George

Sorg-Langhans

Ph.D. Candidate

Princeton University

Economics Department

langhans@princeton.edu | LinkedIn | (609) 865-2241

Hello and Welcome!

I am a Princeton Economics Ph.D. candidate and I am currently searching for a position as a data scientist, applied scientist or economist.

Resume

References

My research combines machine learning and economics, with a focus on deep learning algorithms for large scale economic models. Through my Ph.D. I have developed in-depth knowledge and skills in causal inference, machine learning and their intersection.

In my work, I use cutting edge techniques to shed light on issues of social concern, investigating the benefits of freedom of movement and the causes of inequality. I am passionate about how we can use data science and economics to create innovative solutions to pressing social challenges.

I have 6+ years of experience in programming with a focus on Python and 7+ years of experience in economic modeling. I am a McGraw Fellow at Princeton University, a Scholar of Trinity College Dublin, and a Fellow of the German National Academic Foundation.

I love teaching and have a passion for working collaboratively. Two of my proudest achievements during my time at Princeton were winning the teaching prizes of my department and of the entire Graduate School.

Research

Solving High-Dimensional Dynamic Programming Problems using Deep Learning

(With Jesus Fernandez-Villaverde, Galo Nuno and Maximilian Vogler)

To answer a wide range of important economic questions, researchers must solve high-dimensional dynamic programming problems. This is particularly true in models designed to account for granular data. To break the “curse of dimensionality” associated with these high-dimensional dynamic programming problems, we propose a deep learning algorithm that efficiently computes a global solution to this class of problems. Importantly, our method does not rely on integral approximation, instead efficiently calculating exact derivatives. We evaluate our methodology in a standard neoclassical growth model and then demonstrate its power in two applications: a multi-location model featuring 50 continuous state variables and a highly nonlinear migration model with 75 continuous state variables.

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What are you talking about Mr. President?

Topic Modeling for the Economic Reports of the President

(With Maximilian Vogler)

Despite their power, modern natural language processing techniques have not found widespread use in the economic literature. In this paper we demonstrate their potential in the context of a specific economic application, namely the analysis of the Economic Reports of the President. Specifically, we use both Non-Negative Matrix Factorization and Latent Dirichlet Allocation to extract and study the main topics of each presidential report. Whilst both approaches broadly agree on the topics, the NMF proves more versatile. Overall, the topics we identify are well defined and display remarkable time series patterns, documenting both long run economic trends and highlighting specific policy events. Based on these findings, the economic reports in combination with natural language processing techniques thus present fertile ground for future research.

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Inequality at the Top: Down to the Roots

(With Riccardo A. Cioffi and Maximilian Vogler) [Draft coming soon]

Multiple theories of inequality compete to explain U.S. wealth inequality and the share of wealth held by the top one percent. To what extent does it matter which of these models we rely on? In this paper we analyze the responses of the different theories to a host of policy experiments. To this end, we form a quantitative model that nests the competing channels and assesses the effects of policy experiments by sequentially shutting off all but one of these model mechanisms. Our model is calibrated on the wealth distribution which allows us to starkly contrast the different theories and clearly understand the mechanisms at work. We find significant differences in the predictions of the competing models for wealth inequality dynamics following a policy shock.

Work In Progress

The Dual Role of Migration - Insurance and Volatility in the Schengen Area

(With Jesus Fernández-Villaverde, Galo Nuño, and Maximilian Vogler)

Changes in net migration are a key, but often overlooked, margin through which economies absorb aggregate shocks. This mechanism is particularly important for countries within free movement zones like the Schengen Area in Europe. After a country receives an asymmetric negative aggregate shock, its citizens can easily migrate to other countries in the free movement zone, lowering labor supply locally and increasing it abroad. We empirically show that changes in net migration were, in fact, a major adjustment margin during the European Debt Crisis of 2009-2014. In order to understand the implications of these migration patterns, measure their welfare implications, and gauge their consequences for optimal policy design, we build a large scale business cycle model of the Schengen Area that incorporates migration decisions across different countries. This naturally gives rise to a high-dimensional problem, which the previous migration literature was not able to solve due to the “curse of dimensionality”. By applying the deep learning algorithm developed in Fernández-Villaverde, Nuño, Sorg-Langhans, and Vogler (2020), we overcome this “curse of dimensionality.” We investigate the propagation of economic shock in this model. A vital aspect of our investigation is taking into account the effect of the composition and size of the union. Interestingly, through its membership in such a zone, a country can import or export a substantial amount of unemployment, even in the absence of domestic shocks. This implies that the composition of the migration zone matters, as countries with volatile business cycles receive insurance from the membership while those with more stable business cycles can import volatility.

The Causal Effect of Taxation on Growth - a Machine Learning approach

(With Maximilian Vogler)

The impact of taxation on economic growth has been one of the predominant economic questions and has given rise to entire ideologies and fierce political fights. Given the importance of this question, it might seem surprising that there is still little agreement in the economic literature about the size of this causal effect. This is mainly due to the rampant endogeneity problems plaguing any empirical study in this field. In this paper, we contribute to the rich empirical literature by utilizing recent advances in machine learning approaches to natural language processing. In particular, we analyze the Economic Reports of the President using Non-Negative Matrix Factorization to identify exogenous policy shocks in the spirit of Romer and Romer (2010). Therefore, our approach aims to be more objective, easily replicable and able to incorporate new data without complications.

Teaching

Throughout my Ph.D. I have enjoyed teaching numerous courses. Two of my proudest achievements during my time in Princeton were being awarded first the Economics Department Teaching Prize and then the University Graduate School Teaching Prize.

Consequently, I was nominated and selected to be the McGraw Fellow for the Economics Department. In this position I have taught all incoming Ph.D. instructors for the department over the last two years. I have enjoyed meeting all the instructors and helping them find their footing as new teachers. With the onset of online teaching due to COVID-19, I was part of the McGraw initiative dedicated to developing online resources and infrastructure for the coming academic year.

Finally, I was the course organizer for ECO100 in 2018. It was a challenging but exciting position, since ECO100 is the largest course in Princeton, with over 450 students. In this context, I managed a team of eight Ph.D. instructors and handled all organizational issues for the course.