Macroeconomic Income Adjustment and Tropical

Macroeconomic Income Adjustment and Tropical Forest Conservation: A General Equilibrium Analysis of Malaysia Frank Harrigan, Asian Development Bank This article examines the loss in “metered” aggregate income that could occur if Malaysia surrendered the lumber value of its tropical forest resources to nonlumber uses. We estimate these losses under a variety of assumptions about what “business as usual” and “conservation” might entail. We also consider the sensitivity of income losses to variations in our model’s parameter values and to some of its maintained hypothesis. In a context where lumber’s initial contribution to aggregate income is around 2 percent, we estimate that a switch from lumber to nonlumber uses of tropical forests could cost up to 4 percent of baseline income. Of broader significance is the implication that the associated dynamic general equilibrium multipliers are consistently greater than unity, and often close to two in value. These large income multipliers are observed despite an assumed recovery of income through the reallocation of mobile factors initially employed in lumber activities, and an increase in returns to capital. In this study, a terms of trade deterioration, prompted by the loss of lumber foreign exchange revenue, accounts for about one-half of the total income losses we observe. For economies that rely to a greater extent than Malaysia on lumber foreign exchange revenue, these terms of trade-induced losses could be more important still. From a policy perspective, our results provide a benchmark against which the contingent valuations and other imputations of the monetary value of nonlumber use values of tropical forests may be gauged.  2000 Society for Policy Modeling. Published by Elsevier Science Inc.

Address correspondence to F. Harrigan, Strategy and Policy Office, Asian Development Bank, P.O. Box 789, Manila, Philippines. With the usual disclaimer, the author would like to thank David Demery, J. Malcolm Dowling, Jr., Chris Edwards, Peter McGregor, Kim Swales, and Jeffrey Vincent for helpful comments. The paper also benefited from the comments of participants of the Economics Department seminar program at the University of Melbourne, and from participants comments at the Harvard Institute for International Development inaugural workshop on Malaysian economic development. Received February 1997; final draft accepted September 1997. Journal of Policy Modeling 22(4):491–531 (2000)  2000 Society for Policy Modeling Published by Elsevier Science Inc.

0161-8938/00/$–see front matter PII S0161-8938(97)00158-0

492

F. Harrigan

1. INTRODUCTION Policies that seek to correct environmental externalities raise distributional tensions not only within countries but also sometimes between them. Where a nation owns resources that others regard as part of the global commons, it could be compensated for exporting that part of any conservation benefit stream not captured locally. This principle has appeal, and may be a political precondition for conservation, where the resource economy is “poor” and other countries are “rich.” While disagreement about environmental cost–benefit calculus is likely to be commonplace, partly because of uncertainty about the associated science (Cline, 1991), the economic principles that might govern compensation are clearer. The costs borne by a nation that conserves a global resource will be bounded from above by the sacrifice of consumption that conservation entails. The actual costs will almost certainly be less than this ceiling, because some residents of the resource-owning economy will benefit from conservation. The minimum other nations should be prepared to pay for conservation is what it would cost them to secure equivalent net benefits by alternative means. In this paper, we attempt to measure the loss of “metered”1 income that could occur when an economy surrenders the lumber value of its forests to nonlumber uses. We estimate these costs for Malaysia, one of the world’s largest producers of tropical lumber products. However, our analysis, which is largely counterfactual in nature, has more general applicability. We find that the indirect income loss entailed by a switch of forest resources to nonlumber uses dominates the direct loss, and that the associated general equilibrium income multiplier is greater than one. In this study, a terms of trade deterioration triggered by a loss of lumber foreign exchange revenue has a large adverse effect on household real income. In principle, there is no difficulty in weighing these income losses against the environmental benefits that conservation would secure. But, in practice, there are formidable measurement problems. Many of the markets in nonlumber uses of forests (e.g., 1 Here we use the term “metered” income in the sense of Nordhaus (1991) to refer to incomes that have a counterpart market transaction, and which would therefore normally enter into the measurement of national income. The adjective is intended to signify that there are other incomes that go unmeasured.

TROPICAL FOREST CONSERVATION

493

biodiversity) are largely missing, and obtaining reliable estimates of existence and option values is problematic. While contingent valuation and other methods of imputation promise some progress in the assignment of monetary values to conservation benefits, these exercises are costly, and not without their limitations.2 For these reasons, we do not attempt to measure those offsetting conservation benefits that would accrue to Malaysian residents, nor do we measure those benefits whose incidence might be more widely felt. In the next section of the paper, we describe the contribution of lumber resources to Malaysian economic development. In Section 3, we motivate our approach to the analysis of forest conservation, and describe the main features of the general equilibrium framework that we use in our calculations. We explain the design of our stimulations in Section 4. Section 5 discusses the main results of our paper, and tests their sensitivity to key assumptions. The concluding section briefly considers some of the policy issues raised by our analysis. 2. MALAYSIA’S LUMBER RESOURCES AND ECONOMIC DEVELOPMENT The FAO (1993) estimate that 60.2 percent of the land area of Malaysia was forest in 1990. Of this, 53.5 percent was natural forest and the remainder plantations. Between 1981 and 1990, the FAO estimate that the natural forest area of Malaysia was being depleted at an annual average rate of 2 percent, which is high by both regional and world standards. If deforestation were to continue at this rate, the half-life of Malaysia’s remaining tropical forests would be only 35 years. Alarming as these figures may seem, estimates of the rate of deforestation may not fully account for changes in the stock or quality of forest resources. Often, the degradation of forest resources may be more severe than the loss of forest area suggests (Burns, 1986). Between 1980 and 1990, 2.6 percent of Malaysia’s forest area was logged annually, and the vast majority of this logging was in previously unlogged areas (FAO, 1993). The annual lumber harvest in Malaysia doubled between 1971 and 1989, and lumber stocks could easily have halved over the same period. 2

Economists Plan to Evaluate the World’s Rainforests. Sydney: Reuters, April 18, 1995.

494

F. Harrigan

Logging, however, is only one of many sources of tropical deforestation. In Malaysia, land settlement, shifting cultivation, fuelwood consumption, natural fires, the conversion of native forest for commercial agricultural, and (in earlier years) tin mining have all contributed to deforestation. Unfortunately, it is impossible to accurately identify the share of deforestation attributable to these and to other causes. Nevertheless, it has been estimated that the reduction in the stock of Malaysian lumber due to nonlogging sources may be between two to three times that caused by logging itself (Gillis, 1988). But while land settlement and the conversion of forest area for commercial agriculture were responsible for much of Malaysia’s deforestation in earlier decades, their rate of encroachment on forest areas has since fallen. Today, lumber extraction makes a larger contribution to deforestation than in the past. The environmental impact of deforestation depends on how it occurs. Some sources of deforestation have more benign environmental effects than others.3 There can, however, be little doubt that, in general, deforestation has degraded animal (and human) habitats, interfered with nutrient cycles, resulted in a loss of biological diversity, threatened water resources, caused the loss of nonlumber forest outputs (such as rattan, foodstuffs and potential medicines), eroded educational, recreational, and tourist amenity, and contributed to the accumulation of carbon in the upper atmosphere. To an extent, deforestation in Malaysia has been the result of a cocktail of market and institutional failures that have led to stumpage values4 that do not adequately reflect the nonlumber use and other nonuse (including option and existence) values of tropical forest resources. For example, ill-defined property rights and insecure tenure have created incentives for timber to be “mined” in a context of slow growing stumpage values (see, e.g., Vincent, 1992). However, deforestation has also been a consequence of policies that have consciously traded off the value of 3 For example, the conversion of forest area for commercial agriculture, if it involves land clearance through burning, is unequivocally bad for CO2 concentrations in the upper atmosphere. If, however, lumber is felled and is used in durable wood products, carbon sequestration can continue for some time. Natural tropical forest fires are a particularly damaging form of deforestation. They are often aggravated by earlier deforestation that has eroded forest moisture. In 1994, tropical forest fires covered much of the of Borneo, West Malaysia, and Singapore in a haze for several months of 1994. 4 Stumpage values are essentially the net price of lumber.


495

forest land area against other uses. The transfer of forest land to permanent tree crop agriculture (palm oil and rubber) has undoubtedly generated substantial income for Malaysia. Equally, concession royalties and other taxes extracted from lumber and tree crop sectors have long provided an important source of finance for the Malaysian government. Nor should it be overlooked that land settlement has made a significant contribution to enabling Malaysia’s rural poor (IBRD, 1993). But the benefits of other policies are in doubt. For example, Vincent (1992) concludes that attempts to foster the development of local wood-based industries through log export bans and other measures have been costly. For every sawmill job created in Peninsular (West) Malaysia between 1973 to 1989, the economy sacrificed both value-added and export revenue (Vincent, 1992). Also, by reducing the local price of lumber, and subsidizing inefficient producers, log bans may have accelerated deforestation (Braga, 1992). Our objective in this paper is not, however, to determine whether Malaysia has managed its tropical forest resources prudently, or to suggest how they ought to be managed in the future. Dasgupta and Ma¨ler (1991) explain the complex nature of “sustainable development” and identify its relationship to renewable resource exploitation. And when preferences vary widely within or between societies, the identification of a socially “optima” program for resource use may not even be possible. In isolating the income repercussions of tropical forest conservation, we provide information that policy makers may weigh against the nonlumber value of tropical forests. But, in the absence of complementary information about conservation benefits, no welfare inferences can be drawn from our analysis. 3. THE MODELING APPROACH Conceptually, we portray tropical forest conservation in terms of the permanent withdrawal of an immobile resource from the traded goods sector of a small open economy. Malaysia is, of course, small in the sense that its activity has little impact on the rest of the world, but it is not necessarily small in the sense that it is a price taker in world goods markets (see below). We know from Corden and Neary (1982) that, in general, the withdrawal of an immobile resource from the traded goods sector of an economy will lead to a reduction in (“metered”) aggregate income,

