Supporting information for Bare et al. Assessing the Impact of International Conservation Aid on Deforestation in Sub-Saharan Africa

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1 Supporting information for Bare et al. Assessing the Impact of International Conservation Aid on Deforestation in Sub-Saharan Africa Data We used available data to construct a panel dataset covering the years This dataset contains repeated observations of the same indicators (see below) on the same units of analysis (here countries) over time. The dependent variable in our regression models is deforestation. We used deforestation data for the years from (Hansen, Potapov et al. 2013). Deforestation is defined as the yearly loss of tree cover, from a baseline of the tree cover extent in the year Our model uses forest loss at 20% and 50% thresholds, that is, when tree cover (by percent canopy cover) drops below the defined threshold for a given area. Thus, if tree cover canopy drops from 60% to 51%, it is not recorded as deforestation for the 50% threshold, but if it drops from 51% to 49%, it is recorded as deforestation. Analysis of deforestation at different thresholds is important because it demonstrates land use change dynamics in different types of forest environments. In SSA, many forested areas are open woodland ecosystem and naturally sparse in forest cover, thus necessitating the disaggregation of forest cover types. Eritrea is excluded from the model because it does not have recorded forest cover (Hansen, Potapov et al. 2013). The key predictor variable, conservation aid, derives from the dataset developed by (Miller, Agrawal et al. 2013) based on the AidData compilation (2012), as described in the main text. Conservation aid includes money allocated by multinational and bilateral donors to host countries in SSA. Conservation aid is part of official development assistance (ODA) committed and disbursed by bilateral and multilateral donors. Conservation aid projects may have an immediate positive effect on forest cover with clear, measureable goals and criteria for success (e.g. reforestation or support for protected areas management), but they may also have less definable, longer-range positive effects (e.g. capacity building, environmental education) or may be preventative in nature (e.g. alternative livelihoods). The dataset includes coastal and marine conservation projects as some of these may relate to mangroves or coastal trees, though such projects comprise less than 1% of total aid spending for the study sample. The dataset excludes industrial forestry projects, as these are unlikely to affect forest loss. Forestry activities in general are controlled for in our regression model, with the variable roundwood (see below). As noted, the dataset includes mixed conservation aid projects that explicitly address both ecological and economic objectives, such as sustainable agriculture, local land use planning, and irrigation and watershed management, as well as strict projects with a more narrow biodiversity focus, such as PA management, environmental ministry administration, and scientific research (Miller 2014). However, some of the mixed projects may include large components of development objectives and less substantial focus on conservation objectives. Conservation aid money may include aid projects administered by NGOs in SSA countries, but does not include money raised internally or privately by NGOs. In line with standard practice in analysis of conservation aid projects (see e.g.(miller, Agrawal et al. 2013)), the full project funding amount is included in the dataset even though the proportion devoted directly to conservation-related activities may vary. 1

2 We compiled data on several other predictor variables hypothesized to affect deforestation. Our governance indicator is a composite score of corruption control, rule of law, and government effectiveness, measures we expect are most likely to affect governments capacity to utilize aid. The data comes from the Worldwide Governance Indicators dataset, which is widely used in academia and policy (Kaufmann, Kraay et al. 2009, Langbein and Knack 2010). Since these measures are highly correlated, we created a composite score using principal component analysis. Each aspect is in units of a standard normal distribution, with mean zero, standard deviation of one, and a range of approximately- 2.5 to 2.5 (higher values correspond to better governance). Our democracy variable uses the Polity IV score of regime type, measured yearly, indicating whether it is a democracy or authoritarian regime (Marshall and Jaggers 2010). Democracy is scored on a -10 (least democratic) to + 10 (most democratic) scale. Democracy scores were also modeled in a quadratic function (democracy squared) in order to analyze the effect of the forest transition. Protected areas are included in the model as the percent of a given country s land designated as some form of protected area that is at least 1,000 hectares and IUCN category I-VI, excluding marine protected areas. Other variables affecting forest cover derive from the World Development Indicators, including FDI (foreign direct investment as net inflows in current USD), income (GDP per capita, constant 2005 USD), and net official development assistance (ODA), which refers to all development assistance in the form of loans and grants to promote economic development and welfare (OECD 2015). Demographic variables include population density and rural population (the percent of the country s population living in rural areas). Finally, commodity variables include agriculture, the share of arable land area that is under permanent crops or permanent pastures (World-Bank-Group 2012), livestock, a production index of the volume of production in one country in comparison with a base period of (World-Development-Indicators 2014), and roundwood, referring to the total amount of timber that is harvested for economic gain (FAO 2011). Statistical Analysis All statistical analyses were done in Stata (version 14.0). Descriptive statistics for the variables analyzed in the OLS regression analysis are presented in Table S1. We estimated the following OLS model: y i = β 0 + β i X i + i where y i is national level deforestation rate at either the 20% or 50% forest cover, X i is a suite of independent and control variables, and i is a random error term. The independent variables tested were conservation aid, governance, and the other variables mentioned above in data, as well as an interaction term of the governance and conservation aid variables. All models controlled for fixed effects and time trend. As robustness checks, we ran various models that tested multiple measures of key variables, with the two measures of deforestation threshold (20% and 50%, as developed by (Hansen, Potapov et al. 2013), and in two sample populations, one with all SSA countries and another with only high forest cover countries (forest area greater than the median for SSA countries). These two sample populations were created to evaluate the different deforestation drivers in countries of different forest densities as noted by (Rudel 2013) describing the variation between dense, humid forest countries of the Congo basin and sparse, dry forest countries of East Africa. These two 2

