This file was created by scanning the printed publication. Text errors identified 

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Haynes, Richard W. 1993. Forestry sector analysis for developing countries: issues

and methods. Gen. Tech. Rep. PNW-GTR-314. Portland, OR: U.S. Department

of Agriculture, Forest Service, Pacific Northwest Research Station. 47 p.

A satellite meeting of the 10th Forestry World Congress focused on the methods

used for forest sector analysis and their applications in both developed and

developing countries. The results of that meeting are summarized, and a general

approach for forest sector modeling is proposed. The approach includes models

derived from the existing literature and can be used as a structure for applying

forest sector analysis in developing countries.

Keywords' Supply-demand relations, forest sector modeling, developing countries

(forestry), Third World.

Abstract
1 Introduction

2 Evolution of Forest Sector Models

2 Evolution of Model Form

5 Model Taxonomy

6 Some Examples of Forest Sector Models

17 Forest Sector Analysis in Developing Countries

22 Impact of Strategic Planning

24 Applications of TAMM and the CGTM to Developing Countries

26 Summary

26 General Approach to Forest Sector Modeling

27 A Philosophy to Guide Model Building

29 General Model Attributes

37 Nontimber Considerations in Sector Work

39 In Closing

40 Acknowledgments

40 Literature Cited

47 Appendix 1

47 Example Gap and Equilibrium Models

Contents
Introduction A satellite meeting of the 10th Forestry World Congress (held in Paris. France,

September 1991) focused on the methods used for forest sector analysis and

their applications in both developed and developing countries. This meeting was

motivated by the growing recognition among government and nongovernment

organizations of the need for more systematic and complete analysis of the forest

sector. Where once viewed in extremely local terms, long-term forest land stewardship and economic development issues increasingly involve entire national forest

sectors. Proposed changes in one aspect of the forest sector, such as a new

processing facility or changes in land management or tenure practices, have effects

that ripple through the entire forest sector and may impact other economic sectors.

(By forest sector, we mean the consideration of all aspects of forestry from the basic

land tenure issues, to harvesting, production, and consumption decisions. Such a

view is no different than similar views of various agricultural and industrial sectors

in an economy.) Often the indirect impacts are the most enduring, outlasting the

immediate and direct impacts of a specific project.

Much of the impetus for this work comes from observing the disparity of forest

analysis and planning methods and applications among countries. Countries in

North America and Europe have made much progress in analyzing and managing

their forest resources, but countries elsewhere have neither benefitted from these

analytical advances nor advanced very far in understanding their forest sectors. Until

recently, forest sector analysis in the developing world rarely has involved applying a

model, and this usually has been limited to an inventory exercise. There is need for

improvement, however, as international concern for forest resources is rising, and

expanding populations and growing economies are increasing the demand on the

natural resource base. For these reasons, government and nongovernment agencies

are directing their attention to applying forest sector models to developing countries.

This change in attention will show how the forest sector in developing countries differs

from that in developed countries. Principle differences are the size and structure of

the macroeconomy within which the forest sector exists. For forestry and especially

forest management, the agriculture sector and the nature of agriculture production

are especially important. Unlike developed countries, agricultural production occupies

the majority of the rapidly growing population in the Third World. Because opportunities are limited for yield increases through intensive management, demand for

agricultural production often is satisfied with extensive cultivation of marginal areas,

especially in the tropics. This results in conversion of forest land to agricultural

production. Demand for fuelwood, poorly defined property rights, and extreme

poverty combine to put further pressure on forests, and natural resource degradation

usually is the result.

A complicating factor in forest sector analysis in developing countries is the lack

of data. This seriously limits applying some of the more complex models at the

same time that demand for comprehensive analyses of the forest sector is rising. In

developed and developing countries, increasing recognition of tropical forests as

both economic and ecological resources is amplifying calls for greater recognition

1 The proceedings from the 10th Forestry World Congress

(Haynes and others 1992) included an array of efforts tracing

the development and application of a broad range ^f forest

sector models for both developed and developing countries

and dealing with a wide range of issues

1
Evolution of Forest

Sector Models

Evolution of Model Form

of nontimber products and ecological services. To be widely applicable in the

developing world, forest sector models must be able to tackle these complex issues

while accommodating constraints presented by scarce data and limited resources.

In this paper, I first summarize the results from the September 1991 conference

(Haynes and others 1992) and propose a general approach for forest sector modeling. The general models that I propose are derived from the existing literature

(most of it summarized in the various papers presented at the World Forestry

Congress) and can be used as a structure for applying forest sector analysis in

developing countries. It is important to remember that informal forest sector analysis

methods have long been part of the tools available to foresters. In the next section,

I've traced the evolution of forest sector models and used the experience of the

United States as an example. In the third section. I've proposed two broad classifications for forest sector models and illustrated how they can be applied in

various situations.

This section describes the evolution of the form and use of forest sector models. For

the use of the models, I am concerned with changes both in how models are used

in policy analysis and their applicability to issues in developing countries.

The theoretical and methodological roots of forest sector models evolved from

basic economic issues explored in the 1950s and early 1960s by Samuelson (1952),

Tramel and Seale (1959), Takayama and Judge (1964, 1970), and later by McCarl

and Spreen (1980). The term "forest sector" first was used by Panders and others

(1978) to describe their model of Scandinavian forestry. In the early 1980s, the term

"forest sector model" came into general use largely through the Forest Sector Project

at the International Institute for Applied Systems Analysis (IIASA). This project

developed a global trade for model forest products (Kallio and others 1987). The

IIASA project stimulated research in a number of countries and generally popularized

the version of forest sector modeling, pioneered in the United States by Adams and

Haynes (1980), that relied heavily on economic representations and market solution

algorithms.

Forest sector models2

 are characterized by a systems view of the biological and

social processes in forestry. The importance of this systems view is that it can be

used to explore the biological development of forest resources and the attendant

dynamics of the forest industry over time and under alternative futures and management strategies. A basic premise is that various relations describing (or mimicking)

biological and social (predominantly economic) processes can be specified.

These models have been used to organize various views of the forest sector and

to derive useful information for improving and expanding the perceptions held by

land managers, the public, and government and nongovernment agencies. In some

countries, such as the United States, forest sector models have been used to assess

present and future resource conditions, demands for forest products, and in the last

several decades, prospective price trends. In this role, models have helped guide

land stewardship decisions.

2

 The use of the term "model" here means the development

of abstract representations It takes on a semimathematical

definition only when the discussion turns to model specification and solution

2
In the evolution of forest sector models in the United States, for example, the earliest

of the assessments3

 focused almost exclusively on resource conditions arid were

used to argue for conservation and management of timberland resources. There was

little analytical framework in these early studies, as most were descriptive discussions of subjectively drawn trends in resource conditions and use. After World War II,

harvest levels in the United States began to increase, thereby stimulating and requiring more detailed planning frameworks. More recent assessments have depended on

analytical frameworks incorporating supply-demand representations; price and

quantity determination in both stumpage4

 and product markets; and projection of

timber inventories by region, owner, and timber type.

The evolution of forest sector models from simple Malthusian concepts of catastrophic scarcity to integrated planning models was guided by (and responding to)

a changing policy context. Early policy context argued for conservation and wise

use of renewable resources. Currently, a shift is occurring in various national and

international economic policies away from the progressive vision5

 of government

objectively pursuing the public interest6 to a vision of government economic policies

driven by continual competition among interest groups. This competition has increased since the mid-1980s, evolving from competition among commodity-oriented

interest groups to competition among commodity, noncommodity, and environmental

groups at both local and national levels. Forest policy goals derived from the

progressive movement were to reform forest practices and make them serve the

public interest. Modern forest policy goals involve sustaining the flows of goods and

services from forest lands that meets national (and local) needs. Each policy goal

requires a different analytical paradigm.

3

 Analysis of the demand-supply situation for timber has a

history in the United States dating to 1876 The complete

bibliography of these assessments is given in appendix C of

Haynes(1990)

4

 "Stumpage market" is a term used for those wood markets

where the timber is sold by standing volumes In such

markets, the purchasers bear the cost (and associated risks)

of harvesting the wood An alternative form used in some

areas of Europe is a log market where timber is sold after

harvest but before removal from the forest A third variation,

used in parts of Canada, is a market based on delivered

wood costs These are just different ways of pricing a factor

of production

5

 The progressive movement, at the end of the 19th century

in the United States, sought to introduce scientific methods

and techniques to the solution of public issues

6

 One common manifestation of the public interest has been

the intent of many natural resource management actions to

contribute "to the greatest good for the greatest number"

Nelson (1987) describes this shift in government economic

policies for the United States

3
The experience of the United States illustrates how the changing policy context

influenced the development of forest sector models. The experience also illustrates

how methodological developments contributed to and facilitated the evolution of

forest sector models. The experience in the United States also owes much to the

development of spatial equilibrium commodity models used in the agriculture sector.

The attributes of these latter models are described by Bawden (1966) who also

describes their potential applications to international trade questions. Agricultural

commodity models in the United States include supply and demand representations,

spatial treatment of production and consumption, factor and product market distinctions, and in some cases, well-defined policy variables. Their evolution and especially

their use of spatial market mechanisms are described by Thompson (1981).

The chief advocate for forest sector models in the United States has been the USDA

Forest Service. The Forest Service is the government agency responsible for managing the National Forest System, which has evolved in the 20th century to include

77.2 million hectares of forest and grass lands accounting for roughly one-fourth of

all U.S. forests. Starting in the 1960s, the Forest Service began developing formal

planning frameworks. The basic approach was a trend analysis of demands for forest

products and availability of timber resources (USDA Forest Service 1965, 1974,

1982). Policies and emerging trends were discussed in the context of the gap

between the trajectories of the demands for forest products (expressed as roundwood equivalents) and the prospective availability of timber. In time, this approach

became know as a gap analysis (or the gap model) and was applied in the United

States as the primary forest sector model until the late 1970s. During the 1960s and

1970s when it was commonly used. gap analysis evolved to include price trends.

Much of the evolution of forest sector models beyond gap models was stimulated by

changes in policy issues away from describing policy needs by quantity shortfalls

(that were never observable with real world data) to identifying policy needs by price

impacts. This shift in the United States occurred in the mid-1970s during a period of

rapid changes in commodity prices and concern about the effectiveness of various

policies to alter selected price trajectories. The implication of this shift for model

builders was that future models needed to be built around supply and demand

representations directly including price as an independent variable and that

model-solution processes needed to seek the price and quantity equilibrium. This

class of model has become known as equilibrium models.

Starting in 1977, the Forest Service undertook the development of a forest sector

model designed to explicitly explain regional stumpage price behavior. This model

was built around a spatial equilibrium algorithm.7

 The initial purpose was to develop

7

 The emphasis on regional markets meant that the model

had to deal with the spatial juxtaposition of stumpage

markets Samuelson (1952) generally is credited with

defining the problem, but its solution until the last decade

has always involved complex computational methods

Previous experience with linear programming models both

in agriculture and forestry led model builders in the United

States to select a computationally simpler algorithm (a

reactive programming algorithm developed by King and

Ho [1972]) to solve for spatial equilibrium

4
Model Taxonomy

p regional harvest and price trends as required by the Resources Planning Act (RPA).

