President Macron Speech
Research Objective
-
This quantitative study aims to understand the evolution and socio-economic dynamics of immigration in France between 2010 and 2025. The analysis examines the relationship between migration flows, control policies, territorial distribution, demographic composition, and labor market participation.
-
The objective is to determine whether available empirical evidence aligns with or contradicts prevailing political narratives on immigration, including claims that immigration imposes a burden on state systems or, conversely, represents an economic and social asset.
Research Question
"To what extent do quantitative indicators of migration and socio-economic integration in France confirm or contradict political narratives about immigration?"
Hypotheses
Data Sources
All data used in this analysis originate from official French institutions:
INSEE (National Institute of Statistics and Economic Studies)
-
Socio-economic indicators of immigrant populations
-
Employment, education, household and territorial data
Ministry of the Interior
-
Asylum applications
-
Residence permits
-
Forced removals
-
Demographic statistics
Datasets were selected on the basis of reliability, relevance, and preliminary data cleaning conducted by the institutions, enabling a more efficient analytical process.
Methodology
The analysis was conducted using Python in Google Colab, and libraries pandas and matplotlib were employed for dataset processing, cleaning, and visualization.
Each dataset underwent a uniform preparation sequence:
-
Importation
-
Cleaning: removal of empty rows, redundant headers, irrelevant footnotes
-
Variable selection: retention of key indicators such as Year, Population, Immigrants, Applications, and Employment rate
-
Numeric conversion and dataframe restructuring
-
Visualization: construction of line graphs and bar charts to highlight yearly and cross-category trends
Use of AI in the Analytical Process
The large language model ChatGPT served as a technical assistant during the data preparation phase. Its role included:
-
providing explanations of Python functions and methods
-
assisting in the structuring of cleaning routines
-
identifying coding errors and proposing corrections
-
validating intermediate outputs
The use of AI improved workflow efficiency while maintaining methodological rigour and human oversight.