WPS4015


                Regional Impacts of Russia's Accession
                      to the World Trade Organization

                        Thomas Rutherford, University of Colorado

                                    David Tarr, The World Bank

Abstract: In this paper we develop a computable general equilibrium model of the regions of Russia to

assess the impact of accession to the World Trade Organization (WTO) on the regions of Russia. We

estimate that the average gain in welfare as a percentage of consumption for the whole country is 7.8

percent (or 4.3 percent of consumption); we estimate that three regions will gain considerably more:

Northwest (11.2 percent), St. Petersburg (10.6 percent) and Far East (9.7 percent). We estimate that the

Urals will gain only 6.2% of consumption, considerably less than the national average. The principal

explanation in our central analysis for the differences across regions is the ability of the different regions

to benefit from a reduction in barriers against foreign direct investment. The three regions with the largest

welfare gains are clearly the regions with the estimated largest shares of multinational investment. But the

Urals has attracted relatively little FDI in the service sectors. An additional reason for differences across

regions is quantified in our sensitivity analysis: regions may gain more from WTO accession if they can

succeed in creating a good investment climate.


World Bank Policy Research Working Paper 4015, September 2006

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage
the exchange of ideas about development issues. An objective of the series is to get the findings out
quickly, even if the presentations are less than fully polished. The papers carry the names of the authors
and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper
are entirely those of the authors. They do not necessarily represent the view of the World Bank, its
Executive Directors, or the countries they represent. Policy Research Working Papers are available
online at http://econ.worldbank.org.




This paper was prepared at the request of the Government of the Russian Federation to the World Bank.
We gratefully acknowledge financial support from the United Kingdom's Department for International
Development. We thank Oleksandr Shepotylo for help with the data and estimates of parameters in this
paper and Maria Kasilag for help with the logistics.

                   Regional Impacts of Russia's Accession to the WTO

                                                     by

                                 Thomas Rutherford and David Tarr



                                            I. Introduction

        Russia is the largest economy in the world that is not a member of the World Trade Organization.

Russia applied for membership in the General Agreement on Tariffs and Trade (GATT) in June 1993 and

the GATT Working Party was transformed into the World Trade Organization (WTO) Working Party in

1995. President Vladimir Putin has made WTO accession a priority for Russia, and after languishing for

several years, the Russian accession negotiations began to see real progress under his administration.

        In response to numerous calls for a quantitative assessment of the impact of WTO accession on

Russia, Jensen, Rutherford and Tarr (forthcoming) have estimated the economy-wide and sector impacts

of Russian WTO accession. While this paper has been helpful in identifying the sectors likely to expand

and contract and the reasons for the gains to Russia from WTO accession, geographically Russia is the

largest country in the world. There are parts of European Russia close to markets of Western Europe and

parts of Far Eastern Russia that are close to the markets of China and Japan, while large portions of

Siberia are relatively isolated. We can expect the impacts across the regions to be very diverse, even for

the same industry. Consequently, there is a need for a model of Russia that distinguishes its regions.

        In this paper we develop a ten region model of Russia for the purpose of assessing the impacts

across these ten regions. The structure of the model for each region follows the general structure of the

national model of Jensen, Rutherford and Tarr. In particular, we allow foreign direct investment in the

business services sectors in each region. We also allow for imperfect competition where the sectors that

use goods or services produced under imperfect competition obtain endogenous productivity effects from

additional varieties of goods or services (the Dixit-Stiglitz framework).

        We present results and explain the economic intuition for these results from a computable general

equilibrium model that we believe is appropriate to evaluate the regional impacts of Russian accession to

the WTO. We argue that the gains to Russia from WTO accession will derive from three principal effects,

which are, in order of importance:

(1) Liberalization of barriers to foreign direct investment in services. A growing body of evidence

and economic theory suggest that the close availability of a diverse set of business services is important



                                                      2

for economic growth. 1 The key idea in the literature is that a diverse set (or higher quality set) of business

services allows users to purchase a quality adjusted unit of business services at lower cost. Russian

commitments to multinational service providers will encourage them to increase foreign direct investment

to supply the Russian market. Russian businesses will then have improved access to the services of

multinational service providers in areas like telecommunication, banking, insurance, transportation and

other business services. This should lower the cost of doing business in Russia, increase productivity of

Russian firms using these services and generally improve the competitiveness of the Russian economy.

However, the regions of Russia vary widely in their capacity to attract foreign direct investment.

Differences in results across regions depend to a significant extent on their attractiveness as a location for

FDI.

(2) Russian tariff reduction. Tariff reduction will lead to improved domestic resource allocation since

tariff reduction induces the country to shift production to sectors where production is valued more highly

based on world market prices. This impact, known as the "gains from trade" is a fundamental effect from

trade liberalization and is often stressed by international trade economists in the literature. In addition,

Russian businesses will be able to more easily import modern technologies or a greater variety of

technologies and this will increase Russian productivity. As we show, this second impact is more

important.2

(3) Improved access to the markets of non-CIS countries in selected products. Russia has already

negotiated most-favored nation (MFN) status on a bilateral basis with most of its important trading

partners, so Russia's exporters will not see an immediate reduction in the tariffs they face and this effect

may not be expected to be large. But Russia will have improved rights under antidumping and




1As early as the 1960s, the urban and regional economics literature (e.g., Jacobs, 1969; Chinitz 1961; Vernon 1960;
Stanback, 1979) recognized the importance of non-tradable intermediate goods (primarily producer services
produced under conditions of increasing returns to scale) as an important source of agglomeration externalities
which account for the formation of cities and industrial complexes, and explanations of the difference in economic
performance across regions. The more recent economic geography literature (e.g., Fujita, Krugman and Venables,
1999) has also focused on the fact that related economic activity is economically concentrated due to agglomeration
externalities (e.g., computer businesses in Silicon Valley, ceramic tiles in Sassuolo, Italy). Evidence comes from a
variety of sources. Ciccone and Hall (1996) show that firms operating in economically dense areas are more
productive than firms operating in relative isolation. Caballero and Lyons (1992) show that productivity increases in
industries when output of its input supplying industries increases. Hummels (1995) shows that most of the richest
countries in the world are clustered in relatively small regions of Europe, North America and East Asia, while the
poor countries are spread around the rest of the world. He argues this is partly explained by transportation costs for
inputs since it is more expensive to buy specialized inputs in countries that are far away for the countries where a
large variety of such inputs are located.

2Romer (1994) has emphasized that the impact of trade liberalization on new or higher quality products is much
more important quantitatively than improved resource allocation.


                                                            3

countervailing duty investigations in its export markets, which is the source of the improved access we

model.3

        Our aggregate results for each region are summarized in table 13a. Our central estimates are that

the overall gains to Russia from WTO accession are 7.8 percent of Russian consumption (or 4.3 percent

of GDP). While the average gain in welfare as a percent of GDP for the whole country is 4.3 percent, we

estimate that three regions will gain considerably more: Northwest (6.2 percent), St. Petersburg (5.7

percent) and Far East (5.2 percent). The principal explanation for the differences across regions is the

ability of the different regions to benefit from a reduction in barriers against foreign direct investment.

The four regions with the largest welfare gains are clearly the regions with the estimated largest shares of

multinational investment. All have estimated multinational shares of the service sector that are twice the

national average in maritime services, rail services, truck services, air transportation services,

telecommunications, science and financial services. On the other hand, we estimate that the Urals will

gain only 3.3% of GDP, considerably less than the national average. But the Urals has relatively little FDI

in the services sectors.

        We observe that the reduction in barriers to FDI alone results in an improvement in Russian

welfare on average across regions of 6.7 percent of consumption (or 3.7 percent of GDP). Improved

market access and tariff reduction contribute an improvement in Russian welfare by 0.3 percent and 0.7

percent of consumptioni, respectively, or a combined one percent. Thus, by far the most important effect

derives from the reduction in barriers to FDI in services. We also simulate a reduction in the barriers to

FDI in business services of only fifty percent of the cuts we assume in our WTO scenario. The gains in

Russian welfare are substantially reduced to 4.2 percent of consumption, which again shows the

importance of reducing barriers to FDI in order to increase Russian productivity, competitiveness and

welfare. Thus, the estimated gains from FDI liberalization are almost three-quarters of the total gains from

Russian WTO accession. Thus, while improving its offer to foreign services providers within the context

of the GATS has been one of the most difficult aspects of Russia's negotiation for WTO accession, our

estimates suggest that the most important component of WTO accession for Russia in terms of the welfare

gains is liberalization of its barriers against FDI in services sectors.

        In the sensitivity analysis, we also incorporate data on the investment potential of regions based

on the investment potential rankings of Expert RA. The principal result is that the estimated gains for

Moscow, St. Petersburg and Tumen increase and the estimated gains for Siberia, Northwest, North,



3WTO accession will grant an "injury determination" to Russia in antidumping cases in WTO members countries.
Combined with the decision by the US and the EU to treat Russia as a market economy this will imply Russian
exporters may have considerably improved rights in these cases in the US. But market economy status may be
denied in particular cases, so it will be necessary to see how this is implemented in practice.


                                                            4

Central and the Far East decline. Despite smaller estimated gains in this scenario, Far East and Northwest

are still estimated to receive above average gains. The results suggest that the gains for a region could

vary considerably depending on whether it succeeds in creating an atmosphere conducive to investment.

          Our estimates show that many of our Russian goods sectors should expand and employment in

many services sectors expands. This occurs despite the reduction in all tariffs by 50 percent and a

reduction in barriers against foreign direct investment in business services. Whether a sector expands or

contracts depends on what happens to the incentives of one sector versus another. Economy-wide, Russia

will see an increase in exports equal to the increase in imports. The expansion in exports occurs because

Russia will have to pay for additional imports through hard currency, which increases the demand for

hard currency and causes a real depreciation of the ruble. A depreciated ruble makes exporting more

profitable and decreases the demand for imports until the additional exports are equal to the additional

imports. The export intensive sectors tend to gain the most from ruble depreciation.

          In goods sectors, we estimate that the ferrous metals, non-ferrous metals and chemicals sectors

are the goods sectors that expand the most. These are the sectors that export the most intensively. They

also experience a terms of trade gain from improved treatment in antidumping cases. We estimate that

food, machinery and equipment and construction materials will decline as these sectors export relatively

less and are relatively highly protected.

          In business services, employment effects vary across sectors, with some expanding

(multinationals will demand Russian labor when they locate in Russia and the demand for business

services increases) and some contracting demand for labor (since multinationals use Russian labor less

intensively than Russian companies). But users of business services will become more internationally

competitive as they obtain an increase in the quality and diversity of available competitively priced

business services.

          This paper quantifies and supports the views expressed by the some in the government that when

all changes are taken into account, many goods sectors would expand, despite a reduction in their tariff.

          The paper is organized as follows. In section II, we describe the model and the most important

data. In section III, we describe and interpret the policy scenarios and quantitatively assess the sensitivity

of the results to parameter assumptions. Many of the scenarios we describe are decomposition scenarios

that allow us to assess the relative importance of the various aspects that we consider important to

Russian WTO accession. We provide sensitivity analysis in section IV and briefly conclude in section V.




                                                       5

                              II. Overview of the Model and Key Data


Production and Geographic Structure

          There are 30 sectors in the model that are listed in table 1. There are three types of sectors:

perfectly competitive goods and services, imperfectly competitive goods sectors and imperfectly

competitive business services sectors.

          The geographic decomposition of our model of Russia is shown in table 2. In the first instance,

we obtain data from the publication the Regions of Russia by Rosstat on 88 regions of Russia. The 88

regions have several names in Russian; the most common legal jurisdiction is referred to in Russian as an

"oblast." Oblasts are analogous to states in the United States or provinces in Canada. But there are also

jurisdictions known as territories, federal cities, autonomous districts and an autonomous region. Since we

want to use the term "region" for another purpose in the model, we use the Russian term "oblast" for all

of these 88 geographic areas,4 with the understanding that they are not all oblasts in the Russian sense of

the term.

          We group several contiguous oblasts into what we call Regional Markets (RMs). The mapping of

oblasts into regional markets is also shown at the top of table 2. In this paper, we shall analyze effects at

the level of the Regional Market. Value-added, exports and imports by sector for our ten regions of

Russia are presented in tables 3-11.

          We assume that firms and sectors operate at the Regional Market level, primary factors of

production are not able to move between Regional Markets (unless otherwise notes). We assume a nested

CES structure of demand. Since this implies that the structure of demand is both a homothetic and

weakly separable, consumers and firms in a representative Regional Market r employ multiple stage

budgeting for all goods.


Price determination for competitive goods and services sectors

          Firms in each Regional Market have three choices for sales: sell in their own Regional Market;

sell to other parts of Russia; or export to the rest of the world. This is depicted in figure 1. Firms

maximize revenue for any given output level based on their transformation possibilities between the three

types of goods which is defined by a constant elasticity of transformation production function. For all

firms within the same Regional Market, the product they export to other parts of Russia (including other

oblasts within their own regional market) is homogeneous. It follows from our assumptions of


4Several of the territories are part of oblasts, so it was necessary to adjust the data to avoid double counting of the
territories.


                                                             6

homogeneous demand and production outside of the own Regional Market, that for each competitive

good, say good g, there will be only three prices for good g of Regional Market r: the price of good g in

Regional Market r; the price of good g from Regional Market r in other parts of Russia; and the price of

good g from Regional Market r in the rest of the world.

         The structure of demand for goods or services from competitive sectors is shown in figure 2.

Consumers and firms in a representative Regional Market r first optimize their choice of expenditures on

foreign goods versus goods from Russia. Subsequently they optimally allocate their expenditures between

goods from other Russian Regional Markets and their own Regional Market. Finally, they optimally

allocate their expenditures between goods from the other Russian Regional Markets. This structure

assumes that consumers differentiate the products of producers from different regional markets; but, they

regard as homogeneous the products of producers from different oblasts within the same regional market.


Goods produced subject to increasing returns to scale

         The structure of demand for goods produced under increasing returns to scale is shown in figure

3. Consumers (and firms) in RM r, optimally allocate expenditures on good g among the goods available

from the different regional markets of Russia and the rest of the world producers. Having decided how

much to spend on the products from each regional market, consumers then allocate expenditures among

the producers within each regional market. Since we assume identical elasticity of substitution at all

levels, this is equivalent to firm level product differentiation of demand. That is, the structure is

equivalent to a single stage in which consumers decide how much to spend on the output of each firm in

the first stage of optimal allocation of expenditure.

         We assume that imperfectly competitive manufactured goods may be produced in each region or

imported. Both Russian and foreign firms in these industries set prices such that marginal cost (which is

constant) equals marginal revenue in each regional market. There is a fixed cost of operating in each

region and there is free entry, which drives profits to zero for each firm on its sales in each regional

market in which it sells. Quasi-rents just cover fixed costs in each region in the zero profit equilibrium.

We assume that all firms that produce from the same regional market have the same cost structure--the

standard symmetry assumption.

          Foreigners produce the goods abroad at constant marginal cost but incur a fixed cost of operating

in each RM in Russia. The cif import price of foreign goods is simply defined by the import price; by the

zero profits assumption, in equilibrium the import price (less tariffs) must cover fixed and marginal costs

that foreign firms incur in each regional market.




                                                       7

        Similar to foreign firms, Russian firms also produce their goods in their home regions; they incur

a fixed cost of operation if each RM in which they operate. By the zero profit constraint, if they operate in

a RM, the price of their product must just cover both fixed and marginal costs of operation in that RM.

         In figure 4, we depict the structure of production for imperfectly competitive Russian firms.

Regional firms use intermediate inputs (which can be foreign inputs, inputs from other regions of Russia

or from its own region) and primary factors of production to produce output. We emphasize that business

services are not part of the "other services" nest; rather business services substitute for primary factors of

production in a CES nest.5 We show that the elasticity of substitution between business services and

primary factors of production significantly impacts the results.

        We assume that Russian firms do not have any market power on world markets and thus act as

price takers on their exports to world markets. On the exports to the rest of the world then, price equals

marginal costs. On sales to Russia, firms must use a specific factor in addition to the other factors of

production. The existence of the specific factor implies that additional output or firms can only come at

increasing marginal costs. Imperfectly competitive Russian goods producers sell in all of Russia; but

services firms do not sell in other Russian regional markets.

        We employ the standard Chamberlinian large group monopolistic competition assumption within

a Dixit-Stiglitz framework, which results in constant markups over marginal cost. For simplicity we

assume that the composition of fixed and marginal cost is identical in all firms producing under increasing

returns to scale (in both goods and services). This assumption in a Dixit-Stiglitz based Chamberlinian

large-group model assures that output per firm for all firm types remains constant, i.e., the model does not

produce rationalization gains or losses.

        An increase in the number of varieties increases the productivity of the use of imperfectly

competitive goods based on the standard Dixit-Stiglitz formulation. Dual to the Dixit-Stiglitz quantity

aggregator is the Dixit-Stiglitz cost function which shows the productivity adjusted cost of using the

available varieties in the regional market when varieties are purchased at minimum cost for a given output

level. This cost function for users of goods produced subject to increasing returns to scale declines in the

total number of firms in the industry. The lower the elasticity of substitution, the more valuable is an

additional variety.

        We have assumed that imperfectly competitive firms within a regional market have symmetric

cost structures and face symmetric demand for their outputs. It follows from these assumptions that all



5For example, firms can employ an accountant or a lawyer, or contract for accounting or legal services. They can
employ a driver and buy a truck, or contract for delivery services. These examples make it evident that it is more
appropriate to allow substitution between business services and primary factors of production than to assume a
Leontief structure.


                                                           8

imperfectly competitive firms from a regional market will obtain the same price in any regional market of

Russia in which they operate, although the price will differ across regional markets since the fixed costs

associated with entering any regional market varies across the regional markets.


Services sectors that are produced in Russia under increasing returns to scale and

imperfect competition

        These sectors include telecommunications, financial services, most business services and

transportation services. In services sectors, we observe that some services are provided by foreign service

providers on a cross border basis analogous to goods providers from abroad. But a large share of business

services are provided by service providers with a domestic presence, both multinational and Russian.6 As

shown in figure 5, our model allows for both types of foreign service provision in these sectors. There are

cross border services allowed in this sector and they are provided from the firms outside of Russia at

constant costs--this is analogous to competitive provision of goods from abroad. Cross border services

from the rest of the world, however, are not good substitutes for service providers who have a presence

within the regional market of Russia where consumers of these services reside.7

        Russian firms providing imperfectly competitive business services operate at the regional level

and organize production in a manner fully analogous to imperfectly competitive Russian firms producing

goods. Thus, figure 4 applies to both Russian imperfectly competitive goods and services firms. Other

assumptions we made for imperfectly competitive goods producers, such as entry conditions, pricing and

symmetry are also apply to imperfectly competitive services providers. The only difference is that we

assume that regional services providers sell only in their own regional market. It follows from these

assumptions that there is a unique price for all Russian providers of imperfectly competitive business

services in a regional market.

        There are also multinational service firm providers that choose to establish a presence in a RM of

Russia in order to compete with regional Russian firms directly in the Russian Regional Market. The

decision to locate in a regional market by a multinational must take into account the existence of a fixed

cost of operating in a regional market. As with imperfectly competitive goods producers, quasi-rents must

cover the fixed plus marginal costs of producing in a regional market and we have a zero profit

equilibrium.



6One estimate puts the world-wide cross-border share of trade in services at 41% and the share of trade in services
provided by multinational affiliates at 38%. Travel expenditures 20% and compensation to employees working abroad
1% make up the difference. See Brown and Stern (2001, table 1).
7Daniels (1985) found that service providers charge higher prices when the service is provided at a distance.



                                                         9

         When multinational service providers decide to establish a domestic presence in a regional market

of Russia, they will import some of their technology or management expertise. That is, foreign direct

investment generally entails importing specialized foreign inputs. Thus, the cost structure of

multinationals differs from Russian service providers. Multinationals incur costs related to both imported

primary inputs and Russian primary factors, in addition to intermediate factor inputs. Foreign provision of

services differs from foreign provision of goods, since the service providers use Russian primary inputs.

This is shown in figure 6, where we show multinationals combining imported primary inputs with inputs

of the service good from the oblasts within the regional market. Domestic service providers do not import

the specialized primary factors available to the multinationals. Figure 4 for Russian business service

providers is analogous to figure 6 for multinational service providers except for the nest for imported

primary inputs. Foreign service providers also must use a specific factor to produce the output and this

implies that additional output can only be obtained at increasing marginal costs. Since the structure of

costs for all multinational firms that provide a service s in a given region m is identical and demand is

symmetric, there is a unique price for all multinationals providers of service s in regional market m.

          For multinational firms, the barriers to foreign direct investment affect their profitability and

entry. Reduction in the constraints on foreign direct investment in a region will induce foreign entry that

will typically lead to productivity gains because when more varieties of service providers are available,

buyers can obtain varieties that more closely fit their demands and needs (the Dixit-Stiglitz variety effect).



Factors of Production

         Primary factors include skilled and unskilled labor and three types of capital; (i) mobile capital

(within regions); (ii) sector-specific capital in the energy sectors reflecting the exhaustible resource; and

(iii) sector specific capital in imperfectly competitive sectors. We also have primary inputs imported by

multinational service providers, reflecting specialized management expertise or technology of the firm.

The existence of sector specific capital in several sectors implies that there are decreasing returns to scale

in the use of the mobile factors and supply curves in these sectors slope up.

         The above list of primary factors exist in all regions. In the case of skilled and unskilled labor it is

natural to assume that the representative agent in the region obtains the returns from these factors of

production. Consistent with standard trade models, in our central model we assume that capital and labor

are immobile between regions. However, this model is a regional disaggregation of a national model of

Russia; consequently, it does not seem reasonable to assume that all capital in a region is owned by the

agents in that region. Thus, we make the assumption that a fraction of the capital in any region is held by

agents outside of the region, but the capital is held by residents in the country. We take the fraction of the



                                                       10

capital held by agents in the region to be 50 percent, but this percentage is not crucial to the solution of

the model.

          Regarding the capital of each region that is held outside of the region, it is convenient to think of

a national mutual fund that holds the remaining capital in each region. For all three capital types ((i)

mobile; (ii) specific capital in the energy sectors; and (iii) specific capital in the IRTS sectors) fifty

percent of the capital used in the region is owned by this mutual fund and the balance is owned by the

representative agent in the region. The national mutual fund invests in all regions and obtains an overall

return. The representative agent in the region also holds shares in the national mutual fund.8.

          For each region we report returns to capital as returns to the three types of regional capital held

by the region's representative agent. Plus the region's representative agent obtains a share of the returns

from the national mutual fund. The return to national capital is the region's share of the return of the

national mutual fund reported as a percentage of initial consumption of the region.


Key Data
          Ad Valorem Equivalence of Barriers to Foreign Direct Investment in Services Sectors

Among the key restrictions against multinational service providers that have existed or exist in Russia are:

the Rostelecom monopoly on long distance fixed-line telephone services (scheduled to be removed),

affiliate branches of foreign banks are prohibited, and there is a quota on the multinational share of the

insurance market. 9 Estimates of the ad valorem equivalence of these and other barriers to FDI in services

are key to the results. Consequently, we commissioned 20 page surveys from Russian research institutes

that specialize in these sectors and econometric estimates of these barriers based on these surveys.




8 Define M(r) as the representative agent's share of the returns to the national mutual fund. We calculate M(r) as
follows. For each region r, the initial consumption income C( r) equals its endowment income E( r) minus the share
of the trade surplus B( r) attributed to the region: C( r) = E( r) ­ B( r). Define B( r) = [C( r)/C]* B
where C is aggregate consumption for Russia and B is the aggregate trade surplus. That is, the region's share of the
trade surplus is proportional to its share of aggregate consumption.

E( r) is defined above, but is also defined as: E( r) = L( r) + K( r) + M(r). That is, endowment income for each
region is labor income L( r); plus capital income on regional capital held by the representative agent in the region K(
r); plus the representative agent's share of the returns to the national mutual fund M(r). E( r), L( r) and K( r) are all
known. We calculate M( r) residually for each region r as:
E( r) - L( r) - K( r) = C( r) - B( r) - L( r) - K( r) = M(r) .

9The protocol on Russian accession signed between the European Union and Russia on May 21, 2004 calls for the
termination of the Rostelekom monopoly by 2007 and allows for an increase in the upper limit on the multinational
share of the Russian insurance market.


