IDEAS home Printed from https://ideas.repec.org/p/iza/izapps/pp215.html
   My bibliography  Save this paper

Better Tracking SDG Progress with Fewer Resources? A Call for More Innovative Data Uses

Author

Listed:
  • Dang, Hai-Anh H

    (World Bank)

  • Carletto, Calogero

    (World Bank)

  • Jolliffe, Dean

    (World Bank)

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 H & Carletto, Calogero & Jolliffe, Dean, 2024. "Better Tracking SDG Progress with Fewer Resources? A Call for More Innovative Data Uses," IZA Policy Papers 215, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izapps:pp215
    as

    Download full text from publisher

    File URL: https://docs.iza.org/pp215.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. Dang, Hai-Anh H & Hallegatte, Stephane & Trinh, Trong-Anh, 2023. "Does Global Warming Worsen Poverty and Inequality? An Updated Review," IZA Discussion Papers 16570, Institute of Labor Economics (IZA).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pape, Utz & Verme, Paolo, 2023. "Measuring Poverty in Forced Displacement Contexts," GLO Discussion Paper Series 1245, Global Labor Organization (GLO).
    2. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    3. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
    4. 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.
    5. Dang, Hai-Anh H & Kilic, Talip & Abanokova, Kseniya & Carletto, Calogero, 2024. "Imputing Poverty Indicators without Consumption Data: An Exploratory Analysis," IZA Discussion Papers 17136, Institute of Labor Economics (IZA).
    6. Sarr, Ibrahima & Dang, Hai-Anh H. & Guzman Gutierrez, Carlos Santiago & Beltramo, Theresa & Verme, Paolo, 2024. "Using Cross-Survey Imputation to Estimate Poverty for Venezuelan Refugees in Colombia," GLO Discussion Paper Series 1534, Global Labor Organization (GLO).
    7. 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.
    8. Dang, Hai-Anh H & Kilic, Talip & Hlasny, Vladimir & Abanokova, Kseniya & Carletto, Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment," IZA Discussion Papers 16792, Institute of Labor Economics (IZA).
    9. Hai-Anh H. Dang & Paolo Verme, 2023. "Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 653-679, April.
    10. Bijlsma Ineke & van den Brakel Jan & van der Velden Rolf & Allen Jim, 2020. "Estimating Literacy Levels at a Detailed Regional Level: an Application Using Dutch Data," Journal of Official Statistics, Sciendo, vol. 36(2), pages 251-274, June.
    11. John A. Maluccio, 2009. "Household targeting in practice: The Nicaraguan Red de Protección Social," Journal of International Development, John Wiley & Sons, Ltd., vol. 21(1), pages 1-23.
    12. Claudio A. Agostini & Philip H. Brown, 2010. "Local Distributional Effects Of Government Cash Transfers In Chile," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(2), pages 366-388, June.
    13. World Bank, 2007. "Sri Lanka - Poverty Assessment : Engendering Growth with Equity, Opportunities and Challenges," World Bank Publications - Reports 8050, The World Bank Group.
    14. Talip Kilic & Thomas Pave Sohnesen, 2019. "Same Question But Different Answer: Experimental Evidence on Questionnaire Design's Impact on Poverty Measured by Proxies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(1), pages 144-165, March.
    15. Tomoki Fujii, 2013. "Geographic decomposition of inequality in health and wealth: evidence from Cambodia," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(3), pages 373-392, September.
    16. van de Walle, Dominique, 2005. "Do services and transfers reach Morocco's poor? Evidence from poverty and spending maps," Policy Research Working Paper Series 3478, The World Bank.
    17. Antonio Yúnez Naude & Jesús Arellano González & Jimena Méndez Navarro, 2010. "Cambios en el bienestar de 1990 a 2005: un estudio espacial para México," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 25(2), pages 363-406.
    18. Coulombe, Harold & Wodon, Quentin, 2008. "Assessing the geographic impact of higher food prices in Guinea," Policy Research Working Paper Series 4743, The World Bank.
    19. Melanie Morten, 2006. "Indian Poverty during the 1990s: Resolving Methodological Issues from the 55th NSS Round," ASARC Working Papers 2006-07, The Australian National University, Australia South Asia Research Centre.
    20. David Stifel & Luc Christiaensen, 2007. "Tracking Poverty Over Time in the Absence of Comparable Consumption Data," The World Bank Economic Review, World Bank, vol. 21(2), pages 317-341, June.

    More about this item

    Keywords

    poverty; imputation; Sustainable Development Goals; developing countries;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izapps:pp215. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.