The high share of the shadow economy

The official goal of federal policy on Kaliningrad reflected in the Federal Target Programme (FTP) 2002–10 and in numerous official statements by federal and regional authorities is to catch up with the oblast’s immediate neighbours, Poland and Lithuania. The importance of this goal is stressed not only from the vantage point of economic development but also as a political condition of Kaliningrad’s development as an integral part of the Russian Federation. Russian authorities strive to prevent imbalances in the quality of life that could trigger massive dissatisfaction and separatism. In view of this goal, it is important to find out exactly what the quality of life in Kaliningrad is in relation to its neighbours. The official statistics should be supplemented by the assessments of the shadow economy as well as by the calculations at purchasing power parity (PPP).

The volume, dynamics and structure of the GDP/GRP are by far the most important indicators of the economy of a country or a region. They allow us to assess not only the overall state of the economy, but also the structural disparities of sectoral development and living standards.

Moreover, the GRP is one of the main indicators considered when taking investment decisions.

GRP analysis of this nature is one of the fundamental elements of the social and economic assessments of the region. There are several difficulties concerning the analysis, however, notably the reliability and comparability of the indicator. The problem of reliability of GRP figures published by the Statistics Office is acute. In its turn, it is based on two other problems, the methodology used by the State Committee for Statistics and the distortion of the source data.

While discussing the methodology of the Statistics Office goes beyond the scope of this report, available results of scientific research allow us to deal with the distortions of the source information. To do this, we have to account for the regional grey economy, of which the volume and boundaries can be estimated by a number of different methods. The grey (non-observed) economy consists of three types of economic activity:

• informal activities (predominately made up of goods and services that are allowed for production and dissemination but there is a lack of adherence to national legislation, omissions in the registration of workers, etc.);

• hidden (underground) activities – those that are allowed by the law, but which are intentionally hidden from the state to avoid either obligatory payments (e.g. taxes and tariffs) or necessary procedures (e.g. compliance with safety measures); and.

• illegal activities – those that are prohibited by law (for example, illegal production and distribution of drugs) or those that are recognised as illegal when performed without the necessary licensing or registration (OECD, 2000).

The problem is that the grey economy is vital for properly accessing the Kaliningrad regional economy. A further dimension is added by the image of Kaliningrad as a region with a particularly high volume of grey activities. The first assessment of the grey economy was performed by the experts of the Russian European Centre for Economic Policy (RECEP), specialists of the University Pierre Mendes France (Grenoble) and St Petersburg State University of Economics and Finance (FINEC) within the EU’s TACIS programme. The methodology of the grey economy investigation was the Delphi method, which consists of a number of repeating questionnaires effectively resulting in the formation of a group opinion on an issue of interest. The main factors of the method are the responses given under anonymity principles, controlled feedback (the experts are informed of the results of the previous round of the investigation) and formation of a group opinion of experts on the problem investigated.

There were three rounds of questioning in Kaliningrad, which involved 15 experts from the region, including the representatives of the regional administration, Regional Duma, Controlling and Revision Department of the Ministry of Finance in Kaliningrad, tax police, Federal Security

Service, the Ministry of Internal Affairs, the regional tax committee, the Kaliningrad city administration and economists from Kaliningrad State University. Each of the rounds featured a separate questionnaire with 30 questions on the shadow economy overall and on illegal activities in particular. According to the results of the survey, the average volume of the grey economy sector was 95% of the official one. Thus, the real volume of GRP after the correction was estimated as almost twice as large as the official GRP figure. The shadow GRP’s structure at the stage of creation and utilisation is illustrated in Table 2.8.

Table 2.8 Composition of the shadow economy GRP elements Share of the

element (%) GRP elements Share of the element (%) Final consumption 48.0 Payments to the wage-earners 31.0

Gross savings 25.0 Net production taxes 25.5

Net exports 27.0 Gross profit and gross mixed income 43.5

Total shadow GRP 100.0 Total shadow GRP 100.0

Sources: Eliseeva & Burova (2002); Samson et al. (2002).

The share of the grey economy varies in different sectors. There is a noticeable discrepancy in the experts’ judgment, though: the share of the grey sector in any of the sectors does not reach 95%. If we calculate the volume of GRP, correcting the official figure, we would derive RUB 43,000 mn, which is 36% higher than the published figure. The difference in the volumes of the grey economy given by the experts (95%) and calculated for each of the sectors (36%) is substantial and difficult to explain. The first possible reason is that the structure of the official GRP does not take into account two important activities, people working from home and illegal activities, which are therefore not included in the calculation of GRP by sector. Moreover, psychologically, the experts assess the grey economy as a whole and by sector differently, which confirms once again that the performed investigation only provides some starting points for further research.

Further results of the Delphi survey can be summarised as follows:

• The shadow incomes of Kaliningrad citizens constitute 43% of their average per capita incomes.

• The share of illegal exports is 13%; the share of illegal imports is 15%.

• The average share of illegal activities in the total volume of the grey economy is assessed as 28%. The most common types of illegal activities are the production and distribution of drugs and weapons, smuggling and prostitution.