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          These questionnaires provided us with data, descriptions and assessments of the regulatory

environment in these sectors. 10 Using this information and interviews with specialist staff in Russia, as

well as supplementary information, Kimura, Ando and Fujii (2004a, 2004b, 2004c) then estimated the

ad valorem equivalence of barriers to foreign direct investment in several Russian sectors, namely in

telecommunications; banking, insurance and securities; and maritime and air transportation services. The

process involved converting the answers and data of the questionnaires into an index of restrictiveness in

each industry. Kimura et al. then applied methodology explained in the volume by C. Findlay and T.

Warren (2000), notably papers by Warren (2000), McGuire and Schulele (2000) and Kang (2000). For

each of these service sectors, authors in the Findlay and Warren volume evaluated the regulatory

environment across many countries. The price of services is then regressed against the regulatory barriers

to determine the impact of any of the regulatory barriers on the price of services. Kimura et al. then

assumed that the international regression applies to Russia. Applying that regression and their

assessments of the regulatory environment in Russia from the questionnaires and other information

sources, they estimated the ad valorem impact of a reduction in barriers to foreign direct investment in

these services sectors.11 The results of the estimates are listed in table 2.12 In the case of maritime and air

transportation services, we assume that the barrier will only be cut by 15 percentage points, since pressure

from the Working Party in these sectors is not strong.

          Share of Expatriate Labor Employed by Multinational Service providers. The impact of

liberalization of barriers to foreign direct investment in business services sectors on the demand for labor

in these sectors will depend importantly on the share of expatriate labor used by multinational firms. We

explain in the results section that despite the fact that multinationals use Russian labor less intensively

than their Russian competitors, if multinationals use mostly Russian labor their expansion is likely to

increase the demand for Russian labor in these sectors.13 We obtained estimates of the share of expatriate

labor or specialized technology that is used by multinational service providers in Russia, but which is not



10   This information was provided by the following Russian companies or research institutes: Central Science
Institute of Telecommunications Research (ZNIIS) in the case of telecommunications, Expert RA for banking,
insurance and securities; Central Marine Research and Design Institute (CNIIMF) for maritime transportation
services and Infomost for air transportation services. We thank Vladimir Klimushin of ZNIIS; Dmitri Grishankov
and Irina Shuvalova of ExpertRA; Boris Rybak and Dmitry Manakov of InfoMost; and Tamara Novikova, Juri
Ivanov and Vladimir Vasiliev of CNIIMF. The questionnaires are available at www.worldbank.org/trade/russia-wto.
The same sources provided the data on share of expatriate labor discussed below.
11   Warren estimated quantity impacts and then using elasticity estimates was able to obtain price impacts. The
estimates by Kimura et al. that we employ are for "discriminatory" barriers against foreign direct investment. Kimura
et al. also estimate the impact of barriers on investment in services that are the sum of discriminatory and non-
discriminatory barriers.
12   See Jensen, Rutherford and Tarr (2006) for an explanation of the estimate in telecommunications.
13See Markusen, Rutherford and Tarr (2005) for a detailed explanation on why FDI may be a partial equilibrium
substitute for domestic labor but a general equilibrium complement.


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available to Russian firms, from the Russian research institutes that specialize in these sectors. In general,

we found that multinational service providers use mostly Russian primary factor inputs and only small

amounts of expatriate labor or specialized technology. In particular, the estimated share of foreign inputs

used by multinationals in Russia is: telecommunications, 10% plus or minus 2%; financial services, 3%,

plus or minus 2%; maritime transportation, 3%, plus or minus 2%; and air transportation, 12.5%, plus or

minus 2.5%.


Tariff and Export Tax data

         Tariff rates by sector are taken from the paper by Tarr, Shepotylo and Koudoyarov (2006). Tarr,

Shepotylo and Koudoyarov estimate the tariff rates by sector in our model based on the following data

and methodology. For the purpose of calculating the tariff rates, we obtained data on the quantity and

value of imports for 2001, 2002 and 2003 from the electronic database of the commercial company

Academy-Service.14 This dataset provides information on the Russian tariff structure at the tariff line

level, i.e., the 10-digit level. The source of information on tariff rates is the Decree of the Government of

Russian Federation on import duties #830.15 The decree is available, for example, at www.consultant.ru.

        The average MFN tariff in Russia has increased between 2001 and 2003. On an un-weighted

simple average basis it increased from 11.6% to 12.9%; on a weighted average basis it increased from

11.4% to 14.5%. This average is calculated based on MFN tariffs.

         Collected tariffs are less than MFN tariffs because of a several exemptions in the Russian tariff

structure. Most notably, CIS imports usually enter tariff free (although there are exceptions to this rule),

and personal and private imports also enter tariff free for sufficiently small values of imported shipments.

We also provide estimates of the tariff rates where we adjust for zero tariff collections on CIS imports.

That is, in our formulas for calculating the tariff on a tariff line, we set ad valorem and specific rates on

imports from the CIS countries equal to zero to take into account the special trade regime within the CIS.

We call these calculations our estimated collected tariff rates. We find that overall estimated collected

tariff rates are lower than the MFN rates by about one percent.

         Our overall estimated collected tariff rate was equal to 10.4% in 2001, 10.9% in 2002, and 11.5%

in 2003. On the other hand, based on Ministry of Finance and Customs Committee data, the actual




14http://www.ftinform.com
15We looked at three editions of the decree: first, dated by 11.30.2001 for 2001; the second, dated by 02.06.2003 for
2002 rates, and the third, dated by December 2003 for 2003 rates.


                                                           13

collected rate was 9.5% in 2001, 9.7% in 2002, and 9.8% in 2003. The difference can be attributed to the

fact that we did not take into account any exemptions other than the CIS free trade zone exemption.16

         We believe collected tariff rates more closely approximate the protection a sector receives and the

incentives it faces. Using our estimated collected tariff rates, and based on a Rosstat mapping from the

tariff line data of the Customs Committee to the sectors in our input output table, we calculated a

weighted average tariff rate for the sectors of our model. The results of this procedure for each sector of

our model are reported in table 12a.

         Export tax rates are calculated from the 2001 input-output table of Rosstat and are reported in

table 12a. Since we do not change export taxes in the counterfactual simulations, these parameters are less

important to the results than the tariff rates.


Input-output tables

         The core input-output model is the 2001 table produced by Rosstat. The official table contained

only 22 sectors, and importantly has little service sector disaggregation. In order to disaggregate the table,

we used costs and use shares from our 35 sector Russian input-output table for 1995 prepared by expert

S. P. Baranov. (For details see www.worldbank.org/trade/russia-wto.) When we broke up a sector such as

oil and gas into oil, gas and oil processing, we assumed that the cost shares and use shares of the sector

were the same in 2001 as they were in the 1995 table. For example, steel is an input to the oil and gas

sector. Suppose in 1995, that oil purchased 55 percent of the steel used in oil and gas. Then we assume

that in 2001, oil purchased 55 percent of the steel used in oil and gas.


Regional IO tables

         We generated input-output tables for the regions based on output data from the regions and the

national input-output table. For each industrial sector we took national output from the national input-

output table for 2001, and we used the data in Regions of Russia to allocate the shares of that output

across regions. That is, from the Regions of Russia 2001, we have, by region; industry shares of output

for the year 2000 (table 13.3); thousands of tons of oil recovery, including gas condensate, for the year

2000 (table 13.13); extraction of natural gas (in millions of cubic meters for the year 2000, table 13.14);

thousands of tons of mined coal for the year 2000 (table 13.15). We also have external exports and

imports by region, as well as the commodity structure of exports and imports by region for the year 2001



16To calculate actual collected rate, we used the Ministry of Finance data on collected import duties as a numerator.
As a denominator, we used the overall import volume less import from Belarus as reported by the Russian Customs
Committee. The exclusion of the imports from Belarus is determined by the fact that the electronic dataset which we
used in the calculations reported import volume without imports from Belarus.


                                                         14

(tables 23.1 and 23.2). We also have unpublished data supplied by Rosstat on exports and imports by

sector and by region of Russia.

         We assume that the technology of production is common across regions, so that the same input

output coefficients apply across all regions. We create an input output table for each region of Russia,

where the shares of output and energy for each region are taken from the Regions of Russia and we have

common technology across all regions. We infer regional demand (and supply) of services, assuming that

intermediate and final demand for services have the same intensity of demand in all regions as in the

national model.

         We have to adjust the resulting regional import and export intensities so that regional exports in

aggregate are consistent with national import and export values. We do not need to make any other

adjustments, as the production technologies are assumed consistent across the regions.


FDI Shares

         We explain the methodology further in the appendix, but briefly, we first employed the NOBUS

survey to obtain the shares of workers working in multinationals service sectors in each sector in each

region. We used this as a proxy for the share of output in each service sector in each region. We also

obtained information from (1) our estimates from Russian service sector institutes of the share by sector

of multinational ownership in the key services sectors; 17 (2) Regions of Russia (2003) by Rosstat; and (3)

the "BEEPS survey. Only the NOBUS survey provides data that allows us to estimate shares of

multinational ownership by both region and sector. We thus start with our calculations based on the

NOBUS information.

         When found, however, that when we aggregate the NOBUS shares across regions or sectors, the

other three sources of information show considerably higher foreign ownership shares than the NOBUS

survey. We believe that the NOBUS survey estimates are too small, and adjust them. We adjusted our

estimates from the NOBUS to be consistent with the estimates of the service sectors institutes. The

service sector institute estimates are lower than those from the BEEPS or Regions of Russia, and thus

involve less adjustment of the NOBUS data. We employed least squares adjustment of the NOBUS data

so that the weighted average over all of Russia in each sector is consistent with the national estimates we

received from the specialist service sector research institutes in Russia. This process will give as a

structure of ownership based on the NOBUS survey, with the economy-wide average by sector

determined by the national data. Results are presented in table 12b.




17We thank the service sectors institutes in Russia mentioned above for these estimates.


                                                         15

                                           III. Policy Results

         For each of our ten regional markets, we first discuss our central scenario (results are in table 13).

In our central scenario, we assume that tariffs on goods are cut by fifty percent, that barriers to foreign

direct investment are eliminated or reduced (depending on the sector) and that seven industrial sectors

receive an improvement in their market access between 0.5% and 1.5%. See table 12a for details by

sector. We present the overall welfare effects, the impact on wages and returns to capital, the changes in

exports and the real exchange rate and factor adjustment costs. The gains come from a combination of

effects, so we also estimate the comparative static impacts of the various components to WTO accession

in order to assess their relative importance. We also decompose the results in the components of the gains

to provide a transparent explanation of the results. Next we discuss the estimates of the impact at the

level of productive sectors of the economy. In order to obtain an assessment of the adjustment costs, we

estimate the percentage of mobile labor and capital that must change industries. We also conduct

sensitivity analysis with respect to some key parameters and present these results. A key aspect of the

sensitivity analysis is how the results differ when we assess the ability of different regions to attract FDI

based on the ranking of their investment potential. The sensitivity analysis helps to provide insights into

how the gains may differ across regions.


Welfare Effects of WTO Accession
         In table 13a, we show that the weighted average of the welfare gains across all regions is 4.3

percent of GDP or 7.8 percent of consumption. By region, the welfare gains as a percent of GDP range

from 3.1 percent in Tumen to 6.2 percent in the Northwest region. Except for Tumen, the gains as a

percent of consumption range from 6.2 percent in the Urals regional market to 11.2 percent in the

Northwest regional market. The gains to Tumen as a percent of consumption are much higher because a

larger share of the GDP of Tumen is invested, so consumption is a smaller share of GDP.

         In order to assess what is causing these results we have undertaken several additional simulations

in which we allow only one of the components of our WTO scenario to change, while holding others

constant. That is we evaluate separately the impact of complete removal of barriers to foreign direct

investment in business services, but no other changes; the impact of an increase in market access alone;

and a cut tariff barriers only.

         Explaining Differences across Regions. While the average gain in welfare as a percent of

GDP for the whole country is 4.3 percent, we estimate that three regions will gain considerably more:

Northwest (6.2 percent), St. Petersburg (5.7 percent) and Far East (5.2 percent). The principal explanation

for the differences across regions is the ability of the different regions to benefit from a reduction in



                                                       16

barriers against foreign direct investment. Some regions may attract FDI much more easily than others. A

key parameter in our model is the initial share of multinational investment in each sector in each region.

Multinational firms have widely different shares of the business services sectors in the different regions.

A ten percent expansion of multinational firms will be a much larger absolute amount in a region that has

substantial FDI initially. Thus, larger initial shares of FDI in a region will lead to larger absolute increases

in FDI in the region when the barriers against FDI are reduced.

        In table 12b, we display our estimates of the shares of the industry captured by multinational

firms. The three regions with the largest welfare gains are clearly the regions with the estimated largest

shares of multinational investment (along with a fourth region, the North region, which also gains

substantially). All have estimated multinational shares of the service sector that are twice the national

average in maritime services, rail services, truck services, air transportation services, telecommunications,

science and financial services. On the other hand, we estimate that the Urals will gain only 3.3% of GDP,

considerably less than the national average. But we see from table 12b that the Urals has relatively little

FDI in the services sectors, as the Urals share of FDI ranges from about 50 to 70 percent of the national

average depending on the sector.


        Impact of Foreign Direct Investment Liberalization in Business Services. In this scenario,

presented in table 13b, we reduce barriers against FDI in the services sectors according to the cuts in table

12, but there is no reduction in tariffs or improved market access. Russian commitments to reduce barriers

against multinational service providers will allow multinationals to obtain greater after tax returns on their

investments in Russia. This will encourage them to increase foreign direct investment to supply the

Russian market. Although we expect some decline in the number of purely Russian owned businesses

serving the services markets, on balance there will be additional service providers. Russian users of

businesses services will then have improved access to the providers of services in areas like

telecommunication, banking , insurance, transportation and other business services. We have referenced

several empirical papers in the introduction which show that availability of a diverse set of service

suppliers is crucial to the growth of countries as this should lower the cost of doing business and increase

productivity of Russian firms using these services. We estimate that the gains to Russia from

liberalization of barriers to FDI in services are about 6.7 percent of the value of Russian consumption or

3.7 percent of the value of GDP.




                                                       17

         Impact of Improved Market Access. In table 13d, we present the results of a scenario in

which we allow for improved market access (according to the terms of trade improvements of table 12),

but we do not lower tariffs or barriers to FDI in services sectors. We estimate that the impact of improved

market access at 0.3 percent of consumption (0.2% of GDP). The gains come from both improved prices

for exports. But also a higher value for exports allows Russia to buy more imports and more varieties of

imports increase productivity. The gains are quite small compared with the gains from liberalization of

barriers against FDI in services. In part this is because the ad valorem equivalent of the barriers against

FDI are much higher than the percentage improvement in market access, while Russia already has most-

favored-nation (MFN) status or better on a bilateral or plurilateral basis with virtually all its trading

partners.

         Improved market access has a small but positive effect on all regions except Tumen. The reason

the impact is negative for Tumen is that Tumen exports mainly oil and gas, products that do not gain from

the improved market access. The improved market access results in greater exports for the economy,

which has the effect of appreciating the real exchange rate, and thereby reducing the value of the principal

exports of Tumen. This is analogous to the Dutch disease problem, except it is non-oil and gas exports

that are hurting the oil and gas exports in this scenario.


         Impact of Tariff Reduction. The results for this scenario are presented in table 13e. We lower

tariffs by fifty percent, but there is no liberalization of the barriers to FDI or improved market access. The

estimated welfare gains to the economy are 0.7 percent of consumption or 0.4 percent of GDP.

         The gains to the economy from tariff reduction alone come about for two reasons. Tariff

reduction in Russia will lead to improved domestic resource allocation since tariff reduction will induce

Russia to shift production to sectors where production is valued more highly based on world market

prices. This impact, known as the "gains from trade" is the fundamental effect from trade liberalization

and is often stressed by international trade economists. In addition, Russian businesses will be able to

more easily import a variety of modern technologies and this will increase Russian productivity.


         Summary of Overall Welfare Effects. We observe that the reduction in barriers to FDI

results in an improvement in Russian welfare on average across regions of 6.7 percent of consumption.

Improved market access and tariff reduction contribute an improvement in Russian welfare by 0.3 percent

and 0.7 percent, respectively, or a combined one percent. Thus, by far the most important effect derives

from the reduction in barriers to FDI. We also simulate a reduction in the barriers to FDI in business



                                                        18

services of only fifty percent of the cuts we assume in our WTO scenario (shown in table 13c). The gains

in Russian welfare are substantially reduced which again shows the importance of reducing barriers to

FDI in order to increase Russian productivity and welfare.


Impact on the Productive Sectors

          It is useful to discuss principles of sector analysis before discussing the results. Businessmen in

Russia sometimes complain that the tariff or FDI barriers in their sector will decline and forecast that

WTO accession could adversely impact on their sector. The initial effect of the tariff reduction is to

induce an increase in the demand for imports, and this is the immediate impact that businessmen fear. But

the rest of the world will not provide Russia with a "free lunch," i.e., the increased imports have to be

paid for by increased exports. The increased demand for imports raises the prices of foreign exchange

(more technically, depreciates the real exchange rate) that in turn induces an increase in exports and a

decrease in the quantity of imports. The real exchange rate depreciates until the value of the increase in

exports equals the value of increased imports. The percentage change in the overall value of increased

international exports is presented in the table 13a and equals 9.4 percent in our central scenario.18 The

expansion of exports varies across regions ranging from a low of 2.8 percent from Tumen to a high of 23

percent from the Central region.19

          Thus, not all sectors can decline since Russia has to pay for its imports with hard currency. It is

not the absolute level of the tariff that is important for the impact of WTO accession on the sector; rather

it is the impact of changes in protection on relative prices. The tariff reduction induces output expansion

in many sectors because, first, tariff reduction reduces the costs of imported intermediate inputs, so the

price of intermediate inputs may decline in many sectors. Second, and crucially, tariff reduction induces

a depreciation in the real exchange rate.

          Similarly, all regions within Russia must pay for their imports, from abroad or from other regions.

Analogous to our national model, we assume that each region has a balance of trade constraint such that



18 The change in the value of international exports must equal the change in the value of international imports. Since
international exports exceed international imports in the benchmark equilibrium, the percentage change in exports is
smaller than the percentage change in imports.
19 Since the initial value of exports exceeds the initial value of imports in our data set, a smaller percentage increase
in exports is equal in absolute dollar value to a larger percentage increase in imports.


                                                             19

any increase or decrease in imports is exactly matched by an increase or decrease in exports. Moreover,

total employment in the region is unchanged by the trade or FDI policy changes. Thus, an expansion of

employment in one sector must be offset by a decline in employment in another sector.

          In tables 15-18, we present the estimated results in particular sectors and region. We present the

results for the WTO overall scenario. Our WTO accession scenario involves a proportional reduction in

all tariffs to one-half of their original level, improved market access and complete removal of barriers to

FDI in services. Results are presented for output, exports, imports and employment of skilled and

unskilled labor by sector. We discuss manufacturing and services sectors separately.

          Expanding Manufacturing Sectors. Results for the manufacturing sectors that expand or

contract depend on several industry characteristics. Sectors which are likely to expand are those that

either: export a relatively large share of their output; obtain an exogenous increase in export prices as a

result of WTO accession; are relatively unprotected initially compared to other sectors of the economy; or

experience a significant reduction in the cost of their intermediate inputs, typically because they have a

large share of intermediate inputs that come from sectors that produce additional varieties due to trade or

FDI liberalization.

          The manufacturing sectors that we estimate are likely to expand their output the most are ferrous

metals, chemicals and non-ferrous metals.20 These sectors are among the sectors that we assume will gain

an exogenous increase in the price of its exports upon WTO accession. They are also among those that

export the highest share of their output--they all export over thirty percent of the value of their output on


          20 In the Saint Petersburg, Northwest, Far East and North regions, we estimate a more substantial expansion
of the business services sectors than in other regions, since, as explained above, these regions already experience
more foreign direct investment relative to the other regions of the economy. The expansion of business services in
these regions attracts labor and capital away from other sectors of the region, which explains why output of non-
ferrous metals declines in the Northwest region (and slightly in the Saint Petersburg region). But non-ferrous metals
is a rather small sector in those regions so it does not significantly detract from the economy-wide overall expansion
of non-ferrous metals--which is equal to 61 billion rubles in 2001 rubles.

          The estimated decline in the value of non-ferrous metals output in the Northwest and Saint Petersburg
regions is 100 million rubles for the sum of the decline from the two regions. Since value-added is 308 billion rubles
in Saint Petersburg and 152 billion in the Northwest region in 2001 rubles, this is only about two-tenths of one
percent of the value added of the regions. Economy-wide, value-added of non-ferrous metals is 195 billion rubles in
2001, so these declines are only about one-half of one percent of the value-added of non-ferrous metals.



                                                            20

a national basis. Export intensity is important because a reduction in tariffs generally depreciates the real

exchange rate (see Table 13 for estimates). Since the real exchange rate depreciates, sectors that export

intensively will gain an increase in the value of their exports in terms of rubles.21

         Declining Manufacturing Sectors. The sectors that contract the most are the sectors that are the

most protected prior to tariff reduction and which have a relatively small share of exports. Most notably

this includes food, machinery and equipment and construction materials. All of these sectors do little

exporting and are among the sectors with tariff rates above ten percent. Textiles and apparel, with the

highest tariff in the economy, also declines, but less significantly. But the export and import intensities

vary across regions, so results differ across regions.

         Business Services Sectors. Russian business and labor interests in these sectors are not the same,

and we discuss the impact on labor in these sectors first. Our central estimates, shown in tables 16 and 17,

are that skilled and unskilled employment will expand in several business services sectors, most notably

telecommunications, truck transportation and railway transportation services. The reason is that as a

result of a reduction in the barriers to foreign direct investment in these sectors, we estimate that there

will be an expansion in the number of multinational firms who locate in Russia to provide business

services from within Russia, and a contraction in the number of purely Russian firms. But multinationals

also demand Russian labor, even though they use Russian labor slightly less intensively than Russian

firms.22 But as more service firms enter the market, the quality adjusted price of services falls, and



21Formally, there is no money in the model, so the value of exports increases in terms of the numeraire. The real
exchange depreciates because the increased demand for imports accompanying the decline in tariffs induces an
increase in the price of foreign exchange. In addition, the reduction in barriers to multinational investment in the
services sector depreciates the real exchange rate. This is because multinationals use more foreign skilled labor, and
they must pay in foreign exchange for the foreign skilled labor from domestic sales. The depreciation of the real
exchange rate encourages exports and mutes the import expansion. The depreciated real exchange rate results in the
export sectors having an increased incentive to export even if the tariffs in the export markets are unchanged. This is
one of the primary reasons that international trade economists say that an import tariff is equivalent to a tax on
exports. Given our view that Russia will neither give nor receive a free lunch from the rest of the world in the long
run, we assume that there must be an increase in the value of exports to match the increase in the value of imports
accompanying tariff reduction. The real exchange rate is the principal variable that induces the equilibrium between
the change in imports and exports.
22As discussed above, we have employed estimates of the share of expatriate labor used by multinationals provided
by Russian research institutes in the services sectors. In general the share is small, from about 3 to 15 percent,
depending on the sector. We perform sensitivity analysis, using the high and low estimates provided by the research
institutes.


                                                           21

industries that use services expand their quantity demanded for business services. For

telecommunications, truck transportation and railway transportation services, on balance, the increase in

labor demand from the increase in the demand for business services exceeds the decline in labor demand

from the substitution of multinational supply for Russian supply in the Russian market. Thus, we estimate

that labor in these business services sectors will gain from an expansion in foreign direct investment and

multinational provision of services in Russia.

         These results are not uniform, however, as in maritime and financial services sectors we estimate

a decline in employment. In these sectors the fact that multinationals use Russian labor less intensively

dominates the impact of the greater use of business services.

         Regarding capital, as a result of the removal of restrictions, we estimate there would be

significant increase in foreign direct investment and an increase in multinational firms operating in

Russia. Regarding Russian firms, we must be careful in interpreting what this means. As discussed above,

we define joint ventures between Russian firms and multinationals as a multinational firm. An estimated

decline in Russian firms does not mean their capital moved to other sectors or disappears. In many cases,

it means the Russian firms have become joint venture partners with a multinational firm in the same

sector. Multinationals will often look for Russian joint venture partners when they want to invest in

Russia. Many Russian companies providing business services are likely to see this as a profitable

opportunity and form joint ventures with multinationals. These Russian companies will become part of

the expanding multinational share of the business services market. The Russian firms that become part of

joint ventures with foreign investors will likely preserve or increase the value of their investments.

