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Reshaping Economic Geographyin Latin America and the Caribbean
A Companion Volume to the 2009 World Development Report
March 6, 2009
Washington, D.CTHE WORLD BANK
Contents
Acknowledgements .........................................................................................................5
Accronyms and abbreviations ...........................................................................................6
Reshaping Economic Geography in Latin America and the Caribbean.
A LAC Companion Volume to the 2009 World Development Report ........................................ 7
1. Chapter 1. The Spatial Distribution of Welfare in Latin America and the Caribbean ............. 25
1.1 Space and Economic Development ....................................................................... 26
1.2 The Distribution of Income across Latin America .................................................... 29
1.3 The Distribution of Population in Latin America....................................................... 32
1.4 Historical Determinants of Population Settlements in Latin America........................... 36
1.5 Conclusions ....................................................................................................... 39
2. Chapter 2. The Links between Space and Individual Monetary Welfare ............................. 41
2.1 Density, Distance, and Division in LAC .................................................................. 42
2.2 Density, Distance, and Divisionin LAC: a Quantitative Evaluation .............................. 55
2.3 Density, Distance, Division, and Growth ................................................................ 63
2.4 Conclusions ....................................................................................................... 67
APPENDIX ............................................................................................................... 69
3. Chapter 3. Spatial Disparities in Human Development .................................................... 73
3.1 Characterization of Spatial Inequality in Human Development .................................. 74
3.2 Determinants of Human Capital Formation: the Role of Space .................................. 87
3.3 Human Capital Formation and Neighborhood Effects ............................................... 93
3.4 Conclusions ....................................................................................................... 99
4
Reshaping Economic Geography in Latin America and the Caribbean
4. Chapter 4. Policy Implications ....................................................................................101
4.1 Why the Focus on Institutions, Infrastructure, and Incentives .................................102
4.2 Overall Inequality and Spatial Inequality in Latin America and the Caribbean ............105
4.3 Inequality of Opportunities in Latin America and the Caribbean ...............................107
4.4 Policy Experiences in LAC ...................................................................................110
4.5 How do Territorial Development Programs Fit the “3-Ds” and “3-Is” Frameworks? ......115
4.6 A Key Institution for Latin America and the Caribbean: Land Policy ..........................117
4.7 Conclusions ......................................................................................................119
References .................................................................................................................121
5
Reshaping Economic Geography in Latin America and the Caribbean
This report is the product of a regional multisec-
toral effort which included staff from PREM, HD
and SDN under the overall guidance of the office
of the Chief Economist. It has been conceived as
a regional companion piece to the World Devel-
opment Report 2009, directed by Indermit S. Gill
(ECAVP). Under request of the WDR team, a LAC
team was assembled to analyze the spatial di-
mension of economic growth in the countries of
the region.
The present report was prepared under the di-
rection of Jaime Saavedra-Chanduvi (LCSPP) and
Ethel Sennhauser (LCSAR) by a core team com-
prised of Gabriel Demombynes (LCSPP), Hector
V. Conroy (LCSPP), Omar S. Arias (LCSHD), Jesko
Hentschel (ECSHD), Tito Yepes (LCSSD), Mal-
colm Childress (LCSAR), and Emmanuel Skoufias
(PRMPR).
Background papers for this report were prepa-
red by Francisco J. Pichon (IFAD), Javier Escobal
(Grade, Peru), Carmen Ponce (Grade, Peru),
Leonardo Gasparini (Universidad Nacional de La
Plata, Argentina), Pablo Gluzmann (Universidad
Nacional de La Plata, Argentina), Raul Sanchez
(Universidad Nacional de La Plata, Argentina ),
Leopoldo Tornarolli (Universidad Nacional de
La Plata, Argentina), Jean-Paul Faguet (Lon-
don School of Economics and Political Science),
Mahvish Shami (London School of Economics
and Political Science), Paula Giovagnoli (Lon-
don School of Economics and Political Science),
Frank-Borge Wietzke (London School of Econo-
mics and Political Science), Omar S. Arias (World
Bank), Jesko Hentschel (World Bank), Francis-
co Haimovich (World Bank), Luz A. Saavedra
(University of St. Thomas), and Wilkins Aquino
(Cornell University).
This report has benefited enormously from the
assistance in data processing by Brian Blank-
espoor (World Bank), Elizaveta Perova (World
Bank), Kristian Lopez (Texas A&M University),
and Diana Hincapie (World Bank).
The team is grateful for funding and support
from the WDR as well as for comments and sug-
gestions from Indermit S. Gill and various other
members of the WDR team, including Chorching
Goh (ECSPE), and Somik V. Lall (FEU).
We would like to thank our peer reviewers, Somik
V. Lall (FEU) and William F. Maloney (LCRCE) for
their comments and suggestions. We are also
grateful to the individuals who provided com-
ments and suggestions at the annual meetings
of the Network and Inequality and Poverty and
the Latin America and the Caribbean Economics
Association in Rio de Janeiro, Brazil.
Acknowledgements
6
Reshaping Economic Geography in Latin America and the Caribbean
Accronyms and abbreviations
CCT Conditional Cash Transfers
GDP Gross Domestic Product
INEGI Instituto Nacional de Estadística y Geografía
LAC Latin America and the Caribbean
PPP Purchasing Power Parity
PROGRESA Program for Education, Health and Food
(Programa de Educación, Salud y Alimentación)
TDP Territorial Development Program
VAT Value Added Tax
WDR World Development Report
7
Reshaping Economic Geography in Latin America and the Caribbean
Overview
Recent insights from the reenergized fields of
urban economics and economic geography em-
phasize the importance of spatial interactions and
in particular the tremendous power of economic
concentration as a key driver of economic growth.
The overarching purpose of this report is to apply
those ideas to others of within-country develop-
ment in Latin America, and consider their implica-
tions for policy. The report examines how density,
distance, and division partially explain income
patterns in the region and considers how policy—
guided first by a focus on equality of opportuni-
ties—can foment growth by promoting density,
reducing distance, and tackling divisions.
• Density refers to the concentration of econom-
ic activity.
• Economic distance measures how easily peo-
ple, capital, goods, and services move between
locations.
• Divisions are restrictions on such flows.
This report examines how the themes of the
2009 World Development Report (WDR), Reshap-
ing Economic Geography, apply to differences
within countries in Latin America and the Carib-
bean. The WDR considers the “3-Ds” at two ad-
ditional levels—internationally and at the level of
cities—which are not addressed in this companion
volume. This report is also designed to comple-
ment the LAC Regional Study Sources of Welfare
Disparities Within and Between Regions in Latin
American and Caribbean Countries, which exam-
ines many related issues.
Key messages from this report include the
following:
1. A combination of history, natural geography,
and the forces of economic concentration have
determined the location of economic activity
in the region.
2. The historical location of cities was determined
chiefly by the location of pre-colonial settle-
ments and resource extraction needs during
the colonial period.
3. Today, in line with the patterns observed
around the world, the highest income areas
within countries in Latin America and the Ca-
ribbean are those with high population den-
sity, short economic distance to urban areas,
and low levels of ethno-linguistic division.
4. Empirical evidence shows that these “3-Ds”
are related to patterns of current internal mi-
gration, which are key to the concentration of
population and economic activity that drives
economic growth.
Reshaping Economic Geography in Latin America and the Caribbean
A LAC Companion Volume to the 2009 World Development Report
8
Reshaping Economic Geography in Latin America and the Caribbean
5. Within-country disparities across space in
health, nutrition, and education are profound
but have diminished in most countries. There
is also strong evidence that spillover effects in
human capital operate at the local level.
6. The single best approach to addressing spatial
disparities in all countries is to promote equal-
ity of opportunities, specifically through en-
suring equal access to health, education, and
basic services. Ensuring equality of opportuni-
ties encourages density, by giving people the
human capital they need to prosper if they re-
locate from lagging to leading areas.
7. More broadly, territorial development pro-
grams, and general policies to promote growth
and address spatial disparities, can be devel-
oped using a “3-Is” framework, consisting of a
context-specific mix of institutions, infrastruc-
ture, and incentives.
Chapter 1 of the report introduces a continent-
wide map of income at the local level and con-
siders the historical roots of settlement patterns
in the region. Chapter 2 presents an atlas of in-
come maps for most countries in the region and
analyzes how income patterns relate to density,
distance, and division. Chapter 3 describes the
disparities in human development in the region.
Chapter 4 examines policy implications.
Chapter 1:Geography of Income and Populationin Latin America and the Caribbean
Latin America and the Caribbean encompass
tremendous geographic diversity. The terrain
stretches from the Pampas’ vast flatlands to
the high peaks of the Andes. The region’s cli-
mate ranges from the world’s most arid desert
in northern Chile to the extreme humidity found
in the rainforests of the Amazon and Costa Rica,
and from the glacial cold of Tierra del Fuego to
the blistering heat of Mexico’s Sonora desert.
The continent has an extremely long coastline
to which all but two countries have direct access
and is home to the immense Amazon river, which
pours almost 220,000 cubic meters of water into
the Atlantic Ocean every second.
This geographical diversity is matched by its spa-
tial variation in income levels. A detailed map of
local-level income shows the dispersion of mean
income across the continent (see Figure 1.) The
map shows familiar contrasts of welfare across
neighboring countries, such as the marked differ-
ence in income between Chile and Bolivia. It also
presents the high differences within countries,
for example between the prosperous Southeast
of Brazil and the lagging Northeast.
(The map shows average income of municipalities
in most cases but uses more aggregated units for
some countries. Also, the map shows mean con-
sumption rather than income for some countries,
and for those countries for which neither income
nor consumption estimates were available, the
map displays a welfare index, equal to the inverse
of a country-specific unsatisfied basic needs in-
dex, or UBNI. The countries for which only a UBNI
map was available are shown in green.)
While there is great variation across the conti-
nent, areas of population concentration are gen-
erally areas of economic concentration, and thus
9
Reshaping Economic Geography in Latin America and the Caribbean
Figure 1. Mean Income at the Local Level in Latin America and the Caribbean
Source: World Bank staff with data provided by various authors (see data sources in references). Notes: Argentina: data correspond to an Unsatisfied Basic Needs Index (UBNI) and so a different color scheme has been used (lighter shading indicates lower percentage of basic needs satisfied; shades correspond to deciles of the distribution). Haiti, Suriname, and Trinidad & Tobago: data correspond to national 2007 GDP per capita at 2005 US$ (PPP adjusted). All other countries: figures correspond to survey data estimates at the regional level or small-area estimates based on survey and census data. The resulting estimates of mean per capita income have been rescaled so that the population-weighted average matches 2007 GDP per capita at 2005 US$ (PPP adjusted). In the cases of Ecuador, Guyana, Jamaica, Nicaragua, Panama, and Peru estimates of mean per capita consumption have been used instead of mean per capita income. Grey areas reflect missing data.
10
Reshaping Economic Geography in Latin America and the Caribbean
high incomes. The concentrations of population
and economic concentration can be understood
as the result of the initial conditions established
by the historical location of settlements paired
with the process of long-term economic growth.
We consider the initial conditions and the process
in turn.
Historical Determinants of Population
Settlements in Latin America
The locations of the major cities reflect the pat-
terns of pre-Columbian settlement, the needs of
colonial settlers, and later trade and administra-
tive patterns, which were driven in part by natural
geographical factors. In Mexico, the Aztecs were
organized in many groups whose agricultural pro-
duction relied on the high productivity of the val-
leys. Tenochtitlan—now Mexico City—was located
in a central location that facilitated control and
trade with the various clans. After the conquest,
the Spaniards found it strategic to maintain con-
trol of Mexico City and turn it into a hub linking
Pacific and Atlantic ports.
The locations of the main Andean cities are re-
lated to the system of transport and production
of pre-Hispanic civilizations. Transport in the In-
can empire followed north-south routes which
ran alongside the mountain chains. Pre-Hispanic
groups living alongside the Andes were located
on the adjacent flatlands, where the availability
of water and arable land was superior. In most
cases, the Spaniards founded their cities in loca-
tions where there was already an indigenous set-
tlement in order to be close to potential sources
of precious metals.
The trade flows during the colonial period re-
inforced the prominence of a few coastal cities
along the Pacific coast of Latin America. The
Spaniards limited trade with their colonies to
Spanish goods only, banning trading in all Pa-
cific ports other than Lima and Acapulco. Traded
goods directed to South America were shipped
from Seville to Cartagena and then to Portobello
(a port in Panama that was destroyed by pirates
in the seventeenth century), where they were
then transported by land to the Pacific.
On the Atlantic side of South America, most of
the key cities are located along the coast. They
evolved from ports and trading centers in coun-
tries that did not have large interior pre-Hispanic
cities. Other interior cities, such as those in Argen-
tina, developed later on the basis of monopolies
imposed by Spain that allowed them to compete,
and as part of an alternate route for the transport
of silver from Peru, developed because of attacks
on Portobello.
In summary, cities in LAC were founded during
colonial times mostly as administrative centers to
efficiently extract the abundant natural resources
found in the new territories. At other times, cities
were founded solely on the basis of strategic mili-
tary considerations. The locations of these cities
provided the attraction points for later economic
and population concentration.
Chapter 2:
Density, Distance, and Division
Given the initial conditions of urban settlement
locations, long-term economic development un-
11
Reshaping Economic Geography in Latin America and the Caribbean
folded, influenced by three spatial dimensions—
density, distance, and division. The emphasis on
these “3-Ds” derives from both the theoretical
insights of the new economic geography and the
new urban economics and historical experience.
Around the world, growth and development takes
place via a process of distance reduction through
a concentration of economic activity and popula-
tion, as people move from areas where they were
settled due to historical circumstances towards
areas favored by markets. The 3-Ds can be un-
derstood briefly as follows:
• Density is the economic mass or output gen-
erated on a unit of land that is beneficial for
economic growth if three conditions are met:
1) there are economies of scale in production;
2) transport costs are low; and 3) capital and
labor are mobile.
• Distance measures how costly it is for capital,
people, goods and services to move between
two locations. Distance, in this sense, is an
economic concept, not just a physical one.
• Divisions are particular restrictions on eco-
nomic exchanges across space, such as those
triggered by territorial disputes, civil wars, and
conflict between countries. Within a country, di-
visions can be the result of ethnic segregation,
land-ownership conflicts, and social cleavages,
including economic-class distinctions such as
between slum dwellers and the rest of a city’s
population.
Distance and division reduce the ability of peo-
ple to move to more dynamic areas. The cost a
worker faces when moving to a different area is
not only the one he or she incurs when relocating
but also the costs he incurs when going back to
his place of origin or when sending remittances.
Similarly, an indigenous person who faces dis-
crimination outside of his hometown would be
strongly discouraged from moving into a city
even if it offers good economic prospects for the
general population.
The current geography of income in the region
is the product of the historical location of cities
and long-run growth. If the 3-Ds explain long-
run growth in LAC, they should at least partially
explain current patterns of income within coun-
tries. We examine the extent to which this is the
case using municipal-level estimates of income,
combined with census and Geographic Informa-
tion Systems (GIS) data.
The analysis looks at the relationship between an
area’s mean income and purely geographic char-
acteristics as well as density, distance, and divi-
sion. Measures of purely geographic characteris-
tics included in the analysis are various indicators
of temperature and precipitation at different mo-
ments throughout the year as well as indicators
of altitude and slope of the terrain. The 3-Ds are
operationalized as follows:
• Density is approximated by population density.
Since the units of analysis are fairly small in
most cases, population density provides a good
measure of economic density.
• Distance is measured in two ways: by the min-
imum distance between each administrative
unit and the sea and by the minimum time re-
quired to travel from that administrative unit to
a city of 250,000 people or more.
12
Reshaping Economic Geography in Latin America and the Caribbean
• Division is captured by the proportion of an
area’s population that belongs to an ethnic mi-
nority group.
As a whole, the results show that density, dis-
tance, and division are consistently associated
with mean income. In the great majority of coun-
tries, density is positively associated with high
levels of income while distance and division are
negatively related.
In Bolivia, for instance, municipalities with popu-
lation densities above the national average con-
sistently have mean levels of income per capita
that are also above the national average. In con-
trast, the farther away a municipality is from a
large city—of 250,000 inhabitants or more—the
lower its mean per capita income. The same re-
lationship holds between distance to the sea and
mean per capita, which implies that even though
Bolivia is landlocked the municipalities that are
further away from the sea (and hence from inter-
national markets) tend to be poorer. Finally, those
municipalities whose population has a relatively
larger concentration of indigenous people also
tend to have lower mean income, suggesting that
ethno-linguistic divisions play against this group.
The analysis also controls for “first nature”, i.e.
purely geographic characteristics. These include
temperature, temperature variability, precipita-
tion levels, elevation, slope, and distance to the
equator. These variables appear as statistically
significant correlates of mean income in many
countries, although the signs of the correlation
vary by country for most variables. In several
countries, controlling for other variables, areas
that have a high elevation but low slope (mean-
ing that they are not on a mountainside) also
have higher income.
Indeed, a relatively high altitude generally coin-
cides with a higher mean income in five of the
eleven countries analyzed (in the remaining six,
it has no statistically significant association). This
result may at first seem surprising since people
living in mountainous areas of Latin America typi-
cally have low levels of income. However, high alti-
tude occurs both on plateaus and in mountainous
areas. The results also show that municipalities
with relatively higher slopes—i.e., in mountain-
ous areas—indeed have lower mean incomes in
six of the eleven countries analyzed (in the rest,
the association is not statistically significant).
These results suggest the unsurprising finding
that purely geographic characteristics are corre-
lated with municipal income. However, the hu-
man transformation of the environment can have
a large and strong effect on welfare which offsets
that of first-nature geography. Evidence of the
ability of infrastructure to overcome geographic
obstacles comes from detailed work using a two-
observation panel of poverty maps in Peru (from
1993 and 2005) which finds that differences in
economic growth between the coastal areas and
the poorer Sierra (mountain) and Selva (rainfor-
est) regions cannot be explained by geographic
factors and instead are strongly related to differ-
ences in infrastructure investment.
More Density Via Internal Migration
in Countries in LAC
The World Development Report posits that mi-
gration from leading to lagging areas has been a
13
Reshaping Economic Geography in Latin America and the Caribbean
Summary Results from Regressions of Municipal-Level Mean Income
on Measures of Density, Distance, Division, and Purely Geographic Variables
Bo
livia
Bra
zil a
Ch
ile
Ecu
ad
or
Gu
ate
mala
Ho
nd
ura
s
Jam
aic
a b
Mexic
o
Nic
ara
gu
a
Pan
am
a
Peru
DENSITY
Population
Density+ + + + + — + + +
Population
Density (remote)+ + + + + + +
DISTANCE
Travel Time to
City 250k+— — — — — — — —
Distance to Sea — — — — — —
DIVISION Minority Group — — — — — + — — n.a.
CLIMATE
Temperature + + — + +
Temperature
variability— + — — +
Precipitation + — — — +
Precipitation
variability
TERRAINElevation + + + + +
Slope — — — — — —
Distance to
Equator+ + + + +
Adjusted R2 0.5 0.6 0.6 0.5 0.7 0.3 0.5 0.7 0.7 0.8 0.6
No. Observations 314 6322 330 216 329 294 413 2411 143 585 1823
Source: World Bank staff calculations with data from various authors (see data sources in references), various censuses, and GIS. Note: Results shown summarize the results of country-specific regressions. A “+” sign means the coefficient is positive and statistically significant at least at the 10% level; a “-” sign means the coefficient is negative and statistically significant at least at the 10% level. a) Minority group is defined as “non-Black”. b) Excludes years of education and occupation variables. c) Excludes occupation variables. Literacy substitutes for years of education.
key driver of increasing economic and population
density, which is essential to long-run econom-
ic growth. Theory also predicts that people will
migrate from lagging economic areas to leading
areas within a country in order to realize better
wages and therefore a higher standard of living.
However there may be other factors that condi-
tion decisions to migrate, such as access to better
services in the destination area, escape from con-
flict in the area of origin, and proximity to roads
and transportation options (distance). Analysis
undertaken for this study examines recent pat-
terns of internal migration in LAC countries has
the following findings:
14
Reshaping Economic Geography in Latin America and the Caribbean
Empirical work shows there is substantial variety
in the motives for migration. In some countries
the internal movements are driven mainly by pull
factors like a search for better labor opportuni-
ties (e.g. Bolivia and Nicaragua). In others the
motivation to migrate can also be linked to push
factors such as the lack of access and quality of
services in the source area (e.g. Guatemala) or
security issues (e.g. Colombia).
Second, there is considerable heterogeneity be-
tween countries in terms of rates of internal mi-
gration. The highest internal migration rates are
observed in Brazil, Colombia, Costa Rica, Domini-
can Republic and Peru (around 70%). In El Salva-
dor and Paraguay the migration rates are also high
(over 60%). When comparing with surveys that
gather compatible information, the lowest migra-
tion rates are observed in Argentina, Bolivia, Hon-
duras and Nicaragua (around 50%). There also
exists substantial heterogeneity between coun-
tries in terms of recent migration (within the past
five years). When considering comparable criteria,
the highest recent migration rates are observed
in Colombia, Dominican Republic and Honduras
(around 10%-20%). The lowest recent migration
rates are observed in Argentina and Nicaragua.
Third, migration is a selective process. Migrants
are typically male, skilled, young, white or mes-
tizo, and without children. Relatively lower rates
of migration for indigenous peoples indicate that
division—understood as the historical exclusion of
indigenous groups—is an obstacle to migration.
Fourth, most people migrate to leading economic
areas within their country, but migrants tend to
migrate more often to nearer areas rather than
faraway places. In Honduras, for example, Hon-
durans who migrate principally from poor regions
move to the nearest leading area (e.g. from El
Paraíso to Francisco Morazán or from Copán to
Cortés).
Migration flows are less than what might be ex-
pected giving existing wage differentials. For
example, in the impoverished Mexican states of
Chiapas, Guerrero, and Oaxaca, net migration
amounts to 2-2.5 percent over a period of five
years, and similar rates are found for low-income
areas of Chile.
As a whole, these findings suggest that migration
is a vehicle for increasing economic density and
increasing welfare in LAC. These possibilities are
limited, however, by the twin barriers of distance
and division.
Chapter 3:Disparities across Space inHuman Development
Human development enables people to live a
longer and healthier life, be educated, and have
access to resources for a decent standard of
living –as such, human development is the out-
come and goal of the development process itself.1
This chapter explores the interplay of density and
human development indicators (levels of health,
1 A plethora of studies have analyzed the links between human capital and monetary welfare in Latin America. See, for instance, Perry et al (2006), IDB (2004), and De Ferranti et al. (2003).
15
Reshaping Economic Geography in Latin America and the Caribbean
nutrition and education) across various dimen-
sions of space in Latin America. This chapter
gives special attention to a debate in the Latin
American social policy circles that focuses on spa-
tial disparities in welfare within micro-spaces in
urban areas, dubbed as “neighborhoods,” given
its increasing relevance in a context of high ur-
banization. We aim to explore here the spatial
influences in the process of human develop-
ment (and human capital formation) between
and within localities in what the WDR2009 terms
‘advanced urbanization’ – to draw the implica-
tions for public policy of intra- local area divisions.
This is in line also with a focal point of the social
policy debate in advanced urbanized countries
like the US and in Europe: to explicitly explore
–and tackle– the physical as well as social con-
notations.
Spatial Variation between Countries,
Regions and Neighborhoods
Today, spatial differences within LAC countries,
in human development indicators, remain high,
even for those countries that show more favor-
able indicators at the national level. At this, still
broad, level of aggregation we can observe that
absolute disparities tend to be larger for coun-
tries with lower national averages. Within most
countries in Central America and others like
Brazil one can find areas with human develop-
ment indicators comparable to national averages
in much better performing countries. However,
even in countries with very high national aver-
ages, high inter-regional disparities can emerge
as observed in Panama, Colombia and Mexico for
literacy, in Chile, Panama and Peru for number
60.0
70.0
80.0
90.0
100.0
Urug
uay
Arge
ntina
Chile
Costa Rica
Vene
zuela
Pana
ma
Ecua
dor
Colombia
Para
guay
Mexico
Braz
ilPe
ru
Dominica
n Re
p.
Boliv
ia
El S
alva
dor
Hondu
ras
Nicara
gua
Guatemala
Literacy rates within and across countries in Latin America (for population Ages 15 to 65)
Source: Data comes from latest available national household survey in each country.
16
Reshaping Economic Geography in Latin America and the Caribbean
Source: Bank staff calculation based on estimates from Monin 2004
of years of schooling, in Argentina and Panama
for health insurance access, in Chile, Brazil and
Mexico for access to water, and in Chile, Brazil,
Colombia and Ecuador for access to sanitation.
The disparities tend to be much larger for basic
services access, especially between urban and
rural areas of the countries. This shows that, de-
spite its high importance, most countries are far
from assuring equal access across space to all of
their populations.
Spatial disparities within countries in health in-
dicators can be examined with specialized health
and demographic surveys. In Ecuador and Peru,
we observe marked differences in malnutrition
rates between urban and rural areas, both across
departments as well as within departments. From
a policy perspective, what is most interesting is
whether intra-country spatial disparities increase
0%
10%
20%
30%
40%
50%
60%
Huanca
velic
a
Lam
baye
que
Huánuco
Cusc
o
Junín
Aya
cuch
o
Caja
marc
a
Apuri
mac
LaLi
bert
ad
Puno
Uca
yali
Ánca
sh
Lore
to
Pasc
o
Piura
Moqueg
ua
Am
azo
nas
San
Mart
ín
Madre
de
Dio
s
Lim
a
Are
quip
a
Tum
bes
Ica
Tacn
a
Urban Rural
or decrease over time. Most governments in Latin
America strive for universal or equitable access
of the population to social services and equality
in most indicators examined here. Between the
1990s and the 2000s, most countries expanded
access, and most reduced spatial dispersion be-
tween the 1990s and 2000s. For the majority of
countries for which data is available, there is a
similar trend towards spatial convergence be-
tween the 1990s and 2000s in access to water
and sanitation.
Abstracting from national boundaries, areas
with higher income poverty tend to exhibit both
lower average schooling attainment of the adult
population and also a lower share of the popu-
lation covered by a formal health insurance. In
particular, people in lagging areas tend to have
personal and family characteristics that increase
Regional malnutrition rates in Peru, 2004
17
Reshaping Economic Geography in Latin America and the Caribbean
their chances of dropping out from school or not
accessing health insurance.
In a recent study for several Latin American coun-
tries, Arias, Diaz and Fazio (2006) find that indi-
vidual and family factors are primary predictors
of successful school progression, although spatial
Poverty Rates (S$2 PPP), Average Schooling, and Access to Health Insurance
R² = 0.550
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
(% u
nder
2 U
SD
per
day
lin
e)
(for adults ages25-65)
2.0
Poverty and Average Years of Schooling in Latin America
Poverty Rates (S$2 PPP), Average Schooling, and Access to Health Insurance
Average years of schooling in region
3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0
Po
vert
y R
ate
effects remain important. Foremost, education
tends to be strongly transmitted from parents to
offspring through parental education and wealth.2
Risk of School Dropout: Urban vs. Rural
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
Risk of school drop out and region:Percentage change in risk compared to children in rural areas
%
Nic
ara
gu
a
Co
lom
bia
Bra
zil
Ch
ile
El
Salv
ad
or
Do
m.
Rep
ub
lic
2 School variables were not explicitly part of the analysis due to lack of data so their effect is captured by family socioeconomic characteristics that influence the capacity to access better quality schools.
18
Reshaping Economic Geography in Latin America and the Caribbean
For instance, having a mother with only primary
education increases the risk of school dropout by
as much as 160 percent in Chile and no less than
60 percent in El Salvador compared to having a
college educated mother. Second in importance
is family income, whose effect is often about half
that of parental education.
Spatial inequality in resources affects not only
possibilities but also the incentives to invest in
human capital. The evidence of the impact of
school quality in Latin America suggests that this
is a significant source of variation in the returns
to education. As an example, Arias et al. (2004)’s
study for Brazil measured the impact of educa-
tion quality on schooling returns from cross-state
and inter-cohort variations in pupil-teacher ratios
—proxies for quality of education. Workers edu-
cated in states with a lower pupil-teacher ratio
have higher average returns to education (by 0.9
percentage points per year of schooling). Large
class sizes are not uncommon to Latin American
poor children especially in rural and marginal
urban schools. The pupil-teacher ratio is also
correlated with other key inputs of the educa-
tional process, such as instructional time, edu-
cational materials, and teachers’ education and
experience.
Human Capital Formation and Neighborhood
Effects
We now turn to the discussion of spatial dis-
parities in welfare within micro-spaces in urban
areas, dubbed as “neighborhoods”. This discus-
sion has gained increasing interest in the Latin
American social policy debate –how contextual
surroundings, physical endowments, and social
interactions influence opportunities and mobility
of households. The examination of neighborhood
externalities in human development is important
also for another reason. Above, we showed that
“space” is an important correlate of a number of
human development indicators –however, how
such spatial influences can influence household
non-monetary welfare is largely unexamined.
The neighborhood literature tries to shed light on
exactly these transmission mechanisms.
In work conducted for this study Giovagnoli,
Arias, and Henstchel (2008) examine the impact
of neighborhoods on education and health out-
comes in Bolivia and Peru. They use survey data
matched with recent census data. The authors
examine a number of spatial transmission mech-
anisms and find relatively strong indications of
causal influences for the existence of role model
effects for school drop-outs in Bolivia. Even with
strong assumptions about endogenous self-selec-
tion into neighborhoods, the statistical relation-
ship between space and the likelihood of school
drop-out remains significant. Moving a youth with
given characteristics to a neighborhood where
there is a 10 percent higher school drop out rate
than the original neighborhood, increases the
probability for the newly moved child to drop out
from school between 1 and 3.8 percent. The au-
thors find somewhat weaker, but still relatively
strong, evidence of the importance of educational
externalities.
19
Reshaping Economic Geography in Latin America and the Caribbean
Chapter 4:Policy Implications
Chapter 2 of this report considers how the spatial
income patterns of the region can be understood
in terms of the 3-Ds—density, distance, and
division—and Chapter 3 documents the large dis-
parities in health, nutrition, and education within
countries across the region. The final chapter
deals with the question of how policy can be
informed by these findings, with a focus on how
to integrate leading and lagging regions within a
country.
The policies discussed in this chapter are oriented
towards promoting long-run economic growth.
Theory and historical experience suggest that
growth is spurred by the spatial concentration
of economic activity combined with high levels
of human capital. Thus, policy can encourage
growth by promoting human capital and address-
ing distance and division, which are the two ob-
stacles to increasing density. Following the guide-
lines of the 2009 World Development Report, a
three-pronged approach can be used, summa-
rized as the “3-Is”: Institutions, Infrastructure,
and Incentives.
“Institutions” as used here has a broad meaning,
covering both 1) institutions that ensure equality
of opportunities like education, health care, food
security, and basic services, and 2) institutions
that provide a regulatory framework, such as
property rights, land tenure regimes, and trans-
port and urban development regulations. Ensur-
ing that institutions are spatially blind should be
the primary approach for most countries. In terms
of education, health care, food security, and ba-
sic services like water, sanitation, and electricity,
“spatially blind” means equal access to people
across the country, regardless of location.
“Infrastructure” refers to spatially connective
policies aimed at connecting places and mar-
kets. Prime examples are interregional highways
and railroads to promote trade and improving
information and communication technologies to
stimulate the flow of ideas. These policies should
supplement the focus on institutions, in countries
where lagging areas have large numbers of poor
and few impediments to mobility.
“Incentives” refers to spatially focused policies
to stimulate economic growth in lagging areas,
such as investment subsidies, tax rebates, lo-
cation regulations, local infrastructure develop-
ment, and targeted investment climate reforms,
such as special regulations for export process-
ing zones. This approach can be used in addition
to the focus on institutions and infrastructure, in
countries fragmented by linguistic, political, reli-
gious, or ethnic divisions, which cause areas to
be particularly likely to suffer from coordination
failures and poverty traps.
Why the Focus on Institutions, Infrastructure,
and Incentives
This report has discussed how the experience of
countries around the world has been that growth
and development takes place via a process of
concentration of economic activity and popula-
20
Reshaping Economic Geography in Latin America and the Caribbean
tion, as people move from areas where they were
settled due to historical circumstances towards
areas favored by markets. It also considered
the existing patterns of income and poverty in the
region and showed that economically prosperous
areas in the region’s countries have high popula-
tion density, low economic distance to cities, and
low levels of ethno-linguistic division.
Given that migration from lagging to leading areas
has been a crucial component of development in
the world’s success stories—in the United States,
in Europe, in China, and elsewhere—the key
objective of governments dealing with differences
in welfare across space in all countries should be to
not stand in the way of this process. The primary
objective of policies should be to develop portable
assets that help people migrate to places with
economic opportunities. This can be done through
guaranteeing equal access to basic services—
education, health care, water, and sanitation, for
example—regardless of one’s location. Promoting
such “spatially blind institutions” corresponds to
ensuring equality of opportunity.
But in countries with substantial lagging areas
with high population density additional efforts
to connect with leading areas may be necessary.
Isolation from markets in more dynamic parts of
the country reduces welfare, as workers and pro-
ducers have limited possibilities for offering their
labor and products. In these cases, infrastructure
and other investments that connect peripheral
areas to markets will improve both consumer
welfare and productive efficiency.
Finally, in a third case—countries facing deep di-
visions due to, for example, ethno-linguistic or
religious heterogeneity—the combination of spa-
tially blind and spatially connective policies may
be insufficient. In such cases, there may be a
need to complement institutions and infrastruc-
ture with spatially focused incentives to encour-
age economic production in lagging areas.
