Marian Preda · 2020. 2. 19. · Marian Preda Faculty of Sociology and Social Work, University of...

FORECASTING THE FUTURE OF HUMANKIND UNDER THE CURRENT DEMOGRAPHIC PRESSURES 3Angelica BĂCESCU – CĂRBUNARU, Professor, PhD The Bucharest University of Economic Studies

UNCERTAINTY AND STATISTICAL RISK 11Prof. Alexandru ISAIC-MANIU PhDCentre for Industrial and Services Economics, Romanian AcademyAssoc. prof. Irina DRAGAN PhDUniversity of Economic Studies, Bucharest

QUANTIFY THE IMPACT OF INNOVATION AND SUPPLY, TRANFORMATION, CONSUMPTION OF ELECTRICITY ON ECONOMIC DEVELOPMENT ACROSS EU COUNTRIES 28Florin VĂDUVATitu Maiorescu University, Bucharest, RomaniaRodica GHERGHINABucharest University of Economic Studies, RomaniaIoana DUCATitu Maiorescu University, Bucharest, Romania

TESTING PHILLIPS CURVE FOR SERBIAN AND ROMANIAN ECONOMY 40Srdjan FurtulaFaculty of Economics in Kragujevac, Serbia Danijela DurkalićUniversity of Kragujevac, SrbiaMihaela SimionescuInstitute for Economic Forecasting, Centre for Migration Studies in Prague Business School, Romania

ROMANIAN FOREIGN TRADE DEPENDENCY AND STABILITY 57Elena BănicăNational Institute of Statistics, Bucharest, Romania, Valentina VasileInstitute of National Economy-Romanian Academy, Cristina BobocBucharest University of Economics & Institute of National Economy-Romanian Academy

Romanian Statistical Review nr. 3 / 2018

CONTENTS 3/2018

ROMANIAN STATISTICAL REVIEW www.revistadestatistica.ro


STATISTICAL MODEL FOR PREDICTION OF FUTURE TREND IN HYPERTENSIVE DISEASE IN ADULT POPLATION OF ROMANIA 79Oana-Florentina Gheorghe-FroneaCarol Davila”University of Medicine and Pharmacy Bogdan DorobantuCarol Davila”University of Medicine and Pharmacy Corina IlincaFaculty of Sociology and Social Work, University of Bucharest Stefania MateiDivision of Social Sciences, Research Institute of the University of Bucharest, Marian PredaFaculty of Sociology and Social Work, University of Bucharest Maria DorobantuCarol Davila”University of Medicine and Pharmacy

Romanian Statistical Review nr. 3 / 2018 3

Forecasting the Future of Humankind under the Current Demographic PressuresAngelica BĂCESCU – CĂRBUNARU, Professor, PhD ([email protected])

The Bucharest University of Economic Studies

Corresponding member of the Romanian Academy of Scientists

ABSTRACT The article holds forth forecasting the future of the humankind, with reference

to the investigative methods on its further development.

The article presents the current demographic pressures that affect forecast-

ing depending on the land norm per person, the uneven distribution of waters, the

rainfall regime, the natural environment, the use of nuclear energy, etc.

In conclusion, the future of humankind is a process threatened by natural,

economic, social, technological, epidemiological or military risks, and the developed

countries can no longer maintain their advantages if not pushing forward for develop-

ing countries.

Keywords: demographic pressure, forecasting the future of humankind, ur-

ban agglomerations, food pressure, human development model.

1. INTRODUCTION - DOES A FORECAST ON THE FUTURE OF HUMANKIND EXIST TODAY?

We must recognise that humankind has developed so far without

a particular strategy, i.e. our civilisation left itself ridden by the tempting,

though sometimes misleading wave of the scientifi c and technical progress.

Under the circumstances of the strong competition across nations, the

human society accumulated certain bombs in human development challenges,

such as nuclear bombs, environmental pollution, poverty, etc. which, if not

being subject to disposal on time, could head the world towards disaster, a

disaster which can jeopardise the very existence of life on earth.

Therefore, in the current period people will have to inquire where exactly

is the human society moving, what will be the human society tomorrow, which

are the sprouts of that change and which are the strategies to support the society in

moving on right direction.

When the population is continuously growing and increasingly scarce


natural resources, there is a need for a new conception in the management of

the planet, fi rst of all lucidly perceiving where the humankind is going. This

requires the most realistic forecasts for the future of humankind, identifying

some possible scenarios on a given timeframe that characterise at least the

fi rst quarter of the twenty-fi rst century. Of course, this requires fi rstly a

sober perception of the world we live in, with a scientifi c knowledge on the

forces of good propelling the general progress and evil forces hampering the

development and whose proliferation needs to be cut out.

The main problem of Romanians is to be aware of the crisis to the

real dimension and to try to keep it under control. The costs of delay would be

huge and this depends on the following dilemma:

a) how soon the Romanian political class will understand this reality;

b) how will be used the EUR 20 billion lent, as this amount is to be

returned in due time and is bearing interest;

c) do the decision makers of Romania have the slightest macroeconomic

preparedness to manage this complicated crisis?

d) would national governments be capable to repay this money

without charging costs to people?

2. METHODOLOGY - WHICH ARE THE INVESTIGATIVE METHODS OF THE FUTURE?

Ordinary people are living more anchored in the present, being fi rstly

concerned about what happens today and less on what will be happening in the

future, which means that humankind cannot develop if not based on scientifi c

guidance, a scientifi c forecast of the likely evolution in the future.

As rational human beings, the people are concerned about deciphering

their future and becoming. The human knowledge strivings could be broken

down into three groups, depending on their scientifi c bases:

a) occult, biblical, doomsday foresights;

b) paranormal foresights (e.g. Nostradamus);

c) scientifi c forecasts.

Following on from the study of climate and weather, i.e. meteorology,

foresights have expanded in knowhow and technology and afterwards in

sociology and economy. In international practice, the basic forecasts are:

a) classical explorative methods based on extrapolation of past trends

of economic phenomena and processes;

b) morphological research, meaning the analytical approach of each

party and then reconstituting the combination;

c) scenario method, i.e. the construction of logical sequences to show

how future arises;

d) simulation techniques, i.e. study of various development options;


e) normative methods, where the desired objectives shall be established

based on regulatory norms and rules;

f) systemic analysis methods, integrated methods investigating social

and technical factors;

g) Delphi method, the most commonly used aims to reach a consensus

on an expert group based on an interactive process where the

views of the entire group are to be faced with individual views

“Experts are anticipated to have access to signifi cant knowledge

that enables them to predict the future – or at least, to do so better

than nonexperts.” (Rowe G.)

It should be noted that demographic projections developed by the UN

are among the forecasts with the highest degree of credibility, being the most

realistic and scientifi cally based.

In fact, according to the World Bank, there are today more than 800

million hungry people in the world and nearly half of the world’s population

is struggling in the direst poverty.

We must recognise that quite often our past dictates upon our decisions,

what constitutes a serious approach to refi ne and modernise the methods of

forecasting future.

3. THE CURRENT DEMOGRAPHIC PRESSURES AFFECTING FUTURE

“Humankind is being buffeted by the forces of demographic

change’ (David E. Bloom, 2016)

If we speak of population growth, many of us immediately imagine

scenarios on how will we succeed in procuring living resources. We bear in

mind the literate of the nineteenth century Thomas Malthus, who took the

view of a population going beyond food supply.

The forecasts of a world exhausted by the humankind occur even in

popular culture. In some areas of the globe, the population increase represents

a peak of concerns due to increasing pressures on land, the labour market and

obviously on government budgets. Multiple demographic phenomena, such as

ageing, migration, urbanisation and increased average life duration, make even

more complex the overpopulation process.”The world continues to experience

the most signifi cant demographic transformation in human history. Changes in

longevity and fertility, together with urbanisation and migration, are powerful

shapers of our demographic future, and they presage signifi cant political,

social, economic, and environmental consequences.” (David E. Bloom, 2016).

Some Member States are facing signifi cant migration of labour force, such as

our country. Others are in a position to carry out a “demographic dividend”


based on an expected growth of working-age adults such as China (F. Wang,

I. Mason A. Mason). These crossed dynamics defi ne today the demographic

changes. In the paper “The Population Bomb” published in 1968, Paul Ehrlich

warned upon the global disaster can occur due to overpopulation.

“In July 2014 the UN for the fi rst time issued offi cial probabilistic

population projections for all countries to 2100. These projections quantify

uncertainty associated with future fertility and mortality trends worldwide”

(L. Alkema, P. Gerland, A. Raftery, J. Wilmoth, 2015).

The forecasts made by the UN on the demographic perspectives of

the world population were the subject of a regular review carried out since 1951

by the Population Division of the Department of Economic and Social Affairs of

the United Nations Secretariat. “According to the results of the 2017 Revision,

the world’s population numbered nearly 7.6 billion as of mid-2017 (table 1),

implying that the world has added approximately one billion inhabitants over

the last twelve years. Sixty per cent of the world’s people live in Asia (4.5

billion), 17 per cent in Africa (1.3 billion), 10 per cent in Europe (742 million),

9 per cent in Latin America and the Caribbean (646 million), and the remaining

6 per cent in Northern America (361 million) and Oceania (41 million). China

(1.4 billion) and India (1.3 billion) remain the two most populous countries of

the world, comprising 19 and 18 per cent of the global total, respectively”.

Population of the world and regions 2017, 2030, 2050 and 2100, according to the medium-variant projection based on U.N. data

Table 1

Region Population (millions)2017 2030 2050 2100

World 7 550 8 551 9 772 11 184

Africa 1 256 1 704 2 528 4 468

Asia 4 504 4 947 5 257 4 780Europe 742 739 716 653Latin America and the Caribbean 646 718 780 712Northern America 361 395 435 499Oceania 41 48 57 72

Source: United Nations, Department of Economic and Social Affairs, Population Division

(2017). World Population Prospects: The 2017 Revision. New York: United Nations.

David Bloom (2016, pp. 6-11) states that “ninety-nine percent of

projected growth over the next four decades will occur in countries that are

classifi ed as less developed—Africa, Asia (excluding Japan), Latin America and

the Caribbean, Melanesia, Micronesia, and Polynesia. Africa is currently home

to one-sixth of the world’s population, but between now and 2050, it will account

for 54 percent of global population growth. Africa’s population is projected to


catch up to that of the more-developed regions (Australia, Europe, Japan, New

Zealand, and northern America—mainly Canada and the United States) by 2018;

by 2050, it will be nearly double their size” (fi gure 1).

On 1 July 1990, the usual resident population in our country was

23,206,720 inhabitants and on 1 July 2015 were 19,819,777 inhabitants. This

population will exert increasing pressure on the physical space, food, living

space, renewable and non-renewable natural resources, the environment and

labour market.

