I. Introduction
In recent years, several papers have reconsidered the impact of geography on
development, adding it to a hypothetical consensus list of factors explaining growth
(Fischer, 1993; Barro, 1997). In empirical cross-country studies that focus on
geographical factors, one could characterize Bolivia as a “well-behaved” observation.
For instance, a tropical geographical location, landlocked status, limited agricultural
productivity, and high tropical disease burdens are among key factors Gallup, Sachs,
and Mellinger (1998) identify as obstacles to development. Bolivia displays all these
traits, and simultaneously, has the lowest GDP per capita and Human Development
Index in South America.
This paper argues, however, that considering the impact of geographical variables
within Bolivia makes feasible a considerably richer analysis. The picture that emerges
is occasionally not entirely consistent with the international evidence, but nonetheless
points toward a systematic and significant impact of geography on development.
The key observation behind this focus on within-country variation is that in
Bolivia, geographical regions are defined not by latitude, as in most cross-country
studies, but by altitude, or somewhat equivalently, longitude. This happens because
altitude variations have endowed what would otherwise be a rather homogenous
“tropical” country with at least three distinct geographical areas. The general goal of
this research is to study how this division has affected the country’s economic
development since 1950.
With this objective, the paper first defines and characterizes the three
geographical areas used in the analysis: the Andean, Sub-Andean or Valley, and
Lowland regions. It seeks to establish that there are significant, geographicallyinduced
differences among them. These are found along dimensions as varied as
climate traits, agricultural production and disease patterns, and the languages
predominantly used by the local population.
These inter-regional differences motivate an analysis of how geography has
influenced what may be the central aspect of Bolivian development since 1950: a
significant shift of population and productive activities from the Andean (and to a
lesser extent the Sub-Andean) region to the Lowlands. This event is associated with
the emergence of three dominant urban centers, one in each of the areas discussed.
In studying these developments, the paper focuses on the following topics and
arguments:
1) The distribution of population. This section first describes the migration flows
that account for a significant portion of the Lowlands’ growth. It also presents
results suggesting these flows have been responsive to differences in income
levels.
2) Observed urbanization patterns. This part of the analysis argues that partially
due to migration, Bolivia displays peculiar urban concentration patterns, at
least relative to most of its neighbors and comparable developing countries.
Namely, since 1950 Bolivia has not urbanized around a dominant city, and in
fact the usual concentration measures have evolved in unexpected ways.
3) The distribution of productive activity. This section first describes the
significant shift in economic activity from the Andean (and Sub-Andean) to
the Lowland region, an expected result given the mentioned migration and
urbanization patterns. In trying to account for this development, the section
explores:
a) The way in which natural endowments help explain this shift, paying
special attention to the agricultural products around which (particularly the
early) Lowlands growth was concentrated;
b) The transfers of private and public financial capital which, combined with
the human capital movements described, have made this region’s growth
feasible;
c) How transport costs appear to influence the geographical distribution of
industrial and other productive activities across Bolivia, highlighting the
role of natural resources and urban economies.
4) The distribution of welfare. The focus here is on the extent to which
geographical variables explain regional welfare levels, as measured by GDP
per capita and social development indices. A robust result is that in Bolivia,
contrary to the usual cross-country evidence, more tropical (lower altitude),
further inland areas have higher welfare levels.
5) Convergence. This final section considers whether there has been interregional
convergence along these dimensions, and how the lowlands’ growth
may have affected the distribution of welfare. No robust conclusions emerge
here. Some welfare measures suggest no clear tendency, whereas a specific
poverty measure suggests there has in fact been some divergence between
regions.
Drawing on these results and observations, a final section presents some
conclusions and discusses policy implication. Additionally, this section highlights some
areas for further research.
III. The regional distribution of populationHistorically, the greater share of Bolivia’s population has been located in the
Andean region, with the Sub-Andean and Lowland regions coming next, in that order.
As Figure 1 shows, however, the Lowlands have been gaining importance since 1950,
almost equaling the Sub-Andean regions’ share by 1992. This figure is based on the
censuses of 1950, 1976, and 1992, the three available for the period under study.
Figure 1This pattern is also reflected in Table 3, which presents net migration and
population growth rates by department. As the table shows, the two highest 1976-
1992 growth rates are in Lowland region departments, while the two lowest, one of
them negative, are in the Andean area.
This differential growth is due to variations in fertility rates, but also to migration
patterns. Table 3 also shows that all three Andean departments have negative net
migration rates at least during the 1987-1992 period, while all Lowland ones have positive rates at least during the same years. As in most other aspects, the Sub-
Andean results are “sandwiched” between the other two.
In light of the significant migration flows suggested by this evidence, it is
interesting to explore how responsive these have been to income levels. Unfortunately,
this type of analysis can only be carried out in the case of urban-bound migration,
since detailed data on migrants’ characteristics is available from household surveys
carried out only in the nine departmental capitals.5 Additionally, this section focuses
on urban-urban migration, because an “adequate” characterization of migrants’ regions
of origin and regions of destination is available only at this level.
