May 1990 

Southeastern Forest Experiment Station 

P.O. Box 2680 

Asheville, North Carolina 28802 
Research Direct ions 

in the Study of Timber Markets 

and Forest Policies 

David H. Newman 

Assist ant Professor 

School of Forest Resources 

University of Georgia 

Athens, GA 30602 

David N. Wear 

Research Forester 

USDA Forest Service 

Southeastern Forest Experiment Station 

Research Triangle Park, NC 27709 

ABSTRACT 

Research on the economics of forest production and timber 

supply has been prolific, especidly over the past 10 years. 

A synthesis of this literature defines research progress to 

date and dso defines research directions. Research is 

proposed under four topic areas: (1) aggregate production 

functions for forestry, (2) wood products technologies and 

derived demands for timber products, (3) timber market 

models, and (4) market imperfections, including industrial 

organization, externalities, and risk. 

KEYWORDS: Forat production technology, timber markets, 

market structure. 

timber supply. Three outstanding contributions were 

published in 1980: Nyde's 'Timber Supply, Land 

Allocation, and Economic Efficiency'knd Jackson's 

"The Microeconomics of the Timber Industry," 

provided the theoretical bridge from the financial 

analysis of timber stands to the microeconomic 

analysis of timber supply, while Adams and Haynes' 

Timber Assessment Market Model (TAMM) brought 

together many developments in. econometric 

approaches to market modeling. TAMM incorporated 

an ecclnornic model of timber supply in national 

assessments required by the Resource Planning Act. 

Since 1980, a substantial body of related research has 

extended and broadened their coverage. 

Introduction 

Research in forest economics bas long been dominated 

by questions regarding timber availability and 

long-term timber supply. This focus is logical, as 

government and individual firm attempt to form 

policies and production plans for a long-iked 

renewable resource in a dynamic world. Plguch of the 

early work ww driven by the fear of "tinmber famine9' 

and the obvious economic dislocations that would 

follow, Some feel that these fears were borne out at 

the turn of the century in the Lake States and in the 

1930's in the South, when many firms shut down 

operations and left communities stranded with little 

industrial base. As a result, early researchers 

attempted to assess the potential for national and 

regional forest development and to predict the effects 

of government policies on timber supply. 

The first models to assess timber supply were "gapff 

models which compared rates of harvest against 

standing timber inventories and rates of growth (e.g., 

'6ISE)la Forest Service 1920, 1933, 1965). These models 

generally predicted horrific consequences from then 

current y ractiees with a quick exhausti~n of 

inventories and destitute timber-dependent 

ccrmmuniticl;t. However, many critics noted that these 

overly simplified models gave biased results. They felt 

that better predictions would be possible only with 

the inclusion of ecoaomic and other dynamic forces in 

the estimation of future timber supplies (Clawson 

1979; Vaux and Zivnuska 1952; Zivnuska 1964). 

In the 1980'~~ the emphasis of forest economics shifted 

from stand-level problems and physical views of 

timber supply to regional tirnl5er markets and variable 

Our paper surveys and synthesizes this literature, 

emphasizing developments since 1980. We also discuss 

emerging research issues and directions in the 

microeconomic study of timber supply and forest 

policies. We frame our study with two concerns: (I) 

understanding and projecting timber market activities, 

and (2) estimating the influence of policies on timber 

producers and timber markets where markets do not 

lead to the desired quantities or allocations of 

resources. Our discussion of these concerns is 

structured around four major topics: (1) production 

technology in the timber-growing sector, (2) the 

structure of timber demand, focusing on the use of 

timber as an input into downstream production, (3) 

regional timber market models, and (4) timber market 

imperfections and their effects. In each section, we 

outline the basic theory, survey the literature, and 

identify mztJor research issues and directions. 

This paper's primary emphasis is placed on the 

Southern United States. We therefore deernphasize 

important national and stand- level applications, 

discussing them only where they contribute directly to 

regional studies. Where germane, we refer to recent 

surveys in these uncovered areas. Our intent is not to 

List all the relevant research that has gone on in the 

pat decade but to focus on research that shows the 

greatest promise for Improving analysis of forestry 

supply issues or that departs significantly in its 

approach to these issues. 

The Timber Production finetion and 

Timber Supply 

Timber supply is defined by cornbinkg a biobgical 

model of forest production and a behavioral model of 
timberland owners. The forest production process 

translates physical inputs such as light, water, 

nutrients, and air and management inputs such as 

capital, labor, and entrepreneurial skills into forest 

products. The products considered here are pulpwood, 

sawtimber, and fuelwood. While other products or 

benefits such as forage, water quality, wind protection, 

and recreation may also be considered, for reasons of 

simplicity we ignore these possibilities here. Joint 

production of different forest products, however, is an 

important field of research (see Peterson and Sorg 

1987 for a recent survey of attempts to include all 

forest products into a valuation scheme). The 

behavioral model assumes that timberland owners are 

rational decisionmakers and that they use efficient 

land management met hods. The general economic 

model of production, the production function, is: 

where x is a vector of inputs and is a vector of 

outputs.' For the forestry case, x includes biophysical, 

land, labor, and capital inputs to timber growing and 

y includes products such as pulpwood and sawlogs, t 

refers to the amount of time used to grow trees. 

Aggregate regional production functions translate 

these forestry inputs into forest products and permit 

powerful analysis of timber supply. For example, the 

production function translates an incremental change 

in land availability into a physical output response. In 

addition, changes in production function estimates 

over time measure technological progress in terms of 

changes in input productivity. Conversely, 

productivity declines that may be associated with 

cultural influences such as acid rain and COz-induced 

climate changes can also be examined' 

With few exceptions, however, estimating production 

functions in forestry has proved very difficult. Two 

measurement problems are especially troublesome: (1) 

the temporal separation of forestry inputs and forest 

products, and (2) a lack of data on input quantities. 

These problems, while unique in their degree, are 

simila in form to those in other fields of study. In 

other tseclors, grogress hies been made in estimating 

production technology indirectly by studying the 

behavior of producers heed with various markt 

situations. These procedures allow the analyst to use 

observ&ions of profid-maxinnLzrng behavior to discover 

With reg& to d sseckbns on theory, the uninitiated reader 

should refer to stmdd dcroecmorrtic texts. A basic reference 

is IfirsMeifer (1980); a mre complete theoretical Lre&ment can 

be found in RusseU. and WiWnson (19'79) or Silberberg (1978). 

the underlying production techno lo^. This approach 

shows promise for applications to forestry. To 

illustrate this connection or duality between 

production technologies and decisionmaking, we 

consider two models of producer behavior: (1) revenue 

maximizing with constrained budgets and (2) cost 

minimizing with constrained output. For revenue 

maximization, if we assume that individual timberland 

owners control what they produce but not the price 

they are paid for their output, then optimal 

production can be defined by the production function 

and a budget constraint. Timberland owners will seek 

to maximize revenue, subject to limits on their 

production budget: 

Where p and w are the prices of the M outputs and N 

inputs respectively, R is revenue, and B is the budget 

level. Knowledge of f and solutions to (2) define the 

set of input demand functions. These functions are 

obtained by solving the N first order maximization 

conditions of the system for x as function of prices. 