If in 1990 the global population density per square km was

approximately 40 inhabitants, this could rise to 59 inhabitants per km² in 2025

and the population growth will continue to put pressure on the environment

and emptying villages through migratory exoduses towards city pattern (Food

and Agriculture Organisation). Naturally, it would contribute to improving the

living conditions of the population and the civilization in general, but it also

has multiple implications for the human condition generated by the population

concentration in large agglomerations (fi gure 1).

Figure 1

It should be noted that in recent decades the degree of cities concentration

has increased, so that in 1950 there were 90 cities with more than 1 million

inhabitants and 512 cities in 2016. The forecast for 2030 is of 662 cities (United

Nations Department of Economic and Social Affairs, Population Division

(2016)). This requires a reconsideration of the city in terms of size and structure,

in order to fi ght against a giant, monstrous and overcrowded city, with poor

living conditions and characterised by the humiliation of human personality.

The forcible mass uprooting of small-scale farmers and the rural population

in general, brutally removed from their environment, caused serious economic

and social imbalances, thus widening the gap between towns and villages, thus


becoming natural a rural world far lagging behind and the population belonging to

urban environment. This puts strong pressures on the environment, soil and waters,

agricultural and forestry resources, coastal marine areas and continental shelves, as

well as on the diversity and on the global ocean.

Throughout the history, the humankind put pressure on natural

agricultural resources, as the main source of food that in return has infl uenced

the typologies for human consumption. This food pressure has led to increased

agricultural production, which in turn entailed the multiplication of population

over wide areas of the planet. If we take into account the axiom that within the

system of human needs food has absolute priority, it is clearly emerging the need

for food production to be at the top of the agenda.

Of course, the demographic pressure upon the food area, expressed in

the norm of land needed to feed a person decreased continuously. Thus, if during

the civilisation of hunting the norm of land was 5,000 hectares per person, in the

civilisation of plough was 2 ha, and today, in the period of peak agriculture, the

norm of land reached 0.08 ha. It is recognised that we owe the life on earth mostly

to green plants, 2/3 being forests. Occupying more than 30 % of the world’s

surface, forests provide at the same time more than half of the oxygen produced

worldwide by photosynthesis. Thus, the forests ensure the dynamic balance

through the annual absorption of around 15-20 million tonnes carbon dioxide and,

at the same time, the production of some 10-15 billion tonnes of oxygen.

The human development model depends largely on waters that, the

same with the food, has become a global problem. The relative scarcity of potable

water, in combination with the dramatic effect of local shortages on agriculture

and livelihood, has put water risks and opportunities among the top sustainability

issues (Ernst & Young, 2012; McNally, 2015; PwC, 2011). According to the United

Nations - 2012, 783 million people do not have proper access to drinking water,

and, in sub-Saharan Africa, water is unavailable to over 40% of the population.

More than 850 million people are undernourished and at risk of starvation, and over

1.1 billion do not have access to energy, which necessitates innovative business

models for off-grid rural areas (Schillebeeckx, Parikh, Bansal, & George, 2012).

Forecasting the future depends on the category of existing waters, as follows.

Natural waters, although representing the most convenient source are

unevenly distributed across areas and territories, are restricted to a certain period

of use, requires considerable expense for the calibration of their release, their

quality has deteriorated due to pollution and on large parts of the planet account

for fl ood hazards.

The arranged waters have developed strong economic (high costs) and

environmental restrictions, hazards of fl ooding and earthquakes and limits of use

could increase up to 40-50 years.


Groundwater under the form of underground lakes and rivers, have the

advantage that are less polluting but they are situated in the arid areas where there

is the greatest need of water and their use involves high pumping costs.

The waters of the polar ice caps comprise over 77 % of the world’s

freshwater resources, which could cover the requirements of humankind on several

thousand years. The disadvantage is that they are almost entirely in the polar areas

and have particular implications on the environment, climate, and rainfall patterns

worldwide due to displacement, with only limited possibilities for use in adjacent

areas.

Marine water resources are the safest reserves that can meet the

requirements of freshwater perspective of humankind, but has the disadvantages

of the high costs of desalination and seas and oceans waters pollution. This requires

new desalination technology under economic effi ciency conditions. No economic

growth model can be developed without taking into account the environment,

whose pollution has great implications on diversity, fauna and fl ora, the climate of

the planet, global warming which by the “greenhouse effect” can give 2-4 degrees

in addition to the Earth’s temperature. Under such circumstances, the environment

is acting as a general fi xed capital subject to moral and physical wear out and

should be recovered from the national product. Therefore, measures should be

taken at national and international levels, such as reducing the pollutant nature of

some industrial sub-sectors, stopping deforestation, promoting organic farming,

introducing taxes and fees on the use of natural marine resources and on related

pollution, etc.

The danger of using nuclear energy is the catastrophic degree of pollution

as well as the risk to extend its geographical scope and to be out of control.

Therefore, the realistic solution does not seem to be the non-proliferation and

nuclear test ban, but rather the destruction of all nuclear arsenals and completely

banning nuclear weapons or other similar weapons of mass destruction, which

could endanger the survival of the planet.

4. CONCLUSIONS From all the above we can deduce that forecasting the future of

humankind is a challenging process with risks and pitfalls, i.e. natural risks, such as fl ooding, cyclones, climate changes, earthquakes, etc. as well

as economic risks, such as the economic crisis, unemployment, infl ation,

monetary crisis etc. We must not neglect social risks, such as extreme poverty,

lack of housing, hunger, social tension and technological risks of ambivalent

nature such as those related to progress and non-progress of chemistry, biology,

nuclear energy, computing and cybernetics, etc.

There should also be taken into account the epidemiological risks, such


as the emergence of infectious diseases that may endanger human beings or the

risks of military confl icts between countries and major terrorist movements.

In conclusion, since Earth is only one, developed countries need to

understand that it is no longer possible to further develop nor can maintain the

benefi ts they have if the countries lagging behind from economic and social

standpoint are not taking off towards progress.

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Uncertainty and Statistical RiskProf. Alexandru ISAIC-MANIU PhD ([email protected])

Centre for Industrial and Services Economics, Romanian Academy

Assoc. prof. Irina DRAGAN PhD ([email protected])

University of Economic Studies, Bucharest

ABSTRACT All measurements are subject to uncertainty and the measurement result is

complete only when accompanied by a statement of uncertainty associated with the

measured value. This is the case for most statistical indicators, from the conjunctural

predictions, to the measurement of household’s expenditure and income, to the calcu-

lation of some GDP components, to determining of the price indexes, to determining

the unobservable economy, to determine the population’s perception regarding qual-

ity of life etc., up to voting intentions, to list only a few areas. The uncertainty, in the

fi eld of measurement, must be materialized by a statistical indicator, which expresses

a certain fact, the distance /closeness to the true value of the size subject to the mea-

surement process. Uncertainty also appears as the result of human ignorance, and its

form of manifestation is the variability which, exceeding certain admissible limits, can

generate what we commonly call a risk, namely to make an erroneous decision in a

situation where necessary information is distorted precisely because of too much vari-

ability. The risk in making decisions is present in all human activities, from where the

vastness of the problem as a research fi eld. In the paper we propose the exposure of

uncertainty measurement procedures and the link between uncertainty and risk. The

statistical modeling of risk has as a starting point the assumption that risk can be assimi-

lated with the possibility of suffering a certain loss. Because the possibility is expressed

quantitatively, by probability, the risk appears as a probability function in the occurrence

of an unwanted phenomenon. Finally, certain Taguchi risk applications are presented,

regarding the relationship between this risk and the potential index of a process.

Key words: uncertainty, errors of III and IV type, statistical risk, Taguchi

method

1. CHARACTERISTICS MEASUREMENT Deriving from the measurement science, metrology, this issue has

evolved into engineering sciences but it is also indispensable in the socio-

human fi eld, with the essential distinction that there is no measure unit (like in

Physics) in the social sense.

A statistical indicator can be the numerical expression of an economic

category but also the correspondent of a variable. The statistical indicator is

used to numerically express the sizes, structures, interdependencies or changes

in time of some social-economic phenomena. The overwhelming majority

of the statistical indicators are obtained by statistical processing, according


to standardized methodologies of the observation data, measured on units

(individuals, families, companies and so on) of a survey.

It is common knowledge that there are many types of measurements

(besides the classical physical ones), such as measuring customer satisfaction,

poverty, infl ation, environmental degradation, the health of the population,

school performance, the volume of national or regional production, measuring

profi tability and so on.

All measurements are subject to uncertainty and the measurement

result is complete only when accompanied by an indication of the associated

uncertainty. This uncertainty has a probabilistic basis and refl ects the

incomplete knowledge of quantity value (Eisenhart 1963).

One of the simplest decision-making problems under uncertainty is

the acceptance /rejection of a statistical assumption, a hypothesis that may be

true or false. Uncertainty occurs precisely because of sampling: it works with

a part (or parts) of a certain “whole” (population, batch of products etc.) and

not with the whole collectivity, so the decision to accept /reject the hypothesis

is based on the examination of only that part, which is the sample. In most

situations, indicators that refl ect customer satisfaction, poverty and so on are

obtained based on data collected through surveys.

2. LITERATURE REVIEW We will not dwell on the common defi nitions and common sense of

the term uncertainty (according to Dex: “lack of certainty, uncertainty, doubt,

hesitation”; or according to Macmillan’s Dictionary of Modern Economics:

“the fact that something is not known or has not been decided; uncertainty

about/over/as to; a degree of uncertainty (some uncertainty); uncertainty

surrounds something (people are very uncertain about it)”, and we will tackle

the formal defi nitions and concepts of statistical uncertainty offered by the

profi le standards, as offi cial regulatory documents.

Thus, in ISO 14253-1: 2013 we fi nd the general defi nition of

uncertainty: “a non-negative parameter characterizing the dispersion of the

quantitative values being attributed to a measurand, based on the information

used”. For the measured values ISO 20988: 2007 it states the following

defi nition: “uncertainty (of measurement) - parameter, associated with the

result of a measurement, that characterizes the dispersion of the values that

could reasonably be attributed to the measurand”. ISO 13843:2017 defi nes

the standard uncertainty: “uncertainty of the result of a measurement

expressed as a standard deviation” and ISO 17123-1: 2014 establishes

expanded measurement uncertainty: “half-width of a symmetric coverage

interval centered around the estimate of a quantity with a specifi c coverage


probability.” The target value in this area is recommended through ISO/IEC

Guide 99: 2007 “target measurement uncertainty: measurement uncertainty

specifi ed as an upper limit and decided on the intended use of measurement

results”. Same standards classify uncertainty measurement in two main types:

“Type A evaluation, evaluation of a component of measurement uncertainty

by a statistical analysis of measured quantity values obtained under defi ned

measurement conditions” (VIM 2012). In the case of type A, it is assumed that

the distribution of the observed values is of Gaussian type, with the average m

and the standard deviation equal to the standard deviation of the mean σ. Type B evaluation of measurement uncertainty is the evaluation of a component of

measurement uncertainty (standard uncertainty) determined by means other

than a Type A evaluation of measurement uncertainty (ISO 17123-1: 2014). In

this case, for an assessment of uncertainty, the only available information in

this case is that X lies within a specifi ed range [a, b]. Measurement uncertainty

is often considered as the standard deviation of the probabilities distribution

that could be attributed to a measured quantity. The relative uncertainty is

the uncertainty of measurement relative to the magnitude of a single choice

for the value to be measured. This particular unique single choice is usually

called the measured value, which may be optimal in a well-defi ned sense (e.g.,

mean, median, or modal value). Thus, the relative measurement uncertainty

is the uncertainty of measurement divided by the absolute value of the

measured value, when the measured value is not zero. The uncertainty should

not be confused with the estimate attached to a result of measurements that

characterize the range of values within which the average is supposed to be.

