Strict versus mixed-use protected areas: Guatemala's Maya Biosphere Reserve

Ecological Economics 112 (2015) 14–24

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Analysis

Strict versus mixed-use protected areas: Guatemala's Maya Biosphere Reserve Allen Blackman Resources for the Future, Washington, DC, USA

a r t i c l e

i n f o

Article history: Received 11 March 2014 Received in revised form 16 January 2015 Accepted 22 January 2015 Available online xxxx Keywords: Protected area Deforestation Guatemala Matching

a b s t r a c t Although protected areas, or “parks”, are among the leading policy tools used to stem tropical deforestation, rigorous evaluations of their effectiveness—that is, evaluations that control for their tendency to be sited in remote areas with relatively little deforestation—have only recently begun to appear. Important open questions concern the link between the stringency of protection and park effectiveness. How do mixed-use parks that allow sustainable extractive activities perform relative to strictly protected parks? And what types of mixed-use management perform best? In addressing these questions, it is particularly important to control for nonrandom siting, since different management regimes tend to be sited in areas with different preexisting characteristics. To date, most rigorous studies of this issue have focused on scores of parks in one or multiple countries, a strategy that in principle could be undermined by unobserved park heterogeneity. This paper uses high-resolution 2001–2006 land cover data derived from satellite images along with statistical techniques that control for nonrandom siting to examine the relative effectiveness of strict and various mixed-use protection strategies in a single large park: the two-million-hectare Maya Biosphere Reserve in Guatemala. Our results comport with the emerging consensus that on the whole, mixed-use protection in this park has been more effective in stemming deforestation than strict protection because of the performance of forest concessions within the multiple-use zone. However, we also find that mixed-use protection has had smaller, more heterogeneous effects than indicated by simple methods that do not control for nonrandom siting. © 2015 Elsevier B.V. All rights reserved.

1. Introduction According to the United Nations Food and Agriculture Organization, the rate of deforestation in tropical countries remains “alarmingly high.” For example, in both Latin America and Africa, it averaged 0.5% per year in the first decade of the 2000s, five times the global rate (FAO, 2011). This deforestation has contributed to a host of local and global environmental problems, including soil erosion, biodiversity loss, and greenhouse gas emissions (Harris et al., 2012; Gibson et al., 2011; Chomitz, 2007). Protected areas, or “parks,” are one of the leading policy tools used to address these problems. Yet we have few reliable evaluations of their effectiveness. Most studies measure effectiveness by simply comparing rates of deforestation inside and outside the parks, ignoring the fact that they typically are sited on land that has preexisting characteristics, such as inaccessibility and rough terrain, which inhibit deforestation (Joppa and Pfaff, 2009). As a result, these studies conflate the effects on deforestation of the parks and their siting, thereby generating overly optimistic assessments. Over the past several years, a new evaluation approach has emerged that uses statistical techniques to control for the nonrandom siting of

E-mail address: [email protected].

http://dx.doi.org/10.1016/j.ecolecon.2015.01.009 0921-8009/© 2015 Elsevier B.V. All rights reserved.

conservation policies. In addition, it relies on land cover change data derived from remote sensing images (Blackman, 2013). Not surprisingly, studies using this approach typically find that parks are less effective at preventing deforestation than past studies suggest (Joppa and Pfaff, 2010). Because this evaluation approach is relatively new, however, it has yet to be used to fully explore the determinants of park performance. An important open question is whether and under what conditions strict protection is more effective than mixed-use protection. The former prohibits all extractive activities inside parks while the latter allows activities that are sustainably managed, usually by local communities. Arguments have been made in support of each management strategy. On one hand, strict protection may be more effective than mixed-use protection because, by definition, it prohibits more extractive activity (Locke and Darden, 2005). But on the other hand, the effectiveness of strict protection depends critically on the existence of formal regulatory authorities willing and able to monitor and enforce land cover change restrictions—institutions that are in short supply in developing countries (Balmford et al., 2003; Bruner et al., 2004). Also, strict protected areas can create counterproductive conflicts between regulators and local communities (Nepal and Weber, 1995). To overcome these challenges, mixed-use protection seeks to strengthen local communities' capacities and incentives for

A. Blackman / Ecological Economics 112 (2015) 14–24

forest conservation (Wells and Brandon, 1992; McNeely, 1995). These factors, which vary widely across communities, can drive forest conservation outcomes (Persha et al., 2011; Hayes, 2006; Hayes and Ostrom, 2005). Hence, at the end of the day, the relative effectiveness of strict and mixed-use protection is an empirical question. In addressing this question, it is particularly important to control for the nonrandom siting of each management regime, since compared with strict protection, one would expect mixed-use protection to be located in more accessible areas under greater deforestation pressure (Pfaff et al., 2014). Studies making this correction have begun to appear. Their findings are mixed. Several studies conclude that strict protection outperforms mixed-use protection. For example, using a national sample from Thailand, Sims (2010) finds that strictly protected wildlife sanctuaries and national parks are more effective at stemming deforestation than less strictly protected forest reserves. Using a sample of parks in the Brazilian Amazon, Soares-Filho et al. (2010) conclude that strictly protected parks are more effective than those designated for sustainable use. And using data from Bolivia, Costa Rica, Indonesia, and Thailand (along with consistent definitions of park types), Ferraro et al. (2013) find that although effects vary across and even within countries, in general, strict protection outperforms less strict protection, albeit only slightly in many cases. In contrast to these studies, however, Nelson and Chomitz (2011) find that in a global sample of parks, mixed-use protection generally does a better job of stemming forest fires (a proxy for deforestation) than strict protection. Using a national sample for Mexico, Blackman et al. (2015) find that mixed-use parks are more effective at preventing deforestation than strictly protected ones. And using a sample of protected areas in the Brazilian Amazon, Pfaff et al. (2014) find that sustainable-use parks and indigenous reserves avoid more deforestation than strictly protected parks. A common feature of all of those studies is that each analyzes scores of parks in multiple countries, an entire country, or a large part of one. This approach has advantages. Variation across parks helps identify the effect of protection type. Also, results from studies of multiple parks are more likely to have external validity than those from studies of small samples. But studies of multiple parks also have disadvantages. Variation in unobserved confounding factors—that is, variables that affect both deforestation and the selection of land into different management regimes—can bias identification. For example, say land with poor governance (rule of law) tends to attract illegal logging and also tends to be strictly protected. Furthermore, say governance is unobserved. In an econometric evaluation, this unobserved confounding factor could drive a finding that mixed-use protection outperforms strict protection, even after controlling for observed factors such as distance to population centers. In principle, a smaller sample of parks—within which there is likely to be less variation in such unobserved confounding factors—could mitigate this problem. Toward that end, this paper exploits a natural experiment: a single large park in northern Guatemala, the two-million-hectare Maya Biosphere Reserve (MBR), which comprises three management regimes: a core protected area where extractive activity is strictly prohibited, a multiple-use zone where sustainable extraction is allowed in certified forest concessions, and a buffer zone where agriculture and private tenure are permitted. In addition to its tripartite zoning, three other features make the MBR an attractive case study. First, several permutations of mixed-use management have been employed inside the multiple-use zone. Forest concessions have been awarded to established resident communities, newly formed resident communities, communities located outside the MBR, and private companies. Second, all of the MBR's management regimes have been “put to the test” since the park's founding. The Petén region of northern Guatemala, where the MBR is located, has witnessed rapid deforestation in the past two decades due to immigration and a changing security situation (Shriar, 2011; Schmidt,

