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THE ANALYSIS OF THE INTERCONNECTIONS BETWEEN THE INDICATORS OF THE EXTERNAL PAYMENT BALANCE AND THE MACROECONOMIC AGGREGATES OF RESULTS 3Prof. Constantin ANGHELACHE PhD.Academy of Economic Studies, Bucharest, „Artifex” University of BucharestProf. univ. Alexandru MANOLE PhD.Conf. univ. dr. Mădălina Gabriela ANGHEL PhD. „Artifex” University of BucharestMarius POPOVICI PhD. StudentAcademy of Economic Studies, Bucharest

BIRTH SEASONALITY PATTERNS IN CENTRAL AND EASTERN EUROPE DURING 1996-2012 9Associate Professor, PhD Christiana Brigitte BALAN“Alexandru Ioan Cuza” University of Iasi Professor Emeritus, PhD Elisabeta JABA“Alexandru Ioan Cuza” University of Iasi

MODEL ESTIMATES OF GROSS DOMESTIC PRODUCT IN RELATION TO EXPORT AND IMPORT OF FUELS, FOCUSED ON THE ELASTICITY AND DETERMINATION OF DIRECTLY AND INDIRECTLY ASSOCIATED RATES 21 Professor Habil. PhD. Gheorghe SĂVOIUUniversity of PiteștiSenior Lecturer PhD. Emilia GOGUPhD. candidate Alexandru IONESCUBucharest University of Economic Studies

CORRELATIONS BETWEEN EXPENDITURE AND EMPLOYEES IN R&D ACTIVITY BY PERFORMANCE SECTORS FROM ROMANIA 42Vasilica CIUCĂ CSINational Scientifi c Research Institute for Labour and Social ProtectionPhD Profesor Daniela PAȘNICUNational Scientifi c Research Institute for Labour and Social Protection, Spiru Haret UniversityGabriela TUDOSE CSIIINational Scientifi c Research Institute for Labour and Social ProtectionMihai Robert PAŞNICUBrown University, United States

PATTERNS OF FOREIGN DIRECT INVESTMENT IN TRANSYLVANIA 52Cristina SACALĂAniela Raluca DANCIUVasile Alecsandru STRATBucharest University of Economic Studies

Romanian Statistical Review nr. 1 / 2016

CONTENTS 1/2016ROMANIAN STATISTICAL REVIEW www.revistadestatistica.ro


THE EUROPEAN UNION SOLIDARITY FUND: AN IMPORTANT TOOL IN THE RECOVERY AFTER LARGE-SCALE NATURAL DISASTERS 69PhD Univ. Professor Maria IONCICĂBucharest University of Economic Studies PhD Univ. Professor Eva-Cristina PETRESCU Bucharest University of Economic Studies

INSIGHTS ON EDUCATION - INNOVATION LINKS AND IMPACT 81Mihaela DIACONU”Gheorghe Asachi” Technical University of Iasi, Romania

Romanian Statistical Review nr. 1 / 2016 3

The analysis of the interconnections between the indicators of the external payment balance and the macroeconomic aggregates of results Prof. Constantin ANGHELACHE PhD. Academy of Economic Studies, Bucharest, „Artifex” University of Bucharest Prof. univ. Alexandru MANOLE PhD. Conf. univ. dr. Mădălina Gabriela ANGHEL PhD. „Artifex” University of Bucharest Marius POPOVICI PhD. Student Academy of Economic Studies, Bucharest

ABSTRACT The payment balance (BP) may be defi ned, generally, as a statistic image of the international economic transactions between resident and non-resident agents of a country. These transactions are considered during one period of time (year, trimester, month). In spite of its denomination, BP concerns not only the usual payments, but all transactions, even if a part of them does not comprise the cash payments. Key words: Payment balance, analysis, components, capital, reserve

The payment balance – in a specifi c form, more restraint – represents, in fact, an integrant part of the National Accountability (the account 8 „The rest of the World”). That is why among the indicators of the payment balance and the main macroeconomic aggregates are to be emphasized a series of essential relations for the study of the domestic and external economic imbalances, their fi nancing, as well as their incidence on the economic situation of the respective country. As consequence, BP represents an element of the National Accountability that allows a complete and detailed framework for the collecting and displaying the international economic statistics of the country.


The standard components of BP are grouped in the following main sections: • The current transactions comprise: - exported and imported merchandise (goods) (including afferent

distribution services supplied on the territory of the country up to the customs of the respective country); as consequence, evaluation of merchandise imports and exports should be refl ected by the FOB prices. This component represents the main aspect of the BP current account (trade balance);

- exported and imported services (traveller and merchandise shipping, tourism, insurance, communication, advertisement etc);

- producer incomes received in foreign countries and those paid to foreign partners as work income (salaries), from investments (dividends) and from ownership (interest);

- transfers in non-countertrade received from outland and given up to foreign partners as: emigrant patrimonial transfer, sendings of the emigrants’ funds, donations, inheritances, pensions, free technical assistance, scholarships, taxes, fees etc.;

• Capital and fi nancial transfers refl ecting the changes with external fi nancial assets and liabilities comprise:

- capital transfers; - direct and portfolio investments (bonds, shares); - granted and received credits on long, medium and short term; - reserve assets (detained by NBR), representing a distinct category

of capital of fi nancial (?), DST, convertible currency etc. These are debts managed by the monetary authorities in a certain economy with the aim to directly fi nance the imbalances of BP and in order to intervene on the fi nancial currency market to infl uence the current exchange rate of the national currency;

- other posts (transit accounts, clearing/barter accounts).

There is a third section, „errors and omissions” (net) representing a residual post owed to different causes (sources of uncertain data, currency fl ee the country etc). The result of the current transactions represents the current account balance, where the main component is the trade balance. The balance of the capital and fi nancial account is indicating the types of external fi nancing of the current account defi cit, with credits, investments and un-blocking of the offi cial reserve funds as the most important. For the entries on debit, the signifi cances are inverse.


The balance equation of BP is: SCC + SF + E = 0 (1) or SCC = –SF – E, (2) where: SCC – current account balance of the payment balance measures

the net transfers of real resources (goods, services, incomes) and the current transfers without countertrade between one economy and the rest of the world;

SF – fi nancial account balance of the payment balance represents the net foreign saving or the entries of non-residents’ savings fl ows, not mentioning the outputs of the nopn-residents’ saving fl ows;

E – errors and omissions (net).

Between the macroeconomic aggregates and the indicators of the external payment balance (BP) there are some connections regarding which there is necessary to mention certain relations. As a start, it should be mentioned the identity between offer and goods and services fi nal demand. The total offer of goods and services during a year is made of the domestic output (PIB) and the imports (Imp), while their distribution is made of the domestic aggregated demand, fi nal consumption – CF and gross supply of capital _ FBC, adding also the external demand (Exp).

ExpSFBCFCpbCpvpPIBFBCCF

Im (3)

nExpFBCCFpExpFBCCFPIB .)Im( (4) where: Exp.n = Exp – Imp – the clearance sale of the goods and services trade balance (net export).

As it is known, GDP (PIB, here) is defi ned as gross fi nal production accomplished by the production factors in the internal units (residents and non-residents). The GDP (PIB) structure following the fi nal use offers the most important rates (consumption rate, investment rate, export and import rate). The dynamics of these rates whose evolution emphasize the supporting and supplying factors for the economic development (domestic demand, including the expansion of the branches reliant to the supplies on the external market, as well as external demand).


After 1989, the net export was permanently negative, while the import of goods and services was always in advance. This evolution shows that an increasing signifi cant quota of the domestic consumption was supplied from external resources. This is a tendency which is evidently harder to sustain. The gross national product (PNB) measures the value of the national fi nal production of the economic agents (residents), in the country and outside, so that to GDP (PIB) adds the balance of the production agents incomes with regard to foreign environment (SVFS). PNB = PIB + SVFS = PIB + (VFIS – VFPS), (5) where: VFIS – production agents’ incomes from outside the country; CFPS – production agents’ incomes paid in other countries.

Based on the sign of the balance, PNB might be bigger or less than PIB. The available national income is obtained id the net national product (PNN) is added to the foreign current transfers balance(STCS).

VND = PNB – A + STCS = PNN + (TCIS – TCPS), (6) where: TCIS – foreign current transfers; TCPS – current transfers paid to foreign economic environment; VND – the available income of the economy expressing the economic possibilities for fi nal consumption (CF) and saving activities. It follows that:

VND = CF + EN = PNB – A + (TCIS –TCPS) == PIB – A + (VFIS – VFPS) + (TCIS – TCPS) == CF + FBC + (Exp – Imp) – A + (VFIS – VFPS) + (TCIS – TCPS)== CF + FNC + (Exp – Imp) + (VFIS –VFPS) + (TCIS –TCPS)

Finally, we obtain:CF + EN = CF + FNC + (Exp – Imp) + + (VFIS – VFPS) + (TCIS –TCPS) (7) or EN – FNC = (Exp – Imp) + (VFIS – VFPS)+(TCIS –TCPS) = SCC= –SF, (8) where: SCC – the clearance sale of the current account of payment balance

represents the difference between the net saving activities and the net investment;

SF – the clearance sale of the fi nancial account of the payment balance representing the external fi nancing manners.

The last equation might be written:


EN + SF = FNC, (9) meaning the net investments are fi nanced either fromdomestic savings, or from external fi nancing (credits, foreign investments, un-blockings from the national bank reserves). The disparity of savings – investments of the national accounts indicates the relation between national income and the external payment balance. The previous relation may be supplementary parted into sectors, identifying the specifi c indicators for the private sector („Companies” and „Population households”) and the public sector. To this aim, the fi nal consumption and the gross making of the capital will be decomposed in two component each (Cpv and Cpb, respectively FBCpv and FBCpd), including the calculation and the net governmental incomes TX (Governmental incomes as taxes, less the governmental transfers to the private sector).

SFSCCFBCpbEpbFBCpvEpvFBCpbCpbTXFBCpvCpvTXVNDFBCCFVND

EpbEpv

)()()(()( (10)

Obviously, there is a defi cit of the external current account which implies either insuffi cient private savings relative to the private investments, or insuffi cient public savings relative to government investments, or both of these two. The importance of this identity should be emphasized. It represents the major constraint for the accounts of an economy, and suggests the relation between the accounts of the accounts of the domestic private sector, the government budget and the current account of of BP. In other words, the amount of domestic imbalances is equal to the current account imbalance. In case the economy absorbs more resources (ABS = Cpv +Cpb + FBCpv + FBCpb) than it absorbs, then, inevitably, it will exist a current account defi cit of that country. The national and international publications frequently emphasize the balance relation between saving activities, investments and BP current account sold (implicitly the external fi nancial sold)BP current account sold (implicitly the external fi nancial sold) is frequently emphasized in rates by relating each indices of GDP (PIB) and GNP (PNB).

EB _ FBC = SCC (11) PIB PIB PIB or REB - RFBC = RSCC (12)

Evidently, a saving rate less than investment rate will result in an increase the pressure on payment balance.


Conclusions The distinction between PIB and PNB is important when a great part of the domestic production is realized based on external factors, and when the Romanian economic agents obtain incomes from foreign activities. The magnitude of the external disparity is given by the disparity between the domestic savings which covers only partially the internal investment. There is underlined the contribution of each sector to the current account defi cit generation, thus allowing the analysis of causes and necessary decision making. The defi cit/excedent of the payments balance can be infl uenced through adequate policies that stimulate savings.

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between Some of the Macroeconomic Variables”, Romanian Statistical Review Supple-ment, Issue 1, pp. 88-94, Romanian Statistical Review este revistă indexată în bazele de date internaţionale DOAJ, Index Copernicus, EBSCO, ICAAP, RePEc, ISSN 2359-8972 CNCSIS, categoria B+

2. Anghelache C., Voineagu V., Mitruţ C. (2013) – „Statistică macroeconomică. Sistemul conturilor naţionale”, Editura Economică, Bucureşti

3. Anghelache C. (2008) – „Interconnections Between the External Balance Indicators and the Macroeconomic Outcomes Aggregates”, Metalurgia International, nr. 2/2008, pp. 168-171, ISSN 1582-2214 Editura ştiinţifi că F.M.R., revistă citată în bazele de date internaţionale SCOPUS, EBSCO, THOMSON SCIENTIFIC MASTER JOURNAL LIST, Sci Search

4. Anghelache C. (2008) – „Tratat de statistică teoretică şi economică”, Editura Economică, Bucureşti

5. Anghelache C. (2010) – „Some Macroeconomic Evolution of Romania”, Metalurgia In-ternational, nr. 5/2010, pp. 198-205, ISSN 1582-2214, Editura ştiinţifi că F.M.R., revistă citată în bazele de date internaţionale SCOPUS, EBSCO, THOMSON SCIENTIFIC MASTER JOURNAL LIST, Sci Search

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Birth Seasonality Patterns in Central and Eastern Europe during 1996-2012 Associate Professor, PhD Christiana Brigitte BALAN1 “Alexandru Ioan Cuza” University of Iasi Professor Emeritus, PhD Elisabeta JABA “Alexandru Ioan Cuza” University of Iasi

ABSTRACTFertility is characterised by seasonal and cyclical variations and also by another trend by geographic region and time span. This study aims to identify a birth seasonal-ity model for post-communist Central and Eastern European (CEE) countries and to analyse the features of birth seasonality model by CEE countries during the period 1996-2012. The method used to analyse the seasonal pattern of live births is the de-composition of time series using the moving average fi lter and the identifi cation of seasonal variation by calculating seasonal factors. The results show that seasonality of live births in CEE countries is characterised by two peaks, one in July and another in September, except the Baltic countries and the Czech Republic. Key words: birth pattern, seasonality, seasonal factors, similarities, Central and Eastern Europe JEL classifi cation: C22, J11

INTRODUCTION

Live births present a seasonal pattern with differences by geographic regions under the infl uence of factors that outline this phenomenon. The study of seasonal pattern of births has been a steady concern of demographic researchers. Cummings (2010) argues that since Quetelet (1835) many researchers have studied the seasonality of births in various countries and periods in order to fi nd reasons for this pattern. Demographic studies (Lam and Miron, 1994) found that seasonal variations in birth rates differ by geographic regions, for example the European model is different from the American model. Moreover, within the European model there exist specifi c features by geographic sub-regions.

1. Corresponding author: Christiana Brigitte BALAN, [email protected], Faculty of Economics and Business Administration, “Alexandru Ioan Cuza” University of Iasi, Carol I Avenue, no. 22, code 700505, Iasi, Romania, tel: 0740-132-198


The European model of births seasonality is characterised by high birth rates in spring, especially in April, and in early autumn, in September, and by lowest fi gures in late autumn and early winter (from October to December). This birth pattern is specifi c to countries with hot summers and cold winters. In the Northern European countries, there are peaks in spring (March to May) and depressions in autumn (October to November). In Southern Europe, the birth peak occurs in summer or late summer (Haandrikman and van Wissen, 2008). The seasonality of births is infl uenced by multiple factors. One group of factors is related to physical environmental characteristics (geophysical factors and climatic characteristics such as temperature, light). These factors explain natural cycles. Cummings (2010) shows taking into account diverse cultures and geographical regions that periods of increased conception are preceded by periods of high intensity of light. Roenneberg and Aschoff (1990) and Manfredini (2009) found that births seasonality is greatly explained by temperature and length of photoperiod (PP). Other studies, such as Becker et al. (1986) and Huber and Fieder (2008), believe that seasonality is infl uenced by a combination of temperature and nutrition, while Bailey et al.(1992) and Doblhammer et al. (2000) argue that the amount of rain could explain the profi le of seasonal birth. Regions with important climate changes show higher seasonality than regions with more stable climate (subarctic and equatorial geographic zones) (Friger et al., 2009). An overview of literature dealing with seasonality explained by latitude and sunlight, climate and temperature, and food supply is presented by Laaidi et al. (2011). Another group of factors with infl uence on births seasonality is related to cultural, social, demographic, and economic conditions (festivals, religious rules and calendars, marriage seasonality, seasonal labour migration). These factors explain the artifi cial cycles. The September births’ peak is related to holiday theory, namely, an increase in conceptions around Christmas and the New Year is due to increased amount of time spent by people around winter holidays (Haandrikman and van Wissen, 2008). In the literature, the hypothesis of birth seasonality is validated using either aggregate (Lam and Miron, 1991; Chatterjee and Acharya, 2000; Bobak and Gjonga, 2001) or individual data (Rizzi and Dalla-Zuana, 2007; Friger, Shoham-Vardi and Abu-Saad, 2009). Countries of Central and Eastern Europe have a common past in what regards pro-natalist policies implemented during the communist regime. Therefore, it is of interest to verify if fertility in these countries has preserved the model designed by pro-natalist policies or it has registered a specifi c evolution by countries or group of countries.


The study aims to identify a birth seasonality model for the post-communist Central and Eastern European (CEE) countries and to analyse the differences in birth seasonality between CEE countries and the general European seasonal birth pattern. The method used to analyse the seasonal pattern of live births is the decomposition of time series using the moving average fi lter and the identifi cation of seasonal variation by calculating the seasonal factors. The results show that the seasonal component of live births in CEE countries is characterised by two peaks, one in July and another in September, except the Baltic countries. The following section of the paper (Section 2) presents the data and the methodology and Section 3 discusses the main results. The conclusions are included in Section 4.

DATA AND METHOD

Sample and data The study included the following countries: Hungary, Poland, Romania, Slovenia, Slovakia, Bulgaria, Czech Republic, Croatia and the three Baltic countries: Estonia, Latvia and Lithuania. These eleven countries together with Albania, which is not included in this study, are classifi ed as Central and Eastern European (CEE) countries by OECD (2001). The variables used in this study represent the number of live births by month and by country during the period 1996-2012. We note by ty the number of live births in a month t. The data are available from the Eurostat database under Demography and migration topic in the Fertility section (http://ec.europa.eu/eurostat/data/database).

Method The identifi cation of the seasonal pattern of live births has been made using the sequence chart that allows identifying visually the trend and the seasonal variation for a time series. The monthly data have been corrected for the unequal number of days using the following approach (Manfredini, 2009):

(1) where: – is the corrected number of births per month under the hypothesis of months of equal length; – is the mean number of births per day in month t.


The time series for live births ( ty ) can be decomposed, using a multiplicative model, into the following components: Ct– cyclical component; f(t) – trend component; St – seasonal component; εt – error component. The multiplicative model has the following form:

(2)

The estimation of the seasonal component has been made by calculating seasonal factors using the following approach: 1) The estimation of the trend component from the original data by

applying a moving average fi lter (Jaba, 2002; Brockwell & Davis, 2002):

dyyyyyyyy ptpttttptptt /)21......

21( 1111 +−++−+−− ++++++++= (3)

where: ty – is the moving average for month t; and d – is the period of the seasonal component (the period is 12 months, so d is even, d=2p).

2) The estimation of seasonal component by calculating the seasonal indices and the seasonal factors.

For a multiplicative model, as considered for the analysed time series, the seasonal indices (it) are computed as an average of the ratios tt y/y (the detrended values of monthly births) using observations only for period k, where k = 1, …, d. The seasonal factors ( ˆ

kS ) (the seasonal means) are obtained by adjusting the seasonal indices, namely, as the ratio of the seasonal index to the geometric mean of the indices:

d,...,1k,i/iS d

d

1kkkk ==

= (4)

The product of the seasonal factors equals 1 ( d,...,k,Sd

kk 11

1

==∏=

).

Cluster analysis has been used to assess similarities among countries in accordance with the seasonality of live births. Considering seasonal factors calculated for each country by month, the cluster analysis allows identifying homogenous groups of countries taking into account the seasonality of fertility. The basic criterion for clustering the countries is distance. Countries that are near each other should belong to the same cluster, and countries that are far


from each other should belong to different clusters. Distance between two cases has been assessed using the Squared Euclidean distance. It has been calculated as the sum of the squared differences between all the variables of the two countries.

22 ( , ) ( )i ii

d x y x y= −∑ The clusters that merge are made up of more and more dissimilar countries. The decision of the optimal number of clusters is based on researcher’s subjective reasons. The estimation of seasonal effects has been made using SPSS software using the seasonal decomposition procedure.