All measurements are subject to uncertainty and the measurement

result is complete only when accompanied by a statement of associated

uncertainty. This uncertainty has a probabilistic basis and refl ects the

incomplete knowledge of the quantity value (Petrescu, 2004).

Measurement uncertainty is often considered as the standard deviation

of the knowledge status probabilities distribution over the possible values

that could be attributed to a measured quantity. The relative uncertainty is

the measurement uncertainty in relation to the magnitude of a single choice

for the value for the measured quantity, when this choice is nonzero. The

development of mathematical possibilities of uncertainty representation is a

concern that Helton and Oberkampf (2004) dedicates an interesting study.

According to Eisenhart (1963), measurement is the assignment of

numbers to certain material objects in order to represent the relationships

between them and certain specifi c (particular) properties of the objects in

question. The author distinguishes between the measurement method and the

measurement process. Finkelstein (1982) develops the basic principles of

measurement in various branches of science, including in the socio-human


fi eld, and Murphy (1961) emphasizes that a measurement method does not

become a measurement process until this process is in the state of statistical

stability, meaning that measurements become the “product” of an identifi able

statistical universe. This means that we can allocate a well-defi ned statistical

distribution to these values that represent the measurand. We usually “hope”

that this repartition be the normal one, because the parameters of this statistical

law, m and 2σ , are exactly the average and respectively the theoretical

dispersion of the measured quantity. As well known, they are estimated by

x (arithmetic mean) and by the indicator ∑ −−= − 212 )()1( xxns i , where

ix are the observed values of the variable X. In the case of the normal law

(Gauss-Laplace), the average value can be taken as an acceptable reference

value, constituting a substitute of the so-called “true value” of the measurand,

which is unknown (see also ISO 5725-1 page 6, section 3.5).

Hunter (1980) considers that a measurement operation is in a state

of statistical stability if there are quantitative measures of repeatability and

reproducibility. By “repeatability” he understands a measure of variability

between the values observed in the same laboratory and by “reproducibility”, a

measure of variability between two or more laboratories. The term “laboratory”

is generic (conventional), the laboratory may also be a public opinion polling

organization. The two concepts are defi ned by the aforementioned standard

as representing fi delity under repeatability and reproducibility conditions.

Quantitative measures proposed by Hunter are the associated standard

deviations (SR) and the so-called repeatability /reproducibility limits (r sau R),

defi ned as , where d is the absolute value of the difference between two results

of an observation obtained under repeatability /reproducibility conditions.

An assessment version of uncertainty evaluation and of prediction chances

is performed by Helton and Oberkampf (2004), based on the uncertainty of

input information and its propagation in prediction models to the uncertainty

of the fi nal solutions, in case of using Monte-Carlo simulation methods.

Hoffman and Hammonds (1994) tackled the issue of uncertainty

propagation within predictive models, making a clear distinction between the

uncertainty generated by the lack, respectively information insuffi ciency and

the uncertainty generated by the variability of the input data.

Stigler (1986) develops in a monumental book the issue of mathematical

methods in measuring and modeling uncertainty. Stigler’s emphasis is upon how,

when, and where the methods of probability theory were developed for measuring

uncertainty in experimental and observational science, for reducing uncertainty and

as a conceptual framework for quantitative studies in social sciences. He describes

the scientifi c context in which the different methods evolved and identifi es the

problems (conceptual or mathematical) that retarded the growth of mathematical


statistics and the conceptual developments that allowed major breakthroughs.

Oberkampf et al (2001) deal with the subject of uncertainty and Jacquin (2010)

develops an intriguing approach of predictive uncertainty.

Among the interesting applications of uncertainty determination, can be

mentioned the ones of Valcan (2013) to determine the uncertainty in measuring

the value of phosphorus in water, or Beck and Krueger (2016) discuss the issue

of integrated global climate change models, combining the representations of the

economic system and climate, models that have become important tools in supporting

policy makers on climate matters. Riahi et al (2007) present three versions of

scenarios in economic and social development and the consequences on greenhouse

gas emissions and analyze the feasibility of each of them. Conclusions focus on

uncertainties and costs. Rui et al. (2016) develops the problem of uncertainty and its

measurement on a specifi c domain - the resilience, by non-probabilistic indicators

and metrics to which they associate the uncertainty dimension. Georgescu -Roegen

(2000) addresses the issue of uncertainty and choice based on a paper of Armstrong

(1948) which, for the measurement of uncertainty, assigns a binary system, assigning

0, respectively 1 to complete uncertainty, respectively to complete certainty, then

0.5 to neutral uncertainty, repeating the process on successive intervals. The

author draws the attention to the distinction between uncertainty and probability

and the need to eliminate confusion between the two categories. The author also

highlights the issues related to the nature of expectations and uncertainty, certainty

and quasi-certainty, between expectation and subjective belief. Ferson et al. (2007)

approach the characterization of measurements that include epistemic uncertainties

in the form of intervals. It examines the application of the basic description and

the determination of some algorithms to make inferences on the data observed.

Bachmann et al (2010) raise the level of macroeconomic approaches using

microdata based on business environment making uncertainty indicators based on

both ex-ante, as well as on ex-post forecast errors. Jurado et al (2015) exploit a rich

literature and volume of observational data in order to provide direct econometric

estimates of variable macroeconomic uncertainty over time. Cox and Harris (2006)

approach some developments of previous material on some automatic calculation

of uncertainty programs. The software architecture is harmonized with a reference

work and regulation in the fi eld: “Evaluation of measurement data. Guide to the

expression of uncertainty in measurement” JCGM 100:2008. A critical analysis of

the literature on decision-making risks and the measurement of uncertainty up to

the level of 2010 is carried out by Zio and Pedroni (2012).

Another area of interest, closely related to uncertainty, is that of risk.

Kaplan and Garrick (1981) defi ne the risk and offer a calculation version based

on Bayes’ theorem, differentiating the relative risk, the relativity of the risk, the

acceptability of the risk. The presentation of the different risk types (fi nancial,


technical, bankruptcy, so on) and the calculus methods are developed by Isaic-

Maniu (2006). A development of the risk topic in a technical fi eld - making

composite materials with a risk specifi city, is performed by Taguchi, Ionesiet

et al (2012), Walls et al. (2016) and Montewka et al (2014) address the issue

of risk in determining reliability.

3. MEASURING UNCERTAINTY It is known that one of the simplest decision-making problems under

uncertainty is the acceptance / rejection of a statistical assumption, a hypothesis

that may be true or false. Among the sources of uncertainty we can mention:

an incomplete, partial information on a certain entity, lack of information,

inadequate interpretation of information, mis-attribution of causality. The

uncertainty in the fi eld of measurement gains a concrete outline through a

statistical indicator, through a formula in the end, which expresses a certain

fact, the distance / closeness to the true value of the physical size subjected to

the measurement process. The uncertainty can not be separated from the human

factor. Statistics, among its many meanings, is defi ned as follows: “the science

of decision-making under uncertainty”. Referring to the human factor, Cox

(1957) states “the observer is a part of what he observes, the thinker is a part of

what he thinks. We can not passively observe the statistical universe as mere

spectators, because we ourselves are part of this universe”. Uncertainty appears

as the fruit of human ignorance, and its form of manifestation is variability,

which exceeding certain admissible limits, can generate what we commonly

call a risk: to make an erroneous decision in a situation where, the necessary

information is distorted, precisely because of this exaggerated variability.

Statistically, the standard uncertainty defi ned in ISO 20988: 2007

as the uncertainty of the result of a measurement, expressed by a standard

deviation, is basically an estimation of the internal variability of a set of

observations carried out on a measurand.

“Measurement”, in this context represents the whole set of

operations where experimental data are obtained (the term

“measurement” is commonly used as a synonym for the measured or the

experimental value).

The standard deviation is calculated using the well-known formula

( ) where

[1]

with which is not an unbiased estimator of the theoretical

standard deviation (σ ), but s2 is such an estimator for the variance 2σ .

In order to obtain an unbiased estimator, we will have to remind that


the distribution of the statistic is chi (not chi-square) with (n-1) degrees of

freedom, and that its mean value E(s) is computed as:

[2]

where )(•Γ is Euler’s famous Gamma function (see Patel et al, 1996, pp.

118). Therefore, the non-stacking coeffi cient for s will be precisely the inverse

of the coeffi cient that multiplies the expression under the radical. However,

in practice we do not work with the modifi ed estimator. When calculating the

natural variation range of the measurand, or when we want to build confi dence

intervals - either for the average ( µ ) or variance (2σ ) - we use the statistics x

and s, or we rely on the fact that the statistic has a chi-square

(2χ ) distribution with (n-1) degrees of freedom, as demonstrated by Helmert

in 1876 (Sheynin, 1995, pp. 88). Similarly, we make use that the statistics x

and s2 are independent and x and w = xmax – xmin (the data amplitude x1, x2,

....,xn) are independent and that the statistic has a Student

distribution with (n-1) degrees of freedom. We also know that the average

has an approximately normal distribution with the same mean ( µ ) with the

x generating variable but with the adjusted variance n2σ , where n is the

number of values of which x was evaluated.

The size is named in the dedicated standard (VIM 2012) as the

experimental variance of the mean and is a measure of the uncertainty of x . As is

well-known, statistical applications use the expressions nσµ 3− , respectively

nσµ 3+ , as limits for the mean of a process, where µ is estimated through

the mean of the group’s mean and σ by an average amplitude, either directly

through s. According to the known theory:

[3]

if x is a normal variable then outside the boundaries there will be about

0.27% of the variable’s values; thus, on average, one of each 370 subgroup’s

means will be out of bounds when the process is in control.

Since the average is an almost normal variable, even if the measurand

x proves to be another statistical law, we can say that a quantitative measure

of the average uncertainty is the interval [ nsxnsx 3,3 +− ]. The composite

standard uncertainty is also defi ned as a standard deviation, but in this case,

the measurand is considered a (differentiable) function of certain input

measures x1, x2, ....,xn, ),...,X,Xf(XÕ N21= , every such measure having

its own variability, given by s(xi) its standard deviation. The measurand

Y is indirectly evaluated, through measuring Xi , Ni ,1= elements and the

composite standard uncertainty noted, in ISO/IEC Guide 99: 2007, but


also in Charles et al (2017), with )(yuc the positive square root of the

expression:

[4]

where u( ix )= )( ixs the standard deviation of Xi component estimated

in and the values of Xi are considered uncorrelated and the form of the

function f is not signifi cantly nonlinear.

