Oil, Macroeconomics and Forests: Assessing the Linkages
Sven Wunder and William D. Sunderlin1
ABSTRACT:
How does an oil boom affect the forest cover of tropical oil exporting-countries? Are they more
or less likely than non-oil countries to experience forest loss? What macro-economic linkages
and policies are decisive? This article summarises research on land-use changes in eight tropical
developing countries. Our country-comparative approach reveals that the direct oil impacts on
forests are unquestionably subordinate compared to oil's derived macroeconomic impact. In
most cases, oil wealth indirectly but significantly comes to protect tropical forests. The core
mechanism here is that oil rents cause 'Dutch Disease', decreasing the price-competitiveness of
agriculture and logging, which strongly diminishes pressures for deforestation and forest
degradation. But domestic policy responses to oil wealth are also vital determinants for the
forest outcome. When governments use most oil wealth for urban spending sprees, this
reinforces the core effect by pulling more labor out of land-using and forest-degrading activities.
Yet, in extreme cases when boosting oil revenues finance large road-construction programs or
frontier-colonization projects, the core forest-protective effect of oil wealth can be reversed.
Repeated currency devaluation and import protection of heavily land-using domestic sectors also
contribute to increased forest pressures. These conclusions have ample policy implications,
reaching beyond the group of tropical oil countries. Other international capital transfers, like
bilateral credits, aid or debt relief can have similar impacts. These measures will alleviate
pressures on forests, unless they come to bolster specific forest-detrimental policies. This also
provides some suggestions on what forest-friendly safeguards could realistically be taken in the
design of structural adjustment programs, considering the important trade-offs between
development and conservation objectives.
Keywords:
1. Introduction
Over the last decade, there has been a growing interest in the relationship between
macroeconomics and the environment, including the links to deforestation and forest
degradation. This is due to a growing recognition that the fate of tropical forests is widely
determined by factors outside forests and the forestry sector, which often have been called the
"underlying causes of deforestation" (Contreras-Hermosilla 2000). A wealth of theoretical
economic models has been developed, with multi-country regression models as the single
predominant category (Angelsen and Kaimowitz 1999). However, the FAO deforestation data
normally used in these exercises are highly problematic (Rudel and Roper 1997; Matthews
2001; Grainger 1996) and it is dubious to what extent one can interpret the cross-country pattern
as a time-series perspective for individual countries choosing between different macroeconomic
policies (Kaimowitz and Angelsen 1998). The country-comparative studies (Reed 1992; Reed
1996; Kaimowitz et al. 1998; Wood et al. 2000) have mostly analyzed short-run effects of
macroeconomic change, notably the environmental effects of structural adjustment.
This comparative research by the Center for International Forestry Research (CIFOR), the
results of which are documented more extensively elsewhere (Wunder 2003), looks at the longrun macroeconomic determinants of land-use and forest-cover change. Eight tropical developing
countries were analyzed, five of which (Cameroon, Ecuador, Gabon, Papua New Guinea, and
Venezuela) as detailed primary cases and three (Indonesia, Mexico and Nigeria) as secondary,
summary-style cases. In this article, we will concentrate on the five primary countries, and on
the issue of deforestation, defined as a (temporary or permanent) radical removal of trees to less
than 10% crown cover. This is similar to FAO's definition (FAO 2000a), which comes to focus
forest conversion to other land uses. Selective logging is thus not a direct source of
deforestation, though it may enable conversion and cause forest degradation. How these
processes link to oil wealth and macroeconomic policies is analyzed elsewhere (Wunder 2003a).
All study countries are tropical oil exporters, a choice that was made for two reasons. First, these
economies often fluctuate dramatically due to heavy reliance on a single export commodity with
unstable world-market prices. We also know that the policy responses to boom-and-bust cycles
differ enormously – both over time and across countries (e.g. (Neary and van Wijnbergen 1986;
Collier et al. 1999; Bevan et al. 1999a; Little et al. 1993), especially because oil wealth buys
these countries higher degrees of freedom in defining individual policy responses (Gelb and
Associates 1988; Karl 1997). The ‘macroeconomic laboratory’ of oil-exporting countries thus
offers a good opportunity to study links between external economic changes and the forest.
Second, earlier studies confirmed the basic country-comparative hypotheses that oil- and
mineral-exporting tropical countries have actually more forests left, and lose these on average at
a lower pace than non-mineral countries in the tropics (Mainardi 1998; Sunderlin and Wunder
2000). If Brazil, historically and at the federal-state level itself an important mineral exporter, is
excluded from the calculus, the FAO's Forest Resource Assessment (FRA) 2000 data show that
mineral exporters hold no less than 72.1% of remaining tropical forests (Wunder 2003: ch.2). In
other words, there is something special about oil and minerals that in most, though not all cases
protects forests. One purpose of this article is to explain this mechanism, and how it is affected
by domestic policy responses. The core hypothesis to test in the following is that in the oil
countries, deforestation pressures are lower during oil bonanzas when oil revenues are booming
than during oil-bust periods. This takes the previous reasoning from a cross-country to an intertemporal setting.
The article is structured as follows. Section 2 briefly reviews hypotheses and methodologies.
Section 3 outlines the deforestation data problems, and how they were dealt with in this study.
Section 4 presents empirical results for each of the five primary countries, while Section 5 takes
a closer look at three of the key causal linkages in the model. Section 6 discusses the role of
different policy instruments. Section 7 provides conclusions and further perspectives.