496

F. Harrigan

an accompanying depreciation of the real exchange rate,5 and a movement of resources from the non-traded to the traded goods sectors. Because the magnitude of the resource shock that we consider is large, these interlocking general equilibrium repercussions cannot safely be ignored. It is for this reason that we use an empirical general equilibrium model of the Malaysian economy to illustrate the effects of conservation. As we shall see, a partial equilibrium analysis would give seriously misleading results, and an empirical macro model would not cover well the mechanisms through which income changes are propagated. Our applied general equilibrium model (Demery et al. (1992)) is not unlike the family of models described by Robinson (1991), and more fully articulated by Bourguignon et al. (1992). It identifies traded and non-traded goods sectors within a broader classification of 13 commodities. “Lumber” and “Tree Crop” activities (and commodities) are separately identified. The commodity output of both these activities is traded. A third agricultural activity (“Other Agricultural”) produces commodities (such as fish and fruit) that are primarily destined for domestic consumption. There are three manufacturing sectors, one of which is “Resource Based.” This “Resource-Based” manufacturing sector includes some downstream lumber activities, including the manufacture of wooden furniture, and paper and pulp products. The other manufacturing sectors are classified as “Export Oriented” and “Domestic.” Non-traded goods and activities include “Construction,” “Dwellings,” and “Public Services.” The outputs of “Private Services” and “Utilities” are largely but not completely nontraded. Finally, “Oil and Gas” and “Other Mining” activities complete our sectoral division of economic activities. There are both mobile and immobile factors of production. Capital and land, which are aggregated in a Hicks composite factor, are quasi-fixed, with their sectoral allocation responding to relative rewards through the allocation of net investment. The aggregation of capital and land is dictated by a lack of data. Such aggregation is valid if the relative rewards to capital and land do not change, or if technology is separable in capital and land. We consider the significance of the aggregation of capital and land, and its quasi-fixed nature, further in Section 5 below. Labor is 5 Throughout, the “real exchange rate” refers to the price of nontraded to traded goods, and the “terms of trade” refers to the price of imports to exports.


497

intersectorally mobile. We distinguish between three different skill types (“Unskilled,” “Semiskilled,” and “Professional and Skilled”) and between workers in “Land-Based” and “Nonland-Based” activities. Producers in each sector, except lumber, solve static profit maximization problems subject to multilevel (constant returns CES) technology constraints. These determine input demands and output supplies. In the lumber activity the representative producer faces an output constraint and, given output, minimizes cost. Parameteric variation of this output constraint facilitates the measurement of changes in income associated with lumber conservation. The market for Malaysian lumber is cleared through rationing exports. Unsatisfied domestic demand is met through imports. This treatment seems reasonable given the priority that Malaysia has given to securing lumber for local supply (see above). In other sectors, equilibrium is achieved through relative price adjustments. Where lumber stocks are managed as a renewable resource, producers maximize the expected net present value of their lumber stock by harvesting lumber until the real interest rate equals the lumber growth rate plus the rate of expected appreciation of its stumpage value. This assumes that stumpage values are nonnegative, and that second-order conditions are satisfied. However, a conjunction of insecure property rights, old lumber, and slowly growing stumpage values has encouraged concession holders to “mine” lumber in Malaysia (Gillis, 1988; Vincent, 1992). In circumstances like these, where planning horizons are short, intertemporal first-order conditions are irrelevant, and producer behavior can reasonably be represented in static terms. In our model, aggregate consumption is a function of household disposable income and private sector nonhuman wealth.6 A representative household then allocates its aggregate expenditure between goods using a linear expenditure system. We model import and export demands using Armington (1969) functions. Activity investment demand is exogenous in lumber and tree crop sectors, and is an increasing function of Tobin’s “q” 7 elsewhere. Transactors’ stocks of net wealth (measured at the beginning of the period) are supplemented by their (end of period) financial 6 We assume that all transactors form their expectations adaptively. If we were instead to assume that consumers optimized intertemporally and that they formed their expectations rationally, then income losses would, by definition, be reduced over those we estimate. 7 Specifically, the ratio of the marginal revenue product of capital to its user cost.

498

F. Harrigan

surpluses. Net wealth is then allocated over assets using portfolio selection models in which relative returns and perceived risks are important. End-of-period asset market equilibrium occurs through the endogenous adjustment of asset yields, and the nominal exchange rate. Lumber workers, who are predominantly “unskilled,” sell their labor services in the “Land-Based” labor market that covers workers in agricultural, fishing, and lumber sectors. In the remaining sectors of the economy, there are separate labor markets for each of the three skill categories. In each period, wages adjust to clear the Land-Based labor market and the markets for Semiskilled and Professional workers. In the Unskilled labor market, we allow for the possibility that some workers who loose their jobs may experience a spell of unemployment. However, our wage adjustment function is parameterized so that unemployment reverts to its equilibrium rate quickly. This reflects the flexibility that Malaysia’s labor markets have exhibited for many years.8 Workers migrate from the land-based to other labor markets in response to (lagged) real consumption wage and unemployment differentials. Other than through migration, labor is inelastically supplied, and labor force growth is exogenous. There are four main departures in our treatment of the Malaysian economy from the analytical framework employed by Corden and Neary (1982). First, there is no requirement for trade balance in the sense of zero net exports. Second, and related to this, the terms of trade is endogenous. Third, the “behavior” of government is explicitly modeled. Finally, our model is dynamic rather than comparative static in nature. Each of these modifications lends greater realism to our calculations. External balance in a real trade model, such as Corden and Neary’s, is normally identified with a balanced trade restriction. Usually this “closure” requires that domestic absorption adjusts to ensure that net exports are zero (or some other exogenously determined number). In our model, the current account balance is endogenous. The nominal exchange rate adjusts (together with other asset yields) to satisfy the conditions for asset market equilibrium requirement, which include the requirement that the total 8 In fact, Malaysia for a long time imported significant quantities of labor and faced shortages of both unskilled and skilled workers (Government Press, 1994).


499

demand for foreign exchange be equated with its supply.9 Nevertheless, domestic absorption still responds to the current account balance through the influence the latter exerts on domestic wealth. Deficits, for example, reduce private wealth and consumption, thus allowing resources to be released from the non-traded to the traded goods sectors of the economy. This imparts stabilizing feedback to the balance of payments. In most simulations, we assume that the supply of foreign capital to the Malaysian economy is elastic. This assumption is not unrealistic, given Malaysia’s historical reliance on foreign saving.10 We do, however, also provide estimates of the income losses that could occur if foreign saving were inelastically supplied. In our model, the repercussions of the current account are felt by domestic transactors through accompanying changes in their net private wealth. The endogeneity of the terms of trade has important implications for our analysis. To the extent that conservation leads to a reduction in foreign exchange, the Malaysian economy may have to endure a fall in the price of its nonlumber exports and an increase in the local price of its imports to replace lost foreign exchange. The magnitude of such changes will be larger; the larger is the price elasticity of demand for Malaysian lumber, and the smaller the price elasticity of demand for nonlumber exports. Vincent (1992) notes that there are ready temperate substitutes for industrial roundwood and plywood produced from tropical lumber,11 and notes that competition from temperate woods is one reason why tropical lumber prices have not risen more quickly as world stocks have become depleted. Available econometric evidence supports this view and indicates that imports of tropical 9 The balance of payments condition is, in effect, the external closure, and the exchange rate is determined together with endogenous asset yields to satisfy the condition for asset stock equilibrium (demand ⫽ supply) in each period in each asset market. Ours is a Branson-like model of exchange rate determination in which long-run equilibrium is characterized by the adjustment of the real exchange rate to ensure a current account balance consistent with a stable ratio of external debt to income. The steady-state debt to income ratio depends on initial conditions, the path of autonomous variables, and the model’s structure. 10 Malaysia has frequently run current account deficits in the range of 5 to 10 percent of its GDP, although they have also posted more modest deficits in some years. Debt servicing has not proved problematic due to the fast nature of its income and export growth. Malaysia, of course, experienced a large withdrawal of foreign capital in the wake of the Asian crisis. 11 There are of course specialty woods such as teak, mahogany, and nara for which substitutes are not so readily available.

500

F. Harrigan

lumber are highly sensitive to price (see the studies cited by Vincent, 1992, and Parthama and Vincent, 1992). Therefore, our baseline assumption is that Malaysia is a price taker in world lumber markets. For Malaysia’s manufacturing exports we impose export price elasticities that are comparatively large (⭓5). Our values are close to those estimated by Riedel (1989) for Hong Kong’s manufactures. However, Muscatelli et al. (1992, 1994) suggest that manufacturing export price elasticities may be as low as 0.5. It is possible, therefore, that the nonlumber export price elasticities that we use in our reference simulations are upwardly biased. If such bias is present, we will tend to understate the terms of trade losses that would accompany lumber conservation. Because there is some uncertainty about the magnitude of export price elasticities for both nonlumber and lumber commodities alike, and because these play a central role in determining terms of trade changes, we also test the sensitivity of our results for departures from our baseline parameter assumptions. A loss of lumber output and export revenue would result in an erosion of Malaysia’s tax base. It is unlikely that a fiscal deterioration would go uncorrected. In our simulations, we assume that the Malaysian government is precomitted to real levels of recurrent expenditure, to a path for monetary (and bond stock) expansion, and to targets for public debt (expressed as percentage of GDP). To accommodate these, we assume that the government adjusts (equiproportionately) income taxes and its capital expenditure. The last important departure from a real trade model that we make is to provide a dynamic setting for our simulations. We consider income losses over a 10-year period. A comparative static analysis of the effects of conservation could be misleading because the adjustments to relative price and sectoral balance sheets are likely to be slow working in the presence of stock adjustments. Equally, the modeling of the steady-state effects of conservation is problematic.12 A 10-year period has been chosen because it is not too long to render our assumptions about the growth and structural evolution of the Malaysian economy fanciful, but it is sufficiently long to facilitate analysis in a context where there may 12 We would, for example, have to define precisely what is meant by “balanced resource technological change” (Nordhaus, 1991) and to make conjectures about the composition of Malaysian far into the future.


