Forest Clearing in Rural Livelihoods: Household-Level Global-Comparative Evidence

By R. Babigumira, A. Angelsen, M. Buis, S. Bauch, T. Sunderland, Sven Wunder. 2014

119 citations recorded by the source corpus.

This paper examines the factors that influence rural household decisions to clear forestland. We use a large dataset comprising 7172 households from 24 developing countries. Twenty-seven percent of sampled households had converted forest to agriculture during the previous 12months, clearing on average 1.21ha. Male-headed households with abundance of male labor, living in recently settled places with high forest cover, unsurprisingly tended to clear more, but regional peculiarities abounded. Households with medium to high asset holdings and higher market orientation were more likely to clear forest than the poorest and market-isolated households, questioning popular policy narratives about poverty-driven forest clearing.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Soil Science

View Paper: https://www.semanticscholar.org/paper/4f49891b43fb6bcfa2aadf21a5f6c968e9e38e1a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. A. Angelsen
2. Sven Wunder
3. B. Belcher
4. J. Börner
5. Patrick Meyfroidt
6. S. Engel
7. C. Shackleton
8. P. McElwee

In Management, Monitoring, Policy and Law

1. A. Angelsen
2. J. Börner
3. Sven Wunder
4. Patrick Meyfroidt
5. S. Engel
6. P. McElwee
7. E. Gómez‐Baggethun
8. S. Pagiola

In Soil Science

1. A. Angelsen
2. Sven Wunder
3. G. Shively
4. J. Börner
5. Patrick Meyfroidt
6. M. Wyman
7. B. Belcher

Citations in the corpus

• Approaches and terminology for causal analysis in land systems science (2016, background): “For example, Babigumira et al. (2014) explored the factors that influence smallholder decisions to clear forestland by using a 1 year time lag between explanatory factors and area deforestation measure to avoid endogeneity.”
• Safety Nets, Gap Filling and Forests: A Global-Comparative Perspective (2014, background): “However, this cursory examination must be supplemented by a more comprehensive test of the shock variables within a proper forest-clearing model (see Babigumira, Angelsen, Buis, Bauch, Sunderland, & Wunder, 2013, this volume).”
• Forests, Livelihoods, and Conservation: Broadening the Empirical Base (2014, background): “The last analysis of PEN data by Babigumira et al. (2014) aims to identify links between site context and rural household characteristics and their forest clearing decisions.”