development effects of electrification: evidence from the...
TRANSCRIPT
Development Effects of Electrification:
Evidence from the Geologic Placement of Hydropower Plants in Brazil
Preliminary Draft, not for general circulation: This version 7/30/08
Molly Lipscomb University of Colorado, Boulder
A. Mushfiq Mobarak
Yale School of Management
Tania Barham University of Colorado, Boulder
Abstract
Expansions of electricity grids reflect both cost considerations (where is it cheapest to generate electricity?) and demand-side concerns (where are firms and people located, and where is demand for power likely to grow most?). Demand evolves simultaneously with power generation, and complicates efforts to estimate the effects of electrification. This paper attempts to isolate the portion of the variation in grid expansions in Brazil that is attributable to “exogenous” engineering cost considerations to estimate the development effects of electrification between 1950 and 2000. Brazil relies almost exclusively on hydropower, and hydro-power generation requires intercepting water at high velocity. A portion of the spatial variation in the expansion of the electricity grid in Brazil during this period is therefore driven by river gradients suitable for hydro-power generation.
We predict hydropower plant placement based on geologic characteristics (river gradient and water flow) of locations throughout Brazil and then develop a cost-minimizing “engineering model” to predict the expansions of transmission lines from each of those predicted hypothetical stations every decade. The model generates maps of hypothetical electricity grids for Brazil in each decade which show what the grid would have looked like had infrastructure investments been made based solely on geologic cost considerations, ignoring all demand-side concerns. We use these modeled hypothetical maps to instrument for the actual variation in electrification across Brazil over time.
We begin by showing that the very strong cross-sectional correlation between electrification and population density is largely driven by the correlation between population density and water availability. In fixed effect regressions, the effect of grid expansions on subsequent changes in population density is much smaller, and fixed effects instrumental variables estimates show that even this smaller effect is likely a result of electricity grid expansions following population projections, rather than electrification inducing an increase in density. Electricity is estimated to increase GDP per capita under the fixed effects IV. Since the population density results suggest that other ‘general equilibrium’ effects such as in-migration to electrified areas or selective in-migration by skilled workers and firms are not taking place, we interpret the positive GDP result as an indication of a true causal effect of electricity on some aspect of productivity. *We thank the University of Colorado NICHD Population Center, Corporación Andina de Fomento, Center for Advancement in Research and Teaching in the Social Sciences at the University of Colorado and the Macmillan Center at Yale University for the financial support that made this data collection possible. We also thank Daniel Ortega and seminar participants at Corporación Andina de Fomento, UC Energy Institute and Yale School of Management for comments, and Vanessa Empinotti and Steven Li for excellent research assistance.
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I. Introduction
A fundamental role of governments in developing countries is to provide public services
such as health, education and infrastructure for its citizens. To this end, developing countries
devote about a third of their budget to health, education, and infrastructure programs (World
Bank, 2004). Assisting developing countries in providing services that enhance human
development (such as education, health, water, and electricity) is a primary responsibility of
multi-lateral development agencies, such as the World Bank which spent over 20 billion dollars
in development projects in 2004 alone (World Bank, 2005a).
It is important for social scientists to inform policy-makers about the returns to each type
of public investment so that money is spent effectively to reduce poverty and stimulate economic
growth. Our knowledge about the returns to many health and education initiatives are well
developed, since it has been possible to design small-scale randomized experiments in order to
measure the effectiveness of isolated social interventions, for example, increasing teachers’
salaries or paying parents to take children for preventive health checkups. Designing
randomized experiments for large-scale infrastructure projects (such as building electricity grids)
is beyond researchers’ capabilities, which has limited our understanding of the true impacts of
such projects.
The need for research on the returns to infrastructure is particularly pressing because
there is now renewed support from the development community for large infrastructure projects
as a means to poverty reduction (World Bank, 2003; Ali and Pernia, 2003). This renewed focus
on infrastructure is partly a response to large unmet needs. There are 3 billion people who lack
access to modern energy, 1.1 billion people who lack access to clean water supply, 2.4 billion
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who live without adequate sanitation, 20 percent of the rural population who live more than 2 km
from an all-weather road, and a third of the world’s population which has never used a telephone
(World Bank, 2005b).
This paper exploits quasi-random variation in hydro-power generation and transmission
in Brazil to determine the causal impact of electricity provision on income and population
density. River gradient (controlling for the slope of the land in nearby areas) is an exogenous
source of variation in the potential for hydro-power generation, and the cost-minimizing
expansion paths of transmission lines from the hydropower generation plants create panel
variation in this exogenous component of electricity generation. We will use these exogenous
geographic components of electricity provision to examine the effects of electricity on income
and population density using county-level data for Brazil from 1960 to 2000.
We focus on both population density and GDP per capita as outcome variables to better
understand the precise mechanisms through which electricity affects socio-economic outcomes –
is it that electrification induces in-migration of highly productive people and firms, or does it
enhance the productivity of existing firms and workers. Cross-sectional instrumental variables
estimates show a very large population density response to electrification, although this may be
due to a positive cross-sectional correlation between density and rivers (which is required for
hydropower, but also attracts people for other reasons). In location fixed effects estimates,
electrification induces a 10-15% increase in density, but this may be driven by electric grid
expansion plans following population projections. Finally, in a fixed effects instrumental
variables estimate, the effect of electricity on population density is statistically indistinguishable
from zero. This suggests that the large positive effect of electrification on subsequent GDP per
capita increases that we observe in our fixed effects IV estimates is perhaps not entirely be
driven by the in-migration of more productive workers and firms.
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Section II reviews the relevant literature on the effects of infrastructure on development,
section III explains our estimation strategy, section IV presents an overview of the data which we
are using and we are continuing to collect, section V presents estimates on the role of local GDP
and population in the expansion of the electricity networks, and section VI concludes and
presents a framework for the continuing process we will follow in order to strengthen the
instrument and improve the accuracy of our coefficient estimates.
II. Background and Literature
Brazil is a particularly relevant setting for our project because the country relies almost
exclusively on hydro-power to meet its electricity needs. Eighty-seven percent of the electricity
in Brazil is generated from hydro-electric plants as opposed to 23 percent across the developing
world (World Bank, 2005d). This dependence on hydro-electricity suggests that electricity
provision in Brazil is more closely tied to quasi-random geographic variation associated with
river gradients and flow accumulation than electricity provision elsewhere. Twenty-seven
percent of rural Brazilians still lack access to electricity (World Bank, 2005c). Furthermore,
over the course of the study, 1950-2000, electricity networks in Brazil have exploded in size.
The transmission network in Brazil has grown at an average rate of 8.9 percent per year,
increasing in size from 2,359 kilometers in 1950 to 174,806 kilometers in 2000 (Siese, 1991,
2002). In addition, the Brazilian Congress is currently debating the construction of what would
be the world’s second largest hydro-electric dam in the Amazon, and its current president is
committed to increasing hydro-electric energy in the country (New York Times, 2005).
