Agricultural growth and investment options for poverty reduction in Nigeria

This study uses an economy-wide, dynamic computable general equilibrium (DCGE) model to analyze the ability of growth in various agricultural subsectors to accelerate overall economic growth and reduce poverty in Nigeria over the next years (2009-17). In addition, econometric methods are used to assess growth requirements in agricultural public spending and the relationship between public services and farmers’ use of modern technology. The DCGE model results show that if certain agricultural subsectors can reach the growth targets set by the Nigerian government, the country will see 9.5 percent annual growth in agriculture and 8.0 percent growth of GDP over the next years. The national poverty rate will fall to 30.8 percent by 2017, more than halving the 1996 poverty rate of 65.6 percent and thereby accomplishing the first Millennium Development Goal (MDG1). This report emphasizes that in designing an agricultural strategy and prioritizing growth, it is important to consider the following four factors at the subsectoral level: (i) the size of a given subsector in the economy; (ii) the growth-multiplier effects occurring through linkages of the subsector with the rest of the economy; (iii) the subsector-led poverty reduction-growth elasticity; and (iv) the market opportunities and price effects for individual agricultural products. In analyzing the public investments that would be required to support a 9.5 percent annual growth in agriculture, this study first estimates the growth elasticity of public investments using historical spending and agricultural total factor productivity (TFP) growth data. The results show that a 1 percent increase in agricultural spending is associated with a 0.24 percent annual increase in agricultural TFP. With such low elasticity, agricultural investments must grow at 23.8 percent annually to support a 9.5 percent increase in agriculture. However, if the spending efficiency can be improved by 70 percent, the required agricultural investment growth becomes 13.6 percent per year. The study also finds that investments outside agriculture benefit growth in the agricultural sector. Thus, assessments of required growth in agricultural spending should include the indirect effects of nonagricultural investments and emphasize the importance of improving the efficiency of agricultural investments. To further show that efficiency in agricultural spending is critically important to agricultural growth, this study utilizes household-level data to empirically show that access to agricultural services has a significantly positive effect on the use of modern agricultural inputs.Agricultural growth, agricultural investments, agricultural services, Development strategies, Dynamic Computable General Equilibrium (DCGE), low elasticity, market opportunities, Millennium Development Goals (MDG), modern agricultural inputs, nonagricultural investments, Poverty reduction, Public investments, Total factor productivity (TFP),

The Impacts of Trade Liberalization on Poverty in Nigeria: Dynamic Simulations in a CGE Model

The study examines the effects that trade liberalization will have on poverty in Nigeria. Previous studies have been limited by static and partial equilibrium analysis. We use a Dynamic Computable General Equilibrium Model to analyze this issue. The more favorably affected sectors are capital intensive; therefore, capital income improves over time while land and labor income reduce. This has positive implications for urban households and negative implications for rural households due to the dependence of the latter on mostly land and labor income. As a result, urban poverty decreases in the short and long run while rural poverty increases in both periods. Policies to improve the agricultural sector will thus have to be implemented before or concurrently with trade liberalization in order for it to have a pro-poor effect. In this way, the rural areas which obtain most of their income from this sector will respond more positively to trade liberalization.CGE Model, Trade liberalisation, Nigeria, Poverty, Dynamic, ECOWAS, Import tariffs

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A 2006 Social Accounting Matrix for Nigeria: Methodology and Results

The 2006 Nigeria SAM is a comprehensive, economy-wide data framework, representing the structure of the Nigerian economy; the links among production activities, income distribution, consumption of goods/services, savings and investment, and foreign trade of the economic agents in year 2006. This 2006 Nigeria SAM is a 61 sector square matrix table with the column and row beginning with activities account, followed by commodities account and thereafter accounts for the economic agent in the Nigerian economy. Each cell in the matrix represents the flow of economic activities in monetary terms from a column account (expenditure or outflow) to a row account (income or inflow). Also, each activity and commodity account begins with letter “a” and “c” respectively. This 2006 SAM was built for the dynamic CGE (DCGE) model that examined the growth and investment options available in the agricultural sector for reducing poverty in Nigeria, and was an integral part of the Agricultural Policy Support Facilites activities for strengthening evidence-based policymaking in Nigeria. Given the agricultural policy analysis focus of the SAM and DCGE model, 34 sector of the SAM are under agriculture and included key cash and food crops as well as livestock subsector. The 2006 Nigeria SAM also includes 12 manufacturing (such as beef, textiles, and wood products); 2 mining sector (including crude petroleum and natural gas); and 13 service sectors (such as building and construction, electricity and water, and hotels and restaurants). While the total number of sector for the SAM is 61, the commodities account is 62 as fertilizer was treated as commodity rather than activity. The 2006 SAM data files comprise two worksheets; one for the SAM data and the other containing legend to the SAM data. The value for each of the cell entries is reported in naira million (2006 prices). The data used for building this SAM were obtained from various sources including but not limited to publications of the National Bureau of Statistics (NBS), the Central Bank of Nigeria (CBN), and the Federal Ministry of Agriculture and Water Resources (FMAWR). Data from an earlier SAM of the country developed by United Nations Development Programme (UNDP), 1995 are also used, and was balanced using the cross entropy estimation method. The SAM was built following the International Food Policy Research Institute (IFPRI) standard format (Lofgren et al. 2001).IFPRI1DSGDSource: The 2006 Nigeria SAM was assembled by Regional Strategic Analysis and Knowledge Support System (ReSAKSS) in collaboration with the International Food Policy Research Institute (IFPRI) and the Federal Ministry of Agriculture and Water Resources (FMAWR), Nigeria under the Canadian International Development Agency (CIDA) sponsored Agricultural Policy Support Facility (APSF)

Strengthening macro economic policy modeling and analysis in Nigeria

The Computable General equilibrium (CGE) model has been successfully used to inform macro and micro policymaking in many developed countries. In developing countries like Nigeria, their influence on policy is limited - in spite of their proven usefulness - due to the low number of people who understand them and/or use them appropriately. Therefore, this workshop was designed to assist with these limitations by acquainting participants with introductory knowledge of CGE modeling with use of the GAMS software (Appendix 1). The specific objectives were: · Expose participants to a policy planning tool, the CGE Model · Acquaint participants with the Nigeria 2006 Social Accounting Matrix (SAM) · Study the effect of policy scenarios on growth and poverty eradication in Nigeria. It was attended by 21 participants drawn from various government departments, research institutions, and universities.Non-PRIFPRI5; GRP32; NSSPDSGD5 page

Knowledge management and development targets in Nigeria

Non-PRIFPRI1; NSSPDSG