15

2010). Finally, the MBR has so far attracted little attention from scholars using statistical techniques to control for nonrandom siting. They mostly have focused on parks in higher-income countries, such as Brazil and Costa Rica (Blackman, 2013). Hence, given the MBR's multifaceted management, location, and low profile in the new literature on park performance, an analysis of the park that controls for nonrandom siting offers to improve our understanding of the relative effectiveness of different park protection strategies.1 This paper uses 2001–2006 land cover data derived from satellite images along with a combination of covariate matching and regression to generate estimates of the relative effectiveness of the core protected area and the multiple-use zone, and of various management regimes within the multiple-use zone. Our results are mostly consistent with emerging evidence from qualitative and simple quantitative studies that the multiple-use zone has been more effective than the core protected area in stemming deforestation because of the performance of multiple-use zone concessions (Radachowsky et al., 2004, 2012; Lundin, 2010; Hughell and Butterfield, 2008; Cronkleton et al., 2008; Nittler and Tschinkel, 2005). However, our estimates of the deforestation effects of the multiple-use zone as a whole and of the concessions inside it are smaller, less statistically significant, and more heterogeneous than those that do not control for nonrandom siting.

2. Background Guatemala has been plagued by rapid deforestation. Between 2000 and 2010, clearing averaged 1.2% per year, an order of magnitude higher than the global rate. By 2010, only 38% of the country's original forest cover remained (FAO, 2011). Partly to address this problem, Guatemala has established a system of 322 parks (including small private reserves) on 3.5 million hectares (CONAP, 2013). At 2.1 million hectares, the MBR is by far Guatemala's largest park (Fig. 1). It was created in 1990 to help conserve the Selva Maya, one of Mesoamerica's largest remaining contiguous forests. This forest had been, and continues to be, threatened by rapid population growth and the coincident expansion of agriculture, ranching, forest fires, and illegal logging (Carr, 2008; Clark, 2000; Schmidt, 2010; Shriar, 2011). Deforestation rates in the Petén department that includes the Selva Maya are the highest in Guatemala—more than 4% per year between 2001 and 2006 (Castellanos et al., 2011). Among the three management zones in the MBR, the core protected area is the largest (821,700 ha, constituting 40% of the MBR). The multiple-use zone is slightly smaller (779,500 ha, 38%), and the buffer zone covers a 15-kilometer strip along the southern edge of the park (315,800 ha, 22%). The National Protected Areas Commission (Consejo Nacional de Areas Protegidas, CONAP) administers the MBR. Ten separate strictly protected areas constitute the core protected area. Of these, two—Tikal and Cero Cahuí—predated the MBR. Six others were established in 1990 along with the MBR, one in 1997, and one in 2002 (Table 1). Since the MBR's creation, CONAP has granted 14 forest concessions in the multiple-use zone that together make up twothirds of this management regime (Table 1).2 Twelve were established between 1997 and 1999, one in 2000, and one in 2001. Hence, all but two of these strictly protected areas and concessions predate our 2001–2006 study period. 1 A caveat, however, is that the MBR is so large that spatial variation in some unobservable confounding factors (e.g., tree species and stumpage values) may still be substantial. In fact, the MBR is larger than the land area of all parks in some small countries. For example, it is 1.5 times the size of all Costa Rican parks. 2 Of the 14 multiple-use zone concessions, two—La Colorada and San Miguel—were revoked by CONANP because of chronic noncompliance with management plans. In both cases, the revocation occurred after 2006 (Radachowsky et al., 2012). Because these concessions were valid and active during our 2001–2006 study period, they are included in our analysis. However, our qualitative results are robust to dropping them from our regression sample.

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A. Blackman / Ecological Economics 112 (2015) 14–24

Fig. 1. Maya Biosphere Reserve, by land-use zone.

Each of the 14 forest concessions has a 25-year term and is contingent on performance criteria meant to ensure sustainable use that benefits local communities. The criteria include a forest management plan, legal incorporation of the grantee, a democratically elected leadership, and third-party certification to Forest Stewardship Council standards within three years of the granting of a concession (Finger-Stich, 2003; Lundin, 2010).3 The concessions fall into four categories (Radachowsky et al., 2012) (Table 1). Established resident community concessions (n = 2) were granted to communities inside the multiple-use zone that were settled long before the MBR was established. Historically, these communities have subsisted by harvesting and trading in nontimber forest products, such as chicle, xate, and allspice. New resident communities (n = 4) were settled by recent immigrants around the time the MBR was established. Nonresident community concessions (n = 6) were granted to communities outside the multiple-use zone, the result of government efforts to recruit entities to manage concessions. Finally, industrial concessions (n = 2) were granted to commercial timber companies. 3. Methods 3.1. Empirical Approach As noted above, the main challenge we face in attempting to accurately measure the deforestation effect of the core protected area, the 3 In addition, partnership with a local nongovernmental organization was required until 2001. Forest Stewardship Council is the largest forest certification scheme in the tropics. More than 29 million ha in 41 developing countries has been certified to its standards (FSC, 2014).

