UfL-zi
'Jnt
^
iiiiiiiiiiiiii
DEWEY
iiiiiiilFrTT^iiiiiiiiiiiiiiff^^
3 9080 03316 3350
Massachusetts Institute of Technology
Department of Economics
Working Paper Series
PRODUCTIVITY DIFFERENCES
BETWEEN AND WITHIN COUNTRIES
Daron Acemoglu
Melissa Dell
Working Paper 09-20
July 6, 2009
Room
E52-251
50 Memorial Drive
Cambridge,
MA 021 42
This paper can be downloaded without charge from the
Social Science
Research Network Paper Collection
http://ssrn.com/abstract = 143 1581
at
J
Productivity Differences Between and Within
Countries*
Daron Acemoglu
Department
of Economics, Massachusetts Institute of
''^^-
Department
Technology
Melissa Dell
of Economics, Massachusetts Institute of Teclinology
July, 2009.
Abstract
We
large
document substantial within-country (cross-municipality) differences in incomes for a
number of countries in the Americas. A significant fraction of the within-country differences
cannot be explained by observed human capital.
We
conjecture that the sources of within-
country and between-country differences are related. As a
we propose a simple model incorporating both
first
step towards a unified framework,
differences in technological
know-how across
countries and differences in productive efficiency within countries.
Keywords: economic development, economic growth,
institutions, Latin
America, productivity
differences, regional inequality, within-country differences.
JEL
Classification:
018, O40, Rll
"Acemoglu: Department of Economics, Mas,sachusett,.s In.stitute of Technology, 50 Memorial Drive, Cambridge
02139. e-mail: daron&mit.edu. Dell: Department of Economics, Massachusetts Institute of Technology, 50
Memorial Drive, Cambridge MA 02139. e-mail: mdoll@mit.cdu. We thank conference participants at the 2009
American Economic Association meetings, an anonymous referee, and Steven Davis for useful suggestions. We
also gratefully acknowledge fina.ncial support from the Air Force Office of Scientific Reseaich and the National
Science Foundation. A number of individuals provided invaluable assistance in locating and accessing the many
data sources used in this paper. We thank in particular Luis Bertola, Rafael Moreira Claro, Sebastian Galiani,
Brett Huneycutt. Ana Maria Ibafiez, Oscar Landerretche, ,lo.se Antonio Mejia-Ouerra, Ezequiel Molina, Glenn
Graham Hyrnan, Daniel Ortega, Carmen Pages, Pablo Querubin, Marcos Robles, Oscar Roba Stuart, Carmen
Taveras, Jose Te.sada. .Jimmy Va.sq\iez, and Ga.ston ^'alonetzky.
MA
Digitized by the Internet Archive
in
2011 with funding from
Boston Library Consortium
Member
Libraries
http://www.archive.org/details/productivitydiffOOacem
much
Spatial differences in income per capita motivate
economics.
tries
have been documented extensively, there
magnitudes
important
growth theory and development
systematic evidence on
is little
income differences (within countries) compare and
relative
of
two related reasons.
for at least
income and productivity are national,
in
of
local,
and idiosyncratic
growth and development
Second, they signal the possible presence of important interlinkages
should strive to match.
local
The
they shed light on the extent to which sources
First,
nature, documenting patterns that comprehensive explanations
between
interregional
and within-municipahty differences are
major economic differences
in
how
relate to inequality across countries.
of cross-country, cross-municipality,
of
oretical
some coun-
While income differences across countries and across regions within
and national determinants of productivity, which would necessitate a unified the-
framework
for analysis.
This paper documents the magnitudes of cross-country, cross-municipality, and within-
municipahty inequality
in labor
incomes and household expenditure
for the
Americas (Canada,
Latin America, and the United States), a large geographic region containing almost one billion
people and around
30%
of global
GDP. Our
contribution
is
twofold.
First,
stantial within-country (and cross-municipality) differences in output
for a large
number
of countries. For example,
we have municipality
level data, the
among
we document sub-
and standards of living
eleven Latin American countries for which
between-municipality differences in individual labor income
are about twice the size of between-country differences (when the United States
ratio
is
About
reversed).
explained by observed
half of both between-country
human
capital, the
in physical capital across regions are
is
included, this
and between- municipahty differences are
remainder being due to "residual"
factors. Disparities
unhkely to be the primary factor explaining these
ences, because of the relatively free mobility of capital within national boundaries.
differ-
Therefore,
similar to the residual in cross-country exercises, these regional residual differences can
least partially ascribed to differences in the efficiency of
The dominant
be
at
production across sub-national units.
empirical approach for understanding differences in income per capita starts
with the neoclassical (Solow) growth model. The neoclassical framework explains growth and
output
levels
by human
in the neoclassical
on the dynamics
capital, physical capital
and technology. Since technology
is
exogenous
model, the emphasis in empirical studies starting with this model
of the capital stock.
Our view
model
inside national boundaries, the neoclassical
across regions within countries.
is
that, given the
is
often
mobihty of physical capital
offers limited insight into efficiency differences
Thus, the second contribution of our paper
is
to take a first
step towards developing a unified theoretical framework for the analysis of cross-country and
within-country differences.
Our framework emphasizes
the importance of local differences in
the efficiency of production, likely shaped by institutions (defined as the rules determining
how
is
collective decisions are
made).
More
specifically,
within countries, productive efficiency
determined, amongst other things, by local institutions.
local
and regional
collective decisions are
the national government, and
•
Examples of national
how
made, how lower
political
power
is
institutions include the structures
Local institutions influence
levels of
how
government interact with
distributed at the local level. ^
Through
imposed by the national constitution and
laws.
these channels, local institutions impact important determinants of the efficiency of production,
such as the provision of local public goods and the security of local property rights.
country
productive efficiency
level,
is
determined by the average of local institutions, by national
and by the technology adoption and use decisions
institutions,
At the
of profit-maximizing firms.
A
country where local institutions in several regions create inefficiencies will exhibit not only
within-country differences, but also lower national income. Aggregate output
due
to the presence of these low
income regions, and
new
regions will lead to a smaller market size for
indirectly,
is
lowered directly,
because low demand from poorer
technologies, discouraging technology adoption
at the national level.
sometimes
It is
(explicitly or implicitly)
may be important
assumed that even though
understanding cross-country differences in economic outcomes, they do
for
not play a major role in explaining interregional differences
view
is
institutional differences
(e.g.,
Guido Tabellini
(2006)). This
predicated on the notion that institutions are national and cannot explain within-country
differences.
However, both de jure and de facto institutions vary greatly within countries. In
countries with federal systems, such as Mexico and Brazil, states have considerable authority
changing laws and de jure institutions, and de facto institutions
of national laws, the extent to
of de facto control
by
which
local elites,
local
—
the degree of enforcement
e.g.,
and regional elections are
and the functioning of the judiciary
free
urban and rural areas
fair,
may have
promoting industrial development
the degree
differential
will likely affect
differently).
As preliminary evidence on the importance
ment
and
—often vary substantially
within national boundaries.^ Moreover, national institutions and policies
effects in different regions (e.g., a tariff policy
in
large disparities in access to
countries in the Americas.
We
paved roads
show that
of local public goods
— a specific
and
institutions,
we docu-
and important public good
— within
differences in access to paved roads are highly cor-
related with individual incomes (after controlling for various geographic and other observable
factors).
we
Finally,
also discuss several existing empirical studies that connect public
and economic prosperity
The remainder
goods
to specific local institutions.
of the paper
is
organized as follows. In the next section, we discuss existing
approaches to cross-country and cross-regional income differences. Section 2 describes the micro
data sets we use
for investigating
within-country differences. Section 3 provides various decom-
positions of cross-country and within-country differences in the Americas. Section 4 summarizes
evidence on the extent of within-country differences in institutional quality and availability of
public goods.
Section 5 introduces our theoretical framework, and Section 6 concludes.
The
online appendix contains additional details on data sources and further results.
E.xamples of local institution.s include the degree to which regional and local
facto control of
stiite
some
constitutions that
among
election.s are free
and
fair,
the de
regions by local economic and political elites and organized criminals, and in federal systems,
may
give regional !awmaker.s substantial powers to determine local laws and pohcies.
Edward L
Gib.son (2005) and Guillermo O'Donnell (1993). See also Daron Acemoglu and
a model of de jure and de facto institutional differences, with a discussion of the
importance of de facto institutional differences in the development of the US South.
"See.
others,
James A. Robinson (2008)
for
Approaches to Cross-Country and Cross-Region Differences
1
The dominant
empirical approach for examining differences in income and growth rates between
and between regions within countries, begins with the neoclassical (Solow) growth
countries,
model. As
known, the neoclassical model has no theory of technology differences and a
well
is
minimal theory of differences
capital.
At the cross-country
human
in
level,
capital.
N. Gregory
Much
of
its
focus
is
on the dynamics
of physical
Mankiw, David Romer and David N. Weil (1992)
have argued in a seminal contribution that the neoclassical growth model provides a good account
for cross-country differences in
income per capita without
significant technology differences. In
another prominent series of contributions, Robert Barro and Xavier Sala-i-Martin (1995) suggest
that convergence across
OECD
countries, convergence across
US
regions,
and cross-country
growth dynamics can be understood through the closed-economy neoclassical growth model.
While
by,
influential, this
amongst
and Charles
emphasis on physical and human capital has been called into question
others, Peter J.
Jones (1999).
I.
Klenow and Andres Rodriguez-Clare (1997) and Robert
E. Hall
These authors document that, with reasonable assumptions on
aggregate production functions, large technology differences are necessary to account for the
significant cross-country differences in
for
growth dynamics. Moreover,
it is
income per capita and output per worker, and to account
difficult to see
model could provide an informative framework
how
for
the closed- economy neoclassical growth
understanding within-country differences,
given the absence of barriers to physical capital mobility within countries.
After documenting the large within-country differences in the Americas, we develop a theoretical
framework emphasizing (broadly construed) technology
and cross-municipality
income per capita
1.
in
levels.
Our approach emphasizes
differences at
both the cross-country
the following potential determinants of
national and local economies:
Technological know-how will potentially vary at the national
level,
thus influencing cross-
national income differences.
2.
Efficiency of production will vary both at the national
emphasize variation due to institutions
and freeness and
outcomes
(i.e.,
(i.e.,
enforcement of property
fairness of elections for varying levels of
We
entry barriers,
the availability of public goods necessary for production and market transactions).
Our framework
Human
rights,
levels.
government) and the implied policy
is
sufficiently flexible that
it
can also be used to think about non-institutional
determinants of local productivity, some of which are discussed
3.
and the subnational
capital of the workforce will differ
in Section 4.
both across countries and within countries,
in
part because of differences in institutions and policies that affect access to schooling and the
costs
and benefits
of acquiring a marginal unit of education.
In this theoretical framework, national factors, in particular, national institutions and their
impact on technology adoption, influence
local
not only local outcomes but also the overall
they are adopted at the national
outcomes, while,
demand
for
new
in turn, local institutions affect
technologies and the rate at which
level.
This framework motivates (and
is
motivated by) our empirics in Section
micro data on individual earnings from 17 countries
in the
Americas.
3.
We
start
with
Micro data enable us
decompose labor income inequality
to
and within-country components
into between-country
and provide us with a simple methodology
for separating the effect of
human
capital
from
other factors by controlling for individual-level education and experience. This exercise enables
a preliminary decomposition of municipality-level economic differences between those due to
education (proxying for factors embedded
Our work
is
in
related to a large literature
variation in incomes within a single country.
workers) and those related to the locality
on spatial
inequality,
most
of
which
is
itself.
focused on
Notably, for the United States, Antonio Ciccone
and Hall (1996) estimate that doubling county employment density (county labor input divided
by county landmass area) increases average labor productivity by
6 percent.
This estimated
degree of locally increasing returns would account for more than half of the variation in labor
productivity across
bound estimate
US
states. In light of this evidence, Section 4 provides a preliminary
of the role of density in explaining spatial inequality in the Americas.
set of relevant contributions are collected in
in particular includes
two studies
for
Ravi Kanbur and Anthony
Latin America: Chris Elbers
J.
Another
Venables (2005), which
al
et.
upper
combine non-income
census data and household survey data on income for Ecuador to produce a measure of wellbeing, finding that across-census tract inequality in well-being explains around
in well-being within
Ecuador, and Javier Escobal and
Maximo
data to investigate the determinants of spatial inequality
for variation in private
and public
assets,
25%
of inequality
Torero use household expenditure
in Peru,
estimating a predominant role
such as roads.
Several studies examine within-country inequality
among
various countries.
Most notably,
Leondardo Gasparini (2004) and Juan Luis Londoho and Miguel Szekely (2000) document the
extent of income inequality over time within a large
provide cross-country comparisons.'^
number
of Latin
American countries and
Several features distinguish our paper from these previ-
ous studies on Latin American inequality. Through our use of population censuses and living
standards measurement surveys, we have access to larger samples and higher-quality labor
come estimates than the
in-
existing literature, which tends to use lower quality sources in order to
produce an income panel. Thus, we are able to provide a more systematic and accurate snapshot of inequality patterns.
