A mill specific-rounwood_demand_equation_for_southern_and_central_finland

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Journal of Forest Economics 11 (2005) 95–106
www.elsevier.de/jfe

A mill-specific roundwood demand equation for southern and
central Finland
Olaf SchwabÃ, Gary Bull, Thomas Maness
Department of Forest Resources Management, University of British Columbia, 2045-2424 Main
Mall, Vancouver, BC, Canada V6T 1Z4

Received 10 December 2003; accepted 4 May 2005

Abstract
The majority of the roundwood processed by the highly concentrated forest products
industry in Finland is supplied by non-industrial private forest owners (NIPF). The industry’s
heavy reliance on NIPF roundwood supplies and the NIPF owners’ high dependency on the
industry for revenue motivated this study of the spatial fibre flows in regional markets. To
describe the direction and magnitude of these regional fibre flows we estimate a mill-specific
timber demand equation. This empirical model of roundwood demand can be used as a
benchmark for identifying inefficiencies in wood procurement procedures. This study expands
on the theoretical and empirical literature by increasing the spatial resolution of timber
demand estimates.
r 2005 Elsevier GmbH. All rights reserved.

JEL Classification: Q230
Keywords: Finland; Non-industrial private forestry; Roundwood demand estimation; Spatial
resolution

ÃCorresponding author. Tel.: +001 604 822 0921; fax: +001 604 822 9106.
E-mail address: oschwab@interchange.ubc.ca (O. Schwab).

1104-6899/$ - see front matter r 2005 Elsevier GmbH. All rights reserved.
doi:10.1016/j.jfe.2005.05.001

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96 O. Schwab et al. / Journal of Forest Economics 11 (2005) 95–106

Introduction

Approximately three quarters of the forest land in Finland is owned by non-
industrial private forest owners (NIPF).1 More than 350,000 NIPF owners supply
over 90% of the wood processed by the three major forest products companies in
Finland. The relationship between the NIPF owners and the forest products industry
can be characterized as mutually dependent. The strong reliance of the forest
industry on NIPF timber harvests has created substantial concerns about the
security of current and future timber supplies. These concerns are centred on the
shift from traditional objectives (timber harvesting) to non-timber objectives such as
managing forests for their aesthetic values and recreational use. These new objectives
are gaining widespread acceptance among forest owners and could result in
substantial timber supply shortages in the future (Kuuluvainen et al., 1996).
NIPF owners face two major challenges. First, they usually do not have the
resources to market their timber outside the local area. Second, Finnish laws require
forest owners to provide free public access to resources such as recreation
opportunities, aesthetic values and other non-timber forest products. Consequently,
forest owners can generate revenue only from timber sales.2 Although the relevance
of forest-based income has been declining steadily over the last few decades, rural
households still rely heavily on timber sales to supplement their income (Karppinen,
1998; Saastamoinen and Pukkala, 2001). For these NIPF owners, identifying all
potential customers is essential for negotiating profitable timber sales because
competition between the three major forest products companies and the smaller,
independent sawmills will ensure competitive prices. A comprehensive review of
studies related to NIPF can be found in Amacher (2003).
In contrast to existing roundwood supply models, the spatial resolution of
roundwood demand models has been relatively low. Demand data that was
aggregated at the regional or national level was commonly used (Brannlund, 1989;
¨
Toppinen and Kuuluvainen, 1997; Bergman and Nilson, 1999; Ronnila and
Toppinen, 2000; Kallio, 2001). Models using more disaggregated data on round-
wood demand exist; however, these models still do not provide any information on
the source of the roundwood (Baardsen, 2000; Roos et al., 2001; Nyrud and
Bergseng, 2002; Nyrud and Baardsen, 2003). Therefore, the objective of this study is
to identify the structural relationships that affect the direction and magnitude of
fibre flows in regional roundwood markets by estimating a mill-specific timber
demand equation. Modeling the structure and dynamics of fibre flows in these
regional markets is becoming increasingly important with both the changing
industry structure and recent shifts in the demographics and objectives of NIPF
owners.

1
NIPF are defined as forest owners who do not own or control primary or secondary processing
facilities.
2
Hunting rights being the notable exception.