multiple-use zone, and various categories of concessions within the multiple-use zone, is that these management regimes were not randomly sited. Rather, as illustrated below, plots of land with certain preexisting climatological, geophysical, and socioeconomic characteristics that drive deforestation were disproportionately selected into different regimes. Therefore, measuring each regime's deforestation effect by simply comparing the average deforestation rate for plots subjected to that regime and for a control group of plots not subjected to it, with the latter average serving as the counterfactual—that is, what would have happened absent the regime—is likely to generate biased results. To control for selection bias, we use a combination of regression and matching, an increasingly common strategy in the program evaluation literature (Imbens and Wooldridge, 2009; Ho et al., 2007) and more specifically, in the recent literature evaluating forest conservation policies (e.g., Sims, 2014; Alix-Garcia et al., 2012). That is, we identify unprotected control plots that are similar to treatment plots inside the MBR in terms of observed characteristics that drive forest cover change. We drop unmatched control plots from the regression sample, and then use a probit regression (with municpio fixed effects) to estimate treatment effects. This strategy of combining nonparametric matching (i.e., dropping from the study sample control observations that are dissimilar to the treatment observations) with parametric regression typically generates treatment effect estimates that are more accurate and more robust to misspecification than does regression alone (Imbens and Wooldridge, 2009; Ho et al., 2007). Design replication studies have shown that it can be effective in recovering “true” model parameters derived from experimental studies (Ferraro and Miranda, 2014). We use one-to-one matching with replacement to identify unprotected control plots that have observable characteristics similar to

A. Blackman / Ecological Economics 112 (2015) 14–24 Table 1 Management units in Maya Biosphere Reserve Multiple-use zone and core protected areas. Management unit

Area (ha)

Concessions Established resident communities Carmelita Uaxactun Nonresident communities Chosquitán La Unión Las Ventanas Rio Chanchich San Andrés Yaloch New resident communities Cruce la Colorada La Colorada La Pasadita San Miguel Industrial concessions La Gloria Paxbán Protected areas Cero Cahui El Pilar Laguna del Tigre-Río Escondido Laguna del Tigre Mirador-Río Azul Naachtún-Dos Lagunas San Miguel la Palotada-El Zotz Sierra de Lacandon Tikal Yaxhá-Nakúm-Naranjo

Year established

53,797 83,558

1997 1999

19,300 21,176 64,973 12,217 51,939 25,387

1999 1999 1999 1998 1999 2000

20,815 22,885 18,817 7039

2001 1999 1997 1994

66,458 65,755

1999 1999

650 1000 45,168 289,912 116,911 30,719 34,934 202,865 55,005 37,160

1989 1997 1990 1990 1990 1990 1990 1990 1955 2002

treated plots inside the MBR. To measure similarity, we use Mahalanobis distance, a scale-invariant measure of distance in n-dimensional covariate space. Using a matched sample, we estimate a probit model of the form 0

0

multiple-use zone, and buffer zone. The goal is to compare the deforestation effects of the core protected area as a whole and of the multipleuse zone as a whole. Model 2 includes dummies for location in the core protected area, the multiple-use zone with concessions, the multipleuse zone without concessions, and the buffer zone. The purpose is to compare the effects of mixed-use protection with and without concessions. Finally, Model 3 uses dummies for location inside the core protected area, the buffer zone, and four categories of multiple-use land: without concessions, with established resident community concessions, with new resident community concessions, and with nonresident community concessions. The objective is to compare the effects of different types of concessions in the multiple-use zone. We do not include in Model 3 a dummy for location inside industrial concessions, nor do we include in the regression sample plots inside these concessions. The reason is that our land cover data registers zero deforestation in these concessions during our study period. Therefore, an industrial concessions dummy would be perfectly correlated with the dependent variable. We drop all plots inside industrial concessions to ensure that the baseline (omitted) category is restricted to the subsample of unprotected plots, and not the union of these plots and plots inside industrial concessions. 3.3. Limitations

(Sources: Carrera et al., 2004; CONAP, 2013).

Yi ¼ α þ γm þ Di β1 þ Xi β2 þ εit

17

Our ATT estimates will be biased if our treatment dummies are endogenous—that is, correlated with unobserved confounding factors that affect both the probability that a plot is subjected to a given management regime, and the probability that it is cleared. For example, as discussed in the Introduction, if poor governance is unobserved and is correlated both with strict protection and deforestation, it could bias our treatment effect estimates for strict protection downward. Stumpage values could be a second unobservable confounding factor. To help control for such factors, as noted above, we employ two strategies: we include municipio fixed effects in our regression model, and we focus on a single protected area within which one would expect less variation in unobserved confounders—particularly those having to do with park management.

ð1Þ 3.4. Naïve Estimators

where i indexes plots, m indexes municipios (counties), Y is a binary dummy indicating deforestation, γ are municipio fixed effects, D is a vector of binary treatment dummy variables indicating location in a part of the MBR with a particular protection regime (strict, mixed use, nonresident concession, etc.), X is a vector of geophysical and socioeconomic control variables, α and β are parameters or vectors of parameters to be estimated, and ε is an error term. Each parameter in β1 purports to measure a particular management regime's effect on deforestation. The marginal effect derived from each parameter (calculated for the subsample of treated plots) can be interpreted as the effect of subjecting an unprotected plot to a particular management regime, formally, as the average treatment effect on the treated (ATT). The statistical significance of each ATT tests the null hypothesis that the regime has zero additional effect on deforestation. The statistical significance of pairwise differences in ATTs are tests of null hypotheses of zero difference between pairs. As discussed below, we include municipio fixed effects in the regression to help control for the influence of unobserved confounding factors. Finally, we cluster standard errors at the muncipiolevel to control for potential spatial correlation of errors. 3.2. Models In the context of our regression analysis, we use the term model to refer to different combinations of the treatment dummy variables that constitute D in Eq. (1). We use three models. Each aims to compare the effects of different regimes or aggregations of regimes. Model 1 features treatment dummies for location in the core protected area,

To shed light on the value of our empirical approach, we report ATT estimates from a naïve approach—the simple difference between the average deforestation rate on treated and all untreated plots. As noted above, this estimator is susceptible to bias due to the nonrandom siting. 4. Data 4.1. Sample We use a dimensionless plot of land defined by latitude and longitude coordinates as our unit of analysis. From all such plots within the national territory of Guatemala, we selected the sample used in the matching analysis by dropping five sets of plots. First, to make the sample computationally feasible, we overlaid a 250-meter (m) rectangular grid on a map of the country (i.e., a grid with lines spaced 250 m apart vertically and horizontally) and dropped all plots except those where grid lines crossed. In principle, using a rectangular grid instead of a random algorithm to select sample plots helps control for spatial autocorrelation by ensuring that plots are no closer together than the resolution of the grid. Second, we dropped all plots in the southern half of the country—specifically, those below 15.75° latitude. These plots are farthest from the MBR, which is situated in the country's northern tip. We use this sampling strategy because two mountain chains running east-to-west bisect the country just south of this latitude. The lowlands to the north of it, which include the MBR, have observable geophysical,

