The Contribution of Economic Data 

to Bank-Failure Models 

Working Paper 2003-03 

August 2003 

Daniel A. Nuxoll * 

* This working paper reports the details of one part of a three-part study done in close collaboration with John 

O’Keefe and Katherine Samolyk. See Nuxoll, O’Keefe, and Samolyk (2003). While each author had primary 

responsibility for one part of that study, many of the ideas in this part were developed in close collaboration with 

O’Keefe and Samolyk. 

The opinions expressed here are the author’s and do not necessarily reflect the views of the FDIC. 
Abstract 

The wave of bank failures during the late 1980s and early 1990s was caused in part by a 

series of regional recessions. This paper examines whether the FDIC can use state-level 

economic data to forecast bank failures and finds that these data do not improve models 

that use only bank-level data. The paper also proposes a number of explanations for the 

lack of improvement. 

1 
 

Virtually all economists would accept that economic conditions affect the health 

of banks. An FDIC research team has investigated whether this basic theory can be 

translated into a forecasting model, and specifically whether nonbank economic data can 

be used to improve forecasts of bank health. This project was a systematic effort to 

explore the somewhat surprising result noted by some of the developers of the Federal 

Reserve System’s off-site program: regional economic variables do not improve the 

forecasts of bank health. 1 

This paper reports on one aspect of that project: whether nonbank data can 

improve bank-failure forecasts.2 In general, we find that economic data do not improve 

these forecasts despite the fact that the data are statistically significant.3 Possible 

explanations for this result are explored in the conclusion. 

This paper begins with a short description of the relationship between bank 

conditions and economic events in several regions in the United States. The second 

section describes both the bank-specific and the economic data used in this exercise. The 

third discusses bank-failure models in general before turning to whether economic data 

improve failure models. The conclusion speculates about the possible reasons that statelevel economic data do not improve the forecasts. 

1

 See Cole, Cornyn, and Gunther (1995), 8. 

2

 For the purposes of this paper, bank failures include banks that are resolved by the FDIC and cases of open-bank 

assistance. 

3 It is impossible to demonstrate conclusively that including economic data in forecasting models is futile. A vast 

number of models are conceivable, so anyone who really believes in the usefulness of economic data would argue that 

the problem is that the wrong model has been tested. The failure of one model, or even of numerous models, does not 

necessarily reflect on all models. Obviously, this observation applies to any negative empirical finding. Nonetheless, 

the negative finding of this paper, despite extensive specification searching, must raise questions about any future 

forecasting exercise that does successfully use economic data. In particular, one must ask whether the success is due to 

the power of the model or is the result of a lucky data-mining expedition. 

2 
 

Bank Conditions and Economic Events 

A casual examination of data from the past couple of decades might suggest that 

there is a very weak connection between the health of the economy and the health of the 

banking sector. Figure 1 plots the growth rate of personal income (solid line) in the 

United States as well as the number of bank failures (broken line). The number of 

failures did increase shortly after the recessions of 1973 and 1980–1982. However, 

failures actually peaked in 1989, following a period of steady economic growth. During 

the recession of 1991–1992, fewer banks failed than in preceding years 

However, most accounts that stress the importance of the economy to the banking 

industry stress the importance of local economies. The FDIC’s History of the Eighties 

(1997) refers extensively to local economic conditions in its discussions of the difficulties 

experienced by agricultural banks and of banking problems in the Southwest, New 

England, and California. Until the 1980s, many states severely limited the geographic 

market for banks by legally restricting branching and bank holding companies. 

Consequently, almost all banks tended to draw all their deposits from their home states 

and to make the bulk of their loans in a very circumscribed area. The relevant economic 

conditions for most banks were local, not national. 

Figures 2–5 are analogous to figure 1; however, they plot the growth of personal 

income for an individual state (solid line) against the percentage of banks that failed in 

that state (broken line).4 Figures 2, 3, and 4 plot events in Texas, Massachusetts, and 

4 These figures plot the number of failures as a percentage of the banks in existence in the state the previous 

December. The raw number of failures might be deceptive because Texas, for example, had many more banks than 

Massachusetts. 

These figures plot the growth rate of personal income in the state minus the growth rate of personal income in 

the United States. Rapid growth in the rest of the country probably tends to induce growth in all the states because the 

goods and services produced within a state are sold to customers in the rest of the country. Subtracting the two growth 

rates produces a positive number if the state is growing more rapidly than the rest of the United States, and a negative 

3 
 

 

California—states that had notable banking crises. In contrast to figure 1, these seem to 

confirm the conventional wisdom that recessions cause banking failures. However, 

figure 5 plots events in Ohio, a state that had substantial economic problems but only a 

handful of bank failures.5 Ohio is not unique in this regard. Apparently, banking crises 

tend to occur as a result of economic problems, but not all episodes of economic distress 

produce banking crises. 

A closer examination of the figures does reveal a definite pattern. The economies 

of Texas, Massachusetts, and California all grew more rapidly than the economies in the 

rest of the United States for several years before the problems developed, whereas Ohio 

(and other states that did not have banking crises) did not experience a boom. This fact 

suggests that banking crises tend to follow a boom/bust cycle. This pattern might occur 

because banks tend to lend aggressively in those states experiencing booms. Many of 

those loans then might go sour when the economy deteriorates, and the more aggressive 

banks might suffer large losses and fail.6 Banks in less buoyant markets do not have the 

temptation to gamble on growth. 

