Browse by Theme: Inclusive Governance

Gilbert Muyumbu’s recent blog set out a challenge to the Doing Development Differently (DDD) narrative: does DDD risk reproducing or strengthening the unequal power relations between state and citizen, elite and poor, that cause poverty, insecurity and injustice for so many? Gilbert’s blog sparked a lively debate, online on Twitter – and within the CARE Inclusive Governance Team. Here’s how that discussion went – and four suggestions that emerged for how the DDD agenda can better listen to and engage Southern civil society.

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The Doing Development Differently (DDD) agenda promises a number of changes in the development business. It has a manifesto that advises development practitioners to start with problems, not ready-made solutions; understand and engage with the local politics; support locally-led reform; not be afraid to try, fail, and try again; and think like an entrepreneur, taking risks, and making small bets.

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This report highlights some of the accomplishments and learning over the past few years in promoting Inclusive Governance across the CARE International confederation.

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In August this year, CARE International in Ghana together with its partners – OXFAM and ISODEC – commenced a pilot evaluation of the USAID-funded Ghana’s Strengthening Accountability Mechanisms (GSAM) project, using an innovative approach to impact evaluation called Contribution Tracing. Here’s what we did, and five key lessons we learned.

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In this final blog in the 3-part blog series on Contribution Tracing, we want to show you how an ancient monk, who has been dead for over 250 years, can help us to find data with the highest probative value – in other words, helps us find strong, reliable evidence.

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In the second in this 3-part blog series on Contribution Tracing we turn our attention to finding out which items of evidence are the most powerful.

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This 3-part blog series highlights a new approach to impact evaluation called Contribution Tracing. The blog series explains key steps in Contribution Tracing that can guide evaluators, and those commissioning evaluations, to avoid common data traps, by identifying and gathering only the strongest data.

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