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A. Blackman / Ecological Economics 112 (2015) 14–24

climactic, and socioeconomic characteristics that are different from those of the highlands and Pacific coastal regions to the south. Presumably, the same is also true for unobservable characteristics in this northern region. In any case, together these first two steps generated a sample of 740,711 plots. Third, to avoid conflating the effects of MBR protection with that of other protected areas, we dropped all 93,689 plots in protected areas outside the MBR. Next, we dropped all 209 plots for which data used in the regression analysis were missing. Finally, to ensure that our treatment and control plots have the same baseline forest cover, we dropped all 249,437 plots that were not forested at the start of our study period. We are left with a sample of 397,376 plots: 111,559 inside the core protected area, 118,787 inside the multiple-use zone, 40,633 inside the buffer zone, and 126,397 outside the MBR and all other protected areas. 4.2. Variables For each plot in our sample, we collected or generated data on outcomes (deforestation), treatments (core protected area, multiple-use zone, various categories of concessions within the multiple-use zone), and control variables (discussed below), and we created a plot-level relational database comprising all these data (Table 2). The outcome variable, cleared, is a binary dummy that indicates whether the plot was deforested between 2001 and 2006. It is derived from two highresolution (1:50,000) national land cover maps, both in turn derived from Landsat satellite images created (using the same classification methods) specifically to assess deforestation during the intervening years (Castellanos et al., 2011). Our three principal treatment variables, core protected area, multiple-use zone, and buffer zone, are binary dummies indicating location in the MBR's three principal management zones. Additional treatment dummies (not listed in Table 2 to save space) indicate the location in the various categories of concessions within the multiple-use zone (see Table 2 and Fig. 1). The control variables include climatological, geophysical, and socioeconomic land characteristics that drive deforestation (Boucher et al., 2011; Chomitz, 2007; Kaimowitz and Angelsen, 1998). The climatological characteristics are temperature and rain, which are 50-year (1950–2000) historical averages. Among the geophysical variables, altitude and slope are derived from high-resolution (90 m2) digital elevation maps. Travel time is in relation to the nearest town or city with a population greater than 200. We estimated these times in ArcGIS using iterative techniques that identified the lowest-impedance routes from each plot in our sample to towns and cities, taking into account rivers, bridges, the slope of the terrain, and the location and quality of roads (see Appendix 1 for a more detailed description of our methods

and data). Finally, population is the total population of the municipio in which each sample plot is located for 1994, the most recent census year that predates our study period. We include all six control variables in the procedure used to identify matched control plots. However, we drop two—altitude and population—in estimating Eq. (1). We drop altitude because it is highly correlated with temperature, and we drop population because it is measured at the municipio-level and therefore perfectly correlated with our municipio fixed effects. 4.3. Summary Statistics Variable means indicate that both deforestation rates and land characteristics are quite different in most of the subgroups used in this analysis (Table 3). As for deforestation rates, the average six-year (2001–2006) rate on unprotected plots was an astonishing 19.5%, and the rate in the buffer zone was even higher—25.2%. Within the MBR, the rates were 5.3% in the core protected area and 2.0% in the multiple-use zone. Within the multiple-use zone, rates ranged from 5.2% on land without concessions to 0% on land in industrial concessions. Ignoring the possibility of nonrandom siting, discussed above, these simple summary statistics suggest that compared with the core protected area, the multiple-use zone cut deforestation by an additional 3.3 percentage points, that concessions cut deforestation by more than 4%, and that certain categories of concessions cut deforestation by far more than others. But summary statistics for our control variables also suggest that nonrandom siting is not just a possibility in our case: the subgroups have very different observable characteristics affecting deforestation.4 For example, compared with plots in unprotected areas, those in the core protected area tend to be less populous, more remote, and drier—all characteristics typically associated with less deforestation. And compared with plots in the multiple-use zone, those in the core protected area tend to be wetter, lower, and more populous—all characteristics associated with more deforestation. Hence, these summary statistics point to the importance of controlling for preexisting plot characteristics in estimating the relative effectiveness of the various management regimes. 5. Results Before turning to the regression results, we note that Mahalanobis matching generates a matched sample of unprotected plots that is, on average, quite similar to the sample of plots inside the MBR. Following Rosenbaum and Rubin (1983), we measure similarity using mean standardized bias (MSB), which is the average across all matching covariates

Table 2 Variables. Variable

Description

Units

Scaling factor

Source

Spatial scale

Years

Outcome Cleared

Deforested 2001–2006

0/1

1

UVG

1:50,000

2001–2006

Treatment Zone/area dummies

CPA, MUZ, BZ, and MUZ divisions

0/1

1

CEMEC-WCS

1:50,000

2007

Controls Temperature Rain Altitude Slope Travel timea Population

Average monthly temperature Average monthly precipitation Altitude above sea level Slope (100tan(п angle/180) Travel time nearest population ctr. N 200 Population of municipio

°C mm m % min No.

10−1 1000−1 1000−1 100−1 1 10,000−1

WorldClim WorldClim SRTM SRTM Own calculations CCAD

30s 30s 90 m 90 m 900 m Municipio

1950–2000 1950–2000 2006 2006 n/a 1994

BZ = buffer zone; CPA = core protected area; CCAD = Comisión Centroamericana de Ambietne y Desarrollo; CEMEC-WCS = Centro de Monitoreo y Evaluación del Consejo Nacional de Areas Protegidas-Wildlife Conservation Society; MUZ = multiple-use zone; SRTM = Shuttle Radar Topography Mission. The 2006 digital elevation model data are described in Farr et al. (2007); UVG = Universadad del Valle. Data are described in Castellanos et al. (2011). WorldClim refers to 1950–2000 global climate data, described in Hijmans et al. (2005). a See Appendix, “Travel Time Model.”

