United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-504

May 2001

Assessment of the Competitive

Position of the Forest Products

Sector in Southeast Alaska,

1985-94

Guy C. Robertson and David J. Brooks
Guy C. Robertson is a research economist, Forestry Sciences Laboratory, 2770

Sherwood Lane, Suite 2A, Juneau, AK 99801-8545; and David J. Brooks is a

research forester, Forestry Sciences Laboratory, 3200 SW Jefferson Way, Corvallis,

OR 97331.

Authors
Robertson, Guy C.; Brooks, David J. 2001. Assessment of the competitive position

of the forest products sector in southeast Alaska, 1985-94. Gen. Tech. Rep. PNWGTR-504. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific

Northwest Research Station. 29 p.

This paper provides an assessment of the competitive position of the forest products

sector in southeast Alaska relative to that of its major competitors. An analytical framework relying on the economic concepts of comparative and competitive advantage is

first developed, with emphasis on the relative cost and productivity of productive inputs

such as labor, capital, and raw materials. The assessment is divided into three main

components: (1) forest resource characteristics and production costs in the logging

sector, (2) production costs in the sawmill sector, and (3) relative market position in

end-product markets. Major competing regions are British Columbia in Canada and the

states of Washington and Oregon in the United States. Japan’s market for soft-wood

saw logs and sawn wood is the focus of the end-market analysis. Data consistently

indicate that southeast Alaska has been a high-cost producer of sawn-wood products

operating at the margin of profitability over the assessment period. This is due to a

combination of high labor costs on a per-unit-of-input basis and low productivity for

labor inputs in both the logging and sawmill sectors, and for raw material inputs in the

sawmill sector. Certain species and log grades, however, are capable of generating

considerable profits, and the relation between average profitability for the sum total of

harvests in southeast Alaska and the profitability of specific components of this harvest also is analyzed. Implications of these findings for current efforts to promote

increased value-added timber processing in southeast Alaska are discussed in the

conclusion.

Keywords: Comparative advantage, labor productivity, timber scarcity, value added,

stumpage prices.

Abstract
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1

An assessment of the competitive position of the forest products sector in southeast

Alaska has important implications for various forest policy questions including current

efforts to increase value-added processing in the region as well as the more general

debate over harvest levels for the Tongass National Forest. Primarily because of its

remote location, southeast Alaska generally has been recognized as a high-cost producer of wood products. On the other hand, some species and log grades harvested in

southeast Alaska are capable of sustaining high market prices in some market cycles,

thereby offsetting higher production costs. The objective of this assessment is to provide quantitative measures of production costs and product revenues for softwood

lumber produced in southeast Alaska and to compare these data with similar measures for southeast Alaska’s principal North American competitors. Although most of

the assessment concentrates on average costs for timber harvested and processed

in southeast Alaska, production costs and market prices for specific species also are

included.

The first section of this assessment establishes a framework that can be used to assess the position of the wood products sector relative to that of its competitors and end

markets in southeast Alaska. The economic concepts of comparative and competitive

advantage are used to focus the analysis on the cost and utilization of productive inputs

such as labor, capital, and for the sawmill sector, log inputs. In subsequent sections,

this framework is used to organize data specific to southeast Alaska and to compare

these measures with similar measures used in the coastal region of British Columbia in

Canada and the Pacific Northwest region of the United States.

The body of the assessment focuses on specific aspects of the chain of events starting

from the forest resource to the sale of processed products in final markets. Following a

general overview of the wood products sector in southeast Alaska, logging costs and

the composition of the forest resource are considered. Next, we discuss processing

costs and efficiency in the sawmill sector. This is followed by an analysis of total lumber

production costs. The relative position of southeast Alaska products in the Japanese

market for softwood sawn wood is considered next. We then address the impact of production costs and market prices on the imputed “residual value” of stumpage (market

prices minus production costs) and the observed prices received for the sale of timber

in southeast Alaska, British Columbia, and the Pacific Northwest. As cost factors and

their implications for comparative and competitive advantage are not static over time,

the next to last section of this report concentrates on how southeast Alaska’s regional

comparative advantage in lumber production may be changing over time. The conclusion addresses the policy implications of the assessment.

Two main bodies of economic theory specifically address the position of a regional

wood products sector relative to that of its major competitors. The first is the HecksherOhlin trade theory (and variants thereof), which is summarized in the concept of comparative advantage. The second is found more often in the business and management

literature and relates to the competitiveness of a firm, industry, or sector. Although

competitiveness is defined in various ways, all definitions involve the ability to supply

comparable goods at lower cost than major competitors. Before addressing their application to the southeast Alaska wood products sector, we will discuss each of these

theories separately.

First described by the British economist David Ricardo in the early 19th century, the

concept of comparative advantage today relies heavily on the work of Hecksher (1949

for example), Vanek (1963), and Ohlin (1967). Their work primarily explains trade patterns as a function of country-specific endowments of productive inputs (or “factors”),

Introduction

Analytical

Framework

Comparative and

Competitive Advantage
2

which usually are restricted to labor and capital. Briefly stated, the theory of comparative advantage holds that a country will specialize in producing products that more

intensively use productive factors that country has in relative abundance. Hence, developing countries, such as China, with large labor resources relative to the supply of

capital inputs, will specialize in labor-intensive goods. Countries with large stocks of

fixed and human capital such as the United States, on the other hand, will specialize

in capital or information-intensive goods. This specialization and resulting trade, it is

argued, maximizes welfare by efficiently allocating the productive resources of all

traders.

A corollary to the concept of comparative advantage is that of factor price equalization,

which holds that, in equilibrium, wages (including payments for capital) paid to similar

productive factors will be equal across all trading countries. To continue with the above

example of developing and developed countries, factor price equalization can readily

be seen in the tendency of imports of labor-intensive manufactures from low-wage

countries to depress wages for unskilled labor in the developed nations. Note that the

quality of factor inputs must be similar, and labor skills must be taken into account.

Here, skilled labor can be viewed as the combination of unskilled labor and human

capital inputs (namely, education and experience).

Empirical work addressing comparative advantage has tended to concentrate on only

the broadest aggregate measures, yielding general conclusions about the comparative advantage of nations but little information about specific regions or products. A

common approach is to view the net balance of trade of a country in capital or laborintensive goods as indicative of its comparative advantage in either of these categories.

The share of the net trade of a given good in the country’s total trade volume can then

be used as a measure of comparative advantage. This is often termed “revealed comparative advantage.” Econometric methods can then be used to estimate the relation

between revealed competitive advantage and variously constructed indexes of the

endowment of labor and capital of a country, both fixed and human (see Balassa 1978

for an example of this approach). Not surprisingly, empirical studies have found a consistent relation between capital endowments and net exports of capital-intensive goods.

Bonnefoi and Buongiorno (1990) provide a rare example of the application of this sort

of method to trade in wood products. By regressing net trade of various wood products

on total income and on proxies for capital endowments and forest resource endowments (represented by per capita income and total harvest, respectively), they were

able to explain a relatively high proportion of the net international trade in processed

wood products. They were not as successful, however, in explaining roundwood exports. Additionally, the influence of their capital proxy was generally insignificant in

all but the paper and paper board sector. Given that total harvest was used as a proxy

for forest resource endowments, results of the study simply state that those countries

with high levels of harvest relative to the size of their economies are more apt to export

wood products either in raw or in processed form. An additional conclusion is that countries with high capital endowments are more apt to export paper products.

Bonnefoi and Buongiorno (1990) highlight the difficulty in incorporating forest resources

as a productive factor in a comparative-advantage analysis. The problem involves

measuring the relative abundance or scarcity of a natural resource in economic terms.

A substantial amount of literature addresses this question for nonrenewable resources

(for example, Smith 1980, Solow 1974). Berk (1979) provides an often-cited example of

a forestry-related application, and Catimel (1996) provides a general discussion of the
3

relation of scarcity, comparative advantage, and trade in Canada’s forest products sector. Although stumpage prices (the price of standing timber) and harvest costs have

been proposed as two possible measures of scarcity, both entail certain problems, and

there is no broad agreement on an accepted or unambiguous measure of timber scarcity in the literature on the topic.

The second of economic theory applied in this assessment is that of competitiveness.

Brinkman (1987) provides two definitions of competitiveness: (1) “market competitiveness,” which refers to the ability of an industry to supply comparable goods at a lower

cost than other producers; and (2) “true competitiveness,” which is defined as the

ability of an industry to provide goods to the international market in competition with

other producers such that the activity augments the wealth of the nation. This second

definition addresses the need to consider the total costs and benefits accruing from

production activities. These include government subsidies, and economic externalities

affecting both market and nonmarket values. For this assessment, we concentrate on

the first definition, although issues such as direct subsidies and environmental externalities are recognized as being important to forest management.

The literature on competitiveness is both voluminous and diffuse, ranging from theoretical discussions of the tangible and intangible elements that affect the competitive position of firms, industries, and nations (for example, Coffin and others 1993, Porter 1990)

to more practical analyses of industry cost structures and markets. Indeed, many of the

articles found in the trade journals as well as many university and public sector working papers fall into this latter category. Similarly, this report relies extensively on the

measurement and analysis of production costs and end-market price behavior as

indicators of competitiveness.

