The impact of a financial crisis on the Chicago banking industry: Changes in the strategic groups of banks.

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Master’s thesis

The impact of a financial crisis on the Chicago banking industry: Changes in

the strategic groups of banks.

by

Marith Elisabeth Douw S2505975

mardouw@gmail.com

Date: 13-07-2020

Study: MSc. BA Strategic Innovation Management First thesis supervisor: dr. Charlie Carroll

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2 Table of Contents

1. Introduction ___________________________________________________________ 4

1.1 Using strategic groups to investigate the effects of a financial crisis _______________ 5

2. Research Questions _____________________________________________________ 6 3. Investigating a financial crisis using strategic group theories ___________________ 7

3.1 Strategic groups _____________________________________________________________ 7 3.2 The interdependence view _____________________________________________________ 7 3.3 Differences in dynamics of Strategic groups ______________________________________ 8 3.4 Performance differences in Strategic groups ______________________________________ 9 3.5 The strategies of banks _______________________________________________________ 10

4. Methodology __________________________________________________________ 11 4.1 Time periods _______________________________________________________________ 11 4.2 Sample selection ____________________________________________________________ 11 4.2 Data collection ______________________________________________________________ 11 4.3 Variables __________________________________________________________________ 12 4.3.1 Strategy variables _____________________________________________________________ 12 4.3.2 Performance variables __________________________________________________________ 13 4.3.3 Governmental bailouts _________________________________________________________ 14 4.4. Analysis ___________________________________________________________________ 14 4.5 Manipulating the data for use _________________________________________________ 15

5. Results and Discussion ___________________________________________________ 16

5.1 Pre-crisis results (years 2001-2003)_____________________________________________ 16

5.1.1 Identifying the strategic groups _____________________________________________________ 16 5.1.2 Performance variables ____________________________________________________________ 21

5.2. Crisis period (years 2008-2010) _______________________________________________ 25

5.3 Post-crisis period (years 2013-2015) ____________________________________________ 32

5.4 Differences across groups ____________________________________________________ 39 5.5 Movements across groups: ____________________________________________________ 41 5.6 Regulating the financial system ________________________________________________ 49

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3 Abstract

The financial crisis of 2008 was one of the largest economic events to hit the world in the twenty-first century. Many individuals found themselves jobless, and many firms and financial institutions saw their high successes come crumbling down. The banks found themselves in financial difficulty and some found themselves trying to survive. It is disputed that there were many indications for the upcoming financial crisis, however these were not noticed on time. This research paper studies the impact of the financial crisis on the banking industry in Chicago over three time periods, before the crisis, during the crisis and after the crisis. It is investigated through the use of strategic groups in order to show the way in which the banks were affected. During the financial crisis many bailouts were given out to banks in order to help them survive. This paper will also investigate if biases were present in the giving out of bailouts to banks in specific strategic groups. This research paper provided useful insights by using theoretical implications into a real-life issue. The results from this research have given surprising and useful insights into the way in which banks in Chicago dealt with the financial crisis and also in the way in which the US government gave out bailouts to banks.

Key words: Banks, Chicago, Financial crisis, Strategic groups, groups, US government, bailouts, financial institutions.

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1. Introduction

The financial crisis that gripped the world from 2008 until 2012 and it created devastating effects for many economies throughout the world. The financial crisis of 2008 first started with the collapse of the Lehman Brothers Bank (BBC, 2019). The Lehman brothers first appealed to the US government for funding but it was denied and filed for bankruptcy. This led to consumers no longer trusting the banks and this in turn led to high interest rates as the banks tried to stabilize themselves. This knock-on effect quickly spread to other parts of the world and in particular to the Western world (BBC, 2019). The financial crisis quickly took hold in all parts of United states of America (hereafter referred to as the US), and saw many banks incurring financial difficulties. However, the effects of the financial crisis were not limited to the U.S, and the crisis quickly spread to Europe, further spreading its devasting effects.

It is important to note, that the financial crisis was a long process that went undetected for many years (CRF, 2020). The Lehman Brother bank was not the first droplet that should have been noticed. During the end of the 1990s the government passed the “Community

Reinvestment Act”, which allowed low-income and middle class individuals to access more and higher loans. This resulted in risky loans being given out by the banks, and at the end of the 1990s banks were even encouraged to extend loans to many individuals (The U.S. Financial Crisis, 2018).

Many economists saw the financial crisis before it even happened and the financial crisis can be traced back to the year 2000-2001, when the Federal reserves in the US decided to cut the interest rates. This lead to “an easy credit environment” within which many loans were given out by banks (The U.S. Financial Crisis, 2018). As many companies and individuals had access to an easy source of credit, a lot of money was invested into the US housing market. In 2004, the Securities and Exchange Commission in the US decided to loosen the net capital rule (The U.S. Financial Crisis, 2018). However, the major event that led up to the financial crisis was the bursting of the US housing bubble. When the bubble burst in 2007, a knock on effect was that the mortgages that were given out, were higher than the prices of the house itself. This caused many financial difficulties and led to individuals not being able to pay back their loans and thus causing the banks to find themselves in difficulties. The collapse of the housing market was one of the major events that marked the beginning of the financial crisis (The U.S. Financial Crisis, 2018).

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Many banking entities applied for some form of financial aid during the crisis from the government and in the second half of 2008, over 136 public and private companies and over 25 US federally insured banks filed for bankruptcy (Douglas, 2009). In order to prevent many more banks from filing for bankruptcy many started applying to the US government for financial aid. Some banks received this in the form of a bailout. In total 450 banks failed across the US during the banking crisis (FDIC, 2020). However, it was noted that 85% of the banks that failed were smaller banks who had less than $1 Billion assets (Layden, 2019). During the financial crisis, many banks were facing difficulties, it was then that the FDIC decided to step up and tried to save the largest banks in the US (Smith, 2011). The banks that qualified for financial aid from the FDIC adhered to the “too-big-to-fail” criteria. However the Lehman Brothers Bank did not qualify and the collapse of this bank is seen as one of the events that many consider the start of the financial crisis What many don’t know is that one day after the Lehman Brother bank was allowed to collapse, the Federal reserve bailed out one of America’s largest investment banks, the “America International Group” (AIG) (The U.S. Financial Crisis, 2018). Thus, it was not clear which banks fell under the criteria which were set out by the FDIC.

In September of 2008, America’s biggest bank “the Washington Mutual” was seized by the FDIC and also declared bankruptcy, this was a big signal to the rest of the United States that the banks had reached the end of their era (The U.S. Financial Crisis, 2018).The economy saw itself in a downward spiral, due to the knock-on effects of the banking crisis was having on the economy.