If these functions are substituted for 2; in the 

production function, the optimal output for specified 

prices or the supply function is defined: 

For the cost minimization problem total cost C is 

defined as the sum of input bills for the forest sector. 

If we assumo that producers minimize the costs of 

attaining their production goals, then the cost 

function defines the set of cost-minimking inputs 

necessary to reach this goal: 

Output supply and input demand functions may also 

be derived from the actual expenditures of firms. The 
inappropriate to consider the level of output as an 

exogenous variable.' If so, it may be necessary to 

incorporate variable output, using a profit function. 

Figure 1: The relationship between the shapes of production and cost functions. 

purchme of various combinations of inputs and the 

returns to production reveci! the production 

possibilities or production function. Because the cost 

function: 

c = !?(Y 7 4 (7) 

reflects the minirniumcost combination of inputs to 

produce y7 it can be used to derive all the essential 

elements of the production technology. 

A cost function can describe a production technology 

because cost and production are dual, or mirror 

image, problems. This duality for the case of one 

input and one output is displayed in figure 1. In the 

lower right-hand quadrant, the production. function 

defines the transformation of input z into output y, 

The 45-degree line in the lower left-hand quadrant 

transfers output (y) from the production function to 

the cost function in the upper left-hand quadrant. The 

outlay function (upper right-hand quadrant) is simply 

the price sf the input (zu) times volume of the input 

rased, and the cost function (upper left) relates output 

do these total outlays. For this simple cme it is emy do 

see that the shape of the cost function is determined 

exactly by the shape of the production function. 

The cost function can be used to reveal the 

input:output possibilities for an individud producer, 

If we aggregate across individual producers within a 

region, we can examine regional input demands and 

output supply, At the regional level, it may be 

In summary, a forestry production function provides 

an analytical framework for studying the supply of 

forest outputs. It is an empirical means of fusing 

biological and management inputs and a convenie~lt 

mechanism for examining the inAuence of land use and 

other shifts on timber production. The same 

information can also be derived from an aggregate cost 

(or profit) function. 

Previous Research 

Regional models of forest production have been slow to 

evolve in the forestry literature. Rather, much of the 

modeling work has focused on stand- and forest-level 

models. However, a precedent for this aggregate work 

exists in the rich body of production function research 

developed in the agricultural economics literature (see 

Woodworth 1977 for a review). Several factors explain 

wiry these models have found more expression inn 

agriculture. For major agricultural crops, ownership 

and management types are relatively homogenous and 

crop production cycles are annual or semiannual in 

nature. These factors allow for the greater 

accumulation and richer analysis of compatible data. 

These advantages are not generally present in the 

forest sector. The extended production period hw 

been particularly vexing. As Hirshleifer (1976) 

emphasizes, standing timber is both a product and a 

factor of production. As plant, timber is a form of 

capital that appreciates "on-the-stump." It is, 

however, different from other factors because the firm 

cannot adjust growing stock as freely as it can other 

inputs such as forestry workers and skiddess. In this 

sense, growing stock is similar to large structures and 

other forms of capital that may be fixed in. the short 

run. This fixity of growing stocks must be considered 

in an aggregate timber production or cost function 

model. 

In addition to this basic problem regarding the nature 

sf forest production, some severe data probiems 

discourage the direct estimation of a forestry 

production function. Broad tree specie, ownership, 

2~ this case, being exogenous indjcske that the vaiable is 

not totally controlled by local decisiomIskem- A dl;viiu;iable WE& 

is locally controlled is an. endogenous vwiiable in this context. 
and managernent classes pose serious aggregation 

problem, and strong annual or cross-sectional data 

for regions are typically lacking. Finally, information 

about important factors of production such as labor 

inputs is typicdly not collected. Forest economists 

have attempted to get around some of these problems 

to answer basic questions about prodttctivity and 

technological change. They have examined such topics 

as the specification of production relationships for 

industrial wood products, returns to research and 

technical change in the forest products industry, and 

the development of micrct- and macro-level biological 

forest production functions. However, these functions 

have generally been developed for stand-level 

problem. Regional studies have relied on proxy 

variables in lieu of missing data on input use. 

Stand production function research in forestry has 

developed at two dierent levels, Most recent studies 

focus on the stand-level effects of only biological 

inputs and time (see Alig and others 1984a, 1984b for 

a review). Production functions have been estimated 

for individual species in which site productivity 

responds to management as well as physical inputs 

(Chang 1984; Couto and Nautiyal 1984; Pjautiyal and 

Couto 1981, 1984; Rawat and Nautiyal 1985). This 

allows the effects of management controlled factors 

such as fertilization and planting density, to be merged 

with exogenous site and biological factors in describing 

total forest production. 

Functional forms for these models vary across studies 

to increase precision and tractability for particular 

species and uses. Couto and Nautiyal (1984), for 

example, examine only fertilizer and rotation age for 

Egcalyplzls grandzs and compare six functional forms 

ranging from a simple Cobb-Douglas model to 

quadratic models with interaction terms. Their chosen 

model used logarithmic-reciprocal function, setting 

timber yield (I.: m3/ha) equal to a function of 

nitrogen N, phosphorus P, potirsh A', and time T: 

Chang (1984) estimated loblolly pine yield (ft3/acre) 

as a logstrithmic-reciprocal function of age, planting 

density M, and site index S: 

Optimal management plans can be derived using these 

specifications of forest production with generalized 

forest rotation models. These plans should increase the 

economic efficiency with which planted forest stands 

are managed; they can also improve our understanding 

of the eRects of management inputs on production. 

Since they cannot be aggregated in meaninal ways, 

however, they are useful only for micrc-level decisions, 

Recent studies have meaured regional biological 

forest production directly (Wallace and Newman 1986; 

Wallxe and Silver 1984). In these studies, forest 

survey data ownership and species distribution, along 

with average biological characteristics, were used to 

assess the effects of policy variables and variations in 

ownership and forest type inputs on total forest 

productivity. The basic model is quite simple. 

Productivity Ti is set as a homothetic function of 

biological characteristics and total: acreage in forest 

production. The full model in FVallace and Newman 

(1986) is a log-Knear function of average tree diameter 

D, average stocking percent B, and average site 

quality S times a nonlinear combination of acreage A 

in ownership class (public P, nonindustrial private 

forest N, and industrial I) and species type (loblslly 

pine L, other softwood M, oak-pine 8, and other 

hardwood HI. The general formula, is: 

where the P's, w5s, and y's are estimated parameters, 

the a is an exponential term between 0 and 1, and u7s 

represent other unspecified variables and measurement 

error. In addition, the model is restricted so that xui = C yi = l (thus, for instance. 

wl = wz + u3 -+ ~24) '%'he model is estimated using 

nonlkear eslima"con techniques. 