Although closely related, competitiveness and comparative advantage are not the

same. Comparative advantage is an equilibrium concept. At economic equilibrium, factor prices, and therefore production costs, will be equal for all trading partners. Advantage will be measured solely by the relative volumes of production and trade of different

types of goods. Competitive advantage, on the other hand, relies on the existence of

disequilibria. Here, different technologies and factor wages result in different production costs for similar goods. The country (or region) demonstrating the lower cost possesses a competitive advantage. To the extent that factor wages reflect the economic

fundamentals of a country or region, and not market distortions, competitive advantage will reflect an underlying comparative advantage. In this case, the expected market equilibrium has not yet been reached. Economic theory would predict that this

competitive advantage will result in increased production volumes and relative trade

shares. At the same time, increased local demand for productive factors, and increased

supply of the final product in export markets will result in the equalization of factor

wages. Consequently, competitiveness can be used (with proper care) to predict future

economic developments: countries with a competitive advantage in a given industry

can expect increasing production and export market shares in that industry. Also, if the

supply of productive factors used in that industry is constrained, the country can expect

increasing wages to those factors. For competitive disadvantage, the exact opposite is

the case.

The application of this argument to the wood products sector is complicated by the fact

that timber is a major component in the input mix. As demonstrated by Berk (1979),

and further evidenced by positive stumpage prices, timber is a scarce resource, at least

in the short run. This is especially true for the high-quality, old-growth timber that comprises a proportion of southeast Alaska harvests. High scarcity values for the timber

Competitiveness

Factor Inputs and

Stumpage Prices
4

(referred to as “scarcity rents” by economists) will be reflected in high market prices for

logs or lumber products. Stumpage prices will partially reflect these scarcity rents. The

production of logs, however, will entail the use of other inputs, and the cost of these

inputs also will be incorporated in the stumpage value of the resource. Here, the

stumpage price can be seen as a residual value that measures the final market price of

the logs minus the cost of their production. Using an example that also fore-shadows

the conclusions of this paper, consider a region with high labor costs but also high

scarcity rents associated with its timber. Although the region will be at a competitive

disadvantage in logging per se, it still may have an advantage in log production if the

scarcity value of the timber is high enough to compensate for the higher cost of labor.

This sort of competitive advantage based on scarcity value, however, cannot always be

used as a predictor of future increases in production volumes. The scarce resource is,

by definition, depletable. With old-growth timber, the limited volume of accessible timber may severely constrain increases in production. Even with second-growth or plantation timber, currently available supply will be dictated by investments made years

ago, sometimes under different economic conditions. Before physical depletion of supply is reached, production costs can be expected to rise at an increasing rate as producers are forced to harvest more inaccessible stands. For certain stands, these costs

will exhaust even relatively high timber scarcity rents, and the resulting negative residual values (or zero stumpage prices) will clearly signal a lack of profitability and competitive advantage in the sector. In addition to changing production costs, the sector

also may face cyclic swings in the demand for its product. Such swings are well known

in the wood products sector. The resulting price fluctuations will be reflected in changing stumpage values, and once again, negative profitability during some portions of the

market cycle.

The previously mentioned arguments suggest two ways to measure competitiveness:

production costs and profitability—essentially two sides of the same coin. Production

costs can be estimated by examining the productivity and wages of specific factors.

For example, hourly wages can be combined with labor hours per unit of output to derive a unit cost for labor. The latter can be estimated by examining stumpage prices

or calculating residual values. In the following analysis, these measures for southeast Alaska are compared with those for the Pacific Northwest and British Columbia.

Throughout, it is important to view specific products as a combination of factor inputs,

and the production process as a whole as a process that seeks to efficiently allocate

the use of these factors. Subject to the limitations of technology and often policy, firms

will attempt to maximize the use of factors that are cheap relative to other producers

and minimize the use of those that are expensive.

Rigorous application of the theories described previously to the question of comparative and competitive advantage of southeast Alaska is complicated by the lack of

adequate comparable data for the region and its competitors, the relative absence of

competitive stumpage markets in the region, and the general complexity of the wood

products sector. For southeast Alaska, these complexities include physical and policyinduced resource constraints and the potential to produce various products in different

locations. Rather than presenting an abstract but complete picture of the competitive

position of the region, the present analysis is more similar to putting together a puzzle

where some of the pieces are missing. Consequently, the following sections present

evidence of the relative position of the region in a somewhat piecemeal fashion by

using available data and making comparisons where possible. Different stages in the

production and marketing process are used as the principle means to organize these

data and their comparisons.

The Wood Products

Sector in Southeast

Alaska
5

Figure 1 provides a schematic diagram of the wood products sector, which identifies

major production stages and key elements that combine to determine competitiveness.

Three broad areas of analysis are indicated: (1) the forest resource in which timber

stocks are combined with labor, machinery, and other inputs to produce raw logs; (2)

the processing sector in which logs are combined with other factors to produce products such as lumber and chips (including mill residues); and (3) end markets where

purchasers compare the price and physical characteristics of southeast Alaska products to those from other regions. These purchasers include both manufacturers and

final consumers. Factor costs include wages, interest (the opportunity cost of invested

capital), and costs for other materials and services. The physical characteristics of

timber contribute to the determination of competitive status through their effect on the

quantity and cost of other factor inputs needed to both manufacture and use products

from southeast Alaska, and the range, quantity, and quality of products that may be

supplied. Finally, end-market demand shifters such as construction activity and consumer preferences in conjunction with the availability of substitute products will help

determine product prices.

Along the bottom portion of figure 1, the concept of stumpage price as a residual value

also is displayed. Given the assumption of perfect information and competitive markets,

the difference between final price and production costs will be “bid back to the stump”

or, in other words, concentrated in the stumpage price. The validity of these two assumptions, however, particularly that of perfect information, is open to question. Stumpage prices are perhaps better viewed as emerging from a separate stumpage market in

which purchasers bid based on expected profits and thus include their expectations of

future price fluctuations and their attitude toward risk. In spite of the intervening influence of these and other factors in stumpage markets, the connection between stumpage prices and residual values nonetheless remains a strong one. Both measures will

be examined in the following analysis.

Data for this report were drawn from various sources. The physical characteristics of

the forest resource are analyzed by using historical data on southeast Alaska log production by species and grade. Pond log values, mill prices (or export prices as reported

by the U.S. Department of Commerce [as reported in Warren 1998]), and end-market

prices in Japan comprise our primary sources of price data at different stages of the

production process. For estimates of harvest and processing costs, we have relied on

USDA Forest Service sale appraisal data and industry survey data1 available from outside sources. Our discussion of factor costs focuses on relative wage rates as reported

by the U.S. Bureau of the Census, Annual Survey of Manufactures.

Cost estimates from the sale appraisal process are a major source of information used

in this report. These estimates, however, are based on historical industry conditions (as

reflected in Forest Service cost collection surveys), and these conditions have changed

considerably in recent years as the sector has adjusted to the closure of the region’s

remaining pulp mill in Ketchikan and a general reduction in sawmill capacity. Additionally, new production facilities are being considered. These developments could substantially alter the costs reported in the sale appraisal process as well as the general

economics surrounding the Forest Service’s sale program. Census of Manufactures

1 U.S. Department of Agriculture, Forest Service. 1984-

94. Sale appraisal data. Unpublished data. On file with:

USDA Forest Service, Forest Management Division,

Alaska Region, P.O. Box 21628, Juneau, AK 99802.
6

wage and labor input data, on the other hand, provide a more independent (but significantly less detailed) assessment of costs faced by southeast Alaska producers. These

data reflect more fundamental economic conditions in southeast Alaska, and they generally corroborate the conclusions found in the analysis of the sale appraisal data.

Although in certain years Alaska has imported pulpwood and mill residues, these imports are small and largely irrelevant to the assessment of southeast Alaska’s competitive and comparative advantage. In the absence of imported raw timber, the quantity

and the physical characteristics of the forest resource in southeast Alaska define the

upper boundary of how much and what sorts of wood products the region may produce. For the Tongass National Forest, this boundary will be further constrained by

several forest regulations, including designations of the suitable timber base and constraints on harvest designed to ensure sustainability and guard against high grading

(that is, harvesting only the better trees or stands). Restrictions on exports of raw

material from the Tongass National Forest currently limit log exports of federal timber

to Alaska yellow cedar (Chamaecyparis nootkatensis (D. Don) Spach) and a proportion

of the western redcedar harvest (Thuja plicata Donn ex D. Don). This ban applies to all

sales outside the region. The Tongass National Forest timber base is the major focus

of this part of the report, and the private forests of southeast Alaska will be discussed

primarily in terms of their impact on Tongass National Forest harvests and subsequent

processed goods. Additionally, we have concentrated on only the existing timber stock;

future second-growth timber supplies and issues of forest productivity are not

considered.

