Structural Estimation and Counterfactual Analysis in Models of Human Capital Formation

2022 Fiscal Year Final Research Report

• Project/Area Number: 19K13715
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 07050:Public economics and labor economics-related
• Research Institution: The University of Tokyo
• Principal Investigator: Griffen Andrew 東京大学, 大学院経済学研究科(経済学部), 准教授 (10645055)
• Project Period (FY): 2019-04-01 – 2023-03-31
• Keywords: Structural estimation / Prenatal care / Technological change / Migration

Outline of Final Research Achievements

We have completed two working papers. For the first we have parameter estimates and are currently running counterfactuals. We will use the model to understand how uncertainty, occupational and location experience "lock in" and general equilibrium effects contributed to increases in observed inequality during the period 1980 - 2000. The second project was delayed by COVID-19 because the RCT could not be completed on schedule. However, we received the final data set last year and have been working to estimate the model, which incorporates many new quality measures.

Free Research Field

Empirical microeconomics

Academic Significance and Societal Importance of the Research Achievements

技術変化に関する私たちのプロジェクトは基本的に、技術変化の期間中に個人がスキルの習得と場所をどのように調整するかについてのものです。 来たるべき AI 革命を考慮すると、私たちの研究は、労働者が行う必要がある調整の種類と、これらの変化が不平等にどのようにマッピングされるかを正確に語ることができます。 DRC は 5 歳未満児死亡率が世界で最も高い国の 1 つであり、私たちのモデルは需要パターンの豊富な特徴を提供するだけでなく、出生前ケアを改善するために設計された反事実的な政策の影響推定値も与えることができます。