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ORI GIN AL PA PER
Migration and mobility on the Amazon frontier
Jill L. Caviglia-Harris • Erin O. Sills • Katrina Mullan
� Springer Science+Business Media, LLC 2012
Abstract Migration patterns within tropical forest frontiers are highly complex
and multidirectional, with movements to, from, and within these regions likely
driven by different macro and micro factors. As such, several different conceptual
models have been suggested to explain these dynamics. This paper uses data from a
panel survey of households in a frontier region of the western Brazilian Amazon
along with ‘‘second hand’’ reports on where people have moved to evaluate these
conceptual models. Our rich data set, collected over nearly a decade from hundreds
of households, allows us to compare households who arrived at different ages to
assess predictions of the life cycle hypothesis; those who have been in the state (or
on their properties) for different numbers of years to investigate the turnover
hypothesis; those who arrived with different levels of capital to examine path
dependence as suggested by conceptual models that focus on wealth dynamics; and
the destination and purpose for moves from and within the study region to look for
evidence of the frontier expansion hypothesis. We do not find any evidence for the
turnover hypothesis, perhaps due to the favorable biophysical and market conditions
in our study region. However, patterns in this region are consistent with all of the
other conceptual models, reflecting the overlapping theoretical foundations of the
models, and the complexity of migration and mobility on the frontier.
J. L. Caviglia-Harris (&)
Economics and Finance Department, Salisbury University, Salisbury, MD 21804-6860, USA
e-mail: [email protected]
E. O. Sills
Department of Forestry and Environmental Resources, North Carolina State University,
Raleigh, NC 27695-8008, USA
e-mail: [email protected]
K. Mullan
Department of Agricultural and Resource Economics, University of California, Berkeley,
207 Giannini Hall, Berkeley, CA 94720, USA
e-mail: [email protected]
123
Popul Environ
DOI 10.1007/s11111-012-0169-1
Keywords Migration � Population mobility � Brazilian Amazon � Panel data �Rural households
Introduction
Migration is widely considered a primary contributor to deforestation on tropical
forest frontiers, as growing populations clear forests for homesteads and agriculture
(Carr 2009; Amacher et al. 2009). Movements to and from frontier regions are
driven by various macro and micro factors (Carr et al. 2006; Rosero-Bixby and
Palloni 1998), and the resulting migration patterns impact these regions in highly
complex and multidirectional ways. For example, previous research on the Amazon
suggests that migration to frontier regions is often the result of government policies
(or other exogenous shocks), including expansion of infrastructure and financial
incentives (Hecht and Cockburn 1990; de Almeida et al. 1995). On the other hand,
mobility within frontier regions tends to be determined more by micro factors such
as household characteristics and life cycle stage (Fearnside 2008; Perz 2001). While
soil degradation has been noted as one of the leading causes of land abandonment
and migration further into the frontier (Southgate 1991; Goetz 1997), it is clear that
many other factors also influence second generation migrants, including social ties,
personal goals, and local labor market conditions (Barbieri et al. 2006; Shrestha and
Bhandari 2007; VanWey et al. 2007).
While migration patterns to, from, and within forest frontier regions are clearly
interrelated, they are usually investigated separately because researchers rarely have
information from both the origin and destination regions of migrants (Carr 2004,
2009; Entwisle et al. 2009). While a cross-section of a population at any point in
time can be used to assess the prior mobility decisions of the current population,
panel data provide a more complete understanding of population movements.
Household behavior is not static, but rather involves decisions made over several
years by successive household heads (Davis and Lopez-Carr 2010). Analysis that
ignores these dynamics may misrepresent the size, significance, and even direction
of effects on the decision to remain on a property or migrate to a different region,
and therefore, under- or overestimate the potential effects of different policy
interventions.
This paper investigates the migration patterns of households with farms in several
government-sponsored settlements in the western Brazilian Amazon, based on a
three-period panel of those farms and households. Our study site has undergone
tremendous growth from both development and population viewpoints since the
original municipality of Ouro Preto do Oeste was first ‘‘emancipated’’ in 1970
(Pedlowski 1997). It has also been rapidly deforested, and thus, one motivation for
examining migration and mobility is to understand key drivers of environmental
outcomes. Further, population dynamics can directly affect local politics and the
demand for social programs. There are two key dimensions to these dynamics: (1)
the patterns of migration to the frontier (where are immigrants from, when did they
arrive, how do household characteristics vary with origin and arrival time); and (2)
the patterns of mobility among those already at the frontier (who moves, why do
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they move, and where do they go). We present four common models of migration,
which generate different hypotheses about the drivers of migration and its links to
land use within agricultural frontiers. Using a combination of panel survey data,
second-hand reports on migration location, qualitative data, and descriptive
statistics, we assess evidence for these hypotheses in our study area.
Population dynamics in frontiers
Migration and population dynamics have significant impacts on both the physical
environment and use of public services in developed and developing nations alike,
but these impacts are particularly notable in frontier regions of developing nations
because they are experiencing the most rapid change (Ebanyat et al. 2010).
Migration to the frontier clearly affects population density, but also the composition
of the population and of households (Carr 2004). All types of population mobility—
including in-migration to frontiers, rural–urban moves within the frontier, and rural–
rural moves from old to new frontiers—can impact the use of natural resources and
public services. For example, mobility between rural and urban areas shifts demands
for social expenditures including roads, schools, and health posts.
The potential impact of population growth and density on resource use has long
been recognized. Beginning with Malthus’s (1798) ‘‘Iron Law of Population,’’ and
continuing with Boserup’s (1965) counter hypothesis that population growth could
positively impact the environment by stimulating intensification measures, the
importance of human migration has long been acknowledged. More specifically,
movement into new frontiers often has a large initial impact on natural resource use
(e.g., deforestation). In the Brazilian Amazon, an increasingly important question is
what happens to those frontiers as they age.
Ravenstein’s (1889) ‘‘laws of migration’’ posit that migration is a response to a
combination of push and pull factors. Recent literature on frontier development has
emphasized how these factors may differ by the age of the frontier and time of
settlement, as well as the characteristics of each population cohort (Brondizio et al.
2002; Carr 2009; Perz and Walker 2002; McCracken et al. 1999; Campari 2005;
Barbieri et al. 2006). These differences in turn have varying implications for the
impact of migration on demographic structure and natural resource use. To make
sense of this complexity, four different—but overlapping—conceptual frameworks
are commonly referenced. We summarize and identify key hypotheses of these
frameworks under the labels of (1) life cycle, (2) path dependence or wealth
dynamics, (3) turnover, and (4) frontier expansion.
Life cycle hypothesis
The life cycle hypothesis suggests that individuals and households are more likely to
migrate at different stages of life (Brondizio et al. 2002). While the macroeconomic
setting and current policy can impact migration according to this framework, local
and regional factors play a more important role. Thus, the establishment of family is
an overarching driver of migration with newly established households and single
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individuals prepared for marriage and/or career moves more likely to move.
Household age structure is hypothesized to impact in- and out-migration to both
rural and urban areas (Barbieri et al. 2009).
The household life cycle has also been hypothesized to explain land production
choices for households in the Amazon, suggesting a link between life cycle stage
and selection of cropping system (Perz and Walker 2002; Walker 2004; Walker
et al. 2002; Perz 2001). Both households and properties can be considered to have
life cycles (Campari 2005) and be classified by differences in age and cohort
(McCracken et al. 2002). In this context, household and property life cycles can be
distinguished by the age of the household (often coincident with the amount of time
that a given household has resided on a property) compared to the amount of time
that the property has been occupied. In frontier regions, time in residence and
property age are often highly correlated and, therefore, difficult to separate
empirically. A pure property age effect implies that patterns of land use are driven
by number of years since occupation, while a cohort may be defined by time of
arrival, age of individuals, and community network, among others. Here, we refer
specifically to the ‘‘life cycle hypothesis’’ that relates life stage (or cohort as defined
by individual age groups) to the decision to move to a new property or home and the
land use decisions that can be linked to these moves (Brondizio et al. 2002).
Path dependence or wealth dynamics
In-migration to frontier regions has been found to be largely driven by the poverty
of potential migrants at their original location (Barbier 2004). Path dependence
further suggests that the low initial capital holdings of these migrants strongly
determine future economic outcomes and onward migration choices. The concept of
path dependence applies at multiple scales, from national (Lall et al. 2006) to
household levels (Yesuf and Bluffstone 2009). For example, path dependence can
explain why households who experience failure can become ‘‘trapped’’ in poverty,
while economic success is much more likely for households with higher initial
holdings of financial, structural, and human capital (Fearnside 2008). This relates to
migration because mobility decisions may be determined by the acquisition (or lack)
of capital (Barbieri and Carr 2005). While the concept is relatively simple, it can be
difficult to disentangle true path dependence from first arrival effects. In the latter
case, those who arrive in the settlement first are more successful (Caviglia-Harris
et al. 2009). This may be because these first arrivers acquire properties with better
soils, which allow them to accumulate greater wealth and stay longer on the same
properties (Moran et al. 2002). According to this conceptual framework, migration
is more likely when lower initial natural capital on a property is combined with
lower physical, financial, and human capital of a newly arriving household.
