Content uploaded by David J. Spielman
Author content
All content in this area was uploaded by David J. Spielman
Content may be subject to copyright.
Rural innovation systems and networks: findings from a study
of Ethiopian smallholders
David J. Spielman
•
Kristin Davis
•
Martha Negash
•
Gezahegn Ayele
Accepted: 11 March 2010
Ó Springer Science+Business Media B.V. 2010
Abstract Ethiopian agriculture is changing as new actors,
relationships, and policies influence the ways in which
small-scale, resource-poor farmers access and use infor-
mation and knowledge in their agricultural production
decisions. Although these changes suggest new opportuni-
ties for smallholders, too little is known about how changes
will ultimately improve the wellbeing of smallholders in
Ethiopia. Thus, we examine whether these changes are
improving the ability of smallholders to innovate and thus
improve their own welfare. In doing so, we analyze inter-
actions between smallholders and other actors to provide
new perspectives on the role played by smallholder inno-
vation networks in the agricultural sector by drawing on
data from community case studies conducted in 10 locali-
ties. Findings suggest that public extension and adminis-
tration exert a strong influence over smallholder networks,
potentially crowding out market-based and civil society
actors, and thus limiting beneficial innovation processes.
From a policy perspective, the findings suggest the need to
further explore policies and programs that create more
space for market and civil society to participate in small-
holder innovation networks and improve welfare. From a
conceptual and methodological perspective, our findings
suggest the need to incorporate rigorous applications of
social network analysis into the application of innovation
systems theory.
Keywords Africa Ethiopia Agricultural development
Innovation Participatory rural appraisal
Social networks Social learning Technology adoption
Abbreviations
ADLI Agriculture Development-Led Industrialization
BoARD Bureau of Agriculture and Rural Development
CSI Credit and Savings Institution
ERSS Ethiopia Rural Smallholder Survey
NGOs Nongovernmental organizations
PRA Participatory rural appraisal
SNA Social network analysis
Introduction
Ethiopian agriculture is increasingly characterized by new
policies, actors, and relationships that influence how small-
scale, resource-poor farmers access and use information
and knowledge. These changes are partly due to the
increasing emphasis by the government on agriculture-led
development. While this growing complexity suggests
opportunities for Ethiopian smallholders, little is known
about how those opportunities can be effectively leveraged
to promote pro-poor processes of rural innovation.
D. J. Spielman (&)
International Food Policy Research Institute, P.O. Box 5689,
Addis Ababa, Ethiopia
e-mail: d.spielman@cgiar.org
K. Davis
International Food Policy Research Institute, c/o Agridea,
Eschikon 28, 8315 Lindau, Switzerland
M. Negash
Center for Institutions and Economic Performance (LICOS),
Katholieke University of Leuven, Deberiostraat 34, bus 3511,
3000 Leuven, Belgium
G. Ayele
Ethiopian Development Research Institute, P.O. Box 2479,
Addis Ababa, Ethiopia
123
Agric Hum Values
DOI 10.1007/s10460-010-9273-y
Growth and innovation in Ethiopia’s agricultural sector
are, by most measures, fairly weak. Agricultural GDP per
capita grew just 0.48% per year between 1996 and 2005
and displayed significant volatility year to year. Grain
production per capita grew just 1.38% per year, while
cereal yields stagnated around 1.2 metric tons per hectare.
The use of inorganic fertilizer is limited to just 37% of
farmers, and application rates remain at about 16 kg per
hectare. Use of improved seed varieties is relatively low, as
is the use of many other agricultural technologies. And
while the proportion of Ethiopians living below the poverty
line declined between 1995 and 2005, it remains at 40%
(World Bank 2005). Thus, rural incomes and livelihoods
remain largely unchanged throughout the country, despite
recent upswings resulting from several successive years of
favorable rainfalls and some positive policy reforms related
to commodity marketing, agricultural export promotion,
and social safety nets.
An analysis based on an innovation systems framework
can contribute to addressing the discrepancy between the
changes in policies, actors, and relationships, on the one
hand; and productivity on the other. The framework draws
attention to the diverse actors that contribute to agricultural
innovation processes—public research organizations, pri-
vate companies, nongovernmental organizations (NGOs),
civil society organizations, and smallholders themselves—
by shedding light on the roles and responsibilities, actions
and interactions, and institutions that condition behaviors
and practices. This study examines how Ethiopian small-
holders innovate—how they make use of new or existing
knowledge and technology in their livelihood decisions;
how their social networks contribute to innovation pro-
cesses; and how those decisions, networks, and processes
are influenced by policy- and market-driven factors. This
examination is particularly relevant in light of the slow rate
of technological change in Ethiopia’s agricultural sector
and the slow emergence of alternative institutional and
organizational arrangements to enhance growth and develop-
ment in the sector.
The question this study sought to answer is whether the
new forms of interaction and the increasing diversity of
actors within Ethiopia’s agricultural innovation system are
having an impact on the capacity of smallholders and rural
communities to beneficially participate in innovation
processes.
A conceptual framework
We begin by introducing a conceptual framework based on
the innovation systems approach to studying how society
generates, exchanges, and uses information and knowl-
edge, and how these processes can be strengthened to
promote innovation and distribute the benefits of innova-
tion more widely. The framework represents a significant
change from the conventional linear perspectives on tech-
nological change by emphasizing the importance of
studying an ‘‘innovation system’’ as a single unit com-
prising the actors involved in the innovation process, their
actions and interactions, and the formal and informal rules
that influence their practices and behaviors. We define an
innovation as any knowledge (new or existing) introduced
into and used in an economically or socially-relevant pro-
cess (OECD 1999). For the purposes of this study, the term
innovation included not only the adoption of a new agri-
cultural technology, but also a range of other processes,
such as the reorganization of marketing strategies by a
group of smallholders, the use of a new learning and
teaching method by agricultural extension agents, and the
introduction of a new processing technique by an agroin-
dustrial company. We define an ‘‘innovation actor’’ as
someone who introduces or uses such knowledge—a pro-
cess that entails seeking information from various sources
and integrating elements of the information into social or
economic practices in a way that changes the behaviors and
practices of individuals, organizations, or society. Innova-
tion actors include public sector entities (research organi-
zations, agricultural extension and education services, state
marketing agencies, state-owned enterprises, institutes of
higher learning, international research centers, and foreign
universities); private actors such as traders, entrepreneurs
and for-profit companies; collective action entities such as
farmers’ cooperatives; civil society, including NGOs and
community-based organizations; and, of course, farmers,
members of farm households, agricultural laborers, and
residents of rural communities.
The key commodity linking these actors is knowledge.
Although knowledge is a difficult commodity to charac-
terize, we assigned to it several key properties that were
useful for the purposes of this study. Knowledge may be
scientific or technical in nature, or it may be organizational
or managerial. It may occur in a codified or explicit form,
or it may be tacit or implicit. Knowledge may originate
from foreign sources of discovery or emerge from the use
or reorganization of internal and indigenous practices and
behaviors (Clark 2002; Malerba 2002).
Because innovation results primarily from the exchange
and use of knowledge, the nature of interactions between
and among actors is another important aspect for consid-
eration. Interactions may be spot market exchanges of
goods and services that embody new knowledge or tech-
nology; costless exchanges of knowledge conducted in the
public domain; long-term, durable exchanges that incor-
porate complex contractual arrangements and learning
processes; local- or community-level systems of knowl-
edge sharing; or hierarchical command structures. The
D. J. Spielman et al.
123
study of how actors structure their interactions in the
exchange of knowledge gives the innovation systems
framework its definitive systems perspective.
An important element of an innovation system is the
array of social networks within which innovation actors
interact with one another, or the sets of individuals or
organizations in which each has connections of some kind
to some or all of the other members of the set (see Rycroft
and Kash 1999; Malerba 2005; Mowery and Sampat 2005).
Social networks can define, limit, or enhance an individ-
ual’s opportunities for social learning by influencing
membership or participation in a given innovation process,
thereby affecting access to knowledge (Besley and Case
1994; Foster and Rosenzweig 1995; Munshi 2004;
Bandeira and Rasul 2006). The form, function, and
boundaries of a social network are often determined by
social and economic institutions, conventionally defined
here as the rules, conventions, traditions, routines, and
norms of a given social or economic system (North 1990).
A comprehensive description of the innovation systems
approach was first set forth by Lundvall (1985) and applied
to national comparisons of innovation systems by Freeman
(1987). The concept was further elucidated in Dosi et al.
(1988), Lundvall (1988, 1992), Freeman (1988, 1995),
Nelson (1988, 1993), and Edquist (1997), with empirical
applications focusing primarily on national industrial pol-
icy in Europe, Japan, and several East Asian countries that
were experiencing rapid industrialization during the 1980s.
While their work emerges partly from Schumpeterian tra-
ditions in evolutionary economics, it also draws heavily on
theories of organizational behavior and sociology (Balzat
and Hanusch 2004; Spielman 2006).
Yet the innovation systems approach is still young in its
application to developing-country agriculture. Biggs and
Clay (1981) and Biggs (1989) offer an early foray into the
approach by introducing several key concepts—institu-
tional learning and change, and the relationship between
innovation and the institutional milieu in which innovation
occurs—that become central to later innovation systems
studies on developing-country agriculture. Later studies by
Hall and Clark (1995), Hall et al. (1998), Johnson and
Segura-Bonilla (2001), Clark (2002), Arocena and Sutz
(2002), Hall et al. (2002, 2003), and World Bank (2007)
introduce the innovation systems approach to the study of
developing-country agriculture.
