How to Study the Effects of Automation on Worker Skill Requirements Using EDA

To study the effects of automation on worker skill requirements, using Exploratory Data Analysis (EDA) can provide valuable insights. EDA is a data analysis approach that helps to identify patterns, detect anomalies, test hypotheses, and check assumptions with the help of graphical representations and statistical methods. In this case, EDA can uncover trends and relationships between automation and skill requirements across industries and job roles. Here’s how you can approach the analysis:

1. Define the Problem

The first step is to understand the core research question: How does automation influence the skill requirements for workers? To answer this, you need to break down what is meant by “skills” and how they may change due to automation:

• Are certain skills becoming obsolete?

• Are new skills emerging in response to technological advancements?

• Which industries or job roles are seeing the greatest impact?

2. Data Collection

To study this, you’ll need data on automation, worker skills, and related factors. The sources of data could include:

• Industry reports on automation trends (e.g., McKinsey, PwC, World Economic Forum)

• Government labor data (e.g., Bureau of Labor Statistics, Eurostat)

• Job postings data (from sites like LinkedIn, Indeed, or Glassdoor) to track shifts in job requirements.

• Surveys and academic papers on automation and skill demand.

The data should include information like:

• Job roles or occupations

• Skills (technical, soft skills, or industry-specific)

• Automation levels (e.g., automation adoption rates by industry)

• Worker demographics (e.g., education level, experience)

• Industry-specific data on automation technologies

3. Data Cleaning

Before applying EDA, you need to clean the data. This involves:

• Handling missing values by either imputing or removing rows with missing data.

• Removing duplicates to ensure data integrity.

• Ensuring data consistency in terms of units and naming conventions for skills, industries, etc.

• Encoding categorical variables such as job roles, skills, and industry types.

4. Visualizing the Data

EDA is largely about visualization, so generating meaningful charts is crucial. Below are some of the common visualizations you can use to examine the effects of automation on skill requirements:

• Histogram/Bar charts: To show the distribution of automation across industries or job roles. For example, you can create histograms to visualize the automation adoption rates across different sectors.

• Heatmaps: Useful to identify correlations between skill requirements and automation. For example, you can correlate the increase in technical skills (like AI, robotics, and data analytics) with the rise of automation in various industries.

• Boxplots: To understand the spread of skill levels across job roles before and after automation adoption. For instance, you might compare the average level of technical proficiency required in manufacturing jobs before and after automation.

• Scatter plots: Useful for plotting relationships between two variables, such as the level of automation and the increase in demand for specific skills like programming or machine learning expertise.

• Time Series Plots: If you have temporal data (e.g., trends over years), you can plot how automation has evolved over time alongside changes in skill demand. For instance, you could plot job postings requiring automation-related skills and track how they’ve grown over time.

• Word Clouds: These can be useful to visually capture the most frequently mentioned skills in job postings before and after automation adoption.

5. Statistical Analysis

Along with visual exploration, statistical techniques can help you quantify the impact of automation on skill requirements. Some methods include:

• Correlation analysis: To assess whether automation adoption is correlated with an increase or decrease in the demand for certain skills. For instance, you can check if there’s a negative correlation between the automation of certain tasks and the demand for manual labor.

• Principal Component Analysis (PCA): To reduce the dimensionality of skill data and find the most significant components (i.e., the skills that vary most in response to automation). This is particularly useful when you have a large number of skills to analyze.

• Clustering analysis (e.g., k-means): To group similar job roles based on their automation exposure and skill requirements. For example, you can classify job roles into groups based on skill changes after automation is introduced.

• Regression analysis: To predict changes in skill demand based on automation variables. You might run a regression model to estimate how the level of automation in a specific industry predicts the demand for technical versus soft skills.

6. Analyzing Results

Once you have visualized and run some statistical analyses, you should interpret the results:

• Identify skills that are becoming obsolete due to automation (e.g., manual tasks, basic clerical work).

• Identify emerging skills that are in high demand due to automation (e.g., robotics, AI, data analysis, machine learning).

• Understand the industry-specific impact. For instance, automation might significantly reduce the need for manual labor in manufacturing, while increasing demand for advanced technical skills in IT and engineering sectors.

• Highlight any regional or demographic variations in skill requirements due to automation.

7. Formulate Insights and Hypotheses

Based on your findings, you can derive insights such as:

• Impact of automation on low-skilled jobs: You might find that low-skilled jobs are being automated, but workers need to adapt to higher-skilled roles like machine operation or supervision.

• Skill shifts in high automation industries: In sectors like manufacturing or logistics, certain repetitive tasks may be automated, but there’s a need for workers with advanced technical skills to manage and maintain the automated systems.

• New training and education opportunities: You can also highlight the need for educational institutions to adapt their curriculum to match the new skill requirements emerging due to automation.

8. Communicate the Results

Finally, share your findings through clear, visual storytelling. Summarize the impact of automation on worker skill requirements, the trends that emerged, and the industry-specific findings. You can create an interactive dashboard using tools like Tableau or Power BI to allow for deeper insights into the data, or a detailed report summarizing the main takeaways.

By using EDA, you can not only answer the research question but also uncover deeper insights about the future of work, skill development, and the growing role of automation in the workforce.