VERY PRELIMINARY
Draft 1.0
Comments Welcome
How Big is Too Big?
What Should Finance Do and How Much Should It Be Cut Down To Size?1
Gerald Epstein and James Crotty
With the Assistance of
Iren Levina and Nina Eichacker
Department of Economics
University of Massachusetts
Amherst, MA
USA
September 28, 2011
Abstract
The financial sector has grown significantly over the last several decades and some have
suggested that the sector is now too big. Yet we have no obvious theoretical framework nor clear
metric to measure the social usefulness of financial activities to help us determine the desirable
size of the financial sector. In this paper we explore some ways to conceptualize the appropriate
size and quality of the financial sector and present some initial data on gambling versus
productive finance and the productivity of financial innovation.
I. Introduction
By almost any measure, the size of the financial sector in the United States, and in many parts of
the world, exploded over the past several decades, prior to the financial crash of 2008. (See some
summary data in section II below; see MacEwan and Miller, 2011, on the role of finance in the
crisis). In the aftermath of the crisis, many analysts, some in surprisingly high positions of
authority in the world of financial governance, have argued that the financial sector has grown
too big, that many of its activities have little, or even negative social value, and that the
productivity and efficiency of the world economy could be improved in the financial sector were
1
This paper has been prepared for, Capitalism on Trial: A Conference in Honor of Thomas E. Weisskopf, PERI,
Gordon Hall, University of Massachusetts, Amherst, September 29 – 30. Gerald Epstein takes full responsibility for
the errors contained in this draft. The authors thank the Institute for New Economic Thinking (INET) for generous
financial support.
1
to shrink. Lord Adair Turner, Chairman of the UK’s FSA remarked in an interview with
Prospect Magazine and then in a speech in September, 2009: “…” …not all financial innovation
is valuable, not all trading plays a useful role, and that a bigger financial system is not
necessarily a better one.” (Turner, Mansion House Speech, 2009). Defending his Prospect
magazine remarks, he remarked: “…I do not apologise for being correctly quoted as saying that
while the financial services industry performs many economically vital functions, and will
continue to play a large and important role in London’s economy, some financial activities which
proliferated over the last ten years were ‘socially useless’, and some parts of the system were
swollen beyond their optimal size.” (ibid.)
Paul Volcker was more blunt. He reportedly told a room full of bankers: “I wish someone would
give me one shred of neutral evidence that financial innovation has led to economic growth —
one shred of evidence,” said Mr Volcker.”
Despite this general and, one might add, increasingly wide-spread view of the bloated
state of the financial sector, there has been relatively little research which has tried to analytically
frame and carefully estimate the extent of “unproductive” finance and to estimate the dimensions
of financial bloat and its impacts. (see, however, Arcand, Berkes and Panizza, 2011, and Panizza,
2011, for a recent attempt; the work by Turner, Haldane and colleagues, 2010, is also of
significant interest here).
This paper takes some small steps in the direction of trying to make some sense out of
these perceptions and concerns. A complete study of these issues would address the following
questions:
1) What metrics should be used to determine the appropriate size of the financial system? How
can we tell if it is bloated?
2) How can we determine which activities of the financial sector are socially useful and which
are not?
3) How can we determine which financial innovations are socially useful and which not?
4) What do the answers to these questions imply about the appropriate level and nature of
financial remuneration in the financial industry?
5) What do the answers to these questions imply about the appropriate type of financial
regulation, including the nature and level of financial taxes?
The answers to these questions have important implications for policy. For example, a
number of economists and regulators, including Pollin and Baker, the European Commission,
Lord Turner, and some IMF economists have endorsed the idea that financial taxes should be
increased. An industry response is that this would reduce the size of the financial sector below
the optimal level and hinder useful financial innovation. Most financial reform legislation,
including the Dodd-Frank legislation recently passed in the United States call for increased
capital and liquidity requirements which may shrink the size of the sector relative to what it
would be otherwise. Bankers and others have expressed concern that these need to be levied in
such a way as to preserve “international competitiveness” of the financial sector, and to prevent
activities from going “offshore”. But if, at the margin, the financial sector is not socially
efficient, then a “lack of competitiveness” which causes the sector to shrink is not socially
harmful. Others have called for significant restrictions on the level or form of banker pay in
2
order to generate more fairness and to reduce excessive risk incentives. (Crotty and Epstein,
2009; Crotty, 2009). Critics have responded that these actions might lead to “banker brain drain”,
leading to the movement of the most highly paid bankers abroad. Here again, this is of particular
social concern only if the activities of these highly paid bankers are making a significant social
contribution. The answers to the questions posed above are obviously relevant to these key
policy issues.
Given the breadth and complexity of these issues, in this paper, we will focus mostly on
two aspects of this very broad set of questions: 1) the issue of financial gambling engaged in by
U.S. investment banks in the lead up to the financial crisis and 2) the issue of the social
productivity of financial innovation. These discussions are highly preliminary and can at most
make some initial progress toward assessing the question: how big is too big.
In what follows we will first offer some initial definitions with regard to the social
productivity of the financial sector. In section III we will present a broad overview of the growth
of the financial sector in the last several decades and briefly review some literature that has
raised questions about the social value of its role. In section IV we will present some rough
estimates of the share of income generated by U.S. investment banks from gambling as opposed
to other aspects of their activities. In section V we will discuss the social productivity of
financial innovation and in the final section we will summarize and present some suggestions for
future research.
II. A Socially Productive Financial Sector? Initial Definitions
In this difficult area, we begin with James Tobin’s important essay, “On the Efficiency of
the Financial Sector” first published in Lloyd’s Bank Review in 1984 and reprinted in Essays in
A Keynesian Mode (Jackson, 1987). Tobin defined four different types of efficiency of the
financial system. The first three are: 1) information arbitrage efficiency 2) fundamental valuation
efficiency 3) full insurance efficiency. While these three concepts only really make sense in an
Arrow-Debru type world, and need major reconceptualization in a world characterized by
“Keynesian Uncertainty” (Crotty, 2008) it is the fourth concept of efficiency which is most
immediately relevant to this paper:
“The fourth concept relates more concretely to the economic functions of the financial
industries... These include: the pooling of risks and their allocation to those most able and willing
to bear them... the facilitation of transactions by providing mechanisms and networks of
payments; the mobilization of saving for investments in physical and human capital... and the
allocation of saving to their more socially productive uses. I call efficiency in these respects
functional efficiency…I confess to an uneasy Physiocratic suspicion, perhaps unbecoming in an
academic, that we are throwing more and more of our resources, including the cream of our
youth, into financial activities remote from the production of goods and services, into activities
that generate high private rewards disproportionate to their social productivity. “(Tobin, 1987).