RESULTS

During the 17 years considered in the analysis, the variation in life births shows different trends by countries. For Romania and Hungary, the downward trend over the entire period is clearly highlighted by the sequence charts (Figure 1). In all European countries fertility has declined considerably over the past decades, with important consequences on the structure of population age. The ageing of population in these countries will cause an absolute population decline in the future years (Asandului, 2012) In the CEE countries, the downward trend in fertility is explained by both the demographic transition theory and the social and economic conditions specifi c to former communist countries. Fertility generally declines during periods of political and social instability (Jemna & Cigu, 2014). In Romania, the evolution of fertility is characterised by a cyclical pattern with four peaks in the year 1967, 1974, 1981, and 1989 and a general downtrend, followed by a stable low fertility level after 2000 (Jaba et al., 2013). Poland and Slovakia show a similar pattern in that both countries registered a descending trend until 2003, followed by an increase of live births (up to 2009). The reversal of the fertility trend is explained by the recuperation of postponed childbirths and by the ongoing social and economic stabilization (Potančoková et al., 2008). On the other hand, Croatia and Lithuania show a decreasing trend until 2002, after that life births show a rather steady evolution. Slovenia, Czech Republic, Bulgaria, Latvia, and Estonia had an upward trend of life births during the period 2003-2008. In Bulgaria, a minimum value is observed in November 1997 (4300 live births), the period October 1997 – February 1998 being characterized by the lowest fi gures as compared to other years. In Latvia, November 1997 also presents smaller values compared to other fi gures.


It appears from the graphs of monthly births registered during the period January 1996 – December 2012 that time series show seasonal variation for all the eleven countries.

Monthly life births variation in CEE countries during the period 1996 – 2012

Figure 1


Source: Own processing in SPSS 20.00

The seasonal factors were obtained using the decomposition procedure of the time series considering the multiplicative model. The values of the seasonal factors are presented in Table 1. For most of the countries, the seasonal profi le is defi ned as bimodal with two peaks in July and September, corresponding to conceptions in October and December, respectively. In Hungary, Poland, Romania, and Bulgaria the


highest value is observed in July, while in Slovenia, Slovakia and Croatia, the highest values are observed in September. This model is explained by the holiday theory that generates artifi cial cycles in the live births evolution. The September births’ peak is related to an increase in conceptions around Christmas and the New Year due to the increased time spent around winter holidays. The fertility model in these countries is also shaped by the outfl ow of emigrants abroad for work and by their return home around Christmas holidays, the majority of emigrants belonging to the fertile age group. Most births occur in July and June in the Czech Republic, Estonia, Latvia and Lithuania. The fi ndings for the Czech Republic for the period 1996-2012 are in contrast with the fi ndings of Bobak and Gjonca (2001) that showed that most births occurred in March to May.

Table 1. Seasonal factors for live births in CEE countries considering the multiplicative decomposition model of time series

Czech Republic

Hungary Poland Romania Slovenia Slovakia Bulgaria Estonia Croatia Latvia Lithuania

January 0.9580 1.0031 1.0166 0.9737 0.9992 0.9970 0.9899 0.9555 1.0129 0.9689 0.9744

February 0.9914 0,9935 0,9913 0,9810 0,9725 1,0097 0,9948 0,9933 1,0020 0,9839 0,9823

March 1,0086 0,9743 1,0131 0,9742 0,9772 1,0051 0,9859 1,0359 0,9560 1,0389 1,0081

April 1,0436 0,9648 1,0165 0,9637 0,9833 1,0226 0,9796 1,0136 0,9489 1,0085 1,0189

May 1,0432 0,9546 1,0081 0,9853 1,0128 1,0150 0,9881 1,0333 0,9592 1,0248 1,0250

June 1,0617 1,0053 1,0295 1,0401 1,0104 1,0392 1,0304 1,0665 0,9786 1,0542 1,0430

July 1,0711 1,0706 1,0751 1,1091 1,0598 1,0611 1,0795 1,0600 1,0431 1,0692 1,0917

August 1,0240 1,0296 1,0267 1,0405 1,0205 1,0275 1,0522 1,0187 1,0299 1,0194 1,0237

September 1,0238 1,0660 1,0672 1,0682 1,0663 1,0727 1,0511 1,0303 1,0956 1,0126 1,0199

October 0,9444 0,9940 0,9550 0,9903 0,9986 0,9390 0,9751 0,9359 1,0195 0,9503 0,9523

November 0,9190 0,9686 0,9041 0,9515 0,9502 0,9013 0,9261 0,9337 0,9806 0,9297 0,9377

December 0,9112 0,9758 0,8970 0,9224 0,9490 0,9098 0,9473 0,9233 0,9737 0,9397 0,9230


The number of births decreases in late autumn and early winter, thus, the lowest values are noticed in November and December corresponding to conceptions in February and March. However, in Croatia, the lowest values of live births are observed during the spring months. By applying the cluster analysis, the eleven CEE countries are grouped into clusters according to values of seasonal factors. Using the information provided by the dendrogram plot (Figure 2), we set the appropriate number of clusters to keep the two clusters, each of them divided into two smaller sub-clusters.


The dendrogram seasonal model of life births in CEE countries by clusters during the period 1996 – 2012

Figure 2

S O i i SPSS 20 00


The two clusters and their sub-clusters are: - Cluster 1 is formed of: o Sub-cluster 1: Czech Republic and the Baltic states; o Sub-cluster 2: Poland and Slovakia; - Cluster 2 is formed of: o Sub-cluster 3: Croatia and Hungary; o Sub-cluster 4: Bulgaria, Romania and Slovenia. Countries in the same cluster and sub-cluster show a similar seasonality pattern of monthly live births during the observed time span (Figure 3).


Seasonal model of life births in CEE countries by sub-clusters during the period 1996 – 2012

Figure 3


This birth seasonal model in CEE countries is different from the European seasonal model characterised by high birth rates in spring. In most CEE countries, the highest number of births is observed in July.

Conclusions

This study analyses monthly live births in CEE countries during the period January 1996 – December 2012. The main objectives of this study were to identify the birth seasonality pattern for the observed countries and to assess the characteristics of birth seasonality model by country. Previous studies on the seasonality of births showed that seasonal pattern of births is explained by two main factors: natural cycles (geophysical


factors and climatic characteristics) and artifi cial cycles (cultural, social, demographic, and economic factors). The method used to analyse the seasonal pattern of live births is the decomposition of time series using the moving average fi lter and the identifi cation of the seasonal variation by calculating seasonal factors. The analysis of life births variation in CEE shows different trends, by countries. Moreover, for all the CEE countries, monthly births show seasonal variation. For most of the countries, the seasonal profi le is defi ned as bimodal with two peaks in July and September. This birth pattern is different from the European seasonal pattern characterised by high birth rates in spring.

References

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Model Estimates Of Gross Domestic Product In Relation to Export And Import Of Fuels, Focused on the Elasticity and Determination Of Directly and Indirectly Associated Rates Professor Habil. PhD. Gheorghe SĂVOIU1 University of Pitești Senior Lecturer PhD. Gogu Emilia PhD. candidate Ionescu Alexandru Bucharest University of Economic Studies

ABSTRACT The article is based on several interrogative assumptions related to the positive impact of the crises and the recession on determinations in the econometric models of Romania’s GDP as a variable dependent in relation to the export and import of fuels. After a short introductory section, which details, in a relative manner, the overall goal and the objectives of the paper, a fi rst section makes use of elasticity and the modern solutions of building the coeffi cient of elasticity, proposing an original alternative to existing vari-ants, and afterwards the next section builds on these statistical tools in the econometric modeling of Romania’s GDP, starting from the ratios and value indicators and offering a few original models where the export and import of fuels are the key initial explanatory factors. The fi nal remarks reinterpret the role of the energy resources, as well as that of the related fl ows, in enhancing statistical connections, and especially the role of crises and recessions in validating econometric models, by raising their degree of predictability. Key words: gross domestic product (GDP) elasticity coeffi cient, correlation matrix, econometric model, fuel export / import. JEL Classifi cation: C15, C19, C51, C53.

INTRODUCTION

Real Business Cycle Theory (or the RBC theory), as a variant of Business Cycle, synthesizes a whole class of macroeconomic models where

1. Corresponding author: Gheorghe Săvoiu - [email protected]


the shocks of the cycle of business fl uctuations can be counted as cases of real progress to a large extent (Rothbard, 2000), in contrast to the nominal approach, while acknowledging, and in this way quantifying, the four major economic fl uctuations as being the trend (the general tendency), the cyclicality (the business cycle), seasonality (sub-annual variation) and the random tendency (residual variable impact). Recessions and periods of economic growth, in RBC theory, appear as an effective response to the exogenous changes in the actual economic environment, and Ludwig Mises and von Friedrich Hayek were among the few who empirically predicted the Great Depression (Cooley and Hansen, 1995; Diego and Gertler, 2006). The Austrian Business Cycle Theory Applied in Rothbard’s America’s Great Depression Business Cycle, as well as the economic cycle, had both fervent advocates, if we merely mention Joseph Schumpeter, and vehement opponents, if we refer to Irving Fisher, who also set up an entire theory of fl uctuations in business, called the Theory of Business Fluctuations (TBF). The topic of Business Cycle has become an important subject of analysis and econophysics, too, although there are signifi cant precedents with Louis Bachelier or Benoit Mandelbrot; the emergence of such outstanding econophysicists as Rosario Mantegna and Eugene Stanley allowed analyzing over one million records of stock market indices to fi nd that the real economic value has a special distribution, which deviates from the classical or Gaussian one, without however being able to identify a standard mathematical model of the business cycle (Mantegna, Stanley, 1999; Săvoiu, 2012). All those approaches have virtually suggested the primary aim of the present paper, which deals with a model of Romania’s GDP in keeping with several explanatory factors, or else independently, in connection with the resources, for a period in the country’s economy that was affected by crisis or recession. Recession or crisis are part of the RBC theory, and they generate and augment confl icts. The standard, relatively dominant picture of crisis or recession seems to come near that of a state of confl ict, or a disease of the economy, entirely similar to medical approaches, and, practically like any confl ict or disease, crisis and recession have a solution, which occurs in two phases or stages. During the fi rst phase (the so-called cold phase) of the active confl ict or disease, when physically or materially, there occur specifi c cellular disorders, and the psyche and the autonomic nervous system are facing unexpected situations, the whole body, switched into a phase of stress, reorients itself in order to be able to cope with the confl ict or disease as such. The sufferer’s mind becomes increasingly more concerned with the content of the confl ict or disease, occurring sleep disturbances and lack of appetite; attention focuses on the confl ict or disease, and the additional mental


and physical activity seeks to generate the optimal conditions to resolve the confl ict or disease in an optimal manner. This phase is called cold because of the presence of actions that are uncertain, unstable and oscillating, tending towards the extremities. During stress period, there occurs the constricting of the blood vessels, and the specifi c symptoms of confl ictuality become the cold extremities of the body (especially the hands) and the instability that has set in (shivering and cold sweat.). The longer the cold state of the disease, the greater the risk that the consequences are possibly fatal; and, by extension, the longer the cold phase of the crisis or recession gets, the more evidently the economy feels it is dependent on vital resources and essential activities, on their continuity. In the second stage, when treatment and solutions are already present and available, the body or the economy relax and adjust themselves, while everything returns to a higher temperature, and to the stability that is specifi c to normality. This latter stage may be termed the hot phase of the confl ict or the disease, when solutions have already been put into practice, and are yielding results (the economy heats up, returning to normality, and the body regains its existential stability). The life cycle, from hot to cold, and the cycle of creativity, from expansion to contraction, the cycle of activities and products, the geological cycle or the demographic cycle, they are all realities and certainties of the modern world, capable of altering the process of human mind and thinking, the health of the individual, but also the internal confl ictive character of the economies. The warm or hot phase and the cold phase, expansion and contraction, prosperity and decline, represent the two sides, reverse and obverse, of the economy, the relaxation and the stress of economic confl ict, revealing cyclical fl uctuations caused by the gradual accumulation of decisions, solutions, correct or incorrect behaviours, be it fi nancially, monetary, or investment- and management-wise, having major consequences in resource allocation at the national or global economy (Săvoiu, 2013). This means either a natural continuity, if we refer to prosperity, or a system short-circuit, when it comes to recession, changing the mechanism of resource allocation, price dynamics, freedom of competition, which all coordinate the economic activity of billions of people. Everywhere in the world economy, confl ict seems to start from the tendency to impose a historical limit on resources. Starting from these already mentioned resources, which are nearly always a challenging issue, the authors felt the need to proactively reshape the macro-aggregates of the economy, both as main outputs, and as key dependent variables, by having recourse to: Gross Domestic Product (GDP), Final Consumption (FC ), gross capital investments, or Gross Fixed Capital Formation (GFCF), while


fuels, in their characteristic import and export fl ows, synthesized the major independent factor. What preoccupied the thinking of the authors of the present article was an attempt to simplify or reduce modelling in a caeteris paribus manner, based on explanatory factors, and going as far as the periodization or length of analysis, as a limit of the profound senses of estimating the mechanism of the economy. What happens if the cold phase is eliminated from the life cycle of the organism? This was essentially the fi rst interrogative hypothesis, and the quantifi cation of the level of heating needed by the economy was studied with the statistical instrumental simplicity of the elasticity coeffi cients. Does the cold phase affect modelling itself, as well as the vitality and intensity of the associations in the model? This was the second interrogative hypothesis, and the authors’ propensity towards eliminating it, or keeping it in two types of econometric models, is revealing.

FROM ELASTICITY TO THE COEFFICIENT OF ELASTICITY

Statistical and econometric literature brings together a set of tools, techniques and models, both classic and modern, relating to fl ows of exports and imports, and also to the increasingly intense factor dependence of macro-aggregates of the GDP, FC or GFCF type in fuels, especially those based on oil. One of the fi rst econometric models, accused of apparent perishability, though in fact an example of development with emphasis on permanent diversifi cation and modelling based on factorial reductionism (as a rule, on a single explanatory factor, which increases its character of a caeteris paribus model, and also of a type of modelling successively focused on one single hypothesis of explanatory variable) was, and has remained, the model of elasticity and the elasticity coeffi cient (Săvoiu, 2001; 2013a). Elasticity represented a pre-classical econometrics model, as early as 1838, as intuited by A. Cournot, and authenticated by A. Marshall in 1885, through the coeffi cient of elasticity, prior to the emergence of econometrics as a multidisciplinary science. Conceptualized exhaustively in economics, elasticity, appearing in a famous PhD thesis by Derycke, entitled Élasticité et analyse économique (Derycke, 1964), as a ratio of relative variations which shows the change of a (qualitative) variable as an action concurrent or prior to another (quantitative) variable, while Jan Tinbergen redefi ned it, statistically and econometrically, as a measure of the absolute and relative mobility of a phenomenon, in relation to another one, with which it is in a natural relation, thus emphasizing the paramount importance of correlating or combining the two variables or the


two factors analysed. Elasticity, generalized at the level of the fabric of the transactions in a national economy, such as that of Romania, and in the space of the modern statistical and econometric thinking, can express the sensitivity and reactivity of a consequential or dependent variable (GDP, FC, GFCF, etc.) to the change in other explanatory variables or independent factor (fuel export, fuel import, etc.), and so macroeconomic behaviour is explained and quantifi ed through variables placed in a complex statistical and econometric causality. In a strictly mathematical manner, the elasticity of demand is quantifi ed via the elasticity coeffi cient calculated using a point or portion of the elasticity curve function y = f(x). Elasticity was considered a standard form of non-proportionality, in the sense of algebraic non-equivalence between the relative changes of two variables: y, or dependent (GDP, FC, GFCF, etc.) and x, or factorial (export or import of fuel, etc.). Non-equivalence is expressed by the manner of determining a coeffi cient of elasticity different from |±1|. Hence, the mathematical solution immediately appears of the resulting equation, in a relatively simple manner, namely if: y / y x / x≠Δ Δ (1) This can also be re-written as:

Δy Δx=λ ×(y/x)y x (2)

hence the value of the elasticity coeffi cient promptly appears:

Δy Δxλ = :(y/x) y x (3)

The elasticity coeffi cient of variation quantifi es, for an illustration, the ratio between the variation of the dependent feature (general result as demand or GDP, FC, GFCF, etc.) and the variation of the factor variable, or independent variable (price or income, export or import of fuel, etc.) which allows to express it, as well, as a function of the elasticity of demand y = f(x) transposed in c = f(p) or c = f(v):

Δc Δpλ = :c/p c p and Δc Δvλ = :c/v c v

where: p 0Δ → and v 0Δ → (4)

The elasticity coeffi cient can also be determined as the ratio of the marginal value and the mean value:

dc c:c/p dp p

and dc c:c/v dv v

or c c:c/p p p

and

c c:c/v v v

where: p 0 and v 0 (5)


The elasticity coeffi cient can also be calculated directly from an elastic function y = f(x), being defi ned by a ratio between the logarithmic derivative of y and the logarithmic derivative of x:

( )

( )( )c/ p

d log c dc dp dc p dc p:d log p c p c dp dp c

λ = = = ⋅ = ⋅ where: dp 0→ (6)

and

( )

( )( )c/ v

d log c dc dv dc v dc v:d log v c v c dv dv c

λ = = = ⋅ = ⋅ where: dv 0→ (7)

In the case presented in formulas 4.5 and 6.7, economic demand represents the dependent variable y, and price and income are the explanatory factors x (complementing the factor name λc/p și λc/v, thus becoming the coeffi cients of elasticity-price or elasticity-income of the demand (Pitchford, 1960; Trebici,1985; Săvoiu, 2001; Isaic-Maniu, 2003). By analogy, one can also build annual coeffi cients of elasticity for GDP, FC, GFCF, in keeping with the export or import of fuels, one can model and analyse the main macro aggregates (Cooley and Hansen, 1995; Klump and de La Grandville, 2000; Diego and Gertler, 2006; Brett Goldfarband Li, 2013; Assous, Bruno & Legrand, 2014). Making use of the theory of time series, and its relative indicators, another two simple ways of practical determination are deduced, which are strictly connected with the existence of databases expressed in indexes or rates,

x

Δx:

y

Δyy/xλ turns into:

y xλ =R /Ry/x %% (8)

Thus the coeffi cient of elasticity becomes a mere ratio of rates (Demetrescu, 1967) and the coeffi cient of elasticity-price is transcribed as

( ) ( ) ( )c v% %c/ v R / Rλ = and the coeffi cient of elasticity-income as ( ) ( ) ( )

c p% %c/ p R / Rλ = .

The coeffi cient of elasticity can also be expressed through indices:

C1/ 0 1/ 0 1 0 1 0 1/ 0

C / V V0 0 0 0 1/ 0

C V C C V V I 100: :

C V C V I 100Δ Δ − − −

λ = = =−

(9)

where VC II 0/10/1 , are the indices of demand and income, or

C1/ 0 1/ 0 1 0 1 0 1/ 0

C / P0 0 0 0

P1/ 0

C P C C P P I 100: :

C P C P I 100Δ Δ − − −

λ = = =−

(10)

where

C P1/ 0 1/ 0I , I are the indices of demand and prices (Săvoiu, 2013a).


The signifi cance of the coeffi cients of elasticity-price and elasticity-income describes, in a contrary way, the same economic reality. Thus, the coeffi cient of elasticity-price of demand is an investigative tool that expresses the percentage the demand changes with when there is a 1% variation of the price (the transcription is preceded by the “minus” sign), and the coeffi cient of elasticity-income of demand expresses the percentage of the change in demand when there occurs a 1% variation of the income (the transcription is preceded by the “plus” sign).

THE ELASTICITY OF GDP IN ROMANIA’S ECONOMY IN RELATION TO OVERALL EXPORTS / IMPORTS,

AND SPECIFICALLY IN FUELS

Within the context of correctly identifying the rates of the evolution of the macro-aggregates GDP, FC, GFCF compared to the evolution of rates X, Xb, M, Mb, and also the evolution of fuel import and export in the Romanian economy, we can determine the elasticity coeffi cients based on a database, which is expressed directly in rates, of the economy between 1996 and 2014. At fi rst, it appears that overall, during the 19-year period, the average rate of nominal GDP was 9.09%, refl ecting the 11.88% increase in nominal export, that of nominal import was 10.98%, and fi nal consumption in current prices increased by 8.63%, and the gross fi xed capital all, again in current prices, by 9.48% (with fl awed developments in the crisis and deep recession years 1999 and 2009, yet with a signifi cant economic recovery between 2005 and 2007), while actual increases, or increases in comparable prices, are signifi cantly lower (the average growth value, based on the real GDP growth index in the last 25 years in Romania, was around 1%). The most visible change (increase or decrease) can be seen in the import and export of mineral fuels, which together lead to an elasticity having deeper modelling implications. In this respect, it proved useful to determine the specifi c elasticity of GDP (exemplifi ed here as a calculation relationship by: λ (Xfuel/GDP = RXfuel/RGDP), and the results are outlined below (Table 1).