In the case of correlated variables and nonlinear distribution, the above

formula is completed accordingly. In many situations, the relation Y=f(X1, X2, ......, Xn) can be linearized (by logarithm, inversion or other operations).

For example in case of polytropic functions: NaN

aa XXXcY ⋅⋅⋅⋅= ...21

21,

,0,,0 >∈≥ cRaX ii and thus, by reverting, a linear function is obtained in

the new variables. The process is well-known in the theory of regression and

correlation, where linearization plays an important role in facilitating the

estimation of function parameters.

Extended uncertainty is defi ned according to (VIM 2012), as a size that

defi nes a range around the result of a measurement, a range in which a high

fraction of the distribution of the values that can reasonably be attributed to the Y

measurand. This uncertainty is also called global uncertainty and is denoted as U.

In quantitative terms, U is expressed as )(yuKU c⋅= , where K is an expansion

factor which, according to the VIM 2012 document, can usually take values

between 2 and 3, and uc(y) is the composite standard uncertainty. Thus, the result

of a measurement can be expressed in the form UyY ±= , where y is the best

estimate of the Y measurand, the range [y-U, y+U] being the domain considered

to incorporate an important fraction of the values of this Y measurement.

If we consider that yy = (the mean being the best situation of Y) and

considering that uc(y) is a standard deviation sy, the interval [ ]yy sKysKy ⋅+⋅− ,

is a range of natural tolerances of ( )γ,P type, where P is the proportion of values

found in this interval, γ being the likelihood with which this situation happens:

[5]

where: yi sKyL ⋅−= , ys sKyL ⋅+= , ),,( γPnKK −= ,

1,0 << γP ; f(y) being the probability density function of Y measurand. Thus,

what is the explanation of the fact that K factor is preferred with values

between 2 and 3.

If Y is normally distributed, then we know that the range

[ ]sysy 2,2 +− contains about 95.45% of the Y values and if we take K=3

then [ ]sysy 3,3 +− contains about 99.73% of the values.


The measure γ is the probability of coverage, or even the confi dence

with which we assert that the respective interval sKy ⋅± contains the

proportion P of the values for measurand distribution Y.

4. THE ERRORS AND THEIR ASSOCIATED RISKS Typical statistical errors in the hypothesis verifi cation process

are Neyman and Pearson’s well-known Type I and Type II, usually noted

with α and β, on which we no longer insist, only extend to other possible

interpretations beyond the classical assumption, for example in the fi eld of

computer security: Type I errors (or false positives) that classify the authorized

users as imposters; Type II errors (or false negatives) that classify imposters as

authorized users.

Superior type errors appeared in statistical specifi c literature later.

Type III errors, which according to Raiffa (1970) are the correct resolution

of a false problem. Continuing with the example of computers area, the term

“false positive” can also be used when antivirus software incorrectly classifi es

a harmless fi le as a virus. Incorrect detection may be due to a heuristics or an

erroneous signature of viruses in a database. Similar problems can occur with

antitrust or antispyware software. Type IV errors were proposed by Marascuilo

and Levin (1970) which they defi ned in a way similar to Mosteller as the

mistake of “misinterpreting a correctly rejected hypothesis”. None of these

last two proposed risk categories met with broad acceptance from specialists.

Depending on uncertainty, the risk is characterized by the possibility of

being quantifi ed by probability laws, although it is also dependent on “fl uid”

elements, such as uncertainty and loss, which can not always be expressed

numerically. Here is a brief presentation to some of the defi nitions of risk. Mic Dicţionar Enciclopedic, page 809: “Risk: danger, possible inconvenience”. It

then identifi es a number of concretizations, with examples such as “contractual

risk” - the debtor sustains the damaging consequences of the issuance of the

creditor of his obligations to him as a result of the debtor’s failure to fulfi ll his

obligations because of causes that are not attributable to him. The notion of

work risk appears - the bearing by an owner or the holder of a direct operative

administration right, of damages resulting from the loss or destruction of work

by major force or in case of unforeseeable circumstances; the notion of insured

risk is also mentioned. DEX (1998) states: “The risk represents the possibility

to reach a danger, to face a tribulation or to bear a loss; possible danger”.

Merriam - Webster’s Collegiate Dictionary (1994) also offers historical details

in the sense that the term “risk” and its derivatives began to be used in English.

The risk may or may not be manifested by the action of the so-called

risk factors which, when operating effectively, can cause various losses. It


should also be said that despite the fact that there is a risk in a given situation,

it may not be manifested, that is to say, it does not produce effects (sometimes

the decision-maker acts randomly and may indeed have the chance of not

suffer loses, damages etc.). A risk-taking action is said to be risky or dangerous

(hazardous). Over time, new notions and new activities have been created

in the fi eld of management, thus emerging a new risk management activity

as a technique of evaluation, minimization and prevention of accidental loss

in a business, insurance, safety measures or other appropriate elements, and

obviously a new “risk manager” specialist. The wide range of risks is given

by the fact that besides Nature, man created a contradiction that includes the

economy as a whole, as well as industry, fi nance, commerce and so on, the

natural environment being increasingly anthropogenic, natural components

have been increasingly modifi ed by human activities. The human activities

contain the germs of environmental destruction, and in addition, these are

activities dedicated to the satisfaction of many non-vital needs (Georgescu-

Roegen, 2000). The diffi culty arises because the uncertainty and the risk being

in their structure impregnated with the random element, immediately attracts

the statistical-probabilistic arsenal. Moreover, by minimizing both the risks

and the possible losses from the effective risk manifestation, we need the

operational research methods.

Therefore, the mathematical modeling of risk starts from the assumption

that risk can be assimilated to the possibility of suffering a certain loss. Since

the possibility is quantifi able by probability, the risk appears as a function of the

probability of occurrence of an unwanted phenomenon, but also of the adverse

effects of this event on which we did not anticipate the production. The effects

in turn are manifested - combined: money losses, drops in performance, delays

in executing operations and so on.

The uncertainty occurs precisely because of the sampling: it works

with a part (or parts) of a “whole” (population, lot, fi rms etc.) and not with the

whole collectivity, the decision to accept /reject the hypothesis based on the

examination of only that part, which is the sample.

Among the versions of economic risk we can mention: the risk of

indebtedness, the fi nancial risk, the settlement risk, the bankruptcy risk.

The latter risk requires the theoretical construction of a function (analytical

relation) that can estimate the probability that a particular enterprise, company

etc.) to record losses (suffi ciently large) that would not allow it to pay its

various invoices to utilities (gas, water, electricity) or to be unable to repay

any credits or loans contracted on the market. The economic risk is seen in

conjunction with the so-called “political” and “country” risks. These can not

be expressed by a formula, being a composite indicator, using a conventional


“scoring system” based on various factors taken into account: legislative

stability, economic democracy and the level of offi cial corruption – an issue

of special interest on the side of investors.

Country risk is defi ned in summary as a risk of default, non-recovery,

non-repayment of capital, or situations where a particular political regime in a

country tends to nationalize the assets of private companies, usually foreign.

Various specialized publications as The Economist or Euromoney have

tried since the 1980s to construct a synthetic indicator that would allow the

classifi cation of countries according to this country risk, in ascending order:

the last places are occupied of countries with a maximum risk in the perception

of those interested. Statistically, the curve is modeled using random variables.

Thus, for example, the technical risk (Isaic-Maniu 2006) is determined from

the reliability index. Formally, if T is the variable representing the time-to-

failure of the object, then one may write:

[6]

The complementary value of reliability is precisely the technical risk

of falling down a component or a system:

[7]

Here R(t), in our case for t=T0, stands for the reliability function

associated with the variable T. Consequently, the complement of R is the so-

called non-survival (or non-reliability) function F(t)=1-R(t), which represents

from statistical point of view, the distribution function (df) of T. Here F(t0) is

hence the probability that the system operates less than a desired time t0. If the

reliability R(t0) is low, consequently this technical risk is high. More adequate

to defi ne this technical risk seems to be hazard rate (or failure rate) function

which may be called also „the danger of failure”:

danger of failure”:

)(

)(

)(

)(

tR

tdR

tR

tdF

) t ( R

) t ( f =

) t ( F - 1

) t ( f = ) t (z

means a low level of reliability (z(t) is expressed usually in failures/hour).

[8]

A high value of z(t) means a low level of reliability (z(t) is expressed

usually in failures/hour).

Excessive risk treatment, especially of fi nancial placements, is

performed by Radulescu and Radulescu (2006). If the variable X is the profi t

of an investment, the loss suffered by an insured, or the exceedance of the alert

threshold of the concentration of a pollutant in the air, then the associated risk is:

( )[ ]2

1 )()( mXMXVarXR −== [9]

where )(XMm = is the average value of variable X .


Furthermore,

( )[ ]2

2 )( +−= XTMXR [10]

where T is a so-called “disaster threshold” (Radulescu and Radulescu

2006, p. 199) and it is obvious that the investor wants to have a profi t placed

always above the threshold.

If X is interpreted as exceeding the concentration of a pollutant

relative to a certain maximum allowable T level, then the risk of pollution is:

( )[ ]pTXMXR +−=)(3

, { },...2,1∈p [11]

The notation originates in Lower Partial Moment indicator – the

partially lower order alpha moment in relation to T threshold:

T

X

pxdFxTXTMXTLPM )(),( [12]

X

D

X

XT

TX

[12]

where: )(xFX is the distribution function of X variable.

If T equals the mean )(XMm = and 2=α , then we obtain the so

called semivariance:

2)( XmMXSV [13]

The expressions )( XR and )(XR are nothing but average risks associated to

D

XT

TX

[13]

The expressions )(2 XR and )(3 XR are nothing but average risks

associated to quadratical form 2)(),( TxkTxL −= used by Gauss since

1809 (Kackar, 1985).

5. TAGUCHI RISK AND AN APPLICATION The expression

2)(),( TxkTxL −= was reactivated by Taguchi

(Alexis 1999): T is the target value of measurable characteristic X , and

),( TxL is the loss quality function. Then:

D

T dxxfTxLTxLMxR )(),(,)( [14]

D

XT

TX

[14]

is Taguchi risk, meaning the average value of the variable ),( TxL

(Voda 2009) where )()( ' xFxf X= and D is the defi nition domain of X ,

usuallyD , usually ,0 .

X

XT

TX

.

In our case:

our case:

2

2222

22

)()(

)(2)()()(

)(2)(),(

TXMXVark

TXTMXMXMXMk

TXTMXMkTxLM

[15]

If the variabile is normally distributed ),(~ 2NX , then the Taguchi risk becomes:

X

XT

TX

[15]

If the variabile is normally distributed ),(~ 2σµNX , then the

Taguchi risk becomes: [ ] [ ]22 )(),( TkTxLM −+= µσ , and its estimation

becomes:


[ ]22 )()(ˆ TXskxRT −+= [16]

where ∑−= ixnX 1, and ∑ −−= − 212 )()1( Xxns i

The more symmetrical is the distribution of the characteristic, and its

mean on the observation data )(X is closer to the target value )(T , the lower the

associated risk is.

