Description du projet
Ouvrir la voie à de nouvelles opportunités pour l’analyse de données
Une classe importante de modèles économiques structurels se concentre sur la formation de compétences et de capital humain qui joue un rôle central dans la croissance, entre autres. Le projet NPSkills, financé par l’UE, montrera que ces modèles actuels reposent sur des normalisations apparemment inoffensives qui ont non seulement un impact sur l’interprétation des paramètres et des prédictions, mais peuvent également conduire à de mauvaises stratégies et recommandations politiques. NPSkills développera de nouvelles méthodes statistiques pour combler ces lacunes. Le projet offrira une analyse d’identification complète et se concentrera sur un ensemble de paramètres importants pour les politiques qui produisent des conclusions robustes. Les travaux du projet pourront contribuer aux domaines de la croissance économique, des inégalités et des investissements en faveur des enfants.
Objectif
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.
Mots‑clés
Programme(s)
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Appel à propositions
(s’ouvre dans une nouvelle fenêtre) ERC-2020-STG
Voir d’autres projets de cet appelRégime de financement
ERC-STG - Starting GrantInstitution d’accueil
53113 Bonn
Allemagne