President Luiz Inácio Lula da Silva was recently quoted in the New York Times as saying:
“There was a dereliction in not building hydroelectric projects in the previous government. With
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the projects that are underway, we can permanently guarantee [supplies of energy] for 5, 6 or
even 10 years down the line.”
Despite the growth in multi-lateral lending for infrastructure in the early 2000s (World
Bank, 2003; Ali and Pernia, 2003), there has been very limited academic research on the impacts
of electricity. In the little evidence on electricity that does exist (Balisacan and Pernia, 2002; Fan
et al., 2002; Balisacan et al., 2002; Taylor, 2005; Escobal et al., 2005), it is difficult to make
causal inferences about its impacts, since studies fail to account for the fact the electricity is
often expanded first in areas with the greatest potential for economic growth. The problem is
particularly acute when researchers simply contrast areas with electricity against areas without
using cross-sectional data (Escobal et al., 2005), since the two types of areas are likely to be
different in important but unobservable ways, and people and firms who expect to benefit most
from electricity access are likely to migrate in response to provision. In studies that use panel
data, it is possible to compare changes in outcomes over time in ‘treatment’ (with electricity) and
‘control’ (without) areas, but the treatment areas have not been randomly selected.1
The lack of clear empirical evidence on the consequences of electricity is in sharp
contrast to the knowledge social scientists have developed on the returns to investments in health
and education. This imbalance is mostly attributable to the nature of statistical inquiry. Since
the provision of government services typically responds to existing conditions or expectations of
benefits, to clearly measure the returns to any public project, we need to observe provision in an
“experimental” setting, where the allocation is randomized either by design or by nature. While
it is feasible for social scientists to design small-scale randomized interventions in health or
1 Fan et al. (2002) use Chinese provincial-level data from 1970-97 and show that for every 10,000 yuan spent on electricity development, 2.3 persons are brought out of poverty. Balisacan and Pernia (2002) use Filipino provincial level data from 1985-1997 to argue that the rich tend to benefit from access to electricity. Balisacan et al. (2002) shows that in Indonesia in 1990, a 10% improvement in access to a composite technology measure (presence of public phones, TV postal office, and electricity in the village) raises the income of the poor by roughly 2 percent.
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education,2 large-scale infrastructure projects such as roads or electricity networks are rarely, if
ever, randomly placed.
In a simple observational study, we would over-estimate the returns to building electric
grids if electricity is more likely to be provided in areas where the government expects it to lead
to larger benefits. The U.N. commissioned Canambra Engineering feasibility study reports
(1968) drew up expansion plans for the electricity grid in the southern and south-eastern states of
Brazil based on forecasts of load factors (the key demand side factor) that were primarily linked
to local GDP and projected GDP growth. Expansion of networks is prioritized where forecast
loads are largest. Even in a panel data study that controls for location fixed effects, expansion
plans based on forecasts may result in an upward bias in the estimated effect of electricity
provision on various growth measures (e.g. economic or population growth), which are directly
related to the demand side factors for electricity used for grid expansion plans. Conversely, we
may under-estimate returns in an observational study if the government’s objective is to provide
electricity to areas deemed in greater need or if it emphasizes the expansion of coverage to new
regions or states that were previously left uncovered. This paper attempts to develop a
methodology for estimating the returns to electricity that accounts for the non-random placement
of power grids.
Geographic variation has been used as a source of identification in a few empirical
studies of the effects of public investment in new infrastructure projects. Michaels (2007) uses
whether a county is connected to the interstate highway network to estimate the effects of
lowered trade barriers on the demand for skilled labor. Duflo and Pande (2005) use slope as an
instrument for the placement of dams in Indian districts. Since slope does not vary over time,
they create an interaction of slope and state-wide dam construction in each time period to create 2 See Kremer (2003) for a review of some randomized experiments in education. Other examples include: Miguel and Kremer (2003); Banerjee et al., 2004; Gertler and Boyce, 2001; Shultz, 2001; Kremer et al., 2002; Thomas et al., 2003.
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panel variation in their instrument. Dinkelman (2008) is the most closely related paper to ours,
and uses land gradient as an instrument to show that there are significant cooking technology and
female labor participation impacts of communities’ connection to the electricity grid after 1996
in Kwa-Zulu Natal province of South Africa. In her design, slope increases the cost of providing
electricity, making a connection less likely. In our study we use river gradient as a positive
predictor of hydropower dam placement, and are careful to always include land slope as a control
variable since - as Dinkelman (2008) notes - land slope may be correlated with agricultural
outcomes, the cost of providing other public services, and other characteristics of the population.
In Kwa-zulu Natal the correlation between slope and other socio-economic variables Dinkelman
(2008) gathers data on is low, but this may not be more generally true across Brazil. Also, since
land or river gradient would only have cross-sectional variation, we rely on a simplified cost-
minimizing engineering model of the grid expansion to examine the cross-sectional time series
effects of electricity grid expansion across Brazil over the time period 1960-2000.
III. Estimation Strategy
Even though electricity is not randomly allocated, households’ access to electricity in a
country that relies heavily on hydro-power may have an exogenous geographic component to it
because it would depend on proximity to rivers with a gradient suitable for hydro-electricity
generation. Our research design exploits the quasi-random placement of hydro-electric plants in
Brazil based on variation in geography that affects the possibility of hydro-power generation.
Generating hydro-electricity requires high-velocity water flow, which is contingent on
intercepting water at a steep gradient. The ability to construct reservoirs and dams and the
availability of sufficient water flow are crucial to guaranteeing consistent electricity supply from
a given power plant. Hydroelectric plants must be constructed where river depth and flow is
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sufficiently large to maintain the plant throughout the year (Canambra, 1968). Since geography
(particularly river gradient and flow) affects the suitability of hydro-electric plant construction,
this creates some natural variation in the source points for electricity across Brazil. To the extent
that the physical slope of a river is uncorrelated with socio-economic characteristics of the local
population, this creates a nice natural experiment where observationally similar households are
either more or less likely to be endowed with electricity access depending on the their distance to
an appropriately sloped river. One possible drawback of this methodology is that geography
(and in particular the slope of the land where a household resides) could indeed be correlated
with socio-economic outcomes. Slope can affect land use, agricultural outcomes, and
occupational choices.3 However, river gradient (which affects hydro-power generation and
suitability of dam construction) is typically different from the slope of the land, so we can
separately control for geographic characteristics that are related to socio-economic outcomes,
including the slope of the land, while simultaneously using river gradient as an instrument for
hydropower plant construction.
