2019 social accounting matrix for Ghana: A Nexus project SAM

The 2019 Ghana Social Accounting Matrix (SAM) follows IFPRI's Standard Nexus SAM approach, by focusing on consistency, comparability, and transparency of data. The Nexus SAMs available on IFPRI's website separates domestic production into 42 activities. Factors are disaggregated into labor, agricultural land, and capital, with labor further disaggregated across three education-based categories. The household account is divided into 10 representative household groups: Rural and urban households across per capita consumption quintiles. Nexus SAMs support the improvement of model-based research and policy analysis in developing countries and allow for more robust cross-country comparisons of national economic structures, especially agriculture-food systems

Agriculture, Food Systems, and Nutrition: Meeting the Challenge

Malnutrition is a global challenge with huge social and economic costs; nearly every country faces a public health challenge, whether from undernutrition, overweight/obesity, and/or micronutrient deficiencies. Malnutrition is a multisectoral, multi-level problem that results from the complex interplay between household and individual decision-making, agri-food, health, and environmental systems that determine access to services and resources, and related policy processes. This paper reviews the theory and recent qualitative evidence (particularly from 2010 to 2016) in the public health and nutrition literature, on the role that agriculture plays in improving nutrition, how food systems are changing rapidly due to globalization, trade liberalization, and urbanization, and the implications this has for nutrition globally. The paper ends by summarizing recommendations that emerge from this research related to (i) knowledge, evidence, and communications, (ii) politics, governance, and policy, and (iii) capacity, leadership, and financing

Assessing Africa-wide pangolin exploitation by scaling local data

Overexploitation is one of the main pressures driving wildlife closer to extinction, yet broad-scale data to evaluate species’ declines are limited. Using African pangolins (Family: Pholidota) as a case study, we demonstrate that collating local-scale data can provide crucial information on regional trends in exploitation of threatened species to inform conservation actions and policy. We estimate that 0.4-2.7 million pangolins are hunted annually in Central African forests. The number of pangolins hunted has increased by ∼150% and the proportion of pangolins of all vertebrates hunted increased from 0.04% to 1.83% over the past four decades. However, there were no trends in pangolins observed at markets, suggesting use of alternative supply chains. The price of giant (Smutsia gigantea) and arboreal (Phataginus sp.) pangolins in urban markets has increased 5.8 and 2.3 times respectively, mirroring trends in Asian pangolins. Efforts and resources are needed to increase law enforcement and population monitoring, and investigate linkages between subsistence hunting and illegal wildlife trade

Child-centered food systems: reorienting food systems towards healthy diets for children

Current food systems are failing to guide children towards healthy diets. This paper presents a tool to identify the actions needed to reorient food systems to become more child-centred from a nutrition perspective. To connect the dots between children's lives, their food environments and food supply systems, the tool takes a child-centred, food systems approach. Comprising six methodological steps, the tool starts by measuring and understanding children's realities and then working back up into the system to identify how food environments and supply systems could make relevant foods more or less available, affordable, appealing and aspirational in the contexts of children's lives. The paper spells out the mix of methods needed to make this assessment, gives examples of the data and studies already available and type of insights they provide, and discusses the methodological challenges and gaps. It presents a worked example that shows how following these steps in sequence enables the identification of a package of actions that can act coherently to reorient food systems in the way most likely to have impact on child malnutrition

Methods of nutrition surveillance in low-income countries

Background In 1974 a joint FAO/UNICEF/WHO Expert Committee met to develop methods for nutrition surveillance. There has been much interest and activity in this topic since then, however there is a lack of guidance for practitioners and confusion exists around the terminology of nutrition surveillance. In this paper we propose a classification of data collection activities, consider the technical issues for each category, and examine the potential applications and challenges related to information and communication technology. Analysis There are three major approaches used to collect primary data for nutrition surveillance: repeated cross-sectional surveys; community-based sentinel monitoring; and the collection of data in schools. There are three major sources of secondary data for surveillance: from feeding centres, health facilities, and community-based data collection, including mass screening for malnutrition in children. Surveillance systems involving repeated surveys are suitable for monitoring and comparing national trends and for planning and policy development. To plan at a local level, surveys at district level or in programme implementation areas are ideal, but given the usually high cost of primary data collection, data obtained from health systems are more appropriate provided they are interpreted with caution and with contextual information. For early warning, data from health systems and sentinel site assessments may be valuable, if consistent in their methods of collection and any systematic bias is deemed to be steady. For evaluation purposes, surveillance systems can only give plausible evidence of whether a programme is effective. However the implementation of programmes can be monitored as long as data are collected on process indicators such as access to, and use of, services. Surveillance systems also have an important role to provide information that can be used for advocacy and for promoting accountability for actions or lack of actions, including service delivery. Conclusion This paper identifies issues that affect the collection of nutrition surveillance data, and proposes definitions of terms to differentiate between diverse sources of data of variable accuracy and validity. Increased interest in nutrition globally has resulted in high level commitments to reduce and prevent undernutrition. This review helps to address the need for accurate and regular data to convert these commitments into practice

A 2007 Social Accounting Matrix (SAM) Database for Zambia

This data study includes Zambian Social Accounting Matrix (SAM) for the year 2007. The national SAM is built using new input-output use tables, national accounts, government budgets, and balance of payments, and so provides a detailed representation of the Zambian Economy. It separates 44 activities and commodities; labor is dis-aggregated by education level; and households by rural and urban per capita expenditure quintiles. Information on labor is drawn from the 2006 Living Conditions Monitoring Survey. Finally, the SAM identifies government, investment and foreign accounts. It is therefore an ideal database for conducting economy wide impact assessments, including SAM-based multiplier analysis and computable general equilibrium (CGE) modeling

2010/11 Social Accounting Matrix for Ethiopia

This data study includes social accounting matrix (SAM) for Ethiopia for the year 2010/11. The SAM is an extension of the Standard Nexus Structure. It consists of 63 activity sectors, 67 commodity sectors, three types of factors of production: labor (rural and urban disaggregated by level of education), land, and capital (disaggregated by crops, livestock, mining and other sectors). The household sector is divided spatially into urban and rural households. Rural households are further disaggregated into households that earn crop and/or livestock incomes (i.e., farm households) and those that do no earn incomes from either source (i.e., nonfarm households). Households are further disaggregated into per capita expenditure quintiles. This SAM allows analyzing issues at the detailed level and to better understand the potential impacts of policy changes for both better off and more vulnerable households