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Due to the low level of quality of the Labour Force Survey (LFS) data set, studies devoted to matching the LFS data with data from alternative sources are frequent. In this paper, we propose a novel method of complementing data gaps on wages in the Labour Force Survey data set. The method is based on estimataing the parameters of the multilevel model explaining wages on the basis of the Structure of Earnings Survey (SES) data set. In such a way, we identify the impact of individual characteristics and enterprise-level features on wages. We also find evidence of random differences between the wages of workers from different professional groups. The relative importance of consecutive groups of variables is evaluated on the basis of the estimates of the parameters of the full model and reduced models. The results of the estimation of the parameters are in line with expectations. The estimates of parameters and predictions of random effects are used in order to calculate the theoretical wages of individuals who do not report wages in the Labour Force Survey. When the predicted wages are compared with the observed ones, some discrepancies are observed. Rationales for these discrepancies are provided. Therefore, the use of a correction factor is proposed. Correction factors are provided for different features of workers and different features of enterprises. The use of the microeconometric multilevel model, as well as the correction factor, leads to reasonable wage estimates of workers not reporting them in the Labour Force Survey. The proposed method may be used in order to complement data gaps on wages for other EU countries.

The paper studies a new point of view and the approach to profit as an inherent part of business finance as well as a symbol of every healthy economy. The fundamental function of the profit is a stimulus; it means initial motivator of the business activity. The profit provides core resources for survival at the business start and after the stabilization, it is the synonym for progress. The aim of this paper is to detect significant change-points in times series of EBITDA during the analysed period in every country of the Visegrad Group to recognize the progress years in the monotonic development. We use a method of homogeneity test of time series that delivers significant robust results. We observe the variable EBITDA to eliminate different tax, interest and depreciation policies of these emerging countries. The original research of this article is based on empirical results of business profits of the sample of 3,853 enterprises covered by the broad theoretical review. Firstly, we identify missing values; and detect the outliers by Z-score and Grubbs test. EBITDA of 1,058 Slovak enterprises, 688 Czech enterprises, 1,376 Polish enterprises and 731 Hungarian enterprises is analysed during the period from 2010 to 2018. We eliminate the inconsistent observations and construct average values of EBITDA. Secondly, we prove normality by Jarque-Bera test, and support it by Shapiro-Wilk test, Anderson-Darling test, Lilliefors test to deliver reliable results. Thirdly, we find an independency of distribution that confirm randomness by the Box-Pierce test. And finally, we identify the years that affect heterogeneity of EBITDA in the countries of the Visegrad Four. We uncover some really surprising results. For all countries in the Visegrad Four, the year 2013 is detected as a change-point at a significance level of 0.05. This significant year shifts EBITDA between two homogeneous series with corresponding central lines and recognizes the similar annual development within the groups. In addition, we discuss the results to the areas and factors affecting the business risk. The adjustable area represented by the business dynamism has no significant impact on the development of EBITDA. The uncontrollable macroeconomic factors such as a GDP, unemployment rate, inflation rate, average monthly gross wage, and Ease of doing business index demonstrate the same development of Slovak, Czech, Polish and Hungarian enterprises. We connect our gained results to the undisputed influence of these factors and its derived components on monotonic development of EBITDA. Despite the fact, that the countries are not economically interconnected as they used to be in the past, in has to be underlined that their mutual relations are still very narrow and close and that might be the reason, why identical results are achieved in the countries with divergent development.

The objective of this study is to propose a trustworthy, valid and consistent methodological approach for measuring the efficiency of a logistics platform, where an entire country constitutes a logistic platform. Traditional Data Envelopment Analysis (DEA) is found to be an appropriate tool – if its weaknesses are eliminated. DEA results are highly influenced by the choice of appropriate inputs and outputs variables, but the method itself does not provide guidance for their identification. The authors therefore propose to integrate traditional DEA by combining the Delphi technique with the Analytical Hierarchy Process (AHP) method, which will assist in identifying proper, consistent input/output variables, evaluated by their relevance. The proposed framework allows the performance evaluation of the selected platform’s element or elements. It is thus a useful decision support tool for enterprises (private, public, both) that are managing logistics platforms and trying to improve their productivity in order to sustain or improve their position on the competitive market. This methodology allows comparative efficiency analyses to be estimated for similar countries. The presented methodology on one hand enables tailor-made solutions, but on the other hand is very general, and, with minor adjustments, can be applied by a variety of firms and industries. It can be applied in private sector firms in production and service industries, to analyse the relative performance of diverse logistics and non-logistics services, and in public profit or non-profit organisations.

“Multiple Attribute Decision Making (MADM)” is an expert based field which is working based on real data and experts’ opinions. So many studies have been doing based on MADM methods which they usually use qualitative data based on experts’ ideas. Decisions based on the experts’ opinion shall be carefully designed to cope the real problems uncertainty. This uncertainty will be even more intricate if combining the problem with the ambiguity of the future study. Prospective MADM is a future based type of MADM field which is concentrating on decision making and policy making about the future. Prospective MADM (PMADM) can have both explorative and descriptive paradigms in the studies but it will more useful to be applied for strategic planning. In this regard, experts’ role would be even more challenging because one/some possible future/futures will be partially designed based on their opinions. Future and prediction always complicates the decision environment, especially methodologies founded on experts’ judgement. Considering experts’ preferences, attitude, and background, they may be a major source of inaccurate results. Causal Layered Analysis (CLA) is well-known “Futures Studies” method which is qualitative and usually is supporting other methods such as “Backcasting” and “Scenario Planning”. CLA has a deep point of view to the subjects to support a future with all those changes which are necessary for the main goal/goals. In this study, this idea will be proposed that CLA can be added to PMADM outline to decrease the risk of unsuitable decisions for the future and for this aim a case study about energy and CO2 consumption in policy making level proposed and a hybrid MADM method based on BWM-CoCoSo applied in the PMADM outline for the procedure.

In their response to the necessity to meet the demands of customers, the enterprises are forced to reduce the time of order delivery. Today, almost every enterprise has its own warehouse facilities or outsources warehouse processes. Therefore, the contemporary warehouses play a significant role in production and service networks. The maintenance of high efficiency of warehouse processes determines the competitive functioning of enterprises. Continuous progress in this area sets the pace for these changes. Nevertheless, despite of the desire to reduce costs while increasing the efficiency of the warehouse process, you cannot forget about employees. In addition to efficiency and the level of generated costs, a warehouse employee is one of the factors that not only affects the shape of the logistics system in an enterprise, but also affects all links in the supply chain. This study is intended to research the impact of technological and organizational innovation implemented in the warehousing process on the efficiency of picking processes and staff workload on picking stations. The research was performed with warehouse simulation models developed in FlexSlim 3D Simulation Software. The simulated warehouses represent the warehouses in B2C (Business to Customer) logistics. They are about the layout of bag-type warehouse and the size and shape of the assortment varies. The size of storage zone is the same for all three warehouses. In these warehouses the assortment is arranged randomly. For each model, several simulations have been performed. The conducted research has shown that the results of technological and organizational innovation implemented in the warehousing process should be in general evaluated positively. Both the warehouse productivity and the picking process efficiency increased. The staff workload decreased, which is reflected in greater work comfort for a man and which supports implementation of control activities. However, it should be noted that implementation of the technological and organizational innovation in the warehousing processes adopted in various enterprises changes the labor market, thus it is possible that some problems with maintaining current employment levels will occur.