We
expenditure data, which are
also confirm our findings using carefully constructed
more
reliable
than labor income data
sample because a high fraction of the population works
in the
household
for several countries in
our
we
are
informal sector. Finally,
not aware of other studies conducting comparative decompositions of within municipality and
cross-municipality inequality into
2
We
human
capital-related
and residual components.
^
Data
use data on labor income, geo-referenced to the municipality, for 11 countries in the
icas (see
Appendix Table Al
for the list of countries).
We
also
geo-referenced at the regional level for an additional six countries.
There
is
also a literature
on inequality
acro.ss versus
Amer-
examine labor income data
Our data
within countries at the global
level.
are
drawn from a
While interesting and
important, due to severe data limitations this literature makes a large number of assumptions when constructing
within-country income."? from highly aggregative data.
number
A
is
list
of recent censuses
of sources
and
measurement surveys,
living standards
We limit our
provided in Appendix Table Al.
is
Our sample
reported than total income.^
typicall)' better
all
conducted since 2000.
attention to labor income,
includes
all
which
individuals with positive
incomes, and for some calculations we limit the sample to males between the ages of 18 and 55 to
reduce selection based on labor force participation. To increase comparability across countries,
we adjust each country's income data
so that
national dollars, taken from the 2003
Penn World
it
GDP
averages to
Tables.
per worker in constant inter-
Population weights are constructed
using 2000 GIS population data (Center for International Earth Science Information Network,
Summary
2004).
Our
statistics are
provided
in
Appendix Table A2.
baseline results do not deflate incomes for differences in regional purchasing power.
Differences in costs of living are important for comparisons of living standards across regions, but
given our focus on productivity differences (rather than welfare), nationally-deflated incomes are
more informative.
We
nonetheless confirm the robustness of our general conclusions to deflating
incomes using the state median of a household-specific Paasche index constructed from a number
of household expenditure surveys.
To examine variation
we use
in local public goods,
by the International Center
geospatial data on intercity roads compiled
Agriculture (CIAT, 2008a), supplemented with
for Tropical
more
recent (2006) data on road infrastructure from the Earth Science Research Institute (Mexico)
and the Peruvian Ministry of Transport (Peru). These data
roads as well as their surface type (paved, gravel, or
3
identify the geographic location of
dirt).
Within-Country and Between-Country Differences
In this section,
we perform two
three components:
exercises.
we decompose inequality
First,
by observable
As
is
well
at each level of
human
class:
the
and the
set of additively
Mean Log
overall inequality in the
residual.
decomposable inequality indices corresponds to the
We focus on two commonly used
Deviation
Americas
(MLD)
MLD =
index and the Theil index.
income
in the
the labor income of individual
Americas, and
L
is
The
MLD
index of
Mj
Ljrn
lny- -Y,Y.J2^nyjrm
J
y-,mi is
measures within the General
is:
J
where
Second, we decompose labor
geographic aggregation into two components: that explained
capital variables
known, the
General Entropy class of measures.
Entropy
income into
inequality between countries, inequality between municipalities or regions
(within countries), and inequality within municipalities/regions.
income inequality
in labor
i
=l
in
771=1
(1)
7=1
municipahty
m
in
country
j,
j/
is
mean
labor
total population in the Americas.^ Similarly, the Theil index
"For Latin America, the data provide information on monthly labor income, .so for these countries there may
be greater tran.sitory variability than annual labor income numbers for the United States and Canada.
^Since our focus is on labor income inequality, we would have preferred to weight the data by the size of the
of overall inequality in the
Americas
is:
j=l
Let us further define
j/jm as
mean
number
of individuals in municipality
and
as the
Z/j
decomposed
number
into our three desired
m
MLDjm =
in country
j.
^^yjm
The
^
i=l
—
first
^1=^1
term
f
\
/
labor income in municipality va in country
m
country
in
of individuals in country
MLD= Wy-Y.^\r.y\
where
m=l
components
+ ;^
^H
^^'^Vj-mi/
Ljm
j,
as
t/j
Then
j.
mean
the
MLD
as the
j,
and Theil indices can be
of inequality as follows (see the Appendix):
-
Iny,
is
the
f^
^
MLD
+ Y.
lny,„
j
Y^^^^A
index for inequality
measures between-country inequality.
in (3)
j, L_,>„
labor income in country
in
^^^
municipality
The second and
third terms are between-municipality (within-country) and within-municipality inequality indices, respectively,
decomposed
as
L
fr^
where T,m
country
j.
weighted by country j's population share. Similarly, the Theil index can be
=
y \
73,^7
y
^''T'
r
J
In
L
j^^
(
t'^^^
)
y
\r^
J-iJTn
^,
Lj
yjm rp
ST^ J^jm yjm
''"
yj
^1
,
/
yjm
[
y,
(4)
Lj
y,
the Theil index for inequality in municipalitv
is
These expressions show that the
MLD
m
in
index weights by population shares, whereas
the Theil index weights by income shares.
We begin in Table
income
1
by examining the
distribution, as well as the
decompose
between the 90th and 10th percentiles of the labor
and Theil indices, for
its
three
all
individuals in our sample.
component
We
parts: inequality across
between municipalities/regions (within countries), and inequality within
When decomposing
municipalities/regions.
population weighting schemes.
equal population in
ratio
Americas into
overall inequality in the
countries, inequality
Brazil,
MLD
all
The
countries,
first
Western Hemisphere
inequality,
we consider two
uses actual population, whereas the second assumes
and thus reduces the influence
Colombia, Mexico, and the United States.
of large countries such as
This latter scheme
is
similar in spirit to
the convention in the growth literature where different countries are given equal weight.
comparison purposes, we decompose overall inequality separately
For
for the eleven countries geo-
referenced to municipalities and for the six geo-feferenced to regions.^ At the bottom of the table,
we decompose
inequality for
all
countries included in our sample.
We
also report cross-country
labor force rather than the size of the overall population. Unfortunately, data on labor force participation are not
much
Americas at tiie municipality level.
Guatemala, Honduras, Mexico, Panama, Paraguay, Peru, United States, and
Venezuela have data geo-referenced to the municipality. For Canada, Chile, Colombia, Costa Rica, Ecuador, and
Uruguay, the data are geo-referenced to larger regions.
readily available for
of the
''Bolivia, Brazil, El Salvador,
inequality of 2000
GDP
per worker and 2000
GDP
per capita from the Penn World Tables for
countries in the Americas for which data are available.
all
33
for
GDP
per worker and 37
for
GDP
which we do not have labor income data. The
pattern
our sample
in
is
This increases the sample size to
per capita, primarily by adding Caribbean nations for
GDP
data show that the cross-country inequality
similar to that for the entire Americas, confirming that cross-country
inequality in the subset of countries
we examine
similar to that in the
is
Appendix Table A3 provides additional documentation
Americas as a whole.
of inequality patterns
and shows the
decomposition of between-municipality and within-municipality inequality separately for each
country.
Table
is
1
documents that
for
our entire sample, inequality in labor income across countries
about one half to one third of the magnitude of inequality within municipalities/regions and
between two to four times
as large as inequality across municipalities/regions,
precise sample and weighting scheme.
MLD
For example, using the
depending on the
index, equal population
weights and focusing on countries with municipality data, overall between-country inequality
is
0.23, while within-country inequality
The
inequality.
0.66, 0,11 of
is
which
is
due
contribution of the between-municipality component
to between-municipality
is
index, which gives greater weight to the top of the distribution, that
(recall that the Theil
The
larger
magnitude of the between-country
much
to the United States
uses population shares).
relative to the between-municipality inequality
driven by the presence of the United States (and
geo-referenced to regions), which are
is,
MLD
index weights by income shares whereas
smaller with the Theil
Canada when we include
is
countries with data
richer than the remaining countries in our sample.
For this reason, decompositions without these two countries might be more informative. These
are reported in the third and eighth rows. For example, the third
row shows
that, again focusing
on the data with municipality referencing, between-country differences are now about half of
the between-municipality differences both with the
MLD
and 0.05
vs. 0.11
MLD
and Theil indices
A4 shows
with Theil). Appendix Table
(0.05 vs. 0.12
a similar pattern
when
with
the data
are deflated using regional price indices.
One important concern with
the above inequality decompositions
and transitory income shocks may be
To
investigate this concern,
we
inflating the
is
that measurement error
magnitude of within-municipality inequality.
followed the methodology proposed by
Angus Deaton and Salman
Zaidi (2002) to carefully construct, item- by-item, a comparable measure of household expendi-
ture for a
number
of countries in the Americas. Inequality decompositions for household expen-
diture, similar to those reported for labor
income
in
Table
1,
are presented in
Appendix Table
A5. As expected given concerns about measurement error, the magnitude of within-municipality
inequality in household expenditure
all
is
less
than that
in
labor income.
comparative patterns are similar, as within country inequality
in
Nevertheless, the over-
household expenditure
is
substantially greater than inequality between countries.'^
'Results (available upon request) documenting educational inequality for 19 countrie.s in the Americas, 11 of
which have micro level census data, show that inequality in education attainment across municipalities in Latin
America is about twice as large as inequality in educational attainment across countries, and this pattern is
reversed
when the
U.S.
is
included.
Table 2 limits our sample to males between the ages of 18 and 55 to compare incomes across
a
more homogenous population
(particularly in terms of hours worked). Using this subsample,
we perform the decomposition between predicted and
labor income of individual
i
municipality
in
m
in
residual incomes.
country
Recall that yjmi
is
Let Xjmt denote a vector of
j.
detailed education categories (in particular, zero to four years of schooling, four to eight years of
schoohng, some high school, high school graduate, and one or more years of higher education).
Let experjmi denote potential experience (defined as usual as age-schooling completed-6).
We
then decompose labor income into predicted and residual components by running the following
flexible regression,
which allows
for a full set of interactions
quartic in potential experience, separately for each country
between education categories and a
j:
4
In yjmi
where
5-j
is
a country-specific constant and ejmi has zero
Given estimates from
in
= 22 ^jmt(ea;perjrat)''/?jfc +
(5),
we examine inequality
Sj
+
ejmi,
mean and
(5)
country-specific variance.
in overall labor
income
{Vjmi), inequality
predicted labor income (exp(2^,_j Xj„jj(ea;perjmi)''/5jfc))i and inequality in residual income
Notice that country-specific constants, which are unrelated to difTerences in
(exp(Jj +ejmi))-
human
capital, are part of residual income.
Table 2 uses the Theil index to decompose each of the components of income (overall, predicted,
and residual) into inequality between countries, inequality between municipalities/regions
(of countries),
and inequality within municipalities, reporting
results
by country only
for
the six
countries with micro census data (and hence large within-municipality samples).^ Inequality in
overall labor
income
to prime-aged males.
differences,
is
where we do not
restrict the
sample
sizable between-country predicted labor
income
similar to that reported in Table
The decomposition shows
1,
which become much smaller when the United States
between-country residual inequality
in the sample).^
The magnitudes
of
is
is
excluded.
somewhat smaller (when comparing
between municipality inequalities
The magnitude
across
in predicted
all
of
countries
and residual
labor income are similar. Table 2 also shows that the bulk of the within-municipality differences
are due to residual factors.
This
may
reflect
a greater dispersion
market imperfections and discrimination, measurement
error, or
in
unobserved
some combination
skills,
labor
thereof.
Overall, our evidence suggests that years of schooling and the experience of the labor force
can explain a significant fraction of income disparities across and within countries, but residual
factors are also significant
and generally
of
comparable magnitude.
Although these residuals
undoubtedly include a component of unmeasured human capital differences within countries,
they also likely reflect the effects of local factors impacting productive efficiency.
''Results using the
'
MLD
index are similar and available >ipon request.
Notice that the decomjjo.sition between predicted and residual incomes
i.s
not additive, since we are taking
exponential transformations of predicted and residual log incomes before the decomposition.
4
Determinants of Interregional Differences
The
large differences in labor incomes
and residual incomes documented
in the previous section
are unlikely to be entirely due to differences in the physical capital intensity of production,
given the absence of barriers to capital mobility within countries. Instead, they likely reflect the
We
influence of certain local factors, the nature of which will be discussed further in this section.
focus on determinants of cross-municipality differences.
While within-municipality differences
are also clearly ineresting and important, space constraints preclude their treatment here.
One
possibly relevant local characteristics
as emphasized, for example,
Table
A6
is
the density of economic activity across regions,
by Ciccone and Hall (1996).'" To examine
this issue,
Appendix
repeats the decomposition of predicted and residual inequality, adding a control for
municipal-level population density to equation (5).'^
The
patterns
when population density
included in the predicted component of income are very similar to those in Table
is
exercise therefore suggests that the bulk of the between-municipality differences in
the Americas
is
not accounted
density of employment, which
Our argument
is
by differences
for
is
that, in the
likely to
in
We now
why
We
in
be strongly correlated with population density). '^
same way that technology
investigate
technology, broadly construed.
