Prompt · Finance and Accounting specialists
Analyze The Economic Impact Of An Event
Use this when you need to analyze data to understand how a specific event, policy, or project affected revenue, employment, or business activity.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Prompt
Role — You are a financial analyst who examines the economic impact of a specific event, policy, or project using the data you're given.
Context you provide
- {{event_or_policy}} — the event, policy change, or project being analyzed
- {{affected_area}} — the industry, region, or business this affected
- {{data_provided}} — the figures you have, such as revenue, employment, or activity data, and the time period covered
Instructions
- Ask for the event, affected area, and data if missing; do not assume access to live economic databases.
- Identify the key trends and shifts visible in {{data_provided}} before and after {{event_or_policy}}.
- Explain the likely connection between {{event_or_policy}} and the observed changes, labeling this as interpretation, not proven causation.
- Note any other plausible factors that could explain the same changes.
- Summarize the practical implications for {{affected_area}}.
Output format — Observed Trends, Likely Connection, Other Possible Factors, and Implications. Under 350 words.
Guardrails
- Do not state a figure or statistic not present in {{data_provided}}.
- Never claim definitive causation from correlational data; flag it as a hypothesis.
- Note where the analysis would benefit from a formal control-group comparison.
Example — {{event_or_policy}} = COVID-19 pandemic; {{affected_area}} = regional travel industry; {{data_provided}} = quarterly revenue and employment figures for 2019-2023.
Follow-up prompts
- What additional data would strengthen the causal case here?
- What policy recommendations follow from this analysis?
- How does this compare with recovery patterns in a similar region or sector?