501

be further rapid tropical deforestation and significant structural change in the economy. The dynamic properties of our model resemble closely those of a neo-classical growth model. Aggregate economic growth reflects both factor augmentation and productivity growth (which is assumed to be Harrod neutral). We calibrate the model using 1990 Malaysian national accounts and related data. These data are then reproduced as our base year solution. The model is parameterized, and its exogenous data inputs set to generate a trend growth rate of GDP of 8 percent through to 1999. This growth rate is very close to the actual outcome over the period 1987–94. We configure our model in such a way that about 1 percent of aggregate GDP growth is attributable to growth of the “raw” labor force, and 3.5 percent each to the growth of the capital stock and to total factor productivity. The accumulation of human capital is subsumed in total factor productivity gains. Our assumptions imply substantial capital deepening over the period. In broad terms, we assume that the structure of the Malaysian economy evolves in such a way that it arrives by 1999 to the point the Korean economy had reached by 1990.13 A technical appendix provides fuller details of the model’s structure. 4. SIMULATION DESIGN Recent evidence points to a leveling off in Malaysian lumber harvests following a period of rapid growth (Government Press, 1994). But stationary or even declining harvests do not necessarily imply the sustainable management of lumber stocks (zero net harvests). In Malaysia, lumber harvests would have to fall substantially to be brought into balance with the growth of lumber. Concern over available stocks prompted a temporary ban on log exports that has been in force in Sabah since 1992. Sarawak has also announced plans to severely curtail logging in its forests.14 The absence of good data about stock levels and the shifting nature of policies toward logging raise some uncertainty about what “business as usual” might entail. For this reason, we measure 13 By 1999, we assume that both secondary and tertiary sectors of Malaysia contribute about 38 percent each to aggregate output. This is close to the 1990 Korean figures. To some degree these assumptions have been overtaken by the Asian crisis. 14 In Sarawak, the state government plans that the current (1993) log harvest of 18.8 million cubic meters will to fall to 15.0 million cubic meters by the end of the century.

502

F. Harrigan

income changes from two alternative baselines. The first baseline (A) assumes that lumber harvests and related output remains stationary at their initial (1990) levels through to the end of the decade. The second (B) envisages that harvests will decline at an average rate of 8 percent per year. Such a decline in harvests could occur because of the “hollowing out” of forest areas or because of the adoption of policies intended to lead to the better management of Malaysia’s forest resources. It seems likely that the actual outcome will occur somewhere within the band defined by our baseline assumptions.15 By applying these baselines in the measurement of income losses, we hope to identify a range within which actual losses might lie. Unfortunately, it is not possible to identify our baseline assumptions either with projected rates of deforestation or with reductions in lumber stocks. The stock-flow identities that govern the evolution of lumber stocks require information on opening balances, lumber growth rates, stocks destroyed by nonlogging sources of deforestation, in addition to assumptions about lumber harvested and replanted. Projecting the area under forest cover is more difficult still (see FAO, 1993). The data required for such projections are not available for Malaysia. However, we can infer from the FAO data that, absent other changes, stationary harvests would mean that deforestation would continue at a rate of at least 2 percent per annum. This would imply that by the turn of the century a maximum of only 44 percent of Malaysia’s land area would remain under natural forest cover. An 8 percent decline in harvests will probably secure a larger stock of lumber stands, but this is unlikely to increase Malaysia’s forest land area. Indeed, the Malaysian government currently plans to transfer another 3.5 million hectares of state forests to other land uses, which is a little over 10 percent of its remaining forest area. The issue of whether an incentive-based approach to conservation is preferable to a command-and-control approach is complex. In practice, elements of both approaches may have to be applied, and the dividing line between the two is not always clear.16 Markets in forest and ecological resources are difficult to create but, 15 Recent data indicate that largely because of quotas imposed by state governments sawlog production fell by 5.1 percent on its 1993 level. Exports of sawlogs fell by 14 percent in volume terms. See Government Press (1994). 16 For example, even for tradable permits associated quantities (or units) still have to be administratively determined.


503

equally, the administrative and efficiency costs of regulatory approaches to conservation may be large. Command and control policies make most sense where either there is considerable uncertainty about the effects of incentive based approaches, or the objective is to eliminate an unwanted activity altogether.17 The types of conservation policy we have in mind include permanently gazetting larger areas as protected or reserve forest and, more generally, tightening land use regulations in relation to existing forest areas. In our model, we represent “conservation” in terms of lower lumber harvests (flow outputs), and a commensurate reduction in stock inputs (composite land and capital) used in lumber production. To reflect possible constraints on agricultural extension, we restrict net investment flows in agricultural sectors. Limiting the encroachment of other activities, principally tree crop plantations, on forest area is likely to be an important aspect of conservation. In our model, tree crop output is endogenous, but will depend on the growth of (exogenous) net investment expenditures. The conversion of forest area for plantations entails substantial net investment in land conversion. Therefore, by reducing net investment expenditures in tree crop and other agricultural sectors we can, to the extent that capital and land are complementary, mimic land use restrictions. We measure conservation costs using a “compensating variation” measure. The compensating variation is the amount that households would have to receive to leave their consumption unchanged following conservation. In our model, we identify the compensating variation through the increase in unrequited transfers from the rest of the world (to domestic households) needed to satisfy the consumption constraint. Note that although these compensation payments are nondistortionary in the sense that they have no direct substitution effects, alternative lump-sum transfer could yield different results.18 Our measure of the compensating variation is: NCV ⫽

1999

兺

t⫽1990

dY row t ⫼ (1 ⫹ ␳)t⫺1990

T

兺

t⫽1990

Yt ⫻ 100 (1 ⫹ ␳)t⫺1990

. . .) ⫽ Ct(. . .)∀t), 兩 (Ct(dY row t

T 苸兵1990,1999其,

(1)

17 However, the announcement of a pending ban on logging might succeed only in accelerating deforestation if it further weakens concession holders tenure. 18 This is because their general equilibrium (indirect) effects will usually be different. Note that small changes in the value of the compensating variation can occur when we vary the elasticity to consume from income.

504

F. Harrigan

where dYrow is the change in real endogenous income transfers that, in each period, maintains private real consumption at its preconservation (baseline) level, Ct, ␳ is the discount rate, Y is real gross domestic income (GDI) in the preconservation (baseline) economy, and t indexes time. We report income losses both in base year (T ⫽ 1990) GDI units and in units of the present value stream of GDI (T ⫽ 1999). We measure real income using a GDI rather than a constant price GDP measure because there are, as we shall see, large terms of trade changes associated with tropical forest conservation. GDI is equal to (constant price) GDP plus a terms of trade adjustment defined as the “capacity to import,” less the volume of exports. The “capacity to import” equals nominal exports divided by the import price deflator. This definition of GDI is that used in the World Bank’s World Tables. To draw “welfare” implications from our analysis, our measure of the compensating variation would have to be adjusted to reflect the increase in the nonlumber value of forests created by conservation. Note, however, that because we assume that trees and land area is permanently transferred to nonlumber uses, we do not have to account for the increase in the value of the terminal lumber stock. Our assumptions imply that the lumber value of trees is lost as nonlumber values are created through conservation. Although it would be interesting to consider policies that preserved trees today with a view to supporting larger sustainable harvests in the future, we do not have the stock-flow information that would be needed to calibrate the required model of lumber growth. Also, were the policy issue one of sustainable harvesting, the income losses in Equation 1 would be offset by the present value of the (potentially infinite) sequence of future lumber harvests made possible by the deferral of harvests in earlier periods. 5. SIMULATION RESULTS 5A. The Anatomy of Income Changes To facilitate identification of the various mechanisms through which income losses are generated, we begin by measuring the effects of a 50 percent reduction in lumber flow outputs and stock inputs over their 1990 levels. This entails a uniform reduction in the levels of flow output and stock inputs in the lumber sector in each year. In all simulations, the reductions in flow output and stock inputs take effect in 1991 and continue through to the terminal year (1999). In 1990, investment demand is reduced to reflect


505

the lower level of stock inputs applied in lumber activity in later years. In this simulation, we place no restrictions on agricultural extension and employ baseline A. The compensating variation (NCV) under these assumptions is 2.2 percent (T ⫽ 1990) of the capitalized stream of baseline GDI, and 18.2 percent when measured in base year GDI units (T ⫽ 1990). In Table 1 (row 1) we show the values of the compensating transfers from the rest of the world in each period, expressed as a proportion of contemporaneous GDI. In the remaining rows we show lumber’s baseline and conservation income shares (rows 2 and 3), and we also record the impact of our conservation assumptions on a variety of variables when compensating transfers are suppressed (rows 4 to 14). On suppression of compensating transfers, GDI falls by 2.3 percent of its (capitalized) baseline value. This is very close to the income loss registered in the NCV. This is because the incidence of income losses falls almost entirely on households. Note also that the percentage reductions in private real consumption (row 5) are about twice the magnitude of the percentage declines in income. This is because the incidence of income losses falls almost entirely on households, and the consumption base from which they are measured is less than 60 percent of GDI. Reductions in wealth to some degree accentuate consumption losses. The most notable aspect of these results is that the magnitude of the overall decline in income is large compared to the initial downsizing of lumber activity. Since, over the period 1990–99, the share of lumber in GDI averages 2.1 percent in our baseline, a 50 percent reduction in output and stock inputs leads to a direct withdrawal of income of around 1 percent of GDI, some of which is recovered when mobile factors released from lumber find employment elsewhere in the economy. Therefore, it follows that the indirect loss of income is at least as great as the initial direct withdrawal. In this particular simulation the dynamic general equilibrium income multiplier is of the order of 2. To understand why income losses are magnified, it helps if they are decomposed. We start with a simple household income identity: y⬅

W R · L ⫹ ␪ · c · K. Pc P

(2)

In Equation 2, y is household income measured in consumption units, W is the nominal wage, Pc is the consumption deflator, L

Remittances (%GDI) Baseline share lumber (%GDI) Conservation share lumber (%GDI) GDI Consumption Exports Imports Terms of Trade External debt to GDI (%)2 Real consumption wage Unemployment rate unskilled2 Lending rate2 Aggregate physical capital stock Agricultural labor force

Year 0.03 3.06 3.06 ⫺0.02 ⫺0.12 0.46 ⫺0.46 ⫺0.13 ⫺0.36 ⫺0.23 0.01 ⫺0.01 0.00 0.00

1990 4.48 2.88 1.43 ⫺3.23 ⫺8.46 4.19 ⫺2.23 ⫺4.35 ⫺2.33 ⫺8.41 ⫺1.07 ⫺0.22 ⫺2.55 ⫺0.02

