Including non-binary gender in the calibration strategy for the Canadian long-form sample survey weights

Abstract

Due to global events impacting social and economic landscapes, the spotlight on inequalities endured by marginalized and vulnerable groups has intensified, necessitating action from policymakers to create a more equitable future for all. It is essential that National Statistics Offices (NSOs) provide detailed statistical data which highlights the experiences of these marginalized groups to ensure that fairness and inclusion are key components of evidence-based policy. Aligning with these principles, in 2021 Canada became the first country to collect and disseminate data on gender diversity in a national census giving Canadians the option to select male, female, or non-binary. Due to their small size, non-binary population totals were not used in the 2021 Census long-form sample calibration due to the risk of increasing the variance of estimates. This paper presents an alternative long-form calibration strategy which allows for small populations, such as non-binary individuals, to be incorporated while mitigating methodological concerns. The strategy put forward can incorporate multiple small populations simultaneously while also being adaptable to the calibration systems of other NSOs. The results of a Monte Carlo simulation are presented showing improved data quality for the non-binary population under the alternative calibration strategy.