one should keep in mind that the actual state of affairs may be different from the norms set by the authorities. Under the present conditions pre- vailing in the forestry sector in Russia, an important source of informa- tion could be forest auctions where forest plots are sold for logging.
In 1998, 15% of all the timber cut in Russia has been sold at timber
Table 14. Data from forest enterprises in the Komi Republic (1997).
Name Legal status
Volume of timber cut
(1000 m3)
Average timber price RUB/m3
Average cost RUB/m3
1. Kyltovsky AO 39.7 98.6 136.7
2. Sysolsky AO 219.0 72.7 86.4
3. Palauzsky AO 92.6 86.0 91.0
4. Ust-Vyisky AO 35.1 75.0 137.0
5. Timshersky AO 94.9 90.0 120.0
6. Prupsky AO 122.0 70.0 94.0
7. Ust-Kulomles AO 83.0 68.0 157.8
8. Parma AO 134.1 67.8 139.8
9. Ust-Nemsky Public 145.4 60.8 107.0
10. Pomozdinskles TOO 130.4 73.0 87.0
11. Kortkers AO 74.2 76.0 98.0
12. Yasnogles TOO 42.2 81.8 124.0
13. Parma AOZT 83.2 85.0 110.0
14. Pechorsky AO 75.1 94.9 250.6
15. Obyachevsky TOO 124.1 64.0 100.5
16. Syktyvdinsky AO 201.3 69.6 103.1
17. Ukhtales AO 78.9 95.3 164.2
18. Borovskoy OOO 104.2 65.0 103.0
19. Undorsky AO 26.7 88.6 146.6
TOTAL 1906.1 74.1 117.8
Source: All-Russia Institute of Continuous Education in Forestry's own calculations.
auctions. It should be noted that on average in Russia timber auction prices are about two times higher than stumpage fees (see Table 15).
Selling timber at auctions is organized according to the procedures set by the Forest Code of the Russian Federation (articles 43–45) (GOR, 1997) and by regulations on forest auctions approved by the Russian Forest Service (#99 of August 11, 1997). To carry out a forest auction, an auction commission is set, its members being approved by regional
Table 15. Average timber stumpage fees and auction prices in Russia (RUB/m3).
1996 1997 1998 1999
(1st quarter)
Region
Average stumpage fee Auction price Average stumpage fee Auction price Average stumpage fee Auction price Average stumpage fee
1 2 3 4 5 6 7 8
TOTAL 7.9 26.3 10.1 24.5 10.1 20.0 12.3
1. Arkhangelsk Oblast 8.0 16.8 9.0 12.2 9.3 16.6 9.0 2. Vologda Oblast 11.0 31.2 13.0 – 7.0 11.6 11.0 3. Republic of Karelia 7.0 – 14.0 38.2 12.1 – 15.0
4. Republic of Komi 5.0 10.6 5.0 – 7.2 8.6 7.0
5. Leningrad Oblast 7.3 23.8 9.5 25.8 13.3 37.5 17.2 6. Novgorod Oblast 7.0 31.2 10.0 24.3 15.0 21.6 24.0 7. Pskov Oblast 12.0 40.0 13.0 38.2 13.9 25.8 25.5 8. Bryansk Oblast 17.0 94.3 27.0 60.3 37.0 60.8 48.0 9. Vladimir Oblast 13.0 67.9 12.1 72.0 15.0 64.8 15.1 10. Kostroma Oblast 11.0 21.3 11.0 18.7 11.0 19.0 17.0 11. Moscow Oblast 9.3 39.1 13.1 36.0 14.0 34.7 24.0 12. Smolensk Oblast 13.0 37.9 17.0 51.5 22.2 26.7 22.0 13. Yaroslav Oblast 10.0 42.1 10.0 – 13.6 23.8 14.9 14. Nizhny-Novgorod
Oblast 6.8 22.2 8.5 24.3 20.0 34.9 14.0
15. Perm Oblast 6.0 31.7 6.3 43.5 12.0 33.4 9.0 16. Sverdlovsk Oblast 5.5 42.9 11.6 28.6 10.8 9.2 12.1 17. Tomsk Oblast 5.0 24.9 7.0 10.5 8.0 12.7 10.0 18. Tyumen Oblast 10.8 30.5 14.0 27.2 11.6 28.2 13.0 19. Krasnoyarsk
Region 4.2 12.9 5.0 10.0 6.0 10.3 6.3
20. Primorsky Region 7.0 – 8.9 – 8.5 20.9 21.0
21. Khabarovsk Region 8.0 – 9.0 11.7 18.0 13.0 18.0 22. Kaliningrad Oblast 32.8 123.3 39.9 98.4 87.5 85.8 220.6
authorities. Regional authorities, according to Article 45 of the Forest Code (GOR, 1997), are given all the rights to manage and to use the forests, which are within federal property. Within the auction commission there are representatives of a territorial branch of the regional Forest Service, municipalities and leskhoz. Leskhoz organizes the auction.