Interpretation of the coeEcients for this model 

proceeds in two manners. The biological coefficients 

sigaifS; partial derivatives of growth with respect to 

the particular variable, The acreage coefficients, 

because of the summation constraint, signify only 

reladivc: impacts betmen acreage types and not direct 

impacts. Still, substantial new information is 

generated about aggregate productivity and nurnerous 
conceptual experiments can be performed vis-k-vis the 

acreage breakdowns. 

This type of production model has not explicitly 

considered time or growing-stock effects. It does offer 

substantial promise for applying available resource 

inventory data to the assessment of policy, technical, 

and environmental changes in the region. Its utility 

would be greatly enhanced with the incorporation of 

temporal effects. 

Research Directioazs 

Recent developments in applied duality theory have 

enhanced our ability to examine the production 

technology through cost/profit analyses. These dual 

models have yet to be used to estimate biological 

production technologies, but the approach is 

promising. Cost/profit function analysis requires input 

and output price data which are often more readily 

available and more reliable, and avoids some of the 

measurement problems cited earlier. In addition, 

recent studies in partial equilibrium analysis suggest 

ways of incorporating growing stock as a quasi-fixed 

factor of production in a cost function model (e.g., 

Berndt and others 1981). 

Estimating an aggregate forest production technology 

will require advances in several areas. The most 

important is data quality. Forest survey data have 

been used for several previous analyses, but this 

source is limited in its breadth. These studies have 

relied on proxies for much of the management inputs 

to the production function. Actual memures of these 

inputs are needed do explicit1 y identify production 

functions. This can be accomplished, on a limited 

level, by tying the forest types described in the ibrest 

inventories with capital and labor inputs necessary to 

create them. These inputs may weli have been 

tabulated for other purposes, but they have rarely 

been brought together in a single analysis. 

Time can be directly incorporated into a regional 

production model by memuring the lags between the 

inputs and outputs of forest management. 

Incorporating these lags could enhance our 

understanding of such topics as returns to research, 

tax, and other incentive programs for nonindustrial 

private forest (NIPF) owners, ete, A basic problem 

with this approach is the lack sf compatible annual 

data over long time periods: which would allow the 

econometric i~zterp~e t ation of relevant lags, 

Reliable production functions for timber growing 

would permit direct, analytical means of generating 

timber supply functions. To date, timber supply has 

been modeled in two different ways. One is through 

the use of simplified models of the timber market. 

These studies (discussed in detail later in this paper), 

are highly aggregated and do not distinguish between 

ownership and site groupings, or between short- and 

long-term supply effects. Short-run effects are limited 

because of the relative fixity of timber stocks, and 

long-run results must be defined by a stock- . adjustment mechanism. 

The other approach to timber supply modeling is 

through the mechanical simulation of a production 

function. This engineering approach bas been applied 

to long-run questions but has not yet been used to 

study short- versus long-run stock adjustments. 

Supply models derived from a properly specified 

production function would incorporate these 

important aspects of supply behavior. 

Woad Froduets Industries and Timber 

Demands 

An alternative way to handle production lags is to 

directly incorporate growing stock in the production 

model. Growing stock represents the accumulated 

investment of physical and management inputs in the 

stand. The price sf growing stock then reAeets the 

opportunity costs of these inputs and can be viewed as 

rent on the standing tirnber capital. Timberland 

owners adjust levels of growing stocks by harvesting 

and through timber stand improveme& and 

regeneration, The amount of growing stock held at 

any time should reflect its capitalized value relative to 

product prices. 

This section examines the demand side sf timber 

markets and its derivation from production 

technofo@es for wood products. This Leckzs~sBo~ is 

described using the same esnstructs described above: 

production functions and cost fumcticms. Here 

however, the demand for inputs, such as timber, 

rather than the supply of outputs is emphaized. 

Wood products technologes gro.c.Pde the eEcient 

combination of inputs such as timber, Iabar, and 

capital, and wood product outputs such as plywood, 

lumber, and paper. By applying the same analytic 
technique we used for the timber-growing production 

function, the wood products production function can 

yield timber demand functions: 

where y; is the demand for stumpage of class j, P is a 

price vector for wood product outputs, W is the price 

vector for raw material and other inputs, and D is a 

function. Knowledge of these production relationships 

allows for the direct estimation of changes in timber 

demands as product mixes and levels shift. 

The application of dual production models is much 

more straightforward for wood products 

manufacturing than for forest production, and 

research activity in this area has been prolific. While 

these models have yielded valuable information about 

timber demand and substitution possibilities between 

timber and other inputs, their results have not been 

consistent across regions and model forms. Import ant 

questions therefore remain about the specifications of 

the production model. In addition, the measure of 

timber inputs has been highly aggregated. Both 

concerns suggest needs for future investigations. 

Previous Research 

In the past 15 years, extensive research into the 

structure of manufacturing technologies has been 

fueled by concurrent advances in duality theory and in 

the estimation of flexible functional forms for both 

production and costlprofit functions. The result is a 

much more direct bridge between the theoretical 

constructs of microeconomics and the statistical 

estimates of its parameters. 

Humphrey and Moroney (1975) pioneered the 

application of dual models in natural resource 

economics, analyzing several natural resourceusing 

industries in the United States, including the 

mlid-ww d products industries. In their estimates of 

produc tion technologies "tey used both cost function 

and production function approaches and considered 

labor, capital, and saw materials inputs. They applied 

their models at the national level, but they imposed 

fairly restrictive msurnptions. Subsequent studies 

generalized this bait model by considering fixed and 

variable inputs, more refined definitions of outputs, 

and production ad the regional level. 

These studies have assessed the production structure 

of the major wood products industries, usually by 

two-digit , sometimes four- digit, Standard Industrial 

Classification (SIC) code. They generally used the 

production function dual, the cost function, to 

investigate such concerns as technological bias, 

substitution in production, and derived demands for 

inputs, including tirnber. The transcendental 

logaithmic, or Translog, model has been the most 

popular specification for cost models. This versatile 

model allows direct estimation of elasticities of 

substitution between the primary inputs of 

production: labor, capital, and wood (and sometims 

energy), input demand and output supply elaticities, 

and time or technological change effects. 

The general approach in these studies is to define a 

second-order approximation to a cost function for the 

industry. The derivative property of cost functions 

then defines the demand function for inputs to 

production. Either the cost functions, the derived 

demand equations, or both are estimated by a 

technique that accounts for the error structure 

between equations. For example, the 

constant-returns- to-scale, n-input , translog cost 

function is: 

Where C is cost, p's are input prices, and p's are 

coefficients. In addition, scale effects can be 

incorporated by including output levels, and 

technological change can be addressed with a time 

variable. The partial derivative of (12) with respect to 

the logarithm of input price defines the cost share of 

that input: 

Share equations sum to 1, so only 11-1 equations are 

independent. Once estimated, (12) and (13) describe 

the salient characteristics of the production technology 

using vmious demmd, substitution, and scale 

elmticities, 

Applications of these models to the wood products 

industry have yielded contrasting conclusions about 

tiae production technology. Most notably, analyses 

have disagreed about the relative substitutability of 

stumpage for the other inputs into the production 

process. ARL (1987): Stier (1980), and de Borger and 

Buongiosno (1 985) generally agree that stumpage, 

capital, and labor are gross substitutes in production 
in both the lumber and pulp and paper industries. 