A. Blackman / Ecological Economics 112 (2015) 14–24

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Table 3 Variable means. Variable

Zone or area Unprotected

Core protected area

Buffer zone

Multiple-use zone All

(n = 126,397) Cleared 19.48 Temperature 2.38 Rain 2.49 Altitude 3.99 Slope 10.22 Travel time 3.01 Population 2.90

Concessions

No concessions

Established resident concessions

New resident concessions

Nonresident concessions

Industrial concessions

(n = 111,559)

(n = 40,634)

(n = 118,787) (n = 82,800) (n = 35,987)

(n = 21,385)

(n = 10,256)

(n = 30,771)

(n = 20,388)

5.29 2.52 1.62 1.84 0.72 3.66 0.89

25.20 2.51 1.71 1.73 1.49 3.19 2.63

2.04 2.50 1.38 2.10 0.56 2.63 0.81

0.12 2.51 1.29 2.27 0.64 1.68 0.94

5.13 2.49 1.44 2.34 0.33 2.62 0.68

0.04 2.49 1.38 1.99 0.73 2.47 0.96

0.00 2.46 1.36 2.66 0.37 2.43 0.55

0.68 2.49 1.36 2.27 0.57 2.27 0.82

of the variance-normalized percentage difference between the means of the treatment and control samples. Mahalanobis matching reduces MSB from 59.4% to 1.8% (Table 4). Although a clear threshold for acceptable MSB does not exist, a statistic below 3 to 5% is generally viewed as sufficient (Caliendo and Kopeinig, 2008). Note, however, that after matching, there are still significant differences between means of each covariate for the treated and matched control samples. These significant differences partly reflect the large number of observations in our sample. Our empirical strategy, which combines regression and matching, helps to control for these residual differences (Imbens and Wooldridge, 2009; Ho et al., 2007). 5.1. Mixed-use Versus Strict Protection Results from Model 1 suggest that within the MBR, mixed-use protection is more effective than strict protection and that the buffer zone exacerbates deforestation. The counterfactual level of deforestation for our 2001–2006 study period—the average predicted probability of deforestation on matched unprotected plots—is 9%, or roughly 2% per year (Table 5). Relative to this baseline, the estimated ATTs indicate that the multiple-use zone cuts deforestation by 58%, the core protected area reduces it by 43%, and the buffer zone increases it by 100%. However, the ATT for the core protected area is not statistically significant. Not surprisingly, we can reject at the 1% level the null hypotheses that the buffer zone ATT is the same as the multiple-use zone ATT and the core protected area ATT (Table 6). However, given the sizable standard error for the core protected area ATT, we are not able to reject the null hypothesis that it is equal to the multiple-use zone ATT. 5.2. Do Concessions Matter? Results from Model 2 indicate that within the multiple-use zone, concession land is more effective than nonconcession land in stemming deforestation. Relative to the counterfactual baseline of about 2% deforestation per year, concession land cuts deforestation by 73%, a result that is significant at the 1% level (Table 5). By comparison, the ATT for the multiple-use zone without concessions is about one-fifth that size and is not statistically significant. We can reject at the 1% level the null hypothesis that these two ATTs are equal (Table 7). 5.3. What Type of Concessions Performs Best? Results from Model 3 indicate that of the three types of concessions included in regression, established resident community concessions and nonresident community concessions are most effective in stemming deforestation. Relative to the counterfactual baseline of 2% 4 Given the large number of subgroups, we have omitted pairwise tests for differences in means. However, the vast majority of these differences are statistically significant.

5.16 2.52 1.42 1.72 0.55 3.43 0.79

deforestation per year, nonresident community concessions cut deforestation by 80% and established resident community concessions reduce it by 78%. We can reject at the 5% level the null hypothesis that the two ATTs are the same (Table 8). By contrast, the ATT for new resident community concessions is positive, although not statistically significant. We can reject at the 1% level the null hypothesis that the new resident community concessions' ATT is equal to those of the other two types of community concessions (Table 8). 5.4. Naïve Treatment Effect Estimates In general, the ATT estimates discussed above are less significant, both statistically and economically, than the ATTs generated using a naïve approach (Table 5, Model 4). All eight naïve ATTs are statistically significant at the 1% level. By contrast, only 7 of the 13 regression ATTs are statistically significant. Moreover, with one exception, the regression ATT's are one-fifth to two-thirds smaller than the naïve estimates. The discrepancy between the significance of the regression and naïve ATTs reflects the failure of the latter to control for the fact that, as discussed in Section 4.3, large parts of the MBR are sited on land with (at least some) characteristics associated with lower-thanaverage deforestation rates. As a result, the naïve estimates effectively give various management regimes full credit for their lower-thanaverage deforestation rates, even though these rates actually are partly due to preexisting characteristics of MBR land. The one exception to this narrative is the regression ATT for the buffer zone, which is statistically significant and about three times larger than the naïve estimate. The underlying logic for the discrepancy is Table 4 Matching quality: Standardized bias (SB) for unmatched and matched samples. Variable

Sample

Temperature Unmatched Matched Rain Unmatched Matched Altitude Unmatched Matched Slope Unmatched Matched Travel time Unmatched Matched Population Unmatched Matched Mean all Unmatched Matched

Treated Control % SBa 2.51 2.51 1.53 1.53 1.94 1.94 0.77 0.77 3.14 3.14 1.12 1.12 – –

2.38 2.51 2.49 1.54 3.99 1.88 10.22 2.28 3.01 3.19 2.90 1.20 – –

76.6 −0.8 −160.9 −2.3 −56.9 1.6 −5.8 −0.9 6.5 −2.9 −49.5 −2.4 59.4 1.8

% reduction |SB| t-testb 99.0 98.6 97.1 84.0 54.9 95.2 – –

*** *** *** *** *** *** *** *** *** *** *** *** – –

***p b 1%, **p b 5%, *p b 10%. a Standardized bias is the difference of the means for the treated and untreated subsamples as a percentage of the square root of the average of sample variances in both groups. b Test of null hypothesis that means are equal.

20

A. Blackman / Ecological Economics 112 (2015) 14–24

Table 5 Average treatment effect on treated (ATT) estimates from probit regressions using matched control samplea and from naïve estimator.b Variable

Regression

Model

1

2

3c

4

Core protected area Multiple-use zone Multiple-use zone with concessions Multiple-use zone without concessions Multiple-use zone with established resident community concessions Multiple-use zone with new resident community concessions Multiple-use zone with nonresident community concessions Buffer zone Matched control plots? Municipio fixed effects? Standard errors clustered at municipio-level? Average pr(cleared) for matched control plots (%) Number of observations Pseudo-R2 (%)