Russian capital owners in business services who remain wholly independent of multinational firms, either

because they avoid joint ventures or are not desired as joint venture partners, will likely see the value of

their investments decline.

         This suggests that domestic lobbying interests within a service sector are very diverse regarding

FDI liberalization. We estimate that labor should find it in their interest to support FDI liberalization even

if capital owners in the sector oppose it. But capital owners themselves may have diverse interests

depending on their prospects for acquisition by multinationals.


                                                      22

                                        IV. Sensitivity Analysis


Sensitivity to Investment Potential of the Regions

         In our central scenario, results differ across regions due to a significant extent to the inherited FDI

of the regions--the more existing FDI, the more the regions are capable of attracting new FDI for the

same elasticities. In this scenario we augment the assessment of how regions may adapt and attract FDI

based on the ranking of their investment potential. For investment potential ranking we use the rankings

of Expert RA, which we explain in Appendix B. We use the investment potential rankings to adjust a

parameter in our model (etaf) that reflects the responsiveness (elasticity) of foreign investment supply to

an increase in the price of their product in the region. We assign higher values of etaf to regions with

above average investment potential rankings and conversely for low investment potential rankings. We

present these results in the first row of new results in table 19, where we also indicate how the elasticity

etaf varies across regions based on the investment rankings.

         The principal result is that the estimated gains for Moscow, St. Petersburg and Tumen increase

and the estimated gains for Siberia, Northwest, North, Central and the Far East decline. Despite smaller

estimated gains in this scenario, Far East and Northwest are still estimated to receive above average gains.

The results suggest that the gains for a region could vary considerably depending on whether it succeeds

in creating an atmosphere conducive to investment.



Sensitivity to Results to a 50% Cut in the Barriers to Foreign Direct Investment

         We perform sensitivity analysis with respect to the extent of liberalization of barriers to foreign

direct investment. In this scenario, we cut in the ad valorem tax equivalence of the barriers to FDI in the

services sectors by 50 percent of the cut we executed in our central scenario. In this scenario, we allow

for improved market access and a fifty percent cut in tariff barriers. We find that the gains to the economy

are reduced to about 4.2 percent of consumption or 2.4 percent of GDP. St. Petersburg and the Northwest

regions still are the regions that gain the most, but the rainking of gains among other regions changes

slightly--reflecting different relative gains from FDI, versus tariff reduction or improved market access.


                                                       23

Piecemeal Sensitivity Analysis

         In table 19, we present the impact on welfare of varying the value of key parameters. In these

scenarios, we retain the central value of all parameters except the parameter in question. In general, the

gains to the economy (welfare gains) increase with an increase in elasticities, since higher elasticities

imply that the economy is able to more easily shift to sectors or products that are cheaper after trade and

FDI liberalization.23 There are two parameters in the table that have a strong impact on the results: the

elasticity of substitution between value-added and business services (esubs) and the elasticity of

multinational firm supply (etaf). A liberalization of the barriers to FDI will result in a reduction in the cost

of business services, both from the direct effect of lowering the costs of doing business for multinational

service providers and from the indirect effect that additional varieties of business services allow users to

purchase a quality adjusted unit of services at less cost. When the elasticity of substitution between

value-added and business services is high (esubs = 2 in table 19), users have the greater potential to

substitute the cheaper business services and this increases productivity. The elasticity of multinational and

Russian firm supply (etaf, etad) is primarily dependent on the sector specific factor for each firm type

(foreign or domestic). When etaf is high, a reduction in the barriers to foreign direct investment results in

a larger expansion in the number of multinational firms supplying the Russian market, and hence more

gains from additional varieties of business services. In addition, the share of the services market captured

by multinationals has a strong effect, since a liberalization results in a larger number of new varieties

introduced.



                                                  V. Conclusions

         These results are consistent with the themes of empirical work on multilateral trade liberalization

that suggest that a country will generally gain more from its own liberalization than it gains from

improved access to the markets of its trading partners. Improved market access is a gain to Russia but is

quantitatively less important than its own tariff and FDI liberalization in terms of increases in Russian



23An increase in the elasticity of substitution between varieties reduces the welfare gain. This is because when
varieties are good substitutes, additional varieties are worth less to firms and consumers.


                                                           24

welfare from WTO accession. In addition, we find that in the Russian context, liberalization of barriers to

FDI are quantitatively more important than tariff liberalization. In part, this reflects the starting point of

the analysis, in which we assess that Russia has done more to lower it tariffs on goods than it has to

liberalize its barriers to FDI in services sectors. But it is also explained by the economic geography

literature that suggests that access to a diverse set of service providers is crucial for growth.

         Regional results in our central scenario differ mainly due to inherited FDI, where more existing

FDI allows a region to more easily attract new FDI when barriers against FDI are relaxed. In our

sensitivity analysis, we show that a better investment potential of a region will also lead to larger gains for

a region. So creating a good investment climate can help a region gain more from WTO accession.




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        Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade
        Organization" Review of Development Economics.

Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004a), "Estimating the Ad Valorem Equivalent
        of Barriers to Foreign Direct Investment in the Telecommunications Services Sectors in Russia."
        Available at http://www.worldbank.org/trade/russia-wto.

Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004b), "Estimating the Ad Valorem Equivalent
        of Barriers to Foreign Direct Investment in the Maritime and Air Transportation Service Sectors
        in Russia." Available at http://www.worldbank.org/trade/russia-wto.

Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004c), "Estimating the Ad Valorem Equivalent
        of Barriers to Foreign Direct Investment in Financial Services Sectors in Russia." Available at
        http://www.worldbank.org/trade/russia-wto.

Markusen, James R. (1989), "Trade in Producer Services and in Other Specialized Intermediate Inputs,"
        American Economic Review, 79:85-95.

Markusen, James R, Thomas Rutherford and David Tarr (2005), Trade and Direct Investment in Producer
        Services and the Domestic Market for Expertise," Canadian Journal of Economics, Vol 38 (3),
        758-777.
Marshall, J.N. (1988), Services and Uneven Development, London: Oxford University Press.

Matusz, Steven J, and David G. Tarr (2001), "Adjusting to Trade Policy Reform," in A. Krueger (ed.),
        Economic Policy Reform, Chicago: University of Chicago Press.

Romer, Paul. (1994), "New Goods, Old Theory and the Welfare Costs of Trade Restrictions," Journal of
        Development Economics," 43(1), February, 5-38.

Rossat (2002; 2003), Regions of Russia: Social and Economic Indicators, Moscow: Federal Service of
        State Statistics.

Rutherford, Thomas F. and David Tarr (2002), "Trade Liberalization and Endogenous Growth in a Small
        Open Economy," Journal of International Economics.




                                                    26

Rutherford, Thomas F. (1999), "Applied General Equilibrium Modeling with MPSGE as a GAMS
       Subsystem: An Overview of the Modeling Framework and Syntax", Computational Economics.

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       Russia: 2001-2003," in Trade Policy and WTO Accession for Development in Russia and the
       CIS: A Handbook, edited by David Tarr, in Russian, Moscow: Ves Mir. Available in English and
       Russian at www.worldbank.org/trade/russia-wto.

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Vernon, R. (1960), Metropolis 1985, Cambridge: Harvard University Press.




                                                27

Table 1. List of Sectors


1. Sectors where foreign direct investment from new multinational services providers is
possible

          RLW       Railway transportation
          TRK       Truck transportation
          PIP       Pipelines transportation
          MAR       Maritime transportation
          AIR       Air transportation
          TRO       Other transportation
          TMS       Telecommunications
          SCI       Science & science servicing
          FIN       Financial services


2. Sectors where new foreign firms may provide new goods from abroad

          FME       Ferrous metallurgy
          NFM       Non-ferrous metallurgy
          CHM       Chemical & oil-chemical industry
          MWO       Mechanical engineering & metal-working
          TPP       Timber & woodworking & pulp & paper industry
          CNM       Construction materials industry
          FOO       Food industry
          OTI       Other industries

3. Competitive sectors subject to constant returns to scale

          HEA       Public services, culture and arts
          AGR       Agriculture & forestry
          COL       Coalmining
          HOU       Housing and communal services
          CON       Construction
          ELE       Electric industry
          GAS       Gas
          CRU       Crude oil extraction
          OIL       Oil refining and processing
          OTH       Other goods-producing sectors
          PST       Post
          TRD       Wholesale and retail trade
          CLI       Textiles and apparel




                                              28

Table 2. List of Russian Regional Markets and Oblasts


Russian regional "markets" (markets are aggregates of oblasts defined below)

     msc   Moscow                 (msk,mos)
      stp  Saint-Petersburg       (len,spb)
      tmn  Tumenskaya             (tum,kha,yam)
      vgd  Northwest              (vol,klg,nov,psk)
      nor  North                  (kpa,nen,krl,kom,arh,mur)
      cen  Central                (bel,bry,vla,vor,iva,kal,kos,krs,lip,orl,rya,smo,tam,tve,tul,yar)
      sou  South                  (sar,ady,dag,ing,kab,klr,kar,sev,kdk,sta,ast,vlg,ros)
      url  Urals                  (mar,mor,tat,udm,chv,kir,niz,pen,ulo,ore,sam,bas,per,krg,sve,chl)
      sib  Siberia                (alr,bur,tyv,hak,alt,irk,kem,nvs,tom,oms,eve,tai,ust,kra,sah,kam,mag,kor,chu)
      far  Far East               (agi,chi,hab,amu,sao,pri,eao)


Oblasts (plus Republics, Territories, Federal Cities, Autonomous Regions, Autonomous Districts)

  1. ady   Adygeya, The Republic of                          46. mar         Mari El, The Republic of
  2. agi   Aginsky Buryatsky Autonomous District             47. mor         Mordovia, The Republic of
  3. alt   Altaisky krai                                     48. msk         Moscow city
  4. alr   Altay Republic                                    49. mos         Moskovskaya
  5. amu   Amurskaya                                         50. mur         Murmanskaya
  6. arh   Arkhangelskaya                                    51. nen         Nenetsky Autonomous District
  7. ast   Astrakhanskaya                                    52. niz         Nizhegorodskaya
  8. bas   Bashkortostan, The Republic of                    53. sev         North Osetia, The Republic of
  9. bel   Belgorodskaya                                     54. nov         Novgorodskaya
  10. bry  Bryanskaya                                        55. nvs         Novosibirskaya
  11. bur  Buryatia, The Republic of                         56. oms         Omskaya
  12. chr  Chechnya (sou), The Republic of */                57. ore         Orenburgskaya
  13. chl  Chelyabinskaya                                    58. orl         Orlovskaya
  14. chi  Chitinskaya                                       59. pen         Penzenskaya
  15. chu  Chukotsky Autonomous District                     60. per         Permskaya
  16. chv  Chuvashia, The Republic of                        61. pri         Primorsky krai
  17. dag  Dagestan, The Republic of                         62. psk         Pskovskaya
  18. eve  Evenkiysky Autonomous District                    63. ros         Rostovskaya
  19. ing  Ingushetia, The Republic of                       64. rya         Ryazanskaya
  20. irk  Irkutskaya                                        65. spb         Saint Petersburg City
  21. iva  Ivanovskaya                                       66. sah         Sakha, The Republic of
  22. eao  Jewish Autonomous Region                          67. sao         Sakhalinskaya
  23. kab  Kabardino Balkaria, The Republic of               68. sam         Samarskaya
  24. klg  Kaliningradskaya                                  69. sar         Saratovskaya
  25. kal  Kaluzhskaya                                       70. smo         Smolenskaya
  26. klr  Kalymykia, The Republic of                        71. sta         Stavropolsky krai
  27. kam  Kamchatskaya                                      72. sve         Sverdlovskaya
  28. kar  Karachaevo Cherkessia, The Republic of            73. tai         Taimyrsky (Dolgano-Nenetsky) Autonomous
  29. krl  Karelia, The Republic of                                          District
  30. kem  Kemerovskaya                                      74. tam         Tambovskaya
  31. hab  Khabarovsky krai                                  75. tat         Tatarstan, The Republic of
  32. hak  Khakasia, The Republic of                         76. tom         Tomskaya
  33. kha  Khanty-Mansiysky Autonomous District              77. tul         Tulskaya
  34. kir  Kirovskaya                                        78. tum         Tumenskaya
  35. kom  Komi, The Republic of                             79. tve         Tverskaya
  36. kpa  Komi-Permyatsky Autonomous District               80. tyv         Tyva, The Republic of
  37. kor  Koryaksky Autonomous District                     81. udm         Udmurtia, The Republic of
  38. kos  Kostromskaya                                      82. ulo         Ulyanovskaya
  39. kdk  Krasnodarsky krai                                 83. ust         Ust-ordynsky Buryatsky Autonomous District
  40. kra  Krasnoyarsky krai                                 84. vla         Vladimirskaya
  41. krg  Kurganskaya                                       85. vlg         Volgogradskaya
  42. krs  Kurskaya                                          86. vol         Vologodskaya
  43. len  Leningradskaya                                    87. vor         Voronezhskaya
  44. lip  Lipetskaya                                        88. yam         Yamalo-Nenetsky Autonomous District
  45. mag  Maganskaya                                        89. yar         Yaroslavskaya

*/No data.

                                                            29

Table 3. Value Added in 2000 by Russian Regional Market and by Sector
             (in billions of 2001 rubles)   a/

                                                                                           St.       North-   Sector
Sector b/  Central Far East         North      Siberia      South        Urals Moscow Petersb. Tumen  west     total

MAR           4.7         1.3         1.4         7.2          4.1       14.7     4.0    1.7     6.9  1.0     47.1

AGR         110.2        17.5         7.4       84.8       125.2       155.3    15.2    11.8     8.2  15.1   550.7

AIR           3.6         1.3         1.0         5.3          4.5       10.0   14.9     1.6     2.5  0.6     45.2

CHM          13.9         0.3         1.6       10.6           9.3       35.6     7.7    2.0     0.1  4.2     85.4

CNM          10.3         1.1         0.7         4.6          6.7       12.2     8.8    2.4     0.8  0.7     48.2

COL           0.1         5.2         3.2       32.6           1.7        1.2                                 43.9

CON          76.4         7.1         6.1       54.7       105.2       151.6    138.6   29.7    24.1  11.4   605.0

CRU                       4.5       16.9          9.9        37.7        97.5                  282.9  1.0    450.3

ELE          26.7         7.8         7.1       34.7         22.9        62.9   42.9    10.5    19.1  5.8    240.4

FIN          43.5        16.4       14.3        60.7         44.0      106.8    115.7   20.8    47.0  9.0    478.0

FME          19.0         0.5         1.6         9.0          3.9       33.8     2.3    1.1          11.3    82.5

FOO          24.9         9.2         3.7       17.4         24.0        28.1   33.9    17.7     0.5  4.7    164.2

GAS                       0.2         0.4         0.5          1.5        2.8                   52.2  0.0     57.5

HEA          42.3        17.5       14.6        58.8         45.0      100.6    126.3   21.8    40.6  8.9    476.4

HOU          22.1         8.8         6.7       29.1         24.8        50.9   68.0    11.2    13.7  4.6    239.8

MWO          38.7        11.2         2.6       19.4         18.0      101.8    45.4    18.9     3.0  3.6    262.7

NFM           2.5         8.1         9.3      113.2           4.8       46.0     7.1    4.4          0.2    195.5

OIL          12.2         4.7         1.5       17.4         10.1        39.2     4.4    7.2     3.9         100.6

OTH           7.7         2.0         2.2         8.8          4.9       18.4     6.3    3.3     7.4  1.9     62.8

OTI          13.6         0.6         0.7         2.5          4.0        9.2     9.9    2.6     0.1  0.9     44.0

PIP           1.5         0.5         0.5         2.0          1.7        5.4     1.2    0.7     5.2  0.2     18.7

PST           2.7         1.0         0.8         3.5          2.9        6.3     6.8    1.3     2.8  0.6     28.7

RLW          23.5         6.9         6.3       30.2         20.5        53.8   34.5     9.4    16.1  6.2    207.3

SCI           8.8         3.2         2.4       10.4           7.8       22.6   16.5     4.1     9.8  1.5     87.0

TMS           7.6         2.8         2.2       10.1           8.3       18.7   21.1     3.6     7.4  1.5     83.3

TPP           6.8         4.4       15.1        13.1           2.6       13.0     6.8    7.3     0.6  4.2     73.9

TRD         205.4        78.2       67.0       300.6       223.3       535.3    456.6  101.6   386.8  48.2  2,402.9

TRK          10.2         3.5         3.1       13.6         11.3        24.6   23.0     4.7    12.9  2.2    109.2

TRO           4.5         1.5         1.3         5.7          5.1       11.2   10.7     2.0     5.7  0.9     48.7

CLI           3.8         1.6         1.2         5.0          4.4        8.6   12.9     2.0     1.9  0.8     42.3
Market
total       747.2      228.8       202.7       975.2       790.0     1,778.0  1,241.5  305.6   962.3 151.1  7,382.4

a/Value added defined here does not include taxes.
b/ Sector codes are in Table 1, oblasts in the markets are listed in Table 2.


Source: Regions of Russia, Roskomstat, and authors' calculations.


                                                                  30

Table 4. Value-Added by Sector as a percent of the Value-Added of the Regional Market

                                                                           St.         North-
Sector        Central Far East     North  Siberia South  Urals Moscow Petersb. Tumen    west

MAR               0.6        0.6      0.7    0.7    0.5   0.8     0.3     0.6     0.7    0.6

AGR              14.8        7.6      3.7    8.7   15.8   8.7     1.2     3.9     0.9   10.0

AIR               0.5        0.6      0.5    0.5    0.6   0.6     1.2     0.5     0.3    0.4

CHM               1.9        0.1      0.8    1.1    1.2   2.0     0.6     0.7     0.0    2.8

CNM               1.4        0.5      0.4    0.5    0.8   0.7     0.7     0.8     0.1    0.4

COL               0.0        2.3      1.6    3.3    0.2   0.1

CON              10.2        3.1      3.0    5.6   13.3   8.5    11.2     9.7     2.5    7.6

CRU                          1.9      8.3    1.0    4.8   5.5                    29.4    0.7

ELE               3.6        3.4      3.5    3.6    2.9   3.5     3.5     3.5     2.0    3.9

FIN               5.8        7.2      7.0    6.2    5.6   6.0     9.3     6.8     4.9    5.9

FME               2.5        0.2      0.8    0.9    0.5   1.9     0.2     0.4            7.5

FOO               3.3        4.0      1.8    1.8    3.0   1.6     2.7     5.8     0.1    3.1

GAS                          0.1      0.2    0.0    0.2   0.2                     5.4    0.0

HEA               5.7        7.6      7.2    6.0    5.7   5.7    10.2     7.1     4.2    5.9

HOU               3.0        3.8      3.3    3.0    3.1   2.9     5.5     3.7     1.4    3.0

MWO               5.2        4.9      1.3    2.0    2.3   5.7     3.7     6.2     0.3    2.4

NFM               0.3        3.5      4.6   11.6    0.6   2.6     0.6     1.4            0.1

OIL               1.6        2.1      0.8    1.8    1.3   2.2     0.4     2.4     0.4

OTH               1.0        0.9      1.1    0.9    0.6   1.0     0.5     1.1     0.8    1.2

OTI               1.8        0.2      0.3    0.3    0.5   0.5     0.8     0.8     0.0    0.6

PIP               0.2        0.2      0.2    0.2    0.2   0.3     0.1     0.2     0.5    0.1

PST               0.4        0.4      0.4    0.4    0.4   0.4     0.6     0.4     0.3    0.4

RLW               3.1        3.0      3.1    3.1    2.6   3.0     2.8     3.1     1.7    4.1

SCI               1.2        1.4      1.2    1.1    1.0   1.3     1.3     1.4     1.0    1.0

TMS               1.0        1.2      1.1    1.0    1.0   1.1     1.7     1.2     0.8    1.0

TPP               0.9        1.9      7.5    1.3    0.3   0.7     0.5     2.4     0.1    2.8

TRD              27.5       34.2     33.0   30.8   28.3  30.1    36.8    33.3    40.2   31.9

TRK               1.4        1.5      1.5    1.4    1.4   1.4     1.9     1.5     1.3    1.5

TRO               0.6        0.7      0.7    0.6    0.6   0.6     0.9     0.7     0.6    0.6

CLI               0.5        0.7      0.6    0.5    0.6   0.5     1.0     0.7     0.2    0.5

Total           100.0      100.0   100.0  100.0   100.0 100.0   100.0  100.0    100.0  100.0

See Table 1 for sector codes.


Source: Regions of Russia, Roskomstat.




                                                   31

Table 5. Share of Sector Value-Added by Regional Market of Russia
        (in percent)


                                                                         St.        North- Sector
Good      Central Far East  North    Siberia    South  Urals Moscow Petersb. Tumen   west   Total

MAR         10.0       2.8     2.9     15.2        8.8  31.3    8.5     3.7    14.7  2.0   100.0

AGR         20.0       3.2     1.3     15.4      22.7   28.2    2.8     2.1     1.5  2.7   100.0

AIR          7.9       2.8     2.1     11.6      10.0   22.1   32.9     3.6     5.5  1.4   100.0

CHM         16.3       0.4     1.8     12.4      10.9   41.6    9.1     2.4     0.1  4.9   100.0

CNM         21.3       2.3     1.5       9.6     13.9   25.2   18.2     4.9     1.6  1.4   100.0

COL          0.3      11.8     7.3     74.2        3.8  2.7                                100.0

CON         12.6       1.2     1.0       9.0     17.4   25.1   22.9     4.9     4.0  1.9   100.0

CRU                    1.0     3.7       2.2       8.4  21.7                   62.8  0.2   100.0

ELE         11.1       3.2     2.9     14.4        9.5  26.2   17.9     4.4     7.9  2.4   100.0

FIN          9.1       3.4     3.0     12.7        9.2  22.3   24.2     4.4     9.8  1.9   100.0

FME         23.0       0.6     1.9     10.9        4.8  40.9    2.8     1.4         13.7   100.0

FOO         15.2       5.6     2.2     10.6      14.6   17.1   20.6    10.8     0.3  2.8   100.0

GAS                    0.3     0.7       0.8       2.5   4.8                   90.8  0.0   100.0

HEA          8.9       3.7     3.1     12.3        9.4  21.1   26.5     4.6     8.5  1.9   100.0

HOU          9.2       3.7     2.8     12.1      10.3   21.2   28.4     4.7    5.7   1.9   100.0

MWO         14.7       4.3     1.0       7.4       6.8  38.8   17.3     7.2     1.1  1.4   100.0

NFM          1.3       4.1     4.7     57.9        2.5  23.6    3.6     2.2          0.1   100.0

OIL         12.1       4.7     1.5     17.3      10.0   38.9    4.4     7.2     3.9        100.0

OTH         12.3       3.2     3.6     14.0        7.8  29.3   10.0     5.2    11.7  3.0   100.0

OTI         30.8       1.3     1.6       5.7       9.1  20.9   22.5     5.8     0.2  2.1   100.0

PIP          7.8       2.7     2.5     10.5        8.9  28.9    6.4     3.6    27.7  1.0   100.0

PST          9.3       3.5     2.8     12.2      10.1   22.0   23.9     4.5     9.9  1.9   100.0

RLW         11.3       3.3     3.0     14.6        9.9  25.9   16.6     4.5     7.8  3.0   100.0

SCI         10.1       3.6     2.8     11.9        8.9  26.0   18.9     4.7    11.3  1.7   100.0

TMS          9.1       3.3     2.7     12.1        9.9  22.4   25.3     4.3     8.9  1.8   100.0

TPP          9.3       5.9    20.5     17.8        3.5  17.6    9.2     9.8     0.8  5.7   100.0

TRD          8.5       3.3     2.8     12.5        9.3  22.3   19.0     4.2    16.1  2.0   100.0

TRK          9.3       3.2     2.8     12.5      10.4   22.6   21.1     4.3    11.8  2.0   100.0

TRO          9.2       3.1     2.7     11.7      10.5   23.0   21.9     4.2    11.8  1.9   100.0

CLI          9.0       3.7     2.8     11.7      10.5   20.4   30.6     4.7     4.6  1.9   100.0

Total      329.2      99.4    96.2    453.3     284.4  734.1  465.5  128.4    340.9 68.7   3000.0


Source: Regions of Russia and authors calculations.