The annual elasticity coeffi cients in Romania’s economy quantifi ed as a ratio of rates

Table 1Coeffi cients of elasticity of GDP detailed, as compared to:

Year Exports of goods and

services (FOB)

Exports of goods (FOB)

Imports of goods and services (CIF)

Exports of goods

(CIF)

Export of mineral fuels

(FOB)

Import of mineral fuels

(CIF)

1996 2.99 2.77 8.46 8.88 -1.28 7.681997 1.84 2.09 1.25 1.29 -0.47 -0.191998 -0.21 -0.08 0.21 0.28 -1.33 -1.801999 -1.13 -1.01 0.47 0.57 -1.29 2.482000 2.08 2.03 2.07 2.16 5.32 3.652001 1.07 1.01 1.65 1.78 -0.16 2.392002 2.02 2.16 1.10 1.21 6.34 -0.582003 0.75 0.73 1.36 1.46 -1.40 1.182004 1.23 1.35 1.45 1.48 1.59 2.172005 0.67 0.57 0.85 0.78 2.79 1.502006 0.86 0.73 1.12 1.12 0.41 0.942007 0.58 0.51 0.94 0.94 -0.50 -0.012008 0.36 -0.63 0.37 0.18 2.74 2.152009 0.91 0.71 1.87 2.12 2.82 3.162010 4.60 6.84 3.76 5.23 2.70 5.712011 3.86 4.34 3.10 3.40 5.96 6.012012 11.90 23.09 1.39 2.50 -21.10 50.622013 1.82 0.71 0.39 0.65 0.39 -2.322014 1.93 1.61 1.62 0.61 5.16 -0.02

Source: The data were processed by the authors, based on the calculation relationships above and the Eurostat Database[online] available at: http://ec.europa.eu/eurostat/data/database. Note*: To calculate the elasticity coeffi cients of other macro-aggregates, GDP was substituted, for illustration, by FC and GFCF, and the results were detailed in Table 1 in Appendix 1.

A special case is that of cross-elasticity or transverse elasticity, a case that is underlain by the possibility of calculating a coeffi cient of elasticity of demand in the situation of an x commodity, in keeping with the change in the price of a y commodity. The classic calculation formula is:

p ΔpΔq q Δq Δqy yx x x xλ = : = × = :

q /p Δp p Δp q q px y y y y x x y or qx py

q /px yλ R : R (11)

This relationship can be adapted, for example, also for the macroeconomic result (GDP, FC, GFC, etc.) depending on the total export and import of an economy, or depending on the fuel export and import. Interpreting cross-elasticity or transverse elasticity considers fi ve different situations (Case and Fair, 1999): 1. if λ=∞, the demand and the macroeconomic result are perfectly elastic, and a minimal change in price or a factor change results in a maximum change in the amount or result;


2. if λ>1, the demand and the macroeconomic result are elastic, a change in price or a factor change results in a supra-proportional change in the quantity or result, the products or independent factors being in a state of rivalry, or substitutable products (e.g. honey and sugar, import or export of fuels); 3. if λ=1, the demand and the macroeconomic result are unitary-elastic; a change in price or a factor change by 1% results in an identical change in quantity or result; 4. if λ<1, the demand and the macroeconomic result are inelastic; a change in price or a factor change results in a sub-proportional change in the quantity or result, the products or factors being in a state of complementarity, or solidary (car and petrol, import or export of fuels). 5. if λ = 0, the demand and the macroeconomic result are inelastic; a maximum price change or a factorial change do not result in a change in the amount or result, and the products or factors are independent (a situation of inelasticity).

Coeffi cients of cross-elasticity connected with overall export / import, and export / import of fuels in the Romanian economy between 1996

and 2014Figure 1

The chart displayed in Figure 1 shows an increase in elasticity after the prolonged crisis and the global recession that followed the Romanian economy after 2008, both in terms of coeffi cients of cross-elasticity of fuel import / overall import, and in terms of fuel export / import, exceeding the


usual values for two decades in an excessive and compensatory manner (the data from Table 2 in Appendix 1 describe these special developments).

ECONOMETRIC MODELS FOCUSED ON RATES AND ELASTICITIES IN THE ROMANIAN ECONOMY

Econometric modelling based on elasticity coeffi cients, generating classical models, does not represent a phenomenon ended, which can be illustrated by the mere solution offered in this paper, apt to generate new elasticity coeffi cients and new models, which the latter can extract or derive from the real world. This paper will provide below a number of high-performance models in point of constructive determination based on a concept of elasticity substantiated by determination or the values of R2, extracted directly from the correlation matrices (where the value of R is presented). Statistical correlation and regression are essential in the processes of identifying, selecting and modelling the factorial connections in economy, which fi nally provide the simplest and most effi cient indicator through the correlation ratio Ry/x:

( )( )

2

2 11/ / 2

1

ny Yxi iiR Ry x y x ny yii

−∑== = −

−∑=

(12)

or

/y xR D= (13)

hence, eventually |Ry/x | Î (0,1] (14)

Rapid factor identifi cation is achieved through correlation matrices with a single R, the generator of R-squared (R2) in econometric models. The interpretation of the determination is that of explanatory variation compared to another variation of the result, and in the context of two distinct variables, defi ned by ratios, determination conceptualizes in a much deeper manner elasticity as measurable association or correlation of the relationships of characteristic rates. The correlation matrix of the rates in Romanian economy between 1996 and 2014 contains these types of deep elasticities, and also allows subsequent modellings focused on the statistical tools described and the information drawn from them (Table 2).


Correlation matrix of the rates of the main macroaggregates expressed in nominal rates

Table 2

GDP

Exports of goods

and services(FOB)

Exports of goods(FOB)

Imports of goods

and services (CIF)

Imports of goods

(CIF)

Exports of fuels (FOB)

Imports of fuels(CIF)

FC GFCF

SER01 SER02 SER03 SER 04 SER05 SER06 SER07 SER08 SER09SER01 1.000000 0.540258 0.381985 0.792680 0.750186 0.458764 0.559063 0.973412 0.885559SER02 0.540258 1.000000 0.923102 0.863208 0.873457 0.759431 0.787393 0.434732 0.688476SER03 0.381985 0.923102 1.000000 0.780558 0.820637 0.607965 0.691234 0.326554 0.514521SER 04 0.792680 0.863208 0.780558 1.000000 0.989515 0.630947 0.843220 0.746792 0.888216SER05 0.750186 0.873457 0.820637 0.989515 1.000000 0.590990 0.825852 0.707810 0.846486SER06 0.458764 0.759431 0.607965 0.630947 0.590990 1.000000 0.773560 0.371668 0.530125SER07 0.559063 0.787393 0.691234 0.843220 0.825852 0.773560 1.000000 0.493279 0.737844SER08 0.973412 0.434732 0.326554 0.746792 0.707810 0.371668 0.493279 1.000 0.794444SER09 0.885559 0.688476 0.514521 0.888216 0.846486 0.530125 0.737844 0.794444 1.000

Source: Calculations made based on Eurostat data [online] available at: http://ec.europa.eu/eurostat/data/database.Software used Eviews.

In analysing the interdependencies with potential predictive valences, the fi rst category is represented by the multifactor models derived from the monitoring of the interdependence of GDP rate and import / export along the axis of the dominance of fuels trade. This type of analysis allows retracing and forecasting the values of a variable that can use the information contained in the values of another variable, with which it is related. First, the rates were selected that were derived from the nominal indices specifi c to the three indicators in the sphere of foreign trade in correlation with the rate of GDP during 1996-2014 (SER04, or the series of the annual rate of imports of goods and services -CIF, SER05, or the series of the annual rate of imports of goods –CIF, and SER06, or the series of the annual rate of exports of mineral fuels FOB). The fi rst two high-effi ciency models are focused on the simultaneous elasticity of the GDP rate (SER01) in keeping with the rate of the import of goods and services (CIF) or SER04 and the rate of export of mineral fuels (FOB), or SER06, i.e. in keeping with the rate of the import of goods (CIF), or SER05 and the rate of the export of mineral fuels (FOB), or SER06, models that were slightly improved with coeffi cients and free terms (Figure 2).


High-effi ciency econometric models focused on the simultaneous elasticity of the rate of GDP in keeping with the rates of export and

import Figure 2

Ist model based on SER04 and SER06Dependent Variable: SER01 Method: Least Squares Sample: 1996 2014 Included observations: 19Variable Coeffi cient Std. Error t-Statistic Prob. C 2.352263 2.202390 1.068050 0.3013SER04 0.642174 0.150314 4.272216 0.0006SER06 -0.021958 0.062510 -0.351264 0.7300R-squared 0.631186 Mean dependent var 9.676316Adjusted R-squared 0.585084 S.D. dependent var 11.49117S.E. of regression 7.401918 Akaike info criterion 6.985295Sum squared resid 876.6143 Schwarz criterion 7.134417Log likelihood -63.36030 F-statistic 13.69113Durbin-Watson stat 1.472716 Prob(F-statistic) 0.000342

IInd model based on SER05 and SER06Dependent Variable: SER01 Method: Least SquaresSample: 1996 2014 Included observations: 19Variable Coeffi cient Std. Error t-Statistic Prob.C 3.173642 2.339799 1.356374 0.1938SER05 0.522519 0.145386 3.594025 0.0024SER06 0.007565 0.065430 0.115622 0.9094R-squared 0.563144 Mean dependent var 9.676316Adjusted R-squared 0.508537 S.D. dependent var 11.49117S.E. of regression 8.055814 Akaike info criterion 7.154605Sum squared resid 1038.338 Schwarz criterion 7.303726Log likelihood -64.96874 F-statistic 10.31267Durbin-Watson stat 1.284361 Prob(F-statistic) 0.001326

Software used EViews

If the years are extracted the value impact of which was profoundly negative on the growth of GDP (1999 and 2009 in the table of rates – Table 3 in Appendix 1), we fi nd that without these values, the data reveal an evolution that is not infl uenced in any major way by the crisis or recession, and the new correlation matrix becomes the expression of a signifi cant attenuation, tending to limit of an average intensity of the determination (R2 moves towards ever smaller values: 0.342 and 0.286, respectively) as rates of GDP elasticity compared to fuel export and import. The trend is very interesting that is revealed by these data for Romania’s economy: a positioning in temporal contexts that are not infl uenced by the crisis or the recession, for a determination that tends to 0.25, which shows R values of the 0.5 type, or at the limit of the average intensity of both fuel exports and fuel imports compared to GDP pace (Table 3).


Correlation matrix of the rates of the main macroaggregates expressed in nominal rates, excluding 1999 and 2009

Table 3

GDP

Exports of goods

and services(FOB)


Imports of goods

and services (CIF)

Imports of goods

(CIF)


Imports of fuels(CIF)

FC GFCF

SER01 SER02 SER03 SER 04 SER05 SER06 SER07 SER08 SER09SER01 1.000000 0.358647 0.187366 0.624733 0.538128 0.341723 0.285926 0.951080 0.800243SER02 0.358647 1.000000 0.910220 0.836459 0.856754 0.705714 0.727833 0.190153 0.607877SER03 0.187366 0.910220 1.000000 0.767746 0.837261 0.527606 0.622639 0.106345 0.395635SER 04 0.624733 0.836459 0.767746 1.000000 0.977754 0.564210 0.761939 0.537993 0.834417SER05 0.538128 0.856754 0.837261 0.977754 1.000000 0.501603 0.732879 0.457792 0.757436SER06 0.341723 0.705714 0.527606 0.564210 0.501603 1.000000 0.754140 0.214177 0.450339SER07 0.285926 0.727833 0.622639 0.761939 0.732879 0.754140 1.0000 0.180096 0.594846SER08 0.951080 0.190153 0.106345 0.537993 0.457792 0.214177 0.180096 1.0000 0.640627SER09 0.800243 0.607877 0.395635 0.834417 0.757436 0.450339 0.594846 0.640627 1.000

Source: Calculations made based on Eurostat data [online] available at: http://ec.europa.eu/eurostat/data/database.Software used Eviews.

While R values of 0.705714 and 0.761939 stress the dominance of fuels in Romanian exports and imports, the values of 0.341723 and 0.285926 stress that, in the evolving context that excludes crisis or recession (the cold phase), the organism of the economy loses some of the intensity of the correlation imposed by the confl ictive character of the resources. In other words, the crisis and the recession in Romanian economy became factors that boost direct and indirect (cross) elasticities between export and import fl ows, but also the special ones, between fueld export and GDP, respectively between fuel import and GDP. The econometric artifi cial technique of excluding the years of major impact of the crisis and recession (1999 and 2009) also produces changes signifying diminution, up to the disappearance of the signifi cance of value models of GDP focused on export or import of fuels. The statistical analysis of confrontation of the values of R-squared for the models resulting from databases containing, or on the contrary excluding, the years of crisis and deep recession (1999 and 2009) becomes an interesting one, having important consequences for the originality of this research focused on the principle of caeteris paribus applied in this specifi c context not to the factors, but the records made in databases. A relevant example exploits bi-factorial models focused on simultaneous elasticity of fuel export and import as explanatory factors of GDP, a model that is easy to improve with coeffi cients (free terms). In Figure


3 the radical change can be seen of the values of R-squared and the collapse of F-statistic in both models compared (Voineagu et al, 2007; Andrei et al. 2008; Săvoiu, 2009).

Econometric models confronted in a context of crisis or recession, and out of such a context

Figure 3

Note: SER04 = Import rate of goods and services (CIF) SER06 = Export rate of Fuels (FOB)

Dependent Variable: SER01 – GDP rate Method: Least Squares Sample: 1996 2014 Included observations: 19

Variable Coefficient Std. Error t-Statistic Prob. C 2.352263 2.202390 1.068050 0.3013

SER 04 0.642174 0.150314 4.272216 0.0006 SER06 -0.021958 0.062510 -0.351264 0.7300

R-squared 0.631186 Mean dependent var 9.676316 Adjusted R-squared 0.585084 S.D. dependent var 11.49117 S.E. of regression 7.401918 Akaike info criterion 6.985295 Sum squared resid 876.6143 Schwarz criterion 7.134417 Log likelihood -63.36030 F-statistic 13.69113 Durbin-Watson stat 1.472716 Prob(F-statistic) 0.000342

Note: SER04 = Import rate of goods and services (CIF) SER06 = Export rate of Fuels (FOB)

Dependent Variable: SER01 – GDP rate Method: Least Squares Sample: 1 17 (Included observations: 17 - without 1999 and 2009


SER04 0.519721 0.207291 2.507205 0.0251 SER06 -0.004036 0.064641 -0.062442 0.9511 R-squared 0.390461 Mean dependent var 12.25412 Adjusted R-squared 0.303384 S.D. dependent var 8.955174 S.E. of regression 7.474303 Akaike info criterion 7.019604 Sum squared resid 782.1129 Schwarz criterion 7.166642 Log likelihood -56.66663 F-statistic 4.484091 Durbin-Watson stat 1.462998 Prob(F-statistic) 0.031262

Note: SER05 = Import rate of goods (CIF) SER06 = Export rate of Fuels (FOB)

Dependent Variable: SER01 – GDP rate Method: Least Squares Sample: 1996 2014 Included observations: 19


SER05 0.522519 0.145386 3.594025 0.0024 SER06 0.007565 0.065430 0.115622 0.9094

R-squared 0.563144 Mean dependent var 9.676316 Adjusted R-squared 0.508537 S.D. dependent var 11.49117 S.E. of regression 8.055814 Akaike info criterion 7.154605 Sum squared resid 1038.338 Schwarz criterion 7.303726 Log likelihood -64.96874 F-statistic 10.31267 Durbin-Watson stat 1.284361 Prob(F-statistic) 0.001326

Note: SER05 = Import rate of goods (CIF) SER06 = Export rate of Fuels (FOB)

Dependent Variable: SER01 – GDP rate Method: Least Squares Sample: 1 17 (Included observations: 17 - without 1999 and 2009


SER05 0.376801 0.199260 1.890997 0.0795 SER06 0.024537 0.066278 0.370220 0.7168

R-squared 0.296470 Mean dependent var 12.25412 Adjusted R-squared 0.195966 S.D. dependent var 8.955174 S.E. of regression 8.029922 Akaike info criterion 7.163012 Sum squared resid 902.7150 Schwarz criterion 7.310049 Log likelihood -57.88560 F-statistic 2.949824 Durbin-Watson stat 1.224446 Prob (F-statistic) 0.085306


Crises and economic recession periodically bring into the focus, in the Romanian economy, the importance of fuel export and import, which are necessary for the operation of this complex economic fabric of transactions of great variety. A correlation matrix for the period 1995-2014, focused on value indicators, simplifi es a construction of econometric models and induces some fi nal performances. Within this context, which provides coverage for a period of 20 years, there are a few (one- and two-factor) models that have a reasonable future as forecast tools, and are useful in benchmarking or confrontation analyses. However, their validity is questionable in evolution periods having no signifi cant impact of crises and economic recessions, as was demonstrated above. What was associated and correlated more intensely was the value of the two indicators related to international fuel trade in correlation with the value of GDP (SER01) during 1995-2014 (this time, out of a total of 23 distinct variables, those which were selected as more detailed factorial variables were SER18 or the value of the exports of fuels and mineral oils, including bitumen


materials - FOB and SER19 or the value of imports of mineral fuels, lubricants and related materials - CIF). It appears that SER01 or GDP, this time in terms of value, can be more effi ciently modelled in keeping with SER18 and SER19, whether in uni-factorial or bi-factorial models (Figure 4):

Value econometric models of GDP focused on export and import of fuelFigure 4

Dependent Variable: SER01 GDP Method: Least Squares Sample: 1995 2014Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C 15.40517 7.313419 2.106425 0.0495 SER18) 0.004290 0.000393 10.92405 0.0000 R-squared 0.868934 Mean dependent var 83.26345 Adjusted R-squared 0.861652 S.D. dependent var 46.41009 S.E. of regression 17.26230 Akaike info criterion 8.629567 Sum squared resid 5363.767 Schwarz criterion 8.729140 Log likelihood -84.29567 F-statistic 119.3349 Durbin-Watson stat 1.115011 Prob(F-statistic) 0.000000

Dependent Variable: SER01-GDP Method: Least Squares Sample: 1995 2014Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C 3.467032 8.410828 0.412211 0.6851 SER19 0.021543 0.002009 10.72476 0.0000 R-squared 0.864683 Mean dependent var 83.26345 Adjusted R-squared 0.857165 S.D. dependent var 46.41009 S.E. of regression 17.54001 Akaike info criterion 8.661486 Sum squared resid 5537.737 Schwarz criterion 8.761059 Log likelihood -84.61486 F-statistic 115.0205 Durbin-Watson stat 1.275764 Prob(F-statistic) 0.000000

Dependent Variable: SER01 -GDP Method: Least Squares Sample: 1995 2014 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C 7.901380 8.199153 0.963682 0.3487 SER18 0.002304 0.001212 1.900249 0.0745 SER19 0.010507 0.006104 1.721466 0.1033 R-squared 0.888390 Mean dependent var 83.26345 Adjusted R-squared 0.875259 S.D. dependent var 46.41009 S.E. of regression 16.39144 Akaike info criterion 8.568877 Sum squared resid 4567.550 Schwarz criterion 8.718237 Log likelihood -82.68877 F-statistic 67.65772 Durbin-Watson stat 1.245429 Prob(F-statistic) 0.000000


Final consumption (hereinafter SER20) and gross investments or Gross fi xed capital formation (SER21) correlates with import and export of fuels (previously described by SER18, the value of the export of fuels and mineral oils, including bituminous materials - FOB and SER19, or the value of the import of mineral fuels, lubricants and related materials - CIF), generating themselves econometric models in an even more effi cient, successful manner than the previous ones, which is a rational thing in view of the axis of elasticity, which is more clearly pronounced in these cases (Figure 5 and Figure 6):


Value econometric models of Final Consumption (FC) focused on export and import of fuel

