Journal of Rural Studies 42 (2015) 1e10
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Journal of Rural Studies
journal homepage: www.elsevier.com/locate/jrurstud
Income diversification of migrant colonists vs. indigenous
populations: Contrasting strategies in the Amazon
rez a, *, Richard Bilsborrow b, Bolier Torres c
Cristian Vasco Pe
a
nica, Ecuador
Facultad de Ciencias Agrícolas, Universidad Central del Ecuador and Universidad Estatal Amazo
University of North Caroline at Chapel Hill, USA
c
nica, Ecuador
Universidad Estatal Amazo
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 2 April 2015
Received in revised form
22 August 2015
Accepted 13 September 2015
Available online xxx
How do migrant colonists and indigenous populations differ in their land and labor allocation in the
Amazon, and what does this imply for their income levels/livelihoods and the environment? We address
this by analyzing patterns of on- and off-farm employment of rural populations, both mestizo and
indigenous, in the Ecuadorian Amazon. We use data from an unusual survey that covers both mestizo
and indigenous households. As elsewhere in rural areas of the developing world, off-farm employment is
found to be the principal income source for 68% of the population and accounts for 53% of total
household income on average. Within off-farm employment, farm wage employment is most common
for the poor, who usually have little human (education) or natural capital (agricultural land). For
educated individuals, in contrast, non-farm wage employment is commonly the choice. In the Amazon,
the government (national, provincial, municipal) is the main employer, which is linked to recent large
government investment in infrastructure and decentralization, leading to significant expansion of nonfarm employment opportunities for rural populations close to major towns. The implications of this for
livelihoods, sustainable development and the environment are explored in the conclusions.
© 2015 Elsevier Ltd. All rights reserved.
Keywords:
Off-farm employment
Diversification of livelihood strategies
Mestizos and indigenous populations
Household survey
Amazon
Ecuador
1. Introduction
How do the very different types of peoples living in the remote
rainforest of the Ecuadorian Amazon survive? That is, what livelihood strategies do they adopt, do these differ between indigenous
populations and mestizos who migrate to the region as colonists
from elsewhere in the country? In particular, do they differ in their
dependence on agriculture, the resources of the forest and rivers,
and off-farm work? And finally, what are the implications for
household levels of living and the future survival of the rainforest?
For some decades now, off-farm employment has become one of
the main income sources for rural people in developing countries
(Anderson and Leiserson, 1980). This is often true even in remote
areas where people still rely heavily on environmental and forest
income (Angelsen et al., 2014; Wunder et al., 2014). But is this also
true in the Ecuadorian Amazon?
* Corresponding author. Universidad Central del Ecuador, Facultad de Ciencias
nimo Leiton y Av. La Gasca s/n, Ciudadela Universitaria, 170521
Agrícolas, Gero
Quito, Ecuador.
rez), richard_bilsborrow@unc.
E-mail addresses: clvasco@uce.edu.ec (C. Vasco Pe
edu (R. Bilsborrow), boliert@yahoo.com (B. Torres).
http://dx.doi.org/10.1016/j.jrurstud.2015.09.003
0743-0167/© 2015 Elsevier Ltd. All rights reserved.
The Ecuadorian Amazon, one of the world's biodiversity hotspots (Bass et al., 2010; Myers et al., 2000), is under threat from a
number of factors, including, on the one hand, in-migration, frontier colonization and the advance of the agricultural frontier, and on
the other oil, mineral and timber exploitation (Hicks et al., 1990;
Bilsborrow et al., 2004). Because of factors such as isolation from
city centers and markets, lack of agricultural extension assistance to
farmers, and low fertility, acidic soils, agricultural incomes in the
region are likely to be lower than in the Highlands and on the Coast,
the other two geographical regions of Ecuador (Vasco and Vasco,
2012). The situation is further complicated due to rapid population growth and the resulting ongoing process of farm subdivision
which has turned land into a resource which is no longer plentiful
for farmers in the case of Ecuador (Bilsborrow et al., 2004), where
its Amazon region may be considered a closed territory, first, circumscribed by the Andes mountains on the west and the borders of
Colombia and Peru on the other three sides; and within this region,
large areas were appropriated by the State to create three large
national parks and protected areas while more recently even larger
areas were titled to indigenous communities via common property
regimes, in the early 1990s.
2
C. Vasco Perez et al. / Journal of Rural Studies 42 (2015) 1e10
Despite prior studies, none has compared income levels or
livelihood diversification patterns of colonist and indigenous populations in Ecuador or elsewhere in the Amazon. We address this
gap by taking advantage of detailed data from a new household
survey to analyze the drivers of off-farm employment and other
livelihood strategies of colonist and indigenous (Kichwa and Shuar)
populations in the Ecuadorian Amazon.
The rest of this paper is organized as follows: the next section
presents the theoretical framework and hypotheses, followed by a
review of the literature on livelihood strategies focusing on offfarm employment in the Amazon. The subsequent section introduces the study area, data source, and variables. Finally, the
statistical estimation methodology is described, followed by the
results and conclusions.
2. Theoretical framework
The rural household model (Ellis, 1993; Singh et al., 1986) is a
useful starting point to examine income diversification decisions.
Rural households may be seen to maximize their utility given a
number of constraints, among them income, time and technology.
The model posits that diversification is a function of the returns to
labor time in on-farm employment compared to the returns from
off-farm employment. With a fixed amount of initial assets (land,
capital and technology) and household labor time, a household
compares the returns from allocating labor to on-farm vs. off-farm
work (each with several components), and rationally decides
where to allocate labor (Ellis, 2000). Thus households or individuals
tend to allocate labour to off-farm work provided it yields higher
returns and is no more risky than farm activities (Reardon et al.,
2000). The literature differentiates between risk management (ex
ante) and risk coping (ex post) strategies (Ellis, 2000; Reardon et al.,
2006). In the first, households choose to diversify income sources a
priori to prevent income failures at the household level, while in the
second households diversify activities to cope with unexpected
events that threaten their livelihoods. Evidently, the approach here
draws on the first version, of Ellis and many followers.
