Merging the BIF and the SAIF: 

Would a Merger Improve the Funds’ Viability? 

Robert Oshinsky 

Financial Economist 

Division of Research and Statistics 

Federal Deposit Insurance Corporation 

550 17th Street, N.W. 

Washington, D.C. 20429 

Phone: 202-898-3813 

Fax: 202-898-7149 

E-mail: roshinsky@fdic.gov 

The author gratefully acknowledges the comments and suggestions of 

William R. Watson, Barry Kolatch, Steven Seelig, James Marino, Lynn Shibut, and 

Kevin Sheehan. The views expressed are those of the author and not necessarily those of 

the Federal Deposit Insurance Corporation. 
 

1. Introduction 

As part of the Financial Institutions Reform, Recovery, and Enforcement Act of 

1989 (FIRREA), the Savings Association Insurance Fund (SAIF) was created. Ever since 

its creation, however, it has been considered vulnerable, partly because of its small size 

and partly because of its geographic concentration. SAIF-member institutions constitute 

a much smaller portion of U.S. banking organizations than Bank Insurance Fund (BIF) 

member institutions do. As of year-end 1998, the SAIF had 1,430 members, roughly 

16 percent the number of BIF members, and the SAIF insured an estimated $709 billion 

in deposits, roughly 33 percent of the estimated deposits insured by the BIF.1 In addition, 

SAIF-member institutions are geographically concentrated, unlike BIF-member 

institutions. 

This paper examines the SAIF’s ability to remain solvent using a Monte Carlo 

model that was developed by Oshinsky (1999) to study the effects of banking 

consolidation and megamergers on the BIF. It shows that, in contrast to the BIF, industry 

consolidation has served to reduce the vulnerability of the SAIF, as several large 

BIF-member institutions have increased their SAIF-insured holdings. Nonetheless, the 

SAIF continues to be somewhat more vulnerable to insolvency risk than the BIF. 

The paper also examines a merger of the BIF and the SAIF. It finds that a larger, 

combined insurance fund would be less at risk than either the BIF or the SAIF separately. 

In other words, both the BIF and the SAIF would benefit from a merger of the funds. A 

fund merger would also eliminate the possibility that one fund might become insolvent 

while the other fund remains solvent. For the SAIF, however, the benefits of a fund 

1

 FDIC (1999), 17. 

2 
merger have diminished during the 1990s as industry consolidation has effectively spread 

the risk of large-bank failures across insurance funds. 

Section 2 (“Background”) reviews some of the changes that took place in SAIFinsured institutions between 1990 and 1998. The model is described in section 3 

(“Simulation Model”), and the results for the SAIF are presented in section 4 (“SAIF 

Results”). In section 5 (“Merging the BIF and the SAIF”), the results from a hypothetical 

merger of the BIF and the SAIF are discussed. 

2. Background 

The SAIF insures a much smaller pool of institutions than the BIF. As of 

December 31, 1989, there were 2,598 SAIF-member institutions, compared with 13,230 

BIF-member institutions. By year-end 1998, the SAIF insured only 1,430 institutions, or 

14 percent of all depository institutions, and the assessment base for SAIF-member 

institutions, including SAIF-insured portions of BIF-member institutions, constituted 

only 25 percent of the year-end 1998 banking industry assets. Between 1989 and 1998, 

stress in the thrift industry and ongoing consolidation to meet competitive pressures 

brought about numerous mergers of SAIF members, frequently involving BIF members 

as well as SAIF members. 

Another point of vulnerability for SAIF-member institutions is that they are more 

geographically concentrated than BIF-member institutions. However, geographic 

concentration is not examined here. 

3 
 

2.1 Oakar Institutions 

The SAIF was created with no initial funding. It was further handicapped by 

requirements in FIRREA that some of the SAIF premiums be diverted to pay part of the 

cost of Federal Savings and Loan Insurance Corporation (FSLIC) and Resolution Trust 

Corporation (RTC) resolutions.2 At its inception, SAIF members faced the prospect of 

heavy insurance premiums for a long time; moreover, SAIF members could change to 

BIF insurance only by paying large exit and entry fees.3 Although this provided some 

measure of protection to the SAIF by discouraging migration of SAIF members to the 

BIF, it also discouraged banks from purchasing failed thrifts. Banks that were 

considering the purchase of a thrift would face either high up-front fees or the need to 

maintain separate institutions indefinitely. 