1
For example, Ekboir and
Parellada (2002) examine the social and economic changes
that encouraged the diffusion of zero-tillage cultivation in
Argentina, a process that resulted from a complex series of
events and interactions among farmers, farmers’ organi-
zations, public researchers, and private firms. Hall et al.
(2002) studied the organizational learning processes that
stimulated the diversification of agricultural research
financing in India to include new actors (e.g., medium-
sized firms and producer cooperatives) and new modalities
(e.g., contract research, public–private partnerships). Clark
et al. (2003) detail the factors contributing to the success of
a project in postharvest packaging for small-scale farmers
in Himachal Pradesh, India, by studying the institutional
learning and change processes that were incorporated into
the project design.
Common to all of these studies is the emphasis placed
on the role of diverse actors and interactions within com-
plex systems of innovation, and the institutional context
within which these processes occur. In essence, they argue
that the conventional emphasis on linear innovation pro-
cesses—moving knowledge from scientists to extension
agents to farmers—is an oversimplication of complex
processes that are highlighted by non-linear learning pro-
cesses, feedback loops, and other complex interactions that
occur among far more heterogeneous actors. Policies and
investments in support of linear innovation models, they
argue, are bound to fail if they do not take into account
these complexities in promoting innovation.
But while these studies tend to provide insightful anal-
yses at the project, sectoral, or national level, they do not
address the most basic level of innovation—that of the
farmer. In an attempt to fill part of this knowledge gap,
both conceptually and empirically, we examined how
social networks facilitate the transfer of knowledge exter-
nalities—knowledge made available to an individual as a
result of the practices or behaviors of other individuals—
and how those externalities affected individual decisions to
innovate with respect to farmers’ agricultural practices or
technology adoption choices. An early model of informa-
tion externalities and agricultural technology was described
by Besley and Case (1994) in reference to the adoption of
improved cotton cultivars. The model was later refined by
Foster and Rosenzweig (1995) in a study of high-yielding
varieties (HYVs) of wheat and rice in India during the
Green Revolution.
Several modifications to the social learning/social net-
works model have since entered the literature. Munshi
(2004) adds nuance to the social learning model by dem-
onstrating that information flows related to a new tech-
nology are weaker in heterogeneous populations. Bandeira
and Rasul (2006) add yet another twist by modeling social
learning as a nonlinear process and testing it with a study of
sunflower adoption in northern Mozambique. Similarly,
Darr and Pretzsch (2008) improve on the original models
by comparing innovation processes within situations of
information availability and scarcity. These studies provide
several testable hypotheses. First, a distinction can be made
between the effects of ‘‘learning by doing’’ (a function of
1
See Spielman (2006) for a review of the literature on innovation
systems applications to developing-country agriculture.
Rural innovation systems and networks
123
one’s own innovative capabilities) and ‘‘learning from
others’’ (a function of one’s social networks). Second,
while imperfect knowledge about new agricultural prac-
tices is a barrier to adoption, the barrier decreases as
farmers and their neighbors gain experience. Third, inno-
vation can occur both through strong, cohesive networks
when there is abundant information, and through weakly-
knit networks where there is scarce information. These
hypotheses underlie the key question being asked by our
study—whether new forms of interaction and increasing
diversity within Ethiopia’s agricultural innovation system
are benefiting smallholders and rural communities—
because they highlight the need for more analysis of the
nature and function of smallholder networks and their
contribution to the adoption of new agricultural practices.
Background: smallholder innovation in Ethiopia
Improving smallholder productivity is a central theme in
Ethiopia’s development discourse. Approximately 80% of
the country’s population is rural, and rural poverty is
widespread. A range of factors contribute to this situation,
including: high rural population densities and extreme land
shortages (especially in the relatively fertile highlands
where per capita land area has fallen from 0.5 ha in the
1960s to only 0.2 ha by 2008); recurrent droughts, variable
rainfall, and declining soil fertility that lead to low output;
high variability of agricultural production (with cereal
yields averaging around 1.5 ton/ha); limited access to
modern inputs and infrastructure such as improved seed,
fertilizer, and irrigation; and a weak market for agricultural
commodities (World Bank 2005).
There is ample recent literature to suggest that raising
agricultural productivity, and thus improving rural welfare,
remains a fundamental challenge in Ethiopia (see Diao and
Pratt 2006; Taffesse 2008; Dercon and Hill 2009). The
government—through its economic growth strategy, Agri-
culture Development-Led Industrialization (ADLI)—
argues that a phased approach that focuses on boosting
agricultural productivity before investing in industrializa-
tion is an optimal development strategy for the country.
This translates into a strategy that has focused on the
promotion of new agricultural technologies, the introduc-
tion of better price incentives for agricultural commodities,
and greater investment in rural roads and other infrastruc-
ture (MoFED 2002, 2005).The strategy draws heavily on
the resources and capacities of public agencies that are
pillars of the country’s formal innovation system: public
sector research, extension, and education services, all of
which are recognized as the most prominent sources of
information, technology, and inputs for the Ethiopian
smallholder (Kassa 2005). The strategy also calls for active
engagement with other potential sources of innovation,
such as the private and civil society sectors, cooperatives
and cooperative unions, domestic and foreign firms, rural
investors and entrepreneurs, and NGOs and community-
based organizations.
Yet while the ADLI strategy implicitly recognizes that a
more dynamic and competitive innovation system is criti-
cal to transforming agriculture in Ethiopia, it has yet to
translate that notion into a system with the potential to
improve rural livelihoods. This is due in part to a contin-
uing focus on traditional, linear modes of technology
transfer and a strict focus on production quantities. Spe-
cifically, Ethiopia is struggling with a deeply path-depen-
dent tendency to promote organizational cultures that
inhibit innovation, particularly among public providers of
rural services. These organizations are often deeply hier-
archical, averse to change, focused on linear science, and
driven by unchanging sets of shared beliefs. These beliefs
and customs come in part from Ethiopia’s culture, history,
and politics, and likely began within the feudal system of
Imperial regime, were reinforced during the military Derg
regime (1974–91), and are likely present within much of in
the current regime (1991–present).
2
Public policies, programs, and investments in Ethiopia
are largely driven by one particular set of static shared
beliefs—that food security and food self-sufficiency are
largely synonymous, that the development and dissemina-
tion of new technologies to smallholders will generate the
yield and output increases needed to achieve food security
and reduce poverty, and that the innovation system’s pri-
mary function is to develop and disseminate these new
technologies. This belief eschews a more nuanced under-
standing of innovation systems and processes, and the need
for integration among heterogeneous actors to successfully
promote innovation. These beliefs also fail to recognize the
need for new, more creative approaches to strengthening
individual capacities of the research, education, and
extension systems; transforming organizational cultures
into cultures more responsive to the changing needs of the
agricultural sector; and forging links among smallholders,
extension agents, and actors in private industry and civil
society that comprise the wider innovation system.
Thus, the development of Ethiopia’s innovation system
faces several obvious challenges. The most critical chal-
lenges include (a) how to design and implement policies to
create and strengthen the formal organizations engaged in
the innovation process (universities, private firms, and
research organizations); (b) how to facilitate innovation
2
For a review of the literature on rural governance in Ethiopia, see
Dom and Mussa (2006a, b), Segers et al. (2008), Aalen (2002),
Pausewang et al. (2003), Vaughan and Tronvoll (2003), and Gebre-
Egziabher and Berhanu (2007).
D. J. Spielman et al.
123
among smallholders with support cooperatives, extension
services, and civil society actors; and (c) how to mediate
effectively between and among these actors. Studies on
Ethiopia’s general innovation system (UNCTAD 2002;
IKED 2006; Spielman et al. 2007), its agricultural research
system (Abate 2006), and its agricultural education and
extension systems (Kassa 2004a, b, 2005; Gebremedhin
et al. 2006; Davis et al. 2007) make these points quite
clearly.
Conceptually, these challenges inhibit the very kinds of
innovation being promoted by the government for Ethio-
pian agriculture, as demonstrated by theoretical and
empirical research on innovations systems presented ear-
lier. Practically, these challenges also indicate the need for
incentive mechanisms that promote greater cooperation
and coordination between different public organizations at
different levels (i.e., at the federal and regional levels) and
between public organizations and newer players in the
system (i.e., between public education, research, and
extension on the one hand, and private companies and civil
society organizations on the other).
Methods, sources, and data
Site and household selection
To examine these issues more closely, this study made use
of several methods, data, and data sources. Geographic
sites and households chosen for case studies of smallholder
innovation networks were drawn from the 2005 Ethiopia
Rural Smallholder Survey (ERSS). This section describes
the survey itself, and then defines the site and household
selection criteria. Finally, the section provides an overview
of the focus group and semi-structured interviews con-
ducted for this study.
Ethiopia rural smallholder survey
The ERSS was designed to collect data on the economic
activities and behaviors of smallholders, with emphasis on
efforts to improve rural welfare and income through
increased market interaction. The stratified sample used in
the survey comprised 7,186 households randomly drawn
from 293 enumeration areas (each roughly mapping to a
kebele)
3
from which 25 randomly drawn households were
surveyed. The ERSS sample is considered representative at
the national level as well as at the regional level for
Ethiopia’s four largest regions.
