ICOS2019icos2019.efsa.unsa.ba/images/media/ABSTRACTBOOK_LAYOUT_ICOS2… · Trg oslobođenja - Alija Izetbegović 1, 71000 Sarajevo, Bosna i Hercegovina Federalni zavod za statistiku

BOOK OF ABSTRACTS

ICOSI n t e r n a t i o n a l C o n f e r e n c e o n O ffi c i a l S t a t i s t i c s

2019

BOOK OF ABSTRACTS

ICOS2019 - The International Conference on Official Statistics Sarajevo, Bosnia and Herzegovina, November 14-15 2019

CONFERENCE TOPIC: Emerging trends in statistical methodologies and data dissemination

EDITORS-IN-CHIEFJasmina Selimović, PhD, Dean, School of Economics and Business, University of SarajevoEmir Kremić, PhD, Director, Institute for Statistics of the Federation of Bosnia and Herzegovina

PUBLISHERSEkonomski fakultet u Sarajevu/ School of Economics and Business in Sarajevo, University of SarajevoTrg oslobođenja - Alija Izetbegović 1, 71000 Sarajevo, Bosna i Hercegovinawww.efsa.unsa.ba

Federalni zavod za statistiku/ Institute for Statistics of the Federation of Bosnia and HerzegovinaZelenih Beretki 26, 71000 Sarajevo, Bosna i Hercegovinawww.fzs.ba

NUMBER OF COPIES150

TECHNICAL EDITORAdis Duhović

DESIGN&PRINTAgencija PERFECTABranilaca Šipa 33, 71000 Sarajevowww.perfecta.ba

ORGANIZERS OF THE ICOS2019 CONFERENCEThe ICOS2019 Conference is organized by the School of Economics and Business in Sarajevo and the Institute for Statistics of the Federation of Bosnia and Herzegovina.

PATRONAGEThe ICOS2019 Conference is organized under the patronage of the Government of Federation of Bosnia and Herzegovina.

OUR FRIENDSUnited States Agency for International Development (USAID) The World Bank Group Office in Bosnia and HerzegovinaThe Republika Srpska Institute of StatisticsAgency for Statistics of Bosnia and Herzegovina

All abstracts in this Book of Abstracts were subject to double-blind reviews.

November 14-15, 2019 Sarajevo, Bosnia and Herzegovina


2019

School of Economics and Business in Sarajevo Institute for Statistics of the Federation of Bosnia and Herzegovina

Sarajevo, 2019

Book of Abstracts

of the ICOS2019The International Conference on Official Statistics

Emerging trends in statistical methodologies

and data dissemination

CONFERENCE TOPIC:

November 14-15, 2019 Sarajevo, Bosnia and Herzegovina


2019

School of Economics and Business in Sarajevo Institute for Statistics of the Federation of Bosnia and Herzegovina

Sarajevo, 2019

Book of Abstracts

of the ICOS2019The International Conference on Official Statistics

Emerging trends in statistical methodologies

and data dissemination

CONFERENCE TOPIC:

Vol.1, No. 1 (2019)

Focus and Scope of the Conference

The International Conference on Official Statistics: Emerging trends in statistical methodologies and data dissemination (ICOS2019) is a joint initiative by the School of Economics and Business of the University of Sarajevo and the Institute for Statistics of the Federation of Bosnia and Herzegovina that aims to bring together academics, practitioners and data users to present their research, share their best practices, and discuss avenues for development of official statistics in Bosnia and Herzegovina and the region.

This international scientific gathering will be held in Sarajevo on 14 and 15 November 2019, and will aim to become a biennial event. It will include presentations by prominent international invited speakers, as well as high-quality research findings from applicants selected through rigorous manuscript review by the international Scientific Program Committee.

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Conference International Scientific CommitteeAbdić Ademir, PhD School of Economics and Business, University of Sarajevo, B&HArnaut-Berilo Almira, PhD School of Economics and Business, University of Sarajevo, B&HBalavac Merima, PhD School of Economics and Business, University of Sarajevo, B&HBaldigara Tea, PhD Faculty of Tourism and Hospitality Management, CroatiaBarcaroli Giulio, MSc ISTAT, ItalyCancho Cesar, PhD World Bank, Washington D.C., USADelalić Adela, PhD School of Economics and Business, University of Sarajevo, B&HDisegna Marta, PhD Bournemouth University, United KingdomDumičić Ksenija, PhD Faculty of Economics & Business Zagreb, University of Zagreb, CroatiaEfendić Adnan,PhD School of Economics and Business, University of Sarajevo, B&HEl Ouardighi Jalal, PhD Université de Strasbourg, Bureau d’Economie Théorique et Appliquée, FranceErjavec Nataša, PhD Faculty of Economics & Business Zagreb, University of Zagreb, CroatiaGičević Selma, PhD Institute for Statistics FB&H, B&H; Harvard TH Chan School of Public Health, Boston, USAHarry Miller, PhD Faculty of Science, University of Sarajevo; IUS, B&HJongstra Eduard UNFPA’s Regional Office for Eastern Europe and Central AsiaKennedy Ryan, PhD University of Houston, Political Science, USAKremić Emir, PhD Institute for Statistics FB&H, B&HLovrić Miodrag, PhD School of Economics, University of Kragujevac, SerbiaMehić Eldin, PhD School of Economics and Business, University of Sarajevo, B&HPahor Marko, PhD Faculty of Economics, University of Ljubljana, SloveniaPosadas Josefina, PhD The World BankPugh Geoffrey, PhD Staffordshire University, United KingdomRadicic Dragana, PhD University of Lincoln, United KingdomResić Emina, PhD School of Economics and Business, University of Sarajevo, B&HSelimović Jasmina, PhD School of Economics and Business, University of Sarajevo, B&HSmajlović Lejla, PhD School of Economics and Business, University of Sarajevo, B&HSomun-Kapetanović Rabija, PhD School of Economics and Business, University of Sarajevo, B&HŠabanović Edin, MSc Agency for Statistics B&H, B&HŠćeta Lamija, PhD School of Economics and Business, University of Sarajevo, B&HTanzer Deon, MA International Monetary FundVeledar Emir, PhD University of Florida, USAVerbič Miroslav, PhD Faculty of Economics, University of Ljubljana, Slovenia

Conference organizing Committee

Conference chairsJasmina Selimović, PhD, Dean of the School of Economics and Business, University of Sarajevo, B&HEmir Kremić, PhD, Director of the Institute for Statistics of the Federation of Bosnia and Herzegovina, B&H

MembersMehić Eldin, PhD School of Economics and Business, University of Sarajevo, B&HBašić Meliha, PhD School of Economics and Business, University of Sarajevo, B&HRabija Somun-Kapetanović, PhD School of Economics and Business, University of Sarajevo, B&HHarry Miller, PhD Faculty of Science, University of Sarajevo; IUS, B&HResić Emina, PhD School of Economics and Business, University of Sarajevo, B&HAbdić Ademir, PhD School of Economics and Business, University of Sarajevo, B&HSelma Gičević, PhD Institute for Statistics of the Federation of Bosnia and Herzegovina, B&HDelalić Adela, PhD School of Economics and Business, University of Sarajevo, B&HBalavac Merima, PhD School of Economics and Business, University of Sarajevo, B&HDžebo Nisveta, BSc in Economics Institute for Statistics of the Federation of Bosnia and Herzegovina, B&H

INVITED LECTURERS

MAIN CONCEPTS OF THE SYMBOLIC DATA ANALYSIS FRAMEWORK 11 Edwin Diday

EUROSTAT’S NAVIGATION TOOLS FOR RESEARCH AND MOTIVATION IN TEACHING STATISTICS: EU ENLARGEMENT COUNTRIES’ DATA IN FOCUS 15Ksenija Dumičić

Tangible and Intangible Assets in the Growth Performance of the EU, Japan and the US - A comparative analysis based on the EU KLEMS 18 Robert Stehrer, Amat Adarov

TIME TO QUIT: THE TOBACCO TAX INCREASE AND HOUSEHOLD WELFARE IN BOSNIA AND HERZEGOVINA 20Alan Fuchs, Edvard Orlic, Cesar A. Cancho

THE LOCAL ECONOMIC EFFECTS OF SOFT BORDERSEvidence from the dissolution of the Union of Serbia and Montenegro 22 Vassilis Monastiriotis, Ivan Zilic

SOME ASPECTS OF QUANTITATIVE ANALYSIS OF DEVELOPMENT ROLE OF GROSS EXPENDITURES FOR RESEARCH AND DEVELOPMENT 24Jasmin Komić

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Contents

Official Statistics (improvement, harmonization, data collection, tools, methods, sector-specific statistics, etc.)