Figure 5 Dependent Variable: SER20 FC Method: Least Squares Sample: 1995 2014 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C 15.59421 5.228289 2.982661 0.0080 SER18 0.003265 0.000281 11.63110 0.0000 R-squared 0.882570 Mean dependent var 67.24525 Adjusted R-squared 0.876046 S.D. dependent var 35.05156 S.E. of regression 12.34064 Akaike info criterion 7.958313 Sum squared resid 2741.246 Schwarz criterion 8.057886 Log likelihood -77.58313 F-statistic 135.2826 Durbin-Watson stat 1.270648 Prob(F-statistic) 0.000000

Dependent Variable: SER20 FC Method: Least Squares Sample: 1995 2014 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C 6.345754 5.908479 1.074008 0.2970 SER19 0.016442 0.001411 11.65148 0.0000 R-squared 0.882932 Mean dependent var 67.24525 Adjusted R-squared 0.876428 S.D. dependent var 35.05156 S.E. of regression 12.32159 Akaike info criterion 7.955223 Sum squared resid 2732.789 Schwarz criterion 8.054796 Log likelihood -77.55223 F-statistic 135.7569 Durbin-Watson stat 1.450533 Prob(F-statistic) 0.000000

Dependent Variable: SER20 FC Method: Least Squares Sample: 1995 2014 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C 9.553196 5.722842 1.669310 0.1134 SER18 0.001667 0.000846 1.969231 0.0654 SER19 0.008459 0.004260 1.985568 0.0635 R-squared 0.904676 Mean dependent var 67.24525 Adjusted R-squared 0.893462 S.D. dependent var 35.05156 S.E. of regression 11.44089 Akaike info criterion 7.849746 Sum squared resid 2225.199 Schwarz criterion 7.999106 Log likelihood -75.49746 F-statistic 80.66992 Durbin-Watson stat 1.461172 Prob(F-statistic) 0.000000


Value econometric models of Gross Fixed Capital Formation (GFCF) focused on export and import of fuel

Figure 6 Dependent Variable: SER21 GFCF Method: Least Squares Sample: 1995 2014 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C 1.035077 2.450264 0.422435 0.6777 SER18 0.001313 0.000132 9.982359 0.0000 R-squared 0.847001 Mean dependent var 21.81025 AdjustedR-squared 0.838501 S.D. dependent var 14.39149 S.E. of regression 5.783504 Akaike info criterion 6.442536 Sum squared resid 602.0806 Schwarz criterion 6.542109 Log likelihood -62.42536 F-statistic 99.64750 Durbin-Watson stat 0.979090 Prob(F-statistic) 0.000000

Dependent Variable: SER21 GFCF Method: Least Squares Sample: 1995 2014 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C -3.356877 2.303150 -1.457515 0.1622 SER19 0.006795 0.000550 12.35248 0.0000 R-squared 0.894480 Mean dependent var 21.81025 AdjustedR-squared 0.888618 S.D. dependent var 14.39149 S.E. of regression 4.803010 Akaike info criterion 6.071002 Sum squared resid 415.2402 Schwarz criterion 6.170575 Log likelihood -58.71002 F-statistic 152.5839 Durbin-Watson stat 1.202083 Prob(F-statistic) 0.000000

Dependent Variable: SER21 GFCF Method: Least Squares Sample: 1995 2014 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C -2.764551 2.420084 -1.142337 0.2691 SER18 0.000308 0.000358 0.859964 0.4018 SER19 0.005320 0.001802 2.953231 0.0089 R-squared 0.898879 Mean dependent var 21.81025 Adjusted R-squared 0.886982 S.D. dependent var 14.39149 S.E. of regression 4.838141 Akaike info criterion 6.128419 Sum squared resid 397.9294 Schwarz criterion 6.277779 Log likelihood -58.28419 F-statistic 75.55774 Durbin-Watson stat 1.151099 Prob(F-statistic) 0.000000


All the above examples underline that the econometric modelling of the major macro-aggregates in the Romanian economy over the last 20 years


has a greater predictability power, given by the very presence of the crises or recessions that basically adjusts and amends the trends.

CONCLUSIONS

The increasingly diverse and accentuated practical use of the classical econometric models augmented, in parallel with the amount and consistency of their criticism by those who sought to capitalize on them as forecast and estimate solutions. According to a number of relatively justifi ed criticisms, the econometric model has essential limitations, ranging from using oversimplifi ed economic methods, and the inability of the quantitative model to fi t a theory or another, up to the high error margin of prediction and simulation, and the absence from the fi nal model of explanatory variables related to the response of the authorities and economic policies, which may distort, even in a short term, the behavioural rationality of an economy. The econometric model, in its major sense and acceptation, shows a continuous decline in time of its fi rst form by simple parameterization, which was acknowledged by the existence of the claim “in business few (cor)relations retain, in time, their original mathematical precision” (Săvoiu, 2013). The success of the classic econometric model is not unanimously accepted among economists, either – i.e. among those who ought to enjoy its applicability to the utmost. Thus, Ludwig von Mises and Friedrich von Hayek, major representatives of the neoclassical Austrian school of economics, questioned formalization of economic behaviour through econometric modelling, highlighting the sad balance of the predictions made on the basis of econometric models in recent decades, despite their increasingly modern computing equipment and their ever more sophisticated make-up, with increasingly serious theoretical accents, and practically devoid of the impact of experiment. The originality of the analysis, using coeffi cient of elasticity, of the macro-aggregates in the Romanian economy (GDP, CF, GFCF, etc.), based on the export and import of fuels, generates relatively effi cient, high-performance models, yet posing problems of validation for the calmer periods of economic development. The specifi c marks of this research focused on the caeteris paribus principle, applied to both the factors and the records in databases, relativize the medium-term credibility of econometric models, as it seems they are being regarded as too easily outdated, and even questionable – as is the case of many contemporary econometric models. These fi ndings bring about the need for multiplication of the modelling economic disciplines, by adding experimental economy, a branch of economics that involves performing laboratory experiments to test micro-


economic models, and also fi nancial economics, which includes probability theory and fi nancial economy in the construction of its models in order to round off econometrics in assessing the theories formulated by economics, while even invalidating, in the medium term, some established econometric models.

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Appendix no. 1Annual elasticity coeffi cients of export (X) and import (M) overall, and of export and import of fuels compared to Final Consumption (FC) and

Gross Fixed Capital Formation (GFCF) in Romania’s economyTable 1

Year X/FC M/FC Xfuels/FC Mfuels/FC X / FBCF

M/ FBCF

Xfuels/FBCF

Mfuels/FBCF

1996 0.90 2.54 -0.39 2.30 6.86 19.42 -2.95 17.641997 1.63 1.11 -0.42 -0.17 -43.58 -29.57 11.09 4.441998 -0.16 0.16 -1.01 -1.38 -2.67 2.64 -16.55 -22.491999 -1.06 0.44 -1.21 2.32 -0.47 0.19 -0.53 1.022000 2.64 2.63 6.77 4.65 0.84 0.83 2.15 1.472001 1.14 1.75 -0.17 2.54 0.45 0.70 -0.07 1.012002 2.94 1.61 9.26 -0.84 2.26 1.24 7.12 -0.652003 0.61 1.10 -1.13 0.95 0.65 1.18 -1.22 1.022004 1.23 1.46 1.59 2.18 0.82 0.97 1.06 1.452005 0.62 0.79 2.58 1.39 0.73 0.92 3.01 1.622006 0.95 1.24 0.45 1.04 0.49 0.64 0.23 0.532007 0.64 1.04 -0.56 -0.01 0.34 0.56 -0.30 0.002008 0.54 0.55 4.05 3.18 0.23 0.24 1.74 1.362009 0.88 1.81 2.72 3.05 0.45 0.92 1.38 1.552010 4.59 3.75 2.70 5.69 5.81 4.75 3.41 7.202011 6.53 5.24 10.08 10.17 2.18 1.75 3.36 3.392012 2.63 0.31 -4.67 11.21 -0.53 -0.06 0.93 -2.242013 3.65 0.79 0.79 -4.64 5.07 1.10 1.10 -6.452014 2.03 1.71 5.42 -0.02 2.83 2.38 7.55 -0.03

Source: The data were processed by the authors, based on the calculus relations presented above and the Eurostat Database[online] available at: http://ec.europa.eu/eurostat/data/database.


Annual cross-elasticity coeffi cients of export or import of fuel compared to total import or export, in Romania’s economy

Table 2

YearCross-elasticity coeffi cients between 1996 and 2014

Export of fuels / export

Import of fuels / import

Export of fuels / import

Import of fuels / export

1996 -0.43 0.91 -0.15 2.571997 -0.25 -0.15 -0.38 -0.101998 6.19 -8.53 -6.27 8.411999 1.14 5.26 -2.73 -2.192000 2.56 1.77 2.58 1.762001 -0.15 1.45 -0.10 2.232002 3.15 -0.52 5.75 -0.292003 -1.87 0.87 -1.03 1.572004 1.29 1.49 1.09 1.762005 4.14 1.77 3.28 2.232006 0.47 0.84 0.36 1.092007 -0.87 -0.01 -0.54 -0.012008 7.52 5.79 7.39 5.902009 3.09 1.69 1.50 3.472010 0.59 1.52 0.72 1.242011 1.54 1.94 1.92 1.562012 -1.77 36.43 -15.19 4.252013 0.22 -5.87 1.00 -1.272014 2.67 -0.01 3.18 -0.01Source: The data were processed by the authors, based on the calculus relations presented above and the Eurostat Database[online] available at: http://ec.europa.eu/eurostat/data/database.


Rate of GDP and other major indicators of Romania’s economy between 1996-2014 (previous year = 100)%

Table 3

Year GDP

Exports of goods

and services(FOB)


Imports of goods

and services (CIF)

Imports of

goods(CIF)


Imports of fuels(CIF) FC GFCF

SER01 SER02 SER03 SER 04 SER05 SER06 SER07 SER08 SER091996 1.62 4.84 4.49 13.69 14.37 -2.08 12.43 5.40 0.701997 8.40 15.44 17.59 10.48 10.81 -3.93 -1.57 9.47 -0.351998 17.77 -3.81 -1.37 3.76 4.98 -23.59 -32.06 23.26 1.431999 -9.03 10.24 9.13 -4.26 -5.19 11.66 -22.41 -9.66 -21.972000 20.19 41.93 41.08 41.74 43.71 107.51 73.74 15.87 50.122001 11.54 12.36 11.61 19.00 20.48 -1.85 27.62 10.88 27.302002 7.27 14.65 15.73 8.02 8.81 46.09 -4.19 4.98 6.472003 8.44 6.34 6.17 11.48 12.32 -11.85 9.94 10.48 9.732004 16.01 19.68 21.63 23.22 23.76 25.38 34.69 15.94 24.002005 30.65 20.65 17.51 26.09 23.95 85.47 46.05 33.10 28.412006 22.68 19.51 16.46 25.45 25.46 9.22 21.31 20.49 39.962007 27.42 15.83 13.92 25.84 25.69 -13.83 -0.15 24.89 46.372008 13.55 4.94 -8.50 5.03 2.49 37.13 29.12 9.17 21.372009 -15.44 -14.07 -10.92 -28.92 -32.68 -43.50 -48.76 -15.99 -31.422010 5.26 24.22 35.98 19.79 27.55 14.23 30.04 5.28 4.172011 5.18 19.97 22.45 16.04 17.60 30.84 31.12 3.06 9.182012 0.15 1.83 3.56 0.21 0.39 -3.25 7.80 0.70 -3.492013 8.05 14.63 5.68 3.17 5.25 3.16 -18.63 4.01 2.892014 4.14 8.00 6.67 6.72 2.53 21.36 -0.07 3.94 2.83

Source: The data were processed by the authors, based on the calculus relations presented above and the Eurostat Database[online] available at: http://ec.europa.eu/eurostat/data/database.


Correlations between Expenditure and Employees in R&D Activity by Performance Sectors from Romania Vasilica Ciucă CSI ([email protected]) National Scientifi c Research Institute for Labour and Social Protection PhD Profesor Daniela Pașnicu ([email protected]) National Scientifi c Research Institute for Labour and Social Protection, Spiru Haret University Gabriela Tudose CSIII ([email protected]) National Scientifi c Research Institute for Labour and Social Protection Mihai Robert Paşnicu ([email protected]) Brown University, United States

ABSTRACT

In the context of increased competition in the recent years, the innovation has become a key factor in economic development through better use of opportunities, capturing new markets and creating high quality jobs. A crucial element for achieving innovation is the R & D, due to the stock of knowledge created at the human, cultural and societal level and its use in designing new applications. In order to manage the funds better in R & D, stimulate competitiveness and attract new funds into R & D, given the target of Europe 2020 Agenda, the paper ana-lyzes the correlation between expenditure and employees in R & D by performance sectors from Romania, and presents a mathematical model which might explain the dynamic of the structure of employees in business sector from R&D. It also considers the impact of the results on the capacity for innovation, economic development and future directions of action to increase investment in R & D, effi cient use of intellectual capital and economic specialization. Keywords: innovation, R&D, expenditure, employment, investment JEL classifi cation: J21; J24


CONTEXTUAL ISSUES REGARDING RESEARCH DEVELOPMENT AND INNOVATION

Smart growth, which is one of the three pillars of the Europe 2020 strategy, involves developing an economy based on growth and innovation by improving education, strengthening research performance, promoting innovation and knowledge transfer and full use of information and communication technologies. The role of research and innovation in increasing competitiveness and ensuring high quality jobs through the implementation of innovative ideas into new products and services has been widely developed in the specialty literature (Scherer, F., M., 1986, Grossman, GM and Helpman , E., 1991, et al). According to the statistics in 2012, Romania ranks last in the European Union in terms of public and private investment in research and development, with a percentage of 0.48% of GDP compared to the EU average of 2.03% and the U.S. 2.75%. Moreover, the European Commission warned the Romanian authorities in the document on the country profi le in 2013 that Romania must invest and make reforms in research and innovation, to achieve the target set for 2% of GDP by 2020. For Romania a major challenge is the low level of competitiveness, the Romanian economy is characterized by the predominance of technology-based sectors of medium and low level, with a low demand for knowledge and an underdeveloped innovation culture. The presence of R & D in enterprises is poor, Romania having one of the lowest levels of intensity of activity R & D in business from EU, with a value of 0.17% in 2011 (No. 25 of 27) and an annual average growth rate of -3.4% during 2000-2011. Moreover, no Romanian company is listed in the top 1000 EU companies investing in R&D. Global Competitiveness Report 2011 classifi es the country as being focused on effi ciency (with Bulgaria), while all other EU economies are either in transition to stage focusing on innovation, either already in this stage. Given the above context, Romania ranks modest at Chapter innovation too, with the lowest intensity of knowledge in economy in the EU. This indicator measures structural change which focuses on modifi cations in the sectoral composition of the economy, showing the evolution of the share of sectors, products and services based on knowledge and as a value it reached at 28.35 in 2010, lower than the EU average, ie 48.75 . At European level there have been developed new relevant indicators that highlight important thematic elements in key technology sectors: automotive, ICT, new production technologies, nanotechnologies and safety, and contribution of technology high level (HT) and medium level (MT) on the trade balance. Thus the aggregate contribution of technology (HT + MT) on the trade balance of payments was in Romania to 0.38% in 2011, falling well below the EU average of 4.2%. Moreover,


the indicator on the economic impact of innovation was 0.384 in 2010-2011, which is below the EU average of 0.612. In the context of the Europe 2020 strategy, in particular the initiative “Innovation Union” and the main implementation instrument - Horizon 2020 was developed National RDI Strategy 2014-2020 in Romania and the two instruments for its implementation, the National Plan for RDI 2014-2020 and the National Strategy for Competitiveness (CNS), which marks the beginning of a new cycle and that has set the following general objectives: increasing the competitiveness of the Romanian economy through innovation, growth Romanian contribution to the advancement of knowledge frontier and increasing the role of science in society. One of the cross objectives envisaged “achieve by 2020 a critical mass of researchers needed a RDI conversion factor of economic growth”. Strategy targets were set in the spirit of the convergence of Romania to the EU average, based on the premise that by 2020, public spending on research will gradually increase to 1% of GDP, plus fi scal incentives for private fi rms. Thus, the number of researchers in the private sector full-time equivalent provides a signifi cant increase from 3518 existing in 2011, at 7000 in 2017 and respectively 14500 in 2020, supported by an equally dramatic increase in R & D expenditures of Business sector from 0.17% of GDP in 2011 to 0.6% of GDP in 2017 and 1% of GDP in 2020. For the public sector is expected that the number of researchers (equivalent full time) increase slightly smoother compared to the private sector, from 1.409 in 2011, to 15000 in 2017 and respectively 17000 in 2020, supported by increased public spending on R&D (% of GDP) from 0.31 to 0.63 in 2017 and 1.0 in 2020. We mention that in the period 2005-2011 the total number of researchers in the R & D activity decreased with 30%, and the biggest drop on the sectors of performance was recorded in the business sector (66 %). In the National Reform Plan, is referred in the country-specifi c recommendation (RST 7) to ensure the implementation of a closer association between research, innovation and enterprise, in particular by granting priority status of research and development that are likely to attract private investment. Given these goals we continue to highlight structural weaknesses in the allocation of labor performance R&D sectors, with special regard to the dynamics of private sector staff involved in R&D.

CORRELATIONS BETWEEN EXPENDITURES AND EMPLOYEES IN R & D ACTIVITY BY PERFORMANCE

SECTORS

It will be noted that there is an inverse relationship between the research expenditure in research-development activity and the share of technicians and other similar categories of staff within this activity. In 2005,


the share of technicians, similar categories and other categories of employees in the EU, were approximately 44% in total employees of research and development activity and in Romania 30.9% being on a inferior level in EU. This allocation was based on operational needs of the productive units. These assumptions refl ect, also, the economic specialization and the rationality of economic development. The business sector develops a more applied and experimental activity, meanwhile the public institutions and higher education sector develop more fundamental research activities. What is irrational in this context is the high level of technicians and other supporting allocated to the government sector, meanwhile this sector develop mainly the basic research where the need for administrative and technical support is not relevant. After 7 years, between 2005 and 2012, these correlations have changed: only 5.4% of expenditures were dedicated for basic research in business sector (an effi cient process), a signifi cant decrease comparative to 2005, but a rationale measure and appropriate one for this sector (fi g.1).

Correlations between basic R&D expenditures ratio and technicians and others ratio in 2005 comparative to 2012

Figure 1

Source: NIS data processed by authors, National Institute of Statistics, Romanian Statistical Yearbook, 2006 and 2013

Within the higher education sector, basic research expenditure reached 64% of total research expenditures and technicians who are working in this sector reached a proportion of 24.3% from the total employees in research and development activity.


Within government sector, the number of technicians and other supporting staff increased from 29.6% (of total employees in this sector) in 2005 to 44% in 2012 and the basic research-development expenditure increased from 39.5% in 2005 to 61% in 2012. In private non-profi t sector, the number of technicians and other supporting staff increased from 17.4% in 2005 to 38.2% in 2012 and the basic research-development expenditure increased, also, strongly from 1.1% in 2005 to 47.3% in 2012. If we analyse the correlations between applied research-development expenditures and the number of technicians and other supporting staff, we will highlight the rational way of development of business sector, demanding more technicians and other supporting (54.4%), spending more for applied research (94.4%) then other sectors. Higher education sector is spending less for applied research and is using fewer technicians, meanwhile an irrational way of development is showed by government sector who is spending for applied research less than half of total (39%) and is using a ratio of 44% technicians of total employees (fi g. 2).

Correlations between applied R&D expenditures ratio and technicians and others ratio in 2012

Figure 2

Source: NIS data processed by authors, National Institute of Statistics, Romanian Statistical Yearbook 2006 and 2013; applied research expenditures refer to experimental and applicative research-development expenditures


We should, also, note as a defi ciency the allocation of a high percentage of other supporting labor force (other than technicians) in sectors, such as government (26.6%), private non-profi t (24.8%) and even business (32.9%).

