Although Russia is very rich in natural resources, the share of natural resource rent payments in budget revenues is very low, less than 4% of the consolidated budget. One of the possible ways to increase its effectiveness is to increase the share of natural resource rent payments in budget revenues.
NET-BACK ESTIMATION OF TIMBER RENT The simple net-back method (netting out all costs, including a fair profit,
For the purpose of estimating normal profit, a standard return on equity for the entire Russian logging sector has been restated in the following way. 2 NIPIEILesprom recommends 25% as a normal rate of return on capital in the logging sector, but this seems too high as the rate for the baseline scenario. Using the regional data set as the lower bound estimate and the NIPIEILesprom as the upper bound estimate, the interval for granted rent on roundwood is in the region of US$ 1.6–8.5/m3, and total lost income amounts to US$ 191–1032 million per annum, to which, as above, 35% of the unrealized rent must be added to obtain the probable total lost income.
TIMBER RENT MODELING Next, we turn to the second approach of timber rent estimation. In order
The only difference compared to Table 5 is the distribution between normal and excess profits. As shown in the preceding paragraphs, cost data are generally unreliable and vary from source to source, and the choice of a normal profit margin is an arbitrary decision. Let's assume that a forestry company has acquired the right to exploit a certain wood plot with an area of A hectares.
As mentioned above, the period T of exploitation of a given timber plot is given exogenously. Since substitution possibilities between capital and labor at the level of a logging enterprise are very limited, it is assumed that the function ( , )H K L is of a Leontief type. Then, under conditions of an optimal mix of capital and labor used in production (L=DK), and assuming the following specification of the function ( , )G Q d.
TIMBER RENT ESTIMATION BASED ON NORMATIVE DATA To estimate the production function (2), we first use normative data regarding
TIMBER RENT ESTIMATION BASED ON NORMATIVE DATA To estimate production function (2), we first use normative data on
The productivity of equipment depends on the main rental formation factors (volume per tree, transport distance, etc.), which are shown in Tables 10 and 11. The reference values for the volume per tree and transport distance are: 0.3 cubic meters and 30, respectively km. The volume per hectare depends on the volume per tree, as shown in Table 13.
The data in Table 13 are to some extent conditional, because in practice there are a number of confounding factors, for example the existence of small trees, which are not economical to harvest. The estimation results presented above clearly point to the inadequacy of a Cobb-Douglas specification of the production function. Once the parameters of the production function have been estimated, one can calculate the timber rent for a given set of rent determinants.
These results seem quite unexpected because of the huge differences in capital costs between technologies A and B on the one hand and technology C on the other, and because labor costs in Russia are very low. All attempts to estimate the parameters of the production function from the data of logging companies failed due to the extremely low quality of the data.
TIMBER RENT ESTIMATION BASED ON AUCTION DATA Though timber rent estimation based on normative data is very useful,
Selling timber at auctions is organized according to the procedures established by the Forest Code of the Russian Federation (Articles 43-45) (GOR, 1997) and by regulations for forest auctions approved by the Russian Forest Service (#99 of 11 August 1997). ). Within the auction committee there are representatives of a territorial branch of the regional Forest Service, municipalities and leskhoz. The winner of the auction must cut wood, clean the forest plot and recultivate it within a certain period.
Except for the constant term, all the estimated coefficients are of correct sign and statistically significant at the 1% level. As the sales volume varies significantly from auction to auction, a weighted assessment of the same function has been carried out (19). To overcome the shortcomings of linear regression, non-linear specification of the function defining the auction price should be used for the estimation.
As in the case of the linear specification of the auction price, a weighted estimation of equation (22) under the exogenously given coefficients c4 and c5 is also performed. An important problem related to the regression estimation above is the stability of the estimation results with respect to the values of the exogenous parameters (µ and δ).
ANALYSIS OF ESTIMATION RESULTS The estimation results yield two important outcomes
The difference between the normative approach and the auction approach may be due to the fact that a number of existing taxes in the normative approach (e.g. road tax, taxes for non-budgetary funds, social and municipal taxes, police tax, etc.) were not accounted for, while the auction price implicitly includes all costs. The high level of logging costs reported by logging companies and local forest authorities compared to the estimation results based on data from timber auctions is due to the following reasons: .. a) loggers participating in timber auctions are more efficient; b) auctions reveal real costs; c) companies, when reporting their costs, include road construction costs as running costs, rather than as investments. One of the possible explanations for the discrepancy between estimated and market timber prices could be the existing market structure, i.e. some degree of monopsony.