Here, we begin with Tobin’s concept of functional efficiency and use it to frame our
discussion of the roles the financial sector has been playing in recent decades. To measure these
roles, new measures will have to be developed. Standard measurements of the contribution of the
3
financial sector to the economy, such as value added of the financial sector, rate of return on
equity, and wages are highly problematic because they have reflected mispricing of risk,
excessive leverage and monopoly power, (Haldane, 2010). According to some analysts, notably
William Black, they have also reflected fraudulent activities. Thus, other measures of functional
efficiency and inefficiency will need to be developed.
Though it might be a useful starting point, Tobin’s taxonomy of different types of
financial efficiency is itself highly problematic, for at least two reasons. First, they mostly
assume that financial fundamentals exist that can be readily known and that it makes sense to
judge efficiency with respect to the proximity of values to the fundamentals. From a Keynesian
perspective based on fundamental uncertainty this does not make much sense. Second, Tobin
suggests that the financial sector at worst can be unproductive; but a broader perspective – based
in different ways on the works of Marx and Minsky – would suggest that the finance sector can
engage in exploitation and destroy value. We return to these points later in the paper.
First we present some basic data that show how dramatically the finance sector has grown
in recent decades to place the issue of “financial bloat” in an empirical context.
III. Brief Overview of Recent Trends in The Size of the Financial Sector2
No matter how the size of the financial sector with respect to the rest of the economy is
measured, the trend of massive growth is obvious. Financial sector total financial assets grew
from about a third of total US economy financial assets in the post- World War II decades to 45
percent of total financial assets. Their value was approximately equal to the US GDP in the early
1950s, whereas now it amounts to 4.5 times of the US GDP. Financial sector profit has grown
from about 10 percent in the 1950-60s to 40 percent of total domestic profits in the early 2000s.
This rise in the financial sector as a whole is accompanied by a drastic rise in some of its
segments. Investment banking has drawn special attention during the 2007 - present crisis,
because these financial institutions were at the heart of creating new financial products and
because bankruptcy of some of them and forced acquisitions of others triggered the beginning of
the crisis. Financial assets of the securities industry amounted to a constant 1 percent of total
financial sector financial assets from 1945 till the early 1980s. After that, they rose five-fold and
reached the level of 5 percent of the total financial sector financial assets. Their rise as a share of
GDP has been even more pronounced – from 1.5 percent in the post- World War II decades to 22
percent in 2007. Other measure of the size of the securities industry in the US gives an even
larger figure, with the securities industry total assets reaching 45 percent of GDP in 2007.
The rise in the size of the financial sector and its individual firms is important as it
suggests major changes in the way economies operate. Nevertheless, a rise in the size it does not
necessarily follow that this rise is a “social waste”. Hence, what matters is not the size of the
financial sector per se, but its size with respect to its economic function – the services it
provides.
The Rise of Finance
2
This section is based on the work of Iren Levina.
4
At least since the outbreak of the recent financial crisis, the debate on the size and the role of the
financial sector in the economy, has related both to the size of the finance sector over-all and to
the size of particular financial institutions (the “too big to fail” TBTF issue.
So, the first question is the size of the sector as a whole. No matter how the size of the financial
sector with respect to the rest of the economy is measured, the trend is obvious. Financial sector
total financial assets grew from about a third of total US economy financial assets in the postWorld War II decades to 45 percent of total financial assets. Their value was approximately
equal to the US GDP in the early 1950s, whereas now it amounts to 4.5 times of the US GDP.
Financial sector profit has grown from about 10 percent in the 1950-60s to 40 percent of total
domestic profits in the early 2000s. (Figure 1)
Figure 1
0.50
4.50
0.45
4.00
0.40
3.50
0.35
3.00
0.30
2.50
0.25
2.00
0.20
1.50
0.15
1.00
0.10
0.50
0.05
0.00
0.00
times
5.00
19
45
19
47
19
49
19
51
19
53
19
55
19
57
19
59
19
61
19
63
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
times
Financial sector financial assets as a share of total economy financial assets and GDP (US, 1945-2009)
Source: calculations by authors based on data from Flow of Funds and NIPA, Table 1.1.5
as a share of GDP (left scale)
as a share of total economy financial assets (right scale)
This rise in the financial sector as a whole is accompanied by a drastic rise in some of its
segments. Investment banking has drawn special attention during the 2007-09 crisis, because
these financial institutions were at the heart of creating new financial products and because
bankruptcy of some of them and forced acquisitions of others triggered the beginning of the
crisis. Financial assets of the securities industry amounted to a constant 1 percent of total
financial sector financial assets from 1945 till the early 1980s. After that, they rose five-fold and
reached the level of 5 percent of the total financial sector financial assets. Their rise as a share of
GDP has been even more pronounced – from 1.5 percent in the post- World War II decades to 22
percent in 2007. Other measure of the size of the securities industry in the US gives an even
larger figure, with the securities industry total assets reaching 45 percent of GDP in 2007.
(Figures 2, 3)
5
Figure 2
Security brokers and dealers financial assets as a share of total financial sector financial assets, all sectors
financial assets, and GDP: USA, 1945-2009
25
20
%
15
10
5
20
09
20
07
20
05
20
03
20
01
19
99
19
97
19
95
19
93
19
91
19
89
19
87
19
85
19
83
19
81
19
79
19
77
19
75
19
73
19
71
19
69
19
67
19
65
19
63
19
61
19
59
19
57
19
55
19
53
19
51
19
49
19
47
19
45
0
Source: calculations by authors based on data from Flow of Funds and NIPA, Table 1.1.5
share of total financial sector financial assets
share of all sectors financial assets
share of GDP
Figure 3
Securities industry total assets as a share of total financial sector total assets and as a share of GDP: US,
2001-2008
50
45
40
35
%
30
25
20
15
10
5
0
2001
2002
2003
2004
2005
2006
2007
2008
Source: calculations by author based on Flow of Funds, NIPA, Table 1.1.5, and SIFMA, U.S. Securities Industry
Financial Results. Securities industry consists of NYSE- and NASD-reporting firms.
as a share of total finance total assets
as a share of GDP
The discussion about the rise in size of the financial sector is usually related to that about its
economic contribution – a debate triggered by Turner Review in which Turner posed a question
of the extent to which the rise in the financial sector corresponds to its real economic function.
