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Business-Finance

Published on April 9, 2008

Author: Woodwork

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Power Law Tails in the Italian Personal Income Distribution:  Power Law Tails in the Italian Personal Income Distribution F. Clementi1,3 and M. Gallegati2,3 1Department of Public Economics, University of Rome “La Sapienza”, Via del Castro Laurenziano 9, I–00161 Rome, Italy fabio.clementi@uniroma1.it 2Department of Economics, Università Politecnica delle Marche, Piazzale Martelli 8, I–62100 Ancona, Italy gallegati@dea.unian.it 3S.I.E.C., Università Politecnica delle Marche, Piazzale Martelli 8, I–62100 Ancona, Italy http://www.dea.unian.it/wehia/ 1. Introduction:  1. Introduction Slide3:  PARETO LAW. More than a century ago the Italian economist Vilfredo Pareto stated in his Cours d'Économie Politique (1897) that a plot of the logarithm of the number of income-receiving units above a certain threshold against the logarithm of the income yields points close to a straight line. RECENT EMPIRICAL WORK. Recent empirical work seems to confirm the validity of Pareto (power) law. For example, Aoyama et al. (2000) show that the distribution of income and income tax of individuals in Japan for the year 1998 is very well fitted by a Pareto power-law type distribution, even if it gradually deviates as the income approaches lower ranges. The applicability of Pareto distribution only to high incomes is actually acknowledged; therefore, other kinds of distributions has been proposed by researchers for the low-middle income region. According to Montroll and Shlesinger (1983), US personal income data for the years 1935-36 suggest a power-law distribution for the high-income range and a lognormal distribution for the rest; a similar shape is found by Souma (2001) investigating the Japanese income and income tax data for the high-income range over the 112 years 1887-1998, and for the middle-income range over the 44 years 1955-98. Nirei and Souma (2004) confirm the power-law decay for top taxpayers in the US and Japan from 1960 to 1999, but find that the middle portion of the income distribution has rather an exponential form; the same is proposed by Drăgulescu and Yakovenko (2001) for the UK during the period 1994-99 and for the US in 1998. THE AIM OF THIS ANALYSIS. We look at the shape of the personal income distribution in Italy by using cross-sectional data samples from the population of Italian households during the years 1977-2002. We find that the personal income distribution follows the Pareto law in the high-income range, while the lognormal pattern is more appropriate in the central body of the distribution. From this analysis we get the result that the indexes specifying the distribution change in time; therefore, we try to look for some factors which might be the potential reasons for this behaviour. 2. Lognormal Pattern with Power Law Tail:  2. Lognormal Pattern with Power Law Tail 2.1 The Data Source:  2.1 The Data Source 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). DEFINITION OF INCOME. The basic definition of income provided by the SHIW is net of taxation and social security contributions. It is the sum of four main components: compensation of employees; pensions and net transfers; net income from self-employment; property income (including income from buildings and income from financial assets). Income from financial assets started to be recorded only in 1987. SAMPLE SIZE. The average number of income-earners surveyed from the SHIW-HA is about 10,000. CURRENCY UNIT. All amounts are expressed in thousands of lire, except for 2002, where incomes are reported in euros. 2.2 Empirical Findings:  2.2 Empirical Findings LOGNORMAL PATTERN... The profile of the personal income distribution for the year 1998 suggests that the central body of the distribution (almost all of it below the 99th percentile) follows a two-parameter lognormal distribution: …WITH POWER-LAW TAIL. On the contrary, the tail of the distribution (including about the top 1% of the population) follows a Pareto (power-law) distribution: 3. Time Development of the Distribution:  3. Time Development of the Distribution 3.1 Temporal Change of the Distribution:  3.1 Temporal Change of the Distribution 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 set. ESTIMATION RESULTS. The estimation results show a shift of the distribution and a change of the indexes specifying it. This fact means that the curvature of the lognormal fit and the power-law slope differ from year to year, i.e. Gibrat index (measured as β=1/(σ√2)) and Pareto index change in time. 3.2 The Shift of the Distribution: GDP and Personal Income Growth Rate Distributions:  3.2 The Shift of the Distribution: GDP and Personal Income Growth Rate Distributions ANNUAL GDP… Macroeconomics argues that the origin of the shift of the distribution consists in the growth of the Gross Domestic Product (GDP). To confirm this hypothesis we study the fluctuations in the growth rate of annual GDP: By means of a non-linear algorithm, we find that the probability density function of annual GDP growth rates is well fitted by a Laplace distribution: ...AND PERSONAL INCOME (PI) GROWTH RATE DISTRIBUTION. the same functional form seems to be valid also in the case of PI growth rates: 3.3 The Shift of the Distribution: Universal Features in the GDP and Personal Income Growth