Building a Global Career Opportunity Simulator Using World Bank and ESCO Data

Over the past few years, I have noticed that many discussions about career opportunities focus on a single number: salary.

When people evaluate opportunities in different countries, the conversation often starts and ends with compensation.

While salary is important, it rarely tells the full story.

Economic conditions, labour market dynamics, occupation demand, inflation, and broader country level indicators can all influence what a career opportunity actually looks like in practice.

That observation led me to build the Global Future Impact Simulator.

The idea was simple: combine publicly available economic data with occupational classification data to provide a broader perspective on career opportunities across different countries.

Why I Built It

The initial motivation came from a simple question.

When someone is considering a career move, how do they evaluate whether an opportunity is genuinely attractive?

Most people look at salary first.

However, comparing salaries across countries without additional context can be misleading.

A salary that appears attractive on paper may look very different when considered alongside economic conditions, labour market trends, or country specific factors.

I wanted to build a tool that brought some of this information together in a single place and made it easier to explore.

Rather than searching multiple websites and datasets, users could enter a country, occupation, and salary and receive a broader picture of the opportunity.

Bringing Different Data Sources Together

One of the most interesting parts of the project was combining different types of information.

The simulator uses occupational classification data from ESCO alongside economic indicators derived from World Bank datasets.

These sources serve different purposes.

ESCO helps identify occupations and related career pathways, while economic indicators provide additional context about the environment in which those occupations exist.

Bringing these datasets together created an opportunity to provide insights that neither source could offer independently.

The Challenge of Occupation Matching

One challenge I encountered was occupation matching.

People rarely use job titles in a perfectly standardised way.

Two professionals performing similar work may describe their roles differently.

For example, someone may enter a job title that does not directly match a standard occupation classification.

To address this, the simulator attempts to identify related occupations from the ESCO framework and present relevant matches.

This helps users discover similar occupational categories that may be useful when exploring opportunities.

Developing this matching process was one of the most interesting parts of the project.

Designing for Simplicity

One lesson I learned during development is that users generally prefer clarity over complexity.

It is easy to continue adding new metrics, indicators, and visualisations.

However, more information does not always create a better experience.

Throughout development, I tried to focus on presenting information in a way that was useful without becoming overwhelming.

The goal was to create something that could be understood quickly while still providing meaningful context.

What Surprised Me

One thing that surprised me was how differently people interpret the same information.

Some users focus almost entirely on salary.

Others pay closer attention to broader economic indicators.

Others are interested in how their occupation aligns with recognised occupational frameworks.

This reinforced the idea that career decisions are rarely based on a single factor.

Different people prioritise different aspects of an opportunity.

What I Would Improve Next

If I were starting this project again, I would spend more time on the occupation matching logic earlier in the process.

The most difficult part was not displaying the World Bank data. The harder part was making the output feel useful when a user enters a job title in their own words.

For example, a person may type “cyber security engineer”, but public occupation datasets may describe related roles using different wording. That creates a real challenge when trying to return useful matches without overcomplicating the user experience.

This is an area I want to keep improving.

Looking Ahead

The simulator is not finished, and I can already see areas where it needs improvement.

There are many opportunities to improve the experience, expand available data, and refine how information is presented.

Future enhancements may include additional indicators, improved visualisations, and more advanced occupation matching capabilities.

For me, the most rewarding aspect of the project has been the opportunity to combine multiple public datasets into a practical tool that people can use to explore career opportunities from a broader perspective.

Building the simulator reinforced an important lesson.

Good decisions are often made when information is viewed in context rather than isolation.

Salary will always be an important part of evaluating opportunities, but it is rarely the only factor that matters.

My goal was to build a tool that encourages a more complete view of the picture.

You can explore the simulator here: