What Are the Domestic and Regional Impacts From Ethiopia's Policy on the Export Ban of Teff?

Abstract

In response to global food price volatility and trends toward increased global food demand, Ethiopian policy makers were forced to adopt strategies such as restricting food exports in order to protect domestic food security. However, these policies can have a disproportionate regional impact on domestic markets and can result in lost revenue from exports. For this reason, they have been criticized as inefficient from the perspective of economic development. Here, we examine the sub-national dynamics of a ban on food exports. We do this for the case of Ethiopia's ban on exports of teff, a staple grain in the country that has increasing global demand. We assess the impact of the ban and of proposed policies to relax the ban, across regions within the country and for various market actors along the teff value chain. Using a partial-equilibrium model developed with a detailed modeling of the agro-economic features of the country, we analyze the direct impacts on export revenue, producers' profits, transport patterns, and consumption across the disaggregated regions in Ethiopia due to changes to its teff export policy. In particular, we show that the immediate benefit due to significant increase in international revenue due to large teff export would be enjoyed primarily by food distributors and storage operators while the crop producers' profits increase only negligibly. Simulations also indicate that lifting the export ban would be expected to have significant impacts on domestic transportation of teff between regions (for example from Mekelle to Werder), and to reduce consumption of teff significantly in some regions (for example, Semera, Jijiga), an effect due to the lack of competition in the transportation sector. The granularity of the model helps us capture the possibility of such lopsided benefits which were not captured in earlier studies.

A systematic review of the impact of food security governance measures as simulated in modelling studies

“…No study covered all food value chain echelons, and only two studies 43,44 covered all value chain echelons but one: retail. Most studies covered food production only, consumption only, or a combination of production, consumption and trade (Fig.…”

“…Among treasure-based measures, bearer-directed payments (n = 31) dominated, and among authority-based measures, standard constraints dominated (n = 38). Frequently simulated authoritarian measures were land protection 24,40,52 , tax policies [53][54][55] and trade regulations such as tariffs 15,54,56 , quotas 28,55,57 or bans 28,43,58 . These measures have in common that they affect or are meant to affect the wider public (first row, Table 3).…”

“…Land protection policies 24,40,42,52 Production restrictions for X 59 Export taxes [53][54][55]58,91 , bans 28,43,58,127 or tariffs for X 28 Import tariffs 15,35,[53][54][55][56][57][58]70,82,133,135,136,148 or bans 109,127 or other trade barriers 57,82,109 for X Import tariffs on farm inputs 54,70,136 Value-added tax (VAT) on farm inputs 50 or on X 138,147 Compliance cost for genetically modified food production 129 Land protection policies 24,40,42,52 Open compacts (n = 7) Legalize agroforestry in timber production forests 25 , bio-fuel production on marginal grasslands 26 , or colonization of new land for agriculture 25<...…”

Optimization Based Modeling for the Food Supply Chain's Resilience to Outbreaks

“…The proposed bilevel optimization routine can generate data on bilateral food trade among countries, which account for interdependencies between food products, to inform international food trade policies beyond international meat supply chains (Avraam et al, 2021b). Even though US-FEAST focuses on the US food system, our framework is applicable to a range of countries, including food security in Ethiopiaand sectors with interacting agents across their supply chains, e.g., fuel markets and electricity markets (Huppmann and Egging, 2014;Brijs et al, 2017;Bakker et al, 2018;Sankaranarayanan et al, 2020). The bilevel data-generation routine can disaggregate data, however its accuracy depends on the inputs available to generate it.…”

“…Hence, the large change in cow meat prices in that region compared to other regions is the result of the combination of the cost function assumption and the large decrease in total purchases in west-south central. While individual-level actions are always difficult to predict, at an aggregate level, consumption activities can be assumed to have consistent characteristics that can be represented in models such as ours (Evenson and Gollin, 2003;Sankaranarayanan et al, 2020). Our model reveals the tradeoff between a decrease in demand because of consumers' concerns, and the availability of cheaper meat for those that choose to consume it.…”