JOURNAL ARTICLE

Revisiting Quebec's Quiet Revolution: A synthetic control analysis.

  • Published In: Canadian Journal of Economics, 2025, v. 58, n. 2. P. 548 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Geloso, Vincent; Reilly, Chandler S. 3 of 3

Abstract

The year 1960 is often presented as a break year in the economic history of Quebec and Canada. It is used to mark the beginning of the "Quiet Revolution" during which Canada's French‐speaking province of Quebec underwent rapid socio‐economic change in the form of rapid economic convergence with the rest of Canada and the emergence of a more expansive state (more so than in the rest of Canada). Using synthetic control methods, we analyze whether 1960 is associated with a departure from previous developments. With regards to GDP per capita, GDP per worker, household‐size adjusted income, real wages and enrolment rates in primary and secondary schools, we find that 1960 was not an important date. For all macroeconomic indicators and enrolment rates, the counterfactual scenarios do not significantly differ from the actual data. For life expectancy at birth and completed schooling outcomes by schooling cohorts, we find that 1960 did mark a significant departure—albeit a modest one. We also find signs that size of government changed markedly after 1960. Shifting to other methods such as panel approach or time series strategy do not alter these results. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Canadian Journal of Economics. 2025/05, Vol. 58, Issue 2, p548
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2025
  • ISSN:0008-4085
  • DOI:10.1111/caje.70000
  • Accession Number:185257966
  • Copyright Statement:Copyright of Canadian Journal of Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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