1.1 Using strategic groups to investigate the effects of a financial crisis

In this research, the way in which a financial crisis influenced strategic groups will be investigated. A financial crisis is a disturbance to the financial market, which is typically associated with falling asset prices and insolvency among debtors and intermediaries, which spreads through the financial system and disrupts the market’s (or country’s) ability to allocate capital (Eichengreen et al., 1989).

Strategic groups have been changing over time. At first strategic groups were firms in a similar industry that followed similar strategies (Porter, 1980). In contrast, Cool and Schendel (1988) stated that a strategic group is the set of firms competing within an industry on the basis of similar contributions of scope and resource commitments. Porac and Thomas (1990) further grew on the notion of strategic groups stating that strategic should be identified through analysts’ observations of organizational similarities in an industry. Porac and Thomas (1990) also showed that such groupings are best documented using managers’ perceptions because decision makers tend to define their competitive landscape by matching known competitive characteristics to known organizations.

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in order to track strategic groups over time . This will be done to see how the groups of banks changed their strategies in order to adapt to the crisis and the emerging “new normal” in the banking industry.

In particular, it will be investigated how the bailouts some banks received will affect the structure of the strategic groups and if it thus becomes evident how the Federal Reserve chose which banks to bailout.

2. Research Questions

The effects that a financial crisis has on the formation of strategic groups is a very interesting topic. This thesis aims to look into the practical implications of an economic event on the structure of strategic groups.

The financial crisis had major impacts for the economy of a country, this in turn caused the banks to adjust to the changing economy. Due to this, it will be interesting to see if the impact of a financial crisis changes the strategic groups that were identified before the financial crisis occurred and if the banks were able to adopt to the “new sense of normal”. This leads to the first research question:

RQ1: In what way will a financial crisis influence the strategic groups present in the banking industry?

Another interesting factor to investigate, is whether there was a bias on the US governments side as to which banks received the bailout. Was this equal across groups? Was one group chosen strategically in order to allow for the greatest number of banks to survive? This leads to the second research question:

RQ2: Did the government funding differentially impact the strategic groups in the industry and if so in what way?

It will be interesting to see if government funding changes the strategies within the strategic groups or the structure of the strategic groups themselves. The US government issued

financial aid to banks who were experiencing difficulties in order to prevent athe large banks within the industry from going bankrupt.

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3. Investigating a financial crisis using strategic group theories

3.1 Strategic groups

When looking into the way in which strategic groups have formed and have been researched, it can be inferred that most definitions concerning strategic groups concur that firms can be grouped based on the strategies that they follow, thus firms are similar within groups and different between groups. This is seen in the following definition; “A strategic group can be defined as a groups of firms in an industry that follow the same or similar strategies along key strategic dimensions”- Porter, 1980. Therefore, strategic groups are also referred to as “sets of firms that are homogeneous within groups and heterogeneous between groups with respect to strategy” - Carroll, 2018.

Based on the above definitions, this research will look into strategic groups in light of the interdependent view. The interdependent view shows that firms are vying for transactions with buyers and/or suppliers (Carroll & van Heyningen, 2018). Meaning, that firms try to outbid one another when buying resources and attracting new customers and this in turn may be able to influence the firms’ performance. This will show that they are interdependent, as they will try to influence the performance of firms within their strategic groups but not firms in other strategic groups.

3.2 The interdependence view

The interdependence view shows that strategic groups consist of interacting subsets of firms that reflect the competitive rivalry within the groups (Tang & Thomas, 1992). Hence, these collections of firms “reflect the social structure of rivalry within an industry” (Carroll, 2018). The interdependence view holds that firms are attracted to a strategic position which may lead to profitable vertical transactions (Carroll & Thomas, 2019). This implies that firms pursuing the same strategy will compete with one another. Therefore, firms within one strategic group will compete with one another, but firms in different strategic groups will not (Carroll & Thomas, 2019). The interdependence view also shows the nature of the

interactions between firms and how they could affect the performances of firms. Carroll & Thomas, 2019 showed that by using the interdependence view, it was possible to see which firms would be likely to interact with one another.

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Using the interdependent view on the strategic group of banks, leaves much room for interpretation. Many banks have to take into account barriers such as regulatory issues, charter and capital requirements (Xavier, 2016). Most banks do not have one set of specific products and are located in a multiproduct industry, within which the different segments will have different levels of competition and barriers to entry (Xavier, 2016). This competitive rivalry between banks is one of the most important external factors, as even though firms within a strategic group are likely to react similarly to similar events in the environment, firms within a strategic group are also direct rivals of one another (Carroll, 2018). Firm behaviour is embedded in the systems of inter-firm relations (Simsek, 2003). These networks encompass the firms relationships with other firms, not just horizontally but also vertically (Gulati, 2000). These ties may also be interorganizational and are enduring by nature (Gulati, 2000). In order to fully utilize these ties, firms or in the context of this research banks must be willing to acknowledge both the opportunities and constraints that inter-organizational interaction brings (Gulati, 2000). Thus, the firms within the strategic groups will have to interact with one another.

3.3 Differences in dynamics of Strategic groups

Co-opetition is the combination of cooperation and competition. As stated by Nalebuff and Brandenburger (1997) - “Business is cooperation when it comes to creating a pie and competition when it comes to eating it”. They base their arguments on the fact that

competition and cooperation can be simultaneously done by the firm and that in order for a firm to stay competitive it must use both cooperation and competition. Thus, when looking into the interdependence view, it is important to look into the dynamics of strategic groups. The dynamics in a strategic group can be defined as the differences in performances in a strategic group that may be caused due to group effects (Carroll & van Heyningen, 2018). Firms may also experience interdependence in their interactions between groups in the form that they experience internal cohesion within groups and external isolation between groups (Cormac, 1971).

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9 3.4 Performance differences in Strategic groups

The relationship between strategic groups and financial performance has been seen in the articles of Porter, (1979); McGee and Thomas (1986); Caves and Porter, (1977). Hunt (1972) explained strategic groups as “groups of firms within an industry following similar strategies in terms of key decision variables”. He used the above definition in order to explain the differences in intra-industry performances of firms. Dranove et al., (1998) took a different view on strategic groups and stated that strategic groups only exist if the performance of members is an outcome of group characteristics. This however, was not always the case in reality. While the above views are wide spread, they are based on a logic that does not take all factors of an industry into consideration. Carroll & Thomas (2019) explain that basing the indication that a group exists on performance is based on logical errors.

Thus, profitability is not due to the level of competition within a group, but it is more

attributable to the size of the group. If a few firms are present within the strategic group such as an oligopoly or a duopoly, then the firms will be able to coordinate their interactions with other firms in the group and create a higher level of performance (Carroll & Thomas, 2019). Therefore it may be seen that strategic groups with a high level of firms, will have a lower level of performance as these groups are competing with one another. However, firms which find themselves in a duopoly or an oligopoly within the group may lean more towards a strategy of perfect competition, as they are able to establish cooperative agreements as usually these strategic groups have a lower number of firms (Carroll & Thomas, 2019). This is seen as true-group effects.