Taguchi (Alexix 1999) assumes that if the mean X is very close to the

target value T, then the standard deviation changes as follows:

=X

Tss1 [17]

Certainly, if XT ≈ , then one can suppose that ss ≈1 , and the loss

function is:

2

2

2

2

1),(X

skT

X

TskksTxLM

TX thus, in the exact version, derived from relation [14] we have:

[18]

If TX = thus, in the exact version, derived from relation [14] we have:

2

22)(ˆX

sXkksxRT [19] [19]

Both in forms [18] and [19] occurs the coeffi cient of variation Xs / ,

Taguchi thus motivating his idea to use this indicator as a performance indicator

of a process (in the broadest sense, not just as a technological process).

Appreciation and introduction of the coeffi cient of variation, as a performance

indicator of the process, was disputed, considering that the importance given

by Taguchi to the inverse of this indicator - the signal to noise ratio or the

perturbation coeffi cient, is exaggerated and often unconvincing. It can not be

judged the performance of a process, for example, only from the point of view

of the variance coeffi cient or its inverse.

Formula [17] shows that if 0=T , then 01 =s , that would mean that

all values are identical, which is a limited case. Consequently, we believe

that Taguchi’s hypothesis [17] can only be valid if 0≠T . In case 0=T we

should start from formula (16):

)()(ˆ 22 XskxRT += [20]

that we can rewrite:

22

2

22 )(11)(ˆ XSNkss

XskxRT

[21]

X

[21]

thus highlighting the indicator SN )(XSN - signal to noise ratio. In order

to mimimize risk )(ˆ xRT a function of two variabiles X and 2s has to be

minimized.


A measurable feature, which generate frequent fallouts to the fl ywheel

from the constructional component of a compressor, has fi xed tolerances as

follows: Lower Specifi ed Limit LSL=263.48 mm and Upper Specifi ed Limit

USL=263.68 mm. The target value is T=263.58 mm, exactly the middle of

the [LSL-USL] interval. If the performance level is fi xed to Cp=2 and the

process mean is 58.263x = 58 mm with standard deviation s=0.011 mm, then

the Taguchi index has the estimated value index has the estimated value 40.0ˆ

622

pmpm C

T

LSLTUSLC that is a very weak potential

this case, we can calculate Taguchi risk for

that is a very weak potential index of the process. In this case, we can calculate

Taguchi risk for with formula [18] and it results:

( )[ ] kkkLM T ⋅≈⋅=

⋅⋅= 007.000684.0

403.0

1

6

2.0x

2

. Constant k is expressed in

monetary units.

If the deviation d is exactly 1, then k = Ad representing the cost for a

non-conforming unit of product (with charactersitic values greater than USL

or lower than LSL). The higher the Taguchi risk is, the bigger the cost for the

defective unit. Thus:Cost /defective unit Taguchi risk /unit

1 m.u. 0.007mm2 m.u. 0.014 mm3 m.u. 0.021 mm4 m.u. 0.028 mm5 m.u. 0.035 mm

Here the m.u. symbolize the chosed monetary unit (ROL, EUR, USD

and so on). The relation between the monetary unit and the risk being linear

(R = k · u), as the unit of defective product is cheaper, the associated average

loss, also expressed in monetary units, is lower.

6. CONCLUSIONS One of the most common decisions under uncertainty is the acceptance

/rejection of a statistical hypothesis, which may be true or false. The uncertainty

is generated, in this case, by the fact that only a part of a population is

involved, and that the assertions are based on the indicators obtained from the

observation data of a sample, not from the whole population. It is the case of

most situations of determining the statistical indicators, from the conjunctural

predictions, to the expenditures and incomes of the households, to the

determination of some components of the GDP, to the intentions of voting -

to list only a few of the domains. The risk in decisions making is present in

all human activities, hence the vastness of the problem, as a research fi eld.

The diffi culty is amplifi ed by the fact that the uncertainty and the risk are


impregnated by the random factor, but this draws the statistical probabilistic

tools in dealing with the issue. The statistical modeling of the risk starts from

the assumption that the risk can be assimilated to the possibility of suffering a

certain loss. Because the possibility is expressed quantitatively by probability,

the risk appears as a probability function in the occurrence of an unwanted

phenomenon. The uncertainty and the risk are modeled by random variables.

In many cases, the variace is used as a measure of risk, but in the case of less

symmetric distributions the result is inconclusive, in which case the Taguchi

risk can assess the loss.

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Quantify the Impact of Innovation and Supply, Tranformation, Consumption of Electricity on Economic Development Across EU CountriesFlorin VĂDUVA (fl [email protected])

Titu Maiorescu University, Bucharest, Romania

Rodica GHERGHINA (rodica.gherghina@fi n.ase.ro)

Bucharest University of Economic Studies, Romania

Ioana DUCA ([email protected])

Titu Maiorescu University, Bucharest, Romania

ABSTRACT The objective of the research is to identify the impact of innovation and sup-

ply, transformation and consumption of electricity on economic development. In order

to analyse the relationship between economic growth, innovation and energy con-

sumption in EU countries, the regression technique was used. The output variable is

Gross Domestic Product per capita computed for 26 countries across the EU. The ex-

planatory variables are the potential factors that could have contribution to GDP growth

of countries, as well as the innovation – measured by Global Innovation Index and

Primary Energy Consumption. The result of research can be used in substantiation of

public policies designed to boost the economic development of various countries.

Keywords: economic growth, innovation, supply, transformation and con-

sumption of electricity, GDP

1.INTRODUCTION In this century, the energy policy, through all its components, plays a

major part in ensuring environmental sustainability. The electricity sector has

come a long way in its progress towards a sustainable approach to electricity

generation and supply, but it still has a long way to go. Although the level

of investment in this sector has increased in the past years, it will need to

maintain this trend for its objectives to be achieved. The forecast is that the

electricity prices will increase in the next two decades, consequently decision-

makers must minimise the pressure put on companies and consumers by way

of decreasing costs and avoiding the policies’ ineffectiveness.


Our research includes a review of the literature in the fi eld of the

researched topic, the research methodology, the database used in the research

and the main empirical results, followed by conclusions.

2. LITERATURE REVIEW The impact of innovation and energy consumption, especially

electricity, is the object of numerous scientifi c research endeavours in the recent

years. According to Platchkov and Pollitt, ”the economic engines, technologies

and demand management are essential in understanding the long-term trends

in the fi eld of energy and especially the fi eld of electricity consumption”

(Platchkov and Pollitt, 2011). At the same time, „the energy policy in the current

context should balance availability, the security of supply and environmental

sustainability” (World Economic Forum, January 2015, pp.9).

In this context, in the modern society, „energy consumption is one of

the drivers of economic growth, and electricity consumption, which is one of

the most fl exible types of energy, is a vital input in a country’s socio-economic

development”. (Electricity consumption and economic growth in India Sajal Ghosh*, Indira Gandhi Institute of Development Research (IGIDR), Mumbai, India Received 2 September 2000).

Another important and frequently analysed aspect is the extent to

which certain policies for the rationalisation of electricity consumption of

for tariff-setting may be implemented by governments if consumption is very

different from one industry to another, and their contribution to the country’s

economic growth is also different.

As a matter of fact, the causal relationship between energy consumption

and economic growth was the focal point of the research performed by several

economists and public policies analysts beginning with the 1970s (Kraft and Kraft,

1978; Beenstock and Willcocks, 1981; Samouilidis and Mitropopulous, 1984; Yu

and Choi, 1985; Erol and Yu, 1987; Cheng and Lai, 1997; Adjaye, 2000: Tsani,

2010; Acaravcı, Ozturk, 2010; Akkemik, Göksal, 2012; Adhikari, Chen, 2013).

In a study performed by Morimoto and Hope (Morimoto and Hope,

2001), the authors start from the model developed by Yang (2000) and, based

on the research they performed, reached the conclusion that variations in

electricity supply have a signifi cant impact on the change in the real GDP in

Sri Lanka.

Other authors, in the scientifi c research papers they elaborated, tackle

the investment in innovation and technology, as well as its impact on economic

growth and electricity consumption.

According to Narayan&Prasad, most research studies published

in the fi eld of energy policies confi rm that „electricity consumption triggers


economic growth both in developed countries and in emerging ones, which

might mean that implementing electricity conservation policies would lead

to decelerating economic growth” (Narayan, Prasad, 2008). The explanation

derives from the fact that most countries depend economically on the industries

with high electricity consumption levels.

In another study performed by Caraiani, Lungu and Dascălu, the authors

„investigate the causal relationship between energy consumption and the GDP in

European emerging countries in the interval 1980-2013 and perform an analysis

in which they take into account stationarity, co-integration and causality tests,

and the study results are mixed” (Caraiani, Lungu, Dascalu, 2015).

In a study titled ’’Information and Communication Technology, electricity consumption and economic growth in OECD countries: A panel data analysis”, the authors analyse the connection between using ICT, economic

growth and electricity consumption in OECD countries in the interval 1985–2012

(Salahuddin, Alam, 2016). The results confi rm the fact that both the use of ICT

and economic growth boost electricity consumption, both on the short and on

the long term. The study emphasises at the same time that OECD countries have

not yet achieved the maximum energy effi ciency by expanding ICT. Effi cient

coordination, ICT expansion and the policies for the decrease in greenhouse gas

emissions may potentially allow OECD countries to lower the risks. The use of

green technologies is recommended as potential solution to decreasing electricity

consumption triggered by the intensive use of ICT, especially in data centres.

3. RESEARCH METHODOLOGY The objective of the research is to identify the impact of innovation

and supply, transformation and consumption of electricity on economic

development. The reasons why we decided to study the impact of these areas

of the socio-economic life on the economic development are the following:

• Numerous studies have highlighted the positive impact of research on

the economic development. Research, however, is a very complex area

that incorporates the human factor, education, infrastructure, public

policies, the business environment and its ability to capitalize research,

the openness and capacity of fi nancial institutions towards funding

research etc. Taking these issues into account we considered that the

global innovation index is one of the most representative indicators

calculated on international level that captures the complexity of

the innovation process. It is a composite indicator made up of 79

individual indicators coming from many various areas (see Figure

1), so that it could capture the complexity of the innovation process.

Another reason why we chose this indicator is that, on The Global


Innovation Index website (https://www.globalinnovationindex.org/

analysis-comparison), detailed information about the 79 indicators

taken into account is given, on each country (even with the possibility

to make comparisons between the countries). It is a useful instrument

for adapting public policies so that it will improve the global innovation

index indicator. Taking into account the above, we believe that by

highlighting the impact the Global Innovation Index has on economic

development can create a useful tool for public policy steerage.

