This paper applies the framework for pro-poor analysis to welfare changes from a CGE-microsimulation model to analyze what are the better or worse models for agriculture modernization, and to estimate the contribution of growth and redistribution to changes in poverty in DRC. The findings indicate that labor-using technological change generates absolute and relative pro-poor effects whereas capital-using technological change leads to immiserizing growth. More importantly, the results suggest that labor-using technological change can be independently sufficient for reducing poverty via the income growth effects. This study also highlights how developing input supply networks, securing tenure among smallholders, and improving access to land for women are important for pro-poor agricultural modernization.

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The Ethiopian economy has grown significantly and the government has prioritized industrial skills development and expanded technical and vocational education and training (TVET). However, mismatches between the skills available and the skills required are widespread and the unemployment rate for TVET graduates is high. Little scholarly effort has been made to empirically identify the exact types and domains of skills in which these supply-demand mismatches happen. The present study relies on interviews with 30 vocational trainers, 19 employees, 13 factory managers and 3 garment industry experts. To measure the perception gaps between the supply and demand sides of worker skills and explain why mismatches occur, we conducted an assessment in which assessors from among the factory managers and vocational trainers along with the three industry experts concurrently graded the garment-manufacturing vocational skills of the same workers. For this purpose, we developed a unique instrument that captures the knowledge and skills of workers in real work environments. The analysis reveals that TVET trainers expect students to have comprehensive skills and grade the skills of workers more generously, whereas factory managers expect not variety but quality, and score workers' performance more critically. Differences in the educational backgrounds and practical experience of assessors contribute to these gaps. The evidence from this study suggests that the vocational skills assessment instrument we have developed for our research is valid and can serve as a basis for future large-scale performance assessments.

PurposeThis study uses machine machine learning techniques to assess industrial development in Africa.Design/methodology/approachThis study uses nightlight time data and machine learning techniques to assess industrial development in Africa.FindingsThis study provides evidence on how machine learning techniques and nightlight data can be used to assess economic development in places where subnational data are missing or not precise. Taken together, the research confirms four groups of important determinants of industrial growth: natural resources, agriculture growth, institutions and manufacturing imports. Our findings indicate that Africa should follow a more multisector approach for development, putting natural resources and agriculture productivity growth at the forefront.Originality/valueStudies on the use of machine learning (with insights from nightlight satellite images) to assess industrial development in Africa are sparse.

PurposeThis article focuses on the perception gaps between teachers and students of technical and vocational education and training (TVET) related to garment production and the reasons behind such gaps. Garment production is the priority sector for the Ethiopian government, which plans to make it the driver of export-oriented growth. At the same time, it is among the programs that demonstrate the lowest employment rates.Design/methodology/approachA questionnaire was developed by the authors. It was completed by 162 students and 53 teachers in garment-related programs of seven TVET colleges in Addis Ababa, the capital city of Ethiopia.FindingsThe findings show that while teachers tend to highlight the importance of practical skills, students desire broader coverage of practical and managerial skills and entrepreneurship. The expectations differ not only based on the person's recognition of labor market conditions but also by the conviction of the efficacy of the education and training system itself. Teachers tend to be persistent on conventional approaches of teaching, while the advanced training on new approaches based on the competency-based training (CBT) significantly impacts on their attitude. Meanwhile, students' perceptions are largely based on their job aspirations and motivations for schooling.Practical implicationsThe authors’ findings may serve to improve the relevance of the Ethiopian Occupation Standards.Originality/valueThe unique feature of this study is that the authors measure skills from multiple dimensions. While the authors examine participants' perceptions of occupation-specific skills, they also analyze the relationships of these perceptions with attitudinal and cognitive skills.