(which administrative records may miss). Further, surveys can provide estimates when the size of the target population is unknown and present the opportunity to collect rich additional data. However, survey data is typically collected in less frequent intervals, representative at coarser administrative levels, and can be subject to measurement error (Danovaro-Holliday et al. 2021; Althubaiti 2016; Burton et al. 2009). In the absence of conclusive evidence in favor of either data source, the official WHO/UNICEF estimates of vaccine coverage have used an arbitration procedure whereby administrative data is used if the discrepancy with survey data is smaller than 10 percentage points and survey data otherwise as long as it is deemed “credible” (Burton et al. 2009; 2012; Brown et al. 2013). Other research and policy has preferred survey over administrative data for its purported independence and higher accuracy (Dykstra et al. 2019; Cutts et al. 2016; Sandefur and Glassman 2015; Lim et al. 2008; Gavi, The Vaccine Alliance 2022a) and in rarer cases (typically when researchers had some control over the quality of administrative records) used only administrative data or both data sources complementarily (Banerjee et al. 2010; Barham and Maluccio 2009; Banerjee et al. 2019).

Evidence on the potential size and direction of the misalignment between survey and administrative is scarce in the context of COVID-19 and focuses on the United States or Germany (Wolter et al. 2022; Bradley et al. 2021; Nguyen et al. 2021). These studies found survey estimates to exceed administrative data with some evidence for errors of representation and measurement in the survey data. To the best of our knowledge, ours is the first large scale study investigating this issue in the context of LMICs and testing a comprehensive range of survey design choices.

A third stream of literature is applied (typically microeconomic) research using survey and sometimes administrative vaccination data in LMICs to inform policy. In these studies, vaccine uptake is usually the outcome of interest, regressed on some hypothesized determinant of vaccination behavior or policy intervention. Examples in development economics abound but can be found, for instance, in the literature on cash transfer interventions (Haushofer and Shapiro 2016; Chandir et al. 2022; Kusuma et al. 2017; Celhay et al. 2021; Barham and Maluccio 2009; De and Timilsina 2020; Benedetti et al. 2016; Debnath 2021), the literature on improving the quality and utilization of health services (Banerjee et al. 2010; Björkman and Svensson 2009; Christensen et al. 2021; Blimpo et al. 2022) and many others (Cockx 2022; Levine et al. 2021; Aggarwal 2021; Keats 2018; Palloni 2017; Adhvaryu et al. 2019; Stoop et al. 2019; Miller and Urdinola 2010; Banerjee et al. 2019).<sup>2</sup> Most directly, our study relates to a growing body of research that specifically studies COVID-19 vaccination and aims to inform vaccination campaigns with estimates on vaccine acceptance (Lazarus et al. 2021; 2022; 2023; Solís Arce et al. 2021; Kanyanda et al. 2021; Wollburg et al. 2023; Dayton et al. 2022) and uptake (Wollburg, Markhof, et al. 2022; H. M. Reza et al. 2022). Our study informs this research by testing the reliability of data underlying it.

A final stream of literature is methodological research on phone surveys in LMICs. This research has seen a significant increase in interest since the COVID-19 pandemic and led to calls for the integration of phone surveys into routine data collection schedules in health, economic,

> 2 See also the literature reviews of Andreas et al. 2022, Jain et al. 2022, Machado et al. 2021, Kaufman et al. 2018, Jacobson Vann et al. 2018, Oyo-Ita et al. 2016, Harvey et al. 2015, Groom et al. 2015, and Saeterdal et al 2014.

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