Projects
Global Layoffs Analysis (2022-2025)
The past few years have seen significant workforce reductions across industries, driven by economic uncertainty, technological shifts, and post-pandemic adjustments. This project explores global layoff trends from 2022 to 2025, analyzing between others:
- Most affected regions, companies, and industries
- Timing of peak layoffs
- Relationship between funding and workforce reductions
Methodology
- Cleaned and processed the dataset using SQL (Exploratory Data Analysis)
- Visualized trends and insights using Python (Matplotlib/Plotlyexpress)
- Explored key questions like:
- Which sectors were hit hardest?
- Did well-funded companies lay off more or fewer employees?
- Were there regional differences in layoff patterns?
Key Findings
🔹 Consumer and retail saw the highest layoffs
🔹 Layoffs peaked in early 2023
🔹 Companies with lower funding rounds were more likely to downsize
1. Global Layoffs 1: SQL Cleaning
2. Global Layoffs 2: SQL EDA
3. Global Layoffs 3: Python Visualisation
2023 was the year with highest layout indexes, peaking in the first quarter:
The most affected industries were Consumer, Retail and Hardware:
Denmark, Vietnam and Nigeria show the highest layoff indexes:
Is there a correlation between company size and layoff indexes? (double-click to filter a stage)
While higher-funded companies lay off more people in total, this is largely due to their size. It seems necessary to cross-reference with layoff percentages, including a clear view of averages and quartiles:
Lower funding does seem to reflect on higher exposure to layoffs. A regression plot shows this clearly too:
- Positive correlation in advanced stages like Series G (often signaling pre-IPO readiness or heavy expansion) or the rare Series J (often massive unicorns or declining giants), suggest that some companies raised significant funding but still had high layoff percentages.
- This could reflect overexpansion or overhiring followed by corrections, strategic misalignment (money was raised, but business goals weren’t met), or investor pressure to cut costs post-funding (post-hype) round.
- This could point to overfunded companies in decline, poor capital efficiency or late-stage companies with bloated ops trimming aggressively.
Some conclusions
🔹 According to these graphs, companies with lower funding rounds were more likely to downsize proportionally.
🔹 Early-stage companies experience higher relative downsizing, meaning they are more likely to lay off a larger share of their staff, and in some cases, nearly all of it.
🔹 This aligns with the common startup risk curve — early-stage companies face more volatility, resource constraints, and abrupt strategy pivots that lead to higher relative layoffs.
📌 See the full notebook here: 