−43.36 −57.79⁎⁎

−41.82

−40.81

−14.83 −77.65⁎⁎⁎ 16.60 −80.15⁎⁎⁎ 100.45⁎⁎⁎

−72.84⁎⁎⁎ −89.58⁎⁎⁎ −96.56⁎⁎⁎ −73.51⁎⁎⁎ −99.44⁎⁎⁎ −73.72⁎⁎⁎ −99.85⁎⁎⁎ 29.38⁎⁎⁎

Yes n = 15 Yes 9.93 256,976 16.51

No n/a No n/a varies n/a

100.13⁎⁎⁎ Yes n = 15 Yes 9.39 277,364 15.71

Naïve

−73.29⁎⁎⁎ −15.71

100.13⁎⁎⁎ Yes n = 15 Yes 9.37 277,364 16.51

n/a = not applicable. ⁎⁎⁎ p b 1%. ⁎⁎ p b 5%. a Dependent variable is cleared and independent variables are temperature, rain, slope, and travel time (see Table 1). Control observations are weighted based on the number of times they are used as matches. The omitted category is unprotected plots in northern half of Guatemala. Statistics in the table are marginal effects for treated plots expressed as percent changes from baseline (average predicted probability of deforestation for matched unprotected plots). b Simple difference in mean of cleared for treated and unmatched control plots. c Regression sample for Model 3 excludes all plots in both industrial concessions.

the same, however, but with the twist that in this case, the management regime exacerbates deforestation, such that the ATT is positive. Like land in the rest of the MBR, that in the buffer zone has some characteristics associated with lower-than-average deforestation rates. Therefore, a naïve ATT is biased downward because it does not give the buffer zone sufficient credit for spurring deforestation conditional on these characteristics. In general, the ranking of the regression ATTs and the analogous naïve ATTs is the same, although the relative magnitudes are not. Put slightly differently, the magnitudes of the regression ATTs are more variable than the naïve ATTs. For example, in Model 1, both the regression ATTs and the naïve ATTs indicate that mixed-use protection is more effective than strict protection. However, the differential is larger in the case of the regression ATTs. Similarly, in Models 2 and 3, both regression ATTs and naïve ATTs indicate that concession land is more effective than nonconcession land, and that established resident concessions and nonresident concession are more effective than new resident concessions. However, again, the differentials are larger in the case of the regression ATTs. By definition, the difference in relative magnitudes of the regression and naïve ATTs is due to the fact that the regression ATTs control for differences in the observable characteristics of land in each of these regimes. As discussed in Section 4.3, these differences are significant. 6. Robustness Checks This section investigates the robustness of our results to different sampling strategies, empirical approaches, and model specifications. In general, our results are reasonably robust. Regarding sampling, as discussed in Section 4.1, we used a 250 m rectangular grid to select sample plots. A potential disadvantage of this relatively fine spatial scale is that it may pick

up spatial autocorrelation. We tested the robustness of our results to using samples selected with 500 m and 1000 m grids. In both cases, the results were qualitatively identical to those presented above. Table 9, Models 5–7, presents results from the 1000 m grid sample. Here, as in our main results, we find that the multiple-use zone performs better than the core protected area; concession land within the multiple-use zone performs better than nonconcession land; established resident community concession land and nonresident community concession land perform better than new-resident community concessions and land without concessions; and the buffer zone exacerbates deforestation. Most of the statistically significant ATTs are close to those from the 250 m grid sample. An alternative to our empirical approach (combining matching and regression) is simple matching—that is, calculating ATT as the difference in mean deforestation rates for a sample of treatment plots and a matched sample of control plots, normalized by the baseline deforestation rate to generate a percentage change. We estimate matching ATTs using one-to-one Mahalanobis matching with replacement. We use all six covariates in Table 2 and enforce a common support. We calculate standard errors using Abadie and Imbens's (2006) heteroskedasticityconsistent conditional variance formula. Again, the results are qualitatively quite similar to our main results, with a few differences (Table 9; Model 8). Although we find that the multiple-use zone has a greater effect on deforestation than the core protected area, the matching ATT for the core protected area is statistically significant at the 1% level. Similarly, although we find that concession land within the multiple-use zone outperforms nonconcession land, the matching ATT for nonconcession land is statistically significant at the 5% level. Finally, although we find that within the multiple-use Table 7 Pairwise tests for equality of average treatment effects on the treated (ATT) from Model 2.a

Table 6 Pairwise tests for equality of average treatment effects on the treated (ATT) from Model 1.a CPA CPA MUZ BZ

n/a – ⁎⁎⁎

MUZ n/a ⁎⁎⁎

CPA = core protected area; MUZ = multiple-use zone; BZ = buffer zone. a Test of null hypothesis that marginal effects are equal. ⁎⁎⁎ p b 1%.

BZ

n/a

CPA MUZ conc. MUZ no conc. BZ

CPA

MUZ conc.

n/a ⁎⁎⁎

n/a

– ⁎⁎⁎

⁎⁎⁎ ⁎⁎⁎

MUZ no conc.

n/a

⁎⁎⁎

BZ

n/a

CPA = core protected area; MUZ = multiple-use zone; BZ = buffer zone; conc. = concessions. a Test of null hypothesis that marginal effects are equal. ⁎⁎⁎ p b 1%.

A. Blackman / Ecological Economics 112 (2015) 14–24

21

Table 8 Pairwise tests for equality of average treatment effects on the treated (ATT) from Model 3.a CPA CPA MUZ no conc. MUZ estab. res. conc. MUZ new res. conc. MUZ nonres. conc. BZ

n/a – ⁎⁎ ⁎⁎⁎ ⁎⁎ ⁎⁎⁎

MUZ_no conc. n/a –

⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎

MUZ_estab. res. conc

n/a

⁎⁎⁎ ⁎⁎ ⁎⁎⁎

MUZ_new res. conc.

n/a

MUZ_nonres. conc.

⁎⁎⁎ ⁎⁎⁎

n/a

BZ

⁎⁎⁎

n/a

CPA = core protected area; MUZ = multiple-use zone; BZ = buffer zone; conc. = concessions. a Test of null hypothesis that marginal effects are equal. ⁎⁎⁎ p b 1%. ⁎⁎ p b 5%.

zone, nonresident concessions have the largest ATT, the matching ATT for established resident concessions is not statistically significant. On average, the matching ATTs for management regimes are about 7% larger than the regression ATTs. A disadvantage of a simple matching approach is that we are not able to use fixed effects to control for unobserved confounding factors such as forest governance and stumpage values. Therefore, we calculated Rosenbaum bounds to check the sensitivity of the matching results to such factors (Rosenbaum, 2002; Aakvik, 2001). As detailed in Appendix 2, this procedure indicates that the matching results are unlikely to have been driven by unobserved heterogeneity. Regarding model specification, we first note that our results are robust to using other types of regression models—logit and linear probability models both generate results (available from the author upon request) that are quite similar to those from probit models. As for our choice of control variables, most available observable covariates that are not already included in our regression analyses—including dominant tree types, socioeconomic statistics, and agricultural variables—are strongly correlated with either these fixed effects or other covariates. As a result, models that include them are generally inestimable. 7. Discussion This section discusses our main findings about the relative performance of different protected area management regimes. It also considers spatial spillovers.