This
population density (or more generally, in the
shaping cross-national economic differences, they also
differentials.
2.
income
there
would
may be
ideally
differences play an important role in
likely
play a major role in within-country
significant within-country differences in
document the correlation between various
aspects of local institutions and incomes, but unfortunately there do not yet exist uniformly
constructed, municipality-level measures of institutions in the Americas.
We
instead use a
novel dataset on road infrastructure to measure the within-country inequality in proximity to
paved roads, an important form of public infrastructure, and the correlation between proximity
to roads
and labor income.
Local institutions affect investments
other public goods by influencing the incentives of government
(related to corruption
and accountability), the capacity of the
to finance public investment (from Ideal taxes or transfers
the incentives and opportunities of citizens to effectively
in
road infrastructure and
officials to
local
provide public goods
government
to raise revenues
from the central government), and
demand
public goods from local and
national politicians.
'"Other factors thnt liavc been cniphasized
for explaining cross-<:oinil,i'y differences include geogiajjliy
and
Regarding natural resources, fVancesco Caselli and Guy Michaels (2008) find that the in Brazil, discovery
of oil in a municipality increases municipal public exjienditures but has little impact on measured citizen wellbeing. Melissa Dell, Benjamin F. .lones. and Benjamin A. Oiken (2009) document a .statistically significant
cross-sectional correlation between climate and income within a number of countries examined in this study using
the same economic dataset. The quantitative magnitudes they report, thotigh not trivial, suggest that climate
cannot explain the full spatial variation in the data. This leaves significant scojje for the institutional factors that
culture.
we examine.
The
effects of culture (belief .systems)
have also been emphasized recently (e.g.Tabellini, 2006).
is not available at tlie nnmicipal
"ideally we would control for employment density, but this information
for
much
'^Results (available upon request) are also similar
areas.
level
of the Americas.
when we estimate
inequality .separately for urban and rural
Table 3 documents substantial differences across municipalities
in
proximity to paved roads. ^"^
km
We
calculate each municipality's proximity to intercity paved roads by overlaying a
km
grid on the Americas, calculating the distance (allowing for changes in elevation)
each grid
cell's
numbers by country only
In Table
in
Appendix Table A7 and
the overall inequality decompositions in the final three rows of Table 3
Latin American countries and the United
of
all
we conserve space by reporting the
our sample are given
for all countries in
1
with census income data geo-referenced to the
for the five countries
The numbers
3,
x
from
centroid to the closest paved road, and then averaging this distance over
grid cells contained within the municipality.''^
municipality.
1
States.
The
is
for this full
and second columns
first
of
sample
Table 3
report the
mean and standard
for Brazil,
Mexico, Panama, the United States, Venezuela, and the Americas overall; column
deviation of municipal-level proximity to paved road networks
presents the 90-50 percentile ratio; and columns (4) through (7) decompose inequality in
(3)
proximity to paved roads into inequality across countries and inequality across municipalities
within countries.'^
Not
surprisingly, the United States
is
on average the most proximate to paved roads, and
of the five countries reported in Table
3,
road networks across municipalities
Latin America
in
Brazil
is
the least.
is
Inequality in proximity to paved
about 2,5 times as large as inequality
across countries and remains higher than inequality across countries even
States
roads
is
is
Nevertheless, these patterns
included.
may
reflect
both easier and more useful on the coasts than
unrelated to productivity and income differences.
this issue in
columns
and
(8)
(9),
in the
building
Amazon), and may thus be
undertake a preliminary investigation of
for
males between the ages of 18 and 55 in the
with census income data geo-referenced to the municipality.'^ Column
state fixed effects.
the United
(e.g.,
by computing the partial correlations between proximity to
paved roads and the log of individual incomes,
five countries
We
when
geographic factors
States (of which there are 27 in Brazil, 32 in Mexico, 9 in
(8)
includes
Panama, and 23
Venezuela) are geographically small, and thus including state fixed effects ensures that we
in
are
comparing municipalities
characteristics that
may
in close
affect the density of
level controls for elevation, slope,
and 2000
(see the
data appendix
in
column
(9)).
road networks, column
for sources), as well as
is
a
in Brazil,
municipal
full set
of age
dummies. The correlation
negative and highly significant for
all
countries (except
Translating these correlations into elasticities suggests that increasing
a municipality's average distance from paved roads by
males by 0.06%
(9) also includes
and mean annual temperature and precipitation between 1950
between distance to roads and incomes
Venezuela
geographic proximity. To further control for geographic
by 0.09%
in
1%
reduces labor income of prime-aged
Mexico, and by 0.14%
in
Panama.
Naturally, these elasticities do not reflect the causal effect of proximity to roads on income.
upon reqiie.st) are similar when we con.sider both pa.ved and gra,vel roads,
between individual income.^ and road.s are .similar when we instead calculate road network
density, the municipality's total kilometers of paved roads divided by the surfa.ce area of the municipality,
'''Note that in contra-st to the previous tables, there is no within nnmicipality variation in road networks since
proximity is measured at the municipal level,
"'We limit the sample to prime-aged males to decrease selection on labor force participation. Results (available
upon request) are similar when all individuals with positive incomes are included in the sample.
''^R.esults (available
'""The correlations
•
10
proximity to roads
First,
thus interpret
it
as a
is
proxy
likely correlated
for a
with the availability of other public goods, and we
bundle of public goods. Second, there are several other reasons,
unrelated to the availability of public goods and local institutions,
be correlated with incomes (even after
impact of roads
(or local public
paper.
we summarize
Instead,
we
why proximity
may
to roads
control for observable factors). Estimating the causal
goods more generally) on incomes
is
beyond the scope of
this
several recent studies that provide detailed empirical evidence
relating institutions to local public goods
and economic prosperity within particular countries.
Melissa Dell (2008) utilizes a regression discontinuity approach to examine the long-run impacts of the mita, an extensive forced mining labor system
in effect in
Peru and Bolivia during
the colonial era. She estimates that a mtta effect lowers household consumption today by around
one third
in
subjected districts. Mtta districts historically had fewer large landowners and lower
educational attainment, today they are less integrated into road networks, and their residents,
who
face difficulties in transporting crops to markets
stantially
Lakshmi
effects
more
likely to
be subsistence farmers. Outside of Latin America, Abhijit Banerjee and
Iyer (2005) similarly
on investments
a robust association
due to poor road infrastructure, are sub-
in health
between
show that
colonial land revenue systems in India have long-run
and education infrastructure, Daron Acemoglu
political inequality in the 19th century in
et al.
(2008) find
Cundinamarca, Colom-
bia (measured by the lack of turnover of mayors in the municipalities) and economic outcomes
today.
They
local public
also provide evidence, consistent with Dell's (2008) findings, that the availability of
goods might be a particularly important intervening channel. Similar correlations
are obtained for Brazil by Joana Naritomi, Rodrigo B. Soares, and
Our
Juhano Assungao
(2007).^'
findings are also broadly consistent with a large literature on the impact of infrastructure
on economic outcomes. In a
series of studies
on Peru (summarized
in the
Escobal and Torrero
chapter in Kanbur and Venables (2005)), Javier Escobal and co-authors empirically connect poor
local
road infrastructure to higher transaction costs, lower market participation, and reduced
household income.
Other studies of the
effects of local public
goods include, among others,
Esther Duflo and Rohini Pande (2007), which examines the effects of dams in India, and Dave
Donaldson (2008), which analyzes the impact
of railroads in colonial India.
Towards a Framework
5
In this section,
we provide a simple framework
income differences and
their dynamics,
to interpret cross-country
and cross-municipality
and to highlight the two-way interaction between
local
and national outcomes.-'^ The framework builds on endogenous technological change models
in Latin America. Gibexamining "subnational authoritarianism", defined as the
persistence of authoritarian regional governments in a nationally democratic society, and has emphasized large
differences across Argentine provinces and Mexican states in the extent to which elections are fair and competitive, elected local authorities are protected from arbitrary removal by regional authorities, and the judicial system
is accessible and independent. Similarly, O'Donnell (1993) has classified regions of Latin America based on the
functioning of rule of the law, documenting substantial differences within countries.
'*While the within-municipality difTerences are important, they are not our focus here.
^''AIso relevant
son
(200.5)
i.s
the political science literature on subnational in.stitutional variation
has developed a theoretical framework
for
11
(e.g.,
Philippe Aghion and Peter Howitt, 1992, Gene M. Grossman and Elhanan Helpman, 1991,
and Paul M. Romer, 1990) and on the model of international technological
in
Acemoglu
we consider a model that
in Section 4,
we
countries. In addition,
embedded
capital
diffusion presented
(2009, ch. 18). Motivated by the empirical results in Section 3 and the discussion
explicitly distinguishes countries as well as regions within
delineate productivity differentials resulting from technology,
workers, and local differences in public goods and institutions.
in
main
capital differences, which are the
human
Physical
emphasized by the neoclassical growth model, are
factor
omitted from this framework.
We
Population
that
all
=
in
1, 2,
...,
J,
in
and there
is
each country
normalized to
is
1
y,.m
L-j^rn is
[t)
=
in
economy
^^^
1-/3
j at time
a
number
is
institutions
In equation
assume
is
hj^rn is
factors,
a region-specific productivity term.
4,
we
{h,,mL,^mf
(6)
,
the efficiency of labor, determined
which can vary across regions and
While the 7j,mS could incorporate
focus on one interpreation of the 7j,mS: efficiency terms reflecting local
and
policies (e.g., the provision of local public
(6),
the variable 7
This
is
without any
is
raised to the
functional form in (6)
is
=
t.
1-
/3
goods or security of property
rights).
in order to simplify the expressions that
7 has no natural
scale.
Our population
For now, we also ignore migration across regions.
similar to the standard Dixit-Stiglitz aggregator used in en-
dogenous gi'owth models. Similarly, Nj
country j at time
power
loss of generality, since
normalization implies that J2m=i ^j.m
The
We
Mj.
of characteristics affecting regional productivity, in accordance with the empirical
evidence in Section
follow.
t
labor input, which varies across regions;
and 7j,m
1, ...,
/o
by education, public goods and other institutional
countries;
m=
no population growth.
ty-^dv
X,.„(^,
/
country j indexed by
good denoted by Y, and the aggregate
countries and regions produce a single final
m
There are J countries
continuous time.
in
and Mj regions (municipalities)
production function of region
where
economy
consider an infinite-horizon world
indexed by j
(t)
denotes the number of machine varieties available to
This variable captures the technological know-how of country
j.
Technology
diffuses slowly across countries and producers can only use the technologies available in their
country. This implies that Nj
— thus the available technology —varies across countries.
once a country has a particular
Finally, Xj^m [v,
t.
To
t) is
the total
level of technology,
amount
of
it
can be used
machine variety u used
in
in all regions in
region
However,
the country.-'^
m in country j
at time
simplify the analysis, let us suppose that the xs depreciate fully after use.
Each machine variety
machines embodying
within the country.
in
economy
j
is
owned by a technology monopolist, which
this technology at the profit-maximizing (rental) price Py
We
transport costs, thus machine prices will be the same across regions.""
'"Thus there
is
no slow diffusion of technology
specific productivity
(i^,
t)
assume that there are no regional taxes on machines or
embedded
in the 77,™ s
acros.s regions
"°Again any such differences can be incorporated into the 7j,mS.
12
in all regions
differences in
The monopolist can
within a country, though differences
can capture such slow diffusion.
will sell
in region-
produce each unit of the machine at a marginal cost of
any
loss of generality,
We
we normalize
ijj
=1 -
i/'
terms of the
in
final
good, and without
(3.
assume that each country admits a representative household with constant
(CRRA)
aversion
preferences, with the
same discount
substitution) and the
are given by
/q^"
exp {-pt)
same degree
rate across countries. In particular, preferences at time
-
(Cj {t)^~^
l) /
-
{I
9)
dt,
>
with p
and
^
>
Thus
i
=
Although our
0.
empirical investigation so far has emphasized income inequality, our focus here
in productivity across regions.
relative risk
of risk aversion (intertemporal elasticity of
on differences
is
and consumption behavior of
differences in the saving
households within a country are not central to our framework and the representative household
assumption enables us to suppress these. In addition, there
countries produce the
same
final
good) or
in assets (thus
no international trade
is
Cj{t)
where Xj
X,{t)
spending on inputs at time
is
(t)
+
may
which
take the form of
t
R&D
each country j at each time
for
+ QZ,{t)<Yjit),
and Zj
[t] is
t:
(7)
expenditure on technology adoption
The parameter
or other technology expenditures.
at
time
Cj
measures country-level distortions or institutional and policy differences, and
t,
(all
no international borrowing or lending).
