Determinants of price level differences in Russian regions

ABSTRACT

We examine the factors determining regional price differentiation in the Russian Federation. According to official statistics, the price of a fixed set of goods and services in different regions may currently differ more than two-fold. Employing panel data analysis for 2000–2015, we find differences in regional prices were caused by the following factors: the Balassa–Samuelson effect (differences in wages, regional economic structure, income structure); regional trade costs (distance of the region from other regions); and the level of monopolisation of retail trade. The results obtained in the article can be used in developing and implementing economic policy aimed at the reduction of poverty, in assessing the efficiency of transport and logistical projects, and also in developing and analysing the consequences of monetary policy. Taking into account price differences between regions can therefore improve accuracy in forecasting the consequences of measures of monetary policy.

KEYWORDS:

• Regional price differences
• Balassa–Samuelson effect
• regional economy

Acknowledgements

The authors would like to express thanks to Michael Alexeev for notes and comments made, which helped improve the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. The ‘Red Belt’ is a set of industrialised regions in which the Communist Party of the Russian Federation (KPRF) has proven relatively popular in elections.

2. Information obtained from the collections Regiony Rossii: sotsial’no-ekonomicheskie pokazateli (Regions of Russia: Socio-Economic indicators). From these one can obtain information on regional CPI for the years 1996–2015 and also for 1992. Statistical data on regional CPI for the period 1993–1995 were obtained from the collection Regiony Rossii: Stat. sb. v 2 t. T. (Regions of Russia: Statistical Handbook in Two Volumes, Vol. 2 (Goskomstat Rossii, Moscow, 1999), 861 pp.).

3. This is a fairly drastic assumption since before the price liberalisation of January 1992 the territory of the USSR was divided into three zones of differing price levels. However we choose to disregard these differences since we wish to show only the scale of growth of consumer prices.

4. In January 1998 there was a denomination of the ruble in the ratio of 1000:1, i.e. 1 new ruble began to correspond to 1000 old rubles).

5. The calculations in Table 1 take into account the denomination of 1998 by dividing prices before 1998 by 1000. The Autonomous Districts are not examined separately, since statistics for them were collected only from 1997. For analogous reasons the Republics of Ingushetia and Chechnya are not examined.

6. Information obtained from the collections Regiony Rossii. sotsial’no-ekonomicheskie pokazateli (Regions of Russia: Socio-Economic indicators) for various years (from 2003 to 2016). From this source one can obtain a transparent form of information on the value of a fixed set of goods and services from 2001 to 2015. The handbook for 2003 contains information on the cost of a fixed set of goods and services in December 2001, and also the change in the value of that set from December 2000. This enables one to calculated the value of a fixed set of goods and services in 2000. The data for the value of the fixed set are provided to the end of the year, which in monthly terms corresponds to December.

7. In constructing Table 2 we used the same set of regions as in Table 1 in order to obtain comparability of results.

8. $http://www.gks.ru/fre{e}_{d}oc/ne{w}_{s}ite/prices/ISJ/methodology.pdf$.

9. Adding the Chukhotka Autonomous District and the Kamchatka Territory to the sample changes the slope of the pairwise regression line on the scatter-plot, which leads to unstable results. In respect of the Chechen Republic, data for the regional CPI and the value of a fixed set of goods and services become available only from 2004. In the case of the Republic of Ingushetia there are gaps in the data for types of paid services provided in particular years.

10. We checked robustness of our results for the choice of dependent variable and carried out analogous econometric research for an endogenous variable, measured on the basis of the cost of a fixed set of goods and services. This did not provide significant qualitative differences in the results obtained.

11. We used wages and not income because income, in addition to wages, includes returns from various production factors (property [capital], entrepreneurial ability) and also social payments (transfers), whereas the Balassa–Samuelson effect finds expression only through incomes from labour – that is, wages. Data for these indicators were obtained from the EMISS website https://www.fedstat.ru/indicator/33433.

12. This method of measuring the costs of trade is frequently used in the construction of gravitational models of trade, see Anderson and Van Wincoop (2003), McCallum (1995) and Tinbergen (1962), along with Kaukin and Idrisov (2013), in Russian. Statistics for distances by road (and for some regions by ship) were obtained from the source https://flagma.ru/raschet-rasstoyaniy.html).

13. We used monthly data from January 2002, obtained from the EMISS website https://www.fedstat.ru/indicator/31448.

14. The reciprocal relationship between market forces and the price volatility of food products is discussed in Kornher and Kalkuhl (2013) and also in Felis and Garrido (2015).

15. The volume of paid services to the population https://www.fedstat.ru/indicator/31280 was divided by the GRP.

16. The regression coefficient expressed in Figure 2 is not equal to the coefficient for the variable ‘the share of paid services’ in the models described in Section 4, since in constructing this regression not all factors from the models of Section 4 were accounted for, that is the conditions of the Frisch–Waugh–Lovell theorem were not met.

17. Data obtained from the EMISS website https://www.fedstat.ru/indicator/31501.

18. In the periods 2000–2015 and 2003–2015.