Information and Communication Technologies for Development Evaluation

173 Pages·2019·8.588 MB·English

by  Oscar A. García and Prashanth Kotturi

Information and Communication
Technologies for Development
Evaluation
Written by a team of expert practitioners at the Independent Office of Eval-
uation of International Fund for Agricultural Development (IFAD), this
book gives an insight into the implications of new and emerging technolo-
gies in development evaluation.
Growing technologies such as big data analytics, machine learning and
remote sensing present new opportunities for development practitioners and
development evaluators, particularly when measuring indicators of the Sus-
tainable Development Goals. The volume provides an overview of informa-
tion and communication technologies (ICTs) in the context of evaluation,
looking at the theory and practice, and discussing how the landscape may
unfold. It also considers concerns about privacy, ethics and inclusion, which
are crucial issues for development practitioners and evaluators working in
the interests of vulnerable populations across the globe. Among the contri-
butions are case studies of seven organizations using various technologies
for data collection, analysis, dissemination and learning.
This valuable insight into practice will be of interest to researchers, practition-
ers and policymakers in development economics, development policy and ICT.
Oscar A. García is the Director of the Independent Office of Evaluation (IOE)
of the International Fund for Agricultural Development (IFAD). Before
joining IFAD, Oscar served as the head of the advisory services at UNEP –
Technology, Industry and Economics Division, Paris, providing guidance to
the Partnership for Action on Green Economy. He was senior evaluation ad-
visor for the United Nations Development Programme (UNDP) Evaluation
Office overseeing programmatic evaluations in Africa, Asia, Latin America
and the Caribbean. Mr. Garcia has more than 25 years of professional expe-
rience, combining operational and managerial practice with results-based
management, strategic planning and evaluation expertise.
Prashanth Kotturi joined the IOE in October 2012 and is currently working
as an Evaluation Analyst. Since then, he has worked in lead and support
roles on a wide range of evaluations ranging from project evaluations, coun-
try portfolio evaluations to corporate-level evaluations and evaluation syn-
thesis. Before IOE, Prashanth has worked in the financial services industry
and with microfinance institutions in his home country, India.
Routledge Studies in Development Economics
142 The Service Sector and Economic Development in Africa
Edited by Evelyn F. Wamboye and Peter J. Nyaronga
143 Macroeconomic Policy for Emerging Markets
Lessons from Thailand
Bhanupong Nidhiprabha
144 Law and Development
Theory and Practice
Yong-Shik Lee
145 Institutions, Technology and Development in Africa
Jeffrey James
146 Urban Policy in Latin America
Towards the Sustainable Development Goals?
Edited by Michael Cohen, Maria Carrizosa and Margarita Gutman
147 Unlocking SME Finance in Asia
Roles of Credit Rating and Credit Guarantee Schemes
Edited by Naoyuki Yoshino and Farhad Taghizadeh-Hesary
148 Full and Productive Employment in Developing Economies
Towards the Sustainable Development Goals
Rizwanul Islam
149 Information and Communication Technologies for Development
Evaluation
Edited by Oscar A. García and Prashanth Kotturi
For more information about this series, please visit www.routledge.com/
series/SE0266
Information and Communication
Technologies for Development
Evaluation
Edited by
Oscar A. García and Prashanth Kotturi
First published 2020
by Routledge
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and by Routledge
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Routledge is an imprint of the Taylor & Francis Group, an informa
business
© 2020 selection and editorial matter, Oscar A. García and
Prashanth Kotturi; individual chapters, the contributors
The right of Oscar A. García and Prashanth Kotturi to be identified
as the authors of the editorial material, and of the authors for their
individual chapters, has been asserted in accordance with sections
77 and 78 of the Copyright, Designs and Patents Act 1988.
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explanation without intent to infringe.
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ISBN: 978-0-367-13714-4 (hbk)
ISBN: 978-0-429-02823-6 (ebk)
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Contents
List of Figures, Tables and Boxes ix
List of Contributors xi
Acknowledgements xiii
Introduction 1
OSCAR A. GARCíA AND PRASHANTH KOTTURI
The Challenge of Evaluating the Sustainable Development Goals 2
ICTs with Promise for Evaluators 4
Privacy, Equity and Biases 5
ICT4Eval – The Publication 7
1 Evaluation and the Sustainable Development Goals:
Opportunities and Constraints 9
MARCO SEGONE
The Greatest Opportunity and the Greatest Challenge for the
Global Evaluation Community 10
How Do We Assess Whether Development Interventions
Are Relevant, and Are Having an Impact in Decreasing
Inequality and Improving the Welfare of the Worst-Off Groups? 11
How Do We Carry Out Evaluation Given the Complexity of
the SDGs? 14
How Can We Take Advantage of New Technologies to Address
New Challenges? 18
How Can We Strengthen the Capacities of Governments,
Civil Society Organizations, and Parliamentarians to
Evaluate Whether Interventions Are Having Equitable
Outcomes for Marginalized Populations? 20
Individual and Institutional Evaluation Capacities
Enabled by a Supportive Environment 21
Fostering Demand for and Supply of Evaluation 23
Conclusion 24
vi Contents
2 Information and Communication Technologies for Evaluation
(ICT4Eval): Theory and Practice 27
OSCAR A. GARCíA, JYRKI PULKKINEN,
PRASHANTH KOTTURI ET AL.
Data Collection: Faster, Cheaper, More Accurate 27
Data Analysis: The Machine Learning Revolution 29
Dissemination and Learning: Reaching a Global Audience 29
ICTs in Practice – the Case for Cautious Optimism 65
Sunk Costs vs. an Opportunity 65
Do It Yourself vs. Outsourcing 66
Mainstreaming ICTs into Operations 67
Evaluation 2.0: Turning Dilemmas to Dividends? 68
3 Big Data Analytics and Development Evaluation:
Optimism and Caution 76
MICHAEL BAMBERGER
Some Themes from the Big Data Literature 77
Demystifying Big Data 79
Defining Big Data and NIT 79
Big Data and Data Analytics 80
The Data Continuum 87
The NIT Ecology and the Linkages to Development
Evaluation 88
Where Is the Big Data Revolution Headed? 89
Does Big Data Apply to Development Evaluation? Should
Evaluators Care About It? 91
The Great Potential for Integrating Big Data into Development
Evaluation 92
Big Data and Development Evaluation: The Need for Caution 96
Overcoming Barriers to Big Data Use in Evaluation 102
Future Scenarios for Development Evaluation in the Age
of Big Data 104
New Skills Required for Evaluation Offices, Evaluators
and Data Scientists 106
4 Technology, Biases and Ethics: Exploring the Soft Sides of
Information and Communication Technologies for Evaluation
(ICT4Eval) 111
LINDA RAFTREE
Factors Affecting Information Technology Access and
Use Among the Most Vulnerable 112
Contents vii
Data and Technology Alone Cannot Ensure Inclusion 115
Inclusiveness of Access and Use Affect the
Representativeness of Big Data 116
Bias in Big Data, Artificial Intelligence and Machine Learning 118
Protecting Data Subjects’ Rights in Tech-Enabled, Data-Led
Exercises 121
Improving Data Privacy and Protection in the Development Sector 123
5 Technology and Its Implications for Nations and Development
Partners 128
OSCAR A. GARCíA AND PRASHANTH KOTTURI
Structural Transition and Pathways for Economic Development 128
Income - Who Has Technology Affected the Most? 133
A Luddite’s Nightmare or a Passing Phenomenon? 137
Geography - Implications for Sustainable Rural Development 138
Dealing with Disruptions and Moving Forward 139
Implications for Development Partners 142
Conclusions 146
OSCAR A. GARCíA AND PRASHANTH KOTTURI
The Story This Far 146
Key Takeaways 148
Looking Ahead 149
Index 153

