Cześć!

I am an Assistant Professor (Juniorprofessor) of Quantitative Macroeconomics at the University of Mannheim. I completed my Ph.D. at the European University Institute in Florence.
In my research, I am particularly interested in how the heterogeneity of individuals and frictions in the retail market affect the decisions made by different households and, in turn, how these factors influence macroeconomic outcomes. To capture these complex dynamics more accurately, I use large microeconomic datasets within a macroeconomic context. This approach is supported by tools such as structural models and machine learning techniques.
You can find my résumé here.

Miscellanea

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

In my free time, I enjoy going on bike rides around the scenic villages near Mannheim (and beyond).

Research

1. Shopping Frictions with Household Heterogeneity: Theory & Empirics
[article] [slides]
Revise&Resubmit at the Review of Economic Studies
This paper examines the impact of price dispersion on household consumption, highlighting the role of economic status in shaping purchasing behaviors. Leveraging detailed scanner data, I document high-earning employees pay 1.5 to 7% more than lower-earning ones for the same or similar goods. A causal link between income and the prices is established using the Economic Stimulus Act of 2008. The findings indicate that 8 to 22% of the increase in household spending following a transitory income shock is due to higher prices paid. Despite a broader variety in the consumption baskets of wealthier households, very few goods are tailored to specific income groups. Integrating consumer search with the savings problem, I propose a new model to reconcile the observed patterns and quantify the impact of retail-market frictions on consumption. Counterfactual analysis shows that over two-thirds of households face higher prices due to a price externality.

2. Understanding the Sources of Earnings Losses After Job Displacement:
A Machine-Learning Approach (jointly with Andreas Gulyas)
[article] Revise&Resubmit at the Journal of Labor Economics
Applying a generalized random forest to Austrian administrative data, we uncover significant systematic heterogeneity in worker outcomes after job displacement. A quarter of workers face wage losses of 30%, while another quarter experience no losses or even gain. Among many factors, firm wage premia are the most important determinant of the wage loss heterogeneity, while workers' age is the most important for employment losses. Our findings suggest that earnings losses stem from mean reversion in firm wage premia rather than the destruction of firm-specific human capital. Compositional differences in individual characteristics account for most of the cyclicality in earnings losses.

3. Looking into the Consumption Black Box: Evidence from Scanner Data (jointly with Daniel Runge)
[slides]
Preliminary draft available upon request. Non-preliminary draft available super soon!

4. The Consequences of the Covid-19 Job Losses: Who Will Suffer Most and by How Much? (jointly with Andreas Gulyas)
[article] CEPR Covid Economics (47)
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.

Teaching

At Mannheim, I offer courses at various levels to diverse audiences:

1. Quantitative Macroeconomics with Heterogeneous Households [Ph.D. course]
A Julia tutorial I prepared for my students can be found [here].
2. Advanced Macroeconomics [M.Sc. course]
3. Introduction to Predictive Analytics and Machine Learning in R [B.A. block seminar]
Some prerecorded materials introducing programming in R and statistical learning can be found [here].

How to reach me

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

Elements

i = 0;

while (!deck.isInOrder()) {
    print 'Iteration ' + i;
    deck.shuffle();
    i++;
}

print 'It took ' + i + ' iterations to sort the deck.';