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O. Schwab et al. / Journal of Forest Economics 11 (2005) 95–106 97

Methodology

This section summarizes the steps taken in the analysis of previous work on timber
demand estimation, data collection, and finally, procedures for the estimation of the
mill-specific timber demand equation presented in this study. To analyze wood
procurement patterns of mills in southern and central Finland we used data provided
by UPM-Kymmene Metsa, a subsidiary of UPM-Kymmene Oy. The Metsa group is
¨ ¨
responsible for the purchase, harvest, and transportation of roundwood to UPM-
Kymmene’s processing facilities (UPM-Kymmene Metsa, 2000). The dataset used
¨
for this study contains spatial fibre flow information for all roundwood procured by
UPM-Kymmene Metsa during the 2000 calendar year. In brief, roundwood was
¨
supplied to 105 mills of which 31 were owned by the parent company UPM-
Kymmene Oy. Fig. 1 shows the five timber supply areas: Kainuu, Ostrobothnia,
Central Finland, South-East Finland, and Western Finland.
The remainder of the logs utilized in Finnish manufacturing for the year 2000
came either from Russian and Baltic States log imports (12%) or from logs already
in the mill inventory from the previous year (20%). For each timber supply area, the
roundwood information is further segregated into six roundwood assortments:
Norway spruce [Picea abies (L.) Karst.], Scots pine [Pinus sylvestris L.] and birch
[Betula sp.] sawlogs; and spruce, pine, and birch pulpwood.

Fig. 1. Timber supply areas in southern and central Finland.

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Initially, the database contained 579 observations with a cumulative fibre flow
of 20.5 million m3. However, since the geographic origin of roundwood procured
from imports and from mill inventories could not be determined (approximately
32%); these observations were excluded from the analysis. Furthermore, since
information on log prices were only available for South-East Finland, Central
Finland, and Ostrobothnia, fibre flows from Kainuu and Western Finland were also
excluded (approximately 27%). Therefore, the regression analysis was based on the
remaining 177 observations, accounting for 8.4 million m3 (approximately 41% of
the total fibre flow). To determine the average transportation distance from the
harvesting site to the mill locations we assumed the centroid of each timber supply
area as the point of origin for all timber procured from within this area.
Transportation distances were measured following major roadways on a
1:1,000,000 scale map. Table 1 summarizes the parameters that were selected for
analyzing regional fibre flows, as well as references to the timber demand studies that
this selection was based on.
The econometric model for the mill-specific timber demand equation and the
expected signs of the parameters are shown in Eq. (1), with being the error term.

Volume ¼ f fDistance; Mill capacity; Roundwood price; Price volatilityg þ . (1)
ðÀÞ ðþÞ ðÀÞ ðÀÞ

Table 2 summarizes the parameter types, as well as the range of values before and
after transformations for both the dependent and the independent variables used in
estimating the demand equation.
The dataset was analysed using the statistical software SAS, release 8.02 (SAS,
2001). Initially, ordinary least squares (OLS) regression was applied. To test for the
applicability of the Gauss-Markov assumptions for OLS we used a Breusch-Pagan
test (Greene, 2003; Wooldridge, 2003), which indicated that the error term was
heteroskedastic with a test statistic of 12.72, 4 degrees of freedom and p ¼ 0:01.
Analysing the error term obtained from OLS estimation, we found that the error
variance was proportional to the squared natural logarithm of the volume.
Therefore, to correct for the heteroskedasticity, we re-estimated the demand
equation using weighted least squares (WLS) regression with a weight of:

wi ¼ 1= ln ðVolumeÞ2 . (2)

For this WLS regression we obtained a Breusch-Pagan statistic of 5.87, with 4
degrees of freedom, and p ¼ 0:25.
The functional form specifications of the independent variables were tested using
the Davidson-MacKinnon test (Wooldridge, 2003). Non-logarithmic forms of these
parameters were rejected since the t-statistic for the residuals were significant. Since
this type of functional form specification test cannot be applied to a logarithmic
dependent variable, we calculated a goodness-of-fit measure following Wooldridge
(2003). While an R2 of 0.29 was obtained using the linear form of the dependent
variable, an equivalent of 0.38 was calculated for the logarithmic form of the
dependent variable.