Merrifield and fIayn~ (1983), however, found 

stumpage and labor to be complements in lumber 

production, while stumpage was a substitute for 

capital in production. In later studies of lumber and 

plywood production, Merrifield and Singleton (1986) 

and FVeiar (19817) found stumpage and capital to be 

weak complements and all other inputs substitutes. 

Finally, Humphrey and Moroney (1975) found 

stumpage and capital to be strong complements in the 

pulp and paper industry (SIC 26), with stumpage and 

labor being substitutes. 

These differences likely arise from different levels of 

aggregation of both inputs and outputs in these 

models. On the input side, all timber is typically 

lumped into a single quantity variable. Some studies 

separate softwoods from hardwoods; some do not. 

Even softwood timber is composed of a wide variety of 

tree species and qualities. Constantino and Haley 

(1988) have shown that these attributes greatly 

inAuence the value of a tree in production. On the 

output side, it is common to lump lumber, plywood, 

and other solid-wood products as a single aggregate 

output. These different products are made with very 

different technologies. In both cases, it is reasonable 

to question the validity of aggregating materials of 

variable quality. 

In addition to the horizontal aggregation of product 

classes, production technologies have also been 

aggregated on the vertical scale (for example, across 

logging, hauling, and milling). Production/cost 

models of the solid-wood products industries have 

defined timber input at the stumpage level because 

price data are generally available for stumpage but not 

for delivered logs. However, use of stumpage input 

prices requires the analyst to include logging 

contractors and log transportation as well at; lumber 

and plywood manufacturers in a model of the wood 

products technology. Assumptions underlying this 

kind of aggregation are that the quality of the labor 

and capital used in the lumber industry is identical to 

that in the logging industry and that the degree of 

substitution between capital and labor is the same in 

the two industries. 

Studies examining returns to research and technical 

change in forest production focus on two major 

concerns: technical change bias, and economic returns. 

Early work focuses on the former using methods 

developed by Solow (1957). These studies show wood 

product industries exhibiting positive growth in 

productivity over the past 30 years. The technological 

bias of this growtb is generally labor saving (Greber 

and White 1982; Robinson 1975; Stier 1980). Recent 

investigations have shown large positive economic 

returns arising from the research that has fueled this 

productivity growth (Bengston 1984; Seldon 1988; 

Seldon and Newman 1987). 

Demand information from these aggregate production 

models has also been integrated in timber market 

assessments. Wear (1 988) used factor demand 

equations from an aggregate cost function model in a 

simple partial equilibrium analysis of wood products 

and regional stumpage supply markets. His results 

suggest that the use of these highly specific demand 

models in lieu of commonly applied 

fixed-factor-proportions models significantly changed 

stumpage market projections. Further work is needed 

to apply these derived demand relationships in timber 

market models. 

Research Directions 

Research into the production structure of wood 

products sectors has been prolific. Research in this 

area will continue to refine and generalize production 

models and should focus on translating this 

information into timber market models. Production 

models can be refined by considering more output and 

input detail. On the output side, the separability of 

product groups is unclear, and the vertical as well as 

horizontal integration of production ha not been 

adequately addressed. In addition, emerging 

technolo@es may shift demands for products. A 

related topic is the presence and extent of 

"economies-of-scope,)) or cost- advantages of 

producing a diversidied set of outputs. On the input 

side, recent research suggests that results are highly 

sensitive to the specification of fixed versus variable 

inputs. This indicates a need for more careful study of 

capital investment dynamics in individual wood 

products sectors. 

These models can also be extended to incorporate 

more detailed timber information, Distinctions 

between tree species and producing regions can be 

made and the separability of various species groupings 

tested. This approach can be used do develop segionaf 

demand models for tinnber. In addidim, the point of 

valuation of timber should be examined, The precision 

of production and demand models may be improved 
by considering log and stumpage markets separately. Price 

Another area of concern is the timber data used. Many 

studies use the prices paid for USDA Forest Service 

timber as a proxy for prices paid for private timber. 

Jackson (1987) has compared Forest Service against 

other price series and has shown that stumpage prices 

vary greatly between Federal and State ownerships in 

Montana. He attributes these differences to the P 

complexity of Federal timber sales and an extensive 

roads program on Federal lands. Additional 

contractual obligations may also reduce stumpage 

prices for Forest Service timber. In contrast, Cubbage 

and Davis (1986) found no significant differences 

between the prices reported for Forest Service and 

State timber sales in Georgia. Econometricians hope 

that if a bias occurs, it is in level and not in trend, but 

the issue remains as an important research question. 

Timber Market Modeling 

Timber market models define the interaction of timber 

supply decisions of individual producers with timber 

demands. Timber demand depends directly on the 

economy's consumptive demands for wood products 

such as lumber, plywood, paper, and paperboard. 

Accordingly, we can view the timber stumpage market 

as the interface between timber growers and wmd 

products industries. Research in this area focuses on 

the mechanics that simultaneously determine the level 

of tianber produdion and timber prices. 

Timber market rnodels are a way of sorting input and 

output price effects on market prices and quantities of 

timber produced. In theory, supply and demand can 

be analytically derived from production constructs for 

the wood growing and wood using sectors, 

respectively. In reality, specific data have not been 

available to fully develop market models in this wy. 

Instead, the theory has been used as a heuristic guide 

to variables that should influence supply and demand. 

These variables have been used to specify 

"impressionistic" market models. These models 

typically contain a correct complement of variables, 

but coeEcients are not constrained to reflect the basic 

tenets of microeconomic theory. 

The bisic structure of a market model for stumpage 

derives from equations of supply and demand and a 

market equilibrium condition. We earlier described 

the theoretical foundations for timber supply and 

stumpage demand through their respective production 

Q Quantity 

Figure 2: Stumpage supply and demand. 

functions. '6-Fyith the assumption of cornpe titive 

markets and profit maximization, the application of 

Hotelling's lemma defines the firm's input demand 

function and producer's stumpage supply function 

(Varian 1984). The resulting system of equations can 

be represented by: 

Q" .=3(~" 3 P) (14) 

&d = 4d(~* 9 4) (15) 

qs (P* , P) = qd(~* 9 $1 (16) 

where Qi is the stumpage quantity supplied or 

demanded; p* is the equilibrium market price and p 

and 4 represent other factors that shift supply and 

demand such as final goods prices, capacii;~, price 

expectations, technology, government policies, and the 

wailabitity of land, energy, and capital. 