The resulting model called TAMM (described in a later section) exceeded all expectations (Adams and Haynes 1980, Haynes and Adams 1985). It produced regional

price trends in both product and factor (stumpage) markets that recognized simultaneous market interactions and differences in regional timber resources. This model

built on early econometric studies (such as those by Adams 1974, McKillop 1967,

Mills and Manthy 1974, and Robinson 1974) and linear programming efforts that

attempted to solve spatial market concerns (for example, Haynes and others 1978,

Holley 1970, Holley and others 1975.)

The use of forest sector models continues to evolve in the United States and elsewhere. This evolution is guided by advances in our understanding of market mechanisms (behavioral models of production, consumption, and capacity adjustment),

solution algorithms, timber inventory projection systems, and the role of forest sector

models in policy formulation. Research on forest sector models can require a longterm commitment. In Sweden, for example, a 7-year project to develop a model of

the forest sector is just beginning to produce results (Lonner 1991). This Swedish

work generally follows the basic economic theory in that it seeks long-term equilibria

between demand and supply both in the forest industry product market and in the

wood raw material market.

During the past two decades, several types of forest sector models have evolved.

The two most popular are the gap and equilibrium models, which include a wide

array of contemporary models that have been applied to various forest sector

problems and issues. The primary distinguishing feature is the role of prices.

It should not be assumed that one class of model is necessarily better than another.

Even though equilibrium models evolved, in part, from the inability of gap models to

address specific policy questions, both classes of models appeal to different audiences of policy makers. The gap model has much intuitive appeal because policymakers can gauge the amount of timber needed as the goal of a policy action (this

assumes that the most common policy action would be "to fill the gap"). The equilibrium model appeals to policy makers interested in price changes and estimates of

consumer or producer impacts. Within the Forest Service, all policy discussions in

the last decade have been based on the equilibrium models, but decisionmakers

still find it useful, at times, to frame a policy discussion around the notion of some

quantity (that is, the level of the timber sale program) associated with a particular

issue.

8

 The Forest and Rangeland Renewable Resources Planning

Act (RPA) of 1974 as amended by the National Forest Management Act of 1976 directs the Secretary of Agriculture to

prepare a renewable resource assessment The purpose of

this assessment is to analyze the timber resource situation to

provide indications of the future cost and availability of timber

products to meet the Nation's demands. The analysis also

identifies developing resource situations that may be judged

desirable to change, and it identifies developing opportunities

that may stimulate both private and public investments

5
Both model types are based on discrete time intervals. A third type of model is based

on continuous time relations. Such models use system-dynamics principles (Forrester

1961) One such model is the SOS model of the Scandinavian forest sector (Panders

and others 1978). This model viewed the Scandinavian forest sector as a set of cause

and effect relations. Another model is the TSM model developed by Sedjo and Lyon

(1990) and applied to international forest resource questions. These models have

had only a limited impact on the design of forest sector models. The majority of forest

sector models have been derived from various economic models and consequently

have shared the economists view of market processes.

Some Examples of In this section, I review some example modeling efforts that illustrate some of the

Forest Sector Models points made above. These examples include descriptive types of approaches and

gap and equilibrium types of models,

Descriptive studies—Beyond the relatively narrow modeling literature, there is a

rich body of studies dealing with forest sector issues descriptively. Many of these

studies have been done by government and nongovernment organizations, in some

cases as part of various developmental assistance efforts. For example, the Food

and Agriculture Organization (FAO) of the United Nations, demonstrates at least

two approaches in its analytical efforts: the first is illustrated by studies reporting

on country projects that have planning or policy content; the second approach is

illustrated by projects undertaken as part of the tropical forestry action program

(TFAP).

The FAO has about 300 currently active forestry projects;9

 about 10 percent are

concerned mainly with forest development planning and about 2 percent with supply

analysis. Many more. however, may have a policy or planning component and involve various types of modeling inputs. Some projects have a significant quantitative

planning content. A number of FAO references are documented by the FAO (1991).

There are two general types of FAO documents. First, there are a number of publications dealing with the collection and dissemination of statistics; examples are the

Yearbook of Forest Product Statistics, Forest Product Prices, and periodic outlook

studies. The second group of publications describes the outlook for various national

forest sectors. The objective of the latter studies is to provide an international capability to prepare perspectives on future development of the forest sector as a framework for policy formation and decisionmaking as required by national governments

and nongovernment organizations. This perspective addresses such issues as:

1. Development of the demand for forest products.

2. Supply potential of forests, including consideration of competition for land

resources, and the supply potential of forest industry.

3. Development of trade.

9

 Wardle, Phillip Correspondence dated 5 March 1992

Senior Forestry Economist, FAO

6
4. Implications for investment in forestry and industry.

5. Implications for rural communities, people, and employment.

6. Implications for the environment.

The broad concept behind the FAO forest sector studies is similar to the economic

theory-based approaches used elsewhere. Such studies deal with estimating demand

and supply of forest products and wood raw materials through an analysis of historic

relations among consumption, production, and economic indicators. To the extent

possible, an equilibrium is sought between demand and supply. Finally, such studies

try to assess the implications of the development path for major variables under

given policy issues of interest.

The experience of the European timber trends and prospects studies—Since

1952. a series of studies on the outlook for the forest and forest products sector in

Europe has been prepared and published under the auspices of two intergovernmental bodies: the Timber Committee of the United Nations, Economic Commission

for Europe (UN/ECE), and the FAO European Forestry Commission (United Nations

1953. 1964, 1976, 1986). Preparations are in hand for the fifth of these studies,

known collectively as the European timber trends studies (ETTS), to be published

in the mid-1990s.

The ETTS studies have analyzed the outlook for supply of and demand for forest

products and the balance between the two. They are intended for use primarily by

national governments in policymaking for the forest sector, but they also have been

found useful by academic researchers arid by private trade and industry analysts.

The various ETTS studies are among some of the best contemporary examples of

gap types of forest sector models.

From ETTS I to ETTS IV—The situation facing the authors of ETTS I in the early

1950s was truly challenging: a climate of uncertainty existed over general trends of

the economy as a whole, and there were significant political problems (start of the

cold war). Problems facing analysts of the forest sector included comparable uncertainty about the future and a weak database; only four countries had a forest

inventory more recent than the early 1930s

Egon Glesinger, the first director of the ECE/FAO Timber Division, led a dynamic

team that was able (with modest resources) to assemble a database and mobilize

sufficient expert knowledge of forest resources, forest products markets, and end

uses of products. The outlook period was to 1960; although most of the work was

carried out by the core team, they had access to national data and expertise. This

group reached the conclusion that demand for forest products in Europe would

expand strongly in the 1950s; it was argued that both supplies from the USSR and

a "dynamic forest policy" for Europe were necessary to satisfy the expected demand.

In spite of how reasonable they may seem to us now, these conclusions and recommendations were controversial at the time.

7
All the ETTS studies, from ETTS I to ETTS IV, have been built on the same basic

structure. They begin with separate analyses of the outlook for demand for forest

products and of the likely supply of timber from European forests. Forecasts of

demand for products are converted to raw material requirements (also called raw

material equivalents), thereby taking into account production efficiency. A comparison

of the supply and demand balance for individual countries also takes into account,

to the extent possible, factors such as international trade and recycling. The ETTS

studies always have been complete in their coverage of European countries and of

all timber-based products. Although each study has drawn policy conclusions, these

conclusions have not been particularly specific or controversial since ETTS I. The

value of the ETTS studies has been seen by participants and users alike as providing a broad analytical framework and a compilation of data, within which individual

countries or industries could address their own specific problems.

Over the years, the ETTS database has improved significantly, thanks in part to

the efforts of the body now known as the Joint FAO/ECE Working Party on Forest

Economics and Statistics. Time series data describing timber harvest and production

and trade in forest products have increased in quality and length. Forest resource

data are more consistent and comparable across countries. Improved data have not

led to significant changes in the basic methodological approach, however. Beyond

the use of econometric methods for demand analysis, the basic gap framework

remains much the same. The changes in the methods used for demand forecasts,

starting in the fourth ETTS study (United Nations 1986), were the result of efforts to

achieve consistency with those done by the FAO (1988).

The broad structure of ETTS IV (United Nations 1986) had the following elements:

Quantitative forecasts to 2020, by country, for European forest area, growing

stock, growth, and removals from forests, based on data from national

correspondents.

Econometric projections of consumption of products, by country, with

presentation and analysis of recent trends in end uses and prices.

A description of the situation of the European forest industries in the mid-1980s,

with estimates of future trends in recycling of waste paper and the use of

residues in manufacturing.

A description of trends in interregional trade and an analysis of the capacity

of exporting regions to expand their supply to Europe.

Quantitative forecasts of wood used for energy, developed from a survey of

national correspondents.

These elements are combined in an analytical framework comparing physical

forecasts of roundwood supply and forest products demand, after making specific

assumptions about recycling, trade, and conversion efficiencies (in production). The

result is a set of "gaps" that simply indicate that some or all of the fixed assumptions

underlying the forecasts are unlikely to be realized. These prospective gaps (that will

never actually materialize, because markets adjust continuously) may trigger adjustments in policies or in the decisions of producers or consumers. Unfortunately, this

section was widely misunderstood, with many seeing the gaps as forecasts.

8
One of the few serious criticisms of ETTS IV was that internal consistency in economic terms was lacking because there was no analysis of industrial supply comparable

to the analysis of consumption. Although price is an explicit component of consumption forecasts, it is not an element of roundwood supply forecasts. In addition, the

response of producers (those buying roundwood and selling the products consumed)

is not considered in the analysis. To address this problem, efforts are underway to

develop product supply forecasts that will establish a basis for applying consistent

assumptions about the economic environment and would allow net trade to be a

conclusion rather than an assumption of the study.

A variant of the gap model—Another variant of the gap model is illustrated by a

study done to assess the demand for National Forest timber in Alaska (Haynes and

Brooks 1990).10 As background, the State of Alaska has many characteristics of a

developing economy. The state is highly dependent on resource extraction and

primary industries (forestry, fisheries, oil, and gas development), and natural resource

wealth is seen as the primary engine of economic development. In addition, most

production is sold to external markets. Managers of public forest resources are

challenged in Alaska to ensure that the mix of products satisfies public needs and

that costs are balanced by present or future returns.

The model used is a simple, spreadsheet-based one that estimates the derived

demand for Alaska National Forest timber in three stages: (1) estimates, by product,

of Alaska forest products output; (2) the raw material requirements necessary to

support this production (calculated by using explicit product recovery and conversion

factors); and (3) harvest by type of landowner computed by subtracting projections of

timber harvest by private owners and harvest by non-National Forest public owners

from the total raw material requirements (after adjustments for log and chip imports).