                                                     32

Table 6: Exports by Product and by Regional Market (in billions of 2001 rubles)

                                                                               St.        North-
Good      Central Far East      North    Siberia     South   Urals Moscow Petersb. Tumen   west

MAR           4.9       1.7        1.7       7.1      4.5    13.7     4.3     2.2    6.9    1.2

AGR           3.1       0.5        0.2       2.4      3.5     4.4     0.4     0.3    0.2    0.4

AIR           5.6       2.4        1.8       7.6      6.8    13.2    18.9     3.0    4.4    1.2

CHM          27.5       0.5        2.0     33.2      18.2    92.0    13.7     5.0    0.3   15.2

CNM           1.8       0.2        0.1       1.7      1.2     3.4     1.6     0.6     0.2   0.2

COL           0.1       4.2        2.6     26.4       1.3     0.9

CON           4.0       0.4        0.3       2.9      5.6     8.0     7.3     1.6    1.3    0.6

CRU                     7.1      27.0      15.8      60.3   156.1                   452.8   1.6

ELE           1.1       0.3        0.3       1.4      1.0     2.6     1.8     0.4    0.8    0.2

FIN           0.7       0.3        0.2       0.9      0.7     1.6     1.8     0.3    0.7    0.1

FME          50.5       1.1        3.7     22.4      11.1    83.8     7.1     4.8          28.3

FOO           8.7       9.7        2.5     12.6      23.1    10.1    27.0     9.2    0.1    3.6

GAS                     1.4        3.1       3.6     11.5    21.8                   410.7   0.0

HEA           0.3       0.1        0.1       0.4      0.3     0.6     0.8     0.1    0.2    0.1

HOU           0.3       0.1        0.1       0.4      0.3     0.6     0.8     0.1    0.2    0.1

MWO          22.8      14.2        1.9     43.0      23.3    97.0    95.5    30.4    1.9    5.0

NFM           4.6      15.1      18.4     203.1      11.1   101.6    17.2    11.3           0.6

OIL          35.5      13.7        4.5     50.9      29.5   114.4    12.8    21.1    11.5

OTH           1.7       0.4        0.5       1.9      1.1     4.0     1.4     0.7    1.6    0.4

OTI           7.2       0.3        0.4       1.3      2.1     4.9     5.3     1.4    0.0    0.5

PST           0.3       0.1        0.1       0.5      0.4     0.8     0.9     0.2    0.4    0.1

RLW           0.9       0.3        0.2       1.1      0.8     2.0     1.3     0.3    0.6    0.2

SCI           0.6       0.2        0.2       0.7      0.5     1.6     1.1     0.3    0.7    0.1

TMS           0.9       0.3        0.3       1.2      1.0     2.1     2.4     0.4    0.9    0.2

TPP           3.1      17.1      29.2      35.1       1.7    13.3     2.1    12.7    0.4    8.1

TRD           2.0       0.8        0.7       3.0      2.2     5.3     4.5     1.0    3.9    0.5

TRK           0.4       0.1        0.1       0.5      0.4     0.9     0.9     0.2    0.5    0.1

TRO           0.5       0.2        0.2       0.7      0.6     1.3     1.2     0.2    0.7    0.1

CLI           2.6       1.1        0.8       3.4      3.1     6.0     9.0     1.4    1.3    0.5

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.




                                                      33

Table 7. Sector Exports as a Percent of Total Exports of the Regional Market

                                                                             St.       North-
Good      Central Far East      North   Siberia     South  Urals Moscow Petersb. Tumen  west

MAR            3          2         2          1        2      2      2       2      1     2
AGR            2          1         0          0        2      1      0       0      0     1
AIR            3          3         2          2        3      2      8       3      0     2
CHM           14          1         2          7        8     12      6       5      0   22
CNM            1          0         0          0        1      0      1       1      0     0
COL            0          4         3          5        1      0
CON            2          0         0          1        2      1      3       1      0     1
CRU                       8        26          3      27      20                    50     2
ELE            1          0         0          0        0      0      1       0      0     0
FIN            0          0         0          0        0      0      1       0      0     0
FME           26          1         4          5        5     11      3       4          41
FOO            5         10         2          3      10       1     11       8      0     5
GAS                       2         3          1        5      3                    45     0
HEA            0          0         0          0        0      0      0       0      0     0
HOU            0          0         0          0        0      0      0       0      0     0
MWO           12         15         2          9      10      13     40      28      0     7
NFM            2         16        18        42         5     13      7      10            1
OIL           19         15         4        10       13      15      5      19      1
OTH            1          0         0          0        0      1      1       1      0     1
OTI            4          0         0          0        1      1      2       1      0     1
PST            0          0         0          0        0      0      0       0      0     0
RLW            0          0         0          0        0      0      1       0      0     0
SCI            0          0         0          0        0      0      0       0      0     0
TMS            0          0         0          0        0      0      1       0      0     0
TPP            2         18        28          7        1      2      1      12      0   12
TRD            1          1         1          1        1      1      2       1      0     1
TRK            0          0         0          0        0      0      0       0      0     0
TRO            0          0         0          0        0      0      1       0      0     0
CLI            1          1         1          1        1      1      4       1      0     1

Total        100        100       100      100       100     100    100    100     100  100

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.



                                                   34

Table 8. Sector Export Intensities by Regional Market: Exports of the Sector as a
           Percent of Production of the Sector (in percent)

                                                                                   St.       North-
Good       Central Far East        North    Siberia    South     Urals Moscow Petersb. Tumen  west

MAR             53         57         57          51     53        50      54      57     51   57
AGR              1          1          1           1      1         1       1       1      1     1
AIR             46         48         48          44     45        43      43      48     47   48
CHM             27         21         17          48     27        37      24      35     40   57
CNM              3          3          3           6      3         5       3       4      4     7
COL             23         23         23          23     23        23
CON              3          3          3           3      3         3       3       3      3     3
CRU                        56         56          56     56        56                     56   56
ELE              2          2          2           2      2         2       2       2      2     2
FIN              1          1          1           1      1         1       1       1      1     1
FME             40         35         34          37     43        37      48      75          37
FOO              4         12          8           8     11         4       9       6      3     9
GAS                        27         27          27     27        27                     27   27
HEA              0          0          0           0      0         0       0       0      0     0
HOU              1          1          1           1      1         1       1       1      1     1
MWO             11         25         14          48     25        18      45      32     12   27
NFM             44         45         48          43     58        55      62      67          79
OIL             32         32         32          32     32        32      32      32     32
OTH             12         12         12          12     12        12      12      12     12   12
OTI             15         15         15          15     15        15      15      15     15   15
PST              9          9          9           9      9         9       9       9      9     9
RLW              2          2          2           2      2         2       2       2      2     2
SCI              3          3          3           3      3         3       3       3      3     3
TMS              7          7          7           7      7         7       7       7      7     7
TPP              7         77         33          48     11        17       5      30     12   33
TRD              1          1          1           1      1         1       1       1      1     1
TRK              2          2          2           2      2         2       2       2      2     2
TRO              5          5          5           5      5         5       5       5      5     5
CLI             11         11         11          11     11        11      11      11     11   11
Total          364        534       474          545    488      477      397    459     357  510

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.




                                                      35

Table 9: Imports by Product and by Regional Market (in x 2001 rubles)

                                                                                St.        North-
Good       Central Far East      North    Siberia    South    Urals Moscow Petersb. Tumen   west

AGR           4.6        1.4        0.8        4.3     5.0      7.6    7.9     2.0     1.3   0.8

AIR           0.3        0.1        0.1        0.4     0.3      0.6    0.9     0.1     0.2   0.1

CHM          12.5        4.0        2.8      18.8      9.7     25.5   47.9     8.9     4.9   3.6

CNM           2.7        0.5        0.4        3.3     2.8      4.6   11.6     2.2     0.8   0.7

COL           0.6        0.2        0.2        1.3     0.4      1.2    0.6     0.2     0.2   0.2

CON           8.0        4.0        3.4      10.2     10.0     20.4   15.3     4.7    16.4   1.7

CRU           1.5        0.6        0.2        2.2     1.4      5.1    0.6     0.9     1.1   0.0

ELE           0.3        0.1        0.1        0.4     0.3      0.8    0.5     0.1     0.2   0.1

FIN           1.6        0.6        0.5        2.2     1.6      3.7    4.2     0.8     1.7   0.3

FME           9.5        1.2        0.8        3.1     7.8     11.7   13.4     4.3    2.9    2.8

FOO          19.1        7.2        3.0      14.4     17.9     25.3   79.1    36.2     4.2   7.7

GAS           0.1        0.0        0.0        0.1     0.1      0.3    0.2     0.0     0.2   0.0

HEA           0.9        0.4        0.3        1.3     1.0      2.3    2.8     0.5     0.9   0.2

HOU           3.5        1.4        1.1        4.6     3.9      8.0   10.7     1.8     2.2   0.7

MWO          35.8       14.3        8.2      18.0     31.1     60.3  198.5    49.6    20.1  10.9

NFM           3.9        2.1        1.5      13.2      4.7     10.9    9.2     3.5     2.2   1.2

OIL           4.1        1.1        1.0        4.5     4.0      9.0    6.4     1.6     3.3   0.8

OTH           1.7        0.6        0.5        2.2     1.7      4.1    3.7     0.8     2.0   0.4

OTI           1.2        0.3        0.2        1.1     1.1      2.1    1.5     0.4     0.6   0.2

PST           0.1        0.0        0.0        0.1     0.1      0.2    0.2     0.0     0.1   0.0

RLW           0.1        0.0        0.0        0.2     0.1      0.3    0.3     0.1     0.2   0.0

SCI           0.0        0.0        0.0        0.0     0.0      0.1    0.1     0.0     0.0   0.0

TMS           0.8        0.3        0.2        1.0     0.8      1.8    2.0     0.4     0.7   0.2

TPP           2.7        0.6        1.1        0.7     3.6      2.0   22.1     7.3     0.1   2.8

TRD           1.0        0.4        0.3        1.5     1.1      2.6    2.4     0.5     1.5   0.2

TRK           0.4        0.1        0.1        0.5     0.4      0.9    0.9     0.2     0.5   0.1

TRO           0.1        0.0        0.0        0.2     0.2      0.3    0.3     0.1     0.2   0.0
CLI            25         10          8         32     29        56     84      13     13      5
Total         142         52        35       142       140     268     528    140      81    41

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.




                                                     36

Table 10. Sector Imports as a Percent of Total Imports of the Regional Market

                                                                               St.       North-
Good      Central Far East      North    Siberia     South   Urals Moscow Petersb. Tumen  west

AGR            3          3         2          3        4       3       1       1      2     2

AIR            0          0         0          0        0       0       0       0      0     0

CHM            9          8         8        13         7      10       9       6      6     9

CNM            2          1         1          2        2       2       2       2      1     2

COL            0          0         1          1        0       0       0       0      0     1

CON            6          8        10          7        7       8       3       3     20     4

CRU            1          1         1          2        1       2       0       1      1     0

ELE            0          0         0          0        0       0       0       0      0     0

FIN            1          1         2          2        1       1       1       1      2     1

FME            7          2         2          2        6       4       3       3      4     7

FOO           13         14         9        10        13       9      15      26      5   19

GAS            0          0         0          0        0       0       0       0      0     0

HEA            1          1         1          1        1       1       1       0      1     0

HOU            2          3         3          3        3       3       2       1      3     2

MWO           25         28        23        13        22      23      38      35     25   27

NFM            3          4         4          9        3       4       2       2      3     3

OIL            3          2         3          3        3       3       1       1      4     2

OTH            1          1         1          2        1       2       1       1      2     1

OTI            1          1         1          1        1       1       0       0      1     0

PST            0          0         0          0        0       0       0       0      0     0

RLW            0          0         0          0        0       0       0       0      0     0

SCI            0          0         0          0        0       0       0       0      0     0

TMS            1          1         1          1        1       1       0       0      1     0

TPP            2          1         3          1        3       1       4       5      0     7

TRD            1          1         1          1        1       1       0       0      2     1

TRK            0          0         0          0        0       0       0       0      1     0

TRO            0          0         0          0        0       0       0       0      0     0

CLI           17         20        22        23        21      21      16       9     15   13

Total        142         52        35       142       140     268     528    140      81   41

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.




                                                     37

Table 11. Sector Import Intensities by Regional Market: Regional Imports of the
             Sector as a Percent of Regional Consumption of the Product

                                                                             St.       North-
Good     Central Far East      North   Siberia      South  Urals Moscow Petersb. Tumen  west

AGR            4         4         4          4         4      4      4       4      4     4

AIR            3         3         3          3         3      3      3       3      3     3

CHM           35        36        30         47       29      33     74      58     19   57

CNM           13        16        13         18       10      11     29      25      8   20

COL            7         7         7          7         7      7      7       7      7     7

CON            8         8         8          8         8      8      8       8      8     8

CRU            5         5         5          5         5      5      5       5      5     5

ELE            1         1         1          1         1      1      1       1      1     1

FIN            1         1         1          1         1      1      1       1      1     1

FME           32        17        19         14       36      17     38      37     25   75

FOO           25        23        13         15       20      15     31      94     10   50

GAS            2         2         2          2         2      2      2       2      2     2

HEA            1         1         1          1         1      1      1       1      1     1

HOU            8         8         8          8         8      8      8       8      8     8

MWO           36        37        24         15       27      26     98    100      16   53

NFM           16        40        41         96       35      23     39      41     23   25

OIL            8         8         8          8         8      8      8       8      8     8

OTH           19        19        19         19       19      19     19      19     19   19

OTI            7         6         6          6         6      6      7       7      6     6

PST            2         2         2          2         2      2      2       2      2     2

RLW            0         0         0          0         0      0      0       0      1     0

SCI            0         0         0          0         0      0      0       0      0     0

TMS            6         6         6          6         6      6      6       6      6     6

TPP           13        11        28          4       19       5     60      83      1   78

TRD            0         0         0          0         0      0      0       0      0     0

TRK            2         2         2          2         2      2      2       2      2     2

TRO            1         1         1          1         1      1      1       1      1     1

CLI           73        73        73         73       73      73     73      73     73   73

Total        142        52        35      142        140     268    528    140      81   41

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.




                                                     38

Table 12a. Tariff Rates, Export Tax Rates, Estimated Ad Valorem Equivalence of
Barriers to FDI in Services Sectors and Estimated Improved Market Access
(ad-valorem in %) -- by sector

                                                                              Estimated     Equivalent % barriers
                                                                              change in             to FDI
                                                                Export tax world market                 Post-WTO
                                                  Tariff rates    rates         price      Base Year Accession

Electric industry                                       2.6         0.0           0.0

Oil extraction                                          0.0         7.9           0.0

Oil processing                                          4.5         4.6           0.0

Gas                                                     5.0        18.8           0.0

Coalmining                                              2.2         0.0           0.0

Other fuel industries                                   5.0         2.6           0.0

Ferrous metallurgy                                      5.9         0.4           1.5

Non-ferrous metallurgy                                  8.5         5.3           1.5

Chemical & oil-chemical industry                        7.5         1.6           1.5

Mechanical engineering & metal-working                10.7          0.0           0.0

Timber & woodworking & pulp & paper industry          13.5          6.9           0.0

Construction materials industry                       12.0          1.6           0.0

Textiles and Apparel                                  16.8          4.1           0.5

Food industry                                         14.1          3.1           0.5

Other industries                                      12.4          0.0           0.5

Agriculture & forestry                                  8.4         0.6           0.0

Other goods-producing sectors                         14.6          0.0           0.5

Telecommunications                                                                            33.0          0.0

Science & science servicing (market)                                                          33.0          0.0

Financial services                                                                            36.0          0.0

Railway transportation                                                                        33.0          0.0

Truck transportation                                                                          33.0          0.0

Pipelines transportation                                                                      33.0          0.0

Maritime transportation                                                                       95.0         80.0

Air transportation                                                                            90.0         75.0

Other transportation                                                                          33.0          0.0

Source: Tarr, Shepotylo and Koudoyarov (2005) for tariff rates; Kimura et al. (2004a,b,c) for barriers to FDI;
Roskomstat for export tax rates; authors' estimates for change in world market prices.




                                                        39

Table 12b. Shares of Business Services Sectors in the Regions of Russia Captured
by Multinational Firms (ad-valorem in %) -- by sector

                                                         Other
                 Maritime Rail  Truck  Pipeline    Air  Transp. Telecom Science Financial

Moscow           0.47      0.02   0.03   0.06       0.11   0.02    0.08    0.07   0.13

St. Petersburg   0.70      0.06   0.10   0.06       0.50   0.08    0.30    0.20   0.18

Tumen            0.29      0.04   0.05   0.01       0.46   0.04    0.20    0.12   0.08

Northwest        0.70      0.06   0.10   0.06       0.50   0.08    0.30    0.20   0.20

North            0.70      0.06   0.10   0.06       0.50   0.08    0.30    0.20   0.20

Central          0.41      0.03   0.07   0.05       0.35   0.05    0.20    0.09   0.09

South            0.46      0.04   0.06   0.05       0.30   0.05    0.19    0.10   0.09

Urals            0.15      0.02   0.03   0.02       0.16   0.03    0.09    0.07   0.04

Siberia          0.28      0.02   0.05   0.03       0.26   0.04    0.15    0.09   0.07

Far East         0.70      0.06   0.10   0.06       0.50   0.08    0.30    0.20   0.20



National average 0.35      0.03   0.05   0.03       0.25   0.04    0.15    0.10   0.10




                                            40

Table 13a. Impact of WTO Accession on Regional Markets (% change from base year)

                                        Overall          St.        North-                                   Far
                                       average Moscow Peters. Tumen  west North Central South Urals Siberia East


Aggregate welfare

Welfare (EV as % of consumption)           7.8    7.0  10.6     13.8  11.2   9.8  7.6     8.3   6.2    7.6    9.7

Welfare (EV as % of GDP)                   4.3    4.7    5.7    3.1   6.2    4.7  4.2     4.7  3.3     4.2   5.2



Aggregate trade

Regional terms of trade (% change)         3.3    4.9    6.4    4.4   6.1    5.2  4.8     4.4   3.7    3.6    5.4

Regional exports (% change)                1.9    2.6    2.1    1.8   2.1    2.2  2.2     1.7  1.6     1.6   2.4

Real exchange rate (% change)              2.5    2.6    3.4    2.7   2.9    2.7  2.8     2.8   1.9    1.9    3.0

International exports (% change)           9.4   13.3  19.1     2.8   17.3   7.7 23.0    10.9 10.8     8.0  11.1



Return to primary factors (% change)

Unskilled labor                            4.1    4.7    6.6    4.2    6.1   5.5  3.8     4.9   2.5    4.1    6.2

Skilled labor                              4.2    3.5    7.4    3.8    7.2   5.7  5.3     5.1   2.9    4.4    6.9

National capital                           4.0    4.2    4.9    4.2    4.4   4.2  4.4     4.3   3.4    3.4    4.6

Regional mobile capital                    6.5    6.6  10.2     5.4   10.2   7.6  6.9     6.5   5.5    6.1   8.0

Crude oil resources                        4.9                  5.6   4.1    5.3          5.4   2.9    2.8    5.9

Natural gas resources                      1.8                  2.9  -17.2  -9.1         -5.0  -9.9  -12.3   -9.1

Coal resources                            10.8                             14.1  13.7    13.6 10.6     9.8  14.2

Specific capital in domestic firms       -24.7  -32.3  -26.4   -47.5 -23.7 -27.4 -19.7  -26.3 -18.6  -21.0  -30.4

Specific capital in multinational firms  101.4   60.4   45.6   228.1  79.2 148.3 116.6 130.6 144.1 165.2 118.0



Factor adjustments

Unskilled labor (% changing sectors) A     2.3    2.1    3.2     1.5   4.2   2.1  2.6     1.7   2.3    2.1    2.8

Skilled labor (% changing sector)      A   2.5    2.6    3.9     1.9   4.1   2.5  2.9     2.0   2.4    2.4    3.2



Source: Authors' calculations.




                                                         41

Table 13b. Impact of Full Foreign Direct Investment Liberalization in Services on Regional
Markets: Welfare, Trade and Factor Market Effects
(% change from base year)

                                                         St.        North-                                    Far
                                       Average Moscow Peters. Tumen  west  North Central South Urals Siberia  East


Aggregate welfare

Welfare (EV as % of consumption)            6.7   5.5    8.4    11.5   9.0   8.9   7.3     7.2   5.9    6.8    8.4

Welfare (EV as % of GDP)                    3.7   3.8    4.5    2.6    5.0   4.3   4.1     4.1   3.2    3.8    4.5



Aggregate trade

Regional terms of trade (% change)          1.7   2.1    2.9    3.1    3.1   3.1   2.1     2.0   1.7    1.9    2.9

Regional exports (% change)                 1.8   2.1    1.8    2.0    1.6   2.2   1.4     1.5   1.5    1.9    2.0

Real exchange rate (% change)               1.1   1.0    1.6    1.6    1.4   1.6   1.2     1.2   0.8    1.2    1.5

International exports (% change)            2.9   5.1    5.5    2.5    5.6   3.1   5.2     2.6   1.8    2.3    4.1



Return to primary factors (% change)

Unskilled labor                             3.3   2.6    5.1    3.0    4.5   5.0   3.5     3.7   2.5    3.5    5.3

Skilled labor                               2.1   0.9    3.9    2.5    4.2   4.3   2.6     2.6   1.2    2.5    4.3

National capital                            2.1   1.9    2.6    2.5    2.4   2.6   2.1     2.1   1.8    2.1    2.4

Regional mobile capital                     3.8   3.4    5.6    3.9    5.8   5.2   4.3     4.2   2.9    3.5    5.3

Crude oil resources                         4.0                 4.4    3.9   4.9           3.8   2.6    3.8    4.5

Natural gas resources                       1.7                 2.2   -8.8  -3.9          -4.2  -3.6   -2.1   -6.2

Coal resources                              7.9                              9.9   7.3     8.2   6.4    7.6    9.1

Specific capital in domestic firms        -20.4 -27.4  -19.1   -47.7 -18.2 -23.8  -14.2  -22.2 -14.0  -18.7  -25.1

Specific capital in multinational firms    86.3  53.2   44.2   198.9  68.4 126.9  96.6   106.1 118.6 137.5   100.1



Factor adjustments

Unskilled labor (% changing sectors)        1.4   1.4    1.7    1.4    1.7   1.7   1.1     1.1   1.2    1.6   1.8

Skilled labor (% changing sector)           1.8   2.0    2.3    1.8    2.2   2.4   1.6     1.4   1.6    2.0    2.5



See Table 2 for definition of regional markets

Source: Authors' estimates




                                                           42

Table 13c. Impact of Partial Foreign Direct Investment Liberalization in Services on Regional
Markets: Welfare, Trade and Factor Market Effects
(% change from base year)

                                                         St.         North-                                    Far
                                       Average Moscow Peters. Tumen   west  North Central South Urals Siberia  East


Aggregate welfare

Welfare (EV as % of consumption)           4.2    4.3    6.0    6.4    6.1   4.8    3.7    4.5   3.2     4.0    5.1

Welfare (EV as % of GDP)                   2.4    2.9    3.2    1.5    3.4   2.3    2.0    2.6   1.7     2.2    2.7



Aggregate trade

Regional terms of trade (% change)          2.1    3.5   4.6     2.5   4.2    3.3    3.4    3.0  2.6     2.2    3.6

Regional exports (% change)                 1.0    1.6   1.3     0.6   1.4    1.1    1.6    1.0  0.7     0.4    1.4

Real exchange rate (% change)               1.8    2.0   2.5     1.7   2.0    1.7    2.1    2.0  1.3     1.0    2.1

International exports (% change)            7.3    9.3  14.6     1.6  14.3    6.7   18.5    8.8  9.1     6.2    9.4



Return to primary factors (% change)

Unskilled labor                            1.6    2.5    3.1    1.5    2.7    1.7   1.2     2.1  0.4     1.4    2.3

Skilled labor                              2.1    2.0    3.8    1.3    3.5    2.0   2.7     2.6  1.6     2.0    3.0

National capital                           2.5    2.8    3.3    2.4    2.8    2.5    2.9    2.8  2.1     1.8    2.9