1991 2.97 2.67 1.34 ⫺2.87 ⫺6.05 1.94 ⫺1.45 ⫺3.25 ⫺1.55 ⫺6.38 0.36 1.86 ⫺2.53 ⫺0.63

1992 2.55 2.51 1.26 ⫺3.07 ⫺2.62 ⫺1.03 ⫺1.07 ⫺1.90 1.39 ⫺4.45 0.84 1.00 ⫺2.36 ⫺1.23

1993 2.44 2.32 1.16 ⫺2.75 ⫺3.71 0.56 ⫺0.75 ⫺2.31 1.94 ⫺4.29 0.15 0.02 ⫺2.24 ⫺1.73

1994 2.31 2.18 1.09 ⫺2.40 ⫺4.84 1.98 ⫺0.57 ⫺2.63 1.48 ⫺4.58 ⫺0.04 0.18 ⫺2.12 ⫺2.05

1995 2.16 2.04 1.02 ⫺2.28 ⫺4.39 1.55 ⫺0.49 ⫺2.29 1.67 ⫺4.24 0.17 0.36 ⫺1.99 ⫺2.25

1996 2.02 1.91 0.95 ⫺2.19 ⫺3.88 1.19 ⫺0.39 ⫺2.00 2.02 ⫺3.78 0.18 0.18 ⫺1.87 ⫺2.41

1997

1.83 1.78 0.89 ⫺2.01 ⫺3.79 1.40 ⫺0.22 ⫺1.93 2.03 ⫺3.49 0.08 0.02 ⫺1.75 ⫺2.51

1998

1.72 1.66 0.83 ⫺1.85 ⫺3.78 1.62 ⫺0.10 ⫺1.88 1.83 ⫺3.36 0.03 ⫺0.03 ⫺1.65 ⫺2.57

1999

1 Lines 4 to 14 refer to results from a simulation in which we compare pre- and postconservation outcomes, having relaxed the consumption constraint. A discount rate of 10 percent is applied in the calculation of the compensating variation. Because the discount rate enters both the numerator and denominator of Equation 1, the compensating variation is comparatively insensitive to the choice of discount rate (see also Table 4). 2 This is the level difference in this ratio. Level differences are also reported for the unemployment rate and the interest rate.

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Row

Table 1: Summary of the Effects of Log Harvest Restrictions (% Differences from Baseline A) 1

506 F. Harrigan


507

is aggregate labor units (assume full employment of labor), R is the nominal capital rental rate, K is the stock of capital and 0 ⬍ ␪ ⬍ 1 is the share of “profits” accruing to households. To prevent the algebra from getting cluttered, taxes are suppressed, labor is aggregated over markets and capital over sectors, and household claims on profits are assumed to be parameteric in Equation 2. Applying the difference operator to Equation 2 and manipulation gives19 ⌬y ⬅ ␶1 · (⌬w · (L ⫺ ␺ · LF ) ⫹ ␪ · ⌬ r · (K ⫺ ␺ · KF )) ⫹ ⌬␶ · y0 ⫺ ␶1 · ((w0 · ␺ · LF ⫹ ␪ · r0 · ␺ · KF ) ⫺ (w1 · ␺ · LF ⫹ (1 ⫺ ␭) · ␪ · r1 · ␺ · KF )),

(3)

where ⌬ is the difference operator, w is the real product (not consumption) wage, r is the real product rental, ␶ is the ratio of (current period) local producer prices to consumption prices, ␺ is the percent reduction in lumber activity over the period (expressed as a decimal fraction), ␭ is the share of the lumber capital stock that is immobile, the subscripts 0 and 1 index the periods before and after the reallocation of labor and the loss of lumber capital, and the superscript F denotes factor quantities initially in the lumber sector. The first term in parenthesis on the RHS of Equation 3 is a measure of the change in household income arising from changes in real product factor prices for given endowments of labor and capital. The second term measures the loss in the command of initial household income over consumption goods when consumer prices rise relative to the cost of local output. We attribute this effect to terms of trade changes. The third expression has two components. The first part is the direct loss in household income that would occur if a fraction ␺ of lumber capital and labor income were eliminated, and all factors were immobile. The second term is the recovery of household income that occurs when mobile factors initially employed in lumber are reallocated to other sectors. Using Equation 3 we can now condense the income changes that are reported in Table 1. We do this for the change in GDI 19 Changes in factor incomes are weighted by the current ratio of producer to consumer prices (the “terms of trade”), and the “terms-of-trade” change in the second term of Equation 3 is measured from base income. Income changes can also be decomposed where the weight on factor income changes is the base ratio of producer to consumer prices, and “terms-of-trade” changes could be measured for current rather than base household income.

508

F. Harrigan

in the impact period, 1991, reported in row 1 of Table 1. It is important to understand that these are the income changes that would occur in the absence of compensation. Also, to facilitate comparison, the left and right hand sides of Equation 3 are divided through by base GDI in 1991. All values in Table 2 are calculated directly from the results of model simulations or are defined by the nature of the experiment itself. Table 2 identifies “terms-of-trade” changes, as reflected in the change in the relative price of producer to consumption goods, as the effect that contributes most to the net income loss, accounting for just under half of the total decline.20 Next in importance is the decline in the real product wage that occurs when lumber capital is withdrawn from production. This accounts for around one-quarter of the net decline in income. The incidence of real wage reductions would, of course, be felt most directly by those workers and households in the agricultural sector, and by unskilled workers elsewhere. Therefore, it seems that conservation would be likely to have an adverse effect on the distribution of worker and household income.21 However, the reduction in household income caused by increased taxes is small, and accounts for only 5 percent of the net loss. Our computations also make it clear that a substantial proportion of the direct loss of lumber income is recovered both when mobile factors are reallocated, and when the return to capital increases as its stock shrinks. Taken together, just under 65 percent of the direct loss of lumber income is recovered through the reallocation of mobile factors and through increased profits. The terms-of-trade–induced income losses that are highlighted in Table 2 (and row 8 of Table 1) occur as the price of imports rise relative to the price of exports. The real exchange rate also depreciates as the price of traded to non-traded goods increases. 20 This ranking and the respective contributions may, however, change over time. Specifically, once wages fall in the agricultural sector this will lead to out-migration and there will be income losses as migrants experience temporary unemployment in nonagricultural labor markets. These effects are, however, small, and wage adjustments are fast. Also, when investment is reduced in agriculture to mimic land restrictions, this will represent an additional source of income loss. Space considerations preclude a decomposition for each and every year. 21 There is only one household identified in the version of the model used in these simulations so that it is not possible to measure distributional effects directly. It is unlikely that households with unskilled workers are likely to benefit much from the increase in capital income.

Annotation Value of NCV for 1991, Table, row 1. Calculated as in Equation 3, expressed in real GDI units Calculated as in Equation 3, expressed in real GDI units Calculated as in Equation 3, expressed in real GDI units Note: households total claim on operating surplus is approx. 38 percent, and total profits increase by 1 percent of GDI in our simulation. The factor ␶1 equals 0.868. Calculated as in Equation 3. This is 50 percent of baseline share of GDI recorded in Table 1 Calculated as in Equation 3. Expressed in real GDI units Note: initial share of labor in Lumber value-added is 0.43 percent, and that the product wage of mobile labor falls in the simulation Capital is assumed ex-post immobile (␭ ⫽ 1) (see p. 6) Calculated from model simulation Summation of above

Value (% GDI) ⫺4.48 ⫺2.20 ⫺1.40 ⫹0.33

⫺1.30 ⫹0.51

0.00 ⫺0.27 ⫺4.33 ⫺0.17

Total Terms of trade Labor share of factor product Capital share of factor product

Direct loss of lumber income Recovery of mobile labor income

Recovery of capital income Tax losses Total losses Income changes not elsewhere included.

Household income changes to GDI

Table 2: The Decomposition of Income Changes in 1991

TROPICAL FOREST CONSERVATION 509

510

F. Harrigan

These relative price changes facilitate a movement of resources into the traded goods sector and leads to the recovery of some of the foreign exchange that is lost as lumber exports contract. In raising the cost of consumption relative to the price of domestic output, the deterioration in the terms of trade reduces real income through the Laursen/Metzler effect.22 The dynamics as well as the composition of income losses are of some interest. Although the negative impact of conservation on income and consumption recedes as lumber activity contracts, losses nevertheless persist (see Table 1, rows 1 and 4). Because we have assumed that the withdrawal of lumber capital is permanent, lower product wages in the agricultural labor market and capital incomes in the lumber sector endure. Also, an emerging deficit on the current account reduces private sector wealth and, with a lag, leads to a (stabilizing) fall in private consumption (rows 5–9 of Table 1). Finally, the resource gap that the current account deficit mirrors, causes a small increase in domestic real interest rates. This raises the user cost of capital, lowers Tobin’s “q,” causing the aggregate capital stock to fall (row 13 of Table 1). This further reduces the real product wage and household wealth. The qualitative nature of the resource switching effects that occur with lumber conservation are exactly as would be predicted by a real trade model. Output in those sectors that are comparatively sheltered from world trade (Construction, Utilities, Public Services, Private Services, and Domestic Manufacturing) contract following conservation. The largest gains in output are in those sectors that we assume are most open to world trade (Export Manufacturing). By the terminal year of our simulation (1999), constant price value added in Construction falls over baseline by 2.8 percent and value added rises by 4.2 percent in Export Manufacturing. 5B. The Results with Alternative Baselines and Alternative Conservation Policies Having now explained the basic mechanisms through which conservation reduces “metered” income, we now turn to the issue 22 The income effect of exchange rate changes is often referred to as the Laursen/Metzler effect.