This pattern suggests that any model that uses state-level economic data to 

forecast bank failures should look back over a number of years and should be structured 

to allow for this sort of dynamic. The next section explains how this was done. 

number if it is growing more slowly. This number is a better indicator of the intrinsic strength of a state’s economy. 

Moreover, it yields a clearer relationship between local economic problems and banking problems. 

5 Ohio had five bank failures between 1980 and 1995. In December 1980, 407 banks filed Call Reports. By December 

1995, mostly because of mergers, only 255 filed. 

6 FDIC (1997) provides some documentary and statistical evidence for this theory. 

4 
 

The Data and the Model 

Table 1 describes the variables that were used in the set of specifications of the 

model discussed in this working paper, and table 2 reports the sample medians, means, 

and standard deviations for the variables. Table 1 also describes some alternative 

economic variables that were explored. The first panel of each of the first two tables 

describes Call Report variables and examination variables. These are standard 

examination and Call Report data that have been used by both academic and government 

researchers in bank-failure models. There are three important categories of data: prior 

CAMEL ratings, capital/asset ratios, and loan-quality ratios. Other variables were also 

used, but the results indicated that these three categories of variables have the most 

explanatory power.7 All Call Report data were from the December reports. No effort 

was made to eliminate stale CAMEL ratings.8 

The Call Report and examination data are basically control variables. The 

variables of interest are the economic variables. The economic data were chosen with the 

criterion of whether they were likely to contribute to the accuracy of forecasts of failure 

among all FDIC-insured commercial banks. This criterion eliminated a number of data 

series. 

Some data are simply not available on a timely basis. For example, Gross State 

Product (GSP) might be a better indicator of economic conditions than personal income. 

7 See Hooks (1995) for a discussion of failure models. Demirgüc -Kunt (1989) has an overview of the earlier literature. 8 The effect of examination intervals on forecast accuracy was considered, however. The model had higher Type II 

and lower Type I errors for banks that had not been examined recently. A lower percentage of these banks failed. 

This finding contrasts with Cole and Gunther (1998). Cole and Gunther find that, on average, a failure model 

has more difficulty forecasting the failure of banks that have not been examined recently. The key is that Cole and 

Gunther compare the whole power curves, whereas we are comparing a specific section of the powercurves —the 

section that is associated with observations with specified probabilities of failure. 

5 
 

However, GSP data are available only with a lag of a couple of years, so 1998 data might 

be available only in 2000. 

Second, the data must exist for times and locations in which there were a 

substantial number of bank failures. For example, even with the best data one could 

probably not estimate any sort of reasonable bank-failure model for the states of 

Alabama, Georgia, Mississippi, North Carolina, and South Carolina. These five states 

had a total of 18 bank failures between 1980 and 1995, and there are simply not enough 

observations to estimate accurately any kind of model. Similarly, there was one bank 

failure in 1997 in the whole United States, so it is impossible to estimate any reasonable 

bank-failure model based solely on the data for that year. 

Finally, other data are available for some regions but not for all. These data are 

potentially useful only for a subset of FDIC-insured banks; but this project had a broader 

focus. For example, a wide variety of series on local economic conditions are available 

only for a few states or cities. Forecasts of regional economic conditions might be a very 

useful indicator of the likelihood that banks in a region will fail in the future. Many firms 

produce those forecasts for some states or cities. However, the coverage of these 

forecasts is spotty, so their usefulness is limited. 

Three broad categories of data meet the basic criterion: personal-income data, 

employment data, and banking aggregates.9 Table 1 lists a number of these data series as 

“State Economic Variables” or “Alternative State Economic Variables.” During the 

course of this project, each of these data series was tested. In general, the employment 

9 All dollar-denominated numbers were deflated to 1992, though deflation makes no difference except in the 

specifications that pool the data for different periods. 

6 
 

data were the least useful, and growth in personal income and growth in total loans made 

by banks headquartered in a state improved the failure models the most. 

In order to include the type of dynamic discussed above, we used five years’ 

worth of data in the models. For example, if the growth rate in personal income was 

used, the model included five growth-rate terms—the rates from the year before the Call 

Report, from two years before it, and from three, four, and five years before it. Some 

experimentation revealed that using five years of data produced coefficients that were 

imprecisely estimated. The data are highly correlated, so the model cannot distinguish 

the effects of growth five years earlier from the effects of growth three years earlier. For 

that reason, most work used the growth rate of personal income between three and five 

years earlier, the growth rate two years earlier, and the previous year’s growth rate.10 

This approach produced more precise coefficient estimates and better forecasts. 

Throughout this project, a logistic model was used. This is a standard model 

among both academics and bank regulators.11 The model was fitted on bank failures that 

occurred within two years of the Call Report date, so the model can be said to have a 

two-year horizon. 

After the model was fitted, out-of-sample forecasts were done. Because of the 

two-year horizon, the forecasts were based on data from the Call Report two years after 

the Call Report used to fit the model. For example, the model was fitted on failures in 

1990 and 1991, Call Report data from December 1989, and the CAMEL rating for the 

10 As in figures 2–5, the personal-income growth rate actually used is the difference between the personal-income 

growth rate in the state and the rate in the nation. 