Table 3The analysis uses a conventional regression framework seeking to relate wage
levels and net migration rates across cities. Providing some introductory information in
this regard, Figure 2 plots net migration rates against wage indices for the nine
departmental capitals in Bolivia.6 Apart from one outlier, Oruro, there appears to be
the expected relation between wage levels and net migration rates.
Figure 2To explore this issue systematically, Table 4 presents a profit regression, where a
migration indicator is regressed on a set of individual characteristics (age, years of
education, marital status, and ethnicity), a set of characteristics of the city of origin
(unemployment rate, wage level, and poverty incidence), and an analogous set of
destination descriptors. The default value of the dependent variable is 0, and it is 1 if
the individual has moved from one of the major nine cities to another within the last
five years. The sample is restricted to heads of households.
Table 4As is often the case in these settings, the results suggest that, all else equal, younger,
more educated people are more likely to migrate. The other individual-level
characteristics are not statistically significant.
Focusing on the origin and destination characteristics, the coefficients are all of
the expected sign, suggesting people are more likely to migrate to areas with higher
wage and lower unemployment and poverty levels. These variables, however, are not
significant.
To summarize, this section has presented basic information on the significant
changes in the geographic distribution of population that took place in Bolivia since the
1950s. The main finding is the declining importance of the Andean area and the rising
participation of the Lowland region. These changes are the product not only of
differential fertility rates, but also of significant migration, which at least at an urban
level appears to be potentially responsive to economic conditions. This last aspect that
will be important in linking these developments to the shifts in the distribution of
economic activity discussed in later sections.
IV. Urbanization PatternsThe association between growth and urbanization is one of the central empirical
regularities in development. As Figure 3 shows, Bolivia’s experience in this realm is
qualitatively consistent with the international evidence: there have been persistent
increases in national and region-specific urbanization rates during the period under
study. While no region had an urbanization rate higher than 50% in 1950, two of
them did by 1992. The most rapid increase and highest urbanization level is observed
in the Lowlands.
Figure 3previous section may account for the fact that in one aspect of urbanization, Bolivia
displays relatively idiosyncratic characteristics, at least if its neighbors’ experience is
taken as a benchmark. Namely, unlike Argentina, Chile, Paraguay, and Peru, the
country has not urbanized around a clearly dominant city (this list excludes Brazil, the
remaining neighboring country). In other terms, urban concentration, by any of the
usual measures, is lower in Bolivia than in comparable countries.
To illustrate this, consider Wheaton and Shishido (1981), who provide evidence
on this topic for 38 developing countries. Their results are consistent with the level of
development having at first a positive and then a negative effect on urban
concentration. In their sample, Bolivia would clearly be in the segment where this
relationship is positive.
In order to measure urban concentration, these authors introduce a number of
measures. The first is the Urban Primacy Index, simply the ratio of the population in
the
largest city (P1) to the total urban population (P).
UP=P1/P
Figure 4 shows the evolution of this measure both at a national and regionalspecific
level. The lower part of the figure indicates that the relative importance of the
largest city, La Paz, has declined: from containing almost 40% of the urban population
in 1950, the city’s share declined to slightly over 30% in 1992. In contrast to the
international experience, therefore, economic development has been associated with
declining urban concentration, at least by this measure.
Figure 4The upper part of this figure shows, however, that while the emergence of
smaller cities may partially account for this declining concentration, it also reflects the
rising dominance of one city within each of the three regions discussed. These are La
Paz in the Andean, Cochabamba in the Sub-Andean, and Santa Cruz in the Lowland
region. The growth of these three cities accounts for why all within-region urban
primacy indices have increased since 1950.
These three cities have also grown faster than all others combined: the
percentage of the urban population contained in these metropolitan areas has gone
from about 55 in 1950 to 65 percent in 1992. Thus, their growth accounts for a
disproportionate share of the general urbanization observed in Figure 3.
Because the behavior of the Urban Primacy Index is dominated by only the
largest city in the area under study, the literature also tries to achieve a more
balanced picture using the Herfindahl concentration index, drawing from Industrial
Organization. If Pi is the population of city i, and P is the total urban population in the
area under consideration, the Herfindahl index is given by:
H=_( Pi / P)2
As Figure 5, shows, qualitatively similar results emerge when this measure is used
instead.
It is relevant to note that Figures 4 and 5 are based on Metropolitan Area (MA)
rather than city-level data. This distinction matters only for two cities: La Paz and
Cochabamba, and mainly in the first case. The La Paz metropolitan area consists of
the cities of El Alto and La Paz. The former contains about 40% of their combined
population. If city rather than MA data is used, the only qualitatively important change
arises in Figure 5, because the Herfindahl index in the Andean region no longer rises
between 1976 and 1992. Because El Alto and La Paz are contiguous cities with
integrated labor markets, this distinction does not seem important.
Figure 5An alternate way to consider urban concentration has recently received more
attention, namely Zipf’s “law” or the so-called rank-size rule. Put shortly, this rule
states cities’ populations should be inversely proportional to their rank. In other
words, the nth largest city in the country should have a population size roughly equal to
1/n that of the largest city.
