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Trade, value chains and
food security
P. Montalbano, S. Nenci and L. Salvatici
The State of Agricultural
Commodity Markets
2015-16
Background paper
Trade, value chains and food security
Pierluigi Montalbano, Silvia Nenci, Luca Salvatici
Background paper prepared for
The State of Agricultural Commodity Markets 2015–16
Food and Agriculture Organization of the United Nations
Rome, 2015
The designations employed and the presentation of material in this information product do not
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© FAO, 2015
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ii
Contents
Acknowledgements........................................................................................................................... iv
Executive summary ............................................................................................................................ v
1. Introduction ...................................................................................................................................1
2. What are global value chains? ........................................................................................................1
3. The empirical evidence ...................................................................................................................4
4. GVC analysis based on trade in value added ....................................................................................9
4.1. Indicators ........................................................................................................................................ 11
4.1.1. Participation in GVCs ................................................................................................................... 12
4.1.2. Position in GVCs ........................................................................................................................... 14
4.2. Trade costs magnification ............................................................................................................... 16
5. GVCs analysis based on household panel data............................................................................... 17
5.1. The case of Ugandan maize: data and stylized facts ...................................................................... 17
5.2. The effects of farm households position in the Ugandan maize value chain ................................. 20
6. Conclusions .................................................................................................................................. 22
References....................................................................................................................................... 23
iii
Acknowledgements
This document was commissioned as a background paper for the preparation of the 2015–16 edition
of FAO’s flagship report The State of Agricultural Commodity Markets. It was prepared by Pierluigi
Montalbano, Associate Professor of International Economic Policy at La Sapienza University of Rome,
Rome, Italy ; Silvia Nenci, Assistant Professor in Economics at Roma Tre University, Rome, Italy; and
Luca Salvatici, Associate Professor of Economic Policy at Roma Tre University, Rome, Italy. Its
preparation was guided by a terms of reference prepared by FAO. The paper benefited from
comments by participants at a series of meetings held by FAO in the first half of 2015 to provide
input into the drafting of the report.
iv
Executive summary
A new literature has recently emerged aimed at describing the competitiveness of a country and its
industries by looking specifically at production of value added, as well as level of integration into,
global value chains (GVCs). The emergence of GVCs and new trade patterns suggests revision of
strategies aimed at fostering competitiveness and of trade and development policies that can
directly or indirectly affect food security (FS).
This paper aims at presenting possible directions of research for investigating the relationship
between trade and food security by taking into account the role of production fragmentation and
the degree of participation of farmers in the different stages of the GVCs. To this end, it first
introduces the issue of global value chains by providing definitions and characteristics, with a specific
focus on the agrifood industry. A brief review of the state of art of empirical analysis on the issue
and a synopsis of recent GVC case studies are also provided. Case studies on GVCs based on detailed
micro data for a single product provide detailed examples of the multiple stages involved in moving
a product from the farm gate to the consumer and of the discrepancy between gross and valueadded trade.
Successively, the paper presents two lines of research: the first at the macro level, deals with the
emerging literature on tracing the value added of countries’ trade flows. In recent years there have
been several initiatives and efforts that have tried to address the measurement of trade flows in the
context of the fragmentation of world production, tracing the trade value added by combining inputoutput tables with bilateral trade statistics and proposing new indicators (such as the GVC
participation and position indicators). The interpretation of these indicators and results for individual
countries in the temporal, geographic and industry dimensions is still a work in progress and poses
new challenges to scholars and policy experts. However, even if the available data are not very
detailed, they enable the domestic content exports to be distinguished from foreign content exports
for individual countries as well as for where the countries are located in the value chain.
The second line of research, at the micro level, makes use of the new household panel data, with a
strong focus on agriculture and rural development. The use of household surveys could make it
possible to assess the implications of participating at different stages of the value chain through the
comparison of food security levels for households characterized by similar traits but with different
involvement in the chain. The case of Ugandan maize is presented as a first attempt to analyse the
relationship between farmers’ food security and their level of participation and position in the
agrifood value chain.
The paper concludes by emphasizing how the materialization of GVCs and the change in the trade
paradigm require a new set of policies able to attenuate or overcome the current domestic
constraints and allow small and medium-sized producers to become more competitive and
participate in national and international value chains in a sustainable manner. Accompanying
policies may help to maximize the expected benefits and minimize the risks stemming from value
chain participation. The design of these policies would greatly benefit from the availability of
relevant indicators at a detailed level for the agriculture and food sectors.
v
1. Introduction
The increasing international fragmentation of production that has occurred during recent decades
challenged the conventional wisdom about how trade is viewed and interpreted. This new
organization of production and the emergence of complex chains have determined a reconfiguration
of world trade in terms of participants and comparative advantages. A new literature has recently
emerged aimed at describing the competitiveness of a country and its industries by looking
specifically at their production of value added, as well as their level of integration into global value
chains (GVCs) (Gereffi and Fernandez-Stark, 2011; Miroudot and Ragousssis, 2009; Koopman et al.,
2011, 2014; Stehrer, 2013; Timmer et al., 2014; Baldwin, 2012; Baldwin and Evenett, 2012). The
emergence of GVCs and new trade patterns suggests the revision of strategies aimed at fostering
competitiveness and trade and development policies at large (Cattaneo and Miroudot, 2013).
The aim of this paper is to present possible directions of research for investigating the relationship
between trade and food security by taking into account the role of production fragmentation and
the degree of farmer participation in the different stages of the GVCs. To this end, the topic is
introduced, with a specific focus on the agrifood industry (Section 2), and a synthetic picture of the
state of the art of the empirical analysis (Section 3) is provided. Then, after noting that the bulk of
the empirical evidence is currently based on narratives and case studies, focus is on two lines of
research: the first, at the macro level, deals with the emerging literature on tracing the value added
of countries’ trade flows (Section 4); and the second, at the micro level, makes use of the new
household panel data with a strong focus on agriculture and rural development (Section 5). Finally
some policy implications are presented in the conclusions (Section 6).
2.
What are global value chains?