Table 2 reports an apparently puzzling result: the mean and median growth rates are negative. However, 

these numbers are weighted by the number of banks in each state. The negative number means that the states with a 

large number of banks grew more slowly than the rest of the country. 

11 The Federal Reserve System has, for several years, used an off-site system that includes a logistic model to forecast 

bank strength. 

7 
 

last exam before December 1989. The coefficients were then used with Call Report and 

examination data from December 1991 to forecast failures in 1992 and 1993. In 

principle, the necessary data would have been available at the end of 1991 to forecast the 

failures over the next two years.12 This exercise mimics the way the FDIC or other 

banking agency might use such a model. 

This model was estimated both as a cross section and as a pooled time-series cross 

section. This paper reports on the cross-section models estimated on data from the Call 

Reports of December 1986, December 1989, and December 1992 as well as on a pooled 

time-series cross-section model.13 These results are broadly representative of a much 

wider group of results. 

These models have two types of errors, conventionally called Type I and Type II 

errors. Type I errors occur when the model indicates that a bank will survive, but the 

bank does not survive. Type II errors occur when the model predicts that a bank will fail, 

but it actually survives. More colloquially, Type I errors occur when the model frees the 

guilty, and the Type II errors occur when the model convicts the innocent.14 

These models do not produce an unambiguous prediction that a bank will fail; 

rather, they estimate the probability that a bank will fail. A bank is projected to fail if its 

12 In reality, the Call Report data and the examination data are available with a lag, so the data would have been 

available in early 1992. 

13 The December 1986 cross section, for example, used Call Report data from that date as well as data from the last 

examination before December 1986 and failures from the years 1987 and 1988. 

Because of the two-year horizon, there is both an “even” and an “odd” version of the pooled model (the 

former uses Call Report data from even-numbered years, the latter from odd-numbered years). Both forms were tested, 

and there were no material differences. This paper reports the even version. 

14 The terminology of Type I and Type II errors is adopted from hypothesis testing. Simpler criteria for forecasts can 

be developed. Such criteria, however, would necessarily involve some system of weighting the two types of error. For 

instance, there are criteria involving rank-order statistics, but these can be interpreted as assigning an equal weight to 

the two errors. In bank-failure models, Type I errors are usually thought more serious, but there is no general 

agreement about the appropriate weights. 

8 
 

estimated probability exceeds a specific threshold level. Raising the threshold 

necessarily decreases the number of projected failures. Decreasing the number of 

projected failures also reduces the number of failures accurately forecasted, as well as the 

number of surviving banks projected to fail. Thus, a higher threshold increases the level 

of Type I errors and decreases the level of Type II errors. One can easily graph the tradeoff between Type I and Type II errors by varying the threshold. 

These graphs can be used to compare the accuracy of various models. More 

accurate models have a lower level of Type I errors for any given level of Type II errors. 

That is, the curve representing a more accurate model lies below and to the left of the 

curve for a less accurate model; or equivalently, the curve lies closer to the origin (which 

represents no Type I errors and no Type II errors). 

It should be noted that such curves can intersect. In these cases, one model is not 

unambiguously better than the other.15 

These errors can be measured both in-sample and out-of-sample. In-sample 

graphs use the same data that were used to estimate the model, so they reflect the fit of 

the data. However, for FDIC purposes, the critical criterion is not statistical fit but 

forecasting power. Forecasting is by definition out-of-sample, so out-of-sample errors 

are more relevant to the model’s usefulness to the FDIC. 

For example, the model was estimated on failures in 1990 and 1991, Call Report 

data from December 1989, and the CAMEL rating for the last exam before December 

15 Actually, bank supervisors are most interested in the section of the curve that represents a low level of Type II 

errors. Bank supervisors have limited resources to devote to monitoring banks intensively. Failure models and other 

off-site models can be used to identify those banks that are in the most danger of failing so that supervisors can allocate 

those resources. Type II errors amount to a waste of resources because supervisory resources are diverted to banks that 

are not in danger of failure. Because of the constraint, supervisors are undoubtedly most interested in not wasting 

resources, that is, in low Type II errors. Consequently, they would be most interested in whether, for low levels of 

9 
 

1989. Type I and Type II errors are calculated in terms of the ability of the model to 

correctly identify failures in 1990 and 1991—that is, in terms of the data used to estimate 

the model. The forecasting exercise uses the coefficients of this same model and 

December 1991 data to forecast failures in 1992 and 1993. Because the model was 

developed without data from 1992 or 1993, the Type I and Type II errors of this exercise 

are more indicative of the usefulness of the model. 16 

Results 

Table 3 reports results for four different samples. The first column gives the 

coefficient estimates and standard errors when December 1986 data are used to estimate 

the probability of failures in 1987 and 1988. A positive coefficient means that higher 

levels of the variable are associated with a larger probability of failure. One asterisk 

indicates that the coefficient is significant at least at the 10 percent level, two indicate 5 

percent, and three indicate 1 percent. The chi-squared statistic is for the null hypothesis 

that all three loan/growth coefficients are zero. The three asterisks indicate that the null 

can be rejected at the 1 percent level of significance. 

