Conference Agenda

The conference agenda provides an overview and details of sessions. In order to view sessions on a specific day or for a certain room, please select an appropriate date or room link. You may also select a session to explore available abstracts and download papers and presentations.

Session Overview
11-04: Data Collection Methodology
Thursday, 22/Mar/2018:
2:00pm - 3:30pm

Session Chair: Gbemisola Oseni Siwatu, The World Bank, United States of America
Location: MC 6-860


Production Frontiers & Soil Suitability: An Analysis of Maize Farmers in Eastern Uganda

Sydney Gourlay

World Bank, United States of America

Rural societies rely heavily on smallholder subsistence farming for food security and consumption. Both theory and evidence point to a strong, positive relationship between agricultural productivity and economic outcomes, yet, large gaps exist between realized and agronomically feasible yields. Productivity is hindered by a multitude of factors including lack of knowledge and extension services, market failures, and inadequate use of improved inputs. This paper focuses on the soil suitability of a given crop, maize, for a given agricultural plot. Farmers cultivating maize on land that is not agronomically suitable for maize will face limited yield potential, as is illustrated here using stochastic frontier analysis. This paper sets out to determine the magnitude of forgone production due to cultivation on less than suitable land and to identify which groups of farmers are bearing the burden of this constrained productivity, ultimately allowing for greater targeting of agriculture-based poverty reduction policies.


Mission Impossible? Exploring the Promise of Multiple Imputation for Predicting Missing GPS-Based Land Area Measures in Household Surveys

Talip Kilic2, Ismael Yacoubou Djima1, Gero Carletto2

1Living Standards Measurement Study (LSMS), Survey Unit, Development Data Group, The World Bank; 2The World Bank

Research has provided evidence for the use of GPS as the scalable gold standard for land areas measurement in household surveys. Nonetheless, facing constraints, survey agencies often measure with GPS only plots within a given radius of dwelling locations, potentially introducing biases in land area statistics. This study uses nationally-representative, multi-topic household surveys from Malawi and Ethiopia with near-negligible missingness in GPS-based plot areas to validate a multiple imputation (MI) model for predicting missing GPS-based plot areas. The analysis artificially creates missingness in GPS-based areas of plots beyond relevant distances of dwelling, conducts MI under each scenario, and compares the distributions of the imputed plot-level area and agricultural productivity, with their known distributions. This results in imputed yields distributions statistically undistinguishable from true distributions with up to 82% and 56% missingness, respectively for Malawi and Ethiopia. The study highlights the promise of MI for predicting missing GPS-based plot areas.


Targeting small scale irrigation in Mozambique: Initial evidence and implications for impact evaluations

Paul Christian, Florence Kondylis

The World Bank, United States of America

to be filled


Sampling strategies for assessing impacts of irrigation investment in Rwanda

Florence Kondylis

The World Bank, United States of America

to be filled