Turnover hypothesis
The turnover hypothesis relates farm failure to low soil productivity. The ‘‘cycle of
abandonment’’ explains frontier evolution as a process of farm failure and land
consolidation (Campari 2005). Households that leave failed farms may move to
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urban centers or to new frontiers, leaving behind a sparsely populated old frontier.
Thus, both declining agricultural rent in the old frontier and opportunities for
production in new frontiers help explain movements at the extensive margin (Rudel
2005).
Also described as the ‘‘hollow frontier’’ thesis (Rudel et al. 2002), peasant
pioneer cycle (Pichon 1997a), and invasive forest mobility (Myers 2009), the
turnover hypothesis posits a series of events beginning when colonists move to
forest frontiers, clear land for agriculture, and plant subsistence crops. After several
years of cultivation, crop yields decline and more forest is cleared. This process
continues until the property no longer contains soils adequate for crops. At this
point, the colonists abandon the property or sell it to wealthy landlords, who convert
it to pasture (Rudel et al. 2002). According to Rudel et al. (2002), this sequence has
occurred in a variety of frontiers in Latin America including Brazil, Bolivia,
Colombia, Venezuela, Central America, and Mexico. Walker (2003) describes
possible variations on this sequence including adoption of perennial crops and cattle
with changes in the household labor force and aging of the family, relating this
conceptual framework to the life cycle hypothesis. The hallmark of the turnover
hypothesis is an inevitable sequence of events that is repeated over and over,
resulting in a continuing cycle of poverty and land degradation.
Frontier expansion hypothesis
The frontier expansion hypothesis is based on the observation that resource-
dependent economies tend to undergo boom-bust cycles due to insufficient
reinvestment into other productive assets (Barbier 2005). Thus, resource exploita-
tion may not lead to sustainable growth patterns but instead a ‘‘vicious cycle’’ of
underdevelopment, primarily due to a lack of reinvestment into the manufacturing
and human capital necessary for growth. On tropical forest frontiers, this implies
rapid rates of logging followed by deforestation that generates limited returns.
Adding to the cycle is that the poorest populations tend to be concentrated in these
fragile areas, thus linking this framework with that of path dependence. These
households do not have sufficient capital to leverage frontier resource exploitation
to raise rural incomes and reduce poverty in the long run. Only wealthier households
invest and benefit from key resource markets due to their dominance of credit
markets (Agesa 2000). Under these circumstances, resource rents that are earned
from frontier ‘‘reserves’’ are likely to be reinvested in further land expansion and
resource exploitation.
In this context, migration within (old) frontiers is hypothesized to be driven by
both pull factors to urban centers and push factors to new frontiers (Carr 2009).
Economic development is typically associated with growing urban sectors, while
economic failure results in further migration into frontiers. Thus, the turnover
hypothesis and frontier expansion hypothesis both identify push factors associated
with farm failure as driving the deforestation frontier. Ludewigs et al. (2009) argue
that the conceptual models underlying these hypotheses are really one and the same,
that they are both oversimplified, and that they are applicable only to spontaneous
frontier settlement. However, they remain common conceptual frameworks for
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discussions of population dynamics in the Amazon. And while they have many
similarities, they offer fundamentally different predictions for regional societies.
The turnover hypothesis suggests that a mobile frontier population will repeatedly
fail in one location and move on to new areas. In the frontier expansion framework,
similar patterns may also be observed. However, if investments in capital and
infrastructure are made by the government and wealthy households, this can in
principle result in a thriving population, with households who either migrate to
expanding urban areas or remain on their original rural properties.
Conceptual model comparison
The four conceptual frameworks described above all link land use choices to
migration push and pull factors. They intersect in many respects and are not
necessarily conflicting, yet they are based on different scenarios and have some
distinct predictions about the impacts of mobility on the landscape and population
of old frontiers. Some of the differences originate from historical context, while
others result from placing different weights on household vs. property character-
istics. These can be summarized as differences in (1) where migrants go, (2) the type
of individuals and households who migrate, and (3) the impact of immigration on a
region (Fig. 1). According to the life cycle hypothesis, since the younger generation
is more likely to migrate, the frontier will initially be populated with young
individuals and families. Over time these individuals remain on their properties
while their offspring migrate to new areas, both urban and rural (Caldas et al. 2010;
Fig. 1 Alternative migration conceptual models
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Perz et al. 2008). Thus, the old frontier is left with an aging population. Path
dependence suggests that the old frontier would be left with a relatively wealthy
population due to the movement of unsuccessful households further into the frontier.
According to the turnover hypothesis, however, old frontier regions are likely to be
devastated by mobility, left with little natural, human, or financial capital. Finally,
the frontier expansion hypothesis also predicts that a large proportion of the
population will move further into the frontier, but in this case, the successful farm
households (who invest in human, natural, or financial capital) remain on the old
frontier.
In the following sections, we use a variety of approaches, including comparison
of means (to test for cohort effects), summaries of qualitative data (to investigate
reasons for migration), descriptive statistics on migration (to investigate destination
choices), and regression analysis (to identify the determinants of migration) to
assess the evidence for the hypotheses associated with these four conceptual
frameworks in our case study region in the western Brazilian Amazon. We begin
with a description of the study region and our survey data and continue with an
overview of migration patterns. We then analyze in detail migration and mobility
into, out of, and within the study region and discuss implications for the conceptual
models of migration.
Migration to the Amazon and study site
Large-scale migration to the Brazilian Amazon began in the 1970s with federal
settlement campaigns such as ‘‘Operation Amazonia.’’ With the stated goal of
providing the rural poor with a means to escape landlessness and unemployment,
road construction began in the states of Goias, Maranhao, Mato Grosso, and Para
shortly followed by the construction of the Tranzamazon Highway (completed in
1972) through the states of Maranhao, Para, and Amazonas; and later BR-364
through the state of Rondonia (Hecht and Cockburn 1990; Southgate 1992, 1994;
Pedlowski and Dale 1992). In 1970, the government established the National
Integration Program (PIN) to settle families along these newly constructed
highways. Primary objectives were to establish 100,000 households (or 500,000
individuals) along the Transamazon, although only 8,000 families had settled along
the road by 1980 due to the low soil fertility (Browder 2002; Southgate 1990; Hecht
and Cockburn 1990; Moran 1981). By contrast, the PIN targets for settlement along
the BR-364 in Rondonia were much more conservative. However, due to relatively
rich soils, many more migrants arrived than had been predicted or planned for
(Pedlowski et al. 1997; Millikan 1992).
In fact, population increased faster than the national average in almost all of the
Amazonian states in the first two decades of these federal programs, with rates that
were more than two times the national average in the states that were the focus of
the federally funded highway and PIN programs (Table 1).1 Rondonia stands out
1 This immigration rate is a calculation between censuses and is, therefore, an expression of mobility
‘‘balance’’ (entrances and exits) in ‘‘long’’ periods.
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with the most rapid population growth in the 1970s (333% increase) and 1980s
(134% increase), well above regional averages of 82 and 52% in those two decades
(IBGE 2010; Browder et al. 2008).2 These high percentage growth rates are partly a
reflection of the relatively low initial population numbers.3 However, they
effectively illustrate the rapidly changing situation in Rondonia, which was the
Amazonian state with the smallest population growth rate in the 1990s (24% vs.
regional average of 31%) and from 2000 to 2005 (9% vs. regional average of 11%).
This does not mean that the population of Rondonia (or of the Amazon) has become
fixed in place; rather, mobility in recent decades involves greater movement within
the state and the region as opposed to immigration from other regions of Brazil.
Our study area, Ouro Preto do Oeste, is located in the center of the state of
Rondonia, on the southwestern side of the Amazon River basin and the so-called arc
of deforestation (Alves 2002; Lele et al. 2000). The climate of Rondonia is classified
as humid equatorial with a dry ‘‘winter’’ season. The average annual temperature is
above 25�C with annual precipitation totals of over 2,200 mm (Coronel et al. 2006).
Six municipalities comprise the study area and survey region as follows: Ouro Preto
do Oeste, Vale do Paraıso, Urupa, Mirante da Serra, Nova Uniao, and Teixeiropolis,
covering nearly 6,000 km2 (Fig. 2). Major roads in the municipalities are paved and
extend over 180 km southwest to northeast. Most farms are on secondary roads that
through 2005 remained unpaved and difficult to navigate during the rainy season.
