How to Study the Impact of International Trade on Local Economies Using EDA

Studying the impact of international trade on local economies using Exploratory Data Analysis (EDA) requires a systematic approach to data sourcing, preparation, visualization, and interpretation. EDA helps uncover trends, patterns, and relationships within the data, guiding deeper economic analysis and policy development. This guide walks through the steps to analyze such complex economic interactions effectively.

Understanding the Relationship Between International Trade and Local Economies

International trade affects local economies through multiple channels: employment, income levels, industry performance, tax revenue, infrastructure development, and more. Some regions may benefit from export growth, while others may suffer due to import competition. EDA allows researchers and policymakers to isolate these effects and identify which local economies are most affected.

Step 1: Define the Scope and Objectives

Before diving into data, clearly define what you want to study. Common objectives might include:

• Identifying how international trade affects employment in manufacturing sectors of a specific region.

• Analyzing whether areas with higher export volumes have higher GDP per capita.

• Determining if import penetration negatively impacts small businesses in specific industries.

Having a clear goal ensures that the EDA process remains focused and relevant.

Step 2: Collect Relevant Data

Multiple datasets may be needed to capture the trade-local economy relationship. Common sources include:

International Trade Data

• UN Comtrade Database

• World Bank Trade Statistics

• OECD Trade by Industry and End-use (TIDE)

• U.S. Census Bureau Foreign Trade Statistics

Local Economic Indicators

• Bureau of Economic Analysis (BEA) regional data

• Bureau of Labor Statistics (BLS) employment and wage data

• Census data for demographic and business information

• World Bank Subnational Indicators (for non-US cases)

Industry-Specific and Regional Data

• Input-output tables

• Tariff and non-tariff barrier data

• Location Quotients (LQs) for industries

• Import/export data at the city or county level

Step 3: Clean and Prepare the Data

Raw data often contains missing values, duplicate entries, and inconsistent formats. Begin with the following cleaning steps:

• Remove or impute missing values.

• Convert date fields into datetime objects.

• Normalize trade volumes and economic metrics to account for inflation.

• Aggregate data into consistent time periods (monthly, quarterly, annually).

• Create derived metrics, such as export intensity (exports/GDP) or import competition index (imports by sector/employment in sector).

Step 4: Explore the Data with Summary Statistics

EDA begins with understanding basic statistics:

• Central Tendency: Mean and median GDP, trade volumes, employment rates.

• Dispersion: Standard deviation, interquartile ranges of trade impact across regions.

• Correlation: Examine pairwise correlations between trade variables and economic indicators.

Use groupby functions to segment the data by year, region, or industry to understand localized effects.

Step 5: Visualize the Relationships

Geographic Plots

Use maps to visualize regional disparities:

• Choropleth maps showing trade volume, GDP, or unemployment rates.

• Bubble maps representing trade exposure by metro areas.

Time Series Plots

• Plot trade volume vs. employment rate over time.

• Analyze GDP growth trends alongside export/import dynamics.

Scatter Plots and Regression Lines

• Export intensity vs. wage levels in local industries.

• Import penetration vs. job losses in manufacturing.

Box Plots and Histograms

• Distribution of trade exposure across localities.

• Wage variability in trade-exposed vs. non-exposed regions.

Step 6: Feature Engineering

Create variables that provide deeper insight:

• Trade Exposure Index: [(exports + imports)/total output] by region or industry.

• Shock Variables: Measure sudden trade changes (e.g., due to tariff policy) and analyze before-after effects.

• Comparative Advantage Index: Export share of a local sector relative to national average.

• Net Export Ratio: (exports – imports) / GDP to assess trade balance at local level.

Step 7: Segmentation and Clustering

Cluster regions with similar trade and economic characteristics:

• Use K-Means or hierarchical clustering to identify similar economic zones.

• Label clusters (e.g., “High Export, Low Employment”, “Import Heavy, Industry Diverse”) for targeted analysis.

Principal Component Analysis (PCA) can also reduce dimensionality and help understand which variables most strongly define local economic performance in the context of trade.

Step 8: Analyze Causal Relationships (Preliminary)

While EDA does not confirm causation, it helps identify possible causal hypotheses:

• Use lagged variables to check if increases in imports precede job losses.

• Compare similar regions with varying trade exposure using difference-in-differences style visualizations.

• Analyze the trajectory of key indicators before and after trade agreements (e.g., NAFTA, USMCA).

This sets the stage for deeper econometric or machine learning models if needed.

Step 9: Interpret Findings

Interpret your EDA results in the context of economic theory and policy relevance:

• If trade growth correlates with higher local employment, what industries are driving this?

• Do regions exposed to import competition show lagging wage growth or industrial decline?

• Are smaller economies more vulnerable to trade shocks?

Use insights to guide questions for further study or recommendations for policymakers.

Step 10: Communicate Insights Effectively

Use data dashboards and infographics to present key findings:

• Interactive maps showing trade impacts by region.

• Time-series animations of trade effects pre- and post-policy change.

• Summarized plots comparing trade-exposed vs. insulated regions.

Tools like Tableau, Power BI, Plotly, or custom Python dashboards (Dash/Streamlit) enhance interactivity and usability.

Recommended Tools for EDA in This Context

• Python: Pandas, Matplotlib, Seaborn, Plotly, Geopandas, Scikit-learn

• R: ggplot2, dplyr, tidyr, sf, tmap

• SQL: For managing large relational datasets

• Tableau/Power BI: For quick business-level visualizations

Conclusion

EDA offers a powerful framework to study the multifaceted impact of international trade on local economies. By merging trade statistics with regional economic indicators, cleaning and visualizing the data effectively, and extracting actionable insights, researchers and policymakers can better understand and respond to globalization’s uneven effects. While EDA alone doesn’t establish causality, it provides the essential groundwork for more advanced statistical or machine learning models and evidence-based decision-making.