In the case of the Ecuadorian Amazon, participation in off-farm
employment may be driven by “push factors” such as declining soil
fertility, low yields and small land holdings, which negatively affect
agricultural incomes (Bilsborrow et al., 2004). In this line, Murphy
(2001) notes that off-farm wages can compensate for low farm
earnings resulting from small parcel size and isolation from markets. Nevertheless, partaking in off-farm employment may also be a
consequence of “pull factors”, including higher returns to off-farm
employment, which is partly due to higher wages linked to wage
levels offered by oil companies (Murphy et al., 1997), and more
recently to the growing demand for employees due to decentralization and the growth of the public sector (Jara Alba and
Umpierrez de Reguero, 2014; SENPLADES, 2012). The implications
of this broad theoretical framework for hypotheses regarding the
specific effects of sets of individual, household and community
characteristics on the choice of main income-earning type of work
a person engages in are presented in section 5.2 below.
3. Literature review
A number of studies have examined the diversity of livelihoodseeking behavior in developing countries, often focusing on the
determinants of participation in off-farm work. In the case of Latin
et al. (2001) found that female-headed houseAmerica, Berdegue
holds in Chile with good education and access to credit are more
likely to participate in non-farm work. Escobal (2001) found that
education, credit, electrification and road infrastructure are
important determinants of income diversification in Peru. Also in
Peru, Laszlo (2005) found that Peruvian households in districts with
more population centers and a more developed tourist sector are
more likely to engage in non-farm self-employment. Isgut (2004)
determined that farm wage labor is principally undertaken by
low-educated men. On the other hand, non-farm self-employment
is common among women, while non-farm wage employment is
carried out mainly by those more educated. In Nicaragua, Corral and
Reardon (2001) found that educated men are concentrated in nonfarm wage employment, while both women and household heads
tend to be self-employed. Ferreira and Lanjouw (2001) studied the
determinants of non-farm work in Northeast Brazil, finding that
well-educated men are more likely to engage in well-paid non-farm
jobs, while women tend to undertake low-income non-farm jobs.
Also in Brazil, Jonasson and Helfand (2010) found that the shorter
the distance to population centers, the higher the odds for rural
Brazilians to take part in non-farm work activities.
Several case studies have focused on off-farm employment in the
Amazon. In a qualitative analysis on the Brazilian Amazon, for
example, Steward (2007) noted that young people prefer off-farm
jobs emainly in the public sector-to agricultural jobs, the returns
to which are considerably lower due to low market prices, lack of
credit and lack of extension services. VanWey and Vithayathil (2013)
found that the likelihood of taking part in off-farm work is higher for
males, those more educated, and those residing closer to urban areas.
The determinants of land use of migrant colonists has been the
focus of some considerable research in the Northern Ecuadorian
Amazon (provinces north of Pastaza). Although these studies do not
have off-farm employment as their focus, its importance is noted.
For example, based on the first survey of 419 migrant colonist
households in the Ecuadorian Amazon implemented by Bilsborrow
and Pichon in 1990, Murphy et al. (1997) found that 36% of colonist
households in Sucumbíos and Orellana were engaged in some form
of off-farm work, and that this was positively linked to household
n (1997b) observed that off-farm earnings
income and wealth. Picho
of educated households are considerably higher than those with
less educated household heads. Later Murphy (2001) argued that
off-farm employment is a livelihood strategy of increasingly landconstrained colonists to cope with low harvests and drops in
market prices (the price of the major cash crop, coffee, plummeted
starting in the late 1990s). A second survey of colonist households
was conducted in 1999 on the same approximately 400 plots of
land and found the population had doubled. Research based on this
second, much larger sample revealed changes over time in land use
(increased deforestation) and labor allocation. Barbieri et al. (2005)
reported that the share of households engaged in off-farm
employment grew from about 30% in 1990 to almost 50% in 1999,
seen as related to the reduction in farm sizes during 1990e1999
due to subdivision. Similar results are presented by Bilsborrow et al.
(2004), who linked increasing off-farm work also to the growth of
urban labor markets and improvements in infrastructure, especially roads, factors also noted by Rudel et al. (2002b) in the
southern Ecuadorian Amazon.
Another relevant but small body of literature is that on off-farm
employment of rural households in Ecuador. Using data from the
Living Standards Measurement Survey (1995), Elbers and Lanjouw
(2001) found that persons in the Amazon (albeit a small sample
from the region) were less likely to participate in off-farm activities
than those in the Highlands. In contrast, using data from the national Labor Force Survey eincluding 823 Amazon households(2010), Vasco and Vasco (2012) found that the likelihood of
participation in non-farm work is higher for residents of the
Amazon. Further research based on a more detailed new survey,
such as that used here, is thus desired to clarify this.
In sum, the data suggest rising participation in off-farm
employment in Ecuador over time. As in most developing
C. Vasco Perez et al. / Journal of Rural Studies 42 (2015) 1e10
countries, this seems to be the result of both push factors (e.g.,
population growth, smaller farms, declining soil fertility), and pull
factors (e.g., improved roads and growth of urban labor markets).
Given that these changes are occurring, what is the current status of
indigenous and mestizo colonist households in terms of livelihoodseeking behavior? They have different endowments of land and
labor (see descriptive data below) and very different cultures and
connections to the land, but do these factors lead to very different
forms of labor allocation? Or are other factors more central, such as
education, household size, and location relative to markets? These
are the central issues to be studied here, using data from a new,
detailed household survey.
4. Data and variables
This section briefly describes the study area and the survey data.
In addition, descriptive figures about on-farm and off-farm
employment in Pastaza are presented and discussed.
4.1. The study area
Pastaza is Ecuador's largest but at the same time least populated
province. Most of the population is concentrated in the western part,
in the Andean foothills, while the far larger eastern part deeper in the
Amazon is occupied mainly by indigenous populations. Colonists,
coming principally from the highlands, represent 38% of Pastaza's
rural population, while indigenous peoples account for the rest, with
the Kichwa and Shuar the most populous ethnic groups. The Kichwa
are the most numerous, with an approximate population of 14,000
people. They hold communal land rights to approximately one
million hectares of land. The Shuar number about 5000 individuals,
and have communal land titles to about 200,000 ha in the southern
part of Pastaza. The Shuar of Pastaza are mainly settled along the
road going south from Puyo to Morona Santiago, the province where
they originally migrated from (Fig. 1) (INEC, 2010; Prefectura de
Pastaza, 2012). Population growth along with the advance of the
agricultural frontier has resulted in increased pressure on natural
resources in the province (ECORAE, 2003). As in most of the Amazon
region, soils in Pastaza are very sensitive to erosion and become poor
soon after vegetation is removed (Hicks et al., 1990; Mainville et al.,
2006). As a consequence, yields are low compared to the Coast and
the Sierra or Highlands regions of Ecuador. Given these limitations,
farm households in the Amazon need to have more land to
compensate for its low productivity, which leads them to clear more
forest (when available); selling the timber is an added bonus that
supplements household income in the short-run.