The distribution of harvest by species and log grades is largely determined by the forest resource, particularly if proportionality requirements restricting high grading at the

stand or forest level are in place. In table 1, we present 1995 statistics for harvest by

species and grade for southeast Alaska and British Columbia (similar data are not

readily available for the Pacific Northwest). Estimates for southeast Alaska are based

on Alaska Region log scaling tickets, whereas for British Columbia estimates are from

Forest resource: Processing: External markets:

Pond log

values

FOB Mills mill

Log exports

End uses

Wages

Interest

Other

Factor costs:

Shifters

Substitution

Demand:

Lumber

Chips

Residues

Export, remfg. 

and mkt. cost

(-)

Harvest and

delivery cost

(-)

Market price

(+)

Mfg. cost

(-)

Stumpage

(residual)

Inventory

Operability

Transportation

Physical 

characteristics:

Infrastructure

Transportation

Physical

characteristics: Wages

Interest

Other

Factor costs: Transportation

Handling

Mfg./remfg.

Export,

mktg. and

Remfg.

Harvest Pond log

values

FOB Mills mill

Log exports

End uses

Wages

Interest

Other

Factor costs:

Shifters

Substitution

Demand:

Lumber

Chips

Residues

Export, remfg., 

and mkt. cost

(-)

Harvest and

delivery cost

(-)

Market price

(+)

Mfg. cost

(-)

Stumpage

(residual)

Inventory

Operability

Transportation

Physical 

characteristics:

Infrastructure

Transportation

Physical

characteristics: Wages

Interest

Other

Factor costs: Transportation

Handling

Mfg./remfg.

Export,

mktg. and

remfg .

Harvest

Figure 1—Sources of comparative and competitive advantage in the wood products sector.

The Forest Resource

Physical Characteristics
7

the Vancouver log market.2 3 In general, log class distributions are similar across regions, with a high concentration of volume in the saw log class (number 2 saw logs in

southeast Alaska), and a higher proportion of volume in the lower grades for western

hemlock (Tsuga heterophylla (Raf.) Sarg.) than for Sitka spruce (Picea sitchensis

(Bong.) Carr.). Although there are higher percentages in the premium grade for southeast Alaska, this could possibly be an artifact of the different log classification systems

used for each region.

A more striking difference is found in the comparison of the shares of Sitka spruce

in total production. Because of the relatively small share of Sitka spruce in British

Columbia total volume, spruce volumes supplied in 1995 are small in spite of the overall position of British Columbia as a major forest products producer. In that year, southeast Alaska accounted for about 70 percent of estimated total North American production of Sitka spruce (42 percent from private owners and 28 percent from the Tongass

National Forest) with British Columbia accounting for the remainder. This highlights the

special position southeast Alaska enjoys as the world’s major producer of Sitka spruce.

For hemlock, however, the situation is reversed. Southeast Alaska accounts for around

17 percent (private 11 percent and Tongass National Forest 6 percent) of total western

hemlock production for export to Pacific Rim markets; British Columbia, at 59 percent,

accounts for most of the western hemlock production, and the Pacific Northwest accounts for most of the remaining 23 percent. If Pacific Northwest production for U.S.

domestic markets were included in this calculation, southeast Alaska’s share of total

hemlock production would be significantly smaller. The role of the region as a principal

2 Although the southeast Alaska log ticket data

purportedly comprise a census of total Tongass National

Forest log production, analysis indicates that a small

proportion of log production is not recorded. Nevertheless, it seems safe to assume that the sample is representative. The Vancouver log market, on the other hand,

constitutes only a minority share of total British Columbia

coastal log production. We have no reason, however, to

believe that the sample differs systematically from the

whole.

3 British Columbia log market prices and volumes.

Unpublished data. On file with: British Columbia Council

of Forest Industries, 1200-555 Burrard Street,

Vancouver, BC V7X 1S7, Canada.

Table 1—Distribution of 1995 harvest by species and grade, coastal British

Columbia and southeast Alaska

Species and Premium Low-grade Species share

location saw log Saw log saw log Utility (of total harvest)

Percent

Sitka spruce:

British Columbia 14.6 61.8 10.0 13.6 1.9

 Southeast Alaska 17.4 52.7 11.3 18.6 23.8

Hemlock:

 British Columbia 3.7 46.9 17.0 32.4 15.6

 Southeast Alaska 7.2 41.2 24.8 26.8 58.3

Note: Southeast Alaska log classes are translated as follows: premium saw log = no. 1 saw log, select and

special mill; saw log = no. 2 saw log; low-grade saw log = no. 3 and no. 4 saw logs; utility = utility logs.

Source: USDA Forest Service (1984-94), British Columbia Council of Forest Industries (see footnote 3).
8

supplier of Sitka spruce and a relatively minor sup-plier of hemlock has important implications when analyzed in conjunction with the end markets for each of these species,

and they will be discussed further in later sections of this report.

Harvest costs per thousand board feet log scale4 for southeast Alaska, the Pacific

Northwest, and British Columbia are displayed in figure 2. Figures for southeast Alaska

were estimated by using Forest Service sale appraisal data from independent sales,

and they represent an appraised cost rather than actual transactions (Western Pine

Association).5 Data for the Pacific Northwest and British Columbia were obtained from

a private mill survey and represent actual costs as reported by respondents (Resource

Information Systems, Inc. [RISI] 1996). The figure clearly indicates a similarity in both

levels and trends between southeast Alaska and British Columbia and a large discrepancy between these series and that for the Pacific Northwest. As will be shown in subsequent sections, the similarity between southeast Alaska and British Columbia also

is reflected in delivered wood costs (or pond log values). Though accessibility is a

factor, the increasing trend in harvest costs in southeast Alaska most likely is due to

decreases in both average piece size and volumes per acre. National forest sales in

the Pacific Northwest likely have experienced similar increases in cost because of

these same factors and from a related expansion in the use of helicopter logging systems. Regional Pacific Northwest averages, however, may have been held in check by

an increasing proportion of private volumes in the total mix, and the greater accessibility and more uniform characteristics of the second-growth private resource. In any

event, these data show a large and expanding cost differential between southeast

Alaska and the Pacific Northwest.

British Columbia has the highest harvest costs of the three regions, which likely is due

in part to similar resource characteristics as those encountered in southeast Alaska.

Firms in British Columbia, however, operate in a substantially different macroeconomic

environment, thereby making direct comparisons of costs with U.S. producers problematic. Trends, on the other hand, are more amenable to comparison. Most harvests in

both regions are concentrated in old-growth timber and thus occur at the extensive

margin of timber production. As old-growth stocks are liquidated, increasing costs over

Harvest Costs

4 Log scale (broad feet log scale) refers to a Scribner

measurement in which round logs are converted into the

number of board feet lumber tally (board feet lumber to

tally a square foot of lumber 1 inch thick), which can be

produced from these logs. Initially, board feet log scale

and board feet lumber to tally were theoretically identical,

but increasing lumber recovery and variations among different regions and different log sizes mean that the two

measures are not the same. Converting broad feet log

scale to broad feet lumber to tally entails an estimate of

the physical conversion of logs to lumber at the mill. This

issue is further addressed in the subsequent section on

log conversion efficiency and overrun.

5 Here we have used volume weighted yearly averages

from the Forest Service form FS 2400-17 (line 29, logging costs net of specified road construction). These

figures represent Forest Service estimates of expected

costs and not actual costs incurred by harvest. The lack

of an adequate sample size (particularly in 1986) is a

problem, but the estimates generally conform to expectations and seem to be relatively stable (though slightly

increasing) over time. Note that the extension of this

series to the entire Tongass National Forest requires the

assumption that the long-term contract sales exhibit the

same cost parameters as the independent sales.
9

time can be expected. This is in contrast to the Pacific Northwest, where second-growth

timber constitutes a substantial and increasing majority of total harvest, a trend which

was accelerated by the recent declines in harvests from public lands in the region.

Table 2 displays a more detailed description of southeast Alaska-appraised logging

costs as estimated from the sale appraisal data. The major cost categories include

skidding and loading (44 percent of 1985-94 average total logging costs), transportation

to mill (16 percent), and felling and bucking (12 percent). Specified roads are counted

as separate for the application of purchaser road credits. They accounted for 15 percent

of total appraised logging costs in the 1985-94 period. The total change over the sample

period is reported in the last column of table 2. The $26-increase in skidding and loading

(an estimated 3.7-percent annual rate of increase) is the largest increase for any single

category and accounts for over half of the total increase in logging costs. Costs in this

category will be sensitive to several factors, operability and log size being chief among

them. In contrast to the “logging cost” subtotal, “total logging costs” experienced slight

gains over the sample period, but this is due to a zero reported specified road cost in

the final year, an anomaly which disguises a generally upward trend in these costs as

well (note that only one sale was available for the 1994 sample).

The physical characteristics of the forest will largely determine the quantity and mix of

productive factors used to harvest a given quantity of timber. Although economic theory

predicts the marginal substitution of factors (capital for labor, for example) because of

changes in relative factor wages, production technologies in the real world are less

flexible. Logging firms will combine various productive inputs based on a limited menu

of possible logging techniques. Consequently, the ability to adjust the mix of inputs may

be limited. When combined with wage rates, the use of these factors will determine

total production costs. Table 3 displays labor cost statistics for Alaska and for the

Figure 2—Harvest costs in southeast Alaska, the Pacific Northwest, and British Columbia. All

estimates exclude permanent roading costs (from USDA Forest Service, Alaska Region, timber

sale appraisal data [see footnote 1]; RISI 1996).