2. Theories and methods
Why should oil wealth reduce pressures on forests, i.e. why should the core hypothesis be
expected to hold? Sizeable foreign exchange earnings from oil, sometimes supplemented by
foreign borrowing, raise government spending, which increases aggregate demand. This causes
the real exchange rate to appreciate through growing inflation and/or nominal currency
revaluation. The oil country’s non-traded and quasi non-traded (import-protected) sectors
expand, while non-oil traded goods lose competitiveness and decline. This is the classical
"Dutch-Disease" pattern (Corden 1984), termed after the Netherlands' experience with gas
revenues in the 1960s and 1970s (The Economist 1977). In developing countries, primary sectors
have been the prime victims of the Dutch Disease - especially agriculture, and in particular the
"purely traded" export sectors (Roemer 1984; Neary and van Wijnbergen 1986; Collier et al.
1999). Hence, to the extent that (large parts of) the agricultural and timber sectors are exposed
to foreign competition, an oil boom generally discourages the expansion of agriculture and
logging. That would also tend to reduce area expansion in these sectors, and hence curb
deforestation for agricultural conversion and for a number of other primary activities that
potentially claim forested lands (e.g. shrimp farming, small-scale mining, etc.).
What impact has the oil-led expansion of the non-tradable sectors on forests? By far most nontradables are urban (public and private service sectors, construction, etc.), so most oil booms go
hand in hand with accelerated urbanization. Rural labor is drawn to the cities, abandoning their
natural-resource based rural livelihoods for urban activities that tend to have more benign forest
impacts. Growing cities leave “ecological footprints” on forests, e.g. through timber demand for
construction, fuelwood for cooking or protein-rich foods (meat, dairies) that can cause forest
conversion. Often peri-urban forests are most affected by these pressures. Still, urban sprawl in
itself was a negligible deforestation source in the study countries; in most cases the production
for urban markets became overall more land-intensive (i.e. less land inputs per unit of output),
and part of urban demand is also satisfied through imports.2 Higher urbanization is thus in most
cases good news for national forest conservation.3
As this simple overview already shows, several links between the macroeconomic and forest
levels need to be checked, and various side effects are at work. In a recent analogous study on
the root causes of biodiversity loss, it was envisaged that all macro changes jointly jeopardize
biodiversity (Wood et al. 2000). But it is more realistic that some underlying factors cause
higher environmental pressures while others reduce it, the net effect depending on the balance
between them. One way of analyzing multi-level linkages with multiple, opposed effects are
Computable General Equilibrium (CGE) models - see Kaimowitz and Angelsen (1998: 61-68)
for an overview of applications to deforestation. However, these models have difficulties dealing
with the spatial specificity of deforestation. They also generally require a wealth of knowledge –
yet data gaps proved to be huge for our study countries - and we did not wish to succumb to the
temptation to bridge data gaps by convenient CGE assumptions. For the sake of greater
transparency, we opted for a partial, recursive approach instead (see Figure 1).
FIGURE 1 (see below)
The analysis runs from the external level (foreign-exchange inflows) and the macroeconomic
level down to sectoral production, land use and the forest level. When oil wealth finances large
increments in road budgets or in resettlement projects, subsidies for transport or for agriculture,
these policy responses increase forest loss; the opposite can be the case if budget allocations for
conservation and forestry regulations are strengthened. But structural changes also affect the
forest outcome, sometimes in an ambiguous way. For instance, higher incomes induce
consumers to buy more meat and dairies, which tends to increase cattle-led deforestation - a
trend that is important particularly in Latin America. On the other hand, an income-led shift
from slash-and-burn produced tubers to more land-intensive rice production can help to reduce
forest loss, as has happened in Central Africa. Reduced poverty in a booming economy goes
hand in hand with higher labor costs (reducing deforestation), but there will also be more funds
available for investment, which can promote more forest clearing in capital-scarce frontiers.
These ambiguities related to opposed partial effects can only be resolved case by case, but still
the partial model proved to be a flexible framework for pulling together the patchwork of landuse factors, in spite of incomplete data coverage and variable spatial scenarios.
3. Deforestation and land-use data
While hundreds of deforestation models have been developed over the last decade or so, the
underlying country-level forest and deforestation statistics remain highly insecure. The general
adequacy of the FAO forests statistics has been discussed elsewhere (e.g. Grainger 1996,
Matthews 2001); here we shall simply make some observations for the five case countries.
Figure 2 compares the forest stock estimates for these countries from four of the most used
international sources, the FAO, TREES and IUCN. We can see that different definitions and
sources easily trigger a variation in the 5-15% range, but for countries with large forest-savanna
transition zones (like Cameroon) or many forest fragments in deforested landscapes (like
Ecuador), the differences can be up to 35-40%. More worrying is it that gaps do not only occur
between institutions, but even between FAO's FRA 1990 and FRA 2000. The main explanations
of these disparities between assessments are the differences in canopy-cover criteria, spatial
scales of resolution, time scale and sample coverage (Wunder 2003: ch.3).
FIGURE 2 & TABLE 1 (see below)
The dramatic forest-stock readjustments then also come to dominate deforestation figures. Table
1 shows that the FRA 2000 recorded deforestation figures for the 1990s were strikingly different
from those for the 1980s in FRA 1990. In Cameroon, forest loss would seem to almost have
doubled, while in Venezuela it was reduced to one third and in Gabon to one tenth of the 1980s
figure. For PNG, annual forest loss would still seem to be 113,000 ha, but FRA 2000
retrospectively set the 1975-85 deforestation to zero so that the flow estimate would still be
compatible with the changed forest-stock figures. For Venezuela, the large reduction in
estimated deforestation reflects the change in methods between FRA 1990 and FRA 2000, from
a population-based model prediction to a national-expert consensus around a much more
conservative figure. The footnotes show that the FAO figures also often build on long-term
extrapolations from only a couple of available national forest assessments, implying that at best
estimates say something about "the recent past" rather than specifically the decade of the 1990s.