Most colonists engage in commercial agriculture, including
crops such as coffee, cacao, African palm, sugar cane, palm heart,
naranjilla (a native fruit) and taro (locally known as papa china), in
addition to considerable, and growing cattle ranching. Although
indigenous people practice subsistence agriculture (mainly cassava
and plantains), many also engage in the production of cash crops
which are marketed in Puyo, the provincial capital and by far the
largest town in the Southern Amazon (Prefectura de Pastaza, 2012).
As noted above, population growth and scarcity of agricultural
land have stimulated farm households to seek off-farm work
(Bilsborrow et al., 2004), while the expansion of urban areas has
opened up off-farm employment opportunities, facilitated by an
improved road network. During the last decade, the Amazon has
received significant government investments in education, health,
electrification, and employment resulting from decentralization of
public service, in the framework of the “Plan Nacional del Buen Vivir1”
1
Ecuador's National Development Plan 2013e2017.
3
(SENPLADES, 2013). These investments create non-farm jobs and
dynamize local economies including the informal sector (Steward,
2007). However, many of the most highly qualified and better-paid
positions in the Amazon are occupied by nonpermanent settlers
coming from other regions of Ecuador (Vasco, 2013).
4.2. The survey data
The data used here come from a household survey conducted in
the canton Pastaza, part of the province of Pastaza (see Fig. 1). The
fieldwork took place in MayeOctober 2013. The survey obtained
information on household demographic characteristics, land use,
household assets, and social capital, as well as on-farm and off-farm
employment. Households were selected using two-stage sampling.
First, given that resources were limited, only 17 communities could
be selected (Fig. 1), so the procedure used was controlled sampling
(Goodman and Kish, 1950; Kish, 1965), to ensure a broad diversity
of communities in terms of ethnicity, distance to roads and markets, types of land tenure, population size and density, and the
availability of off-farm and non-farm employment (see Cavendish,
2003). In the second stage, households within a community were
selected using random sampling. A total of 304 households were
selected in 17 sample communities (see Table 1). Although not a
probability sample of communities, it ensures good representation
of the diverse rural livelihoods in Pastaza. To illustrate, larger
communities close to Puyo such as Canelos (Kichwa), Chubitayo
n Bolivar (colonist) offer more non-farm jobs since
(Shuar) and Simo
they concentrate some public sector functions including schools.
Individuals residing in these areas are also more likely to commute
to Puyo to engage in (mostly low qualified) non-farm jobs. Such
jobs are not available in smaller communities relatively close to
n del Llandia (colonist), Pitirishka
Puyo, such as Boayacu and Unio
and Centro Yu (Shuar), where people rely more on cash crops. On
the other hand, in Chapintsa and Sharupi (Shuar), and Santa Cecilia
de Villano (Kichwa), where roads were opened recently and
forested areas still exist, incomes from logging are likely higher
than elsewhere. In settlements farther from Puyo such as Shiram
Popunas (Shuar) and Killoalpa, Jaime Roldos and Nuevo San Jose
(Kichwa), people tend to rely more on subsistence farming and
forest products.
The data include Kichwa (116), Shuar (120) and colonist (68)
households. The survey was administered to the household head
and spouse (if available). The questionnaire seeks data on the
involvement of household members in off-farm work and other
economic activities based on data for 365 individuals from the 304
households who were at least 15 years old and had contributed to
household earned/produced) income during the twelve months
preceding the survey. It is important to note that income here is
comprehensive, including the value of both subsistence and cash
crops (including own-consumption), livestock, environmental income (mainly timber and game), and income from non-farm work.
However, it excludes income from remittances, gifts, renting out
animals, interest and other income from liquid assets, which in
almost all cases are non-existent or tiny in these rural populations
compared to their earned income.2
In this study, off-farm employment comprises farm wage
employment, non-farm self-employment, and non-farm wage
employment. Farm self-employment includes crop and animal
production and raising fish in fish ponds. The common tendency to
not include “environmental income” often leads to a misleading
2
Note remittances from former household members migrating elsewhere are
non-existent or tiny since there are no migrants in the sample to international
destinations.
4
C. Vasco Perez et al. / Journal of Rural Studies 42 (2015) 1e10
Fig. 1. The study area.
picture of rural livelihoods, especially in forested areas (Angelsen
rdova et al., 2013) where it is sometimes a
et al., 2014; Prado Co
significant part of household income. Thus in this study, income
from environmental sources is included, and includes income from
sales of products from timber extraction, hunting and fishing (in
natural rivers, streams, and lagoons).
5. Methodology
This section describes the empirical strategy utilized to estimate
the drivers of participation in the five livelihood strategies and
presents the variables used in the multivariate analysis.
5.1. Empirical strategy
Since decisions regarding labor allocation are made simultaneously rather than individually, a multinomial probit model is
used to estimate the likelihood of an individual participating in
farm self-employment, generating environmental income, farm
wage employment, non-farm self-employment, or non-farm wage
employment as his/her principal source of income. A multinomial
probit model is preferred over other approaches- -i.e., multinomial
Table 1
Communities included in the sample.
