Transcript Income Inequality Dynamics: Evidence from a Pool of Major

Income Inequality Dynamics: Evidence
from a Pool of Major Industrialized
Countries
F. Clementi1,3 and M. Gallegati2,3
1Department
2Department
3S.I.E.C.,
of Public Economics, University of Rome “La Sapienza”, Via del
Castro Laurenziano 9, I–00161 Rome, Italy
[email protected]
of Economics, Università Politecnica delle Marche, Piazzale Martelli 8, I–60121
Ancona, Italy
[email protected]
Università Politecnica delle Marche, Piazzale Martelli 8, I–60121 Ancona, Italy
http://www.dea.unian.it/wehia/
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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1. Outline
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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
THE DATA:
THE CROSS-NATIONAL EQUIVALENT FILE (1980-2002)
THE SURVEY ON HOUSEHOLD INCOME AND WEALTH (1987-2002)

EMPIRICAL FINDINGS:
THE SHAPE OF THE DISTRIBUTIONS
TEMPORAL CHANGE OF THE DISTRIBUTIONS
THE SHIFT OF THE DISTRIBUTIONS
FLUCTUATIONS OF THE INDEXES SPECIFYING THE DISTRIBUTIONS
TOTAL INCOME COMPOSITION PATTERN

INEQUALITY DECOMPOSITION BY INCOME SOURCE
GENERAL FRAMEWORK
STATIC DECOMPOSITION BY INCOME SOURCE
DYNAMIC DECOMPOSITION BY INCOME SOURCE
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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2. The Data
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2.1 The Cross-National Equivalent File (19802002)

CROSS-NATIONAL EQUIVALENT FILE DATA SOURCES. We use income data from the US Panel
Study of Income Dynamics (PSID), the British Household Panel Survey (BHPS), and the German
Socio-Economic Panel (GSOEP) as released in a cross-nationally comparable format in the
Cross-National Equivalent File (CNEF). Our data refer to the period 1980-2001 for the US, and to
the period 1991-2001 for the UK; in order to perform analyses that represent the population of
reunited Germany, we refer to the subperiod 1990-2002 for the GSOEP.

DEFINITION OF INCOME. In this paper, the measure of income for each individual is based on the
pre-government annual income of the household to which they belong, adjusted for differences
in household size using the so-called OECD-scale of equivalence, which deflates household
income by the square root of household size. The household pre-government income is equal to
the sum of household labour income, household asset income, household private transfers, and
household private retirement income.

SAMPLE SIZE. In the most recent release, the average sample size varies from about 7,300
households containing approximately 20,200 respondent individuals for the PSID-CNEF to 6,500
household with approximately 16,000 respondent individuals for the BHPS-CNEF; for the
GSOEP-CNEF data from 1990 to 2002, we have about 7,800 households containing approximately
20,400 respondent individuals.

CURRENCY UNIT. All the variables are in current year currency; therefore, we use the Consumer
Price Index (CPI) to convert into constant figures for all the CNEF countries. The base year is
1995. For longitudinal consistency, all German CNEF income variables are expressed in euros
(1€=1,95583DM).
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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2.2 The Survey on Household Income and
Wealth (1987-2002)

THE DATA SOURCE. The Historical Archive (HA) of the Survey on Household Income and Wealth
(SHIW), made publicly available by the Bank of Italy for the period 1977-2002, was carried out
yearly until 1987 (except for 1985) and every two years thereafter (the survey for 1997 was shifted
to 1998). In 1989 a panel section consisting of units already interviewed in the previous waves
was introduced in order to allow for better comparison over time. As the incomes from financial
assets started to be recorded only in 1987, our data refer to the subperiod 1987-2002.

DEFINITION OF INCOME. The basic definition of income provided by the SHIW-HA is net of
taxation and social security contributions. It is the sum of four main components: compensation
of employees (including net wages and salaries and fringe benefits); net income from selfemployment (including income from self-employment, depreciation, and entrepreneurial income);
pensions and net transfers (including pensions and arrears and other transfers); property
income (including income from buildings and income from financial assets). The following
components of net disposable income are used in this study: labour income (equal to the sum of
compensation of employees and net income from self-employment), pensions and net transfers,
and property income.

SAMPLE SIZE. The average number of income-earners surveyed from the SHIW-HA is about
10,300.

CURRENCY UNIT. All the amounts are expressed in lire, except for 2002, where the income
variables are reported in euros. For longitudinal consistency, we report all the data in 1995 prices
using the CPI, and convert them in euros (1€=1936,27LIT).
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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3. Empirical Findings
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3.1 The Shape of the Distributions

THE BODY OF THE DISTRIBUTIONS. We observe that the lognormal complementary cumulative
distribution function:
 log  y    
1  F  y   P Y  y   1   




with 0≤y<∞, -∞<μ<∞, and σ>0, gives a very accurate fit until the 98th-99th percentile of the
distribution for all the countries.