The equilibrium condition (16) allows straightforward 

estimation of the coeEciesnts of these functions, Figure 

2 depicts this system with linear functions and gives 

the predicted signs of the coeEcients. For demand, 

prices of other inputs into the production 

process-capital (k), labor (w), energy (e), etc.-are 

expected to show substitution effects as firms adjust 

input mix in line with relative input prices. These 

coeEcients would thus be expected to have positive 

coefficients, although factors such as energy could be 

complements and their signs are uncertain. Final 

goods prices (f) for wood products and complements 

to wood products (c) would also positively increase 

stumpage demand while the price of substitute final 
goods-other building materials, plwtics, etc. 

(sb-would negatively affect stumpage demand. 

Technical change occurring in the forest products 

industry causes uncertain shifts in the demand 

function, depending on the type of change that occurs. 

At one level, technical change can reduce costs and 

lead to increaed production and therefore greater 

stumpage demands. At another level, technical change 

can increase production efficiency, reducing the need 

for stumpage in production. Therefore, the sign on a 

demand technology coefficient must be empirically 

determined. 

The supply coefficients are constructed in a similar 

manner to demand coefficients. Increases in input 

costs (w, L, and e) decrease supply as production costs 

increme. Since forests crestte multiple outputs ( e.g. 

wildlife, recreation, sawtimber, pulpwood) these 

outputs may act as substitutes or complements in 

production and thus may negatively or positively shift 

supply. FinaUy, technical change works to lower 

production costs and thus positively shifts supply. 

Figure 2 presents an idealized estimated model. Severe 

data lilrnitations frustrate the complete delineation of 

market models such a that proposed in the figure. On 

the supply side: the lack of strong cost data lifits the 

delineation of direct supply effects from input price 

changes. As a result, many researchers have used the 

standing tider inwntory as an inverse proxy for 

input costs. The rationale for the use of this proxy is 

that production costs are reduced as inventory builds 

up because economies of scale are introduced. 

An understanding of the benefits derived from 

expanding the production capabilities of the southern 

forest requires ident8cation of the characteristics of 

forest output markets, Policy analysis demands 

precise models sf timber markets and in.iiestment 

behavior. This has been an area of active research, 

and several studies have examined timber markets in 

the Southeat, Results may be improved as promising 

deve10pments from other areas of study are applied to 

southern timber markets. 

Previous Research 

Most modeling has focused on national markets for 

iumber, paper, plywoody or other products. These 

models often leave the raw material input as an 

exogenous or predetermined varia"be (Adams and 

Blackwell 1973; McKillop 1967; Mills and Manthy 

1974). Possible reasons for favoring industry studies 

are the general availability of homogeneous market 

data and the seemingly greater application of the 

analytical results to macroeconomic policy decisions, 

Several studies are available that either focus on an 

aspect of southern timber supply and demand or have 

an aggregate southern regional component as part of a 

national market assemment. Robinson (1974) 

examined regional stumpage and lumber markets for 

the South and the Pacific Northwest for the period 

1947-1967. He used two-stage least squares (2SLS) 

regression to estimate an eight-equation linear system. 

However, he was unable to characterize the southern 

lumber market because the own-price coefficient for 

lumber was not significantly different from zero. To 

arrive at a solution for southern stumpage, he 

ultimately assumed a predetermined southern pine 

lumber consumption level and solved for the 

equilibrium stumpage demand price as a function of 

lumber production, chip production, and time. His 

estimates showed a stumpage demand elasticity of -0.5 

and a supply elasticity of 1.06. The supply elasticity 

was higher than expected, and he discounted the 

relatively large value as being related do the use of a 

nonsimltaneous estimation method. He found a more 

theoretically consistent short-run supply elaticity of 

0.32 when both quantity and price were endogenized 

in the estimation method. 

As part of their national Timber Assessment Market 

Model (TAMM) Adams and Haynes (1980), specified 

southern sawtimber stumpage supply functions by 

region and ownership type. The South wa divided 

into two regions, and supply functions for forest 

industry and NlPF were estimated. They used 2SLS 

with stumpage supply modeled for each regionlgroup 

as a linear function of two variables: own price and 

standing timber inventory. Solid-mod stumpage 

demand was driven only by demand in the final gmds 

markt and product conversion factors, ass the 

own-price demand elrzsticity was eEectlvejy assumed to 

be 0. The estimated supply elmticities for the two 

regions ranged from 0-3 do 0,47 70s own price, with 

forest industry being slightly more elastic, The 

inventory elmticities ranged from 0.41 to 8,72, with 

NIPF being more elstic, In a similar earlier study, 

Adarns (1997) estimated somewhat lower elwticities of 

0.24 and 0.13 for own price and inventsay, resgectively~ 

This model was for the entire southern region and for 

all private ownerships and was not disaggregaded, 
Daniels and Hyde (1986) applied a regional supply 

and demand model suggested by Jackson (1983) to the 

total (both softwood and hardwood) North Carolina 

forest sector, They used an indirect least-squares 

formulation to estimate their coefficients. The model 

posited supply as a log-linear function of own price 

and standing inventory, Demand was a function of 

own price and final goods price. The estimated supply 

price and inventory elasticities, 0.27 and 0.16, were 

very similar to those found by Adam (1977). Their 

demand function was virtually perfectly inelastic with 

a price response of -0.03 and final goods price 

elasticity of 0.52. 

Newman (1987) used a profit-maximization approach 

to derive timber demand and supply equations to 

model the southern pulpwood and solid-wood (lumber 

and plywood) stumpage markets. The three-stage 

least-squares estimation method, which simultaneously 

determined coefficients in both markets, was based on 

work done in Scandinavia by Brannlund and others 

(1985) and Kuuluvainen (1986). The major innovation 

of these modeling efforts was that both pulpwood and 

solid-wood production possibilities were included in 

the supply specification, allowing for the delineation of 

substitution possibilities by stumpage producers. 

Newman found solid-wood timber to be a weak 

complement to pulpwood supply as owners jointly 

produce both goods. Pulpwood stumpage was a 

substitute for solid-wood supply. 

Recent work in Sweden and Finland has a strong 

potential for use in econometric supply modeling in 

the South. Loikbnen and others (1986) combined 

survey and market data in a three-step estimation 

procedure: (I) the decision to harvest is simulated 

using all observations and a profit model; (2) the 

amount of timber expected from NIPF owners who do 

sell is estimated, using a linear model; and (3) the 

total production expected from all owners, is projected 

using results from (2) and a tobit model. The 

important value of these models is that they use 

repeated sampling of NIPF owners to gauge the 

intertemporal eEects of market and institutional 

variables on tirnber supply behavior. Similar models 

have examined positive questions such as responses to 

subsidies, ta changes, and other market and 

nonmarket occurrences in Finland and Sweden (Carlen 

and Lofgren 1986). 

Engineering production models of long-term timber 

supply were developed first for California by Vaux 

(1954) and later, more formally, by Hyde (1980). 

Long-term models of timber supply in individual 

Southern States were performed for Georgia by 

Montgomery and others (19751, for East Texas by 

Hickman and Jackson (1981), for Mississippi by 

Bullard and others (1984), and for Louisiana by 

Hotvedt and Thomas (1986). These models assume 

that landowners eBciently guide their management 

behavior in response to prices and costs, and that 

landowners maximize the present vaIue of the timber 

production on their land. 

