Krzysztof Pytka

Cześć!

I'm an Assistant Professor (Juniorprofessor) of Quantitative Macroeconomics at the University of Mannheim. Image of Krzysztof Pytka In my research I am interested how the heterogeneity of individuals affects macroeconomic outcomes. Tools that I use span structural models and machine learning.

Contact

Department of Economics
Universität Mannheim
L7 3-5, Office 2.09
68161 Mannheim, Germany
Phone: +49.621.181.181.7
e-mail: p***@uni-mannheim.de

Résumé

You can find my CV here.

Research

Working Papers

1. The Consequences of the Covid-19 Job Losses:
Who Will Suffer Most and by How Much? (jointly with Andreas Gulyas) [article]

Using the universe of Austrian unemployment insurance records until May 2020, we document that the composition of UI claimants during the Covid-19 outbreak is substantially different compared to past times. Using a machine-learning algorithm from Gulyas and Pytka (2020), we identify individual earnings losses conditional on worker and job characteristics. Covid-19-related job terminations are associated with lower losses in earnings and wages compared to the Great Recession, but similar employment losses. We further derive an accurate but simple policy rule targeting individuals vulnerable to long-term wage losses.

2. Understanding the Sources of Earnings Losses After Job Displacement:
A Machine-Learning Approach (jointly with Andreas Gulyas) [article] [slides]

We document the sources behind earnings losses after job displacement adapting the generalized random forest due to Athey et al. (2019). Using administrative data from Austria over three decades, we show that displaced workers face large and persistent earnings losses. We identify substantial heterogeneity in losses across workers. A quarter of workers face cumulative 11-year losses higher than 2 times their pre-displacement annual income, while another quarter experiences losses less than 1.1 times their income. The most vulnerable are older high-income workers employed at well-paying firms in the manufacturing sector. Our methodology allows us to consider many competing theories of earnings losses prominently discussed in the literature. The two most important factors are the displacement firm's wage premia and the availability of well paying jobs in the local labor market. Our overall findings provide evidence that earnings losses can be understood by mean reversion in firm rents and losses in match quality, rather than by a destruction of firm-specific human capital.

Additional resources using the results from the paper:
  • Web app predicting earnings losses conditioned on individual characteristics
  • General-audience presentation about using the GRF for estimation of heterogeneous treatment effects. [slides]
  • Presentation about using the GRF in business analytics. [slides]

3. Shopping Effort in Self-Insurance Economies (in revision) [article] [slides]

Best Paper Prize for Young Economists at the WIEM’16 conference.

How are income fluctuations transmitted to consumption decisions if the law of one price does not hold? I propose a novel and tractable framework to study search for consumption as part of the optimal savings problem. Due to frictions in the retail market, households have to exert some effort to purchase the consumption good. This effort has two components: 1. effort to search for price bargains; 2. effort required to purchase consumption of a given size. These two motives are necessary to replicate two seemingly contradictory shopping patterns observed in the data, namely: higher time spent shopping by the unemployed and retirees and (conditioned on being employed) the positive elasticity of shopping time with respect to labor income. The former is well known in the literature, while the latter is new and I document it using data from the American Time Use Survey. The model allows me to reconcile the traditional savings theory with households' shopping behavior in a quantitatively meaningful way. As I show frictions in the purchasing technology generate important macroeconomic implications for modeling inequality and, in general, household consumption.

Teaching

At Mannheim I give following lectures:

  1. Quantitative Macroeconomics with Heterogeneous Households [Ph.D. course];
  2. Advanced Macroeconomics [M.Sc. course];
  3. Statistical Learning and Big Data with R [B.A. course].

Here you can find the (old) website with teaching materials.

Misc.

If you're curious about the pronounciation of my given name it goes by [kʂɨʂt̪ɔf] in IPA. But Christoph is totally fine with me.

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