This report recommends caution in the use of
spatially focused incentives. This approach fol-
lows from the mixed results of such policies. In
those cases where spatially targeted interven-
tions have been successful, they have been cou-
pled with both policies that both foster spatially
blind institutions (i.e., ensuring equality of oppor-
tunity) and spatially connective policies. Without
laying the foundations through a principal focus
on institutions and infrastructure, targeted incen-
tives are unlikely to succeed.
Overall Inequality and Spatial Inequality in
Latin America and the Caribbean
Policymakers have sometimes considered reduc-
ing spatial inequality income as a policy objective
in itself, which in turn arises in part as a response
to the historically high levels of overall inequali-
ties in the region.3 We can quantify “spatial in-
equality” in income as income inequality between
subregions of the country. Overall inequality in
income is equal to the sum of within-subregion
inequality and spatial inequality.4 Spatial inequal-
ity in income accounts for only a total minority
of overall income inequality in most countries.
Spatial equality in income amounts to less than
3 See Lopez and Perry, 2008.4 Unlike the Gini index, the overall Theil index can be decomposed
into between and within components.
21
Reshaping Economic Geography in Latin America and the Caribbean
ten percent of overall income inequality in all
but four countries (Haiti, Honduras, Peru, and El
Salvador.) The remainder of income inequality is
accounted for by inequality within the subregions
of each country. This suggests that the margin
for reducing overall inequality by reducing spatial
inequality is limited.
Inequality of Opportunities in Latin America
and the Caribbean
Instead of attempting to address spatial inequal-
ity in outcomes like income, an approach with
direct policy implications is to reduce inequality
of opportunities. Because much inequality of op-
portunities is related to space, it may be neces-
sary to target investments to disadvantaged ar-
eas in order to achieve equality of opportunities.
A focus on inequality of opportunities is attractive
for several reasons. There is generally a stronger
societal consensus around the ideal of equality of
opportunities than around equality of outcomes.
The aim with greater equality of opportunity is to
level the playing field so that circumstances such
as gender, ethnicity, birthplace, and family back-
ground, which are beyond the control of an indi-
vidual, do not influence a person’s life chances.
Quantitative estimates in a recent study suggest
that between one-half and one-quarter of overall
economic inequality in a typical LAC country is
due to inequality of opportunities. Moreover, in-
equality of opportunities, measured in terms of a
child’s access to education, electricity, water, and
sanitation is extremely high in many countries in
the region.5
Inequality in access to infrastructure—water,
sanitation, and electricity—are strongly deter-
mined by location. Although the work in the pre-
vious chapter showed that inequality of economic
outcomes is not principally associated with place
(in terms of national subregion), inequality of op-
portunities is largely a consequence of where a
child lives, chiefly due to differences across the
urban-rural divide. While spatial inequality of
economic outcomes is low relative to overall in-
equality, spatial inequality of opportunities is very
substantial. In many countries, children living
in rural areas face insufficient access to basic in-
frastructure and services and are thus disadvan-
taged as adults.
It is important to recognize that achieving equality
of opportunity will necessarily require very large
investments in health, education, and basic ser-
vices in areas that are currently disadvantaged.
While patterns vary, in many countries, public ex-
penditures per person for health, education, and
basic services are much higher in central urban
areas than in more remote areas. Simply achie-
ving equality of expenditure in these sectors on
a per person basis would generally mean increa-
sing resources devoted to more remote areas.
The costs of providing some services are often
higher in more remote areas. This is particularly
likely to be the case for public services like water,
sanitation, and electricity. However, spending at
higher levels (on a per capita basis) to achieve
equality of opportunity in these areas can be jus-
tified in light of the fact that in other realms of
public spending, more remote areas are often
very disadvantaged. 5 Paes de Barros, et al. (2008).
22
Reshaping Economic Geography in Latin America and the Caribbean
Two additional considerations are in order for
policy to address inequality of opportunities. First,
the package of opportunities that is considered
essential will necessarily vary with a country’s
level of development. The decision as to which
opportunities are affordable and desirable for a
particular country must be made by that partic-
ular society. Second, the technology to provide
equality in a particular opportunity will often vary
across space. To take one example, access to ba-
sic health care might be provided chiefly through
large hospitals in urban areas and clinics in re-
mote rural areas.
Institutions
The shorthand term “institutions” covers a va-
riety of regulatory, universal, and spatially blind
policies. A key aspect of many policies in this
category is that they are focused on improving
skills and health, which they can use wherever
they live. Thus, when people relocate from those
areas not favored by markets to leading areas
with more opportunity, they have portable hu-
man capital assets they can carry with them. This
focus meshes well with a general emphasis of the
equality of opportunities.
One type of program in this category is the con-
ditional cash transfers which have been popular
and highly successful in a number of countries. In
Brazil, Bolsa Familia has improved education and
health outcomes. Cash transfers are given in ex-
change for school attendance, for health checks,
and other welfare-related issues. They thus not
only provide the household with an income, but
also ensure that they have the conditions needed
to secure economic resources for themselves in
the future. Similarly, Oportunidades in Mexico
has spurred school attainment and improved
health for many poor Mexicans.
A different mechanism that can potentially con-
tribute to spatially blind institutions in LAC is
decentralization. Decentralization can improve
service provision through two channels. First,
decentralized governments can be held more ac-
countable because citizens are able to exercise
“exit” and “voice” more effectively. Second, lo-
cal governments have better information and are
thus better able to ensure better provision. How-
ever, the advantages of local level information
must be gauged in light of the economies of scale
and positive externalities lost in comparison to
large scale provision by central government.
Infrastructure
The shorthand term “infrastructure” covers a va-
riety of spatially connective policies. The purpose
of such policies is to promote economic growth in
currently lagging areas by linking them to lead-
ing areas. The emphasis on such policies follows
from the observation that integration—measured
in terms of economic distance—is a key determi-
nant of an area’s economic success.
A primary example of spatially connective poli-
cies is improving the intraregional road network.
In Brazil improvements to the road network be-
tween the 1950s and 1980s reduced transport
and logistics costs. But most of the economic
gains accrued to the Center-West, with only small
gains to the lagging Northeast, at a time when its
23
Reshaping Economic Geography in Latin America and the Caribbean
share of the national network increased from 15
percent to 25 percent. Even so, such investments
did bring economic density closer to the lagging
Northeast.
Incentives
The shorthand term “incentives” refers to spa-
tially targeted programs intended to promote
economic growth in lagging areas. Area incen-
tives, popular in developing countries, have pro-
duced mixed results. In Brazil, where the goal
has been to attract “dynamic” industries to the
lagging North and Northeast by providing fiscal
incentives, expenditures have reached $3–$4
billion a year. A recent impact evaluation shows
that the allocation of these “constitutional funds”
did induce the entry of some manufacturing es-
tablishments into lagging regions—but incentives
were not attractive enough for vertically integrat-
ed industries.6 Between 1970 and 1980 the Mexi-
can government used fiscal incentives to promote
industrial development outside the three largest
urban agglomerations. Firms locating outside
these three large cities were eligible for a 50–100
percent reduction in import duties and income,
sales, and capital gains taxes, as well as acceler-
ated depreciation and lower interest rates. Their
impact on economic decentralization was insig-
nificant because import duties on raw materials
and capital goods were low to begin with; so the
reductions had no effect on location decisions
and lost revenues.7
How do Territorial Development Programs
Fit in the “3-Ds” and “3-Is” Frameworks?
Territorial development programs (TDP) have be-
come popular in many Latin American and Carib-
bean countries. They typically are constituted by
a variety of programs in several sectors. Within
such programs, governments have sometimes
emphasized spatially targeted programs for in-
come generation. Given the mixed experience
with such programs, a preferred approach is for
territorial development programs to emphasize,
in first instance, investments in spatially blind in-
stitutions—including basic services—and to sup-
plement this approach with spatially connective
infrastructure for areas that are higher density
areas with large numbers of poor people. Spa-
tially targeted programs for income generation
should only be used in the more limited case of
areas suffering from problems of great division.
More concretely, this prescription suggests that
a territorial development program should focus
first on improving access to education, health,
and basic services such as water and electricity.
In densely populated poor areas, a TDP should
also improve roads and communications infra-
structure to better connect to leading areas. The
emphasis on connectivity follows from the obser-
vation that remote areas cannot be prosperous in
isolation. Their economic success requires links
to the greater regional and national economy.
It is worth noting that the first two policy goals
suggested here for territorial development pro-
grams—connecting remote areas through infra-
structure and increasing human capital through
6 Carvalho, Lall, and Timmins (2008). Constitutional funds were created in 1989 to finance economic activities in the North and Northeast regions.
7 World Bank (1977) and Scott (1982).
24
Reshaping Economic Geography in Latin America and the Caribbean
large investments in education, health, and basic
services—feature prominently in the 2008 World
Development Report, Agriculture for Develop-
ment. Although the themes of the 2008 and 2009
WDRs are very different, they share these two
themes, both recognizing that enhancing porta-
ble human capital and the connection of outlying
areas are essential policy objectives.
A Key Institution for Latin America and the
Caribbean: Land Policy
Land policies play an important facilitating role
in the spatial development of countries, sub-na-
tional regions, cities and neighborhoods. Densi-
ty in urban activity leads to increasing demand
for land and increasing land prices, based on
the higher economic returns associated with the
spatial agglomerations at the heart of urban cen-
ters. The gradient of land rent for almost every
growing urban center is the same--highest prices
in the center declining as a function of distance
and decreasing density. This generality implies
the need for land policies and land institutions
in urban centers which permit these dense, high
value uses to occur. These include the following:
1) clear property rights and rules of the game for
property markets which are fair and transparent;
2) robust land information systems in registries,
which provide information to market participants;
3) capacities for public acquisition to ensure land
supplies and discourage speculative landhold-
ing; and 4) value-based property taxation which
encourages intensity of use and finances pub-
lic infrastructure to support private investment.
Transparent land governance is critical to inhibit
rent-seeking in urban land development and fair
competition.
25
The Spatial Distribution of Welfare in Latin America and the Caribbean
This chapter presents the major geographic
features of Latin America and the Caribbean and
introduces a highly disaggregated map showing
the distribution of income in the region, along
with a map of the spatial distribution of population
in the region. The chapter also discusses how
geography can affect economic development
and discusses the initial conditions which helped
determine the spatial distribution of population and
economic activity in the region. Initial locations
of major cities were determined by the strategic
and political considerations of colonial settlers.
During the first centuries under colonial rule in
the region, population remained dispersed in rural
areas, engaged in resource extraction activities.
The cities established as administrative and trade
centers became the later attraction points for
population and economic activity.
Those who journey through Latin America and
the Caribbean bear witness to a colorful mo-
saic not only of cultures, cuisines, climates, and
ecosystems but also standards of living. Along
the roads that link the southern tip of Argentina
to the northern reaches of Mexico, a traveler is
confronted with stark differences in welfare le-
vels across international borders, between regio-
ns of a country, between rural and urban areas,
and even between neighborhoods of a city.
This chapter presents the spatial distribution
of income using a map of the region, showing
local areas shaded according to their average per
capita income. It is as if we were to look at a
snapshot of the continent8 taken from a satellite
camera that could photograph the different levels
of income in shades of corresponding intensity.
This map reveals large areas of the continent
where income levels are consistently high across
space and others where they are consistently
low. Such areas cross international boundaries
and follow major geographical features.
The information presented in this chapter comes
chiefly from census and survey data from most
Latin American and some Caribbean countries
collected at different times between 2000 and
2006. These chronological differences generate
comparability issues which are further amplified
by differences in the way questions were asked
in each survey, as well as by the different ways
in which economic concepts are understood in
each country. While a natural consequence of any
study covering a wide range of countries, every
effort has been made to minimize these compa-
rability issues.9
Chapter 1 The Spatial Distribution of Welfare in Latin America and the Caribbean
8 The term “continent” is used here to refer to the Latin America and Caribbean region; the sub-national regions we use as units of analysis, in turn, are referred to as regions.
9 See appendix for details.
26
Reshaping Economic Geography in Latin America and the Caribbean
The remainder of the chapter is organized as
follows. Section 1.1 describes the geography of
LAC and outlines the ways in which geographic
characteristics can influence economic growth.
Section 1.2 examines the spatial distribution of
income in the region via a high-resolution map.
Section 1.3 presents the spatial distribution of
population in the region. Section 1.4 describes
the history behind the modern day distribution
of the population in the region, and Section 1.5
draws conclusions.
1.1 Space and Economic Development
Latin America and the Caribbean encompass tre-
mendous geographic diversity. The terrain stret-
ches from the Pampas’ vast flatlands to the high
peaks of the Andes. The region’s climate ranges
from the world’s most arid desert in northern Chi-
le to the extreme humidity found in the rainfo-
rests of the Amazon and Costa Rica, and from the
glacial cold of Tierra del Fuego to the blistering
heat of Mexico’s Sonora desert. The continent
has an extremely long coastline to which all but
two countries have direct access and is home to
the immense Amazon River, which pours almost
220,000 cubic meters of water into the Atlantic
Ocean every second.
The region’s main geographic features can be
seen in Figure 1.1. In the north, the Mexican de-
serts end at the center of the country, where the
two mountain ranges meet to start a mountai-
nous area that stretches to Guatemala, as well
as a green area that covers the whole of Central
America. In South America the sharp peaks of
the Andes wall in the western end of the Amazon
rainforest, and then expand to form the Peruvian
and Bolivian Altiplano before they rise to even
higher altitudes at the border between Chile and
Argentina and then finally recede at the glaciers
of Tierra del Fuego. On the eastern side of South
America, the hills of Patagonia flatten out to the
northeast and turn into the fertile Pampas. As the
climate turns more humid in northern Argentina
and Paraguay, the landscape becomes greener, as
if anticipating the lushness of the Amazon rainfo-
rest. An arid region stretches from the southwes-
tern part of Brazil, situated between the Amazon
and Paraguay, to the Sertão in northeast Brazil.
How are these geographic features related to eco-
nomic activity? In a famous study on the Medite-
rranean, Fernand Braudel wrote that mountains
“hinder transport, turn coast roads into corni-
ches, and leave little room for serene landscapes
of cities, cornfields, vineyards or olive-groves sin-
ce altitude always gets the better of human acti-
vity.”10 Braudel’s analysis applied to Latin America
provides one reason why geography may mat-
ter. The region is home to the longest exposed
mountain range in the world, the Andes, which
has many summits rising above 6,000 meters.
Their escarpments are so steep that the crossing
from one side to the other was for many years a
long and dangerous endeavor.
The following is an excerpt from a 19th century
traveler’s account of the trip from Villeta to Gua-
duas, on the lower part of the Cordillera Oriental
of the Andes in Colombia:
10 Braudel, 2002, p. 5.
27
The Spatial Distribution of Welfare in Latin America and the Caribbean
Figure 1.1. Physical Map of Latin America and the Caribbean
Source: UNEP/DEWA/GRID-Europe, GEO Data Portal, compiled from NASA/GSFC, http://geodata.grid.unep.ch/
28
Reshaping Economic Geography in Latin America and the Caribbean
[I]t was tremendous—down—down—down! ro-
cks, ravines, precipices, steeps, swamps, thus
again and again; free-stone ascents, which ap-
peared to imbibe the moisture of a warm atmos-
phere, and crumble at the touch; hills under-worn
at the foot, tilted into the ravine and steep gulleys
washed by the mountain floods, leaving the large
rocks naked and tottering, over which, and over
which only, lay the track for man and beast.11
The challenges of such a journey suggest that
one mechanism through which geography can
matter for economic development is by creating
transportation costs which isolate certain regio-
ns. Geographical barriers can effectively create a
long economic distance between one region and
the rest of the world.
The literature has suggested four other channels
through which geography may influence econo-
mic performance.12 First, year-round warm wea-
ther like that found in tropical regions, makes it
extremely difficult to control the spread of infec-
tious diseases, resulting in high mortality rates,
hindering the development of a healthy workfor-
ce. Second, agricultural production in tropical re-
gions is less productive. Although a wide variety
of products are native to these areas, the yields
are generally lower because pests are more diffi-
cult to control, photosynthesis occurs more slo-
wly, and rainfall variability is greater. 13
These three channels—distance, climate, and agri-
cultural productivity—are purely geographic; their
mechanisms operate without human intervention
and are thus based on what is called “first-natu-
re geography”. The fourth reason why geography
can affect economic development incorporates
human action. According to the so-called “curse
of natural resources” hypothesis, countries that
are rich in natural resources concentrate on the
exploitation of those resources, and the invest-
ments made in that sector crowd out investments
in other activities more conducive to growth.14
Another channel through which geographic cha-
racteristics may have affected economic deve-
lopment is institutions.15 Upon conquering new
territories, colonizers established institutions
that replicated European ones wherever geogra-
phic conditions—mostly climatic—allowed them
to settle. In places where the environment was
prone to the spread of infectious diseases, co-
lonizers could not settle and hence established
institutions designed solely for the most efficient
extraction of natural resources. According to this
theory, the institutions implemented during the
colonial period would have shaped modern insti-
tutions and hence affected growth.
The “new economic geography” which is the
focus of this report abstracts completely from
first-nature geography and focuses instead on
“second-nature” geographic characteristics, pro-
posing that concentrations of population paired
with increasing returns to scale and low trans-
port costs, result in the physical concentration of 11 W.M. Duane, 1826, pp. 577–578, cited by Palacios and Safford,
2006, p. 18.12 See IADB, 2000, p. 21. See also Gallup, Sachs, and Mellinger,
1999; Leamer et al., 1999; and Sachs and Warner 2001.13 IADB, op. cit., p. 21.
14 See Sachs and Warner, 2001.15 See Acemoglu, Johnson, and Robinson, 2001.
29
The Spatial Distribution of Welfare in Latin America and the Caribbean
production. The combination of people and firms
in space—economic density—gives rise to exter-
nalities known as agglomeration economies that
foster economic growth. Nonetheless, there is
evidence that pure geography matters as well.
The following section lays out the distribution of
income in the region, which is analyzed in light of
both first-nature and second-nature geography in
Chapter 2 of this report.
1.2 The Distribution of Income across Latin America
Recent statistical developments make it possible
to estimate income (or consumption) at high le-
vels of geographic disaggregation. Using these es-
timates, compiled from a number of researchers,
Figure 1.2 presents a highly detailed picture of
income and consumption levels in the region, and
Figure 1.3 provides a close-up of Central America
and part of the Caribbean.16 Estimates of mean
per capita income/consumption at the smallest
possible unit available are shown in red. Country-
specific basic needs indicators, shown in green,
were used in those cases where neither income
nor consumption estimates were available. An at-
las of country-level maps with the same informa-
tion is presented in Chapter 2 of this report.
Several caveats apply to these maps. For a num-
ber of reasons, the maps are not fully compara-
ble across borders. First, some of the estimates
correspond to income, while others correspond to
consumption. Second, the estimates were cons-
tructed using census and survey data collected
in different years. Third, the definitions of the
income and consumption aggregates vary from
country to country. The income/consumption va-
lues (shown in red) cannot be compared in any
respect to the basic needs index values (shown
in green.) For those countries where a measure
of per capita income/consumption was availa-
ble, figures have been scaled by country-specific
factors such that the population weighted avera-
ge of income/consumption equals the country’s
2007 GDP per capita, adjusted to purchasing-
power-parity equivalent dollars of 2005.
While mean income clearly varies throughout the
region, there are large areas with similar levels. 17
A medium to low-income area encompasses the
northern part of Argentina, most of Paraguay, Bo-
livia, non-coastal areas of Peru, most of Ecuador,
the Pacific coast in Colombia, and the northwes-
tern part of Brazil. Another large area of low inco-
me is found in northeastern Brazil, covering the
states of Maranhao, Piaui, Ceara, Rio Grande Do
Norte, Paraiba, Pernambuco, Alagoas, Sergipe,
and Bahia. A third area of low income extends
across Nicaragua, Honduras, El Salvador, Guate-
mala, and most of the southern Mexican states of
Chiapas, Oaxaca, and Guerrero.
Zones with high levels of income can also be iden-
tified. The north of Mexico and Venezuela form two
of these areas. 18 Another can be found in Brazil,
16 Often called loosely “poverty maps”, small-area estimates are based on the methodology proposed by Elbers, Lanjouw, and Lanjouw (2003) which combines census and survey data to exploit the statistical representativity of the former with the detail of the latter. See Box 2.1, in Chapter 2, for a detailed explanation.
17 The term “income” will be used in this chapter as shorthand for monetary welfare, measured either through income or expenditure.
18 Data for Venezuela are aggregated at the regional level, which eliminates the possibility of analyzing welfare differences within those regions.
30
Reshaping Economic Geography in Latin America and the Caribbean
formed by the states of Mato Grosso, Goias, Sao
Paulo, Rio de Janeiro, and parts of Minas Gerais
and Mato Grosso Do Sul. Finally, although inco-
me data for Argentina are not comparable, assu-
ming the levels of Argentina’s Basic Needs Index
roughly reflect income, another large area of high
income would include Chile, and the southern two-
thirds of Argentina, and Uruguay. 19
Figure 1.2 Municipal-Level Income or Basic Needs in Latin America and the Caribbean
19 The map in Figure 1.2 shows an index of unsatisfied basic needs representative of the whole population.
31
The Spatial Distribution of Welfare in Latin America and the Caribbean
Figure 1.3. Monetary Welfare in Central America, Jamaica, Haiti,
and the Dominican Republic
Source:World Bank staff production with data provided by various authors (see data sources in references).Note: Haiti: data correspond to 2007 GDP per capita at 2005 US$ (PPP adjusted). All other countries: figures correspond to survey data estimates at the regional level or small-area estimates based on sur-vey and census data. The resulting estimates of mean per capita inco-me have been rescaled so that the population-weighted average mat-ches 2007 GDP per capita at 2005 US$ (PPP adjusted). In the cases of Jamaica, Nicaragua, and Panama estimates of mean per capita ex-penditure have been used instead of mean per capita income. Grey areas represent missing data.
Box 1.1 Methodology for Small-Area Estimates
The maps here are assembled from a series of country-specific maps that were developed using small-area techniques. For those countries for which an income or consumption-based map was not available, a map was generated using a basic needs index.
The small area estimates were calculated by various researchers using a technique developed in Elbers, Lanjouw, and Lanjouw, 2003. The technique involves estimating a relationship between income (or consumption) and household and community characteristics in a household survey and using that relationship to estimate simulated values of income/consumption in a population census.The statistical methodology can be roughly described as a two-stage process
• In the first stage, survey data are used to construct an econometric model of household per capita income/consumption. The model is constructed using variables that can be found in both the survey and the census. The coefficients estimated from that model are then preserved and used in the second stage.
• In the second stage, census data together with the coefficients estimated from stage one are used to predict the indicator of interest. The prediction is made at the household level and contains a simulation of the error term of the model from stage one. Several simulations are run and the results are averaged out at the small-area level. Standard errors of the small-area estimates are also computed.
Producing small-area estimates is a complex and painstaking process which requires careful and time-consuming data analysis as well as a long computational time. The last problem has been greatly reduced thanks to the production by World Bank staff of a specialized software, PovMap, that is available at no charge from http://iresearch.worldbank.org
One of the first uses given to small-area estimates was to produce estimates of poverty rates at the municipality level and to present them graphically on a map whose colors vary according to the level of poverty. This popularized small-area estimates under the term “poverty maps” but, as discussed by Bedi, Coudouel, and Simler (2007), their applications go beyond mapping poverty. Among other uses, they are helpful for targeting social interventions and, as in this chapter, for analyzing the spatial determinants of welfare.
32
Reshaping Economic Geography in Latin America and the Caribbean
Population Population
Country Population Density Year Country Population Density Year
Caribbean Countries Central American Countries
Anguilla 11,561 120 2001 Costa Rica 3,810,179 75 2000
Antigua 75,561 270 2001 Dominican Rep 8,562,541 176 2002
Aruba 90,506 469 2000 El Salvador 5,744,113 273 2007
Bahamas 303,611 22 2000 Guatemala 11,200,000 103 2002
Barbados 250,010 581 2000 Honduras 6,535,344 58 2001
Barbuda 1,325 8 2001 Mexico 97,500,000 50 2000
Belize 240,204 10 2000 Nicaragua 5,142,098 42 2005
British Virgin Islands 20,253 134 2000 Panama 2,948,023 41 2000
Cayman Islands 39,410 152 1999 Average 141,442,298 102
Cuba 11,200,000 102 2002
Dominica 71,474 97 2001 Andean Countries
Grenada 102,632 298 2001 Bolivia 8,274,325 8 2001
Guadeloupe 386,565 237 1999 Colombia 43,400,000 38 2006
Guyana 751,223 3 2002 Ecuador 12,100,000 47 2001
Haiti 7,929,048 286 2003 Peru 26,200,000 20 2005
Jamaica 2,607,631 237 2001 Venezuela 23,100,000 25 2001
Martinique 381,427 338 1999 Average 113,074,325 28
Montserrat 4,482 44 2001
Netherlands Antilles 175,653 220 2001 Southern Cone Countries
Puerto Rico 3,808,610 429 2000 Argentina 36,300,000 13 2001
Sain Berthelemy 6,852 326 1999 Brazil 170,000,000 20 2000
Saint Martin 29,079 539 1999 Chile 15,100,000 20 2002
St. Kitts & Nevis 46,111 171 2001 Paraguay 5,163,198 13 2002
St. Lucia 158,076 256 2001 Uruguay 3,241,003 19 2004
St. Vincent 102,631 264 2004 Average 229,804,201 17
Suriname 492,829 3 2004
Trinidad & Tobago 1,262,366 245 2000
Turks & Caicos 20,014 40 2001
Virgin Islands US 108,612 309 2000
Average 30,677,756 214
1.3 The Distribution of Population in Latin America
Population densities vary enormously from coun-
try to country: from a low of 3 people per squa-
Table 1.1. Population Density in Latin American and Caribbean Countries
Source: Own calculations based on data from CityPopulation, http://www.citypopulation.de/ Area for Puerto Rico comes from CIA Fact-book, https://www.cia.gov/library/publications/the-world-factbook/geos/rq.html
red kilometer in Suriname and Guyana to more
than 500 people per squared kilometer in Bar-
bados and Saint Martin (see Table 1.1). A coun-
try-level measure of population density, however,
does not provide a useful guide to concentration
33
The Spatial Distribution of Welfare in Latin America and the Caribbean
for large countries, where the population is highly
concentrated in a limited number of cities. Figure
1.4 shows the location of population by overla-
ying the income map circles representing cities
with more than 100,000 inhabitants. The circles
are scaled by the total population of the city.
Figure 1.4. Mean Per Capita Income and Population in LAC around 2000
Source: World Bank staff calculations.Note: See footnote to figure 1.3. Each red circle represents a settlement of 100,000 people or more and its size corresponds to the relative population of that city.
34
Reshaping Economic Geography in Latin America and the Caribbean
The map shows that relatively few people live in
the largest stretches of hinterland: east of the
Andes and in northern Mexico. But there are two
important deviations from this general pattern. In
Mexico, population is heavily concentrated around
the three largest cities, all of which are significan-
tly far from the coast and between the two moun-
tain ranges that run alongside the Pacific and Gulf
of Mexico coastlines. The second deviation occurs
in Colombia where most of the population resides
in the valleys of the Andes, roughly forming a
strip between the southern Pacific coast at the
border with Ecuador and Caracas, following the
Cordillera Oriental of the Andes.
The corridor between Cordoba, in Argentina, and
La Paz, in Bolivia is another area far from the
seashore where people have settled. Although
our map reveals that there is a substantial num-
ber of cities with more than 100,000 inhabitants
in this area, the total population established the-
re is not nearly as large as in the cases of Mexico
Box 1.2. The Definition of Space
It is important to define what is meant by space when analyzing spatial patterns. There are at least two dimensions along which the definition has to be made. First, the physical size of space, which affects the precision with which any phenomenon can be observed to occur, as well as the degree to which spatial patterns are declared. Imagine, for instance, that we define every block of a city as a spatial unit and that we want to analyze average individual income levels. The size of these spatial units would allow us to observe a great variety of income levels within a city and we would probably observe clusters of blocks with low, middle, and high income. If, on the contrary, we defined our spatial units as the northern and southern sides of the city, we would only observe two levels of mean income and all the clusters of low, medium, and high income blocks would be averaged out. Of course, the decision of how big the spatial units should be depends, as in our case, on the availability of data.
Second, the definition of space in social sciences must go beyond the merely physical aspects to incorporate relevant aspects of human activity. In contrast with a geographical definition of space which only takes into account the first–nature20 characteristics of a location, a socioeconomic conceptualization incorporates also its second–nature characteristics as well as the economic interactions between its inhabitants and the individuals of other locations. Although it may be geographically sensible to define one valley as a spatial unit, from a socioeconomic and even cultural perspective it may make much more sense to divide such space in two if, say, that valley is divided by an international border. Territorial development programs (TDPs) operate in territories that are defined on the basis of these considerations. In a rural TDP, “the territory is a space with identity and a development project socially concerted” (Schejtman and Berdegue, 2004, p. 5).
How we define space affects how we evaluate it. The city of Sao Paulo, for instance, could be considered small from a purely physical definition of space but large from a demographic or economic one. Similarly, Hawaii is physically isolated but economically well integrated if we consider the amount of people, goods, and money that come in and out of the archipelago on a daily basis.
In this chapter, the definition of space used—the sub-national regions—is fully determined by the characteristics of the data, which in turn are dependent on administrative boundaries. Yet, as we will see below, this definition of space reveals certain patterns of welfare that conform to the large and almost purely physically determined areas coarsely defined above.
20 The term “first nature” refers to the characteristics of a place as originally determined by nature: Whether it is a mountainous or coastal area, its mineral resources, and so on. “Second nature”, on the other hand, refers to the characteristics of a place that are the result of human intervention—the availability of roads and other works of infrastructure, for example.
35
The Spatial Distribution of Welfare in Latin America and the Caribbean
Population(millions)
Land Area (millions of square km)
Land in Tropics
(%)
Populationwithin 100 km
of Coast(%)
Coastal Density(population
per square km)
Population within 100 km of Coast or
Ocean-Navigable River (%)
Sub_Saharan Africa 580 24 91 19 40 21
Western Europe 383 3 0 53 109 89
East Asia 1819 14 30 43 381 60
South Asia 1219 4 40 23 387 41
Transition Economies 400 24 0 9 32 55
Latin America and the
Caribbean472 20 73 42 52 45
or Colombia. As with many dimensions of welfa-
re, the Andes mark a clear separation between
more and less heavily populated areas.
Income levels and population do not necessa-
rily coincide in the Latin America and Caribbean
territory. There are areas where both variables co-
incide: High levels of income in areas with high
demographic concentrations—e.g. around large
cities like Sao Paulo and Mexico city—as well as
areas with low levels of income and scarce popula-
tion—e.g. northeastern Nicaragua and in the Ama-
zon. However, there are also areas where income
Table 1.2 Demographic Concentration Along the Coast and Navigable Rivers in Large Regions of the World
Source: Gallup, Sachs, and Mellinger, 1999 (excerpt of Table 1).
Figure 1.5. Coastal Relative Concentration
Source: World Bank staff calculations ba-sed on Gallup, Sachs, and Mellinger, 1999 (see Table 1.2).Note: Columns represent the percentage of each region’s population living within 100 km off the coast, divided by the per-centage of each region’s territory that is within 100 km off the coast.
0
1
2
3
Sub-SaharanAfrica
WesternEurope
East AsiaEconomies
LatinAmerica &Caribbean
South Asia Transition
is high and population is scarce—e.g. in southern
Chile and Argentina—as well as heavily populated
areas with low average incomes—e.g. northeas-
tern Brazil.
Population in the region is concentrated along the
coast. In terms of the percentage of population
living within 100 km of the coast, LAC is roughly
tied with East Asia on the second place with 42
percent of its population living in coastal areas.
Western Europe has the highest ranking, with a
comparable figure of 53 percent.
36
Reshaping Economic Geography in Latin America and the Caribbean
Figure 1.5 presents the percentage of people
living in coastal areas—as defined by such 100
km fringe—divided by the percentage of each
region’s territory that is coastal.21 Latin America
still ranks second in world regions and East Asia
now shows the highest relative concentration.
Western Europe has fallen to the last place preci-
sely because 62 percent of its territory is within
100 km of the coast, compared to less than 20
percent in LAC. Notice, in Table 1.2, that if we
take into account ocean-navigable rivers, almost
90 percent of Western Europe’s population lives
in well connected areas—36 percent more of its
total population than when only considering the
coastal areas. In contrast, Latin America and the
Caribbean have few ocean-navigable rivers and,
as a result, only an additional 3 percent of their
population lives within 100 km of such rivers.
Although the population is located along the
coast, many important cities are not located at
the sea, where connectivity to markets would be
greatest. This leads to the question of how the
locations of population settlements were establis-
hed, which is the topic of the next section.
1.4 Historical Determinants of Population Settlements in Latin America
The current spatial distribution of Latin America’s
population has its origins in colonial times, when
the main cities of the region were founded.22
Unlike Western Europe, wherein cities emerged
naturally over a long period of economic develop-
ment, in Latin America they were created abrup-
tly by the Spanish and the Portuguese.
After the conquest of new territories, European
settlers arrived to the Americas embracing the
ideal of the city life. They were mostly people
who had spent a significant portion of their lives
in European cities, and the newly acquired lands
offered the possibility of founding new cities and
designing them in accord with the Renaissance
ideals. Downtown Mexico City and Lima, with
their perfectly squared blocks and streets tra-
ced in a grid centered around a plaza, are good
examples of the careful planning that accom-
panied the foundation of Latin American cities.
