Feasibility-Constrained Programme Aggregation

In Simple Words

Imagine a restaurant menu listing 43 specific dishes. If a recommendation algorithm tries to predict how much every diner will like each individual dish, it will fail because many dishes are rarely ordered or unavailable. Grouping the menu into 6 broad categories (like pasta, seafood, or salads) ensures every diner gets a reliable recommendation that the kitchen can actually serve.

Research question

Deploying algorithmic recommendation models across dozens of highly specialized public measures causes estimation failure due to tiny sample sizes per measure, empty choice sets for specific client demographics, and operational supply constraints in local training centers.

Participants

N = 460,442 historical jobseekers evaluated across 43 official Swiss labor market training measures.

Study Design

Econometric aggregation of 43 disaggregated administrative training measures into 6 to 8 broad feasibility-consistent categories (e.g., Basic Courses, Language Skills, Computer Skills, Further Training, Employment Programmes, No Programme).

Results

Program aggregation eliminated demographic choice-set restrictions (such as native language requirements), increased sample sizes per category to maintain statistical estimation power, and aligned algorithmic choices with operational local course availability.

Takeaway

Aggregate highly disaggregated intervention types into broader functional categories that guarantee valid choice sets for all client demographics while preserving statistical estimation power.

Related findings

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• Ability-Heterogeneous Wage Returns →Labour & employment

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• Activation Program Threat Effect →Labour & employment

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• Actual vs. Potential Experience Bias →Labour & employment

  Calculating how many years someone has been a driver by subtracting their high school graduation year from their current age creates significant error if they spent five years without a car. Taking a simple formula creates a false picture of driving practice.