Using smartphone app collected data to explore the link between mechanization and intra-household allocation of time in Zambia

2020 | T. Daum | F. Capezzone | R. Birner

Published in Agriculture and Human Values (2020).

A previous draft of this study was published as ZEF-Discussion Papers on Development Policy No. 278.


Digital tools may help to study socioeconomic aspects of agricultural development that are difficult to measure such as the effects of new policies and technologies on the intra-household allocation of time. As farm technologies target different crops and tasks, they can affect the time-use of men, women, boys, and girls differently. Development strategies that overlook such effects can have negative consequences for vulnerable household members. In this paper, the time-use patterns associated with different levels of agricultural mechanization during land preparation in smallholder farming households in Zambia were investigated. A novel data collection method was used: a pictorial smartphone application that allows real-time recording of time-use, which eliminates recall bias. Existing studies analyzing the intra-household allocation of resources often focus on adult males and females. This study paid particular attention to boys and girls as well as adults. The study addressed seasonal variations. Compositional data analysis was used to account for the co-dependence and sum constraint of time-use data. The study suggests a strong gender differentiation for land preparation activities among mechanized households; for households using manual labor, such differentiation was not found. There is some evidence that the surplus time associated with mechanization is used for off-farm and domestic work. The study cannot confirm concerns about negative second-round effects: mechanized land preparation is not associated with a higher workload for women and children during weeding and harvesting/processing. The study provides a proof-of-concept that smartphone applications can be used to collect socioeconomic data that are difficult to measure but of high relevance.