Trade, value chains and food security

December 2015

P. Montalbano, S. Nenci and L. Salvatici

The State of AgriculturalCommodity 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

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The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations (FAO) concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The mention of specific companies or products of manufacturers, whether or not these have been patented, does not imply that these have been endorsed or recommended by FAO in preference to others of a similar nature that are not mentioned. The views expressed in this information product are those of the authors and do not necessarily reflect the views or policies of FAO. © FAO, 2015 FAO encourages the use, reproduction and dissemination of material in this information product. Except where otherwise indicated, material may be copied, downloaded and printed for private study, research and teaching purposes, or for use in non-commercial products or services, provided that appropriate acknowledgement of FAO as the source and copyright holder is given and that FAO’s endorsement of users’ views, products or services is not implied in any way. All requests for translation and adaptation rights, and for resale and other commercial use rights should be made via www.fao.org/contact-us/licence-request or addressed to copyright@fao.org. FAO information products are available on the FAO website (www.fao.org/publications) and can be purchased through publications-sales@fao.org.

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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

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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.

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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 value-added 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 input-output 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.

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

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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 high-value 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

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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).

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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 high-value 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

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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).

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Table 1: GVC case studies: a synopsis

Country Sector/ product

Smallholders Trade Industry structure

Standards/Quality requirements

Vertical coordination (contracting)

Other vertical coordination*

Data Impacts Source

Madagascar

Horticulture

yes (100% contract)

Exports 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)

small farmers’ micro-contracts

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, Randrianarison and Swinnen, 2009

Senegal (3 regions)

Horticulture (fruit and vegetable)

yes (mainly agro-industrial employees)

Exports Competition

public standards: common marketing standards, sanitary and phytosanitary measures, hygiene rules based on HACCP control mechanisms, traceability standards. Private standards

large and smallholder contract-based farming

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 agro-industrial employees)

Maertens and Swinnen, 2009

Ethiopia Biofuel (castor)

yes (100% contract)

Exports contracts to castor producers

specific household survey level data

improvement of food security for participating farmers (specifically in terms of access to food)

Negash and Swinnen, 2013

Sub-Saharan Africa (SSA) (Senegal, Madagascar, Côte d’Ivoire, Ghana, Kenya, Zambia, Zimbabwe

Horticulture (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

mainly contract-farming 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 co-ordination in vegetable sector in Ghana

country survey data + qualitative information

welfare effects (income mobility and rural poverty reduction)

Maertens, Minten and Swinnen, 2012

Vegetables in Madagasc

100% procurement from smallholder

High-value exports

Monopoly in Madagasc

complete contracting with smallholders in Madagascar; own

input supply programmes in

country survey data + qualitative information

positive welfare effects for rural households through both product-

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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

(particularly to EU)

ar; competition in the Senegalese bean sector; monopoly in the Senegalese tomato export sector

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

and labour-market channels (product-market effects have a higher impact on household income); positive impact of vegetable contract-farming in Madagascar on farmers’ income and food-security from a combination of direct and indirect - i.e., technological and managerial spill-over effects.

Kenya Vegetables

yes Exports to EU

Competition

yes (the global Good Agricultural Practices-GAP)

farm-level survey data

positive results on export and domestic farmer revenue from adoption of standards

Asfaw, Mithofer and Waibel, 2009

Viet Nam and Bangladesh

Pangasius catfish

yes (farm owners come predominantly from upper income brackets)

Exports Competition

yes (certification standards)

in Viet Nam a producers affiliated to processing companies ensure greater levels of vertical co-ordination

descriptive cases

certification is likely to result in greater differentiation and polarisation between larger and smaller farm operators (exclusion of small farms from important markets).

Belton, Mahfujul Haque, Little and Sinh, 2011

Costa Rica, Guatemala, El Salvador, Honduras, and Nicaragua

fresh fruits and vegetables

low participation of smallholder producers

yes (quality and safety standards)

new kind of procurement system

descriptive cases exclusion of under-capitalized small growers.

Berdegué, Balsevich, Flores and Reardon, 2005

Kenya fresh vegetables

low participation of smallholder producers

Exports to UK

Competition

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)

descriptive cases

concentration of production and processing in the hands of a few large firms

Dolan and Humphrey, 2004

Chile raspberry sector

yes

exports (mainly to EU and US)

yes (food quality and safety standards)

contract system

credit and technical trainings are provided by governmental institutions

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

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Zimbabwe horticulture

yes exports to UK

yes (food quality and safety standards)

contract system

timely payments, provision of advice and assistance to producers

descriptive cases small-scale producers integrated successfully into a supply chain

Henson, Masakure and Boselie, 2005

Kenya fresh vegetables

exports contract system Jaffee and Masakure, 2005

Source: Authors’ elaboration. * input supply programmes, trade credit, investment assistance, bank loan guarantees, technology and management advise

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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 input-output 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-addedanoecd-wtojointinitiative.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).

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Box 1: Available trade in value added databases

Database Years Sectors (primary) Regions Import matrices* Indicators

TiVA 1995, 2000 2005, 2008, 2009

18 (agriculture, food) 58 a) proportional b) homogeneous c) proportional

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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).

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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 OECD-WTO 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.

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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.

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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.

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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)

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).

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

ag

food

Selected OECD

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

ag

food

Selected other countries

15

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.

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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

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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 input-output 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~pagePK: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.

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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).

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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.

20

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 household-specific 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.

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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).

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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.

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