More Macro-musings
Explorations in everybodys favorite - the labour market!
Total Labor Force Participation
Source: Eurostat
Overall labor force participation rate (100 - inactivity rate) for the working-age population (ages 15-64).
Labor Force Participation by Gender
Source: Eurostat
The chart compares labor force participation rates between males and females (ages 15-64). Click the legend items to show/hide each gender's data.
Note: Male participation rates are calculated as (2 × Total - Female) to ensure the male and female rates average to the total rate.
Labor Force Participation by Age Group
Source: Eurostat
The chart shows labor force participation rates across different age groups. Click the legend items to compare specific age cohorts.
Beveridge Curve
Source: Eurostat
The Beveridge curve shows the relationship between unemployment rate and job vacancy rate. Each point represents a year from 2016 onwards.
A movement along the curve typically represents cyclical changes in the labor market, while shifts of the entire curve might indicate structural changes.
I observe the effect of Covid-19
Dealing with disparate data from separate websites and separate datasets will be the end of me…how I wish there was a NBER for Italy :CCC…. Some reflections on the challenges I faced:
- Had to handle various data markers like ':' for missing values and annotations like 'b' or 'p' in the Eurostat data
- The code structure in TSV files needed careful parsing (e.g., Q,PC,T,Y15-64,IT for demographic breakdowns)
- Converting inactivity rates to participation rates (100 - inactivity)
- Calculating gender-specific rates: Had to use a weighted formula to derive male rates from total and female rates
- Ensuring data consistency across different age groups and time periods
- Handling the transition from employment-based to direct long-term unemployment data for the Beveridge curve
- Representing multiple dimensions of data (gender, age groups) in a clear way
- Creating interactive elements that allow to explore different aspects of the data
- Designing appropriate scales and axes for different types of rates
- Dealing with missing or incomplete data points
- Ensuring proper aggregation of quarterly data into annual averages
- Maintaining consistency in how we handle edge cases across different datasets
- Coordinating between Python data processing scripts and JavaScript visualizations
- Ensuring proper data flow from raw TSV files to processed JSON for the charts
- Implementing responsive design for the visualizations
- Managing multiple chart types (line charts for participation rates, scatter plot for Beveridge curve)
- Identifying meaningful patterns in labor force participation across different demographics