THE UPPER INCOME TAIL. The upper income tail of the income distributions is rather well fitted
by a Pareto or power-law complementary cumulative distribution function:

k
1  F  y   P Y  y    
 y
where k,α>0, and k≤y<∞.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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The binned cumulative probability distribution of the equivalent and personal income along with the lognormal and Pareto fits for
some randomly selected years: (a) United States (1996); (b) United Kingdom (1998); (c) Germany (2002); (d) Italy (2000)
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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3.1 The Shape of the Distributions

THE BODY OF THE DISTRIBUTIONS. We find that the lognormal complementary cumulative
distribution function:
 log  y    
1  F  y   P Y  y   1   




with 0≤y<∞, -∞<μ<∞, and σ>0, gives a very accurate fit until the 98th-99th percentile of the
distribution for all the countries.

THE UPPER INCOME TAIL. The upper income tail of the income distributions is rather well fitted
by a Pareto or power-law complementary cumulative distribution function:

k
1  F  y   P Y  y    
 y
where k,α>0, and k≤y<∞.

UNIVERSAL STRUCTURE. The distribution pattern of the personal income expressed as the
lognormal with power law tail seems to hold all over the years covered by our data sets.
However, we observe a shift of the distributions and a change of the indexes specifying them
over time.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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Time development of the income distribution for all the countries and years: (a) United States (1980-2001); (b) United Kingdom (19912001); (c) Germany (1990-2002); (d) Italy (1987-2002)
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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3.2 Temporal Change of the Distributions:
The Shift of the Distributions

GDP AND INDIVIDUAL INCOME GROWTH RATE DISTRIBUTION. We assume that the origin of the
observed shift of the income distributions over the years covered by our data sets consists in the
growth of the countries. To confirm this assumption, we study the fluctuations in the
(logarithmic) growth rate of GDP and individual income. We find that the distributions of both
GDP and personal income growth rate display a “tent-shaped” form; hence, they are remarkably
well approximated by a Laplace or double exponential distribution:
f  y  ,    P Y  y  
 y 
exp  


 2


1
where -∞<y<∞, -∞<μ<∞, and σ>0. Moreover, after normalization all the points representing both
GDP and personal income growth rates collapse relatively well close to the peak upon the solid
lines representing the Laplace probability density function.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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The probability distribution of GDP and PI growth rates for all the countries and years: (a) United States (1980-2001); (b) United
Kingdom (1991-2001); (c) Germany (1990-2002); (d) Italy (1987-2002)
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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3.2 Temporal Change of the Distributions:
The Shift of the Distributions

GDP AND INDIVIDUAL INCOME GROWTH RATE DISTRIBUTION. We assume that the origin of the
observed shift of the income distributions over the years covered by our data sets consists in the
growth of the countries. To confirm this assumption, we study the fluctuations in the
(logarithmic) growth rate of GDP and individual income. We find that the distributions of both
GDP and personal income growth rate display a “tent-shaped” form; hence, they are remarkably
well approximated by a Laplace or double exponential distribution:
f  y  ,    P Y  y  
 y 
exp  


 2


1
where -∞<y<∞, -∞<μ<∞, and σ>0. Moreover, after normalization all the points representing both
GDP and personal income growth rates collapse relatively well close to the peak upon the solid
lines representing the Laplace probability density function.

UNIVERSAL FEATURES IN THE GROWTH DYNAMICS OF BOTH GDP AND INDIVIDUAL INCOME.
These findings (reminiscent of the concept of universality found in statistical physics, where
different systems can be characterized by the same fundamental laws, independent of
“microscopic” details) lead us to the conclusion that the temporal evolution of both GDP and
personal income is governed by similar mechanisms, pointing in this way to the existence of
correlation between them as one would expect.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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3.3 Temporal Change of the Distributions:
Fluctuations of the Indexes Specifying the
Distributions