Business Statistics and the Use of Administrative Data 26Ema Mišić

EXAMINATION OF THE IMPACT OF HOUSEHOLD INCOME ON EXPENDITURE ON CLOTHING AND FOOTWEAR IN BOSNIA AND HERZEGOVINA AND SERBIA 29Hasan (M) Hanic, Milica (Z) Bugarcic, Lejla (R) Dacic

EVALUATION OF THE EMPLOYMENT PROGRAM OPPORTUNITY FOR ALL OF THE FEDERATION OF BOSNIA AND HERZEGOVINA 32Merima Balavac, Josefina Posadas

Individual and policy mix effects of regional and national R&D subsidies on the cooperative behaviour of Spanish manufacturing firms 34Dragana Radicic, Geoff Pugh, Mehtap Hisarciklilar

LOCAIZED DISSEMINATION SYSTEM 37Serhat ATAKUL

METHODS FOR TREATMENT OF MISSING DATA IN STATISTICAL SURVEYS: OVERVIEW OF EXISTING SOLUTIONS 39Edin Šabanović, Rabija Somun-Kapetanović

Studying EU countries regarding mortality with SYR program 42Filipe Afonso, Edwin Diday, Simona Korenjak-Černe, Aleša Lotrič Dolinar

Application of statistical methodology (Economics, Finance, Health, Education and other related disciplines) and Business Statistics (use of administrative and accounts data, challenges and development)

ANALYSIS OF THE FACTOR OF SAVINGS OF PRIVATE PROFIT ENTERPRISES IN BIH BY APPLICATION OF ECM METHODOLOGY 45Irma Đidelija, Rabija Somun-Kapetanović

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COMPARISON OF STRUCTURAL EQUATION MODELLING AND MULTIPLE REGRESSION TECHNIQUES FOR MODERATION AND MEDIATION EFFECT ANALYSIS 48Lejla Turulja, Nijaz Bajgoric

CONVERGENCE AND HETEROGENEITY IN GLOBAL DIETS 51Le Thai Hong

PREDICTING EMPLOYEE HEALTH AND COST: APPLICATION OF MACHINE LEARNING ON EMPLOYEE HEALTH CLAIMS DATA, INSIGHTS, AND POSSIBILITIES 53Anshul Saxena, Sankalp Das, Muni Rubens, Joseph Salami, Chintan Bhatt, Lejla Turulja, Tian Tian, Peter McGranaghan, Louis Gidel, Emir Veledar

SHARE OF ADULTS WHO ORDER GOODS OR SERVICES ONLINE INFLUENCED BY SHARE OF THOSE WITH DIGITAL SKILLS BROKEN DOWN BY GENDER: CLUSTER ANALYSIS ACROSS EUROPEAN COUNTRIES 55Ksenija Dumičić, Blagica Novkovska, Emina Resić

RELATION BETWEEN BMI OF ADOLESCENTS AND SOCIO-ECONOMIC STATUS OF FAMILY 58Irzada Taljić, Adela Delalić

THE FOURTH INDUSTRIAL REVOLUTION AND ICT SECTOR IN BOSNIA AND HERZEGOVINA 60Sanjin Čengić

Econometrics modeling for policy making, Social and welfare statistics and Big data and machine learning

Determinants of Customer Satisfaction in the Hotel Industry: Application of Factor Analysis and Ordinal Logistic Model 62Dedić Lejla, Merima Balavac

How efficient targeting in Social Services in Federation of Bosnia and Herzegovina? 65Adela Delalić, Ademir Abdić, Muamer Halilbašić

LEADING INDICATOR FOR EMPLOYMENT USING BIG DATA 68Renzo Castellares, Gerson Cornejo

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METHODOLOGICAL RESEARCH IN THE SERBIAN SURVEY ON INCOME AND LIVING CONDITIONS 70Jelena Suzić

MODELLING AND FORECASTING GDP IN BOSNIA AND HERZEGOVINA USING ARIMA MODELS 72Emina Resić, Ademir Abdić, Adem Abdić, Fahir Kanlić

Modelling the Employment in Croatian Hotel Industry using the Box-Jenkins and the Neural Network Approach 75Tea Baldigara

TARGETING BY NUMBERS: THE USE OF OFFICIAL STATISTICS FOR ESTIMATING SOCIAL TRANSFERS DISTRIBUTION IN BOSNIA AND HERZEGOVINA 77Edin Šabanović

THE EFFECTS OF NON-TARIFF BARRIERS ON EXPORT OF CEFTA MEMBER COUNTRIES 79Merima Balavac, Begović Selena

Special session for students and young researchers

ANALYSIS OF FACTORS AFFECTING YOUTH TO VOTE ON ELECTIONS CASE STUDY: STUDENTS OF SCHOOL OF ECONOMICS AND BUSINESS AT THE UNIVERSITY OF SARAJEVO 81Emil Ninković

Analysis of the main factors that cause stress during the education at the School of Economics and Business at the University of Sarajevo 83Zemina Selmani

DETERMINANTS OF SUCCESS OF STUDYING AT THE SCHOOL OF ECONOMICS AND BUSINESS, UNIVERSITY OF SARAJEVO 86Ajla Šušić

ECONOMIC DETERMINANTS OF STUDENTS’ ACADEMIC ACHIEVEMENT IN BOSNIA AND HERZEGOVINA 88Eldar Komar, Emina Kuloglija

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INVITED LECTURERS

MAIN CONCEPTS OF THE SYMBOLIC DATA ANALYSIS FRAMEWORK

Edwin DidayCEREMADE Paris-Dauphine University Place du Marechal de Lattre de Tassigny – 75775 Paris, FranceE-mail: [email protected]

AbstractData science is, in general terms, the extraction of knowledge from data, considered as a science by itself. The Symbolic Data Analysis (SDA) gives a new way of thinking in Data Sciences by extending standard numerical or categorical data to “symbolic data” in order to extract knowledge from aggregated classes of individual entities. For example, the classes are regions and the individuals are their inhabitant, the classes are documents and the individuals are words, the classes are species and the individuals are specimen etc. The SDA is born from the classification domain by considering classes of a given population to be units of a higher level population to be studied. Such classes allow a summary of the population and often represent the real units of interest. In order to take care of the variability between the individuals member of each class, these classes are described by intervals, distributions, set of

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categories or numbers sometimes weighted and the like. In that way, we obtain new kinds of data expressing variability, called “symbolic” as they cannot be reduced to numbers without losing much information. The SDA aim is to study and extract new knowledge from these new kinds of data by at least an extension of Computer Statistics and Data Mining to symbolic data. The theory needs an extension of standard random variables of numerical or categorical values to random variables of symbolic values which leads to models of Dirichlet or of Copula kinds.

First, we recall some basic notions in Data Science: what are complex data? Which kind of internal class variability can be considered? Then, we define “symbolic data” and “symbolic data tables” (see Figure 1) which express the within variability of classes. Often in practice the classes are given. When they are not given, clustering can be used to build them by the Dynamic Clustering method (DCM) which is an extension of the K-means to more general models than just means (as regressions, distributions, etc.). The description of these class yields by aggregation to a symbolic data table. We say that the description of a class is much more explanatory when it is described by symbolic variables (closer from the natural language of the users), then by its usual analytical multidimensional description.

Figure 1. An example of symbolic data table (from the SYR software). The SDA aim is to build such data table from complex and big data tables, to manage them and to extract from them new and useful knowledge, complementary to the standard approach.


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We consider complex data as data which cannot be expressed in term of a standard data table where units are described by quantitative and qualitative variables. Complex data happen in case of unstructured data, unpaired samples, multisource data (as mixture of numerical, textual, image, social networks data). For example, a region can be defined by characteristic variables of its population, of its hospitals, of its schools but these variables are unpaired as they do not concern the same units.

The aggregation, fusion and summarization of such data can be done into classes of row units which are considered as new units. In unsupervised learning classes can be obtained by giving a concise and structured view on the data. In supervised learning classes are used in order to provide efficient rules for the allocation of new units to a class. A third way is to consider classes as new units described by “symbolic” variables which values are “symbols” as: intervals, probability distributions, weighted sequences of numbers or categories, functions, and the like, in order to express their within-class variability. For example, “Regions” expressing the variability of their inhabitant, “Companies” expressing the variability of their web intrusion, “Species” expressing the variability of their specimen. Hence, variability can appear between individuals, but also inside individuals (as cracks or corrosion inside a power plot tower), or for an individual varying in time or position. One of the advantages of this approach is that unstructured data and unpaired samples at the level of row units, become structured and paired at the classes’ level.