It is based on the following aggregate relation for stumpage demand (d):

d=k 1s1k1 p1+LE, (1)

where

kland kp are product recovery factors for lumber and pulp,

sl is lumber output,

p'p is pulp output, and

LE is log exports.

The result of this model was an estimate of the volume of National Forest timber

necessary to balance demand, given the explicit assumptions about markets (see

fig. 1) and the implicit assumptions about prices.

10 This analysis of the timber situation in Alaska was done in

support of the Tongass National Forest land management

planning process

9
Figure 1—The Alaska forest sector, logs and lumber in million board feet, pulp and wood chips

in thousand tons (A) Forest products, (B) timber harvest by product, (C) timber harvest by owner

This model satisfies economic principles in that production equals consumption

(with adjustments for trade), and it satisfies material-accounting principles in that

raw material requirements (derived demand) consider all products and conversion

efficiency. Like most forest sector models, this approach assumes that demand for

products is the primary determinant of the quantity and type of products produced.

Because little of Alaska production is consumed in the State, expected developments

in Japanese markets underlie projections for Alaska. Alaska's share of offshore

markets is projected, and production is assumed to be equal to this quantity.

Similarly, supply is assumed to be equal to the derived demand for raw material in

raw material markets. Given assumptions (exogenous projections) of supply from

other owners, the derived demand for National Forest timber can be computed.

10
The model was based on limited historical data describing relevant components of

the Alaska forest sector. Possible future timber harvests were computed by using an

analysis of trends in factors that determine harvests. The historical period 1965 to

1988 was used as the basis for a projection of nearly comparable length. This effort

to balance hindsight and foresight was used to avoid undue emphasis on the shortterm cycles—both boom and bust—that the Alaska market is subject to. Sharp

cycles, triggered by changes in demand, are characteristic of the forest sector. Data

for the historical period also were used to confirm the estimates of conversion factors

and to validate the methodology for computing derived demand.

The method used to make these projections did not compute equilibrium values for

prices and quantities; in fact, no explicit reference is made to prices. This was an

elaborate trend-based projection of quantities, in which trends in consumption or

trends in exports were the driving assumptions. Alaska markets (or any other) will

adjust, through changes in consumption and supply by private owners, to any level

of supply by public owners. Prices are the mechanism by which the need for these

adjustments is indicated. Because planned timber harvests from public lands

generally do not depend on prices, but are dependent on policies and multiple

objectives, the quantity of National Forest timber necessary to balance the market

was computed as a residual.

Analysts in developing countries will perhaps find this the most useful example of

a simple model, designed to answer straightforward questions. The model was

developed from a set of data fundamental to any analysis of a forest sector

(quantities produced and traded), and the data were examined in a now widely

available format: a microcomputer spreadsheet. It is also an example of a model

that easily can be made more elaborate. Projections of consumption and production,

for example, can be made to depend on prices and costs, rather than on the simple

trends in market shares that were used. These elaborations can be made while the

basic framework of the analysis is maintained.

The timber assessment market model—In the 1970s, the Forest Service developed

a forest sector model for the behavior of regional prices, consumption, and production

in both stumpage and product markets. This is the timber assessment model (TAMM,

Adams and Haynes 1980), which has been used for the past decade to provide forecasts of market activity and to explore the need for and consequences of various

policy actions

TAMM provides an integrated structure for considering the behavior of regional

prices, consumption, and production in both stumpage and product markets. It is

designed to provide long-term projections of price, consumption, and production

trends and to simulate the effects of alternative forest policies and programs. To

an extent far greater than was possible in the past, the use of TAMM has focused

attention on the dependence of projections on (exogenous) input assumptions. In

Forest Service assessments, these assumptions include the major determinants of

the supply and the demand for various forest products. This section provides a brief

description of how TAMM operates. Details about the various input assumptions used

in TAMM are described in Haynes (199C).

11
Figure 2—General structure of TAMM/ATLAS model

The general structure of TAMM is shown in figure 2. It is composed of 10 major

parts with exogenous inputs from four other models (pulp, trade, fuelwood, and area

change). Briefly, product demand, such as softwood lumber, is obtained by multiplying the ratio of product use per unit of activity (such as the number of housing

starts) and the number of units and summing these results over all various end uses

for the product. The activity measures are exogenous and generally are taken from

long-term macroeconomic forecasts. Unlike supply relations, which are compiled

regionally, demand functions are compiled nationally. An example demand function

(ignoring the time subscript) is given by the expression,

(2)

where

Qi is the consumption for product i,

Fi,u represent end use (u) factors as a function of their several arguments,

Pi the price of product i,

Psi.u is a vector of prices of substitute materials in end use u,

Su is a vector of other shifters, and

Zu is a measure of activity in end use u.

12
The demand relations are regionalized by accounting for differences in regional per

capita consumption and the ratio of regional prices to national prices. The product

supply equations (ignoring the regional subscript) are estimated in the form,

Qs = F[(P - C), K] , (3)

where

Qs is the supply volume,

P is the product price,

C is the average variable cost, and

K is the installed capacity.

Average variable costs are composed of wood and nonwood elements converted to

a unit output basis by means of product recovery and productivity factors reflecting

the current (or projected) conversion efficiency of the industry. Each product supply

function includes installed capacity as a proxy for capital stock. Shifts in installed

capacity are modeled as a function of anticipated changes in relative regional profitability or rate of return. Timber supply is modeled as a function of stumpage price

and timber inventory. Finally, timber demand functions are derived from product

supply functions by means of product recovery factors. Pulp fiber requirements

are derived from a pulp model developed by Ince (in press). Trade and fuelwood

projections (except softwood lumber imports from Canada) also are developed

from analysis external to the TAMM system.11

The remaining steps in the annual cycle of TAMM involve timber supply computations

and an inventory projection system (ATLAS; Mills and Kincaid 1992). ATLAS is an

age-based, yield table system that projects acres by detailed strata for periods consistent with the inventory stand-age classes. In ATLAS, the inventory is represented

by acreage cells classified by region, ownership, management type, management

intensity, and age class. Major assumptions include yield functions, projections of

timberland area change and assumptions linking harvest to removals from inventory.

The basic economic representation of timber supply describes the supply of timber at

any point in time as being a function of the private timber inventory levels, stumpage

prices, and the amount of public harvest available at that time.

11 See the 1989 RPA Timber Assessment for further details

(Haynes 1990).

12 The aggregate timberland assessment system was

developed by Mills and Kincaid (1992) It considers only the

timber inventory located on timberland producing or capable

of producing crops of industrial wood and not withdrawn from

timber utilization by statute or administrative regulation.

Currently, areas qualifying as timberland have the capability

of producing in excess of 20 cubic feet per acre per year of

industrial wood in natural stands and may be inaccessible or

inoperable, or both Further, these models include only live

trees of commercial species meeting specified standards of

quality or vigor Cull trees are excluded When associated

with volume, these growing stock inventories include only

trees 5 0 inches in diameter at breast height and larger

13
Figure 3—The CINTRAFOR global trade model

The solution of TAMM simulates a spatial equilibrium in the markets modeled for

each year of the projection period. These solutions do not represent optimal decisions, and the basic market solution algorithm13 cannot be readily used to find

intertemporal production or consumption strategies that are in some sense optimal.

The production, consumption, and price time paths are only estimates of outcomes

of contemporaneous interactions in freely competitive markets.

13 A revised reactive programming algorithm is used (Brooks

and Kincaid 1987) Briefly, reactive programming is a method

for solving continuous demand and supply function by

successive adjustment of quantities produced and their

distribution to demand regions to maximize producer profits

net of transport costs in each supply region

14
The CINTRAFOR global trade model—The CINTRAFOR global trade model74

(CGTM) is the result of 8 years of research at the International Institute of Applied

Systems Analysis (IIASA) and continuing work at the University of Washington. The

model projects production, consumption, prices, and trade for 10 forest products in

43 (supply and demand) regions that the world has been artificially divided into.

Figure 3 describes the various components of the model. When given the demand

and supply for each region, bilateral trade flows among regions, and transportation

costs, the model solves for an equilibrium price, production, consumption, and trade

flow. Equilibrium results for a base year are used to find equilibrium solutions for

subsequent years by considering changes in demand, production, and trade levels

These changes are implemented through submodels for timber growth, production

capacity, and consumption. A detailed description of the model is presented in Kallio

and others (1987) and Cardellichio and others (1988, 1989).

The CGTM utilizes the mathematical programming approach suggested by

Samuelson (1952) and incorporated by Kallio and others (1987) in the IIASA forest

sector model. A partial equilibrium solution is found by summing consumer plus

producer surplus minus transportation costs. Constraints working on the model are

(1) materials balance—in each region for each commodity, consumption equals

production minus net exports; and (2) production capacity—production levels lie

within the industrial capacity of each region.

Ten products are considered in the model: coniferous and nonconiferous saw logs,

coniferous and nonconiferous pulpwood, coniferous and nonconiferous sawnwood,

coniferous and nonconiferous plywood, reconstituted panels, and wood pulp. The last

two products are inputs to the model.

The regional breakdown in the model is the most complete for a global forest sector

model. There are 33 final product demand regions around the globe. Many regions

have estimated demand functions for sawn wood and plywood. Final product demand

is specified in constant-elasticity form by using one of the following equations:

Q/Z = a*Pb or (4)

Q = a*Pb*Zd , (5)

where

Q is the product consumption (million m3

 of product),

Z is an exogenous indicator of market activity (for example, GDP or housing starts),

P is the product price (real local currency per m3

 of product), and

a, b, and d are estimated parameters.

14 This section was written by John Perez-Garcia at

CINTRAFOR, University of Washington, Seattle

15
The final product supply specification is:

P = C + a*Ub, (6)

C = (p + HD)*R1 + MVMC - PcH*R2 . and (7)

U = Qs/K-1 , (8)

where

P is the product price (real value per m3

 of product),

C is variable production cost (real value per m3

 of product),

U is capacity utilization,

a and b are estimated parameters,

p is stumpage cost (real value per m3

 of log),

HD is log harvest and delivery cost (real value per m3

 of log),

R1 is an input-output coefficient (m3

 of log used per m3 of product),

MVMC is minimum variable manufacturing cost (real value per m3

 of product),

PCH is the price of wood chips (real value per m3

 of chips),

R2 is an input-output coefficient (m3

 of product),

Qs is product output (million m3

 of product), and

K-1 is production capacity at the end of the previous year (million m3

 of product).

As in most forest sector models, the supply specification assumes fixed proportions:

a unit of output requires fixed proportions of inputs, that is, inputs, (labor, capital, and

raw material such as timber) are consumed in fixed proportions to each other and to

product output

In the CGTM, changes in production capacity are made on the basis of historical

profitability. A decision rule is employed to handle capacity expansion and contraction: if capacity is less than optimal, it expands, otherwise it contracts. The optimal

capacity level is determined by defining a target capacity utilization.

Log cost is defined as the sum of two components: (1) the cost of stumpage or

standing timber, and (2) the cost of harvesting the timber and delivering it to a mill.

Not all regions have both the stumpage and the harvest and delivery cost structure.

