precisely as in Cooperman et al. Further, because it is not clear exactly how to identify the beginning of the crisis, this paper simply defines all preceding quarters leading up to the failure as “crisis.” Though this certainly is not the most precise method, since the “crisis” period will vary given the varying failure dates in the sample, perhaps the results will shed some light on the issue of defining a crisis period before the failure of a small bank. Because this paper uses quarterly data, it is difficult to control for other develop-

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J.M. Hendrickson / The Quarterly Review of Economics and Finance 40 (2000) 401– 416

ments that may impact interest rates charged on large CDs, or any deposit for that matter. This is particularly true for small banks. Consequently, the Wall Street Journal Index was thoroughly checked for intervening events that may effect interest rates or bank behavior over the sample period. No such events were discovered. It may also be interesting to model this as the absolute rates of change such as 共1兲 CDRATE it ⫽ ␤ 0 ⫹ ␤ 1⌬SIZE it ⫹ ␤ 2 FAILDATE it ⫹ ␤ 3⌬CAPITAL it ⫹ ␤ 4UNEM it ⫹ ␤ 5 FAILDATE it • ⌬CAPITAL it ⫹ ␤ 6FAILDATE it • ⌬SIZE it ⫹ ␮ it i ⫽ 1, . . . , N; t ⫽ 1, . . . , T

where ⌬ is the first difference of the original variable. Though the interpretation of the variables and the estimated coefficients becomes more complex (e.g., the coefficient represents the rate of change with respect to the rate of change) in first differences, this differenced model is estimated and the results may be found in Tables 5 and 6. Notice that the first difference results confirm the findings in levels and indicate a robustness to the estimation of the failure model. 12. See Dowd (1993, Chap. 12) for a thorough discussion of the role of regulators and regulation in US banking performance. Appendix. Variable Definitions

Appendix 1 Variable Definitions Variable Name

Definition

CDRATEt

The six month Treasury rate less the uninsured CD rate.

SIZEt

The ratio of failed bank assets to total bank assets within a county which proxies failed bank size.

FAILDATEt

A control variable equal to one for all quarters prior to the failure and zero after the failure.

CAPITALt

The ratio of capital to total assets as a proxy for risk.

UNEMt

The county unemployment rates to control for local economic fluctuations.

FAILDATEt•CAPITALt FAILDATEt•SIZE

The product of the failure dummy variable and the capital to asset ratio. The product of the failure dummy variable and the failed bank assets to total assets ratio.

Source: See “Data and Methodology” section of text.

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