• Graduate Education
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Development Economics
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • The Economics of Crime
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Inequalities in Health and Healthcare
      • Marketing Research with Purpose
      • Markets with Frictions
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Alumni
Home | Events | Using MVPF for Resource Allocation: Treatment Assignment Across Programs and Beneficiaries under Heterogeneous Costs and Benefits
Seminar

Using MVPF for Resource Allocation: Treatment Assignment Across Programs and Beneficiaries under Heterogeneous Costs and Benefits

  • Series
    Labor Seminars
  • Speaker
    Antonio Dalla-Zuanna (Bank of Italy)
  • Field
    Empirical Microeconomics
  • Location
    Tinbergen Institute, Roeterseiland campus, E1.17
    Amsterdam
  • Date and time

    November 17, 2026
    16:00 - 17:00

Abstract

This paper shows how the Marginal Value of Public Funds (MVPF) can guide treatment allocation to improve social welfare. Under budget constraints, the optimal treatment targets individuals with MVPFs above a threshold that minimizes the opportunity cost of treatment. Using experimental data, we show that prioritizing high-MVPF groups under tight budgets can double Head Start’s social benefits compared to random assignment. Analyzing joint allocation across early (Head Start) and late (Job Corps) skill investment programs, we find that exclusive investment in early interventions is not optimal unless substantially higher welfare weights are placed on young children. Joint paper with Kai Liu.