Measures of river gradient and water flow only vary in the cross-section and are constant
over time at a given location. To generate panel variation in the predicted expansion of electricity
grids, we develop a simplified engineering model of the least-cost placement of transmission
lines and sub-stations (which distribute the electricity generated by our predicted generation
plants). Electricity transmission networks transport electricity from the generation plant to the
regional hub which provides electricity to local distribution networks. Construction of new
transmission lines is expensive and requires extensive planning in order to ensure that the new
lines do not cause system shortages. Our simplified engineering model is decidedly a-behavioral
3 Dinkelman (2008) uses slope as an instrument for electricity provision in South Africa, arguing that it’s more costly to extend coverage to sloped areas. Apart from the relationship of land gradient to land use and agriculture, gradient might also make it costlier to supply other public services,
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in that transmission lines are chosen to simply minimize costs without any demand side
considerations. The actual placement of transmission lines in the real world is based on both cost
and demand-side behavioral and human considerations, but since we consider pull factors from
demand to be endogenous, the idea here is to extract only that portion of the variability in
electricity availability that is attributable to variation in river gradient, water flow, and distance
to the locations that possess those geographic characteristics suitable for generating hydropower.
IV. Data
Brazil has experienced almost exponential growth in its electricity network since 1950
(figure 1), largely fueled by hydropower. The transmission network expanded from 2,359
kilometers in 1950 to 167,443 kilometers in 2000 (SIESE, 2000). We have assembled a database
of the locations of all power plants and all electricity transmission substations across Brazil from
the 1960s until 2000, using the feasibility studies and inventories that major electricity
companies in Brazil undertake prior to planning expansion of their networks. The power plant
and transmission line data come in two forms – (1) tables with inventories of all transmission
lines that typically specify the county of origin, the destination county, length and voltage (for
transmission lines) and location (for hydropower generation plants, or (2) large paper maps of
generation plants and lines by region of Brazil. We digitize and combine all this information
into five different GIS maps of the Brazilian electricity network for the 1960s, 70s, 80s, 90s and
2000.4 Power plants were placed on the digital map according to their reported latitude and
longitude, while transmission substations were assumed to be located at the centroid of their
4 The 1960s network is based on the comprehensive inventory taken by Canambra (1967) and Canambra (1969) for 1965 and 1967. The 1970s network is pieced together from various maps and tables from the different regions of Brazil from a variety of sources (e.g. see Figure 5). The 1980s network is based on another comprehensive inventory by SIESE (1987). The 1990s is again pieced together from various sources (e.g. Furnas 1993), and the 2000 network is based on SIESE (2000).
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county of record. Please see Figures 2-5 for examples of the maps and tables which were used to
construct the data set. 775 major electricity plants have been constructed in Brazil since 1910,
and 546 of which are hydroelectric plants.
A. Unit of Observation:
Constructing a digital map database of the evolution of the electricity network allows us
to overlay GIS maps of elevation and rivers and merge information on slope and water flow to
the power plant and transmission data by location. We define “location” (i.e. our spatial unit of
observation) to be a grid of 32,500 evenly spaced points for all of Brazil set 16km apart from
each other. Our task then is to create measures of electricity availability, land slope, river
gradient, water availability and water flow for each of these grid points. Choosing grid points as
our unit of observation rather than states or counties helps diffuse potential bias arising from
political economy factors that might determine the shapes and sizes of political jurisdictions.
B. Constructing measures of electricity availability:
We have data on generation plants and transmission lines, but not on distribution
networks, which is the final component of the electricity grid that connects users to the sub-
stations. Transmission lines transfer electricity from the generation plants to the regions which
are being supplied, while distribution networks transport electricity from the major local
transmission substation to household, industrial and agricultural consumers of electricity. It was
not possible to map these distribution networks going back to the 1960s because electricity
distribution in Brazil is decentralized across 64 privatized electricity companies, and there is no
central clearinghouse for data on their operations. We do however need to account for the
distribution network since assuming that only areas with substations have electricity would be
unnatural. Based on our conversations with electricity sector professionals in Brazil, we assume
that distribution networks stretch 100 km across, so all grid points within a 50 kilometer radius
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of the centroid of a county containing a transmission substation have access to electricity. Figure
6 illustrates how these ideas are implemented in South Brazil for the 1960s. The dark blue points
are counties which have transmission substations and the light blue circles surrounding them are
assumed to be the distribution networks associated with those substations. All grid points (black
dots) which fall within light blue areas are assumed to have access to electricity. Note that in our
data electricity availability is only a direct function of the location of substations (i.e. the
endpoints of transmission lines), and not the placement of hydropower plants. Building long
transmission lines is costly, so proximity to power plants does indirectly affect a location’s
likelihood of having access to power.
Figures 7-11 map the evolution of the electricity network in Brazil from the 1960s
through 2000. As one might expect, the early development of the electricity network was
focused in relatively affluent and industrial south and from 1970s onwards the grid was
expanded to the populous (but poorer) south-east and north-east. The network has expanded
westward every decade since the 1970s, and by 2000, the coastal areas of the south-east and
north-east had almost universal coverage. The Amazon and Pantanal areas have remained
largely uncovered.
C. Geographic data:
Figure 12 demonstrates our construction of the river gradient instrument. We draw circles
of radius 10km around the evenly spaced grid points throughout Brazil and measure average land
slope in those circles to use as a control variable. We then overlay a map of water bodies, create
2 km buffers on either side of each river, and compute the gradient along the river using
elevation maps. We use this river gradient along with measures of water flow to help predict
plant location (while simultaneously controlling for land slope in the surrounding areas). The
specific indicators of water availability used are average and maximum flow accumulation
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(which measures the amount of water flowing into each point on the river), calculated based on
GIS maps from U.S. Geological Survey’s Hydro1k program.
D. Constructing the instrument:
While the exogenous geographic factors such as river gradient and flow accumulation
help predict hydropower plant location, we need to construct an instrument for “predicted
electricity availability in each decade”, which accounts for the evolution of transmission lines
and substations and varies both over time and in the cross section. We construct a very simple
engineering model of electricity grid expansion in which decisions are made solely based on
geography-induced cost considerations in order to generate a prediction for whether each of the
32,500 evenly spaced grid points has electricity access in each of the five time periods of data
between 1960 and 2000.