Regional mobile capital                    3.9    4.4    6.3    2.8    6.3   4.3    4.0    3.7   3.4     3.7   4.6

Crude oil resources                        2.2                  2.5    1.7    2.1           2.8  1.0     0.1    2.9

Natural gas resources                      0.0                  0.7  -14.7   -8.0          -3.0 -8.7   -12.3   -6.6

Coal resources                             4.3                                6.6    7.8    6.8  4.8     3.5    7.3

Specific capital in domestic firms        -11.5  -15.5 -14.4   -17.0 -13.0  -14.1  -10.3  -12.0 -8.5    -8.2  -16.1

Specific capital in multinational firms    42.0   25.3  18.2    90.4  35.8   70.0   49.9   57.6 54.1    67.1   56.2



Factor adjustments

Unskilled labor (% changing sectors)       2.0    1.7    2.4    1.0    3.3   2.0    2.2    1.5   2.3     2.1    2.4

Skilled labor (% changing sector)          1.9    1.8    2.6    1.0    2.9    2.1    2.3    1.5  1.9     2.0    2.4



See Table 2 for definition of regional markets

Source: Authors' estimates




                                                           43

Table 13d. Impact of Improved External Market Access on Regional Markets: Welfare, Trade
and Factor Market Effects
(% change from base year)

                                                         St.        North-                                   Far
                                       Average Moscow Peters. Tumen  west  North Central South Urals Siberia East


Aggregate welfare

Welfare (EV as % of consumption)            0.3    0.5   0.5    -2.2   0.7  0.1     0.4    0.4   0.3    0.2   0.3

Welfare (EV as % of GDP)                    0.2    0.3   0.3    -0.5   0.4  0.1     0.2    0.2   0.1    0.1   0.2



Aggregate trade

Regional terms of trade (% change)          0.1   -0.4  -0.4    -0.6  -0.4 -0.6    -0.5   -0.5  -0.6   -0.7  -0.3

Regional exports (% change)                 0.0    0.2   0.1    -0.3   0.2 -0.2     0.1    0.1  -0.1   -0.2   0.1

Real exchange rate (% change)              -0.6   -0.5  -0.5    -0.4  -0.7 -0.6    -0.6   -0.6  -0.8   -0.9  -0.4

International exports (% change)            0.6   -1.0   1.2    0.3   3.2  -0.1     2.6    0.0   1.4    0.5  -1.8



Return to primary factors (% change)

Unskilled labor                            -0.3   -0.1  -0.2    -0.3  -0.2 -0.4    -0.2    0.0  -0.3   -0.6  -0.4

Skilled labor                               0.0    0.2   0.3    -0.4   0.2 -0.2     0.2    0.1   0.1   -0.3  -0.2

National capital                            0.2    0.3   0.3     0.4   0.0  0.2     0.1    0.2   0.0   -0.1   0.4

Regional mobile capital                     0.4    0.4   0.7    -0.4   0.9  0.3     0.5    0.3   0.5    0.5   0.2

Crude oil resources                        -1.4                 -0.9  -2.5 -1.9           -1.8  -2.3   -2.7  -1.1

Natural gas resources                      -2.5                 -1.6 -13.9 -9.3           -9.3 -11.8  -12.9  -6.3

Coal resources                             -1.7                            -0.8    -1.0   -0.8  -1.2   -2.1  -0.1

Specific capital in domestic firms         -0.4   -0.5  -0.8    -0.1  -1.2 -0.1    -0.7   -0.3  -0.6    0.1   0.3

Specific capital in multinational firms     2.0    0.4  -0.3     4.5   1.6  2.9     3.2    3.7   3.9    5.0   2.1



Factor adjustments

Unskilled labor (% changing sectors)        0.7    0.5   0.8     0.3   1.2  0.8     0.5    0.3   0.8    1.1   0.8

Skilled labor (% changing sector)           0.7    0.4   0.8    0.3   1.1   0.9     0.5    0.3   0.8    1.1   1.0



See Table 2 for definition of regional markets

Source: Authors' estimates




                                                            44

Table 13e. Impact of Tariff Reductions on Regional Markets of Russia: Welfare, Trade and
Factor Market Effects
(% change from base year)

                                                         St.        North-                                   Far
                                       Average Moscow Peters. Tumen  west  North Central South Urals Siberia East


Aggregate welfare

Welfare (EV as % of consumption)           0.7    0.9    1.6    4.6   1.4    0.7   -0.2    0.7 -0.1     0.6   0.9

Welfare (EV as % of GDP)                   0.4    0.6    0.9    1.0   0.8    0.3   -0.1    0.4  0.0     0.3   0.5



Aggregate trade

Regional terms of trade (% change)         1.4    3.1    3.9    1.9   3.5   2.6    3.2    2.8   2.6     2.3   2.7

Regional exports (% change)                0.2    0.2    0.2    0.1   0.3   0.1    0.7     0.2  0.2    -0.3   0.2

Real exchange rate (% change)              2.0    2.1    2.5    1.4   2.3   1.6     2.4    2.1  1.8     1.5   1.7

International exports (% change)           5.5    9.3    9.0    0.1   8.2   4.8   13.1     7.7  7.3     5.1   9.6



Return to primary factors (% change)

Unskilled labor                            1.2    2.2    2.2    1.4   1.8    0.9    0.6    1.2  0.3     1.3   1.3

Skilled labor                              2.0    2.2    2.8    1.6   2.7    1.6    2.3    2.2  1.6     1.9   2.6

National capital                           1.7    1.8    2.2    1.2   2.0    1.3    2.1    1.8  1.5     1.2   1.5

Regional mobile capital                     2.2   2.7    3.3    1.9   3.1    2.1    1.9    1.9  2.0     1.9   2.5

Crude oil resources                        2.3                  1.9   3.0    2.2           3.6  2.8     1.7   2.0

Natural gas resources                      1.2                  0.7   1.6   2.5            9.9  6.1     1.2   0.2

Coal resources                              3.2                              3.6    6.5    5.0  4.4     3.0   3.3

Specific capital in domestic firms         -3.4   -3.7  -4.6    0.7  -3.5   -3.3   -4.2   -3.2 -3.7    -2.0  -5.4

Specific capital in multinational firms   10.9    5.8    1.0    21.0  6.6  15.5   13.5    17.4 18.6    19.3  13.7



Factor adjustments

Unskilled labor (% changing sectors)       1.3    1.0    1.2    0.6   1.6    1.0    1.5    1.1  1.5     1.1   2.4

Skilled labor (% changing sector)          0.9    0.8    1.0    0.4   1.0   0.8    1.4     0.8  1.0     0.8   2.0



See Table 2 for definition of regional markets

Source: Authors' estimates




                                                            45

Table 14. Decomposition of Welfare Impacts for the Regional Markets  WTO Scenario
Component effects on welfare (EV) as a percent of consumption


                                                            St.      North-                                   Far
                                        Average Moscow Peters. Tumen  west  North Central South Urals Siberia East


Wages

Skilled wages                                 0.7   0.3     1.2   1.9  1.5   1.1     1.1    0.9  0.6     0.9   1.0

Unskilled wages                               1.6   1.0     2.5   3.7  2.9   2.1     2.0    2.0  1.2     1.8   2.1



Capital Earnings

National capital                              2.7   4.1     3.9  -0.2  1.7   3.5     1.5    2.9  1.3     1.5   4.3

Regional mobile capital                       3.2   1.7     4.6   7.9  6.2   3.7     4.3    3.2  3.3     3.6   3.3

Regional energy rents                         0.2                 4.2  0.0   0.4     0.0    0.2  0.1     0.2   0.2

Specific capital in domestic firms           -1.9  -1.3    -2.4  -5.2 -2.7  -2.4    -2.5   -1.8 -2.0    -1.9  -2.1

Specific capital in multinational firms       1.0   0.7     1.1   2.9  1.4   1.3     1.2    1.0  1.0     1.1   1.1



Tax and Terms of Trade Effects

Regional investment                          -0.4  -0.1    -0.7  -2.7 -0.5  -0.7    -0.5   -0.5 -0.2    -0.3  -0.7

Value of stock changes                        0.4   0.4     0.5   1.1  0.6   0.6     0.4    0.3  0.3     0.4   0.5

Value of capital flows (terms of trade)      -0.9  -1.0    -1.3  -1.0 -1.1  -1.0    -1.1   -1.0 -0.7    -0.7  -1.1

Change in lumpsum taxes                       1.1   1.1     1.1   1.1  1.1   1.1     1.1    1.1  1.1     1.1   1.1



Total Welfare Change (% Consump)              7.8   7.0    10.6  13.8 11.2   9.8     7.6    8.3  6.2     7.6   9.7



See Table 2 for definition of regional markets

Source: Authors' estimates




                                                            46

Table 15. Impact of WTO Accession on Output by Sector and Regional Market
           (percentage change)

                                                                           St.         North-
Good     Central   Siberia    South    North     Urals Far East Moscow  Peters. Tumen   west

FME         33.1      13.1     44.8     23.7      15.2       19.7  43.6 352.3           10.5

CHM          8.3      21.5     10.4     -1.9      12.7        0.1  2.9     4.9    45.3  28.6

NFM         10.9       4.5     33.8     10.2      22.7        6.1  35.4   -0.1         -11.6

TMS          9.2       5.0      9.0     16.6      -2.3       16.7  -4.3   15.8    11.3  16.8

TRK          7.7       5.3      6.9       9.6      3.1       11.2   2.9   10.8     7.0  11.9

COL         5.6        3.7      5.5       5.7      4.8        5.2

TRD          6.0       4.1      4.4       4.2      3.9        5.3   4.3    6.5     2.7   8.0

ELE          1.9       1.6      1.8       1.0      1.4        0.7   0.4    1.9     0.3   4.0

SCI         -0.6       1.3     -0.1       2.9     -1.2        3.7  -0.1    4.8    -0.5   4.5

HOU          0.9       1.2      1.9       1.8      0.4        1.9   1.4    1.3     2.2   1.6

PST          1.5       1.0      2.0       1.6      0.5        2.1   1.0    1.1     1.7   1.6

HEA          1.2       1.2      1.6       1.4      1.0        1.6   1.4    1.6     1.2   1.7

CRU                    0.9      1.4       1.7      0.9        1.6                  1.6   1.3

RLW          3.2      -0.1      0.9       0.4      0.8        0.8  -1.1    4.6     0.7   2.6

OIL          4.0      -2.5      3.6       2.2     -1.7        3.0  -0.7   -2.8     6.2

GAS                   -1.4     -0.2     -0.5      -1.0       -0.6                  1.1  -1.9

CON         0.5       -1.6      0.6     -1.4      -0.1       -1.4   1.6   -2.7     2.2  -4.7

TPP         -7.0      -4.6    -10.1     -0.6      -2.3       29.9 -13.2   -8.9    15.3 -10.6

AIR         -1.4      -5.1     -1.4       1.1     -3.4        0.6  -2.5   -2.6     4.8  -2.7

CLI         -1.8      -4.5     -2.1     -3.1      -3.2       -2.9  -3.8   -6.1     1.3  -7.1

PIP         -2.4      -4.6     -3.1     -4.6      -2.8       -4.6  -5.9   -6.0    -0.2  -1.3

MWO         -9.4       0.4     -1.7     -6.1      -9.0       -4.0   8.7   -2.4    -1.7 -11.2

OTI         -3.8      -6.2     -2.6     -4.5      -4.3       -5.9  -0.3   -3.6    -1.5  -6.0

AGR         -3.2      -4.0     -2.2     -4.6      -1.8       -5.4  -3.7   -9.7     2.8 -11.4

FIN         -6.2      -6.7     -6.1     -6.6      -6.7       -6.3  -6.6   -6.0    -7.1  -5.3

MAR         -4.3     -11.7     -6.8     -4.9      -6.1       -3.6  -9.3   -6.0    -5.8  -8.1

TRO         -6.2     -11.0     -7.5     -2.3     -15.6       -1.8 -19.4   -2.2   -10.0  -1.1

CNM         -7.4     -11.4     -5.6     -8.0      -7.1       -9.0 -12.7  -14.1     1.6 -15.7

OTH         -7.0     -13.9     -8.7    -13.3      -6.9      -14.7  -7.0  -17.2    -4.6 -21.1

FOO        -13.5     -11.9    -10.6     -8.8     -11.3      -12.5 -17.1  -13.9     0.4 -17.0

See Tables 1 and 2 for sector and regional market definitions.

Source: Authors' calculations.


                                                  47

Table 16. Impact of WTO Accession on Skilled Employment by Sector and Regional Market
         (percentage change)

                                                                               St.         North-
Good       Central    Siberia    South     North      Urals Far East Moscow Peters. Tumen   west

FME           33.4       13.7      45.0     24.0       16.5     19.4   45.9 355.6            11.3

CHM            8.4       22.1      10.6     -1.6       14.0     -0.1    4.8    5.6    46.1   29.5

NFM           11.3         5.1     34.3     10.8       24.3      6.0   38.0    0.9          -10.7

TMS            9.2         5.5      9.2     17.0        -1.3    16.6   -2.7   16.5   11.9    17.5

TRK            7.1         5.2      6.6      9.3         3.5    10.5    3.9   10.7     7.1   11.6

COL            8.0         5.3      8.0      8.0         7.6     6.9

TRD            5.2         3.8      3.5      3.2         4.7     3.5    5.7    6.0     2.1    7.6

OIL            5.0        -1.5      4.4      3.3         0.0     3.5    1.5   -1.2     7.3

HOU            1.3         1.9      2.4      2.5         1.8     2.0    3.5    2.4     3.0    2.7

ELE            2.1         2.1      2.1      1.3         2.6     0.5    2.1    2.6     0.8    4.8

RLW            3.2         0.3      1.1      0.7         1.6     0.6    0.3    5.2     1.2    3.1

SCI           -1.4         1.2     -0.3      2.8        -0.9     3.1    1.2    4.7    -0.3    4.2

HEA            0.4         1.2      1.4      1.2         1.3     0.9    2.7    1.4     1.5    1.3

CON            0.3        -1.3      0.6     -1.3         0.6    -1.7    3.0   -2.4     2.6   -4.5

CRU                       -1.5      0.3     -0.4         0.1    -0.9                   1.7   -2.9

PST           -0.9        -0.7     -0.3     -1.1        -0.1    -1.2    1.1   -1.2    -0.4   -0.8

TPP           -7.3        -4.5    -10.1     -0.6        -1.8    29.3  -12.0   -8.7    15.6  -10.5

AIR           -2.8        -5.8     -2.4     -0.1        -3.7    -1.0   -2.1   -3.9     4.2   -4.1

CLI           -2.4        -4.3     -2.0     -3.0        -2.8    -3.2   -2.2   -6.1     1.9   -7.2

AGR           -2.7        -3.2     -1.6     -3.8        -0.3    -5.2   -1.5   -8.5     3.7  -10.2

OTI           -4.5        -6.1     -2.7     -4.6        -4.0    -6.3    1.2   -3.7    -1.1   -6.3

PIP           -3.2        -5.1     -4.0     -5.6        -2.0    -6.3   -4.7   -6.4    -0.9   -1.6

MWO          -10.6        -0.3     -2.6     -7.2        -9.2    -5.6    9.5   -3.5    -2.2  -12.2

MAR           -5.3      -12.1      -7.6     -6.0        -5.9    -5.2   -8.6   -6.9    -6.3   -9.0

TRO           -6.6      -11.0      -7.9     -2.7      -15.1     -2.7  -18.5   -2.3   -10.0   -1.2

CNM           -7.5      -11.1      -5.4     -7.7        -6.2    -9.2  -11.2  -13.6     2.2  -15.2

FIN           -8.8        -8.8     -8.5     -9.7        -8.0    -9.8   -7.2   -9.0    -9.3   -8.5

FOO          -13.1      -11.2     -10.1     -8.2      -10.1    -12.3  -15.4  -12.9     1.2  -16.1

OTH           -8.1      -14.3      -9.3    -13.9        -7.0   -15.6   -6.2  -17.9    -4.8  -21.9

GAS                     -16.0      -9.6    -14.1      -12.4    -15.0                  -0.9  -22.7

See Tables 1 and 2 for sector and regional market definitions.

Source: Authors' calculations.                     48

Table 17. Impact of WTO Accession on Unskilled Employment by Sector and
            Regional Market (percentage change)

                                                                                St.         North-
Good       Central     Siberia    South     North      Urals Far East Moscow Peters. Tumen   west

FME           35.2        14.0      45.3     24.3        17.0    20.1   44.1 359.0            12.5

CHM             9.9       22.5      10.9     -1.3        14.4     0.5    3.5    6.4    45.5   30.9

NFM           12.9         5.4      34.6     11.1        24.8     6.6   36.3    1.7           -9.7

TMS           10.7         5.8       9.4     17.3         -0.9   17.3   -3.9   17.4    11.5   18.7

TRK             8.6        5.5       6.8      9.6          3.9   11.2    2.6   11.5     6.6   12.8

COL             9.5        5.5       8.3      8.2          8.0    7.5

TRD             6.6        4.1       3.7      3.4          5.1    4.1    4.4    6.8     1.7    8.8

OIL             6.4       -1.2       4.6      3.6          0.4    4.1    0.3   -0.4     6.9

HOU             2.7        2.2       2.6      2.7          2.2    2.6    2.2    3.2     2.6    3.8

ELE             3.5        2.4       2.3      1.6          3.0    1.1    0.8    3.4     0.4    5.9

RLW             4.6        0.6       1.3      1.0          2.0    1.3   -0.9    5.9     0.8    4.2

SCI             0.0        1.5      -0.1      3.0         -0.5    3.7    0.0    5.4    -0.7    5.3

HEA             1.8        1.4       1.6      1.4          1.7    1.5    1.5    2.2     1.0    2.4

CON             1.7       -1.1       0.8     -1.1          1.0   -1.2    1.7   -1.6     2.2   -3.5

CRU                       -1.2       0.5     -0.1          0.5   -0.3                   1.3   -1.9

PST             0.5       -0.5      -0.1     -0.8          0.3   -0.6   -0.1   -0.5    -0.8    0.3

TPP           -6.0        -4.2     -10.0     -0.4         -1.3   30.1  -13.0   -8.0    15.1   -9.5

AIR           -1.5        -5.5      -2.2      0.1         -3.4   -0.4   -3.3   -3.2     3.8   -3.0

CLI           -1.0        -4.1      -1.8     -2.8         -2.4   -2.6   -3.4   -5.4     1.5   -6.2

AGR           -1.3        -2.9      -1.4     -3.5          0.1   -4.6   -2.7   -7.8     3.3   -9.2

OTI           -3.2        -5.9      -2.5     -4.4         -3.6   -5.8   -0.1   -3.0    -1.5   -5.3

PIP           -1.8        -4.8      -3.8     -5.4         -1.6   -5.7   -5.9   -5.7    -1.3   -0.6

MWO           -9.3         0.0      -2.4     -7.0         -8.9   -5.0    8.2   -2.8    -2.6  -11.3

MAR           -3.9       -11.9      -7.4     -5.7         -5.5   -4.6   -9.7   -6.2    -6.6   -8.0

TRO           -5.3       -10.8      -7.7     -2.5      -14.7     -2.1  -19.5   -1.5   -10.4   -0.1

CNM           -6.2       -10.8      -5.2     -7.4         -5.8   -8.6  -12.3  -13.0     1.8  -14.3

FIN           -7.5        -8.5      -8.3     -9.5         -7.6   -9.3   -8.3   -8.4    -9.7   -7.5

FOO          -11.9       -11.0     -10.0     -7.9         -9.7  -11.8  -16.4  -12.3     0.8  -15.2

OTH           -6.8       -14.1      -9.1    -13.7         -6.7  -15.1   -7.4  -17.3    -5.2  -21.1

GAS                      -15.8      -9.4    -13.8      -12.1    -14.5                  -1.3  -21.9

See Tables 1 and 2 for sector and regional market definitions.

Source: Authors' calculations.



                                                        49

Table 18. Impact of WTO Accession on Exports by Sector and Regional Market
            (percentage change)

                                                                               St.         North-
Good       Central    Siberia    South     North      Urals Far East Moscow Peters. Tumen   west

FME           61.0       30.1      76.6     49.7       34.2     45.1   70.2 423.5            26.3

CHM           31.6       39.8      35.4     18.7       32.1     21.0   23.2   21.8   82.8    44.2

SCI           21.3       18.6      22.0     42.6       17.2     43.0   19.1   41.9   25.6    39.8

NFM           21.2       11.3      47.7     19.3       34.0     14.6   48.3    3.2          -11.2

TMS           14.9         5.8     13.8     28.0        -0.3    27.0   -4.3   21.0   20.9    20.4

CLI           15.6         7.8     14.7     12.1       11.3     12.1    9.3    4.5   22.9     1.8

COL            9.1         1.6      9.4     10.4         3.9     9.7

OIL            9.4        -1.6      9.0      7.3        -0.4     8.1    2.2   -1.1   13.0

TRK            7.0         1.5      5.8      6.5         2.4     7.5    2.6    4.7     8.5    4.4

FIN            5.0         0.2      4.9      6.8         1.4     6.9    3.2    5.7     6.6    4.9

TPP            0.9        -4.8     -2.0      6.0         0.9    36.1   -6.6   -5.2   28.2   -10.2

MWO           -1.4         5.3      7.9      2.2        -3.0     4.2   19.1    4.3   11.0    -7.7

AGR            6.5        -3.3      7.2      4.2         1.0     3.4   -0.6   -4.7   16.8    -9.5

CRU                        1.6      2.4      2.9         1.3     2.1                   2.9    1.3

OTI            2.1        -2.9      4.4      0.3         0.5    -1.7    8.4    2.9     4.6   -1.3

CNM            3.5        -6.6      6.5      3.1        -1.6     2.2   -5.3   -7.1   17.9   -11.4

PST            2.6        -1.7      2.9      1.1        -0.7     0.7   -2.1   -5.0     7.1   -5.8

AIR            0.7        -6.6      0.2      2.6        -3.4     1.8   -2.6   -4.3     9.5   -5.3

HEA            0.9        -2.9     -0.2     -3.0         0.2    -3.0   -0.2   -3.8     1.3   -5.2

CON            0.9        -3.8      1.1     -3.0        -0.2    -3.3    3.7   -6.4     4.7   -9.8

ELE           -0.1        -3.1      0.2     -2.6        -1.4    -3.3   -1.5   -5.7    0.9    -4.9

TRD            0.9        -3.8      0.0     -2.9        -2.3    -2.0   -2.8   -6.2     1.9   -6.5

FOO           -4.6        -9.0     -1.6     -1.8        -6.3    -5.4  -10.8   -6.9   12.9   -14.1

HOU           -3.6        -5.7     -2.4     -5.2        -4.1    -5.5   -2.9   -9.6     0.4  -10.6

RLW           -1.6        -7.4     -4.1     -7.2        -3.2    -7.6   -5.1   -6.9    -1.4   -9.9

MAR           -3.6      -14.7      -6.2     -4.5        -7.1    -3.6   -9.1   -8.5    -3.4  -11.7

TRO           -5.8      -13.4      -7.1     -3.3      -16.4     -3.0  -19.2   -6.6    -7.4   -6.8

OTH           -6.2      -17.2      -8.8    -15.8        -6.3   -18.5   -7.0  -22.7    -1.9  -27.5

GAS                     -40.8     -17.0    -33.2      -31.6    -33.9                   2.3  -71.2

PIP

See Tables 1 and 2 for sector and regional market definitions.