The advantage of this method is that it considers only 

timberland that is economically productive in the 

sense of being able to produce a positive financial 

return at a given market price. As market price 

increases, new acreage is brought into production and 

acreage already in production may increase in output 

as new productivity-enhancing methods become 

feasible. 

As one would expect, these models generally show a 

highly elastic supply potential in the range of current 

prices and quantities. The elasticity indicates that 

much of the current timberland produces much less 

stumpage per acre than is technically feasible. Thus, 

in the long run, the timber production in a region 

could greatly increase with little change in price. The 

much lower short-run elasticities derived in 

econometric analyses reflect the timber supplier's 

difficulties in responding to new prices and costs, and 

the fact that timberland is often not managed at its 

technical optimum. Thus, while the results of 

normative analyses made from policy tests are useful 

for comparative purposes, welfare analysis of the 

benefits from these tests are severely limited by the 

design of the model. 

Engineering approachs and their rich supply 

specifications can be fused with econometric demand 

analysk through a linear or quadratic programming 

sector model. This modeling technique has already 

been used in agricultural and industrial sectors (Hazel1 

and Norton 1986; Takayama and Judge 1971) and on a 

Zi~ted level in the forest sector. Greber and Wisdom 

(1985) developed a static model for solid-wood 

products markets in the coastal plain of Virginia, and 

GiUess and Buongiorno (1987) applied the 

methodology to the U.S. pulp and paper industries. 

The advantage of this technique is that it allows high 

specificity of timber inventories and other technical 

inputs. In this way it is similar to the engineering 

approaches. It departs from a purely normative 
assessment by incorporating econometric demand 

models in a market-simulating objective function. 

This specificity allows the direct analysis of a wide 

variety of questions about optimal investment levels 

under varying condition for various classes of 

ownerships. Another important aspect of this 

modeling approach is that, unlike econometric models, 

it provides a framework for simulating production in 

new policy environments. Application to forestry has 

likely been hampered by the intertemporal nature of 

timber production. However, decomposition methods 

and recursive programming offer possibilities for 

coping with this problem (Duloy and Norton 1975). 

Research Directions 

Although the southern timber market has been 

modeled, important research is still needed to fully use 

these market models. One research task is to develop 

models that; explicitly assimilate the lags from policy 

implementation to supply shifts. Incorporating these 

lags is critical for developing true mewures of the 

economic efficiency of various efforts. A major 

shortcoming in attempts to measure welfare benefits of 

policies designed to shift supply is the ad hoe nature 

with which the shifts are implemented (Adams and 

others 1977; Brooks 1985; Newman 1987). More 

extensive testing of lag structures will be helpful in 

future policy analysis. 

The specification of timber supply models needs to be 

expanded beyond a simple function of price and 

inventory variables and towards a complete 

microeconomic model of supply behavior. This model 

form, while being pervasive in previous studies severely 

limits policy analysis. Binkley (1985) discusses how 

the inclusion of an inventory variable places severe 

restrictions on the size of estimated price elasticities, 

depending on the product of concern, However, it is 

theoretically clear that inventory responds to changes 

in harvest levels. This relationship suggests that 

inventory adjustments should be estimated 

simultaneously with prices and quantities in order to 

avoid simultaneity bias in estimation. 

Another possibly fruitful avenue of research is to 

expand market models to simultaneously incorporate 

the market for land. In current models the use of land 

is either inextricably tied up with the inventory 

variable, or land markets are modeled separately (Alig 

1986). In reality, land and growing stock adjust 

through different mechanisrm and land is an 

endogenous variable for timber producers, and thus 

should be directly incorporated in timber market 

analyses. These adjustments are especially important 

in the Southern United States where competing land 

uses will have an important bearing on future forestry 

production. 

Tirnber Market Structure 

Competitive market models assume several "perfects." 

The competitive market equilibrium requires perfect 

and symmetric information for sellers and buyers, 

perfect competition, and perfect compensation. When 

competition is imperfect and some parties have 

influence over price or when information is asymmetric 

between buyers and sellers, a market agent may have 

market power. If market power exists, it needs to be 

incorporated in our models of economic behavior. In 

addition, market power generally suggests an 

inefficient allocation of resources and often forms the 

basis for government intervention. Suboptimal 

investment may also arise if producers are not properly 

compensated for their outputs or are uncertain about 

future returns. These kinds of market imperfections 

are often associated with timber production. 

The most familiar counter-structure to the 

competitive market is a monopoly, in which a single 

producer can determine market price by adjusting 

output levels. The classical result of this market 

structure is that the profit-maximizing producer keeps 

outputs lower and prices higher than the comgetitive 

case. Total social welfare decreases as returns to the 

monopoly increase. Thus, monopoly is seen as an 

obvious case for market regulation. 

Between competition and monopoly is a gray area, 

where there are sufficiently few producers (or 

consumers) to shift the market away from the 

competitive solution. The mechanism for this 

departure may not be clear. Collusion among a few 

producers, as in the case of the OPEC cartel, can 

sometimes be shown but other models of price 

leadership or collusion are also plausible. In these gray 

areas it is difficult to test for market power, because 

accurate production and cost data must be obtained 

from the producers. If market power can be shown, it 

is not always clear that the prospective returns justify 

regulation. In addition, a regulatory mechanism is 

often difficult to define. 
An especially interesting market structure is created 

when the Federal Government participates in a market 

as a producer. Timber production by the USDA 

Forest Service is such a cme. While the Forest Service 

does not act as a profit-maximizer in thew markets, 

its actions can influence timber prices and the 

harvwting and investment behaviors of other owners. 

The Forest Service likely provides some sort of 

leadership in timber markets, especially in places like 

the Pacific Northwest and the Rocky Mountains. 

In southern forestry, monopsony or oligopsony may be 

more important than the oligopoly and monopoly 

cases discussed above. That is, market power likely 

rests with the purchasers of stumpage rather than its 

producers, Large timbersheds are needed to justify the 

very large capital investments in mills, especially 

paper and plywood mills. As a result, there are often 

few purchasers of raw material in an area. In this kind 

of market, price information processes need to be 

considered when modeling timber markets. 

In addition to market structure, a commonly cited 

market failure in forestry is the presence of 

externalities in production. If they exist, tbe timber 

price will generally not fully compensate the timber 

producer for the extra-market, perhaps amenity, goods 

that are derived from forests. Society benefits from 

the production of these goods, but producers will 

generally not produce optimal quantities of them 

without financial incentives. 

Risk and uncertainty in timber production are often 

cited as causes of suboptimaf investment in timber and 

a reason Lo provide government support for practices 

such as planting and site preparation (Adams and 

others 1982). While these types of incentives would be 

unnecessary in s well-functioning market with perfect 

information, analysts argue that forest owners have 

imperfect knowledge OF the relative profitability of 

forestry investments, This lack of information has led 

to suboptimal investment levels and reduced 

productivity in the southern forests, Many reaons far 

this market failure have been proposed: but the length 

of the forest inves$;rnea^t and the? Ieve'L of risk aversion 

of forest owners are felt to be primary contributors. 