To encourage banks to purchase thrifts, Representative Mary Rose Oakar 

proposed including a section in FIRREA that allowed institutions insured by one fund to 

hold deposits insured by the other fund. More specifically, a BIF-member institution 

would be able to purchase a SAIF-member institution, merge the two institutions, and 

avoid the exit and entry fees by designating an appropriate portion of its deposits as 

SAIF-insured. The final legislation included this section and these SAIF-insured deposits 

are termed Oakar deposits.4 

2

 The SAIF provided $2.0 billion to the FSLIC Resolution Fund, $1.1 billion to defease bonds that provided 

funding to the RTC, and annual interest payments of $793 million on the Financing Corporation (FICO) 

bonds that were used to provide funding for the FSLIC. The interest cost for FICO bonds was spread 

across banks and thrifts on January 1, 1997.

3

 Entry fees are assessed only on institutions migrating between funds. De novo institutions are charged 

only regular assessments.

4

 Similarly, a SAIF-member institution can hold BIF-insured deposits, in which the BIF-insured deposits 

are Oakar deposits. In the present study, institutions holding Oakar deposits are referred to as Oakar 

institutions. 

4 
 

The amendment largely succeeded in minimizing market distortions related to 

mergers of depository institutions. By December 31, 1998, 8.1 percent of FDIC-member 

institutions held Oakar deposits. Of these 839 Oakar institutions, 730 were BIF-member 

institutions; they held $257 billion in SAIF-insured deposits, or 36 percent of SAIFinsured deposits. The other 109 institutions were SAIF-member institutions holding 

$39 billion in BIF-insured deposits, or 2 percent of BIF-insured deposits.5 

Although not envisioned at the time, one result of the Oakar amendment has been 

a commingling of fund risk. As large banks have purchased thrifts, the SAIF’s risk 

profile has become more like the BIF’s risk profile. The ramifications of the 

commingling of the funds are discussed further in section 4 (“SAIF Results”). 

2.2 Market Concentration 

When a bank fails, the FDIC is authorized to bill the cost of the failure to affiliate 

or sister banks. Therefore the model implicitly assumed that the failure of one institution 

simultaneously causes the failure of the affiliate institutions. To proxy the failure of all 

affiliate institutions at one time, the model aggregated institutions by holding company. 

Thus, in this paper, references to market concentration are based on banking companies 

rather than individual banks or thrifts.6 

Concentration in the industry (as measured by percentage of domestic deposits 

held by banking company) as of December 31, 1998, is shown in table 1. This 

5

 FDIC (1999), 17–18.

6

 Throughout this paper, the terms “banking organization,” “bank holding company,” and “banking 

company” will be used interchangeably. The term “institution” refers to individual institutions and not to 

the organization. 

5 
 

Table 1 

Industry Consolidation as of December 31, 1998 

Percentage of Percentage of Percentage of Banking BIF-Insured SAIF-Insured Combined Companies Deposits Deposits Deposits 

25 largest 45.2% 31.9% 42.3% 

100 largesta 64.6 58.2 63.3 

All others 35.4 41.8 36.7 a

 Included in the 100 largest banking companies are the 25 largest 

banking companies. 

table demonstrates a significant difference between the BIF and the SAIF: BIF-insured 

deposits are more heavily concentrated in the 100 largest organizations than 

SAIF-insured deposits. In addition, nearly one-half of the BIF-insured deposits are held 

by the 25 largest organizations, whereas the comparable SAIF figure is less than 

one-third. If the funds were merged and all institutions were insured by a new National 

Insurance Fund (NIF),7 the concentration levels would resemble the BIF because of the 

BIF’s relative size. 

3. Simulation Model 

Oshinsky (1999) employed a Markov process to study the probability of 

insolvency of the BIF. The same model was used for this study. For a detailed 

description of the model, see Oshinsky (1999). 

Because, in general, banking failures are serially correlated, the model was 

designed to simulate periods of small, medium, and large disbursements. Using the 

historical patterns of BIF disbursements, a Markov-switching model defined the 

7

 The name “National Insurance Fund” and the acronym “NIF” are used in this paper to indicate a new fund 

that would be created if the BIF and the SAIF were to be merged. This usage does not mean the funds will 

actually be merged, nor does it mean that if they are, the name of the new fund will be National Insurance 

Fund. 

6 
 

probability of switching among the three levels of disbursements. This framework 

resulted in a projection that groups good years together and bad years together. 