Although this type of urban design had existed
earlier in Europe, most notably in ancient Rome,
in the sixteenth century European cities were far
from conforming to the Renaissance ideals and
offered little possibility for adapting to it.
The new cities were sometimes founded in pla-
ces that coincided with major pre-Hispanic sett-
lements. Mexico City, for instance, was founded
in the place of Tenochtitlan, the center of the
Aztec empire. This central location not only offe-
red the military advantage of being close to many
of the different local tribes but also served as a
hub between the ports of Veracruz and Acapulco.
21 This ratio produces an index of population density in coastal areas relative to the country as a whole. A value of 1 for this ratio means that population density in coastal areas is equal to the population density in the whole region; a value of 2, say, means that it is twice as much.
22 This historical recount draws heavily from Morse, 1962.
37
The Spatial Distribution of Welfare in Latin America and the Caribbean
In many cases, however, the cities were created
ex nihilo in places that were deemed strategic for
military or commercial purposes. According to
one historian, “The site for Lima was chosen for
its good lands, its water supply and firewood, and
the commercial and military advantages of its
proximity to the ocean.”23 The location of Buenos
Aires was chosen for its strategic location at the
entrance to Rio de La Plata. First, it was an im-
portant military stronghold for the Spanish crown
since it was close to the southern limits of the
Portuguese territories. Second, it later proved an
extremely important entry point into South Ame-
rica and a port from which many of the resources
extracted from these colonies could be shipped
to Spain. The city of Salvador was founded on a
place suitable for a deep port and that had both
good winds and a good water supply. Cities such
as Guanajuato, in Mexico, and Potosi, in Bolivia,
were founded in the proximity to large mines of
precious metals even though they were far from
important indigenous settlements and from tra-
ding routes.
Colombia offers a hybrid case. The city of Bogota
was founded in a place where there was both a
substantial population of indigenous people and
the indication that emerald and gold deposits
could be found nearby. However, the topography
of the country effectively divided it in three regio-
ns—western, eastern, and Atlantic—and made it
impossible for Bogota to exert a central dominan-
ce over all three regions.
Either in previously populated or in relatively
empty places, the first cities of Latin America and
the Caribbean were founded with the objective
of extracting the natural resources of the colo-
nies, transporting them to Spain, and establis-
hing military and religious dominance. For many
years the cities were sparsely populated. Arriving
colonizers moved from the cities outward to ru-
ral areas. Such movements were encouraged by
the encomienda system. The new territories were
property of the king but were entrusted for their
exploitation to those willing to take the risk of
settling in new lands—mainly the conquerors the-
mselves. By one historian’s account, “According
to the legal concept, the encomendero had the
right to receive the tributes that the indigenous
communities owed to the king. In exchange for
this concession, the encomendero was obliged to
defend the kingdom and to evangelize those In-
dians entrusted to him.”24 In practice, however,
this system allowed for slavery and exploitation
of the indigenous peoples, and concentrated land
ownership in a few people who thereby acqui-
red social status. “The urgency with which land
was pre-empted was therefore heightened by the
process of social leveling that attended the sett-
lement. […] Spain had few colonists to export,
and the adventurers, those at least who had luck,
prowess, or ingenuity, were soon entrenched.
The sequel, therefore, to the leveling process is
the entrenchment of the privileged few, the con-
querors, at the expense of the latecomers and
the unprivileged many.”25
23 Morse, 1962, p. 321.24 Palacios and Safford, 2006, p.69.25 Morse, pp. 327–328.
38
Reshaping Economic Geography in Latin America and the Caribbean
The cities and the encomiendas were part of an
extraction-based economic system. The cities
were founded not with the goal of facilitating
commercial exchange between them and thereby
creating strong regional economies, but rather
with the intention of serving as the immediate
links between the motherland and the new te-
rritories. The excess agricultural production of
the rural areas and all the minerals extracted
were first sent to the cities and from there to the
Iberian peninsula.
This extraction-based system had two general
consequences. First, it impeded the development
of regional economic linkages. In some instances,
the situation was exacerbated by the geographic
characteristics of the territory, as in Colombia
where the Andes created major divisions in the te-
rritory. In other cases, the Spanish crown openly
prohibited trade among the colonies. Buenos Aires,
for instance, underwent such economic hardships
due to this prohibition that towards the end of the
sixteenth century its citizens asked the Crown to
abandon the city. Spain would not trade with Bue-
nos Aires because it represented undesired com-
petition with its own cattle-raising and so, given
the military importance of the city, the Crown was
forced to allow trade between the city and the su-
rrounding colonies. Later, this turned to Spain’s ad-
vantage as piracy around Portobelo (Panama) and
Acapulco forced shipments to be sent from Lima to
Buenos Aires by land and from there to Spain.
The second consequence is that despite the cen-
trifugal pattern of population expansion observed
during colonial times, the cities of Latin America
maintained their status of centers of political end
and economic life. Land owners kept properties
in the cities and resided there during special ce-
remonial periods and in times where life in the
countryside became especially harsh, such as du-
ring droughts or indigenous uprising.
Over time, as the colonies became independent
countries and, later, when economic activity
shifted from the primary to the manufactu-
ring sector, the cities attracted people from the
countryside. Just as during the colonial period,
the cities were the channel for all economic ex-
change with Europe. Economic intermediaries
came into action and thrived in these cities.
Industries also found it profitable to locate in the
cities. Together, all these factors set in motion a
cumulative process of demographic and econo-
mic concentration around the cities.
The end of the colonial period and the arrival of
the industrial revolution also changed the relative
importance of cities. During most of the ninete-
enth century, the newly independent countries
dramatically reduced their economic exchange
overseas and turned economically inwards. Some
cities that before had been important departure
points for the delivery of products, such as the
ports of Acapulco and Veracruz in Mexico, ceased
being central to the economy and instead smaller
cities like Guadalajara became regional centers
of economic activity. The invention of the train
was also key in allowing some of these cities to
thrive.
39
The Spatial Distribution of Welfare in Latin America and the Caribbean
1.5 Conclusions
This chapter has presented the spatial distribu-
tion of monetary welfare in Latin America and the
Caribbean. The maps revealed the existence of
large areas with similarly low—or high—levels of
welfare. These areas cross international borders
and their limits sometimes coincide with major
geographical features such as the Andes or the
arid areas in northeast Brazil.
As discussed, geography could directly affect wel-
fare levels through several channels, but there
are other important indirect mechanisms in which
geography affects welfare only indirectly, through
aspects such as institutions. The history of popu-
lation settlements in LAC lends some support to
the latter possibility. Cities in LAC were founded
during colonial times mostly as administrative
centers to efficiently extract the abundant natu-
ral resources found in the new territories. Other
times, cities were founded solely on the basis of
strategic military considerations.
41
The Links between Space and Individual Monetary Welfare
The World Development Report 2009 identifies
density, distance, and division as key factors
determining economic growth. This chapter pre-
sents an atlas of country maps for the region
showing both income and the location of popu-
lation. These maps can be used to consider the
extent to which distance and division are obsta-
cles to demographic concentration in each coun-
try. Some countries have the highest densities
of poor people in the wealthier areas, suggesting
that distance and division may not be major pro-
blems there. Other countries have a very large
proportion of all their poor people in low-income
areas, suggesting that economic distances, and
perhaps division, are important concerns. Using
a harmonized dataset for several countries whi-
ch includes geographic, socio-demographic, and
economic data, the chapter finds strong support
for the World Development Report’s thesis: den-
sity is positively associated with welfare whi-
le distance and division are negatively related.
Evidence from Peru’s experience also shows that
infrastructure can help overcome the challenges
posed by geography. Finally, the chapter argues
that the economic performance of one area has
significant effects on the economic performance
of its neighbors.
The World Development Report 2009, Reshaping
Economic Geography, considers the role of three
spatial dimensions—density, distance, and divi-
sion—for long-run economic growth. If these fac-
tors determine patterns of long-run growth, they
should also be related to current patterns of in-
come. This chapter analyzes the relationship bet-
ween an area’s mean level of per capita income
(or expenditure) and several of its geographic,
demographic and socioeconomic characteristics,
paying special attention to the three spatial di-
mensions put forth by the World Development
Report 2009.
This report examines how the themes of the 2009
World Development Report (WDR), Reshaping
Economic Geography, apply to differences within
countries in Latin America and the Caribbean.
The WDR considers the “3-Ds” at two additional
levels—internationally and at the level of cities—
which are not addressed in this companion volu-
me. (See Boxes 2.3 and 2.4 for a brief discussion
of these issues.)
One should be cautious about the reverse causa-
lity problem that such analysis poses. The obser-
vation that places with good infrastructure have
high levels of income, for instance, would not tell
us which caused the other. Does good infrastruc-
ture attract firms that offer high-wage jobs or is it
that people with high levels of income have paid
to have such infrastructure built? Could it be that
Chapter 2
The Links between Space and Individual Monetary Welfare
42
Reshaping Economic Geography in Latin America and the Caribbean
both factors reinforce each other? This analytical
problem is not easily solved, and consequently is
best understood as descriptive.
Why is this analysis important? After all, indivi-
duals can and often do move to the areas that
offer the best economic and personal opportuni-
ties available to them, leaving behind the places
in which they cannot fulfill their own goals. The
answer is that, first, regardless of where people
go, there will always be some characteristics in
that location which will affect their welfare. It is
precisely those characteristics which motivate,
to a great extent, the decision to move and it is
therefore important to know what characteristics
people seek out. A second reason is that people
do not always move, either because they face
restrictions or because they wish to stay in a cer-
tain place—e.g. because of cultural attachment
to the place of origin—and so it is important to
know what characteristics of the place should be
modified to improve welfare.
The next section briefly discusses the three spa-
tial dimensions introduced by the World Develop-
ment Report 2009 and presents a series of coun-
try maps contrasting the spatial concentration of
poor people with the spatial concentration of wel-
fare. This visual analysis provides an indication of
the extent to which distance and division may be
hindering the spatial concentration of people in
each country. In Section 2.2 the chapter presents
a cross-section statistical analysis of the relatio-
nship between welfare and the characteristics of
a territory—both purely geographic as well as the
three spatial dimensions proposed by the World
Development Report. Finally, using Peru as a case
study, section 2.3 examines how these spatial di-
mensions affect economic growth and whether
the economic success of an area can spill over to
other areas. Section 2.4 concludes.
2.1 Density,Distance,andDivisioninLAC
Based on the theoretical developments of the new
economic geography, the World Development Re-
port 2009 has proposed that long-run economic
growth is largely driven by the concentration of
economic activity in cities. This economic concen-
tration—density—together with the reduction of
distances and divisions are key determinants of
economic growth.
Density, defined as “the economic mass or output
generated on a unit of land,”26 is beneficial for
economic growth if three conditions are met: a)
There are economies of scale in production; b)
transport costs are low; and c) there is good fac-
tor mobility.
If there are economies of scale in production,
firms minimize costs by concentrating their pro-
duction as much as possible. However, they
weigh this benefit against the cost of distributing
their products to consumers located far away.
If transport costs are sufficiently low, it will be
optimal for them to concentrate their production
in a few or perhaps even a single location. When
choosing where to locate their plants, firms will
26 World Development Report, 2009, p. 49.
43
The Links between Space and Individual Monetary Welfare
look for places where they can readily access
inputs and where their products will be in high
demand, which in most cases occurs in places
with large concentrations of people and firms. In
order to be able to choose freely where to locate
a plant, however, firms require production factors
to be mobile.
Being close to firms and people has other advan-
tages in addition to being able to exploit econo-
mies of scale. People benefit from an extensive
set of goods and services provided at competitive
prices as well as from a large pool of potential
employers. Similarly, firms can benefit from the
availability of a large set of competitive suppliers
and workers. Moreover, technological innovations
spill over more easily from firm to firm when they
are close to one another. All these externalities—
known as agglomeration economies—make it at-
tractive for firms and people to locate in areas
with high economic density.
Economic concentration thus rises in a self-rein-
forcing mechanism: The more firms and people
establish in one area, the larger that market be-
comes and the larger the incentives become for
firms and people to establish in that area. Howe-
ver, if one of the three conditions listed above is
missing, the benefits of economic density are lost
or cannot be reaped.
When production does not present economies of
scale, the optimal decision for firms will be to lo-
cate a plant wherever there are some consumers.
The same decision could be reached if transport
costs are high since the benefits of large-scale
production could be offset by the cost of trans-
porting the goods to where consumers are. Fi-
nally, if production factors are immobile, firms
may not be able to locate close to other firms and
to people.27 Scale economies in production and
transport costs can be considered to be beyond
policy makers’ control; they are the result of
technological progress and even of nature. Fac-
tor mobility, however, can be affected by policy
through the reduction of distance and divisions.
As defined in the World Development Report
2009, distance “measures how easily capital flo-
ws, labor moves, goods are transported, and ser-
vices are delivered between two locations. Dis-
tance, in this sense, is an economic concept, not
just a physical one.”28 Divisions, in turn, “range
from moderate restrictions on the flow of goods,
capital, people, and ideas to more severe divisio-
ns triggered by territorial disputes, civil wars, and
conflict between countries.”29 Within a country,
divisions can be the result of ethnic segregation,
land-ownership conflicts, and any other social
cleavage, including economic-class distinctions
such as between slum dwellers and the rest of a
city’s population.
Distance and division reduce the ability of people
to move to more dynamic areas. The cost a wor-
ker faces when moving to a different area is not
only the one he incurs when relocating but also
27 This is the case, for instance, of agricultural and livestock activities, one of whose main production factors is land. Although these activities typically do exhibit economies of scale and firms in this economic sector could benefit from the same externalities mentioned above, large-scale production requires large portions of land and hence firms cannot agglomerate around a small area.
28 World Development Report, 2009, p. 75.29 World Development Report, 2009, p. 97.
44
Reshaping Economic Geography in Latin America and the Caribbean
the costs he incurs when going back to his place
of origin or when sending remittances. Similarly,
an indigenous person who faces discrimination
outside of his hometown would be discouraged
from moving into a city even if it offers good eco-
nomic prospects for the general population.30
The Latin America and Caribbean region, with its
diversity of cultures, geographic environments,
and levels of economic development, is affected
by distance and division in ways and intensities
that differ from country to country. As a result,
each country presents different degrees of eco-
nomic concentration which reflect in different
spatial distributions of population and income. As
the comparison between the maps of welfare and
population in Chapter 1 showed, some countries
have most of their population concentrated in
leading areas while others have substantial num-
bers of people located in lagging areas.
These differences are important because they
pose very different challenges for development.
A country that has a large number of people loca-
ted in lagging areas faces a larger challenge than
another country whose lagging areas are scarcely
populated, even if both countries have similar po-
verty rates, population sizes, and income levels.
The reason is that the latter country has most of
its impoverished population concentrated around
smaller dynamic areas where, ceteris paribus, it
will be easier to provide them with basic services.
Furthermore, the latter country can be expected
to achieve higher economic growth because, by
virtue of having a higher degree of economic con-
centration, is in better position to reap the bene-
fits of agglomeration economies.
The World Development Report has proposed di-
fferent policy layers for countries depending on
the extent to which distances and divisions affect
the concentration of the country’s population
around its dynamic areas:
• “1-D” countries are those where the poor live
in sparsely-populated lagging areas. For these
countries, the principal challenge is increasing
density by facilitating movement to density.
Panama, where the large bulk of the popula-
tion lives in leading areas, is a good example of
a “1-D” case.
• “2-D” countries are those that have large num-
bers of poor people concentrated in lagging
areas. For such countries, increasing density
needs to be complemented by efforts to over-
come barriers of economic distance. Brazil,
where large numbers of poor are concentrated
in the Northeast, is probably best categorized
as a “2-D” country.
• “3-D” countries are those with areas with sub-
stantial numbers of poor suffering from barri-
ers due to division. A key example of division
is the exclusion of ethno-linguistic minority
groups from full participation in the economy.
These countries face the challenge of simulta-
neously increasing density, reducing distance,
and overcoming division. Mexico, with its sub-
stantial indigenous population in Chiapas, is
best seen as a “3-D’ case.
30 Divisions such as those resulting from ethnic segregation can do more harm than just reduce factor mobility. Segregation reduces the economic opportunities of the disadvantaged group. It lowers their human capital, their employment opportunities, and their access to a wide range of assets and services. The marginalization to which these peoples are subjected can result in social conflict and, in extreme cases, it may lead to armed conflict. The guerrilla that erupted in the southeastern state of Chiapas, Mexico, in 1994 is a good example of this situation.
45
The Links between Space and Individual Monetary Welfare
These categories are not rigid, and some coun-
tries may predominantly fall into one category
but have substantial areas that fit other catego-
ries. For example, Panama as a whole best fits
the “1-D” category. The country has a small in-
digenous population which is very poor and geo-
graphically concentrated, suggesting the “3-D”
case. Maps showing income and the location of
the poor (or the population as a whole) are a use-
ful tool to consider in which category a country
Box2.1Small-AreaEstimates(a.k.a.“PovertyMaps”)
Until the recent development of small-area estimation techniques, no estimates of monetary welfare at a spatially disaggregated level existed for many countries. This problem was due to a common dilemma between data sources. On the one hand, household surveys contained all the information necessary to produce estimates of economic indicators. However, since surveys collect information from samples of the population, the estimates that can be derived from them are only representative of large geographical areas such as sub-national regions or even whole countries. On the other hand, censuses collect information from the whole population and so the indicators obtained from them are representative of any area, even as small as a block. However, since censuses interview every household in a country, they cannot collect information as detailed as that found in surveys and therefore do not contain information on economic indicators like those mentioned above.
A way around this dilemma was found by combining and taking the best of both data sources. The statistical methodology can be roughly described as a two-stage process (see Elbers, Lanjouw, and Lanjouw (2003) for a detailed exposition):
• In the first stage, survey data are used to construct an econometric model of the indicator of interest—say, household per capita income. The model is constructed using independent variables that can be found both in the survey and the census. The coefficients estimated from that model are then preserved and used in the second stage.
• In the second stage, census data together with the coefficient estimates from stage one are used to predict the indicator of interest. The prediction is made at the same level of analysis as in stage one—e.g. the household—and contains a simulation of the error term of the model from stage one. Several simulations are run and the results are averaged out at the small-area level. Since these predictions contain some degree of uncertainty, standard errors of the small-area estimates are also computed.
Producing small-area estimates is a complex process which requires careful and time-consuming data analysis as well as a long computational time. The last problem has been greatly reduced thanks to the production by World Bank staff of a specialized software, PovMap, that is available for free at http://iresearch.worldbank.org
One of the first uses given to small-area estimates was to produce estimates of poverty rates at the municipality level and present them graphically on a map whose colors changed according to the level of poverty. This popularized small-area estimates under the term “poverty maps” but, as discussed by Bedi, Coudouel, and Simler (2007), their applications go beyond mapping poverty. Among other uses, they are helpful for targeting social interventions and, as in this chapter, for analyzing the spatial determinants of welfare.
falls. Figures 2.1a to 2.1u form an atlas of wel-
fare and concentration of population. Each figure
presents one country with its administrative units
colored according to its own spatial distribution of
mean per capita income31 and dots representing
the number of people living in that area.
31 The different shades represent deciles of the distribution of the administrative units’ mean per capita income. As in figure 1.3, incomes have been scaled so that the population-weighted national average equals the country’s 2007 GDP per capita PPP adjusted to US$ of 2005.
46
Reshaping Economic Geography in Latin America and the Caribbean
These maps have to be read very cautiously for
several reasons. First, the dots representing num-
bers of people are placed randomly within the pe-
rimeter of the area to which they belong. In large
areas, the dots may all correspond to one specific
part of the area—e.g. a city—and yet be evenly
scattered throughout the whole area. Second, the
dots may overlap and hide each other. Small but
heavily populated areas such as Buenos Aires,
Figure2.1aWelfareandPopulation
inArgentina
Mexico city or Sao Paulo could have an enormous
amount of dots and yet look like there is only
one dot there. Third, the number of people that
each dot represents varies from country to coun-
try. Finally, some countries do not have income
(or expenditure) data. In those cases, the maps
present an index of unsatisfied basic needs.
In Argentina, population concentrates around
the main urban centers where welfare levels are
high, as measured by the country’s UBNI. Howe-
ver, there is also a significant amount of people
living in the northern part of the country, where
welfare levels are low.
Source: World Bank staff calculations with census data.Note: Index has been inverted: Lower values represent poorer areas with more unsatisfied basic needs. One dot represents 20,000 people.
Figure2.1bPerCapitaIncomeand
PopulationinBelize
Source: World Bank staff calculations with data from Gasparini et al., 2008.Note: Income figures have been scaled so that their population-weighted average equals Belize’s 2007 GDP per capita in PPP US$ of 2005. Each dot represents 1,000 people.
47
The Links between Space and Individual Monetary Welfare
In Belize, the lack of data prevents us from pre-
senting a map with a high level of geographic di-
saggregation. However, the map shows that the
population is close to being uniformly distributed
across the six regions of the country. Belize and
Cayo, the two wealthiest regions, concentrate 50
percent of the population but Toledo and Stann
Creek, the poorest, together have about 20 per-
cent of the country’s population.
Figure2.1cPerCapitaIncomeand
DensityofPopulationinBolivia
Source: World Bank staff with data from CIESIN available at:http://sedac.ciesin.columbia.edu/povmap/Note: Income figures have been scaled so that their population-weighted average equals Bolivia’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean income among Bolivia’s muni-cipalities. Each dot represents 5,000 people.
In Bolivia, most of the population is concentra-
ted on the Altiplano, where the lowest levels of
income per capita are observed. However, within
this large area, people concentrate around the ci-
ties of La Paz, Cochabamba, Oruro, Sucre, and
Potosi. Santa Cruz, to the east of the Altiplano,
is another city concentrating a large number of
Bolivia’s poor. This suggests that Bolivia does not
face major labor mobility problems.
Figure2.1dPerCapitaIncomeand
DensityofPopulationinBrazil
Source: World Bank staff with data from the 2000 census, IBGE.Note: Income figures have been scaled so that their population-weig-hted average equals Brazil’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean income among Brazil’s municipalities. Each dot represents 50,000 people.
48
Reshaping Economic Geography in Latin America and the Caribbean
In Brazil, most of the country’s population is lo-
cated in large urban areas such as Sao Paulo and
Rio de Janeiro. However, the map reveals another
large concentration of people along the northeas-
tern coast of the country, where some of the
country’s lowest levels of income are found.
In terms of 1-D, 2-D, and 3-D categories, Brazil
is a fairly clear case of a “2-D” country. It has a
large population (including a substantial number
of poor) living in the lower income Northeast. The
challenge the country faces is both promoting
continued density and overcoming the economic
distance between the poorer Northeast and the
leading regions of Sao Paulo and Rio.Figure2.1ePerCapitaIncomeand
DensityofPopulationinChile
Source: World Bank staff with data from Agostini and Brown, 2007a and 2007b.Note: Income figures have been scaled so that their population-weighted average equals Chile’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean in-come among Chile’s municipalities. Each dot represents 50,000 people.
Figure2.1fWelfareand
PopulationinColombia
Source: World Bank staff calculations with data from DANE, 2008.Note: Index has been inverted: Lower values represent poorer areas with more unsatisfied basic needs. One dot represents 25,000 people.
49
The Links between Space and Individual Monetary Welfare
In Chile, there is a high concentration of people
around the metropolitan area of Santiago, one of
the country’s high-income areas. There is another
area with low levels of income and an important
concentration of people. This area stretches from
the south of Santiago to the province of Valdivia,
with a heavier concentration of people around the
city of Concepcion.
In Colombia, the population concentrates heavily
along the country’s three main mountain ranges
and the Atlantic coast. The map shows that, for
the most part, these areas have high levels of
welfare, suggesting that the country has a high
Figure2.1gPerCapitaIncomeand
DensityofPopulationinCostaRica
Source: World Bank staff calculations with data from Gasparini et al., 2008.Note: Income figures have been scaled so that their population-weighted average equals Costa Rica’s 2007 GDP per capita in PPP US$ of 2005. Each dot represents 5,000 people.
level of demographic concentration around its
leading areas. Some areas in the north of the
country have low levels of welfare and relatively
large populations, which suggests that a relative-
ly large pocket of poverty may be found there.
As in the case of Belize, data for Costa Rica do
not allow to make a more geographically disa-
ggregated analysis. However, the map is able to
show that a large number of people are located in
the Central region, which includes the city of San
Jose and has the highest mean per capita income
of all the regions.
Figure2.1hPerCapitaIncomeandDensity
ofPopulationinDominicanRepublic
Source: World Bank staff with data from Regalia and Robles, 2005.Note: Income figures have been scaled so that their population-weighted average equals Dominican Republic’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean income among Dominican Republic’s municipalities. Each dot represents 10,000 people.
50
Reshaping Economic Geography in Latin America and the Caribbean
In the Dominican Republic, although the number
of people living Santo Domingo, La Romana, and
Valverde—the three provinces with the highest
levels of mean per capita income—is slightly hig-
her than in the rest of the provinces, the popu-
lation seems to be evenly spread throughout the
territory.
In El Salvador, the majority of people are con-
centrated around San Salvador, San Miguel, and
La Libertad, the country’s leading states. Howe-
ver, Ahuachapan and Sonsonate, two states with
very low levels of income on the western end of
the country, also concentrate a large number of
people.
In Guatemala, most people concentrate around
Guatemala’s three main cities—Guatemala, Que-
tzaltenango, and San Marcos—which enjoy some
of the country’s highest levels of income. Howe-
ver, this concentration does not seem to be subs-
tantially larger than the concentration observed
throughout the territory. The states of Alta Vera-
paz, Quiche, and Huehuetenango, for instance,
Source: World Bank staff with data from Robles et al., 2008.Note: Expenditure figures have been scaled so that their popu-lation-weighted average equals Ecuador’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean expenditure among Ecuador’s municipalities. Each dot represents 25,000 people.
Figure2.1iPerCapitaExpenditureand
DensityofPopulationinEcuador
In Ecuador, there is a substantial number of
people living in areas with high mean per capita
expenditure—the cities of Guayaquil, Quito, and
Ambato. Nevertheless, there is an even larger
number of poor people scattered along and west
of the Andes, in areas whose levels of mean per
capita expenditure range from low to medium.
Source: World Bank staff calculations with data from 2000.Note: Income figures have been scaled so that their population-weighted average equals El Salvador’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean income among El Salvador’s municipalities. Each dot represents 10,000 people.
Figure2.1jPerCapitaIncomeand
DensityofPopulationinElSalvador
51
The Links between Space and Individual Monetary Welfare
also concentrate a large number of people in low-
income areas.
The map of Guyana shows that the great majority
of the country’s population is located in the lea-
ding region—region 4, Demerara-Mahaica—which
contains the city of Georgetown and has the hig-
hest level of mean per capita expenditure.
In Honduras we see that most of the country’s
population is located in the leading areas —Tegu-
Source: World Bank staff with data from CIESIN available at:http://sedac.ciesin.columbia.edu/povmap/Note: Income figures have been scaled so that their population-weighted average equals Guatemala’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean income among Guatemala’s municipalities. Each dot repre-sents 25,000 people
Figure2.1kPerCapitaIncomeand
DensityofPopulationinGuatemala
Source: World Bank staff calculations.Note: Expenditure figures have been scaled so that their popula-tion-weighted average equals Guyana’s 2007 GDP per capita in PPP US$ of 2005. Each dot represents 10,000 poor people.
Figure2.1lPerCapitaExpenditureand
DensityofPopulationinGuyana
cigalpa and San Pedro Sula. Lagging areas on the
west of the country also have a substantial num-
ber of people, but this population is smaller than
the number of people living in wealthier areas.
Jamaica has a large concentration of people
around the city of Kingston and relatively few
people scattered throughout the rest of the coun-
try—with a slightly denser concentration towards
the center of the country.
52
Reshaping Economic Geography in Latin America and the Caribbean
In Mexico, people concentrate primarily
in the center of the country. Many of the-
se people live in Mexico City but many
others live in the Bajio region—northwest
of Mexico City. These areas tend to have,
in general, high levels of income. Howe-
ver, there are also large concentrations
of population along the eastern mountain
range and in the state of Chiapas, both
with very low levels of income.
Given the substantial number of poor who
live in economically isolated indigenous
communities, particularly in Chiapas,
Mexico is probably best classified as a “3-
D” country. Efforts are needed to increase
density, reduce economic distance, and
also to overcome the division these areas
face.
The Nicaraguan map shows that most of
the country’s population is located bet-
ween the country’s two large lakes. This
area, which contains the cities of Managua
and Granada, has some of the country’s
highest levels of per capita expenditure.
To the north and east of the city of Este-
li—directly north of lake Managua—there
are also relatively large concentrations of
people, this time in low to medium-inco-
me areas.
Panama is a very good example of a
country where most of its population is
concentrated in leading areas. The map
shows how people concentrate chiefly
Source: World Bank staff with data from Robles, 2003.Note: Income figures have been scaled so that their population-weighted average equals Honduras’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean income among Honduras’s mu-nicipalities (incomes from rural areas were adjusted for price differentials with respect to urban areas). Each dot represents 5,000 people.
Figure2.1mPerCapitaIncomeand
DensityofPopulationinHonduras
Source: World Bank staff with data from Cumpa and Robles, 2005.Note: Expenditure figures have been scaled so that their population-weighted average equals Jamaica’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean expenditure among Jamaica’s municipalities (incomes from other areas were adjusted for regional price differentials with respect to Kingston). Each dot represents 10,000 people.
Figure2.1nPerCapitaIncomeand
DensityofPopulationinJamaica
53
The Links between Space and Individual Monetary Welfare
Source: World Bank staff with data from Izaguirre et al., 2005.Note: Income figures have been scaled so that their population-weighted ave-rage equals Mexico’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean income among Mexico’s municipalities. Each dot represents 50,000 people.
Figure2.1oPerCapitaIncomeand
DensityofPopulationinMexico
Source: World Bank staff with data from the World Bank.Note: Expenditure figures have been scaled so that their population-weighted average equals Nicaragua’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean expenditure among Nicaragua’s municipalities. Each dot represents 10,000 people.
Figure2.1pPerCapitaIncomeand
DensityofPopulationinNicaragua
around the areas with the highest le-
vels of income—around the cities of Pa-
nama, Portobelo, David, Bocas del Toro,
and Santiago. Still, there are important
numbers of people in a few low-income
areas, such as east of the city of David
and southwest of the city of Panama.
Paraguay’s population also tends to con-
centrate in the country’s high-income
areas—around the cities of Asuncion,
Ciudad del Este, and Encarnacion, all
at the border with Argentina. However,
there is also a large population of people
in middle-income areas of the depart-
ments of Concepcion, San Pedro, and
Caaguazu.
The spatial concentration of population
in Peru can be seen very clearly in this
map. A large number of people are con-
centrated along the high-income coastal
areas—most notably in Lima. Another
very large number of people live in the
mountains, in areas with very low levels
of income.
The map for Uruguay shows that popula-
tion is roughly evenly distributed throug-
hout the country’s nineteen departments.
However, Montevideo and Artigas, the
two departments with the highest levels
of income, show larger concentrations of
people.
54
Reshaping Economic Geography in Latin America and the Caribbean
Source: World Bank staff with data from Robles, 2005.Note: Expenditure figures have been scaled so that their population-weighted average equals Panama’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean expenditure among Panama’s municipalities. Each dot represents 5,000 people.
Figure2.1qPerCapitaIncomeand
DensityofPopulationinPanama
Source: World Bank staff with data from Robles and Santander, 2004.Note: Income figures have been scaled so that their population-weighted average equals Paraguay’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean income among Paraguay’s muni-cipalities (incomes from other areas were adjusted for regional price differen-tials with respect to Asuncion). Each dot represents 10,000 people.
Figure2.1rPerCapitaIncomeand
DensityofPopulationinParaguay
Source: World Bank staff with data from Escobal and Ponce, 2008.Note: Expenditure figures have been scaled so that their popu-lation-weighted average equals Peru’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean expenditure among Peru’s municipalities. Each dot repre-sents 25,000 people.
Figure2.1sPerCapitaIncomeand
DensityofPopulationinPeru
In Venezuela, population is concentrated in
the areas where most people have high levels
of welfare as measured by the country’s un-
satisfied basic needs index: along the Andes,
and around the cities of Maracaibo and Cara-
cas. Although the data do not make it possi-
ble to identify where poor people live, areas
with low levels of provision of basic services
are scarcely populated, suggesting that most
of Venezuela’s poor are indeed located in the
leading regions.
55
The Links between Space and Individual Monetary Welfare
The previous figures have revealed a highly
diverse distribution of population and welfare
across Latin American and Caribbean coun-
tries. In some cases—e.g. Panama—people
are concentrated around the more dynamic
areas while in others there are large concen-
trations of people in lagging areas—e.g. Bra-
zil, Mexico, and Peru. Other countries—e.g.
Guatemala—seem to have their population
distributed roughly in an even fashion across
their territory. All these various spatial con-
figurations of population density and welfare
levels reflect different degrees of labor mobi-
lity within each country. They are the result
of distance and division problems specific
to each country and their solution has to be
shaped accordingly.
2.2 Density,Distance,andDivisioninLAC:aQuantitativeEvaluation
Why are some areas wealthier than others?
The answer to this question is all but simple.
