Impact of Economic Policy Options on Deforestation in
Madagascar
Companion Paper for the Policy Research Report on Forests, Environment, and
Livelihoods
Timothy S. Thomas
The World Bank
Development Economics Research Group
This volume is a product of the staff of the International Bank for Reconstruction and Development / The
World Bank. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect
the views of the Executive Directors of The World Bank or the governments they represent. The World Bank
does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations,
and other information shown on any map in this work do not imply any judgment on the part of The World
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15 June 2007, Washington, DC
Version 1.0
Introduction
Madagascar is an island nation high on the scale of global environmental significance,
primarily due to its large number of endemic species. Many of these species are
dependent on the forests of Madagascar, which have dwindled to perilously low levels.
For this reason alone, an in-depth study of causes of deforestation in Madagascar is
warranted.
Madagascar is also an impoverished nation. In order to promote economic development,
substantial investment needs to be made to enhance private production and trade,
particularly among farmers, which make up a large portion of the working population.
Some investments that could possibly improve the economy, such as roads, might
possibly have the opposite effect on the environment. Other investments, such as
improved physical security in rural areas or more comprehensive land registration, could
be argued to have either positive or negative impacts on the environment.
This study seeks to determine the possible impact of investing in roads, security, and
titling, while controlling for other factors such as agroclimatic conditions and presence of
protected areas. It is similar to an earlier study by Gorenflo et al. (2005), but extends that
study by including many spatial components including distance to nearest non-forest,
forest proportion, and size of forest patch, and models the influence of nearby forest on
each segment of forest.
Gorenflo et al. (2005) do not attempt to account for potential spatial error or spatial lag in
their model. Failure to do so, in the context of a probit model, could lead to biased
parameters and therefore incorrect conclusions. In this paper, we seek to improve on
their estimation by accounting for these spatial components. To do this, we use a
Bayesian approach which relies on Markov Chain Monte Carlo methods to solve spatial
probits.
Section 2 of this paper describes our data and methodology. Section 3 presents and
discusses our results. Section 4 concludes.
Data and Methodology
We are grateful to Mark Steininger and Grady Harper at Conservation International (CI)
for sharing with us their forest change map, which was derived from 34 LandSat images
taken in 1990 and again in 2000 (Steininger, Harper, and Tucker, 2004). Figure 1 shows
forest cover in 1990, and Figure 2 shows change in forest cover between 1990 and 2000.
Our deforestation data came to us in 30-meter gridcells. For our statistical analysis, this
is clearly too fine. Our computing resources would not be able to process a dataset that
large. It also might provide problems with poor geo-referencing at such a fine scale.
Because we were trying to parallel the study of Gorenflo et al. (2007), we tried initially to
use a grid of points spaced at an interval of one kilometer, and focus on land that had
forest in 1990. Because of computational limitations, we reduced that by using only one
in four gridcells, in a regular two-kilometer grid.
From this deforestation dataset we also computed a number of potentially very important
and spatially explicit explanatory variables. First, we created distance to nearest nonforest, as an indicator of accessibility to forest. Second, we computed the forest
proportion in each gridcell. If there is some deforestation in the gridcell, then the
preceding variable -- distance to nearest non-forest -- must be less than 710 meters (the
distance from the center of a 1 km square to a corner of the square) by definition. What
forest proportion does is that it tells us how far along local deforestation is. Some models
of deforestation suggest that in early years of settlement, farmers will deforest more
rapidly than in later years. The reason for this is that farmers cannot make much income
until they have cleared land on which to plant, so they invest a lot of their labor into
clearing, while once they have some land available for farming, they shift the focus of
their labor to agriculture, and clear more land only as they are able.
The third variable we created from this dataset is forest patch size. We believe that small
patches of forest exist in many places because there is something special about that patch
that keeps it from becoming completely deforested. Perhaps it represents a municipal
park, or maybe a patch of trees by a stream or on a steep hillside that is impractical for a
farmer to use for agriculture. We want to use dummy variables as a a sort of nonparametric way of seeing what patch sizes are "special" in the sense that they are less
likely to be deforested. To generate patch size, we first computed a 500 meter buffer
around forest which existed in 1990. We then calculated the size of every contiguous
patch of forest plus buffer. One very interesting finding is that the ring of forest that is
near all coasts of Madagascar is only discontiguous for a short distance on the northwest
coast, despite appearing very thin in places!
Figure 3 shows the population density, as measured in the 1993 population census. The
map reflects population aggregated to 1,242 firaisana (the smallest administrative unit for
which GIS files exist, similar to a county). This map also shows roads, rivers, lakes, and
major cities. The roads dataset -- along with the footpaths dataset which is not shown in
the figure -- were provided by L’Institut Géographique et Hydrographique National de
Madagascar (FTM), and are derived from remote sensing images from the early 1990s.
The scale for these datasets is 1:500,000.
Figure 4 shows the location of land in some kind of protected status. This dataset is from
the World Database on Protected Areas (WDPA). Figure 5 shows the elevation map
provided by L’Institut Géographique et Hydrographique National de Madagascar (FTM).
This map was derived from contour maps with 50 meter intervals. The slope that we use
in the data analysis was calculated from the elevation map.