The model used the historical BIF bank failures and losses instead of the SAIF or 

FSLIC failures and losses.8 The SAIF’s history is far too short to be useful for this type 

of analysis, and in addition, future SAIF failures will almost certainly differ from the 

FSLIC experience. During the 1980s, when most FSLIC failures occurred, savings and 

loan (S&L) regulation and supervision were lax. In the early 1980s, FSLIC officials felt 

that many S&L insolvencies were caused only by the high interest-rate levels that— the 

officials believed— would soon decline, after which the S&Ls would return to solvency. 

Therefore, many insolvent S&Ls were allowed to continue operating. After the sharp 

drop in interest rates in 1983, many S&Ls did return to profitability, but 10 percent of the 

industry remained insolvent. And unlike other regulators, the Federal Home Loan Bank 

Board (FHLBB) encouraged troubled institutions to grow out of their problems and move 

into new business areas to bolster profits. Deregulation, coupled with significant 

forbearance fueled by pressure from various branches of the government and an insolvent 

insurance fund, exacerbated the crisis and increased both the failure and the loss rates of 

thrifts. In contrast, during the early 1980s, banking regulators maintained more political 

independence than the FHLBB, and since it was not itself insolvent, the FDIC was able to 

close insolvent banks earlier, thus reducing the cost of bank failures. Finally, as of 

December 31, 1998, approximately 37 percent of SAIF-insured deposits were held by 

commercial banks, and another 7 percent were held by thrift subsidiaries of bank holding 

8

 Until 1989, savings and loans were regulated by the Federal Home Loan Bank Board and insured by the 

FSLIC. FIRREA abolished the FHLBB and the FSLIC and created the Office of Thrift Supervision (OTS) 

and the SAIF. 

7 
 

companies. The BIF’s experience is therefore probably a better proxy for future thrift 

experience than the FSLIC’s experience. 

The model projected the financial condition of the SAIF for 50 years. For each 

year, it projected thrift failures (and losses from failures), on the basis of the state of the 

industry and random variation within each disbursement state. To capture the effects of 

industry consolidation, the model simulated the top 100 thrift organizations 

individually— that is, SAIF losses from each of the top 100 thrifts were separated from 

the remaining thrifts. Premium assessments were based on the SAIF’s financial 

condition and on the assessment schedule mandated by FIRREA.9 In addition, the 

SAIF’s earnings, net of operating expenses, were estimated. 

The simulation was performed 1,000 times using a Monte Carlo simulation. The 

number of runs that contained an insolvent year determined the probability that the SAIF 

will become insolvent. To better understand the effects of ongoing consolidation on the 

SAIF and the hypothetical NIF, the model was run under three consolidation scenarios. 

3.1 Failure and Loss Rates 

As stated above, the model used the historical BIF failure and loss rates. The BIF 

has historically experienced much lower loss rates for large banks and lower failure rates 

for very large banks. Table 2 summarizes BIF failure rates by asset-size class. During 

9

 Note that the model does not take into account the SAIF Special Reserve that was required under the 

Deposit Insurance Funds Act of 1996. Under this legislation, approximately one billion dollars was 

removed from the SAIF and placed in a special reserve on January 1, 1999. These funds may only be used 

if the SAIF balance falls below 50 percent of the designated reserve ratio. 

8 
 

Table 2 

BIF Failure Rates, 1934–1997 

(Percentage of Institutions per Year) 

Low High Average 

Top 25 0.0% 4.0% 0.1% 

26–100 0.0 4.0 0.3 

All others 0.0 3.5 0.3 

the 64-year history of the FDIC, there were 2 failures of top 25 banking organizations, 12 

failures of top 26–100 banking organizations, and 1,372 failures of smaller institutions. 

But although the range of failure rates was similar across size classes, the average failure 

rate for top 25 organizations was significantly lower. As seen in table 3, loss rates also 

differ by asset-size class. Historical loss rates for top 100 failures (calculated as the 

Table 3 

BIF Loss Rates, 1934-1997 

(Percentage of Failed-Institution Assets) 

Simple Weighted Low High Average Average 

Top 50 –0.9% 10.2% 3.2% 5.3% 

51–100 0.0 18.3 6.6 6.8 

All others 4.8 bp 60.1 11.9 16.0 

Note: For top 100, the figures are by organization; for all others, the figures are per year. 

percentage of each failed organization’s assets) ranged from a small gain to a loss of 

18.3 percent, averaging 5.4 percent. The loss rates for the remaining institutions 

(calculated as an annual aggregate percentage of small-bank failed assets) ranged from a 

low of 4.8 basis points (0.048 percent) to a high of 60.1 percent, averaging 11.9 percent. 