TEMPORAL EVOLUTION OF GIBRAT AND PARETO INDEXES. We observe that the power-law
slope and the curvature of the lognormal fit are different both in different countries, as well as in
different periods for the same country. This fact means that Gibrat index and Pareto exponent
change in time.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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The time series of Gibrat and Pareto indexes over the years covered by our data sets: (a) United States (1980-2001); (b) United
Kingdom (1991-2001); (c) Germany (1990-2002) ; (d) Italy (1987-2002)
(a)
(b)
2.2
0.5
Year
(d)
Year
(c)
1.4
1.4
3.5
Year
1
Gibrat index (β)
0.9
Pareto index (α)
2.7
2.5
2.3
20
02
20
00
2.1
19
98
0.8
19
95
20
02
20
01
20
00
19
99
19
98
19
97
19
96
19
95
19
94
19
93
19
92
1.5
19
91
19
90
0.8
2.9
19
93
2
0.9
1.1
19
91
1
3.1
19
89
2.5
3.3
1.2
19
87
1.1
3.5
Pareto index (α)
3
Pareto index (α)
Pareto index (α)
1.2
3.7
1.3
Gibrat index (β)
Gibrat index (β)
1.3
Gibrat index (β)
20
01
1.7
20
00
19
91
0.4
19
99
19
96
19
94
19
92
19
90
19
88
19
86
19
84
19
82
19
80
0.8
0.6
19
99
Pareto index (α)
2.7
19
98
0.85
3.2
0.7
19
97
Gibrat index (β)
Pareto index (α)
19
96
0.9
Gibrat index (β)
0.8
19
95
0.95
0.9
19
94
1
3.7
19
93
1.05
1
19
92
1.1
4.2
1.1
Pareto index (α)
Gibrat index (β)
1.15
1.2
Pareto index (α)
3.4
3.2
3
2.8
2.6
2.4
2.2
2
1.8
1.6
1.4
Gibrat index (β)
1.2
Year
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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3.3 Temporal Change of the Distributions:
Fluctuations of the Indexes Specifying the
Distributions

TEMPORAL EVOLUTION OF GIBRAT AND PARETO INDEXES. We observe that the power-law
slope and the curvature of the lognormal fit are different both in different countries, as well as in
different periods for the same country. This fact means that Gibrat index and Pareto exponent
change in time.

CORRELATION BETWEEN PARETO INDEX AND ASSET PRICE. From these behaviours we
consider that there are some factors causing no correlation between the Gibrat and Pareto
indexes, mainly affecting the latter. Therefore, we study the origin of the temporal change of
Pareto index in more detail. To this end, we consider its correlation with the asset prices, such as
the stock prices and the housing prices. The stock market dynamics is characterized by a slight
downward trend during the early 1990s, followed by a rise in the mid-1990s which dropped at the
end of the decade after the bursting of the speculative bubble. A similar behaviour is found in the
temporal path of real housing prices. By comparison with the temporal change of the power-law
exponent, we conclude that both stock market and housing market dynamics have a
considerable effect on the upper income tail.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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Fluctuations of the stock market indexes and the housing prices for the countries and years of our concern: (a) New York Stock
Exchange (NYSE) index and CPI Housing (1980-2001); (b) London Stock Exchange FTSE (Financial Times Stock Exchange) index and
CPI Housing (1991-2001); German Stock Exchange Composite DAX (CDAX) index and CPI Housing (1990-2002); Milano Borsa Italia
(MIB) index and CPI Housing (1987-2002)
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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3.4 Temporal Change of the Distributions:
The Composition of Total Income in the Two
Sections of the Distributions

THE COMPOSITION OF TOTAL INCOME... These results lead us to check the possibility that nonlabour income sources are responsible for the Pareto functional form of the observed empirical
income distributions at the high-income range. To this end, we look at the composition of total
income within the two regimes of the income distributions by calculating the share of each
income component in the lognormal and power-law sections of the distributions for all the
countries and years:
k 

k

where μk is the mean of the kth source of income and μ is the average income of the whole
population in the lognormal and Pareto regimes.
...IN THE LOGNORMAL... As expected, individuals in the low-middle income ranges (98%-99% of
the population) rely mostly on labour income.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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The composition of total income in the low-middle income ranges characterized by the lognormal distribution: (a) United States (19802001); (b) United Kingdom (1991-2001); (c) Germany (1990-2002); (d) Italy (1987-2002)
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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3.4 Temporal Change of the Distributions:
The Composition of Total Income in the Two
Sections of the Distributions

THE COMPOSITION OF TOTAL INCOME... These results lead us to check the possibility that nonlabour income sources are responsible for the Pareto functional form of the observed empirical
income distributions at the high-income range. To this end, we look at the composition of total
income within the two regimes of the income distributions by calculating the share of each
income component in the lognormal and power-law sections of the distributions for all the
countries and years:
k 
k

where μk is the mean of the kth source of income and μ is the average income of the whole
population in the lognormal and Pareto regimes.

...IN THE LOGNORMAL... As expected, individuals in the low-middle income ranges (98%-99% of
the population) rely mostly on labour income.

