Predicting the quality and evaluating the use of administrative data for the 2021 Canadian Census of Population

1.Introduction

Before the World Health Organisation declared a global pandemic in March 2020, natural disasters had impacted or limited Census field operations. In Canada, wild fires in 2016 and flooding in 2011 had necessitated that Statistics Canada prepare local contingency plans using administrative data as a way to compensate for non-response. These events launched a long term research agenda towards the use of administrative data in a combined census approach.

In 2020, the increased use of administrative data in census collection and research towards a combined census were also under development in other countries [1, 2, 3]. However, the advancement of the pandemic in March 2020 accelerated exponentially the research regarding the potential use of administrative data for the 2021 Canadian Census of Population, in light of this global emergency and the associated public health measures.

Statistics Canada developed a statistical contingency plan to mitigate a low response rate in the event that the pandemic affected collection. The plan was to use administrative data to impute non-responding households in areas with a low response rate and where the administrative data are of sufficient quality. The impact of the pandemic on the response rate was unknown and, therefore, the use of administrative data was reserved for processing stages following the traditional collection process. For this purpose, we adapted the modeling approach used by other countries, namely, the US Census Bureau [3] and Statistics New Zealand [4] to identify administrative households with good quality data.

Model development was based on data from the 2016 Census. However, the response rate for the 2016 Canadian Census of Population was a record high for the country (98%) and unlikely to reflect the response mechanisms observed during a pandemic. The contingency plan developed a timely but reliable framework to evaluate direct imputation using administrative data, relative to traditional donor imputation, under a variety of response mechanisms. Moreover, this framework allowed us to evaluate the identification of households with good quality data using preliminary data from the 2021 Census during the collection period and adjust parameter specifications accordingly.

The remainder of this paper proceeds as follows. In Section 2, we describe the modeling approach for predicting the quality of data available for administrative households. Thereafter, in Section 3, we discuss the model development using 2016 Census data. In Section 4, we present the evaluation using preliminary data and subsequent implementation for the 2021 Census. Conclusions are provided in Section 5.

2.Modeling approach for predicting the quality of administrative households

Census data are essential for a country, as all layers of society use census data. In particular, it is often the only source of information for small sub-populations. Producing high quality census data is the objective of any National Statistics Organisation. It became evident that one integral part of the research on how to incorporate administrative data into a traditional enumeration census is the evaluation of the quality of the administrative data itself. We use a modeling approach to rank the quality of the available administrative data at the household level. Broadly, this approach is termed the household model and consists of three components: the person-place model, the household composition model and a distance metric.

The basis of the household model is a database of administrative persons, created for the sole purpose of the Census research, composed of multiple sources acquired by Statistics Canada from other government departments. This database includes a variable predicting if the administrative person is in-scope for the Census, the person’s age and sex at birth, all of which are determined using probabilistic models. As well, auxiliary data are available from a variety of administrative data sources such as tax files, immigration files and vital statistics files. Some but not all of these data sources include detailed address information. From these, a list of unique person-address pairs is created. Note that all possible addresses are included in this list and, therefore, a person may have more than one administrative address. Conversely, a person may have no administrative address.