Our objective is to generate matched predictions for the five specific time periods of data
on the actual electricity grid available to us – 1960s based on inventories conducted in 1965 and
1967, 1970s based on maps from 1973 of the network, 1980s based on a comprehensive
inventory conducted in 1987, 1990s and 2000 from the National Agency for Electricity’s data on
recent construction of major transmission lines. From the perspective of the engineering model,
the specific dates for which we have data (i.e. the years that inventories were conducted) is
essentially arbitrary, which implies that the scale of expansion between two periods – i.e. the
number of new power plants and transmission lines built since the last inventory - would remain
indeterminate. We therefore match the scale of expansion between two periods to match the
scale of investment in hydropower plants observed in the actual data. In other words, we allocate
a budget of 240 power plants to the model in the 1960s because that is the number of
hydropower plants in existence in Brazil by 1967 - our inventory date for that decade. By similar
reasoning, the budget for 1970s was 53 additional power plants, 36 additional plants for 1980s,
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25 additional plants for 1990s, and 24 additional plants for 2000. The model takes these budgets
as given, and chooses the optimal location of hydropower plants and transmission lines based
purely on exogenous geological factors.
The first step in the model is to choose the locations of the budgeted hydropower plants.
The model uses as inputs (i.e. “instruments”) whether the location (a circle of radius 10km
around each grid point) has a river, the average and maximum gradient of the river, maximum
water flow accumulation anywhere within that circle, and an indicator for whether that location
falls in the Amazon. We let the data guide us on the relative importance of each of these factors
in determining plant location. Specifically, we run a probit regression of actual hydropower plant
locations on these instruments to generate predicted probabilities for hydropower plant
placement as a function of geologic conditions (see table 1). As expected, hydropower plants are
most likely to be located in areas with steep river gradient, with greater water flow, and away
from the Amazon. Armed with this prediction, our model places the first 240 power plants in the
highest probability locations based on geologic considerations in the 1960s. Figure 13 maps
these 240 predicted plants as red dots and the entire set of hydropower plants used to generate
the probit predictions as yellow dots against a background of elevation (darker colors are closer
to sea level) and rivers. Our model predicts a large number of power plants along the south-east
to north-east corridor (Sao Francisco river basin) where elevation suddenly moves to high from
the low-lying areas of the coast, implying a steep increase in slope. One drawback is that the
model predicts excessively dense clustering of hydropower plants due to the high spatial
correlation in geologic factors conducive to hydropower plant construction. In a future version of
this model, we plan to introduce a slightly more sophisticated decision-making rule where plants
cannot be constructed too close together along the same river.
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The next step in the model is to predict the locations of substations (i.e. directions of
transmission lines) which deliver the electricity generated at each plant predicted from the
previous step. We make the simplifying assumptions that all power plants have the same
generation capacity, and - guided by the data - that each plant has exactly two transmission
substations which are connected through a single line (the average number of transmission
substations per power plant as of 2000 was 2.5 (SINDAT, 2008)). The electricity network is
fully durable, and new substations and power plants cannot be placed in locations which have
already received electricity in prior decades.
The model arrives at the optimal lowest cost electricity network in each decade by
computing costs for all possible arrangements of transmission lines. There are a finite but
arbitrarily large number of possible permutations of transmission lines, and the numeric method
we use to arrive at the lowest-cost grid in equilibrium is detailed in the Appendix. The model
assumes that cost increases with distance and is very high when building substations in the
Amazon (due to high material transport costs). In a future version, we plan to introduce other
natural geographic barriers (e.g. crossing a mountain) as cost-enhancing. Once the equilibrium
set of transmission lines are determined, we assume that all grid points within a 50 kilometer
radius of any substation will receive access to electricity, accounting for the distribution network
for that substation. In other words, we purposefully remain agnostic about the direction in which
the distribution networks are expanded because that choice is governed by demand-side factors
which are ‘endogenous’ to measures of economic performance. The chosen 50km radius is also
data-driven (based on the average size of actual distribution networks in Brazil), and mirrors our
treatment of distribution networks in the actual electricity data. In subsequent decades, new
power plants are placed in the highest probability circles among those which have not yet
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received electricity, and alternative grid points for transmission substations are proposed from
among the points which remain without electricity from the previous decades.
Figures 14-18 plot the areas predicted to receive electricity by this model by decade.
There is a reasonably good cross-sectional spatial correlation with the actual electricity network
for Brazil (figures 7-11), and there are encouraging signs of correlation in terms of direction of
expansion. Geologic considerations induce the model to cover the south and south-east region
first, and then move towards the northeast and slowly west-ward, which is a pattern we observe
in the actual data. However, overall the model chooses to cover a much greater portion of Brazil
with electricity in the first period (1960s) than we see in the actual data. As a result, there is
much lower panel variation in coverage in the time-series dimension than we see in the actual
data, as highlighted in figure 19. As we will see, this adversely affects our predictive power in
the panel regressions. This problem can be traced back to our simplifying assumption that all
hydropower plants have the same power generation capacity which cannot be expanded. In
allocating a generation plant budget to the model for each decade, we assumed that the scale of
expansion of the electricity grid has to match actual number of power plants built between time
periods. This ignores the fact that many power plants were initially built small and had their
capacity expanded in later decades, with an associated increase in the number of substations
allocated to the plant. The constant capacity (and constant number of 2 substations per plant)
assumed therefore forces the model to over-predict electricity availability in earlier years and
under-predict it in later years. We are currently putting together data on the evolution of the
generation capacity of each plant and the cost of expanding capacity relative to the cost of
building new plants, so that we incorporate a decision on whether to expand capacity on existing
plants versus build new plants into the engineering model.
E. Dependent Variables
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This study examines the effect of electricity on changes in population density, GDP per
capita and industrial GDP per capita from the 1960s until today. County level population counts
are based on the decennial demographic censuses, while county GDP number are estimated by
the Instituto de Pesquisa Economica Aplicada (IPEA) based on various sources of data including
the census of manufacturing and of agriculture. Brazilian counties have split numerous times
over the period of our analysis. Since we do not have digital county maps from 1960, we are
unable to assign the grid points (our spatial unit of observation) to the exact county they fall into
in every decade. However, we do know the area of each county for every decade. In order to
approximate the boundaries of each county over time, we digitally identify the location of all
counties existing in a given decade from the centroid of the county as of 2001 and draw circles
around the centroid of those counties such that the area of the circle equals the area of the county
in the decade of interest. We assume all grid points which fall in the circles belong to the county
in that year. In cases where the grid point falls into more than one county circle, the average
value of the dependent variable is used. Grid points which fall outside of all circles are assigned
to the nearest county circle in that decade. Figure 20 plots the 1960 county proxies, which is the
year with the fewest number of counties. Although our unit of observation is grid points, the
underlying source of data is at the county level, so we cluster all errors by 1960 counties, which
is the most conservative assumption on clustering (for later decades we actually have more
counties and therefore more data).
Table 2 reports the summary statistics for these dependent variables. There is much
greater cross-sectional variation in population density than there is panel variation holding
locations fixed. Population density is also the variable that we feel is measured most accurately
over time. Given the way county-level GDP is estimated by IPEA, the cross-sectional
differences in these series are of higher quality than changes in the panel within locations. In
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other words, after the addition of location fixed effects, it is likely that the signal to noise ratios
in the GDP series decrease dramatically. So for GDP, one has to be more careful making
inferences with fixed effects estimates.