Community
Population
Predominant ethnic
group
Accessible by
Time needed to reach
Puyo (hours)
Latitude
Longitude
Comuna 6 de Diciembre
Boayacu
n del Llandia
Unio
n Bolívar
Simo
Centro Yu
Shiram Popunas
Sharupi
Chapintsa
Pitirishka
Chubitayu
Iskayaku
Shiwa Kucha
s
Jaime Roldo
Killoalpa
Nuevo San Jose
Santa Cecilia
Canelos
45
50
180
5682
50
141
94
420
250
1125
60
310
75
75
150
150
2713
Colonist
Colonist
Colonist
Colonist
Shuar
Shuar
Shuar
Shuar
Shuar
Shuar
Kichwa
Kichwa
Kichwa
Kichwa
Kichwa
Kichwa
Kichwa
Dirt road
Dirt road
Dirt road
Paved road
Dirt road
Trail
Trail
Dirt road
Paved road
Paved road
Trail
Dirt road
River
River
River
Dirt road
Dirt road
3.5
1.0
0.5
0.5
1.5
6.0
3.0
2.0
0.75
1.0
3.0
2.0
8.0
8.0
8.0
3.5
1.0
01º290 5700 S
01º180 5300 S
01 200 1600 S
01º430 0900 S
01 520 3200 S
01 540 0000 S
01 570 4500 S
01 590 2200 S
01 480 2800 S
01 540 2500 S
01 220 0400 S
01 080 0900 S
01 230 1100 S
01 230 5800 S
01º220 2600 S
01º300 01200 S
01º330 2800 S
77ª280 1900 W
77º580 5600 W
77 580 1400 W
77º500 4300 W
77 480 3200 W
77 440 5400 W
77 410 3900 W
77 420 4900 W
77 490 5400 W
77 480 4600 W
77 380 0500 W
77 380 4300 W
76 570 2000 W
76 580 1400 W
76º560 2500 W
77º280 5900 W
77º450 0200 W
Source: Survey data (see text).
C. Vasco Perez et al. / Journal of Rural Studies 42 (2015) 1e10
5
Table 2
Variables and descriptive statistics for mestizo colonists, Kichwas and Shuar.
Variable
Dependent variables
Farm self-employment
Environmental income
Farm wage employment
Non-farm self-employment
Non-farm wage employment
Individual-level variables
Age
Female
Education
Colonist
Kichwa
Shuar
Household-level variables
Household size
Share of women
Share of children
Wealth
Credit
Cultivated land
Land in forest
Community-level variables
Piped water
Distance to urban areas
Description
Overall
Farm self-employment as principal work (0/1)
Environmental income as principal work (0/1)
Farm wage employment as principal work (0/1)
Non-farm wage employment as principal work (0/1)
Non-farm wage as principal work (0/1)
0.254
0.073
0.216
0.082
0.373
Colonists
0.134
0.061
0.207
0.085
0.513
Individual's age (years)
Individual is female (0/1)
Completed years of formal education (years)
Individual is colonist (0/1)
Individual is Kichwa (0/1)
Individual is Shuar (0/1)
39.6
0.23
8.5
0.232
0.350
0.416
41.2
0.30
7.9
e
e
e
Number of household members
Share of women in the household
Share of children (<15 years) in the household
Wealth index
Household has received credit
Area under crops and pasture (ha)
Land in forest (ha)
4.5
0.33
0.32
0.196
0.36
5.77
36.0
3.7
0.36
0.28
1.505
0.37
6.55
13.5
Share of households with access to piped water
time needed to reach the closest town with more
than 10,000 people (minutes)
0.50
154
0.63
50
Kichwa
0.351
0.070
0.250
0.063
0.266
41.1
0.16
7.9
e
e
e
5.3
0.29
0.39
0.655
0.21
3.12
59.3
0.49
232
Shuar
0.230
0.085
0.197
0.092
0.396
38.2
0.25
9.6
e
e
e
4.2
0.36
0.28
0.391
0.48
8.57
30.4
0.43
140
Note: (0/1) identifies dummy variables.
logit–since it does not assume independence of irrelevant alternatives (IIA) (Wooldridge, 2002). We thus use a model of the
following form:
Pic ¼ Pr WORKING ¼ c jXijk ; Hjk; k
¼ F Xijk bc1 þ Hjk bc2 þ Pk bc3 ;
(1)
where Pc is the likelihood of an individual i working in category of
employment c, with c indicating any of the five work categories
described above. X, H and P are vectors of individual, household and
community characteristics, described later. b1, b2 and b3 are the
coefficients of the characteristics that constitute the explanatory or
independent variables, showing the size and direction of effects.
5.2. Variables and hypotheses and descriptive statistics
Table 2 presents the definitions and descriptive statistics of the
variables used in the analysis. The dependent variables of interest
are five binary variables indicating whether an individual has either
farm self-employment, environmental income, farm wage
employment, non-farm self-employment or non-farm wage
employment as his/her principal source of income, referred to here
as main source of employment. The dependent variables are discussed further below in the descriptive analysis. The list of
explanatory variables includes sets of individual, household and
community characteristics. All the variables included here are expected to affect whether the person works and in what type of
work, and hence are relevant in a livelihood theory framework as
used here.
Individual characteristics include age, gender, years of formal
education and ethnicity. A priori, one expects older persons, males
vs. females, and indigenous persons more than migrant mestizo
colonists to engage in agricultural activities on the farm, other
things equal. Educated people are hypothesized to be more likely
to take part in (better paying) non-farm jobs than those with less
education. In rural Ecuador, gender plays an important role in the
division of labor (Martínez, 2000). Since there are 85 women
(23%) in the sample, we include a dummy variable taking the
value of 1 if the person is a woman to control for the effect of
gender on the outcome variables. Thus we anticipate that women
are less likely to be involved in farm work (at least among
mestizos), off-farm agricultural work, and environmental work
activities, and more likely to engage in non-farm self-employment (often managing small local stores called tiendas). On the
other hand, it is not clear a priori whether women are more likely
to work in non-farm work than men when other variables are
controlled for. As ethnicity may relate to different cultural values
and tastes for various types of work in ways which are not
obvious, two dummy variables indicating whether an individual is
Kichwa or Shuar are included in the model., with colonists as the
reference group (¼ 0). It is hypothesized that colonists work more
off-farm than the indigenous, so the coefficients for the latter are
expected to be negative.
Among the household variables, we include household size
and the proportions or shares of women and children in the
household separately to control for possible effects of household
composition on individual decisions concerning principal type of
employment. Thus women are less likely to engage in off-farm
work when they have more children, which is adequately proxied by the number of children in the household. Household economic conditions or wealth is proxied by a wealth index, which is
the first principal component of household assets.3 We prefer this
methodology because, in distinction from simple count indices,
which assign equal values to every asset, it gives higher weight to
assets which provide more information about household wealth
(Filmer and Pritchett, 2001). There is also the possibility that
reverse causality and therefore simultaneity exists between
wealth and an individual's livelihood choice, resulting in endogeneity. However, this is not an issue as it would be with current
income, since wealth is a construct based upon assets
3
Possession of a radio, TV, cell phone, computer, gas stove, refrigerator,
chainsaw, spray pump, car, motorcycle, solar panel and rifle. The first principal
component accounts for 29% of the variation.
