Structural Estimation and Counterfactual Analysis in Models of Human Capital Formation

Research Project

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
Project Status	Completed (Fiscal Year 2022)
Budget Amount *help	¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000) Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000) Fiscal Year 2021: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000) Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000) Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Keywords	Structural estimation / Prenatal care / Technological change / Migration / Human capital / RCT / Counterfactuals / Wage structure / Antenatal care
Outline of Research at the Start	The research will consist of two projects. The first project is on the effects of technological change on wage inequality in the US taking into account the endogenous formation of education, location, occupation, and retirement decisions, and how they affect wage inequality. The second project examines clinic choice for antenatal care in the DRC and tradeoffs between price and distance and the extensive measures of providers’ quality. We hope to understand why households choose clinics that are not productive for health outcomes especially given the high infant death rates.
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.
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。