True-group effects are seen within strategic groups if the performance of a firm within the strategic group is due to the group characteristics, when controlling for firm and industry characteristics (Dranove et al., 1998 ). It is thus implied that when there are no strategic interactions between members in a group, there will be no direct effect of group membership on performance (Dranove et al., 1998). However, the intensity of competition might not be homogeneous within an industry. It may be that one group has perfect competition while another group may have duopolistic competition or oligopolistic competition (Carroll & Thomas, 2019).

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This differs from the view of Hatten & Hatten (1987) who state that there is in fact no group effect in reality, and that the performance differences are due to firm-level effects, which in turn are aggregated when comparing strategic groups.Thus, they warn against bring strategic groups to life without any logical basis for support.Carroll and Thomas (2019) make the point that this is a mistaken belief and that strategic groups should not always differ in

performance, as if there are differences in performance, then discrete groups must exist and if there is a failure to find group performances, this may be a feature of the strategic groups. Hence, it can be concluded that in order to see the influence of a group membership on the profit of firms, the individual firm characteristics must also be taken into account when looking into the strategic group (Lawless et al., 1989).

3.5 The strategies of banks

This research paper will look into how the strategic groups of banks were affected by a financial crisis. In doing this the distance between the strategic groups will be measured in order to show that each strategic group is composed of firms that have a similar strategy to one another and that the different groups have similar strategies within the group, but

different strategies between groups. When looking into the distance between the groups, it is seen that the distances between the groups are significantly large which indicates that the groups within each period are following distinctive strategies. Meaning that no two strategic groups are following the same strategy (Cool and Schendel, 1998).

This may be in part due to the differences in size of the banks within the strategic groups. This shows that banks within the strategic groups could be experiencing true-group effects, such that there were performance differences between all groups in each time period (Carroll, 2018). Thus, banks within the strategic groups may be able to engage in collusive agreements with one another. However, due to the turbulent industry due to the crisis it can be implied that the environment would make it difficult for the banks to make collusive agreements with one another.

It has been seen that banks follow two main kinds of strategies; namely a conservative strategy within which they are risk-averse, and a strategy within which a bank is willing to take risks. This is usually seen within the loan loss provisions that a bank accounts for. A large number of loan loss provisions shows that a bank has adopted a more conservative strategy. Another important aspect within the strategies of banks is whether a bank engages in competition with its rivals (Xiao, 2009). A high level of competition shows that there is a high level of rivalry within a strategic group and this will thus lead to a lower level of profitability (Porter, 1979). Which is in line with the true-groups effect (Carroll & van Heyningen, 2018).

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4. Methodology

4.1 Time periods

In order to see the movements of strategic groups across time, three time frames have been selected, namely from 2001-2003 (pre-crisis period), 2008-2010 (crisis period) and from 2013-2015 (post-crisis period).

When looking into the first research question; In what way will a financial crisis influence the strategic groups present in the banking industry? The three time frames will play a

comparison role and give insights into how banks behaved before, during and after the financial crisis. The pre-test benchmarks will give insights into how banks behave in an economy that has not experienced a financial crisis. The crisis period will show how banks tried to survive in an economy with a high level of turmoil. Lastly, the post- crisis recovery period will tie into the second research question: Did the government funding impact the strategic groups in the industry and if so in what way?. This will show the impact that the governmental bailout had on the movements of firms between strategic groups and if some banks were more likely to receive a bailout than other banks.

Each time period will give insights into the ways in which banks behaved during different states of economic turmoil or the lack thereof, which will be used to answer the research questions set out in this research.

4.2 Sample selection

The financial industry was chosen, as it is an industry with very rich and diverse data. In particular this sample is limited to just one city Chicago, to avoid introducing effects due to regions, regulations, currency, etc. The research questions stated earlier will focus on identifying the strategic groups within the region of Chicago and will look into the

performance differences between the strategic groups. In total data was collected on 99 banks through-out the three time periods.

4.2 Data collection

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12 4.3 Variables

The variables used to identify the strategic groups will now be discussed. 4.3.1 Strategy variables

Strategy variables can be defined as being the objective of a firm or business enterprise and in this sense a strategy encompasses what a firm needs to do in order to achieve its goal. In this research, the strategy variables have been broken down into two distinct groups, resource-based strategy variables and market-resource-based strategy variables. Resource-resource-based market

variables are seen as the resources and capabilities that a business enterprise already has and how they use this in their different strategies.

Market-based strategy variables will look into the ways in which banks obtain their funds in a strategic sense. The market based variables are used to identify banks that are competing for transactions with suppliers and/or buyers of funds. This competition is what generates interdependence between banks in the same industry.

The strategy variables that were chosen are related to interdependence, which means that this research will look into banks whose strategies can influence each other’s performance. Hence, this also means that firms interact with each other and may lead to coopetition between firms.

Table 1: The strategy variables used, the name they were given in the FDIC database and an

explanation of the variables.

Strategy variables FDIC variable names Explanation

Retail deposits divided by total deposits Zcoredep_lg10 This will show how many loans have

been given out to the retail sector.

Total Assets Zassets_log10 This will show the relative size of a

bank, and how many assets the bank has.

Balances due from FRB Zchfrb_sqrt This variable will show how many of the

deposits are held overseas divided by the total number of deposits.

Farm loans divided by total loans Zfarm_sqr This will show how many loans have

been given out to the agricultural sector.

U.S government securities Zscus_sqrt This variable shows how much money

the bank received from the US government in relation to the total number of deposits.

Commercial and Industrial loans divided by total loans

Zlnci_sqrt This will show how many loans have

been given out to the commercial and industrial sector.

Loans and leases that have matured in the last three months.

Zlnls3mo_sqrt This will show how many loans have

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Loan loss provisions/Total assets Zlnprovtoassets When a bank has a lot of resources

(assets), but a high number of loan loss provisions, the banks will not be able to use all of their available resources. The banks will invest a number of their resources but not all in order to try and prepare for external shocks which may hits the banks.

Real estate acquired ZOREO_sqrt Refers to houses that the bank seized

when they foreclosed on the mortgages. This collateral is used to lower the risk to the bank because they get something if the customer cannot pay, however this leads to a high liquidity as banks assets will be held up in real estate.

Source: FDIC, 2020.

4.3.2 Performance variables

Performance variables were used to see which strategic groups were similar or different in their performance. The performance of the strategic groups will be identified using the following variables, return on assets (ROA), return on equity (ROE) and net income. Presumably, the banks strive for good financial performance, but managers cannot directly control their performance. Thus, at best they can pursue strategies that might yield higher levels of performance.