Source: Authors' calculations.
                                                     50

Table 19. Piecemeal Sensitivity Analysis -- Welfare Impacts as a percent of GDP by Region
(% change from base year)

                                            Overall                   St.               North-                                               Far
Parameter being changed                    average Moscow Peters. Tumen                  west    North Central South          Urals Siberia  East

Central Results for reference                    4.3       4.7         5.7       3.1       6.2      4.7      4.2      4.7       3.3      4.2  5.2

regional investment potential variationa/        4.2       5.1         6.0       3.3       5.6      4.0      3.8      4.2       3.2      3.6  4.6

esubconsumer = 1.5                               4.5       5.0         6.1       3.2       6.1      5.0      4.5      5.0       3.5      4.5  5.5
esubconsumer = 0.5                               4.1       4.5         5.4       3.0       5.9      4.1      4.0      4.5       3.1      4.0  4.9

esubs = 2.0                                      5.5       5.9         7.3       4.3       7.2      6.1      5.5      6.0       4.3      5.4  6.6
esubs = 0.5                                      3.5       4.0         4.8       2.4       5.2      2.4      3.6      3.9       2.6      3.5  3.9

sigmadm = 4                                      4.3       4.8         5.8       3.2       6.3      4.8      4.2      4.8       3.3      4.3  5.2
sigmadm = 2                                      4.3       4.7         5.7       3.1       6.2      4.7      4.2      4.7       3.3      4.2  5.2

etaf = 17.5                                      4.8       5.2         6.1       3.5       6.7      5.2      4.7      5.3       3.8      4.8  5.6
etaf = 12.5                                      3.7       4.2         5.2       2.7       5.6      3.9      3.6      4.1       2.7      3.6  4.6

etad = 10                                        4.4       5.2         6.1       3.1       6.4      5.0      4.3      4.6       3.3      4.3  5.5
etad = 5                                         4.0       4.1         5.1       3.1       6.0      4.3      4.1      4.5       3.3      4.1  4.7

esub = 4                                         4.3       4.7         5.5       3.2       6.0      4.6      4.2      4.7       3.4      4.3  5.1
esub = 2                                         4.3       4.7         5.5       3.2       6.0      4.6      4.2      4.7       3.4      4.3  5.1

esubprimary = 1.5                                4.3       4.7         5.7       3.1       6.3      4.7      4.3      4.7       3.4      4.2  5.1
esubprimary = 0.5                                4.3       4.9         5.7       3.1       5.9      4.8      4.0      4.8       3.1      4.2  5.4

etadx = 7                                        4.3       4.8         5.8       3.0       6.3      4.7      4.3      4.8       3.3      4.2  5.2
etadx = 3                                        4.3       4.6         5.6       3.3       6.1      4.7      4.2      4.7       3.3      4.2  5.1

Source: Authors' calculations.

a/We vary etaf by region as follows: Moscow = 20; St. Petersburg = 18.3; Tumen = 17.1; Northwest = 11.3; North = 10;
   Central = 12.5; South = 11.5; Urals = 14.3; Siberia = 10.8; Far East = 10.8.


Notes:
a. The piecemeal sensitivity analysis employs central values for all parameters (see below) other than the tested parameter
   and lump sum tax replacement.
b. Hicksian equivalent variation as a percent of the value of consumption in the benchmark equilibrium.

                               Parameter Central Definitions of the parameter (see figures, especially figure 4)
                                            value    for more precise elasticity structure).

                                     esubs   1.25    Elasticity of substitution between value-added and business services

                                     esub     3      Elasticity of substitution between firm varieties in imperfectly competitive sectors
                                 sigmadm      3      "Armington" elasticity of substitution between imports and domestic goods in CRTS sectors

                              esubprimary     0      Elasticity of substitution between primary factors of production in value added
                             esubintermed     0      Elasticity of substitution in intermediate production between composite Armington

                                                     aggregate goods
                            esubconsumer      1      Elasticity of substitution in consumer demand

                                     etadx    5      Elasticity of transformation (domestic output versus exports)
                                      etad    7.5    Elasticity of Russian service firm supply with respect to price of output

                                      etaf    15     Elasticity of multinational service firm supply with respect to price of output




                                                                         51

Figure 1. Sales for Constant Returns to Scale Sectors: Determined by
     Constant Elasticity of Transformation Production Structure


                               Russia sales
                         (not own Regional Market)
Own Regional Market                                         Rest of world sales




                                                                   T = 5 = etadx

                                  CET



                    Output (CRTS good g in Regional Market r)




                                      52

                 Figure 2. Demand for Representative CRTS good g in Regional Market r


                                                     Armington Aggregate Demand

                                                       for CRTS good g in RM r


                                                                CES

                                                                                    = sigmadm    a/




                                         Russia goods                             Rest of World Imports

                                           CES

                                                             = 1.5 * sigmadm



                     Own Regional Market             Other Russian Imports


                                                            CES

                                                                                  = 2 * sigmadm




                                     Imports from                             Imports from
                                  Regional Market 1     Imports from
                                                      Regional Market m    Regional Market q


a/sigmadm = 3 in CRTS sectors, except in OTH (other goods producing sectors). For OTH we rely on estimates from Ivanova (2005).




                                                              53

                   Figure 3. Demand for Representative Dixit-Stiglitz (IRTS)
                                     good g in Regional Market r




                                  Dixit-Stiglitz Aggregate of IRTS good g in RMr



                                                       CES
                                                                                 = 3 = esub a/




            Imports of g from                                                       Goods g from
               Rest of World          Goods g from             Goods g from
                                           RM 1                   RM m                  RM q


                 CES
                            =3             CES                      CES                 CES
                                                      =3                      =3                   = 3 = esub




          Rest of World firms                                  Firms in RM m
          selling g in RMr            Firms in RM 1                                 Firms in RM q
                                     selling g in RMr          selling g in RM     selling g in RM




a/We take  = 3, except based on Ivanova (2005), we take  = 3.1 in MWO;  = 2.6 in TPP;  = 2.5 in CNM; and  = 1.8 in OTI.




                                                           54

                                 Figure 4. Structure of Production for Increasing Returns to Scale Russian Firms:
                                    Representative Good or Service in a Representative Regional Market (RM) m
                                                                                                                                                                                               Goods: to all Russia



                                                                                                                                                                                               Services to own Regional Market
                                                                                                                                                            Gross output
                                                                                                                                                                                    = etad = 7.5
                                                                                                                                                               CES


                                                                                                Exports to Rest of World             Variable inputs from RMm                  Specific factor (fixed supply)


                                                                                                                                                        = 5 = etadx
                                                                                                                                CET

                                                                                                                               Leontief              =    0 a/




                                          Value-added and Business Services
                                                                                                                        Intermediate Goods
                                                                                                                                                                                                        Other Services
                                                        CES             = 1.25 = esubs
                                                                                                                               Leontief            = 0 a/                                                  Leontief
                                                                                                                                                                                                                                 = 0  a/

                         Business Services              ....          Value-added                                              ....                                                                           ....
                          Cobb-Douglas                                                                 Composite                                   Composite
                                                                     Cobb-Douglas                                                                                                          Other                                Other
                                             =   1                                    = 1    b/      Intermediate                                 Intermediate                         Services 1                            Services 5
                                                                                                      IRTS good i    c/                           CRTS good j    d/

                  Business     ....        Business        Sector                        Skilled                                                                                          CES                                    CES
                 Service 1                 Service 9               Capital
                                                          Specific           Unskilled                                                                                                                 =3                                  =3
                                                                               Labor      Labor
                                                          Resources                                     CES              = 3= esub                     CES      = sigmadm
                    CES                      CES
                              = 1.5                                                                                                                                          Services Intermediates    Cross        Services Intermediates Cross
                                                      = 1.5                                                                                                                 from RMm   from other      Border      from RMm from other    Border
                                                                                    Intermediates Intermediates from  Imported         Intermediates               Imported              Russia       Services                   Russia   Services
                                                                                                                                                                  Rest of world
         Domestic                                                                      in RMm                       Intermediate
                      Cross Border   Domestic Cross Border                                     =3    other Russia=3          =3  e/     from Russia =1.5 sigmadm  Intermediates
          Presence       Services    Presence     Services
          Services                    Services
                       =3=sub
                       =                           =3=sub
                                                   =                                Firms in RMm      Firms from   Foreign-based   Own          Other
                                                                                                                                 Regional Russian Imports
                                                                                                      other Russia    Firms
                                                                                                                                 Market
Multinational   Russian        Multinational  Russian                                                                                            CES
 Services    Services in RMm     Services   Services in RMm                                                                                                   = 2 * sigmadm


                                                                                                                                                                                      Notes:
  CES             CES
          =3e/            =3e/    CES     =3     CES     =3=sub                                                               Imports from                  Imports from
                                                                                                                                             Imports from                             a/  = "esubintermediate"
                                                                                                                               Regional                       Regional
                                                                                                                                               Regional                               b/  = "esubprimary"
                                                                                                                                Market 1                      Market q
                                                                                                                                               Market m                               c/ i = l ,..., g where g is the number of IRTS goods
                                                                                                                                                                                      d/ j = l ,..., l where l is the number of CRTS goods
Multinational    Russian        Multinational    Russian                                                    55                                                                        e/  = "esub"

 Service          Service         Service        Service
                                                Providers
Providers       Providers        Providers
                 in RMm                          in RMm

 Figure 5. Demand for Representative Business Service s
       (Dixit-Stiglitz) Sectors in Regional Market (RM) m




                     Business Service s
                          CES               =1, 5




   Rest of World
Cross Border Services               Own Regional Market Services


                                                CES      =3 = esub



                            Multinational Service         Russian Service
                             Providers in RM m           Providers in RM m

                                   CES        =3 = esub          CES      =3 = esub



                        Multinational firms in RMm     Russian firms from RMm




                                               56

      Figure 6. Structure of Production for Increasing Returns to Scale Multinational Business Service Firms:
                          Representative Business Service in a Representative Regional Market (RM) m
                                                                                                                                                                                               Sales to Own Regional Market


                                                                                                                                                                      Gross output
                                                                                                                                                                                              = 15 = etaf
                                                                                                                                                                         CES



                                                                                                                                            Variable inputs                             Specific factor (fixed supply)
                                                                                                                                                               =


                                                                                                                      All other variable
                                                                                                                             inputs                         Imported primary
                                                                                                                                                                   inputs

                                                                                                                           Leontief                  = 0



                                         Value-added and Business Services
                                                                                                                      Intermediate Goods
                                                                                                                                                                                                        Other Services
                                                        CES            = 1.25
                                                                                                                            Leontief                                                                      Leontief
                                                                                                                                                 = 0                                                                             = 0

                         Business Services              ....         Value-added                                             ....                                                                          ....
                          Cobb-Douglas                                                               Composite                                   Composite
                                                                    Cobb-Douglas                                                                                                         Other                               Other
                                               = 1                                                  Intermediate                                Intermediate
                                                                                      = 1                                                                                            Services 1                            Services 5
                                                                                                    IRTS good i    a/                           CRTS good j   b/

                  Business     ....        Business        Sector                      Skilled                                                                                          CES                                     CES
                 Service 1                 Service 9      Specific Capital  Unskilled                                                                                                                 =3                                   =3
                                                                             Labor      Labor
                                                          Resources                                   CES             =3                             CES            =3
                    CES       = 1.5         CES
                                                                                                                                                                           Services Intermediates Cross            Services Intermediates Cross
                                                      = 1.5
                                                                                  Intermediates Intermediates from Imported                                                from RMm from other Border             from RMm from other Border
                                                                                                                                     Intermediates              Imported               Russia     Services                     Russia    Services
         Domestic     Cross Border   Domestic Cross Border                           in RMm
                                                                                             =3    other Russia=3 Intermediate
                                                                                                                           =3                                  Intermediate
          Presence       Services     Presence    Services                                                                            from Russia =1.5 sigmadm         =3
          Services                    Services
                       =3=esub                     =3=esub                        Firms in RMm      Firms from   Foreign-based
                                                                                                    other Russia     Firms       Own          Other           Foreign-based
                                                                                                                               Regional Russian Imports
Multinational   Russian        Multinational  Russian                                                                                                           Firms
                                                                                                                                Market
 Services    Services in RMm     Services   Services in RMm                                                                                    CES
                                                                                                                                                            = 2 * sigmadm

  CES    =3   e/ CES     =3   e/ CES =3        CES      =3=esub
                                                                                                                            Imports from                  Imports from
                                                                                                                                           Imports from
                                                                                                                             Regional                       Regional
                                                                                                                                             Regional                                   Notes:
                                                                                                                                                            Market q
Multinational    Russian                                                                                                      Market 1
                              Multinational                                                                                                                                             a/ i = l ,..., g where g is the number of IRTS goods
                                               Russian                                                                                       Market m
                                                                                                                                                                                        b/ j = l ,..., l where l is the number of CRTS goods
 Service          Service        Service       Service                                                57
Providers        Providers      Providers     Providers
                  in RMm                       in RMm

                                            Appendix A

                  Data on multinational shares of service sectors

We consulted four sources of information: (1) estimates from Russian service sector institutes of

the share by sector of multinational ownership in the key services sectors; (2) the NOBUS survey;

(3) Regions of Russia (2003) by Rosstat; and (4) the "BEEPS survey. Of these four sources, only

the NOBUS survey provides data that allows us to estimate shares of multinational ownership by

both region and sector. We thus start with the NOBUS information (our results by region and

service sector are summarized in table A4 below).


When we aggregate the NOBUS shares across regions or sectors, however, we find that the other

three sources of information, show considerably higher foreign ownership shares than the

NOBUS survey. We believe that the NOBUS survey estimates are too small, and adjust them.

The estimates of the service sectors institutes are lower than those from the BEEPS or Regions of

Russia, and thus involve less adjustment of the NOBUS data. We employed least squares

adjustment of the NOBUS data so that the weighted average over all of Russia in each sector is

consistent with the national estimates we received from the specialist service sector research

institutes in Russia. This process will give as a structure of ownership based on the NOBUS

survey, with the economy-wide average by sector determined by the national data. The results are

in table 12b. In this appendix we explain the details of how we obtained the and calculated the

data.


1.       Data


We use three sources of data to calculate regional shares of output produced by multinational

companies by regions and sectors, and we combine this with our national estimates from our

Russian service sector experts. The regional data are:


1.1      The National Survey of Household Welfare and Program Participation (NOBUS) that
         was implemented in 87 "oblasts" of the Russian Federation in 2003 by means of random
         sampling procedures. The survey was conducted on the basis of voluntary participation of
         selected households. The households that refused to participate in the survey were
         replaced by the households from the same voting district. Total number of households
         that agreed to participate in the survey is 44,529.



                                                  58

1.2     The Statistical handbook "Regions of Russia: Social and economic indicators: 2003"
        published by Roskomstat. The handbook contains the major economic and social
        statistical indicators for Russia since 1990 by economic region.
1.3     Business Environment and Enterprise Performance Survey (BEEPS) 2002 developed
        jointly by the World Bank and the European Bank for Reconstruction and Development.
        BEEPS is a survey of managers and owners of 6,636 firms across the countries of Eastern
        Europe, the former Soviet Union, and Turkey.


2.      Calculations

2.1.    NOBUS. Respondents in the NOBUS survey report their wage income, the
        industry in which they work and whether the company is Russian owned, foreign
        owned or mixed ownership. For each region and industry we calculate the share
        of wage income earned in foreign or mixed ownership companies compared with
        the share of wage income earned in Russian companies. We take this wage share
        (in foreign plus mixed ownership companies) as a proxy for the share of output in
        each region and industry. Documentation of the details follows.

2.1.1 Variables
        First, we define a variable industry i based on the Question 17, Part C of the survey24. We

assign a three letter code according to the Table A1


Table A1 Mapping of the survey Question 9, part C into IO table sector code

Industry                                    Code IO table code                                  Question 9, Part C
Agriculture & forestry and food             AGF      AGF, FOO                                           1,2
Mining                                      MIN      FME NFM COA OLE                                    3
Manufacturing                               MNF      MWO TPP CNM CLO OTHT OIN                           4
Electric industry                           ELE      ELE                                                5
Construction                                CON      CON                                                6
Trade                                       TRD      TRD                                                7
Catering                                    CAT      CAT                                                8
Communication and transport                 COM      RLW TRK PIP MAR AIR TRO                            9
                                                     TMS PST
Financial services                          FIN      FIN                                                10
Communal & consumer services                PSM      PSM                                                15, 16
Administration & public                     ADM      ADM                                                12, 17
associations
Education & culture & art                   ECM      ECM                                                13
Science & science servicing                 SCS      SCS                                                11
Public health & sports & social             SSM      SSM                                                14
security


24Industries are encoded using a 2-digit code from the list of economic activity classification groups given
in the Questionnaire form and based on National Classifier of Economic Activities OK 029-2001
(OKVED).


                                                    59

         Second, we define a region code j as shown in the Table 2.

         Third, we define a variable ownership based on the Question 9, Part C of the survey. For

each respondent k who worked during the sample period25, we define ownership as foreign if the

respondent answered that she worked at the enterprise with mixed (both Russian and foreign)

ownership, or with foreign ownership. Otherwise, we define ownership as local.

         Finally, for each respondent k who worked during the sample period, we define a

variable wage according to the respondent's answer to the Question 13, Part C of the survey:

what was your wage in rubles in the last 30 days?


2.1.2      Aggregation

All regions
We replicate observations according to the weight kvzvijk that is assigned to each respondent and

aggregate the variable wageijk by regions and industries according to the type of ownership:

 foreignij =    kvzv      ijk*wageijk
              k foreign

localij =   kvzv      ijk*wageijk
           klocal

i is an industry code as defined in the second column of the Table 1

j is a region as defined in the Table 2


and calculate a share of foreign owned enterprises calculated as

 foreign _ shareij =          foreignij
                         foreignij + localij


The results are presented in the Table A2.




25 A respondent worked during the investigated period if she answered "Yes" to the Question 1 or the
Question 2, Part C of the survey.


                                                   60

Table A2 Share of foreign owned companies

Region ADM AGF CAT COM* CON ECM ELE FIN                 MIN** MNF*** PSM SCS SSM TRD

alt       0.00   0.00          0.00  0.00 0.00 0.00 0.00        0.00 0.00 0.00 0.00 0.13

amu       0.00   0.00   0.00   0.00  0.05 0.00 0.00 0.00  0.00  0.04 0.00 0.00 0.00 0.00

arh       0.00   0.01   0.00   0.01  0.01 0.00 0.00 0.00  0.00  0.08 0.00 0.00 0.00 0.00

ast       0.00   0.00   0.00   0.02  0.00 0.00 0.00 0.00  0.03  0.34 0.00 0.00 0.01 0.00

bel       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00  0.00  0.00 0.00      0.00 0.00

bry       0.00   0.00   0.00   0.02  0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

vla       0.00   0.00          0.00  0.00 0.00 0.00       0.00  0.08 0.00 0.00 0.00 0.00

vlg       0.00   0.01   0.00   0.00  0.00 0.00 0.08 0.00  0.13  0.01 0.00 0.00 0.00 0.00

vol       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00        0.00 0.00      0.00 0.00

vor       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00  0.00  0.05 0.00 0.00 0.00 0.01

msk       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.12  0.33  0.00 0.00 0.03 0.00 0.01

spb       0.04   0.00   0.00   0.01  0.01 0.00 0.00 0.00  0.25  0.06 0.00 0.03 0.00 0.03

eao       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

iva       0.00   0.00   0.00   0.01  0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

irk       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00  0.00  0.38 0.00 0.00 0.00 0.00

kab       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

klg       0.00   0.00   0.00   0.00  0.00 0.10 0.00 0.00  0.00  0.00 0.24 0.00 0.00 0.03

kal       0.00   0.00   0.00   0.00  0.00 0.00 0.00       0.00  0.00 0.00 0.00 0.00 0.00

kam       0.00   0.01   0.00   0.00  0.00 0.00 0.03 0.00  0.00  0.16 0.00 0.00 0.00 0.00

kar       0.00   0.00          0.00  0.00 0.00 0.00       0.00  0.00 0.00 0.00 0.00 0.00

kem       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00  0.02  0.00 0.00 0.00 0.00 0.00

kir       0.00   0.00   0.00   0.00  0.00 0.00 0.02 0.00  0.00  0.03 0.00 0.00 0.00 0.00

kos       0.00   0.00   0.00   0.00  0.05 0.00 0.00 0.06  0.00  0.04 0.00 0.00 0.00 0.00

kdk       0.00   0.00   0.00   0.02  0.07 0.00 0.00 0.00  0.00  0.05 0.00 0.00 0.01 0.00

kra       0.00   0.00   0.15   0.00  0.01 0.00 0.02 0.00  0.02  0.06 0.00 0.00 0.00 0.00

krg       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

krs       0.00   0.00          0.00  0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

len       0.00   0.10   0.24   0.09  0.00 0.00 0.00 0.00  0.00  0.07 0.00 0.00 0.00 0.00

lip       0.00   0.00   0.00   0.03  0.00 0.00 0.00 0.00  0.00  0.10 0.00 0.00 0.00 0.00

mag       0.00   0.00   0.00   0.00  0.00 0.00 0.00       0.00  0.00 0.00 0.00 0.00 0.00

mos       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

mur       0.01   0.04   0.00   0.00  0.00 0.00 0.00 0.00  0.00  0.10 0.00 0.00 0.00 0.00

niz       0.00   0.00   0.11   0.01  0.00 0.00 0.00 0.00  0.00  0.01 0.02 0.00 0.01 0.00

nov       0.00   0.00   0.00   0.00  0.00 0.00 0.00 0.00  0.00  0.12 0.00 0.54 0.00 0.00

nvs       0.00   0.00   0.00   0.00  0.05 0.00 0.00 0.00  0.00  0.00 0.00 0.06 0.00 0.00



                                              61

Region ADM AGF CAT COM* CON ECM ELE FIN        MIN** MNF*** PSM SCS SSM TRD

oms    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

ore    0.00 0.00 0.00  0.00 0.00 0.00      0.00  0.00  0.02 0.00 0.00 0.00 0.00

orl    0.00 0.00 0.00  0.00 0.00 0.00 0.31 0.00  0.00  0.02 0.00 0.00 0.00 0.01

pen    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00        0.05 0.00 0.00 0.00 0.00

per    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

pri    0.00 0.19 0.31  0.05 0.00 0.00 0.02 0.00  0.00  0.09 0.01 0.00 0.00 0.02

psk    0.00 0.05 0.00  0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.14 0.17 0.00 0.00

sev    0.00 0.00       0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

ady    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

alr    0.00 0.02 0.00  0.00 0.00 0.00 0.00 0.00        0.00 0.00 0.00 0.00 0.00

bas    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.03  0.01 0.00 0.00 0.00 0.00

bur    0.00 0.00 0.00  0.00 0.01 0.00 0.00 0.00  0.08  0.08 0.00 0.00 0.00 0.00

dag    0.00 0.00 0.00  0.07 0.00 0.00 0.00 0.00  0.00  0.03 0.00 0.00 0.00 0.00

ing    0.00 0.00       0.00 0.00 0.23 0.00             0.00 0.00 0.00 0.00 0.00

klr    0.00 0.00       0.00 0.00 0.00 0.00             0.00 0.00 0.00 0.00 0.00

krl    0.00 0.50 0.00  0.00 0.00 0.00      0.00  0.00  0.00 0.00 0.00 0.00 0.00

kom    0.00 0.05 0.00  0.00 0.00 0.00 0.00 0.00  0.05  0.00 0.00 0.00 0.00 0.01

mar    0.00 0.00       0.00 0.00 0.00 0.00 0.00        0.00 0.00 0.00 0.00 0.00

mor    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

sah    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.05  0.00 0.00 0.00 0.00 0.03

tat    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.21 0.00 0.03

tyv    0.00 0.00       0.00      0.00 0.00 0.00  0.00       0.00      0.00 0.00

hak    0.00 0.00       0.00 0.00 0.00 0.00       0.00  0.00 0.00      0.00 0.00

ros    0.00 0.00 0.00  0.00 0.00 0.02 0.00 0.00  0.00  0.01 0.00 0.00 0.00 0.00

rya    0.00 0.00       0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

sam    0.00 0.00 0.00  0.03 0.00 0.00 0.00 0.00  0.00  0.06 0.00 0.00 0.02 0.02

sar    0.00 0.00       0.00 0.00 0.00 0.00       0.00  0.00 0.00 0.00 0.00 0.00

sao    0.00 0.02 0.00  0.03 0.00 0.01 0.02 0.00  0.38  0.00 0.01 0.11 0.00 0.00

sve    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.01  0.03 0.00 0.00 0.00 0.00

smo    0.00 0.00       0.00 0.00 0.00 0.00       0.00  0.00 0.00      0.00 0.00

sta    0.00 0.00       0.00 0.00 0.00 0.00 0.00        0.00 0.00      0.00 0.00

tam    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

tve    0.00 0.00 0.00  0.00 0.01 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.01

tom    0.00 0.00       0.00 0.00 0.00 0.00 0.00  0.00  0.00 0.00 0.00 0.00 0.00

tul    0.00 0.00 0.00  0.00 0.00 0.00 0.00 0.00  0.00  0.03 0.00      0.00 0.00

tum    0.00 0.00 0.00  0.00 0.01 0.00 0.00 0.00  0.03  0.02 0.00 0.04 0.00 0.00



                                     62

Region ADM AGF CAT COM* CON ECM ELE FIN                       MIN** MNF*** PSM SCS SSM TRD

udm       0.00 0.00    0.00    0.00   0.00   0.00   0.00 0.00   0.22    0.00 0.00 0.00 0.00 0.00

ulo       0.00 0.00            0.00   0.00   0.00   0.00 0.00   0.00    0.00 0.00 0.00 0.00 0.00

hab       0.00 0.15    0.00    0.01   0.00   0.00   0.00 0.00   0.00    0.01 0.00 0.00 0.00 0.01

chl       0.00 0.09    0.00    0.02   0.00   0.00   0.00 0.00   0.15    0.23 0.00 0.00 0.00 0.00

chi       0.00 0.00    0.00    0.00   0.00   0.00   0.00 0.00   0.12    0.02 0.00 0.00 0.00 0.00

chv       0.00 0.00    0.00    0.00   0.00   0.00   0.00 0.00   0.00    0.08 0.00 0.00 0.00 0.00

chu       0.00 0.00    0.00    0.00   0.51   0.00   0.06 0.00   0.00         0.00 0.00 0.00 0.00

yar       0.00 0.00    0.00    0.00   0.01   0.00   0.00 0.00   0.00    0.01 0.00 0.00 0.00 0.00



*        COM (communication and transport) includes RLW TRK PIP MAR AIR TRO TMS PST

**       MIN(mining) includes FME NFM COA OLE

***      MNF (manufacturing) includes MWO TPP CNM CLO OTHT OIN

Source: NOBUS 2003




                                                  63

          10 bigger regions


          Table A3 presents shares of foreign owned enterprises for 10 bigger regions: Moscow, Saint

          Petersburg, Tumen, Northwest, North, Center, South, Ural, Siberia, and Far East regions.

