Exploratory Data Analysis (EDA) offers a foundational approach to examining the relationship between minimum wage increases and poverty rates. By systematically exploring, visualizing, and summarizing key aspects of relevant data, researchers and policymakers can uncover insights, detect patterns, and build hypotheses for deeper analysis. Here’s a structured approach to studying the impact of minimum wage hikes on poverty using EDA techniques.
Define the Research Objective
Before beginning the EDA process, clearly define the research goal:
-
Primary question: How do changes in minimum wage affect poverty rates across regions and time?
-
Secondary questions:
-
Are certain demographic groups more affected?
-
Do effects vary by industry or employment type?
-
Is there a time lag between wage policy changes and observable impacts?
-
Data Collection and Sources
Gathering comprehensive, high-quality data is critical. Key datasets include:
-
Minimum Wage Data
-
Federal and state-level minimum wages (historical and current).
-
Data sources: U.S. Department of Labor, state labor departments.
-
-
Poverty Data
-
State or county-level poverty rates over time.
-
Poverty thresholds and guidelines.
-
Data sources: U.S. Census Bureau, American Community Survey (ACS).
-
-
Economic Indicators
-
Unemployment rates.
-
Inflation-adjusted income levels.
-
GDP, cost of living indices, housing affordability.
-
Data sources: Bureau of Labor Statistics (BLS), Bureau of Economic Analysis (BEA).
-
-
Demographic Data
-
Population by age, gender, ethnicity, and education level.
-
Employment statistics by industry and occupation.
-
-
Policy Context
-
Legislation dates and implementation timelines.
-
Regional exemptions or special policies (e.g., tip credits, youth wages).
-
Data Cleaning and Preparation
-
Standardization: Normalize wage data (e.g., constant dollars using CPI).
-
Missing Values: Handle missing data using interpolation or imputation if necessary.
-
Temporal Alignment: Align datasets by time (e.g., monthly, quarterly, yearly) to ensure comparability.
-
Geographic Matching: Merge datasets by common geographical identifiers (state, county, etc.).
-
Lag Variables: Create lagged variables to examine delayed effects (e.g., poverty rate 1 year after wage change).
Initial Exploration and Summary Statistics
-
Descriptive Statistics:
-
Mean, median, range of wages and poverty rates.
-
Year-over-year changes.
-
-
Distribution Analysis:
-
Histograms of poverty rates and wage levels.
-
Box plots by state or region.
-
-
Trend Analysis:
-
Line plots of minimum wage and poverty rates over time.
-
Identify upward or downward trends.
-
Correlation and Comparative Analysis
-
Correlation Matrices:
-
Explore the strength of relationships between wage increases and poverty rates.
-
Include control variables like unemployment rate or cost of living.
-
-
Comparative Groups:
-
Compare states that increased minimum wage vs. those that didn’t.
-
Use visualizations like side-by-side boxplots or violin plots.
-
-
Before-and-After Comparisons:
-
Analyze data surrounding policy change dates.
-
Use paired line plots for affected and control states.
-
Geographic and Demographic Visualization
-
Choropleth Maps:
-
Visualize geographic differences in minimum wage and poverty.
-
Highlight states with the highest/lowest poverty or wage growth.
-
-
Facet Plots:
-
Break down data by age, gender, or ethnicity.
-
Explore how different groups are affected by wage changes.
-
-
Scatter Plots with Regression Lines:
-
Plot minimum wage vs. poverty rate with trend lines.
-
Include bubble sizes for population or employment rates.
-
Time Series Analysis
-
Overlay Plots:
-
Combine time series of minimum wage and poverty rates.
-
Identify lag effects or inflection points.
-
-
Moving Averages:
-
Smooth out short-term fluctuations to identify long-term trends.
-
-
Seasonal Decomposition:
-
Separate seasonal, trend, and residual components in poverty data.
-
Advanced EDA Techniques
-
Principal Component Analysis (PCA):
-
Reduce dimensionality of multiple economic indicators.
-
Highlight main drivers of poverty beyond wage alone.
-
-
Cluster Analysis:
-
Group states or counties with similar wage-poverty patterns.
-
Identify outliers or unexpected trends.
-
-
Interaction Plots:
-
Explore the joint effect of wage changes and another variable (e.g., unemployment) on poverty.
-
Hypothesis Generation
EDA is not for hypothesis testing but for hypothesis generation. After analysis, formulate hypotheses like:
-
“States that raised the minimum wage by at least 10% saw a statistically significant reduction in poverty rates within 2 years.”
-
“Minimum wage increases have a larger impact on single-parent households than on dual-income households.”
Visualization Tools
Use powerful visualization libraries or tools to aid in EDA:
-
Python Libraries: pandas, seaborn, matplotlib, plotly.
-
R Libraries: ggplot2, dplyr, tidyr, leaflet.
-
Dashboards: Tableau, Power BI, Google Data Studio for interactive exploration.
Cautions and Considerations
-
Causation vs. Correlation: EDA can identify patterns, but deeper causal analysis requires econometric modeling.
-
Confounding Variables: Control for other factors influencing poverty (e.g., tax credits, social programs).
-
Data Quality: Ensure consistency in definitions and measurements across datasets.
-
Regional Variation: Account for local economic conditions, urban vs. rural differences.
Conclusion of EDA Phase
Summarize key findings from your EDA:
-
Which states or demographics show stronger wage-poverty linkages?
-
What time lags or external factors modulate this relationship?
-
What patterns demand further statistical or econometric analysis?
Use the insights gained from EDA to design a rigorous model or policy analysis framework that moves beyond exploration toward causality and actionable recommendations.
Leave a Reply