For example, in the Novgorod region there are about 300 official forestry companies, each of which produces only about 7,000 cubic meters of wood per year on average. In many cases, the activities of brokers are criminalized, which was one of the main reasons for the dissolution of the former Federal Forest Service in 2000. There are a large number of competing loggers who sell wood to a limited number (equal to N) of brokers at a price p.
Unfortunately, there are no reliable data on the number of timber brokers and on the price elasticity of timber supply. TAX SHIELD MODEL Next, we turn to the problem of shifting the tax burden from work and.
TAX SHIFTING MODEL Next, we turn to the problem of shifting the tax burden from labor and
Production in the rest of the economy is a function of labor L and capital KM relative to the production function. Employment in the sector of the rest of the economy is defined by the condition of maximizing the profit it brings. The economy is assumed to be on the upward-sloping parts of the Laffer curve for the labor tax and toll fees, ie.
Then, it follows from (51) that at reasonable values of the parameters, an increase in the share of timber rent appropriated by the state with unchanged total budget revenues leads to a decrease in the tax on paid wages, which in turn leads to higher employment in the remaining part. - economic sector. In addition, the critical distance of timber hauling increases, leading to higher employment and consequently production in the forestry sector. One of the few attempts to estimate εM for Russia was conducted by Konings and Lehmann (2000).
Taking into account that the econometric estimates of the production function parameters presented above refer to the case where no new roads are built, it is assumed that ( )d d∗ =0. In a relatively short-term perspective, a doubling of the state's allocated share of the timber rent thus leads to increasing regional employment.
POLICY IMPLICATIONS Even before the economic consequences of the tax shift are modelled,
For reasons similar to those invoked for the RFS, the leskhozy response is expected to be positive on aspects of forestry, but less predictable on market regulation. Their reaction should be negative to the question of whether to limit leskhozy to their legally defined function of forest management. In contrast to the RFS and the federal administration, the regional administration would be divided on the question of an increase in the basic taxes.
Because higher tree stump fees bring additional revenue and possibly a more efficient timber market, the regional government should be in favor of increases in tree stump fees. 3 The FFS has been under the authority of the Ministry of Natural Resources since May 2000. The regional forestry sector is stimulated by attracting new investors. The regional government should support the reuse of forest revenues to reduce taxes on capital and labor.
They should be indifferent to improvements in forest management, favorable to measures designed to curb oligopsony power in the timber market, hostile to increases in log fees, favorable to the recycling of forest revenues (especially if recycling helps in reducing their tax burden) and favorable dedicated as it helps ensure long-term supplies of raw materials. They will respond positively to the limitation of leskhozia in forest management and recycling of forest revenues, but negatively to proposals that harm their profitability.
CONCLUSIONS There are relatively few estimates of timber rent in Russia, and these are
They should be indifferent to changes in cost accounting rules, although this may eventually have an effect on market prices as well. Logging costs obtained from timber rental estimates are significantly lower than those reported by loggers. The low timber rent values captured by loggers and the state can be explained by local monopsony, i.e., the presence of a limited number of intermediaries between loggers and the market, who capture a significant portion of the timber rent.
Doubling the share of wood leases granted by the state in Novgorod Oblast could lead to the increase of employment by almost 1% and regional output by about 0.35%. Increasing stumpage fees, accompanied by tax shifting, should be supported by federal, regional and local Forest Services and Administrations, although opposed by the forest industry. Accounting for Subsoil Mineral Resources (BEA, Washington, DC);. http://www.bea.doc.gov/bea/an/0200srm/maintext.htm. 2000) Environmental Tax Reform: Making It Work.
Perelet (1997) Natural Resource Accounting for the Yaroslavl Region of the Russian Federation (Environmental Discussion Paper No. 35. NIS-EEP Project, Harvard Institute for International Development, Cambridge, MA). GOR (Government of the Russian Federation) (1997) Forest Code of the Russian Federation (Moscow). 1983) Forest Revenue Systems in Developing Countries: Their Role in Income Generation and Forest Management Strategies (Forestry Paper No. 43, Food and Agriculture Organization of the United Nations (FAO), Rome).