Geographic site selection
Using ERSS data, a set of 16 enumeration areas was ini-
tially identified based on evidence suggesting that multiple
households within each enumeration area were engaged in
what the research team identified as innovative agricultural
practices. These practices were associated with the adop-
tion of the following crop/technology packages: oilseed
(linseed, sesame, sunflower, canola, niger seed); apiculture
(primarily modern beehives); nontraditional beans (mainly
fasiola and haricot beans); potatoes (improved varieties);
and onions, garlic, and leeks. A total of 10 enumeration
areas were selected for further exploration based on criteria
designed to provide a heterogeneous sub-sampling of (a)
agroclimatic or agropotential regions, (b) one or more crop
or technology packages being used in a given site, (c)
administrative regions/regional states, and (d) physical
accessibility of the site (Table 1).
By design, these criteria do not generate a nationally-
representative subsample of the ERSS. More importantly,
these criteria bias the subsample toward those areas where
innovation of some type was observed or reported. This
means that these particular areas differ from what is
occurring in the majority of areas in Ethiopia in terms of
innovation. Specifically, farmers in these areas were cul-
tivating new crops, using new production technologies and
techniques, or capitalizing on new market opportunities to
sell surplus production. These innovative practices, whe-
ther pursued singly or jointly, represented an important
deviation from the norm in other ERSS sites, where
farmers were pursuing crop production, technology, and
marketing practices that were much more narrowly and
traditionally defined. Thus, the selected sites provided us
with a set of informative case studies that had much to say
about what was actually occurring—on the ground and
within local innovation systems—with potential signifi-
cance for national and regional policy that targets areas
where innovation is lagging.
Household selection
Households for further study were selected from each
enumeration area based on a rough index generated from
the ERSS data. The index was composed of equally
weighted values for (a) adoption of one or more of the
identified crop/technology packages, (b) adoption of one or
more complementary cultivation practices (e.g., innovative
water management techniques or use of improved seed), (c)
ownership of modern production assets (hand- or foot-
operated mechanical water pumps and motorized water
3
In Ethiopia, kebeles or peasant associations (PAs) are the smallest
administrative unit below the woreda (district) level. For purposes of
comparison, kebeles correspond to a cluster of villages in most other
sub-Saharan African countries.
Rural innovation systems and networks
123
pumps), and (d) contact with agricultural extension ser-
vices. The five households with the highest index scores
and the five households with the lowest index scores were
selected for separate focus group interviews and were
denoted (for convenience only) as innovators and non-
innovators, respectively. As shown in Table 2, these
groups differed in terms of education level and land
holding size, with innovators exhibiting higher mean val-
ues for both. This approach allowed the research team to
identify groups that, according to ERSS data, were using
agricultural practices different from those used by other
members in their community, thus offering potentially
valuable insights into the role of smallholder innovation
networks.
Focus group interviews and semi-structured interviews
In mid-2006, the research team conducted a total of 20
focus group interviews (two at each of the 10 sites, one
with innovators and one with non-innovators) composed of
five individuals each. Focus group interviews were con-
ducted using pretested participatory rural appraisal (PRA)
tools that focused on identifying sources of production
knowledge and information, inputs and materials, credit
and finance, and market links and price information. See
Spielman et al. (2008) for details. Following the focus
group interviews at each site, additional semi-structured
interviews were conducted with key actors identified by the
focus group participants. These interviews were used to
further validate information provided by the focus group
participants and included key informants in the immediate
locality of the site (e.g., development agents,
4
cooperative
managers, kebele officials, and leaders of community-
based organizations); and in the woreda (district), zonal, or
Table 1 Selected sites for in-depth study
Woreda (region) Crop/technology package Agro-ecological zone
a
Growth/development potential
b
Wemberma (Amhara) Apiculture/onions M1, M2 Medium potential, low risk
Janamora (Amhara) Oilseed/apiculture/potatoes M2 Medium potential, low risk
Hawzen (Tigray) Apiculture/oilseed SM2 Low potential, high risk
Hintalo (Tigray) Apiculture/onions SM2 Low potential, high risk
Ambo (Oromia) Oilseed/potatoes M2 Medium potential, low risk
Becho (Oromia) Beans/oilseed M2 Medium potential, low risk
Tikur Inchini (Oromia) Oilseed SH2, M2, H2 High potential, low risk
Kedida Gamela (SNNP)
c
Beans/potatoes SH2 Low potential, low risk
Badawacho (SNNP) Beans SH1 Low potential, low risk
Soro (SNNP) Oilseed/potatoes SH2 Low potential, low risk
a
M1 is hot-to-warm, moist lowlands; M2 is tepid-to-cool, moist midhighlands; SM2 is tepid-to-cool, submoist highlands; SH1 is hot-to-warm,
subhumid lowlands; SH2 is tepid-to-cool, subhumid midhighlands; and H2 is tepid-to-cool, humid midhighlands. Source: EIAR (Personal
Communication)
b
Source: World Bank (2004)
c
SNNP: Southern Nations, Nationalities, and Peoples regional state
Table 2 Social network analysis: descriptive statistics for focus group participants
Characteristics Innovators Non-innovators Group mean difference test (p value)
Number of observations 49 48
Mean group size 5 5
Female participants (%) 12 28
Mean age (years) 45 (12.8) 46 (16.9) 0.7757
Mean education (years) 3 (3.0) 1.8 (3.0) 0.0373**
Mean land size (hectares) 1.84 (1.6) 1.23 (0.9) 0.0283**
Participants who are household heads (%) 92 90
Participants from women-headed households (%) 4 4
Notes: Standard deviations given in parentheses
* Mean between innovators and non-innovators significantly different at confidence interval of 90%; ** 95%; *** 99%
4
Development agents are trained extension agents who are employed
by the regional bureaus of agriculture, managed by woreda-level
offices of these regional bureaus, and posted directly to the kebeles.
D. J. Spielman et al.
123
regional headquarters (e.g., Bureau of Agriculture and
Rural Development (BoARD) officers, managers of credit
and savings institutions, traders, brokers, staff at NGOs,
and others). Interviews were guided by questions similar to
those posed to PRA participants. Data gathered from the
PRA and semi-structured interviews were then used to
conduct social network analysis of each site, as discussed
in the following section.
Social network analysis: methods
Social network analysis (SNA) is a useful, but relatively
underutilized tool designed for the study of innovation
network data such as that gathered from the interviews
described above. As is the case with the innovation systems
approach, SNA has been around for some time but only
recently has been applied to developing country agricul-
ture. SNA was developed by sociologists and further
enhanced as an analytical technique by the fields of
mathematics and statistics. The rapid growth and spread of
SNA into fields beyond sociology and mathematics was
due to the development of better SNA tools, including
powerful software applications. Thus it is only now being
applied to developing country agriculture. To date there is
no case in the scientific literature of this method being used
together with the innovation systems approach. However,
we have found SNA useful in understanding and mapping
innovation systems because of its analytical focus on
relationships and interactions between people and groups,
and its ability to capture knowledge flows and other attri-
butes contained within such interactions.
SNA allows for the study of relationships among mul-
tiple and diverse actors by providing tools with which to
visualize, measure, and analyze the relationships (Borgatti
2006). In the context of innovation, SNA provides an
understanding of how actors interact, how information and
resources move between and among them, and how actors’
roles and relationships are structured. Data for SNA are
commonly based on measurements of relationships
between actors and sets of actors, in addition to the attri-
butes of individual actors. Because SNA is a relatively new
application in this type of research, we describe it here in
some detail (Table 3). For further details on the method-
ology, see Borgatti (1998), Hanneman and Riddle (2005),
and Scott (2000).
In SNA, each actor in a network—whether an individ-
ual, organization, or some other entity of interest—is
termed a ‘‘node.’’The actor of interest within a network is
known as the ‘‘ego.’’ Links between nodes, termed ‘‘ties,’’
denote some form of interaction between nodes. In a tie
linking an ego to another node, the other node is referred to
as an ‘‘alter.’’ Ties can be analyzed with respect to their
strength, frequency, distance, or other such measures
depending on the focus of inquiry. Ties also reflect the key
unit of analysis in SNA—the ‘‘dyad,’’ or a pair of nodes.
Dyads may be composed of direct ties between nodes, or
indirect connections that pass through a series of inter-
connected nodes, termed ‘‘walks.’’ Dyadic attributes can
include the nature of social or economic relationships
captured by the dyad, the characteristics of interactions in
the dyad, or the ways in which information or resources
flow in the dyad. Each network has a size—determined by
the total number of nodes—and a boundary—a natural
delineation between actors and relationships or an artificial
limit set by the researcher.
Data for SNA can be collected through any number of
conventional data collection tools, including household
questionnaires, focus group interviews, and key-informant
interviews. Data for the study of unimodal networks—for
example, smallholder innovation networks—are compiled
in a square (n 9 n) matrix of n actors (nodes) in which
matrix element n
ij
[ 0 denotes the presence of a tie
between actors i and j, while n
ij
= 0 denotes the absence of
a tie.