Economists have cited several reasons for the differences in failure and loss rates for 

large institutions, including a smaller likelihood of fraud that could cause bank failure, 

economies of scale, liability structure, and better diversification of risk.10 To ensure that 

the effects of consolidation are considered, the model incorporated the different failure 

and loss rates for these three asset-size classes. 

10 Another difference may relate to systemic-risk concerns. When the LDC (less-developed-country) debt 

crisis caused several large banks to become troubled in the early 1980s, regulators practiced forbearance 

rather than allowing multiple large banks to fail. Forbearance has been used to assist smaller banks as well. 

9 
 

3.2 Stratification of the Top 100 Banking Companies 

In Oshinsky (1999), the top 100 banking companies were modeled individually 

while the others were modeled on an aggregate basis. A similar— but not identical— 

procedure was used for this analysis. This analysis assumed that the risk profile of a 

bank or thrift is more closely related to the asset size of its banking company than to the 

bank’s rank within the insurance fund. The 100th-largest BIF-member banking company 

is far larger than the 100th-largest SAIF-member banking company. Thus, the largest 

SAIF-member banking companies were placed in the top 25 or top 26–100 asset-size 

classes only if they would have been in these categories on the basis of an asset-size 

stratification of BIF-member organizations. As a result, in the 1997 simulation for the 

SAIF, there were only 24 companies in the top 25 asset-size class, and only 68 companies 

in the 26–100 asset-size class.11 

When the NIF was modeled, there were no companies with deposit insurance by 

multiple funds. Because some SAIF organizations were large enough to be in the top 100 

when BIF asset cutoffs were used, the 1997 simulation for the NIF included 

25 companies in the top 25 asset-size class, and 80 companies in the 26–100 asset-size 

class. 

11 The SAIF universe includes partial institutions— the SAIF portion of BIF-member Oakar institutions. If 

an Oakar institution were to fail, the losses would be split between the BIF and the SAIF on the basis of the 

proportion of BIF and SAIF deposits. In the model, these partial institutions’ assets and deposits are the 

portions of the Oakar institutions’ total assets and total deposits, based on the proportion of SAIF deposits. 

However, when the top 100 organizations were stratified, total assets of the banking company were used 

since the risk profile for the organization is based on total asset size. 

10 
 

4. SAIF Results 

As in Oshinsky (1999), the SAIF model was run under the following scenarios: 

1990 structure, 1997 structure, and 1998 structure pro forma to June 1999.12 The 

year-end 1990 structure was selected because at that time the significant increase in the 

merger activity of large banking companies had just begun. The year-end 1997 structure 

was selected because 1997 was the last year-end date when no megamergers had been 

consummated. The pro forma simulation incorporated the megamergers consummated in 

1998 and those that had been announced but not yet consummated as of June 30, 1999. 

Table 4 provides the results. In 1990, before Oakar institutions became prevalent, 

Table 4 

SAIF Simulation Results 

Probability Probability Probability Model of Fund < 0 bp of Fund < 50 bp of Fund < 75 bp 

1990 structure 9.6% 44.9% 66.2% 

1997 structure 6.2 23.6 52.0 

Pro forma 7.0 25.6 49.4 

the risk to the SAIF was significant mainly because 64.7 percent of the assets of SAIFinsured institutions were held by smaller institutions that, historically, have relatively 

high failure and loss rates. Through industry consolidation, and in particular mergers 

involving large banking organizations becoming Oakar organizations, more SAIF-insured 

deposits came to reside in the top 100 organizations that have historically experienced 

lower loss rates. In addition, some of these mergers involved top 25 organizations that 

have historically experienced lower failure rates as well. By year-end 1997, the top 100 

BIF-member organizations held 30.2 percent of the SAIF-insured deposits, compared 

with approximately 5.4 percent as of year-end 1990. With a larger portion of the SAIF12 The pro forma simulation adjusts the year-end 1998 structure for mergers announced but 

unconsummated as of June 30, 1999. These mergers include, but are not limited to, Fleet Financial Group, 

11 
 

insured deposits residing in institutions with lower failure rates and loss rates, the risk of 

SAIF insolvency decreased significantly, falling from 9.6 percent to 6.2 percent.13 

The risk to the SAIF increased slightly in the pro forma simulation, which 

included the announced but unconsummated mergers. As of December 1997, the top 25 

totaled $403.4 billion in assets, and consolidation during 1998 and adjustments for 

pending mergers effectively moved $20.5 billion in assets from smaller institutions into 

the top 25.14 In addition, as a result of the mergers, the number of companies in the top 

25 decreased from 24 as of December 1997 to only 19. With slightly more assets in the 

top 25 but significantly fewer organizations, the average size of the SAIF portion of these 

companies increased by over 40 percent. Thus, the chance that a single top 25 failure 

would cause SAIF insolvency was greater. 

