Beer Drinking Nations. The Determinants of Global Beer Consumption.

In this paper we analyze the evolution of beer consumption between countries and over time. Historically, there have been major changes in beer consumption in the world. In recent times, per capita consumption has decreased in traditional " beer drinking nations" while it increased strongly in emerging economies. Recently, China has overtaken the US as the largest beer economy. A quantitative empirical analysis shows the relationship between income and beer consumption has an inverse U-shape. Beer consumption initially increases with rising incomes, but at higher levels of income beer consumption falls. Increased openness to trade and globalization has contributed to a convergence in alcohol consumption patterns across countries. In countries that were originally "beer drinking nations", the share of beer in total alcohol consumption reduced while this is not the case in countries which traditionally drank mostly wine or spirits. Climatic conditions, religion, and relative prices also influence beer consumption.

Private Standards and Employment Insecurity: GlobalGAP in the Senegalese Horticulture Export Sector

Crop Production/Industries, International Relations/Trade, Labor and Human Capital,

Retail alliances in the agricultural and food supply chain

This report summarizes the content and discussions of the JRC-DG AGRI workshop on "The role of Retail Alliances in the Food supply chain" that was held in Brussels on 4-5 November 2020. It presents a typology of retail alliances, describes their functioning and activities, and analyses the impact of retail alliances on the food supply chain, from an economic and legal point of view.JRC.D.4-Economics of Agricultur

Differences in composition of seemingly identical branded food products: Impact on consumer purchasing decisions and welfare

The issue of Differences in Composition of Seemingly Identical branded Products (DC-SIP) refers to cases where a good in one Member State is marketed as identical to a good marketed in other Member States, while in reality that good has significantly different composition or characteristics (European Commission, 2019a). The main concern is that “in some parts of Europe, people are sold food of lower quality than in other countries, despite the packaging and branding being identical”, as stated by President Juncker (European Commission, 2017b). This report provides a conceptual analysis of whether and how consumer purchasing decisions and welfare are affected by the fact that the same brand owner offers seemingly identical branded food products with different composition in different countries’ markets. Based on the conceptual and empirical knowledge developed in the fields of demand theory, behavioural economics, marketing and consumer psychology, this report develops a framework to analyse the formation of consumer quality perceptions, purchasing decisions and welfare. We start from a basic neoclassical utility approach to assess the different possible effects of DC-SIP on consumer purchases and welfare. Given the crucial role of quality perception in determining consumers' valuation of a product, we then perform a more detailed analysis of the factors shaping quality perception, based on the Total Food Quality Model. This sheds light on how food quality perception may differ across countries and individual consumers, and how this relates to the issue of DC-SIP. Finally, the report addresses how information asymmetry regarding DC-SIP may lead to the disconfirmation of consumers' expectations once consumers realise or are informed about differences in composition between product versions. The role of deception and unfairness perception on consumer decision-making and welfare is analyzed in order to understand consumers' reactions to DC-SIP. Drawing on the conceptual and empirical literature across fields, this report shows that the impact of DC-SIP on consumer choices and welfare is not straightforward. While consumers care about food quality, differences between product versions are likely to go unnoticed if consumers are not explicitly informed of them. Moreover, even when differences are noticed, different consumers may not have the same preference rankings for different versions. Finally, the price at which different product versions are offered also matters. This heterogeneity means that average purchasing and welfare implications may differ between countries’ markets, and that individual consumers are likely to be differentially affected. In addition to the question of whether consumers would or would not prefer and purchase a different version than the one offered on their market, the existence of different product versions with potentially different quality valuations may in itself be a source of consumer dissatisfaction. The report explains how DC-SIP may lead to consumer perception of deception and unfairness which may negatively affect brand trust and affect consumer reactions, purchasing behaviour and welfare in the short- or longer term. Consumers may voice their concerns, decide not to buy specific products, products from a certain brand, or even lose trust in global brands and turn to local goods instead. Depending on how strong the feelings of deception and unfair treatment are, these reactions may be very weak or strong, and may be short-term, with consumers quickly reverting to habitual purchasing patterns, or may be long-lasting

Urbanization as a driver of changing food demand in Africa: Evidence from rural-urban migration in Tanzania

