The Glass–Steagall legislation describes four provisions of the United States Banking Act of 1933 separating commercial and investment banking. The article 1933 Banking Act describes the entire law, including the legislative history of the provisions covered herein.
The separation of commercial and investment banking prevented
securities firms and investment banks from taking deposits, and
commercial Federal Reserve member banks from:
dealing in non-governmental securities for customers,
investing in non-investment grade securities for themselves,
underwriting or distributing non-governmental securities,
affiliating (or sharing employees) with companies involved in such activities.
Starting in the early 1960s, federal banking regulators' interpretations of the Act permitted commercial banks, and especially commercial bank affiliates, to engage in an expanding list and volume of securities activities.
Congressional efforts to "repeal the Glass–Steagall Act", referring to
those four provisions (and then usually to only the two provisions that
restricted affiliations between commercial banks and securities firms), culminated in the 1999 Gramm–Leach–Bliley Act (GLBA), which repealed the two provisions restricting affiliations between banks and securities firms.
By that time, many commentators argued Glass–Steagall was already "dead". Most notably, Citibank's 1998 affiliation with Salomon Smith Barney, one of the largest U.S. securities firms, was permitted under the Federal Reserve Board's then existing interpretation of the Glass–Steagall Act. In November 1999, President Bill Clinton publicly declared "the Glass–Steagall law is no longer appropriate".
Some commentators have stated that the GLBA's repeal of the
affiliation restrictions of the Glass–Steagall Act was an important
cause of the financial crisis of 2007–2008. Nobel Memorial Prize in Economics laureate Joseph Stiglitz
argued that the effect of the repeal was "indirect": "[w]hen repeal of
Glass-Steagall brought investment and commercial banks together, the
investment-bank culture came out on top". Economists at the Federal Reserve, such as ChairmanBen Bernanke,
have argued that the activities linked to the financial crisis were not
prohibited (or, in most cases, even regulated) by the Glass–Steagall
Act.
Between 1930 and 1932, Senator Carter Glass (D-VA) introduced several
versions of a bill (known in each version as the Glass bill) to
regulate or prohibit the combination of commercial and investment
banking and to establish other reforms (except deposit insurance)
similar to the final provisions of the 1933 Banking Act.
On June 16, 1933, President Roosevelt signed the bill into law. Glass
originally introduced his banking reform bill in January 1932. It
received extensive critiques and comments from bankers, economists, and
the Federal Reserve Board. It passed the House on February 16, 1932, the
Senate on February 19, 1932, and signed into law by President Hoover eight days later.
The Senate passed a version of the Glass bill that would have required
commercial banks to eliminate their securities affiliates.
The final Glass–Steagall provisions contained in the 1933 Banking
Act reduced from five years to one year the period in which commercial
banks were required to eliminate such affiliations.
Although the deposit insurance provisions of the 1933 Banking Act were
very controversial, and drew veto threats from President Franklin Delano Roosevelt,
President Roosevelt supported the Glass–Steagall provisions separating
commercial and investment banking, and Representative Steagall included
those provisions in his House bill that differed from Senator Glass's
Senate bill primarily in its deposit insurance provisions. Steagall insisted on protecting small banks while Glass felt that small banks were the weakness to U.S. banking.
Many accounts of the Act identify the Pecora Investigation as important in leading to the Act, particularly its Glass–Steagall provisions, becoming law.
While supporters of the Glass–Steagall separation of commercial and
investment banking cite the Pecora Investigation as supporting that
separation,
Glass–Steagall critics have argued that the evidence from the Pecora
Investigation did not support the separation of commercial and
investment banking.
This source states that Senator Glass proposed many versions of
his bill to Congress known as the Glass Bills in the two years prior to
the Glass–Steagall Act being passed. It also includes how the deposit
insurance provisions of the bill were very controversial at the time,
which almost led to the rejection of the bill once again.
The previous Glass Bills before the final revision all had
similar goals and brought up the same objectives, which were to separate
commercial from investment banking, bring more banking activities under
Federal Reserve supervision, and to allow branch banking. In May 1933,
Steagall's addition of allowing state-chartered banks to receive federal
deposit insurance and shortening the time in which banks needed to
eliminate securities affiliates to one year was known as the driving
force of what helped the Glass–Steagall act to be signed into law.
The Glass–Steagall separation of commercial and investment banking
was in four sections of the 1933 Banking Act (sections 16, 20, 21, and
32). The Banking Act of 1935
clarified the 1933 legislation and resolved inconsistencies in it.
Together, they prevented commercial Federal Reserve member banks from:
dealing in non-governmental securities for customers
investing in non-investment grade securities for themselves
underwriting or distributing non-governmental securities
affiliating (or sharing employees) with companies involved in such activities
Conversely, Glass–Steagall prevented securities firms and investment banks from taking deposits.
The law gave banks one year after the law was passed on June 16,
1933, to decide whether they would be a commercial bank or an investment
bank. Only 10 percent of a commercial bank's income could stem from
securities. One exception to this rule was that commercial banks could
underwrite government-issued bonds.
There were several "loopholes" that regulators and financial
firms were able to exploit during the lifetime of Glass–Steagall
restrictions. Aside from the Section 21 prohibition on securities firms
taking deposits, neither savings and loans nor state-chartered banks
that did not belong to the Federal Reserve System were restricted by
Glass–Steagall. Glass–Steagall also did not prevent securities firms
from owning such institutions. S&Ls
and securities firms took advantage of these loopholes starting in the
1960s to create products and affiliated companies that chipped away at
commercial banks' deposit and lending businesses.
While permitting affiliations between securities firms and
companies other than Federal Reserve member banks, Glass–Steagall
distinguished between what a Federal Reserve member bank could do
directly and what an affiliate could do. Whereas a Federal Reserve
member bank could not buy, sell, underwrite, or deal in any security
except as specifically permitted by Section 16, such a bank could
affiliate with a company so long as that company was not "engaged
principally" in such activities. Starting in 1987, the Federal Reserve
Board interpreted this to mean a member bank could affiliate with a
securities firm so long as that firm was not "engaged principally" in
securities activities prohibited for a bank by Section 16. By the time
the GLBA repealed the Glass–Steagall affiliation restrictions, the
Federal Reserve Board had interpreted this "loophole" in those
restrictions to mean a banking company (Citigroup, as owner of Citibank) could acquire one of the world's largest securities firms (Salomon Smith Barney).
