Routledge Studies in Development Economics

1 Introductions Oscar A. Garc­a and Prashanth Kotturi

2 Evaluation and the Sustainable Development Goals: Opportunities and Constraints Marco Segone

3 Information and Communication Technologies for Evaluation (ICT4Eval): Theory and Practice Oscar A. Garc­a, Jyrki Pulkkinenand and Prashanth Kotturi

A Data collection: Faster, cheaper, more accurate

B Data analysis: The machine learning revolution

C Dissemination and learning: Reaching a global audience

D Case studies: Geospatial analysis in environmental evaluation Juha Ilari Uitto, Anupam Anand and Geeta Batra; Simulated field visits in fragile and conflict environments: Reaching the most insecure areas of Somalia virtually Monica Zikusooka; Analysing stories of change: engaging beneficiaries to make sense of data Michael Carbon and Hamdi Ahmedou; Is there a role for machine learning in the systematic review of evidence? Edoardo Masset; Using machine learning to improve impact evaluations Paul Jasper; Using geospatial data to produce fine-scale humanitarian maps Gaurav Singhal, Lorenzo Riches and Jean-Baptiste Pasquier; Using and sharing real-time data during fieldwork Simone Lombardini and Emily Tomkys

E ICTs in practice â" the case for cautious optimism

F Evaluation 2.0; turning dilemmas to dividends?

4 Big data analytics and development evaluation: Optimism and caution, Michael Bamberger

A Some themes from the big data literature

B Demystifying big data

C Where is the big data revolution headed?

D Does big data apply to development evaluation? Should evaluators care about it?

E The great potential for integrating big data into development evaluation

F Big data and development evaluation: the need for caution

G Overcoming barriers to big data use in evaluation

5 Technology, Biases and Ethics: Exploring the Soft Sides of Information and Communication Technologies for Evaluation (ICT4Eval), Linda Raftree

A Factors affecting information technology access and use among the most vulnerable

B Data and technology alone cannot ensure inclusion

C Inclusiveness of access and use affect the representativeness of big data

D Bias in big data, artificial intelligence and machine learning

E Protecting data subjectsâ rights in tech-enabled, data-led exercises

F Improving data privacy and protection in the development sector

6 Technology and its implications for nations and development partners Oscar A. Garc­a and Prashanth Kotturi

A Structural transition and pathways for economic development

B Who has technology affected the most?

C A Ludditeâs nightmare or a passing phenomenon?

D Implications for sustainable rural development

E Dealing with disruptions and moving forward

F Implications for development partners

7 Conclusions Oscar A. Garc­a and Prashanth Kotturi