Dynamics:  3.3 The Shift of the Distribution: Universal Features in the GDP and Personal Income Growth Dynamics RESCALED GDP AND PI GROWTH RATE DISTRIBUTION. After normalization: TWO-SAMPLE KOLMOGOROV-SMIRNOV TEST. To confirm this assumption, we test the hypothesis that the GDP and PI growth rate distributions are the same by performing a two-sample Kolmogorov-Smirnov test. In all the cases we studied, the null hypothesis that the growth rates of both quantities are sample from the same distribution can not be rejected at the usual 5% marginal significance level. the resulting empirical distributions appear similar for GDP and PI growth rates. This effect raises the intriguing possibility that a common mechanism might characterize the growth dynamics of both the quantities, pointing in this way to the existence of correlation between them. 3.4 The Fluctuations of the Indexes Specifying the Income Distribution:  3.4 The Fluctuations of the Indexes Specifying the Income Distribution LINK WITH THE BUSINESS CYCLE. Although the frequency of data (initially annual and then biennial from 1987) makes it difficult to establish a link with the business cycle, it seems possible to find a (negative) relationship between the Gibrat and Pareto indexes and the fluctuations of economic activity, at least until the late 1980s. THE ITALIAN EXPERIENCE. For example, Italy experienced a period of economic growth until the late 1980s, but with alternating phases of the internal business cycle: of slowdown of production up to the 1983 stagnation; of recovery in 1984; again of slowdown in 1986. The values of Gibrat and Pareto indexes, inferred from the numerical fitting, tend to decrease in the periods of economic expansion (concentration goes up) and increase during the recessions (income is more evenly distributed). 3.5 Time Pattern of Income Inequality:  3.5 Time Pattern of Income Inequality GINI COEFFICIENT. The temporal change of Gini coefficient for the considered years shows that in Italy the level of inequality decreased significantly during the 1980s and rised in the early 1990s; it was substantially stable in the following years. In particular, a sharp rise of Gini coefficient (i.e., of inequality) is encountered in 1987 and 1993, corresponding to a sharp decline of Pareto index in the former case and of both Pareto and Gibrat indexes in the latter case. 3.6 Asset Price and Economic Performance :  3.6 Asset Price and Economic Performance SPECULATIVE BUBBLE. We consider that the decline of Pareto exponent in 1987 corresponds with the peak of the speculative bubble begun in the early 1980s, and the rebounce of the index follows its burst on October 19, when the Dow Jones index lost more than 20% of its value dragging into disaster the other world markets. This assumption seems confirmed by the movement of asset price in the Italian Stock Exchange. THE 1993 RECESSION OF ECONOMIC ACTIVITY. As regards the sharp decline of both indexes in 1993, the level and growth of personal income (especially in the middle-upper income range) were notably influenced by the bad results of the real economy in that year, which induced an increase in inequality. 3.7 Breakdown of Pareto Law:  3.7 Breakdown of Pareto Law DEVIATION FROM PARETO LAW. We show that these facts (the 1987 burst of the asset-inflation bubble begun in the early 1980s and the 1993 recession year) cause the invalidity of Pareto law for high incomes; that is: during the mentioned years the data can not be fitted by a power-law in the entire high-income range. 4. Summary:  4. Summary Slide16:  THE SHAPE OF THE INCOME DISTRIBUTION. We find that the Italian personal income microdata are consistent with a Pareto-power law behaviour in the high-income range, and with a two-parameter lognormal pattern in the low-middle income region. THE SHIFT OF THE DISTRIBUTION. The numerical fitting over the time span covered by our dataset show a shift of the distribution, which is claimed to be a consequence of the growth of the country. This assumption is confirmed by testing the hypothesis that the growth dynamics of both gross domestic product of the country and personal income of individuals is the same; the two-sample Kolmogorov-Smirnov test we perform on this subject lead us to accept the null hypothesis that the growth rates of both the quantities are samples from the same probability distribution in all the cases we studied, pointing to the existence of correlation between them. TEMPORAL EVOLUTION OF GIBRAT AND PARETO INDEXES OVER THE BUSINESS CYCLE. By calculating the yearly estimates of Pareto and Gibrat indexes, we quantify the fluctuations of the shape of the distribution over time by establishing some links with the business cycle phases which Italian economy experienced over the years of our concern. We find that there exists a negative relationship between the above-stated indexes and the fluctuations of economic activity at least until the late 1980s. BUSINESS CYCLE EPISODES AND BREAKDOWN OF PARETO LAW. In two circumstances (the 1987 burst of the speculative bubble begun in the early 1980s and the 1993 recession year) the data can not be fitted by a power law in the entire high-income range, causing breakdown of Pareto law. 4. Forthcoming Events:  4. Forthcoming Events Slide18:  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/ Thank you all!:  Thank you all!

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