This section seeks to determine what featu-
res characterize an area with high levels of
monetary welfare, placing a special focus on
investigating to what extent density, distan-
ce, and division are indeed related to eco-
nomic development in LAC. The statistical
analysis presented below combines small-
area estimates of income and expenditure,
census, and GIS data. As explained in Box
2.2, the data have been processed in a way
that ensures a high degree of cross-country
comparability.
Figure2.1tPerCapitaIncomeand
DensityofPopulationinUruguay
Source: World Bank staff calculations with data from 2006.Note: Income figures have been scaled so that their population-weighted average equals Uruguay’s 2007 GDP per capita in PPP US$ of 2005. Colors represent deciles of the distribution of mean income among Uruguay’s municipalities. Each dot represents 10,000 people.
Figure2.1uWelfareandDensityof
PopulationinVenezuela
Source: World Bank staff calculations with census data.Note: Index has been inverted: Lower values represent poorer areas with more unsatisfied basic needs. One dot represents 10,000 people.
56
Reshaping Economic Geography in Latin America and the Caribbean
The analysis uses the small area estimates to analy-
ze the relationship between welfare—as measured
by mean per capita income or expenditure—and
a series of indicators of the three spatial dimen-
sions mentioned above. Density is approximated
by population density. Since the units of analysis
Box2.2AnExtensiveandHighlyComparableDataset
The econometric analysis presented in this chapter (see results in Table 2.1) spans several Latin American and Caribbean countries. For this reason, it is important that the data used be comparable across countries. This demand poses a challenge that is further increased by the level of geographic disaggregation—the municipality—and the wide range of variables the analysis needed to incorporate: welfare levels, socioeconomic and demographic characteristics of the population, and geographic characteristics of the area.
Mean welfare levels were taken from the small-area estimates carried out by various authors. These are the same data that were used in the production of Figures 1.3 and Figures 2.1 (see data sources in references section), and they all have been constructed using the same methodology (see Box 2.1) Still, as noted in Chapter 1, the estimates are not strictly comparable because they are based on data from different years, they correspond to different definitions of the same variable, and because they correspond to different variables—Ecuador, Jamaica, Nicaragua, Panama, and Peru use mean per capita expenditure instead of income.
Socioeconomic and demographic variables were taken from the countries’ censuses. Before generating any variable, all census questionnaires were examined to decide upon a list of variables that could be constructed in the same way for all countries. The list is long and includes the proportions of various demographic groups—determined by gender, age, and ethnicity—in each municipality’s population, levels of education, main economic activity of household heads, and individual access to piped water and sewerage at the dwelling.
Data on geographic characteristics were constructed using GIS (geographic information systems) data and technology (see data sources in references section). In all countries, the variables were generated following the same methodology and data source. The list of variables includes characteristics of the terrain (slope and altitude), climatic indicators (temperature and precipitation), and location (latitude, longitude, and distance to the sea and cities with more than 250,000 inhabitants).
Terrain and climatic characteristics were calculated as the mean, median or standard deviation of all the values observed within the perimeter of every municipality. For the distance variables, each municipality was assigned a point—the most densely populated—from which measurements were made. Distance to the sea was calculated as the Euclidean distance from that point to the closest seashore.
Finally, distance to the closest city with more than 250,000 inhabitants was measured in terms of travel time. The procedure to obtain this measure was based on a GIS layer specially constructed for the World Development Report 2009 (see Nelson, 2008). The layer divides the map of the world in millions of squared cells measuring 1’00’’ on each side. To every cell on the map, the layer assigns a certain number of minutes required to travel through that cell by whatever means is fastest. The amount of time assigned to each cell depends on a number of characteristics of the cell, including slope of the terrain, land cover, presence of rivers and bodies of water, presence of roads and its characteristics, etcetera. The travel time between each municipality’s most densely populated point and any city is given by the trajectory yielding the lowest summation of travel times assigned to the cells on that trajectory. For each municipality, travel times to all proximate cities of 250,000 inhabitants or more were generated in this fashion, and the smallest travel time was determined as the value of interest.
57
The Links between Space and Individual Monetary Welfare
are fairly small in most cases, this variable pro-
vides a good approximation to economic density.
The concept of distance is captured in two ways:
first by the minimum Euclidean distance between
each administrative unit and the sea, and second
by the minimum time required to travel from
that administrative unit to a city of 250,000
people or more. Finally, the concept of division is
captured by the proportion of an area’s population
that belongs to an ethnic minority group. This
is an imperfect measure, as it is not necessarily
true that minority groups are everywhere segre-
gated and the intensity of the segregation could
vary from place to place. In Jamaica, for ins-
tance, the ethnic minority is white and yet they
are significantly wealthier than the majority of
the population.
As Chapter 1 of this report discusses, there are
several different mechanisms by which geogra-
phy could have an impact on economic growth;
however, there is no widespread agreement as to
whether these impacts actually occur and, if they
do, how strong they are. It is often argued that
human action can overcome the disadvantages
posed by nature so that those characteristics are
ultimately irrelevant for economic growth. Under
this view, institutions and “second-nature” geo-
graphic characteristics— that is, characteristics of
an area that result from human interaction—are
the relevant factors explaining growth.
Analyzing the effects of space on welfare thus
requires considering both first and second-na-
ture characteristics. Density, distance, and divi-
sion can be considered second-nature geographic
characteristics—that is, characteristics of an area
that result from human interaction. In its Eucli-
dean sense, distance is a first-nature geographic
characteristic—that is, determined solely by na-
ture. Nevertheless, the concept used here is also
determined by infrastructure—e.g. availability
and quality of roads—and the location of other
population settlements—an area may be far from
the sea but close to a major city. The analysis
presented below also explores the relationship
between first-nature geography and welfare by
including a series of climatic and topographic in-
dicators that, according to the discussion presen-
ted in Chapter 1, could have direct impacts on
welfare.
As discussed above, the econometric analysis pre-
sented below is purely descriptive. It consists of a
series of country-specific linear regressions whe-
re the dependent variable is the natural logarithm
of municipal mean per capita income32 and the
independent variables are the different measures
of density, distance, and division, as well as the
climatic and topographic indicators mentioned
above. The analysis also controls for socioeco-
nomic and demographic characteristics of the
municipalities. These include the fraction of wo-
men in the total population, the dependency ratio
(number of people younger than 15 or older than
65, divided by the number of people between 15
and 65 years of age), average years of education
among people 15 to 65 years old, main economic
activity of the heads of household (percentage
32 In the case of Ecuador, Jamaica, Nicaragua, Panama and Peru, mean per capita expenditure is used instead of mean per capita income.
58
Reshaping Economic Geography in Latin America and the Caribbean
dedicated to each of eight possible groups), and
percentage of the population with access to piped
water and sanitation, among others.
Table 2.1 summarizes the results in four columns
for each country.33 The first column only includes
density, distance, and division indicators; the se-
cond column includes purely geographic charac-
teristics only. The third column pools the density,
distance, and division indicators together with
purely geographic characteristics as explanatory
variables, and the fourth column adds all the so-
cioeconomic and demographic controls mentio-
ned above.
The results give strong support to the arguments
put forth by the World Development Report 2009.
Although a few pages above we saw that demo-
graphic concentrations often occur in low-income
areas, Table 2.1 shows that there is generally a
positive relationship between population density
and mean per capita income. Jamaica and Hondu-
ras are two notable exceptions where population
density seems to be negatively associated to in-
come levels—although the estimated coefficients
are close to 0 or have low levels of significance.
Chile and Panama, in turn, do not show a statis-
tically significant relationship between population
density and income. One possible explanation for
these results is the different sizes of the spatial
units. For instance, a large municipality whose
population is highly concentrated in one city with
relatively high levels of income would have a low
population density together with high mean in-
come levels; if the municipality were to include
only the city, income levels would match much
more closely with population density. Ideally, one
would like to work with spatial units of exactly
the same size, however those data are not avai-
lable.34
Does demographic concentration cause econo-
mic concentration and high levels of welfare or
is it that high levels of welfare in an area attract
people, thereby causing economic concentration?
As argued at the beginning of this chapter, the
answer is likely to be that indeed both mecha-
nisms are at work. Economic concentration ge-
nerates positive externalities which raise income
levels, attracting more people and firms, and
thereby increase economic concentration even
further.
Regarding distance, the results show that the
more remote places enjoy substantially lower
levels of welfare. Being close to the sea means
being close to human activity. This is particularly
true in Latin America, where a large proportion
of the population concentrates along the coast
(see Chapter 1). The results of Table 2.1 show
that in four of the countries analyzed distance to
the sea has a strong negative relationship with
income. Similarly, the distance to a city with a
population of 250,000 people or more has also
an inverse relationship with an area’s mean level
of income. This last variable has not been mea-
sured in an Euclidean fashion but as the shortest
33 See appendix for a more detailed table of results.
34 See Box 1.1, in Chapter 1 for a discussion on the importance of the definition of space.
59
The Links between Space and Individual Monetary Welfare
time it takes to travel between the two places.
It incorporates the availability and quality of the
different means of transportation. The results are
therefore stronger in support of the argument
that places that are economically far from the
centers of economic activity suffer a handicap in
terms of economic development.
Gallup, Sachs, and Mellinger (1999) have argued
that economic concentration in remote areas
could be deleterious because, being separated
from large markets, these (mostly agricultural)
areas face decreasing returns to scale in labor.
To investigate this possibility, the analysis inclu-
ded an interaction term which analyzes the re-
lationship between demographic concentration
in remote places35 and welfare. Although the re-
sults show a positive association, this does not
conclusively prove the above argument wrong
because the disadvantages of agglomeration in
remote places could come only at high levels of
concentration. The remote places analyzed here
have generally low levels of concentration, but
the results indicate that such concentrations have
a positive relationship with welfare that are hig-
her in magnitude than those found in the rest of
the country. One possibility is that the availability
of public services and basic infrastructure—and
their concomitant higher levels of income—be-
come increasingly scarce as distance increases
among the group of remote places.
Of the three spatial dimensions highlighted by
the World Development Report, division is per-
haps the most difficult to capture. This analysis
can only include a simple measure: the propor-
tion of an area’s population belonging to one of
the country’s ethnic minorities.
In almost every Latin American country there are
indigenous groups, which have suffered a long
history of segregation and exclusion. The resul-
ts are strikingly robust throughout the different
countries analyzed: Areas with higher demogra-
phic proportions of minority groups are substan-
tially worse off than the rest of the country.36
The results also show that some purely geogra-
phic characteristics are systematically associated
with higher levels of welfare. In five of the six
countries analyzed, there is evidence that areas
with higher levels of welfare are on average cha-
racterized by higher temperatures. The variability
of temperature throughout the year as well as
the level of precipitation are differently associa-
ted to welfare in every country, which can per-
haps be explained by the different crops cultiva-
ted in every country. Higher levels of welfare also
tend to be associated to higher altitudes but not
to mountainous areas, where lower levels of wel-
fare prevail. Several studies have pointed out the
existence of a negative relationship, at the coun-
try level, between welfare and percentage of the
35 “Remote” is defined here as being one of the country’s farthest places from the sea (50 percent of the distribution) and at the same time one of the country’s farthest places from a city of 250,000 people or more (also 50 percent of the distribution).
36 The only exception to this result is Jamaica, where the minority group is conformed by non-Blacks. Still in this case, the results show that there is some division between Blacks and non-Blacks and that it hurts the former even though they are a majority group.
Sou
rce:
Wor
ld B
ank
staf
f ca
lcula
tion
s w
ith d
ata
from
var
ious
auth
ors
(see
dat
a so
urc
es in r
efer
ence
s),
variou
s ce
nsu
ses,
and G
IS.
Not
e: A
“+
” si
gn m
eans
the
coef
fici
ent
is p
ositiv
e an
d s
tatist
ical
ly s
ignifi
cant
at lea
st a
t th
e 10%
lev
el;
a “-
” si
gn m
eans
the
coef
fici
ent
is n
egat
ive
and s
tatist
ical
ly s
ignifi
cant
at lea
st a
t th
e 10%
lev
el.
Shad
ed a
reas
rep
rese
nt
excl
uded
var
iable
s. a
) M
inor
ity
gro
up is
defi
ned
as
“non
-Bla
ck”.
b)
Exc
ludes
yea
rs o
f ed
uca
tion
and o
ccupat
ion v
aria
ble
s. c
) Exc
ludes
occ
upat
ion v
aria
ble
s an
d liter
acy
subst
itute
s fo
r ye
ars
of e
duca
tion
.
Tab
le2
.1P
ote
nti
al
Dri
vers
of
Inco
me
BO
LIV
IAB
RA
ZIL
CH
ILE
EC
UA
DO
RG
UA
TEM
ALA
HO
ND
UR
AS
JAM
AIC
Aa
,bM
EX
ICO
NIC
AR
AG
UA
PA
NA
MA
PER
Uc
DEN
SIT
Y
Popula
tion
Den
sity
++
++
++
++
++
——
—+
++
++
++
Popula
tion
Den
sity
(re
mote
)+
++
+—
++
++
++
++
—+
++
+
DIS
TA
NC
E
Trav
el T
ime
toCity
250k+
——
——
+—
——
——
——
——
——
——
——
Dis
tance
to S
ea—
——
—+
——
——
——
——
—
DIV
ISIO
NM
inority
Gro
up
——
——
——
——
——
——
++
+—
——
——
—n.a
.n.a
.n.a
.
CLIM
ATE
Tem
per
ature
++
++
——
++
++
++
Tem
per
ature
va
riab
ility
——
++
+—
—+
——
——
—+
Prec
ipitat
ion
++
+—
——
——
——
——
—+
TER
RA
INEle
vation
++
++
++
+—
++
++
+
Slo
pe
——
——
——
——
——
+—
——
——
Dis
tance
to
Equat
or
++
++
++
+—
++
++
——
+
Piped
wat
er &
se
war
age
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Dem
ogra
phic
s +
Eco
nom
ic A
ctiv
ity
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Adju
sted
R2
0.4
0.3
0.5
0.8
0.1
0.6
0.6
0.9
0.1
0.5
0.6
0.9
0.4
0.1
0.5
0.7
0.6
0.3
0.7
0.9
0.2
0.2
0.3
0.5
0.3
0.4
0.5
0.7
0.5
0.5
0.7
0.8
0.5
0.6
0.7
0.9
0.7
0.5
0.8
0.9
0.5
0.5
0.6
0.7
No.
Obse
rvat
ions
314
6322
330
216
329
294
413
2411
143
585
1823
61
The Links between Space and Individual Monetary Welfare
territory that is in the tropics (Gallup, Sachs, and
Mellinger, 1999; IADB, 2000). Similarly, Gallup,
Gaviria, and Lora (2003) find a positive relations-
hip between welfare and distance to the Equator.
The results presented here are consistent for six
of the countries analyzed and two of the countries
where the results are inconsistent—Jamaica and
Panama—are “horizontal” countries with relati-
vely little North-South extension. In sum, even
though this set of geographic characteristics does
not always bear the same relationship with welfa-
re levels across countries, what is striking about
these results is that in every country the mone-
tary welfare of a small area is strongly associated
to its purely geographic characteristics.
The comparison between the adjusted R-squared
of the first and second columns of Table 2.1 is
useful to determine which set of variables—the
indicators for density, distance, and division or
the purely geographic characteristics—has grea-
ter capability of explaining income. The resul-
ts are not conclusive. In some cases, such as
Bolivia, Ecuador, Guatemala, and Panama, den-
sity, distance, and division are better predictors
of income than purely geographic characteris-
tics. In other instances, such as in Brazil, Chile,
Jamaica, and Nicaragua, purely geographic
characteristics have a larger explanatory power.
Box2.3:ImplicationsoftheWDRattheInternationalLevel:RegionalIntegrationinLatinAmericaandtheCaribbean
The 2009 WDR argues that Latin American and Caribbean countries will achieve economic growth if they allow spatial economic concentration. But just as within each country it is important that people and firms move closer to economic activity, in the international arena it is important that countries get closer to markets. Of course, countries cannot change their location but there are several things they can do to integrate both globally and regionally.
Regional integration by itself can hardly be considered a long-term solution to economic growth. The countries that form the “bottom billion,” for instance, have such tiny, slow-growing economies that even under regional integration “markets remain tiny [… and the result is] a poor, slow-growing regional economy” (Collier, 2007: 164). The World Development Report 2009 encourages “regional integration as a mechanism to increase local supply capacity and global integration to improve access to markets and suppliers” (WDR 2009: 9.1). The key is, once more, taking advantage of economies of scale. Regional integration allows firms to form international supply chains through which they can reduce production costs more effectively than they would by relying on national suppliers alone.
In Latin America and the Caribbean the prospects for regional integration are better than among the bottom billion countries. However, the location of each specific country may be a strong factor determining whether a country has more to gain from integrating directly to the world’s biggest markets or from simultaneously integrating regionally.
Mexico, for instance, is very close to the world’s largest market and its Central American neighbors are so much smaller that there is much more to gain from integrating with the U.S. than with Central America. The country has understood this geographic advantage and with NAFTA it has taken a major step toward integrating with the U.S.
(Continue)
62
Reshaping Economic Geography in Latin America and the Caribbean
Box2.3:ImplicationsoftheWDRattheInternationalLevel:RegionalIntegrationinLatinAmericaandtheCaribbean
The situation is different for Central American and Caribbean countries, whose relatively small economies could benefit from regional integration to form more efficient supply chains. However, these countries also need to integrate globally in order to access larger markets, and their geographic location is in their favor. The implementation of the DR-CAFTA is an excellent step in that direction. Indeed, this multilateral trade agreement “is expected to deepen regional trade integration (and increase trade levels) among the Central American nations themselves and with the Dominican Republic. DR-CAFTA additionally should promote greater levels of foreign and domestic investment, by improving the certainty of these countries’ market access to the United States” (Jaramillo and Lederman, 2006: 2).
The Southern Cone countries are farther away from the world’s largest markets but have very large markets of their own. These countries have a lot to gain from regional integration. MERCOSUR is a good step in that direction but it is insufficient: It is also necessary to implement “institutional reforms that facilitate intraregional […] factor mobility—and infrastructure investments that link lagging to leading countries and the region to major world markets.” (WDR 2009: 9.2).
Finally, the geographic location of Andean countries puts them in a particular situation. Although they are not particularly close to the world’s largest markets in a geographical sense, economically this distance may be comparable to the distances between themselves and the rest of South America. Indeed, the Andes and the huge stretches of land separating the Pacific coast from the dynamic markets of the Atlantic coast—Buenos Aires and Southeastern Brazil—can impose very high transport costs, making regional integration extremely difficult. This does not mean that the Andean countries have nothing to gain from regional integration—especially the smaller countries—and indeed they should make efforts to head in that direction, but it does suggest that there will be larger gains for these countries from integrating globally and they can take advantage of their proximity to the sea. A special case is Bolivia which is perhaps the only truly landlocked country in the continent (Paraguay is connected to the sea through the Paraguay and Parana rivers). Bolivia has much to gain from regional integration, including the possibility of effectively integrating to the rest of the world.
Are free-trade agreements all it takes to achieve integration and economic growth? The answer is no. Sound and even institutions within each country as well as connective infrastructure are also necessary. The international evidence shows that “Trade liberalization has got parasitic firms off the back of ordinary people, but it has not enabled other activities to flourish. For that governments need to change a whole range of policies that between them determine firms’ costs” (Collier, 2007: 161). The more specific case of NAFTA has revealed that despite the implementation of a free-trade agreement, “income convergence with Northern partners is severely limited by the wide differences in the quality of domestic institutions, in the innovation dynamics of domestic firms, and in the skills of the labor force” (Lederman et al., 2005: xvi). Furthermore, ten years after the beginning of NAFTA, “Those [Mexican] states with higher initial levels of education, better infrastructure (especially in telecommunications), and better local institutions—in addition to locational advantages—accelerated their rate of convergence with the more prosperous North” (Lederman et al., 2005: xvii).
Latin American and Caribbean countries seem to be taking the right steps toward regional and global integration by implementing free-trade agreements among them and with the United States. However, these agreements will not guarantee a good level of integration and economic growth without clear and sound institutions as well as connective infrastructure that allow the free mobility of people, production factors, and products.
63
The Links between Space and Individual Monetary Welfare
Although specific results for the rest of the inde-
pendent variables are not reported, it is worth
mentioning them here briefly. Access to piped
water and sewerage is everywhere positively
associated with welfare. Many of the socioeco-
nomic and demographic controls are strongly
associated to welfare. Education, for instance,
is everywhere positively associated with income
levels. Although these controls suffer perhaps
more intensely from the problem of inverse
causality, what is interesting is that their inclusion
does not eliminate the association between mo-
netary welfare and the variables used to approxi-
mate density, distance, and division.
2.3 Density,Distance,Division,andGrowth
Does the fact that purely geographic characte-
ristics have a strong and significant relationship
with welfare mean that geography is destiny?
The answer is negative. In the book Is Geography
Destiny?, the authors conclude that “Geography
may be largely immutable, but its impact on an
economy and a society is not. The right policies or
technological developments can overcome many
geographical obstacles and help exploit geogra-
phical advantages.” 37
Indeed, certain geographic conditions are less fa-
vorable than others and this imposes additional
challenges to economic development. The results
presented above speak of this situation. Howe-
ver, there is no evidence to suggest that adverse
geographic characteristics cannot be overcome.
First, although it is clear that welfare levels can-
not cause geographic characteristics, the rela-
tionship observed above could be the result of
an unobserved factor that is correlated with the
observed geographic characteristics. If, for exam-
ple, a country were to selectively make larger
investments in coastal areas than in other regions
and if such differential investment was not incor-
porated in the analysis—through, for example, a
dollar measure of investments made in each area
over a given period of time—the results would
show that coastal geographic characteristics are
positively related to monetary welfare. Hence,
although it is clear that the causality could not
go in the opposite direction—i.e., welfare levels
do not cause geographic characteristics—it is
not necessarily the case that geography causes
welfare.
Second, there is evidence that the human trans-
formation of the environment can have a large
and strong effect on welfare which offsets that
of first-nature geography. Escobal and Torero
(2000), for instance, find that “differences in
living standards in Peru can be almost fully
explained when one takes into account the spa-
tial concentration of households with readily
observable non-geographic characteristics, in
particular public and private assets.” In support
of this and the previous point, the authors also
state that “This does not mean, however, that
geography is not important but that its influence
in expenditure level and growth differential co-
mes about through a spatially uneven provision
of public infrastructure.”37 Gallup, Gaviria, and Lora, 2003, p. 131.
64
Reshaping Economic Geography in Latin America and the Caribbean
An additional limitation of the results presented
above is that they are derived from the compa-
rison across different spatial units and therefore
do not provide an analysis of long-term growth.
Such analysis requires observing how welfare has
evolved over long periods of time. In this case,
it would imply having at least two small-area
estimates for periods of time far apart from each
other. In a background paper for this report,
Escobal and Ponce (2008) produced two small-
area estimates for Peru using the 1993 and 2005
censuses. These data are suitable for studying
short-term growth and hence the results must be
taken with caution if long-term growth implicatio-
ns are to be drawn from them.
As illustrated in Figure 2.2, the data show that
between those two years poverty rates in Peru
increased almost exclusively along the Andes,
suggesting that geography may have an impact
on short-term growth. However, this observation
could be due to geographically-differenced in-
vestments in education, infrastructure, and other
development interventions.
Figure2.2AltitudeandChangesinPoverty1993–2005inPeru
Source: Escobal and Ponce, 2008.
65
The Links between Space and Individual Monetary Welfare
Indeed, the authors analyze growth in mean per
capita consumption as a function of a wide range
of geographic, infrastructural, and socio-demo-
graphic characteristics and find that even after
accounting for a large set of socioeconomic as-
pects as well as infrastructural characteristics—
such as availability of piped water, electricity,
sanitation, and telephones—certain geographic
characteristics are still strongly associated to
changes in poverty rates in Peruvian districts.
A similar result was found even after controlling
for changes in infrastructure. However, when
comparing the changes in consumption between
1993 and 2005 across different regions of Peru—
coastal areas vs. the mountainous and rainfo-
rest areas—geographic factors no longer played
a role. In other words, differences in economic
growth between the coastal areas and the poo-
rer Sierra and Selva regions cannot be explained
by geographic factors and instead are strongly
related to differences in infrastructure invest-
ment (see Table 2.3).
Peru is but one among many countries in Latin Ame-
rica and the Caribbean, however it is an especially
well-suited case for this type of analysis because
the country has huge welfare differences that are
very clearly marked by geographic differences.38
38 Colombia, Ecuador, Mexico, and several other LAC countries also have dramatic differences in welfare across space. In Peru, however, a decomposition of Theil’s inequality index shows that approximately 20% of the overall inequality is due to between-region inequality, a figure substantially larger than anywhere else in the region (see Figure 5.1, in Chapter 5).
OverallDifferenceCosta-Sierra 0.073 0.073 0.073 0.073 0.073
Geography 0.143** 0.240 0.245 0.169 0.138
Infrastructure -0.102*** -0.061*** -0.054*** -0.060***
Economic Environment -0.046 -0.036 -0.029
Private Assets 0.061*** 0.047***
Human Capital and Household Characteristics 0.031***
Residual -0.070 -0.065 -0.065 -0.067 -0.054
OverallDifferenceCosta-Selva -0.033 -0.033 -0.033 -0.033 -0.033
Geography 0.170* 0.207 0.209 0.175 0.079***
Infrastructure -0.042*** 0.000*** 0.007 *** -0.017***
Economic Environment -0.043 -0.033 -0.028
Private Assets 0.008 *** -0.011***
Human Capital and Household Characteristics 0.103***
Residual -0.203 -0.198 -0.199 -0.190 -0.159
Table2.3FactorsExplainingDifferencesinGrowthinPeru’sRegions,1993–2005(Decomposition of changes in log-welfare ratios)
Source: Escobal and Ponce, 2008. Excerpt of Table 13.Note: In each panel, each column represents a regression where the dependent variable is average consumption growth at the muni-cipality level in the two regions being compared, and the independent variables are the set of variables indicated on the left. Reported values represent the fitted value of those variables interacted with a regional dummy and evaluated at sample means. Stars represent the statistical significance of the estimated coefficients’ linear combination: * 10%, ** 5%, *** 1%.
66
Reshaping Economic Geography in Latin America and the Caribbean
Therefore, the previous finding gains strength
in light of the fact that it was derived from a coun-
try with such stark spatial welfare differences.
There is still one final factor that could affect the
welfare of a territory beyond those analyzed thus
far, namely the growth of a neighboring area.
There are several mechanisms by which econo-
mic growth in one place can affect growth, posi-
tively or negatively, in another area. An example
of a positive effect is technology diffusion, which
has been found to occur among relatively sma-
ll areas rather than at the larger scale (see Lo-
pez-Bazo et al., 2004). Other mechanism works
through commercial interaction: An area located
between two other trading areas can benefit from
that trade even if simply by providing goods and
services to transporters. This mechanism, howe-
ver, could work in the opposite direction. Behar
(2008), for instance, cites the bad effects that
political instability in Kenya has on the Ugandan
economy, whose exports need to cross Kenyan
territory before reaching the sea.
Migration from one area to another can have both
positive and negative effects on both places. The
place of origin could be negatively affected by the
loss of valuable human capital—e.g., the so-called
“brain drain”—but at the same time it could bene-
fit from the remittances sent by those migrants.
In turn, the destination could benefit from the
additional labor supply of migrants but could be
negatively impacted in average wages and even
in poverty and crime rates if its economy is not
able to integrate all those new workers.
The World Development Report 2009 posits that
this type of unbalanced economic growth across
space is not only normal but desirable, as it
attains the highest possible rates of growth and
can be coupled with an even distribution of wel-
fare. If this is the case, what is the relevance
of analyzing the economic externalities that one
area may have on another? The answer is not
so that governments can attempt to keep those
interactions from occurring but rather to make
the best use of them. Having a good knowledge
of the economic interplay between the different
parts of a country can help resolve the issue of
whether a lagging area is better served through
direct investments in its territory or through the
investment in a more dynamic area economically
linked to it.
The characteristics of the Peruvian data presented
above make them especially well-suited to analy-
ze whether the mechanisms through which these
economic spillovers take place. Saavedra et al.
(2008) estimate a series of econometric models
in which an area’s economic growth depends on
other areas’ characteristics—which also have a
direct effect on the rate of growth of their corres-
ponding location. To investigate how spillovers
are transmitted and how far across space they
reach, the authors define “neighboring” areas in
different ways: by whether they are adjacent, by
proximity, and by migration flows. Their results
indicate that economic growth in one area spills
over to other adjacent areas and to places with
which migration linkages are strong.
67
The Links between Space and Individual Monetary Welfare
As before, the Peruvian experience is useful be-
cause it is a country with marked spatial diffe-
rences in terms of economic growth and welfare.
The observation of positive and strong economic
spillovers even in such circumstances suggests
that the same type of positive externalities could
be found elsewhere in the continent. This re-
port cannot determine where the most effective
investments for improving the livelihoods of a
country’s population should be made, but it has
provided some evidence that investments in an
economically dynamic area can have important
positive spillovers onto other areas.
2.4 Conclusions
This chapter has discussed how economic con-
centration, called “density” by the World Deve-
lopment Report 2009, is both cause and conse-
quence of economic development. The concentra-
tion of economic activity around relatively small
areas—cities—is beneficial for economic growth
because it allows producers to reap the benefits
from large-scale production, and it presents both
producers and consumers with the benefits of
agglomeration economies.
In order to occur and be beneficial to economic
growth, density requires three preconditions:
the existence of economies of scale in produc-
tion, reduced transport costs, and factor mobility.
The latter can be reduced by distance and divi-
sion, where distance is understood in an econo-
mic sense, as the difficulty to move people, go-
ods, and services across space, and division is
understood as the restrictions to the free flow of
ideas, goods, people, and capital.
This chapter presented an atlas of Latin Ameri-
can and Caribbean countries in which the income
levels of small areas was contrasted with the
density of poverty. The atlas showed there is a
good deal of variation across countries in the ex-
tent to which poverty has concentrated around
leading areas, implying different degrees of labor
mobility within each country.
Using an unprecedentedly extensive and homoge-
nous dataset for several LAC countries, this chap-
ter also analyzed the potential drivers of welfare
at the small-area level. The analysis contained
a series of purely geographic characteristics as
well as several indicators capturing the essence
of the three spatial dimensions discussed above:
density, distance, and division. The results of the
analysis showed that even after controlling for
socioeconomic and demographic characteristics,
density is positively associated with welfare le-
vels while longer distances and stronger divisions
are accompanied by lower livelihoods.
Purely geographic characteristics also proved
to be strongly related to welfare; however, the
chapter presented evidence from other studies
which have found that the challenges posed
by an adverse geography can be overcome
through infrastructure investment and sound
institutions.
68
Reshaping Economic Geography in Latin America and the Caribbean
Box2.4:ImplicationsoftheWDRattheCityLevel:CityDensity,Congestion
In cities, the concentration of economic activity reaches its highest level, and they are where the positive externalities from that concentration are at their best. If there are economies of scale to production, costs are minimized when all production takes place in a single location. Firms will concentrate their production only if they can find workers, financial resources and all the necessary inputs at that location, and if bringing their products to wherever consumers are is relatively inexpensive. In other words, when there are economies of scale, production factors are mobile, and transport costs are low, economic activity tends to concentrate.
Of all the places where they can locate their plants, firms choose those where other producers and sizeable amounts of people are already located. The reason is that the agglomeration of people and firms produces several positive externalities known as “agglomeration economies”: by locating in a city, firms benefit from the availability of suppliers, workers, and consumers; similarly, people benefit from the availability of jobs, goods, and services. Agglomeration economies can be very powerful and generate a cumulative causation process in which the more people and firms are located in a city the more other firms and people want to locate there. However, agglomeration also has some costs associated to it, congestion costs such as pollution, traffic jams, and the increased cost of land.
These negative externalities slow down the growth of cities and transform them. Firms in the service sector benefit more from agglomeration and suffer less from its costs than manufacturing firms, because the latter’s plants take up larger extensions of expensive land, and their products need to be transported through congested streets. Hence, a big city that originally attracted many manufacturing firms, at a later stage of development may start to expel these heavier-industry firms and concentrate more in the service sector. Manufacturing firms—also taking advantage of ever lower transport costs—move to the outskirts of these cities or even to medium-sized cities.
Sao Paulo is a good example of how large cities transform to concentrate more in the service sector. As of the 1970s, manufacturing firms started to relocate out of the city and towards other parts of the country while the service sector continued to grow. As a result, the city’s share in Brazil’s manufacturing production has steadily decreased.
Congestion costs in the largest Latin American cities may be very large. Is this evidence that they are too big? The theory does not provide a definite answer and Paul Krugman has noted, “I am quite sure in my gut, and even more so my lungs, that Mexico City is too big—but gut feelings are not a sound basis for policy.” (Krugman, 1999).
Congestion costs can be greatly reduced through proper city management. The Transmilenio transport system implemented in Bogota is a good example of how some policy actions can effectively reduce congestion costs and thereby increase the benefits of economic concentration. Rather than stopping or artificially fostering the growth of cities, Latin American and Caribbean governments should invest in infrastructure that makes these cities more livable and that allows firms and workers to find the locations that offer the greatest economic opportunities.
69
The Links between Space and Individual Monetary Welfare
APPENDIX
Table A.1 below is a more detailed version of
Table 2.1. It presents the full results of an eco-
nometric analysis describing the relationship
between density, distance, division, purely geo-
graphic characteristics, and socioeconomic and
demographic characteristics on the one hand,
and income levels on the other. This analysis
uses small-area (municipality-level) estima-
tes of mean per capita income as indicators of
overall economic activity in each municipality.
