Dollars in Orbit

Inspiration

I didn’t want to just throw numbers around. I wanted to tell a story about the space economy that actually makes sense to people. Everyone hears “rockets,” but the real drivers are satellites, GPS, and private industries that shape our everyday lives. With talk of federal funding cuts, I wanted to ask: who really powers the space economy, how much does it matter, and what happens if taxpayer money gets pulled back?

What it does

This project started with raw BEA data that was messy and spread across years. I reshaped it, calculated key metrics, and built visualisations to answer four questions:

1. Which part is the biggest? → Private Broad industries clearly dominate.
2. How much does space matter to the U.S. economy? → Calculated the space’s share of the Real GDP (2012–2023) of USA.
3. How much comes from taxpayers? → Federal funding = ~12–13% annually. Govt Share =( Government Real Gross Output/Total Real Gross Output) × 100
4. What if funding is cut? → Ran a correlation between government spending and private broad output (𝑟 ≈ 0.59). It’s not a perfect dependency, but it shows federal support does ripple into private growth.

How I built it

I started with the BEA space economy dataset, which came in a messy wide format with years spread across columns. I cleaned and reshaped it in Excel so each row had Sector, Industry, Year, and Value (Nominal, Real, Gross Output, Price Index).

Next, I built lookup rules to classify industries into Private Broad, Private Narrow, and Government, based on the BEA definitions. That let me slice the data the same way policymakers do.

From there, I created pivot tables to track real versus nominal growth, inflation effects, and the government's share over time. I also pulled official U.S. Real GDP data from FiscalData.gov to compare the space economy’s contribution against the national economy. This provided my analysis with context — not just how the space economy grew, but how meaningful that growth is in relation to the entire U.S. economy.

Finally, I built visualisations in Excel (stacked bars, line charts, correlation plots) that walked through my storyline:

1. Who’s biggest (Private Broad).
2. How much space contributes to GDP.
3. What % comes from taxpayers.
4. What happens if that funding is cut.

Everything was built from scratch by me in ~12 hours, which included cleaning, analysis, charts, and presentation.

Challenges I ran into

1. I did this solo, so I had to pivot between data cleaning, analysis, and building the story flow.
2. Wrangling the data (wide and long) was surprisingly time-consuming.
3. Fighting imposter syndrome — I kept thinking “this is too simple,” but realised simple + clear > messy + overcomplicated.

Accomplishments that I am proud of

1. Building a full storyline — from identifying the biggest driver of the space economy, to showing its share in U.S. GDP, to tying it back to taxpayer funding.
2. Doing the entire process solo: cleaning, analysing, visualising, and presenting in under 12 hours.
3. Creating charts that clearly show the story to policymakers and the public.

What I learnt

1. How much work it takes to make raw economic data actually tell a story.
2. That value added, gross output, and price indexes are more than just numbers — they’re different lenses on growth and productivity.
3. How to connect government spending with private sector outcomes using simple but powerful analysis.
4. That sometimes clear storytelling with strong visuals matters more than building an overly complex ML model.

What's next for Dollars in Orbit

1. Expanding the analysis to include employment data in the space economy — showing not just dollars but jobs at risk.
2. Forecasting space economy growth with more advanced models (ARIMA/Prophet) to see what the next decade looks like.
3. Comparing the U.S. space economy’s growth against other countries to frame global competitiveness.
4. Turning the cleaned dataset and charts into an interactive dashboard so policymakers and the public can explore the numbers themselves.

Built With

• excel