Journal of Economics - Obsahy ročníkov

One of the most intriguing topics in the field of corporate finance is the detection of tax fraud. We consider a unique dataset of outcomes from Slovak tax authority audits, obtaining valuable insights into verified instances of tax manipulation and avoiding the misclassification problem that is common in this stream of literature. We apply artificial neural networks, random forests, XGBoost, and support vector machines to verify the extent to which we can classify tax manipulators on the basis of publicly available financial statement indicators. Our results show that the XGBoost model demonstrated the highest effectiveness, achieving an F1 score of 0.75 in the full sample, slightly lower scores within the industry groups, and excellent results in sector A – Agriculture, with an F1 score of 0.85. Our results indicate that the use of nowadays commonly known machine learning methods along with standard financial variables can provide a useful tool for tax fraud detection and, as such, can contribute to higher efficiency of tax audits.

Although the marketing literature is abundant with studies dealing with the responsiveness of consumption to price changes, not much has been said about the elasticity of household expenditures from a macroeconomic perspective. Focusing on several categories of non-durable and semi-durable goods, we provide a pan-European comparative analysis of both price and income elasticities of demand. Income elasticities consistently dominate the price ones, regardless of the chosen consumption category. Although we explicitly allow for time-variability of elasticities via a state space model, our results show that the demand elasticities are independent of the business cycle, both across states and across the considered consumption categories. Consumption trajectories obviously exhibit some secular tendencies and are insensitive to transitory shocks.

This study mainly investigates the relationship between bank credit and trade credit separately for high-technology and low-technology small and medium-sized enterprises (SMEs) in Portugal with considering the impacts of the COVID crisis. The research results show obvious difference about the relationship between granting trade credit and obtaining bank credit between high-technology and low-technology SMEs. Regarding receiving trade credit and obtaining bank credit, the findings support an independent relationship when considering whether obtaining bank credit or not and a substitute relationship when considering the amount of bank credit. The substitute effect of receiving trade credit on obtaining bank credit reflects the existence of restrictions of bank credit for SMEs; the negative influence of the COVID crisis exacerbates the financial situations and causes strongly substitute effect on low-technology SMEs during and after the crisis. Therefore, the findings yielded enrich the research on externally financing behaviour of SMEs with different technology features from the perspective of bank credit and trade credit.

Ambitious environmental goals set by the EU are striving to make Europe a global leader in decarbonizing the whole planet Earth. Based on the euro area data, we investigate whether the banking sector actively supports these initiatives via its loan portfolio carbon footprint. Our main conclusion is that during the observed period, the banking sector acts as a free rider, with emission reductions occurring independently within corporate economic sectors. Spatial intermediation in the context of the green financing paradox does not play a key role. To aid policy-maker decisions, we propose the Composite Carbon Footprint Indicator (CCFI) as a socio-economic tool for classifying economic sectors. By combining GDP and employment data, we can differentiate between economic sectors based on the risk/reward ratio, reflecting the environmental cost of economic expansion and employment goals.