A value chain is defined as the full range of activities that is required to bring a product or service
from conception, through the different phases of production, delivery to final customers, and final
disposal after use. In the context of food production, these activities include farm production, trade
and support to get food commodities to the end-consumer (Kaplinsky and Morris, 2002).
The value chain analysis extends traditional supply chain analysis by identifying values at each stage
of the chain. It is called a value chain because at each stage of the supply chain, value is being added
to the product or service as it is being transformed. Value chains are labelled as global when they
spread across different geographical locations (Gereffi and Fernandez-Stark, 2011). At each stage,
the value added equals the value paid to the factors of production in the exporting country. For
example, the stages of the fruit and vegetable GVC are represented in Figure 1. It is interesting to
look under the surface of Figure 1. Due to the perishable nature of the product, a high degree of
coordination across the involved actors is required along the chain. Production is organized in small,
medium and large farms that supply exporter companies and large producer exporter companies
that own their farms.
Typically, value chains feature two types of bargaining power relationships: buyer-driven and
producer-driven. In the buyer-driven, producers have few options for selling their goods or services.
These chains typically have low barriers to entry at the producer level. In the developing countries,
buyer-driven value chains are often characteristic of labour-intensive industries like agriculture,
clothing and furniture (Webber and Labaste, 2010). Producer-driven value chains are often
characterized by knowledge intensity, relatively higher levels of technology or skills, scarcity, high
levels of marketing or capital-intensive activities. These chains usually have barriers to entry, often
requiring high research and development expenditures, or costly marketing. They are present in
agricultural sectors when standards, product differentiation, packaging and logistics are important,
or when research and development are critical. Examples from the agricultural sector include:
bananas produced by multinationals, organic products like cotton, branded products like processed
and packaged agricultural products, quality-differentiated products like specialty coffees, or highvalue processed products like essential oils (Webber and Labaste, 2010). A key concept in GVCs is
upgrading. Currently, many developing countries perform activities and stages of production
previously carried out in the developed world.
Figure 1: Fruit and vegetable GVC stages
Source: Fernandez-Stark et al. (2011), Figure 2, page 7.
GVCs have become an important topic in the analysis of relationships among competitiveness, trade,
growth, and development. On the one hand, the participation in GVCs allows greater
competitiveness, better inclusion in trade and investment flows, access to new types of production,
upgrading towards higher value added activities and socio-economic upgrading. All these effects,
direct and indirect, are due to increasing employment, better remunerated jobs, better use of
resources, and better governance and political stability. Through participation in GVCs, countries do
not need to develop vertically integrated industries to participate in global trade. They can just
develop capacities in specific segments (stages of production, tasks or business functions) of the
value chain. Consequently, even small countries with limited capacities across the value chain have a
chance to export (Cattaneo et al., 2013).
However, notwithstanding the above-mentioned positive aspects, to get access, involvement and
participation in a GVC is not an easy task. Small producers in particular face distinct difficulties
entering value chains. Strict standards must be met to gain access to GVCs (Reardon et al., 2009; van
2
der Meer, 2006). Some analysts are particularly critical towards this aspect (see below). According to
them, “national and global lead firms now dictate how products are cultivated, harvested,
transported, processed and stored through a series of public and private standards that producers
must satisfy in order to maintain their access to markets” (Bamber and Fernandez-Stark, 2014).
There is also concern that the structural changes may lead to the exclusion of smallholder and family
farmers from contract-farming schemes and hence from supplying to value chains. As pointed out by
Key and Runsten (1999) contract-farming schemes may be biased towards larger farms because of
smaller transaction costs in buying larger quantities from few suppliers. Furthermore, in many cases,
to make value chains function, farm assistance programmes are required to help small producers
overcome constraints in low-income countries with limited access to capital and technology (Dries
and Swinnen, 2004, 2010; Maertens and Swinnen, 2009a; Minten et al., 2009).
GVC analysis is not limited to manufacturing industries but it is also applied to services and
agriculture (i.e. the agrifood industry). The agrifood system is undergoing rapid processes of
modernization, with important implications for poor countries. The increasing importance of global
agricultural trade registered during the past three decades comes with changes in the way GVCs are
organized, with increasing levels of vertical coordination, upgrading of the supply base and increased
importance of large multinational food companies (McCullough et al., 2008, Swinnen and Maertens,
2007). These changes have important implications for developing countries. Increased demand for
high-value products and increasing food prices create opportunities for developing countries to
realize economic growth through expanding and diversifying their agricultural exports (Aksoy and
Beghin, 2005; Anderson and Martin, 2005).
GVCs play a crucial role in productivity growth and food security through direct and indirect effects.
The development of high-value agrifood chains can be an important opportunity to increase rural
incomes, reduce rural poverty and foster pro-poor growth. One of the most important ways through
which rural farm households in developing countries can benefit from agrifood exports and the
increased value in export sectors is through participating in contract-farming with exporters or
overseas buyers (Swinnen, 2014). Contracts for quality production with local suppliers in developing
countries not only specify conditions for delivery and production processes but also include the
provision of inputs, credit, technology, management advice, etc. (Minten et al., 2009). In this
perspective, standards can affect the organization and structure of supply chains in different ways.
On the one hand, they reduce transaction costs in the chain by reducing information asymmetries
between buyers and suppliers (specifically, about quality, safety and other product characteristics).
On the other hand, they increase fixed production costs and transaction costs related to conformity
assessment that can lead to economies of scale and advantages for larger suppliers (Beghin et al.,
2015).
3
3.
The empirical evidence
From the methodological point of view, most of the studies on agricultural value chains are case
studies, taking place in low- and middle-income countries. To analyse these chains, scholars use
mainly qualitative background information or data from surveys specifically designed for the studies
while quantitative assessments are mostly based on cross-data. In several cases, chain analyses are
merely a description of the sequence of stages. Even if this is useful for mapping actors and
processes, it may not be sufficient to guide the actions of agencies, donors and international
institutions involved with policy-making (Webber and Labaste, 2010).