V. Results
There are a number of distinct mechanisms through which electricity may affect
economic outcomes. The most direct mechanism, and the one most closely related the research
questions motivated in the introduction, is the productivity-enhancing effects of, say, lighting a
school room or refrigerating perishable vaccines, or introducing electricity-powered capital into a
firm’s production process. A less direct but equally plausible mechanism is that electrification
induces the movement of people and firms. And if more highly skilled workers and firms
respond differentially to the availability of electricity, the resulting in-migration to electrified
areas can also lead to a positive correlation between electricity and socio-economic outcomes.
For example, if electricity and worker skill are complementary inputs in the production process
and if workers are mobile, then we may observe a large positive effect of electricity on firm
productivity or area GDP per capita even if electricity is not itself highly productive as an input.
In that case, electrification only leads to a spatial reallocation of resources within a country rather
than an overall productivity boost at the macro level. And in that case our estimates will not
speak directly to the comparison between the returns to spending an extra dollar of funds on
educational investments or electricity investments.5
5 Other ‘general equilibrium’ connections between electricity investments and socio-economic outcomes can also prevent us from isolating direct causal effect of electricity on productivity. For example, imagine that with limited budgets, simple cost-benefit calculus induces governments to allocate a greater share of the budget to electricity in areas where electricity is low-cost to generate (e.g. sloped areas with water, as in our instruments), whereas in high-cost areas a greater share of the budget is allocated to other public services such as health, sanitation or education. In that case, even in the instrumental variables regression we would be comparing electrified areas with low levels of other public services to non-electrified areas with high levels of other public services, which, in this case, would lead
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The multiple potential mechanisms at play suggest that it would be useful to examine
electricity’s effect on a variety of outcome variables that can help isolate specific mechanisms.
For example, we could examine the effect of electricity on migration patterns, especially as it
relates to movements of firms and worker types. This may help identify whether a productivity-
enhancing effect of electricity is due to the in-migration of productive firms to electrified areas
or to increases in productivity at existing firms. Unfortunately, we do not yet have access to
such specific variables over our long panel stretching back to the 1960s. So we begin by
exploring the effects of electricity on two summary outcomes for which it is possible to get long
panels of data – population density and GDP per capita. These two variables are related to the
two main hypothesized mechanisms at play – the movement of people and direct production
function effects. Population density changes likely reflect migration since the magnitudes of the
short-term effects of electricity on mortality and fertility – which are the two other important
sources of change in population – are unlikely to be large enough to affect population density
numbers greatly. GDP per capita changes may reflect both differential migration of skilled
workers and productive firms, or improvements in the productivity of existing firms and
workers. We will thus begin with an examination of population density to see whether the
migration mechanism appears empirically important, which will in turn help us better interpret
the sources of any changes in GDP and industrial GDP per capita.
Our regression analysis of the effect of electricity on population density and GDP use
different types of variation in the data in turn. We begin with a purely cross-sectional approach
and take advantage of variation across all regions and states of Brazil (while always clustering
standard errors by the large 1960 counties). As one would anticipate based on a cursory glance
to an under-estimate of the returns to electricity if those other public services are productive. The solution to this type of a problem is conceptually straightforward – we would need to add control variables for such other productive public services in our regressions.
19
of the maps of the actual electricity network in figures 7-11 and the maps of the predicted
(modeled) electricity network in figures 14-18, our instrument’s fit to the endogenous electricity
variable in this case is excellent. Areas predicted by our geologic instruments and model to have
electricity have a 43 percentage point greater chance of actually having electricity available.
However, this statistically significant correlation is somewhat driven by the Amazon dummy in
our predicted hydro-power plant locations, and the large effect of the instrument reflects the
large differences in electrification between the Amazon and non-Amazon regions of Brazil.
These regions are different in other economically-relevant dimensions as well, so we next show
estimates that include region fixed effects, so that the empirical inference is not based on
differences across the disparate regions. The estimated effect in the first-stage regression
immediately jumps down: Areas predicted by our geologic instruments are 8 percentage points
more likely to have electricity than other areas within the same region. The third column in table
3 shows that state dummies have a very similar effect on the first-stage estimates as region
dummies (predicted locations are also 8 percentage points more likely to actually have electricity
as other locations within the same state). In column 5 we add random effects by grid point in
addition to region dummies, and in the last column fixed effects by grid point. The instrument
remains positive and strongly predictive of the actual electricity distribution. Land slope is also
strongly correlated with hydropower plant placement, which both (a) works to reduce the power
of our instrument (due to the correlation between land slope and river gradient, and that land
slope is a separate control variable in the second stage), and (b) makes it important to separately
control for slope in the second stage if there are other possible links between slope and socio-
economic or demographic outcomes.
The first three columns of table 4 report OLS estimates of electricity availability (lagged
one period) on population density. All specifications always separately control for land slope
20
and standard errors are always (conservatively) clustered by the large county areas in 1960. The
second and third columns add region and state dummies respectively, which expectedly lowers
the magnitude of the effect of electricity on population density. The estimated effects are quite
large – electricity availability in an area is associated with its population density being twice as
large the following decade (at the mean), even relative to other areas within that same state. The
average population density in the non-Amazon regions of Brazil is 23 people per square
kilometer during the entire sample 1960-2000, and this average rises to 33 people in the non-
Amazon and non-Pantanal regions. The instrumental variables estimates of the effect of
electricity on population density (in the last 3 columns) are always larger than the corresponding
OLS estimates, which implies that areas suitable for hydropower plant construction (and
transmission line expansions from those plants) have an even an even larger correlation with
dense population. Moreover, the IV estimates get even larger when region and state dummies
are added, indicating strong population clustering near hydropower suitable sites within each
state.
If we were to ascribe causality to these IV estimates, then the results would be consistent
with electricity being targeted more to areas that were sparsely populated. Such targeting would
appear to be most pronounced within states. In other words, under-populated parts within states
were targeted more than denser parts, as opposed to some states being targeted more than others.
However, since these are cross-sectional estimates, then there is another plausible interpretation
of this data. In the cross-section, our instrumental variables estimates are essentially comparing
areas that have rivers with a steep gradient and voluminous water flow against all other areas that
do not. This control group is actually comprised of three different types of areas: those with flat
rivers, those with narrow, shallow rivers, and those with no river at all. Given the presence of no
or low water areas in the control group, our IV estimates may be conflating the effects of
21
electricity with the effects of having water available. And water may draw dense population
concentrations for reasons completely independent of electricity generation. Note that this
inference problem would be completely mitigated in panel regressions where we control for
location fixed effects, since water availability at flow at each location is essentially a fixed
factor. Thus it is important to present fixed effects instrumental variables estimates in this
context in order to isolate the causal effect of electricity availability.