Table 2: The performance variables used, their FDIC database variable names and an explanation of

the variables

Performance variables FDIC variable names Explanation

Return on Assets ZROE Total income the bank generates divided

by the total equity owned by

shareholders, shown as a percentage.

Return on Equity ZPretaxROA The return on assets shows how efficient

the management of a company is, in utilizing its assets for profitability.

Net Income ZNetinc Net income shows if a firm is obtaining

a profit, a high level of net income is used as an indicator for profitability.

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14 4.3.3 Governmental bailouts

The governmental bailouts were sourced from CNN (2018). The bailouts show which firms received which bailouts and how much money they received. The bailouts will be used to see if the bailout influenced the ways in which banks survived the financial crisis.

4.4. Analysis

In this analysis, strategic groups will be used in order to investigate the research questions. Strategic groups are sets of firms that are similar within, and different between groups in terms of strategy (Carroll, 2018). Therefore, the strategic groups can be broken down into clusters. These clusters must exhibit both internal cohesion and external isolation (Cormac, 1971). Due to this, this research will use a two-pronged test which will look into both internal cohesion and external isolation.

Clustering analysis can be used to identify the structure of the industry and explain the competition seen within strategic groups (McGee & Thomas,1986). However, clustering analysis has many weaknesses (Barney & Hoskission, 1990) and that there are better ways in which to analyse strategic groups. This thesis, will overcome these weaknesses by using a clustering analysis that incorporates both external isolation and internal cohesion into its tests (using a two-pronged approach) which was developed by Dr. C. Carroll.

The first prong refers to the internal cohesion, which Ward’s method of clustering analysis with Euclidean distances. A clustering analysis is a statistical technique that is used to

identify strategic groups (Carroll, 2018). The significance tests used shows that the statistic in Ward’s method is total-group variance. If within-group variance in the real-data is low

compared to the randomized data, then the degree of cohesion is statistically significant. The second prong relates to external isolation, which analyzes the distance between the two closest groups (which were identified using Ward’s method). If the two closest groups are isolated from one another, true-group effects are then possible. The second prong uses

significance tests to compare the gap in the real data to the randomized data. The randomized data can be seen in the Monte Carlo test, and the real data is seen in the permutation test. If the gap is significant then the degree of isolation is statistically significant, meaning that the groups are isolated from one another.

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A MANOVA test will be conducted in order to identify performance differences between groups (Carroll, 2018). The MANOVA test will look into performance variables in combination with one another, in order to capture tradeoffs between variables. An

independent samples t-test was used to look into the differences in performance between the strategic groups, this test compares two groups at the same time on 1 variable at a time. The independent samples t-test will allow interpretation of the complex patterns found in the MANOVA analysis.

Cross-tabulation figures are shown in order to show the changes in group-memberships across time. They have been shown between all three periods in this research. The tables for cross-tabulation have been included in the discussion and results.

4.5 Manipulating the data for use

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5. Results and Discussion

In this section, the results of the analysis will be given and discussed. In the pre-crisis period 78 banks were investigated and split into four strategic groups. In the crisis period 84 banks were investigated and split into two strategic groups. It is important to note that in the pre-crisis period group 3 was an isolate and has been omitted from the MANOVA test and the independent samples t-test in order for the analysis to run correctly. Lastly, in the post-crisis period 56 banks were investigated and split into three strategic groups. In the table below an overview will be given of how many banks were in each strategic group per time period.

Table 3: The number of groups per period, and how many banks were in which group.

Pre-crisis period Crisis period Post-crisis period Groups Number of banks Groups Number of

banks Groups Number of banks 0 68 0 58 0 48 1 11 1 15 1 2 2 4 2 6 3 1

5.1 Pre-crisis results (years 2001-2003) 5.1.1 Identifying the strategic groups

In the pre-crisis time period, k=4 has been chosen as it is significant on both the internal cohesion and external isolation tests at the 5% significance level. However, group 3 was an isolate and has been left out of the MANOVA and interdependent t-test analyses in order for the analyses to run correctly. In the pre-crisis period 78 banks were investigated and

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Table 3: The internal cohesion and external isolation of the pre-crisis period for all number of clusters.

Internal cohesion External isolation

Number of

clusters (k) Observed

Permutation test

Monte

Carlo test Observed

Permutation test Monte Carlo test 1 574.9947 0.028 0.007 -- -- -- 2 213.9681 0.001 0.001 10.1877 0.854 0.026 3 126.0171 0.001 0.001 10.1877 0.979 0.122 4 92.6278 0.001 0.001 0.2057 0.001 0.002 5 69.9006 0.001 0.001 0.0669 0.001 0.001 6 50.7954 0.001 0.001 0.0669 0.001 0.001 7 38.9304 0.001 0.001 0.0669 0.001 0.001 8 30.6127 0.001 0.001 0.0517 0.001 0.001 9 23.2410 0.001 0.001 0.0517 0.001 0.001 10 19.1331 0.001 0.001 0.0517 0.001 0.001 11 16.4949 0.001 0.001 0.0517 0.001 0.001 12 14.1024 0.001 0.001 0.0517 0.001 0.001

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Figure 2: Monte Carlo tests for internal cohesion and external isolation for the pre-crisis period.

Figure 3: Scatter plot for the clusters of the pre-crisis period, showing the differences and similarities between the strategic groups (k= 4)

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Figure 4: Box-plots of the strategy variables for the pre-crisis strategic groups (k=4), (a) Total assets (b) number of retail deposits (c) number of balances due from FRB (d) number of US Government securities (e) number of commercial and industrial deposits (f) number of farm/farmland deposits (g) number of loans and leases that have matured in the last three months (h) loan loss provisions to assets (i) Real estate acquired.

The strategic groups have been broken down into four groups: Strategic Group 0: Small banks

This strategic group is characterized by having the smallest size of banks, out of all of the strategic groups. Small banks are focused on basic customer accounts with individual customers. They are not specialized in a certain area. These small banks will be in competition with one another and will not be in competition with banks in other strategic groups as per the logic of the interdependent view of strategic groups.

Strategic Group 1: Medium banks

This strategic group is characterized as having banks which are medium in size. Medium sized banks are seen as having a similar strategy as the small banks group. Their

distinguishing characteristic is that they are slightly bigger in size. Medium banks are also focused on basic customer accounts with individual customers.