511

Table 3: Variations in the NCV with Baseline and Conservation Assumptions Conservation assumptions Baseline

Harvests

Land use

NCV T ⫽ 1999

NCV T ⫽ 1990

Stationary (A) ⫺8% pa (B) ⫺8% pa (B) Stationary (A) ⫺8% (B) ⫺8% (B) Stationary (A) Stationary (A)

⫺50% ⫺50% ⫺50% ⫺50% ⫺97.5% ⫺97.5% ⫺97.5% ⫺97.5%

— — Restricted Restricted — Restricted — Restricted

2.23% 1.10% 2.16% 3.34% 3.30% 4.32% 4.39% 5.46%

18.23% 8.91% 17.62% 31.20% 26.77% 35.37% 35.41% 44.85%

of how losses change with variations in our baseline and conservation assumptions (see Table 3). Consider first what happens when baseline lumber flows and stocks fall at an annual rate of 8 percent annum (i.e., baseline B), rather than remain constant (as in baseline A).23 Not surprisingly, this reduces income losses and the NCV index falls to 1.1 percent of the capitalized stream of GDI, and to 8 percent measured in base year (1990) GDI units. We noted earlier that the conversion of forest land area, especially for commercial agricultural uses, has been a major source of deforestation in Malaysia. It seems likely, therefore, that conservation would also restrict agricultural encroachment on forest land area and inhibit the growth of agricultural output. If gross investment in the agricultural sector is set to replacement level, growth of agricultural output can then only occur either through technological progress or the application of more labor. This is precisely the way in which production possibilities in agriculture would be constrained if capital and land were complementary and further agricultural extension on forest area was prohibited. Of course, it is possible that agricultural extension could still take place on nonforest lands so that our treatment may exaggerate the effect of land restrictions on income. Moreover, to the extent that capital and land are substitutable, output losses could be ameliorated through the adoption of more capital intensive methods of farming. Although substitution possibilities are likely to increase with the elapse of time, it would appear that in the 23 In this simulation baseline lumber output is in fact a little lower than conservation output in the terminal year but is larger in all earlier years.

512

F. Harrigan

production of Malaysian tree crops capital cannot be easily substituted for other factors.24 In our baseline simulation, the growth of output of the tree crop sector averages 3.7 percent per annum. This is very close to the 3.8 percent increase in the volume output that was observed between 1990–93. On the imposition of land restrictions, output growth falls to an average of 1 percent per annum over the period 1990–99. As a consequence, income is further eroded and the NCV (T ⫽ 1999) increases by about 1 percent. This result is largely insensitive to the baseline assumptions applied.25 By comparing the effects of a 50 percent reduction in harvests with those of a 97.5 percent reduction, some indication of the marginal costs of conservation can be obtained.26 For those cases where baseline lumber harvests are assumed to remain stationary (at 1990 levels), marginal costs are almost constant. Over the first 50 percent reduction in lumber activity, the compensating variation averages 0.0446 percent of GDI for each one percent reduction in activity. Over the next 47.5 percent reduction in the level of lumber activity, the average compensating variation increases to 0.0455 percent of GDI. Where we assume that baseline lumber activity declines by 8 percent per year, a different picture seems to emerge. Now, over the first 50 percent reduction in lumber activity, the average loss is 0.022 percent of GDI for each percent reduction in activity. But this figure rises to 0.046 percent of GDI over the next 47.5 percent reduction. This increase in cost occurs because with declining baseline output, the proportional contraction in lumber output approximately doubles as the level

24 In recent years, output of the tree crop sector in Malaysia has been constrained by shortages of unskilled labor. This in itself has helped slow the pace of forest land conversion. Opportunities for the adoption of more capital intensive techniques seem limited (Government Press, 1994). 25 It should be understood that the apparent invariance of the cost of land restrictions to the underlying level of harvests reflects the assumed independence of capital and land inputs in lumber and agricultural sectors. We cannot draw any inferences from these results about an “optimal” policy mix of restrictions on output and land use. Such a judgment would ultimately rest upon whether the objective is to preserve lumber stocks, forest area, or some combination of both. It would also reflect the desired attributes of the preserved forest area and of the implementation and monitoring costs of the different restrictions. 26 For technical reasons, it was not possible to eliminate lumber harvests completely. In any case, a complete ban on lumber logging would be almost impossible to police. It is likely that illegal logging would persist even under the strictest conservation regime.


513

Table 4: Calculations of NCV (T ⫽ 1999) n ⫽ 0.04 g⫽ ␳

0.08

0.07

0.06

0.05

0.04

0.03

0.10 0.09 0.08 0.07 0.06 0.05 n ⫽ 0, ␳ ⫽ 0.1

0.803 0.615 — — — — 0.604

1.131 1.000 0.802 — — — 0.853

1.421 1.338 1.214 1.009 — — 1.072

1.677 1.638 1.580 1.483 1.289 — 1.267

1.905 1.905 1.905 1.905 1.905 1.905 1.441

2.108 2.143 2.195 2.281 2.454 2.971 1.597

n⫽0 g ⫽ 0.03 1.597 1.564 1.523 1.469 1.398 1.298

reduction increases from 50 to 97.5 percent of initial (1990) output. Therefore, it appears that income losses increase almost linearly with the decline in lumber output. A limitation of our analysis is that we have calculated costs over a time horizon that is short relative to the life cycle of tropical lumber species. It is also short relative to the period over which the benefits from conservation could be expected to accrue. As we have already observed, lengthening the simulation period would render our results sensitive to speculation about the rate and form of Malaysian economic development into the distant future. Nonetheless, by extrapolating from our results we can provide some indication of what costs might be over a longer period. An infinite horizon, approximation to Equation 1 is:27 NCV * ⫽ NCV1990 · ␭ ⫹ (1 ⫺ ␭) · NCV1999 · 兩 ␳ ⬎ g, n

T ⫽ 1999,

(␳ ⫺ g) · (1 ⫹ n) (␳ ⫺ n) · (1 ⫹ g) (4)

where g is the (post-1999) assumed steady-state growth rate of GDI, n is the assumed steady-state growth rate of income compensation, ␳ is the discount rate, NCV1990 is the observed NCV (T ⫽ 1999) value for the period 1990–99, NCV1999 (T ⫽ 1999) is the observed ratio of compensating income remittances to GDI in the terminal year, and ␭(g, ␳) measures discounted GDI over the period 1990–99 relative to the limit of the series. In table 4, we 27 This is the limit of Equation 1 as time approaches infinity assuming all variables follow steady-state trajectories from 1999 onwards.

514

F. Harrigan

summarize the results that we obtain by applying this formula. Results are based on the same assumptions used for Table 1. It can be seen that lowering the assumed steady-state growth rate of output increases measured costs. This is because the prominence of lumber activity in the economy now increases. Although, in general, lowering the discount rate reduces costs, it does so only where the rate of growth of output (g) is greater than the rate of growth of compensating remittances (n). If remittances grow more quickly than aggregate output, suggesting a post-1999 increase in the contribution of lumber to aggregate Malaysian income (something that is highly unlikely), a reduction in the discount rate will, in fact, magnify costs. Note also that varying the rate of growth of compensating remittances has a larger effect on costs the smaller the discount rate, and rate of growth of output. It is reasonable to infer from these results that long-run costs are likely to be less than our finite horizon estimates, but it is difficult to be precise by how much. 5C. Sensitivity to Model Assumptions Finally, we consider how sensitive our results are to our model specification. There is, of course, in any CGE model a large number of “free parameters” and a large number of behavioral assumptions that can be varied. Even where theory informs us about the qualitative effect of such changes, their quantitative implications may not be clear. Therefore, in Table 5, we report and annotate the effects that variations in some of our more important assumptions have on costs. Again, all changes are measured from the results reported in Table 1. As we cautioned earlier, terms of trade effects are likely to be sensitive to our assumptions about export price elasticities for lumber and nonlumber products. In Table 5, we report the result of three experiments that bear on this matter. First, we halve all nonlumber export and import price elasticities, and recalculate income losses. Recall, that some evidence (Muscatelli et al., 1993) suggests that our reference elasticities may be biased upwards. When elasticities are halved, income losses (as measured by the NCV) increase by 0.242 percent of GDI over their reference value (2.2 percent of GDI). Next, in a symmetrical fashion, we double all reference nonlumber export and import price elasticities. This reduces income losses by 0.176 percent of GDI. Taken together, these results suggest that income losses respond nonlinearly to

Imposition of a constraint on the ratio of terminal external debt to GDI No lumber capital income accrues to domestic households

Halving trade price elasticities in all but those sectors where Malaysia is assumed to be a price taker in world markets. Doubling trade price elasticities in all but those sectors where Malaysia is assumed to be a price taker in world markets. Unit price elastic lumber exports

Assumption

Table 5: Summary of Sensitivity Tests

Model more closely reflects a “law of one price” economy, terms of trade losses are now more muted Lumber foreign exchange revenues are invariant with respect to the flow of lumber output, income losses are reduced The current account now exerts stronger effect on terms of trade, income losses are increased. Partial elimination of the income losses that arise because of capital immobility, income losses are reduced

⫺0.18 ⫺1.1

⫺0.29

⫹0.8

Greater independence of domestic from world prices, terms of trade losses are increased

Explanation

⫹0.24

⌬ in NCV percent GDI (base ⫽ 2.2%)

TROPICAL FOREST CONSERVATION 515

516

F. Harrigan

trade price elasticities, with marginal changes in the NCV tapering off as price elasticities increase. If nonlumber trade price elasticities were, in fact, as low as 0.5 (see, Muscatelli et al., 1993), then losses would rise from 2.2 percent to 3.1 percent of GDI. We now examine the effect of the lumber export price elasticity on income losses. Recall that we have assumed that the foreign exchange price of Malaysian lumber is set independently of Malaysia’s supply (see, Section 3). Now we model income losses assuming that there is a unitary export price elasticity for Malaysian lumber.28 Accordingly, as Malaysia’s lumber exports decline the price of Malaysia’s lumber rises to maintain lumber foreign exchange revenues. Not surprisingly, the “sterilization” of lumber foreign exchange losses ameliorates the impact of the deterioration in Malaysia’s terms of trade. The compensating variation falls by about 1 percent of GDI. Reassuringly, this change in income compensation is consistent with the share of total losses that we attributed to terms of trade changes in Table 2. A lower export price elasticity for lumber implies that a larger incidence of conservation costs fall on nonresidents.29 Note, however, that if conservation were to entail the complete cessation of lumber trade, then, irrespective of the price elasticity of demand for lumber, there could be no mitigation of foreign exchange losses. The composition as well as the scale of output losses change when foreign exchange losses are sterilized. A more moderate depreciation of the real exchange rate now means that the local currency price of traded to non-traded goods does not rise by as much as previously. Real profits in non lumber export sectors are, as a consequence, comparatively depressed: Tobin’s “q” falls, and the incidence of output losses falls more heavily on real investment expenditure. Net exports expand by much less than previously, 28 Our objective is to “sterilize” foreign exchange losses as lumber exports are withdrawn. We cannot do this exactly because different users of lumber have different price elasticities of demand, and face different markups over basic price to accommodate taxes, transportation, and distribution margins. In the simulation reported in the text, direct foreign exchange losses are sterilized almost exactly in the base period, but the effectiveness of sterilization is gradually eroded and by the end of the simulation period only 80 percent sterilization of the impact foreign exchange losses is achieved. 29 If the market elasticity for lumber were below unity, collusion between producing nations might both increase their income and help conserve forest area, perhaps even eliminating the need for compensation. If the elasticity facing Malaysia as a single producer were below unity, it could improve its terms of trade by restricting output without coordinating its actions with other producers.