A MATHEMATICAL MODEL THAT MIGHT EXPLAIN THE CHANGE IN STRUCTURE WITHIN THE R&D

SECTORS

In order to try to explain the logic behind the employment structure within the R&D sectors, we will need to work in an environment where all factors are constant except for the labor; we are including here factors such as technological capital. Our primary task is to identify the skills required in order to be successful within such a department. First of all, R&D is a very heterogeneous fi eld and thus before starting any sort of analysis we have to confi ne the area of study. Moreover, it is natural to assume that researchers are the ones who have the abilities to be successful in R&D and thus we will focus our paper on them. Following up, whenever we will compare two researchers we will assume that they are pursuing the same idea, obviously in the same department.We will now take a closer look at an actual process of R&D. Everything starts with an idea proposed by a researcher, a goal that is to be pursued. The second part consists of implementing the idea and pushing it as far as possible. In order to do that, the researcher acts as a manager and guides all three types of labor in the R&D, researchers, technicians and support personnel, towards the fi nal product. For simplicity let’s denote them respectively by A, B and C. By taking a similar approach as in the The Lucas “Span of Control” Model (Lucas, R., 1978), we will have:

(1)

Where is the expected revenue, in terms of knowledge created, coming from researcher i by letting him guide workers of type A, workers of type B and workers of type C. represents the probability with which i will make the breakthrough while is his “ability” to implement it. The other parameters,

, suggest that as employment in the department gets larger, the marginal product of labor diminishes, as more workers are increasingly unwieldy to oversee. Moreover, the level of education of an employee is direct proportional with its returns and thus, being that A > B > C, education wise, we have the inequality . Last, but not least, is a simple coeffi cient representing the technological capital already available for research.


Moving a step further, profi t is the most powerful, and arguably, the only important galvanizing factor on the labor market. Thus we must take it as the prime characteristic. For that we are going to defi ne as being the correspondence between the levels of knowledge and the actual revenue/benefi ts that can be obtained by the fi rm by exploiting it in the present conditions. f depends on the technological capital and other factors currently available to the company. Taking a mathematical look at the function, we can see that it is continuous but not necessarily differentiable, due to small discoveries that can end up with big profi ts. Moreover, it is obvious that it is increasing in x, x being the amount of knowledge. Therefore, the profi ts function for i can be written as:

(2)

The parenthesis represents the labor costs of production, where is the wage for labor of type A, of type B and respectively of type C. Having established these notations and formulas, we are ready to tackle the evolution of the structure of labor within the R&D departments. There has been reported a decrease in the number of researchers in 2011 compared to 2005. The most abrupt change was recorded in the business sector and now, using our simple yet comprehensive model we will try to give an explanation to this phenomenon. When talking about actors in the business sector, we are talking about fi rms whose only goal is to maximize profi t. Thus, when comparing two researchers, the only thing they will take into account is (2), the expected profi t that they could bring to the company. Moreover, it is safe to assume that when hiring researcher i, or promoting him to the rank of project manager, he is given

, and people under his guidance in order to maximize profi t. Thus, due to the unbalanced labor structure, it is highly likely that after employing n researchers and setting up their teams, we would end up with only workers of type A. In this case, setting up an extra team would imply the following profi t:

(3)

(4) Having in mind the diminishing returns and the fact that A are the researchers who could be guiding their own teams instead of working in teams where they are not necessarily working at their full potential, we can conclude that this is not the optimal case. As a result, the employer might want to hire


more workers of type B and C and less of type A in order to maximize profi t, in the short run due to present limitations of the technological capital. Coming back to the statistics in the private sector, the fall in the number of researchers should not be seen as an act of neglecting the R&D departments, but more of a step towards effi ciency in the short term, towards maximizing the profi t. Being that there are a lot of abstract parameters characteristic to every fi rm, we cannot affi rm whether this is the ideal structure, but just try to provide a justifi cation for the change. Moreover, by analyzing the statistics, we can see the same tendency in the governmental sector, but not as steep. The reason for this is that the governmental faces little to no competition and thus profi t is not a factor as powerful as it is in the private sector. The non-profi t sector if pretty much inexistent in Romania having a total of 192 employees of all kinds so we cannot talk of any structure. Society-wise, it would be benefi cial if the employers would look at the total knowledge that can be created by two researchers (1) with the available resources and not the profi t they can bring since not every profi table idea requires vast amounts of knowledge. This paradox is easily solved by looking at the f function that differs from fi rm to fi rm. Although vaguely defi ned, it has certain characteristics, as stated before. The crucial argument is that f is increasing in the amount of knowledge but it is not directly proportional to it. Thus, f aims to emphasize the heterogeneity of technological capital, and other factors, available throughout the R&D departments. In other words, although some discoveries might be made, due to the lack of conditions, they cannot be applied and thus are not bringing profi t to the employer in the near future, which in the business sector, is all that matters. Taking a closer look at , which is the technological capital already available for research, we can observe striking differences between countries. Let’s denote by the level of technological capital in Romania and by atthe respective level in a technological edge country such as Germany. It is safe to assume that , not strict being that some sectors might have certain connections that permit them to freely share knowledge and technology. (1) clearly suggests that a higher at enhances the creation of knowledge, but what is still to be analyzed is (2) and more exactly the f function. Our claim is that . In other words, given the same amount of knowledge to Germany and Romania, Germany will exploit the information at least as much as Romania, being that is has superior technology. One further claim is that where M is fi xed constant depending on and other factors such as total capital. The idea behind this relation is that given a fi xed amount of capital, regardless how much knowledge you have, you cannot exploit the knowledge entirely,


or the returns are extremely small. A simple example could be the discovery of gold on the bottom of the ocean after years of research and analysis. On the other hand, researchers have yet to discover ways to extract the gold. An analogy can be drawn between this case and Romania’s R&D sectors. Therefore it is obvious, that an employer is not interested in the amount of knowledge created but more in the amount of knowledge that he can exploit to its full potential (in the near future), maximizing his profi t. Pushing it even further, by denoting and the limits for Germany and Romania, respectively, we can write that . This implies that an extra piece of knowledge such that , may have little to no returns for Romania due to technological (and other) limitations but have great returns for Germany who are capable of fully exploiting the knowledge. One small, yet signifi cant change might be establishing a rigorous management of the pool of knowledge and property rights in the innovation sector. Thus by giving the creator or the company itself property rights we would “artifi cially” infl ate the values of f, where f ’ is the newly f function which takes into consideration the returns in the long run of the newly created knowledge. This would not only stimulate the fi rms to produce more pure knowledge but also encourage the labor to push itself, knowing that their results will be patented. Although encouraging, (Stiglitz, J., 2014) such a system is very fragile and needs to be tackled in a critical yet mindful way, being that it may discourage companies in the private sector to invest in innovation.

CONCLUSIONS

The role of research and innovation in increasing the competitiveness is considered essential to ensure a smart specialization of the economy and represents a strategic objective for 2020. Due to a poor level of intensity of the R&D activity in business (one of the lowest from EU) and of the reduced technology transfer, the Romanian economy has a low competitiveness based on effi ciency and not on innovativeness as in the other EU countries. The R&D sector is characterized by structural weaknesses which reduce the effi ciency and capacity for innovation in the economy. Thus, by examining correlations between R&D expenditures and number of employees in the R&D sector by performance sectors, between 2005 and 2012, we can conclude: the number of employees, technicians and other support staff in public sector grew (knowing that the sector develops fundamental R&D activities where there is no need for a large number of technicians and


administrative staff); basic research has increased the volume of expenditure; basic research expenditure in the private sector has been reduced; applied research and development costs have increased together with the number of technicians and administrative staff in R&D activities of the business sector. We are dealing with a sector which develops in divergent directions, with rational and irrational evolutions from an economic point of view, which means that the R&D is restructuring and is in a continuous redefi nition. Signifi cant efforts are necessary to achieve a critical mass of researchers by 2020, required for converting the R&D in a factor of economic growth. To conclude, these differences and limitations are challenges that the R&D sectors in Romania must face, especially the private sectors whose only concerns are surviving and increasing their profi t on the labor market. Although changes and discoveries are being made, due to the unbalance between capital, knowledge and labor structure, Romania is having trouble in keeping up with the more advanced countries, slowly but surely falling behind its partners.

References

1. Grossman, G.M. and Helpman, E., (1991). Innovation and growth in the global econo-my. Cambridge, MIT Press.

2. Lucas, R.. On the Size Distribution of Business Firms, The Bell Journal of Economics, 508-523.

3. Pasnicu, D., Tudose G (2014) Dynamics of workforce development with a focus on the R&D- The

Romanian Case Study, Adleton Academic Publishers, New York 4. Scherer, F.,M., (1986). Innovation and Growth: Schumpeterian Perspectives, MIT Press

Books. 5. Stiglitz, J., (2014). Intellectual Property Rights, the Pool of Knowledge and Innovation,

National Bureau of Economic Research. 6. *** Research and Innovation performance in Romania, Country Profi le 2013. European

Commission. 7. *** , Programul Național de Reformă 2014, Romanian Government, Romania. 8. ***, Romanian Statistical Yearbook, 2006, National Institute of Statistics, Bucharest,

Romania. 9. ***, Romanian Statistical Yearbook, 2013, National Institute of Statistics, Bucharest,

Romania.


Patterns of Foreign Direct Investment in Transylvania(CENTER, WEST AND NORTH WEST ROMANIAN NUTS 2 REGIONS)

Cristina SACALĂ Aniela Raluca DANCIU Vasile Alecsandru STRAT Bucharest University of Economic Studies

ABSTRACT

Foreign direct investment (FDI) has gained signifi cant importance over the past decade as a tool for accelerating growth and development of transition economies. It is widely believed that the advantages that FDI brings to the standard of living and prospects for economic growth of the host nation largely outweigh its disadvantages. Despite the growing interest in the subject, to our knowledge, there is still no satisfactory empirical work which can explain the determinants of the spatial distribution of FDI fl ows into the separate regions of Roma-nia, one of the largest new EU-member states. Thus, this research attempts to fi ll this gap by using a primary data from a questionnaire that covers the entire transition period. The main goal of this study is to identify the main determinants of the direct foreign investments in Central, West and North West Romanian regions. Basically, the study is constructed so, that it will provide a list of the main strengths and weaknesses of Center, West and North West regions, that would infl uence a foreign investor to choose the proper location for a future investment when developing his strategy. Keywords: regional disparities; economic development; foreign direct invest-ment; agglomeration; market size.

INTRODUCTION

Administrative divisions in Romania After 1990, Romania shifted its spatial policy from a central-based policy to a regional-based policy, in compliance with EU-standards. According to four criteria (number of inhabitants, surface, cultural identity and functional-spatial relations) Romania was divided in 1998 into eight Development Regions. In this paper we shall analyze only three regions: West development region (Arad County, Caras Severin County, Hunedoara County, Timis County), North-West development region (Bihor County, Bistrita County,


Cluj County, Maramures County, Satu Mare County, Salaj County), Center development region (Alba County, Brasov County, Covasna County, Harghita County, Mures County, Sibiu County).

Romania’s administrative divisionFigure 1

Evolution of the regional FDI Ranking the regions based on their ability to attract foreign investors, Danciu et al (2011) confi rmed the strong domination of the Bucharest-Ilfov region, placed on the fi rst position, followed at a long distance by the West, Center and North- West regions. The heterogeneous development areas, the economic decline recorded by small and medium size towns, and the severe negative impact of economic restructuring upon mono-industrial areas determines even bigger disparities inside the regions. Given the signifi cance of investment fl ows for the regional development, identifying the forces that attract foreign direct investment is a matter of high interest for the policy makers. Certain regional factors may determine which regions receive higher levels of investment, while other regions in the same country receive lower investments. Therefore we address two interrelated research questions: “what are the underlying factors that


drive the regional FDI behavior in Romania?”, and “how signifi cant are the individual characteristics of the regions for the FDI activity?” Considering the importance of investments for the economic development, such questions are essential in shaping the economic policies of the regions, both in periods of economic growth and during recessions.

Literature review: FDI regional determinants The literature on FDI determinants has been motivated by theories of international business and by international trade. The so-called OLI paradigm proposed by Dunning (1977, 1981, and 2001) states that three conditions must be satisfi ed for FDI to occur: Ownership advantages, Location-specifi c advantages and Internalization. In this study, we focus on the location-specifi c advantages. Krugman (1996, 1998) demonstrates that the location of economic activity is determined by two groups of factors. First group includes traditional advantages of particular locations such as agglomeration, market size infrastructure and knowledge. Thus, although all the above forces play some role in the choice of location, empirical studies suggest that their importance may vary depending on region, country or industry. For example, Levinson (1996) and Coughlin and Segev (2000), analyzing the establishment of new plants in US, show that agglomeration is the main factor that could explain the attractiveness of the South-East region for new plants. In respect of transition economies, Campos and Kinoshita (2003) and Pusterla and Resmini (2005) fi nd that agglomerations are one of the principal determinants of the spatial distribution of FDI. Chen (2009) investigates the role of agglomeration in determining FDI location in China. The results suggest that urbanization economics and foreign specifi c agglomeration have positive impact on local FDI. Crozet, Mayer and Mucchielli (2004) , Przybylska and Malina (2000) and Ghemawat and Kennedy (1999) fi nd that market-size positively infl uences FDI fl ows to Poland. According to Chakrabarti (2003), an expansion in the market size of a location leads to an increase in the amount of direct investment in that location through an increased demand. According to Woodward (1992), Japanese–affi liated manufacturing investments in the USA during the 1980s prefer states with strong markets. When referring to the knowledge-based view of the fi rm, Cantwell (1989) states that knowledge-seeking investments vary across locations because they depend on location specifi c factors, such as the number of scientists and educated people in the area, previously established innovations, R&D intensity, the education system, and good linkages between educational institutions and


fi rms. The educational level of a country’s citizens, alongside the existence of universities, research centers, science bases and other institutions that create knowledge in a region, has become increasingly important for the internationalization process, not only at the national level but also at the regional level (Cantwell and Iammarino, 2001, 2005; Chung and Alcácer, 2002). Empirical studies’ support for the importance of infrastructure in FDI location decisions is provided by Wei and et al. (1998), Mariotti and Pischitello (1995), Broadman and Sun (1997) and He (2002). A location with good infrastructure is more attractive than the others (Wei and others,1999; He,2002 ). Based on the above empirical literature which states that market size, agglomeration and knowledge positively encourage the infl ow of FDI, we establish our fi rst four hypotheses as follows: Hypothesis 1: Agglomeration factors are a signifi cant reason for FDI localization: hence the stronger agglomeration factors are represented in a given region, more FDI will engage in that region. Hypothesis 2: Market size factors are a signifi cant motive for FDI localization: hence the stronger market size factors are represented in a given region, more FDI will engage in that region. Hypothesis 3: Knowledge factors are a central reason for FDI localization: hence the stronger knowledge factors are represented in a given region, more FDI will engage in that region. Hypothesis 4: Infrastructure factors are a valuable motive for FDI localization: hence the better infrastructure factors are represented in a given region, more FDI will engage in that region. Second group consist of market forces including all kinds of input costs. Crozet, Mayer and Mucchielli (2004) and Lansbury et al., (1996a) demonstrate that labor costs have a signifi cant infl uence on the pattern of inward investment. Glickman and Woodward (1988) found that there was a negative relation between the interstate distribution of the value of foreign manufacturing investment and the index of state labor costs. For this reason, it is possible to hypothesize a positive relationship between resource-seeking investment and the regional location of FDI. Following on from this, our fi nal two hypotheses state: Hypothesis 5: Labor factors are a signifi cant motive for FDI localization: hence the stronger labor factors are represented in a given region, more FDI will engage in that region. Hypothesis 6: Cost factors are a valuable motive for FDI localization: hence the stronger effi ciency-seeking factors are represented in a given region, more FDI will engage in that region.


EXPERIMENTAL SECTION

Data collection Data used in this study were collected in a more complex statistical survey, conducted at the level of all Romanian development regions (except Bucharest-Ilfov region). The entire data collection process was conducted following the methodology described further. In order to identify and quantify the importance of the main determinants of foreign direct investments in Romania we started by collecting some administrative data from the authorities. We received a database consisting on all foreign direct investments in Romania that met our fi ve previously imposed criteria: • fi rms that have more than 100 employees; • fi rms that were created between 1990 and 2009; • fi rms that were still present on the market at the 1st of January 2009; • more than 50% of the initial capital was foreign; • fi rms that were active in the manufacturing sector; Because of the small volume of the target population, of only 670 fi rms (407 fi rms from the three development regions used in this study) we decided to perform an exhaustive survey instead of a sample survey. Thus, our research involved an eight question questionnaire and it was conducted over the telephone among the top and middle managers of the target companies. Less than half, only 235, of the companies provided us viable responses. When referring to the regions included in this present research only 140 managers agreed to answer to our questionnaire. In this context our research became a sample survey and analyzing the selection mechanism became a very important issue in order to address the statistical representativeness of the obtained results. It is obvious that we could not assume that the action of responding/not responding could be considered a mechanism that generates randomization without performing further analysis. The fi rst shortcoming that we encountered was the result of different response rates among our three regions, ranging from a low 29.7% in the West region to 42.6% in the North-West region (the response rate for the Center region was 33.1%). Also noteworthy is the aggregate response rate of only 34.4%. As mentioned before analyzing the selection mechanism became mandatory in order to ensure a greater reliability of the obtained results. Therefore, we used two methodologies in our attempt of obtaining some form of evidence that the selection mechanism was somehow similar to randomization. The basic idea behind both methods was to show that the distributions of some existing control variables were not signifi cantly different


in the respondents and non-respondents samples. Using the administrative information, mentioned at the beginning of this section, we constructed four control variables, as follows: • dichotomous variable - technology level of activity (High Tech/Low

Tech); • dichotomous variable – EU membership of the investor (EU member/

Non EU member); • ordinal three classes variable - number of employees in 2009 (low

number, medium number, large number); • ordinal there classes variable – 2009 income (low income, medium

income, high income)

firms(HT)High tech ,1

firms(LT) tech Low,0X1i

(1)

investor(EU)memberEU,1investorEU)(NONmemberEUNON,0

X2i (2)

2009inemployeesofnumberhigh,32009inemployeesofnumbermedium,2

2009inemployeesofnumberlow,1X3i (3)

2009inincomehigh,32009inincomemedium,2

2009inincomelow,1X4i (4)

The fi rst method involved hypothesis testing. We used the non-parametric Mann-Whitney test and the Chi-Square test to asses the signifi cance of the existing differences, concerning the four control variables, in the two samples (respondents versus non-respondents). The analysis was performed using the SPSS software package and the results obtained (Figure 1) could not provide enough statistical evidence to deny the null hypothesis (there is no statistical signifi cant difference between the two samples). Thus, we can conclude, based on the previously presented results that our respondents sample was obtained through a selection mechanism that is quite similar to a random one.


Respondents versus Non-respondentsFigure 1

The second method that we propose uses some techniques developed by Rosenbaum and Rubin (1983) propensity scores and also a matching mechanism that is specifi c to the one used in the matched sampling technique. Basically we constructed for each unit of the population a score (with values between 0 and 1) in order to quantify the probability of being in the sample, conditioned by our four covariates Xi, described earlier. The methodology involved the following steps:

• For each fi rm the propensity score was calculated.

),,,1Pr()( ,2,2,2,1 iiiiii xxxxSze (5)

)*(

^

11)(

ijxi eze

(6)

)****(

^

4433221111)(

iiii XXXXi eze (7)

• Each non-respondent was matched with at least one respondent based on their propensity scores. We used as criterion an exact matching algorithm.

1(s)zand0(s)z where)(ze)(zeif0

1(s)zand0(s)z where)(ze)(zeif,1),(

jij

^

i

^jij

^

i

^

ji zzMatch (8)

• For each of the non-respondents we calculated the number of possible matches. The value of the variable k was initialized with zero for each non – respondent.

ji,1,),(if1

ji,0,),(if,0)(

ji

jii zzMatchk

zzMatchkzNoMatch (9)


Using this procedure we have identifi ed 10 propensity scores that could not be matched (number of matches for each of those individuals was zero), meaning that almost 2.5% of target population’s units were not represented in the sample of respondents. Thus, is obvious that the probability of not being able to construct a matched sample (containing only respondents) for any random sample of non-respondents is not null. Therefore the reliability of our sample needs to be regarded with great caution when talking about statistical representativeness and the results obtained should be regarded more as a hypothesis for future research than as a fi nal and clear result. Even though post-weighting is a procedure usually used when employing propensity scores based methods because our procedure revealed units (non-respondents) that could not have been matched based on their propensity scores we have decided not to apply weights to our sample.

