Photo credit: World Bank
It is widely feared that the COVID-19 pandemic will lead to a significant worsening of the food security situation in low and middle-income countries. One reason for this is the disruption of food marketing systems and subsequent changes in farm and consumer prices.
Modern marketing arrangements are increasingly being implemented to assure improved food quality and safety. However, it is not well known how these modern marketing arrangements perform in early stages of roll-out. We study this issue in the case of rural-urban milk value chains in Ethiopia, where modern processing companies – selling branded pasteurized milk – and modern retail have expanded rapidly in recent years.
This paper explores the spatial heterogeneity in dairy production in the highland production area around the capital of Ethiopia, Addis Ababa. We look at how urban proximity – defined as the travel time from the farm to the central market of Addis Ababa – affects the production decisions of Ethiopian dairy farmers. We sampled 870 households from the major rural production zones around Addis Ababa, where villages were stratified according to their distance to Addis Ababa.
The importance of cities is rapidly growing. It is estimated that more than half of the world population was living in cities in 2010; this is up from 30 percent in the 1950s (UN Population Division 2010). Given this rapid urbanization, especially so in developing countries, and the increasing importance of the manufacturing and service sectors in these countries’ economies, more people are making a living outside agriculture. As part of this change, many more people do not grow their own food and rely on market purchases for their food needs.
The purpose of this chapter is to understand the changes that have been happening in the teff value chain in Ethiopia based on carefully fielded primary stacked surveys at different layers in the value chain.1 This chapter uses the same data as in Chapter 11 and takes a dynamic angle on the teff sector, as was presented in Chapter 12. However, the scope of the analysis is much broader than in the previous market-focused chapter.
The purpose of this paper is to identify sources and quantifying distortions to agricultural incentives to produce along the small ruminant value chains in Ethiopia.
National and district level average nominal rate of protection (NRPs) were computed for a five-year period (2010–2015). The authors developed four scenarios based on combinations of the different data generation processes employed in relation to each of the key variables.
We study post-harvest losses (PHL) in important and rapidly growing rural-urban value chains in Ethiopia. We analyze self-reported PHL from different value chain agents – farmers, wholesale traders, processors, and retailers – based on unique large-scale data sets for two major commercial commodities, the storable staple teff and the perishable liquid milk. PHL in the most prevalent value chain pathways for teff and milk amount to between 2.2 and 3.3 percent and 2.1 and 4.3 percent of total produced quantities, respectively.
Exchange rate policies can have important implications on incentives for export agriculture. However, their effects are often not well understood. We study the issue of foreign exchange controls and pricing in the value chain for Ethiopia’s coffee - its most important export crop. Relying on unique pricing and cost data, we find that coffee exporters are willing to incur losses during exporting by offering high prices for coffee locally in order to access scarce foreign exchange.
We show theoretically that the presence of basis risk in index insurance makes it a complement to informal risk sharing, implying that index insurance crowds-in risk sharing and leading to a prediction that demand will be higher among groups of individuals that can share risk. We report results from rural Ethiopia from a first attempt to market weather insurance products to existing informal risk-sharing groups. The groups were offered training on risk management and the possible benefits of holding insurance.
In October 2011,the CGIAR program on Climate Change, Agriculture and Food Security (CCAFS) and the Index Insurance Innovation Initiative (I4) organized a jointworkshop hosted by the International Food Policy Research Institute (IFPRI). The workshop was designed to identify and address issues surrounding index‐based insurance for smallholder farmers and the rural poor in the developing world. Emphasis was placed on identifying key areas of research and learning for the academic and policy community to pursue.
In this paper we examine which farmers would be early entrants into weather index insurance markets in Ethiopia, were such markets to develop on a large scale. We do this by examining the determinants of willingness to pay for weather insurance among 1,400 Ethiopian households that have been tracked for 15 years as part of the Ethiopia Rural Household Survey. This provides both historical and current information with which to assess the determinants of demand. We find that educated, rich, and proactive individuals were more likely to purchase insurance.
We conduct a framed field experiment in rural Ethiopia to test the seminal hypothesis that insurance provision induces farmers to take greater, yet profitable, risks. Farmers participated in a game protocol in which they were asked to make a simple decision: whether to purchase fertilizer, and if so, how many bags. The return to fertilizer was dependent on a stochastic weather draw made in each round of the game protocol. In later rounds of the game protocol, a random selection of farmers made this decision in the presence of a stylized weather-index insurance contract.
We analyze the effectiveness of a new approach in providing weather index-based insurance products to low-income populations. The approach is based on the concept of providing multiple weather securities that pay a fixed amount if the event written on the security (that monthly rainfall at a nearby weather station falls below a stated cutoff) comes true. A theoretical model is developed to outline the conditions in which weather securities could outperform crop-specific weather index-based insurance policies.
This paper attempts to inject more rigorous quantitative methods into value chain analysis. Approaches examined include System Dyanimcs (SD) that model flows and relationships between actors with which one can examine the impact of alternative scenarios over time. Agent-Based Models (ABM) model individual farmers, institutions, and social groupings. In SD models, actors are assumed to be the same whereas in ABM models a set of heterogenous characteristics may be defined for each agent.
The Integrating Very Poor Producers into Value Chains Field Guide (Field Guide) is intended to provide the field-level practitioner with tools and applications to impact very poor households. The intended outcome of the Field Guide is to increase market engagement for very poor households, especially women, through enterprise development activities.
This ILRI discussion paper reviews 20 value chain interventions and discusses the econometric techniques used to address the validity of findings. It explores the use of propensity score matching, instrumental variables, difference in difference, regression discontinuity, and randomized controlled trials. Qualitative and participatory methods are also examined with the idea that they may be able to better capture the complexity of value chain processes.
Wheat is one of the four most important food grains in Ethiopia. As a source of calories in the diet, wheat is second to maize. In terms of the area of production, wheat is fourth, after teff, maize, and sorghum. In terms of the value of production, it is 4th or 5th, after teff, enset, and maize, and approximately tied with sorghum.
Wheat production has expanded rapidly in the past decade. According to the CSA, wheat production has grown at 7.5% per year since 1995-96 and at 9.3% over the past decade.
A major challenge when designing a National Agricultural Investment Plan (NAIP) is deciding how to prioritize between different opportunities, e.g., which value-chains should be promoted over others?