The soil fertility map was taken from Plate 22, the soil fertility constraints, of the Global
Agro-Ecozone (GAEZ) program (FAO/IIASA, 2000). Higher numbers mean more
constraints rather than better fertility. We would expect more fertile soils to be
deforested faster, all other things being equal. If, however, the better soils have been
settled for a long time, then we might see less fertile soils being deforested faster. This
layer appears to be highly correlated with rainfall (as predicted by WorldClim by Hijman
et al., 2004), where low rainfall is correlated with fewer constraints, and hence higher soil
suitability. It also appears to be correlated with elevation and slope. Given that this layer
is not finely differentiated -- being reported at the 5 arc minute level, which is
approximately 9 kilometers at the equator -- in regression analysis it might have lower
explanatory power than some of the other variables with which it is correlated.
Mean expenditure per capita was computed at the firaisana level as part of their poverty
mapping project and report by Mistiaen et al. This was based on the 1993 population
census and a survey taken as part of the poverty mapping study. Mean expenditure per
capita is a variable of interest because policy makers may be concerned about the impact
of economic development -- i.e., raising per capita income -- on deforestation. We
assume that this variable is exogenous, though one could argue that it is jointly
determined: land that is discovered to be agriculturally valuable might not only be
deforested more quickly, but it might lead to higher incomes for farmers, and thus higher
mean incomes for people in those firaisana.
We are grateful to Bart Minten and the ILO program of Cornell University, as well as
FOFIFA and INSTAT in Madagascar which collaborated with ILO, for providing us with
their commune census dataset. From this dataset, we extracted a number of variables of
interest. They were the travel cost to nearest major city (i.e., the CUP), proportion of
land titled, date the commune was created, and physical security within the commune (in
terms of personal safety and property theft).
Since our dependent variable was a dichotomous variable, we neede to use either a probit
or logit. It was necessary to decide upon a method to account for spatial errors and
spatial lags. Maximum likelihood methods do not permit us to solve a spatial probit,
except for possibly using the EM algorithm (Fleming; McMillan), though there are
problems with such a method. Fleming has developed a spatial probit using the GMM
method suggested by Kelejian and Prucha.
We chose to employ Markov Chain Monte Carlo (MCMC) methods developed for the
Bayesian approach to the spatial probit (LeSage). After downloading the MATLAB code
from LeSage's website, we modified some of the routines of LeSage, so that we could
apply Fleming's suggestion for improvement. Details are found in the companion paper
by Thomas (forthcoming).
The deforestation model we have in mind is quite simple. It is similar to that of Arild
Angelsen (2007), who wrote in a companion paper to the tropical deforestation policy
research report a model of net returns to land in various types of agriculture and forestry,
with land being converted in places where agriculture gives more return to land than
forestry. Bell and Irwin (2002) review several models of land use change, and simplify
the decision to convert land as being based on net expected returns from the conversion.
The component that they include that was omitted from Angelsen's paper is the cost of
conversion.
The general idea is that things that improve the accessibility of the forest, thus reducing
deforestation costs -- or that make the land more accessible to end markets, thus reducing
input costs and raising farmgate prices -- make the land more likely to be converted.
Hence we include measures of accessibility to the forest and to markets in our analysis.
Poverty in the vicinity of the land could indicate lower costs for labor, which could raise
the probability of deforestation. However, higher income in the area could also raise the
probability of deforestation by providing needed capital for the initial costs of land use
conversion and inputs into higher productivity to agriculture. Since both are plausible,
ultimately it is an empirical question as to which factor, if either, impacts the decision to
deforest.
Results
Because we believed that there were significant differences between the processes of
deforestation found in the moist forests and the spiny forests, we ran separate analyses on
these two regions. This belief was influenced by the Gorenflo et al. (2007) who found
that the Southwest Region (the region for spiny forests) behaved very differently than all
of the other regions, except possibly the Sambirano region in the far north.
Table 1 presents the spatial probit regression results for the moist forest. The SAR
(spatial lag) regression took more than 3 days of computing time to run, while the SEM
(spatial error) took one-sixteenth the time. This is because the SAR needs to do an
extremely time-consuming matrix operation (Gaussian reduction) that the SEM does not
need to do. In the far right columns of Table 1, we show the quantitative magnitude of
the effect of the parameter when multiplied by two times the standard deviation of the
associated variable. We only display this for continuous variables. With dummy
variables, we can just take the parameter estimate as a good indicator of the influence of
that variable.
Table 2 presents basic statistics on each variable used in the moist forest regressions.
Table 3 shows the probit results for the spiny forest, and Table 4 shows basic statistics for
variables used in the spiny forest regressions.
Moist Forest Results
There are eight different categories of variables which we used in our analysis to measure
their impact on deforestation probability. For ease of exposition, we will discuss the
moist forest results first.
Dynamic component of deforestation
The first category we call the "dynamic component of deforestation". In Tables 1 and 3,
these consist of 3 sub-categories of variables.
Patch size. We believe that small patches of trees are indicative of trees that have been
intentionally left standing. Perhaps this reflects a municipal park; a land holder with a
preference for trees; steep hillsides that are not controlled for in our coarse slope variable;
trees left to protect streams and rivers; or ones that are in poorly drained land or other
agriculturally constrained land. In any case, we believe that when small patches of trees
are still standing, this may be due to a choice made by an economic agent, and therefore
the probability of those trees being deforested is smaller than we might otherwise expect.
