GDP per capita growth rates in Russia have been amongst the highest in the world since the mid-1990s. Previous growth accounting research suggests that this was mainly driven by multi-factor productivity (MFP) growth. In this paper we analyse for the first time the drivers of Russian growth for thirty-four industries for the period 1995 to 2008. We pay in particular attention to derive a proper measure of capital services, instead of the stock measures used in previous research. Using these new measures, we find that aggregate GDP growth is driven as much by capital input as MFP growth. Mining and Retailing take up an increasing share of the input, but have poor MFP performance. In contrast, MFP growth was high in goods producing industries but this sector’s GDP share declined. The major drivers of MFP growth were in high-skilled services industries that were particularly underdeveloped in the Russian economy in the 1990s.
Strong growth, intensive structural change and expanding informality have characterized many developing and emerging economies in recent decades. Yet most empirical investigations into the relationship between structural change and productivity growth overlook informality. This paper includes the informal sector in an analysis of the effects of structural changes in the Russian economy on aggregate labour productivity growth. Using a newly developed dataset for 34 industries covering the period 1995–2012 and applying three alternative approaches, aggregate labour productivity growth is decomposed into intra-industry and inter-industry contributions. All three approaches show that the overall contribution of structural change is growth-enhancing, significant and decreasing over time. Labour reallocation from the formal sector to the informal sector tends to reduce growth through the extension of informal activities with low productivity levels. Sectoral labour reallocation effects are found to be highly sensitive to the methods applied.
In theory, a poverty line can be defined as the cost of a common (inter-personally comparable) utility level across a population. But how can one know if this holds in practice? For groups sharing common consumption needs but facing different prices, the theory of revealed preference can be used to derive testable implications of utility consistency knowing only the "poverty bundles" and their prices. Heterogeneity in needs calls for extra information. We argue that subjective welfare data offer a credible means of testing utility consistency across different needs groups. A case study of Russia's official poverty lines shows how revealed preference tests can be used in conjunction with qualitative information on needs heterogeneity. The results lead us to question the utility consistency of Russia's official poverty lines.
We suggest to use information from the state register of personal cars as an alternative indicator of economic inequality in countries with a large share of shadow economy. We illustrate our approach using the Latvian pool of personal cars. Our main finding is that the extent of household economic inequality in Latvia is much larger than officially assumed. The latest officially available estimate of the Gini coefficient is 0.36 for 2005, which is much lower than 0.55 for 2009 reported in our paper.