The Anatomy of Unemployment: Determinants During and After the COVID-19 Crisis

Unemployment is a factor that heavily influences the output of each economy. It is, therefore, one of the main concerns of any government worldwide. This study identifies key determinants of unemployment. By constructing an econometric model for the registered unemployment rate in Slovakia, the period from 2013 to 2022 was under scrutiny, while the impact of the COVID crisis was considered in the model through a dummy variable. Potential determinants of unemployment were selected based on theoretical knowledge and other scientific works, that is, average interest rates, gross minimum wage, GDP, inflation, exports, imports, government spending, corruption index, COVID-19 crisis, and month of the year. The final relevant factors for unemployment were tested and validated: interest rates, GDP, inflation, government spending, and exports. These study results may be valuable for the government when designing targeted interventions to optimise the unemployment rate in Slovakia or similar economies by influencing other macroeconomic indicators

Detecting the manipulation of earnings in the company: triangulation of methods

Research background: Earnings management is a current topic in the world of financial management. It can be considered as a global phenomenon of today’s modern approach to the reporting of accounting information and related accounting decisions of managers, which may affect the overall results of financial statements. Many companies use earnings management as a tool to maintain stable profit growth or prevent “red numbers” from appearing in financial statements that are not beneficial to the company. Purpose of the article: Understanding what earnings management represents and why it is performed is essential for users of a company’s financial statements. However, detecting manipulation in companies is not easy, because Earnings management is successful if it is invisible. Therefore, statistical models are usually used to detect these practices. The aim of this paper is to show that the use of several methods strengthens the results obtained and is more probably to reveal possible manipulation of earnings in companies. Methods: In this study, we used triangulation of methods to detect Earnings management in companies: one of the most frequently used model in this area, Beneish model, but also the model for Slovak companies M-score SVK, which was created under the inspiration of the Beneish model and finally, the model of the company’s propensity score of manipulation. Findings & Value added: The study provides a global view of the possibilities of applying these three models to detect manipulation in the company. The idea of triangulation of methods is based on the consideration that if all the methods detect possible manipulation, it is very likely that it actually happens in the company

The impact of Data structure on classification ability of financial failure prediction model

The creation of prediction models to reveal the threat of financial difficulties of the companies is realized by the application of various multivariate statistical methods. From a global perspective, prediction models serve to classify a company into a group of prosperous or non-prosperous companies, or to quantify the probability of financial difficulties in the company. In many countries around the world, real financial data about the companies are used in developing these prediction models. In Slovakia, standard data from the financial statements and annual reports of Slovak companies are used for the creation of the company’s failure model. Since in this case there are generally large data files, it is necessary to pre-process the data by the selected methods before the prediction model is constructed. A database of the companies needs to be prepared for the subsequent application of statistical methods, and it is also highly appropriate to focus globally on the detection of potential extreme and remote observations. Therefore, the article will focus on quantifying the impact of the data structure detected, for example, the occurrence of extreme and remote observations in the data set, on the resulting overall classification of the prediction ability of the models created

Allowance for School Graduate Practice Performance in Slovakia: Impact Evaluation of the Intervention

This study aims to evaluate the impact of a selected active labour market policy measure that has been applied in Slovakia—Allowance for school graduate practice performance—on the employability of young jobseekers and their sustainability in the labour market, and thus, it will also empirically contribute to the field of relevant literature. The policy targets unemployed school graduates, and it enables them to acquire professional skills and practical experience that corresponds with their level of education, work habits, and possible direct contact with potential employers. At the same time, this measure addresses a long-standing gap in the Slovakian education system, namely, the insufficient linkages between the educational process, the practices in the field, and the requirements of the labour market. Using fiscal resources to finance this policy, it provides a natural and logical platform to investigate the relevance of the outcome of this measure in the context of its proclaimed objectives. In light of this, we employed a counterfactual approach to compare the results of the participants who were affected the measure (recipients; treated group) and non-participants, as their counterparts (comparison/control group), using an instrumental variable to mitigate self-selection and selection-bias problems. Our findings show that this policy intervention has a short- or medium-term impact on the employability of unemployed school graduates and the sustainability of their careers. In addition, a positive impact on their monthly wages was observed. We also came to the conclusion that, assuming the measure is linked to other labour market policy interventions, which is aimed at employers that are willing to hire young unemployed people, it would be possible to improve the functionality and effectiveness of support for the unemployed through indirect measures

Detection of earnings management in insurance companies in Slovakia

Research background: Manipulation and the use of creative accounting or earnings management have become an increasingly popular topic in the history of researchers. Since 2002, this issue has attracted the attention of scientists and economists around the world. On the Slovak market, more than 30 insurance companies are actually operating, and some analyses have revealed that some of these insurance companies are engaging in activities that do not comply with the law. Purpose of the article: The article aims to use selected models for the detection of fraudulent financial reporting to determine whether there are unfair activities in insurance companies in Slovakia. At the same time, we evaluate the reliability of selected models and recommend the best models for this sector. Methods: Based on the set criteria, the Beneish model with five parameters and the Beneish model with eight parameters are applied to selected 20 companies in the Financial and Insurance activities sector to determine which companies have manipulated the financial statements. The analysis is performed using real data on Slovak companies from the Amadeus database. Findings & Value added: For the Financial and Insurance activities industry, we recommend using the Beneish model with eight parameters. Comparing the two models, we can conclude that this model is more accurate and thorough. The reason is also that this model works with more data from the financial statements