(Turner, 2009)
The other theme discussed is the rise of individual financial institutions with respect to the
financial system as a whole. This shifts the focus of the debate from financial sector being “too
6
big” compared to the economy to individual financial firms being “too big” with respect to the
sector. These claims are usually supported by data on rising concentration ratios. And indeed,
both commercial banking and investment banking industries have been getting increasingly
concentrated. Top five commercial banks received 20 percent of the total sector revenues in the
early 1990s and 40 percent in the late 2000s3. The investment banking concentration ratio also
rose from 35 to 65 percent during the same time period4. An important observation here is that
not only have the concentration ratios been rising, but also that that the top five investment banks
receive almost twice the share of the industry total revenue as the top five commercial banks.
(Figure 4)
Figure 4
Total revenue-based concentration ratio for commercial and investment banking (US, 1992-2009)
70
60
50
%
40
30
20
10
0
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Source: calculations by author based on data from Compustat Database, SIFMA, FDIC SDI, Historical Statistics on
Banking, and Statistics on Banking.
investment banks
investment banks (imputed)
commercial banks
This sector-wide concentration is reinforced by concentration of individual activities. A prime
example is derivatives trading. Top five commercial banks and trust companies hold about 95
percent of notional amounts of total derivative contracts held for trade in the 2000s. This allowed
these five banks to receive 76 percent of the entire sector trading revenues from cash instruments
and derivatives in 1998-2006, on average5. Nevertheless, this exposure to derivatives also made
the top five banks account for 100-105 percent of the total industry losses from cash and
derivatives trading in 2007-2008. These losses did not last long though, and already in 2009 the
3
For commercial banks, the concentration ratio is calculated as a share of total revenue of the top five commercial
banks in total revenue of all commercial banks. Top banks are defined by their asset size, and vary from year to
year. Total revenue is equal to a sum of interest and non-interest income.
4
For investment banking, the concentration ratio is calculated as a share of total revenue of the top five investment
banks in total revenue of the US securities industry. Total revenue is revenue of all broker-dealers doing a public
business in the US (NYSE- and NASD-reporting firms). Imputed revenue is based on NYSE revenue and its share in
revenue of all broker-dealers registered with the SEC. Imputation is due to unavailability of data on total revenue of
the US securities industry prior to 2001.
5
Data on trading revenues is the 4th quarters data, and is not year-to-date.
7
top five banks got 44 percent of total trading revenues, followed by the historical record of 87
percent in the first quarter of 2010. (Figure 5)
Figure 5
Concentration ratio based on trading revenues and notional amounts of derivative contracts held for trading:
US commercial banks and trust companies, fourth quarters of 1998-2010
120
100
91.84
92.90
93.13
93.45
96.10
96.01
96.15
96.67
101.29
96.81
105.20
95.38
97.08
84.10
79.46
80
77.49
79.36
79.18
74.48
69.65
%
96.77
87.40
69.40
79.35
73.25
60
43.89
40
20
0
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
1Q2010
Source: calculations by author based on data from OCC’s Quarterly Reports on Bank Trading and Derivatives Activities,
various years
Trading revenues from cash instruments and derivatives
Notional amounts of derivative contracts held for trading
These two issues – that of the rise in the financial system and its segments and that of the rise in
individual institutions and concentration – are usually discussed separately. Nevertheless it is
important to understand that the two are closely interconnected. On the one hand, a rise in the
financial system fosters rising industry concentration. Over the past several decades the rise in
finance has been mostly driven by wholesale finance, and it is not surprising that demand for
liquidity on a large scale cannot be met by small institutions. On the other hand, the large
financial institutions are responsible for a substantial part of the total growth in finance because
it is these institutions that are the main site of financial innovation and creation of new
instruments. In that sense a rise of individual institutions feeds into a rise in the financial system.
Thus, there seems to be a structural problem, with the current financial system requiring big
financial firms to function, and the big firms reproducing the growing financial system.
This in turn poses challenges for financial regulation. What exactly needs to be reduced in size?
Are these individual firms that are too big with respect to the size of the financial system? Or the
financial sector as a whole?
III. Investment Banking
Overview
The rise in the size of the financial sector and its individual firms is important as it suggests
major changes in the way economies operate. Nevertheless, form a rise in the size it does not
necessarily follow that this rise is a “social waste”. This rise might be partly explained by a rising
importance of the services the financial sector provides, and only partly by some other factors. In
8
principle, it is possible to envisage a situation when the financial sector would grow relative to
the economy as a whole, but it will be due to an increased need for its services. Furthermore,
there is no theoretical foundation for an “optimal size” of the financial sector and its subsegments in relationship to GDP or any other indicator of productive dimension of an economy.
Hence, what matters is not the size of the financial sector per se, but its size with respect to its
economic function – the services it provides. At a theoretical level it is in line with the functional
perspective on banking. One way to look at the rise in the financial sector size is through the lens
of financial institutions activities. Given that investment banks were at the heart of the recent
crisis, we will focus on them for the purposes of our study. How can one infer a relationship
between the rise in investment banking and services it provides?
Investment banks and their services are different from traditional banks transforming liquid
liabilities into illiquid assets – both assets and liabilities of investment banks are highly liquid.
For this reason balance sheets are a poor indicator of what investment banks do and of the
content of their activities. That is why we will focus on structure of investment banks revenue
which is a better reflection of what they do.
Incidentally, even if one wanted to use investment banking industry aggregate balance sheets to
study what these banks do, one would have faced serious limitations due to data opacity. Since
the late 1970s, miscellaneous financial claims have been rising as a share of securities brokers
and dealers financial assets, and since the early 1990s these miscellaneous assets have amounted
to a half of total assets held by investment banks. On the liability side the data are more
transparent. One can infer that since the early 1990s securities brokers and dealers have been
funding themselves through three main channels – equity investment in subsidiaries, security
credit, and repo – with each of the three categories amounting to roughly a third of total
liabilities.