3 populations were also created to evaluate the potential effect of conservation aid in the different types of forested landscapes. As is common in the literature on aid, we lagged conservation aid and other independent variables between one and five years (Bearce and Tirone 2010, Wright and Winters 2010). The following variables were (natural) log transformed to ensure a normal distribution: conservation aid, GDP, ODA, population density, rural population density, and roundwood. To verify that the models also met other assumptions of OLS regression, we tested for model specification (using the linktest and ovtest functions in Stata 14); for multicollinearity (by comparing variance inflation factors, all of which were less than 4.13); for heteroskedasticity (using the Breusch-Pagan (1979) test; and for autocorrelation using the (Wooldridge 2010) test for autocorrelation in panel-data models. The results of these tests suggest our models meet the assumptions of OLS regression. Given that our models entail making multiple comparisons using the same data, care is required to address the problem of type one errors. We explored using Bonferroni or Benjamini-Hochberg corrections, but these were too restrictive for our purposes. While better addressing the possibility of type 1 errors these approaches would severely reduce power to detect an important effect as they increase the number of instances that the null is not rejected when in fact it should have been. These adjustments make less sense in social science applications like our article than they would in, say, a field like genetics where a number of real effects may be expected among a huge number of zero effects (Gelman et al 2012).For the topic of our paper there are less likely to be effects that are truly zero (or something approximating it). However, given the potential for type 1 errors our results should be interpreted with caution, recognizing that they represent a first attempt to explore the capacity of countervailing efforts like conservation aid to reduce deforestation rates at a continental scale. 3

4 References AidData (2012). Bearce, D. H. and D. C. Tirone (2010). "Foreign aid effectiveness and the strategic goals of donor governments." The Journal of Politics 72(03): FAO (2011). Forest Resources Assessment Rome, FAO. Gelman, A., Hill, J., & Yajima, M. (2012). Why we (usually) don't have to worry about multiple comparisons. Journal of Research on Educational Effectiveness, 5(2), Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. Turubanova, A. Tyukavina, D. Thau, S. Stehman, S. Goetz and T. Loveland (2013). "High-resolution global maps of 21st-century forest cover change." Science 342(6160): Kaufmann, D., A. Kraay and M. Mastruzzi (2009). "Governance matters VIII: aggregate and individual governance indicators, " World bank policy research working paper(4978). Langbein, L. and S. Knack (2010). "The worldwide governance indicators: six, one, or none?" The Journal of Development Studies 46(2): Marshall, M. G. and K. Jaggers (2010). Polity IV project: political regime characteristics and transitions, Severn, MD: Center for Systemic Peace. Miller, D. C. (2014). "Explaining Global Patterns of International Aid for Linked Biodiversity Conservation and Development." World Development 59: Miller, D. C., A. Agrawal and J. T. Roberts (2013). "Biodiversity, governance, and the allocation of international aid for conservation." Conservation Letters 6(1): OECD (2015). Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database, Organisation for Economic Co-operation and Development. Rudel, T. K. (2013). "The national determinants of deforestation in sub-saharan Africa." Philosophical Transactions of the Royal Society B: Biological Sciences 368(1625): Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data, MIT press. World Bank Group (2012). World development indicators 2012, World Bank Publications. World Development Indicators (2014). Washington DC, World Bank. doi: / Wright, J. and M. Winters (2010). "The politics of effective foreign aid." Annual Review of Political Science 13:

5 Supplemental Tables Table S1. Descriptive Statistics for Panel Models Variable N Minimum 25 th percentile Mean 75 th percentile Max Std. Dev. Deforestation rate, 20% ( ) Deforestation rate, 50% 503 ( ) Conservation aid 521 ($US millions; ) Governance (Principal components score from three indicators, ) 547 Democracy (Polity IV score of regime type; ) Protected Areas (% of land designated as protected; ) FDI (US$ billions; ) Income (GDP per capita, constant ,798 2, US$; ) Official Development Assistance (US$ millions; ) Population Density ( ) Rural Population (millions; ) Agriculture (% of arable land under permanent crops or pastures; ) Livestock (Livestock production index; ) Roundwood (millions of m 3 of timber harvested for economic gain; )