Vertical coordination mechanisms and consolidation at the buyer end of export chains amplify the
bargaining power of large agro-industrial firms and food multinationals, displace decision-making
authority from the farmers to these companies, and strengthen the capacity of these companies to
extract rents from the chain to the disadvantage of contracted smallholder suppliers in the chains
(Warning and Key, 2002). Furthermore, in many developing countries other obstacles add to
resource constraints and menace competitiveness, such as weak regulatory institutions, poorly
designed and implemented sanitary and phytosanitary regulations, inadequate transportation,
power and water infrastructure, and the absence of important value chain actors (Hazell et al., 2010;
Markelova et al., 2009). Consequently, small and medium-sized producers are generally not well
positioned to respond to changes in market structures and are thus marginalized (Dolan and
Humphrey, 2004; Lee et al., 2012; Maertens and Swinnen, 2009a). Some of the most discussed
cases, namely the fruit and vegetable export sectors in Kenya and Senegal, are characterized by large
shifts from smallholder to large-scale farming or, in the case of the Senegal tomato export sector,
completely based on exporter-owned agro-industrial production (Maertens et al., 2012). Similar
shifts, although mostly partial, are observed in other regions and countries, such as Latin America,
other African countries and the Russian Federation (Beghin et al., 2015).
Other studies document that smallholders continue to be included in modern value chains. For
instance, Fafchamps et al.(2005), using survey data from three African countries, Benin, Madagascar,
and Malawi, find that, contrary to expectations, there is little evidence that returns to scale exist in
agricultural trade in African agricultural markets and hence little scope for vertical integration. As a
consequence, there is no efficiency reason why the presence of small agricultural traders should be
discouraged by GVCs. This is the case of the horticulture sector in Africa and the horticulture sector
and animal production in Asia (Minten et al., 2009, for Madagascar; Henson et al., 2005, for
Zimbabwe; Handschuch et al., 2013, for Chile; Kersting and Wollni, 2012, for Thailand; and Wang et
al., 2009, for China).
The most recent empirical studies document mostly positive effects on food security (Minten et al.,
2009) and household and farm income, once farmers are included in contract schemes and highvalue export chains (Minten et al., 2009, and Subervie and Vagneron, 2013, for Madagascar;
Handschuch et al., 2013, for Chile; Asfaw et al., 2009, for Kenya). The welfare of small producers
who are included in high standard value chains typically improves (see Maertens and Swinnen
2009b for the horticultural export chain in Senegal; Rao and Qaim 2011, and Rao et al., 2012, for
the vegetable farmers in high-standard supermarket channels in Kenya; and Minten et al., 2009, for
the vegetable export production in Madagascar).
A large and growing body of literature looks at the relationship between standards and GVCs. The
empirical evidence shows that the effects of standards on the supplier base of the value chains are
sector, country and standard specific (Dolan and Humphrey, 2000; Gibbon, 2003; Jaffee and
Masakure, 2005; Henson and Jaffee, 2008; Reardon et al., 2009; Miyata et al., 2009; Minten et al.,
2009; Swinnen and Vandemoortele, 2009, 2011; Henson and Humphrey, 2010; Swinnen and
4
Vandeplas, 2011; Rao et al., 2012): in several industries and countries, standards induce the
increased importance of large-scale and vertically-integrated production, but in other countries and
industries small farms remain dominant. Several empirical studies analyse specifically if increasing
standards in international markets may exclude smallholders and family farms from value chains
(Dolan and Humphrey, 2000; Gibbon, 2003; Jaffee and Masakure, 2005; Maertens and Swinnen,
2009b; Ouma, 2010; Reardon and Berdegué, 2002; Berdegué et al., 2005; Weatherspoon and
Reardon, 2003; Unnevehr, 2000; Supervie and Vagneron, 2013; Belton et al., 2011; Dries et al.,
2009).
These studies highlight that small farmers might be unable to comply with stringent requirements
due to a lack of technical and financial capacity (Reardon et al., 2001), which may induce traders and
processing firms to reduce sourcing from small suppliers. Also, transaction costs for monitoring
compliance with standards might be very high in the case of sourcing from smallholders (Swinnen,
2014). These analysts underline how such requirements can represent significant barriers to market
access (Lee et al., 2012), which make them prohibitive for many small and medium-sized producers.
It has been often argued that the gains from high-standards agricultural trade are captured by
foreign investors, large food companies and developing country elites (Dolan and Humphrey, 2000).
Other empirical studies show that with the diffusion of standards, the share of export produced by
small farmers tends to decrease (Gibbon, 2003, Dolan and Humphrey, 2000, for Kenya; Minot and
Ngigi, 2004, and Unnevehr, 2000, for Cote d’Ivoire; Maertens and Swinnen, 2009b, Maertens et al.,
2011, for Senegal; Schuster and Maertens, 2013, for Peru).
In order to analyse and summarize the main characteristics of GVCs, specifically those of the
agrifood sector, which is directly related to the food security issue, we provide a synopsis of recent
GVC case studies available in the literature based on some key common aspects (see Table 1).
5
Table 1: GVC case studies: a synopsis
Country
Madagasc
ar
Senegal (3
regions)
Ethiopia
SubSaharan
Africa
(SSA)
(Senegal,
Madagasc
ar, Côte
d’Ivoire,
Ghana,
Kenya,
Zambia,
Zimbabwe
Sector/
product
Horticultur
e
Horticultur
e (fruit
and
vegetable)
Smallholders
yes (100%
contract)
yes (mainly agroindustrial
employees)
Trade
Exports
Exports
Biofuel
(castor)
yes (100%
contract)
Horticultur
e (general
cases)
low participation
of smallholder
producers in
papaya chain in
Ghana,
banana chain in
Côte d’Ivoire,
tomato chain in
Senegal and
horticulture
export
chains in
Zimbabwe
Exports
Vegetable
s in
Madagasc
100%
procurement
from smallholder
Highvalue
exports
Industry
structure
Standards/Quality
requirements
Monopoly
yes (quality of the
product, i.e. length of
the beans, colour, etc.,
ethical standards, i.e.
no use of child labour
employment practices,
and hygiene
instructions in the
processing plant)
Competiti
on
public standards:
common marketing
standards, sanitary and
phytosanitary
measures, hygiene
rules based on HACCP
control mechanisms,
traceability standards.