We see in Table 1 that the impact of patches up to 1,000 square kilometers (including a
500 meter buffer around the patch) is large, negative, and very statistically significant.
Distance to non-forest and forest proportion. The second and third sub-categories of the
"dynamic component of deforestation", the distance to non-forest and the forest
proportion variables, are defined in a particular way that is initially complicated to
understand, but once understood facilitates interpretation of their respective and joint
effects on the probability of deforestation. We chose to treat these continuous variables
non-parametrically; that is, we converted them to categorical variables. In the
regressions, the omitted categories are where the distance to non-forest that is greater
than 2 kilometers and the forest proportion of the 1 kilometer square is greater than 80
percent forested. Because of the way we defined the grid around the point of
observation, until we get closer than 710 meters to non-forest, the square is actually 100
percent forested. In practice, for all but one point in more than 21,000 points in our
study, for distances greater than 500 meters from non-forest, the gridcell has more than
80 percent of the land covered in forest.
Looking at Table 1 in the columns for the SAR, the impact in moving from greater than 2
kilometers from non-forest to between 1 and 2 kilometers is a shift of 1.15, which is quite
a large shift for a probit. Moving from the 1 to 2 kilometer category to the 500 meter to 1
kilometer category represents a shift of 0.65. Moving even further to less than 500
meters results in a shift of 0.54 from the preceding category. The effects of each of
changing from one distance range to another is quite large, and we see very clearly that in
the moist forest region of Madagascar, nearness to non-forest is an important variable
explaining deforestation. This confirms a tendency for new deforestation to expand
outward from older deforestation, like ripples on a pond.
Focusing again on Table 1 in the columns for the SAR, we expected that some deforestation in the gridcell would lead to higher probability of additional deforestation relative to
no deforestation, which we clearly observe. We did not expect to have an increase of
parameter values as the proportion of forest approched 0, since we expected that after
some point in the land clearing process, labor might shift more from deforestation
activities to agricultural activities.
In retrospect, however, since there is much shifting cultivation in the moist forest region
of Madagascar (Gorenflo, et al.), we would expect deforestation to be ongoing as
cultivators shift to nearby plots and possibly abandon older plots. We still may not have
expected deforestation to occur at an increasing rate, but the rates observed in Table 1 are
very modest increases, and none of the incremental changes in percent forest cover
category are statistically significant 1 (though comparing categories 2 or more apart, we
find that the difference is statistically significant).
In comparing these results to those of Gorenflo et al., we might have expected that since
distance to non-forest and forest proportion are quantitatively large and give such high
statistical significance, that our results would differ substantially from theirs in the
variables they share in common. While our results may differ from theirs in marginal
effects of some variables, in general the only significant difference is that our distance to
footpath variable is positive and not statistically significant, while theirs is negative and
statistically significant. In results not published here, when we dropped distance to nonforest and proportion forest from the regression, the distance to footpath became negative
and statistically significant. It seems that distance to footpath in their regressions must be
serving as a proxy for distance to non-forest.
Transport costs
We do not know the actual cost of transporting trees or many agricultural products to
market. What we have is a proxy variable which measures the cost of transporting a
person to the nearest market city (CUP) from the commune seat. Table 1 shows that as
travel cost to the nearest market city increases, there is a major reduction in probability of
deforestation. This implies that deforestation is responsive to economic incentives.
The response of deforestation to economic incentives is also supported in the probit
results of Table 1, which show that with increasing distance to roads, deforestation
probability declines moderately. However, as discussed briefly in the preceding section,
there is no deforestation response to nearness of footpaths. This, we argued, was due to
their close proxy of nearness to non-forest.
Agroclimatic suitability
We would expect that areas with better agricultural potential would be more likely to be
deforested, controlling for other factors. We see this to be true with the slope variable, in
which higher slope leads to lower probability of deforestation. We see also that higher
elevations have lower probability of deforestation.
The surprising result is that the higher the soil fertility constraints, the higher the
probability of deforestation. Possible explanations might be that the fertility constraints
are not well known by the deforesters; or that the dataset was not indicative of the
measures of soil fertility that interest the farmers; or that more fertile soils have
vegetation that regenerates between satellite images in 1990 and 2000, while the poorer
soils cannot regenerate vegetation quickly enough.
1
Tested only in the non-spatial probit.
Population pressure
There are three variables in our analysis that indicate population pressure. The first is
population density as computed at the firaisana level from the 1993 census. The second
is a dummy variable indicating whether the commune was created after 1960. This may
be a proxy for a newly settled area, which might indicate a certain dynamism in growth.
Finally, distance to footpath may be indicative of the presence of people. That is, while
we know the population in each firaisana, we do not know how the population is
distributed throughout the firaisana. The distance to footpath may be indicative of higher
or lower densities.
Higher population density in the firaisana had a small to moderate positive effect on
probability of deforestation. The post-1960 commune creation dummy variable had the
opposite than anticipated effect, showing lower deforestation probability.
Poverty
We used expenditure per capita as a proxy for poverty. As poverty increased (and
expenditure fell), we noted a small but statistically significant increase in probability of
deforestation. In results not presented here, we tried other poverty measures (headcount
poverty and poverty depth), and the results for the non-spatial probits (which for this
particular dataset have been paralleling the results of the spatial probits) gave similar
quantitatively small results.
