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Analysis of Structural Economic Models: Misspecification and Flexible Estimation

Project description

Opening the door to new opportunities for data analysis

An important class of structural economic models focuses on skill and human capital formation which plays a pivotal role in driving growth, amongst other things. The EU-funded NPSkills project will show that these current models rely on seemingly innocuous normalisations which not only impact the interpretation of parameters and predictions but also can lead to poor strategies and policy recommendations. NPSkills will develop new statistical methods to address these shortcomings. It will offer a comprehensive identification analysis and focus on a set of important policy-relevant parameters that yield robust conclusions. The project's work can aid in the areas of economic growth, inequality and investments in children.

Objective

Structural models are key tools of economists to evaluate and design policies. These models specify economic environments, estimate mechanisms that determine outcomes, and can be used for counterfactual predictions. One important class of models deals with skill and human capital formation, which is an important driver of economic growth and inequality. These models study the determinants of skill formation and the timing of optimal investments in children. Since structural models require simplifying assumptions, they are also prone to misspecification.

The proposed research shows that existing skill formation models rely on seemingly innocuous normalizations, which can severely impact counterfactual predictions. For example, simply changing the units of measurements of observed variables can yield ineffective investment strategies and misleading policy recommendations. I plan to tackle these problems by providing a new comprehensive identification analysis and by focusing on a novel set of important policy-relevant parameters that yield robust conclusions. These issues and solutions might extend to many other structural models with latent variables. In addition, I will provide a new flexible estimator for the policy-relevant features and analyze various data sets to reevaluate policy recommendations with potentially large impacts on costs and benefits of large public investments in children, economic growth, and inequality.

Estimation will rely on other objectives of this proposal, which aim to develop new econometric tools. These tools are important contributions on their own rights and are applicable in a wide range of settings. They allow researchers to obtain more precise nonparametric estimators and more reliable conclusions by using shape restrictions implied by economic theory and data-driven dimension reduction techniques. By also providing guidance on which estimation method to use in practice, these results can have a large impact on empirical research.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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ERC-STG - Starting Grant

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Call for proposal

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(opens in new window) ERC-2020-STG

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Host institution

RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITAT BONN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 050 161,00
Address
REGINA PACIS WEG 3
53113 BONN
Germany

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Region
Nordrhein-Westfalen Köln Bonn, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 050 161,00

Beneficiaries (1)

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