Fabio Clementi

Bio: Fabio Clementi is an academic researcher from University of Macerata. The author has contributed to research in topics: Income distribution & Personal income. The author has an hindex of 19, co-authored 62 publications receiving 1208 citations. Previous affiliations of Fabio Clementi include Sapienza University of Rome & Marche Polytechnic University.

Power law tails in the Italian personal income distribution

Abstract: We investigate the shape of the Italian personal income distribution using microdata from the Survey on Household Income and Wealth, made publicly available by the Bank of Italy for the years 1977–2002. We find that the upper tail of the distribution is consistent with a Pareto-power law type distribution, while the rest follows a two-parameter lognormal distribution. The results of our analysis show a shift of the distribution and a change of the indexes specifying it over time. As regards the first issue, we test the hypothesis that the evolution of both gross domestic product and personal income is governed by similar mechanisms, pointing to the existence of correlation between these quantities. The fluctuations of the shape of income distribution are instead quantified by establishing some links with the business cycle phases experienced by the Italian economy over the years covered by our dataset.

Power Law Tails in the Italian Personal Income Distribution

Abstract: We investigate the shape of the Italian personal income distribution using microdata from the Survey on Household Income and Wealth, made publicly available by the Bank of Italy for the years 1977--2002. We find that the upper tail of the distribution is consistent with a Pareto-power law type distribution, while the rest follows a two-parameter lognormal distribution. The results of our analysis show a shift of the distribution and a change of the indexes specifying it over time. As regards the first issue, we test the hypothesis that the evolution of both gross domestic product and personal income is governed by similar mechanisms, pointing to the existence of correlation between these quantities. The fluctuations of the shape of income distribution are instead quantified by establishing some links with the business cycle phases experienced by the Italian economy over the years covered by our dataset.

Pareto's Law of Income Distribution: Evidence for Germany, the United Kingdom, and the United States

Abstract: We analyze three sets of income data: the US Panel Study of Income Dynamics PSID), the British Household Panel Survey (BHPS), and the German Socio-Economic Panel (GSOEP). It is shown that the empirical income distribution is consistent with a two-parameter lognormal function for the low-middle income group (97%-99% of the population), and with a Pareto or power law function for the high income group (1%-3% of the population). This mixture of two qualitatively different analytical distributions seems stable over the years covered by our data sets, although their parameters significantly change in time. It is also found that the probability density of income growth rates almost has the form of an exponential function.

κ-generalized statistics in personal income distribution

Abstract: Starting from the generalized exponential function $\exp_{\kappa}(x)=(\sqrt{1+\kappa^{2}x^{2}}+\kappa x)^{1/\kappa}$ , with exp 0(x)=exp (x), proposed in reference [G. Kaniadakis, Physica A 296, 405 (2001)], the survival function P>(x)=exp κ(-βxα), where x∈R+, α,β>0, and $\kappa\in[0,1)$ , is considered in order to analyze the data on personal income distribution for Germany, Italy, and the United Kingdom. The above defined distribution is a continuous one-parameter deformation of the stretched exponential function P> 0(x)=exp (-βxα) to which reduces as κ approaches zero behaving in very different way in the x→0 and x→∞ regions. Its bulk is very close to the stretched exponential one, whereas its tail decays following the power-law P>(x)∼(2βκ)-1/κx-α/κ. This makes the κ-generalized function particularly suitable to describe simultaneously the income distribution among both the richest part and the vast majority of the population, generally fitting different curves. An excellent agreement is found between our theoretical model and the observational data on personal income over their entire range.

The κ-generalized distribution: A new descriptive model for the size distribution of incomes

Abstract: This paper proposes the -generalized distribution as a model for describing the distribution and dispersion of income within a population. Formulas for the shape, moments and standard tools for inequality measurement ‐ such as the Lorenz curve and the Gini coefficient ‐ are given. A method for parameter estimation is also discussed. The model is shown to fit extremely well the data on personal income distribution in Australia and in the United States. c 2008 Elsevier B.V. All rights reserved.