Protected areas
Table 1 shows that parks and other protected areas seem to be quite effective in lowering
the probability of deforestation. Often protected areas are located in remote places where
probability of deforestation is low to begin with. However, the multivariate regression
approach attempts to control for all other factors including remoteness, and even
controlling for them, we still note the effectiveness of protected areas in Madagascar.
Land titles
As Table 2 shows, some of the categories of land titles are a little thin, making us
cautious not to draw strong conclusions from the probits in Table 1. Nevertheless, it
seems that some land titling (perhaps the initial phases of a land title program) results in a
sizable increase in deforestation probability, but then once a certain percentage of titles
are issued (here, 10 percent or more), the initial increase in probability disappears. This
outcome may simply be a rural-urban phenomena, because the communes with no titles
have a much lower population density than any of the categories with titles.
Physical security
Table 1 shows that in the moist forest regions, when physical security (i.e., safety from
theft and violent crime) is very bad, deforestation is low relative to when it is only
moderately bad, which in turn is slightly lower than when it is good. The "very good"
category only has a few observations, so we do not want to draw too many conclusions
from the very large jump in deforestation between the "good" and "very good" responses.
Spiny Forest Results
Table 3 shows that the spiny forest region is quite different from the moist forest region.
Gorenflo et al. (noted) noted this as well. We can also visually note differences in Figure
2, where in the south and southwest, deforestation tends to come in very large patches,
while deforestation in the rest of the country tends to be "spotty" and dispersed.
The spiny forest spatial probit results show very large spatial parameters. Normally
weights matrices are row-standardized, which means the rows are normalized so they
sum to 1. When this is done, a value of a spatial parameter that is near 1 indicates a
potential problem with non-stationarity, just as we worry about the same problem in time
series analysis. A value of 1 in such a case means that an error (in the SEM) or a lag (in
the SAR) in one geographic observation is transmitted to every geographic location in the
study area.
In our regressions, however, we "matrix standardized" (Thomas, forthcoming). That is,
we normalized by the row with the highest sum of weights. This was a compromise
between the desire to avoid problems with numerical routines created by "holes" in the
unit interval, and the belief that distance ought to have an absolute impact, rather than a
relative impact, on the effect an observation had on its neighbors. See Bell and Bockstael
(2000) for a more complete and well-presented discussion of these two problems. We
have not read previously about anyone who tried to "matrix standardize", but this seems
to us to be a good compromise solution when researchers believe distance ought to matter
in the strength of a weight.
Since the normalized matrix for the spiny forest regressions had an average row sum of
0.64, weights close to 1 should not bother us in terms of transmitting errors across the
entire geographic landscape. Nevertheless, there are likely to be a number of points in
our analysis that have rows summing to 1 or near to 1, and if these points are clustered
together geographically, then a spatial parameter near 1 says that even small errors or
lags are transmitted throughout the cluster. We would, in fact, expect such observations
to be clustered together, because these are the areas where forests are large and
contiguous.
Looking at the spatial parameters in Table 3, the SEM probit results are very close to the
non-spatial probit results (not published in this paper), and if we decide to disregard them
for the SAR probit results -- which differ significantly from the SEM probit results -then we would also be pointing to a very specific instance where failure to account for
spatial factors resulted in parameter estimates that were biased to such an extent to lead
one to the opposite conclusion of the correct one.
If this were a linear model, we could estimated both the spatial lag and the spatial error
parameter simultaneously. But because it is a probit, it is very difficult to estimate both
in the same regression, because of near identification of the two spatial parameters, and
because the spatial probit relies on simulating the latent variable. We are left with uncertainty about which model to believe. In the moist forest regressions of Table 1, the two
spatial models produced very similar results, and so there was no need to differentiate
between the two. In the rest of the discussion on spiny forest regressions, we will
comment on both SAR and SEM results.
Dynamic component of deforestation
Patch size. Just as we observed in the moist forest region, small patches of forest have a
very strong negative effect on deforestation probability. The difference is that in the
spiny forest regression of Table 3, we had to drop the 100 thousand hectare to 1 million
hectare category because there were no forests of that size in the spiny region, and we
had to drop the 10 to 100 thousand hectare category because the few observations we had
in this category resulted in no deforestation, and a maximum likelihood approach has
difficult with dummy variables with no variation in outcome in a probit. The Bayesian
simulation approach, however, was not affected by inclusion of this variable.
Nevertheless, in the results reported in Table 3, we chose to drop it for the sake of
consistency.
Distance to non-forest. In spiny forests, we see that the difference in deforestation
probability is large and siginificant when going from the category of greater than 2
kilometers from non-forest to 1 to 2 kilometers from non-forest, just as we did in the
moist forests. However, we do not observe large or statistically significant changes when
we move to categories that reflect closer proximity to non-forest, unlike the changes we
observed for the moist forest.
Forest proportion. In the SAR probit for spiny forests, we note very unexpected results
which show that the effect of increased forest proportion on probability of deforestation
is positive. Stated differently, this says that the probability of deforestation drops as soon
as deforestation begins.
In the SEM probit for spiny forest, we note results that are very similar to those of the
spatial probits for the moist forest. That is, there is a small and increasing probability of
deforestation as less forest is observed in a gridcell, though unlike in the moist forest
results, is not statistically significant. The other dissimilarity we note is that in the spiny
forest, we do not see the very large jump that we saw in the moist forest when we move
from the greater than 80 percent forested category to the 60 to 80 percent forested
category.
