Inspiration
The idea for WorkWorth struck me late one night while browsing global news. I was reading about the price of a new smartphone, and it occurred to me how meaningless a simple dollar or rupee figure is without context. A ₹80,000 phone is an expensive purchase in India, but what does its $1,000 price tag really mean to someone in New York, or Tokyo, or Lagos?
I became fascinated by this disconnect between price and affordability. The "aha!" moment came when I decided to reframe the entire question. What if we measured the cost of everything not in currency, which is relative, but in the only truly universal metric we all share: human time? That simple idea—to measure cost in the hours of work required to earn something—was the spark that ignited this entire project.
What it does
In simple terms, WorkWorth is an interactive web app that shows you the real cost of things around the world.
Here’s exactly what it does:
It measures cost in TIME, not money. You select a common product, like a smartphone or a cup of coffee. The app then calculates how many hours or minutes an average person in any country has to work to afford it.
It visualizes global affordability. It displays these "work-hours" on an interactive world map and in clear charts, so you can instantly see where products are "cheapest" or "most expensive" in terms of your time.
It lets you compare countries directly. You can easily see a side-by-side comparison, for example, showing that buying an iPhone might take 40 hours of work in Japan but over 400 hours in another country.
Essentially, it cuts through confusing exchange rates to give you a simple, powerful, and human-centered look at the true cost of living and the value of labor across the globe.
How we built it
This project was built entirely within the Plotly Studio ecosystem, leveraging its powerful agentic AI capabilities for rapid development.
Data Sourcing & Merging: The first step was to gather the necessary data. This project sits on top of two core types of datasets:
Consumer Price Data: Sourced from repositories like Numbeo and The Economist's Big Mac Index, providing the cost of common goods in various countries.
Wage Data: Sourced from public APIs and datasets from organizations like the OECD and World Bank to find the average hourly wage for those same countries. I merged these datasets in a Python script to create a unified source file.
The Core Logic: The entire app is powered by one simple but powerful formula to calculate the "Time Cost":
Time Cost (hours)= Average Hourly Wage / Price of Item
Building with Plotly Studio: I uploaded the final dataset to Plotly Studio. Using natural language prompts, I asked the AI to generate the initial layout, including an interactive choropleth world map and several bar charts. From there, I iteratively refined the design, adding filters and dynamic selectors to create the final user experience. The ability to simply ask for "a dropdown to select a product" or "a bar chart ranking the top 10 countries" dramatically accelerated the development process.
Challenges we ran into
Data & Definitions Sourcing & Harmonizing: Finding and merging comparable global datasets for both prices and wages was difficult due to inconsistent reporting standards and missing information for many countries.
Defining "Average": Choosing a fair metric for the "average wage" (e.g., mean vs. median) was a major conceptual hurdle, as this decision fundamentally changes the story the data tells.
Design & Development AI Token Limit: Working within the competition's strict AI token limit required a highly strategic and efficient design process, forcing us to prioritize features and minimize rework.
Maintaining Simplicity: Resisting the urge to add too many features was a constant challenge. We had to stay focused on keeping the app's core message clear, simple, and powerful.
Accomplishments that we're proud of
A New Metric for an Old Question We're proud of creating the "Time-Cost" metric, a simple yet powerful way to reframe a complex question. By measuring affordability in hours of work instead of money, we successfully translated abstract economic data into a tangible, human-centered insight that everyone can understand.
Unifying a Complex Global Dataset Despite significant challenges, we successfully sourced, cleaned, and harmonized inconsistent price and wage data from dozens of countries. Building this cohesive, unified dataset was a major technical achievement and formed the reliable backbone of the entire application.
Delivering Insight Through Simplicity We created a clean, intuitive, and highly interactive user experience. We're proud that our app allows anyone, regardless of their background in economics, to explore a complex topic and discover genuinely eye-opening insights about the global economy with just a few clicks. The design makes a difficult subject accessible and engaging.
What we learned
The Power of Perspective The most profound lesson was how reframing a single variable from money to time can completely change one's understanding of the world. We learned that while prices are just numbers, the "Time-Cost" is a visceral measure of human experience, revealing economic disparities that raw currency figures often hide.
The Story is in the Data We learned that data doesn't just provide answers, it tells a story that the creator helps to shape. Our decision to use the average wage instead of the median or minimum wage was a critical choice that defined our narrative. It was a powerful lesson in how the context and definition of data are just as important as the data itself.
The Value of Constraints The competition's AI token limit was one of our greatest teachers. It forced us to be strategic, deliberate, and efficient. We learned that constraints can be a powerful driver of focus, pushing us to perfect a single, strong idea rather than getting lost in endless possibilities. It was a practical lesson in lean and purposeful development.
What's next for WorkWorth
Deeper Data & Granularity Our immediate goal is to expand the dataset. We plan to add more products, including essentials like rent and utilities, to create a complete "cost of living" basket. We also want to move beyond country-level data and introduce city-specific analysis, which will offer a much more granular and accurate picture of affordability.
Powerful Personalization The future of WorkWorth is personal. We're developing a feature that will allow users to input their own salary and profession. This will transform the app from a global economic explorer into a powerful personal finance tool, showing you how your personal purchasing power compares to the average, both at home and abroad.
Richer Economic Stories We want to empower users to explore not just the "what," but the "why." The next version will integrate other key economic indicators, like the Gini coefficient (inequality) and the Human Development Index. Users will be able to see how affordability correlates with these factors, telling a much richer and more nuanced story about the global economy.
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