Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation

Arbeitspapier

Smallholder farming dominates agriculture in poorer countries. Yet, traditional recall-based surveys on smallholder farming in these countries face challenges with seasonal variations, high survey costs, poor record-keeping, and technical capacity constraints resulting in significant recall bias. We offer the first study that employs a less-costly, imputation-based alternative using mixed modes of data collection to obtain estimates on smallholder farm labor. Using data from Tanzania, we find that parsimonious imputation models based on small samples of a benchmark weekly inperson survey can offer reasonably accurate estimates. Furthermore, we also show how less accurate, but also less resource-intensive, imputation-based measures using a weekly phone survey may provide a viable alternative for the more costly weekly in-person survey. If replicated in other contexts, including for other types of variables that suffer from similar recall bias, these results could open up a new and cost-effective way to collect more accurate data at scale.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IZA Discussion Papers ; No. 14997

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Microeconomic Analyses of Economic Development ; Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
• Thema: farm labor ; agricultural productivity ; multiple imputation ; missing data ; survey data ; Tanzania

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Dang, Hai-Anh ; Carletto, Calogero
• Ereignis: Veröffentlichung
• (wer): Institute of Labor Economics (IZA)
• (wo): Bonn
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/250658
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