Factor Costs and

Intensity in the

Logging Sector

1995 dollars per thousand board feet log scale
10

Table 2—Southeast Alaska logging costs from sale appraisal sample (1995 dollars per thousand board feet

log saclea)

Year

1985-94

Category and sample 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 change

Dollars

Cost category:

Felling and bucking 28 41 33 29 31 29 28 35 24 24 -4

Skid and load 84 117 95 101 106 121 114 147 133 110 26

Haul dump raft and tow 45 44 36 45 39 37 37 38 44 51 6

Road maintenance 3 3 3 5 4 2 4 5 4 8 5

Temporary roads 11 22 15 14 17 19 14 15 13 7 -4

Other 13 2 4 10 13 13 12 16 20 24 11

Logging cost (subtotal) 184 230 186 205 210 220 210 257 237 224 40

Specified road costs 32 0 45 55 43 65 41 48 65 0 -32

Total logging costs 216 230 231 260 253 285 251 305 302 224 8

Sample information:

Sample size 3 1 8 8 5 7 7 7 10 1

Total volume of sales

(MMbf)b 121 5 88 118 38 55 34 36 115 2

a LS = log scale. See footnote 2 in the text for explanation of measurement units.

b MMbf = million board feet.

Source: USDA Forest Service, Alaska Region, timber sale appraisal data, (see footnote 1).

Table 3—Labor inputs in the logging sector, Alaska, Oregon, and Washington (1987-94)

Year

Labor inputs Yearly

by region 1987 1988 1989 1990 1991 1992 1993 1994 Average growth

– – – – – – – – – – – – – – – – – – – Hours – – – – – – – – – – – – – – – – – – – – Percent

Logging production

hours per Mbf ls:a

Alaska 3.59 2.79 3.32 3.47 3.61 3.23 3.57 5.56 3.64 5.20

Pacific Northwest 2.37 2.45 2.35 2.64 2.58 2.78 3.02 3.38 2.7 4.90

Difference (%) 52 14 41 31 40 16 18 65 35 —

– – – – – – – – – – – – – – – – – – – – – Dollars – – – – – – – – – – – – – – – – – – –

Logging production wage

(1995 $ per hour):

Alaska 17.33 15.91 17.43 16.63 17.53 17.96 16.96 15.24 16.87 -0.50

Pacific Northwest 14.14 13.52 14.22 13.68 14.13 13.73 13.37 13.41 13.77 -.60

Difference (%) 23 18 23 22 24 31 27 14 23 —

– – – – – – – – – – – – – – – – – – – – – Dollars – – – – – – – – – – – – – – – – – – –

Unit labor cost

(1995 $ per Mbf ls):a

Alaska 62.29 44.36 57.84 57.63 63.35 57.99 60.47 84.74 61.08 4.60

Pacific Northwest 33.47 33.06 33.38 36.15 36.51 38.15 40.41 45.27 37.05 4.30

 Difference (%) 86 34 73 59 74 52 50 87 65 —

Difference 28.82 11.3 24.47 21.48 26.84 19.84 20.05 39.47 24.03 —

a Mbf = thousand board feet log scale.

Source: U.S. Department of Commerce, Bureau of the Census, Annual Survey of Manufactures (1985-94).
11

Pacific Northwest as represented by an Oregon-Washington average (comparable data

were not available for British Columbia). These data were obtained from the Bureau

of Census’ Survey of Manufactures and are reported for production workers; that is,

workers directly engaged in production as opposed to those engaged in management

and support.

As shown in the upper rows of table 3, Alaska used an average of 3.6 hours of production worker labor per thousand board feet log scale of lumber output over the 1987-94

period. The Pacific Northwest used 2.7 hours—a difference of 35 percent. Each of the

years reported likewise shows a significantly higher labor intensity in logging production

in Alaska, and though each region shows a generally increasing trend over time, there

is no significant trend in the difference between them. As noted in the analysis of the

sale appraisal data, the most likely explanation for higher labor intensity in Alaska relates to its remoteness and more difficult terrain, conditions which preclude certain

mechanized harvesting operations and which necessitate higher labor inputs even

under comparable harvest systems. Smaller average log sizes in Alaska also may

play an important role in dictating lower labor productivity.

Relative wages are a function of both industry-specific conditions and of other factors

affecting the regional labor market as a whole (which will be discussed in a latter section). As in the case of labor intensity, wages in the Alaska logging sector are consistently and significantly higher than those in the Pacific Northwest, and there is no

discernible trend, either increasing or decreasing, in their relation.

By combining labor intensity and wages, we may obtain a measure of the unit cost of

labor in the logging sector (in other words, the labor costs incurred in the production of

one thousand board feet log scale; see the bottom rows of table 3). The combined effect of the higher labor intensity and higher wages in Alaska is evident in the estimated

average unit labor cost differential (65 percent) between Alaska and the Pacific Northwest. This translates into an extra $24 per thousand board feet log scale in labor costs,

which Alaska must pay on average over and above costs paid in the Pacific Northwest.

This differential applies only to those employment categories that are explicitly coded

as logging; if the case is similar for other labor inputs (such as those engaged in road

construction), then the total per thousand board feet labor cost differential will accordingly be higher. In terms of relative levels, the labor cost data presented here mirrors

the total harvest cost estimates presented above, but the divergence of trends apparent in the total cost data is not evident here; costs in both regions are increasing

at essentially the same rate (see table 3 last column). Given the short timespan considered and the high year-on-year variation in the labor cost data, however, these trend

estimates are far less robust than the estimates of average levels for the entire period.

Other factor costs include capital costs (interest on loans and the opportunity cost of

invested capital), energy costs, the cost of other materials (including stumpage costs),

and various other costs such as road construction and transportation. Although labor

statistics are covered by federal and state reporting agencies, information on other

factor inputs is largely unavailable, and it was not possible in this report to derive unit

cost measures for other inputs. Labor cost differentials may nonetheless be indicative

of the relative cost of other inputs in southeast Alaska. Higher wages reflect a higher

overall cost of living for the region (the cost of living allowance for federal employees

in Alaska, for example, is 25 percent). This higher cost also generally will apply to production activities as well as consumption.
12

Capital costs in the region, as measured by interest rates, may be assumed to be

equivalent to that for investments of comparable risk in the Pacific Northwest as capital markets are well integrated at the national and, increasingly, international levels.6

Capital inputs per Mbf harvested in Alaska, however, may well be higher due to more

difficult operating conditions and the absence of road transportation networks. Indeed,

if we assume similar capital to labor ratios in both regions, then unit capital inputs will

exceed those in the Pacific Northwest by the same amount as the unit labor input. For

similar reasons, inputs of other materials and services generally will be higher in southeast Alaska than in the Pacific Northwest. Thus, even assuming wages for these inputs

are similar to those in the Pacific Northwest, costs will be higher because of lower productivity. Wages for these inputs, however, are likely to be higher owing to the generally higher costs endemic to the local economy.

This section focuses on lumber production, again concentrating on unit labor costs

arising from the combined influence of productivity and wages. More so than in the

logging sector, the distribution of fixed capital investments in the region will help determine the productivity of other inputs (notably labor) and thereby overall production

costs. Closure of the Ketchikan Pulp Company’s mill in Ketchikan in 1997 brought pulp

production in the region to an end; consequently, lumber, wood chips, and panel products remain as the most viable commodity options for the wood products sector in the

foreseeable future.7 A laminated veneer lumber plant is planned for the region, but it is

not yet installed. Changes of this sort may somewhat alter labor productivity statistics

for the sector, but underlying fundamentals determining comparative and competitive

advantage will remain the same. In addition to revenues from lumber sales, the sale or

other use of mill residuals (sawdust and chips) often will play a key role in mill profitability. Because of a lack of data, however, this role could not be directly analyzed in

this report. Nonetheless, the general wage and productivity information, which is part

of the following analysis, will have implications for the production of all mill products,

including residuals.

Total sawmilling costs net of the cost of wood inputs for southeast Alaska, the Pacific

Northwest, and British Columbia are shown in figure 3. The figures here are reported in

terms of thousand board feet log scale, and the sources for the Pacific Northwest and

British Columbia are the same as those cited for logging costs. Figures for southeast

Alaska were obtained from the “midmarket analysis,” a component of the Forest Service sale appraisal process (see footnote 1). They constitute an average manufacturing

cost for all species milled in southeast Alaska, meaning western hemlock and Sitka

spruce for all practical purposes. Once again, southeast Alaska costs are significantly

higher than those for the Pacific Northwest, ranging from 19 percent higher in 1984 to

about twice the Pacific Northwest figure throughout the 1991-94 period.

The Manufacturing

Sector

Total Manufacturing

Costs in the Sawmilling

Sector

6 Because of current policy in southeast Alaska, private

firms have identified uncertainity as a major impediment

to obtaining financing for new production capacity, which

in turn, signifies a higher interest rate within the sector

owing to higher risk. This higher capital cost, however, is

a reflection of the current state of the sector and does not

constitute an exogenous input cost. Given identical risk

and potential for profit, interest rates in southeast Alaska

should match those in the Pacific Northwest and

elsewhere.