If we don't know for sure how much forest is lost where and when, and how the pace of change
is altered over sub-periods, how can we then analyze forest-loss causes? We employed what
FAO calls the "Convergence of Evidence" method, which is really to sit down with all available
data and use common sense to try to make the best of it in terms of deducing what land-use
changes are most likely to have occurred. Notably, we looked critically at all national estimates,
at sub-national trends from two international deforestation databases4 and from in-country
sources, and checked development for pre-identified deforestation 'hot-spots'. In Cameroon, we
used primary data gathered for another CIFOR project. Following (Houghton et al. 1991) and
(Barbier 2001), we also looked at cultivated-area expansion as alternative deforestation
indicators. Except for the savanna regions of Northern Cameroon and the Orinoco region in
Venezuela, most unoccupied land in the study countries is forested, and there was a large spatial
correlation between expansion of agriculture (including livestock) and the loss of forest.
The last column ("own best-guess") in Table 1 is the result of this screening of a large number of
land-use sources; in some cases estimated ranges are preferable results to point estimates. For
Cameroon, Ecuador and Venezuela, our estimates lie in between the FRA 1990 and FRA 2000
ones; for PNG and Gabon they are significantly lower than both FAO figures. Although far from
having hard data (except for Cameroon), we also got some idea about historical forest trends - at
least at the level of "lower" or "higher" forest loss and the main factors driving these changes.
These trends will be employed in the analysis below. This land-use screening also gave a clear
indication of what different land uses deforestation and forest conversion is for. While
"agriculture" was the uniform answer, the sub-sectors were quite different. In Ecuador and in
particular Venezuela, cattle ranching is clearly dominating. In Gabon, PNG and Cameroon,
extensive swidden food-crop systems are the leading source; even the multitude of cash crops
grown in Cameroon only accounted for a minor share of deforested area because these crops are
cultivated in a more land-intensive manner.
Finally, the screening also gave us a more realistic vision of which sectors were definitely not to
blame for the bulk of deforestation. The direct land-conversion effects from oil and mining, a
centerpiece of current and past campaigns to save the rainforest (RAN and Project Underground
1998; WWF and IUCN 1996), was among these negligible sources of forest loss. In Cameroon,
oil was produced off-shore; in Venezuela it comes mainly from non-forested savannah areas. In
Gabon, most oil comes from forests, but the space occupied by the industry was less than 10,000
ha (0.05% of forest area); in PNG it was just 1,200 ha. In Ecuador, 99% of oil production comes
from the Amazon forest, but direct conversion has only been in the range 3,000-6,500 ha (0.04-
0.09% of Ecuador’s Amazon forest). Indirect, access-provision effects and pollution effects have
in some cases been much larger. But examples showed that these effects, just like the direct
ones, can to a large extent controlled when the industry uses “best practice".
4. A resumé of the country stories5
A. Gabon
Gabon provides a textbook confirmation of the core hypothesis. With its transformation to an oil
exporter in the early 1970s, this sparsely populated country became the economy with the peak
per-capita rent in our sample. In spite of a mini-bust in 1986-89 and highly fluctuating oil
revenues in the 1990s, the entire post-1973 period was an era of high oil wealth. Although oil
revenues were unequally distributed within Gabon, they caused economy-wide, massive
structural changes. An appreciating exchange rate eliminated smallholder cash crops like coffee
and cocoa. Non-traded sectors thrived, in particular oil-financed public employment and urban
construction. With the total neglect of rural areas and road building, and substantial urban rentseeking, there was a rural exodus of people in the most productive age groups. The agricultural
fields they abandoned grew back into forest. A quote from a village chief in northeast Gabon is
illustrative of a situation in which reduced cropping and lower human presence went hand in
hand with forest rehabilitation: ‘Nobody lives here any more… The young are leaving, and the
elephants and gorillas run freely through our gardens, destroying what little we grow to eat’.6 A
resettlement program that brought people out of the forest into concentrated roadside settlement
(rather than moving them into the "empty" forestland) reinforced that trend. The two national
forest inventories plus a number of village case studies give us a quite solid basis for saying that
high oil wealth probably led to a minor but absolute net expansion of forest area over the last
three decades. Within this picture, there were local deforestation processes in peri-urban areas,
especially during recent mini-crisis periods, when some people lost their urban jobs and
increasingly turned to “weekend farming” to improve their livelihoods. Still, in net terms
deforestation will likely remain limited until oil revenues enter into a phase of rapid decline.
When that happens, the real value of the currency, of wages and non-traded prices would have to
come down, laying the ground for a rise in domestic production of cash and food crops, which at
present still are widely imported from neighboring countries. Since most of Gabon is covered by
forests, this higher land demand would also provide a significant stimulus to deforestation.
B. Venezuela
Venezuela’s historical transformation from a specialized agrarian exporter in the 1920s to a
mono-exporter of oil from the 1930s and onwards showed many similarities with the Gabon
story. It is another case that confirmed our core hypothesis in absolute terms: There was a
marked net forest regrowth in abandoned agricultural areas from 1920 to 1950, while people
rushed into the cities to take advantage of economic opportunities arising from sudden oil
wealth. In the 1950s, Venezuela then invested greatly in road-building, promoting the expansion
of the cattle sector in particular, which accounted for almost all of the country’s deforestation
since WWII. Our regression results showed that cattle-ranching as a semi-traded sector
(protected for sub-periods and sub-products) expanded pro-cyclically with urban incomes, with
a higher demand for protein-rich food among a rising urban middle class sustaining that
expansion. On the other hand, cropped area stagnated completely over decades, in spite of large
population growth. From the regressions, it became clear that cropped area was negatively
correlated with both rising urban incomes and declining price competitiveness: that is, crop
cultivation was the true Dutch Disease victim. While Venezuela still had quite low forest-loss
rates during 1950-80, the trend was an accelerating one. With the deep economic and political
crisis of the 1980s and 1990s, the speed of forest extraction and clearing increased, even in the
areas south of the Orinoco river that had previously been too remote from the bustling urban
centers. This process was closely related to a depreciating real exchange rate, stimulating
logging, artisan mining and even crop cultivation. Since World War II, Venezuela thus had some
deforestation, though still at a lower pace than neighboring countries with a stronger agricultural
sector.