In summary, we find a strong positive cross-sectional (across Brazil, within-state and
within-region) correlation between electrification and dense population using OLS, and an even
stronger correlation between hydropower generation (and associated grid expansion) and
subsequent population density using IV. It is quite possible that the former is driven by reverse
causality (grid expansions follow population projections, which makes those projections self-
fulfilling), and the latter is driven by people’s attraction to both electricity and water. Whatever
the nature or direction of the causality, there is certainly a relationship between electrification
and in-migration, as evidenced by the very large population density gains. Such large gains in
density are unlikely to be driven by changing fertility and mortality within a decade.
Table 5 examines the effect of electricity on GDP per capita, again in the cross-section.
The OLS estimates indicate a strong positive effect, but that effect is understandably smaller
once region dummies or state dummies are added. Electrifying a location is associated with a
15% larger GDP per capita there the following decade relative to other areas within the same
region. The effect becomes smaller and loses statistical significance under the state dummies
specification, but with the caveat that the methods to estimate county-level GDP by IPEA using
underlying sources that were designed to be reported only at the state level probably carries a lot
of measurement error when we focus on its within-state variation. The instrumental variables
estimates of electricity on GDP per capita are less precisely estimated and quite variable
22
depending on nature of the data variation being used, ranging from a strong positive effect across
Brazil to a negative point estimate (but an effect that is statistically indistinguishable from zero)
within states. Estimates of electricity’s effect on just the industrial component of GDP are much
more stable. The OLS estimates are positive and statistically significant and indicate that within
a region, electricity increases industrial GDP by 45% at the mean year-2000 value. The IV
estimates are of comparable magnitude, but the standard errors are much larger. The IV point
estimate suggests that electricity increases industrial GDP by 20% at the mean within regions,
but this is imprecisely estimated and we cannot rule out that the true effect is zero. As with the
population density specifications, the effect on GDP in the cross-sectional IV estimates may
reflect the effect of both water and hydropower.
We estimate random effects and fixed effects models in tables 7 and 8, taking full
advantage of the panel dimension of our dataset. In table 7 we add random effects by grid point
in addition to the region dummies in the IV model, which assumes that location specific
characteristics are uncorrelated with our predicted electricity instrument. Table 8 relaxes this
questionable assumption and reports a results from a “within” estimator where we effectively
regress the decadal change in the outcome variable (GDP or population density) on the lagged
change in our model’s prediction for whether that particular location has electricity. The random
effects IV model shows that electrification increases GDP per capita by about 20% within
region. The estimate for industrial GDP has a comparable magnitude but the effect is not
statistically significant. Electrification has a large positive effect on subsequent GDP per capita
changes in the fixed effects IV regressions.
The most notable difference between the panel and pooled cross-sectional models is that
the effect of electricity on population density is estimated to be much smaller in the panel. Once
we add location (i.e. grid point) fixed effects without instrumenting in column 3 of table 8, we
23
find that electrification is associated with a 10-15% increase in population density the following
decade. This effect is an order of magnitude smaller than all cross-sectional OLS and IV
estimates. Thus the cross-sectional IV estimates do indeed appear to be biased upward due to a
positive correlation between rivers and density. Moreover, even the remaining 10-15% positive
effect we uncover in this fixed effects regression may be driven by the electricity grid being
expanded to areas where planners expect future population growth (and acting on that
expectation in turn fulfills that growth promise). To account for this, we report within-region
random effects IV and grid point fixed effects IV estimates in the last columns of tables 7 and 8,
and find that the effect of electrification on population density is statistically indistinguishable
from zero in both specifications. Any upward bias from grid expansion plans based on
population projections appears to be small.
If the fixed effects IV results are successful in isolating the causal effect of electricity,
then the GDP and population density results indicate that there is a large GDP per capita increase
following electrification which cannot be fully explained by large-scale movements of people.
Thus electricity likely has a direct positive production function effect in stimulating greater
economic productivity.
We are concerned about the poor fit of the predictive engineering model in the panel
dimension, particularly in the earlier two decades when we vastly over-predict electricity
availability and under-predict its growth. Table 10 therefore re-visits the fixed effects
instrumental variables models on a restricted sample of 1980s-2000, since our model’s
predictions of electricity growth during this period matches reality (although we still
systematically under-predict electricity level during this period). The instrumental variables
estimates with grid-point fixed effects produce incredibly large estimates of electricity on GDP.
Both GDP per capita and industrial GDP are predicted to more than double following
24
electrification. Electricity induces a population density increase of about 50-60% at the mean,
but the standard error associated with this estimate is again very large.
VI. Conclusion
Understanding the role of access to electricity in economic development is crucial to
planning long term investments in infrastructure improvements in developing countries. Taking
advantage of the close link between geology and hydropower plant construction in Brazil, we
produce quasi-experimental estimates of the long-term effects of electrification on local income
and population density over four decades. Our methodology is able to predict quite well the
cross-sectional variation in electricity availability across regions of Brazil based only on geologic
factors and cost-minimizing engineering considerations. Our model still requires refinements in
order to improve its prediction of the panel variation in electrification, particularly in the inter-
temporal dimension.
While this geography-based instrumental variables methodology can help isolate the
causal effect of electrification on economic development, there may be multiple mechanisms that
mediate this relationship that are interesting to distinguish. Electricity may induce the in-
migration of people and firms, or the in-migration of a certain type of (e.g. disproportionately
higher quality) worker or firm, and it may simply improve the productivity of existing workers or
firms. From a national-level policy perspective, the last effect is a pure net gain while the
migration-related effects are in part a reallocation of already productive resources. Our focus on
two different types of dependent variables – GDP and population density – helps to distinguish
between the two types of mechanisms to a certain extent, since population density changes are
closely related to migration, while GDP changes reflect an overall effect that aggregates both
mechanisms.
25
Using cross-sectional variation in the data we find very strong effects of electricity on
population density, in the sense that areas with geologic characteristics conducive to building
hydropower plants are predicted to be more than twice as dense on average than areas that are
not. The panel (fixed and random effects) estimates suggest that that electrification during a
decade induces about a 10-15% increase in population density at those locations in the following
decade. In the fixed effects IV estimates (where we simultaneously add the location fixed effects
and use our instrumental variables strategy to address the non-random placement of the
electricity grid), the effect of electricity on population density is statistically indistinguishable
from zero.
Although the GDP estimates are less stable, electricity appears to have a strong positive effect
on subsequent GDP per capita even in the fixed effects instrumental variables specification. The
accuracy of our first-stage predictions of the panel variation in location of electricity provision
still needs to improve. New data on capacity of each generation plant should help in this regard.
