Social Network Analysis (SNA) is a promising yet underutilized tool in the international development field. SNA entails collecting and analyzing data to characterize and visualize social networks, where nodes represent network members and edges connecting nodes represent relationships or exchanges among them. SNA can help both researchers and practitioners understand the social, political, and economic relational dynamics at the heart of international development programming. It can inform program design, monitoring, and evaluation to answer questions related to where people get information; with whom goods and services are exchanged; who people value, trust, or respect; who has power and influence and who is excluded; and how these dynamics change over time. This brief advances the case for use of SNA in international development, outlines general approaches, and discusses two recently conducted case studies that illustrate its potential. It concludes with recommendations for how to increase SNA use in international development.

Young people face myriad obstacles in finding work, leaving more than 71 million unemployed globally. Digital professional networking platforms, such as LinkedIn, may give youth an effective way to find, retain, and advance in work. We explore platform use in developing economies and present new data on a low-cost, successful way to teach youth how to use these platforms. We end by drawing policy implications for the education and workforce development field.

School-to-work transition data are an important component of labor market information systems (LMIS). Policy makers, researchers, and education providers benefit from knowing how long it takes work-seekers to find employment, how and where they search for employment, the quality of employment obtained, and how steady it is over time. In less-developed countries, these data are poorly collected, or not collected at all, a situation the International Labour Organization and other donors have attempted to change. However, LMIS reform efforts typically miss a critical part of the picture—the geospatial aspects of these transitions. Few LMIS systems fully consider or integrate geospatial school-to-work transition information, ignoring data critical to understanding and supporting successful and sustainable employment: employer locations; transportation infrastructure; commute time, distance, and cost; location of employment services; and other geographic barriers to employment. We provide recently collected geospatial school-to-work transition data from South Africa and Kenya to demonstrate the importance of these data and their implications for labor market and urban development policy.

Prix: Togo, Bénin, Burkina: 250CFA Zone CFA: 300 F Europe et autres pays: 1 euro --Abonnement: Contacter 22 61 35 29 / 90 05 94 28 P.3 P.7 En application du projet PDGM financé par la Banque mondiale La Faculté des Sciences va bientôt former des professionnels du secteur minier La loterie visa américaine Un espoir pour les candidats à l’immigration P.4 Mise en œuvre du Programme d’appui à la décentralisation (PAD) Le groupement AZ-Consult/BETRA/ GERMS pour s’assurer des besoins des villes de Tsévié, Kpalimé et Sokodé

Labor markets desperately need information to function effectively and efficiently, making labor market information systems critical public investments. Yet government systems face significant challenges in collecting quality data, turning it into useable market intelligence, and disseminating it in a timely, relevant manner, a situation more acute in developing countries. The rise of private, real-time labor market information (LMI), such as web-based job posting analytics, social network inferences, crowdsourcing, and mobile phone polling, has garnered interest and questioned the dominance of traditional approaches. This brief explores the use of real-time LMI and presents interviews conducted with international donor officials to gain their perspectives on its applicability in developing countries. I suggest that real-time LMI is unlikely to supplant traditional LMI collection anytime soon, and I dispel notions that these new approaches might leapfrog current data collection challenges. Real-time LMI can provide useful in special cases and for supplemental analysis, an additional lubricant for labor markets that suffer from weak data. Policy that supports the improvement of traditional LMI and promotes access to real-time LMI is warranted.