...AND POWER-LAW REGIMES OF THE INCOME DISTRIBUTIONS. Individuals in the top
percentiles (1%-2% of the population) derive a significant share of their income in the form of
capital income. This difference seems to corroborate our conjecture that returns on capital play
an important role in determining the power-law behaviour in the high-income region.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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The composition of total income in the upper tail of the income distributions: (a) United States (1980-2001); (b) United Kingdom (19912001); (c) Germany (1990-2002); (d) Italy (1987-2002)
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4. Inequality
Decomposition by
Income Source
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4.1 The Contribution of Individual Income
Sources to Total Inequality: General
Framework

METHODOLOGY. To further confirm our conjecture that the capital gains contribution to total
income may be responsible for the observed power-law behaviour in the tail of the distributions,
we perform a decomposition analysis of the level of total inequality for assessing the
contribution of a set of individual income sources. To this end, we express total inequality, I, as
the sum of the contributions of each source of income:
I   Sk
k

where Sk depends on incomes from source k, and represents its absolute contribution to total
inequality. If Sk>0, the kth source of income provides a disequalizing effect, and an equalizing
effect if Sk<0.
INEQUALITY MEASURE. The inequality measure we decompose in this way is GE(2), which is a
member of the Generalized Entropy class of inequality measures:
1
GE  2   CV 2
2
where CV is the Coefficient of Variation, having the formula:
CV 
1
2
1 1

2
y







  n i 1 i


n
where n is the number of individuals in the sample, yi is the income of individual i, and μ the
mean income.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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4.2 The Contribution of Individual Income
Sources to Total Inequality: Static
Decomposition by Income Source

METHODOLOGY. When the GE(2) inequality measure is used, the absolute contribution of each
source to total inequality can be written as:
Sk  sk GE  2   k k GE  2  GE  2 k
where sk=Sk/I is the proportional contribution of income component k to total inequality, ρk is the
correlation between source k and total income, χk=μk/μ is the share of source k in total income,
and GE(2) and GE(2)k are one-half the squared coefficient of variation of total income and
source k respectively. A large value of Sk suggests that income source k is an important source
of total inequality.

STATIC DECOMPOSITION BY INCOME SOURCE OF OVERALL INEQUALITY AT THE LOWMIDDLE... The application of this method for source decomposition of total income going to the
population belonging to the low-middle income section of the distributions points to the
contributory influence of labour earnings in explaining the level of aggregate inequality.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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Total inequality (GE(2)) and income source contribution to total inequality (Sk=skGE(2)) for the lognormal region of the income
distribution: (a) United States (1980-2001); (b) United Kingdom (1991-2001); (c) Germany (1990-2002); (d) Italy (1987-2002)
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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4.2 The Contribution of Individual Income
Sources to Total Inequality: Static
Decomposition by Income Source

METHODOLOGY. When the GE(2) inequality measure is used, the absolute contribution of each
source to total inequality can be written as:
Sk  sk GE  2   k k GE  2  GE  2 k
where sk=Sk/I is the proportional contribution of income component k to total inequality, ρk is the
correlation between source k and total income, χk=μk/μ is the share of source k in total income,
and GE(2) and GE(2)k are one-half the squared coefficient of variation of total income and
source k respectively. A large value of Sk suggests that income source k is an important source
of total inequality.

STATIC DECOMPOSITION BY INCOME SOURCE OF OVERALL INEQUALITY AT THE LOWMIDDLE... The application of this method for source decomposition of total income going to the
population belonging to the low-middle income section of the distributions points to the
contributory influence of labour earnings in explaining the level of aggregate inequality.

...AND HIGH END OF THE DISTRIBUTIONS. At the high end of the income distributions, capital
income plays a significant role in explaining the level of overall inequality.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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Total inequality (GE(2)) and income source contribution to total inequality (Sk=skGE(2)) for the power-law region of the income
distribution: (a) United States (1980-2001); (b) United Kingdom (1991-2001); (c) Germany (1990-2002); (d) Italy (1987-2002)
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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4.3 The Contribution of Individual Income
Sources to Total Inequality: Dynamic
Decomposition by Income Source

DYNAMIC DECOMPOSITION OF GE(2) AGGREGATE VALUE... We also attempt to account for the
impact of individual income sources on changes in inequality. Using GE(2) as the inequality
index, our decomposition of changes in overall inequality builds on the following formula:
GE  2   GE  2 t 1  GE  2 t 
  Sk     k  k GE  2  GE  2 k 


k
k

In this decomposition, the changing impact of a source depends on changes in the correlation
with total income, changes in the share of total income, and changes in inequality of the source;
therefore, a large value of ΔSk suggests that changes in factor k have a large influence in
changes in total inequality.
...IN THE LOGNORMAL... We observe that labour income is an important contributor to changes
in total inequality for the great majority of populations.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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One-year dynamic decomposition of GE(2) inequality measure by income source for the lognormal region of the income distribution:
(a) United States; (b) United Kingdom; (c) Germany; (d) Italy
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
30
4.3 The Contribution of Individual Income
Sources to Total Inequality: Dynamic
Decomposition by Income Source