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Table 1. Parameter description and relation to other studies

Parameter Description Data or proxies used References

Distance Distance from Distance from Hultkrantz (1987),
harvesting site to centroid of timber Faminov and Benson
mill gate supply area to mill (1990), Palander (1995),
gate Roehner (1996), Cea and
´
Jofre (2000), Troncoso and
Garribo (2005)
Mill capacity Physical processing Volume of Hultkranz and Aronsson
capacity roundwood supplied (1989), Bergman and
by UPM Kymmene Lofgren (1991), Brannlund
¨ ¨
Metsa¨ (1991), Brannlund (1993),
¨
´
Cea and Jofre (2000),
Kallio (2001), Størdal and
Baardsen (2002), Troncoso
and Garribo (2005)
Roundwood Market price of UPM data on the Brannlund et al. (1985),
¨
price different roundwood annual average Martinello (1985), Lofgren
¨
assortments roundwood price by and Ranneby (1987),
species (birch, Hultkranz and Aronsson
spruce, pine) and (1989), Bergman and
assortment Lofgren (1991), Brannlund
¨ ¨
(pulpwood, sawlogs) (1991), Hetemaki and
¨
for each timber Kuuluvainen (1992),
supply area Kuuluvainen et al. (1996),
Toppinen and Kuuluvainen
(1997), Toppinen (1998a),
Gomez et al. (1999), Cea
´
and Jofre (2000), Latta and
Adams (2000), Bolkesjo
and Solberg (2003),
Lundmark and Soderholm
¨
(2003)
Price Standard deviation UPM data on Roehner (1996), Gomez et
volatility of roundwood prices annual roundwood al. (1999), Yin and
within each timber price by species and Newman (1999)
supply area assortment for 4–5
sub-units per timber
supply area.

To ensure that potentially endogenous variables as well as omitted variable bias do
not affect the parameter estimates, we implemented an instrumental variable
approach using weighted 2-stage least squares (2SLS) estimation. We introduced
harvesting costs and harvesting cost volatility as additional instrumental variables.

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Table 2. Parameter types, descriptive statistics, and transformations
Parameter Volume Distance Mill Roundwood Price Sawmill
capacity price volatility size index

Type Continuous Continuous Continuous Continuous Continuous Indicator
Unit m3 km m3 FIM/m3 — —
Range 835–804,584 16–434 1,121–2,186,693 164.58–371.25 1.54–9.26 0;1
(before transformation)
Mean 47,627 152 358,666 288.50 11.08 —
Standard deviation 97,150 82 527,425 76.05 6.76 —
Transformation Logarithmic Logarithmic Logarithmic — — —
Range 2.92–5.91 1.20–2.64 3.05–6.34 — — —
(after transformation)

For a discussion of why price-related variables may be endogenous in demand and
supply estimation see Latta and Adams (2000). Both harvesting costs and harvesting
cost volatility meet the identification condition of correlation with price volatility,
and are therefore suitable instrumental variables (Wooldridge, 2003). Following
2SLS estimation, we applied a RESET test (Pesaran and Taylor, 1999), which
indicated that the functional form was correctly specified with a test statistic of 1.51
and p ¼ 0:13. We then performed a Hausman specification test comparing the
parameter estimates from WLS and 2SLS estimation (Greene, 2003; Wooldridge,
2003). This test indicated that the WLS estimates are preferred over the 2SLS
estimates with a Hausman test statistic of 0.53, 5 degrees of freedom, and
Pr4w2 ¼ 0:99. Based on the Hausman and RESET tests we can conclude that none
of the explanatory variables are endogenous, and that no substantial omitted
variable bias is present (Wooldridge, 2003).

Results and discussion

The seemingly imbalanced structure of roundwood markets in Finland would
suggest the existence of market imperfections; however, the majority of studies on
the competitiveness of these markets have rejected this hypothesis (Koskela and
Ollikainen, 1998; Toppinen, 1998a, b; Ronnila and Toppinen, 2000; Tilli et al., 2001).
Therefore, it is reasonable to assume that the spatial fibre flows observed in Finland
are based on a sufficiently competitive market model. Table 3 summarizes the
modeling results, including parameter estimates, standard errors, and levels of
statistical significance for estimating the mill-specific timber demand equation.
The estimated equation has an adjusted R2 of 0.55, and a mean squared error of
0.13. All parameter estimates show the expected sign. However, it should be noted
that, contrary to expectations, price did not have a statistically significant effect on
the volume a mill purchases from a particular timber supply area. Based on these
parameter estimates the mill-specific demand equation can be written as
lnðVolumeÞ ¼ 7:235 À 0:692 Ã lnðDistanceÞ þ 0:496 Ã lnðMill capacityÞ
À 0:034 Ã ðPrice volatilityÞ þ 1:266 Ã ðSawmill size indexÞ þ . ð3Þ

