Surveys are a big ask for respondents.
Sitting for hours, trying to recall events from weeks or months in the past. Missing a half-day of work, growing fatigued.
Our project was a small but systematic evaluation of the benefits of asking only a few questions at a time, regularly across the year, at the respondents’ convenience via an Android smartphone.
We constructed 46 different short survey tasks and randomized the frequency (weekly, monthly, or seasonally) with which farmers in our sample (in Rangpur District, northern Bangladesh) received them. Farmers earned small rewards for completing these tasks – a ‘microtasks for micropayments’ platform – in the form of mobile data, talk-time, and credit toward ownership of the phone.
- An early overview of our findings from the first weeks of the study was published in PLoS One in 2016, available here.
- An analysis of how this kind of high-frequency data collection addresses problems of recall bias and measurement error was published in Population and Environment in 2019, available here.
- The full publicly available dataset is held in IFPRI’s Dataverse Repository here.
This project was led by Andrew Bell and Patrick Ward, with co-investigators Md. Ehsanul Haque Tamal and Mary Killilea. We have several analyses in review in revision and will update as they become publicly available.
If you are interested in working with the data, beyond what is possible in the publicly shared dataset, please get in touch. Additionally, if you are interested in working with our custom ‘Data Exchange’ software – a simple front-end for Android ODK that makes the process of engagement a bit smoother (Figure 1), please send us an email. We are keen to find other applications for it, as well as to find students and researchers interested in further developing it.
Figure 1: The Data Exchange app in practice
Project Acknowledgements: We would like to thank Nafundi for their work in software development, and our partners Banglalink and WIN Incorporated for implementation in the field. We would also like to thank Matthieu Stigler for assisting in the data cleaning process. This work was supported by the Cereal Systems Initiative for South Asia (CSISA) of the Consultative Group on International Agricultural Research (CGIAR), with generous funding provided by the United States Agency for International Development (USAID) and the Bill and Melinda Gates Foundation.