List of Figures, Tables and Boxes
Figures
1.1 National average reduction in income poverty in a
hypothetical country 13
1.2 Reduction in income poverty in a hypothetical country,
by gender 14
1.3 The SDGs as a system of goals and targets 15
1.4 Links between the SDGs through targets:
an aggregated picture 16
1.5 Links among Sustainable Development Goal 10 (inequality)
and other goals 17
2.1 A spatial model represents the real world as a combination
of layers or themes 31
2.2 Globally distributed GEF-supported PAs overlaid with sites
of conservation importance 33
2.3 Forest change data used for global analysis, an example of a
PA in Mexico 34
2.4 Economic valuation of carbon sequestrated at each
GEF-supported project site 36
2.5 Geospatial analysis based on satellite data shows decreasing
vegetation at the mouth of the Kagera River, Uganda 37
2.6 In a systematic review, 20% of the search delivered 80% of
the evidence 48
2.7 Google search trends for the term “machine learning” 50
2.8 Nightlights classified as low (red), medium (yellow) and
high (green) in northern Nigeria 58
2.9 Trained vs. test models 59
2.10 Satellite imagery and neural networks 60
2.11 Predictions at 500m by 500m resolution of food insecurity
for a town in Mali, based on a 2018 Emergency Food
Security Assessment 60
3.1 The components of NIT 80
3.2 The four stages of the data analytics cycle 82
3.3 The data continuum 88