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By means of a laboratory experiment, we show that, contrary to standard consumer theory, financially equivalent balance sheet profiles may be perceived as non fungible in a controlled frictionless environment with no probabilistic attributes. A large majority of subjects indeed have a bias in the perception of wealth, such that balance sheet composition matters: for a given net worth with values of assets and debt that are financially certain and risk-free, a greater asset-debt ratio implies greater perceived wealth. The predominance of this bias is explained by low cognitive sophistication and great inattention. Moreover, biased subjects are less patient, less debt averse, more likely to increase spending out of unexpected gains and report greater propensities to consume. A standard optimal consumption choice model, enriched with a rational but inattentive agent a la Gabaix (2014, 2019), aligns our key experimental findings.

After controversial public debates, fuel price regulations were implemented in Austria prohibiting fuel retailers from raising their prices more than once per day. This paper investigates whether these policy measures affected the price transmission dynamics from crude oil prices to retail fuel prices. We estimate different specifications of nonlinear error correction models to quantify a potentially asymmetric adjustment behaviour and compare the results over three subsamples. Particularly, we estimate our models for a pre-regulation period, a between-regulations and a post-regulation period. At first glance, we obtain conflicting results on the efficacy of this policy measure. While the adjustment to the long-run equilibrium seems to be faster if crude oil prices are relatively low, transitory crude oil price decreases are passed through faster than price increases. Only if we consider the combined effect of a crude oil price shock, we can reveal that crude oil price changes are generally passed through faster in the postregulation period. Further, we find that crude oil price decreases are now passed through slightly faster than crude oil price increases. Hence, we conclude that the Austrian fuel price regulation seems to have fostered competition between fuel retailers.

The prediction of the values of ordinal response variables using covariate data is a relatively infrequent task in many application areas. Accordingly, ordinal response variables have gained comparably little attention in the literature on statistical prediction modeling. The random forest method is one of the strongest prediction methods for binary response variables and continuous response variables. Its basic, tree-based concept has led to several extensions including prediction methods for other types of response variables. In this paper, the ordinal forest method is introduced, a random forest based prediction method for ordinal response variables. Ordinal forests allow prediction using both low-dimensional and high-dimensional covariate data and can additionally be used to rank covariates with respect to their importance for prediction. Using several real datasets and simulated data, the performance of ordinal forests with respect to prediction and covariate importance ranking is compared to competing approaches. First, these investigations reveal that ordinal forests tend to outperform competitors in terms of prediction performance. Second, it is seen that the covariate importance measure currently used by ordinal forest discriminates influential covariates from noise covariates at least similarly well as the measures used by competitors. In an additional investigation using simulated data, several further important properties of the OF algorithm are studied. The rationale underlying ordinal forests to use optimized score values in place of the class values of the ordinal response variable is in principle applicable to any regression method beyond random forests for continuous outcome that is considered in the ordinal forest method.

We study higher-order risk preferences, i.e. prudence and temperance, next to risk aversion in social settings. Previous experimental studies have shown that higher-order risk preferences affect the choices of individuals deciding privately on lotteries that only affect their own pay-off. Yet, most risky and financially relevant decisions in the field are made in the social settings of households or organizations. We aim to narrow the gap between laboratory and field evidence by creating a more realistic decision making environment in the laboratory that allows us to identify the influence of different social settings under controlled conditions. We elicit higher-order risk preferences of individuals and systematic ally vary how an individual's decision is made (alone or while communicating with a partner) and who is affected by the decision (only the individual or the partner as well). In doing so, we can isolate the effects of other-regarding concerns and communication on choices. We observe that individuals become more risk-averse when the partner is able to communicate with the decision maker. However, we do not observe an influence of social settings on prudence and temperance. Our results reveal that the majority of choices are risk-averse, prudent, and temperate across social settings. Wir untersuchen neben Risikoaversion auch Risikopräferenzen höherer Ordnung, d.h. "Prudence" und "Temperance" in sozialen Situationen. Experimentelle Studien haben bisher gezeigt, dass Risikopräferenzen höherer Ordnung die Entscheidungen von Individuen bestimmen, die isoliert zwischen Lotterien wählen, die nur ihre eigene Auszahlung bestimmen. Jedoch werden die meisten finanziell relevanten Entscheidungen unter Unsicherheit in sozialen Situationen getroffen, z.B. in Haushalten oder Organisationen. In unserer Studie verkleinern wir die Diskrepanz zwischen bisheriger Labor- und Feldevidenz, indem wir eine realistischere Entscheidungssituation im Labor kreieren. Sie erlaubt es, den Einfluss unterschiedlicher sozialer Einflüsse unter kontrollierten Bedingungen zu identifizieren. Wir erheben Risikopräferenzen höherer Ordnung von Individuen und variieren systematisch wie eine Entscheidung getroffen wird (alleine oder während der Kommunikation mit einem Partner) und wer von der Entscheidung betroffen ist (nur der Entscheider oder auch der Partner). So können wir den Einfluss von etwaigen "other-regarding concerns" und von Kommunikation auf die Entscheidungen isolieren. Wir beobachten, dass die Individuen risiko-averser entscheiden, wenn mit dem Partner kommuniziert werden kann. Wir beobachten jedoch keinen Einfluss der sozialen Situation auf Risikopräferenzen höherer Ordnung. Unsere Ergebnisse zeigen, dass die Mehrzahl der Entscheidungen über unterschiedliche soziale Situationen hinweg risikoavers, "prudent" und "temperate" ist.