However, by 2005, year-round bus service was available along both primary and
most major side roads.
The federal land reform agency, INCRA, originally planned lots for 500 families
in the Ouro Preto do Oeste region under PIN. However, the region quickly became
Table 1 Population growth rate for Amazonian states and region: 1960–2005
Population growth (percent change)
1960–1970 1970–1980 1980–1990 1990–2000 2000–2005
Acre 34.39 39.09 39.16 38.27 12.68
Amapa 65.82 51.84 66.30 69.59 17.73
Amazonas 32.44 48.60 47.78 35.81 12.13
Rondonia 56.91 333.01 134.41 23.81 9.05
Roraima 38.64 88.26 181.18 53.08 15.34
Para 39.72 55.93 46.11 27.05 10.01
Tocantins NA NA 24.77 27.67 10.21
Amazon region (all states above) 38.52 82.18 52.35 30.84 10.97
Brazil 31.19 27.30 23.64 16.84 7.01
Source: IBGE Instituto Brasileiro de Geografia e Estatıstica (2010), http://www.sidra.ibge.gov.br/bda/.
Accessed March 2010
2 Here, the ‘‘North’’ is defined by the Brazilian Institute of Geography and Statistics (IBGE) to include
Acre, Amapa, Amazonas, Rondonia, Roraima, Para, and Tocantins.3 Population figures for these states in 1960 were as follows: Acre (160,208), Amapa (68,889), Amazonas
(721,215), Rondonia (70,783), Roraima (29489), Para (1,5509,35), and present-day Tocantins (328,486).
Source: IBGE (2010)
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known for its relatively rich soils, and by 1974, 4,000 families had arrived. As in the
rest of the state, this rapid population growth has fallen in recent decades, averaging
1–2% per year over our study period from 1996 to 2005. Most of the growth since
1996 has occurred in the municipalities of Nova Uniao (where new settlements were
created on an old unoccupied ranch of over 25,000 ha) and Urupa (also relatively
recently settled with smaller lots than under PIN).
The first lots to be colonized in the region are located near the largest city center,
called Ouro Preto do Oeste, and in the municipality of the same name. The most
recently occupied lots, settled in the 1980s and the 1990s, are in Urupa and Mirante
da Serra municipalities, respectively. The properties in Mirante da Serra are the
least desirable due to poor soil quality. Market access in the region varies
temporally and spatially. In the 1970s, a single market existed in Ouro Preto do
Oeste. As population density increased in the municipalities, central markets have
Fig. 2 Study region
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developed in each, facilitating household access to agricultural markets, as well as
stores selling common household items. However, Ouro Preto do Oeste remains the
most well-developed urban center, with a hospital, several banks, a bus station, and
a secondary school.
Data collection methods and survey design
Our analysis draws from a three-period panel survey, including 713 total household
interviews in 1996 (Caviglia and Kahn 2001; Caviglia 1999), 2000 (Caviglia-Harris
2005; Caviglia-Harris 2004), and 2005 (Caviglia-Harris et al. 2009), as well as
second-hand reports collected in 2005 on over 200 individuals who had moved out
of the households in the survey sample. We completed the greatest number of
household interviews in 2005, when we interviewed all households resident on
sample lots and also devoted considerable effort to tracking and interviewing
households and a sample of household members who moved from the original
surveyed lots. To accomplish this, we implemented a ‘‘registry’’ of all households
resident on our sample lots three months prior to survey administration. As part of
the registry, we determined which households and household members had moved
and sought information on their current address so that we could include them in the
survey. We also elicited information on the reasons for moves and destinations of
individuals and households who had left the lots. We characterize this information
as ‘‘second-hand reports,’’ since these details were obtained by asking resident
households and neighbors about the people who had left. We matched these registry
data from 2005 with our survey panel data from 1996 to 2000, including
information on household characteristics, land use, land value production, and off-
farm labor. The result is a more complete picture of the migration into, out of, and
within the study area than typically obtained through cross-sectional surveys.
The sample for this survey was first developed in 1996 on a systematic random
stratified basis, using maps created by the colonization agency (i.e., Instituto
Nacional de Colonizacao e Reforma Agraria [INCRA]) as a sampling frame for
each municipality (Caviglia 1999).4 This sample includes considerable variation in
soil type, settlement pattern, topography, and distance (from 0 to 90 km) to the
major federal highway (BR-364) that passes through the region and the city of Ouro
Preto do Oeste. A longitudinal panel was maintained in 2000 and 2005 by revisiting
each of the original lots (Caviglia-Harris 2003; Caviglia-Harris et al. 2009).
The original sample (which we call our ‘‘base’’ sample) consists of the 171
households interviewed in 1996, all but one of which was followed up in 2000. In
2005, the sample was increased to include: (1) stable households (including all
household members living on the lot) that had not moved between any of the survey
waves, (2) mobile households who had moved from one of the lots in the base
sample to a location in the survey region or surrounding municipalities, and (3)
4 In addition, in both years the sample was enriched with observations from a control group of farmers
that participated in a World Bank sponsored non-governmental organization (NGO) devoted to the use of
sustainable agricultural practices: APA (Association of Alternative Producers). Since this sample of
farmers was not drawn randomly, these data are not considered in this paper.
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individual household members who had moved to new properties or urban locations
in the survey region or surrounding municipalities. In other words, we interviewed
residents living on the original survey lots, households, and household members
who had moved from these lots between 2000 and 2005, and absentee property
owners (who had moved from the original rural lots to cities). This, along with
expanding the sample frame to include new settlements in the region, resulted in a
sample size of 372.
The 2005 participant registry was implemented by a trained interviewer with
experience in the region and contacts with nongovernmental and government
agencies. He visited all lots that had been included in the first and second rounds of
the survey, recorded the names of all household members, and identified
individuals and households who had moved since the second wave of the survey.
For both individuals and households who had moved, the interviewer elicited
information on (a) why they moved, (b) where they moved, (c) current
occupation(s), and (d) contact information. This information was used to establish
a feasible sampling plan for households and individuals who had left their lots and
to untangle the relatively few, but very complicated, situations involving
subdivided lots, family feuds, and multiple moves to and from lots, among others.
Registry data identified 461 individuals who had moved from lots (including 459
with at least some information on their destination) and 16 whole households who
had moved (with at least partial information on the current location of 15). Of these
individuals, 333 remained in the survey region and proximate area. To best address
our limited resources while maintaining variation in the tracked sample, we
attempted to locate up to two individuals who had moved from each household,
resulting in a total of 87 completed questionnaires with tracked individuals. In
these cases, the survey was completed with reference to the respondents’ new
households and properties.
The household interview averaged over one hour and elicited information on the
socio-demographic characteristics, production, and consumption of all household
members living on the lot. This included (1) full information on farm production
outputs and purchased inputs, allowing us to calculate income as value added to
household labor and land, (2) hectares reported in different land uses, including
forest, pasture, annual crops, and agroforestry and perennial crops, (3) measures of
wealth, including consumer durables, equipment, livestock, vehicles, and reported
value of parcels, and (4) a standard set of socioeconomic characteristics, including
some ‘‘pre-sample’’ characteristics such as state of birth, number of years in
Rondonia, parents’ occupation, and how lot was acquired. Survey data have been
combined with remote sensing data on land cover in each survey year (Caviglia-
Harris et al. 2009).
These two data sources (the registry and survey data) are used to describe
mobility to and from the survey region. The registry data provide information on
where and when individuals and households migrate. Survey data include interviews
with individuals (and their new households) who had moved within the study region
or neighboring municipalities and could be tracked to their new locations, as well as
interviews with households in the original and expanded samples. Thus, we know
the previous (1996 and 2000) household composition, wealth, income and land use
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for all households and individuals who moved, and the more recent (2005)
household composition and other detailed information for those we were able to
track (Sills and Caviglia-Harris 2009; Caviglia-Harris 2004, 2005).
Migration patterns
This section analyzes moves to and from the survey lots. We begin by summarizing
the origin of survey respondents (according to census defined regions) for each of
the survey years (1996, 2000, and 2005) and changes between these years. We
continue investigating in-migration with a comparison of household, lot, land use
and welfare indicators for households originating from different regions of Brazil,
and households who arrived in different time periods. We then compare the same
characteristics across mobile and stable households in our sample. We further
examine out-migration with the registry and survey data collected in 2005,
including why, which, and when individuals and households move. Finally, we
estimate a multivariate model of the determinants of household moves. The
following section draws conclusions from these findings as they relate to the four
conceptual models introduced in the previous section.