6
C. Vasco Perez et al. / Journal of Rural Studies 42 (2015) 1e10
accumulated over a number of years prior to the survey. This
makes it essentially pre-determined, greatly reducing the likelihood of reverse causality and simultaneity. Substantively, wealth
provides a means to overcome entry barriers to some jobs and to
non-farm self-employment (license fees, equipment and machinery acquisition, among others). Lack of access to credit can be
a major drawback for creating and operating a rural business
(Lanjouw and Lanjouw, 2001). Since there is no direct way to
measure access other than use of credit, to capture the effect of
access to credit, a dummy variable equal to 1 if the household has
received a loan and zero otherwise is included. Lack of land or
having little land is hypothesized to be an important driver of
participation in off-farm employment (Vasco and Vasco, 2012). To
control for this, the natural logarithm of the amount of land under crops plus pasture (to reduce the effect of outliers) is included
in the model. Additionally, and given the importance of forest
income for some people's livelihoods in the Amazon, the natural
logarithm of land in forest is added to the specification, and expected to especially affect participation in environmental income
generating activities.
Finally, we consider the effects of (available) community-level
or contextual factors on the type of employment. First, infrastructure and services are hypothesized to be key determinants of
the creation and development of rural businesses (Lanjouw, 1999;
Vasco, 2013). They affect the likelihood of someone having a nonfarm business, but also in the aggregate, create non-farm
employment opportunities for others in the area. Non-farm
employment is likely linked to distance by road to the nearest
town (see Barbieri et al. (2009), on the Northern Ecuadorian
Amazon). To take these factors into account, the share of households with access to piped water –estimated at the community
level– and the time needed to get from the local community to the
closest town of more than 10,000 people (which in all cases was
Puyo) are included in the model, following some preliminary
investigation. Availability of piped water is likely to be correlated
with location and possibly also ethnicity (share of colonist
households with piped water is higher than that of indigenous).
We checked for collinearity in the model, but the variance test did
not indicate any worrisome covariance (inflating factors all below
2.5).
Table 2 also shows mean values for all variables for the three
ethnic groups. Overall, the Shuar are younger and better educated
than the Kichwa and colonists. Colonist women are more likely to
be engaged in income generating activities than indigenous
women, which could be related to cultural norms in indigenous
communities or to colonist farms being on roads. Kichwa households are larger than their Shuar and colonist peers. It is known
from other work (Bremner et al., 2009; Bilsborrow et al., 2004)
that fertility rates of the Kichwa and Shuar are very high and
higher than those of colonist women in the Amazon. A possible
explanation for Shuar households nevertheless being smaller than
the Kichwa could be that more Shuar have migrated to local towns
or even back to their provinces of origin in the southern Amazon.
Kichwa homes have fewer women but more children than their
Shuar and colonist counterparts. In terms of land use, the Shuar
have more land in crops and pasture than the colonists and more
than double that of the Kichwa, which is related to the Shuar
engaging in cattle ranching far more than any other indigenous
group in the Ecuadorian Amazon (see Gray and Bilsborrow, 2008).
Partly as a result of this, the Kichwa have double the amount of
land in forest compared to Shuar communities, and quadruple that
of colonists. Finally, comparing values for contextual variables,
colonist households have more access to piped water and also live
closer to the provincial capital, Puyo, with the Kichwa farther away
than the Shuar.
6. Results and discussion
6.1. Descriptive analysis
Table 2 above shows the proportions of persons who depend on
each of the five types of work for their main source of income by
ethnic group. Colonists have the lowest shares of farm selfemployment and environmental income and the highest share of
non-farm wage employment (51.3%). This is not surprising since
they are generally settled closer to urban areas, and also on or near
main roads (see Fig. 1). Few colonists (13.4%) have farm selfemployment as their main source of income. Smaller farm sizes
and more land in cultivation and pasture also restrict the possibility
of agricultural land expansion. Farm wage labor is also important,
being the principal income source for 20%.
Most Kichwa obtain income mainly from farm self-employment,
which is not surprising since they reside farther away from Puyo. A
quarter of the Kichwa earn their livelihoods selling labor to other
farms, while a similar proportion have non-farm wage employment
as their main source of income. Both environmental income and
nonfarm self-employment have shares below 10%.
Almost a quarter of the Shuar earn their income principally from
agriculture, while 8.5% have environmental income as their largest
source, higher than for colonists and Kichwa households. 20% of the
Shuar engage in agricultural wage labor, which together with data
for the Kichwa, reflects the existence of an emerging wage labor
market among indigenous peoples. This is a bit surprising since
indigenous peoples in the Amazon are known to meet extra
household labor needs mostly via exchange labor (Lu, 2001).
Nevertheless, 31% of indigenous households report hiring agricultural labor the year preceding the survey.
Table 3 shows average annual income by category of employment and ethnic group. Agriculture and “environmental income”
are higher for Shuar than for colonists and Kichwa. Earlier literature
(Rudel et al., 2002a, 2002b) note that over time more Shuar engage
in commercial agriculture (naranjilla production and cattle ranching) and exploitation of natural resources (especially timber). Income from non-farm self-employment is considerably higher for
Shuar than for colonists or Kichwa. This is consistent with prior
research (Lanjouw, 1999) showing that the Shuar are more likely to
participate in high productivity non-farm self-employment. One
possible explanation is that half of the Shuar participating in nonfarm self-employment are chainsaw owners or timber dealers
(often illegal), businesses that offer higher returns than most other
business activities, such as a tienda, cooking meals and handicrafts.
These figures show that non-farm activities in Shuar territories are
closely linked to timber so that non-farm employment does not
necessarily involve a reduction in pressure on natural resources.
Although the share of colonists engaged in farm selfemployment is the lowest among the three ethnic groups, onfarm agricultural income still accounts for a larger portion (36%)
of household income than for the two indigenous populations
(Table 4). Colonists engage in cash crop production and cattle
ranching, which is far more profitable than subsistence agriculture
practiced by most indigenous peoples. Environmental income is
also important, accounting for 18% of colonists' income, which may
reflect that, in spite of living closer to urban areas and having less
land in forest, colonist households still rely on timber exploitation
to balance their budgets. In spite of being the main income source
for half of the individual colonists, nonfarm wage employment
accounts only for a quarter of colonist households' total income.