Census and geographic data were also used to
generate a set of highly homogenous explana-
tory variables across eleven Latin American and
Caribbean countries (see Box 2.2) that allow
comparison of the results across countries.
The results correspond to simple linear regres-
sions where the unit of analysis is a small ad-
ministrative unit—e.g. a municipality. Each of
the four columns presented for every country
includes a different set of explanatory varia-
bles. The first column includes indicators for
density, distance, and division only (see body
of the chapter for an explanation). The second
column only controls for some purely geogra-
phic characteristics using a piece-wise linear
specification that is based on the distribution of
the corresponding variable across all the units
of analysis of the eleven countries pooled. For
example, the variable labeled “Spline: coldest
10% places” is equal to the mean annual tem-
perature if the municipality belongs to the ten
percent coldest municipalities among all eleven
countries. The geographic variables included in
this piece-wise linear fashion are mean annual
temperature, annual temperature variation,
annual precipitation, elevation, slope, and dis-
tance from the Equator.
The third column of Table A.1 (for each coun-
try) includes indicators of density, distance, di-
vision, and purely geographic characteristics.
Finally, the fourth column includes controls for
socioeconomic and demographic indicators.
These indicators are all averages at the mu-
nicipality level and are the following: fraction
of people with access to electricity at home,
and with sewerage service; mean household
dependency ratio, mean age of the head of
household, fraction of the population in wor-
king age (15–64 years old) who are female,
years of education of people aged 15–64, frac-
tion of people aged 15–64 who are employed,
and dummy variables indicating the main occu-
pation of household heads.
The regressions are run separately for each
country; standard errors have been corrected
for heteroskedasticity using White’s formula-
tion and for autocorrelation by clustering at the
state level. At the bottom of each column,
F-tests for the joint significance of each of the
three sets of variables described above are
included.
Po
pula
tion
de
nsi
ty0.3
19
*0.2
68
**0.0
29
0.0
88
***
0.0
67
***
0.0
14
***
0.0
47
***
0.0
05
0.0
02
0.1
99
0.4
92
**0.0
74
[0.1
52
][0
.11
1]
[0.1
28]
[0.0
19
][0
.01
6]
[0.0
04]
[0.0
17]
[0.0
18
][0
.00
7]
[0.1
21
][0
.22
0]
[0.0
90]
Po
pula
tion
de
nsi
ty(r
em
ote
)4.5
78
***
7.8
73
***
-0.5
94
2.8
44
***
1.0
62
*-0
.17
2-1
.57
90
.37
-0.5
09
*0
.73
3**
0.6
33
*0.0
65
[1.0
21
][1
.48
2]
[0.7
79]
[0.8
40
][0
.56
0]
[0.1
53]
[0.9
71]
[0.4
13
][0
.27
1]
[0.3
39
][0
.31
5]
[0.1
73]
Tra
velti
me
toci
ty 2
50k+
(log
)-0
.114
***
-0.1
28
***
-0.0
13
-0.1
64
**-0
.11
6**
*0.0
2**
0.0
24
-0.1
19
***
0.0
01
-0.1
41
***
-0.1
4**
*-0
.00
9[0
.026
][0
.02
5]
[0.0
22]
[0.0
62
][0
.02
6]
[0.0
09]
[0.0
44]
[0.0
23
][0
.01
1]
[0.0
28
][0
.02
5]
[0.0
22]
Dis
tan
ceto
sea
(lo
g)
-0.0
79
-0.3
02
**-0
.08
30
.06
5-0
.04
*0
.00
6-0
.01
4-0
.02
6**
*0
.00
30
.00
2-0
.02
2**
-0.0
29
***
[0.1
50
][0
.13
1]
[0.0
54]
[0.0
41
][0
.02
1]
[0.0
07]
[0.0
14]
[0.0
09
][0
.00
5]
[0.0
10
][0
.01
0]
[0.0
07]
% E
thn
icm
inori
ty-0
.753
**-0
.536
***
-0.3
12
***
-0.5
58
-0.7
12
**-0
.29
6-0
.47
5**
-0.3
88
**0
.00
1-0
.65
2**
*-0
.89
9**
*-0
.51
6**
*[0
.249
][0
.15
3]
[0.0
87]
[0.3
93
][0
.33
7]
[0.2
36]
[0.2
35]
[0.1
86
][0
.09
1]
[0.1
49
][0
.17
3]
[0.1
18]
Sp
line
: co
lde
st10
%p
lace
s-0
.005
0.0
34
*0.0
09
dro
pp
ed
dro
pp
ed
dro
pp
ed
-0.0
79
*-0
.11
2**
*0
.04
2-0
.01
8-0
.13
***
-0.1
85
***
[0.0
16]
[0.0
17]
[0.0
11]
[0.0
41
][0
.04
0]
[0.0
25
][0
.03
8]
[0.0
27
][0
.02
1]
Sp
line
:80
%m
id-t
em
pp
lace
s0
.05
**0.0
29
*0.0
13
**0.0
21
0.0
59
***
-0.0
08
0.0
13
-0.0
14
0.0
02
-0.0
24
**-0
.02
2**
-0.0
05
[0.0
19]
[0.0
13]
[0.0
05]
[0.0
21]
[0.0
14
][0
.00
9]
[0.0
26
][0
.02
1]
[0.0
11
][0
.01
2]
[0.0
08
][0
.00
5]
Sp
line
:ho
ttest
10
%p
lace
s0.0
14
-0.6
62
-0.2
63
-0.2
03
-0.1
52
-0.0
99
*0
00
00
0[0
.61
4]
[0.5
25]
[0.2
85]
[0.1
53]
[0.1
56
][0
.04
9]
[0.0
00
][0
.00
0]
[0.0
00
][0
.00
0]
[0.0
00
][0
.00
0]
Sp
line
:lo
we
st10
% t
em
p v
aria
tion
-0.4
69
0.2
13
0.2
12
0.2
87
0.1
23
-0.0
63
0.0
23
-0.1
72
*-0
.09
8**
*1
.03
2**
*1
.78
6**
-0.3
52
[0.3
28]
[0.1
18]
[0.1
38]
[0.2
75]
[0.1
76
][0
.05
8]
[0.1
03
][0
.08
8]
[0.0
36
][0
.36
3]
[0.6
65
][0
.31
4]
Sp
line
:m
id80
%te
mp
varia
tion
-0.1
32
-0.1
64
**-0
.12
1**
*0.2
4**
0.2
64
***
0.0
73
**0.0
18
0.0
44
0.0
01
-0.2
43
***
-0.1
76
*0.1
11
***
[0.0
82]
[0.0
56]
[0.0
30]
[0.0
90]
[0.0
76
][0
.03
4]
[0.1
58
][0
.12
7]
[0.0
51
][0
.07
6]
[0.0
91
][0
.03
7]
Sp
line
:hig
he
st10
% te
mp
vari
atio
n0
00
0.2
32
0.2
61
0.1
8**
00
0-0
.04
30
-0.0
43
[0.0
00]
[0.0
00]
[0.0
00]
[0.2
54]
[0.2
40
][0
.07
6]
[0.0
00
][0
.00
0]
[0.0
00
][0
.11
1]
[0.0
98
][0
.04
4]
Sp
line
:lo
we
st10
% p
reci
pita
tion
-0.0
2-0
.686
*0.3
07
-3.3
41
***
-3.4
93
***
-0.9
55
*-0
.833
***
-0.9
65
***
-0.2
15
*0
.30
60
.36
10.6
06
***
[0.3
77]
[0.3
50]
[0.1
86]
[1.1
34]
[0.9
25
][0
.47
2]
[0.2
45
][0
.20
2]
[0.1
24
][0
.31
5]
[0.2
62
][0
.19
3]
Sp
line
:m
id80
%p
reci
pita
tion
-0.0
55
-0.0
77
-0.0
06
0.5
08
***
0.5
77
***
0.1
81
***
-0.2
7**
*-0
.20
7**
-0.1
15
***
-0.0
08
0.0
57
0.0
43
[0.1
04]
[0.1
24]
[0.0
92]
[0.0
99]
[0.0
92
][0
.03
0]
[0.0
97
][0
.08
5]
[0.0
39
][0
.05
8]
[0.0
51
][0
.04
1]
Sp
line
:hig
he
st10
% p
reci
pita
tion
-0.1
83
***
-0.0
88
-0.1
36
0.1
48
0.1
33
0.1
15
0.9
56
***
0.5
83
*0
.16
5-0
.09
10
.09
2*
0.0
53
[0.0
54]
[0.0
77]
[0.0
83]
[0.3
12]
[0.2
35
][0
.09
7]
[0.3
26
][0
.31
9]
[0.1
01
][0
.06
9]
[0.0
47
][0
.03
9]
Me
dia
nele
vatio
n (
log
)0.1
47
*0.0
17
0.0
53
0.0
47
0.1
24
***
0.0
13
0.1
17
**0
.10
6**
0.0
45
***
-0.0
65
-0.0
1-0
.01
4[0
.07
7]
[0.0
63]
[0.0
33]
[0.0
38]
[0.0
32
][0
.01
6]
[0.0
49
][0
.04
9]
[0.0
15
][0
.03
8]
[0.0
20
][0
.02
3]
Me
dia
nsl
ope
(deg
ree
s)-0
.048
***
-0.0
3**
*-0
.01
8**
-0.0
91
***
-0.0
79
***
-0.0
15
**-0
.019
**-0
.01
2**
-0.0
02
0.0
16
0.0
02
0.0
02
[0.0
10]
[0.0
07]
[0.0
06]
[0.0
24]
[0.0
17
][0
.00
7]
[0.0
07
][0
.00
6]
[0.0
03
][0
.02
1]
[0.0
12
][0
.01
0]
Me
dia
n s
lope
squ
are
d (
de
gre
es)
0.0
01
**0.0
01
***
0.0
01
***
0.0
04
***
0.0
04
***
0.0
01
***
00
00
00
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
01]
[0.0
01
][0
.00
0]
[0.0
00
][0
.00
0]
[0.0
00
][0
.00
1]
[0.0
00
][0
.00
0]
Dis
tan
cefr
om
Eq
uato
r(la
titu
de)
-0.0
18
-0.0
36
-0.0
17
0.0
81
***
0.0
87
***
0.0
19
***
0.0
43
***
0.0
35
***
0.0
27
***
0.0
02
0.0
17
0.0
34
**[0
.02
6]
[0.0
20]
[0.0
13]
[0.0
06]
[0.0
08
][0
.00
3]
[0.0
10
][0
.00
8]
[0.0
05
][0
.02
7]
[0.0
25
][0
.01
6]
Fra
ctio
nof
pe
op
lew
ithele
ctri
city
0.5
57
**0
.37
1**
*-0
.12
0.2
16
[0.1
88]
[0.0
99]
[0.1
47
][0
.14
6]
Fra
ctio
nof
hh
s w
ithse
wag
e0.5
34
***
0.1
79
***
-0.0
49
0.2
24
[0.1
53]
[0.0
35]
[0.1
15
][0
.18
9]
mea
nh
hde
pen
den
cyra
tio-0
.04
8-1
.36
7**
*-0
.25
3*
-0.9
64
***
[0.1
70]
[0.0
79]
[0.1
37
][0
.18
5]
mea
na
ge
of
hh h
ea
d-0
.28
1**
-0.2
38
***
0.0
25
0.1
15
[0.1
18]
[0.0
78]
[0.0
92
][0
.09
4]
Ag
eo
fh
ea
dof
hsh
(squ
are
d)
0.0
03
**0
.00
2**
0-0
.00
2[0
.00
1]
[0.0
01]
[0.0
01
][0
.00
1]
frac
age
15-6
4w
ho
isfe
ma
le0.0
67
-0.0
95
-0.2
17
4.2
96
***
[0.6
26]
[0.5
82]
[0.6
31
][0
.65
4]
years
of
ed
uca
tion
15
-64
0.0
86
***
0.2
04
***
0.1
61
***
0.0
34
[0.0
23]
[0.0
20]
[0.0
21
][0
.02
8]
fract
ion
15-6
4re
ad &
wri
te
em
plo
yed
15
-64
-0.0
22
0.0
55
1.0
43
***
0.0
33
[0.1
72]
[0.0
96]
[0.2
61
][0
.45
9]
du
mm
ysk
ille
do
ccup
atio
n0
0.5
68
***
0.1
07
**0
[0.0
00]
[0.0
89]
[0.0
43
][0
.00
0]
du
mm
y a
gricu
ltura
l occ
up
atio
n0.0
96
-0.0
13
0.0
17
-0.0
75
[0.1
08]
[0.0
25]
[0.0
53
][0
.04
7]
du
mm
y in
du
stria
locc
up
atio
n0.0
09
-0.0
04
0.0
19
-0.1
6**
[0.1
23]
[0.0
19]
[0.0
47
][0
.06
3]
Co
nst
an
t6.4
49
**6.5
54
***
8.7
71
***
9.5
88
***
5.9
25
***
3.8
01
**3
.903
***
11.4
51
***
6.0
02
***
5.1
28
***
7.4
23
***
2.5
05
5.8
71
***
0.6
59
-2.3
72
4.8
58
*[2
.222
][1
.88
2]
[2.0
35]
[1.8
17]
[0.3
83
][1
.39
7]
[1.0
46
][1
.77
6]
[0.2
04]
[0.9
30
][0
.79
2]
[2.1
65
][0
.16
1]
[1.8
73
][3
.67
4]
[2.5
32]
Ad
just
ed
R s
qu
are
d0.4
07
40
.28
597
0.5
34
40.8
24
71
0.1
434
10
.585
83
0.6
48
55
0.8
637
10.1
411
70
.471
61
0.5
706
60
.89
35
0.4
076
30
.12
35
0.4
80
87
0.7
47
61
N31
431
43
14
314
63
29
632
263
22
63
22
337
33
03
30
33
02
17
21
82
16
216
F-t
est
s(p
-va
lue
s)3D
s0
.00
00
.00
00
.02
10
.00
00
.000
0.0
04
0.0
00
0.0
00
0.2
95
0.0
00
0.0
00
0.0
00
Ge
ogra
phic
0.0
00
0.0
00
0.0
36
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
Co
ntr
ols
0.0
00
0.0
00
0.0
00
0.0
00
Temperature
VariabilityPrecipitationDensity Distance Division
Mean Annual
Temperature
BO
LIV
IAB
RA
ZIL
CH
ILE
EC
UA
DO
R
Tab
leA
.1P
ote
nti
al
Dri
vers
of
Inco
me(
1o
f3
)
DistanceDensity
Popula
tion
densi
ty0.1
79
*0.1
09
*-0
.003
0.3
57
0.5
54
*-0
.66
*0.0
11
-0.0
27
***
-0.0
07
*0.0
57
***
0.0
67
***
0.0
13
[0.0
87]
[0.0
62]
[0.0
25]
[0.3
82]
[0.2
77]
[0.3
75]
[0.0
22]
[0.0
08]
[0.0
03]
[0.0
15]
[0.0
16]
[0.0
10]
Popula
tion
densi
ty(r
em
ote
)-0
.023
-0.1
-0.0
64
0.9
71
0.7
95
0.5
60.2
9*
0.1
83
*0.1
*1.7
58
***
1.5
72
***
0.4
57
**[0
.175]
[0.1
12]
[0.1
15]
[1.0
21]
[1.0
78]
[0.8
19]
[0.1
36]
[0.0
93]
[0.0
55]
[0.5
17]
[0.3
53]
[0.1
95]
Tra
velti
me
to c
ity250k+
(log)
-0.1
67
***
-0.1
93
***
-0.0
62
*- 0
.082
-0.0
53
0.0
22
-0.0
85
-0.0
15
0.0
24
-0.1
57
***
-0.1
4**
*-0
.033
***
[0.0
41]
[0.0
29]
[0.0
30]
[0.0
59]
[0.0
48]
[0.0
46]
[0.0
60]
[0.0
48]
[0.0
33]
[0.0
36]
[0.0
16]
[0.0
10]
Dis
tance
to s
ea
(log)
0.0
02
-0.0
19
0.0
35
*-0
.067
**-0
.021
0.0
02
-0.0
47
-0.0
22
0.0
23
0.0
15
-0.0
38
0.0
02
[0.0
42]
[0.0
44]
[0.0
18]
[0.0
25]
[0.0
26]
[0.0
26]
[0.0
28]
[0.0
25]
[0.0
15]
[0.0
27]
[0.0
26]
[0.0
13]
%E
thnic
min
ority
-0.4
66
***
-0.4
59
***
-0.2
2**
*-0
.404
-0. 4
21
*-0
.235
1.9
82
**1.7
57
*0.9
06
*-1
.049
***
-0.6
57
***
-0.4
25
***
[0.0
63]
[0.0
48]
[0.0
29]
[0.2
53]
[0.2
23]
[0.1
67]
[0.8
90]
[0.8
84]
[0.4
79]
[0.1
14]
[0.0
89]
[0.0
50]
Splin
e:co
ldest
10%
pla
ces
dro
pped
dro
pped
dro
pped
dro
pped
dro
pped
dro
pped
dro
pped
dro
pped
dro
pped
dro
pped
dro
pped
dro
pped
Splin
e:80%
mid
-tem
ppla
ces
0.0
32
***
0.0
04
0. 0
01
0.0
08
0.0
07
0.0
07
0.0
66
**0.0
52
**0.0
24
0.0
06
0.0
06
0.0
09
[0.0
10]
[0.0
09]
[0.0
06]
[0.0
24]
[0.0
21]
[0.0
18]
[0.0
25]
[0.0
21]
[0.0
16]
[0.0
10]
[0.0
08]
[0.0
06]
Splin
e:hott
est
10%
pla
ces
0.0
54
0.1
72
*0.1
21
***
-0.0
13
-0.0
72
0.0
02
-0.2
07
-0.8
50.3
12
0.1
13
0.0
60.0
7[0
.128]
[0.0
88]
[0.0
33]
[0.0
89]
[0.1
05]
[0.1
08]
[0.8
61]
[1.1
10]
[0.3
42]
[0.0
72]
[0.0
80]
[0.0
51]
Splin
e:lo
west
10%
tem
pva
riatio
n0
00
00
00
00
-0.0
81
-0.0
89
0.0
04
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.1
49]
[0.1
01]
[0.0
90]
Splin
e:m
id80%
tem
pva
riatio
n-0
.226
**-0
.183
***
-0.0
88
**-0
.016
-0.1
62
-0.2
19
*0.2
16
0.2
21
0.2
35
-0.1
58
**-0
.066
-0.0
45
[0.0
97]
[0.0
57]
[0.0
37]
[0.1
69]
[0.1
43]
[0.1
24]
[0.2
58]
[0.2
81]
[0.2
00]
[0.0
71]
[0.0
68]
[0.0
44]
Splin
e:hig
hest
10%
tem
pva
riatio
n0.2
48
*0.3
13
**0.0
56
1.8
82
1.3
19
-0.0
53
00
01.1
2**
0.5
71
**0.3
07
***
[0.1
38]
[0.1
17]
[0.1
08]
[1.2
63]
[1.7
96]
[1.4
26]
[0.0
00]
[0.0
00]
[0.0
00]
[0.4
37]
[0.2
38]
[0.1
11]
Splin
e:lo
west
10%
pre
cipita
tion
00
00
00
00
01.0
77
**0.6
78
**0.8
34
***
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.5
11]
[0.3
31]
[0.2
28]
Splin
e:m
id80%
pre
cipita
tion
-0.1
59
*-0
.081
0.0
09
-0.3
94
***
-0.2
39
**-0
.082
-0.1
49
-0.2
12
-0.1
04
-0.1
18
**-0
.023
0.0
01
[0.0
92]
[0.0
60]
[0.0
26]
[0.1
28]
[0.0
90]
[0.1
07]
[0.1
05]
[0.1
56]
[0.0
99]
[0.0
45]
[0.0
50]
[0.0
25]
Splin
e:hig
hest
10%
pre
cipita
tion
-0.0
87
0.0
12
0.0
02
0.1
82
0.4
49
**0.2
69
-0.1
22
-0.1
47
-0.0
18
-0.0
18
0.0
49
0.0
09
[0.0
63]
[0.0
47]
[0.0
30]
[0.2
62]
[0.1
96]
[0.1
55]
[0.1
32]
[0.1
42]
[0.1
02]
[0.1
03]
[0.0
71]
[0.0
60]
Media
nele
vatio
n(log)
0.1
22
*0.0
72
*-0
.003
-0.0
64
-0.0
58
-0.0
94
*0.0
79
0.0
8**
0.0
26
0.1
01
***
0.0
56
**0.0
36
**[0
.063]
[0.0
41]
[0.0
25]
[0.0
64]
[0.0
53]
[0.0
52]
[0.0
46]
[0.0
36]
[0.0
19]
[0.0
27]
[0.0
25]
[0.0
13]
Media
nsl
ope
(degre
es)
-0.0
44
**-0
.039
***
-0.0
05
-0.0
12
-0.0
13
0.0
31
-0.0
16
-0.0
11
0.0
2**
-0.0
4**
-0.0
2*
-0.0
02
[0.0
19]
[0.0
13]
[0.0
07]
[0.0
30]
[0.0
25]
[0.0
23]
[0.0
16]
[0.0
16]
[0.0
09]
[0.0
17]
[0.0
11]
[0.0
06]
Media
n s
lope s
quare
d(d
egre
es)
0.0
01
0.0
01
**0
00
-0.0
01
00
-0.0
01
**0
00
[0.0
01]
[0.0
00]
[0.0
00]
[0.0
01]
[0.0
01]
[0.0
01]
[0.0
01]
[0.0
01]
[0.0
00]
[0.0
01]
[0.0
00]
[0.0
00]
Dis
tance
from
Equato
r(latit
ude)
-0.1
66
**0.0
95
*0.0
71
0.0
95
-0.0
1-0
.05
-0.0
81
-0.0
55
-0.1
98
0.0
43
**0.0
5**
*0.0
37
**[0
.074]
[0.0
51]
[0.0
44]
[0.0
93]
[0.1
06]
[0.0
96]
[0.2
71]
[0.2
14]
[0.1
56]
[0.0
18]
[0.0
11]
[0.0
14]
Fra
ctio
nof
people
with
ele
ctrici
ty0.3
53
***
0.2
38
*-0
.107
0.1
56
*[0
.078]
[0.1
36]
[0.3
02]
[0.0
88]
Fra
ctio
nof
hhs
with
sew
age
0.2
47
***
0.0
37
-0.0
52
0.3
23
***
[0.0
75]
[0.1
97]
[0.0
59]
[0.0
86]
mean
hh
dependency
ratio
-0.4
46
**-0
.689
*-2
.054
***
-0.6
64
***
[0.1
59]
[0.3
38]
[0.1
17]
[0.1
22]
mean
age
of
hh
head
-0.2
78
**-0
.305
**-0
.177
*-0
.077
***
[0.1
23]
[0.1
24]
[0.1
00]
[0.0
23]
Age
of
head
of
hsh
(square
d)
0.0
03
**0.0
03
**0.0
02
0.0
01
***
[0.0
01]
[0.0
01]
[0.0
01]
[0.0
00]
frac
age15-6
4w
ho
is f
em
ale
2.0
84
***
0.5
78
4.8
62
***
2.0
37
***
[0.6
61]
[2.0
05]
[0.5
62]
[0.5
42]
years
of
educa
tion
15-6
40.0
95
***
0.1
05
***
0.0
94
***
[0.0
13]
[0.0
33]
[0.0
10]
fract
ion
15-6
4re
ad
&w
rite
em
plo
yed
15-6
40.0
24
0.4
67
0.2
83
***
[0.1
38]
[0.4
59]
[0.0
80]
dum
my
skill
ed
occ
upatio
n0
00
[0.0
00]
[0.0
00]
[0.0
00]
dum
my
agricu
ltura
locc
upatio
n0.0
47
-0.0
82
-0.1
04
**[0
.089]
[0.0
81]
[0.0
44]
dum
my
indust
rialo
ccupatio
n0.1
46
0-0
.274
***
[0.0
94]
[0.0
00]
[0.0
72]
Const
ant
8.1
37
***
9.6
89
***
7.4
71
***
11.2
8**
*6.0
45
***
4.3
1**
6.3
44
***
12. 8
66
***
8.7
18
***
8.5
8.4
66
**13.9
12
***
5.3
29
***
3.3
86
**4.5
81
***
3.8
09
***
[0.5
47]
[1.0
49]
[0.6
69]
[2.9
21]
[0.4
16]
[1.6
55]
[1.9
12]
[2.8
51]
[0.2
21]
[4.8
97]
[3.8
46]
[2.7
36]
[0.3
79]
[1.2
79]
[0.7
79]
[0.8
11]
Adju
sted
R s
quare
d0.5
6745
0.3
2101
0.6
6252
0.8
7581
0.1
9726
0.2
0164
0.2
864
0.4
9373
0.3
162
0.3
6934
0.4
7107
0.7
3606
0.5
1117
0.4
7585
0.6
6001
0.8
0879
N329
330
329
329
296
294
294
294
414
413
413
413
2411
2418
2411
2411
F-t
est
s (p
-valu
es)
3D
s0.0
00
0.0
00
0.0
00
0.0
20
0.0
05
0.0
90
0.0
00
0.0
04
0.0
11
0.0
00
0.0
00
0.0
00
Geogra
phic
0.0
00
0.0
00
0.0
00
0.0
17
0.0
14
0.0
23
0.0
00
0.0
00
0.0
02
0.0
00
0.0
00
0.0
00
Contr
ols
0.0
00
0.0
00
0.0
00
0.0
00
GU
AT
EM
AL
AH
ON
DU
RA
SJA
MA
ICA
ME
XIC
O
DivisionMean Annual
Temperature
Temperature
VariabilityPrecipitation
Tab
leA
.1P
ote
nti
al
Dri
vers
of
Inco
me(
2o
f3
)
Pop
ulat
ion
dens
ity0.
256
**0.
393
**0.
096
0.00
3-0
.002
-0.0
030.
032
***
0.02
7**
*0.
017
**[0
.110
][0
.158
][0
.061
][0
.011
][0
.009
][0
.005
][0
.004
][0
.007
][0
.006
]P
opul
atio
nde
nsity
(rem
ote)
-0.2
380.
241
-0.7
1**
2.73
1**
*2.
534
***
-0.1
320.
442
**0.
416
**0.
096
[1.0
71]
[0.8
16]
[0.2
99]
[0.6
95]
[0.5
17]
[0.3
12]
[0.1
90]
[0.1
67]
[0.0
69]
Tra
velti
me
toci
ty25
0k+
(log)
-0.1
43**
*-0
.047
0.01
6-0
.138
**-0
.123
***
0.02
3-0
.068
***
-0.0
63**
*-0
.026
*[0
.035
][0
.049
][0
.025
][0
.045
][0
.038
][0
.014
][0
.012
][0
.016
][0
.013
]D
ista
nce
tose
a(lo
g)-0
.093
***
-0.0
84**
*-0
.02
**-0
.073
***
-0.0
63**
*-0
.011
-0.1
19**
*-0
.102
***
-0.0
65**
*[0
.026
][0
.017
][0
.009
][0
.018
][0
.020
][0
.010
][0
.017
][0
.022
][0
.020
]%
Eth
nic
min
ority
-0.1
95-0
.132
-0.0
93-1
.279
***
-1.0
79**
*-0
.353
***
n.a.
n.a.
n.a.
[0.1
62]
[0.1
27]
[0.0
76]
[0.1
60]
[0.1
24]
[0.0
67]
Spl
ine:
col
dest
10%
plac
esdr
oppe
ddr
oppe
ddr
oppe
ddr
oppe
ddr
oppe
ddr
oppe
d-0
.013
*-0
.012
**-0
.002
[0.0
07]
[0.0
05]
[0.0
05]
Spl
ine:
80%
mid
-tem
ppl
aces
0.00
30
0.01
4-0
.005
-0.0
290.
006
0.00
9**
*0.
017
***
0.00
8**
[0.0
14]
[0.0
14]
[0.0
12]
[0.0
50]
[0.0
38]
[0.0
20]
[0.0
03]
[0.0
03]
[0.0
03]
Spl
ine:
hot
test
10%
plac
es-0
.015
-0.1
16**
-0.0
490.
398
**0.
080.
015
-0.1
44-0
.015
0.03
5[0
.055
][0
.040
][0
.036
][0
.142
][0
.085
][0
.050
][0
.155
][0
.144
][0
.116
]S
plin
e: lo
wes
t10%
tem
pva
riatio
n0
00
00
0-1
.259
**-0
.2-0
.343
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.0
00]
[0.4
60]
[0.2
98]
[0.2
72]
Spl
ine:
mid
80%
tem
pva
riatio
n-0
.233
0.02
7-0
.028
-0.0
990.
123
0.11
70.
046
0.08
9*
0.04
9[0
.161
][0
.149
][0
.105
][0
.164
][0
.152
][0
.080
][0
.061
][0
.050
][0
.060
]S
plin
e: h
ighe
st 1
0%te
mp
varia
tion
00
0-0
.719
**-0
.124
-0.1
730.
067
0.01
90.
057
[0.0
00]
[0.0
00]
[0.0
00]
[0.2
46]
[0.1
98]
[0.1
70]
[0.0
93]
[0.0
79]
[0.0
69]
Spl
ine:
low
est1
0%pr
ecip
itatio
n0
00
00
0-1
.048
***
-0.5
55**
*-0
.279
**[0
.000
][0
.000
][0
.000
][0
.000
][0
.000
][0
.000
][0
.175
][0
.151
][0
.133
]S
plin
e: m
id80
%pr
ecip
itatio
n-0
.299
***
-0.2
43**
*-0
.13
**-0
.003
-0.1
630.
089
0.00
70.
116
*0.
056
[0.0
51]
[0.0
58]
[0.0
60]
[0.1
58]
[0.1
20]
[0.0
60]
[0.0
63]
[0.0
63]
[0.0
48]
Spl
ine:
hig
hest
10%
prec
ipita
tion
0.05
-0.0
110.
04-0
.27
*-0
.057
0.01
80.
126
***
0.08
6*
0.04
4[0
.052
][0
.049
][0
.039
][0
.145
][0
.069
][0
.030
][0
.044
][0
.050
][0
.035
]M
edia
nel
evat
ion
(log)
0.01
80.
031
0.01
50.
073
0.03
70.
051
**-0
.003
0.02
60.
017
[0.0
44]
[0.0
28]
[0.0
26]
[0.0
60]
[0.0
54]
[0.0
23]
[0.0
28]
[0.0
26]
[0.0
18]
Med
ian
slop
e(d
egre
es)
0.00
4-0
.001
0.01
-0.0
72**
-0.0
89**
*-0
.024
**-0
.002
0-0
.003
[0.0
26]
[0.0
26]
[0.0
19]
[0.0
32]
[0.0
28]
[0.0
11]
[0.0
07]
[0.0
06]
[0.0
05]
Med
ian
slop
esq
uare
d(d
egre
es)
0**
00
00.
003
**0.
001
00
0[0
.000
][0
.000
][0
.000
][0
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][0
.001
][0
.001
][0
.000
][0
.000
][0
.000
]D
ista
nce
from
Equ
ator
(latit
ude)
-0.1
17**
*-0
.082
-0.0
72**
0.12
5-0
.004
-0.0
590.
008
0.01
8**
-0.0
01[0
.037
][0
.050
][0
.032
][0
.136
][0
.086
][0
.055
][0
.008
][0
.008
][0
.007
]F
ract
ion
ofpe
ople
with
elec
tric
ity-0
.173
0.58
8**
*0.
061
[0.1
30]
[0.1
07]
[0.0
56]
Fra
ctio
nof
hhs
with
sew
age
0.01
90.
295
**0.
411
***
[0.1
32]
[0.1
03]
[0.0
70]
mea
nhh
depe
nden
cyra
tio-0
.587
*-0
.459
***
-0.6
32**
*[0
.310
][0
.103
][0
.108
]m
ean
age
ofhh
head
-0.0
28-0
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-0.0
72**
*[0
.187
][0
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][0
.019
]A
geof
head
ofhs
h(s
quar
ed)
00
0.00
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][0
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][0
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]fr
acag
e15-
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hois
fem
ale
3.49
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-0.5
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.255
][0
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][0
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]ye
ars
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ion
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***
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9**
*[3
.231
][0
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]fr
actio
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read
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rite
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[0.1
83]
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oyed
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493
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2[0
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][0
.278
]du
mm
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illed
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patio
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][0
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]du
mm
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ricul
tura
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upat
ion
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][0
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]du
mm
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rialo
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00]
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Con
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7.07
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*9.
299
***
[0.2
27]
[0.6
54]
[0.7
53]
[4.5
39]
[0.2
44]
[0.9
78]
[1.2
70]
[1.8
20]
[0.1
99]
[2.5
62]
[1.6
79]
[1.7
55]
Adj
uste
dR
squa
red
0.48
108
0.55
560.
6711
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8765
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7391
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AR
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VariabilityPrecipitationDensity Division
Mean Annual
Temperature
Tab
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)
73
Spatial Disparities in Human Development
This chapter examines the relationship between
space and human development in the region,
taking the usual proxies of levels of health,
nutrition and education of the population. It shows
that spatial disparities in human capital formation
are pervasive in most of LAC, although they have
declined in most countries and most dimensions
of human development. Such convergence is
an encouraging sign that public policy in many
countries is emphasizing the principles of equality
of opportunity. The empirical evidence shows
that space per se is of second order in explaining
the variation in human development outcomes
and the formation of human capital. The chapter
argues that it is most likely at the neighborhood
level where transmission mechanisms between
space and household welfare are increasingly
becoming more relevant for the Latin American
social policy debate. The chapter documents
emerging evidence of the existence of spatial
peer group and role model effects by which
residential segregation diminishes the human
capital formation of children and youth from
poor neighborhoods. The chapter concludes that
it is important for policies to primarily focus on
people’s opportunities for human development,
trying to reach those most disadvantaged through
a progressive expansion of access and services.