DYNAMIC DECOMPOSITION OF GE(2) AGGREGATE VALUE... We also attempt to account for the
impact of individual income sources on changes in inequality. Using GE(2) as the inequality
index, our decomposition of changes in overall inequality builds on the following formula:
GE  2   GE  2 t 1  GE  2 t 
  Sk     k  k GE  2  GE  2 k 


k
k


In this decomposition, the changing impact of a source depends on changes in the correlation
with total income, changes in the share of total income, and changes in inequality of the source;
therefore, a large value of ΔSk suggests that changes in factor k have a large influence in
changes in total inequality.
...IN THE LOGNORMAL... We observe that labour income is an important contributor to changes
in total inequality for the great majority of the populations.
...AND POWER-LAW REGIONS OF THE DISTRIBUTIONS. On the other hand, in the high-end tail
of the distributions capital income makes by far the most significant contribution to overall
changes in inequality, especially from the mid-1990s, as a consequence of the increasing
personal ownership of equities.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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One-year dynamic decomposition of GE(2) inequality measure by income source for the power-law region of the income distribution:
(a) United States; (b) United Kingdom; (c) Germany; (d) Italy
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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5. Summary
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
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
THE SHAPE OF THE INCOME DISTRIBUTIONS. Our analysis of the data for the US, the UK,
Germany, and Italy shows that there are two regimes in the income distribution. For the lowmiddle classes up to approximately 98%-99% of the total population the incomes are well
described by a two-parameter lognormal distribution, while the incomes of the top 1%-2% is
described by a power-law (Pareto) distribution.

THE SHIFT OF THE DISTRIBUTIONS. This structure have been observed in the analysis for
different years. However, the indexes specifying the distributions change in time. Thus we
studied the temporal change of the distributions. Firstly, we analyze the GDP and individual
income growth rate distributions. We find that after normalization the resulting empirical
probability density functions appear similar for observations coming from different populations.
This effect, which is quantitatively the same for countries and individuals, raises the intriguing
possibility that a common mechanism might characterize the growth dynamics of GDP and
individual income, pointing to the existence of correlation between these quantities.

TEMPORAL EVOLUTION OF THE INDEXES SPECIFYING THE DISTRIBUTIONS. Secondly, from
the analysis of the change of Gibrat and Pareto indexes, we confirmed that these quantities
should not necessarily correlate each other. This means that different mechanisms are working
in the distribution of the low-middle income range and that of the high income range. One
possible origin of no correlation is the change of the asset price, such as the stock price and the
housing price, which mainly affects the high income distribution.

DECOMPOSITION OF OVERALL INEQUALITY BY INCOME SOURCE. By disaggregating the level
and time trend of total inequality into contributory influences from various income sources, we
find that the low-middle income section of the distributions comprises almost entirely of labour
income, while earnings from financial or other assets play an important role in the high-income
section. We conclude that this difference in the composition and inequality of the income is likely
to be responsible for the lognormal nature of the former and the power-law behaviour in the latter
region of the distributions.
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
34
5. Forthcoming
Events
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
35

COMPLEXITY, HETEROGENEITY AND INTERACTIONS IN ECONOMICS AND FINANCE
(CHIEF). Ancona, Italy, May 2-21, 2005: http://www.dea.unian.it/wehia/AnconaTI_3.htm

10th ANNUAL WORKSHOP ON ECONOMICS WITH HETEROGENEOUS AND INTERACTING
AGENTS (WEHIA 2005). Colchester, UK, June 13-15, 2005: http://www.essex.ac.uk/wehia05/

ECONOPOHYSICS COLLOQUIM. Canberra, Australia, November 14-18, 2005:
http://www.rsphysse.anu.edu.au/econophysics/index.php

WORKSHOP ON INDUSTRY AND LABOR DYNAMICS. THE AGENT-BASED COMPUTATIONAL
ECONOMICS APPROACH (WILD@ACE). Ancona, Italy, December 2-3, 2005:
http://www.dea.unian.it/wehia/
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
36
Dhannabad!
International Workshop on "Econophysics of Wealth Distributions", Saha Institute of Nuclear Physics, Kolkata, India, 15-19 March, 2005
37