Research

Research plan: We want to better understand the interplay between family and career advancement. Specifically, we aim to investigate how family composition influences career trajectories for both men and women. Understanding household decision-making regarding work and career choices requires considering two crucial aspects: 1) Career as an Intertemporal Concept: Career progression is not instantaneous; it unfolds over time. This intertemporal nature is essential for modeling career paths. 2) Intra-household Decision-Making: Analyzing family's impact on careers necessitates seriously considering how couples or families make joint decisions about work and career goals. The Limited-Commitment Intertemporal Collective (LIC) model is particularly well-suited for this analysis as it explicitly incorporates both these aspects (Chiappori and Mazzocco 2017). We want to develop, solve, and estimate an LIC model using rich Danish administrative data to explore how family structures influence the career paths of men and women in Denmark.

Abstract: In this guide, we introduce the limited commitment model of dynamic household bargaining behavior over the life cycle. The guide is intended to make the limited commitment model more accessible to researchers who are interested in studying intra-household allocations and divorce over the life cycle. We mitigate computational challenges by providing a flexible base of code that can be customized and extended to the specific use case. The main contribution is to discuss practical implementation details of the model class, and providing guidance on how to efficiently solve limited commitment models using state-of-the-art numerical methods. The setup and solution algorithm is presented through a stylized example of dynamic consumption allocation and includes accompanying Python and C++ code used to generate all results.

Abstract: The computational time required to solve and estimate dynamic economic models is one of the main constraints on empirical research. The Endogenous Grid Method (EGM) proposed by Carroll (2006) is known to offer impressive speed gains over more traditional stochastic dynamic programming methods, such as value function iterations (VFI). The existing EGM implementations implicitly require an analytical expression for the inverse marginal utility, which is not known analytically in many interesting cases. We propose a simple and fast approach, which we refer to as the iEGM, that can be applied even when the inverse marginal utility is not known analytically. We show through two applications that the iEGM inherits speed and accuracy properties of the EGM and that the our approach is an order of magnitude faster than VFI. Our approach thus allows for estimation of rich economic models in, say, days rather than months.