Statistic Canada’s Longitudinal Social Data Development Program (LSDDP)

Main Article Content

Jenneke Le Moullec


Statistics Canada has a long and reputable history of data linkage and an established Social Data Linkage Environment (SDLE). Under the agency’s modernization agenda the Longitudinal Social Data Development Program (LSDDP) exemplifies the agency’s efforts to position linkable administrative data as central in the field of social statistics. This is in response to the call for better longitudinal and intersectional social measures in an increasingly complex society.

Objectives and Approach
This presentation will include a detailed description of the LSDDP’s research and development activities which are centered on a linkable pseudonymised administrative data-first approach to social measures in the areas of longitudinal life-course analysis and intersectional social measures. The approach builds on existing activities flowing out of the Social Data Linkage Environment (SDLE), but with a more systematized, deliberate, and replicable approach; a set of analytical tools and processes.

The presentation will describe the following aspects of the LSDDP’s work:

  1. Responsible research and development under the principles of necessity and proportionality

  2. Data development and data structure

  3. Intersectional social indicators

  4. Methods and applied research in life-course analysis

Conclusion / Implications
The LSDDP presents a seismic opportunity responding to the need for holistic and multi-dimensional measurement of society that considers social progress and well-being as it relates to the interrelationships over time among social domains. In absence of longitudinal survey data such approach enables the staying upstream of social issues as well as the identification of intervention points for programs, policies, and other initiatives.

Article Details

How to Cite
Le Moullec, J. (2020) “Statistic Canada’s Longitudinal Social Data Development Program (LSDDP)”, International Journal of Population Data Science, 5(5). doi: 10.23889/ijpds.v5i5.1448.