C. Cameroon
Cameroon had the most pronounced economic cycles in our sample of countries. The prosperous
period of 1978-85, with simultaneously high international prices for the main exportables (oil,
coffee and cocoa), combined with access to foreign capital, turned into misery in 1986 when the
external environment (oil and crop prices, soaring real interest rates) reversed sharply–and in an
equally simultaneous manner. Furthermore, during 1986-94 the fixed value of the CFA franc
prolonged the crisis; Cameroon could not devalue on its own. Since the 50% CFA devaluation in
1994, there has been a slow recovery. In land-use terms, specific case studies that had been
tailored to compare boom and bust, with good coverage of the entire humid forest zone, allowed
us to evaluate the core hypothesis exhaustively. The boom (1979-85), though less pronounced
than in neighbouring Gabon, had a clear urban bias and accelerated rural–urban migration,
which led to a slow down in deforestation. In the crisis period, with its overvalued exchange rate
(1986-94), cash crops in the humid forest zone (mainly cocoa) were stagnant. But a significant
return migration to rural areas and an upsurge in slash-and-burn food production of tubers and
especially plantains was sufficient to cause a strong rise in deforestation in the forest zone. Since
the devaluation of 1995, cash crops have experienced a slow revival, and deforestation continues
to be high. Unlike in Gabon, oil wealth was thus not high enough to outright reverse forest loss
and cause forest area to expand. Economic rents were simply insufficient to fully compensate for
other factors, notably rural population growth. But there was an abrupt reduction in the pace of
deforestation. Cameroon thus provides a strong relative confirmation of the core hypothesis:
during the oil-wealth period, but the rate of forest loss markedly slowed down.
D. Ecuador
The case of Ecuador was rather different. The size of the boom was greater than in Cameroon,
but much smaller than in Venezuela and Gabon. Due to rising production, oil revenues remained
quite high until the mid-1980s. A bust followed, combined with a strong political crisis in the
1990s, so although there have been mini oil-bonanzas, the entire post-1985 period is best
characterised as an economic downturn. Nevertheless, deforestation actually accelerated during
the oil boom, stayed high for a decade after, and then probably decelerated. This is because the
government deliberately spent a large share of oil revenues on things that promoted more
extensive land uses, notably new roads (especially connecting the highlands with the lowlands),
but also transport subsidies and land colonisation programmes and support. As in Venezuela, a
second factor was cattle. A growing urban middle class used part of the new purchasing power
to buy cattle-derived proteins, thus creating the level of demand to match the supply-side factors
that enabled the large-scale extension of pastures. In Ecuador, these contrary factors were
stronger than the core-effect ones―and policy was decisive in creating that balance. Although
there was an upsurge in agriculture after the devaluations of the mid 1980s, as the core
hypothesis would suggest, on the whole this country case was contrary to the hypothesis, in both
absolute and relative terms: forest cover decreased in absolute terms, and the pace of that
process also picked up, in comparison to the pre-boom deforestation rates.
E. Papua New Guinea
PNG became a high-mineral exporter from 1972 onwards. Copper and gold predominated until
1992, when significant petroleum production began. The size of these mineral rents with respect
to the economy as a whole was somewhat greater than in Ecuador. Firm macroeconomic
management succeeded in stabilising their impact over time until the 1990s, but then fiscal
control was increasingly lost. The fixed kina exchange rate introduced in 1975 had greatly
hampered non-mineral exports until 1994, but then a floating kina coupled with economic
mismanagement implied a large real devaluation. In other words, the whole period was
characterised by fairly high mineral revenues, but with different economic management cycles.
In terms of land use, cash crops never became a large source of deforestation: even after the kina
devaluation rural violence, inflexible land-tenure arrangements and infrastructure problems were
too great as impediments. PNG is thus an example where improved price competitiveness was
not enough to kick off the cash crop sector. An exception is the oil-palm sector, which caused
some deforestation. As in Gabon, government policies widely ignored rural infrastructure, which
reduced forest loss. An overwhelming share of mineral revenues went into consumption, some
being wasted, and little being invested to enhance production. On the national level, forest loss
was clearly driven by the shifting cultivation of food crops. The rigid land-tenure structure
favoured land intensification over extensification. Forest-loss rates were stable and low, though
moderate with respect to the small size of the population and the economy. This is because of
the high dualism in the economy: few value-added and service sectors have developed that
might have drawn people out of food-crop agriculture and into urban activities to delink
deforestation from population growth. On the whole, mineral wealth in PNG did not stop
deforestation in absolute terms. It does seem to have slowed forest loss down, in particular by
curtailing the cash-crop sector. Yet none of the other cases in this book was as complex as PNG:
factors such as land tenure, crime and political instability were at least as important as mineral
wealth in shaping land use.
F. Summarizing the country evidence
Over a period of several decades, most developing economies are subject to processes of
profound structural change, such as demographic and economic growth, urbanisation, shifts of
resources from primary to secondary and tertiary sectors, expanding land uses, etc. The core
hypothesis has to be evaluated against these "normal" development trends. Table 2 summarizes
the evidence for the five country cases by juxtaposing their macroeconomic cycles to the landuse cycles. Gabon and pre-WWII Venezuela are confirmative cases in absolute terms - large oil
rents basically wiped out agriculture and many abandoned areas grew back into forests; forest
cover increased in absolute terms. In Cameroon the oil effects were not strong enough for that,
as the boom size was smaller than in Venezuela and Gabon, and vis-à-vis fundamental
underlying trends such as rural population growth. But there was a clear relative reduction - the
pace of deforestation dramatically went down in the oil-boom period, and then picked up during
the crisis. PNG was also a case of relative confirmation, though a more hesitant one; realexchange rate effects were here mediated by shifts in economic policy, and a number of nonmineral factors had a crucial influence. Finally, Ecuador was the only case outright rejecting the
core hypothesis, mainly due to the policy of extensive road building and a nexus of factors that
effectively linked the expansion and land-extensive cattle ranching to oil wealth.