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Table 3. Modeling results for weighted least squares and weighted 2-stage least squares
estimation

Parameter Weighted least squares Weighted 2-stage least squares

Estimate Standard Pr4jtj Estimate Standard Pr4jtj
error error

Intercept 7.235 0.762 o0.0001 7.534 0.867 o0.0001
ln(Distance) À0.692 0.133 o0.0001 À0.690 0.133 o0.0001
ln(Mill capacity) 0.496 0.044 o0.0001 0.481 0.049 o0.0001
Price volatility À0.034 0.012 0.0040 À0.046 0.020 0.0232
Sawmill size index 1.266 0.384 0.0012 1.325 0.393 0.0009

Wood procurement patterns can be described by the relationship of volume and
distance. As shown in Eq. (3) the volume of roundwood procured declines steadily
the further away the timber supply area is from the location of the mill. As expected,
the majority of the roundwood is procured relatively close to the mill. Since
roundwood is a relatively bulky, low volume commodity transportation costs are
one of the main factors in determining the allocation (Troncoso and Garribo, 2005).
In their simplest form, transportation costs consist of a ﬁxed component for loading
and unloading and a variable charge per kilometre traveled. In Europe, the most
commonly used transportation systems are trucking, train transport, and barges/
freight ships (Hecker, 2003). Although a switch in the dominant transportation
system from trucking to train and barge for larger distances was expected, a Chow
test following Greene (2003) failed to detect any structural breaks in the distance
parameter.
Two parameters describing mill characteristics (mill capacity, and sawmill size
index) were found to be statistically signiﬁcant. As expected, mill capacity is
positively related to the volume of roundwood procured within a particular timber
supply area. Since the total volume of roundwood available within the immediate
area around the mill is limited by the biophysical growing capacity of the forest and
the harvesting rates, mills with a high processing capacity are forced to procure
roundwood from within a larger wood procurement area to satisfy their volume
requirements. The sawmill size index is an indicator variable for sawmills with an
annual capacity of greater or equal to 500,000 m3. The positive sign of this parameter
in Eq. (3) indicates an upward shift of the intercept in the demand equation for these
mills. Since only relatively few observations were available for these high-capacity
sawmills (n ¼ 12), it was not possible to determine the functional form of the
parameters necessary for a more complex spline model. This upward shift induced by
the indicator variable can be interpreted as counteracting the negative effect of the
higher prices for sawlogs compared to pulpwood. The ratio of value to volume
becomes one of the key factors in determining the economically feasible maximum
transportation distance. In 2000, the weighted average stumpage price of sawlogs
was 45.62 h/m3, while pulpwood stumpage was valued at 16.53 h/m3 on average
(METLA, 2001). Transportation costs are the same for a cubic meter of sawlogs and