Private standards
Vertical
coordination
(contracting)
small farmers’ microcontracts
Other vertical
coordination*
Data
Impacts
Source
farm
assistance and
supervision
programmes
specific household survey
level data (questions on
demographic situation of
the household, land
assets, nature of the
contract, relationships
with the firm, control and
supervision practices of
the firm, benefits and
disadvantages of working
with contracts, perceived
effects on welfare, and
level of inputs and output)
on the contracted plots
higher welfare, more
income stability and
shorter lean periods
Minten,
Randrianaris
on and
Swinnen,
2009
unique dataset compiled
from data collection at
different levels (existing
data sources, qualitative
interviews, quantitative
and structured interviews,
large farm-households
survey)
positive effects on rural
incomes (income effect
is larger for contract
farmers than for agroindustrial employees)
Maertens
and
Swinnen,
2009
specific household survey
level data
improvement of food
security for
participating farmers
(specifically in terms of
access to food)
Negash and
Swinnen,
2013
country survey data +
qualitative information
welfare effects (income
mobility and rural
poverty reduction)
country survey data +
qualitative information
positive welfare effects
for rural households
through both product-
large and
smallholder
contract-based
farming
contracts to castor
producers
Exports
Monopoly
in
Madagasc
mainly contractfarming
arrangements;
individual contracts
with exporters in
some cases (bean
sector in Senegal,
vegetable sector in
Madagascar and fruit
and vegetable sector
in Kenya); very little
vertical coordination in
vegetable sector in
Ghana
complete contracting
with smallholders in
Madagascar; own
6
input supply
programmes
in
Maertens,
Minten and
Swinnen,
2012
ar and the
bean and
the
tomato
sectors in
Senegal
(specific
cases)
farmers in
Madagascar;
50% in the
Senegalese bean
sector; and 0% in
the Senegalese
tomato sector
Vegetable
s
yes
Viet Nam
and
Banglades
h
Pangasius
catfish
yes (farm
owners come
predominantly
from upper
income brackets)
Costa Rica,
Guatemala
, El
Salvador,
Honduras,
and
Nicaragua
fresh fruits
and
vegetables
low participation
of smallholder
producers
Kenya
Kenya
Chile
fresh
vegetables
raspberry
sector
low participation
of smallholder
producers
yes
(particul
arly to
EU)
ar;
competitio
n in the
Senegales
e bean
sector;
monopoly
in the
Senegales
e tomato
export
sector
Exports
to EU
Competiti
on
Exports
Exports
to UK
exports
(mainly
to EU
and US)
Competiti
on
Competiti
on
vertically integrated
estate production for
largest exporters of
the Senegalese bean
sector; complete
vertical integration
in the Senegalese
tomato export sector
Madagascar;
inputs, credit
and
management
assistance in
the
Senegalese
bean sector
yes (the global Good
Agricultural PracticesGAP)
farm-level survey data
yes (certification
standards)
in Viet Nam a
producers affiliated
to processing
companies ensure
greater levels of
vertical coordination
yes (quality and safety
standards)
new kind of
procurement system
yes (product and
process requirements
by UK supermarkets)
more closely
integrated
(supermarkets
directly connected to
larger importer; the
distribution system
originally based on
spot markets has
been replaced by
tightly integrated
supply chains)
yes (food quality and
safety standards)
contract system
7
descriptive cases
credit and
technical
trainings are
provided by
governmental
institutions
and labour-market
channels (productmarket effects have a
higher impact on
household income);
positive impact of
vegetable contractfarming in Madagascar
on farmers’ income and
food-security from a
combination of direct
and indirect - i.e.,
technological and
managerial spill-over
effects.
positive results on
export and domestic
farmer revenue from
adoption of standards
certification is likely to
result in greater
differentiation and
polarisation between
larger and smaller farm
operators (exclusion of
small farms from
important markets).
Asfaw,
Mithofer
and Waibel,
2009
Belton,
Mahfujul
Haque, Little
and Sinh,
2011
descriptive cases
exclusion of undercapitalized small
growers.
Berdegué,
Balsevich,
Flores and
Reardon,
2005
descriptive cases
concentration of
production and
processing in the hands
of a few large firms
Dolan and
Humphrey,
2004
household survey data
a positive effect on the
quality performance
and net raspberry
income of farmers, once
they overcome the
barriers and implement
a food standard
Handschuch,
Wollni and
Villalobos,
2013
Zimbabwe
horticultur
e
Kenya
fresh
vegetables
yes
exports
to UK
exports
yes (food quality and
safety standards)
contract system
timely
payments,
provision of
advice and
assistance to
producers
descriptive cases
contract system
Source: Authors’ elaboration.
* input supply programmes, trade credit, investment assistance, bank loan guarantees, technology and management advise
8
small-scale producers
integrated successfully
into a supply chain
Henson,
Masakure
and Boselie,
2005
Jaffee and
Masakure,
2005
4. GVC analysis based on trade in value added
In recent years there have been several initiatives to address the measurement of trade flows in the
context of the fragmentation of world production, tracing the trade value added by combining inputoutput tables with bilateral trade statistics and proposing new indicators: see, for instance, the work
done by the World Trade Organization (WTO) and the Organization for Economic Cooperation and
Development (OECD) (http://www.oecd.org/sti/ind/measuringtradeinvalue-addedanoecdwtojointinitiative.htm). The value added reflects the value that is added by industries in producing
goods and services. It is equivalent to the difference between industry output and the sum of its
intermediate inputs. Looking at trade from a value-added perspective better reveals how upstream
domestic industries contribute to exports as well as how much, and how, firms participate in global
value chains (Montalbano, Nenci and Pietrobelli, 2015b).
