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Better tracking SDG progress with fewer resources? A call for more innovative data uses

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  • Dang, Hai-Anh
  • Carletto, Calogero
  • Jolliffe, Dean

Abstract

Existing data are severely insufficient for monitoring progress on the Sustainable Development Goals (SDGs), particularly for poorer countries. While we should continue efforts to produce new, high-quality data, this approach seems not feasible for all poorer countries. We call for a more systematic use of recent innovations with techniques such as data imputation to address existing data challenges. Given some resistance to utilizing new methods for filling data gaps, efforts aiming at changing the current perception and employing a mix of new data collection and data imputation can be useful. We also note that the best and most cost-effective approach would be highly context-specific and depends on various factors such as available budget, logistical capacity, and timeline.

Suggested Citation

  • Dang, Hai-Anh & Carletto, Calogero & Jolliffe, Dean, 2024. "Better tracking SDG progress with fewer resources? A call for more innovative data uses," GLO Discussion Paper Series 1539, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:1539
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    References listed on IDEAS

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    1. Dang, Hai-Anh H. & Serajuddin, Umar, 2020. "Tracking the sustainable development goals: Emerging measurement challenges and further reflections," World Development, Elsevier, vol. 127(C).
    2. Altındağ, Onur & O'Connell, Stephen D. & Şaşmaz, Aytuğ & Balcıoğlu, Zeynep & Cadoni, Paola & Jerneck, Matilda & Foong, Aimee Kunze, 2021. "Targeting humanitarian aid using administrative data: Model design and validation," Journal of Development Economics, Elsevier, vol. 148(C).
    3. Hai-Anh H. Dang & Talip Kilic & Ksenia Abanokova & Gero Carletto, 2024. "Imputing Poverty Indicators without Consumption Data : An Exploratory Analysis," Policy Research Working Paper Series 10867, The World Bank.
    4. Hai‐Anh H. Dang & Stephane Hallegatte & Trong‐Anh Trinh, 2024. "Does global warming worsen poverty and inequality? An updated review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(5), pages 1873-1905, December.
    5. Hai-Anh H. Dang & Peter F. Lanjouw, 2023. "Regression-based imputation for poverty measurement in data-scarce settings," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 13, pages 141-150, Edward Elgar Publishing.
    6. Andy Sumner & Christopher Hoy & Eduardo Ortiz-Juarez, 2020. "Estimates of the impact of COVID-19 on global poverty," WIDER Working Paper Series wp-2020-43, World Institute for Development Economic Research (UNU-WIDER).
    7. Decerf, Benoit & Ferreira, Francisco H.G. & Mahler, Daniel G. & Sterck, Olivier, 2021. "Lives and livelihoods: Estimates of the global mortality and poverty effects of the Covid-19 pandemic," World Development, Elsevier, vol. 146(C).
    8. Ibrahima Sarr & Hai-Anh H. Dang & Carlos Santiago Guzman Gutierrez & Theresa Beltramo & Paolo Verme, 2025. "Using Cross-Survey Imputation to Estimate Poverty for Venezuelan Refugees in Colombia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 177(1), pages 207-251, March.
    9. Isabella S. Smythe & Joshua E. Blumenstock, 2022. "Geographic microtargeting of social assistance with high-resolution poverty maps," Decision Analysis, INFORMS, vol. 119(32), pages 2120025119-, August.
    10. Theresa Beltramo & Hai-Anh Dang & Ibrahima Sarr & Paolo Verme, 2024. "Estimating poverty among refugee populations: a cross-survey imputation exercise for Chad," Oxford Development Studies, Taylor & Francis Journals, vol. 52(1), pages 94-113, January.
    11. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    12. Isabella S. Smythe & Joshua E. Blumenstock, 2022. "Geographic microtargeting of social assistance with high-resolution poverty maps," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(32), pages 2120025119-, August.
    13. World Bank, 2021. "World Development Report 2021 [Informe sobre el desarrollo mundial 2021]," World Bank Publications - Books, The World Bank Group, number 35218, April.
    14. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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    1. Dang, Hai-Anh H & Nguyen, Cuong Viet, 2025. "Employing Data Imputation to Track Poverty and Welfare Trends over Extended Time Periods: An Application to a Poorer Country," IZA Discussion Papers 18236, Institute of Labor Economics (IZA).

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    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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