5
A simple nondirectional tie between two nodes is
represented as n
ij
= n
ji
= 1 in the matrix. A directional
tie—denoting, for example a flow of funds from node i to j
but not from j to i—is represented as n
ij
= 1 but n
ji
= 0.
Directed ties in a network graph are indicated by arrows,
and an undirected graph shows only the lines between
nodes. A valued tie in which matrix elements assume
values in the set of real numbers ða
ij
2<Þcan add further
information to the analysis, with values assigned to each
characteristic of the tie—for example, strength, frequency,
or distance Several useful measures drawn from these
relational data are discussed here. Network density (D), for
example, measures the number of nodes that are actually
tied to other nodes in the network and is expressed as a
proportion of all the possible ties in a network or
D ¼
k
NðN kÞ=2
ð1Þ
where denotes the total number of lines (ties) present and N
is the number of nodes in the network.
Degree centrality (C
d
) measures the number of ties that a
node has relative to the total number of ties existing in the
network as a whole, or
C
d
ðn
i
Þ¼k
i
ðn
i
Þ=ðN 1Þð2Þ
where n
i
denotes the ith node in the network, k
i
(n
i
) denotes
the number of ties to n
i
, and N - 1 represents the size of
the network less the node of interest.
5
SNA data can also be used to study bimodal networks in which
nodes are tied by affiliations (e.g., memberships of actors in different
types of associations) and are compiled in nonsquare (n 9 m)
matrixes in which matrix element a
ij
denotes actor i’s tie with
association j.
Rural innovation systems and networks
123
Closeness centrality (C
c
) measures the reciprocal of the
geodesic distance (the shortest path connecting two nodes)
of node n
i
to all other nodes in the network, or
C
C
ðn
i
Þ
1
¼
X
N
i¼1
dðn
i
; n
j
Þð3Þ
where d(n
i
, n
j
) denotes the number of ties in the geodesic
paths linking n
i
and n
j
.
A ‘‘clique’’ denotes the maximum number of nodes that
have all possible ties present among themselves (Fig. 1).
‘‘Coreness,’’ a related indicator, measures the degree of
closeness of each node to the network core, wherein the
network core is defined as a cohesive subgroup of nodes in
which the nodes are connected in some maximal sense.
Network cores are a function of network structure, meaning
that identification of a core is easier in some networks (e.g.,
in a hub-and-spoke configuration) than in others (e.g., in a
network with evenly disbursed ties or multiple cliques).
Whether a node is a member of a network core is
determined as follows. Each node is assigned a coreness
score based on how close it is to the network’s maximally
connected subgroup. The coreness score is normalized so
Table 3 Social network
analysis elements
Source: Authors; Borgatti
(1997, 1998), Davies (2004),
Hanneman and Riddle (2005)
Element Definition
Node Any individual, organization, or other entity of interest
Ego Actor of interest within a network
Alter Node directly connected to an ego
Ego network Network that only shows direct ties to the ego and not between alters
Dyad Pair of nodes linked by a tie
Walk A series of interconnected nodes
Path Walk where each node and line is only used once
Geodesic distance Shortest path connecting two nodes
Network Graphical representation of relationships that displays points to represent nodes
and lines to represent ties; also referred to as a graph
Network boundary Natural delineation between actors and relationships, or artificial limit set by a
researcher
Network size Total number of nodes in a network
Network centralization Degree to which a network revolves around a single node
Network density Nodes that are actually tied as a proportion of all possible ties in a network
Centrality Measure of the number of ties that a node has relative to the total number of ties
existing in the network as a whole; centrality measures include degree,
closeness, and betweenness
Degree Number of ties a node has to other nodes
Closeness Measure of reciprocal of the geodesic distance (the shortest path connecting two
nodes) of node to all other nodes in the network
Betweenness Number of times a node occurs along a geodesic path
Cliques Maximum number of nodes that have all possible ties present among themselves
Core Cohesive subgroup within a network in which the nodes are connected in some
maximal sense
Periphery Nodes that are only loosely connected to the core and have minimal or no ties
among themselves
Coreness Degree of closeness to the network core of each node
Structural hole Weak connection area between two or more densely connected subgroups in a
network, measured by either effective size or redundancy
Effective size Network size of an ego minus the average degree centrality of its alters
Redundancy Average degree centrality of an ego’s alters, not counting their ties to the ego
Core
Periphery
Periphery
Cli
q
ue
Fig. 1 Illustrations of coreness and cliques. Source: Adapted from
Borgatti (1997, 2006)
D. J. Spielman et al.
123
that the sum of squares is equal to 1. Concentration mea-
sures are then obtained by testing the model for different
sizes of the core. This is done by first placing only the node
with the highest coreness score in the core and all other
nodes in the periphery. The model continues testing for
different sizes of the core, from 1 to N. For each different
size of the core, concentration scores are given for each
node along with a correlation score that correlates the given
coreness scores with the ideal scores of 1 for every core
node and 0 for every peripheral node. A core size of x
nodes that generates the highest correlation score identifies
the core membership. Core members are identified as those
with the highest coreness scores.
Structural holes denote weak connection areas between
two or more densely connected subgroups in a network
(Fig. 2). A test for the existence of structural holes mea-
sures the network’s effective size, or the number of ties
between an ego and all its alters minus the average number
of ties that each alter has to other alters (i.e., ego network
size minus redundancy in the network). The larger the
effective size of the network, the more chances an ego has
to act as a broker between two unconnected alters. A
broker is the middle node of a directed triad. It may occur,
for instance, when in a triad (N = 3) of nodes n
1
, n
2
, and
n
3
, n
1
has a tie to n
2
, and n
2
has a tie to n
3
, but n
1
has no tie
to n
3
. In other words, there is a lack of ties among an ego’s
alters (Borgatti 1997).
Therefore, a node’s ‘‘brokerage position’’ is the number
of nodes not directly connected to it. If a broker in a net-
work with a relatively high effective size is removed from
the network, a large number of other nodes also become
separated from the network. Note that unlike coreness,
there is no particular value against which to determine
whether structural holes exist or whether a node is a broker;
certain network structures may indicate the possible exis-
tence of structural holes, and certain nodes may have an
effective size that indicates the possibility of greater
chances to act as brokers.
Centrality, coreness, and the presence of cliques or
structural holes have important consequences for network
members. Power is relational, and the network structure
can affect power relations and can offer opportunities and
constraints (Hanneman 2001). For instance, an actor with
high closeness centrality will be closely connected to many
actors, and thus be in a position to receive information or
other resources from the network. One’s location in the
network can offer opportunities and impose constraints.
Actors with high centrality have a greater variety of choice,
since they are connected to a large number of other actors.
Other actors, who are cut off from parts of the network due to
structural holes, or because they mustgo througha broker, will
have fewer opportunities and choices than those who are
highly connected. Structural holes can be risky; if the actor
connecting two parts of the network pulled out, there would be
a disconnect between two parts of the network.
Despite the useful set of indicators described above,
SNA has several weaknesses. First, ‘‘complete’’ SNA data
sets, where researchers examine every tie between actors,
are extremely large. The resources needed to create a
complete SNA dataset can be prohibitive, and data sets can
become too large to be handled by conventional data
management tools. Second, the primary emphasis placed
on the ties between actors tends to be limiting. By focusing
only on ties, important data on the attributes of individual
actors is sometimes overlooked. Finally, SNA is criticized
for its weak theoretical grounding. The importance placed
on mathematical relationships tends to overshadow the
efforts to develop and test hypotheses about the underlying
nature of these interactions.
SNA’s application to developing-country agriculture
Several studies that use SNA to examine smallholder
innovation systems and processes illustrate the tool’s value.
Raini et al. (2006) used SNA as a tool to detect disparities in
information flows among Kenyan smallholders, agro-
chemical firms, nongovernmental organizations, govern-
mental agencies, international development agencies, and
universities in the development and application of inte-
grated pest management (IPM) techniques to tomato culti-
vation. Within the social relations underlying the networks
they studied, the researchers found significant differences
that influenced the interaction behavior among IPM users.
Similarly, Clark (2006) used SNA to study the intro-
duction of information and communication technologies in
supply chains for chilies, coffee, and peaches in Bolivia.
The study identified key actors, information flows,
and supply chain bottlenecks, and recommends ways of
improving supply chain efficiency and market access for
Structural hole
Fig. 2 An illustration of a structural hole. Source: Adapted from
Moody (2004)
Rural innovation systems and networks
123
network actors. In conjunction with that study, Douthwaite
et al. (2006) used SNA to develop an interactive tool for
use with farmer groups in Colombia to improve members’
understanding of the importance of network relationships
and to strengthen their capacity to manage their networks
more effectively. Conley and Udry (2001) used SNA to
map networks of 450 individuals in four clusters of villages
in eastern Ghana to demonstrate how farmers’ social
learning processes were based on communications con-
ducted through social networks that were not determined
by geographic proximity. Similarly, Giuliani and Bell
(2005) used SNA to examine clusters of wine producers in
Chile to show that knowledge flows and connections,
instead of being influenced by geographic proximity, were
influenced by firm-level absorptive capabilities (measured
in terms of human resources, experience, and experimen-
tation) such that information tended to flow through a core
group of firms with advanced absorptive capabilities and a
similar knowledge base. Hoang et al. (2006) used SNA to
study the influence of ethnicity, gender, socioeconomic
status, and power relations in rice farming communities in
northern Vietnam; the influence of social networks on
access to information; and the benefits of agricultural
research. Darr and Pretzsch (2006) applied SNA to the
study of smallholder networks within agroforestry projects.
