About Me

Welcome to my website! I am Carl Hallmann, an Economics PhD candidate at Northwestern University. My research interests are in Macroeconomics and Economic History. I will be on the 22/23 Job Market. I am available for interviews during the virtual EEA meeting and the virtual ASSA/AEA meeting.


Email: CarlHallmann@u.northwestern.edu
Address: Dept. of Economics, 2211 Campus Drive, Evanston, IL 60208; USA

Working Papers

Short Time Work and the Unemployment Scar [Job Market Paper]

Short-time work is a policy tool which subsidizes employment during recessions. I assess its welfare effects, who benefits most from it, and whether it is suitable as an automatic stabilizer. For this purpose I develop a heterogeneous agents model, for which the income process is generated by a job ladder search and matching model. I calibrate the model to match the German labor market around the great recession and estimate key parameters governing the value a worker generates after entering STW using German social security data in combination with a survey on the use of STW. In my preferred specification STW has a welfare effect equivalent to 0.37% of GDP for each percentage point of working population in STW. Workers at the peak of their career benefit most strongly, as they stand to lose job and firm specific knowledge, as well as the high wages they negotiated in the past. I find that the effectiveness of STW depends on the type of crisis an economy undergoes. STW is less effective if the crisis is driven by a structural change, financial markets are healthy such that few firms are affected by borrowing constraints, or low wage workers with little firm and task specific human capital are affected. As a consequence its effectiveness varies from crisis to crisis, and it is better suited as a discretionary policy tool than an automatic stabilizer.

Why Britain? The Right Place (in the Technology Space) at the Right Time

with W. Walker Hanlon and Lukas Rosenberger
presented at 2022 NBER Summer Institute

Why did Britain attain economic leadership during the Industrial Revolution? We argue that Britain possessed an important but underappreciated innovation advantage: British inventors worked in technologies that were more central within the innovation network. We offer a new approach for measuring the innovation network using patent data from Britain and France in the 18th and early 19th century. We show that the network influenced innovation outcomes and then demonstrate that British inventors worked in more central technologies within the innovation network than inventors from France. Then, drawing on recently-developed theoretical tools, we quantify the implications for technology growth rates in Britain compared to France. Our results indicate that the shape of the innovation network, and the location of British inventors within it, can help explain the more rapid technological growth in Britain during the Industrial Revolution.

Invention and Technological Leadership during the Industrial Revolution

with Lukas Rosenberger and Emre E. Yavuz

This paper provides the first empirical cross-country evidence on inventive activity during the Industrial Revolution. Idiosyncrasies in the French historic patent law allow us to compare invention rates in Britain and France across sectors based on French patent data from 1791 to 1855. Our key result is a robust, positive association of invention rates in Britain and France at the sectoral level. Furthermore, we provide the first quantitative evidence on technological leadership in invention at the sectoral level. The evidence informs a debate about whether the acceleration of technological progress during the Industrial Revolution mainly was a British or a European achievement, which has implications for theories of growth and innovation.

Work in Progress

Central Bank Accounts For All

I develop a simple model of banking in which central bank accounts for all are equivalent to a central bank digital currency (CBDC). I use the model to outline conditions under which a CBDC can be beneficial for financial stability. In the absence of any policy intervention the equilibrium is inefficient as deposit insurance induces banks to act irresponsible. A CBDC can help if the central bank hands what it receives for issuing currency to banks, and requires the right type of collateral in exchange. This collateral should be valuable, especially when the financial market experiences an episode of distress.

Are Recursive Neural Networks Useful for Macroeconomic Forecasting?

with Federico Puglisi and Emre E. Yavuz

We horse-race a Bayesian VAR with hierarchical priors, one of the state of the art macroeconomic forecasting models, with different neural networks. These include a simple recursive neural network (RNN), an RNN with a gated recurrent unit (GRU), and a GRU regularized such that it shrinks towards a random walk (GRU-VAR). We find that any suffciently flexible, and well regularized model has similar forecasting performance as the Bayesian VAR in one step ahead forecasts. We find that our GRU-VAR easily outperforms the Bayesian VAR in forecasts that go further than one step ahead.


Teaching Assistant at Northwestern University

  • MMSS-300: Foundations of Mathematical Social Sciences (2018, 19, 20)
  • ECON-281: Introduction to Applied Econometrics (2021)
  • ECON-315: Topics in Economic History (2020)
  • ECON-324: Western Economic History (2021,22)

Curriculum Vitae

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