19 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You’ll split time between data work and writing. Mornings often mean pulling data in Microsoft Excel or Microsoft SQL Server, cleaning it, and running initial summary stats.
Afternoons usually involve analysis—forecast models, policy impact simulations, or review meetings—then writing a short report in Microsoft Word and making a presentation in PowerPoint or Power BI for policymakers or colleagues.
Start with Microsoft Excel and PowerPoint. Excel handles quick tables, pivot tables, and basic regressions; PowerPoint is how you show results to non-economists.
Next learn Microsoft SQL Server basics for querying databases and IBM SPSS Statistics or similar (R/Python if allowed) for running regressions and forecasts. Learn Outlook and Word for daily communication and reports.
The U.S. Bureau of Labor Statistics (BLS) reports 17,790 employed economists with a 2025 median annual wage of $124,720. The lowest tenth earned $67,360 and the top tenth $238,060.
Pay varies by employer: government, academia, private sector, or consulting. BLS is the source for these nationwide numbers.
Yes, but use AI carefully. Use it to draft plain-language summaries, suggest visualization ideas for Power BI or PowerPoint, or to check grammar in Word. Always verify any numbers, code, or model outputs the AI gives you against your data and tools like Excel or SPSS.
Do not rely on AI for statistical inference, data cleaning, or legal/ethical decisions; document sources and keep raw data and scripts (SQL, Excel workbooks, SPSS files) so everything is reproducible.
Economists focus on how societies allocate resources, policy impacts, markets, and forecasting at macro or sector levels; you’ll study labor, trade, taxes, or agriculture and write policy reports. Tasks include forecasts, policy evaluation, and expert testimony.
Financial analysts typically focus on company financial statements and investment decisions. Data scientists focus on building scalable predictive models and software. All three may use Excel and SQL, but economists use SPSS, Power BI, and formal economic models more often.
Get a degree in economics or a related field and take courses in statistics, econometrics, and micro/macro theory. Learn Excel thoroughly, basic SQL, and at least one stats tool (IBM SPSS Statistics, R, or Python).
Build a portfolio: class projects, forecasts, policy memos, or Power BI dashboards. Try internships at think tanks, government agencies, or research centers to get supervised research and real datasets.
Being able to turn data into a clear story. That means collecting and cleaning data (Excel, SQL), running appropriate analyses (SPSS or other stats tools), and explaining results in Word and PowerPoint or Power BI so non-specialists understand the policy or business decision.
You must also check assumptions and document methods so your work stands up to review or court scrutiny if used for expert testimony.