Three principles guide this paper in conformity with the Data Science framework. First, new tools are needed to transform huge data bases intended for management to data bases usable for Data Science tools. This transformation leads to the construction of new statistical units described by aggregated data in term of symbols as single‐valued data are not suitable because they cannot


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incorporate the additional information on data structure available in symbolic data. Second, we work on the symbolic data as they are given in data bases and not as we wish that they be given. For example, if the data contains intervals we work on them even if the within interval uniformity is statistically not satisfactory. Moreover, by considering Min Max intervals we can obtain useful knowledge, complementary to the one given without the uniformity assumption. Hence considering that the Min Max or interquartile and the like intervals are false hypothesis has no sense in modern Data Science where the aim is to extract useful knowledge from the data and not only to infer models (even if inferring models like in standard statistics, can for sure give complementary knowledge). Third, by using marginal description of classes by vectors of univariate symbols rather than joint symbolic description by a whole multivariate symbols avoid sparse data and a poor explanatory power.

We suggest several directions of research. The standard data tables contain are a case of symbolic data table. Therefore any statistical method extended to symbolic data contain as a case a standard methods. By extending the Data Science to symbolic data SDA opens the way for of a vast domain of research and industrial applications. Symbolic data are the numbers of the future.


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EUROSTAT’S NAVIGATION TOOLS FOR RESEARCH AND MOTIVATION IN TEACHING STATISTICS: EU ENLARGEMENT COUNTRIES’ DATA IN FOCUS

Ksenija DumičićFaculty of Economics and Business, University of Zagreb, Trg J. F. Kennedya 6, HR-10000 Zagreb, CroatiaE-mail: [email protected]

AbstractEurostat’s mission is to provide high quality statistical data and indicators for Europe, promoting trust, adopting research excellence, encouraging innovation, service easiness orientation and professional independence being apart from any interest outside statistical methodologies excellence influence. Providing the European Union (EU) with homogeneous statistical data, Eurostat enables comparisons between countries. Ensuring permanent improvement of services, Eurostat is “Committed to Excellence” institution. Modern societies function properly using its trustworthy, objective and unbiased statistics. Besides national statistics inevitable importance, the EU statistics for decisions and evaluation at European level are necessary, as well. Since statistics can answer questions


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on variety of social and economic issues, e.g. production, unemployment, climate change, environment issues, sustainable development, research and development, it seems to be very important and of special concern that students, at lower, and especially at middle and high education level, become acquainted with official statistical data sources for many essential and practical reasons. E.g., international statistics enable us getting to know our neighbours in the EU Member States and the EU Enlargement countries, as well, being important to develop an unbiased picture about measures of well-being across countries and improving individuals’ statistical literacy for easier citizenship in democratic societies and for improved lives in general.

Since Eurostat provides invaluable sources for statistical data useful in any area of life or activity of individuals, legal entities, area or administrative units across Europe, the research focuses on available online Eurostat’s tools, which are freely affordable and useful for any research area, and so for educational purposes, especially.

It is assumed that educators and students do not use open and free Eurostat data in their research and when they are completing various education tasks often enough. Educators and students might benefit while using these high quality easy accessed secondary data source more often, and not only by scientists and professionals. For using Eurostat data, one may be either a statistician or a non-statistician. These data, collected using transparent data collection harmonized methodology, are user friendly downloadable, appropriately formatted and ready for data manipulation in different formats, and the announced clear license for their free usage encourages all educators and students across Europe, and even wider, to apply them. The paper promotes the Eurostat website and tools like Data Navigation Tree, Browse Statistics by Theme, Publications, e.g. Key figures on enlargement countries 2019 and others, Glossary’s entries, Statistics A-Z,


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Experimental Statistics, Classifications, etc., and offered Main Tables and Databases options, especially. Many online tools make Eurostat’s data and indicators with different brake-downs, presented by tables and visualised by graphs, easily accessed, with simple possibilities for searching through topics in any area of interest, helping education process efficiently.

Keywords: Eurostat; Data Navigation Tree; European Union Enlargement Countries; Browse Statistics by Theme; Teaching Statistics.

JEL classification: I23, I25, A2


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Tangible and Intangible Assets in the Growth Performance of the EU, Japan and the US - A comparative analysis based on the EU KLEMS

Robert StehrerThe Vienna Institute for International Economic StudiesRahlgasse 3, 1060 Vienna, AustriaE-mail: [email protected]

Amat AdarovThe Vienna Institute for International Economic StudiesRahlgasse 3, 1060 Vienna, AustriaE-mail: [email protected]

AbstractThe presentation provides new results on growth and productivity performance before and after the crisis using the EU KLEMS 2019 Release focussing on the role of ICT and intangibles assets . The EU KLEMS 2019 data covers most EU Member States, the US and Japan, forty detailed industries according to NACE Rev. 2 (ISIC


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Rev. 4) along with nine aggregated industries and spans over the period 1995-2017. In particular, intangible assets outside the boundaries of the national accounts are taken into account. The data are used to study total factor productivity, labour and capital productivity developments in a comparative cross-country and cross-industry dimension with an emphasis on the role of capital investments. Inter alia, the analysis studies the implications of various asset types and particularly the role of ICT and intangible capital, as well as changes in labour services and the composition thereof, as drivers of value added and labour productivity growth. Significant differences in the underlying growth contributions between the pre-crisis and post-crisis periods in growth performances are highlighted.


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TIME TO QUIT: THE TOBACCO TAX INCREASE AND HOUSEHOLD WELFARE IN BOSNIA AND HERZEGOVINA

Alan Fuchs The World Bank E-mail: [email protected]

Edvard OrlicBournemouth UniversityE-mail: [email protected]

Cesar A. CanchoThe World Bank E-mail: [email protected]

AbstractTobacco, a leading cause of death, is linked with high medical expenditures, lower life expectancy at birth, reductions in the quality of life, and other adverse effects. Tobacco taxes are considered an effective policy tool to reduce tobacco consumption and produce long-run benefits that may outweigh the costs associated with a tobacco price increase. However, policy makers avoid using tobacco taxes because of the possible regressive effects. In particular, poorer


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deciles across the income distribution are proportionally more negatively affected than richer ones by the extra tax burden. This paper uses an extended cost-benefit analysis to estimate the distributional effect of tobacco tax increases in Bosnia and Herzegovina. The analysis considers the effect on household income of an increase in tobacco prices, changes in medical expenses, and the prolongation of working years under various scenarios, based on data in three waves of the national Household Budget Survey (2007-2011-2015). One critical contribution is a quantification of the impacts by allowing price elasticities to vary across consumption deciles. The results indicate that a rise in tobacco prices generates positive income variations across the lowest income groups in the population (the bottom 20 percent). At the same time, tobacco price increases have negative income effects among middle-income and upper-income groups. These effects are larger, the higher the income level. If benefits through lower medical expenses and an expansion in working years are considered, the positive effect is acerbated among the lowest income groups. The middle of the distribution sees the income effect turn from negative to positive, and the top 40 percent, although continuing to experience a negative effect, see the magnitude of this effect diminish. Altogether, these effects mean that increases in tobacco prices have a pro-poor, progressive effect in Bosnia and Herzegovina. These results also hold within entities and across urban and rural areas.

Keywords: tobacco, taxes, health, distributional analysis, cost-benefit analysis

JEL classification: H23, H31, I18, O15


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THE LOCAL ECONOMIC EFFECTS OF SOFT BORDERSEvidence from the dissolution of the Union of Serbia and Montenegro

Vassilis MonastiriotisLSE Research on Southeast Europe, European Institute, London School of EconomicsHoughton Street, WC2A 2AE London, United KingdomPhone: ++44 (0)20 7955 6937E-mail: [email protected]

Ivan ZilicInstitute of Economics, Zagreb and LSE Research on Southeast Europe, London School of EconomicsE-mail: [email protected]

AbstractNational borders create geographical discontinuities which extent well beyond the political dimension and into the social and economic organisation of space. Following decades of continuing economic and political integration in the European Union, a sizeable literature has developed which examines how the


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removal, or softening, of borders contributes – not always positively – to economic development and the distribution of economic activity at the national and local levels. However, evidence emanating from the inverse process, of erecting borders, is largely lacking from the literature. The recent developments in the international political economy of Europe, not only with regard to Brexit but also with the rise of economic protectionism more generally, give heightened salience to this issue. Motivated by this, in this paper we exploit the ‘natural experiment’ of the peaceful separation between Montenegro and Serbia to examine the local economic effects of erecting borders. We apply a counterfactual impact assessment design, comparing the economic performance of Serbian municipalities bordering Montenegro vis-à-vis that of other border municipalities in the country. We first compare the performance of border regions using administrative data and discuss the limitations arising from this type of data, especially in the context of known problems of statistical measurement and economic informality in the Balkan region. We then re-examine the issue using satellite date on luminosity, which provide a more ‘objective’ measure of economic activity, and discuss the differences between the two sets of data. Our results are illuminating not only with regard to the spatial distribution of economic costs and benefits of independence in the specific case but, we argue, also prospectively for other cases, including that of the Irish border.