The opposite occurs for the Kichwa who, in spite of being mostly
engaged in farm self-employment, earn their livelihoods principally from non-farm wage employment. A possible explanation is
that the Kichwa practice mostly low-value subsistence agriculture
C. Vasco Perez et al. / Journal of Rural Studies 42 (2015) 1e10
7
Table 3
Average annual individual earnings by employment category and ethnic group (US $).
Farm self-employment
Environmental income
Farm wage employment
Non-farm self-employment
Non-farm wage employment
Total earned income
Colonist
Kichwa
Shuar
All ethnic groups
1307
836
2226
3407
5751
4136
1160
824
1593
2741
5902
3383
2016
1110
1453
5564
5060
3851
1517
962
1663
3936
5440
3790
(1789)
(1312)
(1807)
(3184)
(3060)
(3373)
(1153)
(1301)
(872)
(1555)
(3604)
(3114)
(2732)
(2302)
(641)
(6576)
(3650)
(3847)
(2027)
(1747)
(1079)
(4631)
(3345)
(3998)
Note: Standard deviations in parentheses.
Table 4
Share of households' income by ethnic group (column percentages).
Farm self-employment
Environmental income
Farm wage employment
Nonfarm self-employment
Nonfarm wage employment
Colonist
Kichwa
Shuar
Total
36
18
14
6
25
30
11
14
7
38
32
17
17
6
28
32
15
15
7
31
and dedicate small areas to agriculture. Environmental income
accounts for only 11% of Kichwa household total income. Since
Kichwa communities are farther away from roads and urban centers, this may in turn make it difficult to extract and market forest
products.
Farm self-employment is the principal income source for Shuar
households (32% of total income). Environmental income accounts
for 17% of Shuar households' income. This figure is similar to that of
colonists and indicate that Shuar and colonist households are more
dependent on forest resources than Kichwa. Non-farm wage
employment is the second largest income source for Shuar
households accounting for 28% of total income. There are no major
differences in the shares of farm wage employment and nonfarm
self-employment among the three ethnic groups. Overall, Table 4
reports more similarities among colonists and Shuar households
than between Kichwa and Shuar households.
Table 5 shows types of occupation by ethnic group, which requires some explanation. A quarter of colonists work in the public
sector (24%) while another third are engaged in low remunerative
non-farm wage jobs (domestic servant, bricklayer and driver). Most
Kichwa are school teachers (50%) or (other) public sector employees (19%), which are among the better paid jobs in the area.
This may explain why non-farm wage employment is the first
source of income for Kichwa homes despite the fact that most have
farm self-employment as their principal income source. The Shuar
have a more diversified non-farm job portfolio than the Kichwa,
with teaching, public service, army and domestic service the most
frequent choices.
It is interesting to reflect on the role of public investment in the
Amazon as it employs 33, 70 and 53% of the colonist, Kichwa and
Table 5
Type of occupation by ethnic group (%).
Domestic servant
Soldier
School teacher
Public employee
Bricklayer
Carpenter
Chainsaw operator
Driver
Guide
Mechanic
Janitor
Other
Colonist
Kichwa
Shuar
Total
14.3
2.4
11.9
23.8
9.5
2.4
4.4
9.5
0.0
0.0
4.8
17.0
0.0
0.0
50.0
19.4
2.8
2.8
0.0
0.0
5.6
5.6
5.6
8.2
10.6
10.6
33.3
12.1
6.1
4.5
6.1
0.0
1.5
0
0
15.2
8.8
5.4
31.3
17.0
7.5
3.4
2.7
2.7
2.0
1.4
2.7
15.1
Shuar non-farm workers, respectively. Recent government investments in education and the expansion of public service jobs in
rural areas may have played a significant role in shaping employment patterns in the Amazon. A similar picture is depicted in
~o in the Brazilian Amazon,
Steward (2007) case study of Carva
where Federal Government decentralization created high-paying
(teachers, agricultural extension agents) and low-paying jobs
(nurses, teacher assistants, school cooks, security guards), which
reduced interest in farming. As mentioned above, significant
governmental investments4 in the last decade increased the demand for public and private sector employees in the Amazon,
though many of the qualified, better paid positions continue to be
occupied by a floating population from other regions, leaving lowskilled jobs for local people. An exception to this is elementary
school teachers, who account for 50 and 33% of the Kichwa and
Shuar non-farm workers respectively, showing that school teacher
is the only type of non-farm employment whose availability is not
influenced by location or population density. A possible explanation is the implementation of the “Bilingual Intercultural Education
System,” which prioritizes cultural and linguistic development
among indigenous peoples of Ecuador (SENPLADES, 2014).
6.2. Multivariate results: effects of independent variables
Statistical results from the multinomial probit model are presented in Table 6. First, the effects of the explanatory or independent variables on employment in general are described, looking at
individual variables, household variables and then contextual factors. We are, of course, especially interested in the effects of
ethnicity, as that is something not examined in a cross-cultural
study in the Amazon previously as far as we know.
To begin, age effects are absent once other variables are
controlled for, which is a curious finding but shows it is mostly
other factors linked to age that lead to different types of work (viz.,
education). In contrast, gender has significant effects on the main
type of work: Being a woman significantly reduces the likelihood of
a person's working on one's own farm as the principal income
source by 21%, which is consistent with previous results of Vasco
et al. (2013) based on less detailed data for the Amazon region as
a whole. Women are also 5% less likely to work in environmental
work, such as hunting, fishing and timber extraction, which are
generally considered “men's work”. On the other hand, gender has
no significant effect on whether the person engages in off-farm
agricultural work, or operating a non-farm business as the major
source of income. The former shows that women as well as men
engage in off-farm agricultural work, though women do this closer
to their own farm. The latter is likely related to the small sample
size of persons having a non-farm business (8% of the sample), since
a common non-farm business is operating a small store or “tienda”,
4
Transfers to regional and municipal governments in the Amazon increased from
US $ 927 million in the period 2000e2006 to US $ 4213 million in the period
2007e2014 (SENPLADES, 2014).
8
C. Vasco Perez et al. / Journal of Rural Studies 42 (2015) 1e10
Table 6
Multinomial probit model, on the determinants of being engaged in the five possible work categories of on-farm and off-farm employment (marginal effects).