Depending on the importance of the forest auctions, they are held as on- the-spot trades only or, which is usually the case, they include written bids submitted in sealed envelopes. The particular procedure is set by the auction organizer (leskhoz). For selling small scale volumes of timber to local consumers, on-the-spot trades are used.
According to the regulation on forest auctions, possible bidders should be informed at least 30 days in advance. Unfortunately, this information is usually not advertised in mass media and is of an insider character.
This factor, as well as the role of local authorities (lobbying for the inter- ests of local bidders) make auctions not very competitive, with the num- ber of bidders being, in many cases, not more than 2–3 (according to the regulation, there cannot be less than 2 bidders taking part in a tim- ber auction).
The initial price of timber at the auction is set by leskhoz and equals the minimal stumpage fee set at a federal level, multiplied by a coefficient set by regional authorities. Minimal values of stumpage fees for the Novgorod Oblast are presented in Table 16.
The winner of the auction has to fell timber within a certain period, to clean the forest plot and to recultivate it. The winner cannot resell the right to exploit the forest plot to a third party.
The following information is made public:
• leskhoz name;
• particular forest plot;
• the year;
• forest type;
• forest plot area;
• felling technology;
• skidding distance;
• availability of roads and hauling distance;
• volume per tree;
• topography;
• soil type;
• timber distribution, relative to types and dimensions of trees.
Table 16. Selected Minimal Stumpage fees in Novgorod Oblast.
Minimal rate (RUB/m3) Merchantable wood Species Tax
category
Hauling distance (km)
Wide Medium Thin Firewood
Pine 1 < 10 52.4 37.4 18.8 1.5
2 10.1–25 47.6 34.0 16.9 1.4
3 25.1–40 40.5 28.8 14.6 1.0
4 40.1–60 30.9 22.1 11.2 0.9
5 60.1–80 23.8 16.9 8.5 0.8
6 80.1–100 19.0 13.6 6.9 0.7
7 > 100.1 14.3 10.2 5.0 0.6
Birch 1 < 10 26.2 18.8 9.5 1.7
2 10.1–25 23.8 16.9 8.5 1.5
3 25.1–40 20.4 14.6 7.1 1.4
4 40.1–60 15.7 11.2 5.5 1.2
5 60.1–80 11.9 8.5 4.5 0.9
6 80.1–100 9.5 6.9 3.4 0.7
7 > 100.1 7.1 5.0 2.7 0.4
Source: GOR 1999.
At present, at the auctions the highest quality forest plots are sold, which are close to roads, thus relieving the loggers of road construction costs.
The latter argument is the most important one, stimulating selling timber at auctions. This policy leads to a low level of investment in road con- struction, which will result in serious problems when all "convenient" for- est plots will have been cut.
Information on forest auctions has been obtained from leskhozy. A typi- cal pattern of data on auctions that have been used for econometric es- timation is given in Table 17. All in all, data on 156 auctions that were held in the Novgorod Oblast in 1999 were used in the estimations.
In the estimations, the auction price PA served as a dependent variable, while the explanatory variables included type of trees (coniferous or de- ciduous), timber quality (volume per tree), and hauling distance. It
should be noted that since the price at which a logger sells timber is not observable, it cannot be used as an explanatory variable.
Table 17. Auction data. Economic characteristics of timber auctions sales, ac- cording to the data of Novgorod Forest Service (1999).