By defining commercial banks as banks that take in deposits and
make loans and investment banks as banks that underwrite and deal with
securities the Glass–Steagall act explained the separation of banks by
stating that commercial banks could not deal with securities and
investment banks could not own commercial banks or have close
connections with them. With the exception of commercial banks being
allowed to underwrite government-issued bonds, commercial banks could
only have 10 percent of their income come from securities.
It was not until 1933 that the separation of commercial banking and
investment banking was considered controversial. There was a belief that
the separation would lead to a healthier financial system.
As time passed, however, the separation became so controversial that in
1935, Senator Glass himself attempted to "repeal" the prohibition on
direct bank underwriting by permitting a limited amount of bank
underwriting of corporate debt.
In the 1960s, the Office of the Comptroller of the Currency
issued aggressive interpretations of Glass–Steagall to permit national
banks to engage in certain securities activities. Although most of these
interpretations were overturned by court decisions, by the late 1970s,
bank regulators began issuing Glass–Steagall interpretations that were
upheld by courts and that permitted banks and their affiliates to engage
in an increasing variety of securities activities. Starting in the
1960s, banks and non-banks developed financial products that blurred the
distinction between banking and securities products, as they
increasingly competed with each other.
Separately, starting in the 1980s, Congress debated bills to
repeal Glass–Steagall's affiliation provisions (Sections 20 and 32).
Some believe that major U.S. financial sector firms established a
favorable view of deregulation in American political circles, and in
using its political influence in Congress to overturn key provisions of
Glass-Steagall and to dismantle other major provisions of statutes and
regulations that govern financial firms and the risks they may take.
In 1999 Congress passed the Gramm–Leach–Bliley Act, also known as the Financial Services Modernization Act of 1999, to repeal them. Eight days later, President Bill Clinton signed it into law.
After the financial crisis of 2007–2008,
some commentators argued that the repeal of Sections 20 and 32 had
played an important role in leading to the housing bubble and financial
crisis. Economics Nobel Memorial laureate Joseph Stiglitz,
for instance, argued that "[w]hen repeal of Glass-Steagall brought
investment and commercial banks together, the investment-bank culture
came out on top", and banks which had previously been managed
conservatively turned to riskier investments to increase their returns. Another laureate, Paul Krugman, contended that the repealing of the act "was indeed a mistake"; however, it was not the cause of the financial crisis.
Other commentators believed that these banking changes had no
effect, and the financial crisis would have happened the same way if the
regulations had still been in force. Lawrence J. White,
for instance, noted that "it was not [commercial banks'] investment
banking activities, such as underwriting and dealing in securities, that
did them in".
At the time of the repeal, most commentators believed it would be
harmless. Because the Federal Reserve's interpretations of the act had
already weakened restrictions previously in place, commentators did not
find much significance in the repeal, especially of sections 20 and 32.
Instead, the five year anniversary of its repeal was marked by numerous
sources explaining that the GLBA had not significantly changed the
market structure of the banking and securities industries. More
significant changes had occurred during the 1990s when commercial
banking firms had gained a significant role in securities markets
through "Section 20 affiliates".
The perception is that the Glass-Steagall Act created a sense of
accountability among investors within the financial management industry,
encouraging them to (in effect) shy away from ultra-risky transactions
that could lead to financial meltdown. It provided litigators
validation involving cases against such sub-prime investment instruments
on behalf of their clients who were impacted by such injustices.
Without formal and defensible protection as detailed in the
Glass-Steagall Act, investment companies felt at liberty to move toward
unscrupulous investment tactics that had occurred prior to 2009
involving sub-prime mortgages. Thus a cultural shift was certainly in
order after its repeal regardless of the loopholes that existed prior.
Although the magnitude may be questionable, the repeal of the
Glass-Steagall Act can certainly be considered a factor in the global
financial crisis revealed in 2008.
Following the financial crisis of 2007–2008, legislators
unsuccessfully tried to reinstate Glass–Steagall Sections 20 and 32 as
part of the Dodd–Frank Wall Street Reform and Consumer Protection Act.
Both in the United States and elsewhere around the world, banking
reforms have been proposed that refer to Glass–Steagall principles.
These proposals include issues of "ringfencing" commercial banking operations and narrow banking
proposals that would sharply reduce the permitted activities of
commercial banks - institutions that provide capital liquidity to
investment management firms to shore up over-inflated market valuation
of securities (whether debt or equity). Reconciliation of
over-committed funds is possible by filing claims to the FDIC (Federal Deposit Insurance Company) - hence further increasing the federal budget deficit.
Income inequality metrics or income distribution metrics are used by social scientists to measure the distribution of income and economic inequality
among the participants in a particular economy, such as that of a
specific country or of the world in general. While different theories
may try to explain how income inequality comes about, income inequality metrics simply provide a system of measurement used to determine the dispersion of incomes. The concept of inequality is distinct from poverty and fairness.
Modern economists have also addressed this issue, but have been
more concerned with the distribution of income across individuals and
households. Important theoretical and policy concerns include the
relationship between income inequality and economic growth. The article economic inequality discusses the social and policy aspects of income distribution questions.
Defining income
All
of the metrics described below are applicable to evaluating the
distributional inequality of various kinds of resources. Here the focus
is on income as a resource. As there are various forms of "income", the
investigated kind of income has to be clearly described.
One form of income is the total amount of goods and services that
a person receives, and thus there is not necessarily money or cash
involved. If a subsistence farmer
in Uganda grows his own grain, it will count as income. Services like
public health and education are also counted in. Often expenditure or
consumption (which is the same in an economic sense) is used to measure
income. The World Bank uses the so-called "living standard measurement surveys" to measure income. These consist of questionnaires with more than 200 questions. Surveys have been completed in most developing countries.
Applied to the analysis of income inequality within countries,
"income" often stands for the taxed income per individual or per
household. Here, income inequality measures also can be used to compare
the income distributions before and after taxation in order to measure
the effects of progressive tax rates.
Properties of inequality metrics
In the discrete case, an economic inequality index may be represented by a function I(x), where x is a set of n economic values (e.g. wealth or income) x={x1,x2,...,xn} with xi being the economic value associated with "economic agent" i.
