<aside> 🔗 For full analysis + code: Jupyter Notebook Link

</aside>

An Empirical Deep Dive into the Past 3 Years of Tech Layoffs

We are in the midst of a mass tech-layoff. As we near the end of Q1 2023, approximately 150 thousand people have been laid off in the tech sector! These tech-layoffs aren't new either. Three years ago, Covid-19 caused another mass tech-layoff in early 2020. As a recent graduate looking for tech jobs, the recent wave of layoffs hits close to home. In this project I wanted to put my data analysis skills to use and gain a deeper insight into what is going on behind the past three years of tech-layoffs. My analysis is divided into two main sections:

1. Exploratory Analysis of the Past Three Years of Tech-Layoffs

   The first part of my project aims to provide a high-level understanding of the tech-layoffs over the past three years using data from layoffs.fyi. After using Selenium to webscrape the tech-layoff data, I visualize it in different ways to gain a clearer picture of which industries and companies have been hit the hardest. Moreover, I compare the tech-layoffs during Covid-19 versus the current wave of tech-layoffs.

2. Companies with vs without Tech-Layoffs, Any Difference?

   For the second part, I use NASDAQ-100 index as a proxy for the overall tech industry. This allows me to compare companies that laid off workers versus those that didn't during Covid-19 and now. Using Financial Modeling Prep's data, I am able to compare multiple financial KPIs (key performance indicators) across companies.

Each main section has two subsections: (1) explaining the data collection process and (2) analyzing and visualizing the data.

Key Findings

Below are some of the highlight charts. To see the full graphs, scroll down.

• The tech-layoffs during Covid-19 were concentrated in Q2 2020. The current set of tech-layoffs started in Q2 2022 and are still going strong.
• The tech industries most affected during the Covid-19 tech-layoffs were transportation and travel. Now, the tech retail and consumer industries are the most affected.
• Most tech-layoffs have occurred in early-stage and IPOs. IPOs have laid off many more people in these recent layoffs than during Covid-19.
• Companies that are laying off workers tend to have*
  • lower relative free cash flow,
  • lower return on assets
• Stock prices have dropped more for companies that are firing people now (not true during Covid-19 tech-layoffs)*
• Companies firing now are more likely to have lower gross profit margins*

*based on Q1 2020 to Q4 2022 company financials (posted before March 21st); companies have yet to post Q1 2023 financials.

Tools

Throughout this tool I used several libraries. In particular:

• Selenium for webscraping
• Pandas for formatting and reshaping layoff data.