Measuring poverty dynamics and inequality in transition economies - disentangling real events from noisy data

The author uses instrumental variable methods, and the decomposition of income into transitory and persistent components to distinguish underlying income inequality and changes in poverty from the effects attributable to measurement error or transitory shocks. He applies this methodology to household-level panel data for Russia and Poland in the mid-1990s. The author finds that: 1) Accounting for noise in the data reduces inequality (as measured by the Gini coefficient) by 10-45 percent. 2) Individuals in both countries face much economic insecurity. The median absolute annual change in income or spending is about fifty percent in Russia, and about 20 percent in Poland. But roughly half of these fluctuations reflect measurement error or transitory shocks, so underlying levels of income, and spending are much more stable than the data suggest. 3) The apparent high levels of economic mobility are driven largely by transitory events and noisy data. After transitory shocks are accounted for, about eighty percent of the poor in both Russia and Poland remain in poverty for at least one year. So there is a real risk of an entrenched underclass emerging in these transition economies.