Employment law guides
Plain-language explainers on termination, hiring, non-competes, the gig economy, and reading the data.
Plain-language explanations of global employment law concepts, backed by ILO EPLex, World Bank B-READY, and OECD EPL data covering 145 countries and billions of workers worldwide. See our methodology.
Download the country-level extract cited on this page: employment-protection-statistics.csv (CC BY 4.0).
Understanding OECD Employment Protection Scores
What the 0–6 EPL scale measures, how the three pillars are computed, and what the scores mean for workers and employers.
A Guide to Hiring Employees Internationally
Key compliance factors when expanding globally, notice periods, severance, dismissal difficulty, and how to approach high-protection markets.
Termination Laws by Country, Notice Periods, Severance & Dismissal Rules
A deep dive into the Regular Employment pillar: what it measures, how Germany and the US compare, and the practical implications for employers.
Non-Compete Agreements Worldwide, Enforceability by Country
How non-compete enforceability correlates with broader employment protection regimes, plus an overview of EU directive proposals and US state restrictions.
How to Read Employment Statistics, A Plain-Language Guide
Decode unemployment rates, labor force participation, underemployment, and seasonal adjustment. What each statistic measures, and what it misses.
Labor Market Trends and Workforce Analysis, What the Data Shows
The structural shifts reshaping global labor markets: remote work, gig economy growth, demographic aging, automation risk, and how employment protection frameworks shape each transition.
The Gig Economy and Employment Law
How countries classify gig workers, why it matters for employment protection, and what the EU directive, US state laws, and third-category approaches mean for platform-based work.
Methodology
Our guides are based on publicly available data from authoritative government sources. All statistics, ratings, and figures cited in these guides are drawn directly from official datasets and publications, with sources clearly referenced throughout.
We aim to present complex government data in plain language that is accessible to general audiences. When methodologies differ between data sources or change over time, we note these variations inline. Our editorial process includes regular reviews to ensure accuracy and timeliness of the information presented.
Why PlainEmploy Publishes Guides
The data on this site is faithful to the public record, but public records are rarely self-explanatory. Codes, categorical fields, thresholds, and regulatory terminology can confuse even frequent researchers. Our guides translate those specifics into plain English so that a non-expert reader can interpret a record page correctly and so that a professional reader can quickly confirm our interpretation. Guides are written by our editorial process, drafted by our editorial process for structure, and reviewed before publication to ensure that the underlying regulation, methodology, or historical context is represented accurately.
What You Will Find Here
Every guide focuses on a single, researchable question, usually something a visitor might type into a search engine. We cite the original source wherever we state a specific regulatory threshold, dollar figure, date, or fact-about-the-world. We do not invent numbers, do not quote unreliable secondary sources, and do not paraphrase source material so aggressively that its meaning shifts. If you notice a factual drift or a sentence where our summary disagrees with the linked source, email the correction and we will update the guide.
Guides Are Not Professional Advice
Reading a guide is a good first step, it orients you to the vocabulary, the process, and the known edge cases. It is not a substitute for talking to a licensed professional. Guides here do not establish a professional relationship, do not constitute advice tailored to your circumstances, and do not account for recent changes that may not yet be reflected in the upstream dataset. Use them to understand what you are looking at and to form better questions to bring to a qualified advisor.
How We Decide What to Write
We pick guide topics by looking at real visitor questions, search queries that land on the site without a good destination, emails from readers asking for explainers, and patterns in the data that deserve a sustained write-up. We favor topics where a short, honest, well-cited guide genuinely helps over topics that merely drive traffic. When we get a topic wrong, we correct it and note the update; when we get feedback that a guide missed an angle, we add a section rather than rewriting from scratch.