Noah Cohen-Cline
Assignment 8
This project aims to generate a geospatial analysis and depiction of the correlation between dam
construction and poverty in Brazil. Hydroelectric dams tend to be built in poor and often indigenous
areas along the Amazon river network, generating electricity that is delivered to wealthier, urban areas.
While driving economic development for the country’s larger population centers, the dams have adverse
environmental and social consequences for the populations in their immediate vicinity. The hypothesis
of this project is that an analysis of poverty indicators over time will show that in areas where dams are
concentrated, poverty has either increased or decreased at a slow rate as compared with urban areas
that receive most of the electricity from the dams. This project will depict visually where dams are
concentrated in Brazil, and where poverty is concentrated and how it has changed over the past ten
years. Depending on the outcomes of the analysis, the project may also demonstrate a relationship
between dam locations and concentrations of indigenous and Afro-descendant populations, two of
Brazil’s most marginalized groups. The project will either focus on Brazil as a whole, or one region (e.g.
the Northeast region, which is a demarcation used by the Brazilian Institute of Statistics and Geography,
but is not an administrative level), or on one state (admin level 1).
References
1. Duflo, Esther and Rohindi Pande. “Dams.” The Quarterly Journal of Economics. Vol. 122.2
(May 2007): 601-646.
This article discusses the impact of irrigation dams on agricultural productivity and poverty
levels in India. It uses a rigorous econometric model to examine dam and census data for every
district in the country. Duflo and Pande are also interested in distribution of gains between
winners and losers. For irrigation dams, however, the geographic distribution of winners and
losers is different than for hydroelectric dams: winners tend to be downstream communities
who benefit from irrigation and muted effects of rainfall shocks, whereas losers tend to be those
close to the dam and upstream of the dam, who suffer displacement, restrictions on river access,
flooding and waterlogging of agricultural land and increased disease vectors. I hope that my
research and GIS work, like this paper, will demonstrate inequitable distribution of gains from
dams and serve as a call for policy change.
2. Barham, Tania, Molly Lipscomb and Ahmed Mushfiq Mobarak. “Development Effects of
Electrification: Evidence from the Geologic Placement of Hydropower Plants in Brazil.” Centre
for Economic Policy Research. Discussion Paper No. 8427, June 2011.
This paper measures the impact of hydroelectric infrastructure on economic development in
Brazil. It uses GIS data to determine where plants are placed, and where electricity is delivered.
It provides methodologies that will be useful for my analysis. For example, it notes that
information on distribution networks (local grids that transport electricity from transmission
substations to households) is not available, due to the decentralized planning and management
of these networks, but that a reasonable assumption based on discussions with electricity sector
professionals is that households within a 50 kilometer radius of a substation have access to
electricity.
3. “Dams and Development: A New Framework for Decision-Making.” The World Commission on
Dams, 2000.
This is the final report of the World Commission on Dams, a UN-led panel of experts established
to assess the impacts of dams globally and to generate a framework for future decision-making
around dam projects. The report contains chapters on social impacts and development impacts,
and draws on many case studies (including in Brazil). The report provides comprehensive data
that helped give rise to some of my questions about the distributive effects of dams, for
example, that approximately 80 million people have been displaced by dams globally, and that
“many of them have not been settled or received adequate compensation, if any.” (p.17) There
is a large amount of literature on this topic, and I plan to use GIS to depict some of these issues
visually.
4. Ledec, George and Juan David Quintero. “Good Dams and Bad Dams: Environmental Criteria
for Site Selection of Hydroelectric Projects.” The World Bank. Latin America and Caribbean
Region Sustainable Development Working Paper 16, November 2003.
This paper weighs the pros and cons of hydroelectric dams from both environmental and social
perspectives, and elaborates on qualities or characteristics that make dams especially harmful
or benign. By applying these criteria to dams in Brazil, I may adjust the areas used in proximity
analyses on a per dam basis, or choose to leave some dams out of the analyses altogether.
Methods
I am still thinking through methods of analysis that I plan to use. Some ideas include:
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Symbolization of poverty indicators. I have poverty data from the Brazilian census at the
municipality level (admin level 2), and can use this to depict the distribution of poverty in Brazil.
Depending on how clear this looks visually, I may instead create centroids for municipalities and
use density analysis to show where poverty is concentrated.
Proximity analysis of poor municipalities, predominately Afro-descendant municipalities, and
indigenous villages to dams. I can calculate the percentage of each that fall within a defined
radius of dams (possibly using criteria from Ledec and Quintero to determine radius on a dam by
dam basis), and compare these numbers to relative percentages for Brazil’s population as a
whole.
Proximity analysis of poor municipalities, predominately Afro-descendant municipalities, and
indigenous villages to river segments near dams. I can do the same analysis as above, but rather
than looking only at dams, I can look at proximity to segments of rivers upstream and
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downstream from dams, which represents a more accurate coverage of adversely impacted
areas.
Density analysis of marginalized populations. It may prove useful to perform density analysis to
show where indigenous or Afro-descendant populations are concentrated rather than, for
example, simply demarcating indigenous settlements on the map.
Proximity analysis of electrical coverage. Using the coverage approximation from Barham,
Lipscomb and Mobarak, I can use proximity analysis to depict electricity coverage areas and
determine what percentage of communities in the vicinity of dams are actually covered by
electricity.
Transmission lines. I may use symbolization to show transmission lines and depict where
electricity is delivered versus where it is generated (and relative poverty levels in each area).
Data Layers
Data Layer
Municipalities
Poverty indicators
Cities/Towns/Settlements
Dams
Reservoirs
Rivers
Transmission lines
Transmission substations
Source
Brazil 2010 Census
Brazil 2010 Census
(I have this in shapefile format but can’t remember what the
source is, and the metadata doesn’t say –tips for figuring this out?)
Global Reservoir and Dam Database
Global Reservoir and Dam Database
ArcGIS Online?
Brazilian National Agency of Electric Energy
Brazilian National Agency of Electric Energy