Figure 6
9
Composition of security brokers and dealers' financial assets (US, 1945-2009)
100%
total miscellaneous
financial claims
80%
security credit
60%
corporate equities
and mutual fund
shares
40%
credit market
instruments,
excluding corporate
equities
checkable deposits
and currency
20%
19
45
19
47
19
49
19
51
19
53
19
55
19
57
19
59
19
61
19
63
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
0%
Source: calculations by authors based on Flow of Funds
Figure 7
Composition of security brokers and dealers' financial assets (US, 1945-2009)
120
equity investment in
subsidiaries liability
100
unidentified
miscellaneous financial
claims
direct investment
80
60
%
40
taxes receivable by
business
20
security credit liability
19
45
19
47
19
49
19
51
19
53
19
55
19
57
19
59
19
61
19
63
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
0
trade debt
-20
credit market
instruments, excluding
corporate equities
repo
-40
-60
-80
Source: calculations by authors based on Flow of Funds
We will focus on the top five investment banks and the structure of their revenues.
Not only have the top five investment banks grown as measured by their assets, but also they
have been receiving a rising share of revenues. Net revenues of these banks amounted to only 0.3
percent of GDP in the early 1990s, but increased more than two-fold since then. In 2006, top five
banks’ net revenues amounted to 1 percent of GDP. Where do these revenues come from? What
are the activities that generated these revenues? The study will address these questions.
Figure 8
10
Total assets of top 5 investment banks and their share in GDP, 1993-2007
4,500
35
4,000
30
3,500
25
20
2,500
2,000
15
1,500
10
1,000
5
500
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
0
2007
2006
Source: calculations by author based on Compustat Database and NIPA, Table 1.1.5.
MER
MS
BSC.1
LEHMQ
GS
share of top 5 in GDP
Figure 9
Net revenue of top 5 investment banks and its share in GDP, 1993-2007
140
1.20
120
1.00
100
80
0.60
60
0.40
40
0.20
20
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Source: calculations by author based on Compustat Database and NIPA, Table 1.1.5. Net revenue is calculated as a
difference between total revenue and total interest and related expenses.
MER
MS
LEHMQ
BSC.1
GS
11
share of top 5 in GDP
0.00
2007
% of GDP
billions of $
0.80
% of GDP
billions of $
3,000
Leverage plays a key role both in terms of the profits of investment banks, and the vulnerability
they face that proved their undoing during the financial crisis.
The investment banking segment is highly leveraged – more than financial sector as a whole and
more than commercial banks. Notice, before the crisis, IB leverage was rising, whilst that of the
FS as a whole – falling (Figure 11) It makes investment banks more sensitive to liquidity risk – a
fact that was proven by the 2007 crisis. Hence, leverage can be used as one key measure of
financial institution risk exposure.
Figure 11
Leverage of commercial banks, securities industry, and financial sector as a whole: USA, 2001-2009
40
38.0
35.8
34.0
35
31.6
30
28.5
24.9
24.7
25
24.1
times
22.00
20
15
14.13
14.03
12.51
11.04
10.93
12.07
10.99
11.43
11.49
9.90
9.91
2004
2005
10
10.96
9.80
9.78
2006
2007
10.66
9.03
5
0
2001
2002
2003
2008
2009
Source: calculations by author based on Flow of Funds, FDIC, Table CB14, and SIFMA, U.S. Securities Industry
Financial Results. Securities industry consists of NYSE- and NASD-reporting firms. Leverage is calculated as a ratio of
total assets to equities
financial sector
commercial banks
securities industry
Note: Securities industry consists of all broker-dealers doing a public business in the US.
Figure 12
12
Leverage of top 4 independent investment banks and of the securities industry: US, 1993-2009
45
40
35
times
30
BSC.1
MS
LEHMQ
GS
securities industry
25
20
15
10
5
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Source: calculations by authors based on data from Compustat and SIFMA, U.S. Securities
Industry Financial Results. Securities industry consists of NYSE- and NASD-reporting firms.
Leverage is calculated as a ratio of total assets to shareholders' equity
Figure 13
Global data
Source: Turner Review, p. 19
As we will see in our discussion of financial innovations in the next section, investment
banks played a key role in the creation of toxic structured products.
Figure 14
13
Asset-Backed Securities Outstanding (US)
3,000
Other
2,500
Student Loans
2,000
$ Billions
Manufacturing Housing
1,500
Home Equity Loans
Credit Card Receivables
1,000
Automobile loans
500
0
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010Q1
Source: SIFMA. Note: "Other" contains CDOs of ABS; data prior to 2001 does not
Gambling vs. Functional Efficiency in Investment Banks
We start with investment banks. This is of particular interest given that investment banks
were at the heart of the recent crisis. Investment banks and their services are different from
traditional banks transforming liquid liabilities into illiquid assets – both assets and liabilities of
investment banks are highly liquid. For this reason balance sheets are a poor indicator of what
investment banks do and of the content of their activities. That is why we will focus on structure
of investment banks revenue which is a better reflection of what they do.
14
Even if one wanted to use investment banking industry aggregate balance sheets to study
what these banks do, one would have faced serious limitations due to data opacity. Since the late
1970s, miscellaneous financial claims have been increasing as a share of securities brokers and
dealers financial assets, and since the early 1990s these miscellaneous assets have amounted to a
half of total assets held by investment banks. On the liability side the data are more transparent.
One can infer that since the early 1990s securities brokers and dealers have been funding
themselves through three main channels – equity investment in subsidiaries, security credit, and
repo – with each of the three categories amounting to roughly a third of total liabilities.
Thus, precisely because of the nature of investment banking activities as distinct from
traditional banking reflected on banks’ balance sheets, investment banks can be best understood
through the lens on their income statements, not balance sheets. An analysis of investment banks
income statements and ways in which they make money is at the core of the present study.
Specifically, we will look into composition of revenues coming from different activities and
changes in this composition. This will provide a link between what investment banks do and the
extent to which their growth can be validated by their economic function. Put differently,
composition of investment banking revenues can be used as a proxy for composition of activities
investment banks perform, hence, changes in the former would reflect changes in the latter.