The second and third columns give the results of using December 1989 and 

December 1992 data to forecast failures in the following two years. The final column 

gives pooled results in which December 1984 data are used to forecast failures in 1985 

and 1986, December 1986 data are used to forecast failures in 1987 and 1988, and so 

Type II errors, one model has better levels of Type I errors than another. Bank supervisors are relatively uninterested 

in whether two curves intersect at high levels of Type II errors. 

16 Forecasts for the pooled model were done using a “rolling” system of estimation. For forecasts based on the 

December 1991 Call Report, the pooled model was estimated on data for the period before that date. For December 

1993 forecasts, two years of data were used to develop the model. 

10 
 

forth. The failures in this pooled data set occurred between 1985 and 1996; this period 

includes the vast majority of failures of institutions insured by the FDIC. 

The results generally indicate that banks with lower levels of capital, higher levels 

of past-due loans, and lower loan-loss reserves are more likely to fail. These results are 

completely consistent with conventional wisdom and previous studies. Other results have 

not been as thoroughly documented, but they are consistent with what one might expect. 

Banks with worse CAMEL management and liquidity ratings are also more likely to 

fail.17 In addition, banks that have not been examined recently are more likely to fail. 

Low income is not a consistently significant indicator of failure, but at least the sign on 

net income is consistently negative. 

The capital, asset, and earnings CAMEL ratings are mostly statistically 

insignificant, and their coefficients are often the “wrong” sign. One possible explanation 

is that the capital/asset ratio as well as the loss/reserves ratio conveys the relevant 

information in the capital rating, whereas the two past-due loan terms are redundant with 

the asset rating, and the earnings rating adds nothing to the net-income term. There are 

no variables in this specification that are obvious measures of management quality or 

liquidity. 

The charge-off/bad loans ratio changes sign, and in the pooled model there is a 

statistically significant relationship between high charge-offs and failure. One could 

argue either that high charge-offs indicate that the bank has problems in the loan portfolio 

(and is likely to fail) or that the bank is dealing with the problems in the portfolio (and is 

likely to survive). The statistical results do not differentiate between these two theories. 

17 A CAMEL rating of 5 is worse than a CAMEL rating of 1. 

11 
 

Finally, there is an ambiguous but generally statistically significant relationship 

between a bank’s loan growth and its probability of failure. Small variations in loan 

growth are not economically significant, however, because the coefficients are very 

small. For example, in the pooled regression, consistent loan growth of 18 percent per 

year has about the same effect as a 1 percent difference in the capital/asset ratio.18 

Table 4 reports the results of including the state-level economic data. The chisquared statistic is for the null hypothesis that the coefficients on the state data all equal 

zero. The results in this table are representative in that the state data are usually highly 

significant. If the chi-squared statistic exceed 12.84, the variables are significant at least 

at the 5 percent level. The statistics in table 4 exceed this standard by a wide margin. 

This result is not quite universal; there are some specifications in which state data are 

insignificant in the early 1990s. 

The economic data have the expected signs. The negative coefficient on the 

previous year’s personal-income growth means that failure is more likely when the state 

in which the bank is headquartered is growing less rapidly than the rest of the United 

States. The positive coefficient on the growth that occurred between three and five years 

earlier means that high growth in the past tends to put banks at more risk. The data 

confirm the impression of a boom/bust pattern to bank failures. 

Moreover, the coefficients are economically significant. The effect of a 1 percent 

decrease in the capital-asset ratio has about the same effect as being located in a state that 

18 Mathematically, (–1%) * (–0.373) = 18% * (0.0050 + 0.0079 + 0.0036). This general result is robust across samples 

and specifications. This does not translate directly into a probability of failure because the logistic is nonlinear. 

This result might seem to contradict previous research that indicates that high growth is a high-risk strategy, a 

theory that found its way into the provisions of FDICIA. However, the specification includes past-due loans and is 

limited to a two-year horizon. Possibly, within two years of failure, the quality of the loan portfolio already reflects the 

negative effects of high growth. If that is true, then within two years of failure, loans past due (which resulted from 

rapid past growth) are a better indicator than the growth itself. 

12 
 

was growing 1.4 percent more rapidly than the rest of the nation and is now growing 1.4 

percent less rapidly. 19 

Figure 6 shows the in-sample trade-off between Type I and Type II errors for the 

Call Report data from December 1986. The broken line graphs the trade-off between 

Type I and Type II errors for the model that uses only bank data, while the solid line 

shows the trade-off for the model that includes state economic data. The inclusion of 

state data clearly improves the model. The model without state data can achieve a 10 

percent Type II error (about 1,200 banks incorrectly considered probable failures) only at 

the cost of a 23.2 percent Type I error (84 missed failures). The model with state data 

can attain a 10 percent Type II error with only a 10.2 percent Type I error (37 missed 

failures). These results are not too surprising because the chi-squared statistic in table 4 

indicates that the state data considerably improve the fit of the model. Figure 7 shows 

analogous results for the December 1989 Call Reports. The two curves actually intersect 

in this case. Though the state data are statistically significant, there is no obvious 

advantage to using the additional data in this period. 

Though a model with more data necessarily has at least as good a fit as models 

with less data, the same is not true for forecasting. It is well known among 

macroeconomic forecasters that more sophisticated models often perform worse than 

simpler models. 