7 In addition to these products, several specialty wood

products are manufactured in southeast Alaska, with

music-wood as the most visible example. The dynamics of

this sector, however, are considerably different from those

in main-stream commodity production, and specialty wood

products are beyond the scope of the present analysis.
13

The trends in these series are also similar to trends for logging costs. Declining costs

in the Pacific Northwest are commonly ascribed to closures of inefficient mills over the

last few decades. This occurred in the early to mid 1980s because of a demand-driven

recession in the wood products industries, and again in the early 1990s because of a

supply contraction from expanded forest conservation. Costs for southeast Alaska,

on the other hand, have been increasing at an average 3-percent real growth rate per

year over the period shown. Data from previous years, however, indicate that the early

1980s saw a sharp decline in processing costs. (Because of a lack of comparability and

other statistical problems, these earlier data are not displayed here). The 3-percent real

growth figure, therefore, may be the partial result of the endpoints chosen for analysis

and not of longer term trends. Declining log quality and piece size resulting in increased

processing and handling costs also may be partly responsible for this trend. Although

British Columbia logging costs are similar to those in southeast Alaska, manufacturing costs for British Columbia seem to be closely associated with those of the Pacific

Northwest in both levels and trends. This is not surprising given British Columbia’s

position as a major and highly competitive producer of sawn wood for export markets

both in the United States and throughout Asia. Although productivity in the British

Columbia logging sector largely will be determined by the physical characteristics of

the resource, costs in the sawmilling sector will reflect the efficiency gains needed to

successfully compete in global commodity markets.

Table 4 summarizes labor inputs in the sawmilling sector. The sources and derivations

are similar to those for the logging sector shown in table 3. Again, unit labor costs in

southeast Alaska are considerably higher (49 percent higher, on average) than those

in the Pacific Northwest. This is due to the combination of higher per-unit labor inputs

and higher hourly wages. Although higher factor use in the logging sector is easily

explained by the more arduous physical conditions found in southeast Alaska, the

Figure 3—Lumber manufacturing costs in southeast Alaska, the Pacific Northwest, and British Columbia

(from USDA Forest Service, Alaska Region, timber sale appraisal data [see footnote 1]; RISI 1996).

Factor Costs and

Intensity in the

Sawmilling Sector

1995 dollars per thousand boad feet log scale
14

same argument cannot be made for the sawmilling sector. The relatively low score on

labor productivity in sawmilling for southeast Alaska is somewhat surprising given the

lack of kiln drying and other finished lumber processing in the region, activities which

would tend to raise the number of production hours per Mbf in lumber manufacture.

There are several possible explanations for the relatively higher costs of labor for sawmilling in Alaska than in the Pacific Northwest. First and foremost, Pacific Northwest

mills are noted for their high degree of mechanization, and, with a large proportion of

their activity devoted to the production of standardized dimension lumber, they are

likely to benefit from efficiencies and economies of large scale not obtainable in southeast Alaska. Secondly, though adequate figures for fixed capital accumulation are difficult to come by, it is commonly recognized that the capital stock in southeast Alaska

is relatively old and poor in quality. Mills in southeast Alaska commonly utilize less expensive and older equipment, which results in lower labor productivity. Thirdly, southeast Alaska mills have been operating well below full capacity throughout the period

included in this analysis. This underutilization of installed capacity may have been mirrored by an underutilization of labor resources, with mills hiring more workers per Mbf

than would be necessary at optimal capacity utilization.

Many of the arguments relevant to the cost of other factors in the logging sector also

apply in the sawmilling sector. With the exception of capital costs, mills in Alaska will

face higher prices for goods and services in line with the higher prices faced by all firms

operating in the region. As with labor, the absence of large-scale economies will reduce

Table 4—Labor inputs in the sawmilling sector, Alaska, Oregon, and Washington (1987-94)

Year

Labor inputs Yearly

by region 1987 1988 1989 1990 1991 1992 1993 1994 Average growth

 – – – – – – – – – – – – – – – – – – – Hours – – – – – – – – – – – – – – – – – Percent

Sawmill production

(hours per Mbf lt):a

Alaska 3.97 8.81 4.21 5.48 6.89 5.96 7.75 5.25 6.04 3.10

Pacific Northwest 4.54 4.67 4.65 4.78 4.8 4.88 5.19 4.76 4.78 1.20

Difference (percent) -13 89 -9 15 43 22 49 10 26 —

Dollars

Sawmill production wage

(1995 $ per hour):

Alaska 13.35 14.75 13.76 14.28 14.61 17.21 16.02 17.74 15.21 3.80

Pacific Northwest 13.13 12.76 12.69 12.82 12.98 12.92 13.16 13.57 13.01 .50

Difference (percent) 2 16 8 11 13 33 22 31 17 —

Dollars

Unit labor cost

(1995 $ per Mbf lt):a

Alaska 52.97 130 57.96 78.19 100.59 102.58 124.15 93.07 92.44 7.00

Pacific Northwest 59.64 59.56 59.06 61.26 62.32 63.01 68.28 64.55 62.21 1.70

Difference (percent) -11 118 -2 28 61 63 82 44 49 —

Difference -6.66 70.45 -1.1 16.93 38.27 39.56 55.88 28.52 30.23 —

a MBF It = thousand board feet lumber tally.

Source: U.S. Bureau of the Census, Annual Survey of Manufactures (1985-94).
15

the productivity of capital investments and other inputs. Also, although the link between

physical resource characteristics and the quantity of inputs needed is not as direct as in

the logging sector, the remote location mills in the region will necessitate certain inputs,

particularly in the area of transportation, not needed in the Pacific Northwest. Consequently, both the unit cost of nonlabor inputs and the quantity needed will be higher in

southeast Alaska.

In general, both the trends and levels shown in table 4 are consistent with the data

shown in the previous section on total sawmilling costs (fig. 3): southeast Alaska

levels are significantly higher than those in the Pacific Northwest, and the difference is

increasing over time. In contrast to declines in the total cost data for the Pacific Northwest, per-unit labor cost estimates for the Pacific Northwest show an increasing trend.

This constitutes somewhat of an anomaly in the data, but, as with labor costs in the

logging sector, the trend estimates presented here are not robust. Moreover, the Pacific

Northwest, especially the state of Washington, has experienced rapid increases in

secondary manufacturing of wood products in the last decade (Robertson and Lippke

1996). This will be reflected in higher labor inputs per Mbf and thereby higher costs

(note that increased production hours per Mbf account for most of the increase in costs

in the Pacific Northwest shown in table 4). Because of survey procedures, the cost

estimates shown in figure 3 are less apt to include secondary manufacturing activity

and thereby register this shift in industry structure in the Pacific Northwest.

As with labor inputs, efficiency in the conversion of log inputs to lumber is an important

indicator of competitive advantage in lumber manufacture. High conversion rates will

minimize log inputs and the costs associated with them, and will thus denote an efficient use of both the forest resource (as embodied in stumpage rates) and the labor

and other inputs needed to harvest and deliver logs to the mill. “Overrun” is a measure

of the amount of lumber (denoted in lumber tally, or Mbf lt), that can be produced from

a unit of log input (log scale, or Mbf ls). Brooks and Haynes (1997) cite an estimated

overrun of 1.22 for southeast Alaska in 1994. In contrast, the 1994 Forest Service estimate for the Pacific Northwest is 1.7.8 Resource Information Systems Inc., a private

consultant firm, has estimated the Pacific Northwest west-side overrun at 2.03, though

they do not claim that this represents an unbiased sample (RISI 1996). The smaller

Forest Service Pacific Northwest estimate of 1.7 is about 35 percent higher than that

for southeast Alaska, thereby indicating that, even with equivalent delivered log costs,

southeast Alaska mills would incur considerably higher costs for their log inputs. Overrun estimates for British Columbia are currently not available, but it is commonly assumed that British Columbia conversion efficiency is somewhere in between the Pacific

Northwest and south Alaska, and most likely closer to the former than the latter.

Various factors will determine log conversion efficiency. Species, log diameters and

grades, and the amount of log defect all are important determinants that are beyond the

control of the local sawmilling sector. Other, less tangible, factors such as the local pool

of labor skills and accepted local business practices also may affect conversion efficiency. Mill technology, however, is often a more important determinant and is directly

dependent on the investment and production decisions of mill operators. Choice of end

product is likewise largely dependent on producer decisions. Taking independent factors such as labor skills and forest characteristics as a given, there is still good reason

why producers may decide not to maximize log-conversion efficiency. In regions like

8 Personal communication. 1996. Darious Adams,

professor, Oregon State University, Corvallis, OR 97331.

Log Conversion

Efficiency and Overrun
16

southeast Alaska where manufacturing costs (as opposed to delivered log costs) constitute a much higher proportion of the total cost of lumber production incentives to

minimize the cost of log inputs may be relatively weak, especially if such moves would

entail substantial investments of new capital. Likewise, where relatively large profits

are made on a small proportion of the log volume (high-grade spruce and cedar) but

the bulk of material (lower grade hemlock) presents little profit potential even when

delivered wood cost are excluded from the calculation, incentives to economize on

inputs of this lower grade material will be minimal. Here, labor and capital are the highcost factors, and their use will be minimized relative to that of log inputs.

