world bank (2009) reshaping economic geography in latin america and the caribbean

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


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


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


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.


A LAC Companion Volume to the 2009 World Development Report

8


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


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


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


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


• 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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


[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


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


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


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


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


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


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


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


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


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


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


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


42


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


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


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


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


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


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


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


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


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


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


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


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


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 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


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


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


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


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

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Trav

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——

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0.4

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No.

Obse

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314

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330

216

329

294

413

2411

143

585

1823

61


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


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


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


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


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


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


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


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


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

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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


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


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


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


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


Figure3.2.AverageYearsofEducationinLACforPeopleAged21-30(leftpanel)and61andOlder(rightpanel)

77


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.


Figure3.3:AccesstoWater,percentofpopulation,2006

78


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


79


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


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


80


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


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: Bank staff estimation based on national household surveys.

81


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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

97


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.

98


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


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


(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


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.

104


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


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


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


c. Access to Safe Water

0 25 50 75 100

NicaraguaPeru

El SalvadorParaguayJamaica

BoliviaDominican Republic

GuatemalaHondurasEcuador

ColombiaPanamaMexico

UruguayVenezuela, R.B. de

ArgentinaBrazilChile

Costa Rica


d. Access to adequate sanitation

0 25 50 75 100

NicaraguaEl Salvador

BoliviaGuatemalaHondurasPanamaJamaica

ParaguayMexico

Dominican RepublicColombia

PeruBrazil

EcuadorUruguay

ArgentinaVenezuela, R.B. de

ChileCosta Rica


e. Access to electricity

0 25 50 75 100

HondurasNicaragua

BoliviaPeru

PanamaGuatemalaEl Salvador

JamaicaColombiaEcuador

Dominican RepublicParaguay

BrazilUruguay

Costa RicaVenezuela, R.B. de

ArgentinaMexico

Chile


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.

110


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.

112


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.

113

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.

114


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


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

118


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/

Chile:

Agostini, C., and P. Brown, 2007a. “Cash Transfers and Poverty Reduction in Chile”, mimeo.

Agostini, C., and P. Brown, 2007b. “Desigualdad Geográfica en Chile”, Revista de Análisis Eco-nómico 22(1): 3-31.

Dominican Republic:

Regalia, F., and M. Robles, 2005. “Dominican Repu-blic: The Geography of Poverty and Inequali-ty”, mimeo, IDB-World Bank.

Ecuador:

Robles, M., et al., 2008. “Mapa de Pobreza y Des-igualdad en Ecuador”, mimeo, INNFA, Quito, Ecuador.

Honduras:

Robles, M., 2003. “Estimación de Indicadores de Po-breza y Desigualdad a Nivel Municipal en Hon-duras”, mimeo, IADB/SDS/POV/MECOVI-INE Honduras.

Jamaica:

Cumpa, M., and M. Robles, 2005. “Jamaica: Poverty and Inequality at Special Area Level”, mimeo, IADB/SDS/POV/MECOVI-PIOJ Jamaica.

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:

Escobal, J., and C. Ponce, 2008. “Spatial Patterns of Growth and Poverty Changes in Peru (1993-2005)”, mimeo.

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.

Population density:

Oak Ridge National Laboratory, 2005, LandScanTM Global Population Database. Oak Ridge, TN. Available at http://www.ornl.gov/landscan/

127

References

Land cover:

Joint Research Center, 2000, GLC2000: The Land Cover of the World in the Year 2000. Brussels, Belgium. Available at http://www-gem.jrc.it/glc2000/

Elevation:

Global SRTM 30 arcsec seamless data, version 2, 2005. Available at ftp://e0srp01u.ecs.nasa.gov/srtm/version2/SRTM30/

Slope:

Verdin, K.L., Godt, J.W., Funk, C., Pedreros, D., Worstell, B., Verdin, J., 2007, Development of

a global slope dataset for estimation of lands-lide occurrence resulting from earthquakes: Colorado: U.S. Geological Survey, Open-File Report 2007-1188, 25.

Distance to sea:

Global Insights, 2006, Global coastline, version 6.1

Climate:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution inter-polated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. Available at http://www.worldclim.org/current.htm

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