However, the presence of spatial externalities
and spillovers imply a role for social and human
development policies with a spatial focus, on the
basis of a better understanding of the transmission
mechanisms and their importance in the Latin
American context.
Human development enables people to live a
longer and healthier life, be educated, and have
access to resources for a decent standard of li-
ving—as such, human development is the outco-
me and goal of the development process itself.39
In the framework of the World Development
Report 2009, this chapter explores the interplay of
density and human development indicators (levels
of health, nutrition and education) across various
dimensions of space in Latin America. As noted in
Chapter 1, the WDR distinguishes between local,
national and international areas and looks at the
dimensions of transformations as density, distan-
ce and division. As in previous chapters, we exa-
mine the space-development-welfare relationship
within countries (e.g., inter-regional, urban-rural,
within micro-spaces), including the evidence for
convergence in human development indicators.
We discuss the role of space per se in explaining
these within-country spatial disparities and the
associated mechanisms and policy levers.
Chapter 3
Spatial Disparities in Human Development
39 A plethora of studies have analyzed the links between human capital and monetary welfare in Latin America. See, for instance, Perry et al., 2006; IDB, 2004; and De Ferranti et al., 2003.
74
Reshaping Economic Geography in Latin America and the Caribbean
We give special attention to a debate in the La-
tin American social policy circles that focuses on
spatial disparities in welfare within micro-spaces
in urban areas, dubbed as “neighborhoods”, gi-
ven its increasing relevance in a context of high
urbanization. We aim to explore here the spa-
tial influences in the process of human develo-
pment (and human capital formation) between
and within localities in what the WDR 2009 terms
‘advanced urbanization’—to draw the implicatio-
ns for public policy of intra-local area divisions.
This is in line also with a focal point of the social
policy debate in advanced urbanized countries
like the US and in Europe: to explicitly explore—
and tackle—the physical as well as social conno-
tations of space in shaping individual opportuni-
ties.40 The empirical analyses of such effects face
significant difficulties given the necessity to isola-
te the so called ‘neighborhood effect’ from others
that might be correlated with households’ volun-
tary or involuntary location in a specific commu-
nity. The chapter presents the limited advances
in such empirical analyses to date in the region,
and calls for more research in this area given its
increasing importance for public policy.
The chapter is organized as follows. Section 3.1
uses the available data to characterize the level
and evolution of spatial disparities in human de-
velopment indicators within countries and local
areas. Section 3.2 reviews the main factors that
may be behind these spatial disparities, asses-
sing the role of space per se. Section 3.3 focuses
attention on “neighborhoods”, reviewing the evi-
dence and transmission processes that can lead
to an impact of the local surroundings on indivi-
dual human capital formation. Section 3.4 con-
cludes with a reflection on the implications of the
findings on within country and intra-urban human
development disparities for public policy-building
on the “3-Is” WDR 2009 framework.
3.1 CharacterizationofSpatialInequalityinHumanDevelopment
Human development is a cumulative life-cycle
process. There is a large body of literature do-
cumenting the importance of adequate health
and nutrition from pregnancy throughout the first
three infant years in the development of cognitive
and non-cognitive capacity and readiness to learn
at school and in adult life.41 Human development
enables people to live a longer and healthier life,
be educated, and have access to resources for
a decent standard of living—as such, human de-
velopment is both a means and an end for the
development process itself.
We follow the established literature and characte-
rize intra-country disparities in such human deve-
lopment indicators as malnutrition, infant-mater-
nal health, literacy and schooling levels. Several
of the indicators relate to the outcome dimension
of human development—such as literacy rates.
Several others relate to the factors, or inputs,
that are known to be among the determinants of
40 Examples include the ‘Moving out of Poverty Program’ in the United States (Del Conte and Kling, 2001). Glennerster et al., 1999, recount the UK’s policies on area-based social policy.
41 See Heckman, 1996, 2000; Mayer-Foulkes, 2004; and Perry et al., 2006, for a review of numerous studies.
75
Spatial Disparities in Human Development
such outcomes—such as access to water and sa-
nitation or health insurance which influence heal-
th outcomes. We examine how these regional dis-
parities relate to poverty. The results come from
a systematic analysis of household surveys for
Latin American countries—with a very important
caveat arising from such source: The measure-
ment of spatial intra-country disparities depends
on our ability to arrive at spatially disaggregated
estimates, which differ across countries as explai-
ned below. As such, our discussion concentrates
more on what we are able to say about changes in
spatial disparities—rather than static cross-coun-
try differences—, particularly whether countries
have experienced convergence or divergence in
human development indicators over time.
Spatial Variation in the Continent
We start by examining the spatial variation of li-
teracy, years of education, and access to water
and sanitation resources. Figures 3.1a and 3.1b
show literacy rates among the population aged
15–65 in Latin America as estimated by Gaspa-
rini et al. (2008) based on available household
survey data.
Absolute differences in literacy rates and leng-
th of educational instruction in Latin America
are stark. As Figures 3.1a and 3.1b show, the
Southern Cone represents a relatively homo-
genous picture of high literacy rates with the
exception of several regions in Chile. Brazil shows
stark inner-country differences—with low levels
of literacy in the North-East and levels above 90
percent in the South-East. Figure 3.2 maps the
number of years of education by sub-national
Figure3.1a:LiteracyRatesinLatinAmerica,2006
Source: Panel a: Gasparini et al., 2008.
Figure3.1bMeanYearsofEducation,2006
Source: Gasparini et al., 2008.
76
Reshaping Economic Geography in Latin America and the Caribbean
region around the year 2006 from the analysis by
Gasparini et al., 2008. Overall, results are simi-
lar to the literacy achievements mapped in Figu-
res 3.1a and 3.1b. However, the eastern Andes
area has now stretched to include most of Brazil.
Even Southeastern Brazil, although still the most
highly educated part of the country, is substan-
tially outperformed by Argentina and to a lesser
extent by Chile and Uruguay.
The stark spatial differences examined above are
likely to diminish over the next generation. Figure
3.2 presents years of education in LAC for people
aged 21 to 30 (left panel) and the same infor-
mation for people 61 or older (right panel). The
difference between both maps is visibly stark,
with inequalities in educational attainment signi-
ficantly higher for the older generation. Younger
cohorts across the continent are now obtaining
relatively more years of schooling than older co-
horts. If maintained, this tendency suggests that
welfare levels will rise and become more even
throughout LAC.
Turning to access to basic services—as one im-
portant factor determining health outcomes—we
find somewhat lower regional disparities (Figure
3.3). At this level of disaggregation, within each
country, access to piped water is generally more
equally distributed than education. The same is
true for sanitation. However, such relatively les-
ser inequality does not imply that uniformly high
Source: Gasparini et al., 2008.
Figure3.2.AverageYearsofEducationinLACforPeopleAged21-30(leftpanel)and61andOlder(rightpanel)
77
Spatial Disparities in Human Development
access levels are achieved today: Central Ame-
rica and the Eastern Andes stand out as regions
where the majority of the population does not
command access to an adequate water source.
Moreover, as noted below, the situation changes
dramatically once we consider differences bet-
ween urban and rural areas.
Spatial Variation between Countries,
Regions and Neighborhoods
We now examine the intra-country spatial di-
fferences—already visualized for several indi-
cators above—in finer detail. Figure 3.4 shows
the differences in selected education and heal-
th related indicators across regions within seve-
ral Latin American countries. For each coun-
try, the graph marks the lowest and highest
area indicator value within broad political-
administrative areas (regions), also distin-
guishing between urban and rural zones to
the extent that sampling design permits in
national household surveys. This separates
rural and urban areas for each spatial demar-
cation with statistical representation in house-
hold surveys, for instance, the rural and the
urban Coast in countries like Peru.
Today, spatial differences within LAC countries
in human development indicators remain high,
even for those countries that show more favo-
rable indicators at the national level. At this
still broad level of aggregation we can observe
that absolute disparities tend to be larger for
countries with lower national averages. Within
most countries in Central America and others
like Brazil one can find areas with human de-
velopment indicators comparable to national
averages in much better performing countries.
However, even in countries with very high na-
tional averages, high inter-regional disparities
can emerge as observed in Panama, Colombia
and Mexico for literacy, in Chile, Panama and
Peru for number of years of schooling, in Ar-
gentina and Panama for health insurance ac-
cess, in Chile, Brazil and Mexico for access to
water, and in Chile, Brazil, Colombia and Ecua-
dor for access to sanitation. The disparities
tend to be much larger for basic services ac-
cess, especially between urban and rural areas
of the countries. This shows that, despite its
high importance, most countries are far from
assuring equal access across space to all of
their populations.
Source: Gasparini et al., 2008.
Figure3.3:AccesstoWater,percentofpopulation,2006
78
Reshaping Economic Geography in Latin America and the Caribbean
As discussed below, these disparities might reflect
public investment allocation biases or social
exclusion. These could result in communities with
public schools and health centers that are remote,
with deficient water, sanitation and transportation
infrastructure and/or low basic service quality.
Weak accountability of responsible public agencies
and the lack of voice of those communities in
the political process could be driving forces that
contribute to maintaining such discrepancies
over time. Besides its detrimental impact on the
quality of life, this unequal spatial provision of
services can well be linked to a negative impact
on health and schooling outcomes and ultimately
human capital formation.
Cross-country comparisons of spatial dispari-
ties need to be made with care. Countries differ
in the number of political-administrative areas
and, by sampling design, in the level of statisti-
cal representation of areas in household surveys.
As a result, spatial disaggregation possibilities
vary significantly between countries—for exam-
ple, the household survey for Chile allows for
the calculation of thirteen different regional po-
litical units; those for Bolivia, the Dominican
Republic and Guatemala for nine; while those
for Panama and Nicaragua only for four and for
Ecuador only three. We do distinguish between
urban and rural areas; however, the definition of
what is urban or rural also varies across coun-
tries. Since in any given country higher spa-
tial disaggregation can only increase observed
spatial disparities, the cross-country compari-
son of spatial disparities in Figure 3.4 should be
viewed as indicative only.
Figure3.4:SpatialVariationinHumanDevelopmentIndicators,LatinAmerica
Literacy rates within and across countries in Latin America
(for population Ages 15 to 65)
60.0
70.0
80.0
90.0
100.0
Urug
uay
Arge
ntina
Chile
Costa Rica
Vene
zuela
Pana
ma
Ecua
dor
Colombia
Para
guay
Mexico
Braz
ilPe
ru
Dominica
n Re
p.
Boliv
ia
El S
alva
dor
Hondu
ras
Nicara
gua
Guatemala
Source: Data comes from latest available national household survey in each country.
79
Spatial Disparities in Human Development
Arge
ntina
Years of education within and across countries in Latin America(for population Ages 25 to 65)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Chile
Urug
uay
Pana
ma
Vene
zuela
Peru
Ecua
dor
Costa Rica
Mexico
Dominica
n Re
p.
Para
guay
Colombia
Boliv
ia
Braz
il
El S
alva
dor
Nicara
gua
Hondu
ras
Guatemala
Source: Data comes from latest available national household survey in each country.
Health insurance coverage, Latin America(percentage of population)
0.0
20.0
40.0
60.0
80.0
100.0
Chile
Costa Rica
Arge
ntina
Pana
ma
Peru
Urug
uay
Guatemala
Para
guay
Ecua
dor
El S
alva
dor
Nicara
gua
Source: Data comes from latest available national household survey in each country.
80
Reshaping Economic Geography in Latin America and the Caribbean
Access to water, Latin America(percentage of population)
0.0
20.0
40.0
60.0
80.0
100.0
Arge
ntina
Urug
uay
Costa Rica
Chile
Braz
il
Mexico
Ecua
dor
Boliv
ia
Colombia
Para
guay
Vene
zuela
Dominica
n Re
p.
Guatemala
Peru
Nicara
gua
El S
alva
dor
Hondu
ras
Source: Data comes from latest available national household survey in each country.
Access to sanitation, Latin America
(percentage of population)
0.0
20.0
40.0
60.0
80.0
100.0
Costa Rica
Urug
uay
Vene
zuela
Arge
ntina
Chile
Ecua
dor
Colombia
Braz
ilPe
ru
Boliv
ia
Mexico
Para
guay
Dominica
n Re
p.
Guatemala
Hondu
ras
El S
alva
dor
Nicara
gua
Source: Data comes from latest available national household survey in each country.
Source: Bank staff estimation based on national household surveys.
81
Spatial Disparities in Human Development
Spatial disparities within countries in health indica-
tors can be examined with specialized health and
demographic surveys. Figure 3.5 illustrates this with
available data on infant malnutrition for Ecuador
Figure3.5:RegionalVariationinMalnutritionRatesinEcuadorandPeru
and Peru, in the latter case distinguishing urban
and rural rates for each region. We observe marked
differences in malnutrition rates both across and
within regions and between urban and rural areas.
Source: Bank staff calculation based on WHO (2006) in Ecuador, and 2004 Monin Survey in Peru.
Uses the WHO’s standard of height-to-age
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Chim
bora
zo
%
Boliv
ar
Coto
paxi
Imbabura
Azu
ay
Am
azo
nía
Tungura
hua
Cañar
Loja
Carc
hi
Naci
onal
Manabi
Pich
inch
a
Los
Rio
s
Esm
era
ldas
Guaya
s
ElO
ro
0%
10%
20%
30%
40%
50%
60%
Huanca
velic
a
Lam
baye
que
Huánuco
Cusc
o
Junín
Aya
cuch
o
Caja
marc
a
Apuri
mac
LaLi
bert
ad
Puno
Uca
yali
Ánca
sh
Lore
to
Pasc
o
Piura
Moquegua
Am
azo
nas
San
Mart
ín
Madre
de
Dio
s
Lim
a
Are
quip
a
Tum
bes
Ica
Tacn
a
Urban Rural
Regional malnutrition rates in Ecuador, 2006
Regional malnutrition rates in Peru, 2004
82
Reshaping Economic Geography in Latin America and the Caribbean
However, somewhat different from the overall
pattern observed in the cross-country compari-
son, we find for the case of Peru that the intra-
departmental (urban-rural) variation tends to be
larger among regions in the middle range of the
distribution of malnutrition rates rather than for
the worse-performing regions.
These findings are consistent with the results
from a number of specialized city household sur-
veys that support the sentiment that stark diffe-
rences—especially within increasingly urbanizing
areas—exist, both with respect to absolute living
conditions as well as socio-economic stratifica-
tion. In Cali, for example, a specialized house-
hold survey from 1999 found that poverty rates
within the fourteen city communities—as well as
within larger intra-city areas used for stratifica-
tion purposes—differ significantly (Figure 3.6).
These highly diverging poverty rates are found to
be strongly correlated with indicators such as the
ethnic composition of the population, unemploy-
ment and schooling drop-out rates.
Spatial Variation over Time
From a policy perspective, we are much more in-
terested in whether intra-country spatial dispari-
ties increase or decrease over time. Most gover-
nments in Latin America strive for universal or
equitable access of the population to social ser-
vices and equality in most indicators examined
here. This is a policy goal that falls within the
WDR 2009’s set of ‘spatially blind’ policies. Over
time, governments would strive to increase the
provision of services at the national level while
at the same time reducing spatial disparities in
access. That is, many governments attempt to
raise service access over-proportionally in tho-
se areas with low coverage rates—which tend to
be poorer as examined below. This progressive
expansion pattern of service access is much in
line with policies to reduce existing inequalities
of opportunities. This is a widely accepted policy
goal for policy makers in the right or left of the
political spectrum. Policy instruments used for
such a goal include pro-poor distribution of public
investment, for example, a progressive revenue-
sharing arrangement between central and local
Figure3.6:PovertySegregationinCali,Colombia
Source: Hentschel, 2005.
1914
1 Km
Low: <20%Middle: 21% - 40%High: 41% - 50%V. High: 51% - 70%Ext. High: > 70%
(Headcount Rates)Map 1: Poverty in Cali, 1999
18
17
20
9
3
1
10
15
16
1113
2
4
5
8
6
7
12
83
Spatial Disparities in Human Development
government levels (e.g., fiscal formulas with re-
distributive criteria), centrally funded investment
funds (e.g., social funds) or spatially targeted
social programs (e.g., conditional cash transfers)
which use explicit pro-poor targeting strategies.
We start by examining changes in literacy rates
at the Latin American level. As shown in Figure
3.7, the absolute changes in literacy rates
suggest that some of the lagging areas might be
catching up while others may be staying behind.
Although literacy levels in Mesoamerica and
Northeastern Brazil are substantially lower than
in most of the region, they have also increased
substantially over the period 1992–2006. We also
observe several areas within Bolivia, Colombia,
Peru, Ecuador and Uruguay to show decreased
literacy levels—a phenomenon which may occur
as a result of severe and prolonged crisis and
long-lasting conflict, or probably as a result of
both within-country and international migration,
since migrants have a higher likelihood of being
literate than those staying behind.
Looking more closely at the evolution of intra-
country spatial dispersion of human development
indicators, we can visualize which countries have
been able to improve overall human development
indicators on the basis of larger improvements in
lagging areas. While the cross-country compari-
son of dispersion indices remains problematic for
the measurement reasons stated above, the di-
rection of change of such dispersion indices over
time signals unequivocally whether spatial con-
vergence or divergence in human development
is broadly taking place. For this, we calculate
the percentage change in the standard deviation
between regional indicator values (including the
rural-urban splits) for each country at two po-
ints in time—with a negative change indicating
that the dispersion of regional indicator values
decreased.42 Mapping such changes against the
percentage change in the national mean yields
an intuitive depiction (Figure 3.8) that distin-
guishes four groups of countries: those reducing
regional dispersion while improving national indi-
Figure3.7:LiteracyRatesinLatinAmericaandtheCaribbean,Changes1992-2006
Source: Gasparini et al., 2008.
42 Again, the direction of change is more important than the actual value given that the percentage change in the standard deviation is not independent of the number of observations (spatial units).
84
Reshaping Economic Geography in Latin America and the Caribbean
cators (progressive scenario); those experiencing
increases in dispersion and mean national indi-
cators (regressive scenario); countries with de-
clines in both dispersion and national indicators
(ambiguous scenario); and finally those cases
where dispersion increases with falling national
rates (worst case scenario).
Applying such a mapping exercise to schooling,
water and sanitation indicators, we find that
most countries in the sample achieve national
improvements in a spatially progressive man-
ner. Almost all countries expanded access and
at the same time most reduced spatial disper-
sion between the 1990s and 2000s. As Figure 3.9
(panel a) shows, all countries fall to the right of
the graph, indicating increasing national avera-
ges—especially those like Mexico which started
with low overall indicators. Meanwhile, only four
countries (Brazil, Ecuador, Honduras and Nicara-
gua) expanded average national schooling levels
while at the same time increasing inter-regional
dispersions (fall in the lower right quadrant of the
graph). In all other countries, the expansion of
schooling was generally highest in areas where
schooling levels were the lowest to begin with.
The opposite pattern, as notably exemplified by
Honduras, shows cases in which the relatively
better-off areas achieved the highest proportio-
nal improvement in secondary schooling levels.
Figure3.8:MappingChangesinDispersionandNationalMean
Percentagedecreaseinstandarddeviation
Lowerdispersion
Lowernational
indicator
Lowerdispersion
Highernational
indicator
Higherdispersion
Lowernational
indicator
Higherdispersion
Highernational
indicator
Percentagechangenationalaverage
85
Spatial Disparities in Human Development
Figure3.9,panelsathroughc:Education,WaterandSanitationIndicatorsinLatinAmerica,
ChangesinNationalMeanandRegionalVariation
Years of education
Argentina
Bolivia
Brazil
C. Rica
Ecuador
El Salvador
Honduras
Mexico
Nicaragua
Panama
PeruUruguay
Venezuela
-30%
-20%
-10%
0%
10%
20%
30%
40%
-10% 0% 10% 20% 30% 40% 50%
% Change in Mean
% R
educt
ion in S
tandar
d D
evia
tion
Chile
D.Republic
Water coverage
Argentina
Bolivia
BrazilChileD.Republic
El Salvador
Honduras
Mexico
Nicaragua
Peru
Uruguay
Venezuela-50%
-30%
-10%
10%
30%
50%
70%
90%
110%
-10% 0% 10% 20% 30% 40% 50% 60%
% C hange in Mean
% R
educt
ion in S
tandar
d D
evia
tion
86
Reshaping Economic Geography in Latin America and the Caribbean
For the majority of countries examined here, we
find a similar trend towards spatial convergen-
ce between the 1990s and 2000s in access to
water and sanitation as widely available proxies
for health outcomes. Figure 3.9, panel b, shows
a similar picture of broad convergence results—
almost all countries show increases in the national
mean with almost all also achieving it with a re-
duction in spatial dispersion. Non-progressive dis-
tributed gains in access are seen in Honduras, Ni-
caragua and Venezuela for water and Brazil, Hon-
duras, Nicaragua and Peru for sanitation. A few
countries show access declines, albeit at moderate
levels, with an increase in spatial dispersion, such
as Peru for sanitation and Venezuela for water. 43
43 This might also reflect issues of comparability between household surveys.
Source: Bank Staff estimates based on national household surveys.
Sanitation
Venezuela
Uruguay
Peru
Nicaragua
Mexico
Honduras
El Salvador
Ecuador
D. Republic Chile
Brazil
Bolivia Argentina
-60.0%
-40.0%
-20.0%
0.0%
20.0%
40.0%
60.0%
80.0%
% Change in Mean
% R
educt
ion in S
tandar
d D
evia
tion
-10.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0%
Figure3.9,panelsathroughc:Education,WaterandSanitationIndicatorsinLatinAmerica,
The broad convergence trends at the national
level can well hide a much more heterogeneous
pattern when examined more closely. For ex-
ample, Figure 3.10 illustrates for the case of
Peru regional changes in child malnutrition rates
between 2004 and 2007. As can be observed,
there is no clear pattern emerging with not all
of the worst-off regions having witnessed a fall
in malnutrition. There have been even some in-
creases among regions in the middle range of the
distribution of malnutrition rates with a homog-
enous fall in rates in better off areas.
We next turn our attention to examining what
could be driving the observed pattern of spa-
tial disparities in human development indictors.
The discussion is contextualized by summarizing
the well established possible determinants of hu-
87
Spatial Disparities in Human Development
man capital formation. We then draw on existing
empirical evidence to assess the role of various
mechanisms through which space could give rise
to spatial disparities in human development.
3.2DeterminantsofHumanCapitalFormation:theRoleofSpace
The determinants of human capital investments
have been systematically studied (Becker 1967,
1975). In such a classical view, they fall into two
broad groups: those that affect the capacity to
invest in skills, and those that lower the potential
returns to skill investments. In particular, edu-
cation is seen as an investment with associated
costs made on the basis of expected returns. The
costs include direct outlays such as school fees
and other related expenditures and the indirect
opportunity cost of time (e.g., foregone earnings
from work) as well as any non-pecuniary costs
related to tastes and readiness to learn. Private
benefits largely constitute future higher earnings
in the labor market but can also include increased
capabilities to function in a modern society.
Both the costs and benefits of human capital are
influenced by supply and demand factors related
to family and individual characteristics (chiefly
family income or wealth, parental education and
attitudes towards schooling), public investments
(affecting access to schools and quality in the
educational system), and the functioning of labor
Figure3.10:RegionalEvolutionofMalnutritionRatesinPeru
Source: Bank staff calculation based on national household surveys.
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Huan
cave
lica
Huán
uco
Aya
cuch
o
Junín
Lam
bay
eque
Cusc
o
Caj
amar
ca
Apurim
ac
La L
iber
tad
Pasc
o
Puno
Uca
yali
Ánca
sh
Lore
to
Piura
Am
azonas
San
Mar
tín
Mad
re d
e D
ios
Moqueg
ua
Are
quip
a
Lim
a
Ica
Tum
bes
Tac
na
2004 2007
Regional malnutrition rates in Peru, 2004-07
88
Reshaping Economic Geography in Latin America and the Caribbean
markets (unequal access to good jobs affecting
the returns to schooling). Human capital forma-
tion is subject to important intergenerational and
agglomeration externalities. The interaction of
family, school and context factors (all potentially
influenced by public policies) can result in self-re-
inforcing mechanisms that inhibit human capital
formation among certain groups of population in
a country.44
We can distinguish three broad groups of such
spatial impacts on human capital formation:
1) People characteristics, such as parental back-
ground, race/ethnicity, gender, and individual
tastes and efforts, which in turn can induce
spatial sorting or segregation
2) Differential access to and quality of services
and infrastructure, which can stem from the
costs imposed by geography or location en-
dowments or investment policy biases in the
development of infrastructure
3) Spatial externalities, which can arise from ag-
glomeration or social interaction effects (as
explained further below)
In this framework, space can negatively or po-
sitively affect human capital formation through
various mechanisms that affect the costs and be-
nefits of human capital investments. For example,
direct costs can become more binding for families
in lagging, rural or peri-urban areas with remote
public schools and health centers and/or deficient
transportation. When the returns to education
are only attractive for completion of secondary
school and going to the university, as has been
widely documented in Latin America45, children
in disadvantaged areas with uncertain prospects
to reach these education levels (because of lack
of secondary schools) are more likely to drop out
from school.
Similarly, public investment allocation biases or
social exclusion can prevent poor families from
receiving an adequate quality of health services
and schooling. Residential segregation can trap
children of lagging areas in low education levels,
owing to dismal funding and/or failure to attract
qualified personnel for schools and health centers
in their communities as well as lack of labor mar-
ket connections or discrimination, that lower the
returns to their skills.46 These spatial allocation
biases in public social expenditures might well
worsen during periods of high macroeconomic
volatility, which are known to have a negative im-
pact on children’s health outcomes and possibly
on the quality of schooling.47
Through such linkages, lagging regions might be
unable to tap into externalities in human capital
44 Some examples in the poverty traps literature are Azariadis and Stachurski, 2005; Mayer-Foulkes, 2004; and Bowles, Durlauf and Hoffs, 2006.
45 See Arias, Diaz and Fazio, 2006; Bourguignon, Ferreira and Lustig, 2005; IDB, 2004; and De Ferranti et al., 2003.
46 Although discriminatory practices can hurt the efficiency of profit-maximizing firms, there is evidence that the effects of exclusion on human capital formation and socioeconomic status can persist for generations and across space impervious to competitive market pressures. See, for example, Borjas, 1992; and Heckman, 1997.
47 See, for instance, Lustig, 2000; Schady, 2004; and Paxson and Schady, 2005.
89
Spatial Disparities in Human Development
Figure3.11:PovertyRates(S$2PPP),AverageSchoolingand
AccesstoHealthInsurance-LatestAvailableYear
R² = 0.550
0.0
Povert
y R
ate
(% u
nder
2 U
SD
per
day
line)
Po
vert
y R
ate
(% b
elow
2 U
SD
/day
)
Average years of schooling in region(for adults ages 25-65)
Access to health insurance
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
R² = 0.466
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Poverty and Average Years of Schooling in Latin America
Poverty vs% of population with access to health insurance in Latin America, by regions
2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0
0.0 20.0 40.0 60.0 80.0 100.0
11.0 12.0
90
Reshaping Economic Geography in Latin America and the Caribbean
formation related to sociological factors such as
absence of role models, skills agglomeration and
technological innovation. Children born in disad-
vantaged communities can then be at a higher
risk of experiencing malnutrition, illnesses and
environments less conducive to learning. Lacking
a minimum average skill level (say, some secon-
dary schooling), lagging regions would be less
likely to attract more technology and R&D intensi-
ve private investments, which itself can hold back
the demand for skills and thus the private returns
to education.48 This, in turn, could reinforce a low
skill, limited quality private investment cycle.
Mapping the previous cross-country regional data
on schooling and health insurance access indi-
cators against poverty levels in the regions, we
indeed find, not surprisingly, strong correlations.
Figure 3.11 shows that, abstracting from natio-
nal boundaries, areas with higher income pover-
ty tend to exhibit both lower average schooling
attainment of the adult population and also lower
share of the population covered by formal health
insurance. These are not, of course, causal rela-
tions. People in lagging areas tend to have perso-
nal and family characteristics that increase their
chances of dropping out from school or not acce-
ssing health insurance. We would need to isolate
these effects from the contextual effects related
to these areas being disadvantaged in the access
to and quality of services and infrastructure.
In a recent study for several Latin American
countries, Arias, Diaz and Fazio (2006) carried
out a systematic econometric analysis of how
individual, family and area characteristics affect
the risk that children and adolescents drop out
from school too early (i.e. before completing pri-
mary, secondary or tertiary school). Among the
area characteristics they consider are indicators
of rural-urban location and region of residence
as well as proxies of access to school and basic
infrastructure. Their results provide more direct
evidence, albeit imperfect, to assess the relative
importance of space in successful school progre-
ssion (and ultimately years of schooling, a first
pass measure of human capital).
Consistent with the established literature, their
findings indicate that individual and family fac-
tors are the first order predictors of successful
school progression, although spatial effects re-
main important. Foremost, education tends to
be strongly transmitted from parents to offspring
through parental education and wealth. For ins-
tance, having a mother with only primary edu-
cation increases the risk of school dropout by as
much as 160 percent in Chile and no less than
60 percent in El Salvador compared to a college
educated mother. A low educated father additio-
nally increases school failure risks by up to 140
percent in Chile and no less than 40 percent in
the Dominican Republic. Second in importance is
family income whose effect is often about half the
size of parental education. Gender, ethnicity and
family size and female household headship are
among other individual and family characteristics
that significantly affect school progression.
48 See, for example, Lucas, 1988; Azariadis and Drazen, 1990; Kremer, 1993; and Acemoglu, 1997, for growth and poverty trap models of skill agglomerations, and De Ferranti et al., 2004, for empirical evidence on the correlation between technological and skills investments in LAC.
91
Spatial Disparities in Human Development
Figure 3.12 illustrates the results for some of the
area related variables. While of a second order
of magnitude, physical access constraints remain
important determinants of school completion. The
risk of school failure is 20 to 40 percent higher in
the rural areas. Deficient infrastructure (proxied
by unpaved roads) increases school dropout risk
by 80 percent in Nicaragua and by 30 percent
in the Dominican Republic. In these two coun-
tries, the regional variables remain significant
after controlling for road access thus suggesting
that these rural effects may reflect problems
with school supply. Indeed, in a study for Brazil,
Albernaz, Ferreira and Franco (2002) found that
school quality indicators such as teacher’s educa-
tional level and school infrastructure have signifi-
cant effects on children’s educational performan-
ce. Mizala and Romaguera (2002) summarize si-
milar evidence for other countries in the region.
Moreover, Arias, Diaz and Fazio (2006) also re-
port significant differences in the risk of school
dropout between leading and lagging regions in
the countries, ranging from 10 to 80 percent in
the poorest regions. Again this likely partially re-
flects the effect of more deficient basic infrastruc-
ture, including school supply. Since these results
are obtained after simultaneously controlling for
family and individual characteristics they unders-
core the importance of spatial effects in human
capital formation in the region.
Spatial inequality in resources affects not only
possibilities but also the incentives to invest in
human capital. The spare evidence of the impact
of school quality in Latin America suggests that
this is a significant source of variation in the
returns to education. As an example, Arias et al.
(2004)’s study for Brazil measured the impact of
Figure3.12:RiskofSchoolDropoutandSpatialMechanisms—Circa2004
Source: Arias, Diaz and Fazio, 2006.
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
0
10
20
30
40
50
60
70
80
Dom. Republic Nicaragua
Risk of school drop out and infraestructure:Percentage change in risk compared to those with paved
road access
Risk of school drop out and region:Percentage change in risk compared to children in rural areas
Nic
ara
gu
a
%
Co
lom
bia
Bra
zil
Ch
ile
El
Salv
ad
or
Do
m.
Rep
ub
lic
%
education quality on schooling returns from cross-
state and inter-cohort variations in pupil-teacher
ratios—proxies for education quality. Figure 3.13
illustrates their main finding: workers educated
in states with a lower pupil-teacher ratio (say
by 10 students) have higher average returns to
education (by 0.9 percentage points per year of
schooling). Large class sizes are not uncommon
to Latin American poor children especially in rural
and marginal urban schools. The pupil-teacher
ratio is also correlated with other key inputs of the
educational process, such as instructional time,
educational materials, and teachers’ education
and experience.
92
Reshaping Economic Geography in Latin America and the Caribbean
Figure3.13:SpatialDifferencesinSchoolInputsLeadtoDifferentialReturnstoEducationinBrazil
Note: The bottom figure shows the fitted regression of estimates of Mincerian average education
returns by state, cohort and race and the associated pupil-teacher ratios; variables are depicted
as deviations from their means within cohort.
Source: Arias, Yamada and Tejerina, 2004.
retu
rns
(adju
sted
)
pupil/teacher ratio(adjusted)
white non-white
-10.36 7.88
0
10.0
-10.0
5.0
15
20
25
30
35
40
45
50
1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986
Northeast
South
Pupil-teacher rations in the Northeast and South of Brazil, 1940-1990
State pupil-teacher rations and average returns to education
% Non-White workers among total educated in each region: Northeast: 66%, South: 16%
93
Spatial Disparities in Human Development
3.3 HumanCapitalFormationandNeighborhoodEffects
What are Neighborhood Effects?
