ScenarioAdvanced AI economic transition and participation

AI Productivity Distribution Analyst

Also called AI Prosperity Distribution Analyst

An AI Productivity Distribution Analyst would study who gains and who bears the costs if AI reshapes work at scale. It depends on how AI develops, and no company is hiring for it now.

What they do

The analyst would trace how productivity gains and costs from AI reach different groups: workers, regions, firms, sectors and countries. They would build causal studies from labour market, administrative and company data, and test competing explanations. They would draft alternative policy scenarios and set out who wins and loses under each. They would meet worker representatives and community groups, and bring their views into the analysis. They would work with public bodies and international partners, and write findings so non-economists can check how conclusions were reached.

What a strong hire delivers

In the first months, a strong hire would produce a clear framework for what is measured and why. They would deliver a first distributional study with its assumptions stated, and a small set of policy scenarios with trade-offs laid out. They would also set up working channels with affected groups and institutional partners, so later findings are tested and trusted.

How you can tell they are good

Ask them to walk through a past study of who gained and lost from an economic shift. Good candidates separate cause from correlation, name what the data could not show, and explain how they handled disagreement. Ask how they would show affected groups the reasoning. Be wary of confident headline numbers with no account of attribution.

Why it is hard to fill

If this scenario arrives, the people would be scarce because the job sits between several disciplines. Few economists have worked directly with worker representatives, and few policy staff have done causal distributional work. Titles would vary and none would map neatly. Candidates would often look like partial fits on paper, so judging real work matters more than job history.

Where to find them

Today they sit in labour economics, social policy research, inequality and public finance teams. Others come from worker representation bodies, trade unions, public administration and statistical agencies. Think tanks, university research groups, international institutions and government analytical units would be the main sources. Some would come from evaluation and impact assessment roles in programme delivery.

Interview questions for an AI Productivity Distribution Analyst

  1. Tell us about a study where you traced who gained and who lost from an economic change. How did you establish cause?

    A strong answer names the data, the comparison used and the alternatives ruled out. It admits limits plainly. Worry if the answer relies on correlation, or if they cannot say what would have changed their conclusion.

  2. How would you decide which groups count as affected when the changes cut across sectors and regions?

    Good answers start from the data and from consultation, not assumptions. They consider informal work, small firms and groups with weak representation. Worry if they default to a few familiar categories.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for an analyst to study how the gains and costs of AI-driven change are shared across workers, communities and sectors. You would produce transparent, causal evidence and policy scenarios, and work closely with affected groups and public institutions.

What you will do

  • Design and run distributional studies of AI-related economic change
  • Build causal analyses from labour market and administrative data
  • Develop alternative policy scenarios with clear trade-offs
  • Document attribution and assumptions so others can check them
  • Gather and represent the views of workers and communities
  • Coordinate with public bodies and international partners
  • Publish findings in plain language

What you will bring

  • Published or shared distributional or causal analysis you led
  • Working knowledge of labour economics or social policy
  • Experience handling messy administrative or survey data
  • Track record of working with worker or community groups
  • Ability to explain uncertainty to non-specialists
  • Experience working with public institutions

Nice to have

  • Experience in international or cross-border policy work
  • Familiarity with scenario analysis methods
  • Background in worker representation or public administration

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Depends on a scenario

Large-scale restructuring of work and economic institutions

What AI would need to do
AI materially changes economically valuable task mixes across many sectors.
What organisations would need to decide
Institutions fund worker participation, distributional monitoring and transition programmes.
What would rule it out
Slow adoption or institutional inaction leaves these functions embedded in existing occupations.

We give no date. The role becomes real if both conditions hold.

Skills that matter

  • Public policy and economicsAdvancedAnalyse institutional choices and distributional effects
  • Worker and community participationAdvancedInclude affected workers and communities in decisions
  • Institutional and international coordinationWorkingAlign actors and processes across jurisdictions
  • Frontier scenario analysisWorkingExplore conditional outcomes without claiming predictions

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert. Scenario roles describe responsibilities that depend on stated conditions; they are not forecasts.

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