Further research remains to be done on the mechanisms through which the provision of
electricity affects socio-economic outcomes. We plan to use a broader set of outcome variables
from household and firm surveys to more precisely understand these mechanisms.
26
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29
Data Appendix:
There is no comprehensive source for electricity data over our period of analysis across
Brazil. Most of the network was privatized in the 1990s, and the overseeing agencies, Operador
Nacional do Sistema Eletrico (ONS) and Agencia Nacional de Energia Eletrica (ANEEL) were
formed in the early 1990s and have little institutional memory for the period prior to their
charters. In order to assemble our data set, we traveled to Brazil and met with professionals in
the field at major electricity companies and government agencies in Brasilia, Sao Paulo, Rio de
Janeiro, Curitiba, Salvador, and Foz do Iguaçu (Itaipu). The meetings with local professionals
were informative not only in terms of data with which they provided, but also for the broader
understanding of the development of the electricity network in Brazil.
We collected data from the Ministério de Minas e Energia and ANEEL in Brasilia,
Eletrobras, ONS, the Memoria de Eletricidade, and Furnas in Rio de Janeiro, Compania
Hidroeletrica de Sao Francisco (CHESF) in Salvador, Copel in Curitiba, and Itaipu Binacional in
Foz do Iguaçu. Data on the location and year of creation of plants was assembled from a
database of important power plants from Sistema de Informações Georreferenciadas do Setor
Elétrico (SIGEL) and a historical study of hydroelectricity in Brazil by the Memoria de
Eletricidade.
Data on the state of the network in each decade was assembled from a combination of
sources. Data from the 1960s was procured primarily through feasibility studies conducted by
the Canambra Engineering Consultants who did a comprehensive survey of electricity in Brazil
in the 1960s, focusing on the South and South-Central Brazil. Inventories of the network as of
the publication dates in 1965 and 1967 were included as part of the surveys, and maps were also
included to show the placement of the network. CHESF also provided limited information about
the state of the network in North Eastern Brazil from 1960 through the present.
The 1970s data was put together primarily through maps which were drawn by Furnas
and Eletrobras in 1973. Data from the 1980s is from a comprehensive inventory done by SIESE
in 1987. The survey includes detailed data on both transmission lines and generation plants.
Data from the 1990s is from a listing by SIGEL which is a survey of the current electricity
network done by ANEEL. Data from 2000 is from both SIGEL and SINDAT, the database of
the current electricity network done by ONS.
We had the data from the inventories in each period entered into Excel spreadsheets by
firms in India and Bangladesh. Data on line voltage was used to ensure comparability of
30
inventories conducted by different sources—only lines of at least 69 kilovolts were included as
transmission lines—13 kilo volt lines were considered part of the distribution networks. Data
from the tables were transferred into GIS maps which we then compared against maps which
were drawn of the electricity network in each decade in order to insure the accuracy of our final
decade-by-decade networks.
31
Appendix: Modeling Electricity Networks in Brazil: An Engineering Cost Approach
The model begins by selecting the locations of generation plants based on probabilities estimated in a probit equation based on geographic factors. An indicator for whether or not a hydropower plant occurs within a grid circle is regressed on an Amazon indicator which accounts for the lower probability of building power plants in the Amazon due to high materials costs, the average and maximum slope of the river within the grid circle, the log of the maximum flow accumulation (the number of raster grids which flow into each raster), and a water indicator which takes the value of one if the circle has a river or stream in it, zero otherwise.
The initial placement of transmission substations is randomly selected from the remaining grid points across Brazil. Two substations are allocated to each power plant, and a single line is assumed to pass through the three points.
Slope=.15
Slope=.35
Slope=.61
Slope=.48
Slope=.20 Slope=.54
Slope=.29
The cost of the transmission lines is calculated based on distance and an Amazon indicator. (High materials costs are assumed for transmission stations within the Amazon as transport costs are high). Costs are calculated by dividing each transmission line into 100 equally spaced points.
32
Points occurring within a perimeter of a grid point are assigned the average slope which has been calculated for the region surrounding the closest grid point.
The program randomly chooses alternative points and calculates the cost of the transmission lines through those points. If the line cost for the alternative points is lower, the alternative points are retained.
The process is repeated until a lowest cost allocation is achieved for each decade (30,000 iterations).
Distribution networks surrounding each transmission substation and power plant are generated. All points within a 50 km radius are allocated electricity.
33
The process is repeated for subsequent decades. In each decade, power plants are allocated to the highest probability points which have not yet received electricity. Initial locations for transmission substations are randomly chosen from the points across Brazil which have not yet received substations or power plants.
Alternative points are proposed for the new substations. Substations and plants from previous decades are not altered.
The program is again iterated until the lowest cost equilibrium is reached. Distribution networks within a 50 kilometer radius of the chosen points are again assigned. Grid points assigned to receive electricity in a given decade are given a value of 1, while those which are not projected to receive electricity in that decade are given a value of 0. The vector of projected indicator values for each grid point is then used as our instrument for electricity provision in the instrumental variable regressions.
34
Figures:
Figure 1: Increase in kilometers of transmission lines from 1950 through 2000 Source: SIESE
Figure 2: Inventory from Canambra report 1969 Figure 3: Inventory from SIESE report 1987 Source: Canambra (1969) Source: SIESE (1987)
35
Figure 4: South Brazil Transmission as of 1967 Figure 5: South Brazil Transmission as of 1979 Source: Canambra (1967) Source: PAREE (1979)
Figure 6: South Brazil Electricity Grid as of 1960
36
Figure 7: 1960s Transmission with Distribution Figure 8: 1970s Transmission with Distribution
Figure 9: 1980s Transmission with Distribution Figure 10: 1990s Transmission with Distribution
37
Figure 11: 2000 Transmission with Distribution
38
Figure 12: Construction of the river gradient instrument Figure 13: Predicted Locations of Hydropower plants, actual plants, rivers and
elevation
39
Figure 14: 1960’s modeled power allocation Figure 15: 1970’s modeled power allocation
Figure 16: 1980’s modeled power allocation Figure 17: 1990’s modeled power allocation
40
Figure 18: 2000 modeled power allocation Figure 19: Low level of panel variation in model
Figure 20: 1960 county proxies
41
Table 1: Probit Forecast of Placement of Generation Plants (step 1 of the engineering model that creates the instrument)
Dependent variable: Indicator for location has a hydropower plant
Log of Maximum Flow Accumulation
0.029** (0.014)
Average Slope in the river 0.044 (0.030)
Maximum Slope in the river 0.062*** (0.012)
Amazon Indicator ‐0.753*** (0.066)
Indicator for location has a river in it
‐0.030 (0.063)
Observations 33342 R‐squared . Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
42
Table 2: Summary Statistics
GDP per capita (thousands of 2000 R$ per person) Obs Mean Std. Dev. Min Max
1970 35257 1.383992 1.192956 0.045881 47.90996 1980 32528 3.416521 7.051991 0.060664 455.9149 1990 32528 2.90737 3.221777 0.088616 91.90031 2000 32528 3.671918 4.05901 0.461927 184.9774
Industrial GDP pc (thousands of 2000 R$ per person) Obs Mean Std. Dev. Min Max
1970 32527 0.206988 0.442533 0 27.54974 1980 32528 1.055195 2.576341 0.000513 211.3002 1990 32528 0.603734 2.001518 7.78E‐06 89.60116 2000 32528 0.8449 2.735928 0.004097 112.1848
Population Density (people per square kilometer) Obs Mean Std. Dev. Min Max
1970 32527 11.0904 99.17917 0.015955 8893.941 1980 32528 14.28085 139.1132 0.042236 11729.97 1990 32528 17.15497 152.0696 0.089627 12199.77 2000 32528 20.3986 181.7498 0.131608 12915.98
43
Table 3: First Stage for Instrumental Variables Regressions Dependent Variable is Indicator for whether a grid point actually has electricity in the data
Pooled Cross‐sectional Random Effects Fixed Effects
Modeled Electricity Indicator
0.432*** 0.082*** 0.080*** 0.355*** 0.118*** 0.198*** (0.018) (0.014) (0.013) (0.024) (0.016) (0.025)
Land Slope 0.021*** 0.004*** 0.002* 0.027*** 0.003* (0.002) (0.001) (0.001) (0.003) (0.002)
Region Fixed Effects? N Y N N Y . State Fixed Effects? N N Y N N .
