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Projects

respy is actively used as a research tool. Here is a list of projects to showcase the capabilities and kind of questions which are tackled with respy.

If you want to feature your project in this list, open an issue or pr and submit a title, a summary, links to further resources, and contact details.

Research

Bhuller, M., Eisenhauer, P. and Mendel, M. (2020). The Option Value of Education. Working Paper.

We provide a comprehensive account of the returns to education using Norwegian population panel data with nearly career-long earnings histories. We use variation induced by a mandatory schooling reform for an instrumental variables strategy as well as the validation of a full structural model. We discuss the trade-offs between the two approaches. Using the structural model, we go beyond the standard return concepts such as Mincer returns and the internal rate of return. This allows us to account for the sequential resolution of uncertainty and nonlinearities in the returns to education. Both give rise to option values as each additional year of schooling provides information about the value of different schooling choices and new opportunities become available. We are thus able to estimate the true return to education and find an important role for option values.

Contact: @peisenha, @mo2561057


Eisenhauer, P. and Suchy, R. (2020). Robust Human Capital Investment under Risk and Ambiguity. Working Paper.

We build on the prototypical life cycle model of human capital investment Keane and Wolpin (1994) and study individual decision-making under risk as well as ambiguity. Individuals fear model misspecification and seek robust decisions that work well over a whole range of models about their economic environment. We describe the individual’s decision problem as a robust Markov decision process. Our Monte Carlo analysis indicates that the empirical finding of large psychic cost of schooling is in part due to model misspecification by econometricians who only analyze individual investment decisions under risk. This changes the mechanisms driving schooling decisions and affects the ex ante evaluation of tuition policies.

Contact: @peisenha, @rafaelsuchy


Eisenhauer, P. (2019). The Approximate Solution of Finite-Horizon Discrete Choice Dynamic Programming Models: Revisiting Keane & Wolpin (1994). Journal of Applied Econometrics, 34 (1), 149-154.

The estimation of finite‐horizon discrete‐choice dynamic programming (DCDP) models is computationally expensive. This limits their realism and impedes verification and validation efforts. Keane and Wolpin (Review of Economics and Statistics, 1994, 76(4), 648–672) propose an interpolation method that ameliorates the computational burden but introduces approximation error. I describe their approach in detail, successfully recompute their original quality diagnostics, and provide some additional insights that underscore the trade‐off between computation time and the accuracy of estimation results.

Contact: @peisenha

Thesis

Stenzel, T. (2020). Uncertainty Quantification for an Eckstein-Keane-Wolpin model with correlated input parameters.

The thesis analyzes the uncertainty of the effect of a 500 USD subsidy on annual tuition costs for higher education on the average years of education caused by the parametric uncertainty in the model of occupational choice by Keane and Wolpin (1994). This model output is called a quantity of interest (QoI). The uncertainty quantification (UQ) has two stages. The first stage is an uncertainty analysis, and the second stage is a quantitative global sensitivity analysis (GSA). The uncertainty analysis finds that the tuition subsidy has a mean effect of an increase of 1.5 years and a standard deviation of 0.1 years in education. For the qualitative GSA, I develop redesigned Elementary Effects based on Ge and Menendez (2017) for a model with correlated input parameters. Based on these Elementary Effects, I compute multiple aggregate statistics to quantify the impact of the uncertainty in one parameter on uncertainty in the QoI. However, the analysis does not lead to clear results as there is no consensus about how to interpret the aggregate statistics in this context - even for uncorrelated parameters.

Contact: @tostenzel


Massner, P. (2019). Modeling Wage Uncertainty in Dynamic Life-cycle Models.

The thesis sheds light on the validity of the modeling assumptions of wage uncertainty in dynamic life-cycle models of human capital investment. Since the pioneering work of Keane and Wolpin (1997), a majority of studies in this field followed the approach of assuming serially independent productivity shocks to wages. In the case of Keane and Wolpin (1997), the independence assumption indeed simplifies the numerical solution of the model compared to more complex specifications, such as serial correlation. However, the assumption of i.i.d. productivity shocks seems to be quite narrow in light of findings of the reduced-form literature stream on wage dynamics.

Contact: @PatriziaMassner


Raabe, T. (2019). A unified estimation framework for some discrete choice dynamic programming models.

The thesis lays the foundation for respy to become a general framework for the estimation of discrete choice dynamic programming models in labor economics. It showcases the ability to represent Keane and Wolpin (1994), Keane and Wolpin (1997), and Keane and Wolpin (2000) within a single implementation.

Contact: @tobiasraabe

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