Tree accessibility and transport costs
Transport cost to nearest market city. Just as we noted in the moist forest regression,
increasing travel cost to nearest market city has a large negative impact on probability of
deforestation in the spiny forests. This results is consistent in both the SAR and SEM
regressions.
Distance to roads. In the SAR probit, increasing distance to a road actually increases the
probability of deforestation, contrary to the SEM results, the moist forest results, and
intuition. If the SAR results are correct, it may imply that some very large scale
enterprises -- public or private -- were responsible for deforestation, and that these
enterprises chose locations far from roads for reasons not controlled for in our analysis.
The distance to road parameter for the SEM probit was negative, though not statistically
significant at the 5 percent level, but significant at 10 percent in the two-tailed t-statistic
test.
If intuition can be used to confirm correct models, it appears that the SEM gives very
intuitive results for both distance to roads and forest proportion, and therefore we would
lean toward accepting the SEM over the SAR as the correct model.
Distance to footpaths. The SAR parameter for increasing distance to footpaths was
similar to its results for increasing distance to road: positive, moderate strength, and
statistically significant. This result is counterintuitive. The result was also positive for
the spiny SEM and for the moist forest, but in all cases, the parameter was not
statistically significant.
Agroclimatic suitability
Slope. The results for slope in the SAR and SEM regressions in both the spiny and moist
forest zones are the same: negative, moderate to large, and statistically significant.
Elevation. As elevation increases, the probability of deforestation increases, according to
the results of both the SAR and SEM in the spiny forest zone. This is opposite from the
results for the moist forest, but not necessarily counterintuitive. The elevation in the
moist zone has a much greater range than that in the spiny zone (see Tables 2 and 4). It
may simply be that neither the coast nor the mountains are most likely to be deforested,
but rather something in between.
Soil fertility. Increasing soil constraints has a perverse affect on probability of deforestation in the spiny forest, leading to an increase in probability as the soil become more
contrained. This is identical to the case for the moist forest.
Population pressure
Population density. In both the SAR and SEM results for the spiny forest, we see that
population density seems to have a negative effect on deforestation, with the SAR results
being larger than the SEM, and of statistical significance, unlike the same. In the moist
forest, population density had a positive effect on deforestation probability.
Post-1960 commune. The post-1960 variable has no statistical effect on deforestation
probability in either the SAR and SEM regressions for spiny forest, contrasting with the
results for the moist forest, which show a lagre negative effect.
Poverty
In both the SAR and SEM regressions for spiny forest, the mean expenditure per capita
had a negative but statistically insignificant effect on deforestation probability. These
results are very similar to those of the moist forest, where increased poverty leads to a
slight increase in deforestation.
Protected areas
Parks and protected areas cover a very small portion of the forested part of the spiny
forest. Only 25 observations in our database represented the park area. No deforestation
took place in those 25 cells, so we omitted them from the analysis to avoid potential
problems with perfect prediction by a dummy variable.
Land titles
In our analysis of land titling in the spiny forest, we found that both the SAR and SEM
had remarkable agreement, in that there appeared to be large and negative effects for a
limited amount of titling in the commune (compared to no titling), but large positive
effects for extensive titling in the commune. These results were exactly opposite of what
we observed in our regressions for the moist forest.
Physical security
The SAR results show large and statistically significant effects of physical security on
deforestation, while the SEM results show large but mostly statistically insignificant
results. Focusing on the SAR parameters, we see that just as we observed in the moist
forest, very bad physical security yields low deforestation. However, opposite to the
effect we observed for the moist forest, for bad to very good security, the better the
security, the lower the probability of deforestation. This was the general trend noted for
the spiny SEM regressions, as well.
Conclusions
In this study, we examined the impact of economic, social, and geophysical variables on
the probability of deforestation. As Gorenflo et al. noted in their earlier study and is now
confirmed by our study, the impact of many variables differs remarkably between the
spiny forest zone and the moist forest zone. These results were probably driven by the
clumpy nature of deforestation in the spiny forest, where possibly the deforestation
behavior was influenced by large entities, but unknown to us. Gorenflo et al. did note the
literature claiming rapid and massive deforestation in the spiny forest due to commercial
agricultural interests.
A number of studies have claimed or posited a large positive impact of poverty on
deforestation rates and probabilities. Our study confirms a small and sometimes
statistically insignificant impact of low income on deforestation probabilities, and this
result is consistent in both moist and spiny zones.
On the other hand, reduction in transport costs, either through location closer to roads or
reduced cost to the nearest market city, seems to dramatically increase the probability of
deforestation, at least in the moist forest. In the spiny forest zone, reduction of travel cost
to the market city also leads to increased probability of deforestation, but the effect of
moving closer to roads is mixed, depending on which regression we believe -- though we
argued that the SEM -- which has similar results to the results in the moist forest -- is
probably the correct one. Despite which results we choose to believe for the spiny forest
zone, the moist forest zone is almost three times larger in size, and our results suggest
that any road construction or improvement of transportation that results in lower travel
costs to the nearest market city may result in a surge of deforestation.
Road construction would likely lead to a reduction of poverty in the effected areas,
though Moser et al. (2005) suggest that market opportunities are limited by many other
mechanisms besides roads. This would imply that the impact of road construction on
poverty would be modest at best. Reduction of poverty would lead to a feedback effect
in a slight reduction in deforestation. However, since the direct impact of roads on
deforestation is quite large, according to our regression results, this offset would be
slight.
