Abstract
Models of income distribution more or less succeed in linking the current level of household (or individual) income to household (or individual) characteristics. However, they are typically far less satisfactory in explaining income dynamics. Gibrat’s model proves helpful in highlighting the predominant role of randomness in the short run (here, 2–4 years), and this explains why other systematic influences are difficult to identify. One empirical regularity that does emerge, however, is that small incomes tend to increase more, and with more variability, than large ones. The traditional version of Gibrat’s model does not incorporate this peculiarity, but this shortcoming can be overcome with a relatively minor modification of the original model.
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Financial support from the Italian MIUR and from the EU is gratefully acknowledged.
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De Santis, G., Salinari, G. (2013). The Determinants of Income Dynamics. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_44
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DOI: https://doi.org/10.1007/978-3-642-35588-2_44
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