We now turn our discussion to outright spatial dis-
parities in micro-spaces in urban areas— dubbed
as “neighborhoods”. This discussion has gained
increasing interest in the Latin American social
policy debate—how, within neighborhoods, the
contextual surroundings, physical endowments as
well as social interactions influence opportunities
and mobility of households. How do social and
physical interactions between people in a locality,
or neighborhood, impact on household welfare?
Does living in a poor neighborhood jeopardize hu-
man capital formation, over and above the effects
that can be attributed to individual, household
and access characteristics?
The examination of neighborhood spillovers and
externalities in human development is impor-
tant also for another reason. Above, we showed
that ‘space’ is an important correlate of a num-
ber of human development indicators—however,
how such spatial effects can influence household
non-monetary welfare is largely unexamined.
The neighborhood literature tries to shed light on
exactly these transmission mechanisms.
These questions bear more relevance now sin-
ce, as several Latin America scholars hold, in-
tra-urban segregation in Latin America has risen
in the 1990s, increasingly requiring local social
Box3.1:IncreasedPolarizationintheLiteratureonLatinAmerica
A number of studies have examined urban segregation in Latin American cities with different patterns taking hold in different city contexts. A much cited development was the emergence of so called gated communities of high rent neighborhoods (Sabatini, 2003; Alvarez-Rivadulla, 2007). Apart from the security aspect, and the fact that many such developments had commercial and leisure facilities within their compounds, their emergence is also often associated with an increasing seclusion of better off segments of societies from the public spaces of most Latin American cities (Caldeira, 2000; Rubino, 2005; Ploger, 2007). The way in which such segregation takes place differs between cities. For example, in Montevideo, Buenos Aires, Santiago de Chile and Mexico City segregation of the new poor takes place from central to peripheral neighborhoods at the outskirts of the city. This means that the physical distances between poor and non poor increased compared to the period before the 1980s (CEPAL, 2007; Rubino, 2005; Fadda et al., 2000; Baker 2001). Tellingly, it is also in these cities that most observers associate the new phase of urban segregation with direct physical signs of social isolation such as high transport costs and a reduced number of contacts to inhabitants of other neighborhoods (Sabatini et al., 2001; Fadda et al., 2000; Espinoza 1993). In contrast, in most Brazilian cities, many newly emerging Favelas tended to be located in the city centre with only short distances to high class gated communities. While this implied a reduction in the physical distances to better off neighborhoods, most observers note that it was the social distance between residents of poorer and better off neighborhoods that had increased. For instance, in a widely cited study of urban segregation in Sao Paulo, Teresa Caldeira reports that the emergence of gated communities increased social boundaries both though the physical barriers protecting gated communities, and through a change in the employment relations between middle class families and their lower class servants, as such relations were increasingly managed by private contractors rather than through direct contracts between employer and employees (Caldeira, 2000).
Source: Wietzke, 2008
94
Reshaping Economic Geography in Latin America and the Caribbean
Box3.2:NeighborhoodEffectTransmissionMechanisms
Social Interactions within Neighborhoods
Social role model effects arise when the behavior of younger individuals in a community is influenced by the characteristics and earlier behavior of older individuals in a neighborhood. It thus describes how norms and aspirations are passed on between generations and, as such, ascribes more to longer-term explanations of urban inequalities—e.g., by influencing the educational and professional aspirations of younger individuals.
Peer effects refer to social agents adjusting their behavior to that of other individuals around them. Social learning among peers differs from role model effects because the behavioral adjustments they bring about are reciprocal, and mutually reinforcing—individuals interacting in the same group learn from each other and through repeated feedbacks (Durlauf, 2002, p. 6). Closely connected to role and peer effects is the examination of how individuals or households can use their social relations as a resource to improve their social and economic standing—i.e., the examination of social capital, albeit introduced here with a distinct spatial connotation. In the Latin American context some observers have concluded that the lack of relevant social capital in poor neighborhoods represents another important link in the interplay between residential segregation and social inequality in Latin American cities. Social capital can also facilitate collective action within a community (cf. for example, Rao and Walton, 2004) with tight social relations contributing to the creation of norms of trust and reciprocity.
Physical Interactions within Neighborhoods
In addition to these social processes differences in levels of wellbeing between neighborhoods may also be explained by material factors, including local level endowments of public service supply, physical infrastructure as well as neighborhood endowments of private assets. In the case of public health and sanitation, for example, in densely populated neighborhoods with low levels of public sanitation access, communicable diseases such as diarrhea or infections of the respiratory system could be transmitted across individuals and households. For individual households this could constitute a health hazard that exists independent and above the sanitary behavior of the household. In particular in countries which have not yet completed the health transition from infectious diseases to “modern” illnesses such as cancer or coronary diseases, there is evidence that the risk of biological contagion is inversely related to the level of supply of key public services such as sanitation waste management or water.
Spatial Effects between Neighborhoods
Institutional approaches to understand urban inequality analyze deprivation in disadvantaged neighborhoods, not only as the outcome of processes inside the vicinity but also of external mechanisms of closure and stigmatization against inhabitants in deprived neighborhoods. Based primarily on broad social identifiers, such as ethnicity, social background or residential location, these mechanisms leading to external closure and stigmatization are less grounded in physical interactions between individuals and social groups than in the more immaterial world of cognitive concepts and systems of social meanings. Several authors find that in highly segregated societies, social inequality primarily will manifest itself in between-neighborhood differences (as measured by the absolute economic standing of the census tract) rather than in inequalities within neighborhoods (cf. also Diez Roux, 2001; Wilkinson, 1996).
Source: Adapted from Wietzke, 2008
95
Spatial Disparities in Human Development
policies to address intra-area divisions (Wietzke,
2008). One of the hypotheses advanced is that
spatial isolation in many of the more peripheral
neighborhoods, along with a strong homogenei-
ty of population inside deprived areas, are likely
to reinforce existing social divides between diffe-
rent segments of society. Such hypothesis deri-
ves from the observed pattern of urban polariza-
tion in Latin American cities (see Box 3.1). While
systematic cross-country data are scant, seve-
ral city case studies point towards widening of
gaps within localities if indicators of poverty are
used, but less so for indicators such as education
(Wietzke, 2008).
The dominating approach to analyze such “neig-
hborhood effects” in economics draws on what
Durlauf (2002) calls “membership” or social in-
teractions theories. These theories argue that
similarities in outcomes within residential areas
are caused by interdependencies of individual be-
havior within local communities. For example, in
the case of public health, individuals in the same
community may have very similar health outco-
mes not only because of their own hygiene habits
but also due to the different health conditions of
other households in the vicinity. Likewise, in the
case of behavioral choices such as schooling de-
cisions, similarities in outcomes may be obser-
ved because of processes of social learning and
“collective socialization” among members of local
peer groups. Where these types of behavior lead
to sub optimal choices, such as dropping out of
school, they may lead to lasting inequalities and
disadvantage residents of poorer neighborhoods
in the longer term.
Several transmission mechanisms of neighborho-
od effects can be distinguished (Box 3.2). In the
relevant literature, neighborhoods are thought of
as social units and transmissions of behavioral ha-
bits between residents occurs because social inte-
ractions are concentrated within the well defined
confines of a household’s immediate surroundings
rather than in relations that extend across larger
physical distances. Within neighborhoods, we can,
broadly, separate the ‘social interaction’ approach
in which the behaviors of households are shaped in-
terdependently with behaviors in the vicinity, from
a more physical (or material) interaction, in which
access and supply of private and public assets in
the neighborhood directly impacts on household
welfare. Between neighborhoods, inequalities or
social perceptions can lead to stigmatizing results
that would then impact on all households living
within a particular vicinity.
Empirical Evidence
Rigorous empirical evidence on these transmis-
sion channels is relatively scarce. Sastry (1996)
examines the physical interaction transmission
mechanism referred to above. Using hazard mo-
dels to study the effect of individual household
and community variables on infant and under 5
mortality rates in Brazil, finds significantly lower
levels of child mortality in communities that have
access to clean water, sanitation, waste mana-
gement and specialized health facilities.49 Howe-
ver, this relationship only holds in the generally
49 Cf. also Timaeous and Lush, 1995, for similar evidence for Brazil
96
Reshaping Economic Geography in Latin America and the Caribbean
poorer and more tropical North Eastern States of
Brazil while no significant effect is found in the
more affluent southern states. Alderman et al.
(2003) find that rural households in Peru with
no access to piped water or sanitation still have
better outcomes in terms of child nutrition if they
live near to households which have access to both
utilities. Similarly, when focusing on urban areas,
the authors find households which have access
to both facilities also register positive effects on
children’s nutritional outcomes when they live
in neighborhoods where many households have
access to such facilities, controlling for other
community characteristics such as average levels
of income.
Empirical evidence is also emerging for between
area effects–stigmatization. A particularly clear
example for such structures of social stigmatiza-
tion includes wage and employment discrimina-
tion in the labor market, which ultimately reduce
the incentives to invest in human capital. In a
study of the employment history of university
economics graduates in Chile, Nunez and Gutie-
rrez (2004) find that coming from a lower inco-
me municipality has a significant negative effect
on earnings potential, controlling for other fac-
tors such as academic performance, job type and
employment history, as well as other indicators
of socio-economic status such as school type and
quality, and ethnic background. De Queiroz Ribei-
ro and do Lago (2001), who use census data for
Rio de Janeiro to assess differences in wage inco-
me, find that Favela inhabitants receive systema-
tically lower wages than non-Favela inhabitants,
controlling both for race and education.
Questions do exist as to how much the empirical
evidence to date can be relied on to support the
different transmission mechanisms both within as
well as between neighborhoods. Two main que-
ries arise. First, identification of any of the above
transmission channels through empirical analysis
is difficult. At the centre lies the difficulty to se-
parate true neighborhood effects from correlated
effects associated with characteristics of families
that ‘self select’ themselves into a specific area—
and where the estimated neighborhood effect
itself picks up a specific, unobserved, characte-
ristic of a spatially close population group. Failure
to take such selection bias into account may have
contributed to an overall tendency to overestima-
te the influence residential location has on indivi-
dual outcomes (Oakes 2004; Dietz 2002; Evans
et al., 1992).
Second, most studies that use observational
data to study the effect of social interactions in
a neighborhood effectively circumvent providing
such a definition as they use census tracts or
sampling clusters to delimit neighborhoods. On
analytical grounds, this may not be appropriate,
given that census tracts often do not represent
empirically meaningful units of social or spatial
organization—especially when the social inte-
raction effects within neighborhoods (peer, role
model and social capital effects) as well as the
stigmatizing effects between neighborhoods are
analyzed. These difficulties to define relevant so-
cial demarcations of residential units may lead to
a downward bias in the estimation of interaction-
based neighborhood effects, as many of the so-
cial relationships to which this explanation refers
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Spatial Disparities in Human Development
will be inaccurately captured by variables defined
at the level of census tract or a spatially defined
neighborhood.50
We are only aware of two rigorous quantitative
studies that circumvent these problems by using
micro-data from randomized evaluations. First,
Lalive and Cattaneo (2006) and Bobonis and
Finan (forthcoming) use data from Mexico’s OPOR-
TUNIDADES (formerly PROGRESA) and find that
higher enrolment of program beneficiaries also
increases school attendance among non-benefi-
ciaries, controlling for household, community and
school characteristics. Second, Macours and Vakis
(2008) using randomized data from Nicaragua’s
CCT program find that human capital spillover
effects from social interactions can be large.
Furthermore, in a background paper for this re-
port, Giovagnoli, Arias and Hentschel (2009) exa-
mine the impact of neighborhoods on education
and health outcomes in Bolivia and Peru using
an econometric strategy that take at face value
the inability to fully control for selection biases
(Box 3.3). They use survey data matched with
recent census data, which regrettably are not
readily available for many countries. The neigh-
borhood is defined based on the statistical unit of
the household survey (i.e. primary sampling unit
PSU). However, contrary to most studies, avera-
ge neighborhood variables are calculated using
the census data to avoid the problems that pla-
gued the existing studies as there are not enough
observations at PSU to obtain reliable estimates
of these effects (see Box 3.3).
The authors examine a number of spatial trans-
mission mechanisms and find relatively strong in-
dications of causal influences for the existence of
role model effects for school drop-outs in Bolivia.
Even with strong assumptions about endogenous
self-selection into neighborhoods, the statistical
relationship between space and the likelihood of
school drop-out remains significant. Moving a
youth with given characteristics to a neighborho-
od with a 10 percent higher school drop out rate
than the original neighborhood, increases the
probability for the newly moved child to drop out
from school between 1 and 3.8 percent (depen-
ding on the assumed importance of non-obser-
ved neighborhood selection effects). The authors
find somewhat weaker, but still relatively strong,
evidence of the importance of education exter-
nalities (level of mothers’ education in neighbor-
hood) on the incidence of child diarrhea in the
household (Box 3.3).
As a whole, the limited empirical evidence to date
suggests that neighborhood externality effects
could be an important impediment to human
capital formation in poor neighborhoods in an
increasingly urbanized Latin America. The evi-
dence is still scant and more research should be
conducted on this issue with the aim to inform
appropriate policy interventions.
50 As such, Borjas (1995) and Bertrand, et. al. (2000), who evaluate the effect of skills transmission within ethnic groups and information sharing among different language groups in the US, both look at group level difference within residential locations, while neighborhood effects are only indirectly accounted for through fixed effects incorporated in their estimation equation. Both studies find that outcomes differ significantly among sub-groups in the same neighborhoods, thus suggesting that the mechanisms through which group based interactions affect individual outcomes are not limited to spatial proximity alone.
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Reshaping Economic Geography in Latin America and the Caribbean
Box3.3:NewEvidenceonNeighborhoodEffectsAddressingEndogenousGroupSelection
Giovagnoli, Arias and Hentschel (2009) use a methodology proposed by Krauth (2006) that empirically tests whether a significant and independent neighborhood effect on individual outcomes exists once endogenous group selection is addressed. For instance, they assess the effect of deviations from exogeneity on estimates of the effect of peers in the neighborhood who drop out of school on the probability of dropping out of school. Two neighborhood measures are used: peers’ dropout rate and adult education. The dropout rate measures the percentage of young people in a census tract who are 13-18 and who are not in the school and did not graduate from secondary school. The adult education measure is the percentage of adults in a census tract who are 25-64 years of age who completed at least secondary education. For education outcomes, the authors focused on young children between 13-18 years of age and test whether the individual probability of school dropout can be related to the role of neighborhood factors operating through peer influences and the behavior and characteristics of adult role models (Wilson, 1987; and Crane, 1991).
Using a simple linear probability model and controlling for individual, family and geographical characteristics, the authors found that the direct effect of moving a youth with given characteristics to a neighborhood where 10 percent more of the youth did drop out of school than in his/her initial neighborhood, is to raise the probability the youth will dropout by 3.8 percent. This result is based on the assumption of exogenous neighborhood selection. It can be argued, however, that this observed relationship is merely the result of people sorting into neighborhoods according to unobservable characteristics, and these unobservable characteristics may also be related to the outcome.
Therefore, the authors examined the effect of deviations from the exogeneity assumption, on estimates of the neighborhood characteristics that have influence on individual dropout rates. The results suggest that the effect is still positive and significant (ranging from 1.0 to 3.8 ) as long as the upper bound of the correlation between the neighborhood variable and unobservable variables is lower than 25% of the correlation between the neighborhood variable and the observed control factors, which in a fully specified empirical model is within a plausible range of correlations that could be expected between these variables.
Similarly, the authors examine the adult role models, including as independent variable the percentage of adult neighbors who graduated at least from secondary school. They find that, controlling for other human capital determinants, placing a young person in a neighborhood where 10 percent more of adults finished school will decrease the probability of dropping out of school by 0.89 percent. This result is, however, very sensitive to small deviations from the exogeneity assumption. In fact, this negative effect remains only if the correlation between the neighborhood variable and the unobservable variables is up to 7% as large as the correlation between the neighborhood and the observable variables.
For the case of health outcomes, Giovagnoli et al. (2009) look at the probability that a child aged 0-4 had diarrhea in the last 4 weeks, and we study if living in a neighborhood with a large proportion of well educated mothers is associated with a substantial decrease in the probability of child diarrhea. Results show a significant negative effect on the probability of being surrounded with more educated mothers on her child having diarrhea (coefficient of -0.11 from a linear probability model: the effect of moving a child with given characteristics into a neighborhood where 10% more of the mothers did complete secondary school, is to decrease the probability of the child having diarrhea on 1.1%.). Relaxing exogeneity assumption, the results indicate that a negative effect exists (and is different from zero) as long as the upper bound of the correlation between the neighborhood variable and unobservable variables is at least 16% of the correlation between the neighborhood variable and the observed control factors. Therefore, only if one is willing to accept that this correlation is lower than 16%, we could claim a causal effect of the percentage of mothers in the community with some secondary education or more on the probability of her child having diarrhea. In peer, roles and health models, the authors also explored whether the effects under analysis were stronger as one moves from “high quality” to “low quality” neighborhoods. The authors did not find any statistical evidence that a differential effect exists.
99
Spatial Disparities in Human Development
One important question surrounding empirical
studies of this issue is whether statistical and
cross-neighborhood analyses are able to cap-
ture subtler institutional and cultural processes
that restrict (or empower) the opportunities of
residents of poorer neighborhoods. Such deeper
understanding of the processes that shape both
within as well as in-between neighborhood de-
pendencies would require contextual methods of
investigation of specific neighborhoods to accom-
pany the statistical cross-neighborhood analyses.
This is another area where fertile research in the
region is needed.
3.4Conclusions
This chapter has looked at the characteristics of
human capital indicators’ spatial characteristics
in Latin America—and has explored a number
of venues that explain such variation. Taking a
snapshot at both inter- as well as intra-country
spatial disparities in a variety of different human
development dimensions showed that spatial dis-
parities in human development are pervasive in
most of LAC. While a comparison between coun-
tries tended to show that disparities tended to
be higher for those countries with worse average
human development indicators, such observa-
tion would need to be treated with much care
given the intrinsic problems associated with using
household surveys in our analysis—as the within
country spatial variation of malnutrition rates in
Peru showed that such generalizations might not
be accurate.
Further, when comparing the change in dispari-
ties within countries, we could derive the general
conclusion that in most countries and most di-
mensions of human development, spatial dispa-
rities within countries (but at the broader spatial
level) diminished. This is, from a policy perspec-
tive, a welcome conclusion.
Spatial effects on human development are impor-
tant for public policy formulation. The empirical
evidence shows that space per se is of second
order in determining human capital, so policies
should foremost focus on developing people’s hu-
man capital rather than places. However, the me-
chanisms through which space can hinder human
capital formation are still operative and important
so that there is a need for mitigating public policy.
Moreover, more research is needed to understand
the challenges posed by LAC’s increasing urban
polarization to human capital formation. The cau-
sal empirical identification of the relevant effects
is complicated by selection into neighborhoods
(e.g. due to individual socio-economic characte-
ristics, preferences, rental costs, etc) and selec-
tion into peer groups within neighborhoods (e.g.
due to shared preferences or ethnic background).
But even if these are present, the role that social
interactions can play in hindering or enabling hu-
man capital formation should be explored.
Policies and programs that can include spatial di-
mensions comprise a whole range of policy le-
vers. Most importantly, a progressive roll-out of
public infrastructure and financing would focus on
raising opportunities for those people in the least
advantaged areas—and this is independent of the
working of spatial influences and externalities per
se. Reaching disadvantaged population groups
through such spatially rolled-out policies could
100
Reshaping Economic Geography in Latin America and the Caribbean
(and is) applied to many programs, including cen-
trally managed investment funds (like the social
investment funds popular in the 1980s), condi-
tional cash transfer programs (now predominant
in many countries of the region), and programs
to improve service delivery (like the Plan NACER
in Argentina).
In addition, the presence of spatial externalities,
which we have focused on in the latter part of
the chapter, suggests that targeted (territorial)
programs to improve poor rural areas and margi-
nal urban neighborhoods can have larger effects
than envisioned due to social multiplier effects
(through either social or physical interactions
within communities or spatial effects between
communities). Empirical analysis conducted for
this chapter did provide additional evidence for
the existence of such spillovers but more resear-
ch, especially quantitative research (as well as
policy impact evaluations), would be important
for a holistic approach to human development.
101
Policy Implications
This chapter considers how policymakers in Latin
America and the Caribbean can address spatial
inequality and encourage long-run growth. Evi-
dence shows that the potential for reducing ove-
rall inequality by directly reducing spatial equa-
lity in income is limited. Instead, a conceptual
focus on increasing equality of opportunity, in
large part by improving provision of basic ser-
vices in disadvantaged areas, has much greater
promise. This approach is highly compatible with
the World Development Report’s (WDR) “3-Is”
framework and emphasis on fostering “spatia-
lly blind institutions” as the primary policy tool.
The framework also suggests roles for connective
infrastructure and spatially targeted incentives.
This chapter considers how two topics of particu-
lar importance in the region—territorial develo-
pment programs and land tenure policy—fit into
this framework.
Chapter 2 of this report considers how the spatial
income patterns of the region can be understo-
od in terms of the 3-Ds—density, distance, and
division—and Chapter 3 documents the large
disparities in health, nutrition, and education
within countries across the region. This chap-
ter deals with the question of how policy can be
informed by these findings, with a focus on
how to integrate leading and lagging regions
within a country.
The policies discussed in this chapter are oriented
towards promoting long-run economic growth.
Theory and historical experience suggest that
growth is spurred by the spatial concentration
of economic activity combined with high levels
of human capital. Thus, policy can encourage
growth by promoting human capital and addres-
sing distance and division, which are the two obs-
tacles to increasing density. The 2009 World De-
velopment Report lays out a three-pronged policy
framework to do so, consisting of the “3-Is”: Ins-
titutions, Infrastructure, and Incentives.
“Institutions” as used here has a broad meaning,
covering both 1) institutions that ensure equality
of opportunities like education, health care, food
security, and basic services, and 2) institutions
that provide a regulatory framework, such as
property rights, land tenure regimes, and trans-
port and urban development regulations. Ensu-
ring that institutions are spatially blind should be
the primary approach for most countries. In ter-
ms of education, health care, food security, and
basic services like water, sanitation, and electrici-
ty. “Spatially blind” means equal access to people
across the country, regardless of location.
“Infrastructure” refers to spatially connective po-
licies aimed at connecting places and markets.
Prime examples are interregional highways and
Chapter 4
Policy Implications
102
Reshaping Economic Geography in Latin America and the Caribbean
railroads to promote trade and improving infor-
mation and communication technologies to sti-
mulate the flow of ideas. This approach should
supplement the focus on institutions, in countries
where lagging areas have large numbers of poor
and few impediments to mobility.
“Incentives” refers to spatially focused policies to
stimulate economic growth in lagging areas, such
as investment subsidies, tax rebates, location re-
gulations, local infrastructure development, and
targeted investment climate reforms, such as
special regulations for export processing zones.
This approach can be used in addition to the fo-
cus on institutions and infrastructure, in coun-
tries fragmented by linguistic, political, religious,
or ethnic divisions, which cause areas to be par-
ticularly likely to suffer from coordination failures
and poverty traps.
To address spatial inequality, this 3-Is framework
prioritizes spatially blind institutions, recommends
spatially connective policies for a limited group
of cases, and suggests spatially focused policies
for a narrower set of cases. The primary recom-
mendation for “institutions” matches closely with
the focus on reducing inequality of opportunity
through a spatial lens. Simply put, governments
should aim to ensure that the location of a child’s
birth not dictate his or her fortunes in life.
This chapter begins with a discussion of the ratio-
nale for the focus on institutions, infrastructure,
and incentives. It then moves to consider both
the levels and changes over time of overall and
spatial inequality in the region. Next it considers
how, in order to address welfare inequality, poli-
cies should aim to reduce inequality of opportu-
nity. After that, it considers some experiences in
LAC with policies in the institutions, infrastructu-
re, and incentives framework. Two final subsec-
tions discuss policy issues of particular importan-
ce for LAC: territorial development programs and
land policy.
4.1 Why the Focus on Institutions, Infrastructure, and Incentives
The experience of countries around the world has
been that growth and development takes place
via a process of concentration of economic acti-
vity and population, as people move from areas
where they were settled due to historical cir-
cumstances towards areas favored by markets.
Chapter 1 of this report describes this phenome-
non for Latin America and the Caribbean. Chapter
2 considered the existing patterns of income and
poverty in the region and showed through eco-
nometric analysis that economically prosperous
areas in the region’s countries have high popula-
tion density, low economic distance to cities, and
low levels of ethno-linguistic division.
The 2009 World Development Report outlines
three layers of government intervention that can
be used to promote long-term growth, summa-
rized as the three “Is”: 1) spatially blind insti-
tutions, 2) spatially connective policies (“infras-
tructure”), 3) and spatially focused policies (“in-
centives”). In areas where economic distance and
division are not limiting factors, spatially blind
institutions should be sufficient to drive density
and growth. In places where distance is a pro-
blem, this first “I” should be supplemented by
103
Policy Implications
a second “I”—infrastructure—to connect leading
and lagging areas. Finally, in areas suffering from
division, a third “I”—incentives—may be needed
as well.
Given that migration from lagging to leading areas
has been a crucial component of development in
the world’s success stories—in the United States,
in Europe, in China, and elsewhere—the key ob-
jective of governments dealing with differences
in welfare across space in all countries should be
to not stand in the way of this process. (Patterns
of internal migration in LAC are discussed in Box
4.1.) The primary objective of policies should be
to develop portable assets that help people mi-
grate to places with economic opportunities. This
can be done through guaranteeing equal access
to basic services—education, health care, water,
and sanitation—regardless of one’s location. Pro-
moting such “spatially blind institutions” corres-
ponds to ensuring equality of opportunity.
In some cases, this is not enough and a second
approach is needed. In countries with substantial
lagging areas with high population density addi-
tional efforts to connect with leading areas may
be necessary. Isolation from markets in more dy-
namic parts of the country reduces welfare, as
workers and producers have limited possibilities
for offering their labor and products. In these
cases, infrastructure and other investments that
connect peripheral areas to markets will improve
both consumer welfare and productive efficiency.
The emphasis on these spatially connective poli-
cies follows from the observation that around the
world, the poorest areas are overwhelmingly tho-
se that are cut off from leading areas. There is a
long history of using connective infrastructure to
integrate peripheral areas with national markets.
When accompanied by institutions that integrate
nations, such infrastructure investments can pay
off if policymakers are persistent. In the United
States, Congress passed the Appalachian Regio-
nal Development Act in 1965, relying on spatia-
lly blind and spatially connective infrastructure
to integrate the 22 million people in this lagging
area, which spans 13 states, with the rest of the
country.51 The basic strategy combined regionally
coordinated social programs and physical infras-
tructure. The 1965 Act allocated 85 percent of
the funds for highways—seen as critical to mee-
ting other socioeconomic objectives and cumu-
latively having accounted for more than 60 per-
cent of the appropriated funds through the mid
1990s. Other investments included hospitals and
treatment centers, land conservation, flood con-
trol and water resource management, vocational
education facilities, and sewage treatment works.
Between 1965 and 1991 total personal income
and earnings grew 48 percentage points faster
on average in the Appalachian counties than in
their economic “sisters,” population 5 points fas-
ter, and per capita income 17 points faster.
Finally, in a third case—countries facing deep di-
visions due to ethno-linguistic or religious hete-
rogeneity—the combination of spatially blind and
51 Hewings, Feser, and Poole, 2009; population figure for 1996.
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Reshaping Economic Geography in Latin America and the Caribbean
Box 4.1. Internal Migration in Countries in LAC
Worldwide experience has shown that over the long term migration from lagging to leading areas has been a key element of development success stories. Work done by Skoufias and Lopez-Acevedo (2008) examined how recent patterns of internal migration in LAC countries relate to spatial inequality.
Existing regional inequalities are both the cause and the consequence of migration of workers and families with particular productive characteristics such as education, skill and experience. Theory pre-dicts that people will migrate from lagging economic areas to leading areas within a country in order to realize better wages and therefore a higher standard of living. However there may be other factors that influence decisions to migrate, such as access to better services in the destination area, escape from conflict in the origin area, and proximity to roads and transportation options (distance). This box draws on data from several countries in Latin America in an attempt to examine in more detail how internal migration across regions within a country can change the distribution of welfare across space. A number of points emerge from this analysis:
First, there is substantial variety in the motives for migration. In some countries the internal movements are driven mainly by pull factors like a search for better labor opportunities (e.g. Bolivia and Nicaragua). In others the motivation to migrate can also be linked to push factors such as the lack of access and quality of services in the source area (e.g. Guatemala) or security issues (e.g. Co-lombia).
Second, there is considerable heterogeneity between countries in terms of rates of internal migration. The highest internal migration rates are observed in Brazil, Colombia, Costa Rica, Domi-nican Republic and Peru (around 70%). In El Salvador and Paraguay the migration rates are also high (over 60%). When comparing with surveys that gather compatible information, the lowest migration rates are observed in Argentina, Bolivia, Honduras and Nicaragua (around 50%). There also exists substantial heterogeneity between countries in terms of recent migration. When considering compara-ble criteria, the highest migration rates are observed in Colombia, Dominican Republic and Honduras (around 10%–20%). The lowest migration rates are observed in Argentina and Nicaragua.52
Third, migration is a selective process. Migrants are typically male, skilled, young, White or Mes-tizo, and without children. Relatively lower rates of migration for indigenous peoples indicate that divi-sion—understood as the historical exclusion of indigenous groups—is an obstacle to migration.
Fourth, most people migrate to leading economic areas within their country, but migrants tend to migrate more often to nearer areas rather than faraway places. In Honduras, for exam-ple, Hondurans migrate principally from poor regions to the nearest leading area (e.g. from El Paraíso to Francisco Morazán or from Copán to Cortés).
Migration flows are less than might be expected giving existing wage differentials. For example, in the impoverished Mexican states of Chiapas, Guerrero, and Oaxaca, net migration amounts to 2–2.5 per-cent over a period of five years, and similar rates are found for lagging areas of Chile.
As a whole, these findings suggest that migration is a vehicle for increasing economic density and in-creasing welfare in LAC. These possibilities are limited, however, by the twin barriers of distance and division. Measures to reduce distance and reduce division could help spur migration.
Source: Skoufias, E. and G. Lopez-Acevedo, 2008.
52 These results exclude Panama due to a non-comparable definition of temporal migration
105
Policy Implications
spatially connective policies may be insufficient.
In such cases, there may be a need to comple-
ment institutions and infrastructure with spatially
focused incentives to encourage economic pro-
duction in lagging areas.53
As the analysis in Chapter 2 of this report showed,
division measured in terms of ethnicity is a pri-
mary factor explaining differences across space
in income in the region. In almost all countries
for which sufficient data are available to exami-
ne the question, areas with larger populations of
minority groups are poorer, even after controlling
for a number of other variables. The migration
analysis discussed in Box 4.1 also indicates that
members of indigenous groups are less likely to
migrate. Both facts reflect the historical divide
and in many cases the continued discrimination
faced by members of indigenous and minority
groups in the region.
This report recommends caution in the use of
spatially focused incentives. This approach follo-
ws from the mixed results with such policies. In
those cases where spatially targeted interventio-
ns have been successful, they have been coupled
with both policies that both foster spatially blind
institutions (i.e., ensuring equality of opportuni-
ty) and spatially connective policies. Without la-
ying the foundations through a principal focus on
institutions and infrastructure, targeted incenti-
ves are unlikely to succeed.
4.2 Overall Inequality and Spatial Inequality in Latin America and the Caribbean
Policymakers have sometimes considered redu-
cing spatial inequality in income as a policy ob-
jective in itself, which in turn arises in part as a
response to the historically high levels of overa-
ll inequalities in the region.54 Figure 4.1 shows
overall income inequality in each LAC country for
which data are available. We can quantify “spatial
inequality” in income as income inequality bet-
ween subregions of the country, measured using
the Theil index.55 Overall inequality in income is
equal to the sum of within-subregion inequality
and spatial inequality, and the two components
are shown separately in Figure 4.1. The figure
shows that spatial inequality in income accounts
for only a minority of overall income inequality
in most countries. Spatial inequality in income
amounts to less than ten percent of overall in-
come inequality in all but four countries (Haiti,
Honduras, Peru, and El Salvador.)
Since the mid-1990s, income inequality has in-
creased in some LAC countries and decreased in
others. Table 4.2 shows changes in overall inco-
me inequality for countries in LAC for which suffi-
cient data were available. Overall changes were
decomposed into changes due to three sources:
changes in spatial income inequality, changes
due to population shifts across subregions, and
changes in income inequality within subregions.
53 Specific cases are discussed later in this chapter.
54 See Lopez and Perry, 2008.55 Unlike the Gini index, the overall Theil index can be decomposed
into between and within components.
106
Reshaping Economic Geography in Latin America and the Caribbean
The figure shows that changes in overall inco-
me inequality were driven principally by within-
subregion changes. Changes in spatial income
inequality went in both directions and played a
minor role in overall income inequality change.
Population shifts also had only a small effect, but
in all countries where they were important, they
reduced overall income inequality. This reflects
the fact that population shifts consist chiefly of
migration of people from poorer to wealthier re-
gions, so that individuals move from the lower
end of the economic distribution to the middle,
thus reducing overall income inequality.
Figure 4.1. Income Inequality by Country,
Circa 2005: Overall Inequality, Inequality
Within Subregions, and Spatial Inequality
Source: World Bank calculations with data from Gasparini et al., 2008.Note: The number of subregions is shown in parentheses besides the name of the country.