          Aggregation from the regional level to the 10 big regions is conducted according to the following

          formula:
                                             foreignij
           foreign _ sharekj =          ik
                                       foreignij + localij
                                   ik

          where i is a region that belongs to a bigger region k.


Table A3 Share of foreign owned companies for 10 regions

              ADM    AGF      CAT      COM*      CON    ECM    ELE    FIN   MIN**  MNF***   PSM     SCS    SSM   TRD
Moscow        0.000   0.000    0.000     0.001   0.000  0.000   0.000 0.101 0.222     0.000  0.000   0.025 0.000 0.009
Saint
Petersburgh   0.021   0.082    0.042     0.032   0.006  0.000   0.000 0.000 0.111     0.060  0.000   0.023 0.000 0.030
Tumen         0.000   0.000    0.000     0.000   0.008  0.000   0.000 0.000 0.028     0.021  0.000   0.037 0.000 0.000
Northwest     0.000   0.004    0.000     0.000   0.000  0.040   0.000 0.000 0.000     0.024  0.086   0.192 0.000 0.010
North         0.006   0.115    0.000     0.003   0.002  0.000   0.000 0.000 0.025     0.044  0.000   0.000 0.000 0.004
Center        0.000   0.000    0.000     0.003   0.005  0.000   0.005 0.003 0.000     0.026  0.000   0.000 0.000 0.003
South         0.000   0.000    0.000     0.006   0.023  0.006   0.013 0.000 0.023     0.024  0.000   0.000 0.004 0.000
Urals         0.000   0.005    0.016     0.005   0.000  0.000   0.001 0.000 0.034     0.046  0.002   0.035 0.003 0.005
Sibiria       0.000   0.001    0.069     0.000   0.010  0.000   0.005 0.000 0.021     0.141  0.000   0.013 0.000 0.013
Far East      0.000   0.121    0.077     0.018   0.006  0.001   0.008 0.000 0.096     0.046  0.004   0.012 0.000 0.010


*             COM (communication and transport) includes RLW TRK PIP MAR AIR TRO TMS PST
**            MIN(mining) includes FME NFM COA OLE
***           MNF (manufacturing) includes MWO TPP CNM CLO OTHT OIN



          2.2     Regions of Russia 2003


                  Table 3.5 of "Regions of Russia 2003" reports the distribution of labor force by regions

          and types of ownership in 2002. The types of ownership include state, city, private, NGO, mixed

          Russian26, and foreign27 ownership. Unfortunately, the handbook does not have the sectoral

          distribution of labor by the types of ownership within a region. Therefore, we can calculate only

          the regional distribution of workers.



          26Any mix of state, city, private, and NGO ownership
          27Includes both fully and partially foreign ownership


                                                              64

          The share of the foreign owned firms in the region is defined as



 foreign _ sharej =      foreignj
                          total j


where j is a region as defined in the Table 2

foreignj is a number of workers in foreign owned companies in the region j

totalj is a total number of workers in the region j


Results are presented in the Table A4:



Table A4 Distribution of labor force by type of ownership

                                                   Share of workers by type of ownership
 Code                 Region
                                         State      City      Private     NGO    Mixed
                                                                                 Russian Foreign

 rus        Russian Federation            0.21      0.16        0.50      0.01    0.09    0.03
 cen        Central Federal Okrug         0.20      0.15        0.51      0.01    0.09    0.04
 bel        Belgorodskaya                 0.13      0.19        0.61      0.01    0.06    0.01
 bry        Bryanskaya                    0.19      0.17        0.56      0.01    0.07    0.01
 vla        Vladimirskaya                 0.17      0.17        0.53      0.01    0.09    0.03
 vor        Voronezhskaya                 0.20      0.15        0.58      0.01    0.06    0.01
 iva        Ivanovskaya                   0.22      0.15        0.52      0.02    0.08    0.02
 kal        Kaluzhskaya                   0.22      0.15        0.51      0.01    0.07    0.03
 kos        Kostromskaya                  0.21      0.21        0.49      0.01    0.07    0.01
 krs        Kurskaya                      0.22      0.11        0.58      0.01    0.07    0.02
 lip        Lipetskaya                    0.15      0.18        0.47      0.01    0.07    0.12
 mos        Moskovskaya                   0.22      0.18        0.47      0.01    0.09    0.03
 orl        Orlovskaya                    0.21      0.13        0.48      0.01    0.14    0.02
 rya        Ryazanskaya                   0.17      0.17        0.56      0.01    0.07    0.02
 smo        Smolenskaya                   0.22      0.18        0.51      0.01    0.07    0.01
 tam        Tambovskaya                   0.20      0.18        0.55      0.01    0.04    0.02
 tve        Tverskaya                     0.20      0.17        0.51      0.01    0.09    0.01
 tul        Tulskaya                      0.16      0.16        0.45      0.01    0.16    0.06
 yar        Yaroslavskaya                 0.16      0.18        0.48      0.01    0.15    0.03
 msk        Moscow city                   0.22      0.11        0.51      0.01    0.11    0.06
 nor        North West Federal Okrug      0.26      0.13        0.49      0.01    0.07    0.05
 krl        The Republic of Karelia       0.24      0.21        0.39      0.01    0.10    0.05
 kom        The Republic of Komi          0.21      0.22        0.39      0.00    0.13    0.05
 arh        Arkhangelskaya                0.29      0.21        0.38      0.00    0.06    0.06
 nao        Nenetskiy AO                  0.27      0.19        0.45      0.00    0.03    0.06
 vol        Vologodskaya                  0.15      0.19        0.49      0.00    0.14    0.03
 klg        Kaliningradskaya              0.24      0.16        0.50      0.01    0.06    0.03
 len        Leningradskaya                0.17      0.18        0.54      0.00    0.05    0.06
 mur        Murmanskaya                   0.30      0.20        0.38      0.00    0.10    0.02
 nov        Novgorodskaya                 0.19      0.21        0.45      0.01    0.09    0.06
 psk        Pskovskaya                    0.22      0.18        0.54      0.01    0.03    0.02
 spb        Saint Petersburg city         0.34      0.00        0.55      0.01    0.05    0.05
 sou        South Federal Okrug           0.18      0.15        0.57      0.01    0.07    0.01
 ady        The Republic of Adygeya       0.21      0.18        0.53      0.01    0.06    0.00


                                                  65

                                        Share of workers by type of ownership
Code            Region
                                 State   City    Private     NGO      Mixed
                                                                      Russian Foreign

dag  The Republic of Dagestan    0.25    0.15     0.55       0.01      0.04    0.01
ing  The Republic of Ingushetia  0.34    0.15     0.49       0.01      0.02    0.01
kab  Kabardino Balkaria          0.19    0.19     0.49       0.01      0.11    0.00
klr  The Republic of Kalmykia    0.28    0.22     0.43       0.01      0.07    0.00
kar  Karachaevo Cherkessia       0.30    0.09     0.57       0.01      0.04    0.00
sev  North Osetia                0.30    0.18     0.45       0.01      0.06    0.01
cher The Chechen Republic         ...    ...       ...        ...       ...     ...
kdk  Krasnodarsky krai           0.15    0.17     0.58       0.01      0.08    0.01
sta  Stavropolsky krai           0.22    0.09     0.59       0.01      0.05    0.04
ast  Astrakhanskaya              0.23    0.19     0.46       0.01      0.06    0.05
vlg  Volgogradskaya              0.15    0.18     0.59       0.01      0.07    0.01
ros  Rostovskaya                 0.15    0.15     0.59       0.01      0.09    0.01
pvl  Provlzhsky Federal Okrug    0.21    0.16     0.48       0.01      0.13    0.02
bas  Bashkortostan               0.19    0.22     0.37       0.01      0.22    0.00
mar  The Republic of Mari El     0.23    0.19     0.44       0.01      0.12    0.01
mor  The Republic of Mordovia    0.25    0.16     0.40       0.01      0.17    0.00
tat  The Republic of Tatarstan   0.35    0.05     0.38       0.01      0.16    0.05
udm  The Republic of Udmurtia    0.21    0.18     0.45       0.01      0.14    0.01
chv  The Republic of Chuvashia   0.16    0.17     0.49       0.02      0.14    0.02
kir  Kirovskaya                  0.22    0.16     0.48       0.01      0.10    0.03
niz  Nizhegorodskaya             0.17    0.17     0.55       0.01      0.08    0.02
ore  Orenburgskaya               0.16    0.19     0.54       0.00      0.11    0.01
pen  Penzenskaya                 0.26    0.11     0.55       0.01      0.06    0.00
per  Permskaya                   0.17    0.18     0.51       0.01      0.09    0.03
kpao Komi-Perm AO                0.28    0.15     0.49       0.00      0.07    0.01
sam  Samarskaya                  0.14    0.16     0.51       0.01      0.15    0.03
sar  Saratovskaya                0.21    0.16     0.55       0.01      0.04    0.02
ulo  Ulyanovskaya                0.21    0.17     0.41       0.01      0.19    0.01
ura  Urals Federal Okrug         0.18    0.19     0.49       0.01      0.08    0.06
krg  Kurganskaya                 0.19    0.18     0.56       0.01      0.05    0.01
sve  Sverdlovskaya               0.21    0.18     0.40       0.01      0.07    0.12
tum  Tumenskaya                  0.12    0.20     0.58       0.00      0.09    0.01
hmao Hanty-Mansi AO              0.08    0.21     0.60       0.00      0.09    0.01
ynao Yamal-Nenets AO             0.09    0.22     0.55       0.00      0.12    0.01
chl  Chelyabinskaya              0.19    0.19     0.46       0.01      0.09    0.07
sib  Siberia Federal Okrug       0.22    0.19     0.48       0.01      0.08    0.02
alr  Altay republic              0.28    0.23     0.45       0.01      0.03    0.00
bur  The Republic of Buryatia    0.25    0.22     0.41       0.02      0.06    0.04
tyv  The Republic of Tyva        0.34    0.32     0.30       0.00      0.04    0.00
hak  The Republic of Khakasia    0.23    0.16     0.51       0.01      0.07    0.04
alt  Altaisky krai               0.18    0.20     0.54       0.01      0.05    0.02
kra  Krasnoyarsky krai           0.21    0.20     0.42       0.00      0.11    0.06
tao  Tajmyr AO                   0.36    0.17     0.43       0.00      0.03    0.00
evao Evenkskiy AO                0.49    0.21     0.20       0.00      0.11     ...
irk  Irkutskaya                  0.21    0.20     0.46       0.00      0.10    0.02
uoao Ust-Ordyn Buryat AO         0.15    0.25     0.52       0.00      0.07    0.00
kem  Kemerovskaya                0.16    0.22     0.50       0.00      0.11    0.00
nvs  Novosibirskaya              0.28    0.15     0.48       0.01      0.07    0.01
oms  Omskaya                     0.19    0.17     0.56       0.01      0.07    0.01
tom  Tomskaya                    0.23    0.18     0.49       0.00      0.08    0.01
chi  Chitinskaya                 0.33    0.19     0.38       0.01      0.07    0.02
abao Aginsk Buryat AO            0.35    0.07     0.54       0.01      0.02    0.01
far  Far East Federal Okrug      0.29    0.15     0.42       0.00      0.11    0.02
sah  The Republic of Sakha       0.52    0.01     0.28       0.00      0.18    0.00
pri  Primorsky krai              0.23    0.16     0.47       0.01      0.11    0.03
hab  Khabarovsky krai            0.29    0.18     0.38       0.00      0.11    0.03


                                       66

                                                  Share of workers by type of ownership
 Code              Region
                                        State      City       Private     NGO         Mixed
                                                                                    Russian     Foreign

 amu     Amurskaya                      0.26       0.17        0.47        0.00       0.08        0.01
 kam     Kamchatskaya                   0.28       0.18        0.46        0.00       0.07        0.00
 koao    Koryak AO                      0.29       0.27        0.38        0.00       0.06        0.00
 mag     Magadanskaya                   0.23       0.21        0.46        0.00       0.08        0.02
 sao     Sakhalinskaya                  0.19       0.19        0.52        0.01       0.07        0.02
 eao     Jewish AO                      0.31       0.21        0.40        0.00       0.07        0.01
 chu     Chukotsky AO                   0.47       0.23        0.18        0.00       0.11        0.02
Source: Regions of Russia 2003


Distribution of labor force by type of ownership for 10 bigger regions

Table A5 presents shares of total regional labor force employed by state, city, local private

company, non-government, mixed Russian, and foreign organizations for 10 bigger regions:

Moscow, Saint Petersburg, Tumen, Northwest, North, Center, South, Ural, Siberia, and Far East

regions. Aggregation from the regional level to the 10 big regions is conducted according to the

following formula:
           Labor      _by _ ownershipij
sharekj =  ik
                  Total     _labori
                  ik




where i is on of the 89 regions of Russia that belongs to a bigger regional markets k, and j is a

type of ownership.


Table A5 Distribution of labor force by type of ownership. Ten bigger regions

                                                Share of workers by type of ownership
 Code              Market
                                      State    City  Private   NGO      Mixed Russian      Foreign

 Msc     Moscow                        0.22    0.13    0.49    0.01          0.10             0.05
 Stp     Saint Petersburgh             0.30    0.04    0.54    0.01          0.05             0.05
 Tmn     Tumen Region                  0.12    0.20    0.58    0.00          0.09             0.01
 Vgd     Northwest                     0.19    0.19    0.50    0.01          0.09             0.03
 Nor     North                         0.26    0.21    0.38    0.00          0.09             0.05
 Cen     Center                        0.19    0.16    0.53    0.01          0.09             0.03
 Sou     South                         0.19    0.16    0.56    0.01          0.07             0.02
 Url     Ural                          0.21    0.16    0.46    0.01          0.12             0.04
 Sib     Siberia                       0.23    0.18    0.47    0.01          0.09             0.02
 Far     Far East                      0.26    0.18    0.44    0.01          0.10             0.02
Source: Regions of Russia 2003




                                                 67

2.3     BEEPS

2.3.1     Variables


We define a variable workersijk as a number of permanent, full time employees that a firm k in

industry i from country j had in 2002 (Question 91a1 of the survey).

We define an industry i according to the firm's main area of activity in terms of sales (Question

S.3)

Finally, we define a variable ownership that takes two values: foreign and local. A company k is

the foreign owned company if at least 10% of the company is owned by a private foreign

company or organization (Question S.4c).



2.3.2     Country Aggregation


We aggregate the variable workersijkin two categories:

 foreignij =   wor       ker sijk
              k foreign

localij =   wor       ker sijk
          klocal




Then, the share of the foreign owned firms in an industry i in a country j is defined as

 foreign _ shareij =          foreignij
                        foreignij + localij


The results are presented in the Table A6.




                                                68

                                                                       Sector


                                             Transport       and

                                             communication        Trade    and Renta

            Mining Construction Manufacturing                     repair       servi

Country


Albania     1.00a         0.11          0.18                 0.36          0.10

             0.00b         0.32          0.39                 0.50         0.30

                2c           19            62                   14           42



Armenia       0.33        0.00          0.19                 0.10          0.17

              0.58         0.00          0.39                 0.32         0.38

                 3           11            64                   10           47



Azerbaijan    0.00        0.11          0.24                 0.40          0.09

              0.00         0.32          0.43                 0.52         0.29

                 3           27            49                   10           57



Belarus          .        0.07          0.38                 0.40          0.18

                 .         0.26          0.49                 0.50         0.38

                 0           68            42                   25           80



BiH           0.00        0.00          0.10                 0.17          0.18

              0.00         0.00          0.31                 0.39         0.39

                 3           12            67                   12           56



Bulgaria      0.33        0.00          0.24                 0.16          0.17

              0.58         0.00          0.43                 0.37         0.38

                 3           19            49                   25           93



Croatia       0.33        0.09          0.14                 0.07          0.31

              0.58         0.29          0.35                 0.26         0.47

                 3           23            37                   15           58




                                  69

Czech        0.00    0.18     0.21     0.14    0.12

             0.00    0.38      0.41    0.36    0.33

                3      40        68      21      67



Estonia      0.00    0.15     0.17     0.13    0.21

             0.00    0.37      0.38    0.34    0.41

                3      20        30      16      48



FYROM        0.00    0.00     0.09     0.14    0.19

             0.00    0.00      0.29    0.36    0.40

                2      13        45      14      62



Georgia      1.00    0.30     0.15     0.18    0.12

             0.00    0.48      0.36    0.39    0.33

                1      10        34      17      58



Hungary      0.00    0.11     0.55     0.25    0.13

             0.00    0.32      0.50    0.45    0.33

                4      36        51      16      79



Kazakhstan   0.17    0.06     0.24     0.07    0.16

             0.41    0.24      0.43    0.26    0.37

                6      48        54      15      69



Kyrgyzstan   0.25    0.10     0.24     0.17    0.13

             0.50    0.30      0.43    0.39    0.34

                4      21        49      12      52



Latvia          .    0.17     0.50     0.21    0.08

                .    0.39      0.51    0.43    0.27

                0      12        28      14      80



Lithuania    0.00    0.04     0.15     0.13    0.21

             0.00    0.19      0.36    0.34    0.41

                3      28        40      24      48



Moldova         .    0.00     0.34     0.18    0.07



                           70

                 .    0.00      0.48    0.40    0.25

                 0       5        50      11      74



Poland           .    0.11     0.15     0.17    0.20

                 .    0.31      0.36    0.38    0.40

                 0      76      114       54    168



Romania          .    0.17     0.21     0.25    0.12

                 .    0.38      0.41    0.44    0.32

                 0      24        82      20      68



Russia       0.29     0.08     0.17     0.24    0.16

              0.49    0.27      0.38    0.43    0.36

                 7      80      128       37    135



Slovakia         .    0.18     0.17     0.40    0.22

                 .    0.39      0.38    0.51    0.42

                 0      17        29      15      46



Slovenia     0.00     0.00     0.36     0.00    0.15

              0.00    0.00      0.49    0.00    0.36

                 3      28        47      16      40



Tajikistan   0.20     0.00     0.10     0.17    0.00

              0.45    0.00      0.31    0.39    0.00

                 5      20        48      12      47



Turkey       0.00     0.03     0.17     0.21    0.10

              0.00    0.17      0.37    0.42    0.30

                 8      33      151       28    165



Ukraine      0.00     0.04     0.15     0.14    0.27

              0.00    0.19      0.36    0.35    0.45

                 2      57      139       29    114



Uzbekistan   0.00     0.02     0.39     0.16    0.18

              0.00    0.16      0.49    0.37    0.39



                            71

                   5                            41                                               51                                                                        19                            92



Yugoslavia      0.00                         0.06                                              0.12                                                                      0.23                          0.27

                0.00                          0.24                                             0.32                                                                      0.43                          0.45

                   4                            18                                               68                                                                        22                            73



Total by

sector          0.14                         0.08                                              0.21                                                                      0.19                          0.16

                0.35                          0.27                                             0.41                                                                      0.40                          0.37

                  77                          806                                              1676                                                                      523                           2018

Notes:       a Share of employed by a company with at least 10% of foreign ownership




                                                                    and                               repair

                                                                                                            and        business                                                         country
                                                                                                                                                 and
                                                                                                                                                                                               by


 Country             Mining      Construction       Manufacturing      Transport  communication                Trade           Rental, services     Hotels  restaurants      Other               Total

Albania        1.00a         0.11                0.18              0.36                            0.10               0.11                       0.08                     0.00      0.15
                0.00b         0.32               0.39                           0.50               0.30                0.33                               0.28            0.00       0.36
                   2c           19                 62                             14                 42                               9                     13               9       170
Armenia         0.33         0.00                0.19              0.10                            0.17               0.10                       0.25                     0.00      0.16
                 0.58         0.00               0.39                           0.32               0.38                0.32                               0.45            0.00       0.37
                    3           11                 64                             10                 47                               10                    16              10       171
Azerbaijan      0.00         0.11                0.24              0.40                            0.09               0.13                       0.22                     0.00      0.16
                 0.00         0.32               0.43                           0.52               0.29                0.35                               0.44            0.00       0.37
                    3           27                 49                             10                 57                               8                                9     6       169
Belarus              .       0.07                0.38              0.40                            0.18               0.13                       0.20                     0.07      0.20
                     .        0.26               0.49                           0.50               0.38                0.35                               0.45            0.26       0.40
                    0           68                 42                             25                 80                               15                               5    15       250
BiH             0.00         0.00                0.10              0.17                            0.18               0.29                       0.07                     0.13      0.13
                 0.00         0.00               0.31                           0.39               0.39                0.49                               0.26            0.35       0.33
                    3           12                 67                             12                 56                               7                     15               8       180
Bulgaria        0.33         0.00                0.24              0.16                            0.17               0.18                       0.10                     0.06      0.16
                 0.58         0.00               0.43                           0.37               0.38                0.39                               0.30            0.24       0.37
                    3           19                 49                             25                 93                               22                    21              17       249
Croatia         0.33         0.09                0.14              0.07                            0.31               0.18                       0.10                     0.00      0.18
                 0.58         0.29               0.35                           0.26               0.47                0.39                               0.32            0.00       0.38
                    3           23                 37                             15                 58                               28                    10              10       184
Czech           0.00         0.18                0.21              0.14                            0.12               0.16                       0.13                     0.17      0.16
                 0.00         0.38               0.41                           0.36               0.33                0.37                               0.34            0.39       0.37
                    3           40                 68                             21                 67                               32                    23              12       266
Estonia         0.00         0.15                0.17              0.13                            0.21               0.25                       0.07                     0.22      0.18
                 0.00         0.37               0.38                           0.34               0.41                0.44                               0.26            0.44       0.38


                                                                  72

                                                               and                              repair

                                                                                                      and        business                                                         country
                                                                                                                                           and
                                                                                                                                                                                         by


  Country        Mining      Construction      Manufacturing      Transport  communication               Trade           Rental, services     Hotels  restaurants      Other               Total