The decision on whether to model stumpage or harvest and delivery prices separately

depends on their shares of delivered log prices, the availability of data, and the

success of estimation. Stumpage supply is modeled as,

p = a(qs/l)b

 or (9)

P/Pbar = a(qs/V)b

 , (10)

where

p is the stumpage price (real value per m3

 of wood),

pbar is a 3-year moving average of past stumpage sales prices,

qs is the stumpage supply (million m3

 of wood),

16
I is the growing stock volume (million m3

 of wood),

a and b are estimated parameters, and

V is uncut volume in public forests.

Changes in inventory levels are assumed to result in a one-to-one change in

stumpage supply (the inventory elasticity is equal to one), as expressed in the

stumpage supply equation.

The timber supply relations are shifted from period to period by using a growth-drain

identity to update inventory:

It+ 1 = It+ Gt- Ht , (11)

where

G is timber growth (million m3 of wood), and

H is timber harvest (million m3

 of wood)

These dynamic elements of CGTM allow model solutions to be linked among time

periods but do not imply an optimal Intel-temporal market equilibrium solution. The

dynamic structure in the model captures many of the adjustments that would be

expected in a more complete presentation of the forest sector.

Forest Sector Analysis in Forest sector analysis in the Third World has a short history and has been largely

Developing Countries15 descriptive. Perceived failures of forest-based industrial projects coupled with rapid

deforestation in developing countries (most of which are in the tropics) has led to

increased concern about the global environment and the ability of the Third World to

sustain its explosive population in the near future. These concerns in turn have led

to a burst of theoretical discussions on tropical forestry issues, which resulted in a

global campaign in the 1980s. The campaign since has evolved into the Tropical

Forestry Action Plan (TFAP). This plan was engineered mainly by FAO, the World

Bank, and the World Resource Institute; it is the most ambitious environmental action

plan ever launched on a global scale (Winterbottom 1990).

Despite these widespread concerns about deforestation and failures of forest-based

industrial projects in the Third World, few formal analytical studies of the forest sector

are available. Furthermore, the few formal studies that exist have been conducted

under questionable assumptions and severe data restrictions.

The objective of this section is twofold: first, to review the recent literature on forest

sector analysis in developing countries, and second, to evaluate these studies based

on their contributions to the objectives of forest sector analysis. The review focuses

on published works because unpublished studies are not readily available.

15 This section was condensed from a background paper

written by Atse M Yapi, a graduate student in agricultural

economics at Oregon State University, Corvallis, OR

97331-3601 On file with. Social and Economics Value

Program, Forestry Sciences Laboratory, Pacific Northwest

Research Station, P 0 Box 3890, Portland, OR 97208-3890

17
Gap models—Gap models typically attempt to quantify the gap between the potential

demand for and supply of different forest products over time at current price levels.

Aggregate consumption and production at either the national or regional level often

are considered in place of demand and supply per se. And for a given price level, a

gap between consumption and production is forecast. Next, policies and programs

are proposed to fill the gap.

The key advantage of gap modeling is its great flexibility in accommodating different

levels of data availability. It is perhaps this feature that has made the gap approach

the most popular method in developing countries where trained professionals and

statistics on forest sector are very limited (Harou 1992).

The gap modeling approach has been very popular as an analytical framework in

analyzing forest sectors in developing countries. To stress the popularity of the

framework, Constantino (1992) writes, "As a matter of fact, even if a higher degree

of economic sophistication is sought in the analytical exercise, it is most useful to

always go through the gap stage and then proceed from there." Examples of gap

models abound in forest sector reviews either conducted or cosponsored by FAO,

the World Bank, and national governments.

Enabor and Umeh (1992) present three gap-type studies of the forest sector of

Nigeria conducted by FAO in 1979, the World Bank in 1983, and the Nigerian Green

Revolution Committee in 1983. All three studies were commissioned by the Federal

Government of Nigeria. The main objective was collecting and treating technical

and economic data on forest resource conditions and uses to assess future forest

resource requirements of the country relative to possible future supply trends.

Interestingly enough, all three studies arrived at the same major finding: the forest

product needs of Nigeria are unlikely to be met within the country in the near future

because the natural forests will very likely be exhausted before long. The consequent

recommendations made by FAO and the World Bank include strict forest management with special emphasis on plantations for fuelwood and industrial wood. The

study by the Green Revolution Committee reported, however, that the intensive

forest management concentrated so much on artificial regeneration of forests that the

management of natural forests was neglected. Later in 1987. Enabor evaluated the

demand and supply trends of forest products in Nigeria and concluded that external

sources of supply very likely would be sought to meet forest product requirements in

the country (Enabor 1987).

The forest sector development planning model developed by Buongiorno and others

(1981) for Indonesia can be loosely considered as a gap-type model. Although

advanced mathematical programming techniques were used, the basic structure of

the model was designed to determine patterns of timber and timber product supplies

that would be consistent with given levels of domestic demands and exports of wood

products.

18
Buongiorno (1992) reports his experiences with forestry sector planning models from

Malaysia and Nigeria. These two case studies have many features of a gap model.

The model for Malaysia uses six development strategies, which consist of six different

rates of conversion of forest lands to agriculture. By simulation, the model identifies

the best strategy to satisfy domestic and export demands of wood and wood products

given log production data. There is no explicit objective function in the model; therefore, a set of criteria is used to evaluate alternative development strategies. But the

final policy decision is a "compromise reflecting many socio-political considerations,

weeding out the least promising strategies by debating them." The model for Nigeria

is a timber demand and supply model designed not to determine equilibrium prices

and quantities, but to identify investment strategies in the forest sector. Future requirements for and supplies of timber products are forecast by using current consumption and production trends. Then, investments most likely to fill the gap between

expected future demands and supplies at minimum costs are determined. The gap

analysis is conducted for fuelwood, poles, saw logs, veneer logs, industrial residues,

and pulpwood.

Gap models are conceptually simple. They are, in fact, simple ways of organizing

data and presenting results that easily could be explained and understood by lay

people concerned with forestry policies. The simplicity of the gap framework and its

great flexibility in accommodating different levels of data are the key advantages

that made the method so popular in developing countries with limited statistics and

trained professionals. The possibility for errors is great with gap models, however;

they often are based on the assumption that the levels of forest product exports will

be maintained over time when the gap between expected demands and supplies in

a given country is calculated, and this assumption may not hold throughout the

projection period.

Another shortcoming of the gap modeling approach is its inability to guide the implementation of the recommended policies. Gap models are excellent in establishing

policies. But establishment of policy is one thing and policy implementation is quite

another. This is important. For example, the above gap analysis for Nigeria revealed

an urgent need for an intensive forest management policy with emphasis on reservation and regeneration The implementation of this policy, however, took the forms

of (1) an ambitious expansion of the area under reservation from 10 to 25 percent of

the total forest area, and (2) a decision to reforest each year an area at least equal

to that deforested (Enabor and Umeh 1992). Enabor and Umeh (1992) point out that

both the rate of expansion of the reservation area and the reforestation decision were

unrealistic, given (1) the land use and ownership patterns existing in Nigeria,1e and

(2) the country's financial and technical capabilities to regenerate the forest resources

16 Unlike French-speaking Africa, where forest law can

declare any unoccupied forest land state property (although

local populations could still exercise many usage rights),

Nigerian forest regulations fully recognize the claims of local

populations to forest ownership Thus, forests cannot be

gazetted as state forests without prior consultation with local

representatives This consultation process may be time

consuming and uncertain in outcome, thus the prospect

of any ambitious reforestation plan is reduced

19
Market equilibrium models—Market equilibrium models are characterized by a

supply and demand adjustment process, which endogenously determines both price

and quantity. Adams and Haynes (1987) distinguish three types of market equilibrium

models: (1) nonspatial market equilibrium models, where only one country or region

is considered; (2) quasi-spatial market equilibrium models, where goods flow only in

one direction (from one region to another region); and (3) spatial market equilibrium

models, where various spatially isolated markets compete among themselves. Market

equilibrium models have been developed and successfully applied to the forest

sector in developed market economies, especially in North America. It is hoped that

as forest statistics and technical expertise improve in developing countries, market

equilibrium models could develop and supplement the basic gap models.

Market equilibrium modeling approaches have been applied in several cases to the

forest sector in developing economies: the Vincent study (1987) on Japan-South Sea

trade in forest products, the Adams study (1985) on the African-European trade in

tropical logs and sawn wood, and the Vincent (1992b) simple trade model.

Both the Adams (1985) and Vincent (1987) studies are competitive spatial equilibrium

models designed to address trade issues, such as the impacts of trade barriers and

exchange rates policies on trade patterns as they relate to national welfare. Adams's

model serves as a guide to Vincent's model, and both use the same solution

algorithm—reactive programming. Vincent's study was less aggregative than Adams's,

however; Vincent considered individual countries (Japan, Indonesia, Malaysia, and

the Philippines) as trading partners, but Adams grouped individual countries into

regions as trading partners.77

Vincent's (1992b) simple trade model is a nonspatial modeling approach that could

prove useful for analyzing forest-products trade policies in developing countries. This

model is simple because, according to the author, it can be developed and analyzed

in less than 24 hours. The framework does not estimate functional forms for demand

and supply but relies on predetermined estimates of demand and supply elasticities.

This probably explains the simplicity and the quickness of the model. It is nonspatial

(that is, does not determine bilateral trade flows) but does predict net trade balances.

Vincent applied the model to analyze how log-export restrictions affected peninsular

Malaysia. Model solution was obtained by using the Gauss-Seidel procedure; but the

author insisted that the model was solved almost instantaneously.

Applying market equilibrium models in developing countries is more challenging than

applying gap models as they require not only the adoption of assumptions difficult to

justify even theoretically, but also the use of many proxy variables and incomplete

17 Adams considered the following three regions West Africa

comprising Angola, Benin, Ghana, Guinea, Guinea-Bissau,

Ivory Coast, Liberia, Nigeria, Senegal, Sierra Leone, and

Togo, Central Africa comprising Cameroon, Central African

Republic, Congo, Equatorial Guinea, Gabon, and Zaire, and

East Africa comprising Kenya, Madagascar, Mauritius,

Reunion, Tanzania, and Uganda

20
time series For example, Adams (1985) justified his use of the competitive market

framework by assuming that such a behavior may exist among countries rather than

within individual countries. Also, because of data limitation, he adopted demand and

supply equations that are not only crude but are also aggregated over countries that

differ quite substantially from one another. For these reasons, results may be unreliable; and for individual countries, they may bear little or no relevancy at all Nevertheless, the results produced by these competitive models cannot be overlooked. At a

minimum, they provide preliminary results to compare with future research results.

General equilibrium models—General equilibrium (GE) models are multisectoral

models. They are useful because of their ability to capture the intersectoral economic

adjustments whenever they exist in the economy. Input-output (I-O) models are

perhaps the best known models and convey the idea behind general equilibrium to

a certain degree. But unlike I-O models, GE models have the distinguishing feature

of being able to handle, among other things, nonlinearity in production technologies,

optimizing behavior by consumers as well as producers, and price adjustment mechanisms (Vincent 1992a). General equilibrium models often are referred to as computable general equilibrium (CGE) models, perhaps to make them less intimidating

and more accessible Occasionally, CGE models have been advocated as a viable

alternative to forest sector analysis.