Figures 8 and 9 show the results of two forecasting exercises. Figure 8 shows the 

results of using the model estimated on December 1986 Call Report data to estimate the 

probability of failure in 1987 and 1988. In principle, an economist could have estimated 

this model in January 1989 and used the coefficients and Call Report data from 

19 (–1%) * (–0.373) = (–1.4%) * (–.134) + 1.4% * (0.053 + 0.076) 

13 
 

December 1988 to forecast failures in 1989 and 1990. Figure 8 shows that the addition of 

state data does not improve the model.20 Figure 9 tells a similar story for forecasts of 

1992 and 1993.21 These figures are representative of a large number of specifications. 

While it is possible to find specifications in which state data actually improve forecasts 

for some periods, we were unable to find a specification in which state data consistently 

improved forecasts. Indeed, for most specifications, the inclusion of state data actually 

produced noticeably worse forecasts for some periods. A model that included state data 

would have misled regulators during some periods.22 

Possible Explanations 

There are a multitude of possible explanations for these results. First, the 

specification might be incorrect. All empirical work runs this risk. 

Arguably, this project has been more affected by the opposite problem: during 

the course of this project, numerous specifications were tested with the goal of finding 

the correct specification. The search was done in terms of both statistical significance 

and forecasting power. Such searches can easily produce spurious results that owe more 

to overfitting than to any underlying economic relationship. Consequently, the results of 

20 In the section of the graph of most interest to bank regulators, the model with state data does notably worse. Bank 

regulators cannot reasonably accept high levels of Type II error because a forecast with high Type II error would 

suggest that a substantial fraction of the banking industry would fail in the next two years. Bank regulators do not have 

the resources to monitor intensively a substantial fraction of the banking industry. For example, if bank regulators 

could actively monitor 10% of the population, they would be most interest in the section of the graph with about a 10% 

Type II error. With a 10 percent Type II error (about 1,100 nonfailures forecasted to fail), the model without state data 

would have missed 17 failures (a 6.23 percent Type I error), and the model with state data would have missed 28 

failures (a 10.26 percent Type I error). 

21 The model is estimated using December 1989 Call Report data and 1990 and 1991 failures. That model is used 

along with December 1991 Call Report data. Again, in most of the section of the graph of interest to regulators, the 

model that uses only bank data does better. 

14 
 

a specification search might be suspect. In fact, the current project searched and did not 

find results. The problem is not overfitting, but the lack of results. 

Second, it is possible that the conventional wisdom is simply wrong. This is 

highly unlikely because almost any specification shows that state economic data are 

highly significant, in-sample. 

Third, it is possible that we have too little data on the relationship between local 

economic conditions and banking failures. During the period between 1980 and 1995, 

there were four major waves of banking failures: in the oil states of the Southwest, in the 

Northeast, in California, and in the agricultural sections of the Midwest. There were 

other minor crises (for example, in Alaska and Utah), but these affected relatively few 

banks. The limited number of events might not be sufficient to estimate a general 

relationship. 

Fourth, as the savings and loan crisis demonstrated, the failure of a bank is a legal, 

not an economic, event. A bank does not fail when it becomes insolvent; a bank fails 

when the relevant bank supervisor says that the bank has failed. Some have argued that a 

policy of forbearance was replaced by a policy of closing problem institutions quickly. 

In 1991, this change was legally mandated by the prompt corrective action provisions of 

FDICIA. 

This policy change would change the timing of bank failures relative to economic 

events. Events in Texas and Massachusetts are suggestive. If supervisors close banks 

only after banks have had a chance to work out problems, then there will be a lag 

between economic crises and banking crises. There was approximately a three-year lag 

22 Using a pooled model does not change this basic result. In fact, in-sample fit is worse in some periods in a pooled 

model. Including more data only improves the average fit of a model; the use of more data does not necessarily 

15 
 

in Texas (c.f. figure 2). However, if supervisors close banks quickly, the lag will be 

shorter. Banks in Massachusetts failed soon after that state went into recession; figure 3 

shows practically no lag. If this is the case, the forecasting model performs poorly 

because the model is looking for the same lag in Massachusetts that it found in Texas. 

Fifth, bank data are economic data. Any reasonable model of bank failure 

includes a number of variables that reflect the health of the local economy. Loans past 

due 30–89 days, for example, are procyclical and seem to be roughly contemporaneous.23 

Because bank balance sheets reflect local economic conditions, the question is whether 

other economic data, such as personal-income growth, add anything to that information. 

Bank data do have one important advantage in failure models. The bank is 

affected by the economy in which it does business. And virtually no bank markets 

coincide with state boundaries. Most banks have only a few branches and do business in 

a relatively small area, so their health depends less on the condition of the state economy 

than on whether the local factory shuts down or whether local farmers can survive a 

drought. On the other hand, some banks lend out-of-state, exposing part of their portfolio 

to events in other states.24 Of course, money-center banks do business throughout the 

world, so the less-developed-country crisis is an inherent part of the history of banks 

improve the fit in each year. 23 There is a weak relationship (a correlation coefficient of –0.233) between loans past due 30–89 days at banks 

headquartered in the state and personal-income growth in the state. The relationship is statistically significant and of 

the same order of magnitude as the correlation of either employment growth or the unemployment rate with personalincome growth. 

