collection on time use, including efforts to scale up real-time data collection on time use through different approaches.<sup>5</sup> The paper is structured as follows. Section 2 discusses the study design. Section 3 provides an overview of the _Time Tracker_ and metrics on performance in the field. Section 4 compares time allocation across the smartphone and 24-hour recall diary arms, as well as a comparison with time reported in the 7-day recall module administered to both arms. Section 5 presents additional details on intraday variation in reporting and simultaneity of activities across treatment arms. Section 6 concludes with a discussion on future research needs and thoughts on scaling-up smartphone-based time use data collection in large-scale surveys in lower-income settings.

# 2. Study design

The Malawi Methodological Experiment on Time Use Measurement was implemented from July 2022 to March 2023, across 72 enumeration areas (EAs) that were selected across the nine most populous districts in Southern and Central Malawi, with probability proportional to 2018 Population and Housing Census EAlevel household counts. The study period covers part of the dry season, which ends in October, and most of the rainy season, which goes from November to April, in order to capture seasonality in time use as much as possible.

Furthermore, the EA sample was split evenly across Southern and Central Malawi, and by urban/rural status. In each EA, a full household listing was conducted to identify the households that had at least one adult man and one adult woman (working age 15-64). From this universe of households, five households were selected at random for each of the two treatments on time use data collection: (1) an Android-based smartphone diary with the pictorial _Time Tracker_ app, self-reported by respondents, and (2) a traditional time use diary with a 24-hour recall, administered by interviewers as part of a face-to-face interview. Within each household, following the administration of the household roster module of the household questionnaire, one adult man and one adult woman were randomly selected as the study participants.

This design yields a target sample of 720 households and 1,440 participants, split evenly by treatment arm and gender. However, due to app performance issues in the first two weeks of data collection (i.e., the first five EAs) that were sorted out swiftly but that led to a partial loss of smartphone data (i.e., for a subset of reported days for the respondents residing in these EAs), the number of sampled households for the smartphone arm was increased by 1 in approximately 25 percent of the remaining EAs to hedge against potential risk of data loss.<sup>6</sup> However, the feared data loss did not end up materializing, thanks to the

> 5 This includes other approaches to high-frequency data collection; see McCullough et al. (2023) who implement a high-frequency phone survey on time use in Ethiopia.

> 6 There was a total of 19 EAs in which six, as opposed to five, households were selected at random for the smartphone arm to hedge against the potential risk of data loss, following the experience with the first five EAs. Out of the 19 EAs, 11 EAs were visited immediately after the first five EAs, in August-September 2022. Based on the field experience and the incoming data from these 11 EAs in which there was ultimately no data loss, we reverted to sampling five households in each EA for the smartphone arm. Later, in January 2023, we had a single failure in data download from a smartphone on day 5, which we were able to resolve after

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