TABLE 2 (see below)
5. Tracing the linkages
The juxtaposition of periods in Table 2 exhibited some but not all the patterns one would expect
from the core hypothesis. This is hardly surprising, given the long causal chain described in
Figure 1. The main transmission levels are from oil wealth to the real exchange rate/ relative
prices, from prices to sectoral production (agriculture and timber, respectively), and from
production to forest cover/ quality. This is not the place to fully document all these linkages for
all study countries (incl. various data problems), but some general comments seem beneficial.
In most countries, oil wealth produced the expected appreciation of the real exchange rate, with
a shift in relative prices in favour of non-tradables (Wunder 2003: ch. 10). There were often
some lags involved, and some of the regression equations suffered from autocorrelation of the
residuals. In various countries, foreign borrowing was an integral oil-wealth feature as they
borrowed against their new oil wealth to exacerbate foreign-exchange inflows or prolong a
boom period, i.e. delay the adjustment to reduced oil revenues. Higher capital inflows had the
partial effect of causing an additional real currency appreciation. The management of nominal
exchange rates also proved important. In the simple Dutch Disease model, currency devaluation
has no impact on relative prices or competitiveness, since domestic prices adjust instantaneously
so that the real exchange rate remains unchanged. In the real world, domestic prices were sticky,
and overvalued exchange rates could persist for years. Hence, repeated devaluation (as in
Ecuador in the mid-1980s) were vital in reviving traded sectors, while fixed exchange rates
exposed them in other countries, notably in Cameroon 1986-94 and in PNG prior to 1994.
Agriculture is a prime victim of Dutch Disease phenomena, but the link to price competitiveness
is often clearest for the purely traded export sectors. In Gabon and in Venezuela, exchange-rate
appreciation practically liquidated the cash-crop export sectors; in Cameroon and PNG it was
the most important among several factors of crisis. But distinguishing food versus cash crops in
tropical developing countries is becoming increasingly anachronistic due to the rapid growth of
a commercial food-crop sector serving domestic markets. For many farmers, food crops are
becoming the biggest cash sources. This sector was more ambiguously linked to real-exchange
rate effects; it was semi-traded due to imperfect substitutes, to partial trade protection over time
and sub-products, but also to "natural" protection from trade provided by high transport costs.
Rather than suffering from declining output prices, this sector tended to be hurt by higher wages
caused by rising labor opportunity costs. To the extent that these semi-traded goods were linked
to higher income (e.g. meat, dairies, luxury staples), domestic suppliers profited from higher
demand, just like the producers of non-tradables.
Would any effect on agricultural production directly translate into pressures on forests? As
explained above, forests were the "default vegetation cover" in most regions, so higher land
demand would in most cases cause corresponding forest loss. However, the prior transmission
from higher agricultural production to higher land demand would be somewhat variable. In
PNG, lack of physical access and legal rights to new land would promote much intensification
on already occupied soils, so that the relative rise in land demand would be clearly inferior to the
rise in production. The opposite was the case for cattle ranching; in both Ecuador and Venezuela
the aggregate cattle carrying-capacity declined over time, reflecting the spread of ranching to
still more marginal lands with lower per-hectare returns. Still, in all the countries of study
agriculture was the big land-use competitor of forests, and gains for the former mean losses for
the latter. This produces a somewhat uneasy implication: most of what is good news for
agriculture tends to be bad news for forests, and vice versa. As we will see in the following, this
also means that policies that are biased against agriculture may, unintentionally, become forestconserving - and more effectively so than the ones that aim directly at forest conservation.
6. Decisive policy responses
As shown in Section 4, the ultimate impact of oil wealth on forests would mostly be the
enhanced forest protection that the core hypothesis (and the cross-country statistical evidence)
predicts; yet specific outcomes ranged from "absolute confirmation" to "relative confirmation"
and "rejection". Different country-specific preconditions were in part responsible - for instance,
large boom size was conducive to forest protection, and a beef-and-cowboy tradition in Latin
America exerted significant pressures on forests. In Central Africa and PNG, the pressures came
from land-extensive swidden cultivation, though with some adaptation in the PNG case. Yet,
beyond of the preconditioned impacts, distinct country-specific policy responses strongly shaped
the variable outcomes. The study countries' governments were not passively reacting to the
changing fortunes of the world market, and to corresponding adjustments programs designed by
the Bretton-Woods institutions. On the contrary, the rent-rich petro-states developed a high
ability to steer free of external pressures, delaying adjustment, resisting cutbacks and reforms,
etc. Even internally, they were able to "buy off" different regional and other interests, though
this often came at the cost of increased rent-seeking and corruption. In some cases oil wealth in
itself was conducive to armed conflict with separatist movements trying to appropriate rich
natural resources (Wunder 2003: ch.10, Collier 2000; Ross 2001). On aggregate, it is fair to say
that the oil-rich countries had greater room for political manoeuvre than most developing
countries, and they used this political space in ways that turned out to be important for forests.