Strategic Group 2: Large banks

This strategic group is characterized as having banks which are large in size. The industry focus of large banks is dispersed throughout many industries such as the commercial,

industrial and retail industries, as well as customer accounts. The large banks have a different strategy focus than the small and medium banks. The large banks had a higher number of assets than the small and medium banks and this could be because larger banks involve large transactions. The larger banks would not consider the small and medium banks as

competitors, as they do not follow the same strategy as the larger banks. In comparison to small and medium banks, large banks experience a higher level of efficiency, which indicates that as they are larger in size, they will have departments for specific services and will be able to work more efficiently than smaller and medium banks.

Strategic Group 3: Extra- large banks

This strategic group has banks which are of extra-large size. These banks have the highest number of total assets compared to any of the other strategic groups. The extra-large banks

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operate in all market industries (customer accounts, farmland, retail, commercial and

industrial). As seen in the scree-plot, the bank that was classified as being an extra-large bank was an outlier, meaning that there was only one bank in this extra-large group. As this bank is operating in many different industries, it is likely to benefit from economies of scale. The bank in this group would not be in competition with the other strategic groups as they most likely follow different strategies, which is in line with the interdependent view.

Figure 5: Distribution of each performance measure, for each group in the pre-crisis period (k = 4). (a) Return on Equity, (b) Return on Assets and (c) Net income

Table: 4: Results of the MANOVA test for the pre-crisis period (k=3, has been corrected for the outlier group).

In the following analysis a new variable was created Pre4MAN, which omits the outlier seen in group 3.

a b

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22 Between-Subjects Factors N Pre4MAN 0 68 1 11 2 4

Tests of Between-Subjects Effects

Source Dependent Variable

Type III Sum of

Squares df Mean Square F Sig.

Corrected Model ZROE .001a ₂ _.001 _.204 _.816

ZPretaxROA .105b ₂ _.053 _.115 _.892 ZNetinc 8.431c ₂ _4.215 _82.828 _.000 Intercept ZROE .000 1 .000 .134 .715 ZPretaxROA 3.161 1 3.161 6.906 .010 ZNetinc 4.181 1 4.181 82.160 .000 Pre4MAN ZROE .001 2 .001 .204 .816 ZPretaxROA .105 2 .053 .115 .892 ZNetinc 8.431 2 4.215 82.828 .000 Error ZROE .233 80 .003 ZPretaxROA 36.621 80 .458 ZNetinc 4.071 80 .051 Total ZROE .234 83 ZPretaxROA 48.414 83 ZNetinc 12.620 83

Corrected Total ZROE .234 82

ZPretaxROA 36.726 82

ZNetinc 12.502 82

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Table 5: Results of the independent samples t-tests for the pre-crisis period (K=3, as the sample was corrected for the outlier).

Group Statistics

Pre4MAN N Mean Std. Deviation Std. Error Mean

ZROE 0 68 .001855 .0505462 .0061296 2 4 -.004778 .0326073 .0163036 ZPretaxROA 0 68 .372563 .6569470 .0796665 2 4 .250461 .3214358 .1607179 ZNetinc 0 68 -.121798 .0201404 .0024424 2 4 1.372023 1.1481273 .5740636 Multivariate Testsa

Effect Value F Hypothesis df Error df Sig.

Intercept Pillai's Trace .955 123.326b _12.000 _69.000 _.000

Wilks' Lambda .045 123.326b _12.000 _69.000 _.000

Hotelling's Trace 21.448 123.326b _12.000 _69.000 _.000

Roy's Largest Root 21.448 123.326b _12.000 _69.000 _.000

Pre4MAN Pillai's Trace 1.479 16.553 24.000 140.000 .000

Wilks' Lambda .011 49.405b _24.000 _138.000 _.000

Hotelling's Trace 45.950 130.191 24.000 136.000 .000

Roy's Largest Root 44.947 262.192c _12.000 _70.000 _.000

a. Design: Intercept + Pre4MAN b. Exact statistic

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Group 3 was an isolate. In order to run the MANOVA and independent samples t-test correctly, it was omitted from the analysis. Net income was higher for the larger bank group (t (70) = -12, 174, p = 0,000). This can be attributed to the larger banks being able to operate at a higher level of efficiency. This is in line with the predication of true-groups, the smaller banks group had the lowest performance and the larger bank groups had higher levels of performance. The larger banks had less competitors within their strategic groups and could engage in collusive agreements, while the smaller banks had a high number of competitors.

Independent Samples Test

Levene's Test for Equality of

Variances t-test for Equality of Means

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25 5.2. Crisis period (years 2008-2010)

5.2.1 Identifying the strategic groups

In the crisis period k=2 clusters have been chosen, which is significant on all internal cohesion and external isolation tests, at the 5% significance level. Using k=2 allows for the strategic groups to be compared across the three time periods. 73 banks were present in the crisis period, these banks have been investigated according to their distributions on strategy and performance variables.

Table 6: The internal cohesion and external isolation of the crisis period for all number of clusters.

k clusters _Observed: permutation test: Monte Carlo test: _Observed: permutation test: Monte Carlo test: 1 _623.1808 0.694 0.002 -- -- -- 2 _399.2529 0.001 0.002 1.2388 0.002 0.001 3 _266.4610 0.001 0.001 1.2388 0.102 0.001 4 _207.2672 0.001 0.001 1.2388 0.230 0.007 5 _169.2805 0.001 0.001 1.2388 0.341 0.019 6 _132.7913 0.001 0.001 1.2388 0.443 0.046 7 _103.2268 0.001 0.001 0.1935 0.001 0.001 8 82.4884 0.001 0.001 0.1935 0.001 0.001 9 70.2337 0.001 0.001 0.1935 0.001 0.001 10 61.8809 0.001 0.001 0.1935 0.001 0.001 11 55.3660 0.001 0.001 0.1935 0.001 0.001 12 49.6872 0.001 0.001 0.1935 0.001 0.001

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Figure 7: Monte Carlo tests for internal cohesion and external isolation for the crisis period

Figure 8: Histogram showing the distribution of strategic groups in the crisis period. Blue: Strategic group 0 Red: Strategic group 1

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Figure 9: Distribution of the strategy variables for the crisis strategic groups (k=2), (a) Total assets (b) number of retail deposits (c) number of balances due from FRB (d)

c d

e f

g h

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Number of U.S Government securities (e) number of commercial and industrial deposits (f) number of farm/farmland deposits (g) number of loans and leases that have matured in the last three months (h) loan loss provisions to assets (i) Real estate acquired

The strategic groups have been broken down into two groups:

Strategic group 0: Small banks

This strategic group is characterized as having banks which are small in size, meaning that they have a low amount of total assets. These banks are focused on customer accounts and contain the most banks out of the two strategic groups. As there is a large number of banks in this group, the banks will be in competition with one another, and not with banks in other strategic groups, as in the interdependent view the banks within this strategy group will compete with one another when they are pursuing the same strategy (Carroll & Thomas, 2019).