Leskhoz Volume per tree,
cub.m Volume per ha,
cub.m Hauling distance,
km Tree type
formula
1 2 3 4 5
Batetsky 0.36 200 14 6Á2Îñ2Å
Borovichi 0.40 250 25 2Ñ4Å2Á2Îñ
Valdai 0.45 250 10 6Å1Ñ2Á1Îñ
Volotovsky 0.35 130 15 5Á5Îñ+Å
Leskhoz Auction date Number of trades (plots) Volume of sales, cub.m 0 deciduous, 1 coniferous Auction price rub/cub.m Minimal stumpage fee, rub/cub.m Plot area, ha
1 6 7 8 9 10 11 13
Batetsky 1 quarter 5 1000 1 42.00 23.20 5
49 17300 0 18.90 9.92 86.5
2 quarter 3 700 1 23.18 14.82 3.5
8 1500 0 21.31 10.56 7.5
3 quarter 3 1000 1 23.18 14.82 5
14 5100 0 26.74 10.60 25.5
4 quarter 3 1300 1 62.05 18.50 6.5
30 10300 0 34.30 11.62 51.5
Borovichi 1 quarter 3 19100 1 80.98 15.74 76.4
21 6600 0 35.76 9.02 26.4
2 quarter 3 6500 1 87.07 17.61 26
11 3500 0 45.88 9.17 14
3 quarter 3 4600 1 94.66 18.10 18.4
11 1800 0 46.57 9.15 7.2
4 quarter 3 8600 1 95.53 17.16 34.4
9 1600 0 46.84 9.07 6.4
Continued from p. 36
Leskhoz Auction date Number of trades (plots) Volume of sales, cub.m 0 deciduous, 1 coniferous Auction price rub/cub.m Minimal stumpage fee, rub/cub.m Plot area, ha
1 6 7 8 9 10 11 13
Valdai 1 quarter 2 2700 1 103.30 13.63 10.8
7 700 0 41.00 12.57 2.8
2 quarter 1 103.30 13.63
0 41.00 12.57
3 quarter 1 1800 1 130.84 22.24 7.2
2 0 41.00 12.57
4 quarter 1 3400 1 154.32 26.68 13.6
11 2900 0 65.81 15.72 11.6
Volotovsky 1 quarter 4 1
8 2900 0 24.38 20.40 22.3
2 quarter 1 1
2 1100 0 29.63 7.50 8.5
3 quarter 2 1
7 5000 0 35.93 9.16 38.5
4 quarter 4 1
As the first step, the following linear specification has been used:
1 2 3 4
PA =c +c TYPE+c Q+c d, (19)
where TYPE is a dummy variable, equal to zero if deciduous timber was sold at the auction and equal to unity otherwise. The estimation results are as follows (t-statistics in parentheses):
7.1458 18.3557 125.2861 0.3187 , ( 0.5062) (7.2177) (3.5923) ( 3.4373), PA
TYPE Q d
=
= − + + −
− −
(20)
R2 = 0.3754.
Except for the constant term, all of the estimated coefficients are of correct signs and statistically significant at the 1% level.
As the volume of sales differs substantially from one auction to another, a weighted estimation of the same function (19) has been carried out.
The results of this estimation are as follows (t-statistics in parentheses):
17.6627 21.8858 141.8184 0.1724 , ( 1.7364) (9.4919) (5.6018) ( 1.7383), PA
TYPE Q d
=
= − + + −
− −
(21) R2 = 0.9269.
It is clearly seen that weighting by volume leads to a significantly higher value of R2. Though the signs of the estimated coefficients are the same as before, their values have somewhat changed and the coefficient at the hauling distance is statistically significant only at the 10% level.
However, in spite of the quite satisfactory results of the linear regression estimation, it does not allow for the estimation of the price at which log- gers sell timber and of logging costs. As has been mentioned above, the available data on logging costs are extremely unreliable. To overcome the shortcomings of linear regression, nonlinear specification of the function defining auction price should be used for the estimation. Since in a competitive market, auction price should equal timber rent, the fol- lowing nonlinear specification for auction price, corresponding to (10), may be used:
1 2 3 c4 exp( 5 )
PA =c +c TYPE+c Q c d . (22)
This specification of the estimated function has a clear economic in- terpretation. The first two terms on the right-hand-side of the equa- tion correspond to the timber market price for either deciduous or coniferous trees, while the third term on the right-hand-side corre- sponds to logging costs, which depend on timber quality and hauling distance (c3<0).
However, in estimating equation (22), we face difficulties, due to its non- linearity. To overcome this problem, the above given results of estimating the production function based on normative data (coefficients µ and δ) have been used. This suggestion is supported by the notion that the coefficients c4 = −µ 1 and c5=δ in (22) are essentially technological and, generally speaking, are not subject to significant market influence.
Since presently, the most widespread technology used in logging is technology A, the following values of µ and δ, obtained within the
normative approach for this technology, have been used in subsequent estimations: µ = 0.7297, δ = 0.0052.
The estimation of equation (22) under exogenously given coefficients c4 and c5 produces the following result (t-statistics in parentheses):
195.5105 22.4353 127.6107 1 , (5.7221) (6.9888) ( 4.6829),
A d
P = + TYPE− Qµ− eδ⋅
− (23)
R2 = 0.4237.
As in the case of linear specification of the auction price, a weighted es- timation of equation (22) under exogenously given coefficients c4 and c5 has also been carried out. Estimation produces the following result (t-statistics in parentheses):
183.1009 21.4318 117.2127 1 ,
(5.5716) (7.5717) ( 4.5003),
A d
P = + TYPE− Qµ− eδ⋅
− (24)
R2 = 0.9472.
Again, weighting significantly increases the value of R2. An important problem concerning the estimation of the above regression is the stabil- ity of the estimation results with respect to the values of the exogenous parameters (µ and δ). To check it, the dependence of the estimated coefficients c c1, 3 on µ has been calculated (Fig. 1). It is clearly seen that the estimated coefficients are relatively stable with respect to changes in µ. With respect to δ, estimation results are significantly more robust.
6. ANALYSIS OF ESTIMATION RESULTS