50x2030 WORKING PAPER SERIES

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The Women's Empowerment Metric for National Statistical Systems (WEMNS) Module: Development and Psychometric Assessment of a Preliminary Phone Survey Instrument

Within the Women’s Empowerment Metric for National Statistical Systems (WEMNS) project, our objective was to develop, test, and refine a succinct survey instrument for use by national statistical offices and survey practitioners in low- and middle-income countries (LMICs) to measure women’s and men’s empowerment. We followed a multi-phased approach to identify empowerment constructs for measurement.

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Improving the Quality of Survey Estimates from Longitudinal Studies: An Application to LSMS Panel Surveys

Longitudinal surveys are an invaluable source for analyzing the current state of and changes in human populations over time. This paper focuses on the case of panels with a rotating sample design, representing a powerful hybrid solution for facing the impact of panel dynamics on sample representativeness. Using data from the Uganda National Panel Survey, the longest Living Standards Measurement Study panel survey, it experimentally evaluates the suggested technique.

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Adding Soil Sampling to Household Surveys: Information for Sample Design from Pilot Data

Information about the soil is essential to support policy and management decisions in pursuit of the sustainable development goals. This study assesses the scope to estimate administrative-area means of soil properties through the integration of soil analysis in household surveys. This is done on the basis of estimated variance components for two key soil properties (pH and soil organic carbon: SOC) in topsoil and subsoil.

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Yielding Insights: Machine Learning-Driven Imputations to Filling Agricultural Data Gaps

This paper addresses the challenge of missing crop yield data in large-scale agricultural surveys, where crop-cutting is often limited due to cost constraints. The analysis covers several crops, providing insights into the importance of different predictors, including farmer-reported yields and geo-spatial variables, and the conditions under which the approach is valid. The study contributes valuable insights into improving cost-efficiency in agricultural surveys and the potential of imputation methods.

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Substantial impacts of climate shocks in African smallholder agriculture

Climate change is affecting the frequency and severity of extreme weather events. Global data on loss and damage used in research, policy and media primarily come from macrostatistics based on disaster inventories. Here, we propose a different approach, based on survey microdata.

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Making Time Count: A Machine Learning Approach to Predict Time Use in Low-Income Countries from Physical Activity Tracking Data

Understanding men’s and women’s time use is a key factor in addressing issues and formulating policies related to division of labor, domestic work and related gender disparities. Leveraging unique survey data collected in rural Malawi, this study investigates the possibility of predicting men’s and women’s time allocation to an extensive set of activities, using sensor signal data captured by accelerometers.

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Measuring Women's Empowerment in National Surveys: Development of the Women’s Empowerment Metric for National Statistical Systems (WEMNS)

Monitoring progress toward SDG5 — achieving gender equality —remains challenging unless we incorporate women’s empowerment metrics into nationally representative and multi-topic surveys. The Women’s Empowerment Metric for National Statistical Systems (WEMNS) was designed for the 50x2030 Initiative as well as other large multi-topic surveys.

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Attenuating measurement errors in agricultural productivity analysis by combining objective and self-reported survey data

This paper uses unique survey data from Mali to validate an alternative approach to estimate the relationship between crop yields and inputs. The estimation relies on predicted objective crop yields that stem from a machine learning model trained on a random subsample of surveyed plots, and the analysis demonstrates that it is possible to predict sorghum yields with attenuated non-classical measurement error, resulting in a less-biased assessment of the relationship between yields and agricultural inputs.

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The Impacts of Disasters on African Agriculture: New Evidence from Micro-Data

Disasters affect millions of people each year and cause economic losses worth many billions of dollars globally. Reporting on disaster impacts in research, policy, and news primarily relies on macro statistics based on disaster inventories. The macro statistics suggest that a relatively small share of disaster damages accrues in Africa. This paper, instead, uses detailed survey micro-data from six African countries to quantify disaster damages in one key sector: crop agriculture.

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