Global Layoffs Trends Analysis (2020-2025)

Date: January 2026

Tools Used: Excel, SQL, Tableau

Context: Self-Initiated Portfolio Project 

GitHub

Executive Summary: 

This report analyzes global workforce reduction trends between 2023 and 2025, using publicly available layoff data. The objective is to identify patterns across industries, geographies, company stages, and time periods. Key findings highlight concentrated layoffs in specific industries, uneven geographic impacts, and distinct temporal waves of workforce reductions. The insights from this analysis support strategic workforce planning, investment risk assessment, and policy evaluation.

Business Task

The primary business objective is to understand how workforce reductions are distributed across industries, regions, and company characteristics over time. This analysis aims to inform stakeholders about structural labour market shifts and identify risk signals associated with company stage, funding levels, and industry concentration.

Key Stakeholders

- Business leaders and strategy teams

- Investors and financial analysts

- Policy and labour market analysts

- Talent and workforce planning teams

Data Sources

The analysis uses a global layoffs dataset compiled from publicly reported workforce reduction announcements. The dataset spans from 2023 to 2025

and includes company-level information such as industry, country, total employees laid off, company stage, funding raised, and announcement dates.The data may be subject to reporting bias, delayed disclosures, and inconsistencies across regions.

Tools and Methodology

Data preparation and analysis were conducted using SQL for data cleaning and transformation, Excel for validation and exploratory checks, and Tableau 

for visualisation. The methodology included duplicate removal, standardisation of categorical fields, date normalisation, and aggregation across multiple dimensions such as time, geography, industry, and company attributes.

Data Preparation and Cleaning

Key data preparation steps included removing duplicate records, standardizing company names and categorical variables, converting date fields into a consistent format, and validating numerical fields such as layoff counts and funding amounts. Records with unverifiable or incomplete critical information were excluded to improve analytical reliability.

Analysis and Key Findings

Finding 1: 

Layoffs are highly concentrated in a small number of industries, indicating structural employment pressures rather than evenly distributed workforce adjustments.

Finding 2: 

Workforce reductions exhibit clear time-based patterns, with certain periods experiencing sharp spikes in layoffs, followed by stabilization.

Finding 3: 

A limited number of companies account for a disproportionate share of total layoffs, highlighting concentration risk.

Finding 4:

Later-stage and well-funded companies contribute significantly to total layoff volumes, suggesting that scale effects, rather than early-stage instability alone, are at play.

Recommendations

- Monitor industry-level workforce trends to anticipate downstream impacts on talent availability.

- Incorporate layoff concentration metrics into investment and risk assessment frameworks.

- Use time-based layoff patterns as early indicators for organizational restructuring cycles.

- Apply geographic insights to inform regional workforce and policy planning.

Limitations and Assumptions

The analysis relies on publicly reported data, which may underrepresent smaller companies or regions with limited disclosure practices. Funding data is incomplete for some organizations, and layoffs may be announced after internal decisions are made, introducing timing lags.