• On international level there is a trend of changing the economic

structure of countries by refocusing towards energy effi cient economic

activities. This is given by, on the one hand, the interest for reducing

the pollution because, generally, energy intensive industries are

polluting, while also the classic technologies of energy production are

polluting. On the other hand, the industries that require lower energy

consumption generally have a higher added value.

3.1 Description of regression model In order to analyse the relationship between economic growth, innovation

and energy consumption in EU countries, the regression technique was used. The

output variable is Gross Domestic Product per capita computed for 26 countries

across the EU. The explanatory variables are the potential factors that could have

contribution to GDP growth of countries, as well as the innovation – measured

by Global Innovation Index and Primary Energy Consumption and Supply,

transformation and consumption of electricity.

Linear regression is a very powerful statistical technique. Linear models can

be used for prediction or to evaluate whether there is a linear relationship between

two numerical variables. The interest of regression analysis consists in fi nding a

weighted combination of some set of variables that reproduces or predicts as well

as possible the values that we have observed on the response or outcome variable. If

this aim is achieved, the model we develop will predict well not only in the sample

data set at hand but also in new data sets.

Regression analysis is used to describe the relationship between: a

single response variable: Y, and one or more predictor variables: X1, X2,..., Xn.

In linear models, the response variable Y must be a continuous

variable. The predictors X1,..., Xn can be continuous, discrete or categorical

variables.

The equation for a linear regression is:

inini22i110i x...xxy

Where:

(Y) Response variable


(X) Predictor/explanatory variables - xi denotes the i-th observation

on the independent variable X.

Unknown parameters:

0β (Intercept): point in which the line intercepts the y-axis;

iβ (Slope): increase in Y per unit change in X.

They are assumed to be unknown parameters to be estimated from the data.

i=1,2,…,n.

The slope describes the estimated difference in the y variable if the

explanatory variable x for a case happened to be one unit larger. The intercept

describes the average outcome of y if x = 0 and the linear model is valid all the

way to x = 0, which in many applications is not the case.

More broadly, if we have come close enough to identifying the true model

(which, of course will still be only a crude approximation of the phenomenon

we are studying), the science can move forward because we can then be pretty

confi dent that the model is good enough to guide further research, clinical

decisions, and policy. The remarkable acceleration in data analysis has been a

direct consequence of improved computing power.

3.2. Description of the variables Dependent variable (Y) - as the variable of interest in this study - is

the Gross domestic product (GDP per capita Euro) Independent variables (Xi) are considered the followings:

Global Innovation Index (GII) in 2014 was composed of 81 indicators.

These indicators are divided into two categories: input and output indicators.

Input indicators come from fi ve specifi c elements of national economies,

which have a strong impact on innovation, namely: “Institutions, Human capital and research, Infrastructure, Market sophistication, and Business sophistication”

(https://www.globalinnovationindex.org/about # -gii framework).

Output indicators are grouped into two categories, considered the effects

of the innovation process: “Knowledge and technology outputs and Creative

outputs” (https://www.globalinnovationindex.org/about # -gii framework). “

Based on these indicators are then calculated (Figure 1):

• “Innovation Input Sub-Index: is the simple average of the fi rst

fi ve pillar scores

• Innovation Output Sub-Index is the simple average of the last two

pillar scores

• The Innovation Effi ciency Ratio is the ratio of the Output Sub-Index over

the Input Sub-Index” (https://www.globalinnovationindex.org/about # -gii framework)

• Global Innovation Index: “is the simple average of the Input and

Output Sub-Indices” (Dutta, Lanvin and Wunsch-Vincent, 2014, p.43).


Structure of Global Innovation Index

Figure 1

Source: https://www.globalinnovationindex.org/about-gii#framework

Other explanatory variable considered in the model is Supply,

transformation and consumption of electricity. This indicator is noted in

the multilinear regression model with the symbols STC_NRG. The analysed

data are for 2014. To ensure the comparison between countries we considered

variation of the indicator Electrical Energy Available for Final Consumption

(Eurostat), compared to 2005. Energy statistics data covers all major sectors of

the economy that are involved in the production, trade, energy transformation

and energy consumption (the energy sector, industrial sector, transport,

commercial and public services, agricultural/forestry/fi shing and residential).

According to Regulation (EC) No 1099/2008 on energy statistics „Energy

supplied - electricity: consists in the sum of the net electrical energy production

supplied by all power stations within the country, reduced by the amount used

simultaneously for heat pumps, electrically powered steam boilers, pumping

and reduced or increased by exports to or imports from abroad”.

Prior to any analysis, the data should always be inspected for: Missing

values, Outliers, Unusual (e.g. asymmetric) distributions, Changes in variability.

To ensure comparability between countries for GDP, the indicator

GDP per capita will be used. The outliers were also removed from the sample

(Luxembourg and UK and Czech Republic).


GDP per capita, Global Innovation Index and Primary Energy

Consumption

Table 1Country GDP_per

capita_2014_Euro

Glob_Innov_

Index_2014

(%)

Supply, transformation and

consumption of electricity _2014

STC_NRG

(%, 2005=100)Belgium 35900 51.69 100.4

Bulgaria 5900 40.74 107.8

Czech Republic 14700 50.22 101.6

Denmark 46200 57.52 91.5

Germany 37100 56.02 98.2

Estonia 15200 51.54 114.3

Ireland 41000 56.67 102.7

Greece 16200 38.95 97.2

Spain 22400 49.27 93.7

France 32200 52.18 97.7

Croatia 10200 40.75 102.9

Italy 26500 45.65 93.6

Cyprus 20400 45.82 100.1

Latvia 11800 44.81 114.9

Lithuania 12400 41.00 115.8

Hungary 10600 44.61 110.5

Malta 19000 50.44 109.4

Netherlands 40000 60.59 98.6

Austria 38500 55.01 105.3

Poland 10700 40.64 119.4

Portugal 16700 45.63 97.6

Romania 7500 38.08 108

Slovenia 18100 47.23 97.8

Slovakia 13900 41.89 105.7

Finland 37600 60.67 97.7

Sweden 44400 62.29 93.5

Source: Eurostat and The Global Innovation Index 2014

4. RESEARCH RESULTS The regression command is lm for linear model. We will store that

model in a variable called linear_regression. The dependent variable is

followed by a tilde “~” followed by the independent variable.

In R (Alexandru, Caragea, 2016) the lm function computes the

coeffi cients. The output includes a conventional table with parameter

estimates and standard errors, as well the residual standard error and multiple

R-squared. Multiple R² measure the strength of the relationship between the

set of independent variables and the dependent variable. An F test is used to

determine if the relationship can be generalized to the population represented

by the sample. A t-test is used to evaluate the individual relationship between


each independent variable and the dependent variable.

The function plot of regression model will produce a set of four plots:

• residuals versus fi tted values

• Q-Q plot of standardized residuals

• scale-location plot (square roots of standardized residuals versus

fi tted values

• plot of residuals versus leverage that adds bands corresponding to

Cook’s distances of 0.5 and 1.

The multiple regression equation takes the following form:

i210 14STC_NRG_204_Index_201Glob_Innov4_Eurocapita_201GDP_per

The estimated equation can be written as follows:

14STC_NRG_20417.3-4_Index_201Glob_Innov1391.21289.0-4_Eurocapita_201GDP_per

Coeffi cients interpretation

Table 2

Variables Coeff. /

Std. error

Intercept-1289.0

(19115.6 )

Glob_Innov_Index_20141391.2

(149.3)

STC_NRG_2014-417.3

(141.0)

The coeffi cients estimates are b0=-1289.0 (intercept), b1=1391.2

(Glob_Innov_Index_2014) and b2=-417.3 (STC_NRG_2014). The

signifi cance of the estimates is tested with the provided p-values. The null

hypothesis for this test is that all coeffi cients are 0, but the p-value for each

of the estimates is lower than 0.05 so we obviously reject this hypothesis

and conclude that all the estimates can be distinguished from 0 and that the

equation does have explanatory power.

The intercept is the expected mean value of the dependent variable

when all independent variables are zero. So, if the variables Glob_Innov_

Index_2014 and STC_NRG_2014 take both null values, the dependent

variable GDP_per.capita_2014_Euro has a mean value of -1289.0 (Table 2).

As the Global Innovation Index increases with one unit, the GDP per

capita increases with 1391.2 Euro, when all other variables remain constant.

As the Supply, transformation and consumption of electricity increases with

one unit, the GDP per capita decreases with 417.3 Euro, when all other

variables remain constant.


Measures of Fit Quality

• Coeffi cient of Determination, R2 (Multiple R-squared: 0.8774). R2

represents the proportion of the total sample variability explained by

the regression model. That means the explanatory variables (Global

Innovation Index and Supply, transformation and consumption of

electricity) explains 87.74% of response variation (GDP per capita).

• Adjusted R2 (Adjusted R-squared: 0.8662). The adjusted R2 takes into

account the number of degrees of freedom and is preferable to R2.

• Here the p-value of the model is 9.411e-11 (<0.05), so reject

the hypothesis that the slope is zero (in this situation, there is a

correlation between variables, 95% confi dence intervals).

• Pearson’s product-moment correlation coeffi cients as a measure

of the linear correlation between two variables indicate that the

GDP per capita is strongly correlated with Global Innovation Index

(0.9102, p-value = 2.788e-10) and also with Supply, transformation

and consumption of electricity (-0.6274468, p-value =0.0007871);

there are no collinearity between independent variables.

Colinearity Analysis

Figure 2

• The assumptions on the residuals needed to consider the linear model valid.

We need an even scatter of residuals when plotted versus the fi tted values, and a

normal distribution of residuals. R produces 4 plots we can use to judge the model

(fi g. 3).


Residuals Analysis

Figure 3

Analysing the charts generated by software R it can highlight the

following conclusions:

• Residuals are normal distributed, as the fi gure 3 (Normal Q-Q) shows.

• The “Residuals vs Fitted” chart show if there is a trend to the

residuals. When a linear regression model is suitable for a data set,

then the residuals are more or less randomly distributed around the

red line. For the regression model presented in this study, the chart

Residuals vs Fitted shows that the model is suitable for a data set.

5. CONCLUSIONS

In this research we have revealed the impact that innovation (Global

Innovation Index) and power consumption (Supply, transformation and

consumption of electricity) have on economic development (GDP per capita).

Using multiple linear regression we have shown that two independent variables

explain a rate of 87.74% evolution of the dependent variable.

The result of research can be used in substantiation of public policies

designed to boost the economic development of various countries. In our

opinion, the model developed in this research has a grate practical value

because public policy makers have at their disposal a very useful benchmark

tool for Global Innovation Index and his component indicators (see https://

www.globalinnovationindex.org/analysis-indicator). These can give good

orientation for public strategy and low framework in various fi elds like: political


environment, regulatory environment, business environment, education,

R&D, information and communication infrastructure, general infrastructure,

ecological sustainability, credit, investment, trade and competition, knowledge

workers, innovation linkages, knowledge absorption, knowledge creation,

knowledge impact, knowledge diffusion, intangible assets, creative goods and

services and online creativity.