Profi le of the investor for Center, West and North West Regions In this section, we will construct the general framework by describing the obtained results at regional level. The questionnaire consists of eight questions: seven simple questions and a complex one with 18 sub-questions clustered in four groups. The main part of our analysis will focus on the sixth question (the complex question): “Which were the reasons that made you decide invest in this region?” The four main classes mentioned before are as follows: (1) Infrastructure, (2) Labor force, (3) Agglomeration factors and (4) Cost factors The answer to all eighteen questions, included in the four clusters, is a scale with fi ve values: 1–This factor was not taken in consideration, 2 – Very little importance, 3 – Little importance, 4 – Important, 5 – Very Important. Further in our analysis we have modifi ed the scale for each question by building a dichotomous variable because the low volume of our sample and also in order to respect our proposed approach based on strengths and weaknesses. The fi rst class of factors “Infrastructure” includes fi ve topics as follows: (1) transportation costs, (2) quality of the roads, (3) the existences of the airports nearby, (4) the existence of viable land for the investment and (5) favorable conditions for distribution of the products. Transportation costs were considered as being important and very important by about 39% of all investors. Also the existence of viable land for the investment was considered by 53.5% of the interviewed managers as being a crucial reason in the location choosing strategy. Concerning for the authorities might be the fact that almost 70% of the respondents do not consider the existence of airports nearby as being a factor that might require attention when locating an investment. However the existence of favorable conditions for distribution is considered as being important or very important by almost half of the investors.


Level of importance for each Infrastructure related factorTable 1

InfrastructureRegion F1 F2 F3 F4 F5Center 32.00% 14.00% 20.40% 50.00% 40.80%West 41.80% 21.00% 41.90% 62.80% 54.70%

North-West 43.40% 6.50% 28.10% 47.70% 52.10%General 39.00% 13.80% 30.10% 53.50% 49.20%

Companies who assign the transportation costs a greater importance are more inclined to choose North-West or West. This fact shows that those fi rms are interested in the European Highway system and therefore they locate in Romania near the Hungarian border. The quality of the Romanian roads is a problem for the large majority of the investors. Also respondents located in the West consider the existence of an airport as a signifi cant factor in the decision making process. Favorable conditions for distribution are of signifi cant higher importance for investors who decided to locate their facilities in the West and North-West. The second class of factors “Labor force” includes 4 topics as follows: (1) the existence of available labor force, (2) the low cost of the labor force, (3) the existence of qualifi ed labor force, (4) the high level of education of the population.

Level of importance for each Labor related factorTable 2

Labor forceRegion F1 F2 F3 F4Center 84.00% 82.00% 78.00% 45.80%West 83.80% 76.80% 86.10% 44.20%

North-West 95.70% 82.60% 60.80% 15.90%General 87.80% 80.46% 74.96% 35.30%

Important to emphasize is that the fi rst three topics were considered a major factor in the process of strategic planning (important or extremely important) by almost 90% of the companies from our sample. The most important is the existence of available work force, followed by low cost of this workforce and by the existence of qualifi ed workforce. The high level of education is not as important due to the fact that most of the companies bring their specialists requiring local work force for the lower levels of the company. However in the regions West and Center the importance of this factor is signifi cantly higher probably because of the main urban areas (Cluj, Arad, Timisoara). Also noteworthy is the fact that investors who located their


investments in the West region considered the existence of qualifi ed labor force an important characteristic in a signifi cant higher percentage than the rest. Concluding this class of factors we can assert that aspects concerning the existence of labor force at reasonable costs are one of the main advantages of our country. The third class of factors “Agglomeration” was divided into three main topics: (1) The existence of suppliers in the region, (2) The existence of other companies with the same activity fi eld in the region and (3) The existence of other foreign companies in the region. The importance of these three factors in the opinion of our respondents, at regional levels, will be displayed in Table 3.

Level of importance for each Agglomeration related factor level Table 3

AgglomerationRegion F1 F2 F3Center 35.30% 28.00% 40.00%West 32.60% 25.60% 21.00%

North-West 39.10% 41.30% 26.10%General 35.60% 31.60% 29.03%

As we can see from the fi gures these factors are considered as being important by appreciatively about a third of the responding managers. The existence of other foreign companies in the region is regarded with signifi cant greater attention by companies who chose the Center region. The existence of other companies with related fi elds of activity is regarded as being important by a signifi cant larger percentage of the investors who decided to invest in North-West. The fourth class, called “Cost factors” was divided into three main topics: (1) tax incentives for investors, (2) low rent levels or low land acquisition price, (3) availability of cheap raw materials in the area.

Level of importance for each Cost related factor Table 4

Cost factorsRegion F1 F2 F3Center 31.90% 52.10% 22.00%West 16.30% 44.20% 25.60%

North-West 21.70% 52.20% 21.70%General 23.30% 49.50% 23.10%

The low level of rents or the low land acquisition price and the general operating costs are considered as important determinants of the


future investment by almost 50 % of the respondents. Tax incentives and the availability of raw materials are considered as being an important determinant for the made investment by about a quarter of the respondents. A signifi cantly larger percent of the respondents from the Center region considered the tax incentives offered by authorities as being important when deciding to locate their investment. This factor was of importance for a signifi cantly lower percentage of respondents from the West region.

Model specifi cation In order to asses the importance of each factor (item) in the location choosing process we have used a methodology based on a logistic econometric modeling process. We have decided to express the binary answer (YES/NO) to the question, “Since Bucharest-Ilfov region is the most attractive in terms of location of FDI, have you taken into account the region to locate your investment, at the beginning of your decision process?”, as a function of individual items or items’ combinations. Noteworthy is the fact that we have divided all the 18 items into fi ve main classes: infrastructure factors, labor related factors, agglomeration factors, cost related factors and other factors. Thus, we have decided to include each of the factor classes in our model with an individual item or with an aggregate construct. Therefore the structural form of our model is listed below:

Choice (1/0) = 0 + 1 FACTinfr + 2 FACTlab + 3 FACTagl+ 4 FACTr&d+ 5 FACTcost + 6 FACT mark + 7 DUM+e (10)

)*(

^

11)(

ijxi ezch (11)

)*******(

^

77665544332211011)(

iiiiiii XXXXXXXi ezch (12)

Using a multi-step method implying correlation analysis, factor analysis and several logistic regressions we have chosen the best construction for the involved independent variables. Each time Factor Analysis was performed using a PC Extraction method and a Varimax rotation method. Factor scores were created using the Regression Method (1) available in SPSS (the results were a little better with this refi ned method than with the simple weighted sum method (2)).


(1) F = Z*R-1 *S (13) Z – Matrix of standardized observed variable scores R – Correlation matrix for the observed variables S – Factor structure matrix (2) F = Z*S (14) Because some of the independent variables were the initial items measured on a fi ve point scale and some of them were constructed using combinations of the initial items (Factor scores) we have decided to standardize all of them before estimating the model. The independent variables used in the model are as follows: • FACTinfr From the fi rst class of items we have decided to use the answer to the question “Have you taken in consideration, when locating your investment, the existence of favorable conditions for distribution of your products?” The choosing of this factor was infl uenced, beside the results of the statistical analysis mentioned above, by its connection with market seeking behavior which is an important characteristic of foreign companies deciding to invest in Romania. • FACTlab The second class of factors was included in the model using the estimated score for the fi rst Factor resulted from the Factor Analysis conducted on the class of Labor related items. The new construct is highly correlated with the last two items and it might be regarded as a measure for the existence of qualifi ed and highly educated workforce. The values of this independent variable (the score) are standardized.

Labor items Factor AnalysisTable 5

Component matrix (Factor loadings) 0.298 -0.102 0.825 0.839Component score Coeffi cient Matrix 0.112 -0.188 0.556 0.583

• FACTagl From the third class of items, after the same process involving correlation analysis, factor analysis and logistic regression analysis we have decided to use the answer to the question “Have you taken in consideration, when locating your investment, the existence of other foreign companies in the region?” The values of the independent variable included in the model resulted also through standardization. • FACTr&d The fourth independent variable of the model is the item inquiring about the importance of the existence of research centers and universities in


the neighbor area of the investment. The values of the independent variable included in the model resulted also through standardization. • FACTcost The fourth class of factors was included in the model using the estimated score for the Factor resulted from the Factor Analysis conducted on the class of Cost related items. The new construct similarly correlated with all three initial items and it might be regarded as a general measure for cost related factors. The values of this independent variable are standardized.

Cost items Factor AnalysisTable 6

Component matrix (Factor loadings) 0.667 0.770 0.678Component score Coeffi cient Matrix 0.445 0.514 0.453

• FACTmark The sixth independent variable of the model is the item inquiring about the importance of the existence of a potential market in the region (based on the fi eld’s literature market seeking fi rms are one of the most important types of companies that decide to invest in foreign countries). The values of the independent variable included in the model resulted also through standardization. • DUM This variable refl ects the technology level of the investment, with values of 0 for Low Tech investments and 1 for High Tech activities.

RESULTS AND DISCUSSION

The logistic model was estimated using SPSS at the level of the entire sample and also at the level of each region. After running the four logistic regressions the estimated values of the parameters are as follows:

Models parameter estimationTable 7

General Center West North-WestVariable βi Sig βi Sig βi Sig βi Sig

FACTinfr 0.394 0.115 0.213 0.598 0.563 0.341 1.325 0.047FACTlab -0.356 0.134 0.176 0.680 -1.014 0.072 -0.476 0.344FACTagl -0.249 0.322 0.006 0.988 -0.460 0.499 -0.874 0.163FACTr&d 0.518 0.040 0.602 0.124 0.007 0.991 0.754 0.271FACTcost -0.391 0.174 -0.472 0.371 0.160 0.791 -1.572 0.048FACTmark 0.393 0.134 0.200 0.693 0.771 0.159 0.270 0.640DUM -1.262 0.073 -1.661 0.096 -20.041 0.999 -1.313 0.377Const -1.388 0.000 -0.966 0.023 -1.432 0.007 -2.498 0.001


Therefore, analyzing comparatively these four models we will be able to asses the importance of each factor at the level of different regions. In the general regression model, we found that those investors, for whom infrastructure, knowledge and market factors are the main motives for investing in Romania, considered Bucharest-Ilfov region at the beginning of the location process. However, investors for whom low input costs, availability of labor force and resources are signifi cant factors for setting up a business activity in Romania, tend not to take Bucharest-Ilvov area in consideration. These fi ndings confi rm that Romanian regions do indeed differ substantially in attracting foreign capital and that regional characteristics matter in the selection of primary location choice in Romania. When looking at the second model the fi ndings for the comparison of the choice of location between Bucharest-Ilfov versus Center region indicate that only two variables have signifi cant parameters. The parameter of FACTr&d is positively signifi cant at a 15% level. This suggests that foreign knowledge-seeking investors located in the Center region took Bucharest-Ilfov area in consideration at the beginning of their investment. The second one is the industry dummy, called DUM, which is negatively signifi cant (the parameter) at a 10% level. This might indicate that investors from high-tech industries located in the Center region came here without looking at Bucharest Ilfov region. The fi ndings for the comparison of the choice between Bucharest-Ilfov region versus the North-West region shows that only two of the variables’ parameters used in the model turned out to be negative and statistically signifi cant: FACTagl and FACTcost. This suggests that fi rms who regard costs and labor related factors as being important are more likely to go into the North-West area without considering Bucharest-Ilfov region. We also note that the parameter of the FACTinfr is statistically signifi cant and has a positive sign. This shows that investors for whom infrastructure is the important motive for establishing their business in the North West region looked at the Bucharest-Ilfov area when deciding the location of their future investment. The fi ndings for the comparison of the choice between the Bucharest-Ilfov region and the West region show that only two of the variables used have statistically signifi cant parameters. Because of the negative parameter of the labor factor the probability of the infl ow of FDI into the West area is higher than to the Bucharest region. Firms looking for new market opportunities, located in the West region took Bucharest-Ilfov region in consideration in their strategies before coming to Romania. Also noteworthy is the fact that for each of the three regions different factors considered in the location decision process by foreign investors required a comparison between that specifi c region and Bucharest-Ilfov region.


CONCLUSION

As expected the results indicate that there are substantial differences in the attractiveness of Romanian regions, when the initial infl ows of FDI are evaluated. It is shown that if input costs and the availability of labor and resources are seen by investors as important factors for investing in Romania, then all three regions are more favorable for the infl ow of foreign capital than the Bucharest-Ilfov area. Companies that regard the existence of cheap and available raw materials, the existence of other companies working in related fi elds of activity and the existence of tax incentives as being possible determinants for their future investment are more inclined to invest in the North-West region (without even considering the Bucharest-Ilfov region). Firms who decide for an investment in the West region are more inclined to consider in the environmental scan stage Bucharest-Ilfov region as an option if they are more interested in fi nding new markets and on the contrary are not interested in the Bucharest-Ilfov region if they go for labor related aspects. Further study should analyze if Center, North-West and West regions, are preferable location (in comparison to the Bucharest area) for the infl ow of FDI when geographical factors are important motives for investors. Access to west borders (European Highway system), makes this area very attractive for foreign capital (Nandakumar and Wagué, 2001) because geographical proximity imply lower communication costs and fewer diffi culties in managing business activities (Woodward, 1992; Louri et al., 2000). Furthermore, the Bucharest region is more likely to be considered as a preferable destination by a foreign fi rm if market factors, infrastructure factors and knowledge factors are viewed as important motives for locating the future investment. This fi nding is hardly surprising, because the Bucharest metropolitan area is the largest market within the country. Economic development of the region’s main cities together with future improvement in road infrastructure, as well as proximity to the country western border favor Transylvania (Center, West and North West Regions) on the map of foreign investment localization.

Author Contributions Despite the growing interest in the subject of FDI location, to our knowledge, there is still no satisfactory empirical work which can explain the determinants of the spatial distribution of FDI fl ows into the separate regions of Romania, one of the largest new EU-member states. Thus, this research


attempts to fi ll this gap by using a primary data from a questionnaire that covers the entire transition period. The main authors contribution is to identify the main determinants of the direct foreign investments in Central, West and North West Romanian regions and to provide a list of the main strengths and weaknesses of Center, West and North West regions, that would infl uence a foreign investor to choose the proper location for a future investment when developing his strategy.

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The European Union Solidarity Fund: An Important Tool in the Recovery After Large-Scale Natural Disasters PhD Univ. Professor Maria IONCICĂ ([email protected]) Bucharest University of Economic Studies PhD Univ. Professor Eva-Cristina PETRESCU ([email protected]) Bucharest University of Economic Studies

ABSTRACT

This paper analyses the situation of the European Union Solidarity Fund, as an important tool in the recovery after large-scale natural disasters. In the last millen-nium, the European Union countries have faced climate change, which lead to events with disastrous consequences. There are several ex-post fi nancial ways to respond to the challenges posed by large-scale natural disasters, among which EU Solidar-ity Fund, government funds, budget reallocation, donor assistance, domestic and/or external credit. The EU Solidarity Fund was created in 2002 after the massive fl oods from the Central Europe as the expression of the solidarity of EU countries. Romania has received fi nancial assistance from the EU Solidarity Fund after the occurrence of major natural disasters, regional and neighbouring country disasters. The assessment of large-scale natural disasters in EU is very important and in order to analyse if there is a concentration of large-scale natural disasters in EU we used the Gini coeffi cient. In the paper, the method of the statistical analysis and the correlation between several indicators were used to study the fi nancial impacts of large-scale natural disasters in Europe, and especially in Romania. Keywords: Romania, European Union Solidarity Fund, large-scale natural disasters, damage, aid JEL Classifi cation: Q54, Q56

INTRODUCTION: LARGE-SCALE NATURAL DISASTERS, ECONOMIC LOSS AND DISASTER RISK

SOURCES OF FINANCING

The average number of natural disasters worldwide has increased from about 30 per year in the 1950s to more than 400 since 2000 and the economic loss has increased from: 53.6 billion USD (1950-59), 93.3 billion


USD (1960-69), 161.7 billion USD (1970-79), 262.9 billion USD (1980-89), 778.3 billion USD (1990-99) arriving in the last decade at 420.6 billion USD (Kunreuther and Michel-Kerjan, 2008). Regarding the EM-DAT data (2015) we can observe that in 2011 natural disasters in the world: were reaching an alarming peak of total economic damage of 364.07 USD billion (see Figure 1).

Natural disasters in the world and total economic damage (USD billion)Figure 1

The rising trend of natural disasters and of their economic consequences can be explained by population growth, increase in population and housing units in vulnerable areas, also more complete reporting due to improvements in information technology and more important a rise in climatic disasters: extreme weather events, including storms, fl oods, droughts, heat waves, sea waves, heavy rainfall, and wet ground slides (Oh and Reuveny, 2010). Mahul and Ghesquiere (2011) consider that the upward trend is principally due to increase in population and assets exposed to adverse natural events, to growing urbanization, environmental degradation, and expected increase in the number and intensity of hydro-meteorological events resulting from climate change. In the last century, Europe was affected by several natural disasters with important economic consequences (see Figure 2).


Large-scale natural disasters in Europe and total economic damage (USD billion)

Figure 2

In view of socio-economic and climate changes Rojas et al. (2013) estimate that Czech Republic, Romania and especially Hungary will likely experience large fl ood damages by the end of this century and the United Kingdom, France and Italy in Western Europe as well as Romania, Hungary, and Czech Republic in Eastern Europe show the highest absolute damage estimates and are likely to bear the highest costs of adaptation In order to respond to the challenges posed by the large-scale natural disasters Governments have access to various sources of fi nancing following a disaster. Mahul and Ghesquiere (2011) have categorised these sources in ex-post, such as donor assistance, budget reallocation, domestic or external credit, or tax increase and ex ante fi nancing instruments, that need a planning and includes reserves or calamity funds, budget contingencies, contingent debt facility, parametric insurance, CAT-bonds, traditional insurance. Jongejan and Barrieu (2008) consider that it is impossible to defi ne a universal panacea for the natural catastrophe coverage: an insurance arrangement that works in one country might not work in another. In Europe, the major natural catastrophes from the beginning of the century have created the need of solidarity and thus it was created in 2002 an important fund, vital for many countries from EU: the European Union Solidarity Fund.


METHODOLOGY, DATA AND RESULTS OF THE RESEARCH RELATED TO NATURAL DISASTERS IN

EUROPE

In the research were studied large scale natural disasters in Europe, and in Romania using reliable, actual and specialized secondary sources: European Commission site, CEA, EM-DAT: CRED/OFDA International Disaster Database Brussels, Université Catholique de Louvain, data from Romanian Waters National Administration and statistic data from the National Institute of Statistics (Statistical yearbook 2013 and TEMPO-Online time series) and specialized litterature. The assessment of large-scale natural disasters in Europe is diffi cult, but necessary. In order to analyse if there is a concentration of large-scale natural disasters in European Union the Gini coeffi cient was used. The method of the statistical analysis and the correlation between several indicators were used to study the fi nancial impacts of large-scale natural disasters in Europe, and especially in Romania. The intensity of the correlation between the damages caused by natural disasters and the environment protection expenditure in Romania was analysed using Spearman correlation coeffi cient.

The European Union Solidarity Fund: important instrument in the recovery after large scale natural disasters in Europe

The European Union Solidarity Fund (EUSF) was set up to express European Union solidarity to disaster-stricken regions within Europe in an effi cient and fl exible manner. The European Union Solidarity Fund was created in 2002, after the massive fl oods from the Central Europe and since then 24 different European countries have received aid for an amount of over 3.784 billion € (see Table 1).


