Purpose The purpose of this paper is to provide more reliable estimates of the relationship between government spending and economic growth in the European Union (EU) during the period of 1995-2015. Design/methodology/approach The methodology consisted of several different stages. In the first stage for an assessment of dynamics of government spending and economic growth indicators over two decades, descriptive statistics analysis was employed. Correlation analysis helped to identify the relationships between government expenditures (GEs) and economic growth. In the third stage, for modeling the relationship and the estimation of causality between GE and economic growth, Granger causality testing was applied. Findings The research indicated that eight EU countries have a significant relationship between government spending and economic growth. Research limitations/implications This study has been bounded by general GE and economic growth only. The breakdowns of general GE on the basis of the activities they support have not been considered in this paper, which is the main limitation of the research. Despite the limitation, it might be maintained that the research highlights key relationships in the EU countries. Originality/value These insights might be useful for policy makers. In countries with unidirectional causality running from GE to economic growth, the government can employ expenditure as a factor for growth. The governments should ensure that resources are properly managed and efficiently allocated to accelerate economic growth in the countries with unidirectional causality from GDP to GE.

1434 INDEPENDENT JOURNAL OF MANAGEMENT & PRODUCTION (IJM&P)http://www.ijmp.jor.br v. 8, n. 4, October -December 2017 ISSN: 2236-269X DOI : 10.14807/ijmp.v8i4.659 Due to the shortage and mismatching statistical information, the paper is limited with only four following indicators: smart economy, smart mobility, smart environment, and smart governance. The analysis of indicators shows that Lithuanian major cities in all groups of criterion are below average while values of indicators in the case of Swedish major cities are much higher than average.

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Santrauka. Šiame straipsnyje analizuojami galimi intelektinio kapitalo vertinimo aspektai Lietuvos įmonėse. Pirmoje straipsnio dalyje pateikiama intelektinio kapitalo definicijos interpretacija. Ši sąvoka analizuojama pagal jos apibūdinimo pagrindą ir galimus valdymo būdus. Taip pat pateikiama intelektinio kapitalo struktūra, atskleidžiant pagrindinius intelektinio kapitalo vertinimo aspektus. Antroje dalyje itin daug dėmesio skiriama intelektinio kapitalo vertinimo aspektams, identifikuojant pagrindines problemas ir pateikiant galimus jų sprendimo būdus. Pateikiant bendrus intelektinio kapitalo matavimo ir vertinimo modelius bei rodiklius, siekiama juos pritaikyti Lietuvos įmonių intelektiniam kapitalui vertinti. Atliekant tyrimą remtasi anketinės apklausos duomenimis, ekspertiniu (intelektinio kapitalo rodiklių rangavimo) vertinimo metodu. Tyrimo rezultatai parodė, kad intelektinio kapitalo indeksas tirtose mažoje (dirba 9 darbuotojai) ir vidutinėje (dirba 90 darbuotojų) įmonėse skiriasi. Mažoje įmonėje gauta koeficiento reikšmė lygi 0,398, t. y. žemas vertinimas, atitinkamai vidutinėje įmonėje -0,704, t. y. vidutinis vertinimas. Tokiu atveju galima teigti, kad intelektinio kapitalo vertinimo rezultatą lemia įmonės dydis, veiklos pobūdis bei pačių vadovų požiūris į intelektinio kapitalo matavimo poreikį. Intelektinio kapitalo matavimo ir vertinimo tikslingumą galėtų lemti nuostata, kad jis tiesiogiai susijęs su konkurencinio pranašumo įgijimu ir išlaikymu.Reikšminiai žodžiai: kapitalas, intelektinis kapitalas, nematerialusis turtas, intelektinio kapitalo struktūra, intelektinio kapitalo matavimo rodikliai, intelektinio kapitalo vertinimo schema.