The above arguments do not explicitly consider the role of wood chips in the sawmill

product output mix. Pulp production, however, played a dominant role in the timber

economy of the region over the period examined in this report. Consequently, sawmill

overrun rates, as well as overall productivity, may have been substantially impacted by

the need to maintain a steady supply of mill residue to augment wood fiber supply for

the pulp mills. Vertical integration of lumber and pulp manufacturing facilities increases

the possibility that this is the case. More generally, the ability to enhance profits through

the sale or use of wood chips and other mill residue will alter sawmill behavior and the

relation between factor inputs and product outputs. And finally, the ability to legally (and

profitably) export certain species in raw log form may affect the behavior of a mill in situations where it has this option. Hence, the low conversions efficiencies of the southeast Alaska sawmilling sector is the likely result of various contributing factors, many of

which may make good sense given the product mix and economic conditions prevailing

in the region.

Another approach to estimating relative lumber production costs can be made through

the comparison of southeast Alaska pond log values and British Columbia log prices.

Pond log values are derived by subtracting manufacturing costs from final product

prices (FOB mill) as reported for Alaska in the sale appraisal process. Assuming perfect competition in the British Columbia log market, log prices should represent a

similar measure, with mills bidding up prices until all profits (that is, revenues over and

above production costs and a normal rate of return on investment) are dissipated. A

comparison of southeast Alaska pond log values and British Columbia log market

prices is shown in figure 4. In terms of both absolute levels and changes over time,

prices in the two regions are remarkably similar. If we assume that end-product prices

are about equivalent across regions, the implication is that processing costs are also

about equivalent in Alaska and British Columbia, or that lumber production is considerably more profitable in British Columbia. When considering the similarity between

estimated harvest costs in the two regions and the large discrepancy between sawmilling costs (see previous sections), it seems that the latter argument, that British

Columbia mills are more profitable, is the more likely of the two. The lower production

costs of British Columbia lumber producers are not reflected in higher log prices. This,

in turn, implies a partial break in the link between residual value and prices of intermediate products, with log prices in British Columbia being largely determined by harvest

cost and stumpage prices rather than final product price net of mill costs. Once again,

however, the caveat concerning different macroeconomic environments in Canada and

the United States applies.

By combining estimates of harvest costs with those for sawmilling, we can derive an

estimate of the total cost of producing a unit of lumber net of stumpage costs. This estimate is shown in figure 5 for the three regions, and represents the sum of the estimates

presented above. This measure, however, is an aggregate of various species and

Total Costs

Pond Log Values and

British Columbia Log

Market Prices
17

Figure 4—Log values in southeast Alaska and British Columbia (from USDA Forest Service, Alaska

Region timber sale appraisal data [see footnote 1]; British Columbia Council of Forest Industies (see

footnote 3). 1995 dollars per thousand board feet log scale

Figure 5—Total production costs (logging + lumber) (from USDA Forest Service, Alaska Region, timber

sale appraisal data [see footnote 1]; RISI 1996). 1995 dollars per thousand board feet log scale
18

products and the results cannot be directly extended to particular cases without careful consideration. Southeast Alaska is the highest cost producer in most of the years.

Primarily because of its higher harvest costs, British Columbia is the high cost producer

in 1991 and is close to southeast Alaska in other years until 1993, when trends for the

two regions diverge. Throughout the period shown, costs for southeast Alaska generally are increasing, which is in contrast with stable to declining costs in the Pacific

Northwest and a rising and then declining trend in British Columbia.

End markets will ultimately determine the value of southeast Alaska products relative

to that of its competitors. The following analysis concentrates on Japan’s market for

North American softwood logs and sawn wood. The strategy here is to analyze product

prices to ascertain the relative position of southeast Alaska products in this market.

Japan traditionally has been the principal market for southeast Alaska logs and lumber

and the largest export market for Pacific Northwest and British Columbia products if

cross border trade between Canada and the United States is excluded. As such,

Japan’s market is the most logical place to compare relative prices and products from

the three producing regions. However, with the collapse of Japan’s prices for higher

grade logs and lumber in the last few years, southeast Alaska producers have devoted

an increasing share of their production to supplying U.S. consumers in the lower 48

states.

Figure 6 displays yen-based price indexes for major North American log export species

in Japan’s market. The use of an index was chosen because the price data were not

comparable among different species (different sizes and grades were used for each).

Before 1993, western hemlock, Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco)

and Sitka spruce log prices fluctuate in a broadly similar fashion. During this period, it

End Markets

Figure 6—Japan wholesale log prices (yen price index, January 1986 = 100) (Japan Lumber Reports

1945-94).

Year

Index
19

seems that geographic origin rather than species is the primary factor in the correlation of price changes, with the two Alaska species moving much in tandem and (nonAlaska) hemlock and Douglas-fir likewise following each other closely.

After 1993, the indexes begin to diverge, with Sitka spruce prices stabilizing at a high

level, Douglas-fir prices rising through rapid fluctuations, and prices for western hemlock logs (from Alaska and other sources) showing declines. This provides a partial

indication of the market position of southeast Alaska products, where Sitka spruce occupies a high-quality niche position relative to hemlock. Western hemlock is imported

by Japan in larger volumes from both the Pacific Northwest and British Columbia than

from southeast Alaska, and it is more prone to substitution from other species, especially at the lower end of its grade distribution. This conclusion is further borne out in

figure 8, which displays comparable indexes for Sitka spruce, Douglas-fir, and western

hemlock lumber. Here Sitka spruce clearly emerges as a separate product subject to

the same general fluctuations over time but displaying a much greater overall trend in

price increase. This, in turn, indicates an important component of the potential competitive advantage of southeast Alaska: its ability to fill this lucrative and relatively unique

market niche. Although in direct competition with spruce production in British Columbia,

Sitka spruce production in southeast Alaska comprises most of the North American

total, and the region likely enjoys a certain degree of market power, which may counterbalance local diseconomies in logging and sawmilling.

Southeast Alaska hemlock, on the other hand, supplies a broader commodity market

and faces greater competition from other producers and species. Given the relatively

low volumes supplied by Alaska, it will be a true price taker in this market. Consequently, future prospects for hemlock production in southeast Alaska will largely depend on

developments external to the region, including substitution by softwood species from

Figure 7—Japan wholesale lumber prices (yen price index, January 1986 = 100) (Japan Lumber Reports

1945-94).

Year

Index
20

the Russian far east, Northern Europe, and the conifer plantations in the Southern

Hemisphere; the expanding market share of engineered or fiber-based products; and a

general trend toward the use of lower valued species in higher valued applications. Increasingly, the cost and quality of “value-added” inputs (capital and labor), and not the

characteristics of raw log inputs, will determine the success of wood products manufacturers supplying these commodity markets. Consequently, hemlock resource in southeast Alaska may provide a necessary condition for wood products commodity manufacture and export, but the region will have to compete on the basis of other inputs.

Although we have given little attention in this report to log exports originating on private lands in southeast Alaska, these exports constitute an important source of competition for southeast Alaska processed wood products, especially for higher grade

spruce (Flora and others 1992). Evidence for this can be found in the close correlation

between the fluctuations in Sitka spruce log and lumber prices in Japan shown in

figures 6 and 7. The likely conclusion here is that spruce lumber is supplying much the

same end markets whether it is processed in southeast Alaska or in Japan. Given the

supposed market power of the region in Sitka spruce production, anticipated declines

in private log production and exports could result in higher prices for domestically

produced lumber. For lower valued log grades and species (primarily hemlock), the

same argument does not hold. This is not surprising given the small share of southeast

Alaska products in these markets; declines in hemlock log exports from private lands

will not be sufficiently large to significantly impact market prices for hemlock lumber.

Figure 8—Total lumber production costs (with stumpage), 1994 (from USDA Forest Service, Alaska Region

timber sale appraisal data [see footnote 1]; RISI 1996). 1995 dollars per thousand board feet lumber tally

southeast Alaska British Columbia Pacific Northwest

Region
21

Under the assumption of perfect competition in stumpage markets, stumpage prices

will be about equal to market prices net of all production and delivery costs.9 Ideally,

competition among stumpage purchasers will result in cost minimization and zero

“economic” profits (in other words, returns over and above a competitive return on invested capital) in the processing and delivery sectors. Consequently, economic rents

will be maximized and concentrated in the stumpage price. To the extent that this

holds, stumpage prices will be directly linked to residual value. Given a definition of

competitiveness that relies on the cost of producing a good relative to its final market,

the residual value, and thus the stumpage price, will constitute the single most indicative measure of regional competitive advantage in log production.

Depending on the proportion of local timber further processed in the region, stumpage

prices also may provide an indication of competitiveness in the wood-processing sectors. This is especially true when, as is the case in each of the regions considered in

this report, a significant proportion of harvest is barred from export in raw log form.

Competition from log exporters is thus eliminated from stumpage markets for export

constrained timber, and prices will reflect only the demand (and hence profitability) of

local processors. Here, high stumpage prices will indicate local industries whose costs

are well below the final value they generate, and conversely, low stumpage prices will

indicate an industry operating at the margin of profitability. Note that if timber is not

locally scarce, then the profitability of local processors will signal increased production

either through additional investments by existing firms or the entry of new firms. Eventually, economic profits will be eliminated either through wage increases for relatively

scarce inputs, or final market price decreases because of the increased product supply.