We introduce a new microsimulation model built on household transport data to study the distributional effects of carbon-based fuel taxation of private road transport in Germany. Our data includes annual mileage at the car-level, the distinction between fuel types, as well as car-specific fuel consumption, allowing for a very detailed analysis. The model allows focusing on different types of households as well as identifying effect heterogeneity across the income distribution. We compare the recent fuel tax scheme with three policy reform scenarios to empirically test several hypotheses regarding distributional effects of carbon pricing. We find that the legal status quo of the fuel tax has overall regressive effects, with the tax on petrol acting regressive and the tax on diesel acting progressive. A transformation of the current tax into a revenue-neutral carbon-harmonised fuel tax yields a progressive distributional effect, while an introduction of a new carbon tax on transport fuels is neither clearly regressive nor progressive. Combining both tax schemes also has non-regressive effects. Our results suggest that policy makers face various options for pricing road transport greenhouse gas emissions without causing an overall disproportionate tax burden on low-income households.

What are the effects of austerity on distributional policy? We exploit the autonomy of Italian municipalities in setting non-linear income taxes and the exogenous introduction of a fiscal rule to show that austerity increases income tax progressivity. Consistent with this evidence, we find that in a panel of countries austerity correlates with higher marginal tax rates on top- but not on average-earners. The increase in progressivity in Italy is driven by high-skilled mayors, while low-skilled mayors raise taxes uniformly. In the election after the reform, high-skill mayors have higher reelection odds than low-skill mayors, while there was no difference beforehand.

Conditional yield skewness is an important summary statistic of the state of the economy. It exhibits pronounced variation over the business cycle and with the stance of monetary policy, and a tight relationship with the slope of the yield curve. Most importantly, variation in yield skewness has substantial forecasting power for future bond excess returns, high-frequency interest rate changes around FOMC announcements, and consensus survey forecast errors for the ten-year Treasury yield. The COVID pandemic did not disrupt these relations: historically high skewness correctly anticipated the run-up in long-term Treasury yields starting in late 2020. The connection between skewness, survey forecast errors, excess returns, and departures of yields from normality is consistent with a theoretical framework where one of the agents has biased beliefs.

We consider the problem of designing experiments for the comparison of two regression curves describing the relation between a predictor and a response in two groups, where the data between and within the group may be dependent. In order to derive effi- cient designs we use results from stochastic analysis to identify the best linear unbiased estimator (BLUE) in a corresponding continuous time model. It is demonstrated that in general simultaneous estimation using the data from both groups yields more precise results than estimation of the parameters separately in the two groups. Using the BLUE from simultaneous estimation, we then construct an efficient linear estimator for finite sample size by minimizing the mean squared error between the optimal solution in the continuous time model and its discrete approximation with respect to the weights (of the linear estimator). Finally, the optimal design points are determined by minimizing the maximal width of a simultaneous confidence band for the difference of the two regression functions. The advantages of the new approach are illustrated by means of a simulation study, where it is shown that the use of the optimal designs yields substantially narrower confidence bands than the application of uniform designs. Discussion Paper / SFB823;2/2021

We study the collusive efficacy of competition clauses (CC) such as the meeting competition clause (MCC) and the beating competition clauses (BCC) in a general framework. In contrast to previous theoretical studies, we allow for repeated interaction among the retailers and heterogeneity in their sales capacities. Besides that, the selection of the form of the CC is endogeneized. The retailers choose among a wide range of CC types - including the conventional ones such as the MCC and the BCCs with lump sum refunds. Several common statements about the collusive (in)efficacy of CCs cannot be upheld in our framework. We show that in the absence of hassle costs, MCCs might induce collusion in homogeneous markets even if they are adopted only by few retailers. If hassle and implementation costs are mild, collusion can be enforced by BCCs with lump sum refunds. Remarkably, these fundings hold for any reasonable rationing rule. However, a complete specification of all collusive CCs is in general impossible without any further reference to the underlying rationing rule.