In the economic literature on inequality four properties are generally postulated that any measure of inequality should satisfy:
Anonymity or symmetry
This assumption states that an inequality metric does not depend
on the "labeling" of individuals in an economy and all that matters is
the distribution of income. For example, in an economy composed of two
people, Mr. Smith and Mrs. Jones, where one of them has 60% of the
income and the other 40%, the inequality metric should be the same
whether it is Mr. Smith or Mrs. Jones who has the 40% share. This
property distinguishes the concept of inequality from that of fairness
where who owns a particular level of income and how it has been
acquired is of central importance. An inequality metric is a statement
simply about how income is distributed, not about who the particular
people in the economy are or what kind of income they "deserve".
This property says that richer economies should not be
automatically considered more unequal by construction. In other words,
if every person's income in an economy is doubled (or multiplied by any
positive constant) then the overall metric of inequality should not
change. Of course the same thing applies to poorer economies. The
inequality income metric should be independent of the aggregate level of
income. This may be stated as:
where α is a positive real number.
Population independence
Similarly, the income inequality metric should not depend on
whether an economy has a large or small population. An economy with only
a few people should not be automatically judged by the metric as being
more equal than a large economy with many people. This means that the
metric should be independent of the level of population. This is
generally written:
The Pigou–Dalton,
or transfer principle, is the assumption that makes an inequality
metric actually a measure of inequality. In its weak form it says that
if some income is transferred from a rich person to a poor person, while
still preserving the order of income ranks, then the measured
inequality should not increase. In its strong form, the measured level
of inequality should decrease.
Other useful but not mandatory properties include:
Non-negativity
The index I(x) is greater than or equal to zero.
Egalitarian zero
The index I(x) is zero in the egalitarian case, when all values xi are equal.
Bounded above by maximum inequality
The index I(x) attains its maximum value for maximum inequality. (all xi are zero except one) This value is usually unity as the number of agents n approaches infinity.
Subgroup decomposability
This property states that if a set of agents x is divided into two disjoint subsets (y and z) then the I(x) is expressible as:
where μ(x) and μ(y) are the mean incomes of x and y.
and the w functions are scalar weighting function of the sets y and z. In a stronger statement, wy = μy / μx and wz = μz / μx.
Common income inequality metrics
Among the most common metrics used to measure inequality are the Gini index (also known as Gini coefficient), the Theil index, and the Hoover index. They have all four properties described above.
An additional property of an inequality metric that may be
desirable from an empirical point of view is that of 'decomposability'.
This means that if a particular economy is broken down into sub-regions,
and an inequality metric is computed for each sub region separately,
then the measure of inequality for the economy as a whole should be a
weighted average of the regional inequalities plus a term proportional
to the inequality in the averages of the regions. (in a weaker form, it
means that it should be an explicit function of sub-regional
inequalities, though not necessarily linear). Of the above indexes, only
the Theil index has this property.
Because these income inequality metrics are summary statistics
that seek to aggregate an entire distribution of incomes into a single
index, the information on the measured inequality is reduced. This
information reduction of course is the goal of computing inequality
measures, as it reduces complexity.
A weaker reduction of complexity is achieved if income
distributions are described by shares of total income. Rather than to
indicate a single measure, the society under investigation is split into
segments, such as into quintiles
(or any other percentage of population). Usually each segment contains
the same share of income earners. In case of an unequal income
distribution, the shares of income available in each segment are
different.
In many cases the inequality indices mentioned above are computed
from such segment data without evaluating the inequalities within the
segments. The higher the number of segments (such as deciles instead of
quintiles), the closer the measured inequality of distribution gets to
the real inequality. (If the inequality within the segments is known,
the total inequality can be determined by those inequality metrics which
have the property of being "decomposable".)
Quintile measures of inequality satisfy the transfer principle
only in its weak form because any changes in income distribution outside
the relevant quintiles are not picked up by this measures; only the
distribution of income between the very rich and the very poor matters
while inequality in the middle plays no role.
Details of the three inequality measures are described in the
respective Wikipedia articles. The following subsections cover them only
briefly.
The Gini index is a summary statistic that measures how equitably a resource is distributed in a population; income
is a primary example. In addition to a self-contained presentation of
the Gini index, we give two equivalent ways to interpret this summary
statistic: first in terms of the percentile level of the person who
earns the average dollar, and second in terms of how the lower of two randomly chosen incomes compare, on average, to mean income.
The Gini is the sum, over all income-ordered population
percentiles, of the shortfall, from equal share, of the cumulative
income up to each population percentile, with that summed shortfall
divided by the greatest value that it could have, with complete
inequality.
The range of the Gini index is between 0 and 1 (0% and 100%),
where 0 indicates perfect equality and 1 (100%) indicates maximum
inequality.
The Gini index is the most frequently used inequality index. The
reason for its popularity is that it is easy to understand how to
compute the Gini index as a ratio of two areas in Lorenz curve
diagrams. This measure tries to capture the overall dispersion of
income; however, it tends to place different levels of importance on the
bottom, middle and top end of the distribution. As a disadvantage, the
Gini index only maps a number to the properties of a diagram, but the
diagram itself is not based on any model of a distribution process. The
"meaning" of the Gini index only can be understood empirically.
Additionally, the Gini does not capture where in the distribution the
inequality occurs. As a result, two very different distributions of
income can have the same Gini index.
20:20 ratio
GDP per capita PPP vs 20:20 ratio in countries
The 20:20 or 20/20 ratio compares how much richer the top 20% of
populations are to the bottom 20% of a given population. This can be
more revealing of the actual impact of inequality in a population, as it
reduces the effect on the statistics of outliers at the top and bottom
and prevents the middle 60% from statistically obscuring inequality that
is otherwise obvious in the field. The measure is used for the United
Nations Development Programme Human Development Indicators.
The 20:20 ratio for example shows that Japan and Sweden have a low
equality gap, where the richest 20% only earn 4 times the poorest 20%,
whereas in the UK the ratio is 7 times and in the US 8 times. Some
believe the 20:20 ratio is a more useful measure as it correlates well
with measures of human development and social stability including the
index of child well-being, index of health and social problems, population in prison, physical health, mental health and many others.
Palma ratio
GDP per capita PPP vs Palma ratio in countries
The Palma ratio is defined as the ratio of the richest 10% of the population's share of gross national income divided by the poorest 40%'s share. It is based on the work of Chilean economist Gabriel Palma
who found that middle class incomes almost always represent about half
of gross national income while the other half is split between the
richest 10% and poorest 40%, but the share of those two groups varies
considerably across countries.