Keywords: Soft borders; Secession; Independence; Economic effects, Counter-factual impact assessment; Regional growth; Satellite data; Luminosity

JEL classification: R11, R15, R58, F15, F50, O18


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SOME ASPECTS OF QUANTITATIVE ANALYSIS OF DEVELOPMENT ROLE OF GROSS EXPENDITURES FOR RESEARCH AND DEVELOPMENT

Jasmin KomićFaculty of Economics, University of Banja LukaMajke Jugovića 4, 78000 Banja Luka+387 51 430 010

Institute of Statistics, Republic of SrpskaVeljka Mlađenovića 12d, 78000 Banja Luka+387 51 332 701e-mail: [email protected]

Investing in research and development has a particularly important impact on growth and development and it can be considered as an extremely important investment for the future. This implies the significance of quantitative analysis in this area, which should provide a quantitative confirmation of the importance of investing in research and development to socio-economic development.

Monitoring and analysis of investing in research and development implies the provision of appropriate, comprehensive, coherent and methodologically


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consistent data, while respecting the dispersion and presence of inequalities worldwide. Coverage is one of the main problems in this research not only in our country. Thus, it is necessary to respect the existing and introduce new standards for data collection, processing and presentation.

The author has conducted several of these analyses that will be covered in this presentation. In the analyses there were several different samples of countries over several observation periods, with a number of observed variables and composite indices. Some of the results and experiences obtained through these analyses will be presented with particular reference to the indicator for monitoring development flows, as well as its versions, as a creation by the author.

Based on cluster analysis, factor analysis, regression analysis and time series analysis, multiple quantitative confirmation was obtained that investing in research and development significantly affects growth and development.

Taking into account the data and results of the quantitative analysis, it is evident that globally there is a deep gap in terms of the amount and dynamics of expenditures for research and development. This strongly affects the deepening differences and the extremely high dispersion with regard to growth and development, living standards of the population, and civilizational, cultural and all other values, which leads to slowing development of the human society as a whole. Thus, modalities for overcoming this situation need to be found. A possible approach is to respect the principle of a tolerant partnership based on knowledge.

Keywords: analysis of development, gross expenditures, research and development


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Official Statistics (improvement, harmonization, data collection, tools, methods, sector-specific statistics, etc.)

Business Statistics and the Use of Administrative Data

Ema MišićStatistical Office of the Republic of Slovenia/SURSLitostrojska c. 54, Ljubljana, SloveniaPhone: +386 1 2340 684; Fax: +386 1 2415 344E-mail: [email protected]

Abstract For the last decade, data from administrative sources, together with data from registers, have been one of the key sources for most national statistical offices and have become an increasingly dominant way of obtaining data for statistical purposes. With the digitization and creation of new types of data, such as big data, additional sources and possibilities appear.


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The Slovene statistical office also has a very long tradition of using data from various administrative sources, which is a well-established business practice in many statistical fields. The possibility of obtaining and free use of data from various official and administrative sources is also stipulated in the National Statistics Act. Through all these years, a good cooperation with providers and owners of these databases has been established through the statistical system and practice.

The widespread use of data from official and administrative sources for statistical purposes has also been enforced due to some of their key advantages, such as lower costs of acquiring them, reducing the burden on businesses, and in some cases also shorter time for acquiring them. Due to all of these positive characteristics, they have very quickly established themselves and play an important role in business statistics. Business statistics observe enterprises and their parts, and it is very important that the reporting burden is not too high.

In Slovenia data from administrative sources started to be used for the needs of business statistics back in the 1980s with the establishment of the Business Register and the Statistical Register of Employment as key infrastructure tools, continued with the use of tax and accounting data for the monitoring of selected variables, and lastly with the establishment of a spatial information system, which also provides information on the issued building permits. The data from the value added tax declarations have proved to be a very good source for estimating the movement of incomes in selected activities for short-term business statistics and for selecting reporting units at Intrastat. An agreement reached between several institutions on the use of uniform forms for the submission of the annual accounting statements by business entities for three purposes, i.e. for public disclosure of these data, for tax control and for statistical purposes, provided to SURS data for conducting the survey on structural business statistics. Today, in the field of business statistics, two thirds of the data for the needs of various


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statistical surveys are obtained from the already available administrative sources and registers.

In this paper, I want to present the use of data from official and administrative sources when conducting surveys in the field of business statistics. In doing so, I wish to highlight our good practice and positive experiences, challenges we have encountered or we are still facing and our plans for the future. A rapidly evolving, globally integrated business environment requires new indicators, quick responsiveness and cost reduction. Because of this, we are studying the possibility of using new data sources, such as financial transaction data. Collected and processed data are becoming increasingly important and valuable in the 21st century - new black gold not only for statistics. Keywords: business statistics, administrative sources, data, register

JEL classification: M, O


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EXAMINATION OF THE IMPACT OF HOUSEHOLD INCOME ON EXPENDITURE ON CLOTHING AND FOOTWEAR IN BOSNIA AND HERZEGOVINA AND SERBIA

Hasan (M) HanicBelgrade banking academy, University “Union” Belgrade /Professor EmeritusBelgrade, Republic of SerbiaPhone: +38163234304E-mail: [email protected]

Milica (Z) BugarcicBelgrade banking academy, University “Union” Belgrade/Teaching AssistantBelgrade, Republic of SerbiaPhone: +381644641881E-mail: [email protected]

Lejla (R) DacicFaculty of Management and Business Economics, University of Travnik / Teaching AssistantTravnik, Bosnia and HerzegovinaPhone: +38762640400E-mail: [email protected]


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Abstract The subject of this research is a comparative analysis of the impact of income on household expenditure on clothing and footwear in Bosnia and Herzegovina and Serbia. The aim of this paper is to quantify the impact of income as the main explanatory variable as well as selected socio-economic and demographic characteristics of households, on Bosnian and Serbian households’ expenditures on clothing and footwear.

The data sources used in this paper are the results of household budget surveys in B&H and Serbia conducted in 2015, which represents the last year for which data are available in B&H The data are completely comparable given that household budget surveys in both countries are conducted according to the uniform methodology of the Statistical office of the European Union (EUROSTAT).

Apart from the descriptive analysis which includes a comparative overview of the structure of household expenditures in B&H and Serbia in the observed year, classical statistical methods and methodological instrumentation were used, representing an integral part of econometric analysis based on a model formulated in the form of a linear regression equation and other functional forms of Engel’s functions which can be reduced to a linear model by suitable transformations. Seven different functional forms of Engel’s demand equation were used to estimate the impact of income on clothing and footwear expenditures. In addition to absolute expenditures, relative expenditures and the share of expenditures on clothing and footwear in total expenditures of B&H and Serbian households were used as a dependent variable. Socio-economic and demographic characteristics of households are included into a classical linear regression model using dummy variables whose influence is quantified separately and in interaction with income as the main explanatory variable.


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The results of the analysis include estimates of the significance of the impact of income on absolute expenditure and the share of expenditure for the observed product group in total household expenditure, and the impact of socio-economic and demographic factors, the Engel elasticities, as well as other sample indicators that are relevant for testing the underlying research hypothesis.

The results obtained imply numerical values of regression coefficients and income elasticities that can be used in structural models for quantifying the impact of changes in personal consumption on the scope, structure and dynamics of growth of the gross domestic product of the Republic of Serbia and B&H. Numerical values of income elasticities are of particular importance for manufacturers of footwear and clothing as they can serve to predict the impact of consumer income on the volume and value of sales of the observed products.