7.1. Strict Versus Mixed-use Protection Our findings that the MBR multiple-use zone is more effective in stemming deforestation than the core protected area, and that concession land inside the multiple-use zone is more effective than nonconcession land, comport with recent studies based on naïve estimators and anecdotal evidence (Radachowsky et al., 2004, 2012; Lundin, 2010; Hughell and Butterfield, 2008; Cronkleton et al., 2008; Nittler and Tschinkel, 2005). This common ground should not be surprising given that, as discussed in Section 5.4, our broad qualitative results—although not our quantitative ones—are generally consistent with those generated by a naïve approach. Explanations in these recent studies focus on differences in the management capacity and political will of the two sets of agents managing the core protected area and mixed-use zones: undermanned and underfunded regulatory authorities in the former area versus communities and industries in the latter. 7.2. What Type of Concessions Perform Best? We also find that within the multiple-use zone, new resident community concessions performed worse than both established resident concessions and nonresident concessions. Using simple comparisons of remotely sensed deforestation rates along with years of experience working in the MBR, Radachowsky et al. (2012) reach the same conclusion and provide an explanation focused on

Table 9 Robustness: Average treatment effect on treated (ATT) estimates from probit regressions using matched control sample (1000 m grid)a and from matching estimator (250 m grid).b Variable Model Core protected area Multiple-use zone Multiple-use zone with concessions Multiple-use zone without concessions Multiple-use zone with established resident community concessions Multiple-use zone with new resident community concessions Multiple-use zone with nonresident community concessions Buffer zone Matched control plots? Municipio fixed effects? Standard errors clustered at municipio-level? Average pr(cleared) for matched control plots (%) Number of observations Pseudo-R2 (%)

Regression 5

Matching 6

−42.43 −50.23⁎

90.14⁎⁎⁎ Yes n = 13 Yes 8.70 17,619 15.79

−41.23 −69.23⁎⁎⁎ −5.40

89.50⁎⁎⁎ Yes n = 13 Yes 8.72 17,619 16.46

c

7

−40.37

−6.14 −79.45⁎⁎⁎ 47.17 −80.03⁎⁎⁎ 89.42⁎⁎⁎ Yes n = 13 Yes 9.35 16,356 16.45

8 −44.78⁎⁎⁎ −71.51⁎⁎⁎ −88.70⁎⁎⁎ −47.98⁎⁎ −85.00 −9.06 −99.57⁎⁎⁎ 1.43 Yes n/a No n/a Varies n/a

n/a = not applicable. ⁎⁎⁎ p b 1%. ⁎⁎ p b 5%. ⁎ p b 10%. a Dependent variable is cleared and independent variables are temperature, rain, slope, and travel time (see Table 1). Control observations are weighted based on the number of times they are used as matches. The omitted category is unprotected plots in northern half of Guatemala. Statistics in the table are marginal effects for treated plots expressed as percent changes from baseline (average predicted probability of deforestation for matched unprotected plots). b Difference in mean of cleared for treated and matched control plots. Standard errors calculated using Abadie and Imbens' (2006) heteroskedasticity-consistent conditional variance formula. c Regression sample for Model 3 excludes all plots in both industrial concessions.

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A. Blackman / Ecological Economics 112 (2015) 14–24

institutional and historical factors.5 They maintain that the four new resident community concessions, with relatively recent immigrants to the MBR, always have had mixed incentives to comply with the requisites of the sustainable forest concessions that regulatory authorities promoted starting in the mid-1990s. On one hand, concessions legitimized these communities' continued presence in the MBR. But on the other hand, they restricted expansion of the cattle ranching and agriculture on which they subsisted. Partly as a result of these mixed incentives, during the late 1990s and 2000s, all four new resident community concessions witnessed accelerating population growth, land appropriation, expanded cattle ranching, social conflict, and deforestation. By contrast, nonresident concessions did not feature sizable local communities. Therefore, they had fewer disincentives for sustainable forestry. Finally, although established resident concessions engaged sizable local communities, they subsisted principally on nontimber forest products, which actually provided positive incentives for sustainable forest management. 7.3. Spatial Spillovers Although our focus has been on the relative performance of strict and mixed-use protection in the MBR, our buffer zone results also are noteworthy. We find that this management regime increases deforestation by 90 to 100%, another result that jibes with studies based on naïve estimators and qualitative evidence (Hughell and Butterfield, 2008; Shriar, 2011). This conclusion should not be surprising since, as the name implies, the buffer zone's purpose is to deflect population and deforestation pressures from the core protected area and multiple-use zone. However, if the buffer zone did in fact absorb at least some deforestation that otherwise would have occurred farther north in the MBR, then (the absolute value) of our estimated treatment effects for the multipleuse zone and core protected area would be biased upward because we did not net out these negative spillover effects. Measuring and controlling for such spillovers, however, are beyond the scope of this study. By the same token, spatial spillovers between different management regimes inside the MBR but outside the buffer zone—for example, between the multiple-use zone and core protected area—could, in principle, bias our ATT estimates. Although we are not aware of any anecdotal evidence for such spillovers, in principle, they could affect our results. 8. Conclusion As discussed in the Introduction, the MBR can be considered a test bed of different protected area management strategies, including strict protection and several variants of mixed-use protection. Most past evaluations of the relative effects of these strategies have been based on simple comparisons of average deforestation rates. The premise of the present paper is that such evaluations are not necessarily reliable because they do not control for the nonrandom siting of the management regimes that has resulted in their facing very different pre-existing deforestation pressures. Simple summary statistics demonstrate that there are indeed significant differences across these regimes in the preexisting climatological, geophysical, and socioeconomic characteristics that drive deforestation. We have used a combination of regression and covariate matching to control for these confounding factors. Having done that, our results are mostly consistent with the emerging conventional wisdom derived from naïve estimates but offer a more nuanced perspective. In general, all of our estimated treatment effects are smaller, less statistically significant, and more heterogeneous than those based on a naïve approach.