In-migration to the study site
Household data collected in each of the survey years indicate that a majority of the
households in our survey (between 75 and 85% depending on year) immigrated
from the wealthiest part of Brazil, the federally defined ‘‘South’’ and ‘‘Southeast’’
regions (Fig. 3). This is in contrast to the eastern Amazonian states that were settled
largely by relatively poor colonists from the northeast (Walker 2004). The southern
origin of many colonists in Ouro Preto do Oeste may partially explain the stability
of the region (and the low attrition rate from our panel) and its relative economic
success (to be explained in more detail). Table 2 compares the percent of household
heads that we surveyed in the year 2000 that are originally from different regions of
Brazil, with comparable percentages for these municipalities from the national
census in parentheses. The correlation coefficient of 95% between the percentages
of migrants from different regions found in our survey and in the census confirms
the representativeness of our sample. There are a few notable differences between
the municipalities within the survey region. Ouro Preto do Oeste, Urupa, and Nova
Uniao received relatively more migrants from more economically advantaged
regions, with over 90% from the South and Southeast.
Data collected in 2005 include the most variation in settlement date both because
the sample includes original lots that were sold and purchased by new residents and
because the sample includes households in new settlements added to the sampling
frame in that year. Using these data to cross-tabulate origin with decade of
migration (in Table 3), we confirm that of the migrants who have stayed in the
region, the percent from the South and Southeast has remained fairly steady, in the
range of 72–77%.
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Migrants from these more economically developed regions, in particular the
Southeast, have been relatively more successful since migration (Table 4). Total
household income and most wealth measures (including count and principal
component of durable goods and size of cattle herd) tend to be higher for households
from the Southeast. This is in spite of statistically similar lot characteristics such as
soil quality and distance to the city center, and lower initial human capital (as
measured by years of formal education). Lot size, land use, and deforestation levels
also differ in some respects across these groups. All have approximately 3–4 ha in
crops. The area of pasture owned by households seems mostly related to the size of
the properties, and thus those who migrated from the Southeast, who have
significantly larger lots (due to earlier arrival dates), have the greatest area of
pasture. The amount of forest on the lot also differs according to property size, with
the least forest remaining on the smallest properties. However, the percentage of
forest remaining on lots is a fairly consistent 10% except on farms owned by those
who have migrated from within the Amazon (i.e., the North). These households
Fig. 3 Migration to Ouro Preto do Oeste from census defined regions within Brazil, 1996
Popul Environ
123
Table 2 Migration to the greater Ouro Preto do Oeste region from census defined regions, percent of
household heads from 2000 survey data (n = 170) compared to percent of adults from 2000 Census (in
parenthesis)
Origin Mirante da
Serra
Nova
Uniao
Ouro Preto
do Oeste
Teixeiropolis Urupa Vale do
Paraıso
Center 6.25
(7.10)
0.00
(3.64)
0.00
(4.67)
2.86
(5.43)
0.00
(4.38)
6.25
(3.96)
North 3.13
(0.65)
3.70
(0.56)
2.33
(1.42)
0.00
(0.34)
0.00
(0.42)
3.13
(1.08)
Northeast 15.63
(8.77)
7.41
(7.45)
4.65
(8.85)
20.00
(6.37)
15.00
(6.47)
15.63
(10.73)
South 15.63
(15.80)
14.81
(11.68)
11.63
(11.06)
25.71
(10.00)
10.00
(13.76)
15.63
(7.90)
Southeast 59.38
(27.32)
74.07
(34.94)
81.40
(31.32)
51.43
(33.20)
75.00
(33.27)
59.38
(34.23)
The census figures listed in this table do not include children born in Rondonia. In our survey data, the
percentages of individuals born in Rondonia are 40.30, 41.65, 42.44, 44.62, 41.57, and 42.05 in alpha-
betical order of municipality name. Survey figures represent the origin of the household head
Table 3 Regional migration to
Ouro Preto do Oeste, Rondonia
by settlement year, 2005 survey
data
Sample includes the all
household interviewed in 2005:
the base sample, households
added to the sampling frame,
and those tracked to new
locations. Households that
migrated from other countries or
did not answer the question are
not included in the table
(n = 14)
Households % Households
Migration between 1960–1979
Center 5 3.31
North 17 11.26
Northeast 17 11.26
South 16 10.60
Southeast 96 63.58
Total 151 100
Migration between 1980–1989
Center 4 2.40
North 15 8.98
Northeast 20 11.98
South 35 20.96
Southeast 93 55.69
Total 167 100
Migration Between 1990–1999
Center 5 17.24
North 0 0.00
Northeast 3 10.34
South 2 6.90
Southeast 19 65.52
Total 29 100
Popul Environ
123
Tab
le4
Des
crip
tiv
est
atis
tics
for
ho
use
ho
lds
inte
rvie
wed
in2
00
5b
yre
gio
no
fo
rig
in,
mea
n,
and
stan
dar
dd
evia
tio
n(i
np
aren
thes
is)
repo
rted
Var
iab
len
ame
Defi
nit
ion
So
uth
east
(n=
22
3)
So
uth
(n=
59
)
No
rth
east
(n=
46
)
No
rth
(n=
35
)
Cen
tral
(n=
14
)
Ho
use
hold
cha
ract
eris
tics
—su
rvey
da
ta
Ag
eA
ver
age
age
of
the
ho
use
ho
ldh
ead
s,y
ears
49
.98
(13
.69)
44
.17
(12
.52)
50
.33
(12
.9)
28
.83
(8.3
6)
33
.25
(10
.5)
Educa
tion
Aver
age
educa
tion
level
of
the
house
hold
hea
ds,
yea
rs
2.8
8
(2.1
1)
3.7
5
(2.0
6)
2.4
6
(2.1
2)
4.8
1
(1.8
9)
4.5
0
(1.6
2)
Fam
ily
Nu
mb
ero
fh
ou
seh
old
mem
ber
sli
vin
go
nth
elo
t5
.65
(3.5
1)
4.0
0
(2.3
8)
5.1
5
(3.4
)
4.8
0
(3.5
2)
4.3
6
(1.7
4)
So
uth
ern
ori
gin
=1
ifo
rig
ino
fth
eh
ou
seh
old
hea
dis
the
So
uth
or
So
uth
east
cen
sus
regio
ns;
0o
ther
wis
e
1 (0)
1 (0)
0 (0)
0 (0)
0 (0)
Yea
rm
igra
teY
ear
the
ho
use
ho
ldm
igra
ted
toR
on
don
ia1
98
0
(7)
19
82
(5)
19
79
(6)
19
78
(5)
19
83
(8)
La
nd
use
—su
rvey
da
ta
Lo
tsi
zeL
ot
size
,h
ecta
res,
GIS
esti
mat
e6
1.6
1
(57
.01)
47
.72
(80
.49)
58
.93
(60
.18
)
30
.56
(37
.85)
35
.50
(34
.65
)
Agri
cult
ure
Agri
cult
