<abstract xmlns="http://www.w3.org/1999/xhtml"> Typically, big changes in the economic system lead to alterations on families’ disposable income and thus on their spending for different types of products, including food. These may imply in the long run a structural modification of the population’s diet quality. After the fall of the socialist system, in the past two decades, Central and Eastern European countries, including Hungary, went through a profound and sometimes difficult transition of their political and economic systems, shifting from a centralized plan to an open-market economy, and, perhaps more importantly, the European Union integration. Economic change in lower-income and transitional economies of the world appears to coincide with increasing rapid social change. With respect to nutrition, there is evidence that these countries are changing their diets and that changes seem to happen at a faster pace than ever before (e.g. Ivanova et al., 2006). In this paper, we analyse the evolution of Hungarian dietary patterns based on socio-economic status (SES) data between 1993 and 2007. Data allows defining and profiling several clusters based on aggregated consumption data, and then inspecting the influence of SES variables using OLS and multinomial logit estimations </abstract>

Centre for Economic and Regional Studies, Hungarian Academy of Sciences, Corvinus University of Budapest

Centro de Estudos e Formação Avançada em Gestão e Economia (CEFAGE-UE), University of Évora, Portugal

Socio-Economic Status and the Structural Change of Dietary Intake in Hungary

<abstract xmlns="http://www.w3.org/1999/xhtml"> Small and medium-sized enterprises play an important role in employment and also significantly contribute to GDP production. Therefore, an important function of economic policy in all countries is to create an economic milieu that supports the SMEs’ operation. By analysing several economic indices of SMEs in Hungary, we could identify that there are significant differences between the regions. About 40 percent of the enterprises are located in the Central Hungary Region. By examining specific indices of these firms, we can tell that enterprises operating in this region provide higher performance in the point of Return and Gross Value Added. The aim of this study is to assert that regional differences can be found not only in the performance of firms, but also in their capital structure. As a proof of this, we analysed the regional breakdown of capital structure based on a database which contains corporate income tax declaration data of Hungarian joint small and medium-sized enterprises (168,070 firms), and then we separated different financing characteristics by using cluster analysis. Finally, we discovered those endogenous and exogenous factors that could generate the disclosed regional differences and which interact with the performance of enterprises. </abstract>

Department of Finance and Accounting, Faculty of Economic Sciences, Kaposvár University, Kaposvár, Hungary

Department of Economics, Faculty of Economics and Human Sciences, Sapientia-Hungarian University of Transylvania, Miercurea Ciuc

Regional Differences in the Capital Structure of Hungarian SMEs

<abstract xmlns="http://www.w3.org/1999/xhtml"> The aim of this paper is to analyse sectoral interdependencies and to identify the key sectors in the Romanian, Hungarian and Slovak economy, drawing a comparison between these three countries. In order to do these investigations, input-output analysis is applied, as it is based on a model which presents interactions between sectors of the economy. This method can also be used for determining the role of each sector in the national economy regarding its contribution to the total output, incomes, exportimport and so on, and for quantifying direct and indirect impact on the whole economy caused by any change produced in a sector’s activity. As the results of the analyses show, several similarities and differences appear in the economic structure, the sectoral interdependencies and the key sectors of the analysed countries. For example, in Romania, intersectoral transactions are axing mainly on the Trade and Manufacturing sectors, while in Hungary and Slovakia on the Manufacturing and Other professional, scientific and technical services sectors. Key sectors - identified by applying output and income backward linkages - also differ as in Romania the output backward linkage is the largest in the case of the Trade sector, in Hungary, in the Food sector and in Slovakia in the Electricity, gas, water and waste management sector. In the case of the income linkages, Social, collective and personal services rank in the first place in all three countries </abstract>

Sectoral Interdependencies and Key Sectors in the Romanian, Hungarian and Slovak Economy – An Approach Based on Input-Output Analysis

<abstract xmlns="http://www.w3.org/1999/xhtml"> Our study examines some of the key aspects of education funding in Hungary. The theme of this publication is a current issue because the financing of Hungarian education has been dramatically changed from October 2013 on. Enrolment-based funding has been replaced by the average salary-based normative support, and the new “teacher career model” has been introduced. The study demonstrates the changes in financing using a model school, the calculations being based on the Budget Act of each year. We look at the trends of the previous system of financing, analysing the data from 2003 to 2012. We can compare the new, average salary-based funding with the normative support in 2012. By comparing the two types of funding, we attempt to find out whether the changes in Hungarian public education represent an opportunity for true transformation or they will remain a mere alteration in the calculation method of funding. </abstract>

Kaposvár University, Szent Benedek High School and Vocational Training School

Changes in Education Funding in Hungary

<abstract xmlns="http://www.w3.org/1999/xhtml">This paper is a review of the most recent literature regarding the econometric modelling of the impact of warranties on demand. The reviewed literature is limited to the papers that apply the random-coefficient logit model based on Berry, Levinsohn and Pakes (1995) to estimate differentiated products demand. An important feature of these demand system models that is a clear advantage to earlier demand functions is to account for the endogeneity of prices. We focus on those model specifications that take into account endogeneity of both prices and warranty. Another goal for modelling the effect of warranties is to explore the economic rationale for warranty provision. Four theories have been proposed in the literature: insurance, sorting, signalling and incentive theories. This paper aims at decomposing the effect of these theories, to account for different underlying assumptions and to separately determine the implications as presented in the recent literature </abstract>

Quantifying the Economic Value of Warranties: A Survey

<abstract xmlns="http://www.w3.org/1999/xhtml"> We study the performance of some widely-used design construction algorithms like the coordinate exchange and swapping-cycling as well as some of their versions. We measure performance in two ways, namely, first, by measuring jointly both running time and the efficiency achieved, and, second, by fixing the running time of the algorithms and measuring the efficiency achieved while allowing the number of choice situations to vary. In addition, we also analyse the performance in terms of heterogeneous designs. A somewhat surprising outcome of our analyses is that a simplified version of the joint swapping-cycling algorithm outperforms the coordinate-exchange algorithm irrespective of the performance measure. </abstract>

Sapientia Hungarian University of Transylvania, Faculty of Economic and Human Sciences, Department of Economic Sciences, Miercurea Ciuc

Sapientia Hungarian University of Transylvania, Faculty of Economic and Human Sciences, Department of Business Sciences, Miercurea Ciuc

A Comparison of Algorithms for Conjoint Choice Designs

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