The importance of the links between the forest sector and the rest of the macroeconomy has been the driving force in arguing for the use of CGE in developing

countries. Constantino (1992), for example, argues that the forest sector has become

such a major part of the Indonesian economy that it would no longer be feasible to

treat the sector as exogenous from the macroeconomy. Changes in the forest sector,

he asserts, are likely to cause changes in other sectors of the economy.

Vincent (1992a) sees most environmental problems stemming from forest industrial

development in developing countries as related more to nonforestry policy failures

than to the forest products industry itself Consequently, he advocates the use of

CGE models, not as substitute for forest sector models, but as a "best available

alternative" to forest sector analysis, provided that the forest sector is significant in

the country's economy, so that the economic adjustments between different sectors

of the economy could be adequately captured.

The framework for CGE has been used a few times to analyze the impacts of

selected policies on the forest sector in developing countries. Most of these studies

unfortunately have been recorded as unpublished reports. Vincent (1992a) mentions

four such studies.^8

18 See Vincent (1992a) for references to these unpublished

reports

21
The application of CGE models to forestry issues in developing countries is quite

appealing, especially in those countries where forest sectors command important

shares in foreign exchange earnings, employment, and contribution to gross domestic products. As data availability and technical expertise develop, CGE models also

could develop to supplement forest sector models in developing countries. But as it

is. the applicability of CGE models to forestry analysis in developing countries is

likely to remain limited for many years. Even in developed countries, their application

to macroeconomic issues is not without controversy (Vincent 1992a). Most CGE

models are fairly complex, leading to the question of whether they could be easily

understood by the policymakers likely to use them. And they are mostly static.

This may limit their ability to analyze dynamic processes characteristic of a natural

resource sector such as the forest sector. Finally, with the rapid depletion of forest

resources in developing countries, it seems likely that the share of forest sectors will

decrease over time. If so, then CGE models will become less and less relevant as a

tool for forest sector analysis in developing countries.

Data needs—What seems to be most lacking in developing countries are the data

necessary to develop complete forest sector models. World Bank and FAO data

sources are helpful, but they concern mostly international trade variables with virtually no reference to internal market factors such as prices and quantities of forest

products and other commodities of interest traded on local markets. Analysts therefore have no alternative but to rely on data of questionable quality from unreliable

sources. This limits the confidence that can be placed on the estimates and predictions produced by the models. In this context, Buongiorno and others (1981) warn in

their conclusion, "because the data are still crude, this paper must be viewed as a

description of methodology rather than as a recommendation for policy." Adams

(1985) also cautions about the results of his effort saying, "bilateral trade flows, as

determined by the competitive spatial equilibrium model, appear to be generally

consistent with the limited available flow data for 1980, though further testing of the

results is clearly needed." Vincent (1987) is no exception. He found that data from

sources other than the FAO Yearbook, especially sources from within the South

Seas nations themselves, should be sought. Better data exist, but they can be

accessed on!y by doing research in the nations themselves or by establishing

cooperative links with researchers in those nations. Once the data problems are

brought under control, then more attention can be given to other problems, such as

the appropriateness of theoretical specification and associated solution procedures.

Impact of Strategic I" addition to changing methodological approaches, there also has been an evolution

Planning in the use of models in strategic planning by both government and nongovernment

organizations. This is particularly evident in the use of simulation techniques to

analyze alternative futures and management strategies. One goal of these efforts

is to derive useful information that can improve and expand the perceptions held

by land managers and the public. As such, the approach differs from traditional

modeling (and particularly forecasting) approaches by both treating uncertainty

directly in the analysis and stressing the information needs of the public.

22
In the modeling literature, this approach has been called scenario planning (Wack

1985). Scenario planning, as it is usually applied, does not attempt to predict the

future. Instead it postulates a set of plausible futures, each dependent on the

assumptions underlying that future. By doing so, the technique focuses on what

might happen (positive or negative) and how to deal with it. This can be contrasted

with planning for predetermined future. Scenario planning has been around in both

the general literature and forestry literature since the mid 1980s.79 Some of the

earlier applications dealt with the turbulent oil markets of the 1970s (Wack 1985,

Schwartz 1991). In forestry, scenario planning techniques have been a part of the

RPA efforts since the 1983 RPA timber assessment update (Haynes 1990, USDA

Forest Service 1982). Most forestry applications take a classical sensitivity analysis

approach, a limited number of key exogenous and endogenous elements are varied

and key projection results examined for differences. These differences allow the

identification of emerging problems and provide a way to measure the effectiveness

of possible solutions to various problems.

In scenario planning as used in forestry, there are four types of information—

endogenous variables, two classes of exogenous variables, and model coefficients.

First, the endogenous variables are those determined in model solutions. In forest

sector models, these typically include product and stumpage prices, consumption and

production levels, and levels of timber harvest and resource conditions (and levels)

by owners. There are two classes of exogenous variables. One class includes those

predetermined variables that will not be influenced by events within the system being

modeled. The second class of exogenous variables includes those variables whose

values could change depending on what might happen within the system being

modeled or where outside forces could intervene to change them. The first type of

exogenous variable might include variables such as gross national product (GNP),

housing starts, or projections of timberland area. The second class of exogenous

variables represents input assumptions that are less certain. In forestry, these might

include levels of public timber harvest, export policies, growth and yield representation, stand transition matrixes, and so forth. This last type of variable also is called

a policy variable. In that interpretation, these variables are under the control of those

doing the systems modeling and are used to introduce into the various biological and

social processes changes that lead to different values for the endogenous variables.

In forestry models and elsewhere, exogenous variables often are referred to as

assumptions.

The fourth category of information includes all model coefficients. In model-building

terms, these are a special class of exogenous variables. These often are the least

manipulated variables, and they usually are developed and tested independently of

each other. Finally, one truth to remember is that the endogenous variables are the

consequences of the assumptions. Changes in assumptions, in the case of forest

sector models, will change our view of prospective market changes.

19 Prior to the mid-1980s, these same techniques were

described in the simulation literature as a policy simulations

approach (Naylor 1970) At that time, the simulation approach

was seen as having an advantage over the more commonly

applied optimization approaches in that it did not require

knowledge about the policymaker's welfare preferences or

particular targets but provided the policymaker with

information necessary for decisionmaking

23
The models used in these types of applications usually are called systems models,

and many forest sector models fit the general form. The basic systems model can be

written as,

(12)

where

Applications of TAMM

and the CGTM to

Developing Countries

Yt is a vector of current endogenous variables,

Yt-1 is a vector of lagged endogenous variables,

Xt is a vector of predetermined exogenous variables,

Zt is a vector of postulated exogenous variables, and

Vt is a vector of stochastic disturbance terms.

Vectors N1t and N2t are model coefficients. Model solution involves solving for Yt in

terms of Yt-1, Xt, Zt, and Vt. Given Xt and Zt, evolutionary behavior can be explored

in which the model generates its own values for the endogenous variable Yt over

many future periods. By manipulating the values of the postulated exogenous variables Zt, the time paths of the endogenous variables are determined for each

alternative policy.

In unmodified form, models such as TAMM and the CGTM are not applicable as

planning and projection models to the conditions of most developing countries. In

TAMM for example, implementation of its demand and supply elements, at both the

product and stumpage levels, requires data not generally collected in developing

countries. The focus of TAMM is primarily on (U.S.) domestic market conditions,

intracountry trade, and competition among essentially independent producing regions

for a spatially dispersed consumer market. External trade is treated as an "input" to

an elaborate model of the domestic sector. In contrast, external trade usually, though

not always, is critical in the forestry planning of developing countries. Regional

differences may be important on the production side but not so critical on the

demand side (excepting some of the very largest developing nations). TAMM

employs the economist's classical competitive market structure as the context for

market resolution and establishment of a trade equilibrium. This view of the trading

environment (as competitive, organized, and operating entirely within appropriate

legal structures) may not be a reasonable representation of some developing

countries.20 Finally, TAMM is a simulation model. It could be employed in some

programming framework to determine "optimal" public policies, but it was not

designed for this approach to policy analysis and its structure would make such

an application cumbersome.

20 A developing country's "industrial" (primarily export)

economy may operate in this way, that is, analogous to the

economy of a developed, market nation, but the means by

which goods are produced and distributed to the indigenous

and often highly dispersed populations in rural areas may be

quite different It also is evident that in some countries, large

volumes of wood flow from forest to market through strictly

extra-legal channels

24
The basic philosophy behind TAMM and its associated policy planning process

nonetheless would seem to be of considerable potential value. For purposes of

development planning, the key feature that distinguishes TAMM from earlier market

modeling efforts is its linkage of the product and resource levels in a way that allows

interaction among resource growth and inventories, market behavior, and policy

actions at both the market and resource levels. For planning in developing countries

that will at least consider, if not emphasize, stewardship or maintenance of the

productivity of the resource base, this approach would seem to be mandatory.

Construction of each model component must be tailored to the specific conditions

of the country in question and the goals of the planning effort, but some general

guidelines can be adduced.

The basic resource model should provide a reasonable representation of both the

biology of the forests in question and of the form and effects of human intervention

and actions. In the current version of TAMM, use of an age class-based inventory

model with density-dependent yield functions has, to this point, proven satisfactory.

Regeneration activities and additions to the timberland base are among the most

important actions in terms of influencing long-term timber supply. The age-class

format handles these changes well. But the model does not distinguish among

species within a given forest type, and it only approximates stand responses to

selection harvesting. These would be significant limitations in modeling the resource

in, for example, a developing tropical country, where in some natural forests species

differences are important and partial cutting the rule

Concerns in the choice of form for the market portion of the model are no different

from those faced by market modelers generally: aggregation of regions, sectors,

and products; need for an explicit spatial structure; format of the modeling process

(econometric simulation, price-endogenous linear or nonlinear programming); and

the extent to which offshore trade should be made endogenous. The TAMM format

has worked reasonably well for its specific situations. The broad issues involved

here have been addressed in several excellent reviews of forest sector and trade

modeling approaches, including Kallio and others (1987) and Cardellichio and others

(1987). The only useful caveat I can offer is the need to retain flexibility in the

modeling approach in the face of the analyst's inevitable inability to foresee all types

of policy questions likely to be asked. Indeed, the development and initial application

of a model often raises new questions outside (or at the margins of) the abilities of

the original structure This is not to say that one must always build the ultimate

model. Rather, that the bias in modeling be toward adoption of more, not less,

flexible approaches where decisions arise in model construction.

25
Finally, a case needs to be made for the utility of market-sector simulation models,

like TAMM, in the context of development planning. All too often, in my view, development studies entail only the construction of large optimizing models to provide

blueprints for specific activities and expenditures. These studies focus heavily on the

details of enactment of policies. At the same time, their outputs are seldom subject

to any sort of "checking" for their impacts on the broader context of the forest sector.