ure
(per
ennia
lsan
dan
nual
s)on
the
lot,
hec
tare
s
3.7
1
(6.1
5)
3.0
8
(4.6
7)
3.6
5
(4.4
8)
2.2
8
(3.5
2)
4.7
4
(8.8
7)
Pas
ture
Pas
ture
on
the
lot,
hec
tare
s5
0.7
4
(51
.09)
35
.45
(61
.32)
43
.74
(52
.61
)
25
.21
(35
.22)
29
.07
(29
.36
)
Fo
rest
Pri
mar
yfo
rest
on
the
lot,
hec
tare
s6
.09
(10
.99)
7.6
7
(21
.43)
10
.85
(21
.32
)
2.7
9
(5.5
)
1.5
7
(2.8
7)
Ag
rofo
rest
ryA
gro
fore
stry
or
Inte
rcro
ppin
go
nth
elo
t,h
ecta
res
0.7
2
(2.6
6)
0.4
(1.2
7)
0.1
7
(0.8
)
0.2
4
(0.9
6)
0 (0)
Popul Environ
123
Tab
le4
con
tin
ued
Var
iab
len
ame
Defi
nit
ion
So
uth
east
(n=
22
3)
So
uth
(n=
59
)
No
rth
east
(n=
46
)
No
rth
(n=
35
)
Cen
tral
(n=
14
)
Lo
tch
ara
cter
isti
cs—
GIS
da
ta
Av
erag
esl
op
eA
ver
age
slo
pe
gra
die
nt
on
the
lot
5.4
9
(3.1
5)
5.1
1
(2.4
4)
4.9
7
(2.3
5)
7.7
2
(6.3
5)
6.0
2
(3.5
)
So
ilD
om
inan
tso
ilty
pe
on
lot,
der
ived
fro
mab
ilit
yto
support
agri
cult
ure
(1-g
ood,
2-m
oder
ate,
3-r
estr
icte
d,
4-u
nsu
itab
le,
5-i
nca
pab
le)
2.3
1
(0.7
2)
2.4
5
(0.5
8)
2.4
(0.6
7)
2.4
5
(0.7
6)
2.3
8
(0.7
7)
Dis
tan
ceD
ista
nce
toth
eci
tyce
nte
r(k
m)
37
.61
(18
.67)
44
.76
(17
.83)
43
.59
(18
.28
)
42
.94
(18
.76)
46
.77
(15
.3)
Ele
vat
ion
Aver
age
elev
atio
no
nth
elo
t228.4
(43
.36)
25
1.0
1
(51
.27)
23
4.7
2
(33
.93
)
24
4.0
8
(41
.48)
24
6.1
6
(42
.31
)
Wea
lth
an
dw
elfa
rem
easu
rem
ents
—su
rvey
da
ta
Inco
me
Rev
enu
esfr
om
ann
ual
and
per
enn
ial
cro
ps,
mil
k,
off
-far
mla
bor,
and
liv
esto
ck;
20
00
reai
s
10
,79
8
(84
38)
7,0
67
(64
69)
8,6
43
(76
21)
14
,45
7
(43
987
)
6,4
68
(43
83
)
Val
ue
of
veh
icle
saV
alu
eo
fv
ehic
les,
20
00
reai
s6
,85
3
(12
840
)
8,8
77
(25
621
)
6,6
34
(18
115
)
3,4
58
(58
06)
1,7
64
(17
97
)
Dura
ble
saC
ou
nt
of
ho
use
ho
ldd
ura
ble
s6
.3
(4.1
9)
5.3
1
(3.8
6)
5.5
7
(4.2
9)
4.4
6
(3.1
8)
3.8
6
(2.1
4)
Cat
tle
Num
ber
of
catt
leow
ned
on
the
lot
105.8
(10
4.6
9)
68
(79
.48)
92
.74
(13
0.5
)
61
.2
(12
6.2
6)
59
.86
(53
.9)
Pri
nci
ple
com
po
nen
taF
irst
pri
nci
ple
com
po
nen
tco
nst
ruct
edfr
om
veh
icle
s,d
ura
ble
s,li
ves
tock
,re
ales
tate
,an
d
wh
ether
on
the
elec
tric
ity
gri
d
0.3
9
(2.1
9)
-0
.53
(2.0
3)
-0
.23
(2.1
8)
-0
.77
(1.7
5)
-1
.08
(1.1
4)
Su
rvey
sam
ple
incl
ud
esth
eex
tend
edra
nd
om
sam
ple
;th
eb
ase
sam
ple
and
ho
use
ho
lds
add
edto
the
sam
pli
ng
fram
ea
Veh
icle
sin
clu
de
mo
torc
ycl
es,ca
rs,tr
uck
s,an
dtr
acto
rs;
du
rab
leas
sets
incl
ud
ech
ainsa
ws,
TV
s,re
frig
erat
ors
,ce
llp
ho
nes
,sa
tell
ite
dis
hes
;li
ves
tock
incl
ud
eca
ttle
,sh
eep
,
go
ats,
pig
s,ch
icken
s;re
ales
tate
repre
sen
tsth
ev
alu
eo
fru
ral
lots
and
urb
anp
rop
erty
.T
he
firs
tco
mp
on
ent
load
sp
osi
tiv
ely
on
all
asse
ts
Popul Environ
123
have approximately 20% of their lot remaining in forest. While these differences
may suggest cultural differences (i.e., those originating from the Amazon using
more sustainable methods and/or deriving more benefits from the forest), the
amount of forest cleared for pasture and agriculture is probably also largely driven
by the time of settlement (McCracken et al. 2002; Pichon 1997b; Caldas et al. 2010;
Caviglia-Harris and Harris 2011).
To investigate these differences by migration cohort, we divide the 2005 data by
decade of immigration to Rondonia (Table 5). This table shows that households
with heads born in Rondonia are significantly younger and more educated than those
who arrived during the initial settlement wave (i.e., before 1980) or who arrived
after 1980. These younger households from Rondonia also have smaller lots, lower
levels of income, and lower levels of wealth by all measures. On the other hand,
immigrants who arrived pre- and post-1980 have similar levels of income, even
though those who arrived earlier have higher levels of wealth and larger properties
by 2005. Comparisons of the arrival years of pre- and post-1980 immigrants, and
their ages in 2005, indicate that the earlier arrivals were approximately 21 years old,
while the later arrivals were around 26 years old, on average, when then moved to
Rondonia.
Finally, we use the 2005 data to assess differences between households who have
moved over the survey time period (mobile households) and those who have not
(stable households) to provide context for the analysis of out-migration from the
original lot sample. In Table 6, note that mobile households include those that remain
in the survey region and surrounding municipalities as well as those that move further
away and include both full households who moved as well as new households
established by individuals who moved (for reasons such as marriage). Thus, mobile
households include second generation migrants who made either rural–rural or rural–
urban moves. We find that mobile households are significantly younger, have fewer
family members living on their new lot, and have less income. These households also
have lower levels of wealth by all measures. Stable households, on the other hand,
have significantly larger lots, although the biophysical conditions are not
significantly different than on the new lots of mobile households.
Out-migration from original properties
The literature reviewed in ‘‘Population dynamics in frontiers’’ suggests that mobility
is likely linked to welfare, household composition, life cycle, and property
characteristics. We investigate these factors with both survey and registry data from
2005. With this information, we can assess destinations and the push and pull
factors most important for individuals and households leaving their original lots in
the study region.
The primary reasons for moving were employment (including higher education)
and purchase of new or better land5 (Table 7). Several others were reported to move
5 Several households moved to the new settlements recently occupied by large groups of individuals with
the support of the Landless Workers Movement ‘‘Movemento dos Trabalhadores Sem Terra’’ (MST), and
recognized by the Federal Colonization and Agrarian Reform Institute ‘‘Instituto Nacional de
Colonizacao e Reforma Agraria’’ (INCRA).
Popul Environ
123
Ta
ble
5D
escr
ipti
ve
stat
isti
csfo
rh
ou
seh
old
sin
terv
iew
edin
20
05
by
tim
ep
erio
dw
hen
mig
rate
dto
Ro
nd
on
ia,
mea