517

and private consumption falls by much less. However, as reduced real investment cuts into the capital stock, the growth of real wages in inhibited. This changes the temporal profile of consumption losses. Real consumption losses, though smaller, now show much more persistence. Next, we consider the effect that our “external closure” has on measured income losses. External debt rises in our reference simulation, but by only 1.83 percent of GDI by the terminal year of our simulation (see Table 1). Although this could probably be easily financed, it is worth considering what might occur if there were a constraint on foreign saving. In such circumstances, the income losses associated with conservation would increase. Domestic absorption, including household consumption, would then have to fall to accommodate the reduction in the supply of saving from nonresidents. To a degree, we can mimic the required adjustments by precluding substitution between domestic and foreign liabilities. The current account deficit now contracts and the income compensation needed to compensate domestic consumers rises by 0.22 percent of GDI. The smaller current account deficits that now occur reduce the ratio of terminal debt to GDI by 0.5 percent. We infer from these responses that if a constraint on foreign borrowing were to be expressed in terms of a cap on the ratio of external debt to GDP, say set equal to its baseline terminal value, this would increase overall income losses from 2.2 percent to about 3.0 percent of GDI.30 Finally, we consider what would happen to income losses if households received no rents or operating surplus from lumber activities. This has the effect of eliminating some income losses because we have assumed that lumber capital income is entirely lost when lumber values are surrendered to other forest uses. Imposition of this assumption, reduces income losses fall by nearly 0.3 to 1.9 percent of GDI. However, because labor productivity is still adversely affected by the immobility of the lumber capital stock, not all indirect losses are eliminated in this simulation. If we were to assume that lumber capital were fully mobile, the results of Table 2 (row 5 less row 6) suggest that income losses 30 It is not possible to impose this constraint directly in the model. We arrive at this inference by dividing the 1.8 percent increase in terminal debt (Table 1) by the reduction of 0.5 percent reported in the text. If income losses respond linearly to changes in debt, multiplying (1.8 ⫼ 0.5) by the 0.22 percent increase in income losses, gives an overall increase in income losses of about 0.8 percent of GDI.

518

F. Harrigan

of up to 0.8 percent of GDI might be recovered in the impact period (1991).31 6. CONCLUSIONS In this paper we have estimated the loss in “metered income” that Malaysia might experience if it conserved its tropical forest resources. Because no account has been taken of the possible benefits of conservation that arise through the increase in the nonlumber value of trees preserved, our estimates should not be interpreted as net costs, nor should they be identified with prospective changes in “welfare.” Our principal findings are summarized in Tables 1, 2, and 3, and the results of our sensitivity analyses in Tables 4 and 5. For a given conservation policy, “metered” income losses are smallest if mobile resources are efficiently and quickly reallocated, foreign exchange revenues are resilient to the loss of lumber output, and “business as usual” would have entailed fast deforestation. However, even under these conditions, a 10-year moratorium on lumber harvesting and associated land use restrictions could easily generate income losses of over 2 percent of the capitalized stream of GDI. A more probable estimate of the income losses would be between 3 to 4 percent of GDI. Of more general significance are the large values of the dynamic general equilibrium income multipliers that these numbers reflect. Where lumber makes a significant contribution to foreign exchange earnings and these are adversely affected by conservation, direct income losses are amplified by terms of trade losses that more than outweigh the income, which is recovered as mobile factors are reallocated, and profits rise. To facilitate the calculation of the compensating variation we have assumed that Malaysian residents are directly compensated by nonresidents by an amount sufficient to maintain their preconservation consumption stream. In reality, arrangements like this would be impractical. Therefore, in concluding, it is worth briefly considering mechanisms through which the delivery of environmental benefits could be tied to compensating resource transfers. 31 This estimate, however, takes no account of the general equilibrium repercussions that would follow. Also, to the extent that labor and capital are efficiently allocated to begin with, income recovery would necessarily be less than 0.8 percent of GDI.


519

“Debt for nature swaps” are one way in which resource transfers have been linked to environmental rehabilitation and protection programs. However, “debt for nature swaps” have typically relied on the resources of nongovernment organizations and, in practice, have only involved small projects and resource transfers. It seems unlikely that such arrangements could work for large areas of forest, and large debt write offs. Coordination problems and the risk of moral hazard would be just two obstacles that would prevent “debt for nature swaps” working at a macro scale. Another route through which widely shared environmental concerns, including tropical deforestation, could be addressed is through the establishment of an appropriate multilateral structural adjustment facility. However, the scale on which such a facility would have to be funded makes this unlikely.32 Perhaps, more realistically, trade policy could be used to encourage sustainable forest management and pricing practices. However, a carrot rather than a stick approach is required (see Repetto, 1994). Impeding trade in tropical lumber (e.g., through boycotts, quotas or tariffs) would depress stumpage values, and thereby hasten deforestation. Besides, such actions would probably now contravene WTO rules. On the other hand, arrangements linking tropical forest conservation to improved market access for both the processed and manufactured goods of tropical lumber nations would be both environmentally beneficial and trade creating.33 APPENDIX General Model Description M4 is, broadly speaking, of the family of financial CGE models described by Robinson (1991) or by Bourguignon et al. (1992). It has 13 goods markets (including a separate market for Forestry), four labor markets, and five asset markets. There are six institutional transactors in the model who participate in asset markets 32 If, for example, Malaysia were to receive a transfer of 2 percent of its GDI to compensate it for abandoning the exploitation of its lumber resources this would cost around US$14 billion (1994 prices) over 10 years. By comparison, the recently created Global Environmental facility has an endowment fund of only US$4 billion. 33 This assumes that any accompanying acceleration in industrialization will not itself degrade the environment by more than deforestation did. For this reason, concessional access might also be given to best practice and environmentally clean technologies. Repetto (1994) argues that developing countries should also adopt the “polluter pays” principle.

520

F. Harrigan

and for whom flow and stock accounts are separately collated. The model has no extrinsic dynamics, other than through its adaptive treatment of expectations, but it has extensive intrinsic dynamic relationships, governing the accumulation of all physical and financial stocks. Different kinds of closure restrictions may be easily imposed in M4, but here we restrict attention to those that we use in the text. In general, markets clear in each period (one calendar year), though there are some elements of quantity adjustment in goods markets, and wages in the market for unskilled labor respond sluggishly to excess labor supplies. Asset markets are cleared at the end of each period through the adjustment of interest rates and the nominal exchange rate. In the following description we simplify many elements of the model to expedite presentation. Producer Decisions In each activity, except Public Services and Dwellings (which is an account for imputed rent), the representative producer maximizes profit or minimizes cost subject to multilevel technology constraints. The solution of these problems generates the demand for all variable factors of production, including intermediate commodity inputs and different categories of labor. In the production of Public Services output a fixed coefficient technology is employed. In Dwellings, net output is simply equated with imputed rent, calculated as the product of the user cost of housing and the beginning of the period stock of private dwellings. The multilevel technology tree has three layers in each sector. Activity gross output is modeled as a CES function of an aggregate intermediate input and value added. The aggregate intermediate input is an aggregate of composite commodities, and composite commodities are themselves Armington aggregates of domestic and imported commodities. Value added in nonland-based activities is produced by a Cobb-Douglas technology employing fixed capital (including land and other fixed factors) and composite labor. In long-based activities, value added is produced with a CES technology where substitution elasticities are low (0.25). Composite labor is a CES aggregation of professional and skilled, semiskilled, and unskilled occupational categories. For each activity, these relationships generate factor input demands and associated unrestricted and restricted (at the valueadded level) cost functions. Ignoring the complications that are


521

created by margins, by distinctions between traded and non-traded goods, and by joint production these relationship yield: pjmji ⫽ pxwi · er · markup pjdji ⫽ pxi · markup pxi ⫽ pces(pji,pvi) | i 僆 LOP pxi pji pvi pvi

⫽ ⫽ ⫽ ⫽

pvi pjjji wi wni

⫽ ⫽ ⫽ ⫽

pxwi · er | i 苸 LOP pces(pjjji) rpcd(wi,vai,ki[t⫺1]) | i 僆 LOP, i 僆 L rpces(wi,vai,ki[t⫺1]) | i 僆 LOP, i 苸 L

ipces(pji,pxi) | i 苸 LOP parm(pjdji,pjmji) pces(wni) ␶ni · wn,

where: pces is an unrestricted CES cost function; rpces is a restricted CES cost function; rpcd is a restricted Cobb Douglas cost function; ipces is an inverted, unrestricted CES cost function; parm is an Armington price function, the index LOP denotes sectors where the law of one price operates and L indexes land-based activities (Export Agriculture, Domestic Agriculture, Forestry). pxw is the exogenous world commodity price in foreign currency units; er is the nominal effective exchange rate; px is the domestic commodity price; pv is the price of value added; pj is the price of the intermediate aggregate; w is the nominal wage; va is quantity value added; k is the capital stock; pjd is the price of domestic intermediate commodities, and pjm is the price of imported intermediate commodities (both in purchasers’ prices). The last equation lets wages paid to identical labor categories in different sectors vary (see text). The purchasers’ prices of domestic commodities sold to different uses are calculated as markups on px in exactly the same way as pjd. The index n is used here to identify different labor categories and markup is used where goods or factor prices are marked up over factor cost or their cif price. Note that time subscripts have been dropped except where lagged (beginning of the period) quantities or prices enter into the determination of a variable. The counterpart factor demand relationships of our model are: ji ⫽ fces(pxi, pji, xi) vai ⫽ fces(pxi, pvi, xi) | i 僆 LOP vai ⫽ irces(pvi, wi, ki[t⫺1]) | i 僆 LOP, i

