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Identifying the Causes of Tropical Deforestation: Meta-analysis to Test and Develop Economic Theory

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Tropical Forestry Handbook

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Abstract

This chapter discusses methods for summarizing and distilling lessons from the empirical economics literature on tropical forestry, giving particular attention to the method of meta-analysis (“the study of studies”) and to the topic of tropical deforestation. Meta-analysis can be used to take stock of the literature, test hypotheses about the effects of explanatory variables on a dependent variable, and predict the value of a dependent variable across space and time. We discuss previous reviews of the literature on deforestation and then illustrate how to test hypotheses with meta-analysis. Specifically, we examine the so-called “win-win” hypothesis that economic development is good for both people (resulting in higher incomes) and forests (resulting in lower rates of deforestation). Consistent with previous literature reviews, we find that the drivers of deforestation vary by region. However, we reject the win-win hypothesis in all regions: meta-analysis of the literature clearly shows that there are trade-offs between development and forest conservation. In Latin America, there is some evidence for the alternative hypothesis of an “environmental Kuznets curve” of deforestation. The meta-analysis also reveals possible publication biases, including different patterns of results in economics versus other publication outlets, which are important to keep in mind when drawing conclusions from the literature.

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Notes

  1. 1.

    For an example, see the “SEED” database, available here: http://www.cgdev.org/publication/data-set-what-drives-deforestation-and-what-stops-it-meta-analysis-spatially-explicit

  2. 2.

    Nelson and Kennedy (2009) typify the economic approach to meta-analysis with their focus on statistical modeling decisions. In contrast, Rudel (2008) focuses more on the process of identifying and extracting data from the studies, arguing “that to carry out insightful and credible meta-analyses, researchers must address a set of recurring questions about (1) case study selection, (2) coding procedures, (3) variable selection, and (4) conjoint causation.”

  3. 3.

    This illustrates one possible way to search for publication bias. Ferretti-Gallon and Busch (2014) demonstrate another approach by comparing “results where a variable was the focus of a study vs. results of studies where that variable was included only as a control,” under the assumption that the first type of study would be less susceptible to bias against reporting statistically insignificant results. They note “that all four of the meta-variables for which the sign diverged based on whether or not the variable was the focus of the study were related to somewhat controversial or ideologically charged topics for which publication bias might be more suspected: community forestry, land tenure security, poverty, and rural income support.”

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Acknowledgments

We thank Cody Burnett and Viola Glenn for research assistance in identifying and retrieving primary studies and Mitch Renkow and Tom Holmes for constructive comments on earlier versions of this chapter.

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Authors and Affiliations

  1. Forests and Livelihoods, Center for International Forestry Research, Jl. CIFOR, Situ Gede, 16115, Bogor, Jawa Barat, Indonesia

    Stibniati Atmadja

  2. Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA

    Erin Sills

Authors
  1. Stibniati Atmadja
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  2. Erin Sills
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Correspondence to Stibniati Atmadja .

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Editors and Affiliations

  1. Inst. Weltforstwirtschaft, Bundesforschungsanstalt für Forst- und Holzwirtschaft, Hamburg, Germany

    Michael Köhl

  2. Programa Regional REDD, Deutsche Gesellschaft für Internationale, Antiguo Cuscatlán, El Salvador

    Laslo Pancel

Appendix

Appendix

Histograms of Highly Significant Variables from Table 3

See

Table A1 Correlation matrixa
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Figs. A1 and A2

Fig. A1
figure 5

Histogram of t-statistics of the income coefficient (β1), by region of observation: other regions (Asia, Africa, interregional) vs. Latin America

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Fig. A2
figure 6

Histogram of t-statistics of the income coefficient (β1), by the way forest cover was measured; other measures (area of forest cover change, area of forest cover) vs. rate of forest cover change

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Table A2 Model of the probability that income2 is included in the forest cover model
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Atmadja, S., Sills, E. (2015). Identifying the Causes of Tropical Deforestation: Meta-analysis to Test and Develop Economic Theory. In: Köhl, M., Pancel, L. (eds) Tropical Forestry Handbook. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41554-8_252-1

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