Thus, for example, a cost-minimizing or employment-maximizing development plan

might be examined by a market simulation model for its impacts on trade, prices,

consumption, and so forth. This is the general approach used in parts of the USDA

Forest Service's land management planning process for the National Forests. Outputs

from the plans (which are, in effect, resource development strategies for the individual

forests) are examined by various market simulation models to better understand

the interactions and impacts on other elements of the forest sector (such as private

forest owners, trade, prices, and consumption). A similar approach in the context

of planning for developing countries would seem to have equal merit.

The last decade has seen the development of forest-sector-model-based on systems

analysis and quantitative economic techniques (developed since the 1950s.) Since

the mid-1970s these models have been used to judge relevant policy issues and

provide forecasts that are the primary components of long-term assessments prepared by various government and nongovernment organizations. These applications

have overcome considerable skepticism on the use of models for forecasting and

decisionmaking. But some skepticism continues, despite the appearance of analytical

rigor, that policy recommendations are based on opinions rather than on robust

analysis.

Continued evolution of forest sector models in the next decade will result in more

elegant models in the sense that greater reliance will be placed on using theory to

guide econometric and model specification. Coincidentally, evolution of strategic planning as a function of scenario planning will challenge analysts to explore ways to

integrate analyses with intuition in the development and use of forest sector models.

The evolution of forest sector modeling has reached a stage of maturity where there

is general agreement that such models should include treatment of the following

elements:

1. Product demand

2. Product supply

3. Factor supply (often only wood supply is modeled)

4. Factor demand

5. An explanation of the forest resource (both timber inventory and the forestland

base)

It is not necessary that all five components be present, but some implicit treatment

of the missing elements should be included. The two general classes of forest sector

models—gap and equilibrium models—fit this general approach. For summary purposes, various attributes of these two types of models are summarized in figure 4.

A simple but actual policy-relevant example of both types of model is shown in

appendix 1.

Summary

General Approach

to Forest Sector

Modeling

26
Figure 4—Attributes of two general classes of models.

This section begins with a brief summary of modeling philosophy and then discusses

models attributes including examples of how models might be applied under different

circumstances.

A Philosophy to Guide Remember that models reflect the builder's values (and biases) and they are only a

Model Building means to an end (a set of tools that can be used by an analyst). From an economics

perspective, increasing quantification and precision in model building should not be

allowed to narrow the focus and shift the underlying economic concerns away from

its roots in the broad social science view of political economy.

Good model building is an evolutionary process, especially as models of fairly

complete systems are dealt with (for reference these are called systems models).

It is this understanding of evolutionary behavior and its dependence on assumptions

that is important in systems modeling. Such systems models, although inexact, can

and do provide a qualitative understanding of complex biological and social

processes.

27
Some guidelines follow that are helpful in building system models.

1. Methods should be selected to fit problems (or systems); problems should not be

distorted to fit methods.

2. Model testing or validation is critical; although there is no agreement on the

necessary and sufficient rules for producing an overall evaluation, some useful

suggestions for model validation include the ability to reproduce historical trends

and turning points in selected variables. Another area where testing is critical is

when models include complicated processes and it is difficult to predict a priori the

effectiveness of various interventions to induce changes in selected endogenous

variables. Only through repeated experiments can the model builder gauge the

sensitivity of a model to various changes.

3. Models should be inherently complex and not simply produce as output a carbon

copy (or barely modified version) of the various assumptions and exogenous projections provided as inputs; that is, models should not simply parrot their input assumptions An important aspect of this is that although specific relations may seem

simple (in a mathematical sense), such as a yield or a demand function, it is the links

between that relation and all other relations in the model, expressed by changes in

endogenous variables, that introduce model complexity.

4. Models should be robust. Robustness, as a semimathematical concept, is where

the endogenous variables (the results) are relatively stable even "when assumptions

are not quite satisfied" (Keepin 1984). The quality of robustness in models of biological and social systems deals with the sensitivity of the implications drawn from

the results. If the implications change markedly with only slight changes in input

assumptions, then the projection is not robust. The point is that careful sensitivity

testing is required of all models, especially those with complex processes.

5. The utility of system models in providing insights and understanding of the behavior

of forestry processes becomes even greater for long-term projections. It is in this

context that the effects of various time-sensitive (dynamic) relations become critical;

these dynamics must include consideration of different scenarios representing different views of the future, and a recognition of the inherent uncertainty of any projection. The future should be seen as a moving target. Understanding the uncertain

nature of future events and making this uncertainty a part of the modeling process

leads to a shift in focus of decision analysis away from concern for predicting

outcomes toward understanding the forces that eventually produce an outcome.

This philosophy, generally shared among model builders, leads to some common

expectations about the attributes to look for in sector models. Perhaps foremost

is that models need to address the relevant questions, not the other way around.

Models need to be as simple as possible and intuitively understandable. They need

to contain relevant policy variables (or criteria). Good models are used. Models need

to be developed at a geographic scale relevant to the eventual users, including those

who will use the results in making decisions. Sometimes the major role of a model

may be to systematically organize the description of a problem. Models must be

robust, in that their ability to reflect basic structural changes is more important than

superior analytical performance that assumes stability. It is better to have a simple

model than none at all; this is especially true even when data are bad or limiting. In

such a case, a model can help assess the value of improving or expanding the data

in addition to providing information to decisionmakers and analysts.

28
The final point is to acknowledge that much of this section reflects the philosophical

approach of "model simple, think complex" (Behn and Vaupel 1977). There is an

alternative approach of building big monolithic models that seek to be comprehensive

and serve many purposes. Such models lack transparency and aggravate problems

associated with documentation, verification, and credibility. Increased detail is a poor

substitute for imagination in model conceptualization. Modelers need to be careful

that, in spite of their best intentions, forest sector models do not end up as big

models because of pressure to add detail.

General Model Attributes The starting point for any forest sector model is deciding what questions it might be

used to answer. An honest appraisal of potential questions will help focus the scope

of model building. Some key questions about model attributes that have to be

answered at this stage include:

1. How much product detail (both commodity and noncommodity) is necessary?

2. What treatment of the stumpage or resource sector is appropriate?

3. How does national consumption (or the sum of regional consumption) relate to

the rest of the world?

4. How important are prices and in what ways?

5. If prices matter, how are they determined (what market processes will be

modeled)?

6. How will time be treated?

7. What variables are of interest to policymakers?

8. How will issues related to nontimber goods and environmental issues be treated?

By the end of the Paris meeting, there was general agreement that the simplest

model and the model most universally applicable in developing countries is the gap

model. This model and various applications were described in several proceedings

papers from that meeting (Haynes and others 1992). Gap models have been an

important step in the evolution of price equilibrium models as demonstrated by the

experience in the United States. The gap model consists of several major attributes:

product demand, product supply, stumpage supply, resource trends, prices, and

policy measures (see fig. 4). In its most comprehensive form, the gap model differs

from equilibrium models only in the treatment of prices.

In an economic sense, forest sector models represent the simultaneous solution

of two competitive markets: the final product market and various factor markets

(roundwood, labor, capital). In a practical sense, the various factor markets usually

are reduced to just the roundwood market because neither the labor nor the capital

market is limiting (or said another way, opportunities for substitution are greater).

This distinction imposes some specification needs on the variables used in the

empirical relations representing the various markets. The following discussion

depends, too, on maintaining the distinction between product and factor markets.

29
Product demand—Product demand in the gap model can be treated in various ways

including econometric approaches, subjective assumptions, and projections taken

from exogenous sources such as FAO. Each approach is illustrated in various

proceedings papers (see, for example. Constantino 1992, Lonnstedt and Peyron

1992, Sidabutar and Chandrasekharan 1992). Consumption figures can be adjusted

for trade to get domestic (or regional) production. Another consideration is the spatial

scale used in developing the demand estimates; for example, Do we attempt to

estimate the national demand for sawn wood products? and How do we treat different species or subnational differences? The answers to these questions begin to

define the scale and detail of the forest sector model. In the United States, much of

the demand work is done nationally but differences in the demand for hardwood and

softwood species are recognized as are differences in regional consumption. In an

economic context, these demand relations are in the traditional form (quantity as a

function of prices and exogenous shifters). Often these relations represent major

consuming segments of the economy, such as housing or manufacturing. One

approach is called the end-use model where, for example, wood use per house is

estimated and total consumption is computed by multiplying that by the number of

housing units expected to be built.

In addition to the end-use approach, several simple forms of the demand relations

are used relatively widely. The first is a model derived from multiplying estimates

of per capita consumption by expected population (a variant uses consumption

per some unit of GNP). In this approach, trends in consumption are expressed by

changes in per capita consumption. In Mexico, for example, per capita consumption

for industrial uses and fuelwood averages 0 27 cubic meter per year. Wood use in

Mexico has been increasing at the rate of 0.34 percent per year and is expected to

rise by 2010 to 0.29 cubic meter per person. If we assume that Mexico's population

will continue its relatively rapid rate of growth of 2.3 percent per year for the next two

decades, total consumption will increase from 22.63 million cubic meters in 1989 to

39.65 million cubic meters in 2010.

Another model is called the FAO approach and was developed by Baudin and

Lundberg (1987) and is best described by Baudin and Solberg (1989). The FAO

approach generally can be described as a log-linear model of the form,

ln Q=a

1 +b1*ln(Z

t) + u , (13)

where Zt is a vector of explanatory variables such as GNP or GDP and prices

(measured by Baudin and Lundberg as the real unit border values for the largest

trade flows).

For Mexico, the FAO approach would lead to the following equation for coniferous

sawn wood:

In Q = 5.219 + 0.379 In GDP - 0.95 In price , (14)

Such an equation has an adjusted R-square of 0.88 and significant terms except

for price The price term, however, has the correct sign. This example illustrates a

common problem with price terms in demand relations for forest products. Because

these products often are extremely inelastic, the implication for the price coefficients

is that they are nearly zero. Given how small the coefficients may be, it is likely that

the signs of the coefficient may be wrong. Another way to put this is that it is very

likely that the price coefficient will vary around zero, thereby making it difficult to

estimate both the correct sign and a significant variable in the same equation.

30
Product supply—In an economic context, product supply is the quantity of products

produced as a function of prices and costs. Typical product supply functions are of

the form (for sawn wood in this example),

Qs = a1+b1*ps+c1*Z', (15)

where ps is the price for sawn wood (preferably at the mill; that is, prices at the mill,

FOB) and Z' includes explanatory variables, such as measures of processing costs,

installed capacity, and so forth. In the case of costs, it is best, but also unusual, to

have a measure of total processing costs. A more common approach is to use available measures of parts of the total costs, such as labor costs, or proxies, such as

wage rates.