Previous Research 

The importance of externalities in timber production 

has received &he most attention in the literature3 with 

most of this work completed at the stand management 

level. Wartman (19751, Calish and others (1978), 

Mguyen (19791, Berck (1981), Bowes (1983), and 

Strang (11983) all have examined the effects on optimd 

forest rotations when these nonmarket values are 

included in the rotation decision. The ultimate effects 

on. stumpage supply are uncertain, depending on the 

form of the revenue function and the tradeoEs involved 

between land, timber, and extra-market goods. 

The analysis of externalities is important at the 

regional as well as at the stand or forest level. 

Apparent regional growth declines in nonindustrial 

pine forests in the Southeast, for instance, have caused 

great concern and external market factors such as acid 

rain are being investigated as possible contributors 

(Shegeld and others 1985). Substantial modeling is 

needed to understand the regional supply effects of 

such factors and how policy inputs can modify 

potentially deleterious effec ts. 

In other regions, researchers have studied situations 

where purchmers of tider exert market poMyer. 

Monopsonistic or oligopsonist ic market structures can 

arise in forestry from the relatively high costs of 

transporting unprocessed logs and the spatially 

concentrated nature sf the wood products industries. 

The effects of monopsony on stumpage supply in 

specific markets have been analyzed by Mead (1966, 

1968) in the Pacific Northwest of the United States 

md by Johansson and Lofgren (1983: 1985) in 

Sweden. As expected, results suggest that "ick of 

competition depresses stumpage prices and reduces 

total production. The South is generally considered to 

hwe the most active tixnber markets in the country, 

but there is a great need to understand the extent to 

which market power is expressed there, 

A problem related to imperfect market structures is 

the imbalance of information betwen buyers and 

sellers of timber. Many N8PF owners are infrequent 

participants in the stumpage market and thus may be 

unfamiliar with market prices, Timber buyers, on the 

other hand, follow market conditions closely and are 

able do take advantage of perceived changes. To 

correct this perceived market failure, price reporting 

services have been provided by individual States and 

organizations, The best known service is Timber 

Mart-South (TMS), begun in 19'77. Wallace and SiBver 

(1980, 1981) assessed the quality of TMS data: and 

Boyd and Hyde (1889) msessed TMS's escieney. The 

latter study showed that ThIS has produced 

meaurable social benefits. Id has reduced price 
variation and facilitated planning by both buyers and 

sellers. 

Of special importance to researchers is the highly 

detailed listing of stumpage and delivered mill prices 

for different timber species and products. Although 

there is some debate about the quality of these data 

and their compatibility with other sources 

(Cardellichio and Binkley 1983), they offer the 

possibility for greatly improved regional supply and 

price forecrzsting. Prior to the advent of TMS, the 

only data sources were annual reports of Forest 

Service timber sales and those from some State such 

as Louisiana and Arkansw (Ulrich 1981). There is 

substantial concern that firest Service data are not 

representative of private timber sales and thus give 

biased elasticity estimates when used in econometric 

supply and demand analyses (Jackson 1987). Bias is 

especially likely in the South, where kderal timber 

sales make up such a small percentage of the total 

timber transactions. The TbiS regional prices have 

been used in the most recent Forest Service 

projections (WDA Forest Service 1988), More 

extensive testing of different price scenarios and other 

questions relating to market structure will be possible 

in the future as more data points are accumulated. 

The analysis of risk and uncertainty in investment 

decisions is generally based on the work of 'Von 

Neumann and Morgenstern (1953). This theory 

assumes that investors use expected returns from an 

investment to guide their decisions and invest in the 

projects that maximhe their expected wealth. The 

method for deriving this return involves the 

calcuIation of probabilities for t he potential outcomes 

of an investment and then calculating the mean (or 

expected) value of the return. If all outcomes are 

possible, the risk-neu tral investor should be indiEerent 

between investing in the proJeed or receiving a certain 

payoR equal to that expected return, A. risk-averse 

individual would demand a higher return than a 

risk-neutral individual might accept, The imp;2icstions 

of these resuits have been discussed in the forestry 

context by Ckrang (1930), Kao (1982, 1984), and 

Johansson and Lo-fhSren (1985) and in a generalized 

agricultural setting by Antle (1985), 

The implications of this analysis are quite important. 

In the presence of risk, it is not optimd to attempt do 

maximize output as this will Bncreia~e the level 06 

investment and thus create a greater potential loss 

(Daniels 1984). Since NIPF owners have smaller 

landholdhgs than public and industrial owners, they 

are unable to spread their risk level. For them, 

reducing forest investments is a rewonable response. 

Thus, research that sses methods to reduce NIPF 

risk may prove useful in increasing aggregate for& 

productivity. 

Timber insurance is one method to reduce the risk 

perceived by NIPF owners. Public and private 

organizations have attempted to proGde timber 

insurance program in the South aver the past 70 

years, but no program currently exists. ERorts have 

been hampered by the lack of adequate actuarial data, 

on natural hazard losses and the expen= of obtaining 

it (McAndrew 1984). As a result, owners are unable to 

insure themselves against the small probability of a 

catastrophic loss and have likely reduced their 

investment levels accordingly. 

Another method to reduce the eEects of risk is 

through portfolio diversification. Owners of real 

wets, such as tirnber, can decide on the level of risk 

that they wish to carry by the combkation of assets 

that they own (Cass and Stiglitz 1970), Thus, 

individuals who are more risk adverse can balance the 

relatively risky =set, timber, with more secure i16wts 

such as long-term bonds. A number of recent studies 

have examined the riskiness sf returns from grovving 

timber in the South using the Capital Asset Pricing 

Model (Redmond and Cubbage 1988; Thomson 1987; 

Zinkhan 1988). The riskiness of returns from forest 

investments, as measured by the variance of stumpage 

prices wa compared with those of other wets such a 

common stocks. Although there is some question as to 

whet her these =sets can be meaningfully compared, 

the: results show timber returns to be less risky than 

stocks. 

The major risks msociated with growing timber come 

from natural agents such as fire or insects, These risks 

alter management behavior in two ways, First, since 

the expected revenues from timber management are 

reduced by the potential devmtation of these agents, 

forest investments are either nod marhe or they are 

reduced. Second, rotation lengths are shortened 

because risk of loss is an increming function 06 the 

harvest age (Martell 198% Reed 8984)- The regional 

consequence of both of these effects is to Bower 

standing inventory, The eReco; on dotal productivity is 

uncertain because shortened rotations lead to higher 

growth rates. The importance of these results for 

research is that aggregate models which use inventory 

as an independent variable may misstate the actual 

timber supply eRee ts from inventory changes. 
Risks and uncertainty create substantial problems in 

modeling decisionmaking. Often the standard 

methodological assumptions needed to make problems 

tractable become untenable. For example, 

experimental evidence indicates that individuab may 

systematically violate key behavioral assumptions of 

the utility model (Machina 1987). A related question 

is the manner in which landowners perceive changes in 

their environment. Nonpermanent policy changes 

apparently have different supply effects than those 

that are perceived as permanent (Carlen and Lofgren 

1986; Lofgren 1987). Differentiating these types of 

uncertainty makes policy analyses more difficult. In 

addition, computational problems arise because it 

becomes necessary to model investment decisions over 

an extended number of time periods (Kao 1984). 