There is rising concern that the ongoing wave of urbanization will have profound effects on eating patterns and increase the risk of nutrition-related non-communicable diseases. Yet, our understanding of urbanization as a driver of food consumption remains limited and primarily based upon research designs that fail to disentangle the effect of living in an urban environment from other socioeconomic disparities. Data from the Tanzania National Panel Survey, which tracked out-migrating respondents, allow us to compare individuals’ dietary patterns before and after they relocated from rural to urban areas and assess whether those changes differ from household members who stayed behind or moved to a different rural area. We find that individuals who relocated to urban areas experience a much more pronounced shift away from the consumption of traditional staples, and towards more high-sugar, conveniently consumed and prepared foods. Contrary to what is often claimed in the literature, living in an urban environment is not found to contribute positively to the intake of protein-rich foods, nor to diet diversity. Though we do not find a strong association with weight gain, these changes in eating patterns represent a clear nutritional concern regarding the potential longer-term impacts of urbanization. Our results however also indicate that the growth of unhealthy food consumption with urbanization is largely linked to rising incomes. As such, health concerns over diets can be expected to spread rapidly to less-urbanized areas as well, as soon as income growth takes off there. Our findings clearly call for more in-depth research that may help to improve health and food and nutrition security as well as correctly predict food demand and adapt trade, agricultural and development policies.JRC.D.4-Economics of Agricultur

(How) can economic experiments inform EU agricultural policy?

This report provides an overview of the potential contribution of economic experiments to the Common Agricultural Policy evaluation toolbox. The methodology of economic experiments is briefly described in chapter 2. Chapter 3 presents examples of experimental studies relevant for the analysis of the Common Agricultural Policy. Chapter 4 discusses whether there is room for economic experiments in the EU evaluation toolbox. Chapter 5 identifies the main challenges when implementing economic experiments. Chapter 6 provides a set of recommendations, arising from the discussions between the authors of this report and policy officers from DG AGRI, relative to the introduction of economic experiments in the CAP evaluation toolbox.JRC.J.4-Agriculture and Life Sciences in the Econom

An EU-Wide Individual Farm Model for Common Agricultural Policy Analysis (IFM-CAP)

This report presents the first EU-wide individual farm-level model (IFM-CAP) aiming to assess the impacts of CAP on farm economic and environmental performance. The rationale for such a farm-level model is based on the increasing demand for a micro-simulation tool able to model farm-specific policies and to capture farm heterogeneity across the EU in terms of policy representation and impacts. Based on positive mathematical programming, IFM-CAP seeks to improve the quality of policy assessment upon existing aggregate and aggregated farm-group models and to assess distributional effects over the EU farm population. To guarantee the highest representativeness of the EU agricultural sector, the model is applied to every EU-FADN (Farm Accountancy Data Network) individual farm (around 60 500 farms). The report provides a detailed description of the IFM-CAP model prototype in terms of design, mathematical structure, data preparation, modelling livestock activities, allocation of input costs and the calibration process. The theoretical background, the technical specification and the outputs that can be generated from this prototype are also briefly presented and discussed. The report also presents an application of the model to the assessment of the effects of the crop diversification measure. The results show that most non-compliant farms (80 %) chose to reduce their level of non-compliance following the introduction of the diversification measure owing to the sizable subsidy reduction imposed. However, the overall impact on farm income is rather limited: farm income decreases by less than 1 % at EU level, and only 5 % of the farm population will be negatively affected. Nevertheless, for a small number of farms, the income effect could be more substantial (more than –10 %).JRC.J.4-Agriculture and Life Sciences in the Econom

Upscaling the productivity performance of the Agricultural Commercialization Cluster Initiative in Ethiopia

This report presents the results of an impact analysis of several scenarios related to the Agricultural Commercialization Cluster (ACC) initiative. This initiative was introduced by the Government of Ethiopia during the first Growth and Transformation Plan (2010-2015) as a mechanism to improve agricultural productivity and production within specific geographies by targeting a limited number of high-value commodities. The farm-household model FSSIM-Dev (Farm System Simulator for Developing Countries) is applied to a representative sample of 2,886 individual farm-households spread throughout the country, taken from the 2013/14 Ethiopia Socioeconomic Survey. Simulation results show that upscaling the ACC productivity performance to the respective regions would lead to an increase in production of the main products ranging between 1.8% and 62.6%, depending on scenario, region and commodity. The average (across all ACC scenarios considered) country-level production increase for wheat, teff, maize and barley are assessed to be 29.6%, 21.1%, 12.8% and 12.6%, respectively. These impacts are driven by the rise in land productivity, rather than area expansion (through putting fallow land into cultivation) and/or area reallocation. The increase in crop yields would also have a positive impact on both income and poverty level of farm households. The average increase in gross income at the country and individual farm-household levels are assessed to be around 14% and 9%, respectively. These impacts could be more pronounced for individual farms: for example, 85% of the farms would experience an increase in gross income of up to 17% to 32%, depending on the nature of scenarios considered. The largest income change occurs in farms specializing in field crops, and in medium-large farms (i.e. farms with total production value of larger than ETB 9,000). The increase in both production and income would also raise food consumption and improve nutritional indicators.JRC.D.4-Economics of Agricultur