The Palma ratio addresses the Gini index's over-sensitivity to
changes in the middle of the distribution and insensitivity to changes
at the top and bottom,
and therefore more accurately reflects income inequality's economic
impacts on society as a whole. Palma has suggested that distributional
politics pertains mainly to the struggle between the rich and poor, and
who the middle classes side with.
The carbon Palma ratio, which is derived from the income Palma
ratio and described as the ratio of the total emissions of the top 10%
of emitters to those of the bottom 40%, is proposed as a new indicator
to inform the international community and the general public about the
distribution inequality of carbon emissions among individuals. The ratio
is calculated both within and between countries using an elastic
relationship between individual emissions and income. The results show
that the carbon Palma ratios in most developing countries are generally
high, implying that they should concentrate more on coordinating
regional and income inequalities and primarily encouraging high emitters
to reduce in order to enhance emissions and income equity at the same
time. The carbon Palma ratios in developed countries are comparatively
lower; however, their greater historical obligations to warming indicate
that they significantly reduce emissions of all people, in order to
increase national mitigation contributions systematically. On a global
scale, the current carbon Palma ratio is noticeably higher than within
any country, indicating an exceedingly severe inequality when individual
emissions are considered outside territorial boundaries.
The Hoover index is the simplest of all inequality measures to
calculate: It is the proportion of all income which would have to be
redistributed to achieve a state of perfect equality.
In a perfectly equal world, no resources would need to be
redistributed to achieve equal distribution: a Hoover index of 0. In a
world in which all income was received by just one family, almost 100%
of that income would need to be redistributed (i.e., taken and given to
other families) in order to achieve equality. The Hoover index then
ranges between 0 and 1 (0% and 100%), where 0 indicates perfect equality
and 1 (100%) indicates maximum inequality.
Galt score
The Galt score is a simple ratio of a company's CEO pay to the pay of that company's median worker. A company which pays its CEO many times more than its median employee will have a high Galt score.
The score is calculated using the total compensation of the CEO, including salary, bonuses, the value of stock awards and employee stock options, as well as non-equity incentive plan compensation, and nonqualified deferred compensation.
Coefficient of variation (CV) used as a measure of income inequality
is conducted by dividing the standard deviation of the income (square
root of the variance of the incomes) by the mean of income. Coefficient
variation will be therefore lower in countries with smaller standard
deviations implying more equal income distribution.
It has the advantages of being mathematically tractable and its
square is subgroup decomposable, but it is not bounded from above. This
simple form of measurement is not being commonly used mostly for its two
considerable limitations. The first one could be attributed to CV not
having and upper limit, unlike the Gini coefficient, therefore causing
difficulties with interpretation and comparison. Secondly, as the mean
and standard deviation may be heavily affected by anomalous borderline
values, the coefficient would not be an appropriate choice of income
inequality measure for a case of abnormal data distribution.
Compared to the Gini coefficient in practice, CV puts higher
weight on the right tail of the scale, making it sensitive to the rich.
Coefficient of variation may be a suitable choice of measure iIf the
goal of a study is to analyze the wealth concentration at the top of the
distribution.
Variance of the Natural Logarithm of Income
The variance of log Income is described as variance applied to the distribution of log incomes.
This scale invariant measure of relative inequality is sensitive to the
left tail, making it ideal to use when studying the levels of poverty
of the lower income half (the poor).
Wage share is the ratio between Compensation of employees and GDP. In other words, it is the total of employees' income divided by the national income.
Sen poverty measure
Sen
poverty measure combines the Gini coefficient for people living below
the poverty line with headcount ration of poverty and the average income
of these below the poverty line.
This measure has been developed by Nobel Prize winner Amartya Sen but
has not yet been used in the field of income inequality hypothesis.
Although it has been greeted with enthusiasm, the Sen poverty index does
not fulfill number of ideal conditions, e.g. it fails to satisfy the
transfer axiom, it is not decomposable or subgroup consistent.
As described in a section below, Theil-L is an income-distribution's
dis-entropy per person, measured with respect to maximum entropy (which
is achieved with complete equality). (In an alternative interpretation
of it, Theil-L is the natural-logarithm of the geometric-mean of the
ratio: (mean income)/(income i), over all the incomes. The related
Atkinson(1) is just 1 minus the geometric-mean of (income i)/(mean
income),over the income distribution.)
Because a transfer between a larger income & a smaller one
will change the smaller income's ratio more than it changes the larger
income's ratio, the transfer-principle is satisfied by this index.
A Theil index of 0 indicates perfect equality. A Theil index of 1
indicates that the distributional entropy of the system under
investigation is almost similar to a system with an 82:18 distribution. This is slightly more unequal than the inequality in a system to which the "80:20 Pareto principle" applies. The Theil index can be transformed into an Atkinson index,
which has a range between 0 and 1 (0% and 100%), where 0 indicates
perfect equality and 1 (100%) indicates maximum inequality. (See Generalized entropy index for the transformation.)
The Theil index is an entropy measure. As for any resource
distribution and with reference to information theory, "maximum entropy"
occurs once income earners cannot be distinguished by their resources,
i.e. when there is perfect equality. In real societies people can be
distinguished by their different resources, with the resources being
incomes. The more "distinguishable" they are, the lower is the "actual
entropy" of a system consisting of income and income earners. Also based
on information theory, the gap between these two entropies can be
called "redundancy". It behaves like a negative entropy.
For the Theil index also the term "Theil entropy" had been used. This caused confusion. As an example, Amartya Sen
commented on the Theil index, "given the association of doom with
entropy in the context of thermodynamics, it may take a little time to
get used to entropy as a good thing." It is important to understand that an increasing Theil index does not indicate an increasing entropy, instead it indicates an increasing redundancy (decreasing entropy).
High inequality yields high Theil redundancies. High redundancy
means low entropy. But this does not necessarily imply that a very high
inequality is "good", because very low entropies also can lead to
explosive compensation processes. Neither does using the Theil index
necessarily imply that a very low inequality (low redundancy, high
entropy) is "good", because high entropy is associated with slow, weak
and inefficient resource allocation processes.
There are three variants of the Theil index. When applied to
income distributions, the first Theil index (Theil-L) relates to how
income is distributed to income earners, whereas the second Theil index
(Theil-T) relates to how income-earners are distributed to incomes.