European Union Solidarity Fund Interventions 2002-July 2015 - damage and aid granted

Table 1Total Damage mil.€ Total Damage % Aid mil..€ Aid %

1 AUSTRIA 4,368.00 4.57% 170.74 4.51%2 BULGARIA 1,092.00 1.14% 39.20 1.04%3 CROATIA 802.00 0.84% 22.79 0.60%4 CYPRUS 165.00 0.17% 7.60 0.20%5 CZECH REPUBLIC 3,579.00 3.74% 160.90 4.25%6 ESTONIA 48.00 0.05% 1.30 0.03%7 FRANCE 7,571.00 7.92% 203.70 5.38%8 GERMANY 22,004.00 23.02% 971.40 25.67%9 GREECE 3,032.90 3.17% 112.70 2.98%

10 HUNGARY 1,238.00 1.29% 37.60 0.99%11 IRELAND 521.00 0.54% 13.00 0.34%12 ITALY 30,230.00 31.62% 1,318.90 34.85%13 LATVIA 193.00 0.20% 9.50 0.25%14 LITHUANIA 15.00 0.02% 0.40 0.01%15 MALTA 30.00 0.03% 0.96 0.03%16 POLAND 2,994.00 3.13% 105.60 2.79%17 PORTUGAL 2,308.00 2.41% 79.80 2.11%18 ROMANIA 4,033.00 4.22% 119.00 3.14%19 SERBIA 1,105.00 1.16% 60.20 1.59%20 SLOVAKIA 764.00 0.80% 26.10 0.69%21 SLOVENIA 1,273.00 1.33% 48.30 1.28%22 SPAIN 1,332.00 1.39% 31.00 0.82%23 SWEDEN 2,297.00 2.40% 81.70 2.16%24 UNITED KINGDOM 4,612.00 4.82% 162.30 4.29%

TOTAL 95,606.90 100.00% 3,784.69 100.00%average 3983.62 157.70

Created using data from European Commission, EU Solidarity Fund Interventions since 2002, Last update: 10 July 2015 (by country), available at: http://ec.europa.eu/regional_policy/sources/thefunds/doc/interventions_since_2002.pdf

The European Union Solidarity Fund has been used for recovery after large disasters, covering the damages caused by fl oods, forest fi res, earthquakes, storms, drought and other natural catastrophes. The European Union Solidarity Fund fi nance essential emergency operations, non-insurable damages, such as: restoring infrastructures, securing of prevention infrastructures, such as dams, measures to protect cultural heritage, cleaning up, costs of emergency services and temporary accommodation. The EU Member States and the countries negotiating membership can receive aid from the European Union Solidarity Fund in the event of natural disasters. The affected country must apply to the European Commission within 12 weeks of a disaster. The fi nancial aid proposed by the Commission must be approved by the Council and the European Parliament. There are three categories after the occurrence: major disasters, regional disasters and neighbouring country. The major disaster was considered if total direct damage caused by a disaster exceeds €3 billion (at 2011 prices) or 0.6 % of the country’s gross national income, whichever is lower. For the regional


disasters the eligibility threshold is 1.5% of the region’s gross domestic product (GDP), or 1% for an outermost region. The amount of aid is based on total damages, function of the major disaster threshold. 2.5% is paid for the part of total direct damage below the major disaster threshold and 6% is paid for the part of damage exceeding the major disaster threshold (EC, EUR-Lex, 2014). Under new rules adopted in 2014 (Regulation (EU) No 661/2014), working procedures have been simplifi ed and eligibility criteria clarifi ed, and extended to cover drought. The European Union Solidarity Fund annual budget: is € 500 million (2011 prices), plus any funds remaining from the preceding year and the European Union Solidarity Fund is funded outside the EU’s normal budget (i.e. by additional money raised by EU countries). Other changes are related to the possibility of advance payments, shorter administrative procedures and the introduction of reporting requirements as measures to encourage disaster risk prevention strategies. In Europe, the phenomena witch produced most of the damages were fl oods, followed by earthquake. In 2002, there were the most important damages (14300.00 million €) and the biggest aid granted from the European Union Solidarity Fund (707 million €) as a result of fl oods produced in the countries from Central Europe: Austria, Czech Republic and Germany (see Figure 3).

European Union Solidarity Fund Interventions 2002-July 2015 - damage and aid granted (million €) by date of occurrence of the event

Figure 3



On the second place function of the damage, there is the earthquakes Emilia-Romagna, produced in May 2012 in Italy, with damages of 13 274 million € and 670.2 million € aid granted from the EUSF (see Figure 3). In the period 2002- July 2015, function of the damage the fi rst 10 countries most affected by natural catastrophes are in order: Italy, Germany, France, United Kingdom, Austria, Romania, Czech Republic, Greece, Poland and Portugal. Function of the aid granted from EU Solidarity Fund, the fi rst 10 countries receiving aid for recovery after natural disasters are in order: Italy, Germany, France, Austria, United Kingdom, Czech Republic, Romania, Greece, Poland and Sweden.

European Union Solidarity Fund Interventions 2002-July 2015 by country

Figure 4


Using the Gini coeffi cient (Pop et al., 2011, p. 194), the degree of concentration of damages and the degree of concentration of aids granted from European Union Solidarity Fund were studied. The coeffi cient varies between 0, which refl ects complete equality and 1, which indicates complete inequality, complete concentration of the amounts.


C G c=1

12

−

−∑n

pn i [1]

n = the number of terms of the series pi = the share of item i D i = the value for the item i D = the total value

DDp i

i = ,

∑

=

=n

iiDD

1

CG c aid = 0.41 CG c damage = 0.37

For the period 2002-july 2015 the results indicate a coeffi cient of concentration of 0.37 in the case of the damage and a coeffi cient of concentration of 0.41 in the case of the aid from European Union Solidarity Fund, indicating a moderate concentration in both cases.European Union Solidarity Fund is an important tool in the recovery after large scale disasters in Europe, essential for the recovery after large scale natural disasters for the EU countries.

Romania, natural disaster risk reduction and European Union Solidarity Fund

Romania is exposed to many natural hazards: earthquakes, fl oods, storm, landslide drought and extreme temperature. In the case of earthquakes the economic losses can be high, especially, because densely populated areas are affected. The infrastructure and the residential buildings present a high vulnerability to seismic risk. Bucharest is considered as one of the capitals with the highest seismic risk. The event with the most diffi cult consequences was the earthquake from 1977, which had killed 1641 persons and the amount of total damage was of 2 billion $ (see Table 2).


Top 10 Disasters 1900-2015 in Romania - Total damageTable 2

Disaster No Type Date Total damage ('000 US$)1977-0048 Earthquake 4/3/1977 2,000,0002010-0251 Flood 21-06-2010 1,111,4282005-0365 Flood 12/7/2005 800,0001970-0029 Flood 11/5/1970 500,0002000-9328 Drought 00-06-2000 500,0002005-0473 Flood 14-08-2005 313,0002005-0214 Flood 21-04-2005 200,0001998-0193 Flood 15-06-1998 150,0002001-0288 Flood 19-06-2001 120,0001997-0166 Flood 4/7/1997 110,000

Guha-Sapir, D. Below, R. and Hoyois Ph. (2015) EM-DAT: The CRED/OFDA International Disaster Database – www.emdat.be – Université Catholique de Louvain – Brussels – Belgium

However, the most frequent disasters with important economic losses were related to fl oods (see Tab. 2). As far as it concerns the fl ood risk, 1028000 ha of land are exposed to fl ooding, 928935 citizens live in high fl ood risk areas and 903 localities are situated in high fl ood risk areas (Romanian Waters National Administration, 2013). Floods are a devastating phenomenon and in this century there were major fl oods. In 2005, 2008, 2010 and 2014 fl oods have affected large areas and the country received aid from European Union Solidarity Fund (see Table 3).

European Union Solidarity Fund Interventions since 2002 for RomaniaTable 3

Occurrence Nature of disaster Category Damage(million €)

Aid granted(million €)

Total aid granted

(million €)April 2005 Spring Floods major 489 18.8July 2005 Summer Floods major 1.050 52.4 July 2008 Floods regional 471 11.8 June 2010 Floods major 876 25.0

August 2012 Drought and fi res major 807 2.5April 2014 Spring Floods neighbouring country 168 4.2July 2014 Summer Floods regional 172 4.3 119

Data from European Commission, EU Solidarity Fund Interventions since 2002, Last update: 10 July 2015 (by country), available at: http://ec.europa.eu/regional_policy/sources/thefunds/doc/interventions_since_2002.pdf

The damages caused by natural disasters generate important expenses related to restoring and recovery and also expenses related to the prevention of natural calamities.


Using Spearman correlation coeffi cient we have analyse the intensity of the correlation between the damages caused by natural disasters and the environment protection expenditure in Romania (see Table 4).

Correlation between the damages caused by natural disasters and environment protection expenditure in Romania

Table 4

YearsDamage

(million €)Yi

Environment protection expenditure (mld. Lei)

Xi

r y i r x i di2

1 2008 471 16.5 3 3 02 2010 876 18.4 1 2 13 2012 807 20.8 2 1 14 2014 340 16.4 4 4 0

Created using data from: European Commission, EU Solidarity Fund Interventions since 2002, Last update: 10 July 2015 (by country), available at: http://ec.europa.eu/regional_policy/sources/thefunds/doc/interventions_since_2002.pdfNational Institute of Statistics (2015) TEMPO-Online time series, Bucharest: INS.

Spearman correlation coeffi cient: for a sample of size n, the n raw scores Xi, Yi are converted to ranks: rx i , ry i, and Sp is computed from (Ioncică et al. 2006, p. 19):

[2]

Where d i = rx i - ry i is the difference between ranks. Sp damages- prevention expenses = 0.8

The value of 0.8 of the Spearman correlation coeffi cient indicates a powerful, straight connection between the damages caused by natural disasters and the environment protection expenditure in Romania. The result can be explained by the fact that the environment protection expenditure in Romania includes “investments and internal current expenditure for carrying out the activities of environment observation and protection and refer to environment damages prevention or repair” (National Institute of Statistics, 2014). Normally, as the value of damages resulted from natural disasters is larger, the environment protection expenditure augments.


CONCLUSIONS

In the world, from the middle of the last century till now, the economic loss generated by natural disasters has increased almost 8 times, from 53.6 billion USD (1950-59) to 420.6 billion USD in the last decade. The augmentation can be explained by a series of factors: population growth, increase in population and housing units in vulnerable areas, and more important a rise in climatic disasters. There is also a positive factor explaining the increase: more complete reporting due to improvements in information and communication technology. In order to prevent and to cover the economic damages the Governments have access to various fi nancial instruments: international funds, budget reallocation, domestic or external credit, donor assistance, tax increase, reserves or calamity funds, budget contingencies, insurance etc. In Europe, after the major fl oods from the beginning of the century, the European Union Solidarity Fund was created. Since then 24 different European countries have received aid for an amount of over 3.784 billion € for the recovery after numerous natural catastrophes: fl oods, earthquakes, forest fi res, droughts, storms and other. The assessment of large-scale natural disasters in Europe is very important and in order to analyse if there is a concentration of large-scale natural disasters in EU we used the Gini coeffi cient regarding the damages and the aid granted. For the period 2002-july 2015 the results indicate a moderate concentration in both cases. In this period, Romania was also affected by natural disasters and has received the assistance from the European Union Solidarity Fund. Between the damages caused by natural disasters and the environment protection expenditure in Romania there is a powerful, straight connection (confi rmed by the value of the Spearman correlation coeffi cient). The reconstruction after natural disasters is very diffi cult and the European Union Solidarity Fund is a vital element in the recovery after major calamities.

Acknowledgments This research was funded by the European Commission through the ENHANCE project: Enhancing Risk Management Partnerships for Cata-strophic Natural Disasters in Europe (Grant Agreement number 308438). The sole responsibility for the content of this document lies with the authors.


The paper has been presented at the 9th International Confer-ence on Applied Statistics, November 13, 2015, Bucharest, organized by Econometrics and Statistics Department of Bucharest Academy of Economic Studies in collaboration with the National Institute of Statis-tics and Romanian Society for Statistics.

References

1. CEA (2007) Reducing the Social and Economic Impact of Climate Change and Natural Catastrophes Insurance Solutions and Public-Private Partnerships, Brussels: CEA.

2. European Commission (2014) Regulation (EU) No 661/2014 of the European Parliament and of the Council of 15 May 2014 amending Council Regulation (EC) No 2012/2002 establishing the European Union Solidarity Fund.

3. European Commission (2014) EUR-Lex, Brussels available at: http://eur-lex.europa.eu/legal-content accessed November 2015

4. European Commission (2015) EU Solidarity Fund Interventions since 2002, Last up-date: 10 July 2015 (by country), available at: http://ec.europa.eu/regional_policy/sourc-es/thefunds/doc/interventions_since_2002.pdf accessed November 2015

5. Guha-Sapir, D. Below, R. and Hoyois Ph. (2015) EM-DAT: The CRED/OFDA Interna-tional Disaster Database Brussels : Université Catholique de Louvain – Belgium. avail-able at: www.emdat.be. accessed November 2015

6. Ioncică, M. (coord.) et al. (2006) Economia serviciilor: probleme aplicative, Bucharest: Ed. Uranus.

7. Jongejan, R. and Barrieu, P. (2008) Insuring Large-Scale Floods in the Netherlands, The Geneva Papers on Risk and Insurance - Issues and Practice, 33: 250-268.

8. Kunreuther, H. C. and Michel-Kerjan, E. O. (direction of the study) et al. (2008) Man-aging Large-Scale Risks In A New Era Of Catastrophes. Insuring, Mitigating and Fi-nancing Recovery, Philadelphia: Wharton Risk Management and Decision Processes Center from Natural Disasters in the United States.

9. Mahul O. and Ghesquiere F. (2011) Sovereign Disaster Risk Financing in Developing Countries – Financial Protection of the State Against Natural Disasters, Insurance Eco-nomics 63: 2-8

10. National Institute of Statistics (2014) Statistical yearbook 2013, Bucharest: INS. 11. National Institute of Statistics (2015) TEMPO-Online time series, Bucharest: INS ac-

cessed November 2015 12. Oh, C. H. and Reuveny, R. (2010) Climatic natural disasters, political risk, and interna-

tional trade, Global Environmental Change 20: 243–254 13. Pop N. Al. (coord.) et al. (2011) Marketing internațional. Teorie și practică, Bucharest:

Ed. Uranus. 14. Rojas, R., et al. (2013) Climate change and river fl oods in the European Union: Socio-

economic consequences and the costs and benefi ts of adaptation. Global Environ-mental Change http://dx.doi.org/10.1016/j.gloenvcha.2013.08.006 accessed Novem-ber 2015.

15. Romanian Waters National Administration, (2013). Planul national de amenajare a bazinelor hidrografi ce din Romania, Bucharest.


Insights On Education - Innovation Links And Impact Mihaela DIACONU ([email protected]) ”Gheorghe Asachi” Technical University of Iasi, Romania

ABSTRACT This paper analyzes the characteristics of innovative enterprises in the EU in terms of education of employed personnel and its incidence on innovation output, given the key role of education in the innovation process. We use data from the Com-munity Innovation Survey which allows the analysis of enterprises in a broader context of the objectives and strategies pursued by them. Our study identifi es signifi cant direct link between increasing the proportion of personnel with university studies employed by innovative fi rms and increasing turnover. The role of practices promoted by fi rms such as employment of qualifi ed personnel is conducive for innovative output. These fi ndings have important implications for policy makers and managers within the EU. Keywords: education, enterprise, human capital, innovation, universities. JEL classifi cation: O15, O31.

INTRODUCTION

Given the rapid technological change, the ability of organizations to innovate or develop new products or services has signifi cant infl uence on their long-term performance. Innovation is a key to organizational success, resulting in increased production and profi ts and gaining more market share in terms of better satisfying the customer requirements. Increasing innovativeness leads to success and to the ability to cope with continuous changes in the market. Innovative initiatives tend to depend signifi cantly on the knowledge and experience of employees, which outlines the key inputs in the innovation process. The idea that educated employees play a key role in innovation activities, and they, in turn, lead to increased productivity is not new. That follows from the theory of endogenous growth, addressing the link between education and economic growth, based on the contributions that Nelson and Phelps (1966) have had and subsequent Schumpeterian developments. Economic growth is expressed in terms of human capital stock which, in turn, affects the ability of innovation and increasing productivity. Education and research are core activities in academia, giving relevance to the economy as far as externalities are provided due to the formation of individuals and generated knowledge, having the characteristics of quasi-public


goods. In fact, maximizing knowledge externalities is the main reason for the existence of universities focused on training and research. Involvement in research projects by collaboration is based on interactions that characterize universities and businesses, affecting innovation. In this framework, the recruitment of graduates in the business sector is also included, which is the most obvious channel of interaction between the two. Another type of interaction is through setting up of new enterprises (by spin-offs and start-up), promoting innovation and productivity growth. The transition to the knowledge economy involves increasing in universities supply to the growing demand for knowledge and highly qualifi ed staff in the business sector. From this perspective, universities play an indirect role in productivity growth and expansion of industry and services (Foray and Lissony, 2010). Universities also contribute directly to innovation by providing solutions and devices or through involvement in research activities. In the following, we assume that increasing in employment of highly qualifi ed personnel boosts innovation activity, grows production by knowledge development at the fi rm-level (Smith et al., 2005) and supports absorptive capacity (Cohen and Levinthal, 1990). Following this idea, we can formulate the hypothesis that fi rms can obtain better results in innovation when they use more specialists than fi rms less interested in employing of skilled labor force, and the fi rst register, as a result, increasing production. Although the data availability has always been the touchstone in analyzes associated with causal links that are established between labor quality and innovation output, some studies highlight that, among other factors, human capital is a powerful catalyst in innovation. For example, Brynjolfsson and Saunders (2010) show that fi rms with a greater number of high skilled workers are likely to adopt new technologies and innovative systems. McMorrow et al. (2009) demonstrate the direct relationship between investments in research and development, education (measured by the number of years of schooling) and the total factor productivity. Romer (2001) argues that scientists and engineers are relevant for research activity. Using macroeconomic models, Barro (1997) and Aghion and Howitt (1998) show that human capital increases the probability of innovation and stress the importance of employees working in research and development etc. In order to identify such causal relationships expressed above, we consider the analysis of innovative fi rms operating in the EU area, using the latest data provided by Eurostat in Commission Innovation Survey in 2014 (for the period 2010 - 2012) analyzing, in section 2, how the ”human resource” indicator progresses, knowing that it is viewed as a facilitator of innovation. In section 3 we analyze causal links associated with employment of personnel with university education by innovative enterprises and their objectives in innovation activity framed in their strategies and section 4 concludes.


HUMAN CAPITAL - FACILITATOR OF INNOVATION

Although the role of human capital - with reference to skills, abilities and knowledge acquired by individuals - in innovation is not so often embedded in empirical studies in its relation to innovative output, it is explicitly highlighted in indicators of innovation. For instance, it is recognized and included as a facilitator of innovation in composite indicators used to assess the performance of innovation, considering the latter to be a result of components and relations established within the national innovation system. The composite indicators developed by the World Bank, the World Economic Forum or by the European Commission etc. which, according to the methods of aggregation used, compose various scoreboards, have the advantage of incorporating elements in connection with the activities of the national innovation systems affecting innovation outcomes. The European Innovation Scoreboard (EIS) is the most used indicator in Europe, which enables comparisons in innovation performance between countries, being the synthetic expression of a set of sub-indicators (the enablers, fi rm activities and innovation output) with direct infl uence on the EIS composite indicator. In the period 2006 - 2013 there was an increase in the size of EIS from 0.493 in 2006 to 0.554 in 2013 (European Commission, 2014a). However, we can observe stability in its size in some countries situated in various performance groups, but signifi cant differences registered as well between countries when the sub-component indicators are compared. That fact can be explained looking the number of innovative enterprises - indicator of innovativeness with major impact on the EIS - that varies from one country to another as we show in the fi gure below:

The proportion of innovative enterprises in the total enterprisesFigure 1

0

10

20

30

40

50

60

70

80

BE BG CZ DK DE EE IE EL ES FR HR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK

Data source: European Commission (2014b) - CIS 8 (all NACE core activities related to innovation)


Firms with technological (product and process) and non-technological (organizational and marketing) innovation are included in the EIS. The Oslo Manual defi nition is used in the EIS and CIS: “innovation is the implementation of a new or signifi cantly improved product (good or service) or process, a new marketing method or a new organisational method in business practices, workplace organisation or external relations” (OECD, 2005, p. 46). Signifi cant differences in the size of composite sub-indicators have impact on the variation in the EIS. For instance, some countries with increasing in EIS innovativeness indicator above its level at the EU average have also experienced an improvement including in “human resources” sub-indicator (European Commission, 2014a). The latter measure the availability of educated and highly skilled labor force and correspond to the group of innovation facilitators, with reference to the main drivers of innovation performance that are outside the process of the enterprise decision-making. Figure 2, shows the evolution of the “human resources” sub-indicator in correspondence with an output indicator - sales due to innovation activities in countries of the European Union: ”Human resource” and ”innovation output” indicators in member states

of the EUFigure 2.