Strategic group 1: Medium and large banks

In this strategic group medium and large sized bank are present. The two differently sized banks are present in this industry due to the merger of the medium group and large group in the pre-crisis period. The financial crisis blurred the lines between the groups and this is why this group has two sizes of banks. There is some diversity in the size of medium and large banks, however the clusters have significantly more internal cohesion than randomized data. The medium banks may be following a similar strategy to the small banks, but the larger banks strategies will be more distinctive, and this is seen in the distribution in the histogram. These banks serve more industries than the smaller banks group, and thus may be able to operate more effectively.

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Figure 10: Distribution of each performance measure, for each group in the crisis period (k=2). (a) Return on Equity, (b) Return on Assets and (c) Net income

Table 7: Results of the MANOVA test for the crisis period (k = 2).

Between-Subjects Factors N crisis2 0 58 1 15 Multivariate Testsa

Wilks' Lambda .620 14.087b _3.000 _69.000 _.000

Hotelling's Trace .612 14.087b _3.000 _69.000 _.000

Roy's Largest Root .612 14.087b _3.000 _69.000 _.000

crisis2 Pillai's Trace .347 12.244b _3.000 _69.000 _.000

Wilks' Lambda .653 12.244b _3.000 _69.000 _.000

Hotelling's Trace .532 12.244b _3.000 _69.000 _.000

Roy's Largest Root .532 12.244b _3.000 _69.000 _.000

a. Design: Intercept + crisis2 b. Exact statistic

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Type III Sum of

Corrected Model ZROE 2.773a ₁ _2.773 _.950 _.333

ZPretaxROA 32.796b ₁ _32.796 _27.148 _.000 ZNetinc .026c ₁ _.026 _.037 _.848 Intercept ZROE 1.661 1 1.661 .569 .453 ZPretaxROA 44.736 1 44.736 37.032 .000 ZNetinc 1.176 1 1.176 1.670 .200 crisis2 ZROE 2.773 1 2.773 .950 .333 ZPretaxROA 32.796 1 32.796 27.148 .000 ZNetinc .026 1 .026 .037 .848 Error ZROE 207.256 71 2.919 ZPretaxROA 85.771 71 1.208 ZNetinc 49.969 71 .704 Total ZROE 210.174 73 ZPretaxROA 135.397 73 ZNetinc 51.493 73

Corrected Total ZROE 210.029 72

ZNetinc 49.995 72

a. R Squared = .013 (Adjusted R Squared = -.001) b. R Squared = .277 (Adjusted R Squared = .266) c. R Squared = .001 (Adjusted R Squared = -.014)

Table 8: Results of the independent samples t-test for the crisis period (k=2).

crisis2 N Mean Std. Deviation Std. Error Mean

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The groups of medium and large banks had significantly lower ROA (t (71) = 5,21, p = 0,000). This could be due to the interdependent view which states that performance within a group, is more attributable to the size of the group, as groups with a lower number of banks will be able to coordinate more with one another (which in this case is the group of medium and large banks).

Levene's Test for Equality of

Variances t-test for Equality of Means

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32 5.3 Post-crisis period (years 2013-2015) 5.3.1 Identifying the strategic groups

This research will use k =3 clusters in the post-crisis period. k=3 clusters will be used as it is significant on all levels at the 0,05-significance level. When using k=3 the strategic groups are well defined and are spread out in a way that will be easily comparable across the different time periods.

Within the post-crisis period a total of 66 banks were split up into 3 strategic groups, based on their distributions according to strategy and performance variables.

Table 9: The internal cohesion and external isolation of the post-crisis period for all number of clusters.

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Figure 11: Permutation tests for internal cohesion and external isolation of the post-crisis period

Figure 12: Monte Carlo tests for internal cohesion and external of the post-crisis period.

Figure 13: Scatter plot of the groups for the post-crisis period (k=3).

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a _b

c d

e f

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Figure 14: Distribution of the strategy variables for the post-crisis strategic groups (k=3), (a) Total assets (b) number of retail deposits (c) number of balances due from FRB (d)

Number of U.S Government securities (e) number of commercial and industrial deposits (f) number of farm/farmland deposits (g) number of loans and leases that have matured in the last three months (h) loan loss provisions to assets (i) Real estate acquired

The strategic groups have been broken down into three groups: Strategic group 0: Small banks

The small banks strategic group has the smallest number of assets per bank, but they have the largest number of banks seen in the strategic groups in this time period. These banks are most likely following a customer account strategy within which they focus on individual

customers. The banks within this group are not in competition with the other strategic groups in this time-period, the banks are in competition with one another (Carroll & Thomas, 2019). Thus, this group follows the logic of the interdependent view, which states that banks within a strategic group will compete with one another, but banks in different strategic groups will not (Carroll & Thomas, 2019).

Strategic group 1: Large banks

Banks in this strategic group can be characterized as being large banks. They are present in many industries such as the retail, commercial, industrial and the farmland industries. These banks will be most likely to experience economies of scale and scope, due to their large scale of activities. There are two banks present in this strategic group and thus this group could be classified as a duopoly, within which these two banks are in competition with one another, and these banks do not compete with banks from other strategic groups. As there are only two banks in this strategic group, they may be able to form collusive agreements with one another (Carroll, 2018), although this is not certain.

Strategic group 2: Medium banks

In this group medium sized banks are present; they follow a very similar strategy to small banks. There are 6 banks present within this group. These banks are larger in size than the small banks. These banks follow a similar strategy to small banks, but differentiate

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themselves along dimensions seen in the box-plots above. These banks could be performing as an oligopoly due to the small number of banks present within this group, this is not certain but this can be inferred due to true-groups effect.

Figure 15: Distribution of each performance measure, for each group in the post-crisis period (k=3). (a) Return on Equity, (b) Return on Assets and (c) Net income

Table 9: Results of the MANOVA analysis for the post-crisis period (k=3).