Which policies, then, proved to be most effective in protecting forests? We used Gabon as a base
example, and then added policy ingredients from other study countries, to produce a tencomponent so-called 'Improved Gabonese Recipe’ for achieving maximum forest conservation:
Box 1: The improved Gabonese policy recipe for forest conservation
1. Neglect demands for new road-building and road-maintenance in rural areas
2. Draw as much labour as possible out of rural areas by spending all your money in the cities
3. Sell gasoline domestically at prices that produce similar rents to exported fuels
4. Let the real exchange rate appreciate markedly, and then keep it systematically overvalued
5. Tax logging companies heavily so as to keep their operations at the margins of profitability
6. Over-tax export agriculture by confiscatory so-called ‘price-stabilisation schemes’
7. Intensify food crops and/or liberalise food imports
8. Force rural people to settle in concentrated roadside agglomerations
9. Waste your agricultural budget on agro-industrial ‘white elephants’ and ignore smallholders
10. Nourish a rent-seeking environment in which few people find it worth while to produce
7. Conclusion and perspectives
10.3.1 Summing up
This book set out to investigate the core hypothesis that oil and mineral wealth helps protect
forests through its macroeconomic impacts. While it is difficult to summarise all the
observations made in the above discussions, ten main lessons stand out:
1. Most of the countries studied conformed to the core hypothesis in relative terms (‘oil wealth
reduces pressures to degrade and convert forests’). Some did so even in absolute terms (‘oil
wealth reverses pressures to degrade and convert forests’), while two of them did not conform to
the hypothesis (‘oil wealth does not reduce pressures to degrade and convert forests’).
2. Absolute conformers (Gabon, pre-WWII Venezuela) were characterised by very high oil
rents both per capita and with respect to the size of their non-oil economy. The negative cases
were both from Latin America, and a roads–colonisation–cattle nexus drove their contrary
outcome.
3. Regarding deforestation, conversion to agricultural land (including pastures, fallows and
degraded croplands) was the dominant cause in all countries. Depending on the country, most of
the land expansion occurred at the expense of forest cover, though savannahs were also targets
of conversion in some cases. Agricultural production, especially for exports, was clearly held
back by oil wealth. This happened through a fairly solid link between oil revenues and
competitiveness, a strong link between competitiveness and cash crops and a more variable link
to semi-traded food crops. Expansion of agricultural land could not be measured or analysed on
a year-to-year basis. Where land scarcity induced intensification, land expanded at a slower rate
than production. Still, it was clear that in oil countries, as in most of the tropics, the bad fate of
agriculture was mirrored by the good fate of forests. The by far strongest causal force in tropical
deforestation is agriculture. Indeed, those who largely blame deforestation on other, readily
identifiable villains (loggers, miners, urban developers, etc.) miss the key reason why forests are
disappearing.
4. Regarding forest degradation, the expansion of selective logging (whether ‘unsustainable’ or
not) was crucial, through both direct impacts and access-providing effects (e.g. for bushmeat
extraction, conversion). Yet timber production was even more sensitive to fluctuations in
competitiveness than agriculture. During oil booms with real appreciation it declined, while
during busts with currency devaluation it expanded. This effect was stronger for exports, while
domestic timber consumption rose in response to the oil-induced urban construction booms.
Thus, through this mechanism, oil wealth also partially protected forests from degradation by
reducing the incentives for logging and for over-harvesting of wood and non-wood forest
products.
5. Trade policy played a changeable role in land use, but for a number of key sectors
protectionist policies stimulated a significant over-expansion that had large costs in terms of
forest clearing and degradation. First and foremost, cattle-ranching in Latin America expanded
into marginal areas with poor returns and declining carrying capacity, often because restrictions
on the imports of meat and dairy products sheltered domestic production from competition, at
least during sub-periods. Certain land-extensive food crops were also allowed to expand (or
saved from decline), at times by erratically applied import prohibitions or prohibitive tariffs, in
spite of an appreciated real exchange rate. Finally, log-export bans or impediments, and some
import restrictions, promoted the over-expansion of highly inefficient home-market timber
sectors that consumed a lot of wood for low-value products.
6. Oil production in itself is a negligible direct source of deforestation, compared to national
land use. Its direct degradation impacts are variable, and have in many cases declined over time
through better practices. The same is true of mining, though its effects can be more significant:
there are some examples of severe forest loss caused by mining. Like logging, oil and other
minerals can provide access for other forest degradation and conversion. Again, this effect is
variable between countries.
7. Government spending of oil revenues generally had an urban bias protecting forests. Some
potentially damaging forms of spending, like directed settlement projects inside forest areas,
were not related to greater oil wealth. Others like agricultural spending did rise with oil wealth,
but did not greatly affect forests due to inefficiencies and low intensity of land use. Yet the same
is often true of the reverse situation of inefficient spending on public forest and conservation
agencies. On the whole, much of this funding came to be part of the urban nexus of
employment, rent-seeking and inefficiency, which drew resources away from productive sectors,
including those exerting pressures on forests.
8. A minor exception to this pattern of government spending without direct implications for
forests was some of the Latin American forest colonisation projects. A major exception was oilwealth spending on roads (complemented by transport subsidies), which as a policy choice
proved to have a catalyst effect for the forest outcome. Generally, there was a correlative pairing
of scenarios, with ‘no roads – no deforestation’ versus ‘substantial road-building – high forest
loss’, both currently7 and over the next decade after road completion.
9. Urbanisation was greatly accelerated by high oil rents and was an important part of the ‘oilwealth package’ that reduced the pressures on forests in most cases. As such, it was linked to
poverty alleviation: rural–urban migration was a major route out of poverty, and the higher
levels of remuneration for urban labour reduced the incentives for rural forest- and landdependent activities. Conversely, in times of crisis the village, together with the forestconsuming production of food crops, became a rural safety net when urban incomes fell. At the
same time, higher incomes also shifted demand away from land-extensive staple crops, such as
tubers and plantains. This suggests a clear link between reduced poverty, changing consumer
preferences and reduced pressures on forests, which indeed was confirmed in PNG and in the
Central African cases.