The originality of this research is reflected in the fact that, for the first time, an econometric analysis of household consumption for a particular product group was conducted on the basis of large data samples contained in unpublished databases of the Republic Statistical Office of Serbia and the Agency for Statistics of B&H. It is also reflected in the use of the functional forms of Engel’s models that have not been used so far by authors researching this field from this region to quantify the impact of income and socio-economic and demographic variables on the expenditure of households on clothing and footwear in B&H and Serbia. For the first time, a comparative analysis of income elasticities will be conducted for the group “Clothing and Footwear” and the two basic subgroups in the observed countries.

Keywords: personal consumption, Engel functions, household budget survey, income elasticity

JEL classification: C21, C51, D10, D12


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EVALUATION OF THE EMPLOYMENT PROGRAM OPPORTUNITY FOR ALL OF THE FEDERATION OF BOSNIA AND HERZEGOVINA

Merima BalavacAssistant Professor, School of Economic and Business, University of SarajevoTrg oslobođenja 1, 71000 Sarajevo, Bosnia and HerzegovinaPhone: +387 33 564 393E-mail:[email protected]

Josefina PosadasSenior Economist, World Bank, Washington DC, United StatesPhone: + 202-473-1000E-mail:[email protected]

AbstractUsing administrative data from tax records and public employment services, this paper examines whether the largest wage subsidy program deployed in 2014 in the Federation of Bosnia and Herzegovina, Opportunity for All, was effective at increasing employment. Given the non-experimental design, the paper relies on propensity score matching estimators. It contributes to the literature


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on impact evaluations of active labor market policies (ALMPs) by exploiting the work histories of job seekers to identify the control group. In the preferred specification, the program was effective in increasing employment among program participants, relative to the control, by 12.1 percent, 12 months after completing the subsidized period. However, the results are highly sensitive to the assumptions of the starting date of the job spell in the control group, which carries information about previous work history. When changing the assumptions about starting date of the subsidized job spell of the control group, results remain positive in the short run, but they turned out to either be larger or even negative for the medium-run. In all cases, heterogeneity analysis reveals that the program is most beneficial for job seekers about 40 years of age and older, and those who are low-skilled workers.

Keywords: low wage subsidy, employment, unemployment, duration, ALMPs, Bosnia and Herzegovina

JEL classification: J23, J24, J38, J64, J68


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Individual and policy mix effects of regional and national R&D subsidies on the cooperative behaviour of Spanish manufacturing firms

Dragana RadicicLincoln International Business School Brayford Way, Brayford Pool, Lincoln, United KingdomPhone: +441522882000E-mail: [email protected]

Geoff PughStaffordshire University Business SchoolCollege Road, Stoke-on-Trent, United KingdomPhone: +441782294000E-mail: [email protected]

Mehtap Hisarciklilar School of Economics, Finance and Accounting, Coventry University Priory Street, Coventry, United KingdomPhone: +442476887688E-mail: [email protected]


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AbstractThis study investigates the individual and “policy mix” effects of regional and national R&D subsides on the propensity of Spanish manufacturing firms to cooperate with customers, suppliers, competitors and universities in the period 2010-12. One of the main features of R&D and innovation policy is the existence of a large number of policy instruments. The interplay between policies and instruments is termed policy mix. The concept of policy mix has recently gained ground among researchers and policy makers. Policy mix is associated with different dimensions: policy; governance; geographical; and time. We assess the effectiveness of innovation policy at two governance levels – regional and national. In this context, we also test the hypothesis of complementarity between regional and national public support. In theory, it is often assumed that the interactions between different policies and instruments are complementary, although empirical evidence remains scarce.

In common with many studies of innovation and the effects of public innovation support, we highlight heterogeneity by firm size. Accordingly, we report separate estimates of regional subsidies, of national subsidies and of the joint effects of regional and national subsidies for both small and medium-sized enterprises (SMEs) and for large firms. We expect that the effects of public support on innovation and, hence, innovation-related activities including cooperation, will be influenced by the resource advantages of large firms – both human and financial – and by SME advantages with respect to flexibility and reaction speeds.

Our panel analysis uses Spanish firm-level dataset, the Encuesta sobre Estrategias Empresariales (ESEE, or Survey on Business Strategies). Dynamic probit models reveals uniformly strong persistence in firms’ cooperative behaviour, suggesting that cooperation additionality may occur both in the short term and in the long term. Moreover, accounting for the path dependency of firms’ cooperative


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behaviour reveals findings otherwise obscured. In particular, among SMEs with a recent history of cooperation with HEIs, we find that R&D subsidies – regional, national and both in a policy mix – induce large positive effects on cooperation with HEIs. In general, however, we do not find that R&D subsidies promote firms’ cooperation and we find no evidence of systematic complementarities in policy mixes.

We argue that “psychic costs” may help to explain the extreme SME “Cooperators” / “Non-cooperators” heterogeneity with respect to cooperation with HEIs. If recent experience of cooperation has attenuated or even eliminated the psychic costs of cooperation, then public policy may have a relatively easier task in moving SME “Cooperators” further along their existing direction of travel than in moving “Non-cooperators” across a behavioural threshold. A corollary is that public subsidy effects on SME cooperation with HEIs may be an adjustment phenomenon, i.e. acting most strongly while initial experience of cooperation has attenuated the psychic costs of such cooperation but more weakly as the gap closes between actual and private optimum levels of cooperation.

Keywords: R&D subsidies, Spain, Cooperation, Behavioural additionality, Policy mix

JEL classification: O25, O30, O32, O38


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LOCAIZED DISSEMINATION SYSTEM

Serhat ATAKULTurkish Statistical InstituteDevlet Mah. Necatibey Cad. No:114 06420Çankaya / Ankara / Republic of TurkeyPhone: +90 555 792 02 76 ; Fax: +90 312 417 04 32>E-mail: [email protected]

AbstractAn NSI should pay attention to informing the society about the scope of official statistics. This can be done by improving communication with users and finding tailor made solutions for target user groups. Official statistics are used by many different user groups each having somewhat different data needs. Finding new presentation methods special to each user group may help to promote the use of statistics. To this end, TurkStat developed a model to increase the use of official statistics across the country. The model is based on preparing various information packages according to user needs. Users can basically be grouped into two categories: users at the national level and users at the local/provincial level. There are also other classifications such as administrators, press members, academics, non-profit organizations and the business community.

Localized or specially prepared custom packages are sent to its group of users every day. The staff responsible for dissemination in 26 regional offices and a team in the central office carry out the operation. A total of 23,000 individuals


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from different target groups are informed by almost 27 different version of the same press release. The feedback was excellent; many positive comments, especially from local authorities, were received and many people contacted the central office as well as the regional offices to get involved in the system. TurkStat recognized that localized and specialized information attracts much more attention from users. There was not any less data on the website before this dissemination model created. However, many of the users simply did not know where to find the pieces of information before the information was made visible for them.

Keywords: localized dissemination system, tailor made dissemination.


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METHODS FOR TREATMENT OF MISSING DATA IN STATISTICAL SURVEYS: OVERVIEW OF EXISTING SOLUTIONS

Edin ŠabanovićAgency for Statistics of Bosnia and HerzegovinaZelenih beretki 26, 71000 Sarajevo, Bosnia and HerzegovinaPhone: +387 33 911 940; Fax: +397 33 220 626E-mail: [email protected]

Rabija Somun-KapetanovićSchool of Economics and Business Trg oslobođenja–Alija Izetbegović 1, 71000 Sarajevo, Bosnia and HerzegovinaPhone: +33 275 925; Fax: +387 33 275 994E-mail: [email protected]

AbstractAnyone who deals with the collection and the analysis of statistical data sooner or later faces a problem of missing records in data sets. In a typical statistical data matrix, data is missing for some variables and/or for some cases. Missing data is a pervasive problem in every kind of statistical surveys in economy, medicine, biology, psychology, physics etc. no matter they are based on complete coverage or a sample. Ideally completed data sets are very rare in real world and statisticians


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and researchers runs into a problem of violations of the initial assumption about the representativeness of the sample. This is a main reason why some researchers sometimes ignore or underestimate the importance of the presence of missing data and do not address them in their publications or reports. Additional and more technical problem is related to limitations of classic data analysis methods and software, which assume complete data matrices in most cases.

From the late 70`s on, a number of statistical methods and software have been developed in order to solve missing data problems. The progress in this statistical domain is very extensive and still in rapid development. Three different strategies for dealing with missing or erroneous data in statistical surveys can be identified, which are deletion, imputation and using as it is.

