Layoffs, Economy and Employee Sentiments: A Data-Driven Exploration

Directory Structure

.
├── README.md
├── code
│   ├── economic-data-analysis.ipynb
│   ├── layoffs-fyi-scraper.ipynb
│   ├── subreddit.ipynb
│   └── tech-layoffs-analysis.ipynb
├── data
│   ├── README.md
│   ├── layoffs.csv
│   ├── layoffs_combined_cleaned.csv
│   └── layoffs_data.csv
├── output
│   ├── LayoffsAnalysis.pptx
│   ├── layoffs_country.png
│   ├── layoffs_industry.png
│   ├── layoffs_industry10.png
│   ├── layoffs_plot.png
│   └── pie-chart.png
└── tableau
    └── LayoffsAnalysis.twbx

Overview

The global economy experiences continuous fluctuations, impacting various facets of society, including employment opportunities and job security. In recent times, the economic downturn has raised concerns about its effects on the job market. Tech layoffs, in particular, have become increasingly common.

Motivation

Amidst this backdrop, we embarked on an exploration to understand whether economic data could have predicted layoffs across all industries, with a specific focus on the tech sector. Our investigation delves into several critical aspects. We examine the intersection of economic trends, particularly Gross Domestic Product (GDP) and interest rate fluctuations, to discern their impact on the tech industry. Our analysis spans two significant periods: the beginning of COVID-19 in 2020 and the burst of the tech bubble starting in 2022. These external factors significantly influenced workforce dynamics.

Data Sources

1. Fred API: Provides economic indicators.
2. layoffs.fyi: Scraped data on layoffs.
3. Kaggle: Publicly available datasets on tech layoffs.
4. Reddit r/technews and r/technology

You can find more information on the extracted data under:

.
├── data
│   ├── README.md

Sentiment Analysis

We used VADER(Valence Aware Dictionary and sEntiment Reasoner) to perform sentiment analysis on top Reddit posts from r/technology to gauge evolving layoff psychology. Using the data from the analysis , we tried to correlate with Mean GDP and also observe the impact of the sentiments against upvote ratio and number of comments.