Allowance for School Graduate Practice Performance in Slovakia: Impact Evaluation of the Intervention

This study aims to evaluate the impact of a selected active labour market policy measure that has been applied in Slovakia—Allowance for school graduate practice performance—on the employability of young jobseekers and their sustainability in the labour market, and thus, it will also empirically contribute to the field of relevant literature. The policy targets unemployed school graduates, and it enables them to acquire professional skills and practical experience that corresponds with their level of education, work habits, and possible direct contact with potential employers. At the same time, this measure addresses a long-standing gap in the Slovakian education system, namely, the insufficient linkages between the educational process, the practices in the field, and the requirements of the labour market. Using fiscal resources to finance this policy, it provides a natural and logical platform to investigate the relevance of the outcome of this measure in the context of its proclaimed objectives. In light of this, we employed a counterfactual approach to compare the results of the participants who were affected the measure (recipients; treated group) and non-participants, as their counterparts (comparison/control group), using an instrumental variable to mitigate self-selection and selection-bias problems. Our findings show that this policy intervention has a short- or medium-term impact on the employability of unemployed school graduates and the sustainability of their careers. In addition, a positive impact on their monthly wages was observed. We also came to the conclusion that, assuming the measure is linked to other labour market policy interventions, which is aimed at employers that are willing to hire young unemployed people, it would be possible to improve the functionality and effectiveness of support for the unemployed through indirect measures

Being an outlier: a company non-prosperity sign?

Research background: The state of financial distress or imminent bankruptcy are very difficult situations that the management of every company wants to avoid. For these reasons, prediction of company bankruptcy or financial distress has been recently in a focus of economists and scientists in many countries over the world. Purpose of the article: Various financial indicators, mostly financial ratios, are usually used to predict the financial distress. In order to create a strong prediction model and a statistically significant prediction of bankruptcy, it is advisable to use a deep statistical analysis of the data. In this paper, we analysed the real financial ratios of Slovak companies from the year 2017. In the phase of data preparation for further analysis, we checked the existence of outliers and found that there are some companies that are multivariate outliers because are significantly different from other companies in the database. Thus, we deeply focused on these outlying companies and analysed whether to be an outlier is a sign of financial distress. Methods: We analysed whether there are much more non-prosperous companies in the set of outlier companies and if their financial indicators are significantly different from those of the prosperous companies. For these analyses, we used testing of the statistical hypotheses, such as the test for equality of means and chi-square test. Findings & Value added: The ratio of non-prosperous companies between the outliers is significantly higher than 50 % and the attributes of non-prosperity and being an outlier are dependent. The means of almost all financial ratios of prosperous and non-prosperous companies among outliers are significantly different

Cluster analysis of the economic activity of Slovak companies regarding potential indicators of earnings management

Research background: All over the world, any information about the earnings manipulation is very important for all the stakeholders of the companies. Therefore, it is necessary to detect this situation in a certain way. The global practice has shown that it is appropriate to create detection models and it would be very useful to specify individual sectors or the groups of sectors of economic activities of companies. Purpose of the article: The article aims to the financial ratios of Slovak companies that are globally used in the detection of earnings management. Based on hierarchical cluster analysis we identify groups of economic activities (according to the international NACE classification) with similar financial characteristics. Methods: For efficient earnings manipulation detection, high-quality and up-to-date financial data is required. We used financial data of real Slovak companies from the year 2018 obtained from international database Amadeus. After a precise pre-preparation of the dataset, we use the standard clustering procedures. Using the analysis of the dendrogram, the groups of the companies with their economic activities are identified. Findings & Value added: The results of the analysis show that there exist logical groups of NACE categories of economic activity of companies with similar characteristics. Regarding potential earnings manipulation, companies in these groups are as similar as possible. Therefore, financial characteristics can be analyzed together, and more accurate detection models could be created for them

Jones' Model and Its Modifications in the Conditions of the Slovak Republic

One of the key tasks of financial accounting from its beginnings to the present day is to determine the performance of the company. The financial statements should provide users with a true and fair view of the financial position and financial performance of the entity during the period. At present, profit represents the most frequently accepted measure of a company’s financial performance. An important prerequisite for profit as a reliable measure of performance is its quality, which can be influenced by various factors or techniques resulting from earnings management. This paper aims to compare the detection capability of the Jones model and its modifications for assessing the occurrence of earnings management in the conditions of the Slovak Republic. We use the regression analysis and comparison method, based on which we compare the detection capability of the Jones model and its modifications for assessing the occurrence of earnings management in the conditions of the Slovak Republic. The contribution of the paper lies in the observation of the Jones model and its modifications to determine a suitable model for assessing the existence of earnings management in companies in Slovakia, which will be the subject of future research

Business Failure Prediction for Slovak Small and Medium-Sized Companies

Prediction of the financial difficulties of companies has been dealt with over the last years by scientists and economists worldwide. Several prediction models mostly focused on a particular sector of the national economy, have been created also in Slovakia. The main purpose of this paper is to create new prediction models for small and medium-sized companies in Slovakia, based on real data from the Amadeus database from the years 2016–2018. We created prediction models of financial difficulties of companies for 1 year in advance and also a model for 2 years prediction. These models are based on the combination of two methods, discriminant analysis and logistic regression that belong, among others, to the group of the most commonly used methods to derive prediction models of financial difficulties of the companies. The overall prediction powers of the combined model are 90.6%, 93.8% and 90.4%. The results of this analysis can be used for early prediction of the financial difficulties of the company, that could be very useful for all the stakeholders