Inequality and Volatility Moderation in Russia: Evidence from Micro-Level Panel Data on Consumption and Income

Abstract

We construct key household and individual economic variables using a panel micro data set from the Russia Longitudinal Monitoring Survey (RLMS) for [1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005]. We analyze cross-sectional income and consumption inequality and find that inequality decreased during the 2000-2005 economic recovery. The decrease appears to be driven by falling volatility of transitory income shocks. The response of consumption to permanent and transitory income shocks becomes weaker later in the sample, consistent with greater self-insurance against permanent shocks and greater smoothing of transitory shocks. Finally, expenditure and income inequality in Russia are not far apart.

Does Labor Supply Respond to a Flat Tax? Evidence from the Russian Tax Reform

“…Therefore, any action along this margin would have to be for individuals entering the market at high income levels. Third, in contrast to Central and Eastern European countries, aggregate employment in Russia does not move as much as real wages in response to output fluctuations (Boeri andTerrell 2002 andStolyarov 2009), suggesting that employment participation may not be very responsive to wages.…”

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“…For example, Gorodnichenko, Sabirianova-Peter and Stolyarov (2009) Income data also tend to suffer from recall bias, seasonality and other issues to a greater extent than consumption data (Deaton, 1997). Although consumption data also suffer from seasonality, the ability to smooth consumption over a few months makes consumption data a much better measure of actual income.…”

Heavy tails and upper-tail inequality: The case of Russia

“…Emerging economic markets are likely to be more volatile than their developed counter-parts and subject to more extreme external and internal shocks (see, among others, Gorodnichenko et al 2010, for the discussion of extreme macroeconomic disturbances accompanying Russia's transition to a market economy following the collapse of the Soviet Union). The higher degree of volatility suffered by emerging countries leads to the expectation that heavy-tailedness properties and distributions of their key economic and financial variables and indicators may be different from those in developed economies.…”

“…The problem of infinite variance in income and wealth distributions is important because it may invalidate or make problematic direct applicability of standard inference approaches, including regression analysis, (auto-)correlation based approaches and least squares methods (see the discussion in Ibragimov et al 2015;Ibragimov and Prokhorov 2017). In a similar fashion, infinite fourth moments for these variables need to be taken into account in regression and other models involving their volatilities or variances, e.g., in the analysis of permanent and transitory components of income variability and their cross-country comparisons (see Gorodnichenko et al 2010) and the study of autocorrelation properties of financial time series and volatility clustering (see the discussion in Cont 2001; Mikosch and Stariča 2000, and references therein). Finiteness of first moments is important because it points out to optimality of diversification and robustness of a number of economic models for the variables considered (see Ibragimov 2009;Ibragimov et al 2013Ibragimov et al , 2015Ibragimov and Prokhorov 2017).…”