nan
dst
and
ard
dev
iati
on
(in
par
enth
esis
)re
po
rted
Var
iable
nam
eD
efinit
ion
Born
inR
ondonia
(n=
26
)
Mig
rate
db
efo
re
19
80
(n=
14
4)
Mig
rate
daf
ter
19
80
(n=
19
0)
Ho
use
hold
cha
ract
eris
tics
—su
rvey
da
ta
Ag
eA
ver
age
age
of
the
ho
use
ho
ldh
ead
s,y
ears
26
.15
(5.3
1)
51
.67
(12
.42
)
46
.11
(14
.17
)
Ed
uca
tio
nA
ver
age
edu
cati
on
lev
elo
fth
eh
ou
seh
old
hea
ds,
yea
rs
5.2
3
(1.9
3)
2.4
6
(1.9
)
3.4
6
(2.1
7)
Fam
ily
Nu
mb
ero
fh
ou
seh
old
mem
ber
sli
vin
go
nth
elo
t4
.23
(2.0
3)
6.0
4
(3.8
8)
4.8
5
(2.9
3)
So
uth
ern
ori
gin
=1
ifth
eo
rig
ino
fth
eh
ou
seh
old
hea
dis
the
So
uth
or
South
east
censu
sre
gio
ns
of
Bra
zil;
0oth
erw
ise
0.0
0
(0.0
0)
0.7
9
(0.4
1)
0.8
2
(0.3
9)
Yea
rm
igra
teY
ear
the
ho
use
ho
ldm
igra
ted
toR
on
don
ia1
97
8
(4.3
8)
19
74
(3.2
2)
19
85
(4.0
0)
La
ndu
se—
surv
eyd
ata
Lo
tsi
zeL
ot
size
,h
ecta
res,
GIS
esti
mat
e3
1.3
5
(36
.46
)
59
.14
(53
.25
)
56
.54
(68
)
Ag
ricu
ltu
reA
gri
cult
ure
(per
enn
ials
and
ann
ual
s)o
nth
elo
t,
hec
tare
s
1.8
8
(2.4
)
3.6
9
(4.3
7)
3.6
1
(6.8
7)
Pas
ture
Pas
ture
on
the
lot,
hec
tare
s2
7.4
2
(34
.38
)
47
.29
(47
.22
)
45
.24
(57
.59
)
Fo
rest
Pri
mar
yfo
rest
on
the
lot,
hec
tare
s1
.85
(3.6
)
7.1
7
(13
.68
)
6.5
3
(15
.81
)
Agro
fore
stry
Agro
fore
stry
or
Inte
rcro
ppin
gon
the
lot,
hec
tare
s0.1
5
(0.7
8)
0.5
8
(2.5
2)
0.5
3
(1.9
)
Popul Environ
123
Ta
ble
5co
nti
nu
ed
Var
iable
nam
eD
efinit
ion
Born
inR
ondonia
(n=
26
)
Mig
rate
db
efo
re
19
80
(n=
14
4)
Mig
rate
daf
ter
19
80
(n=
19
0)
Lo
tch
ara
cter
isti
cs—
GIS
da
ta
Slo
pe
Av
erag
esl
op
eg
rad
ient
on
the
lot
7.0
7
(6.2
5)
5.4
8
(3.0
2)
5.4
(3.1
8)
So
ilD
om
inan
tso
ilty
pe
on
lot,
der
ived
from
abil
ity
to
sup
po
rtag
ricu
lture
(1-g
oo
d,
2-m
od
erat
e,
3-r
estr
icte
d,
4-u
nsu
itab
le,
5-i
nca
pab
le)
2.4
8
(0.7
2)
2.2
4
(0.7
)
2.4
3
(0.6
8)
Dis
tan
ceD
ista
nce
toth
eci
tyce
nte
r(k
m)
43
.97
(18
.25
)
34
.00
(15
)
43
.91
(19
.92
)
Ele
vat
ion
Av
erag
eel
evat
ion
on
the
lot
23
4.4
7
(44
.84
)
22
5
(44
)
23
9.5
5
(43
.65
)
Wea
lth
an
dw
elfa
rem
easu
rem
ents
—su
rvey
da
ta
Inco
me
Rev
enu
esfr
om
ann
ual
and
per
enn
ial
crop
s,m
ilk
,
off
-far
mla
bor,
and
liv
esto
ck;
20
00
reai
s
7,0
07
(85
04
)
10
,91
0
(85
20
)
10
,19
1
(19
80
4)
Val
ue
of
veh
icle
saV
alu
eo
fv
ehic
les,
20
00
reai
s3
,67
3
(63
09
)
8,3
88
(16
40
2)
6,2
26
(16
38
5)
Du
rab
lesa
Cou
nt
of
all
du
rab
les
4.5
4
(2.6
9)
6.4
7
(4.4
6)
5.6
7
(3.7
3)
Cat
tle
Nu
mb
ero
fca
ttle
ow
ned
on
the
lot
66
.15
(10
8.1
9)
10
3.7
1
(10
5.6
2)
87
.84
(10
4.4
)
Pri
nci
ple
com
po
nen
taC
onst
ruct
edfr
om
veh
icle
s,dura
ble
asse
ts,li
ves
tock
,
real
esta
te,
and
whet
her
on
the
elec
tric
ity
gri
d
0.3
6
(2.1
8)
-0
.06
(2.1
5)
-0
.71
(1.3
7)
Su
rvey
sam
ple
incl
ud
esth
eex
pan
ded
ran
do
msa
mp
le;
that
is,
the
bas
esa
mple
and
ho
use
ho
lds
add
edto
the
sam
pli
ng
fram
ea
Veh
icle
sin
clude
moto
rcycl
es,ca
rs,tr
uck
s,an
dtr
acto
rs;
dura
ble
asse
tsin
clude
chai
nsa
ws,
TV
s,re
frig
erat
ors
,ce
llphones
,sa
tell
ite
dis
hes
;li
ves
tock
incl
ud
eca
ttle
,sh
eep
,
go
ats,
pig
s,ch
icken
s;re
ales
tate
rep
rese
nts
the
val
ue
of
rura
llo
tsan
du
rban
pro
per
ty.
Th
efi
rst
com
po
nen
tlo
ads
po
siti
vel
yo
nal
las
sets
Popul Environ
123
Ta
ble
6D
escr
ipti
ve
stat
isti
csfo
rh
ou
seh
old
sin
terv
iew
edin
20
05
by
mig
rati
on
stat
us,
mea
nan
dst
and
ard
dev
iati
on
(in
par
enth
esis
)re
po
rted
Var
iab
len
ame
Defi
nit
ion
Mig
rati
ng
ho
use
ho
lds
and
ind
ivid
ual
s(n
=7
0)
Sta
ble
ho
use
ho
lds
(n=
12
1)
Ho
use
hold
cha
ract
eris
tics
—su
rvey
da
ta
Ag
eA
ver
age
age
of
the
ho
use
ho
ldh
ead
s,y
ears
40
.59
(15
.23)
54
.83
(11
.48
)
Educa
tion
Aver
age
educa
tion
level
of
the
house
hold
hea
ds,
yea
rs3.5
7
(2.3
3)
2.5
7
(2.0
5)
Fam
ily
Nu
mb
ero
fh
ou
seh
old
mem
ber
sli
vin
go
nth
elo
t3
.91
(2.8
4)
6.4
3
(3.9
4)
So
uth
ern
ori
gin
=1
ifth
eo
rig
ino
fth
eh
ou
seh
old
hea
dis
the
So
uth
or
So
uth
east
censu
sre
gio
ns
of
Bra
zil;
0oth
erw
ise
0.7
1
(0.4
6)
0.8
(0.4
)
Yea
rm
igra
teY
ear
the
ho
use
ho
ldm
igra
ted
toR
on
don
ia1
98
1
(6)
19
79
(6)
La
ndu
se—
surv
eyd
ata
Lo
tsi
zeL
ot
size
,h
ecta
res,
GIS
esti
mat
e2
8.2
0
(45
.34)
79
.13
(70
.31
)
Ag
ricu
ltu
reA
gri
cult
ure
(per
enn
ials
and
ann
ual
s)o
nth
elo
t,h
ecta
res
2.3
4
(8.3
2)
4.4
7
(5.3
4)
Pas
ture
Pas
ture
on
the
lot,
hec
tare
s2
3.3
8
(40
.73)
62
.96
(61
.55
)
Fo
rest
Pri
mar
yfo
rest
on
the
lot,
hec
tare
s2
.29
(7.7
3)
10
.21
(16
.89
)
Ag
rofo
rest
ryA
gro
fore
stry
or
Inte
rcro
ppin
go
nth
elo
t,h
ecta
res
0.1
4
(0.9
9)
0.9
2
(2.9
9)
Popul Environ
123
Ta
ble
6co
nti
nu
ed
Var
iab
len
ame
Defi
nit
ion
Mig
rati
ng
ho
use
ho
lds
and
ind
ivid
ual
s(n
=7
0)
Sta
ble
ho
use
ho
lds
(n=
12
1)
Lo
tch
ara
cter
isti
cs—
GIS
da
ta
Av
erag
esl
op
eA
ver
age
slo
pe
gra
die
nt
on
the
lot
5.2
2
(3.5
7)
5.5
0
(2.8
4)
So
ilD
om
inan
tso
ilty
pe
on
lot,
der
ived
fro
mab
ilit
yto
sup
po
rt
agri
cult
ure
(1-g
oo
d,
2-m
od
erat
e,3
-res
tric
ted
,4
-un
suit
able
,
5-i
nca
pab
le)
2.3
4
(0.7
)
2.3
0
(0.7
5)
Dis
tan
ceD
ista
nce
toth
eci
tyce
nte
r(k
m)
42
.00
(18
)
37
.46
(19
.7)
Ele
vat
ion
Aver
age
elev
atio
no
nth
elo
t234
(40
)
23
0.4
5
(44
.08
)
Wea
lth
an
dw
elfa
rem
easu
rem
ents
—su
rvey
da
ta
Inco
me
Rev
enu
esfr
om
ann
ual
and
per
enn
ial
crop
s,m
ilk
,o
ff-f
arm
lab
or,
and
liv
esto
ck;
20
00
reai
s
9,7
11
(30
713
)
12
,83
9
(90
47)
Val
ue
of
veh
icle
saV
alu
eo
fv
ehic
les,
20
00
reai
s6
,577
(16
428
)
8,0
40
(14
088
)
Du
rab
lesa
Cou
nt
of
all
du
rab
les
4.7
6
(3.4
)
7.4
5
(4.3
6)
Cat
tle
Nu
mb
ero
fca
ttle
ow
ned
on
the
lot
54
.03
(11
7.8
7)
13
1.1
2
(11
8.4
5)
Pri
nci
ple
com
po
nen
taC
on
stru
cted
from
veh
icle
s,d
ura
ble
asse
ts,li
ves
tock
,re
ales
tate
,
and
wh
ether
on
the
elec
tric
ity
gri
d
-0
.52
(1.9
4)
0.8
4
(2.2
2)
Su
rvey
sam
ple
incl
ud
esth
eb
ase
sam
ple
and
tho
seh
ou
seh
old
san
dh
ou
seh
old
mem
ber
sto
mo
ve
from
thes
elo
ts(t
hat
cou
ldb
etr
ack
ed)
aV
ehic
les
incl
ud
em
oto
rcycl
es,ca
rs,tr
uck
s,an
dtr
acto
rs;
du
rab
leas
sets
incl
ud
ech
ainsa
ws,
TV
s,re
frig
erat
ors
,ce
llp
ho
nes
,sa
tell
ite
dis
hes
;li
ves
tock
incl
ud
eca
ttle
,sh
eep
,
go
ats,
pig
s,ch
icken
s;re
ales
tate
rep
rese
nts
the
val
ue
of
rura
llo
tsan
du
rban
pro
per
ty.