苸L

522

F. Harrigan vai jjji ni nnni jdji jmji

⫽ ⫽ ⫽ ⫽ ⫽ ⫽

irpcd(pvi, wi, ki[t⫺1]) | i fces(pjjji, pji, ji) fcd(wi, pvai, vai) fces(wni, wi, ni) arm(pjdji, pjmji, jjji) arm(pjmji, pjdji, jjji),

苸 LOP, i 僆 L

where: fces is a CES factor demand equation; fcd is a Cobb Douglas factor demand equation; irpcd is an inverted, restricted Cobb Douglas cost function; irpces is an inverted, restricted CES cost function, and arm is an Armington demand function; x is gross output; j is the aggregate intermediate input; jj are composite intermediate commodities; n is composite labor; nn is labor by skill; jd is domestic output sold to intermediate uses, and jm is imported output sold to intermediate uses. Factor and Institutional Incomes Here we deal explicitly only with households, government, private, and public corporations and the rest of world. The relationships for other transactors are suppressed to expedite presentation. Factor incomes are calculated as the product of factor prices (less taxes, plus subsidies) and the stocks of factor inputs employed, summed over all activities. Operating surplus is determined residually after subtracting gross emoluments from nominal value added. The aggregate operating surplus of the economy is then distributed between its different institutional transactors. Household’s disposable income is calculated as: yh ⫽

兺n 兺i win · nnin ⫺ taxes ⫹ transfers ⫹ asset income ⫹ unincorporated business income.

Included with “transfers” are the unrequited transfers from overseas residents that adjust to accommodate our consumption constraints (see text). Asset income here and for other transactors is calculated as the product of lagged (beginning of period) stocks and lagged interest rates, so that asset income (which may be negative) is received only after transactors have held stocks for one period. Although not shown, asset revaluations are accommodated in our calculation of all incomes. “Corporate” net income is calculated as: yc ⫽ operating surplus ⫹ transfers ⫹ asset income ⫺ taxes,

and an analogous calculation is made for public sector corporations.


523

Finally, government income is derived principally from taxes, but government also has claims on the operating surplus of those activities in which it invests. Government asset income is typically negative, and represents service payments on outstanding public sector debt. The computation of taxes and transfers in M4 is highly disaggregated. Income taxes received by government are separately calculated by transactor, and indirect taxes are disaggregated by transactor and commodity end uses. yg ⫽ taxes ⫺ transfers ⫹ operating surplus ⫹ asset income.

Final Demands Consumption—aggregate household consumption expenditure is modeled as a function of household disposable income, private sector nonhuman wealth and the bank deposit rate as follows: c ⫽ ␣0 · y␣h 1 · wl␣[t⫺2 1] · exp[⫺␣3 · id],

where c is nominal consumption, wl is (beginning of the period) nominal wealth, and id is the bank deposit rate. Aggregate nominal consumption expenditure is spread over “wants” via the linear expenditure system. A “wants aggregation matrix” and Armington demand equations are then applied to derive domestic and imported commodity demands. Briefly: cz ⫽ les(c,pz⫽1, . . . , Z) cid ⫽ arm共兺 cz · ⍀iz,pcdi,pcwi兲 z

c ⫽ arm共兺 cz · ⍀iz,pcwi,pcdi兲. m i

z

Here les is the LES system of demand equations and pz is the price of the wants category z, which is an aggregate of the composite prices of individual commodities (not shown). Composite prices are, as usual, an aggregate of the purchasers’ prices of domestic and imported consumption (pcd and pcw). The elements W belong to the wants aggregation matrix. Note that while c is expressed in nominal terms, cz, cd and cm are all expressed in quantity units. Investment—investment demands are placed by activities. These demands are then translated into demands for commodities via a capital aggregation matrix (B) and split into their domestic and imported components using Armington relationships.

524

F. Harrigan

Private investment demand in all but land based activity is a function of Tobin’s q (q), which we define as the ratio of the marginal revenue product of capital to its user cost, net of taxes, and gross of subsidies. We employ the following demand equation: iai ⫽共 ␦i ⫹ gi · qiφi兲 · (ki[t⫺1] · ␺i),

which ensures that in the steady-state (q ⫽ 1) private capital grows at the target steady-state rate, g. In this expression, d is the physical depreciation rate in sector i; y is the share of private capital in the total (beginning of the period) capital stock of the sector, and q is Tobin’s q. In land-based activities, investment demands, and hence, capital accumulation, are determined exogenously. After application of the B matrix and the Armington equations the total demands for commodities for investment purposes is: idi ⫽ arm共兺Bji · (iaj ⫹ ␨ · iajpc ⫹ iagj),pidi,pimi兲 j

imi ⫽ arm共兺Bji · (iaj ⫹ ␨ · iajpc ⫹ iagj),pimi,pidi兲. j

Note that exogenous investments (by government (g) and public corporations (pc)) are added to private investment before the translation from activity to commodity demands. Public corporations “target” investment demands are uniformly adjusted by the factor z (itself an endogenous variable) to satisfy the public sector debt constraint (see below and text). Exports—the demand for exports are as follows: exi ⫽

⫺␤i0

␥ · pex 冢pwx · er冣 i

i

· YW ␤i i1 | i 僆 LOP

i

exi ⫽ xi ⫺

兺j jdij ⫺ cdi ⫺ idi ⫺ gi | i 苸 LOP,

where pex is the purchaser’s price of exports and YW is an index of world demand for commodity i. In the law of one-price markets, exports adjust residually to clear goods markets. Savings and Financial Surpluses The savings of each transactor is defined as the difference between their total income from all sources (including asset income) less their current account expenditure. Savings of the household sector is: sh ⫽ yh ⫺ c,


525

and for government: sg ⫽ yg ⫺ g,

where g is total recurrent expenditure on goods and services. Total recurrent government expenditure is largely determined through government’s exogenous decisions about employment and wages in the public sector. Recurrent expenditure (in real terms) adjusts passively to accommodate the supply of output of Public Services. The savings of each transactor less their capital account expenditure equals their financial surplus. So for households, government, and corporations (private and public) we have: fsh ⫽ sh ⫺ housing investment ⫺ unincorporated investment fsg ⫽ sg ⫺ government investment fsc ⫽ yc ⫺ all other private investment fspc ⫽ ypc ⫺ adjusted public corporations investment expenditure.

The end of period balance sheet position of each transactor is then: Ah[t] ⫽ Ah[t⫺1] ⫹ fsh Ag[t] ⫽ Ag[t⫺1] ⫹ fsg Ac[t] ⫽ Ac[t⫺1] ⫹ fsc Apc[t] ⫽ Apc[t⫺1] ⫹ fspc,

where beginning of the period stocks are adjusted for asset revaluations. Note that these stocks are positive for creditor sectors but negative for debtor sectors. Typically, in our model, households and the rest of the world are net asset holders, with remaining sectors being net debtors. Rest of the world savings is identified with the current account balance, and this is added to the beginning of the period stock of net foreign indebtedness (adjusted for revaluations) to give end of period net foreign indebtedness. In domestic currency units: ca ⫽ ⫺(exports ⫺ imports ⫺ debt service payments) Af [t] ⫽ Af [t⫺1] ⫹ cat

Portfolio Selection Government sets fixed growth paths for the money and bond stocks, and its residual funding needs are then satisfied through overseas borrowing. That is: fsg ⫹ wkg ⫺ dM ⫺ dB ⫽ er · dFD*g,

526

F. Harrigan

where wk are working capital demands by government, dM is the change in the money stock (currency), dB is the change in the market value of the bond stock, and er.dFD*g is the change in the stock of foreign currency denominated public sector debt. Each transactor in the model demands working capital. The demand for working capital is generally assumed to be a function of their level of nominal transactions and the prevailing loan/ deposit rate. For example, households demand for working capital is a function of their nominal consumption expenditure and the operating surplus of unincorporated business. These demands for “transactions’ balances” then determine the sector’s demand for currency. In effect our LM curve is vertical. “Working capital” is financed together with other liabilities for debtor sectors and is drawn down from creditors’ stocks of net assets. For creditor sectors (e.g., households), asset portfolio selection can be represented as:34

冤冥冤冥冤 Dj Bj ⫽⌰· Ej er · FA*j iw

id ib ⫹ ie • e ⫹ er 1 ⫺ ␪m

冥

␪d ␪b · (Aj[t] ⫺ wkj[t]) , ␪e ⫺ ␪b ⫺ ␪e

where D are interest bearing deposits, B is bonds, E is equities, FA* are net foreign assets in foreign currency units: id is the bank deposit rate; ib is the market return on bonds; ie is the return on equity; iw is the exogenous world nominal interest rate, and er is the nominal effective exchange rate. The matrix ⌰ satisfies the usual sign and adding-up restrictions. For debtor sectors, the corresponding portfolio selection equations are:

冤

冥冤

冥冤

冥

Lj il ␻l Ej ␻e ie ⫽⌸· ⫹ · (Aj[t] ⫹ wkj[t]) • er · FD*j iw ⫹ er 1 ⫺ ␻l ⫺ ␻e

where L are loans and il is the loan rate. Again, the usual crossequation portfolio restrictions are satisfied by the parameters of this system of demand equations. The ratio of interest-bearing 34 Portfolio selection in the model is, in fact, hierarchical and features many more assets and liabilities than are identified in this simple description of the model. Although our portfolio selection equations satisfy the restrictions identified in the text, they also permit model users to place upper bounds on the share of wealth/debt held in any single asset/ liability.