The contemporaneous relationship is strongest, but there are two important caveats. First, the 

contemporaneous relationship is between past-due loans at theend of the quarter and income growthduring the quarter. 

The timing problem is inherent in any comparison between stocks and flows. Second, the difference between 

contemporaneous, leading, and lagging correlation coefficients is tiny. The correlation coefficient between income 

growth and loans past due at the end of the previous quarter is –0.216, and between income growth and loans past due 

at the end of next quarter, –0.211. 

As one might expect, there is a lagging relationship with loans past due 90+ days and income growth. 

Credit card banks are omitted from this analysis. 24 Banks lent far from the home state even before interstate banking. 

16 
 

during the 1980s. Currently, the federal agencies do know where banks have branches 

and how much money has been deposited in those branches. However, even if these data 

were completely accurate (and they are probably not), the major risk to banks is loans, 

not deposits. Given these problems, it is virtually impossible to measure economic 

conditions accurately within a bank’s market. 

The bank’s balance sheet, however, indicates economic conditions precisely 

within the bank’s market. Of course, that information is noisy because the quality of the 

management and other factors affect the balance sheet. Arguably, this is an advantage, 

for the question is not the health of the economy where the bank does business but how 

the economy affects the bank. That effect depends heavily on the quality of management. 

Hence, state economic data might add little to forecasting models because they do 

not add any information to the data already in the model. 

It must be stressed that these explanations are conjectural. Some are logically 

impossible to verify (for example, logically one can never show that there is no additional 

information in data). Moreover, the proposed reasons are undoubtedly incomplete.25 

Though the reasons for our failure to find an effect are not obvious, it is obvious 

that we did not find a way to use state economic data to improve forecasts of bank failure. 

25 Most importantly, the surge of failures between 1980 and 1995 was unique in FDIC history. This suggests either 

that regional recessions did not occur before 1980 (which seems implausible) or that something happened after 1980 to 

make banks more susceptible to failure. 

17 
 

 

References 

Cole, Rebel A., Barbara G. Cornyn, and Jeffery W. Gunther. 1995. FIMS: A New 

Monitoring System for Banking Institutions. Federal Reserve Bulletin 81, no. 1:1– 

15. 

Cole, Rebel A., and Jeffery W. Gunther. 1998. Predicting Bank Failures: A Comparison 

of On- and Off-Site Monitoring Systems. Journal of Financial Services Research 13 

no. 2:103–17. 

Demirgüc-Kunt, Asli. 1989. Deposit-Institution Failures: A Review of the Empirical 

Literature. Federal Reserve Bank of Cleveland Economic Review 25, no. 4:2–18. 

Federal Deposit Insurance Corporation (FDIC). 1997. History of the Eighties—Lessons 

for the Future. 2 vols. FDIC. 

Hooks, Linda M. 1995. Bank Asset Risk—Evidence from Early Warning Models. 

Contemporary Economic Policy 13, no. 4:36–50. 

Nuxoll, Daniel A., John O’Keefe, and Katherine Samolyk. 2003. Do Local Economic 

Data Improve Off-Site Models That Monitor Bank Performance? FDIC Banking 

Review (in press). 

18 
 

Table 1 

Variables Used in the Failure Model 

A. Call Report Variables 

Capital 

Net Income 

Due 30 

Loans Past Due 90+ 

Reserves 

Charge-offs 

Loan Growth— 

Past Year 

Loan Growth— 

2 Years Earlier 

Loan Growth— 

3–5 Years Earlier 

Equity as a percentage of total assets 

Net income as a percentage of total assets 

Loans past due 30–89 days as a percentage of total assets 

Loans past due 90 or more days plus nonaccruing loans plus other real estate owned (repossessed real estate) 

as a percentage of total assets 

Loan-loss reserves as a percentage of loans past due 90 days plus nonaccruing loans plus other real estate 

owned 

Charge-offs as a percentage of loans past due 90 days plus nonaccruing loans plus other real estate owned 

Percentage growth in total loans (deflated by the GDP deflator) between the December Call Report and the 

December Call Report of the previous year 

Percentage growth in total loans (deflated by the GDP deflator) between the December Call Report of the 

previous year and the December Call Report of the year before that 

Average percentage growth in total loans (deflated by the GDP deflator) between the December Call Report of 

two years earlier and the December Call Report of five years earlier 

B. Examination Variables 

CAMEL C Capital component of CAMEL rating 

CAMEL A Asset component of CAMEL rating 

CAMEL M Management component of CAMEL rating 

CAMEL E Earnings component of CAMEL rating 

CAMEL L Liquidity component of CAMEL rating 

Examination Interval Number of days between the Call Report date and the last examination, divided by 365 

C. State Economic Variables 

P.I. Growth— Percentage growth in total personal income during the past year minus the comparable number for the United 

Past Year States 

P.I. Growth— 

2 Years Earlier Percentage growth in total personal income two years ago minus the comparable number for the United States 

P.I. Growth— Average percentage growth in total personal income three to five years ago minus the comparable number for 

3–5 Years Earlier the United States 

D. Alternative State Economic Variables 

Per Capita Personal Income Growth 

Disposable Personal Income Growth 

Per Capita Disposable Personal Income Growth 

Employment Growth 

Unemployment Rate 

Growth in Total Loans reported by all banks headquartered in the state 

Growth in Total Assets reported by all banks headquartered in the state 

Notes for alterative state economic variables: Five years of data were used. All variables were run both as levels and as differences from the 

comparable U.S. value. All variables were run both as levels and as differences from the comparable U.S. value. All dollar values were deflated by 

the GDP deflator. Total loans and assets are from the December Call Report. Thrifts and credit card banks were excluded. 

