Assessment of gross urban product in Russian cities and its contribution to Russian GDP in 2000–2015
The article presents a new methodology for estimating gross urban product (the gross domestic product by city or metropolitan level) in Russia under extremely low statistical data availability about economy performance at the local level. These estimates provide new analytical instruments for assessing disparities in economic development between more than 1,000 Russian cities and other areas, and cities’ contributions to GDP as well as for comparing indicators of Russian cities with those of foreign countries.
This paper studies structural transformation and its implications for productivity growth in the BRIC countries (Brazil, Russia, India, and China) from the 1980s onwards. Based on a critical assessment of the reliability and consistency of various primary data sources, we bring together a new database that provides trends in value added and employment at a detailed 35-sector level. Structural decomposition analysis suggests that for China, India and Russia reallocation of labour across sectors is contributing to aggregate productivity growth, whereas in Brazil it is not. This confirms and strengthens the findings of McMillan and Rodrik [NBER working paper 17143, 2011]. However, this result is overturned when a distinction is made between formal and informal activities within sectors. Increasing formalization of the Brazilian economy since 2000 appears to be growth-enhancing, while in India the increase in informality after the reforms is growth-reducing.
We expect economic growth to remain strong in Poland and Latvia in 2016. Despite this robust growth, the new Polish government is likely to soften monetary and fiscal policies to further stimulate the economy, in our view. In 2015, the Latvian economy demonstrated strong resilience to external shocks.
The fundamental idea underpinning spatial econometric models of economic growth is as follows: regional growth is determined not only by social, economic, geographic traits of a region but also by spillovers from other regions, most importantly adjacent ones. If one region starts booming, it can left neighbors unaffected (neutral mechanism), spur their growth (cooperation mechanism) or slow their growth by pulling resources over (competition mechanism). What mechanism and to which extent occurs in practice matters for designing balanced economic policy and evaluating efficiency of regional policy investment. Classic spatial econometric models make strong although simplifying assumption that the same mechanism matters for all regions in the same manner, and there is no variation in spillovers intensity across regions. This assumption seems plausible for relatively small and homogenous regions of European countries, but it looks excessively strong for large and diverse Russian regions. In this paper we attempt to relax this assumption and propose a new model, fitting better in Russian conditions and bringing only slight sophistication from the estimation point of view. We introduce sensitivity parameter governing regional exposure to externalities. We assume this parameter to be a linear function of region-level observables, like area, population density or urbanization rate. These hypotheses have been confirmed at least partially. We found that dense and urbanized regions were more sensitive to spillovers. In other words, a region surrounded by the fast-growing areas, will grow the more intense, the more its population density and the higher the level of urbanization.