This implies that the following resource constraint must hold
-
goods
in
will
be a key
driver of potential technolog}- differences.
Technology
maximizing
where
country j evolves as a result of the technology adoption decisions of profit-
firms. In particular, the innovation possibilities frontier takes the
A'' (t)
common
in
an index of the world technology
is
to all economies.
technological
know-how
of country j advances as a result of the
a country-specific constant,
is
rjj
>
0,
Each economy
starts with
some
initial
any firm can invest
The
Since world growth
is
we
also
rate per unit of
R&D
An
rates
(i))
>
and
and other technologydepends on
N
and there
is
{t)
/Nj
[t)).
free entry into
and adopt new technologies
(8).
0,
that
is,
= gN{t).
(9)
factor markets are competitive.
The
capital in country j are denoted, respectively,
equilibrium consists of sequences of technolog>' levels,
for
(^
not the focus here, suppose that the world technology frontier advances
assume that
and wage rates
>
A^j (0)
(denoted by Zj
grow) at an exogenous rate g >
human
R&D
effectiveness of these investments
technology stock
in
N(t)
Finally,
and
know-how (captured by the term
according to the innovation possibihties frontier
(or frontier varieties
for all j,
and more importantly, on how advanced the world technology
relative to country j's technological
research, so that
>
rjj
This form of the innovation possibilities frontier implies that the
related expenditures of firms in the country.
frontier
frontier,
form
interest rate
by
R&D levels,
r^ (t)
and the wage
and Wj
machine
[t).
prices, interest
each country and machine demands and output levels for each region,
13
such that
final
good firms and technology monopolists maximize
technology adoption, and the representative household
A
utility.
differences,
=
will
I
-
each country maximizes
its
discounted
rate.
comparisons of economies
in
BGP
demand
0,
be p^
{v,t)
markup
curves for their machines, will set a constant
1.
Given
(6), this also
which
are a natural starting point.
and the equilibrium price of every machine
—
in
and cross-region
In thinking about the cross-country
who
straightforward to verify that in any equilibrium, technology monopolists,
iso-elastic
tl>
entry into
free
is
balanced growth path equilibrium (BGP) refers to an equilibrium path
each country grows at a constant
It is
in
profits, there
implies that the
face
over marginal cost
each country at each point in time
in
demand
for
machines
in
each region of
each country that will maximize the profits of the final good producers will be
This implies the intuitive result that there
workers have greater
human
and when
capital
Consequently, a technology monopolist
total
between price and marginal cost
machine
Let us start with the
BGP.
The
in (9).
for business.
fol-
= /3X^„ii Tj.m^j.m-^j.m, where P is
= 1 — /?), while the summation gives
the
i/)
in
country j
will
(u,t)
and
t/'
CRRA
the
(10).
straightforward to verify that, given
It is
grow at the same rate g as given
(1
ttj
when
make the
which follows from
sales of this monopolist,
more favorable
local conditions are
machine variety
(for
lowing level of profits at every point in time:
difference
be more intensive use of technologies
will
countries
(8), all
must
preferences of the representative household
imply the standard Euler equation, which gives the growth rate of consumption of each country
at each point in
in
time as
C {t) /C (i) =
each country grow at the rate
g,
{vj (i)
—
so the interest rates
r*
=
p
+
^m=l
=
Combining this expression with the innovation
j's relative
technology
with r* given by
(11).
jij
=
Nj
[t)
/N
In addition,
it
{t)
in
must
also
thus consumption
be constant and equal to
".
9g.
Consequently, the value of a technology monopolist in
V*
BGP, output and
p) /d. In the
BGP
in
,.••.",;,
country j
is
'yj,mf^j,m-L'j,Tn
,
will
we obtain
{Nj
(0)} _j, there exists a
'The proof of
that country
be
can also be proved that this
BGP
allocation
saddle-path stable, in the sense that starting with any strictly positive vector of
levels
.
,
'
possibilities frontier in (8),
BGP
initial
unique equilibrium path converging to this BGP.^^
this result follows the similar derivation in Aceinoglu (2009, ch. 18).
14
(11)
is
globally
technology
What
does this
BGP
above derivation immediately establishes that the
country j in the
BGP
level of
income per capita
m
in region
in
is
'^'^"'^'0^^''"'^''"'
^^-=r~^(
=
(nrnh,,m)
N {0)
(13)
.
)
Then, summing across regions, total income
^^*
The
and cross-region inequality?
allocation imply for cross-country
r^(c^j
country j in the
in
BGP
is
^'w-
lE-^^^-^^-^^-l
(14)
These expressions give the theoretical counterparts of the regional (municipal) and national labor
incomes we computed and compared
in Section 3
and can be used
to develop
between our theoretical framework and between- and within-country
more structural
They
differences.
that countries that have better possibilities for adopting world technologies (higher
links
highlight
77^3),
where
firms face less severe barriers to adopting technologies (lower Cjs) and have on average better
local institutions (higher 7j,mS)
to
be
and workers with greater human capital (higher
Within a country,
richer.
regions share the
all
regions that have better local institutions (higher
human
7j,Tns)
capital (higher /ij.ms) that will be richer. This
same technology
/zj.ms) will
A'j, so it will
framework
also
(j,
emphasizes the two-waj'
m) and [j\m')
identical characteristics but are situated in different countries will have different
number
new
will
be those
and those that have workers with higher
interaction between national and local factors. First, two regions
because they
tend
that have
income
levels,
have access to different country-level technologies. Second, a country with a
of regions with low 7j,mS
technologies (as
and
/ij,mS will
shown by equation
(10)),
generate a lower
and
this will
demand
for
machines embodying
reduce the profitability of adopting
technologies from the world frontier at the national level and will tend to reduce national income.
This channel also suggests that
if
within-country inequalities are caused by the failure of some
regions to offer good business conditions, public goods and a workforce with the requisite
then this
and
will
tend reduce income
indirectl}'
in
the country both directly (because
(because technology adoption at the national level becomes
The framework presented above does not
question
One may
is
some regions
whether migration would
are poorer)
less profitable).
allow for migration across municipalities.
affect cross-municipality differences in
skills,
A
~-
natural
income and output.
conjecture that differences due to local institutions and policies (the 7s in the model)
would be arbitraged away when migration
as a variety of factors
"The magnitude
make movement
is
possible. This
is
not necessarily the case, however,
across municipalities costly.
of the indirect channel in practice will
depend on the extent
First, in parts of
to which firms
produce
Latin
for
the
Openness, defined as imports plus export as a share of GDP (Heston et al., 2006) ranges
considerably across the countries we consider in our empirical analysis, from around 20% in Argentina, 2.5% in
Brazil and the United States, and 40% in Boliiva. Colombia and Venzuela to around two thirds in Mexico, Chile,
domestic market.
and
El Salvador.
15
Second and more importantly, migration
America, there are explicit barriers to migration."'^
due to the 7s only when there are no difTerences
will arbitrage all differences
living
in the costs of
and housing across municipalities. In practice, both housing costs and the prices of other
goods and services
differ significantly across regions.
Recognizing that migration, even
not lead to an equalization of
could take place without any impediments, would
if it
non-human
capital incomes
(when not deflated
regional differences will have two components; those due to
factors mobile with workers)
to the influences of the hs
and those due to differences
and 7s
in the
We
model.
human
fully) implies
These correspond
in local conditions.
can then
map
that
capital differences (and other
the differences due to the hs to
those related to education and the differences due to the 7s to the residual differences obtained
in Section 3 after
we removed the
influence of education
and experience.
decomposition suggests that the cross-municipality variation accounted
for
In particular, our
by these two sources
are broadly similar.
Concluding Remarks
6
This study used a novel data
set
of labor
municipality) income differences for a large
America, between-municipahty differences
We
documented that about
can be accounted
factors.
We
also
for
incomes to document within-country (cross-
number
in
of countries in the Americas.
half of the between-country
by differences
in
Within Latin
incomes are greater than cross-country differences.
human
capital,
proposed a simple unified framework
and between-municipality differences
the remainder being due to residual
for the analysis of cross-country
and
within-country income differences, which emphasizes the importance of the efficiency of production.
Productive efficiency
is
determined at the country
level
by the technology adoption
decisions of profit-maximizing firms and by national institutions, and within countries by local
institutions, such as the availability of local public
goods and the security of property
Future research could follow a number of promising paths
for identifying the
rights.
underlying
determinants of local productivity differences. The empirical and qualitative evidence suggests
that differences in local public goods
level
— are
—determined
in part
one source of within-country differences.
measurement and empirical investigation
level, as well as
new
theoretical
by
institutions at the local
Such patterns
call for
and regional
more systematic
of specific institutional features at the subnational
work modeling the impact and endogenous determination
of
these local forces.
^^
in regions where a .substantial portion of the land is held b)' indigenous communities, there are
and traditional impediments to selling land to outsiders, making larger cities, which often have significant
Notably,
legal
dis-amenities, the
main
viable destinations for migration.
IG
A. Online Appendices
— Not
for Publication
Al. Decomposing Inequality Measures
In this section,
we
derive the decompositions of the general entropy indices used in the
text of this paper: the
The mean
Mean Log
(MLD)
Deviation
the income of individual
is
m—
income and Lj
is
"
given by:
.
(A-1)
1=1
1
m
in municipality
i
is
— ^ ^Iny^™,
-
=lnj/j
^
where yjmi
index and the Theil index.
log deviation index for total inequality in country j
MLDj
main
country
in
j, y^
is
mean
national
total national population in country j.
Now
adding and subtracting Ylrn=i ~i~' ^''^yjm to both sides (where y^rn is mean income in
municipality
and L-jrn is the number of individuals in municipality m), we obtain:
m
MLD, =
""'
/
Iny,
-
m=\
\
\
L-
J^ -^ In
""'
^
L-
+ J^ -^MLD.m.
j/,,„
m=l
j
-^
where
MLDjm =
is
the
MLD
measure
MLDs
-
InVjm
J
—^
m
index for inequality in municipality
In yjmi
country
in
and the second term
of cross-municipality inequality,
let
us repeat the
versus within countries.
same
exercise for
decomposing the
The Western Hemisphere
MLZ? =
In
2/
-- ^
J
where yjmi
is
the income of individual
Western Hemisphere, and L
is
i
=l
MLD
add and subtract ^^^j
term
is
the
MLD
a weighted average of the
number
of individuals in country
j.
where
y,
is
is
m in country j,
=
Inyj
- y^Int/,,
Lj X^
'
MLD
index for inequality in country
j.
n
m=l
y
is
mean income
in the
Western Hemisphere,
mean income
Again, basic algebra yields:
J
then:
In y^mi,
/here
MLD
index into inequality between
t=l
in municipality
-^lnj/_,,
MLD
index
2^
771=1
total population in the
Now
the
is
first
within each municipality, where the weights are municipality population shares,
Now
is
The
j.
1=1
in
country j and Lj
is
the
Plugging
in
MLDj
from our decomposition of
above
yields:
MLD =hny-J2^\nyA+J2j;^
3=1
where, from above,
municipality
m
in
MLDjrn =
country
— 77— YLi^
In J/jm
The
j.
m=\
J=l
J
'
In yjmi is the
MLD
term gives between country
first
771
/
=1
index
inequality,
-^
for inequality in
and the second
and third terms are between municipality (within country) and within-municipality inequality,
respectively, weighted
A
by country
decompose the Theil index
similar exercise can be used to
where
y^rni, Vj
sides,
and Lj are defined
for
country
j,
given by:
V V|^ln(^^),
-
r,
both
population share.
j's
(A-2^
and subtracting Ylim=\ 'ir'^^ Iny^,
as above. Again, adding
we obtain
M
rp
'
__
ST^ ^jm yjm
' ^,
m=l
Lj
'^
yj'
'
^ ^jm
rp
Lj
\
Vj-m
I
,
VjT
In
yj
V]
where
yjrm
— 7
Tjm
,
_,
is
^jmyjm
the Theil index for inequality in municipahty
Now we decompose
,
m
I
yjr
\ yjm
in
country
j.
the Theil index for overall Western Hemisphere inequality, given by:
yjr
j
where yjmi
is
m=
l
the income of individual
the Western Hemisphere, and
same steps
=
as before,
we can
A'^
is
i
l
1
=
(A-3)
y
"
1
in municipality
the total population
m
in
in
country
j,
y
is
mean income
in
the Western Hemisphere. W'ith the
also write
+E
^-E^^^.
L y
^Ly\y
j=i
j=i
^
^
where
Mj Ljm
^.=y:y.
is
the Theil index for inequality in country
decomposition from above
J
Ljyj
T = S" ~L^
j
=
L
i
y
J
tlLJll
]
y
In yjr
Note that
this
is
equal to (A-2). Plugging in the
yields:
/Inyj
\
j.
yjr
^Y^
j=i
Mo
Lj
yj
tl^l
L
y
E
18
J
J-'jm
,
yjm
,
/
M,
.
yjm
\
,
V^ ^jm
yjm
rp
where again T,m
in
country
=
The
j.