The deletion of cases with missing data is the earliest and most simple technique, which has several serious drawbacks. The focus of statistical research has been later directed to imputation methods, the process by which missing values in data set are replaced by appropriately computed values (so called “educated guessing”). Single imputation, multiple imputation and artificial intelligence techniques are three classes of methods within this strategy. These methods show substantial progress in developing statistical procedures for missing data and the most important of them are being included in most software programs.The last strategy for missing data is to use the data as it is without any treatment and to precede the data analysis on incomplete data sets. Such techniques are rare in the literature and used in limited applications in the practice.

In this paper, we define missing data problems and describe the basics and terminology of missing data theory in line with Rubin`s contributions to this statistical area. We then outline the key ideas of all solutions to missing data problem. We focus on the first two missing data strategies where main methods


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for missing data treatment within each strategy will be presented in light of their strengths and drawbacks. In the last part of the paper we present the software programs available for the implementation of missing data treatment methods. We finalize with critical assessment of existing practices of missing data treatment in the official statistics in Bosnia and Herzegovina and conclude with proposal for improvements in nearest future.

Keywords: missing data, deletion, imputation, uncertainty

JEL classification: C150, C180, C520, C830


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Studying EU countries regarding mortality with SYR program

Filipe AfonsoSymbad – Le Symbolic Data Lab, 5 rue de copenhague - CS 13918 - 95731 Roissy CDG CedexE-mail: [email protected]

Edwin DidayCEREMADE, University Paris-Dauphine, Paris, FranceE-mail: [email protected]

Simona Korenjak-ČerneUniversity of LjubljanaFaculty of EconomicsKardeljeva ploščad 17, 1000 Ljubljana, SloveniaPhone: ++ 386 1 5892 626; Fax: ++ 386 1 5892 698E-mail: [email protected]

Aleša Lotrič DolinarUniversity of LjubljanaFaculty of EconomicsKardeljeva ploščad 17, 1000 Ljubljana, SloveniaPhone: ++ 386 1 5892 782; Fax: ++ 386 1 5892 698E-mail: [email protected]


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AbstractPositioning of a country by mortality considering mortality level and also its structure over death cause offers important information to policy makers in the areas of health, labour market and society in general. To thoroughly capture both dimensions, we need a tool that can handle more complex data. One of such possibilities is offered in symbolic data analysis (SDA). SDA is based on richer data description of aggregated data that enables to preserve more information, such as internal variability of the aggregates. Special methods and tools are developed and adapted to analyze such data.

To analyse gender-age-cause-specific mortality in 2015 for 28 European countries we use symbolic data analysis methods and tools implemented in SYR software. We use SYR software in two steps: firstly, to produce proper data presentation capturing both dimensions, and secondly, to group countries by their mortality. In the first step, mortality levels by gender and age groups are discretized in a way that emphasizes differences among countries using a specific discretization method based on Fisher’s algorithm extended to symbolic data. This enables the description of the countries and clusters of countries by histogram-valued variables that preserve internal variation among countries in clusters and uniforms variable descriptions. With that, histogram-valued representations of mortality levels and bar-charts of relative structure of mortality over cause of death can be used in the analysis simultaneously. Positioning countries inside groups is based on the Principal Component Analysis and a variant of k-means, both extended to symbolic data. Tools adapted for symbolic data descriptions and implemented in SYR software enable us to study and more automatically detect the importance of mortality level and death cause structure for country grouping, main differences among groups, and also ranking the countries and variables based on their characteristics. Results show that countries inside clusters are mostly also geographically related and that the discriminating


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factor among clusters is not only mortality level but that clusters differ also by causes of death.

Keywords: symbolic data analysis, aggregated data, cause of death, clustering

JEL classification: C18, C55, C8, C80, C81, C82


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Application of statistical methodology (Economics, Finance, Health, Education and other related disciplines) and Business Statistics (use of administrative and accounts data, challenges and development)

ANALYSIS OF THE FACTOR OF SAVINGS OF PRIVATE PROFIT ENTERPRISES IN BIH BY APPLICATION OF ECM METHODOLOGY

Irma ĐidelijaEconomic Faculty of the University “Džemal Bijedić” in Mostar BiH, 88000 MostarPhone: ++ 387/061 918 412E-mail: [email protected]

Rabija Somun KapetanovićSchool of Economics and BusinessUniversity of Sarajevo, 71000 SarajevoPhone: ++ 387/033 275 925 E-mail: [email protected]


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AbstractThe purpose of this research is to examine the factors that influence the saving of private profit enterprises in BiH.

This study examined the impact of macroeconomic and financial determinants savings of private profit enterprises based on data from the Statistics Agency of BiH and the Central Bank of BiH for the period 2000q1-2016q3. For this purpose, the seasonally adjusted variables used in the analysis were first made using the X-13 ARIMA and TRAMO / SEATS approaches. The stationary rate of seasonally adjusted variables was examined by: expanded Dickey-Fuller unit root test, Phillips-Perron unit root test, and Kwaitkowski-Phillips-Schmidt-Shin stationarity test. Only nonstationary variables of the first order of integration are included in the model, which are coinegrated, which is the condition for applying the Error correction model (ECM). The application of the ECM methodology first required the determination of the corresponding number of lags in the VAR model, and then Johansen’s cointegration test. As the independent variables in the ECM, the following were used: GDP, general government revenue, general government expenditure, money supply (M2), deposit interest rate of enterprises, unemployment rate. The aforementioned variables are included because they have satisfied the basics of the ECM methodology, which is the same line of integration of variables and cointegreted.

The results of the survey show that on longterm only the coefficients with the revenues of the general government in the previous period and the deposit interest rate of the economy in the previous quarter have a statistical significance different from zero. The negative coefficient with the revenues of the general government suggests that the increase in general government revenue in the long run negatively affects the level of the enterprise’s savings. The value of this coefficient of -0.138 indicates that a unit increase in general government revenue,


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an increase of one million KM, will lead to a decrease in the enterprise’s savings level of 0.138 million KM. The deposit interest rate of the economy in the previous quarter has an unexpectedly negative impact on the level of enterprise’s savings in the long run. All short-term coefficients are expected to be smaller than the corresponding long-term coefficients, suggesting that only part of adjusting the deviation from the balance sheet is done in the short run, that is, adjusting the requirements for more than one quarter. In the short-term part of the model, only general government revenues and expenditures have shown that they have a statistically significant impact on the enterprise’s savings. The relatively high value of the custom determination coefficient, 0.607 suggests that almost 61% of variations in the enterprise’s savings level are explained by variations in the set of independent variables included in this ECM model. The obtained research results can be important indicators for adequate measures of monetary and fiscal policy of BiH, and can continue to be indicators for the overall macroeconomic decisions of the state.

Keywords: savings, econometric analysis, economic policy measures.

JEL classification: C32, E21


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COMPARISON OF STRUCTURAL EQUATION MODELLING AND MULTIPLE REGRESSION TECHNIQUES FOR MODERATION AND MEDIATION EFFECT ANALYSIS

Lejla TuruljaUniversity of Sarajevo, School of Economics and BusinessTrg oslobodenja – Alija Izetbegovic 1Phone: +387 33 275 970; Fax: +387 33 275 900E-mail: [email protected]

Nijaz BajgoricUniversity of Sarajevo, School of Economics and BusinessTrg oslobodenja – Alija Izetbegovic 1Phone: +387 33 275 900; Fax: +387 33 275 900E-mail: [email protected]

AbstractMultiple regression (MR) is a well-recognized technique that has been widely used since the beginning of the 20th century in which the period has been


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continuously developed and improved. On the other hand, structural equation modelling (SEM) has become a prevalent technique among researchers, especially in social sciences. It is known as the second generation multivariate method. SEM, in relation to MR, allows not only testing the relationship between independent and dependent variables but also it estimates measurement models and structural models. In this regard, the primary objective of this paper is to compare structural equation modelling and multiple regression analysis for interaction effect and mediation analysis purposes. The analysis of the moderating impact implies verifying that a third (moderating) variable affects the strength or direction of the relationship between an independent and dependent variable. On the other hand, the analysis of the mediation impact refers to verifying that the third variable (mediator) mediates the relationship between an independent and dependent variable.

This paper empirically compares the structural equation modelling and multiple regression analysis by testing the moderating effect of environmental turbulence on the relationship between firm’s innovation and business performance; and by examining the mediation influence of innovation between environmental turbulence and firm’s business performance. In this study, the sample included companies in B&H and respondents were managers. A questionnaire consisting of measurement indicators adopted from previous studies was used to collect data. For all questions, seven-point Likert scale with ‘’Strongly disagree’’ to ‘’Strongly agree’’ anchoring the scales.