The interpretation of these indicators and results for individual countries in the temporal, geographic
and industry dimensions is still a work in progress and poses new challenges for scholars and policy
experts, although the data are generally not sufficiently detailed to trace detailed value chains
(Nenci, 2014; Sturgeon, 2015). However, at a more aggregate level, they enable the domestic
content exports to be distinguished from the foreign content exports for individual countries, as well
as where the countries are located in the value chain (e.g. whether they are upstream, providing
inputs such as chemicals or farm equipment or downstream, processing or marketing the
production).
Koopman et al. (2014) have proposed a unified and transparent mathematical framework to break
down completely gross exports into the various components, including exports of value added,
domestic value added that returns home, foreign value added, and other additional double-counted
terms. Measures of vertical specialization and trade in value added in the existing literature can be
derived from this framework and expressed as some linear combinations of these components. In
order to decompose gross exports, inter-country input output (ICIO) tables have to include three
main elements: data distinguishing intermediate and final flows at the industry level within and
between each country; the value added in production of each industry in all countries; the gross
output of each industry in all countries. Such an ICIO table goes beyond a collection of single country
input output tables. It specifies the origin and destination of all transaction flows by industry as well
as every intermediate or final use for all such flows. To compute these detailed interindustry and
intercountry intermediate flows, there is need for (i) separation gross bilateral trade flows at the
sector level into intermediate, consumption and investment goods, and (ii) allocation of intermediate
goods from a particular country source to each sector in all destination countries in which it is used
(Koopman et al., 2014).
9
Box 1: Available trade in value added databases
Database
TiVA
Years
1995, 2000 2005,
2008, 2009
Sectors (primary)
18 (agriculture, food)
Regions
58
Import matrices*
a) proportional
b) homogeneous
c) proportional
Indicators
39
WIOD
1995–20011
59 products produced
(agriculture, forestry,
fishing, food) and used by
35 industries (agriculture,
food)
41
a) Imputation
b) homogeneous
c) proportional
-
GTAP (version
9)
2011
57 (20)
129
a) proportional
b) homogeneous
c)proportional
-
* A) Types of imports (intermediate, final) by sector
B) Import use by final destination (domestic, export)
C) Source by importing sector
OECD-WTO TiVA (http://oe.cd/tiva)
The TiVA database separates gross trade into components of value added either directly in exports, domestic
value added that is embodied in exports of intermediate goods that eventually return to the home country and
value added by foreign country exports capturing interdependencies. In the TiVA database, jointly developed
by the OECD and the WTO, the agriculture sector also includes hunting, forestry and fisheries. The food sector
is also only a single industry and includes beverages and tobacco.
The exercise starts with individual country input-output tables, combines them with bilateral trade data that
identify source-destination and use them to build Inter-Country Input-Output (ICIO) tables. The current TiVA
version provides 39 indicators for 58 countries (34 OECD countries and other 23 economies including
Argentina, Brazil, China, India, Indonesia, the Russian Federation and South Africa, and a residual “Rest of the
world” region), with a breakdown into 18 industries and accounts for more than 95 percent of world output.
The industry classification is based on the ISIC Rev. 3.1. The time coverage includes the years 1995, 2000, 2005,
2008 and 2009 (OECD-WTO, 2012). The homogeneity (proportionality) assumption is made to disaggregate the
use of imported intermediate goods.
World Input-Output Database (WIOD)
The World Input-Output Database (WIOD) project (WIOD: Construction and Applications) was set up to create
such an all-encompassing database, which provides a tool that can address both the quest for indicators by
policy-makers and the need for empirical observations to test and quantify theories by academic researchers.
The project ran for three years (May 2009–April 2012) and 11 international partners were involved.
The database combines detailed information on national production activities and international trade data. It
relies on national supply and uses tables (SUTs) as basic building blocks rather than input-output tables. A
supply table provides information on products from each domestic industry and a use table indicates the use of
each product by an industry or final user. Tables are constructed for each country and this reflects how much
of each of 59 products is produced and used by each of 35 industries. Note that an SUT does not necessarily
square with the number of industries equal to the number of products because it does not require that each
industry produces one unique product only.
This type of information is available in the WIOD database for 40 countries (all 27 European Union EU
countries and 13 other major countries), plus estimates for the rest of the world (RoW); for the time period
1995–20011 in current basic prices and in basic prices for the previous year. It should be emphasized that all
data in the WIOD are obtained from official national statistics and are consistent with the National Accounts.
The full database is publicly available and free of charge at: http://www.wiod.org/database/index.htm (for
further details see Timmer et al., 2015).
10
Global Trade Analysis Project (GTAP)
The latest Global Trade Analysis Project (GTAP) database contains 57 sectors, 20 of which are agricultural as
defined at the WTO and 129 individual countries and 20 composite regions. Annex 1 contains a detailed listing
of the sectors and Annex 2 lists the countries and regions.
For each country and region, there are input-output tables indicating the production relationship within each
country, much like the input-output tables that are the foundation of the TiVA database. But, unlike the OECDWTO effort (OECD-WTO, 2012), the input-output tables in GTAP are not from official government sources. This
implies that strict comparability between the TiVA database and the one from this project may not be possible.
4.1.
Indicators
The new trade in value-added databases is instrumental to the computation of indicators that can
inform on which products have the longest international value chains, which countries are more
integrated in world markets and for which products, and the contribution of services to exports of
agriculture and food products (Liapis and Tsigas, 2014). Accordingly, gross exports can be broken
down into the following components:
 Foreign value added (foreign value added as a share of exports) indicates what part of a
country’s gross exports consists of inputs that have been produced in other countries. It
includes reimported domestic value added that was exported in goods and services used to
produce the intermediate imports of goods and services used by the industry (i.e. exported
intermediates that return home).1
 Domestic value added is the part of exports created in-country. It is the share of the
country’s exports that contributes to GDP (domestic value added trade share).
There are two main reasons for a country’s exports of value added to be smaller than its gross
exports to the rest of the world:
a) The production for its exports may contain foreign value added or imported intermediate
goods.
b) Part of the domestic value added that is exported may return home after being embodied in
the imported foreign goods rather than being absorbed abroad.