Growing components of revenue would reflect types of activities accounting for the overall
growth in investment banking business.
This study cannot be done at the aggregate level due to data limitations. According to the
data from SIFMA – the only data source on the aggregate securities industry income statements
– “other revenue related to the securities business” and “other revenue” combined amount to a
rising share of total revenue. This share grew from 40 percent in 2001-2004 to almost 70 percent
in 2008. This data source can obviously not be used to decompose the structure of revenues. For
this reason, given that investment banking is a highly concentrated industry with the top five
investment banks receiving up to 65 percent of total revenues, we will focus on the top five
investment banks and the structure of their revenues. Not only have the top five investment
banks grown as measured by their assets, but also they have been receiving a rising share of
revenues. Net revenues of these banks amounted to only 0.3 percent of GDP in the early 1990s,
but increased more than two-fold since then. In 2006, top five banks’ net revenues amounted to 1
percent of GDP. Where do these revenues come from? What are the activities that generated
these revenues? The study will address these questions.
Functional Efficiency of Investment Banking: Gambling vs. Social Productivity
Methodological Issues:
We attempt to distinguish between “gambling” (of functionally inefficient) and “socially
useful”( or functionally efficient) activities of investment banks.
To make this distinction we will first analyze the implications of major financial theories and
their implications for how to evaluate the distinction between trading-related and gambling.
Efficient markets theory: Standard efficient markets theory would suggest that all trading is
efficient and contributes to value-added. Hence, for this approach, the distinction between
trading and gambling would not be relevant since gambling would be seen as serving a social
purpose (enhancing utility in an efficient way.)
15
New Keynesian and Assymetric Information and noise-trading Approaches: These approaches
suggest that with informational imperfections, there may be “noise-trading” which does not
disappear because of the absence of perfect arbitrage. Hence there can be temporary movements
of financial assets away from their fundamental values and that trading activity which moves
asset prices in this way is not socially useful. (See, for example, Stiglitz, 2010 and the body of
work that he and co-authors have contributed to in this area). Utilizing this approach then, one
would attempt to estimate “fundamental or equilibrium” values of assets and then the trading
activity that is associated with significant movements away from these activities. This form of
trading would not be functionally efficient.
Rent Extraction and Value Destroying Approaches: Crotty (2010) develops an analysis of rainmaker incomes from trading activities that rely on imperfect labor markets, oligopoly structures
of financial markets, the creation and profiting from financial bubbles, and the value extraction
from other stake-holders of the firm (also see the work of Summers, Vishney and Schleifer and
others in this area). From this perspective, then, trading can be a mechanism by which to
redistribute rents among stakeholders, and even destroying wealth in the process.
In what follows, we will not be able to fully apply these methodological distinctions. In what
follows, we will simply be able to make a very first pass at estimating trading and gambling.
First Approximation estimates of trading/gambling:
As a first approximation, we define “gambling” is as trading and other “trading-related”
activities of investment banks. “Socially useful” (or “functionally efficient”) activities constitute
the rest of banks’ operations. Due to differences in categorizing sources of revenues by different
banks, an application of this conceptual criterion requires a firm-specific analysis. In what
follows we have space to only present the summary data based on the firm specific analysis. We
construct a data set for the seven largest investment banks for 2006-2008. To show the evolution
of the structure of investment bank activities, we need to compare the findings to the earliest
possible time period. Investment banks went public in different years, and some of them went
bankrupt or were acquired in 2008, for this reason the time period for which SEC filings are
available differ across the firms. More specifically, the 10-k reports are available for Goldman
Sachs that are filed in 2000-2009, Merrill Lynch – 1994-2009, Lehman Brothers – 1994-2008,
Morgan Stanley – 1997-2009, Citigroup – 1994-2009, Bear Sterns – 1995-2008, JP Morgan –
1994-2009. In our data set, we use the first 3 and the last 3 years available in the SEC filings.
When there is a change in a firm’s reporting standards, we try to construct the data set consistent
with the current methodology used in the latest reports.
The tables below present the results of our calculations of gambling as a share of net revenues
for the 5 largest US investment banks.
These data suggest that revenue from “gambling” as a share of total revenue was highly
significant for these investment banks. If one looks at the height of the bubble just before the
crash, say in 2006 or 2007, “gambling” revenue is often as much as 50% or more of total
revenue.
16
These data bear on the issue of proprietary trading that is so important to the Volcker rule. First,
consider Morgan Stanley. The data shows that in 2008, Morgan Stanley’s trading and investment
revenues were about 2% of total revenue as was widely reported in the press. Though we do not
claim that this is precisely “proprietary trading” as defined narrowly, the fact that this figure for
Morgan Stanley for 2008,was so widely cited by bank analysts does suggest that our data is in
line with quoted estimates. Now, note that this 2% figure is from 2008, the year the system
crashed. But in 2006, at the height of the bubble, trading income as a share of total revenue was
more than 19%.
A similar story holds for Goldman Sachs. In 2008, trading income as a share of gross revenue
was re-ported in the media to be around 10% and according to our figures, about 15%. But if one
goes back to the boom years of 2006, it was more than a third of the gross revenue, almost 35%.
As a percentage of net revenue, trading income was much higher, 36% in 2008; in 2006 and
2007, it was a whopping 64% or more of net revenue. For Citigroup, our numbers are even
rougher than for Morgan Stanley and Goldman, but they tell an interesting tale. Trading and
investment revenue as a share of gross revenue in 2006, at the height of the bubble, was only
about 5% of gross revenue, the number cited by many in the press. If one uses the more
appropriate net revenue figure then this share jumps to over 9%.
Interestingly, if one looks at the contributions to Citi’s revenue losses during the crash, according
to these admittedly crude estimates trading and principle investments played a significant role. In
2008, for example, trading and principle investment losses amounted to 20% of gross revenue
and over 40% of net revenue. If one counts these trading losses as a percentage of the declines of
total and net reve-nue, these numbers become much higher. For example, between 2007 and
2008, Citigroup’s total revenues fell by almost $50 billion and net revenues fell by almost $26
billion. In 2008, Citigroup lost $22 billion which amounts to 44% of total revenue losses and
more than 80% of net revenue losses. Contrary to the bankers and pundits that claim that
“proprietary trading” did not cause the crisis, these losses led to a tax payer bailout and
constitute, in fact, one of the main components of what most of us mean by “the financial crisis.”