Their study, based on an analysis of data from four sample
sites in rural Ethiopia and Kenya composed of approxi-
mately 200 households each, revealed that group cohe-
siveness, group activity, and member motivation were
positively related to technology adoption, in addition to
persuasive interventions from the public extension system.
However, questions remain as to whether an increasing
diversity of actors within an innovation system has an
impact on the capacity of smallholders and rural commu-
nities to beneficially participate in innovation processes,
and how such changes can be leveraged to promote pro-
poor processes of rural innovation. This question can be
partly answered by examining innovation network hetero-
geneity and integration, or the relationships among differ-
ent types of actors that form the core and periphery
structures of a network. More specifically, by examining
such measurable indicators as the coreness, centrality, and
tie strength associated with a group of similar actors (for
example, public sector institutions or private market
agents), we can better understand the extent to which a
given network is heterogeneous in its composition or
integrated in its structure. This allows us to interpret the
extent to which these characteristics contribute to, or
detract from, innovativeness within smallholder networks.
Thus, this study provides a descriptive analysis of the
inherent characteristics, measurable relationships, and
implications of the relationships among actors within
smallholder innovation networks.
Results
Overview of findings
The first set of findings is drawn from the focus group
interviews and semi-structured interviews conducted in the
10 enumeration areas. These are examined in the context of
three specific enumeration areas using SNA in the sub-
sequent sections, but are generally applicable across all 10
areas.
First, findings suggest that smallholder innovation pro-
cesses combine a diversity of public, private, and civil
society organizations, the extent of which is illustrated in
Fig. 3. The ties in this figure indicate interactions in rela-
tion to the exchange of production knowledge and infor-
mation, inputs and materials, credit and finance, or market
links and price information. Necessarily, this is not a
nationally representative figure in any sense, nor is it an
empirical illustration of any one site at any one time.
Rather, it is a synthesis of key actors and interactions
present in smallholder innovation systems based on the
combined findings from this study. The point is to char-
acterize the entire range of possibilities in an innovation
system based on composite data before breaking it down
into site-specific networks.
Second, findings show that public service providers play
what might be termed the most prominent role in small-
holder innovation processes, at least within the localities
and communities examined here. BoARDs and their
development agents, woreda and kebele administrations,
government-backed credit and savings institutions, and
farmer cooperatives—all public, quasi-public, or state-
supported rural service providers—are closely linked with
smallholders, with each other, and with the process of
promoting and financing the use of information and tech-
nology. This finding is not surprising in itself, but the
magnitude and consistency with which these service pro-
viders are linked into smallholder networks draws attention
to their role. Simply stated, extension and related public
services are compelling forces in rural Ethiopia. This has
important implications for how innovation networks can
interact.
Third, findings suggest that although these actors are key
providers of information, inputs, and credit related to
improving smallholder output and productivity, their role is
far less evident with respect to developing marketing links
or transmitting price information to smallholders. Of
course, this finding is again limited to the localities and
communities examined here, but nonetheless consistent
with findings from several studies cited earlier. Fourth,
findings show that within these localities and communities,
private sector actors—market traders, brokers, money-
lenders, and private companies—were also somewhat
D. J. Spielman et al.
123
peripheral to smallholder innovation networks. In the case
study sites where market actors actively operated, their ties
to smallholders, public sector service providers, and civil
society organizations were typically weak or nonexistent.
Thus the government is unlikely to adequately meet its goal
of commercializing smallholder productions while market
actors remain peripheral to networks.
Finally, findings suggest that in those case study sites
where civil society organizations operated, their ties to
these same actors were relatively stronger. This finding
applies to various organizations, including local and
international nongovernmental organizations (NGOs),
NGOs more closely associated with the government of
Ethiopia, and community-based organizations established
under the auspices of NGO activities. Moreover, NGOs
were often tied not only to local public sector service
providers but also to a range of other actors beyond the
immediate locality, such as research institutes and univer-
sities. While many of the government actors were central to
the network core and form cliques and have high centrality,
these NGOs had far-reaching ties that are much more likely
to bring new information and opportunities to innovation
networks. This is consistent with the notion of ‘‘the
strength of weak ties’’ described by Granovetter (1973).
Findings from these 10 cases demonstrated that differ-
ences exist among smallholder innovation networks, both
within and between communities, with respect to such
elements as network size, network density, and distance
from different nodes and with respect to the influence that
these networks have on smallholder innovation. Thus, we
dig deeper into these findings with several site-specific
cases in the subsections that follow. The three cases below
were chosen for illustrative purposes only, that is, because
they capture different types of smallholder innovation
systems with different SNA attributes.
Wemberma: the importance of being core
The woreda of Wemberma is a highland district in the
Amhara region where surpluses of maize and wheat are
grown. Wemberma illustrates how innovation processes in
the woreda combine technological changes (adoption of
improved seed-fertilizer packages for maize and diversifi-
cation into new crops/technologies such as onions and
apiculture) with organizational changes (close strategic
coordination among public service providers of inputs and
credit) and institutional changes (individual marketing of
crop surpluses through local market actors and collective
marketing through cooperatives). Wemberma also illus-
trates how smallholders (both innovators and non-innova-
tors) depend on a small number of key nodes for
production inputs, credit, and information—namely, the
local BoARD, the local cooperative and the Amhara Credit
and Savings Institution (CSI), as shown in Fig. 4. These
three institutions, along with the kebele administration,
operate as a closely tied network for the smallholder:
access to inputs from the BoARD requires access to credit
from the cooperative or CSI, which in turn depends on a
Fig. 3 Hypothetical
innovators’ social network.
Source: Authors. Note: ARC:
Agricultural Research Center;
BoARD: Bureau of Agriculture
and Rural Development; CBO:
community-based organization;
CSI: credit and savings
institution; DA: development
agent; ESE: Ethiopian Seed
Enterprise; Iquob: rotating
savings and credit association;
Kebele: kebele administration;
MFI: microfinance institution;
NGO-G: government-associated
NGO; NGO-I: international
NGO; NGO-L: Local NGO;
RSO: Religious or social
organization
Rural innovation systems and networks
123
referral from the kebele administration. At the same time,
smallholders in Wemberma depend on an even smaller
number of key nodes for market information and links—
nodes that are almost entirely delinked from the produc-
tion-related network.
One way of representing this phenomenon is to examine
the network structure in Wemberma in terms of cliques. An
analysis of subgroups within the Wemberma network
shows that three cliques exist, each with a minimum net-
work size of four, and each revolving around the provision
of key agricultural inputs (seed, fertilizer, and credit) with
some degree of redundancy:
1. Smallholders, BoARD, development agent, coopera-
tive, and kebele administration.
2. Smallholders, BoARD, CSI, and kebele administration.
3. BoARD, CSI, government-associated NGO, and keb-
ele administration.
The BoARD and kebele administration are the closest
actors to each other in the sense that they share member-
ship in all three cliques. However, market-related actors
(traders, brokers, and their associations) do not share any
membership with these actors, indicating that market actors
are relatively unconnected to other network actors.
Another way of representing this phenomenon is with an
analysis of coreness in the network. Since core membership
(described earlier) is identified by the core size of x nodes
that generates the highest correlation score, Table 4 pro-
vides measures of those nodes that belong to the network
core in Wemberma. Here, smallholders, the kebele
administration, and the BoARD (all of which are nodes
found in the network’s three cliques) are closest to the
network core, followed by cooperatives, the CSI, and the
development agent. This implies that core membership in
the network is satisfied by the presence of six nodes (all of
which are denoted by asterisks). Interestingly, all of these
actors are public sector organizations, implying that mar-
ket-related actors can be viewed as peripheral to the net-
work. In numeric terms, these market-related actors are
represented by low coreness scores ranging from 0.006 to
0.213, while the public sector organizations are represented
by higher coreness scores ranging from 0.306 to 0.419.
Implicitly, we may conclude that smallholder innovative-
ness in this network is driven by public sector organiza-
tions rather than private market actors.
Another finding from Wemberma is that innovation
networks vary within communities. Closer examination of
networks associated with the two focus groups studied in
Wemberma reveals important differences (Fig. 5, panels a
and b). First, innovators have more ties to a larger number
of actors than non-innovators. Because these ties are not
inter-connected themselves, this makes the innovators
networks larger but less dense than non-innovators. This is
denoted in Table 4 with an ego density score for the
innovator’s network of 35.71 (out of a possible 100)
compared to 66.67 for the non-innovator’s network. Sec-
ond, innovators’ networks are more centralized and closer,
denoting greater proximity (shorter walks) to other actors.
Fig. 4 Map of Wemberma
woreda’s innovation network.
Source Authors. Note The size
of each node is determined by
the node’s degree centrality, or
the number of ties that the node
has relative to the total number
of ties in the network as a
whole. BoARD: Bureau of
Agriculture and Rural
Development; CSI: credit and
savings institution; DA:
development agent; Iquob:
rotating savings and credit
association; Kebele: kebele
administration; NGO-G:
government-associated NGO
D. J. Spielman et al.
123
This is denoted in Table 4 as higher scores for network
centralization, Freeman’s normalized closeness, and nor-
malized degree centrality for the innovators’ networks. This
suggests that innovators have greater access to sources of
knowledge/information, inputs/materials, credit/finance,
and market links/price information, and that access gives
them a potentially greater number of livelihood options and
opportunities than possessed by non-innovators. Third, non-
innovators have fewer ties to traditional or informal insti-
tutions (such as iquob—funeral groups—or local money-
lenders) compared with innovators, as shown in Fig. 5.This
suggests that non-innovators have less access to informal
sources of credit, finance, and risk management.