This is the market mechanism mentioned previously, which relates competitive advantage to revealed comparative advantage via the market equilibrium process. For the

regions and tree species considered in this report, however, timber is a scarce resource, and positive stumpage prices apply.

The relation between stumpage values and production costs is shown in figure 8. Harvest and manufacturing costs are the same as those cited previously in this report,

except in this case, harvest costs are converted to a thousand board feet lumber tally

basis. Stumpage prices are the prices received at the time of harvest (actually a “cut

price”), also expressed in terms of thousand board feet lumber tally. For southeast

Alaska, these prices are for federal timber, and in the Pacific Northwest, they are for

federal timber in California, Oregon, and Washington west of the Cascade crest. British

Columbia prices are for timber harvested on crown lands in coastal British Columbia

(as reported by RISI 1996). Total production costs are relatively similar for all regions,

but the distribution of these costs across the different categories is great. Following

the arguments presented above, the assumption here is that high production costs

in southeast Alaska and British Columbia lead to low stumpage values, and low production costs in the Pacific Northwest lead to high stumpage prices. The data presented in figure 4 represents a simplification, which aggregates across a wide range

9 Because stumpage sales often occur a year or more

before the anticipated time of harvest, stumpage prices

will incorporate future expectations and attitudes to risk

on the part of buyers in addition to current market

conditions and firm profitability. Also, policies regarding

stumpage price adjustments in line with market price

fluctuations along with other peculiarities of specific

market arrangements also will influence bid and realized

prices at stumpage auctions.

Stumpage Prices

and Residual Values
22

of species and products. Likewise, especially in the case of the Pacific Northwest and

British Columbia, the various figures reported in figure 4 do not come from the same

source and may be only loosely comparable. Nonetheless, these numbers provide a

clear depiction of the different aggregate costs in the competing regions, and the

relation among these costs, final market values, and stumpage prices.

With the highest total production cost of the three regions and a substantially lower

stumpage value, the marginal position of southeast Alaska as a high cost producer is

evident. As stumpage price cannot fall below zero (in theory at least), the profitability of

the sector will be highly susceptible to relatively small variations in end-product prices.

British Columbia has somewhat lower production costs, but its stumpage values are

only slightly higher than those in southeast Alaska. The explanation for this discrepancy

may lie in differing end products (that is, if British Columbia generally produces lower

valued lumber) or in differences in reporting procedures, firm profitability, and tax structures. The Pacific Northwest demonstrates both the lowest production costs and by far

the highest realized stumpage value of the three regions. Here, efficiency in production

allows for substantially increased returns to stumpage. To the extent that fluctuations

in end-product prices are directly transmitted to stumpage, the Pacific Northwest can

sustain large declines in end-product prices without a loss of profitability for the sector.

Historical values in cut prices for the Tongass National Forest, the Pacific Northwest,

and British Columbia are shown in figure 9. Pacific Northwest prices are the harvest

prices reported for national forests in the Pacific Northwest Region, and logs from these

harvests are restricted from raw log export. Because log exports also are restricted for

British Columbia and the Tongass National Forest, all the harvest prices reported here

will be for export-restricted logs and will thus incorporate competitiveness factors in the

Figure 9—Cut prices for southeast Alaska, Pacific Northwest, and British Columbia national forests, all

species (from USDA Forest Service and British Columbia Ministry of Forest as reported in Warren 1999) 1995 dollars per thousand board feet lumber tally
23

sawmilling as well as logging industries. As is true with the 1994 data shown in figure 4,

figure 9 displays the marginal character of southeast Alaska operations relative to the

Pacific Northwest and supports the conclusion that the Pacific Northwest generates a

much greater residual value per thousand broad feet harvested and processed. Perhaps surprisingly, British Columbia cut prices are only slightly higher than those for

southeast Alaska, thus reflecting the comparable harvest costs prevailing in that region,

but also perhaps different timber sale procedures and the failure of British Columbia

processors to incorporate profits from their relatively efficient sawmilling operations into

their stumpage bids. Cut prices reported for southeast Alaska are confounded by retroactive rate redeterminations and other factors peculiar to the region, thereby resulting

in negative prices in some years.

Much of the preceding discussion has been based on regional aggregates across species and log grades. More detailed data generated in the sale appraisal process allows

for an analysis of residual values on a species-by-species basis for the Tongass National Forest (fig. 10), but comparable analyses are presently not possible for the other

regions. These values were calculated by subtracting species-specific manufacturing

costs from selling values (as reported in the midmarket analysis in the Alaska Region

sale appraisal process) and then further subtracting average harvest costs calculated

from the independent sale appraisals (see above). These data display a strong differentiation in value by species. Hemlock and spruce generally move in tandem and

demonstrate an overall upward trend but marked cyclic fluctuations. Hemlock is the

lowest valued species with estimated residual values dipping below zero in the mid1980s and early 1990s. These fluctuations roughly correspond to price movements in

the international pulp market (note that utility logs and a proportion of low-grade saw

logs are assumed to be used as pulp and are appraised accordingly). Sitka spruce

demonstrates substantially higher residual values, obtaining levels between $200 and

Figure 10—Southeast Alaska average residual value by species (sawtimber and pulpwood) (from

USDA Forest Service, Alaska Region, timber sale appraisal data [see footnote 1]). 1995 dollars per board feet lumber tally
24

$400 per thousand board feet throughout the 1990s and late 1980s. This estimate is for

all log grades, and residual value estimates for better spruce grades would be considerably higher. Alaska yellow cedar generates extremely high residual values owing to

its high selling value and low manufacturing price (essentially a port delivery cost as

Alaska yellow cedar is exempt from log export restrictions and is exported almost exclusively in raw log form). Because of the predominance of hemlock and spruce in the

volume mix, the volume weighted average closely follows the hemlock price but is

higher because of the higher price of other species.

By multiplying the per thousand board feet residual shown in figure 10 by total harvest volume for each species, an estimate of total residual value generated in southeast Alaska by individual species can be calculated (fig. 11). Again, the high proportion

of hemlock volumes means that total residual values will be sensitive to relatively small

fluctuations in the price of hemlock. In half of the years, the contribution of hemlock to

residual value is negative, and only in 1988 and 1999 does it provide a substantial positive contribution to total value. Also, given the extremely large residual values associated with Alaska yellow cedar and, to a lesser extent, western redcedar, the total residual value generated by these species accounts for well over half of the Tongass

National Forest total in the last few years. This highlights the economic importance of

Alaska yellow cedar and western redcedar, species that often garner considerably less

attention owing to their relatively small volumes.

Overall positive trends for the residual values shown in figures 10 and 11 indicate increasing profitability for the sector as a whole. Much of this, however, is due to the

fact that in the early 1980s, a major global slump occurred in demand for wood products followed by a recovery and then further upward pressure on North American softwood prices because of harvest restraints in the Pacific Northwest. A sharp decline in

prices for most grades and species in southeast Alaska has occurred in the latter half

of the 1990s.

Figure 11—Southeast Alaska total estimated residual value by species (from USDA Forest Service,

Alaska Region, timber sale appraisal data [see footnote 1]).
25

Taken together, the residual value estimates for individual species indicates that the region is capable of generating significant value in certain species but that the profitability

of the timber sector at large will be extremely vulnerable to market price fluctuations in

hemlock. This result is based on species averages across all log grades. If log grades

also were considered, price fluctuations for lower grades of both spruce and hemlock

likely also would prove to be a major determinant in sector profitability. As shown in the

earlier section on forest resource characteristics, most of the material harvested from

the Tongass is in mid to lower grades of spruce and, especially, hemlock. Because of

the heterogeneous nature of southeast Alaska forest stands and specific regulations

designed to limit high-grading on federal lands, the ability of the sector to adjust the mix

of grades and species to different market conditions is limited. Consequently, potential

profits from the sale of higher valued species and grades, particularly cedar, often may

be sacrificed to support the harvest and processing of lower quality material.

How do the data and arguments presented previously relate to the concepts of comparative and competitive advantage? The first conclusion that can be made is that

southeast Alaska is at a competitive disadvantage in the provision of labor as an input

in the production process. This argument is likely true for capital (and other nontimber

inputs) as well. If this is the case, then the region has a competitive disadvantage in

value-added processing as a whole, as labor and capital alone comprise the valueadded component of any good. From the standpoint of comparative advantage, this

means that labor and capital are scarce relative to the supply of other inputs, notably

timber. Given recent layoffs and mill closures, it is hard to argue that labor shortages

characterize the current situation in the southeast Alaska timber sector. Nonetheless,

it must be remembered that comparative advantage is a long-term equilibrium concept,

and short-term fluctuations in labor markets do not apply. In general, the high wages

paid for labor (and other inputs) in southeast Alaska reflect the premium needed to attract and keep workers in the region. From an economic standpoint, this premium is

synonymous with relative labor scarcity—there are relatively more jobs to do than qualified workers to do them, and producers will consequently bid up the price of labor.