Farm self-employment
Individual variables
Age
Age squared
Female (0/1)
Education
Kichwa (0/1)
Shuar (0/1)
Household variables
Household size
Share of women
Share of children
Wealth
Credit (0/1)
Cultivated land
Land in forest
Community variables
Piped water
Distance to urban areas
Number of observations
Wald test c2
Log likelihood
Environmental income
Farm wage employment
Non-farm self-employment
Non-farm wage employment
0.004
0.000
0.214***
0.022***
0.156**
0.028
0.003
0.000
0.052***
0.005**
0.032
0.024
0.004
0.000
0.049
0.024***
0.097
0.062
0.001
0.000
0.303
0.011***
0.011
0.003
0.003
0.000
0.186**
0.064***
0.210***
0.070
0.018
0.431**
0.282
0.032
0.112*
0.081***
0.034**
0.000
0.095
0.066
0.022***
0.012
0.009**
0.019***
0.018
0.201**
0.313**
0.035*
0.098*
0.028**
0.032**
0.003
0.051
0.178**
0.047***
0.021
0.030**
0.003
0.003
0.082
0.081
0.042*
0.046
0.093***
0.044**
0.076***
0.025*
0.086
0.028
0.026
0.056**
0.162**
0.087*
0.026
0.085**
365
278***
398.634
Note: *, ** and *** stand for significance at the 0.1, 0.05 and 0.01 levels, respectively. (0/1) identifies dummy variables.
which is invariably done by women. Finally, being a woman
strongly increases the likelihood of engaging in non-farm wage
employment, discussed further below.
Not surprisingly, education has powerful effects on the main
type of livelihood of a person, with each year of education
increasing the likelihood of engaging in non-farm wage work by
over 6% (so increasing completed years of schooling from 4 to 12, or
completed secondary school, would about double this probability).
At the same time, each additional year of education reduces the
likelihood of participation in each of the other four types of work.
Of farm work (on one's own farm or someone else's) by 2.2e2.4%, of
self-employment by 1.1%, and of work producing environmental
income by 0.5%. The effect on reducing having a non-farm business
is different from earlier research for rural Ecuador as a whole
(Elbers and Lanjouw, 2001; Lanjouw, 1999; Vasco, 2013), where it
was concluded that education is positively correlated with the
likelihood of owning a business compared to other economic activities. A possible explanation is that entrepreneurial activities in
the rural Amazon (mainly operating a store, meal preparation,
handicraft elaboration and timber-related activities) involve lowlevel technology, and hence do not require much education. Overall, these results for education are expected, albeit perhaps surprisingly strong in a region with only one small city and low
technology economic activities.
Moving on to ethnicity, it also has some intriguing differences in
effects. Thus the Kichwa are 16% more likely to have their main
income source be on-farm agriculture than colonists (or Shuar),
while at the same time they are 21% less likely to engage in nonfarm work. There are no differences in the effects of Kichwa vs.
colonist in involvement in the other three work types as the main
source of income. These findings reinforce the descriptive evidence
of Table 4 and indicate that the Kichwa are the most dependent on
agriculture and the least engaged in urban work, while the Shuar
and colonists are virtually indistinguishable. This reinforces findings of other studies showing the Shuar are the most like colonists
of the various indigenous groups in the Amazon region (e.g. Gray
and Bilsborrow, 2008; Lu et al., 2012).
Moving on to the household variables, household size has no
effects, in contrast to household age-sex composition. Thus the
higher the proportion of women, the higher the probability of the
person under study having farm self-employment or off-farm
agricultural labor as his/her largest source of income. Agricultural
labor is usually done close to home. The result for the proportion of
children in the household –that a higher proportion is linked
positively to the person being an agricultural laborer or working in
self-employment–makes sense since those types of work are more
compatible with child care, and tend to be close to home. Household assets as a proxy for wealth has a negative effect on the person
seeking “environmental income” or working as a farm laborer, but a
strong positive effect on engaging in non-farm self-employment.
The former is consistent with earlier literature (Barbier, 2010)
associating poverty with higher reliance on environmental resources, while the latter supports the view that persons in
wealthier households are more likely to set up businesses, consistent with Reardon et al. (2006) who found better-off households
more easily overcome “entry barriers” that non-farm self-employment entails.
The land variables have interesting effects on type of work also.
Cultivated land is positively linked to the likelihood of participation
in farm self-employment (more cultivated land means more time
needed for on-farm work) and “environmental income” (those with
more cultivated land are also likely to have more total land,
including forested land). Having more land in cultivation also is
linked to less working for others in farm or non-farm work, as
hypothesized, but an increased likelihood of working in selfemployment. The latter is because farming and running a nonfarm business out of (usually) the house are complementary economic activities since they are usually done in close proximity.
More land in forest, other things equal, is linked to having less land
cultivated and hence a lower likelihood of working on one's own
farm or in other low-income agricultural work, and instead more to
environmental work (since much is based on the forest) and nonfarm work. This is consistent with research in the Northern Ecuan, 1997a; Pichon and Bilsborrow, 1999), and
dorian Amazon (Picho
suggests that non-farm wage earnings can relax pressures to clear
forest to meet household needs. The direction of causation, however, is not clear, as more wealth in forested land may free up
people to work away more (the coefficient being almost identical to
that of wealth).
With regard to the contextual variables, environmental occupations linked to the forest are more likely in isolated communities
where provision of services eproxied by the share of households
C. Vasco Perez et al. / Journal of Rural Studies 42 (2015) 1e10
with access to piped water– is limited. An increase of 10% in the
share of households having access to piped water is associated with
a reduction of 8% in the likelihood of an individual to obtain income
from the environment. This is not a causal association, however, but
an effect of location, which is supported by the coefficient for distance from Puyo. On the other hand, piped water is associated with
a greater likelihood of non-farm work, which is mostly in urban
areas where piped water is available Distance to the one significant
urban area is only marginally negatively linked to non-farm work,
but is positively linked to both working on one's own farm and selfemployment, as expected. These two contextual variables capture
overlapping phenomena, though are not seriously collinear (see
earlier in paper).
7. Multivariate results by type of livelihood
The discussion of the results above has presented the results
showing differences in the effects of variables on the five types or
categories of livelihoods. Here we summarize the results from the
perspective of each type of livelihood, providing a brief typology of
the main factors linked to each livelihood choice, beginning with
the factors affecting work on one's own farm. Thus for “farm selfemployment”, the person is statistically likely to be male, with low
education, a larger share of adult women in the household, and to
have a farm with more cultivated land and little forest land. The
person is also likely to be Kichwa, and to live far from the one
significant urban place, the provincial capital, Puyo.