6 Table S2. Conservation Aid Recipient Countries in Sub-Saharan Africa, Rank Country Total Aid Committed (current USD) Total forest loss 20% threshold 1 Kenya $276,226, % 9.83% 2 Uganda $252,052, % 9.73% 3 Madagascar $240,398, % 11.20% 4 Tanzania $235,015, % 8.08% 5 Congo, Rep. $166,568, % 1.31% 6 Mozambique $141,492, % 8.86% 7 Cameroon $130,702, % 1.72% 8 Nigeria $118,407, % 3.76% 9 South Africa $105,164, % 26.95% 10 Ghana $96,192, % 9.17% 11 Benin $79,454, % 75.12% 12 Namibia $72,397, % 60.63% 13 Senegal $72,039, % 17.72% 14 Zimbabwe $69,929, % 22.73% 15 Cent. Afr. Rep. $69,085, % 1.25% 16 Zambia $62,313, % 4.89% 17 Niger $59,286, % 16.59% 18 Guinea $57,353, % 7.03% Total forest loss 50% threshold 19 Burkina Faso $54,764, % % 20 Malawi $54,684, % 13.28% 21 Ethiopia $54,065, % 3.00% 22 Botswana $50,720, % 3.75% 23 Gabon $48,532, % 0.91% 24 Mali $35,160, % 97.83% 25 Chad $33,312, % 14.30% 26 Cote d'ivoire $26,986, % 15.12% 27 Lesotho $24,293, % 2.26% 28 Guinea-Bissau $21,929, % 8.98% 29 Rwanda $21,539, % 4.84% 30 Burundi $19,378, % 5.57% 31 Mauritania $18,052, % % 32 Liberia $11,234, % 6.21% 6

7 33 Swaziland $10,680, % 33.13% 34 Sierra Leone $9,691, % 8.53% 35 Dem. Rep. Congo $8,346, % 3.80% 36 Sudan $7,230, % 6.36% 37 Gambia, The $2,952, % 45.00% 38 Equatorial Guinea $1,662, % 1.90% 39 Togo $667, % 4.42% 40 Angola $539, % 2.96% 41 Djibouti $18, % 0.00% 42 Somalia $0 2.39% 7.15% 7

8 Table S3. Results for regression model with deforestation as dependent variable and conservation aid and democracy interaction All countries All countries High forest cover countries High forest cover countries 20% deforestation 50% deforestation 20% deforestation threshold 50% deforestation threshold Time lag 1 year 2 year 3 year 1 year 2 year 3 year 1 year 2 year 3 year 1 year 2 year 3 year Conservation aid.0149** * * * Governance Conservation Aid*democracy Democracy Democracy ** E * Protected areas ** * * * FDI * GDP / capita Total ODA Pop. density Rural population 4.588* * * 4.466* * Agricultural area E Livestock Roundwood Constant ** * -74.7** ** 65.07* Model F-value Model P value 0.0*** 0.0*** 0.0*** 0.0*** 0.0*** 0.0*** 0.0*** 0.0*** 0.0*** 0.0*** 0.0*** 0.0*** Observations R-squared No. of countries Note: Conservation aid, GDP, ODA, population density, rural population density, and roundwood variables were (natural) log transformed. Coefficients for year control variables not shown. ***P<0.001l; ** P<0.01; * P<0.05; +P<

9 Table S4. Results for regression model with deforestation as dependent variable, conservation aid and governance interaction, and low forest cover countries Low forest cover countries Low forest cover countries 20% deforestation threshold 50% deforestation threshold Time lag 1 year 2 year 3 year 1 year 2 year 3 year Conservation aid.0257** Governance Conservation Aid*governance Democracy Democracy Protected areas * FDI GDP / capita Total ODA Pop. density Rural population Agricultural area Livestock Roundwood * Constant Model F-value Model P value 0.0*** 0.0*** 0.0*** *** ** Observations R-squared No. of countries Note: Conservation aid, GDP, ODA, population density, rural population density, and roundwood variables were (natural) log transformed. Coefficients for year control variables not shown. ***P<0.001l; ** P<0.01; * P<0.05; +P<

10 Table S5. Results for regression model with deforestation as dependent variable, conservation aid and democracy interaction, and low forest cover countries Low forest cover countries Low forest cover countries 20% deforestation threshold 50% deforestation threshold Time lag 1 year 2 year 3 year 1 year 2 year 3 year Conservation aid.0241* Governance Conservation Aid*democracy Democracy Democracy Protected areas * FDI GDP / capita Total ODA Pop. density Rural population Agricultural area Livestock * Roundwood Constant Model F-value Model P value 0.0*** *** 0.0*** ** ** Observations R-squared No. of countries Note: Conservation aid, GDP, ODA, population density, rural population density, and roundwood variables were (natural) log transformed. Coefficients for year control variables not shown. ***P<0.001l; ** P<0.01; * P<0.05; +P<0.10. Table S6: Results for regression model with conservation aid as dependent variable 10

11 All countries All countries 20% deforestation threshold 50% deforestation threshold Time lag 1 year 2 year 3 year 1 year 2 year 3 year Deforestation ** Governance * * Democracy Democracy * * Protected areas FDI 1.071* * GDP / capita Total ODA Pop. density Rural population Agricultural area Livestock * * Roundwood Constant Model F-value Model P value 0.0*** *** 0.0*** 0.0*** *** ** Observations R-squared No. of countries Note: Conservation aid, GDP, ODA, population density, rural population density, and roundwood variables were (natural) log transformed. Coefficients for year control variables not shown. ***P<0.001l; ** P<0.01; * P<0.05; +P<