10. However, there were also some confounding factors, mostly but not exclusively in Latin
America. Greater urbanisation and higher incomes also increased the demand for construction
timber and, first and foremost, protein-rich foods (in particular beef), which require extensive
pasture areas. At the same time, poverty alleviation also alleviated constraints on rural capital,
thus promoting higher investments in cattle and cash crops. Wherever this contrary effect
became significant―because of any combination of profitable investment opportunities in
deforestation, an urban middle class with greater purchasing power, or a special taste for
hamburgers, charcoal broilers or precious-wood furniture―the overall effect of poverty
alleviation on deforestation became ambiguous.
Literature
Angelsen, A., and D. Kaimowitz. 1999. Rethinking the causes of deforestation: lessons from economic models. The
World Bank Research Observer 14 (1):77-98.
Barbier, E. B. 2001. The economics of tropical deforestation and land use. Land Economics 77(2): 155-72.
Bevan, D., P. Collier, and J.W. Gunning. 1999a. Nigeria and Indonesia: World Bank comparative study: The
political economy of poverty, equity, and growth, Oxford.
Collier, P., W. Gunning, and Associates. 1999. Trade shocks in developing countries. 2 vols. Vol. Volume 1:
Africa; Volume 2: Asia and Latin America. New York: Oxford University Press.
Contreras-Hermosilla, A. 2000. The underlying causes of forest decline: CIFOR Occasional Paper No. 30, Bogor:
CIFOR, June.
Corden, W.M. 1984. Booming sector and Dutch disease economics: survey and consolidation. Oxford Economic
Papers 36(3): 359-380.
FAO. 2000a. FRA 2000. On definitions of forest and forest change: FRA Working Paper 33. Rome: FAO.
Gelb, A. , and Associates. 1988. Oil windfalls: blessings or curse? New York: Oxford University Press.
Grainger, A. 1996. An evaluation of the FAO tropical forest resource assessment, 1990. The Geographical Journal
vol. 162, No. 1.
Houghton, R.A., D.S. Lefkowitz, and D.L. Skole. 1991. Changes in the landscape of Latin America between 1850
and 1985. I. Progressive loss of forests. Forest Ecology and Management 38: 143-172.
Kaimowitz, D. , and A. Angelsen. 1998. Economic models of tropical deforestation: A review: Bogor: Center for
International Forestry Research.
Kaimowitz, D., Erwidodo, O. Ndoye, P. Pacheco, and W. D. Sunderlin. 1998. Considering the impact of structural
adjustment policies on forests in Bolivia, Cameroon, and Indonesia. Unasylva 49(149): 57-64.
Karl, T. L. 1997. The paradox of plenty. Oil booms and petro-states. Berkely/ Los Angeles/ London: University of
California Press.
Little, I.M.D., R.N. Cooper, W.M. Corden, and S. Rajapatirana. 1993. Boom, crisis and adjustment. The
macroeconomic experience of developing countries. Oxford/ New York: Oxford University Press.
Mainardi, S. 1998. An econometric analysis of factors affecting tropical and subtropical deforestation. Agrekon
37(1): 23-45.
Matthews, E. 2001. Understanding the FRA 2000: Forest Briefing No. 1, Washington D.C.: World Resources
Institute.
Neary, P. , and S. van Wijnbergen. 1986. Natural resources and the macroeconomy: A theoretical framework. In
Natural Resources and the Macroeconomy, edited by N. a. S. J.P., van Wijnbergen: Oxford, 13-45.
Reed, D. (ed.), ed. 1992. Structural adjustment and the environment: WWF International, Boulder: Westview Press.
Reed, D. (ed.). ed. 1996. Structural adjustment, the environment, and sustainable development: London: Earthscan
Publications/World Wildlife Fund.
Roemer, M. 1984. Dutch disease in developing countries: Swallowing bitter medicine: Development Discussion
Paper No. 156, Harvard Institute for International Development.
Rudel, T. , and J. Roper. 1997. The paths to rain forest destruction: cross-national patterns of tropical deforestation,
1975-1990. World Development 25(1): 53-65.
Sunderlin, W. D., and S. Wunder. 2000. The influence of mineral exports on the variability of tropical deforestation.
Environmental and Development Economics 5: 309-332.
The Economist. 1977. The Dutch Disease. November 26.
Wood, A., P. Stedman-Edwards, and J. Mang, eds. 2000. The root causes of biodiversity loss. London: Earthscan
Publication.
Wunder, S. 2003a. The cheapest supplier: Foreign exchange inflows, competitiveness and the expansion of logging
in five tropical countries (draft in progress). Paper read at Abstract for paper submitted to "The Forest
Science/Policy Interface in Europe, Africa and the Middle East", at Copenhagen.
Wunder, Sven. 2003. Oil wealth and the fate of the forest. A comparative study of eight tropical developing
countries (forthcoming). London: Routledge, pp.433, ISBN 0-415-27867-8.