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Type III Sum of

Corrected Model ZROE .028a ₂ _.014 _.543 _.584

ZPretaxROA 1.278b ₂ _.639 _1.276 _.287 ZNetinc 31.123c ₂ _15.561 _186.679 _.000 Intercept ZROE .009 1 .009 .345 .560 ZPretaxROA .864 1 .864 1.727 .194 ZNetinc 23.333 1 23.333 279.914 .000 post3 ZROE .028 2 .014 .543 .584 ZPretaxROA 1.278 2 .639 1.276 .287 ZNetinc 31.123 2 15.561 186.679 .000 Error ZROE 1.371 53 .026 ZPretaxROA 26.526 53 .500 ZNetinc 4.418 53 .083 Total ZROE 1.591 56 ZPretaxROA 28.022 56 ZNetinc 35.701 56

Corrected Total ZROE 1.400 55

ZNetinc 35.541 55

a. R Squared = .020 (Adjusted R Squared = -.017) b. R Squared = .046 (Adjusted R Squared = .010) c. R Squared = .876 (Adjusted R Squared = .871)

Multivariate Testsa

Wilks' Lambda .156 91.786b _3.000 _51.000 _.000

Hotelling's Trace 5.399 91.786b _3.000 _51.000 _.000

Roy's Largest Root 5.399 91.786b _3.000 _51.000 _.000

post3 Pillai's Trace .918 14.714 6.000 104.000 .000

Wilks' Lambda .118 32.505b _6.000 _102.000 _.000

Hotelling's Trace 7.173 59.776 6.000 100.000 .000

Roy's Largest Root 7.130 123.588c _3.000 _52.000 _.000

a. Design: Intercept + post3 b. Exact statistic

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Table 10: Results of the independent samples t-test for the post-crisis period (k=3).

post3 N Mean Std. Deviation Std. Error Mean

ZROE 0 48 -.067652 .1706589 .0246325 2 6 -.002046 .0197326 .0080558 ZNetinc 0 48 -.131687 .0398020 .0057449 2 6 .261241 .4328436 .1767077 ZPretaxROA 0 48 .001791 .7438850 .1073706 2 6 .468079 .3133415 .1279211

Net income is significantly higher for the largest two banks in the large group (t (52) = -6,51, p = 0,000). This may be due to the fact that the financial crisis provided these banks with the necessity to cooperate in order to gain profit, this may not be certain but it is consistent with the logic surrounding the financial crisis.

Levene's Test for

Equality of Variances t-test for Equality of Means

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39 5.4 Differences across groups

Figure 16: Differences between Pre4 and Crisis2 groups in a crosstabulation

Figure 17: Differences between Crisis2 and Post3 groups in a cross tabulation

Figure 18: Differences between Pre4 and Post3 groups in a crosstabulation

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group in the pre- and post-crisis periods. The biggest difference between the small and medium groups was the number of assets that the small and medium banks had, this was slightly higher for the medium banks. The only time that the medium group did not follow this logic was in the crisis period when the medium and large banks formed one strategic group. This was due to the financial crisis, which blurred the lines between the groups. When comparing the pre- and post-crisis period. A considerable number of banks entered and left the industry, and while the lines between groups became blurred, the nature of the

strategic groups before and after the crisis looked quite similar. When the crisis hit many banks found themselves struggling. The banks were not able to keep up with the fast-paced growth that they experienced before the crisis (Buch & Dages, 2018). Which may have led to the banks looked for new ways in which to achieve efficiency and gain profits after the crisis. The level of performance between the groups changed between the pre- and post-crisis

periods, whereas the differences between performance between the groups in the pre-crisis were relatively low (except for the group 3 outlier), the differences in performance seen in the post-crisis were higher. This may be due to the fact that the crisis caused banks in the large bank groups to cooperate, which meant that they were able to achieve higher levels of profit than before the crisis.

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41 5.5 Movements across groups:

In the following table, it is shown how the banks moved or didn’t move across groups. The strategic group of each bank had been noted for each period. S refers to the small strategic group, m refers to a bank being in the medium bank group, L refers to the large-sized bank group and XL refers to the bank being in the extra-large sized bank group.

Table 11: Linking strategic group membership across periods

Bank code Bank name Pre-crisis Crisis Post-crisis

173333 Bank One National _Association XL -- --

75633 BMO Harris Bank National _Association L M/L L

210434 The Northern Trust _Company L M/L L

445339

American National Bank and Trust Company of Chicago

L - -

455534 LaSalle Bank National

Association L M/L -

15536 Park National Bank M M/L --

141734 ShoreBank M S --

259031 Corus Bank National _Association M M/L --

336932 Associated Bank Chicago M - -

397531 Amalgamated Bank of _Chicago M S S

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656733 MB Financial Bank _{National Association} M M/L M

678137 Manufacturers Bank M - -

716833 Marquette Bank M S S

1187430 American Home Bank M S -

1842065 The PrivateBank and Trust _Company M M/L M

2732

Albany Bank and Trust Company National Association

S S S

4839 ABC Bank S S S

5331 Hyde Park Bank and Trust

Company S S --

12937 Oak Bank S S S

18135 South Central Bank _{National Association} S S S

26233 First National Bank S - -

27838 Heritage Bank S - -

48730 The First Commercial

Bank S S --

76135 Highland Community

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90234 Park National Bank and _{Trust of Chicago} S -- --

97037 Uptown National Bank of _Chicago S -- --

111139 Korea Exchange Bank S -- --

125538 Mid Town Bank & Trust

Company of Chicago S -- --

129732 North Community Bank S S M

132376 First Bank of the Americas

S.S.B. S -- --

150679 Universal Federal Savings Bank

S -- --

201834 Lakeside Bank S S S

209139 NAB Bank S -- --

215130 The National Republic _{Bank of Chicago} S S S

217237 State Bank of India S S S

218832 University National Bank S -- --

230777 Fidelity Federal Savings

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235174 Mutual Federal Bank S S S

238876 Preferred Savings Bank S -- --

250476

Illinois-Service Federal Savings and Loan Association

S S S

254670 Columbus Savings Bank S S S

268734 Broadway Bank S M/L --

277240 First Nations Bank S S S

286279 Diamond Bank FSB S S --

287539 Chicago Community Bank S S --

309972 Washington Federal Bank

For Savings S S S

323035 Archer Bank S S -

347639 Devon Bank S S S

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365875 American Union Savings

and Loan Association S.B. S S S

375379

North Side Federal Savings and Loan Association of

Chicago S S S

424343 DuPage National Bank S S S

447070 Hoyne Savings Bank S S S

450472 Loomis Federal Savings

and Loan Association S S S

474339 BankChicago S - -

483274 First Savings Bank of

Hegewisch S S S

490179 Pulaski Savings Bank S S S

520777 Community Savings Bank S S S

533937 Covenant Bank S S -

536479 Lincoln Park Savings Bank S S -

608844 State Bank of Illinois S S S

615879

Central Federal Savings and Loan Association of Chicago

S S S

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664831 North Bank S S S

673431 Seaway Bank and Trust

Company S S S

686271 First Chicago Bank &

Trust S M/L -

718930 Metropolitan Bank and _{Trust Company} S S -

731377 First Security Federal _{Savings Bank} S - -

741273 Park Federal Savings Bank S S S

757377 Liberty Bank for Savings S S S

969675

Second Federal Savings and Loan Association of Chicago

S S -

998675 First East Side Savings _Bank S - -

1013575

Chesterfield Federal Savings and Loan Association of Chicago

S - -

1362246 The Foster Bank S S S

1404883 Burling Bank S S S

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2006024 International Bank of _Chicago S S S