Although the study does not indicate significant differences in the use of MR and SEM when the results of the estimation of moderating and mediating influence are concerned, some empirical limitations of MR as a statistical technique widely used in management and similar social research are supported. The results emphasize the benefits of SEM that relate to the assessment of simultaneous


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impacts and relationships of multiple variables at once, as well as the possibility of incorporating latent constructs into the analysis.

In this way, a better understanding of the use of two popular methods, SEM and MR, in management research is provided. Besides, a comparison of the assumptions of these techniques is presented, as well as the obtained results and conclusions. The expected contribution of this paper is a deeper understanding of the relationship between SEM and multiple regression analysis when it comes to testing the influence of moderating and mediating variables.

Keywords: structural equation modelling, multiple regression, SEM, MR, innovation

JEL classification: C4


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CONVERGENCE AND HETEROGENEITY IN GLOBAL DIETS

Le Thai HongBournemouth UniversityExecutive Business Centre, 89 Holdenhurst Road, Bournemouth BH8 8EB, United KingdomE-mail: [email protected]

AbstractWorldwide obesity has almost tripled since 1975. This trend is the consequence of demographic, epidemiological and nutrition changes that have taken place as countries develop and become more globalised. This research examines the global trends in food consumption for main food categories since 1961 using data from the Food and Agriculture Organisation of the United Nations (FAO). Preliminary data review discloses a steady rise in food availability worldwide for the past 50 years, coupled with significant alterations in the structure of the global diets in terms of both macronutrients and individual food aggregates. Evidence shows that national diets are evolving over time and across countries in ways that are both similar yet distinct giving rise to patterns that can be investigated statistically by the means of cluster analysis. This research is the first attempt in food economics literature to present the application of an innovative space-time clustering technique inspired by fuzzy logic and copula functions. Agglomerations of countries characterised by similar dietary trends are identified


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and the findings are further analysed to uncover both economic and cultural factors that most matter in explaining the pace of dietary change as well as the convergence that is observed globally. The findings will inform the public policy debate on the relationship between diet and obesity and provide evidence to those interested in formulating national policies to promote healthy diets.

Keywords: nutrition transition, globalisation, clustering, copula, obesity


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PREDICTING EMPLOYEE HEALTH AND COST: APPLICATION OF MACHINE LEARNING ON EMPLOYEE HEALTH CLAIMS DATA, INSIGHTS, AND POSSIBILITIES

Anshul Saxenaa, Sankalp Dasa, Muni Rubensa, Joseph Salamia, Chintan Bhatta, Lejla Turuljab, Tian Tiana, Peter McGranaghana, Louis Gidela, Emir Veledara

aBaptist Health South Florida, 8950 N Kendall Dr, Miami, FloridaE-mail: [email protected] of Sarajevo, School of Economics and Business, Trg oslobođenja – Alija Izetbegovic 1, Sarajevo, BiH

AbstractIntroduction: Employers opting for self-insured health plans could reduce healthcare costs by identifying and preventing potential preventable hospitalizations. This study looked at machine learning (ML) techniques to identify low risk patients who are on the path to become high risk, and eventually high cost.


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Methods: This study included 6000 employees and spouses (1.2 million claims) who were covered by employer sponsored health insurance plan during 2011-2016. For analysis, sample was divided into train (85%) and test (15%) sets. A high cost claimant was defined as someone with an annual spending of ≥$10,000 in medical claims. To predict high risk or high utilizers in the next year, we used and compared the following machine learning methods: bootstrapped random forest, gradient boosted tree, deep neural networks, and logistic regression with LASSO.

Results: Most expensive claims were related to cancer, cardiovascular conditions, and surgical procedures. Most frequent claims were for medical evaluations, musculoskeletal conditions, non-traumatic joint disorders, non-specific chest pain, hypertension, diabetes, upper respiratory infections, and after care procedures. Mean age of the participants in the training set was 49.65 years, and 68.16% were females. About 77.61% were employees and rest were spouses. Mean number of ER visits and mean net amount paid were 5.04 days and $6175.70, respectively. In training set 15.6% had yearly expenditure ≥$10,000, compared to 9.7% in the test set. Bootstrapped random forest performed better than other techniques (validation AUROC: 80.33%). This model classified 10% as medium or high risk for spending ≥$10,000 in the test set. Actual mean amount for medium risk category and high risk were $14,912.68 and $28,130.58, respectively.

Conclusion: Our study showed that application of ML methods could predict high cost claimants in advance. Integrating these results in a self-service dashboard could provide cost-effective decision support solution for patient navigators or care coaches, and may save between 10% and 20% of total expenditure.

Keywords: machine learning, logistic regression, health prediction, cost prediction

JEL Classification: I12


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SHARE OF ADULTS WHO ORDER GOODS OR SERVICES ONLINE INFLUENCED BY SHARE OF THOSE WITH DIGITAL SKILLS BROKEN DOWN BY GENDER: CLUSTER ANALYSIS ACROSS EUROPEAN COUNTRIES

Ksenija DumičićFaculty of Economics and Business, University of ZagrebTrg J. F. Kennedya 6, HR-10000 Zagreb, CroatiaE-mail: [email protected]

Blagica NovkovskaFaculty of Economics, University of Tourism and ManagementBlvd. Partizanski Odredi No. 99, 1000 Skopje, North Macedonia E-mail: [email protected]

Emina ResićSchool of Economic and Business, University of Sarajevo71000 Sarajevo, Bosnia and HerzegovinaE-mail: [email protected]


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AbstractThe purpose of the research is to analyze the impacts to the percentage of individuals ordering goods or services online, in the European Union (EU-28) countries, for those aged 16 to 74, as the main variable under study. Since 2007 to 2017, it is increasing in EU-28 linearly by 2.75 percent yearly, with the coefficient of determination of 0.994. Based on this linear trend, it is expected to reach 63 percent in 2019.

Statistical exploration, correlation and Ordinary Least Squares linear regression and hierarchical cluster analysis were performed on Eurostat data for 2017. After Montenegro, the Former Yugoslav Republic of Macedonia, Serbia and Turkey were added, and after Luxembourg was excluded because of the serious outlying data for Gross Domestic Product per capita in Purchasing Power Standards (Index (EU-28 = 100), altogether 31 countries remained included in the analysis. The descriptive statistical analysis for 2017 recognized the range from 13 percent, for Montenegro, and 82 percent, for United Kingdom. Also, it gave the overall average of 47.48 percent per country, with the coefficient of variation of 44.33 percent, with a nearly symmetrical distribution. Just to focus the EU candidates other than Montenegro, which has the minimum value of the indicator considered, the Former Yugoslav Republic of Macedonia has 15 percent, Serbia 31 percent and Turkey, reached 21 percent, and they clustered together with Romania and Bulgaria, Greece and Croatia.

After positive correlations were found, the simple linear regression models were developed and they tell us that: if the GDP per capita in PPS would increase by one percentage point, the percentage of those who are purchasing goods and services online, as the dependent variable, would increase by 0.49 percentage points (t=6.40, p-value≈0.000, R2=0.5858); if, the percentage of households who have internet access at home, for the population considered is aged 16 to 74,


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would increase by one point, the dependent variable would increase by 2.27 percentage points t (t=11,30, p-value≈0.000, R2=0.8148); and finally, if the percent of individuals who have basic or above basic overall digital skills would increase by one point, the dependent variable would increase by 1.41 percentage points (t=12.34, p-value≈0.000, R2=0.8401). One multiple linear regression model was built, with the percentage of households who have internet access at home and the percent of individuals who have basic or above basic overall digital skills, as the regressor. This model was significant at the overall level (F=121.30, p-value≈0.000, R2= 0.8891, regression CV=0.45%, VIF=3.27), and at each estimated regression coefficients, as well. All the regression assumptions of normality, homoscedasticity and no autocorrelation of the residuals for all four models were fulfilled. The leverage of more than 0.2 was the highest for Turkey, Montenegro and Romania. The gender impact was analysed regarding the digital skills as opposed the dependent variable, and it might be concluded that the positive correlation is stronger for female, 0.9205, than for male, 0.8920. This result may be used in improving selling approaches and techniques, with perspective to improve the business results and customer satisfaction.