Haiti (9)
Jamaica (3)
Honduras (6)
Chile (13)
Guyana (10)
Colombia (5)
Brazil (5)
Nicaragua (4)
Bolivia (8)
Belize (6)
Mexico (8)
Dominican Rep. (9)
Paraguay (5)
Panama (4)
Ecuador (3)
Costa Rica (6)
Peru (7)
Venezuela (7)
Argentina (6)
El Salvador (5)
Guatemala (9)
Uruguay (5)
Theil Index of Income Inequality
Countr
y and N
um
ber
of
Subre
gio
ns
Inequality WithinSubregions
Spatial Inequality
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
The information presented in Figures 4.1 and 4.2
suggests that policy principally intended to reduce
spatial income inequality at the subregional level
is limited in its potential to reduce overall inequa-
lity. For 16 out of the 18 countries in Figure 4.2,
the country’s level of spatial inequality in 2005
was less than just the change in overall inequality
that took place over a roughly ten-year period. As
noted previously, those changes were driven lar-
gely by changes in income inequality within su-
bregions. The decomposition shown in Figure 4.2
does suggest that convergence between regions,
i.e. declines in spatial income inequality, played
non-negligible roles in reducing inequality in El
Salvador, Peru, and Paraguay. However, given the
relatively small role of spatial income inequality,
there is little possibility of further declines along
these lines. Even the complete elimination of in-
come inequality between subregions would make
only a small dent in overall inequality.
Of course, the level of spatial income inequality is
partially determined by the level of disaggrega-
tion. At the limit of disaggregation—where “space”
is defined as simply the area immediately around
each individual’s personal body space—spatial in-
equality is equal to overall inequality among indi-
viduals. But research has shown that even taking
spatial units defined at the level of communities,
most income inequality is not spatial (between
communities) but is rather within communities.56
56 See, for example, Elbers et al., 2004. Separately, Kanbur (2006) notes that the fact that most overall inequality is within-group inequality does not necessarily mean that it is most cost-effective to focus on reducing within-group inequality rather than spatial inequality. The real question is which approach will have a larger impact on inequality per dollar of expenditure.
107
Policy Implications
What this suggests is that to the extent that poli-
cymakers are seeking to reduce overall inequality,
they are mistaken to focus primarily on reducing
spatial inequality in income as a means to that
end. Seeking to reduce spatial inequality in in-
come may in itself be desirable due to particular
circumstances. Such cases are discussed later in
the chapter, under the heading of “Incentives.”
4.3 Inequality of Opportunities in Latin America and the Caribbean
Instead of attempting to address spatial inequali-
ty in outcomes like income, a preferred approach
is to reduce inequality of opportunities. Because
much inequality of opportunities is related to spa-
ce, it may be necessary to target investments to
disadvantaged areas in order to achieve equality
of opportunities. A focus on inequality of oppor-
tunities is attractive for several reasons. There is
generally a stronger societal consensus around
the ideal of equality of opportunities than around
equality of outcomes. The aim with greater equa-
lity of opportunity is to level the playing field so
that circumstances such as gender, ethnicity, bir-
thplace, and family background, which are be-
yond the control of an individual, do not influence
a person’s life chances. Quantitative estimates
in a recent study suggest that between one-half
and one-quarter of overall economic inequality in
a typical LAC country is due to inequality of op-
portunities, measured in terms of a child’s access
to education, electricity, water, and sanitation.57
Figure 4.2 Changes in Overall Income Equality, mid-1990s to c. 2005:Decomposition of Sources of Change
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
Bra
zil
El Sal
vador
Ecu
ador
Boliv
ia
Peru
Mex
ico
Para
guay
Panam
a
Nic
arag
ua
Chile
Dom
inic
an R
ep.
Jam
aica
Uru
guay
Arg
entina
Hondura
s
Cost
a Ric
a
Colo
mbia
Ven
ezuel
a
Chan
ge
in O
vera
ll In
equal
ity
Due to Changes inSpatial Inequality
Due to PopulationShifts Across Regions
Due to Changes inInequality Within Regions
Source: World Bank calculations with data from Gasparini et al., 2008.
57 Paes de Barros et al., 2008.
108
Reshaping Economic Geography in Latin America and the Caribbean
One way to think about the importance of inequa-
lity of opportunities is to consider the Human Op-
portunity Index, which synthesizes into a single
indicator the measurement of basic opportunities
in a society and how equitably those opportuni-
ties are distributed.58 Figure 4.3 shows country-
Figure 4.3 Human Opportunity Indices for Selected Educational and Housing Indicators
a. Completion of sixth grade on time
0 25 50 75 100
GuatemalaNicaragua
BrazilEl Salvador
HondurasDominican Republic
ParaguayCosta RicaColombiaPanama
PeruBolivia
Venezuela, R.B. deUruguayEcuador
ChileArgentina
MexicoJamaica
Human Opportunity Index (percent)
b. School attendance ages 10-14
0 25 50 75 100
GuatemalaHondurasEcuador
NicaraguaEl Salvador
ColombiaParaguayPanama
Costa RicaMexicoBolivia
PeruJamaica
Venezuela, R.B. deArgentinaUruguay
BrazilDominican Republic
Chile
Human Opportunity Index (percent)
c. Access to Safe Water
0 25 50 75 100
NicaraguaPeru
El SalvadorParaguayJamaica
BoliviaDominican Republic
GuatemalaHondurasEcuador
ColombiaPanamaMexico
UruguayVenezuela, R.B. de
ArgentinaBrazilChile
Costa Rica
Human Opportunity Index (percent)
d. Access to adequate sanitation
0 25 50 75 100
NicaraguaEl Salvador
BoliviaGuatemalaHondurasPanamaJamaica
ParaguayMexico
Dominican RepublicColombia
PeruBrazil
EcuadorUruguay
ArgentinaVenezuela, R.B. de
ChileCosta Rica
Human Opportunity Index (percent)
e. Access to electricity
0 25 50 75 100
HondurasNicaragua
BoliviaPeru
PanamaGuatemalaEl Salvador
JamaicaColombiaEcuador
Dominican RepublicParaguay
BrazilUruguay
Costa RicaVenezuela, R.B. de
ArgentinaMexico
Chile
Human Opportunity Index (percent)
Source: Paes de Barros et al., 2008.
by-country values of the index for several com-
ponents. Over the long term, policies that reduce
inequality of opportunities in such terms as these
reduce overall inequality.
An important conclusion that emerges from the
Paes de Barros et al. (2008) study is that inequa-
lity in access to infrastructure—water, sanitation,
and electricity—are strongly determined by loca-58 See Paes de Barros et al., 2008, for details.
109
Policy Implications
tion. Although the work in the previous section
showed that inequality of economic outcomes is
not principally associated with place (in terms of
national subregion), inequality of opportunities is
largely a consequence of where a child lives. The
authors of the study explain that this is chiefly
due to differences across the urban-rural divide.
While spatial inequality of economic outcomes is
low relative to overall inequality, spatial inequa-
lity of opportunities is very substantial. In many
countries, children living in rural areas face insu-
fficient access to basic infrastructure services and
are thus disadvantaged as adults.
The natural conclusion for policymakers that flo-
ws from this framework and analysis is that to
reduce inequality, they should seek to reduce in-
equality of opportunity, in part by improving ac-
cess to basic services in areas that lack them.
Such places are very often lagging areas. This
policy prescription corresponds very closely to
the 3-Is framework which prescribes first and fo-
remost spatially blind institutions to respond to
differences in living standards across areas. Im-
proving “spatially blind” access to basic services
will necessarily improve equality of opportunity.
It is important to recognize that achieving equality
of opportunity will necessarily require very large
investments in health, education, and basic ser-
vices in areas that are currently disadvantaged.
While patterns vary, in many countries, public ex-
penditures per person for health, education, and
basic services are much higher in central urban
areas than in more remote areas. Simply achie-
ving equality of expenditure in these sectors on
a per person basis would generally mean increa-
sing resources devoted to more remote areas.
The costs of providing some services are often
higher in more remote areas. This is particularly
likely to be the case for public services like water,
sanitation, and electricity. However, spending at
higher levels (on a per capita basis) to achieve
equality of opportunity in these areas can be jus-
tified in light of the fact that in other realms of
public spending, more remote areas are often
very disadvantaged.
Two additional considerations are in order for po-
licy to address inequality of opportunities. First,
the package of opportunities that is considered
essential will necessarily vary with a country’s
level of development. The decision as to which
opportunities are affordable and desirable for a
particular country must be made by that parti-
cular society. Second, the technology to provide
equality in a particular opportunity will often vary
across space. To take one example, access to ba-
sic health care might be provided chiefly through
large hospitals in urban areas and clinics in remo-
te rural areas.
In summary, given that the scope for addressing
overall inequality by reducing spatial inequality
of income is limited, a better approach is to seek
to achieve equality of opportunities, in terms of
access to health, education, and basic services.
Because access to basic services is often deter-
mined by location, achieving equality of opportu-
nities will generally require investments targeted
to previously neglected areas. This leveling of
the playing field constitutes fostering “spatially
blind institutions,” in the language of the 3-Is
framework.
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Reshaping Economic Geography in Latin America and the Caribbean
4.4 Policy Experiences in LAC
This section considers experiences with progra-
ms that fall under the categories of Institutions,
Infrastructure, and Incentives in Latin America
and the Caribbean.
Institutions
The shorthand term “institutions” covers a variety
of policies. A key aspect of many policies in this
category is that they are focused on improving
skills and health, which people can use wherever
they live. As the previous section noted, this fo-
cus meshes well with an emphasis on promoting
equality of opportunities.
One type of program in this category is the con-
ditional cash transfers which have been popular
and highly successful in a number of countries. In
Brazil, Bolsa Familia has improved education and
health outcomes. Cash transfers are given in ex-
change for school attendance, for health checks,
and other welfare-related issues. They thus not
only provide the household with an income, but
also ensure that they have the conditions nee-
ded to secure economic resources for themselves
in the future. Similarly, Oportunidades in Mexi-
co has spurred school attainment and improved
health for many poor Mexicans.
A different mechanism that can potentially con-
tribute to spatially neutral allocations in LAC is
decentralization. Decentralization can be defi-
ned as the devolution by a central government
of specific functions to democratic sub-national
governments. Centralized policy making often fa-
vors particular regions or cities at the expense of
others, and burdens all regions with overly uni-
form policies and public services too unresponsi-
ve to local needs and conditions.
Decentralization can improve service provision
through two channels. First, decentralized gover-
nments can potentially be held more accounta-
ble. Second, local governments have better infor-
mation and are thus better able to ensure better
provision.
Where it works effectively, decentralization helps
alleviate the bottlenecks in decision making. De-
centralization can help cut complex bureaucratic
procedures and can increase government officials’
sensitivity to local conditions. Moreover, decentra-
lization can help national government ministries
reach larger numbers of local areas with services;
allow greater political representation for diverse
political, ethnic, religious, and cultural groups in
decision-making; and relieve top managers in
central ministries of some tasks to concentrate on
policy. In some countries, decentralization may
create a geographical focus at the local level for
coordinating national, state, provincial, district,
and local programs more effectively and could
provide better opportunities for participation by
local residents in decision making. Decentraliza-
tion may lead to more creative, innovative and
responsive programs by allowing local experi-
mentation. It can also increase political stability
and national unity by allowing citizens to better
control public programs at the local level.
Decentralization does have potential disadvanta-
ges. Decentralization may not always be efficient,
111
Policy Implications
especially for standardized, routine, network-ba-
sed services. It can result in the loss of econo-
mies of scale and control over scarce financial
resources by the central government. Weak admi-
nistrative or technical capacity at local levels may
result in services being delivered less efficient-
ly and effectively in some areas of the country.
Administrative responsibilities may be transferred
to local levels without adequate financial resour-
ces and make equitable distribution or provision
of services more difficult. Decentralization can
sometimes make coordination of national poli-
cies more complex and may allow functions to be
captured by local elites. Also, distrust between
public and private sectors may undermine coope-
ration at the local level.
Although decentralization has been embraced as
a policy initiative in many countries, the empi-
rical evidence on the effects of decentralization
is limited and mixed in its findings. The case of
Bolivia suggests that decentralization can poten-
tially play a positive role (see Box 4.2). Overall,
it suggests that rule-based allocations can help
Box 4.2. Experiences with Decentralization
Many countries in the region have pursued policies of decentralization in recent years, in part in an attempt to improve service delivery in lagging areas. Experiences have been mixed.
The case of Bolivia offers a case where a decentralization program initiated in 1994 appears to have contri-buted to the effective use of funds. Before decentralization, 308 Bolivian municipalities divided 14 percent of all centrally devolved funds, while the three main cities received 86 percent. After decentralization the shares reversed to 73 per cent and 27 per cent respectively. The per capita criterion resulted in a massive shift of resources away from the richest, most developed urban centers.
Investment under centralized government was thus hugely skewed in favor of a few municipalities that recei-ved enormous sums, a second group where investment was significant, and half of districts which received nothing. Decentralization increased government responsiveness to real local needs. After 1994, investment in education, agriculture, and water and sanitation was higher where illiteracy rates, malnutrition rates, and sewerage non-connection rates were higher; and urban development investment was higher in places where public infrastructure such as marketplaces was scarcer.
Decentralization served to re-orient public investment from a regressive pattern of systematically favoring better-off municipalities, and thus increasing already-high levels of spatial inequality, to one that favored poorer, worse-provided municipalities. It is notable that these changes were driven by the actions of Bolivia’s 250 smallest, poorest, mostly rural municipalities investing newly devolved public funds in their highest-priority projects.
In other cases, the record of decentralization is less favorable. In Argentina, for example, although school decentralization had an overall positive impact on student test scores, the gains did not reach the poor. Speci-fically, math test scores increased 3.5 percent and Spanish tests rose 5.4 percent on average after 5 years of decentralized administration. However, the gains from decentralization were exclusively in schools located in non-poor municipalities. In fact, decentralization did not improve at all test scores in schools located in poor municipalities. These results imply that decentralization increased inequality in education outcomes.
Source: Faguet, 2004; Faguet and Shami 2008; Galiani et al., 2008.
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Reshaping Economic Geography in Latin America and the Caribbean
improve services at the local level. This point
is also found in the wider literature on intergo-
vernmental fiscal transfers and decentralization.
Another general lesson from the literature is that
decentralization will typically benefit most areas
with local authorities with strong capacity. For
example, Galiani et al. (2008) find that school
decentralization in Argentina did improve test
scores, but only among better-off municipalities,
and they speculate that this is due to the higher
quality of administration in non-poor areas.
At least five conditions are important for succes-
sful decentralization:
• the decentralization framework should link lo-
cal financing and fiscal authority to the service
provision responsibilities and functions of the
local government, so that local policymakers
can bear the costs of their decisions and deliver
on their promises;
• the local community should be informed about
the costs of services and service delivery op-
tions involved and the resource envelope and
its sources, so that the decisions they make are
meaningful. Participatory budgeting, such as in
Porto Alegre, Brazil, is one way to create this
condition;
• there should be a mechanism by which the
community can express its preferences in a
way that is binding on policymakers, so that
there is a credible incentive for people to par-
ticipate;
• there should be a system of accountability that
relies on public and transparent information
which enables the community to effectively
monitor the performance of the local govern-
ment and react appropriately to that perfor-
mance, so that politicians and local officials
have an incentive to be responsive; and
• the instruments of decentralization—the le-
gal and institutional framework, the structure
of service delivery responsibilities and the in-
tergovernmental fiscal system—should be de-
signed to support the political objectives.
Infrastructure
The shorthand term “infrastructure” covers a va-
riety of spatially connective policies. The purpose
of such policies is to promote economic growth in
currently lagging areas by linking them to leading
areas. The emphasis on such policies follows from
the observation that integration—measured in
terms of economic distance—is a key determinant
of an area’s economic success. This was shown
in the country-by-country econometric analysis
in Chapter 2 of this report, and is confirmed by
country case studies. In Mexico, for example, tho-
se areas least integrated in national markets are
the poorest (see Box 4.3). The detailed analysis
of Peru presented in Chapter 2 of this report also
showed that investments in infrastructure can
promote growth by reducing economic distance.
A primary example of spatially connective policies
is improving the intra-regional road network. In
Brazil improvements to the road network between
the 1950s and 1980s reduced transport and logis-
tics costs. But most of the economic gains accrued
to the Center-West, with only small gains to the
lagging Northeast, at a time when its share of the
national network increased from 15 percent to 25
percent. Even so, such investments did bring eco-
nomic density closer to the lagging Northeast.
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Policy Implications
Box 4.3 Low Market Access in Mexico’s Lagging South
Quantitative information on regional or local market integration is scarce. Summary statistics—such as the road length in a state or province or the straight-line distance to ports or urban agglomerations—are poor proxies for the complexity of a national or regional transportation network. To improve on them, a geo-graphic representation of Mexico’s transport network is used to compute an index of accessibility for each municipio in the country as a simple measure of potential market integration.
This index summarizes the size of the potential market that can be reached from a particular point given the density and quality of the transport network in that region. For any point in the country, it is the sum of the population of urban centers surrounding that point, inversely weighted by the travel time to reach that center. It is computed using an up-to-date digital map of transportation infrastructure from the Mexi-can statistical agency (INEGI). For each road segment, the database indicates the number of lanes and whether it is paved or unpaved—and for railroad lines, the number of tracks. For each category of road or rail, average travel speeds are estimated to calculate how long it will take to traverse each segment in the transport network. Urban population comes from the INEGI database of the location and population size of about 700 cities and agglomerations in Mexico. These urban centers accounted for about 68 million of Mexico’s 97 million people in 2000.
The map of market access shows high values of the index around the federal district, thanks toconcentrations of people and infrastructure. A quarter of Mexico’s GDP is generated within two hours’ travel time from center of the federal district. The southern states of Chiapas, Guerrero, and Oaxaca, the poorest areas, have low market access.
Market access in Mexico is highest around the national capital and low in the lagging southern states
Source: Deichmann et al., 2004; from World Development Report 2009.
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Reshaping Economic Geography in Latin America and the Caribbean
Incentives
The shorthand term “incentives” refers to spatially
targeted programs intended to promote economic
growth in lagging areas.59 Although there have
been few rigorous evaluations of such programs,
the limited evidence suggests that they have had
mixed results. In Brazil, where the goal has been
to attract “dynamic” industries to the lagging Nor-
th and Northeast by providing fiscal incentives,
expenditures have reached $3–$4 billion a year.
A recent impact evaluation shows that the alloca-
tion of these “constitutional funds” did induce the
entry of some manufacturing establishments into
lagging regions—but incentives were not attrac-
tive enough for vertically integrated industries.60
Between 1970 and 1980 the Mexican government
used fiscal incentives to promote industrial deve-
lopment outside the three largest urban agglome-
rations. Firms locating outside these three large
cities were eligible for a 50–100 percent reduction
in import duties and income, sales, and capital
gains taxes, as well as accelerated depreciation
and lower interest rates. Their impact on econo-
mic decentralization was insignificant because
import duties on raw materials and capital goods
were low to begin with; so the reductions had no
effect on location decisions and lost revenues.61
Table 4.1. Examples of Spatially Targeted Development Programs for Income Generation in LAC
Country Type Description
Brazil Investment subsidies
Constitutional funds (interest rate subsidies)—induced entry of some firms,
but not for firms in vertically integrated industries (Carvalho et al., 2006).
Tax incentives for the Zona Franca de Manaus created jobs, but at very high
cost, approximately $28,000 per year.
MexicoReductions in import duties
Import duty and tax exemptions for de-concentrating manufacturing out
of the three largest agglomerations—unsuccessful as tax rates were low to
begin with (World Bank, 1977; Scott, 1982)
Chile
Industrial estates/
Free trade zones
Free trade zones in Zonas Extremas with exemptions for customs, VAT,
corporate profit and real estate taxes—successful in the high tax, high tariff
period until the mid 1990s, performance declined with national import duty
reduction from 35 percent in the 1980s to 6 percent in 2000 (World Bank,
2005b).
59 Latin America has extensive experience with ambitious regional development programs, which are discussed in numerous World Bank reports, including the LAC flagship reports published in 2005 and 2006 (De Ferranti et al., 2005; and Perry et al., 2006).
60 Carvalho, Lall, and Timmins, 2006. Constitutional funds were created in 1989 to finance economic activities in the North and Northeast regions.
61 World Bank, 1977; and Scott, 1982.
115
Policy Implications
4.5 How do Territorial Development Programs Fit the “3-Ds” and “3-Is” Frameworks?
Territorial development programs have become
popular in many Latin American and Caribbean
countries. The presence of spatial externalities
suggests that targeted (territorial) programs to
improve poor rural areas and marginal urban
neighborhoods can have larger effects than en-
visioned due to social multiplier effects, through
either social or physical interactions within com-
munities or spatial effects between communities.
Empirical analyses conducted for Chapter 3 of
this report provide additional evidence for the
existence of such spillovers. Table 4.2 presents
several examples of such programs in the region.
They typically are constituted by a variety of
programs in several sectors. Within such pro-
grams, governments have sometimes emphasi-
zed spatially targeted programs for income ge-
neration. Given the mixed experience with such
programs, a preferred approach is for territorial
development programs to emphasize, in the first
instance, investments in spatially blind institutio-
ns—including basic services—and to supplement
this approach with spatially connective infras-
tructure for areas that are higher density areas
with large numbers of poor. Spatially targeted
programs for income generation should only be
used in the more limited case of areas suffering
from problems of division.
More concretely, this prescription suggests that a
territorial development program should focus first
on improving access to education, health, and
basic services such as water and electricity. In
densely populated poor areas, a territorial deve-
lopment program should also improve roads and
communications infrastructure to better connect
to leading areas. The emphasis on connectivity
follows from the observation that remote areas
cannot be prosperous in isolation. Their economic
success requires links to the greater regional and
national economy.
Many people in the region do suffer from a clear
case of ethno-linguistic division—the historical
and in many cases ongoing discrimination and
exclusion faced by indigenous and other minority
populations in many countries. For concentrated
populations of people facing such divisions, terri-
torial development programs can consider spa-
tially targeted programs for income generation in
addition to programs to expand access to basic
services and improve spatially connective infras-
tructure. Experience in many countries shows
that such policies do not succeed if pursued in
isolation—i.e., if they are not accompanied by
institutions and infrastructure. Consequently, it is
vital that in those situations, a territorial develo-
pment program should be truly comprehensive
and improve all three Is.
It is worth noting that the first two policy goals
suggested here for territorial development pro-
grams—connecting remote areas through infras-
tructure and increasing human capital through
large investments in education, health, and basic
services—feature prominently in the 2008 World
Development Report, Agriculture for Develop-
ment. Although the themes of the 2008 and 2009
WDRs are very different, they share these two
messages, both recognizing that enhancing por-
116
Reshaping Economic Geography in Latin America and the Caribbean
table human capital and the connection of out-
lying areas are essential policy objectives.
Two territorial development projects that fit
within the “3-Is” framework are current projects
in the Lake Titicaca region of Bolivia and the
State of Acre in the Brazilian Amazon. Lake Ti-
ticaca is a UNESCO World Heritage site and has
big potential as a world-class attraction. Howe-
ver, lack of accessibility and infrastructure in-
vestments create a situation with very low scale
tourism, poverty, and social conflict. The area is
a “2-D” case, where the focus should be on in-
creasing density and reducing distance. A project
under implementation is promoting a common
vision for challenges and opportunities for better
living conditions and development. The project
involves a focus on both institutions to increase
equality of opportunity and infrastructure. It uses
an integrated approach including waste water
management and sanitation, transport infrastruc-
ture provision, cultural heritage, and housing.
Table 4.2. Examples of Territorial Development Programsin Latin American the Caribbean
Name Country Objective
PROSAP ArgentinaAgricultural development based on increasing the amount of land under cultivation and on
increasing the productivity of land by extending and improving irrigation infrastructure.
PROLOCAL Ecuador
Fight rural poverty, reduce inequality, and foster inclusion by facilitating poor people’s
opportunities for jobs, production, and income, and by encouraging good management of
natural resources, along with other sustainable practices.
PROMATA Brazil
Support sustainable development by increasing the availability and quality of the basic
services provided by municipalities and by diversifying production and promoting sustainable
management of natural resources.
PRODAP II El SalvadorIncrease income and improve the living conditions of the poor rural population, strengthen
their grassroots organizations, and increase the participation of beneficiaries.
PRODECO Paraguay
Improve the quality of life of the population living in extreme poverty and of vulnerable
groups such as young people, women, and indigenous people through participation and
institutional decentralization.
Lake Titicaca
ProjectBolivia Reduce poverty and social conflict.
Acre Project Brazil Promote development and protect the environment.
Source: Pichon, 2008.
117
Policy Implications
The small State of Acre in the Brazilian Amazon
region presents a heterogeneous microcosm of
critical development challenges as well as poten-
tial for sustainable development. Acre is home to
forest-dependent and highly diverse indigenous
communities who connect by river transporta-
tion. It is a “3-D” case, where density, distance,
and division all need to be addressed. The state
faces the dual challenge of promoting develo-
pment and conserving the world’s most critical
and diverse ecosystem. A “3-I” approach is used
involving institutions, infrastructure, and incenti-
ves. It supports the State government to deliver
basic services, foster community participation
and ownership, and promote productive activities
that are consistent with conservation and river
accessibility.
4.6 A Key Institution for Latin America and the Caribbean: Land Policy
Land policies play an important facilitating role
in the spatial development of countries, sub-na-
tional regions, cities and neighborhoods. Density
in urban activity leads to increasing demand for
land and increasing land prices, based on the hig-
her economic returns associated with the spatial
agglomerations at the heart of urban centers. The
gradient of land rent for almost every growing
urban center is the same: high prices in the cen-
ter, which decline as a function of distance and
decreasing density. This generality implies the
need for land policies and land institutions in ur-
ban centers which permit these dense, high value
uses to occur. These include the following: 1) clear
property rights and rules of the game for property
markets which are fair and transparent; 2) robust
land information systems in registries, which pro-
vide information to market participants; 3) capa-
cities for public acquisition to ensure land supplies
and discourage speculative landholding; and 4)
value-based property taxation which encourages
intensity of use and finances public infrastructure
to support private investment. Transparent land
governance is critical to inhibit rent-seeking in
urban land development and fair competition.
Most Latin American cities are ringed by den-
se, informal settlements of recently arrived im-
migrants who fuel the region’s informal sectors
and provide low-wage labor for urban growth.
These settlements create significant challenges
for achieving the promise of inclusive growth.
At the household level, a lack of property rights
discourages investment into housing and cons-
truction, discourages the emergence of broad
housing finance and holds back infrastructural
development. Informality of land rights also may
discourage labor force participation and school at-
tendance by children as families physically must
guard their homes to asset occupation and per-
sons without addresses may be denied education
and employment. At the level of the city, chaotic
informal development also may distort efficient
use of public investments and optimally efficient
land use, holding back the development of decent
housing and increasing costs to economic activi-
ty for all groups. Bank-financed projects such as
the COFOPRI experience in Lima cut through bu-
reaucracy and red-tape to provide clear property
rights to informal settlements.
Density in lagging rural areas is also complicated
by land policy and tenure distortions. In many
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Reshaping Economic Geography in Latin America and the Caribbean
areas of LAC rural poverty is highly concentra-
ted among land-poor and landless households
who live in areas of extremely high inequality of
land ownership, a relic of the colonial landhol-
ding systems. Lack of land access among the
poor in dense rural areas creates two principal
problems for growth and poverty alleviation. One
problem is the classic low-asset poverty trap in
which households without a minimum threshold
of assets can never accumulate sufficient assets
(financial, physical or human/social) to leave po-
verty, perpetuating an intergenerational cycle.
Even migration under such circumstances is not
necessarily an escape from poverty, as many low-
skilled migrants simply follow seasonal agricultu-
ral activities and cannot afford sufficient educa-
tion for children to find higher-paying labor mar-
ket niches. The problem of low-skill, low-wage
migration has its roots in the rural poverty traps;
and in LAC rural poverty traps are often a function
of land access and the linkages of land access to
asset-accumulation. Bank-financed land access
projects in places like NE Brazil provide financing
and productive investments for low-asset groups
in dense rural areas to achieve asset accumula-
tion and pathways out of poverty.
As distance from growing urban centers increa-
ses, land prices drop and the intensity of invest-
ment in land falls accordingly. In middle-distance
areas land markets play an important role in allo-
cating land to efficient users who supply urban
areas with raw materials and food. LAC’s highly
inequitable land distribution and size-segmented
land markets have created a pattern of massive
underutilization of land and exclusionary agricul-
tural sectors often with notably low productivity.
Creating market linkages at middle-distances
calls for land policies which make efficient pro-
duction feasible and which in many cases means
supporting the emergence of small-holder and
medium-size agricultural units. Although there
is still a long way to go in the region to achieve
these conditions, incipient initiatives on the re-
clamation of illegally acquired lands (Colombia,
Brazil, Bolivia, Paraguay), macro-zoning for im-
proved land use (Brazil) and rural land taxation
(Paraguay), are pointing the way towards a re-
conciliation of the land allocation pattern with
the needed structure of production for rural-ur-
ban linkages. Connective infrastructure can only
achieve its promise for linking rural and urban
regions when the land tenure structure and the
land market work in concert to create inclusive,
productivity-enhancing growth.
Another important area at middle distance are
coastal zones which are tending to apprecia-
te quickly as spatial “ribbons” but which create
complex economic/environmental tradeoffs that
require a great deal of land information, public
planning of the land allocation process and highly
transparent and participatory processes to achie-
ve desirable social welfare outcomes.
At greater distances, land’s market value tends
to drop but its value for ecosystem services tends
to rise because mountainous areas, forests and
wetlands generate most of the region’s water su-
pplies, raw materials and minerals. Land policy
in these regions focuses on the maintenance of
protected areas and the buffering of these zones
from degradation and predation. These areas are
also home to most of the region’s indigenous po-
119
Policy Implications
pulations, who are among the least mobile and
most asset-constrained. There is a strong case
for interventions that target the protection of the-
se areas spatially and the development of these
groups economically in situ. The Bank-financed
support for Brazil’s protected areas program and
the Bank-financed support for Brazil’s Indigenous
Peoples’ Lands program—which together have put
about 40 percent of the Brazilian Amazon (over
200 million ha.) of land under legal protection—
are examples of the application of these types of
land policies for remote, but environmentally and
culturally critical regions.
In summary land policy and land institutions
channel and guide land markets and public land
management in ways which can either comple-
ment spatial development processes or impede
them. In LAC both tendencies are present and
the Bank-financed interventions seek to support
complementation as much as possible. Vested
interests, coordination failures, and the path-
dependency of an exclusionary historical legacy
create significant barriers to the efficient alloca-
tion and use of land for spatial development. An
increasingly wide set of experiences in the Bank’s
partner countries show that these barriers can be
overcome when political will, broad participation
and technical solutions can be brought together.
4.7 Conclusions
This chapter has laid out a three-pronged strategy
for dealing with gaps between lagging and leading
areas in Latin America and the Caribbean. A major
element of the orientation of the framework pre-
sented here is that achieving spatial equality in in-
come per se should not be a goal of policy in most
cases. Instead, governments should strive to pro-
vide equality of opportunity by promoting spatially
blind institutions, thus equipping citizens with tools
that will allow them to prosper and move towards
leading areas. In the more limited set of cases
where the poor are concentrated in densely popu-
lated lagging areas, governments should also seek
to link those areas to leading areas using spatially
connective policies. Finally, in deeply fragmented
societies where spatial divisions cannot otherwise
be overcome or there are severe obstacles to mo-
bility, spatially targeted incentives can be used to
supplement the other two types of measures.
This chapter highlights two policy issues of parti-
cular concern in the LAC region. First, it considers
the role that territorial development programs,
which are very popular in the region, can play
in addressing spatial disparities. Such programs
can be constructed around the “3-Is,” emphasi-
zing first education, health, and basic services in
all cases, plus investments in connective infras-
tructure for cases where economic distance is a
major factor, and reserving spatially targeted in-
centives for the rarer cases where ethnic division
is an otherwise insurmountable barrier. Second,
the chapter considers in detail the issue of land
policy, an issue of particular importance in LAC.
Weak land institutions are a central problem in
many countries in the region, and strengthening
land policy could go a long way towards addres-
sing spatial disparities.
121
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DATA SOURCES
Small-area estimates:
Bolivia and Guatemala:
Socioeconomic Data and Applications Center, Po-verty Mapping Project of the Center for Inter-national Earth Science Information Network, http://sedac.ciesin.columbia.edu/povmap/
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Jamaica:
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Mexico:
Izaguirre, C., et al., 2005. “Actualización del Mapa de Pobreza de México, 2005”, mimeo, PNUD, Mexico.
Panama:
Robles, M., 2005. “Pobreza y Desigualdad a Nivel de Áreas Menores en Panamá”, mimeo, IADB/RE2/SO2-MEF/DPS Panama.
Paraguay:
Robles, M., and H. Santander, 2004. “Paraguay: Po-breza y Desigualdad de Ingresos a Nivel Dis-trital”, mimeo, IADB/SDS/POV/MECOVI-DGEEC Paraguay.
Peru:
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GIS data:
Travel time:
Nelson, A., 2008. “Accessibility model and popula-tion estimates”, background paper and digi-tal files prepared for the World Development Report 2009 Reshaping Economic Geography, World Bank, Washington, D.C.
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This publication was printed by LEDEL SAC Printers in May 2009 in Lima, Peru.