A third "symmetrized" Theil index (Theil-S) is the arithmetic
average of the two previous indices. The formula of the third Theil
index has some similarity with the Hoover index (as explained in the
related articles). As in case of the Hoover index, the symmetrized Theil
index does not change when swapping the incomes with the income
earners. How to generate that third Theil index by means of a
spreadsheet computation directly from distribution data is shown below.
An important property of the Theil index which makes its
application popular is its decomposability into the between-group and
within-group component. For example, the Theil index of overall income
inequality can be decomposed in the between-region and within region
components of inequality, while the relative share attributable to the
between-region component suggests the relative importance of spatial
dimension of income inequality.
Comparison of the Theil index and the Hoover index
Illustration of the relation between Theil index and the Hoover index
for societies divides into two quantiles ("a-fractiles"). Here the
Hoover index and the Theil are equal at a value of around 0.46. The red
curve shows the difference between the Theil index and the Hoover index
as a function of the Hoover index. The green curve shows the Theil index
divided by the Hoover index as a function of the Hoover index.
The Theil index indicates the distributional redundancy of a system,
within which incomes are assigned to income earners in a stochastic
process. In comparison, the Hoover index indicates the minimum size of
the income share of a society, which would have to be redistributed in
order to reach maximum entropy. Not to exceed that minimum size would
require a perfectly planned redistribution. Therefore, the Hoover index
is the "non-stochastic" counterpart to the "stochastic" Theil index.
Applying the Theil index to allocation processes in the real world does not imply that these processes are stochastic: the Theil yields the distance
between an ordered resource distribution in an observed system to the
final stage of stochastic resource distribution in a closed system.
Similarly, applying the Hoover index does not imply that allocation processes occur in a perfectly planned economy: the Hoover index yields the distance
between the resource distribution in an observed system to the final
stage of a planned "equalization" of resource distribution. For both
indices, such an equalization only serves as a reference, not as a goal.
For a given distribution the Theil index can be larger than the Hoover index or smaller than the Hoover index:
For high inequalities the Theil index is larger than the
Hoover index. This means for achieving equilibrium (maximum entropy) in a
closed system, more resources would have to be reallocated than in case
of a planned and optimized reallocation process, where only the
necessary minimum share of resources would have to be reallocated. For
an open system the export of entropy (import of redundancy) would allow
to maintain the distribution dynamics driven by high inequality.
For low inequalities the Theil index is smaller than the
Hoover index. Here, on the path to reaching equilibrium, a planned and
optimized reallocation of resources would contribute more to the
dynamics of redistribution than stochastic redistribution. This also is
intuitively understandable, as low inequalities also weaken the urge to
redistribute resources. People in such a system may tolerate or even
foster an increase the inequality. As this is would be an increase of
redundancy (an decrease of entropy), redundancy would have to be
imported into (entropy would have to be exported from) the society. In
that case the society needs to be an open system. In order to increase
the redundancy in the distribution category of a society as a closed
system, entropy needs to be exported from the subsystem operating in
that economic category to other subsystems with other entropy categories
in the society. For example, social entropy may increase. However, in
the real world, societies are open systems, but the openness is
restricted by the entropy exchange capabilities of the interfaces
between the society and the environment of that society. For societies
with a resource distribution which entropywise is similar to the
resource distribution of a reference society with a 73:27 split (73% of
the resources belong to 27% of the population and vice versa),
the point where the Hoover index and the Theil index are equal, is at a
value of around 46% (0.46) for the Hoover index and the Theil index.
Atkinson index
Atkinson
index (also known as the Atkinson measure or Atkinson inequality
measure) is a measure useful in determining which end of the
distribution contributed most to the observed inequality.
The Atkinson ε parameter
is often called the "inequality aversion parameter", since it regulates
the sensitivity of the implied social welfare losses from inequality to
income inequality as measured by some corresponding generalised entropy
index. The Atkinson index is defined in reference to a corresponding
social welfare function, where mean income multiplied by one minus the
Atkinson index gives the welfare equivalent equally distributed income.
The index can be turned into a normative measure by imposing a coefficient
ε to weight incomes. Greater weight can be placed on changes in a given
portion of the income distribution by choosing ε, the level of
"inequality aversion", appropriately. The Atkinson index becomes more
sensitive to changes at the lower end of the income distribution as ε
increases. Conversely, as the level of inequality aversion falls (that
is, as ε approaches 0) the Atkinson becomes less sensitive to changes in
the lower end of the distribution. The Atkinson index is for no value
of ε highly sensitive to top incomes because of the common restriction
that ε is nonnegative.
Ratios
Another
common class of metrics is to take the ratio of the income of two
different groups, generally "higher over lower". This compares two parts
of the income distribution, rather than the distribution as a whole;
equality between these parts corresponds to 1:1, while the more unequal
the parts, the greater the ratio. These statistics are easy to interpret
and communicate, because they are relative (this population earns twice
as much as this population), but, since they do not fall on an absolute
scale, do not provide an absolute measure of inequality.
Ratio of percentiles
Income
of a given percentage as a ratio to median, for 10th (red), 20th, 50th,
80th, 90th, and 95th (grey) percentile, for 1967–2003 in the United
States (50th percentile is 1:1 by definition)
Particularly common to compare a given percentile to the median, as in the first chart here; compare seven-number summary, which summarizes a distribution by certain percentiles. While such ratios do not represent the overall level of inequality in the population as a whole, they provide measures of the shape
of income distribution. For example, the attached graph shows that in
the period 1967–2003, US income ratio between median and 10th and 20th
percentile did not change significantly, while the ratio between the
median and 80th, 90th, and 95th percentile increased. This reflects that
the increase in the Gini coefficient of the US in this time period is
due to gains by upper income earners (relative to the median), rather
than by losses by lower income earners (relative to the median).
Income
of the given percentiles from 1947 to 2010 in 2010 dollars. The two
columns of numbers in the right margin are the cumulative growth
1970–2010 and the annual growth rate over that period. The vertical
scale is logarithmic, which makes constant percentage growth appear as a
straight line. From 1947 to 1970, all percentiles grew at essentially
the same rate; the light, straight lines for the different percentiles
for those years all have the same slope. Since then, there has been
substantial divergence, with different percentiles of the income
distribution growing at different rates. For the median American family,
this gap is $39,000 per year (just over $100 per day): if the economic
growth during this period had been broadly shared as it was from 1947 to
1970, the median household income would have been $39,000 per year
higher than it was in 2010. This plot was created by combining data from
the US Census Bureau and the US Internal Revenue Service. There are systematic differences between these two sources, but the differences are small relative to the scale of this plot.
