Big River Analytics

Introduction

Local labour market information (LMI), including employment, unemployment, and participation rates, are important for planning activities conducted by municipal and First Nation governments, service providers, and many more organizations. Census of Population (Census) data, published by Statistics Canada (StatCan), provides LMI at the Census Division (CD) and Census Subdivision (CSD) levels. However, the Census is only conducted every five years. This project asks participants to build a model which estimates intercensal LMI at the CD and CSD levels.

Problem Description

LMI are not publicly available at the CD and CSD levels of geography for years when the Census is not conducted. StatCan’s Labour Force Survey provides data on a monthly basis. However, StatCan does not publish Labour Force Survey data at the CD or CSD levels. As such, LMI at the Economic Region level are often used in the place of intercensal data on CDs or CSDs. Figure 1 shows the CSD of the District of Kitimat, as well as the larger CD and Economic Region it is within, as an example illustrating how Economic Regions may not serve as good proxies for CSDs.

In this project, participants will build a model that can produce estimates of intercensal LMI statistics at the CD and CSD levels. This model should be able to produce estimates only using data available at the time the estimate is made. For instance, estimates for the 2020 unemployment rate in the District of Kitimat should not rely on the 2021 Census.

Use Cases

Big River Analytics frequently works with municipal and First Nations governments seeking to inform their decision-making with LMI. Here are a few examples of projects Big River Analytics have conducted that would have benefited from intercensal LMI estimates at the CD or CSD levels:

• Kitimat & Kitimaat Village Child Care Supply & Demand Study: Big River Analytics and Stantec estimated the supply and demand for child care services, in order to anticipate and address unmet demand. This analysis involved modeling population changes, which used LMI to inform migration projections.

• Bi-Annual LNG Monitoring Reports: Big River Analytics produces regular reports to measure whether the employment opportunities from Liquified Natural Gas (LNG) projects benefit women, Indigenous people, and local residents. These reports often rely on LMI at the Economic Region level, or Census data that may be years old.

Extensions

The Problem Description provides a summary of this project at a basic level. There are a variety of ways in which this project can be extended. Participants taking on this project can address some or all of these extensions, or propose their own.

• Testing the Model: participants should develop a method for testing their model. This testing method will be important in the model development, as participants will need to measure the effects of changes they make while developing the model. A testing method will also be important for the application of the model, as the model will be used to produce LMI estimates for various different regions. A testing method will allow Big River Analytics to confirm that the model produces reliable estimates for each new CD and CSD the model is applied to.
• Incorporating More Data: the model developed in this project does not only have to rely on StatCan data. Other publicly available data may also be utilized, such as population projections from provincial governments, Natural Resource Canada’s Major Projects Inventory, and online job postings.
• Calculating Necessary Sample Sizes: StatCan’s Labour Force Survey sample size varies across Economic Regions. The small samples can pose an issue for data quality. If participants are able to calculate the minimum sample size needed to produce reliable intercensal estimates at the CD and CSD levels, Big River Analytics will take their findings to StatCan, and use it to demonstrate the need for larger sample sizes.

Links

• Statistics Canada. (2019). Census Profile, 2016 Census. https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/prof/index.cfm?Lang=E
• Statistics Canada. (2022). Labour Force Survey. https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=3701