527

deposits to loans is (under flexible interest rate regimes) an exogenous parameter of the model, and the loan rate and deposit rate are linked through a fixed markup relationship. End-of-period stock equilibrium requires money market, bond market, equity market, and loan-deposit market clearing, and that foreign net indebtedness equals the sum of the net claims on individual transactors. Hence, M⫽ B⫽ Eh ⫹ Ef ⫽ D·␩⫽ Af ⫽

Mh ⫹ Mc ⫹ Mpc Bh ⫹ Bc ⫹ Bpc Ec ⫹ Epc Lc ⫹ Lpc er · (FD*g ⫹ FD*c ⫹ FD*pc ⫺ FA*h) ⫹ Ef,

where h is the fixed ratio of loans to deposits and Ef, is exogenous (long-run) equity capital inflows. Material Balances In each sector we require that total output demanded equals total output supplied in each period: xj ⫽

兺i jdji ⫹ cdj ⫹ idj ⫹ exj ⫹ gj | j 僆 LOP.35

In the market for Public Services, government recurrent expenditure on goods adjusts to satisfy material balances for given supply and in the Dwellings “market,” the demand for housing is automatically equated with imputed rent. Labor Market Closure In all labor markets except the market for unskilled nonagricultural labor, wages (wn) adjust to equate the demand and supply of labor in each period. In the nonland-based, unskilled-labor market the rate of wage change is related to expected consumption goods price inflation, excess demand for labor and a trend capturing productivity growth. Hence, in those markets that clear we have: lfn · (1 ⫺ nruen) ⫽

35

兺i nnni,

The margins of the banking sector are added to the output of private services.

528

F. Harrigan

where lf is the labor force and nrue is the (exogenous) natural rate of unemployment. In the nonland-based market for unskilled labor: d ln wn ⫽ ␪0 ⫹ ␪1d ln cpie ⫺ ␪2 ln

(1 ⫺ uen) , (1 ⫺ nruen)

where ue is the actual unemployment rate, and cpi is the consumer price index. We model net migration from land-based to nonlandbased labor markets as: d ln

冢

冣

冢

POPLt wL 1 ⫺ ueLt ⫽ φ0 · ln t ⫺ ␻* ⫹ φ1 · ln ⫺ rue* POPt wt 1 ⫺ uet

冢

⫹ φ2 · ln

冣冣

POPLt⫺1 wL 1 ⫺ ueLt⫺1 ⫺ ␵0 ⫺ ␵1 · ln t⫺1 ⫺ ␵2 · ln , POPt⫺1 wt⫺1 1 ⫺ uet⫺1

from which net migration can be calculated as:

冤

nmgt ⫽ exp ln

冥

POPLt⫺1 POPLt ⫹ d ln · POPt⫺1 · (1 ⫹ ng) POPt⫺1 POPt

⫺ POPLt⫺1 · (1 ⫹ ngL) ⫽ POPLt ⫺ POPLt⫺1 · (1 ⫹ ngL),

where POP is the end of the period population stock, nmg is net migration from nonland-based to land-based activities (L), and a bar above a variable represents its mean value in nonland-based markets. The terms v* and rue* denote, respectively, the wage gap and the log ratio of employment rates in equilibrium. The term ng is the natural rate of growth of the labor force. Our net migration equation is an error correction formulation of a model in which we postulate the equilibrium distribution of the population stock is a function of relative market wages and unemployment rates. The labor force in each market is updated through: lfn[t⫹1] ⫽ lfn[t] · (1 ⫹ ngn[t]) ⫹ εn · nmgn[t] |

εn ⫽ 1, εL ⫽ 1. 兺n nmgn[t] ⫽ 0, 兺 n僆L

Other Intrinsic Dynamics The capital stock in each activity is depreciated in each period and augmented by lagged investment: ki[t] ⫽ ki[t⫺1] · (1 ⫺ ␦) ⫹ (iai[t⫺1] ⫹ iagi[t⫺1] ⫹ ␰ · iai[tpc⫺1]).

Private sector, nonhuman wealth is defined in nominal units as the value of the capital stock owned by the private sector, plus outside money plus the private sector housing stock less net foreign


529

indebtedness (net of government debt). wlt ⫽

兺i pki · ki[t] · ␺i ⫹ outside money ⫺ housing stock ⫺ (Af[t] ⫺ er · FD*g ⫺ er · FD*pc).

Note that in the current specification of the model neither public sector debt nor assets enter into the calculation of private wealth. In each period, technological progress occurs at the value-added level of each sector’s production function and is labor augmenting (Harrod neutral), but with variable rates across activities. Technological progress is assumed to be “high” in manufacturing (0.08); moderate in services (0.04), and low in primary sectors (0.02), yielding an aggregate rate of about 0.05. Together with labor force growth of 3 percent pa., this generates trend growth of just over 8 percent pa. Expectations of the change in consumer price inflation and of the nominal exchange rate are adaptive and are of the usual form: d cpi ⫺ ␲冣冢cpi d er ·冢 ⫺ er 冣 , er

␲t⫹1 ⫺ ␲t ⫽ ␭␲ · • e • e er t⫹1 ⫺ er t ⫽ ␭er

t

t⫺1

•

t⫺1

e t

• e where p is expected inflation and er is the expected depreciation of the exchange rate.

Constraints In addition to our market clearing conditions, we require that, in each period, the Forestry sector activity outputs and fixed input stocks satisfy our exogenous restrictions on their values: xaF ⫽ xaF kF ⫽ kF.

To satisfy the output constraint, Forestry exports adjust residually (see above), and investment in Forestry accommodates the constraint on fixed inputs. Although negative investment does lead to the value of the capital stock (and wealth) being written down, it does not, given the irreversibility of capital investment decisions, release resources for use elsewhere in the economy. Our consumption constraint is accommodated through fixing the value of c/cpi at the appropriate baseline level for each year, inverting the aggregate consumption to find the necessarily level

530

F. Harrigan

of household disposable income, and then by calculating the level of unrequited transfers from the rest of the world (a component of transfers) that will yield this income, given all other elements of income and adjusting for taxes. Finally, target ratios of nominal public sector debt to nominal GDP are satisfied through endogenous adjustment factors that operate on “target” income tax rates and public corporations’ “target” capital expenditures. Data and Baseline Calibration The initial database for the model was a set of social accounts, flow of funds, and asset stock data collated for the period 1983–84. These data were subsequently updated to 1990 and the model has be recalibrated on these more recent data and reproduces these data as its 1990 solution. These updated data are consistent with available Malaysian national accounts information for 1990. Model Solution The model is solved using an implementation of the LevenbergMarquard algorithm that is described in Appendix 4 of Harrigan et al. (1991). Essentially, through a recursive ordering of the equations, variable elimination is achieved and the model is then solved as a constrained nonlinear, least-squares optimization problem. Overall, the model has about 2,500 endogenous variables, and although all of the system’s equations must be retained for the calculation of the numerical derivative information used by the algorithm, the rank of the associated Hessian matrix can be reduced to just over 50 for an appropriate ordering of equations.

REFERENCES Armington, P. (1969) A Theory of Demand for Products Distinguished by Place of Production. IMF Staff Papers 16:159–178. Braga, C.A.P. (1992) Tropical Forests and Trade Policy: The Case of Indonesia and Brazil. In International Trade and the Environment (P. Low, Ed.). World Bank Discussion Paper 159, Washington, DC, pp. 173–194. Bourguignon, F., Branson, W.H., and De Melo, J. (1992) Adjustment and Income Distribution: A Micro-Macro Model for Counterfactual Analysis. Journal of Development Economics 31. Burns, D. (1986) Runaway and Treadmill Deforestation, vol. 2. IUCN/IIED Tropical Forestry Policy Paper, London, UK.


531

Cline, W.R. (1991) Scientific Basis for the Greenhouse Effect. Economic Journal 101:904– 919. Corden, M., and Neary, J.P. (1982) Booming Sector and De-Industrialisation in a Small Open Economy. Economic Journal 92:825–849. Dasgupta, P., and Ma¨ler, K.G. (1991) The Environment and Emerging Development Issues. Proceedings of the World Bank Annual Conference on Development Economics, 1991, Supplement to the World Bank Economic Review and the World Bank Research Observer. Demery D., Harrigan, F.J., and McGregor, P. (1992) M4: A Micro-Macro Simulation Framework for Malaysia. Report to the Economic Planning Unit of the Prime Minister’s Department of Malaysia, Kuala Lumpur. FAO (1993) Forest Resources Assessment 1990. Rome: FAO, United Nations. Gillis M. (1988) Malaysia: Public Policies and the Tropical Forest, In Public Policies and the Misuse of Forest Resources (R. Repetto and M. Gillis, Eds.). Cambridge: Cambridge University Press. Government Press. (1990) The Sixth Malaysian Plan. Malaysia: Kuala Lumpur. Government Press. (1994) Economic Report. Malaysia: (Treasury) Kuala Lumpur. Harrigan, F., McGregor, P., Swales, J.K., Perman, R., and Yin, Y.P. (1991) Amos: A Micro-Macro Model of Scotland. Economic Modelling 8:424–479. IBRD. (1993) East Asian Miracle. New York: Oxford University Press. Muscatelli, A., Srinavasan, T.G., and Vines, D. (1994) The Empirical Modelling of NIE Exports: An Evaluation of Different Approaches. The Journal of Development Studies 30:279–302. Muscatelli, A., Srinavasan, T.G., and Vines, D. (1992) Supply and Demand Factors in the Determination of NIE Exports: A Simulataneous Error-correction Model for Hong Kong. Economic Journal, 102:1467–1477. Nordhaus, W.D. (1991) To Slow or Not to Slow. Economic Journal 101:920–937. Parthama, I.P., Vincent, J.R. (1992) United States Demand for Indonesian Plywood. Bulletin of Indonesian Economic Studies 28:101–112. Reidel, J. (1988) The Demand for LDC Exports of Manufactures: Estimates for Hong Kong. Economic Journal 98:138–148. Repetto R., and M. Gillis, Eds. (1988) Public Policies and the Misuse of Forest Resources. Cambridge: Cambridge University Press. Repetto R. (1994) Trade and Sustainable Development. In Critical Issues in Asian Development (M.G. Quibria, Ed.). Hong Kong: Oxford University Press. Robinson, S. (1991) Macroeconomics, financial variables and CGEs. World Development 19:1509–1525. Vincent, J.R. (1992) The Tropical Timber Trade and Sustainable Development. Science 256:1651–1655.