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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.

EVs count and characterization by NTA and Flow cytometry. Variables are expressed as minimum, first quartile, median, third quartile, maximum. (PDF 343Â kb)

Additional file 7: Figure S3. Schematic representation of the construction of the vector pool.

Rolling out text analytics applications or individual components thereof to multiple input languages of interest requires scalable workflows and architectures that do not rely on manual annotation efforts or language-specific re-engineering per target language. These scalability challenges aggravate even further if specialized technical domains are targeted in multiple languages. In recent work, it has been shown that cross-lingual projection of sentiment models in deep learning frameworks based on bilingual sentiment embeddings (BLSE) is feasible without any annotated data in the target language, capitalizing on monolingual embeddings and a bilingual translation dictionary only (Barnes et al., 2018). We use their framework and apply it to multilingual text analytics problems in the pharmaceutical domain in order to (i) investigate under which conditions the BLSE approach scales to technical domains as well, and (ii) assess the impact of different configurations of underlying lexical resources. For the language pair English/Spanish, our findings corroborate the strength of cross-lingual projection approaches such as BLSE in technical scenarios, given the availability of bilingual resources that provide broad lexical coverage, on the one hand, and complementary domain- and task-specific knowledge, on the other.

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.