Th
efi
rst
com
po
nen
tlo
ads
po
siti
vel
yo
nal
las
sets
Popul Environ
123
in with relatives at another homestead or to marry. Relatively few were reported to
have moved for health (or depression), divorce, farm failure, or retirement. Overall
these responses suggest that most individuals and households are moving to take
advantage of better opportunities (i.e., improved land or work) rather than because
of farm failure or health crises.
The age and gender distribution of mobile individuals are reported in Table 8.
The most mobile age group was 20–29. This is true for both males and females and
most likely represents children of the original owners moving to obtain a lot and/or
residence for their new families. A greater percentage of females are found to
migrate between the ages of 10–19 (likely due to marriage) and a greater percentage
of males to migrate (or die) over the age of 60.
Approximately, 30% of the individuals who had been recorded on survey lots in
2000 moved by 2005. The majority (approximately 72%) of these mobile
individuals remained in the survey region and adjacent municipalities, while 15%
left the Amazon region entirely (Fig. 4). This means that at most 13% of mobile
individuals could have moved to the major cities or new frontiers in the Amazon.
Destinations of the 66% of mobile individuals who remain in the survey region are
presented in Table 9. Interestingly, a large proportion of mobile individuals remain
Table 7 Reported reasons that household members migrated between 2000 and 2005 (number of
responses = 454)
Number Percent
Life improvements—up and out
(includes moves to work or study and purchase of new home)
303 66.74
Life regression—down and out
(includes moves to family house, injury, sickness,
and the inability to support family on the lot)
80 17.62
Family life cycle changes
(includes marriage, divorce, and retirement)
71 15.64
Table 8 Age and gender cohorts of household members reported to leave the interviewed lots between
2000 and 2005 (number of responses = 456)
All ages 0–9 10–19 20–29 30–39 40–49 50–59 60? Age unknown
Female 218 16 49 83 23 15 11 1 20
Male 238 21 33 70 37 24 13 14 26
Total 456 37 82 153 60 39 24 15 46
Percent femalea 48 7 23 38 11 7 5 1 9
Percent male 52 9 14 29 16 10 6 6 11
Percent total 100 8 18 34 13 8 5 3 10
a The percent of females for ‘‘all ages’’ is calculated as the percentage of migrants that were females
relative to the total number of individuals to move. The percentage of the age groups to migrate are
calculated relative to the total female population to move (i.e., n=#females in age group/218). The
calculations for percent male are made in the same manner
Popul Environ
123
in the same municipality, suggesting the importance of social and family ties.
Overall, there is little evidence of large-scale movement to the new deforestation
frontier, although this varies by municipality. Mirante da Serra appears to have the
least stable population. This municipality had the greatest percentage (48%) of
Fig. 4 Migration of Individuals within and out of the Ouro Preto do Oeste region between 2000 and 2005
Table 9 Migration patterns among the 66% (277 individuals) of the migrating population to remain in
the study region
2000?2005 ;
Mirante
da Serra
Nova
Uniao
Ouro
Preto do
Oeste
Teixeiropolis Urupa Vale
do
Paraıso
Percent of
survey
population
to migrate
Percent of
deforestation
in the
municipality
Mirante da
Serra
33 30 1 0 0 0 23 72
Nova Uniao 0 45 6 2 0 0 19 77
Ouro Preto
do Oeste
0 9 17 5 0 3 12 84
Teixeiropolis 0 2 0 21 3 0 9 90
Urupa 7 9 0 10 39 2 24 75
Vale do
Paraıso
4 0 0 0 3 26 12 83
Total 44 95 24 38 45 31 100 100
Popul Environ
123
mobile individuals to move outside the survey region and adjacent municipalities
and had one of the greatest percentages of total moves (23%); however, more than
half of the individuals who moved still remained within the municipality borders
(52% of the 48% that moved). Vale do Paraıso, on the other hand, is recorded as
having the most moves to the northern Amazonian states (suggesting increasing
pressure on forests) and the highest percentage of moves outside of the Amazon
region (not in the table). Counter to the frontier expansion hypothesis, there is also a
positive correlation noted between stability and deforestation. Those municipalities
with the highest levels of clearing are also those that have the most stable
populations. For example, only 9% of the survey population recorded in
Teixeiropolis (the municipality with 90% deforestation) in 2000 migrated by
2005, while 23% of the population in Mirante da Serra (with 72% of the forest
cleared) migrated by 2005.
Finally, to identify the determinants of migration, we estimate a probit model6 of
the probability of a household moving to a new property, using our base sample of
households and their characteristics in 1996 to predict whether or not the entire
household moves by 2005. We focus on the probability of moving over the full
survey period because the percent of whole households moving between each
survey wave is so low (about 1% per year in the late 1990s and 2% per year in the
early 2000s, notably lower than rates reported by Shively and Pagiola (2004),
Rodriguez-Meza et al. (2004), and Walsh et al. (2003)). Explanatory variables
included in this model consist of age of the household heads to investigate the life
cycle hypothesis; origin, education, and the year of migration of the household
heads to test the path dependence hypothesis; income and wealth indicators to test
the turnover hypothesis; and land use and lot characteristics to test the frontier
expansion hypothesis.
The probit estimation results (Table 10) suggest that land use and biophysical
conditions of the lot (soil, elevation, and slope) are not significant determinants of
the decision to move. Instead, distance to the city center, wealth (number of durable
goods and vehicles owned), and age of the household head are the key determinants.
Consistent with previous inferences from the descriptive statistics, households with
fewer assets and younger heads are more likely to move. More specifically, as noted
by the marginal effects, an increase of R$1000 reais in vehicle value leads to a one
percent decrease in probability of moving, and an increase in one durable good leads
to a four percent decline in this probability. In addition, we find that households
located closer to the city center are more likely to move, perhaps because they can
more readily sell their properties. Finally, every year the household head ages
reduces the probability of moving by about a half a percent.
6 Probit models are standard regression methods used for binary dependent variables. Estimating models
of binary outcomes by ordinary least squares would produce biased results since the dependent variable is
bound between 0 and 1. Probit models are estimated using standard maximum likelihood procedures and
can be interpreted as the probability that a certain event, here moving off the lot, occurs. The marginal
effects of the explanatory variables vary with the levels of those variables, but the coefficients can be
interpreted as showing the direction and statistical significance of the effects. Since a Probit regression
coefficient does not indicate the change in the probability of the event when the independent variable
increases by one unit, the marginal effects (partial derivatives) for each coefficient at the means of the
explanatory variables are calculated and presented in Table 10 along with the coefficients.
Popul Environ
123
Table 10 Probability of household moving between 1996 and 2005
Covariate Definition Coefficient Marginal
effects
Standard
error
Household characteristics—survey data
Age Average age of the household heads,
years
-0.018* -0.005 -0.011
Education Average education level of the
household heads, years
-0.067 -0.018 -0.062
Family Number of household members
living on the lot
-0.015 -0.004 -0.026
Southern origin =1 if the origin of the household
head is the South or Southeast
census regions of Brazil; 0
otherwise
-0.029 -0.008 -0.314
Year Migrate Year the household migrated to
Rondonia
-0.008 -0.002 -0.020
Land use—survey data
Deforestation Summation of agriculture (perennials
and annuals) and pasture on the lot,
hectares
-0.001 0.000 -0.004
Lot characteristics—GIS data
Average Slope Average slope gradient on the lot -0.013 -0.003 -0.037
Soil Dominant soil type on lot, derived
from ability to support agriculture
(1-good, 2-moderate, 3-restricted,
4-unsuitable, 5-incapable)
0.211 0.056 0.186
Distance Distance to the city center (km) -0.015* -0.004 -0.079
Elevation Average elevation on the lot 0.004 0.001 0.003
Wealth and welfare measurements—survey data
Income Revenues from annual and perennial
crops, milk, off-farm labor, and
livestock; 2000 reais; in thousands
of reais
-0.002 -0.001 -0.022
Value of vehicles# Value of vehicles, 2000 reais; in
thousands of reais
-0.040* -0.011 -0.027
Durables# Count of all durables -0.139** -0.037 -0.063
Constant 15.986 -0.186 39.626
% Correct predictions 76%
Pseudo R2 (Ben./Lerman) 0.68
n 171
The full set of covariates presented in Tables 3, 4 and 5 is not included in the estimation of migration to
avoid collinearity
*, **, *** Significance at the 90, 95, and 99% confidence intervals, respectively# Vehicles include motorcycles, cars, trucks, and tractors; durable assets include chainsaws, TVs,
refrigerators, cell phones, and satellite dishes
Popul Environ
123
Implications for migration theory
Perhaps, the best known narrative about colonization of the Amazon is that poor
landless families from impoverished regions of Brazil migrated to the forest frontier
to escape drought and unemployment, but then struggled to make their livelihoods
on the frontier. Ouro Preto do Oeste does not follow this standard narrative, in that
the majority of farmers who settled in this region are from the relatively wealthy
South and Southeast of Brazil. This makes it likely that they arrived with higher
levels of initial capital, which in combination with the relatively good soils and
advantageous precipitation patterns in Ouro Preto do Oeste, may explain the
significant increases in income and wealth in the study region. While there are no
significant differences in original lot conditions and characteristics, by 2005, there
are notable differences in lot size, land use, and wealth for households originating
from the South and Southeast compared to other households. There are similar
differences across households who immigrated in different decades: households
who migrated prior to 1980 (during the first wave of immigration to the Amazon)
have larger properties, greater wealth, and higher levels of income. This is
consistent with our findings regarding mobility, because lower levels of wealth and
income are associated with greater probabilities of individuals and households
moving off their lots (and possibly out of our sample). Thus, the remaining stable
population is dominated by wealthier households originally from the South and
Southeast, suggesting that these successful households will place the greatest
pressure on natural resources in the study region, while less successful households
may be contributing to the expanding frontier.