The implications of the findings to smallholder inno-
vation networks in Wemberma are (a) public service pro-
viders are key nodes with respect to the provision of
production information and resources, (b) market actors are
largely peripheral, and (c) within-community variations
exist in terms of the structure and role of innovation net-
works. In a surplus output woreda such as Wemberma,
those findings suggest that the network may be insuffi-
ciently configured to provide smallholders with ties to the
marketing side: neither market links nor price information
are transmitted through the subnetwork of public service
providers to any significant extent, and the subnetwork of
private market actors is relatively disconnected from other
actors relevant to smallholders. As a result, smallholders
operate with little access to market-related information.
The core—periphery structure suggested by a marketing
network that is largely separated from the tightly linked
production network can potentially constrain the ability
of smallholders to innovate effectively—to change their
on-farm practices and strategies—in response to changes in
the market.
Soro: a case of diversified networking in action
The Soro woreda in Southern Nations, Nationalities, and
People’s Region (SNNPR) is a major enset (false banana,
or Ensete ventricosum) growing region. Food staple crops
such as wheat, teff, and maize are also cultivated in the
woreda. In recent years, Soro’s BoARD has introduced
several improved varieties of these cereals, along with
higher-value crops such as oilseed and potatoes, and new
water-harvesting techniques.
Findings from Soro indicate that its innovation network
is more diverse than that of Wemberma in terms of the
number and types of actors, with public service providers
playing a less central role in the network (Fig. 6). Soro’s
network includes additional research-oriented actors
(ARC1, ARC2 and ARC3) and NGOs which add hetero-
geneity in a manner that is not present in Wemberma. And
Table 4 Key network
measures, Wemberma
*Network core and clique
members
a
Highest possible value for
each measure is given in
parentheses
Note: BoARD: Bureau of
Agriculture and Rural
Development; CSI: credit and
savings institution; DA:
development agent; Iquob:
rotating savings and credit
association; Kebele: kebele
administration
Actor Normalized
coreness
score
Possible
core size
Correlation
score
Smallholders* 0.468 1 0.441
Kebele* 0.419 2 0.586
BoARD* 0.419 3 0.723
Cooperative* 0.361 4 0.802
CSI* 0.319 5 0.853
DA* 0.306 6 0.910
Traders 0.213 7 0.899
Government-associated
NGO
0.201 8 0.896
Moneylenders 0.078 9 0.798
Iquob 0.078 10 0.712
Cooperative union 0.060 11 0.616
Brokers 0.037 12 0.495
Traders association 0.036 13 0.362
Brokers association 0.006 – –
Measure
a
Innovators Non-innovators
Ego network size (no. of nodes) 8 (13) 6 (11)
Ego density 35.71 (100) 66.67 (100)
Network centralization (%) 39.74 (100) 23.64 (100)
Freeman’s normalized closeness centrality 68.42 (100) 64.71 (100)
Normalized degree centrality 61.54 (100) 54.55 (100)
Rural innovation systems and networks
123
although smallholders in Soro still depend on the BoARD
for access to production information and inputs, it is the
local and international NGOs and market-related actors
who are particularly active in this network. Interestingly,
this may apply to both innovators and non-innovators, both
of whom exhibit similar social network characteristics in
terms of their scores for ego density, network centraliza-
tion, Freeman’s normalized closeness, and normalized
degree centrality given in Table 5.
One way of illustrating these differences using SNA is
to examine how key nodes form ‘‘bridges’’ between core
Fig. 5 Ego network of
innovators (panel a) and non-
innovators (panel b),
Wemberma. Source: Authors.
Note: The size of each node is
determined by the node’s degree
centrality, or the number of ties
that the node has relative to the
total number of ties in the
network as a whole. BoARD:
Bureau of Agriculture and Rural
Development; CSI: credit and
savings institution; DA:
development agent; Iquob:
rotating savings and credit
association; Kebele: kebele
administration
Fig. 6 A map of Soro woreda’s innovation network. Source Authors.
Note Ties indicate relationships between nodes. Node size is
calculated based on degree centrality. ARC-1/ARC-2/ARC-3: three
agricultural research centers active in the Soro network; BoARD-
Region: Regional Bureau of Agriculture and Rural Development;
BoARD-Zone: Zonal Bureau of Agriculture and Rural Development;
CBO: community-based organization; DA: development agent;
Kebele: kebele administration; NGO-G: government-associated
NGO; NGO-I: international NGO; NGO-L: local NGO
Table 5 Structural holes and brokerage measures in Soro woreda
network
Actor Effective
size
Broker
measure
BoARD-Zone 9.00 40
NGO-I (World Vision) 7.89 31
Cooperative union 5.17 12
Smallholders 5.00 12
NGO-L 1.68 1
DA 1.68 1
Cooperative 1.00 0
Kebele 1.00 0
Safety net 1.00 0
NGO-I 1.00 0
NGO-I 1.00 0
NGO-L 1.00 0
ESE 1.00 0
BoARD-Region 1.00 0
Private company 1.00 0
ARC-1/ARC-2/ARC-3 1.00 0
Traders 1.00 0
Measure
a
Innovators Non-innovators
Ego network size
(no. of nodes)
5 (10) 5 (9)
Ego density 20.00 (100) 20.00 (100)
Network centralization (%) 45.10 (100) 39.54 (100)
Freeman’s normalized
closeness centrality
48.64 (100) 51.54 (100)
Normalized degree centrality 27.78 (100) 27.78 (100)
a
Highest possible value for each measure is given in parentheses
Note: ARC-1/ARC-2/ARC-3: three agricultural research centers
active in the Soro network; BoARD-Region: Regional Bureau of
Agriculture and Rural Development; BoARD-Zone: Zonal Bureau of
Agriculture and Rural Development; CBO: community-based orga-
nization; DA: development agent; Kebele: kebele administration;
NGO-G: government-associated NGO; NGO-I: international NGO;
NGO-L: local NGO
D. J. Spielman et al.
123
network actors and more peripheral actors. In Soro, the
bridges include an international NGO (World Vision), the
BoARD, and the cooperative union. Without those bridges,
the Soro network would break into separate networks. This
implies that structural holes exist within the network,
where single nodes lie along the only walks between one
part of the network and another. Implicitly, this means that
information and resources from peripheral actors (e.g.,
regional agricultural research centers) must pass through
these bridges to reach smallholders.
Measurement of the network’s effective size (described
earlier) provides a test for the existence of structural holes
in a network. In the case of Soro, World Vision and the
BoARD both have a relatively high number of ties com-
pared with their alters (see Table 5). This indicates high
effective sizes of their ego networks, suggesting that their
locations are structural holes in the network. If World
Vision and the BoARD were removed from the network,
numerous other actors would also be lost. Another way of
testing this is to examine brokerage measures for these
bridging actors. The zonal BoARD, World Vision (NGO-
I), the cooperative union, and smallholders show relatively
high brokerage scores, implying that they play a relatively
larger role in connecting other nodes compared with other
actors (Table 5).
The Soro case illustrates how a heterogeneous network
provides smallholders with a greater diversity of options in
accessing information, inputs, credit, or other resources,
and how certain actors play critical bridging functions in
making those options available to smallholders. Soro also
illustrates how networks may be characterized not only by
a greater variety and number of actors but also by more
integration—that is, fewer separate subnetworks and less of
a core-periphery structure. This heterogeneity potentially
translates into a greater number of livelihood options and
opportunities for smallholders in Soro, whereas integration
can bring about greater stability in the network. And while
context specificity makes it difficult to justify comparisons
between sites (or to generalize to Ethiopia more generally),
the Soro case suggests that heterogeneous and integrated
networks, all else being equal, provide farmers with greater
livelihood options.
Ambo: a case of both strong and weak ties
Ambo is a highland woreda in Oromia Region west of
Addis Ababa, where teff is grown as the main crop,
alongside improved varieties of wheat, barley, maize, lin-
seed, and potatoes. The woreda’s innovation network is
relatively large and diverse compared to other woredas
covered in the study, which could be due in part to its
relative proximity to Addis Ababa, Ethiopia’s commercial
center. In addition to the usual public service providers, the
network includes local and international NGOs, agricul-
tural research centers, a private company, and several
banks operating within the woreda. This case offers an
opportunity to examine how valued SNA data can be used
to describe the strength of ties among network actors and
with respect to the transmission of specific types of infor-
mation or resources. Data gathered from the PRA exercise
described earlier provide values for tie strength as follows:
1 = not so important, 2 = somewhat important, and
3 = very important.