Nevertheless, stochastic modeling is an area of 

research that has received extensive attention and 

offers important possibilities for future productivity 

assess men ts . 

Research Directions 

Many questions remain regarding the influence of 

market imperfections on markets for timber. While 

these markets have been modeled as competitive cases, 

incongruous results have led researchers to cite market 

failure as reason for policy action. However, the 

finding of a market failure also invalidates the model 

upon which the original analysis is based. Where 

imperfection is suspected, its existence, cause, 

structure, and influence need to be understood. 

Rejection of the competitive case is only the first step 

towards policy actions. An understanding of the 

alternative structure is required to build policy 

instruments and to forecast their costs and benefits. 

Difficulties in regional analysis of market structures 

arise because the degree of oligopsony/monopsony 

necessarily varies over space. This poses serious 

aggregation problems for assessing the regional 

market. Research at a theoretical level is needed to 

address, in effect, the separability of market structures 

in a regional analysis. Ultimately, the question is: 

How meaninghl is a regional pricelquantity 

equilibrium or, conversely, what is the appropriate 

scale for regional market analysis? 

Studies of market structure will need to focus on the 

concentration of firms at a local level. In contrast to 

many natural resources such as copper, coal, and iron, 

which have few supply centers relative to demand 

sites, the demand for timber is local and prices are 

very low relative to unit transportation costs. 

Processing centers are necessarily close at hand, and 

there are often few within a reasonable hauling 

distance from a forest stand. 

Research is needed on the inAuence of risk and 

uncertainty on investments in timberlands. First, 

however, the linkage between these investments and 

timber supplies needs to be explicitly modeled. The 

market changes caused by these investments must 

simultaneously be incorporated into these analyses. 

Mathematical programming techniques already 

discussed can be produced to simulate a host of 

important policy-related scenarios, Issues such as tax 

and other financial and policy changes, environmental 

disturbances, land use changes, and similar concerns 

can be specifically addressed using these techniques. 

Stochastic modeling which has been applied more to 

forest growth and yield modeling than to landowner 

decisionmaking, offers good opportunities to 

investigate the manner in which landowners act upon 

new information about investment potentials. 

Stochastic models also provide a good conceptual 

framework to assess the acceptance of policy and 

technical innovations. In addition, incorporation of 

risk into landowner decision models should help to 

explain investment behavior. 

Research on methods for reducing investment risk is 

needed. Insurance as a means for reducing risk has 

been studied but was abandoned when USDA funding 

for the programs and research was withdrawn. A need 

for timber crop insurance may exist, however, to keep 

Conservation Reserve plantings from reverting to 

agricultural use. If so, research is needed into the 

many difficulties in instituting timber insurance 

programs. 

A. final promising research area deals with the 

assessment of policy innovations that attempt to 

reduce the efects of market imperfections on timber 

supply. These studies would help policymakers 

estimate program efFiciency. Research could 

characterize timber markets and measure welfare 

gains, providing important information on the costs 

and benefits of programs. A subsidy that reduces the 

costs of production and shifts supply, for example, 

should be considered a cost. The direct shift in supply 

resulting from the subsidy cannot at the same time be 
considered a benefit. Only the addition to supply that 

would not be incurred if the subsidy had not been 

made can be considered a benefit. 

Summary and Conclusions 

In this paper, we have attempted to review the 

literature and evaluate research needs in four different 

areas of microeconomic analysis of timber production. 

Research in forest economics has been prolific over the 

last 10 to 15 years, as views of timber markets and 

forestry have moved away from a biophysical paradigm 

and adopted a socioeconomic model. Still, while the 

state of knowledge has advanced, several areas remain 

for researchers. These areas are summarized below. 

The Timber Production Function and Tirnber 

Supply. This area of study, which is the basis for the 

supply side of timber markets, is relatively 

underdeveloped. Evidence rests in the simple form of 

contemporary timber supply models. Advances in 

timber supply modeling will come from basic research 

into the timber production technolou and a rigorous 

derivation of supply from these production models. 

Wood Products Technologies and Timber 

Demand. Research into the production technologies 

for wood products has been prolific in recent years. 

Further research into levels of technological and 

input /out put aggregation is needed to fully 

understand the effects of shifts in production 

technologies on timber demands. In addition, models 

which use market data at the delivered log level, and 

which therefore separate logging and processing 

sectors, will likely improve the precision of demand 

estimates. Basic research into the relationship be tween 

Federal and private timber price trends is also needed, 

not only for the demand side, but for market modeling 

in general. That is, we need to know if the commonly 

used Federal price data are an adequate index of 

private timber prices. 

Timber Market Modeling. The development of 

precise market models is essential for undertaking 

policy analysis in the timber sectors. Many of the 

suggestions made above logically apply to this area as 

well. That is, better grounded models of timber 

supply behavior, information on the reliability of 

tirnber price data, and appropriately aggregated 

structures are needed, The actual use of these models 

will determine the desired model structure. For 

example, if the model is to be used to study a new 

policy, then a programming-type market model with 

econometric demand equations and a mechanistic, 

rather than econometric, supply side may be best. 

This type of programming model, with investment 

dynamics included, has not been developed. Means of 

incorporating the detailed supply and demand 

information discussed in the previous sections into a 

market framework is another important area for future 

research. 

Timber Market Imperfections. Further research 

lies in three distinct areas: market structure, 

externalities, and risk and uncertainty. Understanding 

the effects that oligopsony and monopsony may have 

on price formation (and on the appropriate form of 

market models) is critical to conducting accurate 

policy analysis. The effects of externalities, both with 

regard to the valuation of nontirnber products from 

forest management, and the effects of pollutants on 

growth and yield, need to be studied from a regional 

timber supply perspective. Risk and uncertainty is 

inextricably tied up with the investment calculus of 

individual landowners and will have an extremely 

important bearing on future timber supply from the 

South. 
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the study of timber markets and timber supply. Gen. Tech. Rep. I the study of timber markets and timber supply. Cen. Tech. Rep. I 

I SE-62. Asheville, NC: U .S. Department of Agriculture, Forest 1 Sl3-62. Asheville, NC: U.S. Department of Agriculture, Forest I 

1 Service, Southeastern Forest Experiment Station. 20 pp. I Service, Southeastern Forest Experiment Station. 20 pp. 1 

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I Research on forest production and timber markets is reviewed and direc- I Research on forest production and timber markets is reviewed and directions for future research are suggested. I tions for future research are suggested. 

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KEYWORDS: Forest production technology, timber markets, market struc- I KEYWORDS: Forest production technology, timber markets, market structure. I ture. 

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