TY - RPRT TI - Insights for Influence: Understanding Impact Pathways in Crisis Response AU - Clark, Louise AU - Carpenter, Jo AU - Taylor, Joe AB - The Covid-19 Responses for Equity (CORE) programme was a three-year initiative funded by the Canadian International Development Research Centre (IDRC) that brought together 20 projects from across the global South to understand the socioeconomic impacts of the Covid-19 pandemic, improve existing responses, and generate better policy options for recovery. The research covered 42 countries across Africa, Asia, Latin America, and the Middle East to understand the ways in which the pandemic affected the most vulnerable people and regions, and deepened existing vulnerabilities. Research projects covered a broad range of themes, including macroeconomic policies for support and recovery; supporting essential economic activity and protecting informal businesses, small producers, and women workers; and promoting democratic governance to strengthen accountability, social inclusion, and civil engagement. The Institute of Development Studies (IDS) provided knowledge translation (KT) support to CORE research partners to maximise the learning generated across the research portfolio and deepen engagement with governments, civil society, and the scientific community. As part of this support, the IDS KT team worked with CORE project teams to reconstruct and reflect on their impact pathways to facilitate South-South knowledge exchange on effective strategies for research impact, and share learning on how the CORE cohort has influenced policy and delivered change. This report presents an overview of these impact pathways and the lessons learnt from a selection of the projects chosen to represent the diversity of approaches to engage policymakers, civil society, and the media to generate and share evidence of the effect of the pandemic on diverse vulnerable groups. CY - Brighton DA - 2023/11/10/ PY - 2023 DP - opendocs.ids.ac.uk LA - en PB - Institute of Development Studies ST - Insights for Influence UR - https://opendocs.ids.ac.uk/opendocs/handle/20.500.12413/18172 Y2 - 2023/11/13/12:39:09 ER - TY - JOUR TI - Data-powered positive deviance: Combining traditional and non-traditional data to identify and characterise development-related outperformers AU - Albanna, Basma AU - Heeks, Richard AU - Pawelke, Andreas AU - Boy, Jeremy AU - Handl, Julia AU - Gluecker, Andreas T2 - Development Engineering AB - The positive deviance approach in international development scales practices and strategies of positively-deviant individuals and groups: those who are able to achieve significantly better development outcomes than their peers despite having similar resources and challenges. This approach relies mainly on traditional data sources (e.g. surveys and interviews) for identifying those positive deviants and for discovering their successful solutions. The growing availability of non-traditional digital data (e.g. from remote sensing and mobile phones) relating to individuals, communities and spaces enables data innovation opportunities for positive deviance. Such datasets can identify deviance at geographic and temporal scales that were not possible before. But guidance is needed on how this new data can be employed in the positive deviance approach, and how it can be combined with more traditional data to gain deeper, more meaningful, and context-aware insights. This paper presents such guidance through a data-powered method that combines both traditional and non-traditional data to identify and understand positive deviance in new ways and domains. This method has been developed iteratively through six development projects covering five different domains – sustainable cattle ranching, agricultural productivity, rangeland management, research performance, crime control – with global and local development partners in six countries. The projects combine different types of non-traditional data with official statistics, administrative data and interviews. Here, we describe a structured method for data-powered positive deviance developed from the experience of these projects, and we reflect on lessons learned. We hope to encourage and guide greater use of this new method; enabling development practitioners to make more effective use of the non-traditional digital datasets that are increasingly available. DA - 2022/01/01/ PY - 2022 DO - 10.1016/j.deveng.2021.100090 DP - ScienceDirect VL - 7 SP - 100090 J2 - Development Engineering LA - en SN - 2352-7285 ST - Data-powered positive deviance UR - https://www.sciencedirect.com/science/article/pii/S2352728521000324 Y2 - 2022/08/24/11:15:24 ER - TY - RPRT TI - DPPD Handbook. A step-by-step guide for development practitioners to apply the Data Powered Positive Deviance method AU - DPPD AB - The Method Positive Deviance (PD) is based on the observation that in every community or organization, there are a few individuals who achieve significantly better outcomes than their peers, despite having similar challenges and resources. These individuals are referred to as positive deviants, and adopting their solutions is what is referred to as the PD approach¹. The method described in this Handbook follows the same logic as the PD approach but uses pre-existing, non-traditional data sources instead of — or in conjunction with — traditional data sources. Non-traditional data in this context broadly refers to data that is digitally captured (e.g. mobile phone records and financial data), mediated (e.g. social media and online data), or observed (e.g. satellite imagery). The integration of such data to complement traditional data sources generally used in PD is what we refer to as Data Powered Positive Deviance² (DPPD). The digital data opportunity Recent developments in the availability of digital data provide an opportunity to look for positive deviants³ in new ways and in unprecedented geographical and on temporal scales. A number of studies⁴ have described the challenges related to the application of the PD approach in development. Given these challenges, there are obvious opportunities for innovation in PD and our particular interest here is in the innovative opportunities offered by non-traditional data, following the increasing “datafication” of development and the growing availability of big datasets in a variety of development sectors⁵. DPPD builds on this and expands our ability to extract value from non-traditional digital data while providing a systematic process for leveraging local know-how and the collective wisdom of communities. Data Powered Positive Deviance The DPPD method described in this Handbook emerged from a process of research and testing and follows the same stages as the PD approach. The difference is that DPPD integrates pre-existing, non-traditional data across the five stages, requiring a series of new and specific methods and practices that are not required in the PD approach. The first stage is also somewhat different because it not only defines the problem, but it also checks if it is suitable and feasible to use the DPPD method for the proposed project. Table 1 lists the five stages of the DPPD method. This Handbook dedicates a section to each stage. Stage 1 Assess problem-method fit Stage 2 Determine positive deviants Stage 3 Discover underlying factors Stage 4 Design and implement interventions Stage 5 Monitor and evaluate DA - 2021/11// PY - 2021 PB - DPPD Initiative UR - https://static1.squarespace.com/static/614dae085246883818475c39/t/619f7f163ed02a77d13fd1bd/1637842759939/DPPD+Handbook+Nov+2021.pdf Y2 - 2021/11/25/15:51:12 ER - TY - RPRT TI - Lessons Learned from Applying the Data Powered Positive Deviance AU - Pawelke, Andreas AU - Glücker, Andreas AU - Albanna, Basma AU - Boy, Jeremy AB - This report presents six learnings from four pilot projects conducted by the Data Powered Positive Deviance (DPPD) initiative, a global collaboration between the GIZ Data Lab, the UNDP Accelerator Labs Network, the University of Manchester Center for Digital Development, and UN Global Pulse Lab Jakarta. The pilots seek out grassroots solutions to development challenges that range from the interaction between livestock farming and deforestation to gender-based violence and insecurity in dense urban environments in Ecuador, Mexico, Niger and Somalia. The learnings relate to the early stages of the DPPD method, originally proposed by Albanna & Heeks [1], and focus mainly on the access to, and use of digital data. They are summarized as follows: 1. Remain flexible in the face of data unavailability 2. Leverage existing partnerships for data access 3. Map and fill know-how gaps early 4. Scale with caution 5. Look at deviance over time 6. Look beyond individual or community practices and behavior The report is written for development practitioners, data analysts, domain experts, and more generally anyone interested in using new data sources and technologies to uncover successful local solutions to development challenges. DA - 2021/10// PY - 2021 PB - DPPD ER -