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Applying the Lorenz Curve and Gini Coefficient to Measure the Population Distribution

Author

Listed:
  • Fazley K. Siddiq

    (University of New Brunswick)

  • Halyna Klymentieva

    (Dalhousie University)

  • Taylor J. C. Lee

    (University of New Brunswick)

Abstract

The population distribution in Canada across subnational jurisdictions exhibits significant inequality. Precipitous growth within concentrated urban agglomerations, culminating in severe rural depopulation, has perpetuated existing subnational population growth disparities. More densely populated metropolitan areas are experiencing an increasing proportion of the aggregate population, while ruralized jurisdictions succumb to a dramatic population exodus. Accordingly, the geographic dispersion of the Canadian population has become increasingly skewed, and thus a substantial proportion of the national populace is concentrated within a handful of metropolitan jurisdictions. The localization of growth within urbanized zones renders the geography of the population distribution increasingly volatile, thereby carving a fault line into the landscape of the national economy. Using census data from 1991 to the most recent (2021), this study formulates a novel quantification of the unequal distribution of population in Canada over time, utilizing an adaptation of the Lorenz curve and the Gini coefficient. The findings exhibit significant and growing inequality in the population distribution with a substantial proportion of the national populace concentrated within a few metropolitan areas. Rising rent and real estate prices have led to increasing gentrification, fundamentally altering the economic and social reality of the country.

Suggested Citation

  • Fazley K. Siddiq & Halyna Klymentieva & Taylor J. C. Lee, 2023. "Applying the Lorenz Curve and Gini Coefficient to Measure the Population Distribution," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 29(3), pages 177-192, August.
  • Handle: RePEc:kap:iaecre:v:29:y:2023:i:3:d:10.1007_s11294-023-09874-x
    DOI: 10.1007/s11294-023-09874-x
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    References listed on IDEAS

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    Cited by:

    1. Yoshioka, Hidekazu & Yoshioka, Yumi, 2024. "Generalized divergences for statistical evaluation of uncertainty in long-memory processes," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).

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