Table 1
Gambling vs. Functionally Efficient Activities
Five Large Investment Banks
GS (Goldman Sachs)
millions $
Commissions
Trading and principal investments
1998
1999
2000
1,368 1,522
2,379 5,773
2,307
6,627
Securities services
Net revenue
730
772
940
8,520 13,345 16,590
17
… 2006
2007
2008
25,562 31,226 9,063
2,180 2,716 3,422
37,665 45,987 22,222
"Gambling" as a share of net
revenue, %
52.5
60.4
59.5
73.7
73.8
56.2
Note. Gambling = commissions + trading and principal investment + securities services, for
1998-2000, and gambling = trading and principal investment + securities services, for 20062008, due to a change in methodology.
MS (Morgan Stanley)
millions $
Commissions
Principal transactions
Other
Net revenue
"Gambling" as a share of net
revenue, %
……. 2006
1994
1995
1996
874.3
421.9
101.9
5,554.1
25.2
1,022.5
478.9
93.5
6,419.6
24.8
1,163.1
449.3
107.8
7,462.4
23.1
3,770
13,612
545
29,799
60.2
2007
2008
4,682
6,468
1,161
27,979
44.0
4,463
1,260
6,062
24,739
47.6
Note. Gambling = commissions + principal transactions + other.
BSC (Bear Stearns)
millions $
Commissions
Principal transactions
Net revenue
"Gambling" as a share of net revenue,
%
1993
1994
1995
…… 2005
2006
2007
421
1,157
2,143
73.6
483
1,134
2,417
66.9
547
860
2,075
67.8
1,200
3,836
7,411
68.0
1,163
4,995
9,227
66.7
1,269
1,323
5,945
43.6
Note. Gambling = commissions + principal transactions.
LEHM (Lehman)
millions $
Commissions
[Market making and]
principal transactions
Net revenue
"Gambling" as a
share of net revenue,
%
1993
…… 2005
2006
2007
1,858 1,508 1,649 1,677 1,316
1,269 1,199 1,696 1,697 1,967
1,728
7,811
2,050
9,802
2,471
9,197
4,892 4,016 4,905 5,426 5,218
63.9 67.4 68.2 62.2 62.9
14,630 17,583 19,257
65.2
67.4
60.6
1989
1990
1991
1992
Note. Gambling = commissions + [market making and] principal transactions.
18
MER (Merrill Lynch)
1991 1992 1993
…….. 2006
2007*
millions $
Commissions
2,166 2,422 2,894
5,985 7,284
Principal transactions
1,906 2,166 2,920
7,248 -12,067
Other
340
281
285
2,883 -2,190
Net revenue
7,246 8,577 10,558
33,781 11,250
"Gambling" as a share of net
revenue, %
60.9 56.8 57.8
47.7
-62.0
Note. Gambling = commissions + principal transactions + other.
* Losses (negative numbers) require cautious interpretation of these percentages.
2008*
6,895
-27,225
-10,065
-12,593
241.4
As we will see in the next section, these revenues were achieved partly by selling toxic products
that were at the core of the financial meltdown. Hence, one can reasonably argue that not only
were these activities unlikely to be socially productive, they are actually quite destructive.
V. What is the Functional Efficiency of Financial Innovations? Initial Estimates
Bankers often fight against financial regulation by arguing that regulations will stifle
regulations. What is the functional efficiency of financial innovations? What is the impact of
these financial innovations on the real economy? Are they associated with higher profits for the
innovating firms? More importantly from a social point of view, are they associated with more
investment, more rapid economic growth, or higher productivity growth? Do they reduce
instability, or risk? Unfortunately, there have been very few rigorous empirical analyses of this
topic. As a theoretical matter, there is no presumption that more financial innovation contributes
to higher social welfare. Complex mathematical analyses have shown that financial innovations,
in principle, can either increase or decrease social welfare (Elul, 1995; Frame and White, 2004).
Theory vs. Practice: Financial Innovation, and CDOs, CDSs, and Synthetic CDOs
While mainstream authors discussed above have touted the social benefits of financial
innovation, heterodox economists have taken a more critical stance toward them. Crotty shows in
great detail the destructive nature of many of these “innovations”, and how their existence
deliberately made price discovery harder, and transparency more difficult: in that way they could
generate higher revenues for their issuers. This flies in the face of justifications for innovation
based on effiecient markets theory (see Crotty’s 2008 article “Structural Causes of the Global
Financial Crisis: A Critical Assessment of the New Financial Architecture,” and the “Rainmaker
Financial Firm”)
In the 1989 article “Financial Innovation and Financial Fragility,” Michael Carter applies
Minsky’s theory of financial fragility to the financial innovation and instruments of the 1980s,
and concludes that those new instruments – junk bonds, mortgage backed securities, interest rate
swaps and others – contributed to increased financial fragility. Other papers that have examined
the consequences of the Global Financial Crisis have shown evidence that several – if not all – of
19
the outcomes that Minsky predicts have held true for different sectors of the population that have
been affected by the sub-prime mortgage crisis and its aftermath.
Empirically, there has been very little evidence provided on these key questions. Lerner
(2006) does find that financial innovation raises the profits of the innovating financial firm, at
least in the short run. But what about social impacts? Frame and White (2004) published a
comprehensive survey of the determinants and effects of financial innovation. As their paper
shows, there has been relatively little study of financial innovation. As a result, there is virtually
no evidence that financial innovations contribute to lower cost of capital, more investment, or
higher rates of economic growth. Indeed, in light of the enormous costs associated with the
current crisis, we have a great deal of emerging evidence on the high costs associated with some
financial innovations.
Micro Level Data
In the most comprehensive studies to date, John D. Finnerty and his colleague created a list of
securities innovations organized by type of instrument and function/motivation of the issuers:
debt, preferred stock, convertible securities, and common equities) (Finnerty 1988, 1992, 2002).