00.10.20.30.40.50.60.70.80.9

1

BE BG CZ DK DE EE IE EL ES FR IT CYLV LT LU HUMT NL AT PL PT RO SI SK FI SEUK HR

Sales of innovations Human resources


As we can observe, the size of the ”human resource” sub-indicator registers signifi cant differences among the EU countries, but such differences caught in “innovative sales” appear to be less obvious. This is because the infl uences of other factors such as fi nancing problems faced by innovative fi rms, the access to the new markets for goods, the demand size etc. express combined infl uence on innovative activities and output registered by fi rms


as well. Obviously, since the ”human resource” sub-indicator includes information on education of workforce in the labor market and does not refers to the labor force actually used by enterprises, it is not an appropriate measure to be used to highlight its effect on the innovative output. This is another reason why, from the data in fi gure 2, the link between ”human resource” and innovation performance cannot be established. The CIS8 data published by Eurostat in 2014 for the period 2010 - 2012 enable us to identify and compare the most important factors that hinder innovation activities and, in that context, the role exercised by the lack of qualifi ed personal” can be identifi ed. Figure 3 shows the proportions of (non-mutually exclusive) responses given by innovative enterprises on the most important obstacles perceived by them in their activity:

Innovation obstacles perceived by innovative fi rms Figure 3

0

50

100

150

200

250

BG DE EE EL HR HR CY LT HU MT NL AT PL PT RO Si SK SE

O1 O2 O3 O4 O5 O6 O7 O8 O9


Innovation obstacles - symbols used in fi gure 3Table 1

Symbol Enterprises:O1 - considering high costs of access to new markets highly importantO2 - considering innovations introduced by competitors highly importantO3 - considering dominant market share held by competitors highly importantO4 - considering a lack of adequate fi nance highly important05 - considering a lack of demand highly importantO6 - considering strong price competition highly importantO7 - considering a lack of qualifi ed personnel highly importantO8 - considering strong competition on product quality highly importantO9 - considering high costs of meeting regulations highly important

Source: European Commission (2014b) - CIS 8.

In general, the most important obstacles to innovation activity appear to be those related to the fi rms’ market, including strong price competition and the lack of demand. Also, the lack of adequate fi nance seems to be highly


important alongside high costs of access to new markets and of meeting regulations. Although the lack of qualifi ed personnel does not appear to be the most important, it is considered as a factor of hampering innovation by innovative and non-innovative enterprises as well. This fact can be emphasized more strongly using the fi rms’ responses to the CIS questionnaire by countries and type of enterprise:

The lack of qualifi ed personnel - important factor of hampering innovation

a. innovative enterprises b. non-innovative enterprisesFigure 4.

0

10

20

30

40

50

60

70

BG DE EE EL HR HR CY LT HU MTNL AT PL PT RO Si SK SE

Enterprises considering a lack of qualified personnel highly importantEnterprises considering a lack of qualified personnel not relevant

0

10

20

30

40

50

60

70

BG DE EE EL HR HR CY LT HU MT NL AT PL PT RO Si SK SE

Enterprises considering a lack of qualified personnel highly importantEnterprises considering a lack of qualified personnel not relevant


The data in fi gure 4a show that the most affected are enterprises in Romania (33.4%), Estonia (28.1%) and Lithuania (22.4%), which are at a signifi cant distance from fi rms in other countries in terms of the perceived lack of qualifi ed personnel with adverse impact on innovation. In the same sample of countries (fi gure 4b), non-innovative enterprises are identifi ed to be the most affected by the lack of qualifi ed personnel (Romania - 33.9%, Estonia - 23.6%, Lithuania - 18.7%). Both sides in fi gure 4 (a and b) display comparisons between innovators and non-innovators resulting, as we have expected, that the innovative enterprises can be more affected by the lack of qualifi ed personnel, given the role of education in increasing productivity and facilitating the dissemination of technology.

EMPLOYEES EDUCATION AND BUSINESS STRATEGIES AND GOALS - CORRELATIONS AND

INTERPRETATIONS

To a large extent, human capital incorporates the level of education acquired through schooling and formal training in fi rms. The fi rst component refers to the number of years of schooling that the employees possess. Firms


benefi t of the education level of employees, given that education boosts the capacity of understanding, creation and processing of information. Moreover, the workforce that have a certain level of education show greater ability to absorb knowledge and exploit opportunities than workforce with lower levels of schooling. A direct link between education and innovation is demonstrated by some authors (Knight et al., 2003). Liu and Buck (2007) included the education level in order to explain innovation output in China identifying a signifi cant effect as well. Therefore, a higher level of employees education facilitates the absorption ad transferring of knowledge in innovations and that increases turnover obtained by fi rms, reduces costs, increases market share etc., in according with the fi rms’ strategies. Starting from this conceptual support, we consider of interest to investigate whether growing in employees education leads to increasing in enterprises performance in which they work. In this respect, we use the available data in the period 2010 - 2012, published in 2014 in the CIS 8, expressing education of employees in terms of proportions of employees with university education in total employees in the EU countries. In this context, we refer to innovative fi rms in all core NACE activities related to innovation and we use fi rms that have a certain proportion of employees with university education in the total employees. The classifi cation of innovative fi rms according to this criterion is found in the CIS 8 in the form of the following indicators:

Enterprises classifi cation by % of employees with university educationTable 2

Symbol Enterprises:u_0 - with 0 % of employees with university educationu_1_4 - with 1 % to 4 % of employees with university educationu_5_9 - with 5 % to 9 % of employees with university educationu_10_24 - with 10 % to 24 % of employees with university educationu_25_49 - with 25 % to 49 % of employees with university educationu_50_74 - with 50 % to 74 % of employees with university educationu_m75 - with more than 75 % of employees with university education


The number of innovative enterprises by number of employees with university education, in various member and candidates states at the EU, which we dispose, are summarized in table 3, alongside the total turnover (in thousand euros) of enterprises:


Number of innovative enterprises by type of employees with university education

Table 3.

Countryu_0 u_1_4 u_5_9 u_10_24 u_25_49 u_50_74 u_m75 Total Turnover

(turn)Belgium 335 733 966 1908 1602 999 1395 7938 457163679Bulgaria 321 969 460 726 533 322 589 3920 40026723Germany 17038 10786 16563 24126 9862 6871 5149 90395 4287731000Estonia 39 204 146 317 282 317 355 1660 16306721Greece 699 1625 1044 1746 1241 764 720 7839 106003346Croatia 374 485 436 528 374 238 189 2624 39102552Italy 23210 16002 6461 9394 5376 3103 1907 65453 1654964825Cyprus 33 14 62 220 163 99 77 668 12479178Latvia 49 212 236 294 311 149 190 1441 14373881Hungary 334 860 743 1168 778 522 514 4919 121944563Malta 110 33 93 72 46 25 19 398 7254101Netherlands 27 1308 2031 4611 1825 1448 1717 12967 521901584,00Poland 924 422 1147 3859 2778 1451 1925 12506 374017826Portugal 1846 2924 1298 1597 734 587 661 9647 135833755Romania 20 1183 1220 1655 894 404 589 5965 60020588Slovenia 82 239 288 580 366 233 170 1958 36970577Slovakia 79 364 352 665 366 267 209 2302 76580698Serbia 633 861 598 692 463 229 227 3703 3647475Data source: European Commission (2014b) - CIS 8 (all NACE core activities related to innovation).

According to the methodology used in the CIS 8 on these indicators, each enterprise falls into one category. However, we fi nd from the data analysis that they are highly correlated so, introducing them as independent variables to explain the performance achieved by innovative fi rms could lead to inadequate results. We use, in a fi rst stage, the principal component analysis (PCA) in order to overcome this shortcoming, but also to highlight, in a broader context, existing associations between variables refl ecting the use of personnel with university education by enterprises and their strategies and goals. By that method, we can reduce the number of variables that relate both to the use of personnel with university education by innovative fi rms and their goals; to identify whether signifi cant correlations establish between the two types of indicators, namely the personnel used and goals; to identify if there are variables associated with the two types of indicators and the main components which result from their combination. If the last hypothesis is confi rmed, we are able to identify the extent by which increasing in fi rm performance can be explained by at least one component formed from PCA. In order to perform PCA analysis, we introduce the data above, alongside the indicators related to the goals of innovative fi rms such as reducing of costs, increasing of market share, increasing of profi ts and turnover. Since these dimensions do not incorporate information on the novelty of the products or services, that are of interest in terms of infl uences exercised by highly qualifi ed human factor, we introduce these aspects in analysis identifi ed in the CIS 8


as elements of enterprises’ strategy. Another indicator that refers to the goods or services novelty is the number of enterprises that consider that introducing new or signifi cantly improved goods or services is highly important. We use the indicator which refers to the ”enterprises that consider developing new markets outside Europe highly important” (m_OEU) as a measure of products novelty, having the possibility of making comparisons with the ”enterprises that consider developing new markets within Europe highly important” (m_EU) indicator. The designations and symbols of the variables incorporating goals and strategy elements of innovative enterprises, which we include into analysis are presented in the following table:

Strategies and goals of innovative enterprises - as highly important and not relevant

Table 4Symbol Enterprises:

Strategiesm_EU - that consider developing new markets within Europe highly importantm_OEU - that consider developing new markets outside Europe highly important

Goalsdc - considering a decrease in costs highly importantn_dc - considering a decrease in costs not relevantims - considering an increase in market share highly importantn_ims - considering an increase in market share not relevantipm - considering an increase in profi t margins highly importantn_ipm - considering an increase in profi t margins not relevantit - considering an increase in turnover highly importantn_it - considering an increase in turnover not relevant


We use the same sample of countries for the values of variables above in which operate fi rms in all NACE core activities related to innovation. Descriptive statistics are summarized in the following table:


Descriptive statistics - goals and strategies Table 5

Symbol Mean Std.dev. Min Maxm_EU 4926.83 9852.31 174.00 34347.00m_OEU 6457.78 13190.09 190.00 48559.00dc 7355.11 13427.77 260.00 47691.00n_dc 469.61 892.15 11.00 3130.00ims 5348.06 8879.12 182.00 31595.00n_ims 1271.56 2700.54 24.00 9317.00ipm 6650.06 13342.24 266.00 63152.00n_ipm 773.67 1519.30 23.00 6019.00it 7925.72 14393.85 54.00 54281.00n_it 475.00 899.45 11.00 3193.00

Source: author’s calculation. Descriptive statistics analysis shows that there are discrepancies between the values of the indicators regarding the goals of innovative fi rms and their strategies in many countries. In order to emphasize better those disparities and to group the variables that are not infl uenced by the same factors, we use principal component analysis (PCA). In this respect, we select Kaiser’s criterion for choosing the number of factorial axes, namely the components which result form PCA. The Bartlett’s test is another indication of the strength of the relationship among variables. Table 6 shows the results for the KMO and Bartlett tests. The value for KMO is good, meaning that pattern of correlations are relatively compact and hence factor analysis should yield distinct and reliable factors. The Bartlett’s test of sphericity is signifi cant as well (p < 0.001), and therefore principal component analysis is appropriate.

KMO and Bartlett’s Test Table 6.

Kaiser-Meyer-Olkin measure of sampling adequacy 0.688

Bartlett’s test of sphericity

Approx. Chi-Square 1209.55df 136Sig. 0.00

Source: author’s calculation. Two main components are obtained from PCA analysis and they cumulatively explain 98.7% of the total variance. We implement varimax rotation to facilitate the interpretation of these components and the results are summarized in table 7:


Rotated component matrixTable 7

Variable ComponentDesignation Symbol 1 2

Enterprises with more than 75 % of employees with university education u_m75 0.912 0.339Enterprises with 10 % to 24 % of employees with university education u_10_24 0.902 0.429Enterprises with 5 % to 9 % of employees with university education u_5_9 0.887 0.444Enterprises with 50 % to 74 % of employees with university education u_50_74 0.885 0.464Enterprises with 25 % to 49 % of employees with university education u_25_49 0.841 0.532Enterprises considering an increase in profi t margins highly important imp 0.818 0.564Enterprises considering an increase in turnover highly important it 0.755 0.653Enterprises that consider developing new markets outside Europe highly important m_OEU 0.735 0.673Enterprises considering an increase in profi t margins not relevant n_ipm 0.333 0.927Enterprises with 1 % to 4 % of employees with university education u_1_4 0.417 0.898Enterprises with 0 % of employees with university education u_0 0.454 0.885Enterprises considering an increase in turnover not relevant n_it 0.521 0.848Enterprises considering a decrease in costs not relevant n_dc 0.527 0.841Enterprises considering an increase in market share not relevant n_ims 0.677 0.734Enterprises that consider developing new markets within Europe highly important m_EU 0.677 0.732Enterprises considering an increase in market share highly important ims 0.691 0.718Enterprises considering a decrease in costs highly important dc 0.692 0.712Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser normalization.Source: author’s calculation.

In order to interpret the results, we consider the values greater than 0.5 (in absolute value) only, which are marked in bold in table 7. As it can be seen, the fi rst component is closely correlated with eight variables describing, on the one hand, the highest proportion of employees with university education (between 5% and 75%) and, on the other hand, goals of growing in profi t and turnover, framed in a strategy of developing of new markets outside Europe. Signifi cant correlations of these three variables can be observed including with component 2, but there are stronger correlations with component 1. In the same framework, component 2 is highly correlated with other variables that incorporate, to a largest extent, elements associated with enterprises goals, signifi cant correlations are also identifi ed with two variables related to fi rms operating with the lowest proportions of personnel with university education. A fi rms common goal, which is signifi cantly correlated with component 2, emerges especially to resist on the goods market, given that the strongest correlations establish with enterprises that consider that ‘’an increase in profi t margins is not relevant’’ (which can mean that increasing in turnover or, conversely, reducing in costs is not relevant) and ‘’an increase in turnover is not relevant’’ (which can mean that increasing in profi ts by cutting in costs could be more relevant). The correlation between component 2 and the variable refl ecting that an increase in market share is highly important is stronger than the correlation between the same component and the variable


associated with enterprises considering an increase in market share is not relevant but, in the same time, the ‘’enterprises that consider developing new markets within Europe highly important” variable is stronger correlated with component 2. As a result, we can interpret the two components as incorporating high innovative enterprises (component 1) and medium-low innovative enterprises (component 2). The fi rst comprises the highest proportions of personnel with university education, having increasing in profi t and turnover as the main goal included mostly in a strategy of ‘’developing new markets outside Europe’’. The second component relates especially to those fi rms that do not aim at increasing in profi t or turnover, but the maintenance and development of markets with a high probability of being local or regional, incorporating the lowest proportion of employees with university education. A more detailed analysis on the links between the two components in the European space can be achieved by one of the graphical tools permitted by PCA method, that allows the distribution of the fi rms operating in various countries to be studied and their placement compared to other fi rms from other countries at the same time. The graph obtained by combining the component 1 (on the ‘’ox’’ axis) with the component 2 (on the ‘’oy’’ axis), which shows the distribution of fi rms by country in the four quadrants created by the ox-oy two-dimensional space, is shown in fi gure 5. In this framework, the place where each country is distributed can be seen, depending on the positioning of the two principal components under consideration.

Graphical representation of the combination of components 1 and 2Figure 5

Table 5. Descriptive statistics - goals and strategies

Belgium

Bulgaria

Estonia

GreeceCroatia

Cyprus

LatviaHungary

Malta Netherlands

Poland

Portugal

Romania

Slovenia

Slovakia

Serbia

-0.4

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4


The fi rms by country can be distributed as follows: • fi rms from Germany can be identifi ed (not shown in the fi gure, with

signifi cantly higher coordinates) in quadrant 1 (+, +), namely in the positive space created by the two components (1 and 2). According to European Commission (2014), Germany is one of the innovation leaders;

• in quadrant 2 (+, -) fi rms operating in Belgium and Netherlands are placed. Those countries are in the group of the innovation followers. Surprisingly, we found that fi rms operating in Poland (a moderate innovator country) are placed in the same quadrant;

• quadrant 3 (-, -) incorporates fi rms from innovation followers (Estonia and Cyprus), but especially from moderate innovators (Portugal, Greece, Hungary, Malta, Slovenia, Slovakia) and modest innovators (Romania, Latvia and Bulgaria).

The results in the graph above are consistent with the grouping of the EU countries by innovation performance, according to the EIS indicator so, they confi rm the accuracy of our analysis. In order to identify how increasing in enterprises’ performance can be explained by at least one of the components resulted from PCA analysis we use a multiple linear regression model specifi ed by the following general equation: ii22i11i0i +C+C+=Turn (1) in which, Turni - dependent variable - turnover registered for each group of fi rms operating in the country i; C1i, C2i - the two principal components scores considered as exogenous variables; 1, 2 - parameters that summarize the principal components contribution to the dependent variable; i - an independent and identical distributed error term for i with zero mean and 2 variance.

OLS regression coeffi cients Table 8

Dependent variable ”turnover”Coeffi cient Std. error p

Component 1 924006408.94 28325274,119 0.00Component 2 457574384.01 28325274,119 0.00(Constant) 442573504.00 27527218,466 0.00R-squared 0.989F-statistic 662.55Durbin-Watson 1.53

Source: author’s calculation.

Our results indicate that although both groups - high innovative enterprises (expressed by the component 1) and medium-low innovative


performers (expressed by the component 2) - are factors that explain increasing of turnover, the greater coeffi cient of the component 1 indicates a greater contribution of that component to the turnover increase. The standard econometric tests show good results, and both variables introduced are highly statistically signifi cant. This result demonstrates that the higher proportion of employees with university education is found within the high innovative enterprises and represents an innovation and increasing production facilitator.

CONCLUSIONS

Our results confi rm that innovation is fostered as human capital increases and indicate direct effects of independent variables, namely the employee schooling in universities, on the turnover obtained by enterprises. Employment of personnel with university education falls into the fi rm’s decision-making process, alongside formal training performed in that context. Direct effects also identifi ed attest the results obtained by other authors mentioned above and we could expect that the link between schooling and innovation to be stronger if various levels of education had been introduced. This limitation of our study, as a result of the lack of data, would be interesting for future research, including other fi rm-specifi c practices such as providing of formal training. In fact, the role played by enterprises in employment of specialists but also in improving of human capital through specifi c practices such as providing formal training is obvious. In the same context of the limitations of the present study we place the impossibility to refl ect stronger the incidence of human capital on the novelty of products or services due to lack of data at the fi rm level. However, beyond the practical implications for the management of innovative fi rms in which the selection, recruitment and training of specialists, as well as fi nancing of research and development (R&D) projects are of importance, we must notice that competences in the innovation system are build, to a large extent, in universities. Therefore, it would be interesting to consider shaping a response to the following question in the future: what types and levels of education and training are most appropriate for certain types of (product or process/radical or incremental innovation? This requires the study of interactions between the components and activities of innovation system, so that human capital and fi nancial resources to be effectively allocated. Taking into account the results of PCA analysis, and factors that hamper innovation as well, we reveal that the innovative fi rms particularly operating in countries that are in the group of modest innovators, including Romania, have diffi culties regarding the lack of specialists. For instance,


although the gap, mainly in tertiary education and doctoral studies, with the highest innovation potential, have declined steadily after 2007, there is still a signifi cant gap between the sub-indicators at the EU average and those related to Romania. Training and increasing the number of specialists is crucial from the perspective of using indigenous capacity for innovation and technology absorption especially in these countries, including Romania. Universities have a double major role, a formative one of highly qualifi ed labor force and of performing basic and applied research. Maintaining low expenditure for education and research has serious negative consequences for the performance of universities in the innovation system. Although some initiatives have been taken in defi ning of strategies in research areas and by seeking to strengthen the links between universities and industrial innovation by implementing science parks located in several universities to promote local economic development, they have remained in declarative stage in Romania. On the contrary, fi nancing of various programs has been stopped or the implementation of new projects has been delayed. In addition, the effi ciency of public spending on R&D, the quality and fairness in evaluating of projects that request public fi nancial support, the socio-economic relevance of some public funded research projects and their relevance to the specifi c needs of the Romanian industry is still questioned. Universities are the most important public organization performing research not only in Romania, but also in other countries. Government funding of R&D projects is achieved through grants. The innovation system also includes public research institutions performing the same type of research as universities, including applied and technological development. In other words, different organizations perform the same activities in the innovation system, even if they are confronted with inadequate infrastructure for a high performance research, and inappropriate distribution of researchers in various areas and regions, defective collaboration between researchers of distinct organizations and between them and the business environment. The current weaknesses in the innovation system consist also in underfunding of public R&D, the lack of a legal framework for assessing of R&D effectiveness, and a weak correlation between R&D and the needs for restructuring and industrial development.

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ROMANIAN STATISTICAL REVIEW ... · Emilia GOGU PhD. candidate Alexandru IONESCU Bucharest...

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