Fixed Effects? N N N N N Y Observations 130111 130023 130023 130108 130020 130820 Number of Locations for Fixed Effects 32528 32506 32705 R‐squared 0.257 0.475 0.543 . . 0.164 Robust standard errors clustered by 1960 counties in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Observations are by 16km spaced grid points. Standard Errors are clustered by county.
44
Table 4: Effect of Electricity on Population Density OLS Instrumental Variables
Lagged Electricity 47.023*** 36.511*** 20.956*** 67.663*** 72.703*** 98.326***(4.061) (3.710) (2.340) (8.164) (28.211) (37.689)
Land Slope 1.657 0.707 ‐0.401 0.548 0.472 ‐0.700 (1.092) (1.074) (1.019) (1.088) (1.089) (1.049)
Region Fixed Effects? N Y N N Y N State Fixed Effects? N N Y N N Y Observations 130111 130023 130023 130111 130023 130023 R‐squared 0.020 0.025 0.048 0.017 0.019 0.025 Robust standard errors clustered by 1960 counties in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Observations are by 16km spaced grid points. Errors are clustered by county.
45
Table 5: Effect of Electricity on GDP per Capita
OLS Instrumental Variables
Lagged Electricity 0.667*** 0.451*** 0.165 2.084*** 0.455 ‐0.601 (0.170) (0.122) (0.105) (0.443) (1.450) (1.482)
Land Slope 0.043** 0.003 ‐0.028** ‐0.033 0.003 ‐0.025*
(0.017) (0.019) (0.014) (0.024) (0.021) (0.015) Region Fixed Effects? N Y N N Y N State Fixed Effects? N N Y N N Y Observations 130111 130023 130023 130111 130023 130023 R‐squared 0.043 0.095 0.118 0.027 0.095 0.116 *** p<0.01, ** p<0.05, * p<0.1 Robust standard errors clustered by 1960 counties in parentheses Notes: Observations are by 16km spaced grid points. Errors are clustered by county.
46
Table 6: Effect of Electricity on Industrial GDP per Capita
OLS Instrumental Variables
Lagged Electricity 0.318** 0.383*** 0.253*** 0.463 0.158 0.287 (0.138) (0.051) (0.052) (0.302) (0.544) (0.567)
Land Slope 0.016 0.016 0.000 0.008 0.018 ‐0.000 (0.011) (0.014) (0.011) (0.015) (0.015) (0.011)
Region Fixed Effects? N Y N N Y N State Fixed Effects? N N Y N N Y Observations 130111 130023 130023 130111 130023 130023 R‐squared 0.025 0.041 0.071 0.025 0.040 0.071 *** p<0.01, ** p<0.05, * p<0.1 Robust standard errors clustered by 1960 counties in parentheses
Notes: Observations are by 16km spaced grid points. Errors are clustered by county.
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Table 7: Random Effects Instrumental Variable Regressions with Bootstrapped Errors
GDP per Capita Industrial GDP per
Capita Population Density
Lagged Electricity Provision 2.096*** 0.790** 0.458*** 0.229 30.088*** 3.865 (0.124) (0.313) (0.054) (0.209) (4.428) (6.287)
Land Slope ‐0.034** 0.001 0.008 0.017** 2.567** 0.920 (0.014) (0.012) (0.007) (0.007) (1.085) (1.101)
Region Fixed Effects? N Y N Y N Y Observations 130111 130023 130111 130023 130111 130023 Number of fid 32528 32506 32528 32506 32528 32506 R‐squared . . . . . . *** p<0.01, ** p<0.05, * p<0.1 Standard errors in parentheses Notes: Observations are by 16 km spaced grid points by decade. Errors are bootstrapped with 50 repetitions. Lagged predicted electricity has been used as the instrument.
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Table 8: Fixed Effects Regressions OLS Panel Regressions Instrumental Variable Regressions
GDP per Capita
Industrial GDP pc
Population Density
GDP per Capita
Industrial GDP pc
Population Density
Lagged Electricity ‐0.056 0.051 4.856*** 2.453*** 0.382 ‐3.572 (0.129) (0.074) (1.396) (0.917) (0.473) (11.348)
Fixed Effects? Y Y Y Y Y Y Observations 130108 130108 130108 130820 130820 130820 Number of fid 32527 32527 32527 32705 32705 32705 R‐squared 0.063 0.040 0.008 0.044 0.038 0.006 Robust standard errors clustered by 1960 counties in parentheses *** p<0.01, ** p<0.05, * p<0.1
Notes: Observations are by 16 km spaced grid points across Brazil by decade. Errors are clustered by county as of 1960. Where a grid point is overlapped by more than one county circle, the assignment choice of clusters between adjacent counties is random.
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Table 9: Fixed Effects Regressions for 1980s‐2000
GDP pc Industrial GDP pc
Population Density
Lagged Electricity Access Indicator
8.312*** 4.276*** 10.487 (2.622) (1.653) (26.090)
Observations 98115 98115 98115 R‐squared ‐0.090 ‐0.106 0.005 Number of fid 32705 32705 32705 *** p<0.01, ** p<0.05, * p<0.1 Robust standard errors clustered by 1960 counties in parentheses