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a cubic meter of pulpwood. Due to the higher value per cubic meter sawlogs can be
transported a longer distance before transportation costs exceed the value of the
wood. Therefore, compared to a pulpmill, a sawmill could economically justify a
substantially larger wood procurement area.
Contrary to expectations, the roundwood price was not found to be statistically
significant in our data analysis. The parameter for log price did show the expected
negative sign in the initial OLS estimation, indicating that mills purchase less
roundwood in those areas with higher average roundwood prices. However, the level
of statistical significance was very low (p-value of 0.89); therefore we dropped this
parameter from the empirical timber demand equation. We suspect that the effect of
the highly variable transportation costs (average transportation distance 182 km,
standard deviation 82 km) outweighs the relatively low level of variation between the
average roundwood prices at the roadside for these three timber supply areas
(Ostrobothnia 48.20 h/m3; Central Finland 49.92 h/m3; South-East Finland 47.32 h/
m3). We expect that roundwood price would become statistically significant if it was
possible to link these roundwood prices to fibre flows with a higher spatial resolution
within each timber supply area.
Price volatility was calculated for each roundwood assortment and timber supply
area as the standard deviation of roundwood prices (FIM/m3) from 4 to 5 smaller sub-
units within each timber supply area. These more differentiated roundwood prices
could not be used directly since information on the regional roundwood flows was not
available at the level of these sub-units. The parameter estimate for price volatility
showed the expected negative sign, indicating that less roundwood was procured from
those timber supply areas with a high degree of variation in timber prices. There are
two possible explanations for this response; one based on the wood procurement
response of the mill, and the other based on the roundwood harvesting behaviour of
the forest owner. From the perspective of the mill purchasing roundwood this
parameter can be interpreted as risk aversion. Supply security, that is, the ability to
procure a certain volume of roundwood at a stable price, is an important factor in
roundwood procurement planning. The relevance of this concept of supply security
can be seen from the arrangements that exist between the forest products industry and
forest owners. For example, UPM-Kymmene Metsa offers comprehensive forest
¨
management and harvesting services to forest owners as a means of securing a steady
supply of roundwood (UPM-Kymmene, 2001). Similar reasons are given for the
prevalence of centrally negotiated timber sales agreements between the Swedish pulp
and paper industry and forest owners (Bergman and Lofgren, 1991; Bergman and
¨
Nilson, 1999). In regions with high price volatility this type of supply security is
reduced substantially, thereby shifting timber demand to timber supply regions with
less price fluctuations. From the perspective of the forest owner, a high degree of price
fluctuation can discourage harvesting activities (Gomez et al., 1999). Therefore, the
observation that less roundwood is procured from timber supply regions with a high
degree of price variability can be interpreted as an effect of the lower volumes of
roundwood being harvested (or sold on the stock) by forest owners.
When applying this mill-specific timber demand equation to sawmills and
pulpmills respectively it should be noted that under certain circumstances sawlogs

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Fig. 2. Pulpwood share of total roundwood procurement.

and pulpwood can be substitutes. This substitutability can be shown by calculating
the procurement of pulpwood as a percentage of total roundwood purchases for
individual mills (Fig. 2).
Although the majority of roundwood is being purchased by mills at the extremes of
this ratio, there are some mills that procure a mix of both sawlogs and pulpwood.
While approximately 58,000 m3 of roundwood are procured by mills with a minor share
of pulpwood purchases (5–15%), almost 2.8 million m3 of roundwood are procured by
mills that meet between 5 and 15% of their volume requirements with sawlogs.
This is reasonable, since pulp mills can substitute sawlogs for pulpwood and
sometimes do so because of the additional costs of log sorting and transportation that
would be incurred when separating out sawlogs during harvesting operations focused
on pulpwood. The substitution of sawlogs in the production of pulp and paper can
also be expected in situations where no sawmill is located within a reasonable distance
form the harvesting site, or when the market price of sawlogs falls below the price of
pulpwood. On the other side of the spectrum, the majority of the mills procure only
sawlogs, suggesting that pulpwood is generally not an adequate substitute for sawlogs
(Brannlund, 1989; Nyrud, 2002; Størdal and Nyrud, 2003).
¨

Conclusion

The mill-specific timber demand equation presented in this paper makes it possible
to predict the volume of fibre flows between timber supply areas and a sawmill or

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pulpmill for a defined area in southern and central Finland. These predictions are
based on the distance between a particular mill and the timber supply area, the
production capacity of the mill, the price volatility within each timer supply area,
and an indicator variable for high-capacity sawmills. In addition to providing some
insight into the structure and dynamics of regional roundwood markets, this type of
econometric analysis can be used as a benchmark to identify inefficiencies in wood
procurement procedures, and to estimate input parameters for optimum harvest
allocation models such as the one presented by Troncoso and Garribo (2005).
The empirical work presented in this study shows that it is possible to increase the
spatial resolution of timber demand models substantially by estimating a timber
demand equation for individual mills. By expanding this mill-specific timber demand
equation to include roundwood imports and time series information on the current
parameters, and by linking it with recent advances in predicting the roundwood
harvesting behaviour of NIPF owners, it will be possible to study the inter-
dependencies that exist between the forest products industry and forest owners and
to predict the effect that changes in the market structure will have on the financial
viability of small-scale forestry.

Acknowledgments

We would like to acknowledge the support of UPM-Kymmene Metsa, who ¨
provided the data this paper is based on. The authors would also like to thank three
referees for their comments on an earlier version of the paper.

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A mill specific-rounwood_demand_equation_for_southern_and_central_finland