Finnerty's initial study (Finnerty, 1988) dealt with both consumer and corporate financial
innovations and listed eleven motivations/functions: (1) Tax advantages, (2) reduced transaction
costs, (3) reduced agency costs (4) risk re-allocations, (5) increased liquidity, (6) regulating or
legislative factors, (7) level and volatility of interest rates, (8) level and volatility of prices, (9)
academic work, (10) accounting benefits and (11) technological developments. In his later work,
Finnerty reduced the functions to six: In his later work, Finnerty reduced the functions to six:(1)
reallocating risk, (2) increasing liquidity, (3) reducing agency costs, (4) reducing transactions
costs, (5) reducing taxes or (6) circumventing regulatory constraints. One should add two other
motives: first, firms have a motive to create a proprietary innovation that is complex and murky
enough to give it proprietary advantages for at least an initial period of time (Tufano, 2002; Das,
2006). We will call this (7) the "proprietary" or "redistributive" motive. An eighth motive,
implicitly proposed by James Tobin, is to open new ways to gamble on trends or to limit losses
when such gambling occurs. We will call this the (8) "gambling motive." Clearly, many of these
have nothing to do with reducing transactions costs or increasing social efficiency.
Table 2, taken from Crotty and Epstein (2009) uses the three Finnerty studies to calculate that
number and percentage of innovations that are at least partly motivated by tax, accounting and/or
regulatory "arbitrage" or "evasion." Our estimates reveal that roughly one-third of these
"innovations" are motivated by these factors, rather than simply efficiency improvements. This
estimate, in fact, is almost certainly a gross under-estimate of innovations motivated by tax and
regulatory arbitrage, since Finnerty (and Emery) presented a selected set of innovations which
they suggested would have "staying power" due to their "addition to value." Their list is not
anywhere near a complete list of new types of securities.
Table 2
Financial "Innovations" Motivated by Tax or Regulatory Evasion
Study
Total
Number
20
Percentage
Number of
Security
Innovations
(1)
motivated at
least partly
be tax or
regulatory
reasons
of total
innovations
motivated by
tax or
regulatory
reasons
(2)
(2)/(1) x 100
(%)
Finnerty,
103
45
44
1988
Finnerty,
65
21
34
1992
Finnerty and
80
25
31
Emery, 2002
Sources: Finnerty, 1988; Finnerty, 1992; Finnerty and Emery, 2002
and authors' calculations. (Crotty and Epstein, 2009)
I believe these believe these are likely to be a significant underestimate of the innovations due
to tax evasion, regulatory arbitrage, and even fraudulent activities. Our proposed research is
likely to come up with a significantly larger number. Part of the reason is reflected in the quote
from the recent study by Tufano. Indeed, Tufano (2002) reports a much larger number of
innovations than suggested by the Finnerty, et. al., lists:
"In preparing this chapter, I asked my research assistant to compile a complete listof security
innovations so that I could update an estimate from the mid-80s that showed that 20% of all new
security issues used an “innovative” structure. One place to begin this exercise was Thompson
Financial Securities Data (former SDC), a data vendor that tracks new public offerings of
securities. He provided me with a list of 1,836 unique“security codes” used from the early 1980s
through early 2001, each purporting to be adifferent type of security. Some of the securities
listed were nearly-identical products offered by banks trying to differentiate their wares from
those of their competitors. Others represented evolutionary improvements on earlier products.
Perhaps a few were truly novel. " (Tufano, 2002, p. 7). In short, it is likely that a much larger
percentage of new products are implemented for tax, accounting, regulatory, casino and
redistributive motives than are indicated in this table.
Hence, much of what passes for financial innovation does not contribute to the increase of the
social product, but is used to bypass regulations, avoid taxes, and shift income from some people
or institutions to others.
In addition, financial innovations can actually be destructive: indeed, several of them were at the
center of the recent financial crisis. In what we follows we give a very brief history of two of
them: CDOs and CDSs.
21
Financial Innovation, CDOs, CDSs and the Financial Crisis6
Introduction
Though argued –that the development of the collateralized debt obligation (CDO), credit default
swap (CDS), and synthetic CDO in the lead-up to the global financial crisis represented the best
of financial innovation (efficient placement of risk, increased efficiency in the mortgage market,
creation of credit, generation of large returns for investors), these securities echoed the opaque
(and often doomed) securities from the nineties that Frank Partnoy describes in Infectious Greed.
Yet, as Partnoy and Michael Lewis show, bankers designed and market these financial products
primarily to help them avoid regulation, earn massive service fees, and increase profits for
themselves in the shortest term, with little concern for the long-term effects of these more
complex and less transparent securities on either their own shareholders or their investors.
CDOs, CDSs, and synthetic CDOs present clear instances of the divergence between the theory
and practice of financial innovation.
The synthetic CDO was the quintessential financial innovation of the financial crisis. As Partnoy
describes:
“the Synthetic CDO was the ultimate in financial alchemy. A Synthetic CDO was like a standard
cash-flow CDO, except that a bank substituted credit default swaps for loans or bonds. In other
words, the ‘assets’ of the [special purpose entity] were credit default swaps. As a result, the
companies whose debts formed the basis of a Synthetic CDO had no relationship at all to the
deal; most likely, the companies would not even know about it. Neither the investors in the SPE,
nor the banks, ever had to touch the companies loans or bonds.” (Partnoy, 2009, 383)
The synthetic nature of this asset – representing a further step away from concrete finance that is
easy to visualize and comprehend – was emblematic of larger trends in finance. Barnett-Hartt
and Partnoy both describe the historical evolution towards these assets made from other assets as
a systematic process. Barnett-Hartt notes that there was a significant decrease “in collateral
backed by fixed-rate assets and the increased use of synthetic assets,” (Barnett-Hart, 2009, 14)
from 1999 until 2007. She concludes that “CDOs began to invest in more risky assets over time,
especially in subprime floating rate assets. Essentially, CDOs became a dumping ground for
bonds that could not be sold on their own – bonds now referred to as ‘toxic waste.’” (BarnettHart, 2009, 14) And according to Partnoy, as of 2002, synthetic CDOs:
“were a mainstay of corporate finance. In 2001, banks created almost $80 billion of Synthetic
CDOs. During 2002, even after the bankruptcies of Enron, Global Crossing, and WorldCom –
companies whose debts were referenced in the credit default swaps of numerous Synthetic CDOs
– financial institutions were continuing to do these deals.” (Partnoy, 2009, 383 – 384)
Regardless of the increasingly toxic nature of these synthetic CDOs, their popularity
increased. Tett writes:
“Derivatives versions of CDOs enabled investors to place bets on whether mortgage bonds
would default or not.... They would lead to a frenzy of speculation, all based on the
fundamental premise that the default risk of bundles of mortgages had been virtually erased
6
This section draws on work by Nina Eichacker.