Share of income
Share of pre-tax household income received by the top 1%, top 0.1% and top 0.01% in the US, between 1917 and 2005
A related class of ratios is "income share", the percentage of the
national income received by some specified richest or poorest
percentage-segment of the population.
It's often reported
for the highest-income 1%, etc., of the population, but the Lorenz
Curve (discussed in the Gini section) graphs the cumulative income of
the bottom-income X% of the population, for all X from 0 to 100.
An important and obvious property of the Lorenz curve is that, if
country A has a higher cumulative national-income share for (say) the
poorest 1%, in comparison to country B, then:
If the two countries' Lorenz curves don't cross, then country
A's Lorenz curve is everywhere higher than that of country B, meaning
that, for any X from 0 to 100, the poorest X% of the population have a
greater national-income-share in country A than in country B.
If the two countries' Lorenz curves cross, then it's a
near-certainty that, on the poor side of the crossing-point, country A
has a higher Lorenz curve, meaning that, for any poorest X% of the
population up to the crossing-point, country A's population has a
greater national-income-share.
When inequality does harm, it does most of it at the poorer end of
the population. At an any given income-ordered percentile, a greater
national-income-share means a greater received percentage of the
equal-share (the national mean income).
So the country with a greater cumulative national-income-share
for the poorest 1% will have a greater cumulative received percentage of
equal-share for the poorest X%, either for every percentile X, or (if
the Lorenz curves cross), for the percentiles on the poorer side of the
crossing-point.
Some sources report several Lorenz-curve data-points (cumulative national income-shares up to several percentiles).
As mentioned above, taking the ratio of income share to subpopulation size corresponds to a ratio of mean subpopulation income relative to mean income.
Because income distribution is generally positively skewed,
mean is higher than median, so ratios to mean are lower than ratios to
median. This is particularly used to measure that fraction of income
accruing to top earners – top 10%, 1%, 0.1%, 0.01%, and also "top 100"
earners or the like; in the US top 400 earners is 0.0002% of earners (2
in 1,000,000) – to study concentration of income – wealth condensation,
or rather income condensation. For example, in the chart at right, US
income share of top earners was approximately constant from the
mid-1950s to the mid-1980s, then increased from the mid-1980s through
2000s; this increased inequality was reflected in the Gini coefficient.
For example, in 2007 the top decile (10%) of US earners accounted for 49.7% of total wages (
times fraction under equality), and the top 0.01% of US earners
accounted for 6% of total wages (600 times fraction under equality).
Approaches
Normative approach
Normative interpretation of inequality through inequality indexes means that there is a relationship between an inequality index
and a social-evaluation ordering defined on the incomes — incomes
(nominal or real) of the members of society. Incomes are typically
assigned to individuals rather than households by using an adult
equivalence scale.
Normative approach has been discussed by Charles Blackorby,
Walter Bossert and David Donaldson in the work "Income Inequality
Measurement: The Normative Approach".
Statistical approach
Statistical interpretation of inequality is based on fiscal data and the parameter α from the model of Pareto (1895) as an incomeinequality measure. This approach is further discussed in work "Income Inequality Measurement: The Statistical Approach" by Giovanni M. Giorgi.
Spreadsheet computations
The Gini coefficient, the Hoover index and the Theil index as well as the related welfare functions can be computed together in a spreadsheet. The welfare functions serve as alternatives to the median income.
Group
Members per group
Income per group
Income per individual
Relative deviation
Accumulated income
Gini
Hoover
Theil
1
A1
E1
Ē1 = E1/A1
D1 = E1/ΣE - A1/ΣA
K1 = E1
G1 = (2 * K1 - E1) * A1
H1 = abs(D1)
T1 = ln(Ē1) * D1
2
A2
E2
Ē2 = E2/A2
D2 = E2/ΣE - A2/ΣA
K2 = E2 + K1
G2 = (2 * K2 - E2) * A2
H2 = abs(D2)
T2 = ln(Ē2) * D2
3
A3
E3
Ē3 = E3/A3
D3 = E3/ΣE - A3/ΣA
K3 = E3 + K2
G3 = (2 * K3 - E3) * A3
H3 = abs(D3)
T3 = ln(Ē3) * D3
4
A4
E4
Ē4 = E4/A4
D4 = E4/ΣE - A4/ΣA
K4 = E4 + K3
G4 = (2 * K4 - E4) * A4
H4 = abs(D4)
T4 = ln(Ē4) * D4
Totals
ΣA
ΣE
Ē = ΣE/ΣA
ΣG
ΣH
ΣT
Inequality measures
Gini = 1 - ΣG/ΣA/ΣE
Hoover = ΣH / 2
Theil = ΣT / 2
Welfare function
WG = Ē * (1 - Gini)
WH = Ē * (1 - Hoover)
WT = Ē * (1 - Theil)
In the table, fields with a yellow background are used for data
input. From these data inequality measures as well as the related
welfare functions are computed and displayed in fields with green
background.
In the example given here, "Theil index" stands for the
arithmetic mean of a Theil index computed for the distribution of income
within a society to the individuals (or households) in that society and
a Theil index computed for the distribution of the individuals (or
households) in the society to the income of that society. The difference
between the Theil index and the Hoover index is the weighting of the
relative deviation D. For the Hoover index the relative deviation D per
group is weighted with its own sign. For the Theil index the relative
deviation D per group is weighted with the information size provided by
the income per individual in that group.
For the computation the society usually is divided into income
groups. Often there are four or five groups consisting of a similar
number of individuals in each group. In other cases the groups are
created based on income ranges which leads to having different numbers
of individuals in the different groups. The table above shows a
computation of inequality indices for four groups. For each group the
number of individuals (or households) per group A and the total income
in that group E is specified.
The parameter pairs A and E need to be sorted for the computation
of the Gini coefficient. (For the Theil index and the Hoover index no
sorting is required.) A and E have to be sorted so that the values in
the column "Income per individual" are lined up in ascending order.
Proper use
When using income metrics, it has to be made clear how income should be defined. Should it include capital gains, imputed house rents from home ownership, and gifts? If these income sources or alleged income sources (in the case of "imputed rent")
are ignored (as they often are), how might this bias the analysis? How
should non-paid work (such as parental childcare or doing one's own
cooking instead of hiring a chef for every meal) be handled? Wealth or
consumption may be more appropriate measures in some situations. Broader
quality of life metrics might be useful.