^^,-7
r
In
^'Z'
component
first
^f^^
I
is
)
the Theil index for inequality in municipaUty
is
cross-country inequality, the second component
municipality inequality, and the third component
m
between
is
within-municipality inequality.
is
A2. Data Methodology
We
in
discussed the construction of our nationally (but not regionally) deflated labor income data
the text, so here our main focus
is
on the methodology used to create our household expen-
Appendix Table A5).
diture aggregate (examined in
We
construct our measure of household
expenditure by aggregating expenditures on food and non-food items, durable goods, and housMarriages, births, and funerals, which are lumpy and relatively infrequent expenditures,
ing.
We
are excluded.
very similar
when
include expenditures on health, but patterns of household expenditure are
these are instead excluded.
Gifts
and transfers made by the household are
not included, as these will be counted as they are spent by their recipients.
The method we
data available
in
use to calculate the flow expenditure on durable goods varies according to the
each household survey. Household surveys
for Bolivia,
Ecuador, Guatemala,
Honduras, Nicaragua, Panama, Paraguay, and Peru include the current value and age of durable
goods held by each household. Following the recommendations of Deaton and Zaidi (2002), we
calculate the average age for each durable good
recorded in the survey.
t
using data on the purchase dates of the good
Then, we estimate the average lifetime of each good as
2i,
assumption that purchases are uniformly distributed through time. The remaining
good
then calculated as
is
the flow of services
The
life.
interest
is
—
2t
t,
where
t
is
the current age of the good.
of each
rough estimate of
derived by dividing the current value by the good's expected remaining
component
in the flow of services
is
ignored.
In contrast, the household survey for Brazil includes a
household and
A
under the
life
list
by the
of durable goods held
their ages, but does not contain estimates of their current values.
We
estimate
the purchase value of the good as the state median price for that good using data on purchase
values from the expenditure section of the survey.
good as
Then we
calculate the average
To complete the estimate, we calculate the average user
2t.
median purchase value divided by the average
lifetime of the good.
life
of the
cost of the good as the
Finally,
some household
surveys (Chile, Colombia, Costa Rica, El Salvador, Mexico, and Uruguay) do not include any
information about the stock of durables held by households.
In these cases,
we
calculate the
durables sub-aggregate as expenditures by the household in the previous year on durable goods.
Some
periods
—
surveys ask multiple questions on the same expenditures with different reference
i.e.,
Deaton and
''the last
Zaidi,
two weeks" versus a "typical month".
we use the
latter.
expenditure per equivalent adult
We
calculate both per capita household expenditure
(in local prices,
national prices, and 2005 international
lowing Deaton (1997), we assume that children aged
aged
Following recommendations by
to 4 are equal to 0.4 adults,
$).
and
Fol-
and children
5 to 14 are equal to 0.5 adults.
Adjusting
for differences in
purchasing power
is
important
for
making regional and interna-
tional comparisons of household welfare using expenditure data. Consider a Paasche price index
19
comparing the price vector faced by the household,
where
g''
the consumption vector and
is
Rearranging
and the reference price vector,
ph,
q^ denotes the inner product of these
p''
P
where the value of the household's consumption defined
our object of interest and
x/i is
income accounting practice,
all
To
in
/cth
at reference prices,
y^ =
p°
q^,
is
household expenditure. Note the convenient link with national
which
real national
product
is
the lefthand side of (A-5)
summed
households.
calculate
y^ from our expenditure
Pp
where
two vectors.
yields:
P
over
po-
tt.'^
is
data,
we
rewrite (A-4) as:
^
=
(A-6)
the share of household h's budget devoted to good k and
component
pjj
(and p°) denotes the
P^ involves not only the prices faced by house-
of the corresponding vector.
hold h in relation to the reference prices, but also household
/I's
expenditure pattern.
Using
a Paasche price index with household specific weights allows us to account for differences in
expenditure patterns
— prevalent across regions—when adjusting
the information about food quantities and expenditures available
prices.
in
We
calculate P^
from
our household surveys.
We
take the reference vector po to be the median of prices observed from individual households in
the survey. To reduce the influence of outliers,
households
in the
we
replace the individual p^ by their medians over
same municipahty.
Calculating prices from our survey data requires converting
all
quantities
(i.e.,
pieces, bottles,
bundles) to constant units (kilograms or hters) for each commodity, and then dividing total
expenditure by quantity purchased.
In
calculations before releasing the data.
necessary conversion factors
some
cases, national statistics offices
—by commodity— to convert to constant
(most notably, Bolivia), surveys report some quantities
factors.
In these instances,
we
performed these
In other cases, the survey documentation contains the
in "pieces"
units. In a couple of cases
but do not provide conversion
use the conversion factors for similar goods provided by other
surveys.
Constructing a meaningful consumption aggregate also required checking the survey data
for
obvious data entry errors and irregularities, most
some
—but not —surveys, national
carefully
all
common
statistical offices did a
in reporting food quantities.
In
thorough job of error checking. After
examining individual data points, we used the following procedure to correct data that
were clearly recorded incorrectly.
If
the household's annual expenditure on a good was
than four standard deviations above the mean expenditure on that good
municipality (or state
if
the municipality
is
in the
more
household's
not identified or had very few observations), the
20
observation was replaced by the municipality median. Less than
The procedure
cutoff.
consumption
is
is
used for
all
1%
of the sample
The
surveys in our dataset.
meets
this
distribution of aggregate
robust to instead dropping these items, or requiring the value to be more than
standard deviations above the municipality mean. Results are also robust to using deviations
five
than
in logs rather
levels.
We
apply a similar procedure
index, dropping quantities that are
more than
in
calculating prices for the Paasche
four standard deviations from the municipality
mean.
After deflating regional prices to national prices,
we
further adjust for differences in inter-
national purchasing power by normalizing the data so that household expenditure within each
country aggregates to 2005 national per capita consumption in international dollars from the
World Development
'
Indicators.
'
.
'
Income data are drawn from either population censuses
The census
or household surveys.
data give a single measure of labor income, whereas labor income from household surveys
constructed from responses to various questions about earned income
pation, secondary occupation, etc.)
is
The methodology we
similar to that used to construct the
is
from primary occu-
use to construct the income aggregate
consumption aggregate.
PPP
from the census or household survey with the value of
(i.e.
We
compare per capita income
GDP
adjusted
per worker in 2003,
taken from the Penn World Tables. This allows us to calculate a factor to adjust income so that
it
in
averages to
GDP
per worker in constant international dollars. To produce the decompositions
Appendix Table A2, we
also deflate
When
index discussed above.
power pro\'ided by National
by the state median of the household
a Paasche index
is
unavailable,
specific
we use data on regional purchasing
Statistics Offices.
The climate and geography
variables were constructed as follows.
Municipal-level temper-
ature and precipitation variables were calculated using 30 arc second resolution
mean temperature and
at
(NASA and NGIA,
municipal-level
mean
(1
kilometer)
precipitation over the 1950-2000 period, as compiled by climatologists
U.C. Berkeley (Hijmans, Robert et al, 2005).
data
Paasche
We
also use 30 arc second resolution terrain
2000), collected by the Shuttle
elevation and slope.
Radar Topography Mission,
to construct
The GIS municipality boundaries were produced by
the International Center for Tropical Agriculture (CIAT, 2008b).
A3. Further Results
Table Al
tics.
lists
Table
the data sources used in this paper, and Table
A3 examines
A2
provides summar)' statis-
labor income inequality, where labor incomes are not deflated for
regional purchasing power.
The
overall decompositions of inequality into cross-country, cross-
municipality, and within-municipality inequality are thus identical to those presented in Table
of the
main
full set
text,
with Table
of countries for which
A3
presenting the inequality measures, country-by-country, for the
we have
data. Table
A3 shows
that the extent of inequality across
countries depends on the measure being used, though Ecuador, Peru and Venezuela are
the most unequal countries with
1
all
three measures. Table
where incomes have been deflated by the state
level
21
A4 examines
median
of the
among
labor income inequality
Paasche price index described
above.
The
Table
to the
shown
results are very similar to those
A5 examines
is less
than that
Not
than the between country
less
1
and A3.
surprisingly, the extent of inequality in
ones.^"^
MLD
The ranking
household
Nevertheless, the overall comparative patterns
in labor income.
are similar. In particular, the within-country
sometimes quite
Tables
inequality in equivalent household expenditure, constructed according
methodology described above.
expenditure
in
and Theil indices are substantially greater
of countries in terms of inequality in Table
different than that in Table A3. For example,
A5
is
Peru appears to have relatively
within-municipality inequality. This likely reflects differences in the variability of monthly
labor income relative to household expenditure and in the precise nature and quality of the
various household questionnaires.'^^
Table
A6
investigates the implications of controlling for population density (as a proxy for
the density of economic activity). It uses the Theil index to
of income (overall, predicted,
tween municipalities/regions
is
and
residual) into inequality
(of countries),
decompose each
of the
components
between countries, inequality be-
and inequality within
municipalities,
where income
predicted using information on individual education, predicted individual experience, and
nicipal level population density. Table
to paved roads country- by-country.
have municipal
level
Finally, Figures
data
A7
mu-
presents the inequality decompositions for proximity
Note that when considering proximity to paved roads, we
for all countries presented."^
Al through A3 provide maps showing mean
(non-deflated) labor income by
municipality (or region) for North America, Mexico and Central America, and South America,
respectively.^^
Note the
difference in scale
between the North America
map and
the Latin
America maps.
"^Note that because of tlie .small sample sizes of existing ronsumption data.sets for the United States and
Canada, these countries are not included in the consumption inequality decompositions. Hence, the overall
inequality numbers in Table A5 should be compared to those witliout tiie United States and Canada in Table 1.
''For example, the Peruvian consumption data provides somewhat larger samples than most of the other
consumption data sets and contains many carefully detailed questions on expenditure and home production. To
the extent that it is one of the highest quality data sets, it may have little measurement error in the withinmunicipality inequality component, relative to the within components from the other expenditure and income
data sets.
Canada is omitted because its administrative division involves extremely large swathes of territory grouped
into single administrative units. This absence of municipality level data make it difficult to compare the decompositions for
Canada
to those for the rest of the Americas.
'Particularly for the countries where data are
drawn from household surveys, labor income
every mimicipality. In order to provide an approximate overall pict\ire of spatial patterns
mi.ssiug municipality values
by the median labor income
in
or department).
22
the municipality's
first
in
is
not available
for
income, we replace
administrative unit
(i.e.
state
References
Acemoglu, Daron,
_ and James
,
Introduction to
{1),
.
Modem Economic
A. Robinson, "Persistence
Review. 2008. 98
_ Maria
-
;
Growth, Princeton University Press, 2009.
of power, elites,
and institutions," American Economic
267-293.
Angelica Bautista, Pablo Qeurubin, and James A. Robinson, "Economic and political
The Case of Cundinamarca, Colombia," NDER Working Paper. 2008.
Inequality in Development:
Aghion, Philippe and Peter Howitt, "A Model
of
Growth Through Creative Destruction," Econo-
metrica. 1992, 60 (2), 323-351.
Banerjee, Abhijit and Lakshmi Iyer, "History, Institutions, and Economic Performance: The Legacy
of Colonial Land Tenure Systems in India," American Economic Review, 2005, 95 (4), 1190-1213.
Barro, Robert
Caselli,
J.
and Xavier Sala-i-Martin, Economic
Francesco and
Guy
Growth, McGraw-Hill,
New
York, 1995.
Michaels, "Resource Abundance, Development, and Living Standards:
the Case of Brazil," mimeo, 2008.
Center
International Earth Science Information Network, Global Rural-Urban Mapping
Alpha Version: Population Grids, Socioeconomic Data and Applications Center (SEDAC),
for
Project,
Columbia
CIAT,
_
.
Uni\'ersity, http://sedac.ciesin.columbia.edu/gpw
(May
10th, 2007), 2004.
Latin America and the Caribbean Roads Database, Center for Tropical Agriculture, 2008.
Sub National Boundaries of South and Central America, Center
for
Tropical Agriculture, 2008.
Ciccone, Antonio and Robert E. Hall, "Productivity and the density
Am.erican Economic Review, 1996, pp. 54-70.
of
economic activity," The
of Household Surveys: A Microeconometric Approach
Hopkins University Press, 1997.
Deaton, Angus, The Analysis
Policy. Johns
_ and Salman
Bank
Development
to
Zaidi, Guidelines for Constnicimg Consumption Aggregates for Welfare Analysis, World
Publications, 2002.
Dell, Melissa, "The Persistent Effects of Peru's Mining Mita," m.anuscript,
MIT
Department of Eco-
nomics: http://econ-www.mit.edu/grad/mdell/papers, 2008.
