How to Visualize the Effects of Minimum Wage Increases on Employment Using EDA

To visualize the effects of minimum wage increases on employment using Exploratory Data Analysis (EDA), the goal is to use statistical and visual tools to explore and reveal patterns, trends, and relationships in the data. This process will help you better understand how minimum wage hikes may influence employment levels, both in the short term and over the long term. Here’s a step-by-step guide on how you could approach this using EDA:

1. Collect Data

The first step in any EDA process is to collect data. For analyzing the effects of minimum wage increases, you will need relevant datasets. These could include:

• Minimum Wage Data: Historical data on minimum wage rates at both federal and state levels, as well as local adjustments.

• Employment Data: Employment rates, unemployment rates, and job creation data over time, ideally segmented by industry, age group, and region.

• Economic Indicators: Data on inflation rates, GDP growth, and other macroeconomic factors to account for external influences.

• Demographic Data: Information on population size, poverty rates, and income distribution, which could help contextualize employment trends.

Common sources for this data include:

• U.S. Bureau of Labor Statistics (BLS)

• Federal Reserve Economic Data (FRED)

• U.S. Census Bureau

• State labor departments

2. Clean the Data

Before jumping into analysis, it’s crucial to clean and preprocess your data. Key steps include:

• Handling Missing Data: If some data points are missing, decide whether to fill the gaps or remove the affected rows.

• Converting Data Types: Ensure that numerical variables (e.g., minimum wage, employment numbers) are in the correct format (e.g., integers or floats).

• Outlier Detection: Identify and handle any extreme outliers that may distort your analysis, especially in economic data where anomalies can skew results.

3. Create Key Variables

You’ll need to construct a few important variables that represent the relationship between the minimum wage and employment:

• Percentage Change in Minimum Wage: Compute the percentage change in minimum wage over time.

• Employment Growth Rate: Calculate the percentage change in employment or unemployment rates over the same period.

• Control Variables: Factor in other variables, such as inflation, GDP growth, and population size, to control for broader economic conditions.

4. Initial Univariate Visualizations

Start by exploring the individual distributions of key variables. This will give you a basic understanding of how they behave over time.

• Histograms: For understanding the distribution of wages, employment, and other key variables.

• Box Plots: To detect outliers and understand the spread of minimum wage data and employment rates.

• Time Series Plots: Plot the minimum wage over time alongside employment rates to visually inspect any correlations.

Example Visualization:

python
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import matplotlib.pyplot as plt
import seaborn as sns

# Example code for plotting time series
plt.figure(figsize=(10, 6))
plt.plot(data['Year'], data['Minimum Wage'], label='Minimum Wage', color='blue')
plt.plot(data['Year'], data['Employment Rate'], label='Employment Rate', color='green')
plt.title('Minimum Wage vs Employment Rate Over Time')
plt.xlabel('Year')
plt.ylabel('Rate')
plt.legend()
plt.show()

5. Investigate Relationships Between Variables

Once you have explored individual variables, you can begin to investigate how minimum wage increases affect employment. Use bivariate analysis to uncover potential patterns.

Scatter Plots:

• Plot the relationship between the percentage change in minimum wage and the percentage change in employment.

• Scatter plots will help you visually assess whether a positive or negative correlation exists between the variables.

python
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sns.scatterplot(x='Percentage Change in Minimum Wage', y='Employment Growth Rate', data=data)
plt.title('Relationship between Minimum Wage Increase and Employment Growth')
plt.xlabel('Percentage Change in Minimum Wage')
plt.ylabel('Employment Growth Rate')
plt.show()

Heatmap of Correlation Matrix:

• Create a correlation matrix to determine the strength of the linear relationship between multiple variables, such as minimum wage, employment rate, inflation, and GDP growth.

• This can help you identify which variables are most strongly related to employment changes.

python
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corr_matrix = data.corr()
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Matrix')
plt.show()

6. Segment Data by Region or Industry

It’s important to recognize that the effect of minimum wage increases may not be uniform across regions or industries. To explore this:

• Facet Plots: Use facet plots to break down the data by region, state, or industry to compare how different areas are impacted.

• Grouped Bar Plots: Create grouped bar plots that show employment trends across different minimum wage levels or sectors.

python
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sns.catplot(x='Region', y='Employment Rate', kind='bar', data=data)
plt.title('Employment Rate by Region')
plt.show()

7. Examine Trends Using Time-Series Analysis

Minimum wage increases tend to happen at periodic intervals, and employment changes may not be immediately visible. Conducting a time-series analysis can help you detect longer-term trends and cyclical effects.

• Rolling Mean: Calculate the rolling average of employment rates or minimum wages over a specified window (e.g., 3 years) to smooth out short-term fluctuations.

python
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data['Rolling Employment Rate'] = data['Employment Rate'].rolling(window=3).mean()
plt.plot(data['Year'], data['Rolling Employment Rate'])
plt.title('3-Year Rolling Average of Employment Rate')
plt.show()

8. Control for Confounding Variables

Since many factors contribute to employment rates (such as macroeconomic conditions), it’s crucial to control for confounders:

• Multivariate Regression: Perform a regression analysis to assess the effect of minimum wage increases on employment while controlling for other variables like inflation, GDP growth, and population changes.

• Interaction Effects: Explore whether the effect of minimum wage changes varies depending on other factors, such as region or age group.

9. Model and Hypothesis Testing

Once you’ve conducted EDA, you may want to run hypothesis tests to evaluate the statistical significance of your findings. Some common tests include:

• T-tests or ANOVA: To test if employment rates differ before and after minimum wage increases.

• Granger Causality Test: To determine whether changes in minimum wage can predict employment changes, or vice versa.

10. Final Visualizations

After performing your analysis, create final visualizations that summarize your key findings:

• Line Plots for Trends: Highlight key trends in both minimum wage and employment data over time.

• Bar Charts for Regional or Sectoral Effects: Use bar charts to show the differential impacts of minimum wage increases in various regions or industries.

• Cumulative Distribution Functions (CDFs): To show the cumulative impact of wage increases on employment or other related outcomes.

Conclusion

By using Exploratory Data Analysis, you can gain insights into how minimum wage increases might influence employment. Visualizations such as time series plots, scatter plots, and correlation matrices allow you to uncover patterns in the data and generate hypotheses for further analysis. Always remember to consider potential confounders and use statistical tests to ensure the robustness of your findings.