As expected, the indicator Supply, transformation and consumption of

electricity has an inversely proportional impact on economic growth. Developed

countries implement technologies and develop electrical appliances becoming

more effi cient in terms of energy. Therefore, public policies should stimulate

reduce energy consumption by fostering the development and acquisition of

performance technologies and electrical appliances, not by reducing living

standards as a result of not using this. In future research we intend to monitor

this economic model because we believe that more technology reorients their

areas so that changing from fossil fuel energy to electricity. This trend is

present in auto industry. This guidance will result in an alert rhythm growth in

electricity consumption which in our view will change the economic model.

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Testing Phillips Curve For Serbian And Romanian EconomySrdjan Furtula ([email protected])

Faculty of Economics in Kragujevac, Serbia

Danijela Durkalić ([email protected])

University of Kragujevac

Mihaela Simionescu ([email protected])

Institute for Economic Forecasting, Centre for Migration Studies in Prague Business School, Romania

ABSTRACT The subject of this work is the Phillips curve paradigm in macroeconomics

with an emphasis on its development in New Keynesian theory. The main objective

of the work is to reach scientifi cally relevant and practically useful knowledge on the

concept of the Phillips curve and development its application to the related macro-

economic variables in Serbia and Romania. The dependence between infl ation and

unemployment rate will be analysed on empirical data were chosen in Romania and

Serbia using Bayesian linear regression models. For this study, these two countries

which experienced a period of transition were chosen. Furthermore, Serbia aspires to

join the EU, while Romania has been its member since 2007. The results of this paper

will show that the model of traditional Phillips curve exists only in the short term.

Key words: Phillips curve, unemployment, GDP defl ator, BVAR models.

1. INTRODUCTION The Phillips curve appeared as a response to the question of relations

between infl ation and unemployment. Designed in the 1960s, the Phillips

curve became analytical instrumentation used for the elaboration of macro-

economic analysis that discussed the question of relations of infl ation and

unemployment. For this reason there was a need for a study of this kind of

analysis, and for this purpose we analysed the infl uence of different schools

of economic thought in modifi cation of the Phillips curve and its effect on the

Republic of Serbia and Romania.

Empirical evaluation of the level of unemployment and infl ation of

econometric methods clearly identifi es different results. Based on the defi ned

object of research and consistent with the goal of research, we started from the

hypothetical framework:

H1: The traditional Phillips curve, which shows the trade-off between

infl ation and unemployment, cannot be applied in contemporary long term


conditions in the Republic of Serbia and Romania. The results that appeared

as a product of conceived objectives and tasks led to adequate scientifi c

knowledge. A key contribution of this work is refl ected in the defi nition and

implementation of the Phillips curve as an analytical instrument of the empiri-

cal facts through by using the Bayesian linear regression model, and also in

the development of scientifi c thought about the phenomenon of the Phillips

curve.

2. LITERATURE REVIEW It is known that economists deal with the relationship between the

individual indicators, and comparative analysis of macroeconomic indicators.

However, improving the position of one of the macroeconomic aggregates

is infl uenced by the deterioration of the situation of the other. It is also

proved in the relationship between infl ation and unemployment, which are

key macroeconomic indicators for each national economy. Moreover, the

successful development of a national economy is ensured by the performance

of commercial banks (Gavurova, Belas, Kocisova, Kliestik, 2017; Balcerzak,

Kliestik, Streimikiene, Smrčka, 2017).

In 1958, English economist Alban Phillips (A.W.Phillips), a professor

at the Faculty of Economics in London, published an article in the journal

Economica entitled The relationship between unemployment and the rate of change of money wages in the UK, from 1861 to 1957 (Phillips, AW 1958).

This article created initial spark that launched Phillips; there, he pointed

out the negative correlation of the unemployment rate and infl ation rate of

wages. After the initial form of Phillips curve was formed, it was developed at

different schools of economics.

Keynes and his ideas dominated in the context of macroeconomic thinking

process from 1929 until the 1970s. During the 1970s, the growth of unemployment

became a major problem but the Keynesians were unable to explain this rise in

infl ation and unemployment (stagfl ation) at the same time. Their reasoning was

related mainly to the dispute concerning the adjustment of prices and wages on the

market. Normally, the model became the basis for further research (non)existence

of trade-off between infl ation and unemployment.

The results of the initial analysis of the Phillips served many economists

to develop their research in the same direction. Based on the original Phillips

curve and the idea of change of unemployment and wages, the critics of

Keynesian theory, American economists Paul Samuelson and Robert Solow,

indicated the negative correlation between unemployment and price infl ation

on the basis of data for the United States. Thus, they modifi ed the Phillips

curve so that it became an analysis of the relationship of unemployment and


price infl ation. They concluded the same fact as Philips that unemployment

and price infl ation moved approximately as the ratio of unemployment and

wage infl ation. (Samuelson, Solow, 1960).

Further development of these curves was processed by New Keynesian

schools. In addition to these positions, New Keynesian model overcame some

limitations and accepted a new postulate in the improvement of the analysis

and the introduction of monopolistic competition, nominal and real rigidities

in prices and wages, thus contributing to the approximation of the real

developments in the economy.

New Keynesian Phillips curve assumes that infl ation expectations are

rational and not adaptive, which is withheld from Lucas’ theory. As a result,

there was a New Keynesian Phillips curve which was different from the

Phillips curve developed by Friedman and Phelps. It also differed from the

Phillips curve developed by Lucas and Rapping (Lucas, Rapping, 1969). Their

version of the Phillips curve coupled with rational expectations suggested that

only non-anticipated infl ation (change in money supply) could affect output. In

1969, Lucas and Raping are empirically demonstrated that the Phillips curve

was not stable over time (Lucas, Rapping, 1969), i.e. it changed with time. So,

economists Mankiw and Rice (2001) pointed out that the relationship between

infl ation and output still remained a puzzle for macroeconomists, a conclusion

also formulated by Simionescu (2017).

In recent years, many economists used infl ation expectations and

price adjustment in their research. The New Keynesian Phillips curve model

was built on the works of John Taylor (John Taylor, 1980), Rotemberg Julio

(Julio Rotemberg, 1982), and Calvo Guillermo (Guillermo Calvo 1983).

Rotemberg’s work highlighted the microeconomic framework within which

the reduction of the cost of price changes were discussed. The Calvo model

(Calvo, 1983), based on the price of company, pointed out that every company

kept a fi xed price while employers did not receive the “random signal” about

the price change. During the formation of the new price, the company took

into account that the prices of other fi rms had to change. Bearing in mind that

the prices of other companies were set up in the past, the company took into

account the previous prices in the formation of the current price.

The results which indicate the existence and formation of the Phillips

curve were fi rstly empirical. However, it should explain that empirical result

on the basis of theoretical knowledge. Richard G. Lipsey was the fi rst who

processed Phillips curve empirically and formed the initial equation as a

gradual adjustment of imbalances on the labor market (Palley, T., 2012):

w=f (u−u*) f(0)=0, f′<0, f′′<0 (1)

Where w - is the nominal infl ation wages, u - the actual unemployment


rate, u * - unemployment rate (friction and structural) that corresponds to full-

time employment (natural rate). Based on the econometric model of Lipsey, it

is shown that the excessive demand for labor causes wages infl ation, while a

surplus of labor supply causes defl ation of wages. The equation was quickly

accepted; however, the empirical data showed instability, hence this situation

led to/resulted in modifi cations of the originally defi ned theoretical curve.

In recent research, Phillips curve model is widely used for theoretical

analysis of monetary policy. Thus John Roberts (Roberts, 1995) pointed out

that the main contribution to the New Keynesian Phillips curve explicitly

emphasized the role of nominal rigidities of the model. In his work, Roberts

performed theoretical comparison of Phillips curves over time. What he said

referred to the fact that the New Keynesian models included expectations of

future infl ation, while the Lucas supply curve included current expectations

and current infl ation. The reason for consideration of future infl ation related

to the New Keynesian model in which prices were “sticky” or rigid. As noted,

alternative Phillips curve model was based on rational expectations of rigid

price and was present in models of Taylor, Calvo and others. As Robert

pointed, all those models were the model of New Keynesian Phillips curve,

which included the forward-looking in current infl ation expectations

πt=βEtπt+1+γxt (2)

In their work, Gali, and Gertler (Gali, Gertler, 1999) provided a good

description of the process of infl ation by using alternative approach to assess

whether the New Keynesian Phillips, especially focused on the role of delayed

infl ation. They discussed the popular version of the Phillips curve “New

Keynesian Phillips curve”, which included rational expectations.

Using Calvo model of price adjustment, Woodford (2003) showed

that the aggregation of linear optimal price-adjustment of individual fi rms

could show the current and expected future infl ation and aggregate marginal

cost, (mc). In this way the New Keynesian model introduced a component of

marginal costs (Hornstein, 2008):

πt =γfEtπt+1+λmct+ξt (3)

This equation shows the structural New Keynesian Phillips curve

model where λ and γf are functions of structural parameters, including the

probability of price adjustment, where α, ξt is a random variable. Random

variables are usually interpreted as an exogenous shock to the company.

Solving this equation by “looking ahead” leads to the conclusion that the

current and expected future marginal costs are bearers of current infl ation.

Although the New Keynesian Phillips curve model is superfi cially

similar to the traditional Phillips curve, this model nevertheless introduces

different implications in terms of practical questions for the optimal conduct


of monetary policy and the costs of defl ation. (Rudd, Whelan, 2005). Gali and

Gertler (1999) interpreted hybrid model of Phillips curve by the equation:

πt=λst+ γfEt{ πt+1}+γbπt (4)

Performing these results, they indicated that New Keynesian Phillips

curve model could be a good way to predict the dynamics of infl ation.

The development of the Phillips curve and models for predicting the

relationship of infl ation and unemployment (output gap), developed the concepts

such as “sticky” information and “sticky” prices, as well as attempts to explain the

model of dynamic price adjustment under the assumption that information and

prices moved slowly. (Gertler et.al, 2002). With the advent of “sticky” prices, many

papers on prices, wages and the information that “stick” to the market appeared.

Thus the estimations of sticky information based on the Phillips curve in the USA

were made (Khan, Zhu, 2006) together with optimal fi scal and monetary policy on

the basis of the sticky prices (Schmitt-Grohe Uribe, 2004), and the theory of real

wage rigidity in the New Keynesian model (Blanchard , Galí, 2007).

Using the data for the period 1983 to 2013, Orrenius and Kumar (2015)

showed that the ratio of prices and wages was nonlinear and convex; it decreased

when the unemployment rate was below the average rate, while signifi cant

increase in the wages caused change of unemployment above the historical

average. They also came to the conclusion that the short-term unemployment rate

had a strong relationship with the average and median wage growth, until the long-

term unemployment rate appeared, which affected the median wage growth only.