The comparison of inequality measures requires that the segmentation
of compared groups (societies etc.) into quintiles should be similar.
Distinguish properly, whether the basic unit of measurement is
households or individuals. The Gini value for households is always lower
than for individuals because of income pooling and intra-family
transfers. And households have a varying number of members. The metrics
will be influenced either upward or downward depending on which unit of
measurement is used.
Consider life cycle effects. In most Western societies, an
individual tends to start life with little or no income, gradually
increase income till about age 50, after which incomes will decline,
eventually becoming negative. This affects the conclusions which can be
drawn from a measured inequality. It has been estimated (by A.S. Blinder
in The Decomposition of Inequality, MIT press) that 30% of
measured income inequality is due to the inequality an individual
experiences as they go through the various stages of life.
Clarify whether real or nominal income distributions should be used.
What effect will inflation have on absolute measures? Do some groups
(e.g., pensioners) feel the effect of inflation more than others?
When drawing conclusion from inequality measurements, consider how
we should allocate the benefits of government spending? How does the
existence of a social security safety net influence the definition of
absolute measures of poverty? Do government programs support some income
groups more than others?
Inequality metrics measure inequality. They do not measure possible
causes of income inequality. Some alleged causes include: life cycle
effects (age), inherited characteristics (IQ, talent), willingness to
take chances (risk aversion), the leisure/industriousness choice,
inherited wealth, economic circumstances, education and training,
discrimination, and market imperfections.
Inequality metrics are anonymous. They ignore certain effects of income mobility,
in which the identity of "who is rich" and "who is poor" is considered.
For example, at a particular time, Alice may have $10 and Bob may have
$2. At some time later, Bob may have $10 and Alice may have $2. The
inequality index will be the same in both cases and rather high.
However, the inequality of the average will be zero, since Alice's and
Bob's average holdings are equal ($6). The $8 which has changed hands is
a measure of wealth mobility and the average inequality is generally
higher than the inequality of the average.
Keeping these points in mind helps to understand the problems caused
by the improper use of inequality measures. However, they do not render
inequality coefficients invalid. If inequality measures are computed in a
well explained and consistent way, they can provide a good tool for quantitative comparisons of inequalities.
Inequality, growth, and progress
Evidence
from a broad panel of recent academic studies shows that there is a
nonlinear relation between income inequality and the rate of growth and
investment. Very high inequality slows growth; moderate inequality
encourages growth. Studies differ on the effect of very low inequality.
Robert J. Barro, Harvard University
found in his study "Inequality and Growth in a Panel of Countries" that
higher inequality tends to retard growth in poor countries and
encourage growth in well-developed regions. Underscoring the need for initiatives like the United Nations Sustainable Development Goal 10, aim at reducing inequality. According to Pak Hung Mo, income inequality has significant negative effect on the rate of GDP
growth. In their work "Income Inequality and Economic Growth", they
found out that the most important is the transfer channel while the
least important is the human capital channel. However, the direct impact
of income inequality on the rate of productivity growth
accounts for more than 55 percent of its overall total effect. This
indicates that the effects of income inequality on economic growth are
much more complicated than what we have perceived or modeled.
In their study for the World Institute for Development Economics Research, Giovanni Andrea Cornia and Julius Court (2001) reach slightly different conclusions.
The authors therefore recommend to pursue moderation also as to the
distribution of wealth and particularly to avoid the extremes. Both very
high egalitarianism and very high inequality cause slow growth.
Considering the inequalities in economically well developed countries,
public policy should target an 'efficient inequality range'. The authors
claim that such efficiency range roughly lies between the values of the
Gini coefficients of 0.25 (inequality close to the least unequal European counties) and 0.40 (close to the level of inequality of US.
According to the work "Inequality of Opportunity, Income
Inequality, and Economic Growth," the relationship between income
inequality and economic growth is mediated by the level of equality of
opportunity, as identified by intergenerational mobility. After a number
of recently developed internationally comparable measures of
intergenerational mobility they confirmed that the negative impact of
income inequality on growth increases as intergenerational mobility
decreases. The findings imply that excluding intergenerational mobility
leads to misspecification, which explains why the empirical literature
on income inequality and growth has been so inconclusive.
Another researcher has shown that in perfect markets inequality does not influence growth.
The precise shape of the inequality-growth curve obviously varies
across countries depending upon their resource endowment, history,
remaining levels of absolute poverty and available stock of social
programs, as well as on the distribution of physical and human capital.
A maximum wage, also often called a wage ceiling, is a legal limit on how much income an individual can earn. It is a prescribed limitation which can be used to effect change in an economic structure, but its effects are unrelated to those of minimum wage laws used currently by some states to enforce minimum earnings.
Implementation
No
major economy has a direct earnings limit, though some economies do
incorporate the policy of highly progressive tax structures in the form
of scaled taxation.
A vote to implement a maximum wage law in Switzerland failed with only a 34.7% vote for approval.
Maximum liquid wealth
A maximum liquid wealth policy restricts the amount of liquid wealth an individual is permitted to maintain, while giving them unrestricted access to non-liquid assets. That is to say, an individual may earn as much as they like during a given time
period, but all earnings must be re-invested (spent) within an
equivalent time period; all earnings not re-invested within this time
period would be seized.
This policy is only arguably a valid maximum wage implementation, as it does not actually restrict the wages a person is allowed to maintain, but only restricts the amount of actual currency
they are allowed to hold at any given time. Proponents of the policy
argue that it enforces the ideals of a maximum wage without restricting
actual capital growth or economic incentive.
Proponents believe wealth that is not re-invested in the economy is harmful to economic growth; that actual liquid currency not re-invested timely is indicative of an unfair trade, in which an individual has paid more for a good/service than the good/service was worth. This stems from the belief that currency should represent the actual value of a good or service.
When this policy is imposed, individual savings can only be held as solid assets like stocks, bonds, business, and property.
Opponents argue that since a maximum liquid wealth policy makes no
allowance for individual savings, it therefore assumes the
non-importance of a bank and the loans that banks provide. Loans being essential to the economy, opponents argue, banks are an essential economic institution. Proponents of the maximum liquid wealth policy respond that government
could be directly responsible for supplying loans to individuals; they
also add that such an arrangement could result in vastly lower interest rates.