19 
Table 2 

Basic Statistics for Model Variables 

A. Call Report Variables 

Median Mean St. Dev 

Capital 8.31 8.97 4.19 

Net Income 0.99 0.82 2.52 

Loans Past Due 30 0.84 1.14 1.11 

Loans Past Due 90+ 0.91 1.64 2.31 

Reserves 0.74 0.87 0.63 

Charge-offs 0.24 0.52 1.21 

Loan Growth—Past Year 3.45 3.97 34.09 

Loan Growth—2 Years Earlier 3.53 4.36 32.93 

Loan Growth—3–5 Years Earlier 9.09 13.73 61.89 

B. Examination Variables 

Median Mean St. Dev 

CAMEL C 2 1.78 0.85 

CAMEL A 2 1.97 0.96 

CAMEL M 2 2.09 0.79 

CAMEL E 2 2.13 1.06 

CAMEL L 2 1.67 0.71 

Examination Interval 0.65 0.88 0.90 

C. State Economic Variables 

Median Mean St. Dev 

P.I. Growth—Past Year –0.33 –0.56 2.78 

P.I. Growth—2 Years Earlier –0.38 –0.57 2.73 

P.I. Growth—3–5 Years Earlier –1.67 –2.61 6.22 

Notes: Medians, means, and standard deviations were calculated across all banks (excluding credit card banks) 

between 1984 and 1996.The Call Report variables and the state economic variables are as of the end ofDecember, 

and the examination variables are for the last exam before the end of December. 

20 
 

 

Table 3 

Coefficient Estimates for Different Samples—State Data Not Included 

1986 1989 1992 Pooled 

Intercept –2.898 *** –3.745 *** –7.046 *** –5.010 *** 

0.529 0.761 1.736 0.284 

Capital –0.463 *** –0.588 *** –0.489 *** –0.373 *** 

0.043 0.059 0.105 0.023 

Net Income –0.065 –0.054 –0.121 –0.123 *** 

0.040 0.046 0.096 0.021 

Loans Past Due 30 0.177 *** 0.301 *** 0.143 0.159 *** 

0.043 0.050 0.098 0.022 

Loans Past Due 90+ 0.117 *** 0.180 *** 0.133 *** 0.130 *** 

0.023 0.031 0.047 0.011 

Reserves –1.258 *** –0.247 * –0.102 –0.243 *** 

0.250 0.145 0.441 0.047 

Charge-offs 0.268 *** –0.471 * –0.492 0.077 *** 

0.082 0.251 0.922 0.017 

CAMEL C –0.203 –0.085 –0.434 –0.034 

0.128 0.206 0.441 0.078 

CAMEL A –0.132 –0.263 –0.172 0.140 * 

0.130 0.200 0.406 0.076 

CAMEL M 0.362 *** 0.474 *** 0.602 * 0.280 *** 

0.119 0.160 0.318 0.067 

CAMEL E –0.083 0.109 0.623 * –0.063 

0.099 0.161 0.355 0.061 

CAMEL L 0.756 *** 0.442 *** 0.650 ** 0.587 *** 

0.096 0.141 0.275 0.055 

Examination Interval 0.444 *** 0.016 0.364 0.411 *** 

0.068 0.180 0.293 0.042 

Loan Growth—Past Year 0.0075 ** 0.0085 * –0.0012 0.0050 *** 

0.0035 0.0048 0.0092 0.0017 

Loan Growth—2 Years Earlier 0.0110 *** –0.0019 0.0095 0.0079 *** 

0.0030 0.0035 0.0078 0.0015 

Loan Growth—3–5 Years Earlier –0.0081 * 0.0207 *** 0.0037 0.0036 * 

0.0048 0.0038 0.0093 0.0020 

Chi-Squared on Loan Growth 20.721 *** 29.433 *** 1.211 7.0510 * 

Number of Banks 11872 11191 10777 66714 

Number of Failures 362 228 47 1096 

Notes: For each variable, the coefficient is in the first line. The standard error of the coefficient is in the second line. 

* Significant at the 10% level. 

** Significant at the 5% level. 

*** Significant at the 1% level. 

The Call Report variables are as of the end of the year, and the examination variables are for the last exam before the end of the 

year. Failures occurred within two years of the Call Report. 