Between 1990 and 1997, the model projects that the SAIF reserve ratio would fall 

below 50 basis points and 75 basis points less frequently. The probability of falling 

below 50 basis points dropped by nearly 50 percent, from 44.9 percent to 23.6 percent. 

In contrast, the probability of falling below 75 basis points dropped by only 21 percent. 

Between the 1997 and pro forma scenarios, the probability of the SAIF falling below 

either level changed little. These results are closely related to the changes in the 

composition of the institutions insured by the SAIF. Today, a relatively large portion of 

SAIF-insured deposits still reside in smaller institutions, which could readily reduce the 

Inc.’s acquisition of BankBoston Corporation and Union Planters Corporation’s acquisition of First Mutual 

Bancorp, Inc.

13 Note that the benefit of lower failure rates and loss rates of larger companies was partially offset by the 

movement of a large percentage of the industry into a few companies. Although the larger banking 

companies fail less often, the failure of one company could single-handedly bring about insolvency of the 

SAIF. 

14 The top 25 organizations in the pro forma simulation held $53.4 billion more than the top 25 in the 1997 

structure simulation, but $32.9 billion of this amount was banking industry growth during 1998. Therefore, 

the mergers moved only $20.5 billion into the top 25. 

12 
 

 

fund balance below 75 basis points during a period of stress. However, as the SAIF’s 

condition became more closely linked to top 100 organizations during the 1990s, it 

became less likely that the SAIF reserve ratio would drop below 50 basis points.15 

The simulation results support the hypothesis that during the 1990s, the risk 

profile of the SAIF has become more like that of the BIF.16 To facilitate a comparison of 

the funds, the BIF results from Oshinsky (1999) are shown in Table 5. 

Table 5 

BIF Simulation Results* 

Probability Probability Probability Model of Fund < 0 bp of Fund < 50 bp of Fund < 75 bp 

1990 structure 3.9% 17.8% 43.7% 

1997 structure 6.0 17.1 34.0 

Pro forma 6.5 16.6 32.1 

* Oshinsky (1999), 14,16. 

Whereas the SAIF was projected to be far more vulnerable than the BIF in 1990, 

the difference between the projected insolvency rates for the BIF and the SAIF fell to a 

mere 20 basis points in the 1997 scenario and 50 basis points in the pro forma scenario. 

Likewise, the projected probability of the SAIF reserve ratio falling below 50 basis points 

and 75 basis points fell to levels that are somewhat higher— but more similar to— the 

BIF. 

5. Merging the BIF and the SAIF 

The BIF and the SAIF provide identical insurance coverage to two groups of 

institutions that are becoming more similar. The risk profiles of both funds might be 

15 Note that this result is also driven in part by the model’s assumption about reserves. In particular, the 

model assumed that the SAIF would not reserve for a possible failure of a top 25 bank failure in the 

following year; however, the model allows for over-reserving of smaller institutions, in keeping with 

historical experience at the FDIC.

16 Note that the results rely heavily on the assumption that future SAIF failure rates and loss rates will 

resemble historical BIF experiences. 

13 
improved were they to be merged. To test this theory, the model was run assuming that 

the funds had been merged. Table 6 provides the results, which show the same general 

pattern as the BIF results: the risk of insolvency increased during the 1990s. 

Table 6 

Probability of NIF Insolvency 

Probability Probability Probability Model of Fund < 0 bp of Fund < 50 bp of Fund < 75 bp 

1990 structure 3.7% 23.4% 56.0% 

1997 structure 5.2 17.8 38.6 

Pro forma 6.2 16.9 35.8 

From 1990 to 1997, the probability of insolvency increased— even though the portion of 

insured deposits held by smaller institutions (with higher failure and loss rates) 

dropped— because more banking companies became large enough to materially damage 

the insurance fund single-handedly. Industry consolidation during 1998 and the 

announced mergers effectively moved $125.9 billion in assets, adjusted for industry 

growth, into the top 25 organizations, which held $3.0 trillion as of December 1997. As 

with the SAIF, the mergers caused the number of top 25 organizations to decrease, in this 

case from 25 as of 1997 to 20. 