With respect to the provision of production knowledge
and information, innovators in Ambo view their ties with
the Oromia Credit and Savings Share Company (CSI) as
stronger than other ties relating to the same services (see
Fig. 7, panels a and b). The importance placed on the role
of the Oromia CSI in Ambo is, according to feedback from
smallholders interviewed for this study, a result of the share
company’s intensive engagement in the woreda. The
company does more than disburse loans for purchasing
oxen, seed, and fertilizer; fattening livestock; renting land
for commercial cultivation; or engaging in petty trade. It
also operates a training program to educate farmers on the
company’s various savings and loan programs, and on how
to use loans effectively (e.g., how to engage in profitable
livestock fattening). In short, the company provides both
financial and training services in Ambo.
This finding is an interesting contrast to the observation
at other sites that few smallholders, when asked about their
key sources of production knowledge/information, consider
their local credit and savings institutions as important as
the BoARD or other public service providers. Thus, by
examining individual actors’ perceptions of the strength of
ties, the study of the Ambo woreda shows that the roles
played by actors in a network can vary. The provision of
information and resources from farmers need not follow a
set pattern that is consistent from site to site. Rather, dif-
ferent actors can play different, possibly overlapping or
complementary, roles that may nonetheless contribute to
increasing the number of livelihood options and opportu-
nities for smallholders.
Conclusions
This study asks whether changes in Ethiopia’s agricultural
sector are improving the ability of smallholders to inno-
vate, and thus improve their own welfare. The study pre-
sents an analysis of smallholder innovation networks in
rural Ethiopia. Using tools drawn from social network
analysis (SNA) within an innovation systems conceptual
framework, it examines how various types of networks
relate to the innovation practices of smallholders in case
studies conducted in 10 localities across Ethiopia. Findings
Rural innovation systems and networks
123
offer several insights with respect to development theory
and methodology, on the one hand, and development pol-
icy and practice, on the other.
From a theoretical and methodological perspective, the
study demonstrates the potential contribution of the inno-
vation systems approach to understanding how innovation
occurs in developing-country agriculture, and how small-
holder innovation networks are central to these systems.
The study adds to this by demonstrating the utility of social
network analysis in analyzing the relations between inno-
vation system actors, visually, mathematically, and through
descriptive analysis.
By applying SNA within an innovation systems
approach, this study has provided some novel perspectives
and tools for future investigations of rural innovation,
knowledge flows, and access to resources. SNA concepts
such as centrality, brokerage, coreness, and cliques provide
unique insights into rural innovation networks are critical
to such investigations. This study also illustrates the need
for new tools that allow researchers to (a) compare dif-
ferent network architectures and (b) test hypotheses relat-
ing to the relationships between different architectures
and their impacts on innovation. While the present study
does not fully address this topic, it raises obvious questions
that could open the door for further methodological
development.
From a policy perspective, the study reveals the central
role played by interconnected public organizations in
Ethiopia’s smallholder innovation system, and the periph-
eral role played by market and civil society actors, at least
in the localities and communities examined in the 10
enumeration sites. While these observations are not
nationally representative, they do suggest the need for
further study on the role of non-state actors in the agri-
cultural sector, particularly in light of the government’s
strategic emphasis on smallholder commercialization as a
means of enhancing productivity and reducing poverty.
In summary, SNA provides useful insights into the
inherent characteristics, measurable indicators, and impli-
cations of possible means to enhance smallholder innova-
tion networks in Ethiopia. This form of analysis may also
offer insights that can be useful in developing policies to
strengthen smallholder innovation processes in Ethiopia,
and to develop a more dynamic and competitive agricul-
tural sector in the country.
References
Aalen, L. 2002. Ethnic federalism in a dominant party state: The
Ethiopian experience, 1991–2000. Report R2002:2. Bergen,
Norway: Chr. Michelsen Institute.
Abate, T. 2006. Successes with value chains. Addis Ababa, Ethiopia:
Ethiopian Institute of Agricultural Research.
Arocena, R., and J. Sutz. 2002. Innovations systems and developing
countries. DRUID Working Paper No. 02–05. A
¨
alborg, Den-
mark: Danish Research Unit for Industrial Dynamics, A
¨
alborg
University.
Balzat, M., and H. Hanusch. 2004. Recent trends in the research on
national innovation systems. Journal of Evolutionary Economics
14: 197–210.
Bandeira, O., and I. Rasul. 2006. Social networks and technology
adoption in northern Mozambique. Economic Journal 116: 869–
902.
Besley, T., and A. Case. 1994. Diffusion as a learning process:
Evidence from HYV cotton. RPDS Discussion Paper No. 174.
Princeton, NJ: Princeton University.
Fig. 7 Smallholder ego
network for knowledge and
information (panel a) and inputs
and materials (panel b), Ambo.
Source Authors. Note Line
thickness denotes the strength of
tie on a scale of 1–3. BoARD:
Bureau of Agriculture and Rural
Development; CSI: credit and
savings institution; Kebele:
kebele administration
D. J. Spielman et al.
123
Biggs, S. D. 1989. A multiple source of innovation model of
agricultural research and technology promotion. Agricultural
Administration (Research and Extension) Network Paper. Lon-
don: Overseas Development Institute.
Biggs, S.D., and E.J. Clay. 1981. Sources of innovations in
agricultural technology. World Development 9(4): 321–336.
Borgatti, S. 1997. Structural holes: Unpacking Burt’s redundancy
measures. Connections 20(1): 35–38.
Borgatti, S. 1998. Social network analysis instructional web site.
http://www.analytictech.com/networks/. Accessed 1 Mar 2006.
Borgatti, S. 2006. Social network analysis: Overview of the field
today. http://www.analytictech.com/mb874/Slides/Overview.pdf.
Accessed 14 May 2006.
Clark, N. 2002. Innovation systems, institutional change and the new
knowledge market: Implications for third world agricultural
development. Economics of Innovation and New Technology
11(4–5): 353–368.
Clark, L. 2006. Building farmers’ capacities for networking (Part II):
Strengthening agricultural supply chains in Bolivia using
network analysis. KM4D Journal 2(2): 19–32.
Clark, N., A. Hall, R. Sulaiman, and G. Naik. 2003. Research as
capacity building: The case of an NGO facilitated post-harvest
innovation system for the Himalayan hills. World Development
31(11): 1845–1863.
Conley, T., and C. Udry. 2001. Social learning through networks: The
adoption of new agricultural technologies in Ghana. American
Journal of Agricultural Economics 83(3): 668–673.
Darr, D., and J. Pretzsch. 2006. The spread of innovations within
formal and informal farmers groups: Evidence from rural
communities of semi-arid Eastern Africa. Paper presented at the
Tropentag 2006 Conference on International Agricultural
Research for Development University of Bonn, Germany.
Darr, D., and J. Pretzsch. 2008. Mechanisms of innovation diffusion
under information abundance and information scarcity. On the
contribution of social networks in groups vs. individual exten-
sion approaches in semi-arid Kenya. Journal of Agricultural
Education and Extension 14(3): 231–248.
Davies, R. 2004. Scale, complexity and the representation of theories
of change: Part II. Evaluation 11(2): 133–149.
Davis, K.D., J. Ekboir, W. Mekasha, C.M.O. Ochieng, D.J. Spielman,
and E. Zerfu. 2007. Strengthening agricultural education and
training in Sub-Saharan Africa from an innovation systems
perspective: Case studies of Ethiopia and Mozambique. Wash-
ington, DC: World Bank.
Dercon, S., and R.V. Hill. 2009. Growth from agriculture in Ethiopia:
Identifying key constraints. Paper prepared as part of a study on
Agriculture and Growth in Ethiopia. (Department for Interna-
tional Development, London).
Diao, X., and A.N. Pratt. 2006. Growth options and poverty reduction
in Ethiopia. An economy-wide model analysis. Food Policy 32:
205–228.
Dom, C., and M. Mussa. 2006a. Review of implementation of the
decentralisation policy: A sample survey in four sentinel
weredas of sigray region. Oxford, UK: Mokoro, Ltd.
Dom, C., and M. Mussa. 2006b. Review of implementation of the
decentralisation policy: A sample survey in six weredas of
Amhara Region. Oxford: Mokoro, Ltd.
Dosi, G., C. Freeman, R. Nelson, G. Silverberg, and L. Soete, eds.
1988. Technical change and economic theory. London: Pinter.
Douthwaite, B., A. Carvajal, S. Alvarez, E. Claros, and L.A.
Hernandez. 2006. Building farmers’ capacities for networking
(Part I): Strengthening rural groups in Colombia through
network analysis. KM4D Journal 2(2): 4–18.
Ekboir, J., and G. Parellada. 2002. Public-private interactions and
technology policy in innovation processes for zero tillage in
Argentina. In Agricultural research policy in an era of privat-
ization
, ed. D. Byerlee, and R. Echeverrı
´
a. Oxon, UK: CABI.
Edquist, C., ed. 1997. Systems of innovation approaches: Technol-
ogies, institutions and organizations. London: Pinter.
Foster, A.D., and M.R. Rosenzweig. 1995. Learning by doing and
learning from others: Human capital and technical change in
agriculture. Journal of Political Economy 103(6): 1176–1209.
Freeman, C. 1987. Technology policy and economic performance:
Lessons from Japan. London: Pinter.
Freeman, C. 1988. Japan: A new national system of innovation. In
Technical change and economic theory, ed. G. Dosi, C. Freeman,
R. Nelson, G. Silverberg, and L. Soete. London: Pinter.
Freeman, C. 1995. The national innovation systems in historical
perspective. Cambridge Journal of Economics 19(1): 5–24.