Persons depending on “environmental income” as their main
source of income also tend to be male, with low education but the
share of women is not relevant, and have more land both in cultivation (complementary to extracting timber) similar characteristics
but have more land in forest. They are also relatively far from Puyo,
but have no infrastructure, a difference from those depending on
income from their own farm work.
In terms of who works as farm wage laborers, these are persons
with little education or land, and more likely from households with
large proportions of women and children. At the macro level, these
could be households in areas where population growth and
resulting land subdivisions (Bilsborrow et al., 2004) have reduced
plot sizes over time to a point where the land plots can no longer
support livelihoods from agriculture. In any case, those with more
land have less (need to engage in paid) work away from the
household farm, whether in agriculture or not. Overall, these results are in agreement with earlier research (Vasco et al., 2013;
Vasco and Vasco, 2012) concluding that farm wage employment
is likely for land-constrained, uneducated people who also lack the
financial capital to start a business or the human capital (education)
to engage in much non-farm wage employment.
Moving on to non-farm self-employment, persons in this activity are likely to have significant land in cultivation, little education, a larger proportion of children in the household (childcare
being often compatible with this work), wealth (to confront entry
barriers for setting up a business), and to be far from Puyo. The
latter is likely due to the fact that those living near urban areas find
it easy to shop in stores in town, while those who reside farther
away will tend to buy necessities when they run out, from a small
local retail store or “tienda” (40% of the total number of businesses
in the sample) rather than travel far to the main urban place, Puyo.
Finally, non-farm wage employment is perhaps the most interesting of the five livelihood approaches–both because it is likely to
expand most with future economic development and because it
already provides a large if not the largest proportion of household
income for the three rural study populations (see Tables 3 and 4).
Persons more likely to be engaged in non-farm wage employment
tend to be female, with more years of education, and mestizo
9
colonists or Shuar but not Kichwa. They are likely to have less land
in cultivation than others, since that would absorb household labor,
but more land in forest. They are likely to live closer to the urban
area (Puyo) and to live in wealthier communities with more
infrastructure, such as a piped water system serving most dwellings, which is consistent with previous studies (Elbers and
Lanjouw, 2001; Lanjouw, 1999; Vasco and Vasco, 2012) highlighting the role of infrastructure in facilitating non-farm job opportunities in rural areas.
8. Concluding remarks
This study has analyzed the differences in livelihood choices of
indigenous and migrant colonist households and the factors
affecting these differences in Pastaza, Ecuador, one of the world's
biodiversity hotspots in the Amazon basin. The results reveal that
off-farm employment (mostly non-farm) is the main income source
for 68% of the individuals in the sample and accounts on average for
53% of household income. Within off-farm employment, off-farm
wage employment yields low earnings but and is the main income source for those with little land/assets or education. On the
other hand, non-farm wage employment, which flourishes near
urban areas, is engaged in especially by those with more education,
especially women.
The notions of colonists using cropping and cattle ranching
involving continuous clearing of forests over time and of indigenous peoples practicing subsistence agriculture and collecting
forest products with little impact on the natural environment are
no longer accurate, as increasingly more households have off-farm
employment as their largest source of household income. This is
likely due to both push factors, including scarcity of agricultural
land due to population increase (Bilsborrow et al., 2004), which has
led to subdivision of farms which has in turn driven people into offfarm employment, as well as pull factors, notably the generally
higher earnings available in non-farm employment, which is partly
due to the expansion of public sector employment due to the
expanded role of the state in the region. These jobs are especially
available to those with more education.
The findings in the paper lead to a challenging question: Does
the expansion of non-farm employment have the potential to
combine rural development with protection of the natural environment, a winewin policy? Certainly earnings from both nonfarm self-employment and non-farm wage employment are
generally higher than those of farm work, which should in itself
reduce pressures to clear forest to expand agriculture and extract
timber. However, indiscriminately promoting non-farm selfemployment may include extraction, transport and trade of timber,
particularly in areas with remaining forest cover and road networks
that facilitate transporting timber to markets. A second concern has
to do with dependence on public sector employment, which overall
offers the highest earnings within non-farm wage employment and
has some potential to absorb rural surplus labor. First, further
expansion of public employment depends partly on the country's
economic growth and capacity to generate jobs, including for more
teachers to increase the supply of educated members of the labor
force, both males and females. But there is a Catch-22 here, as this
in turn requires more public resources, to train more teachers and
pay them to go to rural areas (such as the Amazon) to educate more
of the rural population at the secondary education level, to increase
the supply of persons qualified to take advantage of the higher
demand for such more educated workers generated by continuing
economic growth. The absorption of more such workers in urban
areas in turn adds to economic growth.
In this context, policies should also be oriented to promote
environmentally friendly entrepreneurial activities in the Amazon
10
C. Vasco Perez et al. / Journal of Rural Studies 42 (2015) 1e10
region, such as ecotourism, community-based tourism, scientific
tourism, and processing and industrialization of renewable forest
products. These entrepreneurial ventures have the potential, properly managed and taxed, to create benefits for local populations,
especially indigenous populations. Training in tourism and hospitality and making available credit for community-based initiatives
could help in achieving this goal, but more research and experimental pilot projects would be useful to explore practical uses of the
extraordinary Amazonian biodiversity and ancestral knowledge as
potential income sources for indigenous peoples. Finally, even
though an important share of the sample population in this project is
already involved in non-farm wage employment, many of the better
and more qualified positions in both the public and private sectors
are currently filled by a floating population coming from other regions of Ecuador, reflecting the limited qualifications of the vast
majority of the labor force in throughout the Amazon. In this regard,
increasing investment in higher education would help to not only
improve human capital but should also significantly increase income levels of the rural population. For the indigenous populations
especially, it is not access to basic (primary) educationdwhich is
widely available in almost all indigenous communities– but access
to much better quality primary and secondary education (and
increasingly in the future, university level education) that is needed.
Acknowledgements
nica at
Field work was funded by the Universidad Estatal Amazo
Puyo. Carlos Wampash, Luis Chongo, Juvenal Espín and Shiram
Vargas assisted with the data collection. We are grateful to three
anonymous reviews and the editor for most helpful comments on
an earlier version.
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