How to Apply EDA for Studying the Impact of Globalization on Local Industries

Exploratory Data Analysis (EDA) is a crucial step in understanding complex datasets and deriving actionable insights. When investigating the impact of globalization on local industries, EDA enables researchers, analysts, and policymakers to detect patterns, uncover relationships, and build hypotheses from real-world data. Here’s a detailed, structured approach to applying EDA for this purpose.

Understanding the Scope

Before conducting EDA, it’s essential to define what “globalization” and “local industries” mean in the context of the analysis. Globalization may include trade liberalization, foreign direct investment (FDI), cross-border mergers, outsourcing, and international supply chains. Local industries refer to region-specific businesses, SMEs, and sectors primarily serving domestic markets.

The primary goals of this EDA process may include:

• Identifying changes in local industry outputs due to increased global trade.

• Studying shifts in employment patterns.

• Examining the inflow of foreign capital or technology.

• Measuring dependency on international supply chains.

Data Collection

A robust EDA starts with the right datasets. For studying the impact of globalization, data may be sourced from:

1. Economic Databases:

   • World Bank, IMF, UNCTAD, OECD for globalization indices and trade metrics.

   • National statistical agencies for local industry performance indicators.

2. Trade and Investment Data:

   • Import/export records.

   • FDI statistics.

3. Employment and Wage Data:

   • Labor force surveys.

   • Industrial wage records.

4. Sector-Specific Reports:

   • Industry association publications.

   • Market research data.

5. Globalization Indicators:

   • KOF Globalization Index.

   • DHL Global Connectedness Index.

These datasets are often heterogeneous and need to be cleaned and merged for effective analysis.

Data Preparation

Data preparation is the foundational step of EDA:

• Cleaning: Remove or impute missing values, handle outliers, and ensure consistency in units and formats.

• Transformation: Normalize variables, log-transform skewed data, and categorize continuous variables if needed.

• Merging: Join datasets based on common keys (e.g., region, sector, time).

• Feature Engineering: Create relevant variables like trade exposure ratio, export dependency index, or FDI-to-GDP ratio.

Univariate Analysis

Start with examining individual variables:

• Histograms and Density Plots: Understand the distribution of industrial outputs, employment rates, or trade volumes.

• Boxplots: Detect outliers and spread in data like productivity across different industries.

• Summary Statistics: Mean, median, variance, and skewness provide a numeric overview of key metrics.

Examples:

• Compare the mean productivity of a sector before and after trade agreements.

• Visualize wage distribution in manufacturing sectors affected by outsourcing.

Bivariate and Multivariate Analysis

Analyzing relationships between two or more variables reveals how globalization variables correlate with local industry performance:

• Scatter Plots: Evaluate relationships, e.g., between FDI inflows and job creation in specific sectors.

• Correlation Heatmaps: Discover linear relationships between variables such as export ratios, profit margins, and employment rates.

• Boxplots by Category: Compare industry performance across countries or regions with varying globalization levels.

Examples:

• Correlate tariff reductions with changes in domestic output.

• Assess wage variation in industries with different levels of foreign investment.

Time Series Analysis

Given the temporal nature of globalization’s impact, time series analysis provides deep insights:

• Trend Analysis: Visualize how key metrics like employment or exports have evolved over time.

• Rolling Averages: Smooth out volatility in data to highlight underlying trends.

• Event Analysis: Study impacts before and after major policy changes (e.g., entry into trade agreements).

Examples:

• Analyze productivity trends in textile industries before and after joining WTO.

• Observe employment changes in IT services following trade liberalization.

Segmentation and Clustering

Grouping data allows for identifying patterns across different industry segments or regions:

• K-Means or Hierarchical Clustering: Segment industries based on globalization exposure and performance metrics.

• Principal Component Analysis (PCA): Reduce dimensionality and identify the main contributing factors to industry performance.

Examples:

• Classify industries as high-risk or low-risk from globalization impact.

• Segment regions by adaptability to global supply chains.

Geospatial Analysis

For localized impact studies, geographic data visualization is vital:

• Choropleth Maps: Display regional variations in economic indicators like unemployment or industrial growth.

• Heat Maps: Show concentrations of foreign investment or outsourcing activities.

Examples:

• Map areas with high industry decline post-globalization.

• Visualize FDI concentration across states or provinces.

Case Study Approach

Applying EDA in real-world scenarios helps clarify its value. Consider this example:

Case: Impact of Globalization on the Indian Textile Industry

• Collected data from 1990 to 2020 on exports, employment, and FDI.

• Used time series to track post-liberalization trends.

• Analyzed correlation between export growth and wage changes.

• Clustered states based on textile employment and global exposure.

Findings might include increased productivity and exports in globally integrated regions but job losses in areas unable to compete with international pricing.

Hypothesis Generation

EDA is not the end goal—it sets the stage for more advanced modeling. Based on EDA insights, hypotheses might include:

• “Regions with higher FDI inflows show significantly higher productivity growth.”

• “Increased exposure to international trade correlates with wage polarization in local manufacturing.”

• “Globalization leads to higher regional inequality within the same sector.”

These hypotheses can later be tested using econometric models, machine learning, or causal inference techniques.

Visualization Tools

Effective EDA depends heavily on visualization. Recommended tools include:

• Python Libraries: Pandas, Matplotlib, Seaborn, Plotly.

• R Packages: ggplot2, dplyr, shiny.

• Business Intelligence Tools: Tableau, Power BI for interactive dashboards.

• GIS Tools: QGIS, ArcGIS for spatial analysis.

Common Challenges and Mitigation

1. Data Availability: Globalization data may be incomplete or non-standardized. Mitigate with interpolation and data harmonization techniques.

2. Causality Confusion: EDA only reveals patterns, not causality. Supplement with rigorous statistical testing.

3. Confounding Variables: Control for variables like policy changes, technological innovation, or domestic reforms during the analysis.

Final Thoughts

Applying EDA to study the impact of globalization on local industries requires a multidisciplinary approach that blends economic understanding with data science techniques. It involves meticulous data handling, careful visualization, and logical reasoning to generate meaningful insights. While EDA doesn’t offer definitive answers, it equips stakeholders with the foundational knowledge to make informed decisions, craft better policies, and prepare for deeper analytical models that quantify the dynamics of a globalized economy.