Publication: Better tracking SDG progress with fewer resources? A call for more innovative data uses
Loading...
Published
2025-09
ISSN
2452-2929
Date
2025-09-23
Author(s)
Editor(s)
Abstract
Tracking progress on the Sustainable Development Goals (SDGs) is hampered by significant data and measurement challenges worldwide. Countries face issues of both insufficient data—such as missing or rarely updated statistics—and poor-quality data, which may be unusable or inconsistent across regions and over time. Current approaches are unlikely to fully populate the SDG data framework, prompting calls for innovative solutions like citizen science and artificial intelligence to address these gaps. Recent reviews reveal that less than 10% of the necessary data is available for monitoring poverty (SDG 1), and overall SDG data coverage is generally below 20%. Data gaps are even more pronounced for goals related to environmental quality. These problems are most acute in poorer countries, where the need for reliable data is greatest to inform welfare improvements. For example, over half of children under five in Sub-Saharan Africa are not registered at birth, and many of the poorest countries lack basic health indicator data. Vulnerable groups—including women, girls, people with disabilities, migrant workers, and refugees—are often missing from SDG progress reports. Collecting more and better-quality data is essential for effective policy interventions. SDG 1 specifically requires annual reporting on global poverty, a need underscored by the COVID-19 pandemic’s impact on poverty reduction efforts. However, the traditional solution of simply gathering more data is often impractical for low- and middle-income countries, which typically conduct household surveys only every few years due to limited resources and capacity. This restricts their ability to produce timely poverty estimates, especially during crises, and highlights the urgent need for new approaches to SDG data collection and monitoring.
Link to Data Set
Digital Object Identifier
Associated URLs
Associated content
Other publications in this report series
Journal
Journal Volume
Journal Issue
Collections
Related items
Showing items related by metadata.
Publication Data Gaps, Data Incomparability, and Data Imputation(World Bank, Washington, DC, 2017-12)This paper reviews methods that have been employed to estimate poverty in contexts where household consumption data are unavailable or missing. These contexts range from completely missing and partially missing consumption data in cross-sectional household surveys, to missing panel household data. The paper focuses on methods that aim to compare trends and dynamic patterns of poverty outcomes over time. It presents the various methods under a common framework, with pedagogical discussion on the intuition. Empirical illustrations are provided using several rounds of household survey data from Vietnam. Furthermore, the paper provides a practical guide with detailed instructions on computer programs that can be used to implement the reviewed techniques.Publication From Tragedy to Renaissance : Improving Agricultural Data for Better Policies(Taylor and Francis, 2015-02-13)Agricultural development is an essential engine of growth and poverty reduction, yet agricultural data suffer from poor quality and narrow sectoral focus. There are several reasons for this: (1) difficult-to-measure smallholder agriculture is prevalent in poor countries; (2) agricultural data are collected with little coordination across sectors; and (3) poor analysis undermines the demand for high-quality data. This article argues that initiatives like the Global Strategy to Improve Agricultural and Rural Statistics bode well for the future. Moving from Devarajan’s statistical tragedy’ to Kiregyera’s statistical ‘renaissance’ will take a continued long-term effort by individual countries and development partners.Publication Imputing Poverty Indicators without Consumption Data(Washington, DC: World Bank, 2024-08-19)Accurate poverty measurement relies on household consumption data, but such data are often inadequate, outdated, or display inconsistencies over time in poorer countries. To address these data challenges, this paper employs survey-to-survey imputation to produce estimates for several poverty indicators, including headcount poverty, extreme poverty, poverty gap, near-poverty rates, as well as mean consumption levels and the entire consumption distribution. Analysis of 22 multi-topic household surveys conducted over the past decade in Bangladesh, Ethiopia, Malawi, Nigeria, Tanzania, and Viet Nam yields encouraging results. Adding household utility expenditures or food expenditures to basic imputation models with household-level demographic, employment, and asset variables could improve the probability of imputation accuracy by 0.1 to 0.4. Adding predictors from geospatial data could further increase imputation accuracy. The analysis also shows that a larger time interval between surveys is associated with a lower probability of predicting some poverty indicators, and that a better imputation model goodness-of-fit (R2) does not necessarily help. The results offer cost-saving inputs for future survey design.Publication Poverty Imputation in Contexts without Consumption Data(World Bank, Washington, DC, 2021-11)A key challenge with poverty measurement is that household consumption data are often unavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldom feasible to collect full consumption data for estimating the poverty impacts. While survey-to-survey imputation is a cost-effective approach to address these gaps, its effective use calls for a combination of both ex-ante design choices and ex-post modeling efforts that are anchored in validated protocols. This paper refines various aspects of existing poverty imputation models using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam. The analysis reveals that including an additional predictor that captures household utility consumption expenditures—as part of a basic imputation model with household-level demographic and employment variables—provides poverty estimates that are not statistically significantly different from the true poverty rates. In many cases, these estimates even fall within one standard error of the true poverty rates. Adding geospatial variables to the imputation model improves imputation accuracy on a cross-country basis. Bringing in additional community-level predictors (available from survey and census data in Vietnam) related to educational achievement, poverty, and asset wealth can further enhance accuracy. Yet, there is within-country spatial heterogeneity in model performance, with certain models performing well for either urban areas or rural areas only. The paper provides operationally-relevant and cost-saving inputs into the design of future surveys implemented with a poverty imputation objective and suggests directions for future research.Publication Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost(Washington, DC: World Bank, 2024-03-26)Survey data on household consumption are often unavailable or incomparable over time in many low- and middle-income countries. Based on a unique randomized survey experiment implemented in Tanzania, this study offers new and rigorous evidence demonstrating that survey-to-survey imputation can fill consumption data gaps and provide low-cost and reliable poverty estimates. Basic imputation models featuring utility expenditures, together with a modest set of predictors on demographics, employment, household assets, and housing, yield accurate predictions. Imputation accuracy is robust to varying the survey questionnaire length, the choice of base surveys for estimating the imputation model, different poverty lines, and alternative (quarterly or monthly) Consumer Price Index deflators. The proposed approach to imputation also performs better than multiple imputation and a range of machine learning techniques. In the case of a target survey with modified (shortened or aggregated) food or non-food consumption modules, imputation models including food or non-food consumption as predictors do well only if the distributions of the predictors are standardized vis-à -vis the base survey. For the best-performing models to reach acceptable levels of accuracy, the minimum required sample size should be 1,000 for both the base and target surveys. The discussion expands on the implications of the findings for the design of future surveys.
Users also downloaded
Showing related downloaded files
No results found.