For example, two countries can have identical ratios of value-added exports to gross exports but very
different ratios for a) and b). Those countries that are mainly upstream in GVCs, such as product
design, tend to have a large value for b) but a small value for a). In comparison, those countries
mainly specializing in assembling imported components to produce final products tend to have a
small value for b) but a large value for a).
The availability of new aggregate data on trade in value added decomposition, as well as the
availability of new indicators of countries’ participation and position in long international value
chains, can inform the debate about the relationship between trade liberalization and food security.
1
“Domestic value added in exports” (part of a country’s GDP in its exports) and “domestic content in exports”
can then be distinguished: the former excludes the pure double-counted intermediate exports that return
home, whereas the latter is the former plus the pure double-counted term.
11
To this end, in the following subsections, the main indicators proposed by the current literature on
trade in value added are presented.
4.1.1. Participation in GVCs
Hummels et al. (2001) propose a basic index of vertical specialization termed VS, defined as the
import content of exports and measured as the value of imported inputs in the overall exports of a
country (the remainder is the domestic content of exports).
Liapis and Tsigas (2014) compute the VS, i.e. the foreign value-added (FVA) included in total gross
exports for major OECD and non-OECD agricultural (as defined at the WTO) exporting countries
included in the TiVA database (Figure 2). Since agricultural trade is a relatively small share of total
trade, the relatively small FVA share is not surprising. FVA share is considerably larger when
expressed as a ratio relative to gross exports generated by each sector. The import content of
exports varies by country and sector but there does not seem to be a big difference in the FVA of
gross exports for the countries depicted regardless of income level. For most countries, perhaps not
surprisingly, food sectors are more integrated with foreign suppliers that have a higher imported
content in their exports relative to the agricultural sectors.
12
Figure 2: Agriculture and food products foreign value-added share of total gross exports – selected
countries (2009)
Source: Figure 3, page 9, in Liapis and Tsigas (2014).
A second measure, also proposed by Hummels et al. (2001) and termed VS1, looks at vertical
specialization from the export side, and measures the value of intermediate exports sent indirectly
through third countries to final destinations. A third measure is the value of a country’s exported
goods that are used as imported inputs by the rest of the world to produce final goods that are
shipped back home. This measure was proposed by Daudin, Rifflart and Schweisguth (2011). Because
it is a subset of VS1, it is termed VS1*.
VS is a partial measure of a country’s participation in GVCs because it only looks backwards in the
value chain by solely measuring the importance of foreign suppliers of its intermediate goods.
Conversely, VS1 only looks forward to the participation in GVCs by supplying inputs to other
countries for further exports. By combining the two shares, it is possible to assess the importance of
global production chains in country (or industry) exports. The higher (or lower) the value of the
index, the larger (or smaller) is the participation of a country in GVCs.
13
Liapis and Tsigas (2014) compute the participation index as the sum of the vertical specialization
share (i.e. VS) and the percentage of exported goods and services used as imported inputs to
produce other countries’ exports (i.e. VS1) for the agriculture and food sectors (Figure 3). Among
countries with large agricultural exports, Viet Nam, Brazil and Argentina, have the highest
participation rate in agriculture. The agricultural sector of these countries is particularly engaged in
international value chains. In the food sector, Viet Nam, New Zealand, the Netherlands and Argentina
have the highest participation rate.
Figure 3: Participation Index for agriculture and food sectors – selected countries (2009)
6.00%
Selected OECD
5.00%
4.00%
3.00%
ag
2.00%
food
1.00%
0.00%
7.00%
6.00%
Selected other countries
5.00%
4.00%
3.00%
ag
2.00%
food
1.00%
0.00%
Source: Figure 4, page 10, in Liapis and Tsigas (2014).
4.1.2. Position in GVCs
The position of country (or industry) exporters in GVCs, i.e. the GVC position indicator, measures the
level of involvement of a country (or industry) in vertically fragmented production. It is determined
by the extent to which the country (or industry) is upstream or downstream in the GVCs, depending
on its specialization (Koopman et al., 2011).
14
A country lies upstream either if it produces inputs and raw materials for others, or provides
manufactured intermediates or both. A country lies downstream if it uses a large portion of other
countries’ intermediates to produce final goods for exports (i.e. it is a downstream processor or
assembler, adding inputs and value towards the end of the production process). The position
indicator is proxied by Liapis and Tsigas (2014), measuring the length of GVCs, that is how many
production stages are in the chain or how many different borders the goods cross before final
consumption. These measures are particularly useful at sector level. A country is located upstream in
the value chain if its contribution is mainly in producing inputs for others. In contrast, a country that
uses a large amount of intermediates from abroad occupies a downstream position in the chain.
Since two countries can have identical values for the GVC position index in a given sector while
having very different degrees of participation in GVCs, it is important to look at both indicators in
order to have a balanced picture of the degree of integration of a country in GVCs. Also, in this case,
reference is made to the analysis of Liapis and Tsigas (2104) for the position of selected countries in
the agriculture and food sectors (Figure 4):


In the agriculture sector, among the countries with higher participation rates, Brazil is
located more upstream in the value chain compared with Viet Nam. China has the largest
upstream index, while India has among the lowest. China is involved in longer GVCs,
producing mostly inputs used in the agricultural activities of other countries. India, on the
other hand, produces mostly agricultural goods going to final consumers after a few
production stages.
In the food sector, China also has the longest value chains and with Malaysia, specializes in
providing inputs for use in the food production sector of other countries, while Viet Nam and
the Philippines are more downstream, processing imported food for final consumption with
relatively few production stages.
15
Figure 4: Distance to final demand in selected countries for agriculture and food sectors (2009)
Source: Figure 5, page 11, in Liapis and Tsigas (2014).
4.2.
Trade costs magnification
“Goods produced within these supply chains are traded across several countries, incorporating at
each stage an additional layer of added value. The shipments of intermediate goods between the
participating countries and industries provide the material support for this “trade in tasks”. This
explains why trade in tasks is much more sensitive to the tariff duties imposed on those international
transactions, especially when – due to tariff escalation—the duties charged on relatively elaborated
parts and components are high.