Again for Mexico, a supply function can be estimated in the form,

0s = -201771.3 + 0.235 price proxy -871.033 wages + 104.95 year. (16)

The price proxy in equation (16) is the unit value of roundwood imports. Equation

(16) has an adjusted R-square of 0.82 and most terms (except for wages) are significant. In general, the computed price elasticities for the supply relations are four to

five times greater than the demand elasticity. This result is not unique to Mexico, as

it also is seen in similar relations for the United States. This equation for roundwood

production in Mexico also contains a time trend. Analysts often will try to interpret

such variables as a proxy for technological change. Such an interpretation can lead

to trouble because a time trend reflects a uniform rate of change while the adoption

of technological change often is at a nonuniform rate (that is, takes places in bursts

of activity).

Product supply, however, often is treated by converting expected consumption of

each product into roundwood equivalents through the use of local conversion factors

Once in roundwood form, this is the derived consumption of roundwood for the

selected country (if it included price terms, it would be a derived demand function)

Stumpage (roundwood) supply—This is usually treated as a function of timber

availability and can be determined from forest regulation (harvest) models, government fiat, trends from the past decade, proportion of timber inventory or timberland

area, and so forth. It also can be treated in an economic context where timber harvested (or delivered) is a function of timber prices, inventory characteristics, and

other variables.

Often key variables in stumpage (roundwood) supply relations are various attributes

that describe the inventory of available timber. Such attributes are total inventory,

amount over minimum harvest age, average size (diameter), percentage of sawtimber, and so forth. The tendency is to want to add a number of such attributes to

the relations, but experience suggests that most attributes are collinear and just the

basic attributes work best. In countries such as the United States, stumpage (roundwood) supply functions are defined for different types of forest land-owners (public,

forest industry, and nonindustrial) in an attempt to recognize different propensities in

behavior. In this case, total supply is just the sum (in a linear sense) of the individual

supply relations.

31
Resource trends—Often not present, but a key component of one of the selected

issues, is sustainable forest development. The simplest models are stock adjustment

models constructed around the basic growth-drain identity shown in equation (11).

The model in equation (11) is the simplest form in which the inventory term is

expressed as total inventory. A common variant of this model is to express the

inventory on a per-hectare basis. This requires adding a variable for total area of

forest land to the equation. Inventory models can become complex, as illustrated in

the United States where an age-based, yield-table system is used that projects acres

by detailed strata for periods consistent with the inventory stand-age classes.

The actual application of equation (11) is complicated by the treatment of forest land

area, species differences, definition of what forest (and forest lands) are included

in the inventory term, choice of time intervals, and relations between harvests and

removals of timber from inventories (some harvest may be assumed to come from

forest land or species not included in the definition of inventory).

In each simulation period, inventory change is the result of growth, change in the

area of forest land, and timber harvest. Inputs to the inventory model include estimates of harvest, acreage shifts, and growth parameters. The levels of harvest are

derived through interaction with the product components of the forest sector model.

Assumptions about the changes in forest land area are a required input. Alig (1992)

illustrates several different approaches to developing these assumptions.27 Inventory

data can be derived from timberland inventory plot data or from extensive aerial

inventories.

I have used a real example to illustrate how the inventory identity can be used and

how it can be integrated into a simple but functional forest sector model.22 A recent

concern has been economic impacts of the possible introduction of defoliator insects

in the U.S. West as a result of importing larch (Larix sp.) from Siberia and the former

Soviet Far East. These economic impacts were estimated in a market context by

using timber supply and demand relations (taken from the 1989 RPA timber assessment [Haynes 1990]) for the Pacific Northwest, Pacific Southwest, and the Rocky

Mountains. Economic impacts were estimated by first computing the equilibrium price

and quantity by decade and region, and than recomputing the modified equilibrium

price and quantity following a shift in the stumpage supply functions, assumed to be

induced by changes in growing stock inventories. Basic economic impacts are slow

to develop and depend on the extent that inventories and, hence, timber supplies are

reduced owing to less growth.

The supply and demand functions in the model are of the general form,

qd= a1-a2ps, and (17)

qs = b1+b2ps+b3l , (18)

21

 Palo (1987) proposes a scenario approach for developing

assumptions about the area of forest land and rates of

deforestation

22 This model was developed as part of the pest risk

assessment of the importation of larch from Siberia and the

former Soviet Far East (USDA Forest Service 1991)

32
where

qd and qs are the quantities demanded or supplied,

ps is the stumpage price, and

I is the timber inventory available for harvest.

This form for stumpage supply was adopted because inventory levels are one of the

main determinants of stumpage supply In the analysis, I assumed that defoliator

insects will reduce net annual growth of selected species by 15 percent and that the

rate of spread is 20 kilometers/year

Graphically, the analysis involved shifting the stumpage supply functions back by the

amount of change in softwood growing stock inventories that resulted from reduced

forest growth, caused by introduction of defoliator insects. Changes in inventories act

to shift stumpage supply functions in the longer term. and changes in prices help

establish supply levels in the near term. These changes in inventories were

computed by using a growth drain identity (equation 11); both the original and

modified inventory numbers are shown in the following tabulation

Original Modified

Year Inventory Growth Harvest Inventory Growth

Million cubic feet

1990 116,713 2,784 2,714 116,713 2,784

2000 118,983 2,784 2,557 117,304 2,616

2010 119,983 2,784 2,684 114,909 2,445

2020 120,453 2,784 2,737 111,649 2,411

2030 120,303 2,784 2,799 107,768 2,411

2040 120,103 2,784 2,804 103,838 2,411

Solving the supply and demand functions for 2040, for both the original case (without

defoliators) and the modified case (where the supply functions has been shifted by

the ratio of original inventory to the modified inventory), shows that the 15-percent

change in inventories results in a 10-percent increase in stumpage prices Other

economic measures, such as the welfare impacts and employment losses, can be

computed from these results and were computed in the original application of this

small forest sector model.

Prices—There are several ways to treat prices. The simplest is to deal with price

trends descriptively, inferring continuation or change in those trends based on

changes in the various quantity measures. A more complicated way, but more

pleasing to economists, is to solve for puce as the simultaneous solution of supply

and demand functions.

33
Some concepts and aspects of price analysis are important in forest sector modeling.

First there is the concept of price arbitrage; that is, prices of different species and

grades of lumber for example, varying in some fixed proportion to each other or to

roundwood prices. Arbitrage leads to conditions where prices of one species and

grade will not exceed prices for other species and similar grades because of the

possibilities of substitution. Another form of this arbitrage is between similar grades.

In the United States, for example, this concept has been used to develop price

projections for various grades of lumber (Haynes and Fight 1992).

Another persistent problem has been the relation between prices for finished products

and the prices of factors used in producing that product. By definition, the return to a

factor is an economic rent and presumes that the factor is fixed in supply, hence, the

return to the factor is market determined and therefore consistent with market equilibrium concepts. Empirically, this implies that factor prices can be derived from product

prices.

The above implication has important ramifications for policy analysis; for example,

a government program affecting the supply (availability) of timber impacts the final

product prices, but policy questions relate to changes in stumpage or roundwood

prices. In agriculture, this same situation has led to empirical attempts to estimate the

relation between farm level and final market prices. In various types of price analysis,

some of these forms of arbitrage are institutionalized through what have been called

price markup rules (George and King 1971). Classic examples might include the

relations among prices for various grades of shop lumber. These rules have been

used in past forestry studies (Haynes 1977) on the relation between lumber prices

and stumpage prices and implications for the derived demand for stumpage.23

These price markup rules are of the general form,

Pi= b1+ b 2 p j , (19)

where Pi and pj are prices of lumber and roundwood, in this case, and b1 and b2

are estimated coefficients The significance of the estimated coefficients, b1 and b2,

makes a statement about the form of the relation between the two price series It

would be a fixed amount if b2 was zero,24 a constant proportion if b1 was zero, or

some combination if both b1 and b2 were nonzero coefficients. If equation (14) can

be estimated and the coefficients b1 and b2 are significant, then the elasticity in the

factor market (es) is given by the relation,

ej=ei[1/(1-b2)]Pi/pj. (20)

Finally, the term [1/(1-b2)]Pi/pj has been called the elasticity of price transmission

(George and King 1971).

23 In this case, price markup rules are called marketing

margins and are an accepted descriptive model of the

relation between factor and product markets

24 In economic studies, the actual value of the b2 coefficient,

while different than zero but not statistically significant is

interpreted as being zero

34
There are several practical problems related to prices that together or separately

make estimating demand relations difficult. First, there is the lack of price data in

many countries—data that represent consumer behavior. In many cases, the only

price data collected are part of a country's trade data or "list" prices from importers

or wholesalers. It is best when the price data reflect actual transactions as closely

as possible to the final consumers. Second, it is difficult econometrically to estimate

price coefficients that are both statistically significant and have the correct sign. The

reason is that most demand elasticities in forestry are inelastic, so that the various

price coefficients are close to zero. One alternative to this problem is to assume (or

estimate independently) an elasticity and then, using the equation for elasticity and

prices and quantity for a specified period, solve for the slope of the demand function.

Roundwood demand—In many forest sector models, the various comparisons for

policy purposes are done in terms of roundwood volumes; that is, the volume of logs

or other round products required to produce the lumber, plywood, wood pulp, paper

and other similar products. Some call this relation the wood materials balance

relation because it attempts to account for all roundwood used to produce the full

array of forest products (see, for example, fig. 10.2 in USDA Forest Service 1982).

The conversion from product to roundwood measurement scales is one place where

technology assumptions can be put in a forest sector model.25 Over time, product

recovery can be assumed to improve as the result of technological improvements. In

models like the SOS model (Panders and others 1978), changes in technology is an

endogenous process, but for the most part, it is treated as an exogenous variable.

Time intervals—Forest sector models typically are intertemporal and often based

on discrete time intervals. One of the first questions is, What model elements (consumption, product production, capacity, or the resource situation) change with time?

Another question is, How will the model builder deal with the different time intervals

often found in different aspects of the forest sector? For example, market interactions

usually are modeled annually, but inventory models often are specified by decade

(depending if and how age classes are specified).

Gap models typically trace consumption and availability trajectories overtime, although specific gaps between the two trajectories are measured at specific points

in time. The solution of equilibrium models represents an equilibrium in the markets

modeled for each year of the projection period. These solutions often do not represent, and the basic market solution algorithm cannot be readily used to find, intertemporal production or consumption strategies that are in some sense optimal. The

production, consumption, and price time paths in the equilibrium model are only

estimates of outcomes of contemporaneous interactions in freely competitive

markets. One alternative proposed by Lyon and Sedjo (1983) is the use of optimal

control theory to trace the optimal transition path from old growth to managed stands

through time. The control theory approach has not been widely used for modeling

forest sectors.

25 Other places where technology assumptions can be made

include assumptions about wood use (for example, wood use

per house) in demand relations and labor productivity assumptions that can be embedded in the cost assumptions

35
Figure 5—Example policy measures: (A) U.S supply

and demand and (B) stumpage price index.