Two further methods were applied to estimate the level of the shadow economy in the region.

Tatarinov (2002) constructed and analysed the input-output matrix and concluded that the shadow economy must form 55% of the official level in 2000 (i.e. on the top of the official economy). Despite being based on the most mathematically advanced procedure, the results of the input-output matrix analysis are substantially devalued by the use of the official data for the trade flows with the Russian regions. The estimation of household incomes based on the representative sample realised under the leadership of Fedorov in 2001 revealed an excess of 47% (Samson, 2002). Later on, Gareev, Zhdanov & Fedorov (2005) estimated the real GRP at 40% above the official level for 2003.

Although the estimation of 95% appears excessive, a wide consensus is reached around the estimation of 40-50%. In other words, the shadow economy forms about one-third of Kaliningrad’s total GRP. It can be assumed with reasonable certainty (and it also follows from

the available calculations) that the share of the shadow economy is slowly decreasing over time owing to reasons such as the strengthening of state control and more reasonable taxation, in particular a lower social tax and the 13% flat-rate personal income tax. Estimating the real GRP in 2004–05 at 40% above the official level thus seems reasonable.

The second obstacle on the way of positioning Kaliningrad in Russia and in Europe is the methodology of GRP comparisons with various states. This comparison can be done only on the basis of PPP. The PPP reflects the correlation of the world and internal prices of all the goods produced by an economy. This approach to international comparisons is especially important for Russia, which has a significant gap between the exchange rate and PPP. In 2001, this gap was 3.5 times (with the exchange rate at 29.3 RUB/US$ and the PPP at 8.3 RUB/US$). The GRP of the Kaliningrad region, calculated at PPP in 2001 was $6,900 per capita, which is 6.2 times higher than the GRP calculated at the official exchange rate (Table 2.9).

Table 2.9 Official data on the GDP/GRP per capita

1999 2000 2001 2002 2003 2004 Kaliningrad’s GRP per capita (RUB) 17,096 25,931 35,979 43,631 54,889 69,228 RF’s GDP per capita (RUB) 28,492 42,902 53,709 66,111 80,766 102,005 Place among Russian regions 53 44 39 43 44 37 Sources: KRCS (2004 and 2006) and the Russian Committee for Statistics.

Therefore, Kaliningrad’s population is more well off than it may seem from the official statistics. The official data does not accurately reflect the real situation, as is repeatedly noticed by outsiders, whether foreigners or Russians. According to the KRCS’s data, Kaliningrad is chronically lagging behind the Russian average.

A Russian citizen or a foreigner who has been in Russia (outside of Moscow) would confirm that it is completely counter-intuitive to assume that Kaliningraders live 1.5 times worse than Russians do on average. Two factors are crucial to achieve a more adequate representation of the economic reality. First, the shadow economy must be accounted for, as we have already done. Second, calculations of both purchasing power and any indirect evidence on household consumption should be taken into account. Several approaches are possible for PPP calculations. The straightforward one is to take the Russian data from the international comparisons, in which Russia has participated since 1993, and then to account for the difference between the all-Russia GDP per capita and Kaliningrad’s GRP per capita.

apita russiaperc

dpercapita kaliningra russia

d kaliningra

GDP PPP GRP

PPP = ∗ (1)

The figure of $4,400 was obtained for 2000 using this procedure (e.g. Smorodinskaya, 2001a;

Smorodinskaya & Zhukov, 2003). Similarly, the figure of $5,337 can be obtained for 2002.²

1 . 66111 5337

43631

8087∗ = (2)

2 International comparison data is available at www.gks.ru.

Yet these figures do not account for a specific economic regime, detachment or the geographic location of Kaliningrad and, consequently, substantial price differences on many products. A more subtle approach would be to conduct direct GDP/GRP(PPP) per capita comparisons as was done within the project of comparing the purchasing power in the Kaliningrad region and Lithuania by the research group under the leadership of Ivan Samson in 2002. This approach, although more laborious, reveals more exact and trustworthy PPP information since it compares purchasing power directly in the regions with comparable consumption structures. The research revealed that the rouble/lit purchasing power ratio in Kaliningrad and Lithuania in the first half of 2001 equalled to 0.95. As the calculations were based solely on household consumption without calculating expenses, the final figure should be closer to 85% (Samson et al, 2002).

Based on the data of the research, the GRP(PPP) per capita in Kaliningrad in 2000 should be estimated at $6,025, or 37% higher than the figure obtained by direct deduction from the Russian average according to the KRCS data.

The PPP calculations show that Kaliningrad finds itself approximately at the Russian average. It lags behind Lithuania, although less significantly than might be expected. It is roughly equal to the level of Poland’s Warminsko-Mazurskie Voivodship, Kaliningrad’s immediate neighbour with a number of severe structural problems and the highest level of unemployment in Poland.

The findings are also consistent with the data on regional household consumption. For instance, Kaliningrad finds itself among the Russian regions with the highest per capita consumption of automobiles (ranking second) and meat.