Out-migration can be as important as in-migration in frontier regions, but there is
rarely much information available on the magnitude of out-migration, determinants
of who migrates, and information on where they go. We find that a majority of the
households who moved from our original survey lots actually stayed within the
survey region and surrounding municipalities. Further, a large number that are in the
younger age cohorts leave to purchase new properties, attend school, or obtain
employment. Relatively few move to either the new frontier or the largest city in the
state (Porto Velho).
Next, we review the implications of our results for the four conceptual models of
migration described above, identifying evidence for the hypotheses that we have
labeled life cycle, path dependence or wealth dynamics, and frontier expansion, but
not for the turnover hypothesis.
Implications for the life cycle hypothesis
The life cycle hypothesis posits that younger households and individuals are more
likely to move, migrating to new rural frontiers or urban areas thus leaving behind
an older population. In support of this hypothesis, our data suggest that most
immigrants to the study area were relatively young (household heads in early to mid
20 s), and now younger individuals and households with younger heads are more
likely to move off of their lots to destinations both within and outside the study
region.
Popul Environ
123
Implications for path dependence or wealth dynamics
Models of path dependence or wealth dynamics predict that immigrants to the
frontier will (on average) tend to be poor. However, to the extent that there is some
variation in initial capital, those who are relatively wealthy will stay and accumulate
further capital, while the relatively poor will remain poor and may be forced to
migrate again. We use region of origin as a proxy for initial capital holdings and
examine its impact on the decision to move, as well as any other differences
between households originating from different regions of Brazil. We find that
households originally from the South and Southeast (the wealthiest regions) are
significantly wealthier once established in the frontier, even though the quality of
their land is not significantly different and their education level is lower than other
households. We also find households with higher levels of wealth to be less mobile
(i.e., less likely to move during the survey period).
Implications for the turnover hypothesis
The turnover hypothesis suggests households who migrate to the frontier only
remain there temporarily. Farm failure, due to poor initial soil conditions, ultimately
forces them to abandon (or sell) their properties and move further into the frontier.
As a result of the large number of unsuccessful farms, the region is left ‘‘hollowed
out’’ and degraded after the wave of migrants pushes through. We model the
probability of households moving off their lots to test this theory. To assess the
validity of this theoretical framework for our study region, we focus on the influence
of soil quality (i.e., the potential for farm failure) and household income (i.e., a
signal of farm failure) on the decision to move. The turnover hypothesis is not
supported by our estimation results, as soil structure, characteristics of the lot, and
land use are similar across all cohorts and are not significant determinates of the
decision to leave the lot. That is, we do not find that the classic indicators of farm
failure predict mobility. Instead, land ownership appears to help households create
wealth over time at rates affected by region of origin.
Implications for the frontier expansion hypothesis
The frontier expansion hypothesis also posits that migration is triggered by farm
failure; however, with capital investment, the remaining population and region can
thrive. We investigate the decisions of entire households and individuals to move to
test this hypothesis. In this case, we are interested in the impacts of wealth
accumulation on the mobility of both households and individuals. In support of this
hypothesis, we observe that those who have invested in physical capital (i.e.,
durable goods and vehicles) are less likely to move. Those who do move tend to do
so for life improvements rather than because they are unable to support a family on
their current property (as indicated by second-hand reports). Furthermore, we find
that a majority of migrants stay within the same municipality. Only a small subset of
the sample migrated to other parts of Amazonia where they might contribute to the
expanding forest frontier, and a similarly small subset migrated because they were
Popul Environ
123
unable to maintain their livelihoods on the old frontier. This conclusion is not
necessarily counter to the frontier expansion hypothesis. Rather, it suggests that one
reason for the relative stability of this region of the Amazon is that economic rents
from deforestation have been reinvested into productive capital creating possibilities
for regional improvement and development.
Conclusions
Population dynamics can have profound impacts on physical landscapes and the
demand for public services. Nowhere are these impacts more pronounced than in the
tropical forests that comprise the world’s last remaining frontiers. Understanding
household mobility and migration into and out of these regions is critical for
appropriately guiding development and conservation policy.
This paper investigates migration into and out of an old frontier region in the
western Brazilian Amazon, using a survey panel that was maintained at the lot and
household level and included the tracking of individuals and households who moved
within the study region and surrounding municipalities. We supplement the survey
data with second-hand information on all individuals who moved from the lots in
our original sample. This rich data set provides insights from comparisons of
household characteristics, land use, biophysical conditions of the lot, and wealth and
welfare indicators for different cohorts within our population; information on
reported moves and reasons for these moves; as well as allowing us to estimate a
multivariate model of the probability of moving from lots. We assess whether our
findings are consistent with four common conceptual models of frontier migration,
which generate the life cycle, path dependence or wealth dynamics, frontier
expansion, and turnover hypotheses.
Our results indicate that individuals and households are most likely to migrate at
particular stages in their life cycle. However, where they migrate, and their resource
use upon arrival, depend on their own economic status and that of the origin region.
These elements are best explained by combining the alternative conceptual models
of frontier migration. A common theme across these models is of migrants driven to
frontier areas by a lack of resources at their place of origin. Instead, we observe
migration into the region from relatively affluent parts of Brazil, improving well-
being among these immigrants, and a broadly stable population that moves mostly
within the region.
Preceding theory and evidence suggest that these observed patterns of mobility,
resource use, and economic outcomes may well be linked. Our data support the path
dependence hypothesis as we find poor immigrants from relatively wealthy regions
are more likely to invest in further capital accumulation and less likely to migrate
onwards. In this context, the frontier expansion hypothesis would in turn predict
relative stability for individual households and economic growth in the region as a
whole. Our results are largely in line with these predictions and demonstrate a
corresponding pattern of migration primarily to urban centers or other rural areas
within a thriving old frontier, rather than migration out to new deforestation
Popul Environ
123
frontiers or the sprawling slums around the Amazon’s largest cities. Overall, it is
clear that mobility in this frontier region is not unidirectional but rather
multifaceted, including moves to urban areas, new frontiers, and rural properties
within the original survey region. Therefore, it is not surprising that within this
complexity, we find evidence for several of the leading theories of migration on the
forest frontier.
Acknowledgments This research was generously supported by the National Science Foundation, under
grants SES-0452852 and SES-0076549, as well as the National Security Education Program, the
Organization of American States, the Institute for the Study of World Politics, and the McClure Fund
Foundation. We would like to thank our survey team: Stella Maris de Souza Freitas, Eliane S. Pedlowski,
Ivone Holz Seidel, Taıs Helena Akatsu, Luciana Bussolaro Baraba, and Tania Rodrigues Luz for their
tireless efforts to complete the household surveys in 2005 as well as the local residents of Ouro Preto do
Oeste for their participation. We would like to thank Marcos Pedlowski for logistical support, assistance
with our survey registry, and for selecting a remarkable set of women to serve on our team. We would like
to thank Crisanto Lopes de Oliveira for all of his work on our survey registry. We would also like to thank
Carlos Jose da Silva for serving as a driver and guide to our GIS team. His local knowledge was
invaluable. Finally, we would like to thank Daniel Harris and Suzanne McArdle for the creation of the
maps that are included on this paper. A majority, if not all, of the data used in the analysis can be found at
the archive of social science data for research and instruction at the Inter-university Consortium for
Political and Social Research of the University of Michigan. All location identifiers have been removed.
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