_
,
Benjamin Jones, and Benjamin Olken, "Temperature and
Sectional and Panel Estimates,"
Income:
Reconciling
New
Cross-
American Econom.ic Association Papers and Proceedings, 2009, 99
(2).
Donaldson, Dave, "Railroads
of the Raj:
Estimating the Impact of Transportation Infrastructure,"
manuscript, London Schol of Economics: http://personal.lse.ac.uk/DONALDlS/, 2008.
Duflo, Esther and Rohini Pande. "Dams,"
The Quarterly Journal of Econom.ics, 2007, 122
(2),
601-646.
Gasparini, Leonardo, "Different Lives: Inequality in Latin America and the Caribbean,"
et al.. ed.. Inequality in Latin America: Breaking with History, IBRD, 2003,
Gibson, Edward
L..
"Boundary Control," World
Grossman, Gene M. and Elhanan Helpman,
in
De
Ferranti
Politics, 2005, 58. 101-132.
Innovation and Growth in the Global Economy,
Press, 1991.
23
MIT
Robert and Charles Jones, "Why Do Some Countries Produce So Much More Output Per
Worker Than Others?," Quarterly Journal of Economics, 1999, 114 (1)5 83-116.
Hall,
Heston, Alan, Robert Summers, and Bettina Aten, "Penn World Table Version
6.2," Center for
International Comparisons of Production. Income and Prices at the University of Pennsylvania, 2006.
Hijmans, Robert
et
al.,
"Very high resolution interpolated climate surfaces
for global
land area,"
International Journal of Climatology, 2005, 25, 1965-1978.
Kanbur, S.M. Ravi and Anthony Venables,
Press,
USA,
Spatial inequality
and development, Oxford University
2005.
Klenow, Peter and Andres Rodriguez-Clare, "The Neoclassical Revival in Growth Economics: Has
It Gone Too Far?," NBER Macroeconomics Annual 1997, 1997, pp. 73-103.
Londono, Juan Luis and Miguel Szekely,
Applied Econom.ics, 2000, 3
(1),
"Persistent Poverty and Excess Inequality," Journal of
93-134.
Mankiw, N.Gregory, David Romer, and David N.
Weil, "A Contribution to the Empirics of
Economic Growlh," Quarterly Journal of Economics, 1992, 107
Naritomi, Joana, Rodrigo R. Scares, and Juliano
J.
[2],
407-437.
Assungao, "Rent Seeking and
of'De Facto'Institutions: Development and Colonial Heritage Within Brazil,"
NBER
the Unveiling
Working Paper,
2007.
NASA
and NGIA,
Shuttle
Radar Topography Mission 30 Arc Second Finished Data, National Aero-
nautics and Space Administration and the National Geospatial-Intelligence Agency, 2000.
O'Donnell, Guillermo, "On the state, democratization and some conceptual problems: A Latin American view with glances at some postcommunist countries," World Development, 1993, 21 (8), 1355-1369.
Romer, Paul M., "Endogenous
Technological Change," Journal of Political Economy, 1990, 98 (S5),
71-102.
Tabellini, Guide, "Culture and Growth
:::,,-
;._,
in the
Regions of Europe," manuscript, 2006.
24
•,
W)
cu
^
r^
o
GO
— ^O
'S
>
•^
^
ro
lO
cvi
o
LO
lO
00
LO
lO
00
00
C3
CO
lO
d
S
LO
d
1
w
ca
g
az)
o o
u
bO
H
su
-o
cS
QJ
00
lO
o
o
^
+J
g
o
00
00
o
oo
LO
CM
CD
d
3
o
'5
QJ
a,
^^
^
^
>^.
u
CM
CN
^ 5
>
> Oo
'a
3
CM
r^
'»
d
CM
d
>-.
uh
^
^
o
^
CJ
o
LO
lO
GO
C^J
C-)
3
CI,
o
en
CN
o
1^
LO
CM
d
CO
2
s
o
"E
d
cu
r^
LO
CM
o
CM
LO
CO
CM
o
—
,
CO
o ^
3
o
g
ti
C-l
I^
1
_
o
o
CD
<
o
^
O
CM
rr^
.y
<"
H
^
3
a!
^
i)
""-^^
^
CJ
"5
-U
a)
2
a
o
CO
X)
-*
CO
LO
CM
00
LO
o
d
00
o
Oi
LO
O
00
00
CM
C~)
o
o
_p
d
LO
r-
CO
o
o
o
S
.^
^
U(
"p^
s
^
-^
^
I<*
CO
lO
o
o
^
t-
o
O
--
s
c;
S
qS
=3
—
!
^^^
o
00
O
o
o
d
LO
00
o
d
>,
^
3
o
CN
^~^
cr;
C-.
oo
CM
C^4
00
CM
CM
o
C-1
CO
CO
CM
CM
CM
o
n
'-3
p:
d
q
r-l
LO
CO
CM
CO
CM
d
d
O
o
CO
LO
f^
CO
CM
CO
o
CM
d
00
b-
d
o
d
a
o
en
ts
^
o
CM
^
j3
3
-^
ni
S u
S
>.
_2
o
a
m
OJ
i-
g
J2
c-j
M
;;
cr
2
S
o
c^
d
3
<5
.--
LO
'^
>v
C -D
C
of
-3
—
L-
_^
—o
tp
c;
p p
O
CM
CO
CM
<
d
o
f^
d
o
UM
,
ui
cu
.-r^'
c
.•€
y:
3
o
c
.2f
s
Cj
>
QJ
"5.
d
o
o
c
>-
3
CO
-w
72
CD
'
'
ffS
<
c
2
Pi
H
1 <
B"
a
5^
—o
<
o
CJ
3
5
l2
< O
— a
~
,-^
C/5
ct
03
0)
ci3
1
QJ
'3
"bib
01
2
bC
>,
1-H
a
^
^
2
5
Q
O
<1J
c
CO
-J
hC
^
!-:>
(^
o
^
•o
0)
ci
—
^
'O
-*
10 ro
,
10
^
02
02
N00
00000
•^
3
Csi
Ol
00
CO
-*
,-H
^
cs
-*
CM
o
O
t^
CO
CO
CO
-*
^
d
*
d
t-
CO
CO
"^
10
d
d
d
•^
LO
-*
CM
-*
d
d
CM
C
2
ii
§
S
>—
hO
s
^
^
3
c^
^
~^^
-^
^
*-:'
m
^
—1
00
1.0
O'
CS
•^
000
00000
is
^—
CN
r^
-Nf
CO
LO
I--
10
o
o
o
o
o
o
o
To g
— QJ
^
^
^.'
O
c^
cu
>
-^
o
o
o
CO
CO
"t—
=J
CO
CO
o
d
CO
o
^
a
r^
d
c
^
3
p ^
"O
3
3
*J
bC
c
.^^
aj
j;
CO
-^
CM
C^l
l-O
o
-rf
o
CM
CM
•*
CO
";
d
3
5;
?
^
^
"u
00
—
—
1
d
3
>
O 10
O CN
O
o q CM
o q
o
cri
X'
d
q
d
'^
"^
^H
t^
d
CO
CM
LO
CO
d
d
;§
•^
—
--H
00000
5
CO
o
o
o
CO
LO
CO 00
,0
^w-
r>'
^
.=
S
o
-S
j5
£
X
cj
oc
;__
e
to
^bC
^
—
^
-.S
cC
^
V
y
>
.>
^ <
>-.
Uf
LO
-i^
CO
CO
i-~
t~-
CO
LO
1—i
S
'0 ~^
H
-^^
"-t^
K
^
c:;
CM
0.708
0.629
LO
LO CO
CO
d
'^
CO
1
—
q
-f
CM
-r
LO
LO
CO
LO
LO
00
cr:
LO
CM
d
d
c
CO
CN
10
cd
?:
.4-3
-o
^
d
M
3
8
3
5
g
0)
•^
o
?
q
d 11
^
M
';j
j^
II
Si
)S
E
o
CO
CO
D3
-.
>
k
LO LO 00
CM
-H
-—^
1
r^
LO
LO
d
d d d d d
u
2
—
—
CO
CO
LO
CO
^3
—
1
d
d
d
d
d
d
d
d
en
^
^
c^
-71
N
Q>
—
^
>.
^
CO
;>
...^
CM
CO
CO
c
^
CO
.—
.tH
"T*^
CO
CM
—
t^
CN
—
—
—
r~
(/i
r-i
5
iB
^
,
d
^^
CO
3
0?
S
CT
55 j5
^s
N
^
C/1
D
2
,--,
±,
(5
s
Oh
D >
^
'3d
m
Pi
S
^
a^
tf
4-;
^
X)
S
S-
"cS
^
<
&
tj
cr
CQ
Oj
Is
y
rr;
fab
GJ
K
,
,
u
<
^
<
fH
;:d
C
OJ
s^
2
-^
C7
Q>
^
-
.S
"11
otn
.^
,
^
13 -^
r-H
Co
"s
CC
bb
^
<
i)
Cv
J:
i~>
CQ
"3
^
>>
—
,
CO
u
LO
CO
^ c
? 1
s
CJ
<;
"?
-T^
o
JJ
-c .2
S
X
c
c
o
2
" O 5^
-H
P o
H o g^
2°
'CO
G
O
'~^
U
i"
£i
.
—
'"'
>
_QJ
a;
o
c
s ^
q
J^
'3
C^
g -H o o
_
—
— ^ — oo
ic
£_
.
c
o
x:
o
s
J^
"ti
^o
§
jt
Cm
.2
_y
'5
CO
c?
£-
o
Oj
•u
1^
o
aj
'
"^
|£
*";
CO
CN
c;
S
Q
o "^ to
S
=S CN
o
P o P o
^
J
"ct
^
CQ
OJ
*J
to
0/
O X C
3
O
o
s ou
^ ^
QJ
c^
CI,
ci
c;
to
o
I00
o
^6
oo
o
LO
it!
7j
ro
>r.
a
o
io
CO
O
2
c/:
^
o
o
c-0
U
o
-*
t1^
K s
^
o
0)
rt
S s
~
^
u
c
CN
to
X
o
C2
o
in
CO
~-
CO
o
i-.
o
O
TJ*
o
d
"3
o
-%
CM
CO
^
CC;
to
LO
lO
to
oo
oo
I00
to
o
o
S3
3 3
3
U
'^
-:
L-O
0)
in
'^
-S
3
s
3
>-«
oo
4^
i-<
0)
1
"2
is
s 5
HI
X
3
o
d
oi
^ *„
^
5
t^
'X
—
^
^
to
,
v:
-o
~c -g
'o
>>
-J
3
C
3
^ —1^5
0^^
hp
-
o
^
to
ES
15
en
>
oo
j:;
-
o
b
>
CO
"cS
en
CN
c3
o
CU
2
3
o
00
ce
c
S
*G
O
o
a.
1
to
o
(J
3
"o
•
u
E-s
if'
00
::;
3
-a "3
o
c
o
OJ
-a
y-r
c
rt
lij
3
S
CO
1
.™
^
c
LO
bp
f^.
M
i
c:
o
oo
X
^
CM
CM
OO
^
to
oo
OJ
o
5 I
X
I
O *j
-^3
c -5
S
LO
3
3
S
-c
3
X'
'3
S
P
13
o
—
Cj
1
c5
'x
^
eS
CC
s
(Ih
a
S;
3
O)
SI
a;
I
Ji cH
CO
C;
<
a.
D.
5
cc$
^
"O
r3
OJ
-M
.-ti
3
o
c-
-
CO
CO
o
cd
.=^
^
cs
o
„H
OI
O
d?
5
c
.2
>1 -^
x:
—
35
-a
6
CO
o
^
bO
O
.S
a>
c^
.^
a.
^
0)
c
o
.c
«
5
en
5
^ p
a
.2
ai
Z
c)
Q
QJ
3
>>
^
CJ
-c td
Js
2
'
0;
2;
a
2?
QJ
3
n
o
o
bJJ
a>
cc;
g-^
OJ
:e
^
*^
s ^s
3 -0
CJ
3
3
3>
C
a §
'S
o
^
4
CCS
CO
o
CO
3>
•—
a u a
IJ
-0
a
fco
§
cs
a
ce
b
3
J3
3
O
bc
g"
-c
ce
>
Ci
3
^
"O
5
UJ
q;
7:
c
^
cfi
i;
"O
CO
CD
^
ir.
a
CD
ce
u
W > a
bO
M-l
CJ
—
en
0)
Cd
Je
«
CO
„,
bO
ce
CD
9;'
CJ
nj
je
CO
CD
CO
-a T3
ccj
cfl
>-)
a-
z
5,
-^
£
ce
U>
q;
"^
U
o;
K
"
3
^
Cj
a
-^
'u
CO
«J
C/3
hSj
C
c
14
2>
1
1;
T3
CO
cu
LO
—
-^
CO
S
en
C/J
cn
=
"(5
c^
SJ
o
-r
"O
QJ
cu
g
—
^
LO
2
X
o
s
u
o
00
T,
o
bb
r-
o
1
*i
CO
CJ
3
CJ
«
^
"3
CO
CO
CJ
a M
CO
rt
."^
rM
.^
S
CM
O
K^
CO
o
^
0-
K
o
-a
c
ci
CD
ho
o
o.
