Welfare outcomes and the advance of the deforestation frontier in the Brazilian Amazon
Abstract
Frontier expansion in the Brazilian Amazon is often described as “boom-bust” development. We critically assess this characterization by mapping and estimating statistical models of welfare as a function of deforestation at the municipal level. After controlling for potential confounding variables and spatial autocorrelation, estimation results are consistent with a frontier “boom” generated by exploitation of natural resources, followed by a “bust” during which forests continue to fall but there is no compensating gain in welfare. However, average per capita welfare increases again with deforestation at very high levels. This second turning point in average welfare, along with the strong bivariate correlation between deforestation and municipal GDP/km², may encourage local leaders to equate deforestation with development. This confirms the need for international incentive payments for global public goods, such as biodiversity and carbon sequestration, that are provided by the Amazon forest.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the late 2000s, seminal studies evidenced a boom-and-bust development pattern in the Brazilian Amazon.
“In the late 2000s, seminal studies evidenced a boom-and-bust development pattern (Rodrigues et al., 2009, Celentano and Veríssimo, 2007and Celentano et al., 2012).”
From Transition in environmental governance in the Brazilian Amazon: emergence of a new pattern of socio-economic development and deforestation (2016) · cites this paper as Celentano et al., 2012
Census sector data presents a finer spatial scale than the municipal data traditionally used in socio-economic studies.
“As IBGE raw data are produced for 37,295 census sectors covering the entire Amazon, whereas there are only 775 Amazonian municipalities, the census sector data presents a finer spatial scale than the municipal data traditionally used in socio-economic studies (e.g.Rodrigues et al., 2009, Celentano et al., 2012and Weinhold et al., 2015).”
From Transition in environmental governance in the Brazilian Amazon: emergence of a new pattern of socio-economic development and deforestation (2016) · cites this paper as Celentano et al., 2012
Cited by
Sorted by citations
Tritsch, I., & Arvor, D. (2016) · Land Use Policy · doi:10.1016/j.landusepol.2016.09.018 · Cited by 80 · see citing works
Boom-bust patterns in the Brazilian Amazon
Weinhold, D., Reis, E. J., & Vale, P. M. (2015) · Global Environmental Change · doi:10.1016/j.gloenvcha.2015.09.013 · Cited by 41 · see citing works
- Authors
- Celentano, D., Sills, E., Sales, M., & Veríssimo, A.
- Affiliations
- Imazon (BR)
- North Carolina State University (US)
- Venue
- World Development
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 40
- Issue
- 4
- Pages
- 850-864
- ISSN
- 0305-750X (ISSN-L)
- OpenAlex
- W2034226764
- Cited by
- 80
- References
- 0
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 4 · 2023: 4 · 2022: 3 · 2021: 6 · 2020: 7 · 2019: 18 · 2018: 2 · 2017: 6 · 2016: 6 · 2015: 9 · 2014: 2 · 2013: 3
- FWCI
- 1.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- World Development (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Conservation, Biodiversity, and Resource Management
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Natural Resources and Economic Development
- Physical Sciences › Environmental Science › Global and Planetary Change › Land Use and Ecosystem Services
- Keywords
- deforestation, frontier expansion, welfare, Brazilian Amazon, municipal welfare, natural resource exploitation, spatial autocorrelation, biodiversity, carbon sequestration, global public goods
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
