50x2030 WORKING PAPER SERIES

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Measuring Fisheries Outputs: Evidence From a Randomized Recall Bias Experiment

This paper explores the impact of recall periods on the accuracy of self-employment income data collected with household surveys in developing countries. Through randomized survey experiments conducted in Cambodia, it examines the differences in fishery output measurements between various recall periods and the potential for mobile phones to minimize recall bias.

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Supplemental Document for Measuring Livestock: A Review of Data Gaps and Methodological Opportunities for Livestock Data Collection in Household and Agricultural Surveys

Supplemental material to support the 50x2030 Working Paper titled "Measuring Livestock: A Review of Data Gaps and Methodological Opportunities for Livestock Data Collection in Household and Agricultural Surveys."

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Measuring Livestock: A Review of Data Gaps and Methodological Opportunities for Livestock Data Collection in Household and Agricultural Surveys

This review identifies key data gaps and methodological challenges in collecting data on livestock in the context of household and agricultural surveys, with emphasis on livestock enumeration, product quantification and valuation, and labor.

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Research on the Measurement of Harvest and Post-Harvest Losses: On-Farm Storage Loss Model to Improve National Estimates and Reduce Data Collection Costs

As part of 50x2030 research, the food loss research component explores approaches that allow to combine farm or rural household surveys with statistical modelling methods to improve estimates and reduce data collection needs.

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Women’s Empowerment Metric for National Statistical Systems (WEMNS): Development and Psychometric Properties of a Face-to-Face Survey Module

The Women’s Empowerment Metric for National Statistical Systems (WEMNS) survey module for use by national statistical offices and survey organizations in low- and middle-income countries (LMICs) measures women’s and men’s empowerment. We present the process of developing the WEMNS module and the results of a psychometric assessment of face-to-face surveys.

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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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