Keywords: purchasing goods and services online, digital skills, liner regression model, regression diagnostics, hierarchical cluster analysis

JEL classification: C31, L86


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RELATION BETWEEN BMI OF ADOLESCENTS AND SOCIO-ECONOMIC STATUS OF FAMILY

Irzada TaljićUniversity of Sarajevo, Faculty of Educational SciencesSkenderija 72, 71000 Sarajevo, Bosnia and HerzegovinaPhone: +387 61 257 581 E-mail: [email protected]

Adela DelalićUniversity of Sarajevo, School of Economics and BusinessTrg Oslobođenja-Alija Izetbegović 1, 71000 Sarajevo, Bosnia and HerzegovinaPhone: +387 33 253 780 E-mail: [email protected]

AbstractAdolescence is a very sensitive period towards growing up conditions. That made the reason to do this kind of research and examine whether BMI status of adolescents aged 13-15 years is related to the socioeconomic status (SES) of families and the place of residence. The study included 630 adolescents. Purpose-designed SES questionnaire was used as a method. All of included indicators are categorical variables. Some of them are dichotomous (gender, area of living, etc.) and some have more than two categories (number of parents, education of parents, etc.). In order to test whether BMI class differs across the categories, we have performed


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Mann - Whitney U or Kruskal – Wallis nonparametric tests. Aiming to determine the strength and statistical significance of the relationship between BMI status and certain indicator, we have determined polychoric correlation coefficients as the only appropriate estimation of correlation between two categorical variables. Results show that BMI status is independent of the most of included socioeconomics indicators. However, based on the results of chi – square test, we have discovered the existence of relationship in case of area of living and father’s employment. Considering crosstabulation results, the share of overweight children in urban area is 21.8% while in rural area there are 12.8% overweighed children. The percentage of overweighed children is 10% higher if father is employed.

Based on the results of Mann – Whitney U test, we have concluded that area of living does have an impact on BMI class while father’s employment does not. However, considering the results of conducted tests in case of area of living and the fact that p-value in chi – square test for independency of BMI status and father’s employment was close to 0.05 we estimated polychoric correlation coefficients between BMI status and mentioned indicators. According to polychoric correlations, there is statistically significant and relatively weak negative correlation between BMI status and area of living while the correlation between BMI status and father’s employment is positive and weak, but statistically significant.

The similar analysis was performed separately for boys and girls. Again, there was proved the different distribution of BMI status in rural and urban area and existence of the relationship between BMI status and area of living, for both groups, boys and girls.

Keywords: socioeconomic status, BMI status, residence, adolescents, polychoric correlations

JEL classification: I10


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THE FOURTH INDUSTRIAL REVOLUTION AND ICT SECTOR IN BOSNIA AND HERZEGOVINA

Sanjin ČengićInstitute for Statistics of Federation BiHZelenih beretki 26, 71000 SarajevoPhone: ++387 33 407 026; Fax: ++ 387 33 226 151E-mail: [email protected]

AbstractToday, we are able to hear about The Fourth Industrial Revolution that is happening all around the World. It is related to almost every aspect of human life, not only business, but private as well. The Fourth Industrial Revolution is basically German Strategy for industry development and represents a World in which individuals move between digital domains and offline realities with the use of information technology. This would not be able to conduct without enterprises dealing with information and communication technologies. Businesses will need to ensure they have the right mix of skills in their workforce to keep pace with the changing technology. Information and Communication Technology (ICT) sector became one of the most interesting areas for scientists, economists, lawyers and others where significance and role of ICT has been discussed. ICT determine competitive power in the knowledge economy and are the technologies underpinning the digital transformation of the economy and of society. Aim of this paper is to give an overview of the current situation in Bosnia and Herzegovina regarding The


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Fourth Industrial Revolution and enterprises in ICT sector. From aspect of the Fourth Industrial Revolution, main characteristic and literature review would be given through analysis of available articles and books. On the other hand, data about enterprises and their business in ICT sector will be discussed, where some of them are: number of enterprises, revenue, export, number of employees and value added generated in this industry. Further, paper will provide analysis of importance and role that ICT sector has in Bosnia and Herzegovina and for the economy. Main characteristics of ICT sector would be mentioned and explained using data obtained from Agency for Statistics of Bosnia and Herzegovina and financial statements of enterprises. Through paper recommendations on how to respond to challenges that Fourth Industrial Revolution brings would be given, as well as recommendations how to strengthen ICT sector considering issues which mentioned sector is dealing with. Descriptive statistics would be used in order to analyse data obtained. For the preparation of this paper all available scientific and academic literature relating this or similar topic are used, as well as books, articles and web sources.

Keywords: Fourth Industrial Revolution, ICT, Bosnia and Herzegovina

JEL classification: O32, O33


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Econometrics modeling for policy making, Social and welfare statistics and Big data and machine learning

Determinants of Customer Satisfaction in the Hotel Industry: Application of Factor Analysis and Ordinal Logistic Model

Dedić LejlaSchool of Economics and Business, University of Sarajevo71000 SarajevoE-mail: [email protected]

Merima BalavacSchool of Economics and Business, University of Sarajevo71000 SarajevoE-mail: [email protected]


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AbstractIn a highly competitive hotel industry, where business units are offering homogeneous products and services, hotels must find ways to distinguish their products and services among others. Actually, what hotels need to achieve is awareness of their clients’ needs and ensuring that those same needs are met in the best way possible. The primary goal of the research is to determine factors that affect hotel guest satisfaction. Primary research was conducted by interviewing randomly selected individuals. Data collection took 30 days during September 2018. The total number of units collected is 143.

For the purposes of this research, a confirmatory factor analysis was applied. Factor velocities were used as inputs for ordinal logistic regression and sensitivity analysis. For the model of hotel guests’ satisfaction, variables value for money, an ambiance of the hotel, specific services, quality of services and location are statistically significant at the level of 1%. Reservation service, person’s age, gender and the reason for the visit do not have statistically significant effect on customers’ satisfaction.

It is expected that the variable value for money has a non-linear effect on the satisfaction. As expected, the result shows that the value for money increases satisfaction as long as the product and services reflect price, whereas the reversal is close to the average price. In other words, the average prices generate the highest customer satisfaction. Considering that according to social theories the generational group X represents a price-sensitive and cautious group, we have expected a greater effect of variable value for money on satisfaction in this group. However, we have found that the value for money has the greatest effect on the satisfaction in the generational group Z.


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This result can be justified by the fact that youth in the sample (20 years old and younger) are still financially dependent and therefore more price-sensitive compared to other groups. Overall, a business organization should invest in improving the quality of hotel products and services, ambiance of the hotel, specific services, location, as well in harmonizing the value provided with the amount of money paid. This should be done in order to achieve the highest possible benefit for all stakeholders involved.

Keywords: value for money, non-linear effect, average prices, generational groups

JEL classification:


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How efficient is the targeting in Social Services in Federation of Bosnia and Herzegovina?

Adela DelalićSchool of Economic and Business, University of Sarajevo71000 Sarajevo, Bosnia and HerzegovinaE-mail: [email protected]

Ademir AbdićSchool of Economic and Business, University of Sarajevo71000 Sarajevo, Bosnia and HerzegovinaE-mail: [email protected]

Muamer HalilbašićSchool of Economic and Business, University of Sarajevo71000 Sarajevo, Bosnia and HerzegovinaE-mail: [email protected]

AbstractThe incidence of relative poverty in Federation of Bosnia and Herzegovina has remained almost unchanged in the last decade and it is becoming evident that poverty reduction strategies have not yielded the desired results. Federal and


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cantonal ministries for labor and social affairs, through their sectors for social welfare and welfare of the family and children, take care of various social services programs in FB&H. The role and task of social services is not to reduce poverty per se, but rather to define criteria and procedures by which financial assistance will reach the most vulnerable segments of society.

The aim of this study is to draw attention to the inefficiency of social services targeting in FB&H. For that purpose, the permanent financial assistance, as the most representative and the most stable social service, was selected and legal criteria for qualification in FB&H have been investigated. The results indicated a high inconsistency in the legal criteria for qualification and also in amounts of permanent social assistance by cantons.

Based on microdata from Household Budget Survey 2015, the general and extreme poverty thresholds were established and identified general and extreme poor households. The characteristics of identified categories of poor households were analyzed and it was found that 0% of extreme and 2.4% of generally poor are beneficiaries of permanent financial assistance. The main reason for such inefficient targeting of the most vulnerable categories of population was recognized in the Federal Law on Core Issues of Social Care, Care for the War-Disabled Civilians and Care for the Families with Children, that stipulates that only persons and families that (cumulatively): are incapable for work, have insufficient income and there are not family members who are legally obligated to support them. This legal provision resulted in the fact that a significant share o

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