Parks and other protected areas appear to have excellent success in limiting deforestation
that might otherwise occur. The recently pledged expansion of parks, especially with
careful choice of location, could potential offset any deforestation increases resulting
from road construction.
Population density had conflicting impacts on deforestation: small to moderate increases
in deforestation in the moist zone, and moderate to large decreases in the spiny zone.
Land titling seemed to have large impacts on deforestation, but the impact is not
predictable, with opposite effects observed in moist and spiny zones, and proportion titled
influencing deforestation non-linearly. More study needs to be done to draw robust
conclusions.
Communes that were highly insecure had low deforestation in both moist and spiny forest
zones, but outside of the highly insecure zones, moist forests seemed to have increased
deforestation with increased security, while spiny forest had decreased deforestation with
increased security.
On the technical side, we included a number of quantitatively large and statistically
significant variables that are omitted from many deforestation studies. These included
distance to non-forest; forest proportion; dummy variables for small patches of forest;
and spatial error or spatial lag. Given that the parameters in a probit are biased by not
controlling for spatial factors or any other form of heteroscedasticity, and that they are
biased for omitted variables, it is remarkable to us that our results are so similar to those
of Gorenflo et al. (2007) in the moist forest zone, even though they omitted all of these
factors. If similarity between spatial and non-spatial results is consistently noted across
studies, researchers may want to reconsider whether the technical challenges associated
with spatial econometric analysis are worth dealing with in light of the small rewards for
the effort.
References
Angelsen, Arild. 2007. “Forest Cover Change in Space and Time: Combining the von
Thünen and Forest Transition Theories”, World Bank Policy Research Working
Paper no. 4117 (February), World Bank, Washington, D.C.
Association Nationale pour la Gestion des Aires Protégées (ANGAP). Map of Protected
Areas.
Bell, Kathleen P. and Nancy E. Bockstael. 2000. “Applying the Generalized-Moments
Estimation Approach to Spatial Problems Involving Microlevel Data,” The Review of
Economics and Statistics 82(1):72-82.
Bell, Kathleen P. and Elena G. Irwin. 2002. "Spatially Explicit Micro-level Modelling
of Land Use Change at the Rural-Urban Interface", Agricultural Economics 27:217232.
FAO/IIASA. 2000. Global Agro-Ecological Zones CD. Downloaded at
http://www.iiasa.ac.at/Research/LUC/GAEZ/index.htm.
Fleming, Mark. 2004. “Techniques for Estimating Spatially Dependent Discrete Choice
Models”. Advances in Spatial Econometrics: Methodology, Tools,and Applications,
ed. by Luc Anselin, Raymond J.G.M. Florax, and Sergio J. Rey. New York:
Springer-Verlag, 145-168.
Gorenflo, L.J., Catherine Corson, Kenneth M. Chomitz, Grady Harper, Miroslav Honzák,
and Berk Özler. 2007 (Forthcoming). "Exploring the Association Between People
and Deforestation in Madagascar", in R. Cincotta, L. Gorenflo, and D. Macgeean.
(editors), Human Population: The Demography and Geography of Homosapiens
and their Implications for Biodiversity. Berlin: Springer.
Government of Madagascar. Population Census.
Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis. 2004. The WorldClim
interpolated global terrestrial climate surfaces. Version 1.3. Available at
http://biogeo.berkeley.edu/
ILO (Cornell University) in collaboration with FOFIFA and INSTAT. Commune
Census. Downloaded from http://www.ilo.cornell.edu/ilo/data.html.
Kelejian, Harry H. and Ingmar R. Prucha. 1999. “A Generalized Moments Estimator for
the Autoregressive Parameter in a Spatial Model”, International Economic Review
40(2):509-533.
L’Institut Géographique et Hydrographique National de Madagascar (FTM). Map of
Elevation.
L’Institut Géographique et Hydrographique National de Madagascar (FTM). Map of
Footpaths.
L’Institut Géographique et Hydrographique National de Madagascar (FTM). Map of
Roads.
LeSage, James. 1999a. “The Theory and Practice of Spatial Econometrics”, mimeo.
Downloaded July 2003 from http://www.spatial-econometrics.com/html/sbook.pdf.
LeSage, James. 2003. MatLab code for spatial analysis. Downloaded July 2003 from
http://www.spatial-econometrics.com/.
McMillen, Daniel P. 1992. “Probit with Spatial Autocorrelation,” Journal of Regional
Science 35(3):417-436.
Moser, Christine, Christopher B. Barrett, and Bart Minten. 2005. "Missed opportunities
and missing markets: Spatio-temporal arbitrage of rice in Madagascar", mimeo.
Mistiaen, J.; Özler, B.; Razafimanantena, T.; Razafindravonona, J. "Putting welfare on
the map in Madagascar", Africa Region Working Paper Series No. 34. Washington,
DC: World Bank.; 2002.
Steininger, Mark, Grady Harper, and ??? Tucker. 2004. Deforestation and Land-Use
Change Maps for Madagascar in 1990 and 2000.
Thomas, Timothy S. Forthcoming. "Lost in Space: A Primer for Probits When
Geography Matters (as It Almost Always Should)", Companion Paper for the Policy
Research Report on Forests, Environment, and Livelihoods.