Multistage production magnifies the effects of trade costs on world trade. There are two separate
magnification forces. The first exists because goods that cross national borders many times incur
multiple tariffs and transportation costs. The second exists because tariffs are applied to gross
imports, even though value added by the direct exporter may be only a fraction of this amount.
Generally speaking, the magnification effect is stronger for developing market economies than for
developed economies due to the lower domestic value-added share in most developing countries’
manufacturing exports.
Diakantoni and Escaith (2012) argue that after having fallen into relative obscurity, at least from a
normative perspective, effective protection rates (EPRs) may be back to the central stage as
international trade moves from "trade in (final) goods" to "trade in tasks". From a national account
perspective, what is internationally traded is value added (the primary inputs) and the adequate
measure of trade distortion is no more the nominal tariff structure on the output, but the effective
rate of "protection" on value added.
EPRs are again important because they can be interpreted as the percentage increase in sectoral
value added per unit of output, which is made possible by the nominal tariff schedule relative to a
16
situation of free trade. In other terms, EPRs go beyond nominal rates of protection and highlight the
impact of trade policy because they take into account the interconnectivity of a sector with other
domestic and foreign industries. The increasing availability of time series data in international inputoutput matrices opens the way for a deeper analysis of the respective economic and trade policy
factors behind changes in EPRs and the implications for partner countries.
5. GVCs analysis based on household panel data
Another fruitful strand of the literature on GVC analysis benefits from the increased efforts by
international organizations to provide new household panel data with a strong focus on agriculture
and rural development (e.g., the World Bank LSMS-ISA project2). Using new empirical evidence from
household surveys it is possible to assess the implications of selling at different stages of the value
chain, comparing farmer households that are able to export their crops with subsistence households
(Niimi et al., 2007; Magrini and Montalbano, 2011; Balat et al., 2009).
Montalbano et al. (2015) provide a first attempt to look seriously at the relation between farmers’
FNS and their level of participation and position in the agrifood production stage of the value chain.
5.1.
The case of Ugandan maize: data and stylized facts
Maize is the third main crop grown in Uganda (after banana and cassava). In the period 1990–2013,
maize production in the country more than doubled and the consumption gradually increased,
especially in urban areas, because of the increasing prices of traditional staple food (USAID, 2010).
The Uganda LSMS-ISA was implemented by the Uganda Bureau of Statistics, in collaboration with the
World Bank. The survey sample includes 3 123 Ugandan households selected from the Uganda
National Household Survey 2005/2006 that were reinterviewed in 2009/2010 (with a response rate
of 83.5 percent); in 2010/2011 (response rate: 82.1 percent); in 2011/2012 (response rate: 75.4
percent). The questionnaire design comprised three sets of data: the household level data, including
modules related to household characteristics (including consumption expenditure and welfare); the
agricultural data with detailed information on household crop production, farming inputs, practices,
etc., and the community data (retail prices included).3 The overall survey is representative at the
national, urban/rural and main regional levels. Unfortunately, the only item of information in the
survey that can be directly related to household value chain participation and position concerns
different commercial partners.4
Using the Uganda LSMS-ISA, Montalbano et al. (2015) adopted the following identification strategy
to focus on the impacts of heterogeneity in small farmer’s participation and position in agrifood
production: they test empirically whether or not heterogeneity in FNS among farmer households is
linked to heterogeneity in their selling strategy. The latter is used as a proxy for farmers’ participation
2
Available at
http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTLSMS/0,,contentMDK:23512006~pa
gePK:64168445~piPK:64168309~theSitePK:3358997,00.html.
3
There is also a market questionnaire that was administered only in 2009/2010.
4
The specific question is the following “who bought the largest part of maize sold?” Agricultural questionnaire
- Sections 5A and 5B, respectively for the first and second crop season.
17
and position in the chain. To clarify the point, it is useful to make reference to Figure 5, which depicts
the various selling options of the farmer in the case of the maize value supply chain (VSC) in Uganda.
Figure 5: Farmers’ multiple strategy within the maize VSC in Uganda
Source: Montalbano et al. (2015), Figure 1, page 15. Elaboration from ICG (2008); IFPRI (2008);
Ahmed and Ojangole (2014).
As it is apparent from Figure 5 that Ugandan maize farmer households’ selling strategy is mixed. They
sell maize directly to neighbours, relatives and/or consumers at the local rural market (i.e. they do
not participate in the supply chain and so are considered outside the chain); and/or to rural local
traders (i.e. they participate in the supply chain, even if far from final destinations), and/or to
district/urban traders (i.e. they participate in the supply chain in a more downstream position, more
closely to the final markets/exports).
18
Table 2: Maize farmer household descriptive statistics by participation/position in the production
stage of the value chain according to Uganda LSMS-ISA
Source: Montalbano et al. (2015), Table 2, page 17.
19
According to Figure 5, Montalbano et al. (2015) group the maize net producer households according
to their selling strategy as follows:



Those who sell maize only to local consumers (i.e. outside the value chain).
Those who sell maize to local consumers and local traders (i.e. inside the value chain in an
upstream position).
Those who sell maize to local consumers, local traders, and district traders (i.e. inside the
value chain in a downstream position, i.e. closer to the final market/exports).
Table 2 shows that farmer households closer to the final market (downstream column) are
associated, on average, with a higher level of maize unit price, a higher level of per capita income and
food consumption, a higher household diverse dietary score (HDDS), and are less subject to shocks
(i.e. they are more food secure). On the other hand, it is apparent that the households with
differentiated selling strategy are very heterogeneous in their structural characteristics. This is
evident if we look at their maize production and selling conditions. Farmers selling closer to the final
markets are associated with, on average, larger maize areas, harvests and saleable yields. They are
also characterized by their production inputs, farmers selling closer to the final markets using, on
average, more pesticides, fertilizers and improved seeds. They also hire more labour and face higher
transport costs.