In regression terms, this “rule” posits that the estimated coefficient of an
equation relating the log of city rank to the log of city population size should be equal
to approximately –1. Several authors have noted this “rule” works remarkably well.
Krugman (1986) points out that for the 130 largest metropolitan areas in the United
States, estimation yields a coefficient of –1.003 with a standard error of only 0.01. He
suggests Zipf’s law does not work as precisely in other developed countries, but that it
still provides a good fit if the “primal” city (e.g., London and Paris in the case of the
United Kingdom and France, respectively) is removed from the sample.
To explore this issue in the Bolivian context, Table 5 presents simple rank-size
rule regressions, using data on the 20 largest urban centers in 1950, 1976, and 1992.9
As the table shows, Bolivia does not quite adjust to the expectation. Even in 1950, the
coefficient of interest is significantly different from one, and its divergence increases
with each census, reaching a point estimate of –1.6 in 1992. Eliminating the largest or
the three largest cities does not have uniform effects on these results, although in the
last two periods it moves them closer to expectation. When the three largest cities are
removed, the coefficient of interest changes to –1.53, -1.40, and –1.43, respectively.
Table 5Though the reasons behind the relative “failure” of Zipf’s “law” in Bolivia are hard
to ascertain, its worsening performance can at least be partially traced to the growing
and converging importance of the three largest metropolitan areas: La Paz, Santa
Cruz, and Cochabamba. As indicated above, these three centers’ share of the urban
population increased from 55 to 65% between 1950 and 1992, and the three are
clearly much larger than all other Bolivian cities.
Table 6 shows this was not always the case, and is suggestive of how these cities’
growth affects the rank size rule’s performance. For each census year, this table
features the seven largest cities and their respective populations.
Table 6A first point to note is that while La Paz and Cochabamba have been on the “top
three” list since 1950, Santa Cruz only joined in 1976, moving straight from fifth to
second place between these two census years. This partially reflects the strong westeast
net migration patterns described above. The final column in Table 6 shows each
city’s population growth rate. The two cities with the highest growth rates are Santa
15
Cruz and Cochabamba, which has determined that they have been able to approach La
Paz in size.
While La Paz was 3.1 and 4.6 times as large as the second and third largest cities
in 1976, these ratios had fallen to 1.6 and 2.2 by 1992. Particularly when regressions
focus on larger cities, these two observations alone account for part of the rank-size
rule’s worsening performance.
Another notable point about Table 6 is the relatively high “flexibility” in rankings.
Of the seven cities considered, only two, La Paz and Tarija, have not experienced a
change in their position between 1950 and 1992. As a subsequent section will argue,
many of these changes, including of course the rising importance of Santa Cruz, can be
explained by making reference to natural resources and their impact on shifts in the
distribution of economic activity.
Returning to urban concentration, an interesting question is whether Zipf’s rule
“does better” within regions. Table 7 presents evidence in this regard, focusing on the
15 largest cities within each area.10 An interesting result is that in this case the
coefficient estimates are even further from –1. This result is particularly strong in the
Sub-Andean region, where the coefficient on the log of city rank approaches –2.
Table 7To summarize all results on urban concentration, the following points can be
made:
1) As a preliminary and well-established fact, and consistent with the
international experience, Bolivia has experienced a rapid urbanization
process in the period under study.
2) At a national level and at least since 1950, however, Bolivia does not display
a positive growth/urban concentration association that is frequent in
developing countries in general, and among its neighbors in particular. This
seems to be mainly due to the relative decline of La Paz as a population
center, and the rise of Cochabamba and particularly Santa Cruz as alternate
cities.
3) Within each geographic area identified in this paper, however, urban
concentration is clearly on the rise and significant. In none of these areas is
the urban primacy rate below 0.5, and in the Andean region it is above 0.7.
4) When the rank-size rule is used to explore urban concentration in Bolivia,
the results are not entirely consistent with the expectation, and in fact seem
to have been diverging from it over time. A partial reason for this, once
again, appears to be the relative rise of Cochabamba and Santa Cruz with
respect to the historical primacy of La Paz.
5) Finally, the ranking of Bolivia’s cities by size appears to be relatively
“flexible”, with five of the seven largest cities changing their position in the
period under study.
Source: http://docs.google.com/viewer?a=v&q=cache:FIJHgM6qspQJ:www.iadb.org/document.cfm%3Fid%3D788015+bolivia+migration+patterns&hl=en&gl=sg&pid=bl&srcid=ADGEESgJIMsvDSdmjiqvtXF7txs1Sjp3ht8rgADFdt3p1q9IRB_aLyvh2wxqm9-x6o_3LMpAP6rzS_3rm-6DPiVMlJBgxZxuzrQcWupXcjsnK51i38ahP-cM6f2yh0GoGj_x4LYNwirn&sig=AHIEtbSJSQrhnpD3MME96uIwXuPOjl3Jxw
Note: Only the main points related to migration were extracted from the article and placed here; tables, figures and additional information not directly or significantly related to migration were excluded.