UNEP-WCMC. 2003. World Database on Protected Areas. Available at http://sea.unepwcmc.org/wdbpa/.
Figures
Figure 1. Map of Forest Area in 1990.
U
%
Cities
Land cover in 1990
forest
spiny forest
mangrove
nonforest
water
clouds
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
Figure 2. Map of Deforestation Between 1990 and 2000.
Land cover change, 1990 to 2000
forest to forest
forest to nonforest
nonforest to nonforest
spiny forest to spiny forest
spiny forest to nonforest
mangrove to mangrove
mangrove to nonforest
water during at least one period
clouds during at least one period
Figure 3. Population Density Map
50º
48º
46º
44º
Cities
Rivers
Main roads
Main roads
Lakes
Population density
<2
2-5
5 - 20
20 - 50
50 - 100
> 100
U
%
12º
U
%
U
%
U
%
U
%
U
%
14º
U
%
U
%
U
%
U
%
U
%
U
%
16º
U
%
U
%
U
%
18º
U
%
U
%
U
%
U
%
20º
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
22º
U
%
U
%
U
%
U
%
24º
U
%
Figure 4. Protected Areas in World Database on Protected Areas (WDPA).
U
%
Cities
Main roads
Main roads
Parks and Reserves (WDPA)
Marine Park
National Park
Special Reserve
Strict Nature Reserve
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
U
%
Figure 5. Elevation Map.
Tables
Table 1. Moist Forest Deforestation Probit Regressions.
Variable
Param
Prob
from
t-stat
Moist SAR
Constant
Expenditure per capita
Protected area dummy
Population density
Footpath, distance to
Road, distance to
Slope
Soil fertility
Elevation
Distance to non-forest: 1km-2km
Distance to non-forest: 500m-1km
Distance to non-forest: <500m
Forest proportion: 0.6-0.8
Forest proportion: 0.4-0.6
Forest proportion: 0.2-0.4
Forest proportion: <0.2
Travel cost to CUP
< 5% titled (omitted: 0%)
5 - 10% titled
10 - 25% titled
25 - 50% titled
> 50% titled
Bad conditions of safety & theft
Average (omitted: very bad)
Good
Very good
Commune created 1960 or later
Unknown creation date
Patch < 10,000 has.
10,000 < Patch < 100,000 has.
100,000 < Patch < 1,000,000 has.
λ (spatial parameter)
iterations
-4.3295
-3.55E-04
-0.5367
2.86E-03
7.69E-06
-9.02E-06
-9.76E-03
8.82E-03
-2.62E-04
1.1482
1.7967
2.3427
0.3888
0.4288
0.4566
0.5496
-2.18E-06
0.3118
0.2873
-0.0391
0.1043
-0.1781
0.4123
0.4270
0.5199
0.7478
-0.4731
-0.0843
-0.4661
-0.6887
-0.1177
0.3386
1,000
0.000
0.056
0.000
0.001
0.183
0.002
0.004
0.000
0.000
0.067
0.011
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.348
0.265
0.161
0.000
0.000
0.000
0.000
0.000
0.106
0.000
0.000
0.079
0.000
Param
Prob
from
t-stat
Moist SEM
-5.2847
-5.81E-04
-0.6555
3.98E-03
-3.86E-06
-1.26E-05
-9.85E-03
8.80E-03
-3.78E-04
1.6052
2.5502
3.1816
0.4786
0.5655
0.6306
0.7492
-2.32E-06
0.3253
0.2947
-0.0886
0.1398
-0.2567
0.4092
0.3843
0.4854
0.7454
-0.5336
-0.1592
-0.3716
-0.6141
-0.0668
0.3379
0.000
0.009
0.000
0.000
0.345
0.000
0.002
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.234
0.238
0.086
0.000
0.000
0.000
0.000
0.000
0.022
0.000
0.000
0.240
0.000
Twice variable
std dev times
parameter
SAR
SEM
-0.079
-0.129
0.105
0.054
-0.170
-0.168
0.353
-0.261
0.146
-0.027
-0.237
-0.169
0.352
-0.377
-0.519
-0.551
1,000
Notes:
1) For variables with squared term, the twice the standard deviation formula was modified so that the high
and low values had to stay within the bounds of the max and min. Also the lower and upper values for the
linear term were squared to provice upper and lower values for the squared term.
2) These regressions were run with the assumption of homoscedasticity, and fixing the variance to be equal
to 1.
3) The first 100 iterations were omitted
Table 2. Moist Forest Variable Statistics.
Variable
Constant
Expenditure per capita
Protected area dummy
Population density
Footpath, distance to
Road, distance to
Slope
Soil fertility
Elevation
Distance to non-forest: 1km-2km
Distance to non-forest: 500m-1km
Distance to non-forest: <500m
Forest proportion: 0.6-0.8
Forest proportion: 0.4-0.6
Forest proportion: 0.2-0.4
Forest proportion: <0.2
Travel cost to CUP
< 5% titled (omitted: 0%)
5 - 10% titled
10 - 25% titled
25 - 50% titled
> 50% titled
Bad conditions of safety & theft
Average (omitted: very bad)
Good
Very good
Commune created 1960 or later
Unknown creation date
Patch < 10,000 has.
10,000 < Patch < 100,000 has.