Relative earnings limit
A relative earnings limit is a limit imposed upon a business, to the amount of compensation
an individual is allowed, as a specific multiple of a company's lowest
earner; or directly relative to the number of individuals a company
employs and the average compensation provided to each individual
employee, not including a certain percentage of the company's top
earners. The former implementation has the advantage of limiting wage gaps. The latter implementation has the advantage of encouraging employment opportunities, as increasing employment
would be a way for employers to boost their maximum earnings. A
compromise would be to base the limit upon the number of employees had
by a specific company and the compensation of that company's lowest
earner.
A weakness in this method is that a company can simply hire
outside firms to keep low wage employees off their payroll, while only
having the top earning employees on the company's payroll, effectively
bypassing the limits. However, the hiring of external employees will
come at a higher total cost and will reduce company profits, something
against which executives are often measured and compensated.
To moderate self-employed individuals, the maximum could be based on the average compensation of the nation's employed (GDP
per capita) and a specific multiplier. As the number of self-employed
individuals with no employees and who earn an excessive amount of money
would be extremely limited, such a measure is unlikely to be
implemented.
Direct earnings limit
A direct earnings limit is a limit placed directly, usually as a number in terms of currency, upon the amount of compensation any individual is allowed to earn in a given time period.
Scaled taxation is a method of progressive taxation that raises the rate at which the principal sum is taxed, directly relative to the amount of the principal. This type of taxation is normally applied to income taxes, although other types of taxation can be scaled.
In the case of a maximum wage, a scaled tax would be applied so that the top earners in a society would be taxed extremely large percentages of their income. Modern income tax systems, allowing salary raises to be reflected by a raise in after tax income, tax each individual note of currency in each particular bracket at the same rate. An example follows.
Example Calculated amounts shown for top of bracket. Any currency may be substituted for dollars.
Tax bracket
Width of bracket
Marginal tax rate
Tax paid on bracket
Accumulated after tax income
Effective tax rate (rounded):
Nil – $40,000
$40,000
15.00%
$6,000
$34,000
15.00%
$40,000 – $100,000
$60,000
35.00%
$21,000
$73,000
27.00%
$100,000 – $175,000
$75,000
50.00%
$37,500
$110,500
36.86%
$175,000 – $250,000
$75,000
60.00%
$45,000
$140,500
43.80%
$250,000 – $500,000
$250,000
75.00%
$187,500
$203,000
59.40%
Above $500,000
—
90.00%
—
—
Over 59.40%
History
In 1350
positions in the church were in high demand, due to deaths from the
Black Plague eradicating the clergy. Archbishop of Canterbury, Simon Islip issued a letter condemning "priests [that] care more for money than for the safety of their soul",
stating that priests were forgoing their duty to the poor in order to
serve the rich in private chapels. Islip instituted a maximum annual
wage for priests, as well as a fine for the 'giver' and 'receiver' of
those caught offer private tithes above the maximum.
In England, the Statute of Artificers 1563 implemented statutes of compulsory labor and fixed maximum wage scales; Justices of the Peace could fix wages according "to the plenty or scarcity of the time".
To counteract the increase in prevailing wages due to scarcity of
labor, American colonies in the 17th century created a ceiling wage and
minimum hours of employment.
In the early Soviet Union, in the period 1920–1932, communist party members were subject to a maximum wage, the partmaximum. Its demise is seen as the onset of the rise of the nomenklatura class of Soviet apparatchiks.
The idea that any individual could earn money by their labor, instead
of earning for the community, undermined the initial principles of
communism.
In 1933, Washington State Representative Wesley Lloyd proposed an amendment to the U.S. Constitution that would have limited annual incomes to $1 million. His contemporary colleague John Snyder
introduced a companion amendment that would have limited personal
wealth to $1 million. Neither proposed amendment, however, received
enough votes to begin the ratification process.
In 1942, during World War II, US President Franklin D. Roosevelt proposed a maximum income of $25,000 ($447,760 in 2022 dollars) during the war:
At the same time, while the number
of individual Americans affected is small, discrepancies between low
personal incomes and very high personal incomes should be lessened; and I
therefore believe that in time of this grave national danger, when all
excess income should go to win the war, no American citizen ought to
have a net income, after he has paid his taxes, of more than $25,000 a
year. It is indefensible that those who enjoy large incomes from State
and local securities should be immune from taxation while we are at war.
Interest on such securities should be subject at least to surtaxes.
This was proposed to be implemented by a 100% marginal tax on all
income over $40,000 (after-tax income of $25,000). While this was not
implemented, the Revenue Act of 1942
implemented an 88% marginal tax rate on income over $200,000, together
with a 5% "Victory Tax" with post-war credits, hence temporarily
yielding a 93% top tax rate (though 5% was subsequently returned in
credits).
After decades of social democratic governments, the Swedish children's author Astrid Lindgren faced an infamous marginal tax rate
of 102% in 1976, in effect creating a wage ceiling. Though the example
was partly due to inverted loop holes in the tax code, the figure was
seen as an important catalyst for the results in the election that year,
in which the Social Democratic Party lost power after 40 consecutive
years in power. After a "tax rebellion" and demanded the top marginal
tax rates were reduced to 50% in the late 1980s.
Since the 1990s, the chief proponent of a maximum wage in the United States has been Sam Pizzigati; see References, particularly (Pizzigati 2004).
In his 2000 run for the Green Party presidential nomination, Jello Biafra called for a maximum wage of $100,000 in the United States,
and the reduction of the income tax to zero for all income below that
level. Biafra claimed he would increase taxes for the wealthy and reduce
taxes for those in the lower and middle classes.
Many Green parties have a maximum wage in their manifesto, which they
argue would prevent conspicuous consumption and the subsequent
environmental damage that they believe ensues, while allowing the
financing of jobs and a guaranteed minimum income for the poorest workers.
In the United Kingdom until 1901, individual clubs had set their own wage policies. That year, the Football League ratified a maximum weekly wage for footballers of £4 (2012: £368).
This severely limited the ability of the best players in the country to
forgo the need to take paid employment outside of football and, this in
turn, led to the formation of The Players' Union in 1907.
By the summer of 1928 players could earn a weekly maximum of £8 (2012: £408), although clubs routinely found ways to increase this. Arsenal player Eddie Hapgood supplemented his income by fashion modelling and advertising chocolate.
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