Apparently, the household selling strategy is not random but it is likely driven by some specific
household characteristics that can be observable or hidden to the researcher. At the same time, it
may be the case that households able to sell closer to final markets are better off in their food
security outcomes precisely because of their selling strategy (it can be related to a number of factors,
including the reduction in transaction and intermediating costs, and increase in their market power).
Montalbano et al. (2015) adopt the following identification strategy to test for the presence of a
causal relationship between per capita food consumption and participation and position of farmer
households in the production stage of the maize value chain, by controlling for a number of
household characteristics. They also exploit the panel dimension of the data to control for time
invariant (e.g. ability) and linear time-variant (e.g. experience) hidden household heterogeneity.
Finally, to take self-selection into account in value chain participation and position, based on
observable and hidden farmer characteristics, they apply a two stage strategy. First, they estimate
three Linear Probability Models (LPM) of selling maize inside the value chain, upstream or
downstream, based on farmer characteristics, maize characteristics, households, year and regional
fixed effects. Subsequently, they include the residuals from LPM in the panel estimation to control
for selection bias.
5.2.
The effects of farm households position in the Ugandan maize value
chain
Results are presented in Table 3. The first column includes the results of the panel regression
controlling for the value chain regressors, a set of household covariates used as additional controls
and including the full set of household and time fixed effects. The second column adds a householdspecific time trend to track possible unobservable evolving linear abilities for each household. The
third column corrects also for possible selection bias and multicollinearity by including the residuals
of a first stage LPM computed by using a full set of production characteristics normally applied in the
literature to distinguish among farmers allocated to the different stages of the maize value chain.
More specifically, in column (a) value chain participation is accounted for through dummy variables
corresponding to selling maize upstream or downstream, while in columns (b) a quantitative
specification, the share of maize sold inside the value chain at the different stages of the value chain
is implemented.
20
Table 3: Panel regression of HH (log) pc food consumption by value chain participation/position
Source: Montalbano et al. (2015), Table 4, page 19.
The estimates show a very good fit of the per capita food consumption at the household level as well
as a clear ability to explain both observable and hidden determinants of heterogeneity in household
food consumption along the value chain. In addition, it is evident that household participation and
position in the production stage of the Ugandan maize value chain (i.e. proxied by their selling
strategy) retains its significance in explaining heterogeneity in household consumption. More
specifically, Ugandan households that participate in the maize value chain (by selling to local traders)
register, on average and ceteris paribus, a per capita consumption about 30 percent higher than
farmer households outside the chain. It is worth noting here that without considering the effect of
selection (columns 2 and 3), the effect of value chain participation would be downward biased.
However, the difference between selling downstream and upstream is not significantly associated
with per capita food consumption. Although preliminary, these results are consistent with some
recent empirical analyses for other African countries (Sitko and Jayne, 2014; Fafchamps and Vargas,
2005; Fafchamps et al., 2005).
21
6.
Conclusions
Value chain analysis sheds light on the actors participating in each link, how they participate or could
participate in the chain, and opportunities to facilitate or improve those linkages. This is particularly
crucial in agriculture, where governments face the challenge of including small farmers in modern
value chains so that they can benefit from the globalization of markets. Increasingly, the value chain
approach is being used to guide high-impact and sustainable initiatives focused on improving
productivity, competitiveness, entrepreneurship, and the growth of small and medium firms. The
value chain concept is therefore not only relevant when applied to growth, but also with the equity
dimension of the modernization of agrifood systems (Webber and Labaste, 2010).
The growth of GVCs and the change in the trade paradigm require a new set of policies. Domestic
policies might not be at the appropriate level to harness best the challenges of competitiveness
within GVCs. More specifically, small and medium-sized producers in developing countries usually
face competitiveness bottlenecks (i.e. low productivity, poor product quality, lack of standards
compliance, high transaction costs and limited scale) that limit their potential involvement. These
competitiveness bottlenecks are difficult for smallholders to overcome because they face major
constraints: lack of access to markets, lack of training (technical and entrepreneurial), lack of
collaborative networks (among small producers and with chain stakeholders) and lack of finance
(Bamber and Stack, 2014).
In order to attenuate or overcome these constraints and allow small and medium-sized producers to
become more competitive and participate in national and international value chains in a sustainable
manner, appropriate policies should be adopted. Measures to improve producer competitiveness
and sustain value chain inclusion on project completion can be grouped under four pillars,
corresponding to the key constraints: (1) access to markets; (2) access to training; (3) access to
finance; and (4) collaboration and cooperation building (Bamber and Stack, 2014).
Appropriate policies should adopt three complementary objectives: joining, maintaining
participation, and moving along (upstream or downstream) value chains. In addition, accompanying
policies should help maximize the expected benefits and minimize the risks, ensuring that the chain
of results from competitiveness to growth and development is preserved. Unfortunately, this task is
made difficult by the heterogeneity of sectors: the organization of GVCs tends to be sector-specific
(and sometimes even firm-specific), and there are no one-size-fits-all policies (Sturgeon and
Memedovic, 2011).
Case studies on GVCs based on exhaustive micro data for a single product have provided detailed
examples of the multiple stages involved in moving a product from the farm gate to the consumer
and have provided accurate examples of the discrepancy between gross and value-added trade.
However, they are not able to provide a comprehensive picture of the gap between value added and
gross trade or of the country’s participation in cross-border production chains (Liapis and Tsigas,
2014). The new databases on trade in value added provide additional information on country and
industry GVC indicators. Unfortunately, they are at a very aggregated level (in the OECD-WTO TiVA
database the agricultural sector is one aggregated sector, “agriculture hunting forestry and fishing”,
while what is referred to as food is a separate sector “food products, beverages and tobacco”). In
order to gain deeper knowledge of agriculture and food GVCs, it will be necessary to develop
relevant indicators for more disaggregated agricultural and food sectors with more product detail.
The interpretation of these indicators and results for individual countries in the temporal, geographic
and industry dimensions is still a work in progress and poses new challenges to scholars and policy
experts.
22
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