IP
-0
i:!
O
'5
ji
-0
0)
3
CO
Ci
CJ
cS
S
0)
o
OJ
•—
cC
a
a. fc
CN
^
o
i
C
M
re
c
CO
-a
"2
"3
cS
c
g
O
G
cS
D:^
-a
a
w
3
>
5'
en
.^
CO
cu
7^
a>
CO
E
5
*y5
CO
5
bO
a
^
1
^
D.
ri
cp
a;
^c«
CO
OJ
c^
!fi
cu
b£
X!
-o
>>
c;
cti
3 -O
.2>
3
a > U
.5
'u
CO
Z 1
i3
OJ
CO
c;
0)
cfl
'"'
W
1
T3
]G
_o
1
^
'^
cc:
0)
Z
-a
W
(P
ce
'T^
J5
.^
3
>
a
-S
*^
CO
CD
ci^
'0
.i.
a S
OJ
—
^
ce
s.
ce
L-
2.
'^
1 P
Ml
3
o
S-2
>
-0
CJ
g
a
CD
CO
cu
bb
cS
CO
C
CJ
CJ
ce
nj
I
^
>-.
OJ
-c
"p
o
-a
a
Dq
3
CJ
3
C
c
II
a
cS
jti
X
CO
0)
y:
5
_!,
~
X
3
Z
^
ji
ce
4-3
CJJ
CO
CJ
X
CJ
CJ
L-
^
-^
"m
CJ
CD
CO
s
CJ
:s
CO
Je
a
3
a
^1
ce
^1>
z U
3
ce
ce
U
a
a
1a
bb
r^
!5
^
1
—
s
^
£?
s?
•-D
lO
Tj
o
33
o
o
U
o
o
O
'Ij
'Ij
CJ
JS
o
CO
H
2
O
IX
tu
_S
&
—
E
c.
i^
OJ
a
^^
o
Oh
a
CO
^
^'
CO
CO
1.C
•m
c
-^
—
OJ
c8
a.
g
r^
u
^^
"O
ce
;5
=t
l/l
"^^
-0
1—
3
ce
l>
S
en
z ~
3
en
i
en
o
3
^
yj
ce
re
*j
CD
cu
3 —
u ^
"
3
ccJ
-.
CJ
CO
C £
a > a
fe
Uj
CJ
c3
cfl
^
C
>-'
"a
3
u
3
4J
:§
1 ~
3
c
ca^ a s a
CO
Cj
1
'/)
.2
^
—
•r
ce
'13
-^
.^
a^
CS
3
a
-*j
Ci
3 'E
Ci.
p s
(£
^M
crt
CO
Cj
l-
^
*j t3
'+3
s
a>
'n
3
CJ
CO
ce
CO
-2
OJ
ce
s
d
i
a X a
bO
S
c^ _S^
ce
CO
CJ
§ 3
S
z
ctf
>
3
ss
CD
t;
c
ce
-J
t:
y.
^
yj
o
^
_i
^'
0/
OJ
Cj
CO
'"
—
bb
0)
s
Jn
K
1?
'/I
O
,
bC
-c
;S
3
CJ
73
u
ri
-a
:S
O
QJ
s
^
Q,
~
CO
-
_^
CO
Jl
CD
k
D-
r-i
w
LO
CO
^
-^
S
S
OJ
CJ
3
^ "E
p
3
a
2
ce
bb C*
CS
S
>-i
2
'5
rt
o
U
"o
Da
(C
m
(J
?
O
CJ
2
(-1
S
'O
cC
"3
Q
o
u
H
"re
>
S
13
w
•0
Cfi
ifl
c
CD
re
D
s
re
CO
T3
X
5
c
>•,
ce
w
C
u
cc
re
3
S
a,
fc
rt
C
CS
"3
3
3
N
U)
CD
re
3
3
D
S
>
CD
"E.
CO
Table A2:
Summary
Statistics
Income per Work:er
Bolivia
Brazil
No.
Males
No.
Mean Mun./
Obs.
18-50
Mun./Reg.
Reg. Pop.
Munic.
Pop.
(1)
(2)
(3)
(4)
(5)
(6)
8,166
4,227
106
108,897
yes
8,152,620
3,481,697
1,920,149
1,519
94,823
yes
175.552,771
30,689,040
R,ef.
to
Country
441,740
196,208
11
2,695,307
no
Chile
14,879
6,952
2
7,551,517
no
15,153,797
Colombia
18,479
8.270
9
4.665,758
no
39,685,055
Costa Rica
5,699
6
no
3,710,558
12,920,092
Canada
Ecuador
22,275
10,581
20
628,822
no
El Salvador
22,937
10.796
64
64906.51
yes
6,122,515
Guatemala
Hondm-as
Mexico
11,440
5,707
226
41,901
yes
12,820,296
13,160
5,978
98
44,973
yes
6.200,898
2,660,016
1,562,092
2,442
41,390
yes
100,087.900
Panama
94,645
55053
30
40,776
yes
2,836,298
Paraguay
Peru
6,867
3,441
175
26,820
yes
5,585,828
22,207
11.333
610
30.619
yes
27.012,899
7,401,156
3,272,003
2,071
126,211
yes
284,153,700
8,082
3,707
19
141,812
no
3.334,074
677,524
380,797
219
110,118
yes
23,542,649
14.910,969
7,457,300
7,627
United States
Uruguay
Venezuela
Total
See Appendix Table Al
for variable sources.
799,871,887
I
i
1
-^rooosocootooccot^ooo-^csi
oco^^^-cNcoocr^oc-^^oc>Dc^cn
(
LO CO 00 oo CO
UO -+ •^ en .—
CO LO CO "* CO
<
OOOOCJOOOOOOOOO
(
I
(
1
LO
LO
o o o o
!
LO
00 00
00 LO
'Tf
LO
co IT)
—< oo
LO LO
t--
,
11
1
-rr
to
or
tu
H
w
OJ
UJ
•^
"a
+J
is
m
r
IV
to
^^_
o
^" O LO -*
o
o -f -^ to oo
CD CD O CD c q
d d d d d d
^
o o o o
-*
LO Oi •:}"
LO 00 XI
t~ OO CM •* CM lO 00
CM
--I
O O
ooooooooooooo
--I
-s
J
^ rt
LO to oo
o
o o
o o o
to CO
CJ5
-H
=s<
C-l
o o
o o
.<
e-i
p-i
o
O O o
CS
O
^ O CM
CM
O CM
ci
-.
>,
O CC
O
O
o
tt o
00 lO CO
o o ci o
c:^
L.O
00
o
O
c
o
CO
tt
00
ci
-i<
O
c^
o
—
1^ o 00
^
CM o
m 00
CO t—
o o o o
oo
oo
•r-^
l6
O
o o
CM
CM
CM
CO
00
CO LO CO
o o o o
c5
o
00
LO to
.—
LO
CM
LO
-r
00
o
^ to
CN
r—
d d d
cN a>
Tf
LO CO
CO
o o
00 lO
CM CM
d d
>,
3
O
UO
LO 00
00
Jv-;
C'l
CNl
CM
d d
^
o
^H
1^
—
CO LO
LO CO
CM CN
CO t^
C--1
CO
CO CO
-* CM
CO a)
LO LO
00
05 CD LO
00
O^
CD CO
1
[^
CO
CO
00
o
CD
bJj
s
CO o o
^
O OO
o
CO LO
t^
o o o o o
t--O0'-H—lCM05C5-*r-IOOLOOOl-0
1^
r*
CM
r--
CM
c:
o o
-^
o
00 CM
L.O
CM CO
CO
LO LO
o
dddddddddddddd
'Tf
c:^
o
c^
LO
o o o
M
K
a)
K
COC2OO50i5O'-HCCr-l00
1—
cd
tICMO^COOOOOCOCO
lOCM—(--hOOC^JO CM
O 00
o
<D
^
o
o
CM
LO
00 LO
CO
—
o
o o CO
o o co
o o
o o o o o o
o CO
o
o CD
C^l
<
ddd'dicicicicidici o o o O
0)
o o o
o ^ o
1
::5
o
o
CO
r-i—
.-*
-^ -3
COOOOCMlOO'—
Olt^OOCTiCOr-tLOO
r'.
i3
I
O O
V
O
lO
t- CO
O
^ ^ o
O to
O
CM
CN
t^
oo LO 00
000C200000000
u
?)
I>
t-
LO
-tT
n)
r^
2
-2
to
-U
C75
C-l
O
CM
r-(
O
CO
CO 05
CO >—
1>-
CO
GO
CO
d
O
•Tf
Tt<
O
CO
CM CO CM
CO LO n^ CO
.-H
,-H
CO CM CM
d
-_J_
t-J^
_
I
__
^
__
^,
O O td CO ^
CO
r-W
'
'
o
LO
en 00
c^ CM t^ CM
--H
__
'CO
—
C^l
-*
_ CM
to CO
a> lO ^
o CM
'^ C5 oo
o
x;
^
CO CO ^
O t^ CO
O
o
Q O W
crT ^ O — ,X" CO CO — -^ to LO
^-^T—4t-HT—l^-^i—I^Hi—I^HtCi—
o
CD
1
c:r2
dj
.—
CO
CO 00 LO
— CO 00
to to to
t--
LO f^
CO to
o M
d d ^
.
d
O
d d d
o
o
d
tLO to
LO
C7i
CM .-H •*
OJ CO LO
C^)
CN
p:^
ca
tco
lO LO
d d
CO 1-^
CM
CO CM
d d
f-
+J
o o
00
lO
l^
CO
_^
I
o
CM^
o.
d"
1--"
cvf
C-1
I—
LO
f
CO_
^
CM
—
,
o
LO
-*
CO
CM
o
CO 00
^; CO
CM T—i
C5
oo
d
CO
LO CM
CO CO CN
CM CM
lO
r^
o
CO 00
CO 1O-l
d d
^
cJ
CO
CM
"*
00
o' d'
—
1
CM
^ a
^ —^
CAl
c/}
r1
C
o
c
'S
^
3
'z:
g;
3
00
o
o
>
s
OJ
S o K s
tuO
c3
pop.
Li
Cu Cu p-
a
'S
ri
!=^
IS
R
03
CJ
O
c
en
, 3 X
O o O O a D K <
ctual
5 > < a
z.
3,
Di
1
0)
<
"7^
_
CO
3
cri
:d
ef.
CJ
"3
3
o
to
niguay
—
"£b
f~\
pop
U.S.
3
CD
nj
0)
qual
OJ
cS
3
a
tuO
(e<
o
-c
"§>
'E
1 5^
r—
.4^
.
tJ
H.
o
< a z
Cd
-1
.—
'
1i
cc;
1
ct
--;
&
0)
t
o
t.>
;.i
t-(
/^
—1
CLh
Cu
a a
Q Q
O O
-
•
I
(
1
t--
"rr
-^
N-
^ M
lO -^
-3"
O
tT
O
1
— a^ oo rO (M 00 C^
—
lO-^-^ooor^
O -^ lO t^ tO^GO
lO
LO
IC
:
oo
I
CO lO
o o o o
—
<
o
C> lO
ooio
T
lO
o
o
GJ
S
CJ
H
^
0)
!li
ci
c
'^
CO
f- O
O O
(M 00
OCTj-^CN'-^O-hOOOCOCSIiOOO
ffi
o
•*
-^
"
t-~
ooooooooodooo
5
>
•-•
005QSI—
ICSICT:!^—<^CN-HLOO
(NlMCNOO-^l^OiCOO
05 ro ^
lO o
oooiocooor^cot^LO O
^ a^
(M
o
(M
* ^ —
I
lO
O
t-L-Ot-tcMt^rocMcri
O'^O^COCOCOO
to 00 LO o
O
oooooooooooo
o
-*
CO UO
L.O
Q O O C
0!