In spite of this competitive (and comparative) disadvantage, southeast Alaska is able to

profitably produce certain types of wood products. This is because of the scarcity rents

generated by the better species and log grades of the region. Hence, the advantage

of the region can be seen as lying in its ability to supply these scarce raw materials

(logs) and not in providing value-added inputs in combination with them. This assertion

should, in turn, be revealed in the behavior of local firms. Economic theory (as well as

common sense) predicts that firms faced with a competitive disadvantage in processing

will provide the minimum of processing necessary before export. In doing so, they will

minimize the losses incurred in their noncompetitive processing sector and thus maximize profits. The fact that virtually all private harvests of suitable quality in the region

are exported in raw log form bears this prediction out, as does the fact that local mills

have been wholly unsuccessful at bidding private logs away from the export market.

Although data on factor use in the sawmilling sector presented previously do not

directly indicate that mills are likewise minimizing inputs, the lack of lumber finishing

and secondary manufacturing facilities in the region further suggests a comparative

disadvantage in processing.

Certain determinants of comparative and competitive advantage do not change over

time. Location is one such factor. The remoteness, climate, and difficult topography

of southeast Alaska are major factors that impact both the price and productivity of

labor and, by extension, other nontimber inputs. Moreover, although the region is

considerably closer to Japan and other export markets in northern Asia, this has not

Comparative and

Competitive

Advantage Revisited
26

translated into a cost advantage in trans-Pacific shipping owing to diseconomies

associated with the smaller volume of trade, weather conditions, and the lack of

backhaul opportunities (Wisdom 1990).

Comparative advantage, however, is not a static concept, and the relative advantage

of nations and regions will be in constant flux. Alaska is no exception to this rule. Prices

for most goods and services are considerably higher in Alaska because of several factors, with the absence of economies of scale and transportation costs being chief

among them. As for wages, there is an added factor related to the need to provide

wage premiums over and above cost of living adjustments to induce workers to move

to the state in spite of a harsher climate, remoteness, and other perceived disamenities

(Greenwood and others 1991). Recent years, however, have seen a steady reduction

in many of these factors. Growing population has allowed for increasing economies of

scale in transportation, retail and services, and other sectors, and the positive amenities of life in Alaska have increasingly come to be recognized by local residents and

potential immigrants.

Although the data specific to the wood products sector presented in this report give

little indication of relative trends in factor input prices, broader measures of the Alaska

economy clearly indicate a steady reduction in prices relative to the rest of the United

States. Figure 12 shows real wage indexes in the manufacturing sector for Alaska,

Oregon, and Washington. Although the Pacific Northwest states demonstrate a similar

increasing trend, Alaska’s index shows a dramatic decline, particularly in the 1983-92

period during which the index fell by 28 percent. Current manufacturing wages in

Alaska are about equivalent to those in the Pacific Northwest, and these developments

likely will be mirrored in the wood products sector of southeast Alaska in the coming

years. Ideally, a regional producer price index would be the best way to gauge changes

in factor input prices in Alaska relative to those in other regions. Such indexes are not

Figure 12—Real manufacturing wages for Alaska and the Pacific Northwest (index, 1975 = 100) (U.S.

Bureau of the Census, Annual Survey of Manufactures [1985-94]).
27

available at the state level. Consumer price indexes (CPI), however, are available for

certain urban areas, Anchorage Alaska, among them. In the last decade or so, the

Anchorage CPI has fallen slightly more than 10 percent relative to the U.S. average

index. This is equivalent to about a 10-percent decline in absolute prices for the

Anchorage area. Prices in Alaska are still considerably higher than elsewhere, but

they are falling in relative terms.

The data on falling relative prices in Alaska should be treated with some caution. Because it is a composite of wages in all manufacturing industries, the wage index may

reflect changes in industry composition (increasing shares in hi-tech manufacturing in

the Pacific Northwest and fish processing in Alaska, for example) as well as actual declines in real wages within a given industry. Likewise, the Anchorage CPI may not be

indicative of changes happening in southeast Alaska, as Anchorage is relatively far

from the region and is substantially larger and therefore enjoys greater economies of

scale. Nevertheless, it is plausible that continued economic growth in southeast Alaska

will result in declines in producer prices and that the region will see a gradual but

steady increase in its relative advantage in wood products processing. Changes in

statewide wages and consumer prices may partially reflect just such a process. Likewise, the desire of residents to continue to live and work in the region should not be

discounted. Southeast Alaska is perhaps unique in the number (in per capita terms) of

individual entrepreneurs who paln to attempt small-scale wood products manufacturing

in the region. The willingness of these owner operators to work long hours for little pay,

however, likely does not extend to a willingness to work for lower wages in a large mill

or related facility.

The comparative and competitive advantage of southeast Alaska lies in its stocks of

high-grade spruce and cedar timber. In terms of log production, it seems that southeast Alaska is competitive with British Columbia, the only other major source of Sitka

spruce. Other species, even hemlock, can be profitably harvested given log grades

of suitable quality. In all cases, it is the scarcity value of the resource that allows for

profitable operations. In sawmilling, the evidence shows that costs in southeast Alaska

are significantly higher than those in both the Pacific Northwest and British Columbia.

In an open market, southeast Alaska lumber producers would be unable to compete in

stumpage and log markets with more efficient mills in other regions. With the prohibition

of log exports from federal lands, however, processors are able to charge a portion of

their higher costs against the scarcity value of the timber resource, thereby resulting in

reduced stumpage prices. This allows the national forest to bear the costs of regional

diseconomies in processing. Although this is not efficient in an economic sense, the

Tongass National Forest is managed to meet various local and national objectives; as

part of the balance among objectives, returns to the Treasury are foregone in favor of

opportunities for local employment.

The above arguments have important implications for current efforts to increase valueadded manufacturing in the region. In general, efforts to cultivate manufacture of commodity products in direct competition with major producers in other regions will be a

challenge. This is especially true for products where value-added inputs, rather than

timber scarcity rents, comprise a relatively large proportion of the final product value.

The development of niche industries relying primarily on Sitka spruce may be more

successful (as evidenced by the music wood and other specialty producers currently

operating on Prince of Wales Island). Anticipated declines in private harvests in southeast Alaska and Sitka spruce production in British Columbia may further bolster the

chances of success in these ventures. Alaska yellow cedar has commanded extremely

Conclusion
28

high prices in Japan’s market, and southeast Alaska has a near monopoly in its

production. These prices could bear increased processing (with diseconomies once

again being charged to the stump), but processors must compete with log exports. In

the absence of strict export bans, it is doubtful that they can do so successfully in large

numbers. In any case, Alaska yellow cedar volumes are not large enough to generate

significant employment in manufacturing commodity products. Moreover, attempts to

increase processing of this species must be balanced against probable reductions in

residual values and thus stumpage prices.

Most of southeast Alaska timber inventory, however, is concentrated in lower valued

species and log grades. Successful policies directed to this portion of the resource

likely would involve efforts to promote cost minimization and economies of large scale

in processing. An alternative would be to reduce or forego processing of this material

altogether, thereby allowing the region to concentrate on those areas where it has a

demonstrated advantage. This could be obtained either by relaxing those harvest restrictions that prohibit partial harvests (favoring better species and grades) or by relaxing processing requirements for certain lower grades. Such policy changes will have to

be evaluated in relation to other forest policy goals in the region and the ecological consequences of alternative harvesting methods. Should declines in wages and the price

of other inputs result in changes in the comparative advantage of the region in the long

term, then it is likely that increased value-added processing of lower grade materials

may evolve on its own accord. Ideally, such an industry would draw on both private and

public timber supply in the region.

Balassa, B. 1978. The changing pattern of comparative advantage in manufactured

goods. The Review of Economics and Statistics. 61: 315-319.

Berk, Peter. 1979. The economics of timber: a renewable resource in the long run.

Bell Journal of Economics. 10: 447-462.

Bonnefoi, B.; Buongiorno, J. 1990. Comparative advantage of countries in forest

products trade. Forest Ecology and Management. 36: 1-17.

Brinkman, G. 1987. The competitive position of Canadian agriculture. Canadian

Journal of Agricultural Economics. 35: 263-288.

Brooks, D.; Haynes, R. 1994. Timber products output and timber harvests in Alaska:

projections for 1992-2010. Gen. Tech. Rep. PNW-GTR-334. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 32 p.

Catimel, J. 1996. Timber scarcity, comparative advantage, and economic prosperity:

an analysis from a Canadian perspective. Res. Note 42. Ottawa, ON: Canadian

Forest Service. 31 p.

Coffin, G.; Larue, B.; Banik, M.; Westgren, R. 1993. Competitiveness in the

Canadian food industry. Canadian Journal of Agricultural Economics. 41: 459-473.

Flora, D.; Woller, U.; Neergard, M. 1992. Tradeoffs and interdependence in the

Alaska cant and log markets. Res. Pap. PNW-RP-422. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 11 p.

Greenwood, M.; Hunt, G.; Rickman, D.; Teyz, G. 1991. Migration, regional

equilibrium, and the estimation of compensating differentials. American Economic

Review. 81: 1382-1390.

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Warren, D. 1999. Production, prices, employment, and trade in Northwest forest

industries, fourth quarter 1997. Resour. Bull. PNW-RB-230. Portland, OR: U.S.

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Department of Agriculture, Forest Service, Pacific Northwest Research Station. 25 p.
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