100,000 < Patch < 1,000,000 has.
Mean
Std dev
1
323
0.136
14.3
4,026
12,590
6.2
60.5
655
0.107
0.146
0.668
0.148
0.124
0.103
0.066
97,775
0.420
0.074
0.060
0.012
0.022
0.211
0.439
0.190
0.016
0.078
0.149
0.048
0.051
0.059
0
111
0.343
18.4
3,530
9,397
8.6
20.0
499
0.309
0.354
0.471
0.355
0.329
0.304
0.249
118,947
0.494
0.261
0.237
0.111
0.148
0.408
0.496
0.392
0.126
0.269
0.356
0.214
0.219
0.236
Min
Max
1
1
149.7
3,189
0
1
0.853
487.9
0 25,949
0 57,493
0
61.0
0
100
0
2,408
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0 850,000
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
Table 3. Spiny Forest Deforestation Probit Regressions.
Variable
Param
Prob
from
t-stat
Spiny SAR
Constant
Expenditure per capita
Population density
Footpath, distance to
Road, distance to
Slope
Soil fertility
Elevation
Distance to non-forest: 1km-2km
Distance to non-forest: 500m-1km
Distance to non-forest: <500m
Forest proportion: 0.6-0.8
Forest proportion: 0.4-0.6
Forest proportion: 0.2-0.4
Forest proportion: <0.2
Travel cost to CUP
< 5% titled (omitted: 0%)
5 - 10% titled
10 - 25% titled
25 - 50% titled
> 50% titled
Bad conditions of safety & theft
Average (omitted: very bad)
Good
Very good
Commune created 1960 or later
Unknown creation date
Patch < 10,000 has.
λ (spatial parameter)
iterations
observations
-2.6809
-5.74E-04
-9.19E-03
3.93E-05
8.17E-06
-0.0601
0.0136
1.36E-03
0.7320
0.7022
0.8847
-0.0678
-0.1499
-0.2442
-0.2911
-3.61E-06
-0.0640
-0.6225
0.9383
0.5099
1.0619
0.8299
0.6232
0.4454
0.1187
-0.0146
-0.7235
-1.6716
0.8117
1,000
5,661
0.000
0.129
0.002
0.001
0.008
0.000
0.000
0.000
0.001
0.000
0.000
0.237
0.081
0.029
0.068
0.002
0.163
0.001
0.055
0.001
0.000
0.000
0.000
0.006
0.341
0.447
0.097
0.029
0.000
Param
Prob
from
t-stat
Spiny SEM
-3.6047
-6.26E-04
-4.69E-03
1.16E-05
-1.15E-05
-0.0364
0.0177
1.68E-03
0.7575
0.7821
1.1308
0.0401
0.0993
0.1713
0.1564
-1.91E-06
-0.0916
-1.1728
0.4548
0.6250
1.1196
0.6815
0.1963
-0.3111
-0.3089
0.0509
0.0595
-0.9658
0.9707
0.000
0.247
0.108
0.297
0.090
0.012
0.000
0.000
0.003
0.003
0.000
0.360
0.216
0.107
0.254
0.178
0.219
0.004
0.251
0.049
0.000
0.003
0.222
0.155
0.229
0.403
0.464
0.072
0.000
Twice variable
std dev times
parameter
SAR
SEM
-0.064
-0.391
0.175
0.131
-0.376
0.454
0.282
-0.070
-0.200
0.052
-0.184
-0.227
0.592
0.349
-0.292
-0.155
1,000
5,661
Notes:
1) For variables with squared term, the twice the standard deviation formula was modified so that the high
and low values had to stay within the bounds of the max and min. Also the lower and upper values for the
linear term were squared to provice upper and lower values for the squared term.
2) These regressions were run with the assumption of homoscedasticity, and fixing the variance to be equal
to 1.
3) The first 100 iterations were omitted
Table 4. Spiny Forest Variable Statistics.
Variable
Constant
Expenditure per capita
Population density
Footpath, distance to
Road, distance to
Slope
Soil fertility
Elevation
Distance to non-forest: 1km-2km
Distance to non-forest: 500m-1km
Distance to non-forest: <500m
Forest proportion: 0.6-0.8
Forest proportion: 0.4-0.6
Forest proportion: 0.2-0.4
Forest proportion: <0.2
Travel cost to CUP
< 5% titled (omitted: 0%)
5 - 10% titled
10 - 25% titled
25 - 50% titled
> 50% titled
Bad conditions of safety & theft
Average (omitted: very bad)
Good
Very good
Commune created 1960 or later
Unknown creation date
Patch < 10,000 has.
Mean
Std dev
1
236
15.2
2,664
9,477
1.4
39.7
178
0.113
0.180
0.659
0.168
0.107
0.058
0.023
50,823
0.366
0.048
0.011
0.026
0.058
0.205
0.471
0.207
0.047
0.127
0.022
0.008
0
56
21.3
2,232
7,998
3.1
16.7
104
0.316
0.384
0.474
0.374
0.309
0.235
0.150
40,488
0.482
0.215
0.106
0.160
0.233
0.404
0.499
0.405
0.211
0.333
0.148
0.092
Min
Max
1
1
139.2
611
1.324
628.4
0
15,749
0
44,090
0
40.0
10
100
0
969
0
1
0
1
0
1
0
1
0
1
0
1
0
1
2,500 207,500
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1