Introduction

Data & Policy - a peer-reviewed open access journal published by Cambridge University Press (cambridge.og/dap) in association with the Data for Policy Conference - is calling for contributions to a special collection of papers on 'Data for Development Policy: risk, equity, and solving complex problems with limited resources.'

This collection will look directly at the impact of measurement and methods on international development policy decision-making, and how current summary and siloed statistical measures such as the SDGs may obscure risk and uncertainty, and can perpetuate sampling and data biases and impede cost-effective priority setting. These unintended consequences challenge the equitable and efficient use and interpretation of development data.

Measuring economic development has always been challenging, beginning with defining and agreeing on the development dimensions beyond income that matter to well-being. But measurement is also constrained by financial and time costs that result in variable country statistical capacities, the allure of simple measures of central tendency to track progress, and the compartmentalized interventions that fail to leverage complementary inputs to growth, such as education together with health. But increasing climate variability and the COVID-19 pandemic of 2020 have vividly surfaced the critical importance of also measuring risk and the distribution of outcomes, and of understanding resilience over time in vulnerable populations as “development” is not a guaranteed one-way process. These new measurement challenges involve probabilities, distributions, longitudinal data, understanding how and why risk perceptions differ from statistical risk, and combining physical (e.g. precipitation) and social science (e.g. household consumption) units of measurement; that is, potentially more costly and complex measures.  But there are also novel data sources – satellite, social media, the internet – and machine learning methods that can reduce costs.  The challenge will be to exploit these new sources and methods while staying theoretically grounded and aligned with local priorities to most cost-effectively pursue data for development, while resisting the opportunistic use of data and algorithms that exclude sub-populations and can embed other biases.

The papers will discuss how we currently use data to measure ongoing development challenges and emerging threats including climate and inequality, tensions across global north and south communities in data priorities and access, data source trade-offs across novel and traditional data for measuring and informing policy to address those challenges, and how scientists and researchers can better translate the complexities of these measures to decision-makers to increase their utility.

Policy significance of this collection

Valid, reliable, comprehensible, and yet cost-effective measures of development are necessary for evidence-based policy and other decision-makers to effectively prioritize resources across competing budgetary demands and stakeholders.

Key themes

• Trade-offs for evidence-based decision-making in low-resource settings;
• Measurement (e.g. what constitutes development, risk and resilience, equity);
• Sampling and estimate construction biases;
• Novel and traditional data source tradeoffs;
• Inductive (data driven) and deductive theory.

Timetable

Authors are welcome to submit manuscripts as soon as they are ready, with a final deadline of March 3, 2023. Articles will be published as soon as possible after acceptance, in the interest of allowing authors to disseminate their work without unnecessary delay, and added to a curated page for the collection of articles. An editorial reflecting on their insights will be published in 2023.

Submission process

Authors should submit articles through the Data & Policy ScholarOne site, using the 'Data & Development Policy' special collection option when prompted in the submission forum.

Before submission, authors should familarise themselves with the Instructions for Authors (which include LaTeX, Overleaf and Word templates for author convenience). Note that Data & Policy publishes the following types of articles, which authors will be prompted to select from on submission:

• Research articles that use rigorous methods that demonstrate how data science can inform or impact policy by, for example, improving situation analysis, predictions, public service design, and/or the legitimacy and/or effectiveness of policy making. Published research articles are typically reviewed by three peer reviewers: two assessing the academic or methodological rigour of the paper; and one providing an interdisciplinary or policy-specific perspective.
• Commentaries are shorter articles that discuss and/or problematize an issue relevant to the Data & Policy scope. Commentaries are typically reviewed by two peer reviewers.
• Translational papers are contributions that show how data science principles, techniques and technologies are being used in practice in organisational settings to improve policy outcomes. They may present original findings but are less embedded in the scholarly literature as research articles. They are typically reviewed by two peer reviewers, who assess the rigour and policy significance of the paper.
• Data papers that provide a structured description of an openly available dataset with the aim of encouraging its re-use for further resesarch

Note that the journal encourages authors to make replication data and code available in an open repository, where this is possible (see the research transparency policy). All authors must provide a Data Availability Statement in their article that explains where the replication material resides, if it is available, and if not, the reason why it cannot be made accessible.

Guest Editors

• Didier Alia (University of Washington)
• C. Leigh Anderson (University of Washington)
• Federico Trindade (University of Washington)
• Masaru Yarime (Hong Kong University of Science and Technology)