They indicate that within the MBR, although mixed-use protection is more effective than strict protection, some variants have performed much better than others. Specifically, taking into account results from our all of the (nonnaïve) results we presented, including our preferred regression models (Table 5) and the two robustness checks (Table 9), we find that from 2001 to 2006, compared with the deforestation rate that would have occurred absent any protection, the multiple-use zone as a whole cut clearing by 49 to 72%, the core protected area had no discernible effect in most models, and the buffer zone increased deforestation by 87 to 100%. Furthermore, we find that within the multiple-use zone, concessions cut clearing by 68 to 89%, while nonconcession land had no discernible effect in most models. Finally, among the multiple-use zone concessions included in our regressions, nonresident concessions, which cut clearing by 78 to 100%, were the most effective at stemming clearing, followed by established resident concessions, which cut clearing by more than three-quarters in most models. By comparison, new resident concessions did not have a discernible effect in most models. Our results add to the thin but quickly growing body of rigorous evidence on the deforestation effects of strict versus mixed-use protection and community forestry in developing countries. Our finding that the multiple-use zone outperformed the core protected area supports prior research suggesting that mixed-use management can, in some circumstances, be preferable to strict management. But our finding that within the multiple-use zone, the effects of different types of concessions were quite heterogeneous echoes one of the main themes of the literature on community forestry: the effect of community-based management on environmental outcomes is variable and depends critically on institutional and historical factors. Additional rigorous evaluations are needed to understand whether and how our findings generalize across and within countries. We close by briefly reiterating three caveats to these broad conclusions, all discussed above. First, (the absolute values of) our treatment effect estimates for various multiple-use management regimes would be biased upward if these regimes had (negative) spillover effects in other zones. Second, our assessment of the performance of the various types of mixed-use concessions was incomplete. Because we observed no deforestation inside industrial concessions from 2001 to 2006, we were not able to include them in our regression model. In principle, these concessions may well have outperformed all other varieties. Finally, we cannot rule out the possibility that unobserved confounders biased our treatment effect estimates. Acknowledgments Funding for this research was provided by the InterAmerican Development Bank (Contract No. INE/RND/RG-K1225-SN1/11), the National Space and Aeronautics Administration through the SERVIR Applied Science Team (Grant No. NNX12AR57G), the Swedish International Cooperation Agency through the Environment for Development Initiative and the Swedish Research Council Formas through the Human Cooperation to Manage Natural Resources (COMMONS) program. I am grateful to Jessica Chu, Adam Stern, and Sam Stolper for expert research assistance; David Hughell and Rebecca Butterfield for providing data and for conducting the case study that motivated the research; Leonardo Corral, Michael Collins, David Kaimowitz, participants in IDB and EfD conferences and seminars, and three anonymous reviewers for helpful comments; Pablo Imbach and Sergio Vilchez for assembling an initial data set; and Sally Atwater for editorial assistance. Remaining errors are my own. Appendix 1. Travel Time Model

5

Focusing on community forests in Guatemala and the Yucatan Peninsula in Mexico, Bray et al. (2008) also reach a conclusion that echoes ours. They find that long-inhabited community forests, including Carmelita and Uaxactun in the MBR, are at least as effective as strict protection in stemming deforestation.

For each of the plots in our sample, we used Arc-GIS to calculate travel time to the nearest population center. First, we divided the study area into 900 m2 pixels and assigned an impedance to each pixel to account

A. Blackman / Ecological Economics 112 (2015) 14–24

for average slope and the presence of a road. We used the following formulas to assign impedances: for pixels with a highway, impedance was equal to 1 plus the square root of slope (in degrees); for pixels with major or local roads, impedance was equal to 2 plus the square root of slope; for pixels with trails, impedance was equal to 3 plus the square root of slope; and for all other plots impedance was equal to 10 plus three times the square root of slope. We assumed that primary rivers could be crossed only on an existing bridge and ignored secondary and tertiary rivers and waterways. Calculated in this manner, impedance in our study area ranges from 1 to 28 and can be interpreted as the inverse ratio of the rate of travel in hundredths of a kilometer per hour (kph). Thus, the rate of travel on highway traversing a perfectly flat plot is 100 kph, and the rate of travel without a road on a steep plot is 3.5 kph. Having assigned impedances to each pixel, we used standard iterative techniques to find two minimum impedance routes: (i) from each plot to the nearest road, and (ii) from the nearest road to the nearest of 320 population centers with a population greater than 200. We calculated the weighted distances on each route and summed them. Finally, we converted this summed weighted distance into travel times in minutes. Because our assumptions imply a linear relationship between impedance-weighted distance and the time needed to travel that distance, this conversion simply involved dividing by a constant to arrive at total travel times in minutes. The sources for the data used to calculate travel times are as follows. Our population data are from Tageo.com (http://www.tageo.com/ index-e-gt.htm). Our roads and river data were purchased from KAART Data (http://www.kaartdata.com/data-licensing/guatemala/). 30 arc second (approximately 900 m resolution) slope was calculated in ArcGIS using a shuttle radar topography mission (SRTM) 90 m2 digital elevation model from the Consultative Group on International Agricultural Research-Consortium for Spatial Information (CGIAR-CSI) (http://srtm.csi.cgiar.org/). Appendix 2. Rosenbaum Bounds Rosenbaum bounds indicate how strongly unobserved confounding factors would need to influence selection into the treatment—here, the location of a plot in a certain management zone—in order to undermine a matching result. To be more specific, the Rosenbaum procedure generates a probability value for a Mantel and Haenszel statistic (in the case of binary variables) for a series of values of Γ, an index of the strength of the influence that unobserved confounding factors have on the selection process. Γ = 1 implies that such factors have no influence, such that pairs of plots matched on observables do not differ in their odds of being treated; Γ = 2 implies that matched pairs could differ in their odds of treatment by as much as a factor of 2 because of unobserved confounding factors; and so forth. The probability value on the Mantel and Haenszel statistic is a test of the null hypothesis of a zero ATT given unobserved confounding variables that have an effect given by Γ. So, for example, a probability value of 0.01 and a Γ of 1.2 indicate that ATT would still be significant at the 1% level even if matched pairs differed in their odds of treatment by a factor of 1.2 because of unobserved confounding factors. We calculate Γ*, the critical value of Γ at which ATT is no longer significant at the 10% level, for each statistically significant matching ATT in Table 9 (core protected area, multiple-use zone, etc.). In each case, Γ* is larger than 3. Hence, our sensitivity tests suggest that unobserved confounders would need to be quite strong to undermine our statistically significant matching ATTs. In other words, such confounders are unlikely to drive the significant matching results. References Aakvik, A., 2001. Bounding a matching estimator: the case of a Norwegian training program. Oxf. Bull. Econ. Stat. 63 (1), 115–143.

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