was implemented, and multiple enumeration training sessions were conducted throughout the year.<sup>5</sup> Finally, the sample size was reduced. The 2022 HIES is representative at the national, division, and rural and urban levels as before the 2016 rounds. The 2016 survey was an exception, with a sample representative at the Zila level.

The 2022 microdata provides a more comprehensive picture of Bangladeshi households' consumption levels and patterns but risks monitoring welfare indicators over time. In this setting, accurate and robust methodologies are crucial to address comparability over time, help rebuild trends, and evaluate policies' effectiveness. Survey-to-survey imputation methods have emerged as a promising avenue to address this issue, leveraging data from one survey to fill in gaps in another, thereby providing more frequent and robust estimates of poverty and inequality trends over time (Dang, 2021).

Traditional imputation methods, like regression-based and simple matching techniques, operate under the assumption that the missing data in a survey can be accurately imputed from another data source. However, such assumptions might not always hold, especially in cases where there are significant changes in the country's population structure or survey design (Dang, 2021) or a lack of consistency in collecting survey data over time (Mathiassen and Wold, 2021). Moreover, traditional imputation methods do not necessarily account for complex relationships between variables and could introduce bias when the model is misspecified.

There is a growing consensus in the literature towards adopting more advanced imputation methods, particularly in a data-scarce environment. One approach is implementing hot deck imputation methods bolstered with statistical learning techniques (Budhwani et al., 2022). These methods can handle missing data in large datasets with many variables and have the potential to capture the relationships between variables more accurately. Although not a perfect substitute, these methods can also address the challenges posed by survey costs or implementation and enable back-casting of consumption from a more recent survey for better comparison with older surveys (Dang, 2021; Edochie et al., 2022).

One of the main advantages of matching-based imputation methods is that they are relatively simple to implement and require minimal assumptions about the underlying data-generating process. Another advantage is that these preserve the structure of the original dataset (Andridge and Little, 2010). In other words, the imputed data in the receiver survey will have similar distributions and relationships to the original data in the donor survey.

This paper presents a two-step random hot deck imputation methodology, expanding upon the approach delineated by Fernandez, Olivieri, and Sanchez (2023). This strategy, initially grounded in the framework established by Lucchetti et al. (2020), aims to reconstruct the evolving poverty and inequality trends in Bangladesh. This method utilizes LASSO PMM (Predictive Mean Matching) and resampling techniques to predict a comparable consumption aggregate for 2010 and 2016 to the data collected in the 2022 HIES.

> 5 Two refresher training sessions were conducted during the 12-month survey period: the first refresher training took place on March 22 – 24, 2022, and the second on August 28 – 30, 2022. This is in addition to the initial training sessions conducted before the survey: the Enumerators Training session from December 4 to 24, 2021, and the Field Officials Training from December 27 to 29, 2021.

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