The probability that the NIF reserve ratio would fall below 75 basis points 

dropped substantially from 1990 to 1997 and dropped slightly from 1997 to the pro forma 

scenario. Likewise, the model projected that the probability of the NIF reserve ratio 

falling below 50 basis points dropped 24 percent between 1990 and 1997 and dropped 5 

percent between 1997 and the pro forma scenario. Not surprisingly, the NIF results are 

more similar to those of the BIF than the SAIF. However, unlike the insolvency rates, 

the NIF reserve ratio was projected to fall below 50 basis points and 75 basis points 

somewhat more frequently than the BIF. This reflects the differences between the 

composition of institutions insured by the SAIF and the BIF; in particular, SAIF-insured 

14 
 

deposits are more likely to be held by smaller institutions, which have higher failure rates 

and loss rates.17 

For purposes of comparison, table 7 provides the probability of insolvency for all 

three funds. In each scenario, the NIF was projected to be less risky than either the BIF 

Table 7 

Probability of Insolvency 

Model BIF SAIF NIF 

1990 structure 3.9% 9.6% 3.7% 

1997 structure 6.0 6.2 5.2 

Pro forma 6.5 7.0 6.2 

or the SAIF alone: that is, both funds, not just the SAIF, benefited from a merger. 

However, a merger of the funds would be no panacea: the risk of insolvency dropped, but 

it did not plummet toward zero. For the SAIF, the value of combining the funds would 

have been highest in 1990, before many large-bank mergers had effectively reduced the 

risk of the SAIF. The benefit to the BIF would have been relatively small at that time, 

but has increased somewhat with the advent of megamergers. 

At first glance, the results indicate that the comparative advantage of the NIF over 

the BIF and, to a lesser extent, the SAIF appears to have narrowed since 1997. 

Compared to the BIF, the NIF was only 0.3 percent less likely to be insolvent in the pro 

forma scenario but 0.8 percent less likely to be insolvent in the 1997 scenario. This 

result, however, does not capture changes in the depth of insolvency. In the pro forma 

scenario, the average depth of insolvency for the BIF was 101 basis points, but it was 

only 81 basis points for the NIF. Thus a combined insurance fund would more quickly 

be able to recover from insolvency than the BIF. This was less of a factor in 1997, when 

17 In addition, this result may be driven in part by the model’s treatment of reserves for top 25 

organizations. See footnote 15. 

15 
 

the average depth of insolvency was 71 basis points for the BIF and 57 basis points for 

the NIF.18 

From a public-policy perspective, the benefit of a fund merger exceeds the 

benefits for each fund individually. In the pro forma scenario, the model projected a 6.5 

percent probability of insolvency for the BIF and a 7.0 percent probability of insolvency 

for the SAIF; however, the probability that at least one fund would become insolvent was 

10.3 percent. Of the 65 runs that projected BIF insolvency, only 32 also projected that 

the SAIF would become insolvent. This indicates that there is a reasonably good chance 

that one fund could become insolvent while the other fund remains solvent. This 

situation could lead to a loss of public confidence during a period when funds are 

available at the FDIC but in the wrong place. The results also indicate that there is a real 

chance that members of the BIF and the SAIF will be faced with materially different 

assessment rates for the exact same level of insurance coverage in the future. Such 

distortions in the marketplace would most likely reduce the efficiency of depository 

institutions as banks and thrifts expend resources on arbitraging tactics in pursuit of 

smaller insurance assessments. 

In conclusion, these results show that a merger of the funds would substantially 

decrease the probability of a failure of at least one deposit insurance fund. In addition, it 

would provide benefits to both the BIF and the SAIF. A fund merger would reduce— but 

by no means eliminate— the probability of fund insolvency. 

18 For the SAIF, the average depth of insolvency was 37 basis points in the 1997 scenario and 54 basis 

points in the pro forma scenario. Thus the SAIF would typically be able to replenish itself more quickly 

than the NIF. 

16 
 

Bibliography 

Federal Deposit Insurance Corporation (FDIC). 1999. The FDIC Quarterly Banking 

Profile. Fourth Quarter 1998. 

Marino, James A., and Rosalind L. Bennett. Forthcoming. The Consequences of 

National Depositor Preference. FDIC Banking Review 12, no. 2:19-38. 

Oshinsky, Robert. 1999. Effects of Bank Consolidation on the Bank Insurance Fund. 

Working Paper 99-3. FDIC. 

17