                 3          20                30                             16                48                               28                    15               9       169
FYROM         0.00       0.00              0.09               0.14                          0.19                0.09                       0.14                    0.17       0.13
              0.00        0.00              0.29                           0.36              0.40                0.30                               0.36            0.41       0.34
                 2          13                45                             14                62                               11                    14               6       167
Georgia       1.00       0.30              0.15               0.18                          0.12                0.16                       0.17                    0.09       0.15
              0.00        0.48              0.36                           0.39              0.33                0.37                               0.39            0.29       0.36
                 1          10                34                             17                58                               19                    12              23       174
Hungary       0.00       0.11              0.55               0.25                          0.13                0.27                       0.20                    0.06       0.23
              0.00        0.32              0.50                           0.45              0.33                0.45                               0.41            0.24       0.42
                 4          36                51                             16                79                               30                    15              17       248
Kazakhstan    0.17       0.06              0.24               0.07                          0.16                0.14                       0.18                    0.10       0.15
              0.41        0.24              0.43                           0.26              0.37                0.35                               0.40            0.32       0.36
                 6          48                54                             15                69                               37                    11              10       250
Kyrgyzstan    0.25       0.10              0.24               0.17                          0.13                0.00                       0.50                    0.09       0.17
              0.50        0.30              0.43                           0.39              0.34                0.00                               0.53            0.30       0.38
                 4          21                49                             12                52                               14                               8    11       171
Latvia           .       0.17              0.50               0.21                          0.08                0.12                       0.00                    0.00       0.16
                 .        0.39              0.51                           0.43              0.27                0.33                               0.00            0.00       0.37
                 0          12                28                             14                80                               25                               9     7       175
Lithuania     0.00       0.04              0.15               0.13                          0.21                0.09                       0.27                    0.33       0.16
              0.00        0.19              0.36                           0.34              0.41                0.29                               0.45            0.52       0.36
                 3          28                40                             24                48                               23                    26               6       198
Moldova          .       0.00              0.34               0.18                          0.07                0.67                       0.00                    0.14       0.17
                 .        0.00              0.48                           0.40              0.25                0.58                               0.00            0.35       0.37
                 0           5                50                             11                74                               3                                9    22       174
Poland           .       0.11              0.15               0.17                          0.20                0.13                       0.21                    0.04       0.15
                 .        0.31              0.36                           0.38              0.40                0.34                               0.43            0.20       0.36
                 0          76              114                              54              168                                47                    14              25       498
Romania          .       0.17              0.21               0.25                          0.12                0.27                       0.12                    0.11       0.18
                 .        0.38              0.41                           0.44              0.32                0.45                               0.33            0.32       0.38
                 0          24                82                             20                68                               26                    17              18       255
Russia        0.29       0.08              0.17               0.24                          0.16                0.23                       0.21                    0.12       0.16
              0.49        0.27              0.38                           0.43              0.36                0.42                               0.42            0.33       0.37
                 7          80              128                              37              135                                40                    19              58       504
Slovakia         .       0.18              0.17               0.40                          0.22                0.11                       0.00                    0.17       0.18
                 .        0.39              0.38                           0.51              0.42                0.31                               0.00            0.39       0.38
                 0          17                29                             15                46                               38                    12              12       169
Slovenia      0.00       0.00              0.36               0.00                          0.15                0.12                       0.07                    0.00       0.15
              0.00        0.00              0.49                           0.00              0.36                0.33                               0.26            0.00       0.36
                 3          28                47                             16                40                               33                    15               6       188
Tajikistan    0.20       0.00              0.10               0.17                          0.00                0.00                       0.00                    0.00       0.05
              0.45        0.00              0.31                           0.39              0.00                0.00                               0.00            0.00       0.21



                                                             73

                                                                     and                              repair

                                                                                                            and        business                                                         country
                                                                                                                                                 and
                                                                                                                                                                                               by


 Country               Mining      Construction      Manufacturing      Transport  communication               Trade           Rental, services     Hotels  restaurants      Other               Total

                      5           20                48                             12                47                               18                    10              14       174
Turkey             0.00        0.03              0.17               0.21                           0.10               0.26                       0.07                    0.05       0.12
                   0.00         0.17              0.37                           0.42              0.30                0.45                               0.25            0.21       0.33
                      8           33              151                              28              165                                34                    73              22       514
Ukraine            0.00        0.04              0.15               0.14                           0.27               0.22                       0.21                    0.03       0.17
                   0.00         0.19              0.36                           0.35              0.45                0.41                               0.42            0.19       0.38
                      2           57              139                              29              114                                65                    28              29       463
Uzbekistan         0.00        0.02              0.39               0.16                           0.18               0.04                       0.12                    0.10       0.17
                   0.00         0.16              0.49                           0.37              0.39                0.20                               0.33            0.32       0.38
                      5           41                51                             19                92                               25                    17              10       260
Yugoslavia         0.00        0.06              0.12               0.23                           0.27               0.15                       0.17                    0.06       0.17
                   0.00         0.24              0.32                           0.43              0.45                0.37                               0.38            0.24       0.38
                      4           18                68                             22                73                               26                    18              17       246
Total by
sector             0.14        0.08              0.21               0.19                           0.16               0.16                       0.14                    0.08       0.16
                   0.35         0.27              0.41                           0.40              0.37                0.37                               0.34            0.28       0.37
                     77         806              1676                            523               2018                673                                454             409       6636
Notes:         a Share of employed by a company with at least 10% of foreign ownership

               b Standard deviation
               c Number of observations
Source: BEEPS 2002




                                                                   74

                                           Appendix B

                    Investment Ratings of the Regions of Russia

        To assess the investment climate and barriers to foreign direct investment, we use "The

Rating of Investment Climate of Russia's Regions in 2002-2003" published by Expert Rating

Agency (Expert RA). Expert RA is a rating agency founded by Expert Magazine that has

published its "Investment Rating of Russia's Regions" every year since 1996. The evaluation of

the investment climate is carried out for each of the 89 regions of Russia based on more than a

hundred statistical indicators of regional development from various sources that include Rosstat,

the Ministry of Finance, the Ministry of Economic Development, and The Central Bank.


        The rating of investment climate is formed based on two primary measures: investment

risk and investment potential of each region. The aggregate measure of investment risk is

calculated based on the seven types of risks: legal, political, economic, financial, social, criminal,

and ecological risks. The rating of a region by each type of risks is defined based on the index of

risk calculated as the deviation from the all-Russia average level of risk that is set equal to 1 and

aggregated to the overall index of risk An investment potential of a region is derived based on

the eight regional indicators of economic potential in the following categories: labor, consumer

demand, production, financial potential, institutional quality, innovations, infrastructure, and

natural resources. The region's potential rating in each category is calculated according to the

regional share in that category. The overall rating of investment potential is derived as a weighted

average of investment potential in each category. For our sensitivity analysis, we chose the

"investment potential rankings."




                                                 75

Table 2 Investment Risk in 2003

                      Investment Index of      Seven components of investment risk
                      risk rating investment Leg Polit Econo Fina Soc Crimi Ecolo
Region                in 2003     risk       al  ical mic    ncial ial nal   gical
Novgorodskaya               1        0.861     3   44      9   20     7   39     19
Yaroslavskaya               2        0.871     1   74     48   22     2   45     50
Saint Petersburg city       3        0.872     52  76      6    2     3   40     44
Belgorodskaya               4        0.876     58  26     14   24     4   16     11
Orlovskaya                  5        0.885     24    6    15   23     8   33     74
The Republic of
Tatarstan                   6        0.886     64    2    23   27    17   43     40
Vologodskaya                7        0.897     30  23     16   10     6   41     70
Moskovskaya                 8        0.902     9   28      8   14    40   24     43
Nizhegorodskaya             9        0.907     2   72     52   16     9   29     18
The Republic of
Bashkortostan              10        0.923     4     7     3   35    38   63     42
The Republic of
Mordovia                   11        0.924     49    1    49   47    42   14     23
Rostovskaya                12        0.944     44  21      4   31    25   46     21
Lipetskaya                 13        0.949     42  55     21   25     5    5     73
Permskaya                  14        0.953     10  35     26   11    11   69     67
Moscow city                15        0.959     76  34      1    1     1   85     26
Tomskaya                   16        0.976     7   40      7    3    37   77     54
Leningradskaya             17        0.977     11  58     10    9    50   26     72
Kaluzhskaya                18        0.979     26  36     20   18    13   47     71
Krasnodarsky krai          19        0.986     8   24     12   26    35   67     56
The Republic of
Chuvashia                  20        0.988     18  46     64   33    27   23      8
Samarskaya                 21        0.992     28  63     36    8    18   66     60
Kaliningradskaya           22        0.992     45  68     29    6    12   72     36
Saratovskaya               23        1.022     53  13     35   63    26   20     39
Tverskaya                  24        1.023     34  48     67   53    20   25      3
Voronezhskaya              25        1.037     17  67     56   50    28   27     33
Nenetskiy AO               26         1.04     75  11      2    4    58    8     83
Arkhangelskaya             27        1.049     16  27     33   38    49   18     76
Penzenskaya                28        1.055     15  30     74   67    36    1     29
Smolenskaya                29        1.058     38  62     45   51    10   55     45
Ryazanskaya                30         1.06     70  57     58   49    15    7     66
The Republic of
Khakasia                   31         1.06     6   56     24   30    54   59     49
The Republic of
Adygeya                    32        1.062     29  10     79   60    53   12      6
Kirovskaya                 33        1.066     5   49     72   58    48   13     31
Murmanskaya                34        1.073     51  22     39   48    19   42     79
Kemerovskaya               35        1.078     27  33     25   55    16   52     78
Kostromskaya               36        1.078     48  53     66   59    34   17     28
Volgogradskaya             37        1.095     59  79     51   15    32   61     41
Orenburgskaya              38        1.105     22  60     42   28    55   56     57
Yamal-Nenets AO            39         1.11     25    5    17    7    44   53     88
Hanty-Mansi AO             40        1.113     61    9     5    5    14   79     87
Stavropolsky krai          41        1.113     36  78     18   57    23   70     15
The Republic of
Buryatia                   42        1.117     12  32     13   61    62   54     48
Chukotsky AO               43        1.117     89    3    62   13    56   22     80


                                            76

                       Investment Index of     Seven components of investment risk

                       risk rating investmentLeg Polit Econo Fina Soc Crimi Ecolo

Region                 in 2003     risk      al  ical mic    ncial ial nal   gical
The Republic of
Udmurtia                    44        1.121    57  19     65   17    76   30     53
Khabarovsky krai            45        1.123    13  37     38   37    24   82     68
Pskovskaya                  46        1.127    39  52     34   34    80   34     13
The Republic of Karelia
                            47        1.129    55  43     30   39    57   50     65
Tambovskaya                 48        1.136    21  41     60   69    47   19     55
The Republic of Sakha
                            49        1.147    47  12     75   42    75   15     64
Kurskaya                    50        1.148    78  61     61   52    43   32     32
Omskaya                     51        1.157    63  45     44   19    65   73     51
Sverdlovskaya               52        1.162    74  47     19   21    67   57     69
Amurskaya                   53        1.168    46  69     46   68    22   65     52
Astrakhanskaya              54         1.17    33  65     31   12    79   75     34
Tumenskaya                  55        1.183    41  18     11   46    30   86     62
Ulyanovskaya                56        1.187    77  50     59   64    51   31     12
Krasnoyarsky krai           57        1.194    19  77     41   40    39   58     81
Vladimirskaya               58        1.196    54  71     40   45    83   28      7
Chelyabinskaya              59        1.196    62  42     37   41    31   44     86
Altay republic              60        1.198    69  39     28   70    63   49     27
Tulskaya                    61        1.199    43  85     47   43    46   11     82
Irkutskaya                  62        1.209    66  73     27   44    33   76     77
Aginsk Buryat AO            63        1.225    89    4    68   73    73    4     24
Novosibirskaya              64        1.231    14  16     32   29    82   83     30
Primorsky krai              65        1.242    65  83     53   32    41   80     59
Bryanskaya                  66        1.255    23  66     76   65    21   36     85
Chitinskaya                 67        1.263    37  31     81   62    52   71     61
Kurganskaya                 68        1.271    68  54     71   66    68   64     14
The Republic of Mari El
                            69        1.329    32  81     80   78    59   35     10
The Republic of
Kabardino Balkaria          70        1.332    60  82     43   84    64    6      5
Sakhalinskaya               71        1.341    79  80     22   56    81   68     46
Komi-Perm AO                72        1.342    67  25     69   85    61    9      9
Ivanovskaya                 73         1.38    31  64     73   71    87   37     17
Altaisky krai               74        1.387    35  84     54   75    84   51     47
The Republic of Komi        75        1.419    72  59     50   54    74   78     84
Jewish AO                   76        1.419    56  20     82   81    29   84     38
Evenkskiy AO                77        1.435    81  15     86   74    78    3     58
The Republic of
Kalmykia                    78        1.448    50  38     88   76    71   48     20
The Republic of North
Osetia                      79        1.449    20  70     55   80    45   87     22
Tajmyr AO                   80        1.474    89  17     78   36    60   10     89
Magadanskaya                81        1.525    80  51     83   72    85   62     63
Kamchatskaya                82        1.563    83  75     84   79    66   74     37
The Republic of Tyva        83        1.604    71    8    70   86    77   81     25




                                           77

                      Investment Index of        Seven components of investment risk

                      risk rating   investment Leg Polit Econo Fina Soc Crimi Ecolo

Region                in 2003       risk       al    ical mic     ncial ial  nal    gical
The Republic of

Karachaevo Cherkessia      84           1.621    89    86     57     83   69    38     16
The Republic of
Dagestan                   85           1.625    40    87     63     82   70    60       4
Ust-Ordyn Buryat AO        86           1.644    82    29     85     88   72     2       1
The Republic of
Ingushetia                 87           2.493    89    88     77     87   86    88       2
Koryak AO                  88           2.861    73    14     87     77   89    21     75
The Chechen Republic
                           89          13.791    89    89     89     89   88    89     35




Table 3 Investment Potential in 2003

                  Investment Share     in     Eight components of investment potential
                  potential    overall
                  rating    in potential Lab Cons Produ Finan Institu Innov Infrastr Reso
Region            2003         of Russia or  umer ction  cial  tional ation ucture urces
Novgorodskaya          65      0.433      66  65     56    64      57    48     30     79
Yaroslavskaya          33      0.803      31  34     33    35      26    25     27     80
Saint Petersburg
city                    2      6.572       2   3      5    4       2     3       1     89
Belgorodskaya          25      1.105      36  31     27    41      36    41      9     9
Orlovskaya             58      0.516      55  56     57    57      62    38     21     76
The Republic of
Tatarstan               9      2.049      10   9      7    8       7     10     35     34
Vologodskaya           39      0.73       46  35     19    27      39    39     63     62
Moskovskaya             3      4.511       3   2      3    3       3     2       4     48
Nizhegorodskaya         6      2.239       6  10     15    15      9     4      33     57
The Republic of
Bashkortostan          15      1.822      12   8     10    6       18    14     47     25
The Republic of
Mordovia               62      0.452      53  68     55    60      69    47     34     66
Rostovskaya            12      1.969       5   7     16    12      6     11     13     29
Lipetskaya             38      0.74       47  38     21    31      53    65     14     73
Permskaya              11      1.97       22  14     14    11      16    12     58     5
Moscow city             1      17.781      1   1      1    1       1     1       2     89
Tomskaya               45      0.681      48  37     37    33      41    22     70     28
Leningradskaya         21      1.228      29  36     18    32      23    6       5     52
Kaluzhskaya            40      0.726      40  55     48    55      44    15     17     71
Krasnodarsky krai      10      2.017       4   5     12    9       5     27      6     30
The Republic of
Chuvashia              52      0.615      39  53     47    53      49    36     19     83




                                             78

                  Investment Share    in    Eight components of investment potential
                  potential   overall
                  rating   in potential Lab Cons Produ Finan Institu Innov Infrastr Reso
Region            2003        of Russia or  umer ction cial  tional ation ucture urces
Samarskaya             8     2.16        7   6      6    10      8     7      20     45
Kaliningradskaya       37    0.741       52  58    53    52      29    54      3     40
Saratovskaya           20    1.262       9   21    23    21      19    19     23     35
Tverskaya              44    0.682       37  48    41    47      33    29     26     63
Voronezhskaya          28    1.032       23  22    29    30      28    16     18     55
Nenetskiy AO           85    0.091       86  83    74    78      81    83     86     69
Arkhangelskaya         43    0.685       41  33    43    39      42    46     66     19
Penzenskaya            49    0.628       27  46    51    51      55    35     39     49
Smolenskaya            54    0.565       51  47    45    56      56    57     15     74
Ryazanskaya            48    0.628       42  49    40    45      46    42     22     56
The Republic of
Khakasia               73    0.31        71  70    69    71      71    75     67     27
The Republic of
Adygeya                75    0.254       75  73    76    76      73    79     25     86
Kirovskaya             55    0.56        34  45    44    42      47    51     64     53
Murmanskaya            32    0.926       43  40    38    36      37    30     31     13
Kemerovskaya           13    1.95        16  11    13    16      24    26     57     4
Kostromskaya           69    0.344       68  69    64    69      65    49     59     72
Volgogradskaya         22    1.175       19  16    20    17      22    20     43     32
Orenburgskaya          30    0.957       24  27    22    29      35    55     44     18
Yamal-Nenets AO        16    1.682       67  41     9    13      25    73     80     2
Hanty-Mansi AO         5     2.554       26  15     2    2       27    37     81     6
Stavropolsky krai      27    1.035       14  19    31    24      20    32     51     41
The Republic of
Buryatia               57    0.521       57  54    62    58      61    66     74     15
Chukotsky AO           70    0.339       81  81    77    73      82    81     78     14
The Republic of
Udmurtia               41    0.725       32  39    28    37      30    40     41     60
Khabarovsky krai       23    1.143       20  29    25    22      34    34     61     10
Pskovskaya             63    0.443       59  62    67    67      63    68     12     77
The Republic of
Karelia                60    0.473       65  57    54    61      54    62     36     36
Tambovskaya            59    0.515       49  44    58    54      64    43     32     67
The Republic of
Sakha                  19    1.372       64  32    26    23      43    44     85     1
Kurskaya               35    0.766       45  51    42    49      51    53      7     21
Omskaya                31    0.937       25  20    34    20      21    21     53     43
Sverdlovskaya          4     2.771       8   4      4    5       4     5      48     8
Amurskaya              50    0.617       56  61    60    62      66    71     65     12
Astrakhanskaya         56    0.536       60  52    59    44      52    59     46     26
Tumenskaya             34    0.801       38  25    50    28      11    23     62     51
Ulyanovskaya           46    0.649       50  42    46    50      48    18     42     54




                                           79

                  Investment Share    in    Eight components of investment potential
                  potential   overall
                  rating   in potential Lab Cons Produ Finan Institu Innov Infrastr Reso
Region            2003        of Russia or  umer ction cial  tional ation ucture urces
Krasnoyarsky krai      7     2.175       13  13    11    7       14    17     75     3
Vladimirskaya          36    0.75        30  50    39    43      32    24     16     70
Chelyabinskaya         14    1.886       11  12     8    14      12    9      24     24
Altay republic         83    0.126       78  79    82    80      78    80     72     50
Tulskaya               29    0.977       33  30    30    38      31    13      8     61
Irkutskaya             17    1.551       17  18    17    18      17    31     73     7
Aginsk Buryat AO       88    0.052       84  84    87    84      88    82     83     85
Novosibirskaya         18    1.373       15  17    24    19      13    8      50     37
Primorsky krai         24    1.126       18  24    35    25      10    33     45     17
Bryanskaya             47    0.644       28  43    52    48      50    50     10     68
Chitinskaya            51    0.616       63  63    66    59      60    64     69     11
Kurganskaya            67    0.421       62  60    65    63      59    45     49     65
The Republic of
Mari El                71    0.32        70  71    70    72      67    61     54     44
The Republic of
Kabardino Balkaria     68    0.403       61  59    68    65      72    69     28     47
Sakhalinskaya          61    0.472       69  64    49    46      40    56     55     39
Komi-Perm AO           87    0.061       82  82    85    85      83    77     84     82
Ivanovskaya            66    0.426       44  67    63    66      58    60     37     84
Altaisky krai          26    1.037       21  23    32    34      15    28     52     23
The Republic of
Komi                   42    0.69        54  28    36    26      45    52     79     22
Jewish AO              78    0.19        80  77    78    81      77    74     60     38
Evenkskiy AO           84    0.094       87  88    88    89      89    89     89     31
The Republic of
Kalmykia               82    0.138       76  78    80    74      76    76     76     64
The Republic of
North Osetia           64    0.435       58  66    72    68      70    70     11     58
Tajmyr AO              77    0.235       85  85    86    83      85    85     71     16
Magadanskaya           72    0.311       74  75    73    75      74    72     82     20
Kamchatskaya           74    0.269       73  72    71    70      68    58     68     42
The Republic of
Tyva                   81    0.138       79  76    81    79      79    78     88     33
The Republic of
Karachaevo
Cherkessia             76    0.239       72  74    75    77      75    67     56     59
The Republic of
Dagestan               53    0.613       35  26    61    40      38    63     40     46
Ust-Ordyn Buryat
AO                     86    0.066       83  86    84    87      84    89     77     81
The Republic of
Ingushetia             79    0.177       77  80    83    82      80    84     38     87
Koryak AO              89    0.047       88  87    79    86      86    89     87     78
The Chechen
Republic               80    0.155       89  89    89    88      87    89     29     75




                                           80

                                    Appendix C

    Key Elements of the GAMS code for creating the

                     Regional Input-Output Tables


Below are the key elements of the GAMS code for creating the regional input-output tables:


positive
variable
      y(g,r)                   Regional shares of output (imputed),
      rtm(mrg,g,r)             Regional shares of margin provision,


      cd(r)           Region shares of final consumption,
      id(r)           Regional shares of investment
      gd(r)           Regional shares of government demand;


*     Define shares of industrial and energy output based on
*     economic indicators and industrial share statistics from
*     the Regions of Russia.            Notice that this covers industrial
*     goods, fuels, refined products and agricultural output:


y.fx(g,r)$indsec(g) = indic(r,"Indus")*indshare(r,g) /
                    sum(rr,indic(rr,"Indus")*indshare(rr,g));


y.fx(fuel,r) = regfuel(r,fuel);
y.fx("oil",r) = oil(r,"oil")/sum(rr,oil(rr,"oil"));


y.fx("agr",r) = indic(r,"totagr")               / sum(rr, indic(rr,"totagr"));


*     Use "Commissioning of total living area" to allocate
*     construction:


y.fx("con",r) = indic(r,"housing") / sum(rr,
indic(rr,"housing"));


                                           81

*       Use industrial output to allocate other goods and services:


y.fx("oth",r) = indic(r,"indus")/sum(rr,indic(rr,"indus"));


*       Consumptino demand levels are assumed proportional to
*       expenditure per capita times population:


cd.l(r) = indic(r,"exppc")*indic(r,"pop") /
        sum(rr,indic(rr,"exppc")*indic(rr,"pop"));


*       Investment shares are likewise based on indicator data:


id.l(r) = indic(r,"invest")/sum(rr, indic(rr,"invest"));


*       Government demand is assumed proportional to GRP:


gd.l(r) = indic(r,"grp")/sum(rr, indic(rr,"grp"));


Here, then, is the system of linear equations which determine service sector supplies -- note that

these equations are defined in terms of submatrices taken from the national input-output table,

hence we retain the input-output characteristics from the national table when setting up the

regional statistics, and we only calibrate regional shares of output:


positive
variable         a(g,r)           Armington activity level;


equations pamkt, pdmkt, pmmkt;


*       Armington composite goods supply and demand:


pamkt(g,r)$(a.lo(g,r) ne a.up(g,r))..


        a(g,r) * a0_(g) =e= sum(gg, id0_(g,gg)*y(gg,r))
                 + ac0_(g)*cd.l(r) + ai0_(g)*id.l(r) + ag0_(g)*gd.l(r);




                                                 82

*      Market for domestic supply:


pdmkt(g,r)$(y.lo(g,r) ne y.up(g,r))..


       (d0_(g)+s0_(g))*y(g,r) =e=
               sum((mrg,gg), rtmd0_(g,mrg,gg)*rtm(mrg,gg,r)) +
ad0_(g)*a(g,r);




*      Market for imported supply:


pmmkt(mrg,g,r)$(rtm.lo(mrg,g,r) ne rtm.up(mrg,g,r))..
       rtm(mrg,g,r) * rtm0_(mrg,g) =e=
          sum(gg, y(gg,r) * md0_(mrg,g,gg))
               + y(g,r)       * ei.l(r,g) * md0_(mrg,g,"x")
               + cd.l(r)*md0_(mrg,g,"c")
               + id.l(r)*md0_(mrg,g,"i")
               + gd.l(r)*md0_(mrg,g,"g");


After having constructed input-output tables, we then integrate and rebalance trade statistics to

match these production and consumption statistics.




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