20 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, meetings, and writing. Mornings often mean cleaning and analysing data in Microsoft Excel or IBM SPSS Statistics, running regressions or behavioural experiments. Afternoons usually have meetings with product teams, policy clients, or students, plus drafting reports in Microsoft Word and slides in PowerPoint for presentations.
Some days are fieldwork: collecting socioeconomic or survey data, supervising student projects, or testifying in court. Deadlines and conferences mean bursts of writing and PowerPoint preparation, then follow-up emails in Microsoft Outlook and dashboards in Power BI.
Start with Microsoft Excel for data cleaning, pivot tables, and basic regressions—most jobs expect it. Next, learn IBM SPSS Statistics for survey and experimental analysis, and Microsoft PowerPoint and Word for communicating findings.
If you’ll make dashboards or present policy options often, add Microsoft Power BI. Knowing Microsoft Access helps if you work with messy relational databases. Employers list these systems in job descriptions.
According to the U.S. Bureau of Labor Statistics (BLS), 17,790 people are employed in this occupation with a median pay of $124,720 per year. The lowest 10% earn about $67,360, and the top 10% earn about $238,060, per BLS data.
Use those numbers as a range: academic roles often start lower, think tanks and private firms pay toward the middle or high end, and senior roles in industry or consulting reach the top tenth.
AI helps automate repetitive tasks: cleaning datasets, running basic statistical models in SPSS or Python, and generating initial draft text for reports and slides. Use it to speed up analysis and to create visual drafts in Power BI and PowerPoint.
AI won’t replace the job’s core: designing experiments, interpreting causal effects, advising on policy or product feasibility, or providing expert testimony. Learn to use AI as a tool, and focus on domain knowledge, experimental design, and communication—things AI can’t fully do.
Behavioral economists focus on psychological drivers of decisions—biases, heuristics, experimental interventions—while general economists study markets, trade, policy, and broad forecasts. Both analyse data, but behavioral work often uses experiments and survey methods.
Data scientists focus on coding, machine learning, and product metrics. A behavioral economist still needs strong statistical skills (SPSS/Excel), but also designs experiments, writes policy recommendations, and supervises research—tasks that need subject-matter expertise and writing for non-technical audiences.
Day one: clear statistical basics (reading regressions), Excel proficiency (pivot tables, formulas), and written communication in Word and PowerPoint. Also learn how to collect data and use Outlook for project coordination.
You can learn advanced SPSS techniques, Power BI dashboarding, or litigation support writing on the job. Prioritize experimental design, critical thinking, and the ability to explain results to non-experts—those skills affect what you’ll work on the most.