2343167 Pan American Bank S S -

2360904 Pacific Global Bank S S S

2435615 Ravenswood Bank S M/L -

2533043 American Metro Bank S S S

2595975 Builders Bank S M/L M

2634735 Citizens Bank and Trust _{Company of Chicago} S S -

2713461 New Century Bank S S -

297378 GreenChoice Bank fsb - - S

759045 First Eagle Bank - - S

2239288 Wintrust Bank - - M

2806877 The Federal Savings Bank - - S

2817985 PNA Bank - S S

3153233 Metropolitan Capital Bank

& Trust - S S

3201853 New City Bank - M/L -

3216017

Beverly Bank & Trust Company National Association

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3339925 Edgebrook Bank - S S

3348057 Community First Bank - _Chicago - M/L -

3391718 Belmont Bank & Trust

Company - S S

3437483 Signature Bank - S S

3559714 American Eagle Bank of

Chicago - S S

3593307 Gold Coast Bank - S S

4184186 Urban Partnership Bank - - S

As seen in the above table there was a considerable amount of movement between groups. This may be due to the fact that before the financial crisis, many banks were not aware of the risks of over-lending, and thus it became attractive for banks to enter the market (Smith, 2011). When the crisis occurred in 2008, many banks saw themselves facing difficulties. Due to the financial crisis the lines between the strategic groups were blurred, and this caused the merger of the medium and large banks into one strategic group. In the pre-crisis only one firm was present within group 4, this firm was an outlier and exited the market. In total 22 banks failed from the pre-crisis to the crisis-period. However, some new banks also entered, showing that the distribution of banks per group stayed relatively the same.

When looking into the way in which strategic groups changed between the pre-crisis period and the post-crisis, one of the most noticeable differences is the formation of the M/L group in the crisis period, which in the post-crisis period split back into the M and L group that was seen before the crisis period. Many banks would not have come out of the crisis unharmed, and thus the banks may be opting for a new strategy within which efficiency plays a key role.

Table 12: Number of banks per group per period.

Before the crisis During the crisis After the crisis Group Number of banks Group Number of banks Group Number of banks S 70 S 59 S 49 M 11 M/L 15 M 6 L 4 L 2 XL 1

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and internal cohesion for all groups being investigated across the three time-periods. Usually large groups are the most competitive and due to this competitive nature, these groups will have the lowest performance, as is the nature of the true-group effects. This is also seen within this analysis and is consistent with the true-group effects. As seen in table 12, the small bank group is the largest group and as seen in the analysis also has considerably the lowest performance.

The pre-crisis extra-large group disappears, while it can be classified as a monopoly, there is no guarantee that this was in the banks’ best interest. The medium group contains a small number of firms and thus it could be conceivable, but not certain, that this group could operate as an oligopoly. The large bank group stayed small from the time before the crisis to the period after it. This group of banks had the highest level of performance through-out the duration of this research, with the exception of the crisis period. The large group of banks showed a higher level of performance after the crisis. This may be due to large banks being motivated to collaborate and achieve a higher level of performance. Therefore, the higher performance of these groups could be due to true-group effects, although this is not certain but it follows the logic of true-group effects.

5.6 Regulating the financial system

Some banks within the sample received a bailout. In the following table a list can be seen of which banks accepted a bailout and how much the bank received.

Table 13: List of banks that received a bailout in Chicago, the date that they received the bailout and the amount that they received.

Date Bank Amount

Pre-crisis group Crisis group Post-crisis group

17/11/2008 Northern Trust Corp. $1,576,000,000 _L _M/L _L

5/12/2008 MB Financial Inc. $196,000,000 M M/L M

19/12/2008 Marquette National Corporation $35,500,000 _M _S _S

30/1/2009 PrivateBancorp, Inc. $243,815,000 _M _M/L _M

6/2/2009 PGB Holdings, Inc. $3,000,000 M S -

10/4/2009 Metropolitan Capital Bancorp,

Inc. $2,040,000 S S -

22/5/2009 Illinois State Bancorp, Inc. $6,272,000 S S S

26/2/2009 Metropolitan Bank Group, Inc. $71,526,000 S S -

31/7/2009 Chicago Shore Corp $7,000,000 _M _S _--

20/11/2009 Metropolitan Capital Bancorp, Inc.

$2,348,000 S S -

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For a bank to be eligible for a bailout, they had to qualify under section 13 of the Federal Reserve act, which gives the Federal reserve in “unusual and exigent circumstances”

authority to lend to borrowers if at least five Fed governors approve (Smith, 2011). This gave the US government the authority to provide bailouts to banks. Many believed that some of the banks were “too-big-to-fail”, meaning that if the biggest banks in the industry collapsed this would lead to catastrophic events in the long-run for the economy (Buch & Dages, 2018). The US government adopted the “too-big-to-fail” belief as its criteria for allocating bailouts. Due to the “too-big-to-fail” criteria set out by the US Federal government, large banks were more likely to be bailed out than smaller banks, which was most likely done by the

government in order for the competition to be maintained in groups with a small number of banks. However, this criterion was taken vaguely, and not implied for all “big banks” such as the Lehman Brothers bank (Smith, 2011).

Many thought the “too-big-to-fail” criterion too vague, and this played a part in the failure of over 300 small banks throughout the US (Smith, 2011). Based on what happened to the banks during the financial crisis, Barack Obama implemented the “Frank-Dodd act” in 2009, which allowed the US government the authority to resolve financial crises in the future through the use of lending (Smith, 2011). The Frank-Dodd act, also acted as a regulating measure within which banks could be regulated in such a way that they would no longer become too-big-to-fail, trying to overcome the problem of the economy depending to much on any particular bank.

Although many thought that the bailouts were beneficial to the economy, many also thought differently, which led to the “Dilemma of bailouts”. One of the most prominent arguments for bailing out the large banks was that if a large bank failed it could contaminate the whole financial system (Smith, 2011). This was in contrast with the view of many individuals who believe that bailouts are guarantees of creditor positions which are thought of by the “too-big-to-fail” banks, when in reality the banks face failure because the managers of these banks are greedy and irresponsible (Smith, 2011).

Thus, there are different views on the criteria were used in order to assess which banks received bailouts, but that too-big-to-fail banks were favored is a general consensus in all views. When the government acted in name of section 13 of the Federal Reserve Act, they provided large banks with a “free, valuable subsidy of their funding costs and accepts the moral hazard that such guarantees create” (Smith, 2011). Bailouts were not the only way in which the US government tried to regulate the financial system. Many of the medium to small banks were taken over, and merged with larger banks which was motivated and supported by the government (Smith, 2011).