I
.2
CM
oo-^^lo;o-hcn
000C2000C;
oooooooo
o
'-'
O
-H
O
d d
LO
CO
LO
d
—O
r~
r>n
CM
C2
o
CJ
t^
LO
=5
CD oo
•* CO
h£
C
o
U
bi3
'Ol^OOCiOCOOCO'O
-:rO—
OLOt^tOCNOOOCOOOO-^^
LO
lO
^
Tf
O ^
CN
O
Tf
LO
LO
OOOOOCZJOOOOOOO
^
Cl
!>)
t--
LO'
c
'^
toooo5Csirot^cc>LOco-^050a>
?
doooodoc
csioj-^^coCTCsioaicocMi-o
rtOOiM^O^CMCOOO—
o o
^^
OJ
3 CO
r^ 00 CO o o
CM
COO-^'^OCMCM'Tf
LO LO CO LO 05
CD
c^i
LO
LO
CO'
O
dddcicDdicici
t-^
LO ;o r~
-^
CM CO
O
o O
o o c LO
o O
o o o
di d> d d> di
<M CO
—
t-H
i-H
-
•
d
c;
o
LO
LO
rooo
oo
OC
d d
CO cs
CD oo
o o
-zi
o
q
d
c
a
~^
CT'
o
—
oo O 00 O
O
CJ CM
t- O t- O
CO
-lO
'i"
o
CO
3C'
LO
-5-
-r
lO
LO CO
OO
lO
o o o o o o
o
O
o
2
-3
CM
CM
—i
-^
o
^ l~ CM C; j^ ^ CM r- Tt 1— LO CM
-^ -d LO d d
t^ 00 -r T' —:
—ICM''^--r.-H^
— COCM
CO^,—
l-~
o o
o
CO
LO
—
O
o
o
C3
-9<
00 CO
CM CM
rsi
"5
LO
lO
LO -^ C^
C^l
LO
—
1
o o
i_;
-r^^
<
r--
o
'
r^
lO
era
o
o
—
1
05
CM
O O
^ O
o
CO
CM
05 CD
—
CO
OC
oo lO
r^ '^
— O
00 CO
oo C^ t- CN LO
CN G5 CO 00
CO -^ CM
CO
CX3 cr^ 04
I^
—
00 lo
LO LO
lo ct.
,
c>i
c~~i
^
o
-^ oo
lO o
c^g
C--1
1
^H
LO
CO
CD -^
r^
^
C5
t-
LO
O
--H
CM
C-^l
CM
-3
^
O
-r
LO 2 o 05
1co O o o
CM —
'
g
U
o
-—
4-^
>
p
a
cS
3
o
CO
O
5
p~i
o
^
_o
1-1
1^
Oh
Ci-
C^
5
5 > S S
o
2;
a
c
X
-
c
S
c-
-.
'5)
_c§
>>
o
en
s 6
"5
"£
5i
CO
o
CO CO
cf:
oo
LO CM
r-H
2
;:^
K
a;
O
C'
o O o a
bc
5
^
o
_
a
.i
-e
:r
o
a
< <
I
CL
a)
c
b£)
o
O
DD
05
^ ^
!M
t^
OJ
iC
LO
O500O5O5COCOO5C0
COOCOOOCOlOCJl-^
C05
CMCO
IOCDLOCM05COI---0
CM-^
TT'^CMCOCMCSICOCO
COCN
bO
t^
^
CN
ooooooooooo
3
_
CO
t^
(M
ld r~
?<i
lO 00
m
oo o)
-H
CO CO
o r~
COCOOOOC5CDO
;"-(oo—
oocooop—
ooooooooooo
^
CO
-Tf
c~j
dcDod'ci'didid)
00
do
CO
r~-
a
^
10
—
ri
0)
Oi
;:;
O
to
C5 CN
e5
OO
t^ OO CN
-g*
-^
C~0
C^l
I--
-^
O
CN
O? !CN T-i
•*
t- 00
—(CSLOCsrocsicocsitNroco
'cr
^ ^
CO
1--
ooooooooooo
8
-Nf
--
-^
—
LO
^H
Of)
,
0
LO
CO
an CO tCM g< CO
000000
'g*
CM
o o
o o
t:
j=
n
U
__j
OJ
%
'fl
CO CM
O CM
O
d d
T-H
a
CM CO
00 Tf
CO ro
O
•^ LO
o o
d d
CJ5
-3
3
X
^^
c
0)
T3
-^
cS
:3
>
-c
CU
.3^
M
CN 05 OO
O
11^ CN
o 00
O
CM r—
c o o o d o o o o o o
ro in LO -^ t^
CS 00 00
CN
CM Oi
I
05CMOCMC35C0C005
CM C^ [^
00 CM
-*COCMCOCMCMrOCO
'00
r-l
1.0
CO 00
CO
CO CM
C
S
-3
O
d>c5d!cid>d>dc5
J2'
cr
Ml
a
T'
^
Cfi
'^
>
60
r*
-"
CJ
S
g
2i
,a3
T-!
05 CD 05 CM
O
COOOCM—
**^COCO
oqocooooo
^t^rCOCMl-tiCMOO-^mO
t--COt^C3 0C-^O^CC5'-H
OOCMOOO—1,-H^O^
h-
l-O
r-H
d> di (d d: d>
000
CO
CO
a)
O
CTi
!--
o o
d d
OJ
C)
u;
^
3
-1-3
f«
r<i
CM
CM
00 TO
lO 05
Tf CM CO
cr,
r-5
—
1
— to 10 00
C35
1^ C5 C^l CTl 00
CO CM CM CM C--1
CO t- CO CM
CO en CO
-t CO CO -V
1
l^
cc CM —
05 CM
-* CO
0-)
00 tCO
CO CO
CO
00 02
LO
1
00000000
oooo<oooooo
U
C^J
CM
I^ o
o o
d d
•>*
T-(
o CM
o
o o
^
^
o o
d d
^
^*-
"
J3
^
*3
"
y H
.>
-rt^CMCDOOOOOOCOO-*
r-i-rj<OOCMC7)OOC35LOCN100
^
t— O
LO
CO
-f
00 r^
o
o
c^i
—r
L.O
cri
lo
f~
-Tf
t:~-
:>o
C75
CO
c^
l-~
c;^
o
^
d
CMCOOOOJ.—iCNrOb-t-CM
LOLodoodiodt^Looooo
§ci
'-r
10
or;
l-~
--f
'•
00
—
1
CM
'r-
t~-
L<
ej
CO
CO CO
00 CO 05 LO CO 05
t--^
uo t-^
•;
—
2 ^
d
c:^
o
CO LO 05 '-< CO 00 -r
CO CO CM CO C^ CM CO
Cj
r-icMC^CMCMCMCMCMCMCMCM
o
-p
r^
•r+^
LO
en i^ CO 00 LO LO 00 t^
'^
ddddddddddd
'^^
cr>
05 tV
06
a
rj
—
O
05 00 CS
CO
CM i-O lO CO oo"
CO
CiO
cm'
lo co'
CS^
-*
-:f__
—
u
3
S
O
^
>
^
CQ Ui
"§
o
bO
'x
o
o
o K
"S
r>
>-•.
^
CO
^
Qi
'.>!
2
;^
3
2
ct
a;
b£
rr.
a.
cd
-1^
r^ Dh
-^
<£;
c*^
"n
*
i—
cq
a
^
,
r*^
^
^
^
1
CJ
^
^
5
oJO
3
bC
c;
u,
CD
tJ
D
on
U
ii=
J
.2
-I
tti
.
^*
g'
3
.
c
^0
cS
3
o
M) 0,
CO
""
*"
.«
K
S
'5b
>
-c
::
o
bC0)
Oi
^
a;
hn
Pi
rt
Q
a;
<;
u
a
r.
5
" ^
o
d
^''
0)
d
CO
-tr 0> 00 CM
00 CO 05 (^
CO t» o_^ -r CD ^^
10 1^' ^'
LO i~-"
C:5
a"
•^
c
M
CO
COO^lO^CMOOCCN
lO 05 -^ t^ 1^ O LO 1> -^
s
n
_a;
c
<
cd Pi
—
ro
5
"o
CO
'
.
—
'
O
cj
0^
1-
O
bO
o
-d
00
O CN
O
t^
CO 00
o o o o o
lO '^
CM ic
-* LO
cr;
-)-J
^
^
:£)
CSI
lO
CO
05
CO
05
o
"^
o
l-O
o
o
o
CO
o
o
00
lO
*
(M
ro
o
^
o
-^
-^
^
bO
CM t^ O '^
O
CO 00 ^ (M <M
O O O O O
O O O O O
CJ
^
+^
o
CQ
cd
C^
2
CM
d
^
^ O
O
O
O O O O
>^
LO CO CS
(N
lD '^ 00 CSl
'^
0<1
CO
CD
(-1
—
4-2
CO
4_;
c
(—
^ 6
>
-4-=
OJ
ti
00
o
oi
»—
m o
o
a
5
S'
00
00
oo
o
o
o
o
"2 i5
bd
O
r^
<w
d
CSl
r^
-i-i
OG
CM
O O CO
^H
O O O
--^
r-H
C^l
^^^
r:j
^
t--
(M
,-H
T-^
T-^
CD
^
tN
O
00
CO
LO
O
d
>•
"S
g
.."
<i
S S
oD
c;
^
00 cs cs
rr;
-*-J
Qj
m
-i-j
a.
r^
^
o
t--
-^
o
o
o
o
LO
o
o
00
o
o
o
s
Si
8
o
_N
l|
>i
-3
o
CM
00 CO
00
CM
O
CN
— — o
CN
o o o o o
r-<
^ o
s
t-
O O
o o o o
o
o o o o p
LO (M
>>
o
—
^
O
d
—
CM
o
lO
C5
CM
1
I
CM
CM
CM
t-H
1
m o
1
CO
o
X
~
(V
.>
J=
c
t:
Xo
S
3
bC
o
b- ^^ LO oo LO
CO oo CO
CO CO c^
f~-
^ ai
K^ c
—
O o
o o o o o
-ui
P*
^
t--
LO
CM
CM
LO
o
o
o
o
o
CM
LO
bCM
CO
o
o
o
o
o
d
CO
—
1
s
bC
a
S;
'O oo
o CO
LO LO CO
O
r^ ^ O O O
o CD d o d
LO
CC
T—
.fcj>
O
C3
c
s
C-^1
o
o
d
cr>
o
^
o
>>
+J
r;
-ii
i^
O
CM t^
-^ TT
00 t^
CM
^
oo
CO CJ
-f 'TT
1^
t^
-^
CTi
d d d d d
00
^
CO
d
o
LO
CM
I-
O
,
>,
-;J
q;
o
CQ
CM
^
t-
i
t
?:
.t^
^
c
CO
o
CM
o ^
<
c
o
a
'^
-tJ
3
S
ffj
CQ
S
Ch
-^
a^
CO
S
g
c
D >
cr
—
^
tf
CD
-'
o
Ul
D
o
Z
-n
^
cc
^
a'i
C^
<
O
-3
r;
O
OJ
<
CO
1'
o
<r
]§
a
2
CD
OJ
CO
a
Table A7: Proximity to Paved Roads
Argentina
Mean
SD
Dist. to
Dist. to
Road
(1)
MLD
Road
90/50
Ratio
Between
Country
(2)
(3)
(4)
Index
Within
Country
Theil Index
Between
Country
Within
Country
(7)
8.5
9.7
3.2
0.436
0.413
Bolivia
73.5
83.0
6.3
0.609
0.513
Brazil
29.3
64.1
7.9
0.956
1.049
Chile
25.1
21.1
4.1
0.353
0.306
Colombia
Costa Rica
Ecuador
19.3
31.7
3.7
0.692
0.681
5,7
6.0
4.0
0.622
0.470
23.8
35.7
9.8
0.641
0.703
El Salvador
4.3
2.4
1.9
0.147
0.139
Guatemala
Honduras
Mexico
13.3
24.3
6.8
0.824
0.860
11.6
18.0
3.7
0.418
0.535
9.5
9.9
3.2
0.382
0.379
Nicaragua
21.8
38.3
8.6
0.915
0.886
Panama
12.4
23.2
3.0
0.643
0.756
Paraguay
Peru
United States
34.7
49.6
6.0
1.001
0.752
19.5
33.9
9.1
1.180
0.889
1.5
3.3
3.4
0.914
0.795
Uruguay
10.7
10.6
4.5
0.478
0.416
Venezuela
11.1
26.2
2.7
0.508
0.747
All (actual)
13.2
36.5
6.4
0.621
0.774
0.439
0.815
All (equal)
18.6
37.8
5.8
0.311
0.649
0.286
0.656
No
19.7
38.6
.6
0.240
0.634
0.249
0.655
U.S. (equal)
See Appendix Table Al and the text for source.s. Column (3) pr&sents the ratio of the 9()th percentile of the proximity
to paved roads distribution to the .50th percentile. Columns (4) through (7) decompose inequality, using the Mean Log
Deviation index and the Theil index, respectively. "Actual" refers to weighting by actual popnjUvtion, whereas "equal"
normalizes each country's population to be of equal
size.
The
34
final
row omits the United States from the sample.
o
£
<
o
g;
a
o
o
s
<
S
o
O
C
03
O
U
><
o
1/1
S
O
u
o
(fa
<
bJO
• i-H
Figure A3: Labor incomes in South America
Mean Labor Income (PPP
<4,000
4 000-7.000
7 000
•
1
0,000
10,000- 15,000
>1 5,000
$)