Gebre-Egziabher, T., and K. Berhanu. 2007. A literature review of
decentralization in Ethiopia. In Decentralization in Ethiopia, ed.
T. Assefa, and T. Gebre-Egziabher, 9–68. Addis Ababa: Forum
for Social Studies.
Gebremedhin, B., D. Hoekstra, and A. Tegegne. 2006. Commercialization
of Ethiopian agriculture: Extension service from input supplier to
knowledge broker and facilitator. Improving Productivity and Market
Success (IPMS) of Ethiopian Farmers Project Working Paper No. 1.
Nairobi, Kenya: International Livestock Research Institute.
Giuliani, E., and M. Bell. 2005. The micro-determinants of meso-
level learning and innovation: Evidence from a Chilean wine
cluster. Research Policy 34: 47–68.
Granovetter, M. 1973. The strength of weak ties. American Journal of
Sociology 78(6): 1360–1380.
Hall, A.J., and N.G. Clark. 1995. Coping with change, complexity and
diversity in agriculture: The case of Rhizobium inoculants in
Thailand. World Development 23(9): 1601–1614.
Hall, A., M.V.K. Sivamohan, N.G. Clark, S. Taylor, and G. Bockett.
1998. Institutional developments in Indian agricultural research
systems: Emerging patterns of public and private sector activ-
ities. Science Technology and Development 16(3): 51–76.
Hall, A., R. Sulaiman, N. Clark, M.V.K. Sivamohan, and
B. Yoganand. 2002. Public-private sector interaction in the Indian
agricultural research system: An innovation systems perspective
on institutional reform. In Agricultural research policy in an era of
privatization, ed. D. Byerlee, and R. Echeverrı
´
a, 155–176. Oxon,
UK: CABI.
Hall, A., R. Sulaiman, N. Clark, and B. Yoganand. 2003. From
measuring impact to learning institutional lessons: An innovation
systems perspective on improving the management of interna-
tional agricultural research. Agricultural Systems 78: 213–241.
Hanneman, R.A. 2001. Introduction to social network methods.
Riverside, CA: University of California—Riverside.
Hanneman, R.A., and M. Riddle. 2005. Introduction to social network
methods. Riverside, CA: University of California. http://faculty.
ucr.edu/*hanneman/. Accessed 14 May 2006.
Hoang, L.A., J.-C. Castella, and P. Novosad. 2006. Social networks
and information access: Implications for agricultural extension
in a rice farming community in northern Vietnam. Agriculture
and Human Values 23: 513–527.
International Organisation for Knowledge Economy and Enterprise
Development (IKED). 2006. Ethiopia: Innovation and growth in
international comparison. Paper prepared for the Triple Helix
Conference on Transforming University-Industry-Government
Relations in Ethiopia Addis Ababa, May 20–24.
Johnson, B., and O. Segura-Bonilla. 2001. Innovation systems and
developing countries: Experiences from the SUDESCA Project.
DRUID Working Paper No. 01–12. A
¨
alborg, Denmark: Danish
Research Unit for Industrial Dynamics, University of A
¨
alborg.
Kassa, B. 2004a. Postgraduate training in agricultural sciences in
Ethiopia. Higher Education Policy 17: 49–70.
Rural innovation systems and networks
123
Kassa, B. 2004b. Linkages of higher education with agricultural
research, extension and development. Unpublished manuscript,
Alemaya University, Ethiopia.
Kassa, H. 2005. Historical development and current challenges of
agricultural extension with particular emphasis on Ethiopia.
EEA/EEPRI Working Paper No. 2/05. Addis Ababa: Ethiopian
Economic Association/Ethiopian Economic Policy Research
Institute.
Lundvall, B. 1985. Product innovation and user-producer interaction.
A
¨
alborg, Denmark: A
¨
alborg University Press.
Lundvall, B. 1988. Japan: A new national system of innovation. In
Technical change and economic theory, ed. G. Dosi, C. Freeman,
R. Nelson, G. Silverberg, and L. Soete. London: Pinter.
Lundvall, B. 1992. National systems of innovation. London: Pinter.
Malerba, F. 2002. Sectoral systems of innovation and production.
Research Policy 31: 247–264.
Malerba, F. 2005. Sectoral systems. How and why innovation differs
across sectors. In The Oxford handbook of innovation, ed.
J. Fagerberg, D.C. Mowery, and R.R. Nelson. New York: Oxford
University Press.
Ministry of Finance and Economic Development (MoFED), Federal
Democratic Republic of Ethiopia. 2002. Ethiopia: Sustainable
development and poverty reduction program. Addis Ababa,
Ethiopia.
Ministry of Finance and Economic Development (MoFED), Federal
Democratic Republic of Ethiopia. 2005. Ethiopia: Building on
progress: A plan for accelerated and sustained development to
end poverty. Addis Ababa: MoFED.
Moody, J. 2004. The structure of a social science collaboration
network: Disciplinary cohesion from 1963 to 1999. American
Sociological Review 69(2): 213–238.
Mowery, D.C., and B.N. Sampat. 2005. Universities in national
innovations systems. In The Oxford handbook of innovation, ed.
J. Fagerberg, D.C. Mowery, and R.R. Nelson. New York: Oxford
University Press.
Munshi, K. 2004. Social learning in a heterogeneous population:
Social learning in the Indian green revolution. Journal of
Development Economics 73: 185–213.
Nelson, R.R. 1988. National systems of innovation: Institutions
supporting technical change in the United States. In Technical
change and economic theory, ed. G. Dosi, C. Freeman,
R. Nelson, G. Silverberg, and L. Soete. London: Pinter.
Nelson, R.R. ed. 1993. National innovation systems: A comparative
analysis. Oxford, UK: Oxford University Press.
North, D.C. 1990. Institutions, institutional change and economic
performance. Cambridge, UK: Cambridge University Press.
Organization for Economic Cooperation and Development (OECD).
1999. Managing national innovation systems. Paris: OECD.
Pausewang, S., K. Tronvoll, and L. Aalen, eds. 2003. Ethiopia since
the Derg: A decade of Democratic pretension and performance.
London: Zed Books.
Raini, R. K., C. P. Zebitz, and V. Hoffmann. 2006. Social network
analysis as an analytic tool with in integrated pest management
(IPM) stakeholders’ practices. Paper presented at the workshop
on Resources, Livelihood Management, Reforms, and Processes
of Structural Change, Gobabe, Namibia September 18–23.
Rycroft, R.W., and D.E. Kash. 1999. The complexity challenge:
Technological innovation for the 21st century. New York: Cassell.
Segers, K., J. Dessein, S. Hagberg, P. Develtere, M. Haile,and J. Deckers.
2008. Be like bees. The politics of mobilizing farmers for
development in Tigray, Ethiopia. African Affairs 108(430): 91–109.
Scott, J. 2000. Social network analysis: A handbook, 2nd ed. London:
Sage.
Spielman, D.J. 2006. A critique of innovation systems perspectives on
agricultural research in developing countries. Innovation Strat-
egy Today 2(1): 25–38.
Spielman, D. J., K. E. Davis, M. Negash, and G. Ayele. 2008. Rural
innovation systems and networks: Findings from a study of Ethiopian
smallholders. IFPRI discussion paper No. 759. Washington, DC:
IFPRI.
Spielman, D. J., M. Negash, K. Davis, and G. Ayele. 2007.
Agricultural innovation in Ethiopia: A systems overview of
opportunities and constraints.InReversing rural poverty in
Ethiopia: Dilemmas and critical issues. Proceedings of the 9th
annual conference of the Agricultural Economics Society of
Ethiopia (AESA), eds. E. Wale, S. Regassa, D. Gebre-Michael,
and B. Emana. Addis Ababa, Ethiopia: AESE.
Taffesse, A.S. 2008. Decomposition of growth in cereal production in
Ethiopia. Mimeo. Ethiopia: Addis Ababa.
United Nations Conference on Trade and Development (UNCTAD).
2002. Investment and innovation policy review: Ethiopia.
UNCTAD/ITE/IPC/Misc.4. Geneva: United Nations.
Vaughan, S., and K. Tronvoll. 2003. The culture of power in
contemporary Ethiopian political life. Swedish International
Development Cooperation Agency (SIDA) Studies No. 10.
Stockholm: SIDA.
World Bank. 2004. Four Ethiopias: A regional characterization
assessing Ethiopia’s growth potential and development obsta-
cles. Washington, DC: World Bank.
World Bank. 2005. Ethiopia. Well-being and poverty in Ethiopia: The
role of agriculture and agency. Washington, DC: World Bank.
World Bank. 2007. Enhancing agricultural innovation: How to go
beyond the strengthening of research systems. Washington, DC:
World Bank.
Author Biographies
David J. Spielman is a research fellow with the International Food
Policy Research Institute, and is based in Addis Ababa, Ethiopia.
Kristin Davis is a research fellow with the International Food Policy
Research Institute, and is based in Zurich, Switzerland.
Martha Negash was a research officer with the International Food
Policy Research Institute in Addis Ababa, and is currently a PhD
candidate at the Katholieke University of Leuven, Belgium.
Gezahegn Ayele is a senior researcher with the Ethiopian Develop-
ment Research Institute in Addis Ababa, Ethiopia.
D. J. Spielman et al.
123