22
by the process of bundling and then slicing them into tranches. If banks chose to hold more
and more of the risk in these tranches on their books, selling only the more popular tranches
of notes, such as mezzanine, that was no worry because the risk had been so effectively
dispersed that the chance the banks would ever take a hit from it seemed so remote as to be
unfathomable.” (Tett, 2009, 97)
Actors within the financial sector – investment bankers, credit rating agencies, and hedge
funds – manipulated instruments, data, and people, ignored ample evidence of the
groundlessness of the CDO and mortgage-backed security sector in general, and all the while
argued that institutions incapable of understanding the securities that they, the intermediaries,
peddled must not regulate them.
Investment banks had several key roles in the lead-up to the global financial crisis. First,
many of them started mortgage brokering divisions in order to provide raw RMBS material for
cash-backed CDOs. Banks underwrote and sold CDOs and synthetic CDOs (and tranches of
CDOs and synthetic CDOs). They created hedge funds and structured investment vehicles (and
special purpose entities) to purchase their proprietary RMBS, and fill their portfolios primarily
with CDOs. They also bought and sold CDSs, depending on how astutely they evaluated the fate
of the housing market, and the prospects of massive and sustained default by sub-prime
mortgage owners. Through all of this, they paid rating agencies to give their assets high ratings
despite the increasingly rotten value of the underlying securities, and put a tremendous effort into
marketing CDOs and synthetic CDOs, regardless of whether they believed those securities were
sound (in the case of the ignorant investment banks), or rotten (in the case of the mercenary
ones).
Some investment banks successfully profited from the situation by double-dealing.
Deutsche Bank assigned one bond trader, Greg Lippmann, to market CDSs to hedge funds (since
unregulated hedge funds could buy such a security while other institutional investors could not).
Lippmann lobbied hard for hedge fund managers to take advantage of the oncoming collapse of
the CDO market – which would occur after devastation in the housing market – rather than
somehow attempting to warn other Deutsche Bank clients that were long on the securities of the
inherent risk in their investments.
Goldman Sachs took things further than Deutsche Bank. Goldman aggressively
marketing synthetic CDOs filled with destined to fail securities – RMBS, CDOs backed by
RMBS, and commercial mortgage backed securities – to hedge funds and other institutional
investors, including public pension funds. (Morgenstern, 2010) It took care to insulate its bond
sales staff from the knowledge of the full risk of the RMBS backed CDOs by shifting the duty of
selling RMBS CDOs and other permutations of the CDO instrument to those unfamiliar with the
origins of product, while continuing to pressure these new sales staff to sell as many as possible.
Behind the scenes, Goldman’s bond traders’ emails described RMBS backed CDOs as crap and
worse, while management repeatedly warned the bond sales staff not to give clients any
indication in writing that these bonds may be risky, even as clients grew increasingly testy about
the securities once they began to decline in value. (FCIC, 2011)
23
As with investment banks, the themes of greed, ignorance, and perverse incentives to destroy
value in the interest of short-term personal gain are present in the story of how hedge funds
behaved in the lead-up and aftermath of the sub-prime mortgage bubble and global financial
crisis. Hedge funds were the largest share of customers for the equity – riskiest – tranche of
CDOs and synthetic CDOs – in the 2007 OECD article “Structured Products: Implications for
Financial Markets,” Adrian Blundell concludes, after reviewing private industry data, that
“Hedge funds have around 46% of [total exposure to CDO tranches], followed by banks at 25%,
asset managers at 19%, and insurance at 10%.” (Blundell, 2007, 45) Further, hedge funds as of
2007 held the largest percentage of those riskiest tranches – 19.1% compared to banks at 4.9%,
asset managers at 1.7%, and insurance companies at 9.8%. (Blundell, 2007)
The official justification for the lack of regulation of hedge funds is their presumed
sophistication. Jennifer Taub clarifies that the term ‘sophisticated’ as it applies to hedge funds
refers to nothing sartorial or intellectual – merely that hedge funds have a lot of capital to invest
on behalf of their clients. However, this assumed sophistication has not, on average, kept hedge
funds from acting either recklessly or individualistically with respect to RMBS and CDOs. Much
like investment banks, many hedge funds put their clients at risk by investing in those risky
assets, and by using risky short-term financing to pay for those risky assets. Failures or changes
in either market had the potential to create financial calamity for the hedge funds, and by
extension, their investors.
In another similarity to investment banks, a few savvy hedge funds foresaw the inevitable
downturn in housing prices and the resulting rise in defaults. Like Goldman Sachs and Deutsche
Bank, these hedge funds used CDSs to short CDOs and synthetic CDOs. Magnetar and John
Paulson’s Paulson and Co. were chief examples of hedge funds that worked with banks to
underwrite CDOs and synthetic CDOs based overwhelmingly on risky and doomed securities –
CDOs, RMBS, and CDO2s based on tranches of CDOs that banks had been unable to sell. While
Paulson worked chiefly with Goldman Sachs and Deutsche Banks, Magnetar worked with
multiple banks, including Merrill Lynch, J.P. Morgan, UBS, smaller pension funds, and other
intermediaries with CDO holdings to put together riskier than usual bundles of debt. (Eisinger
and Bernstein, 2010) In both sets of dealings, the hedge fund made sure to insure itself against
future losses by holding the affiliated CDS, and merely waited for the housing market to collapse
and to be paid billions of dollars by those long on the bet.
V. Conclusion
In this paper, we have tried to sketch out several pieces of evidence that bear on the issue
of how productive is finance: we tried to share of the revenues of investment banks coming from
gambling activities; we looked at the share of financial innovations that seem to be motivated by
tax or regulatory arbitrage; and we studied the history of several financial innovations that were
crucially connected to the financial meltdown. Obviously, none of these methods is definitive
and much more work needs to be done before we can truly determine the “functional efficiency
of the financial system”.
But we hope we have made the case that a bit of banker brain drain might be a very good
thing indeed.
24
25
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