FIGURES AND TABLES
Figure 1. Linking resource booms to the forest
Level of
analysis
Causal mechanism
(1) External
Oil boom
(price or quantity)
(2) Macroeconomic
External
borrowing
Higher national income
(transitory or permanent)
Structure of
demand (+/-)
Higher domestic spending
(consumption, investment)
Policies and budgets:
. Road budgets (+)
. Transport subsidies (+)
. Agricultural budgets (+)
. Resettlement budget (+)
. Trade protection (+)
. Forestry budgets (-)
. Conservation budgets (-)
Poverty 
Labour
costs 
Savings
(+)/(-)
Real currency appreciation/
Relative price of NT goods rises
NT production
rises*
Quasi NT
production rises*
Semi T sector ambiguous *
T production
declines*
(3) Sectoral
Accelerated
urbanisation*
(5) Environmental
(+)
(-)
Agricultural
production
declines*
Timber
production
declines*
Expansion of
cultivated
area is
reduced*
(4) Land-use
Notes:
T
NT
*
Construction
boom*
Deforestation
is reduced*
Traded sector
Non-traded sector
Relative to pre-existing trends (growth and structural change)
Core causality mechanism - 'Dutch Disease protecting forests'
Opposite causality mechanism - 'Dutch Disease de-protecting forests'
Factor expected to accelerate forest loss and degradation
Factor expected to decelerate forest loss and degradation
Forest area
logged is
reduced*
Other landusing sectors
decline*
Other land
clearing and
extraction is
reduced*
Forest degradation is
reduced*
Figure 2. Differences in the tropical forest-cover estimates by TREES1, IUCN2 and
FAO3, 4
Venezuela
Papua New Guinea
Gabon
Ecuador
Cameroon
-40
-30
-20
-10
0
10
20
Difference from TREES estimates (%)
IUCN
FAO FRA 1990
FAO FRA 2000
Notes:
1. TREES figures from Mayaux et al. (1998)
2. IUCN, WCMC and CIFOR’s Conservation Atlas
3. FAO FRA 1990 figures from FORIS (wet, moist and montane forest)
4. FAO FRA 2000 figures are preliminary, from FAO (2001a)
Table 1. Comparing deforestation estimates (in ‘000 hectare)
FRA 2000
FRA 1990
Own ‘best guess’
(2)
Yearly
deforestation
1980-1990
Yearly
deforestation
( ~1990s)
Total
forests
2000 (1)
Yearly
deforestation
1990-2000
Cameroon
23,858
222 (3)
122
150-200
Ecuador
10,557
137 (4)
238
>180
Gabon
21,862
10 (5)
116
0
PNG
30,601
113 (6)
113
50-70
Venezuela
49,506
218 (7)
599
250-400
Sources: FAO (1993, 2001a), http://www.fao.org/forestry/fo/country/index.jsp; own estimates
Notes:
(1) Including plantations
30
40
(2) Preferred point estimate and/or likely range under variable assumptions
(3) Cameroon, FAO sources: 1975-99 extrapolation; Faure 1989 estimate of 200,000 ha/yr for agricultural
conversion
(4) Ecuador, FAO sources: 1985-92 extrapolation; two remote-sensing national forest maps (the latter
unpublished)
(5) Gabon, FAO sources: 1970-99 extrapolation; Nguépi 1999 15,000/yr ha; Wunder 2000.
(6) PNG, FAO sources: 1975-00 intrapolation; 1975-85 zero deforestation assumed; Forest Inventory and
Mapping System
(7) Venezuela, FAO sources: 1985-95 extrapolation; comparison of two Carrero maps (the latter
unpublished).
Table 2. Comparing macroeconomic and deforestation trends
COUNTRY
MACROECONOMIC CYCLES
Gabon
1960 – 73: Pre-boom
1974 – 85: Boom
1986 – 89: Mini-bust
1990s:
Fluctuations
Venezuela
1920/30s: Rise of petro-economy
1956 – 58: Mini-boom
1974 – 83: Boom
1984 - : Crisis & mini-booms
Cameroon
1960 – 78: Pre-boom
1979 – 85: Boom
1986 – 94: Bust, fixed CFA
1995 - :
Devaluation, recovery
1960 – 73: Pre-boom
1974 – 81: Boom (rising)
1982 – 85: Boom (declining)
1986 – 95: Bust
1996 - : Mini-booms
1972 – 94: Mineral boom (rising),
fixed overvalued kina
1995 – : Oil boom, financial capital
outflows, devalued kina
Ecuador
PNG
DEFORESTATION
CYCLES
1970 – 90s: Net forest
regrowth
Recent: Some
periurban clearing,
probably little net
change
1920 – 50: Forest
regrowth
1950 – 80s: Slow loss
1980s/90s: More rapid
loss
1973 – 85: Slow loss
1986 – 94: High loss
After 1994: Probably
high loss
Before 1975: Moderate
loss
1975 – 90: High loss
1990s: Probably
slower loss
Whole period:
Probably stable, low
loss
linked to food-crop
expansion
After 1994: Perhaps
loss acceleration
EVALUATION OF THE
CORE HYPOTHESIS
Absolute confirmation
(short and long run)
Pre-WWII: Absolute confirmation
Post-WWII: Relative confirmation
Relative confirmation
Rejection
– absolute and relative
Relative confirmation
(hesitant)
1
The authors are Senior Researchers at the Center for International Forestry Research (CIFOR) in Bogor,
Indonesia. We wish to thank Ahmad Dermawan and Ramadhani Achdiawan at CIFOR for research and
computational assistance, and Ken Chomitz, David Kaimowitz, Thomas Rudel, Stuart White XXXX OK?
Add! ….. for comments on earlier text drafts.
2
The highest level of our analysis is the nation state, so in the present article we don't look at the global
consequences of oil wealth and of changed international trade patterns.
3
Sunderlin and Wunder (2000: 318-20) demonstrated for a cross-country data of 66 countries that high
urbanization is one of the structural characteristics related to the group of mineral-rich countries with a
predominantly higher share of forest cover and a lower rate of forest loss.
4
This refers to Geist and Lambin (2001) and unpublished collection kindly made available by Thomas
Rudel.
5
Each of the following sub-sections summarises main lessons from chapters 4-8 in Wunder (2003),
omitting the secondary cases from book chapter 9).
6
Chief Mboula Thaopile, Nioungou village, cited in Adams and McShane (1996: 207).
7
This generalisation is abstracted from what was called Scenario 2, namely large rents with incipient road
building, which tends to be a transitory phase.
14