# **1. Introduction**

This paper seeks to identify interviewer effects using household data collected in Sudan. The data is from a pilot agricultural survey conducted in 2018 by the Central Bureau of Statistics (CBS) in collaboration with the World Bank in Kassala State. Kassala lies in the eastern part of Sudan bordering Eritrea and is viewed as having high smallholder agricultural potential. It was chosen for the pilot agricultural survey in part for this reason but also because of its diverse agriculture including rainfed, irrigated and mechanized crop production, as well as livestock husbandry. The drier north of the state is pastoral whereas the wetter central and southern parts of the state are primarily sorghum-based agro-pastoral livelihood systems.

The pilot survey is a good opportunity to identify interviewer effects because of the authors’ close involvement throughout the survey. This involvement included questionnaire and sample design; enumerator and supervisor training and piloting; post survey debriefing with enumerators and supervisors; and data cleaning and analysis.

# _Interviewer effects_

Interviewer effects are the effects that interviewers can have on survey or census data quality. In the case of surveys, which are the focus here, interviewer effects are the difference between the true population parameter and the survey estimate that is attributable to interviewer errors. Interviewer effects can broadly be divided into three categories: coverage errors, non-response errors and measurement errors. Coverage errors are errors in the sample frame as a result of mistakes in listing or under coverage of difficult to access households. Non-response errors result from interviewer’s inability to solicit a response from a sampled respondent. Finally, measurement errors, which are the focus of this paper, occur when an interviewer does not solicit or record the correct response from the respondent (West and Blom, 2017).

There is a long history of the study of interviewer effects on survey data collection (Kish, 1962). In their recent comprehensive review of the literature on interviewer effects, West and Blom (2017) identify six types of interviewer characteristics that have been related to measurement error in the literature. These characteristics are race/ethnicity, age, sociodemographic matching with interviewer’s characteristics, survey-specific experience, current survey experience, and gender.

# _CAPI and PAPI_

The prevalence of computer assisted personal interviewing (CAPI) should theoretically reduce interviewer induced measurement error through automated skip logic as well as real time response validation. Caeyers _et al._ (2012) conducted a randomized field experiment to compare CAPI with pen and paper interviewing (PAPI). The authors indeed found fewer data errors in the CAPI-collected data than PAPI data as well as lower mean and variance of consumption estimates.

However, there are still sources of interviewer related measurement errors that are not likely to be attenuated by CAPI. These include an interviewer’s ability to understand and explain more complex questions, the skill with which they engage respondents to maintain their enthusiasm and truthfulness throughout an interview. Furthermore, an interviewer’s background 2