The Price of Inequality: How Today's Divided Society Endangers Our Future

Abstract: The Price of Inequality: How Today's Divided Society Endangers Our Future. Joseph E. Stiglitz, 414 pages, New York: W. W. Norton & Company, 2012.I LOVE THESE GUYS''(Joseph Stiglitz) is an insanely great economist,'' so writes Paul R. Krugman, who should know. These two like to write books for the populace at large on the topic of macroeconomics. Krugman also likes to spout offon Sunday news shows like ''Meet the Press,'' and on all the cable news programs. You can see Krugman arguing with Bill O'Reilly on Meet the Press. From YouTube: ''Don't call me a liar, pal, that's what you do all the time,'' says O'Reilly; ''This is not your show, so you can't cut offmy mic,'' says Krugman. Judging by their expressions, I was glad Tim Russert (6- 3-) was there in the studio at the time to protect Krugman (5- 7-) (O'Reilly stands 6- 4-). Conservative pundits dislike both economists, but pretty much leave Stiglitz alone.Krugman makes it a point to stay out of government affairs. Stiglitz does just the opposite, and he sometimes gets burned. Stiglitz was part of Bill Clinton's Council of Economic Advisers and was Chief Economist for the World Bank. After much criticism of the way the International Monetary Fund (IMF) conducted lending to developing countries, he was pretty much fired from the World Bank. Comparing the two, you would have to say that Stiglitz is more the bleeding heart, while Krugman says that what he dislikes most is the dishonesty he sees in political-economic discourse. Both are champions of the common man.Since Stiglitz can make the very valid claim for being ''an insanely great economist,'' most everything he says in his books should be taken seriously. There are some real gems in this book. I especially like the perspective he lends to some of the more peculiar things that happened before, during, and after the financial crisis. People hear about these things in the news from some announcer who makes them sound like just more news-bites, but they are, after all, unprecedented (and largely absurd). Take ''robo-signing'' for instance (page 198).WHAT GOES FOR NEWSRobo-signing was part of what happened during the foreclosure process after the massive numbers of defaults of subprime mortgages. Big banks intentionally did not follow mandated law. Apart from ignoring debtors' rights, an ensuing mess followed. Robo-signing was nothing less than a blatant attempt at rewriting property law, and resulted in lying to the courts about the state of each property's title, literally hundreds of times. No bank officer was charged with a crime. By contrast, the savings and loan crisis of the 1980s led to 829 individual convictions and 650 prison sentences. Nowadays the big banks just pay fines as part of settlements where they admit no guilt, and it's just part of doing business.Take algorithmic or ''flash'' trading in the stock exchanges (page 164). These are buys and sales made in nanoseconds on the basis of extracting information from the patterns of prices and trades. Nothing like real information gathered through market research on an industry, or on a firm in a certain industry, backs up these trades. But traders swear that ''price discovery'' is happening this way, and that all this backs up the efficient markets model. So on May 6, 2010 stock prices plummeted to a point where the Dow Jones temporarily lost 10% of its value. There are reasons to believe that these trades ''make markets not just more volatile but also less 'informative' '' (page 166). Still, the talk on the street is all about efficient markets (echoing Alan Greenspan's failures at the Federal Reserve).Take failed privatizations. When electric power in California was liberalized and Enron manipulated prices and public power to its advantage, this was a story about the company's accounting practices, not about privatizing something that had no business being privatized. …

Colloquium: Statistical mechanics of money, wealth, and income

Abstract: This Colloquium reviews statistical models for money, wealth, and income distributions developed in the econophysics literature since the late 1990s. By analogy with the Boltzmann-Gibbs distribution of energy in physics, it is shown that the probability distribution of money is exponential for certain classes of models with interacting economic agents. Alternative scenarios are also reviewed. Data analysis of the empirical distributions of wealth and income reveals a two-class distribution. The majority of the population belongs to the lower class, characterized by the exponential (``thermal'') distribution, whereas a small fraction of the population in the upper class is characterized by the power-law (``superthermal'') distribution. The lower part is very stable, stationary in time, whereas the upper part is highly dynamical and out of equilibrium.

The distribution of wealth and …scal policy in economies with …nitely lived agents

Abstract: We study the dynamics of the distribution of wealth in an overlapping generation economy with …nitely lived agents and inter-generational transmission of wealth. Financial markets are incomplete, exposing agents to both labor income and capital income risk. We show that the stationary wealth distribution is a Pareto distribution in the right tail and that it is capital income risk, rather than labor income, that drives the properties of the right tail of the wealth distribution. We also study analytically the dependence of the distribution of wealth, of wealth inequality in particular, on various …scal policy instruments like capital income taxes and estate taxes. We show that capital income and estate taxes can signif