Policy measures—There are two issues: the variables of interest to policymakers

and how the effectiveness of policy actions will be judged. In gap models, the

primary policy focus is on the difference (gap) between consumption and timber

availability at specified points in time. In equilibrium models, various price and

quantity variables are available for use as policy variables. The projected path of

prices often is used to identify potential problems or to measure the effectiveness

of specific policy actions including various forms of market interventions. Although not

a feature of gap models, potential policy implications are associated with interpreting

the solution of equilibrium models. First, although these solutions do not represent

intertemporal production or consumption strategies that are in some sense optimal,

they do represent how the markets might evolve from considerations of economic

efficiency. Second, economic models allow the estimation of various welfare

measures that can be used to address the economic equity questions of who

gains and who loses.

These two issues are illustrated in figure 5 by using data from a Forest Service

assessment (USDA Forest Service 1982). Figure 5a illustrates the results of a

gap analysis for projected supply-demand balances for U.S. softwoods. Figure 5b

illustrates a stumpage price projection for softwoods in the Southern States of the

United States associated with balancing supply and demand in the stumpage market.

36
To a policymaker, the inference in figure 5a is that projected demands will exceed

available supplies and policy actions will be needed to reduce the gap. Often the

policy discussion is framed around reducing some shortfall of, say, 2.7 billion cubic

feet in 2000. Implied, although seldom addressed, are the associated price changes

expected, with failures, to fill the gap. Figure 5b illustrates a case where stumpage

prices are used as the policy variable of interest. If, for example, continuation of

recent timberland management practices is assumed, then stumpage prices are

expected to increase (net of inflation) at 2.1 percent per year between 1990 and

2030. The policy issue is framed around the societal acceptance of that rate of price

increase. If it is not acceptable, programs could be instituted to increase softwood

tree planting and conversion of low-value hardwood stands on privately owned

timberlands that would stabilize stumpage prices in the near decades (1990-2010)

and, as the planted stands mature, eventually lower stumpage prices (see fig. 5b).

Both cases illustrate similar policy inferences but for different reasons. In the case of

the gap analysis, perceived quantity shortfalls drive policy actions. In most countries,

these shortfalls are not observable, as price changes cause market adjustments that

offset supply shortfalls. In the equilibrium analysis, perceived price changes drive

policy actions. The lack of policy actions usually is observable in the sense of

undesirable price changes.

The frequent use of forest sector models in various policy applications imposes

specific requirements on the modeling process. First, the models have to include the

variables being considered or likely to be considered. Second, to be useful, models

used in a policy context should help clarify the problem, outline the alternatives, and

compare consequences. Third, declines in the quality of policy modeling must be

guarded against as model size and complexity increase (Alien and others [1992]

review these concerns).

Use of the land is an important variable in forest sector analysis because it

establishes the base from which the supply of timber and the appropriate mix of

nontimber forest outputs can be quantified. The forecast of present and future

land uses also makes an intersectoral approach for the sector quite useful. In

most developing countries, an important forest policy variable is the expansion of

agricultural land. If policies are maintained that promote low-capital or extensive

agriculture to free scarce resources for industrial development or other sectors, the

forest area is bound to shrink rapidly. Forest land is cleared for cultivation because

land often is the cheapest agricultural input. Forest land also is readily available

because the traditional common property ownership of many forests is disintegrating

with changes in traditional ways of life and giving way to an open access regime to

land. Increasing insecurity of tenure favors the use of land for short-term benefits,

which usually involve deforestation. Understandably, the modeling of land-use

changes in such situations is difficult. Yet deforestation often is related to a single

variable: population growth (Palo 1987). The FAO is now projecting global

deforestation by using that simple population variable and validates it by relating

these estimates to forest area measured through satellite imagery and ground

checking.

37

Nontimber

Considerations in

Sector Work
In the United States, Alig (1992) developed a model that simultaneously projects

shifts among major land uses, forest cover types, and ownership by using economic

(GNP) and demographic (population) variables. Methods for projecting area changes

are based on positive or normative approaches: land rent theory, expert opinion, and

optimization models of econometric techniques have all been used to that effect.

Expert opinion can be a useful approach in many countries facing rapid land-use

change. Parks (1992) has applied the same type of approaches to questions about

forest land-use changes in several areas in Southeast Asia.

In modeling the forestry sector, it is possible to integrate change in land use (an

exogenous variable that can be influenced by policies external to the sector) and

changes in timber management (a forestry policy variable) with the production of

nontimber outputs, such as forage, wildlife (game and nongame), water yield, and

fish production (Joyce and others 1992). But such a model is not easily formulated

and then interpreted by the decisionmakers. The limitation of the modeling approach

in integrating nontimber products is more obvious than for timber products, especially

in developing countries where the database is minimal. It was felt by the participants

at the Paris meeting, however, that the description and quantification of nontimber

outputs of each forest type were pertinent information to be provided in the sectoral

review.

Much of the discussion has not recognized the dual role that forests play in developing countries where they provide fuel, crude building materials, and timber input

for wood-processing industries and for exports while at the same time providing

protective resources. They help to maintain soil quality, stabilize hillsides, modulate

climate, protect waterways and marine resources from accelerated siltation, and

promote ecological diversity and wildlife conservation while providing many medicinal

plants vital for health. Although the protective role of forests is very important, it is

the productive role of forests that, in view of the need for economic development,

seems to be valued the most in developing countries (Ehui and Hertel 1989, FAO

1967, Ross and Donovan 1986, Vincent and Binkley 1991).

There is an emerging view of forest-based economic development that is based

less on the productive role and more on the protective role of forests. This is a view

of development based on concepts of social forestry26 and comes as a reaction

to perceived failures of forestry projects coupled with severe deforestation and

increasing rural poverty in developing countries. Westoby (1978) was one of the

earliest proponents of this development approach when he recanted his support for

forest-based industrialization27 in favor of forestry activities that support agricultural

productivity, rehabilitate land, and raise rural welfare. Both the World Bank and FAO

also have come to observe the poor performance (in terms of benefits to the most

26 "Agroforestry," "social forestry," "community forestry," and

many other similar terms refer to the same thing and are

used interchangeably in the literature (Gregersen and others

1985, Winterbottom 1990, World Bank 1978)

^Westoby (1962) was an early proponent of forest-based

industrialization in developing countries endowed with large

forest resources This view of the role of forestry in

developing countries influenced international institutions and

financing agencies and led to a proliferation of forestry

industrial projects in the Third World

38
poor) of forest industries in developing countries and recommend, like Westoby,

social forestry as a better way to promote sustainable forestry sector development

in the Third World (Douglas 1983, Westoby 1978, World Bank 1978). The World

Bank, FAO, and Westoby agree on their basic assessment of the major problem

with forestry in poor countries; however, they differ significantly in their explanations

of why the problem arises. For FAO, the problem is a planning problem deeply

rooted not only in the poor technical and managerial capability of forest services

in developing nations, but also in the failure to integrate rural communities in the

planning process. The Bank views the forestry problem in developing countries as

a financial misallocation problem whereby rural community development projects

do not receive proper attention.

Forestry sector analysis can be approached in different ways. Much of this discussion assumes that one way is to develop a single analytical framework, a model, for

trying to incorporate most of the important variables affecting the sector in general

(see Constantino 1992, Haynes and Adams 1992, Sidabutar and Chandrasekharan

1992). The other type of approach is to focus on one or two major issues such as

fuelwood (Hyde and Seve 1992) or trade (Vincent 1992b). Partial models that tackle

particular issues have to be linked at some point to the broad picture of the sector to

allow decisionmakers to have an overall view of the sector situation. For practical

purposes, small models are useful for addressing specific questions that can in

turn help in making decisions. Yet most participants at the Paris meeting felt that a

general approach, organizing and using all important information for the sector, is

necessary to give a complete picture of the situation. The minimal approach (Harou

1992) provides general guidelines for a gap-type approach, such as defined earlier,

but with enough flexibility to integrate more specific studies or models as required.

Other models are available such as input-output (Essman and Pretzsch 1992) or

national income accounting, which are particularly effective when an environmental

account is introduced (Harou and Constantino 1992). Input-output and national

account approaches are useful for policymakers because they focus on important

policy variables such as employment, income, and demand in the sector. In addition,

intersectoral issues are easily quantified by using these approaches. Although the

biodiversity and carbon sequestration aspects of the forestry sector are very relevant,

they seldom are addressed. Whatever the model used and general approach taken,

Enabor and Umeh (1992) make it clear in their paper that such an exercise provides

useful information for pressuring politicians to consider the forestry sector more

carefully and, consequently, not to forget the sector in their annual budgetary

allocation.

A changing physical and political landscape is requiring that management and

investment initiatives in the forest sector consider ecological, institutional, legal,

and sociological issues while reconciling concerns about efficiency, equity, and

conservation. For international donor institutions and governments, analysis supporting such activities must incorporate these issues and be widely applicable in a

developing country context. It is hoped that this paper will be a building block for

research aimed at developing a framework for such analysis that will satisfy the

changing needs of policymakers and planners.

39

In Closing
Acknowledgments

Literature Cited

Two points should be repeated in closing. First, the development of successful

forest sector models seems to be enhanced if the modeling approach recognizes

evolutionary behavior and its dependence en assumptions. Second, success

depends, in part, on a decisionmaker serving as an advocate for modeling; this

role of advocacy should not be diminished.

Parts of this document were extracted from papers by Darius Adams, David Brooks,

Patrice Harou, and Kit Prins that were prepared for a lUFRO-sponsored meeting at

the 10th World Forestry Congress. In addition, John Perez-Garcia (University of

Washington) provided a brief description of the CINTRAFOR Global Trade Model,

Philip Waddle (FAO) provided brief descriptions of FAO activities, and Aste Yapi

(Oregon State University) provided a literature review of forest sector models in

developing countries. David Brooks (Pacific Northwest Research Station) and Patrice

Harou (The World Bank) provided thorough reviews of an earlier draft that improved

the paper. Funding for part of this study was provided by The World Bank. Finally,

Judy Mikowski provided outstanding assistance in completing this document.

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46
Appendix 1

Example Gap and

Equilibrium Models

Given

Current price (P1 )

Current quantity (q1)

Demand elasticity (ea)

Supply elasticity (es)

Growth in demand

Growth in supply

$0.28/cubic foot (ft3

)

2106.1 million ft3

0.1446

0.3081

0 percent per year

-0.5 percent per year

Question 1: What is the gap between consumption and production after 10

years?

Current +10 years

Million ft 3

Consumption 2106.1 2106.1

Production 2106.1 2003.1

Gap — 103.0

Question 2: What Is the expected rate of price increase necessary to balance

supply and demand in 10 years?

The first step is to compute the supply and demand functions from the given data

by using the equation,

q-q1==eq1/p1 (p-p1),

where qi and pi are the known (current) price and quantity pair and

e is the estimate of elasticity.

From the example data, the demand function is,

q= 2414.45-1101.24p.

The supply equation (computed using 2003.1 as qi) is,

q= 1385.94+2233.45p.

The second step is to compute the new equilibrium price that balances supply and

demand.

Current +10 years

Dollars/ft3

$0.28 $0.31

*U.S. Government Printing Office: 1994 — 589-269/00015 47