21 
Table 4 

Coefficient Estimates for Different Samples—State Data Included 

1986 1989 1992 Pooled 

Intercept –4.057 *** –4.295 *** –7.641 *** –5.190 *** 

0.633 1.049 2.787 0.325 

Capital –0.436 *** –0.569 *** –0.573 *** –0.373 *** 

0.044 0.060 0.121 0.023 

Net Income –0.081 ** –0.006 –0.037 –0.102 *** 

0.041 0.048 0.099 0.022 

Loans Past Due 30 0.102 ** 0.262 *** 0.196 * 0.128 *** 

0.045 0.053 0.108 0.023 

Loans Past Due 90+ 0.096 *** 0.211 *** 0.119 ** 0.127 *** 

0.023 0.032 0.050 0.011 

Reserves –0.984 *** –0.255 * –0.095 –0.215 *** 

0.252 0.148 0.448 0.048 

Charge-offs 0.224 *** –0.347 –0.151 0.067 *** 

0.084 0.255 0.737 0.017 

CAMEL C 0.002 0.005 –0.445 0.075 

0.129 0.204 0.455 0.078 

CAMEL A –0.203 –0.265 –0.125 0.118 

0.135 0.198 0.420 0.077 

CAMEL M 0.503 *** 0.512 *** 0.634 * 0.337 *** 

0.122 0.163 0.354 0.067 

CAMEL E –0.119 0.245 0.654 * –0.086 

0.101 0.167 0.374 0.062 

CAMEL L 0.573 *** 0.341 ** 0.583 * 0.553 *** 

0.098 0.145 0.297 0.055 

Examination Interval 0.389 *** –0.011 0.424 0.373 *** 

0.072 0.177 0.332 0.044 

Loan Growth—Past Year 0.0126 *** 0.0076 * 0.0008 0.0062 *** 

0.0033 0.0045 0.0107 0.0017 

Loan Growth—2 Years Earlier 0.0113 *** –0.0036 0.0017 0.0078 *** 

0.0031 0.0041 0.0127 0.0015 

Loan Growth—3–5 Years Earlier –0.0177 *** 0.0171 *** –0.0008 –0.0013 

0.0053 0.0041 0.0114 0.0022 

P.I. Growth—Past Year –0.213 *** –0.142 ** –0.361 ** –0.134 *** 

0.019 0.076 0.151 0.010 

P.I. Growth—2 Years Earlier –0.100 ** –0.048 0.067 0.053 *** 

0.048 0.113 0.129 0.018 

P.I. Growth—3–5 Years Earlier 0.326 *** 0.181 *** 0.517 ** 0.076 *** 

0.061 0.053 0.237 0.017 

Chi-Squared on P.I. Growth 143.526 *** 28.485 *** 22.187 *** 114.442 *** 

Number of Banks 11872 11191 10777 66714 

Number of Failures 362 228 47 1096 

Notes: For each variable, the coefficient is in the first line. The standard error of the coefficient is in the second line. 

* Significant at the 10% level. 

** Significant at the 5% level. 

*** Significant at the 1% level. 

The Call Report variables are as of the end of the year, and the examination variables are for the last exam before the end of the 

year. Failures occurred within two years of the Call Report. 

22 
Figure 1 

Personal Income Growth and Bank Failures 

United States (1970–1995) 

-3 

-2 

-1 

0 

1 

2 

3 

4 

5 

6 

7 

Percentage Income Growth 

0 

50 

100 

150 

200 

250 

300 

Personal Income Growth 

Bank Failures 

70 75 80 85 90 95 

Year 

23 
Figure 2 

Personal Income Growth and Bank Failures 

Texas (1980–1995) 

-6 

-4 

-2 

0 

2 

4 

6 

8 

10 

12 

14 

16 

Percentage 

Personal Income Growth 

Bank Failures 

75 80 85 90 95 

Year 

24 
Figure 3 

Personal Income Growth and Bank Failures 

Massachusetts (1980–1995) 

Percentage 

8 

6 

4 

2 

0 

-2 

-4 

80 85 90 95 

Year 

25 

Personal Income Growth 

Bank Failures 
Figure 4 

Personal Income Growth and Bank Failures 

California (1980–1995) 

Percentage 

80 85 90 95 

Year 

-2 

-1 

0 

1 

2 

3 

4 

5 

6 

7 

8 

Personal Income Growth 

Bank Failures 

26 
Figure 5 

Personal Income Growth and Bank Failures 

Ohio (1980–1995) 

-4 

-3 

-2 

-1 

0 

1 

2 

3 

Percentage 

Personal Income Growth 

Bank Failures 

80 85 90 95 

Year 

27 
0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Figure 6 

In-Sample Fit Percentage of Type I Errors Failures in 1987 and 1988 

Bank Data Only 

State Economic Data 

10 20 30 40 50 60 70 80 90 100 

Percentage of Type II Errors 

28 

0 
0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 Percentage of Type I Errors 

Figure 7 

In-Sample Fit 

Failures in 1990 and 1991 

Bank Data Only 

State Economic Data 

0 10 20 30 40 50 60 70 80 90 

Percentage of Type II Errors 

29 

100 
Figure 8 

Out-of-Sample Forecasts Percentage of Type I Errors 

Failures in 1989 and 1990 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Bank Data Only 

State Economic Data 

10 20 30 40 50 60 70 80 90 100 

Percentage of Type II Errors 

30 

0 
Figure 9 

Out-of-Sample Forecasts Percentage of Type I Errors Failures in 1992 and 1993 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Bank Data Only 

State Economic Data 

10 20 30 40 50 60 70 80 90 100 

Percentage of Type II Errors 

31 

0