Some papers are concerned with analysis of employment and infl ation in Europe

and variation in the NAIRU (non-accelerating infl ation rate of unemployment)

(Posta, 2015), while some authors deal with sectorial neo-Keynesian curve and

dynamics of the European monetary policy (Norkute, 2015). In more recent work,

the focus is on the Phillips curve in an open and closed economy, looking forward

and looking backward, as well as the hybrid Phillips curve (Abbas, Bhattacharya,

Sgro, 2016). It is clear that the issues related to infl ation and unemployment

might affect business environment, not only the consumers and human resources

(Kliestik, Kocisova, Misankova, 2015; Simionescu, 2016).

3. INFLATION AND UNEMPLOYMENT POLICY IN SERBIA AND ROMANIA

One of the challenges facing monetary policymakers is related to the

selection of appropriate monetary policy strategy, which will be conducted in the

future. Economic policy makers can apply various alternatives in the conduct

of monetary policy such as the strategy of targeting monetary aggregates,

exchange rate targeting, infl ation targeting and monetary strategy based on

an implicit monetary anchor (Swank & Velden, 1997). The implementation


of any of strategies can leave positive and negative effects. Since 1990, some

countries have adopted infl ation targeting as a monetary policy regime, and

among them are the observed countries, Serbia and Romania.

Monetary Policy Framework for selected counties

Table 1Monetary Policy Framework

Exchange rate

arrangement

Infl ation targeting

frameworkOther (EMU)

FloatingAlbania, Hungary,

Romania, Serbia, Turkey-

Free FloatingPoland, Sweden,

United Kingdom

Austria, Belgium, Cyprus, Estonia, Finland,

France, Germany, Greece, Ireland, Italy,

Latvia , Luxembourg, Malta, Netherlands,

Portugal, Slovakia, Slovenia, SpainOther managed

arrangement Czech Republic -

Source: IMF data, Annual report on exchange arrangements and exchange restrictions 2014 :

https://www.imf.org/external/pubs/nft/2014/areaers/ar2014.pdf

Infl ation targeting as a monetary policy strategy includes fi ve key

elements (Mishkin, 2007): 1) public announcement of the numerical value of

infl ation during medium time period, 2) institutional commitment to price

stability as the primary objective of monetary policy, to which all other goals are

subordinated, 3) a comprehensive strategy in which many variables, not only the

money supply or the exchange rate are used for decision-making and creating

monetary policy instruments, 4) transparent monetary policy, 5) responsibility of

central banks in the implementation of infl ation targets.

Regarding the Republic of Serbia, monetary authorities decided to

change the monetary strategy of exchange rate and adopt a strategy of infl ation,

targeting combined with a fl oating exchange rate regime due to the growing

negative effects of macroeconomics. (Josifi dis et el., 2009). Bearing in mind

that the strategy of exchange rate targeting started from rigid to crawling,

Serbia offi cially abandoned exchange rate targeting as a nominal anchor in

September 2006 when the National Bank of Serbia announced preparation for

the implementation of the infl ation targeting strategy.

By applying the infl ation targeting strategy and appropriate conduct

of monetary policy, the infl ation (measured by the GDP defl ator) declined

from 10.62% in 2008 to 2.68% in 2015. This is the lowest infl ation rate in the

observed period from 1996 to 2015. However, both goals of macroeconomic

policy cannot be achieved at the same time, which is shown in the movement

in the unemployment rate. The unemployment rate increased signifi cantly in


Serbia and it is still in double digits. It varied from 13.63% in 2008 to 17.66%

in 2015. It culminated in 2012, when it was 23.9%. The BTI report (BTI,

2016) states that the poverty rate in Serbia is high because of large families,

a great number of one-parent families, high unemployment and the large

Romani population. Also, the report estimates that the grey economy employs

about 1 million of population, which is about 40% of GDP. Conducting the

policy of stable infl ation and exchange rate prevents the use of expansionary

monetary policy in Serbia, while the unemployment is a burning political and

economic problem in Serbia.

Instruments of monetary policy in Serbia and Romania

Table 2Country National Bank of Romania National bank of Serbia

Used instruments of

monetary policy of

targeting infl ation

1. open market operations

2. standing facilities

3. reserve requirements

1. open market operations

2. standing facilities

3. reserve requirements

4. interventions on the foreign exchange market

Source: National Bank of Serbia, National Bank of Romania

Another country in transition that has been taken into consideration in

addition to Serbia is Romania. Due to similar economic structure, transitional

reforms and implementation of the strategy of infl ation targeting, these

countries are interesting for comparison.

The National Bank of Romania (NBR) moved on to direct infl ation

targeting in August 2005. Romania conducts fl uctuating exchange rate. The

BTI report (BTI, 2016) for Romania states that the central bank is a strong and

independent institution that withstands the pressure of the government to change

monetary policy. NBR carried out anti-infl ationary policy and strict banking

supervision. The infl ation rate in Romania reached a historically low level in 2014

(1.69%). The infl ation rate varied from 15.5% in 2008 to 2.92% in 2015. Although

infl ation is within the target values in Romania and EU member states, the political

decision to adopt the Euro and EMU membership has not yet been made.

In Romania, the imbalances caused by the transition to a free market

economy and the economic downturn caused quite an explosion of unemployment

in the early years of the transition (Dănăcică, 2014). But, if we talk about

unemployment in Romania compared with Serbia, its rate in Romania is quite

smaller than in Serbia. The unemployment rate in Romania did not exceed 8%

throughout the period from 1996-2015, and ranged from 5.79% (2008) to 6.81%

(2015). The main reason for the extremely low unemployment rate in Romania

is primarily found in the migration of Romanian working population to some

EU countries, such as Italy and Spain. Like in Serbia, the situation with the


Romani population also deserves special attention in Romania; particularly as a

separate issue in the fi eld of education, health and social protection. Corruption

and red tape continue to permeate the business environment in Romania.

Based on the monetary policy instruments in the countries that

implement infl ation targeting (in our case, Serbia and Romania) it can be seen

that countries generally apply open market operations as a basic monetary

policy instrument. Taking into account the Keynesian thinking, we can get the

impression that the Keynesians created a starting point in creating a monetary

policy based on discretion, and raising interest rates as the main monetary

policy instrument.

4. DATA AND RESULTS The dependence between infl ation and unemployment rate will

be analyzed on empirical data in Romania and Serbia, two countries that

experienced the transition to a market economy. However, more issues were

reported on the Serbian labour market where unemployment became a more

acute problem.

Due to the short data series, we built few Bayesian linear regression

models for infl ation rate and unemployment rate in both countries.

The Bayesian linear regression model has the form:The Bayesian linear regression model has the form:

Y = dependent variable (n * 1 vector) Y = dependent variable (n * 1 vector)

X = explanatory variables (n * k matrix)

Y = dependent variable (n * 1 vector)

- error term

- coeffi cient

Y = dependent variable (n * 1 vector) - variance corresponding to the normal distribution of the errors

For the estimation, we employed Gibbs sampling method using the

priors of Lindley and Smith (1972):

m and V- mean and variance-covariance matrix for the normal

distribution of the coeffi cient

a and b- parameters corresponding to the inverted gamma distribution

of the variance

The variables used in this study are: unemployment rate in Serbia (us),

infl ation rate in Serbia (is), unemployment rate in Romania (ur), infl ation rate

in Romania (ir). When the infl ation rate was considered as dependent variable

in both countries, a low and positive correlation with unemployment rate was


observed. The BVAR models with prior normal distributon of the coeffi cients

confi rm these results (Appendix 1 and 2). The positive correlation between

infl ation and unemployment rate in both countries is contrary to economic

theory. This positive correlation could be benefi cal for the economy only if

both indicators have low values. Lower values for infl ation and unemployment

were observed in Romania compared to Serbia. In this case, fi scal policymakers

should come with a particular set of challenges. This type of correlation was

also met in the US during 1970s, because the president Nixon deleted the U.S.

dollar from the gold standard and controlled the salaries and the prices. Our

results for Romania are consistent with the conclusion of Florea (2014) that

showed also a positive relationship between infl ation and unemployment in

Romania on long-run due for age group 20-24 years, because of the recent

economic crisis and of policy interventions. It seems that Phillips curve is

valid only on short-run.

We checked if there is any causality between infl ation and

unemployment rate in both countries. Granger causality must be checked

only of stationary data series. Therefore, we tested the presence of unit roots

in our data sets. According to ADF test, the data series for both variables in

both countries are stationary in fi rst difference at 5% level of signifi cance

(Appendix 3). Therefore, we checked for Granger causality for the data series

in fi rst difference. For both countries, we got that there is not any type of

causality between absolute changes in infl ation and unemployment rate in

Romania and Serbia (appendix 4). Johansen test was employed to verify if

there is any cointegration relationship between variables with data series in

level (appendix 5). We obtained that infl ation rate and unemployment rate are

cointegrated of order 1 at 5% level of signifi cance. So, a long-run correlation

between infl ation and unemployment rate exists in both countries, but it is a

positive one.

5. CONCLUSION

It is developing a growing interest in predicting infl ation, keeping

in mind that the proper management of infl ation is considered the primary

objective of macroeconomic policies, especially monetary policy. This

is especially important when one considers that the Republic of Serbia on

the road to EU accession and thereby seeks to maintain infl ation within the

permitted limits. The paper presents the different manifestations and causes

that move the understanding of the Phillips curve.

The tested hypothesis has confi rmed the initial assumption that there

is no trade-off between infl ation and unemployment in Serbia and Romania

in nowadays. Reality has shown that economic policy in Serbia is focused on

infl ation targeting policy rather than a policy of unemployment. Given the fact


that infl ation can never be neutralized, it is necessary to keep it under control.

In the case of Romania, situation is a little different, they consider both policy:

unemployment is controlled and infl ation is in limits last two years. This work

gave scientifi cally valid and practically useful empirical contribution to the

understanding of the relationship of infl ation and unemployment. If the Phillips

curve presented a concept come to life and was widely applied in practice, the

monetary authorities could more effectively predict the rate of infl ation. Results

of the research are showed that Phillips curve well describes the dynamics of

infl ation, and can be used as an effective tool for predicting infl ation. Whether

the New Keynesian linear, hybrid or curve that includes marginal costs, they

all come down to relationships infl ation and unemployment and in particular

their predictions. In this paper, we used GDP defl ator as presenter of infl ation

considering the most literature. Data for infl ation can (for example HICP) but

the essence of their relationship is shown in the fi nal result with Phillips curve.

Limitations of this model are refl ected in the fact that infl ation and

unemployment are infl uenced by many other factors, and that there is no

correlation only between these two macroeconomic variables so their changes

can cause other variables of economic policy, such as exchange rates, interest

rates, etc. However, this model represents a simplifi ed picture of reality and

makes certain conclusions for policy makers. This model gives a framework

of monetary authorities to better manage and anticipate infl ation.

From the analysis of the movement of the infl ation rate and the

unemployment rate in the observed period, it be said that in addition to

selected long term necessary to analyse and shorter periods of time in order

to see a short-term movement of infl ation and unemployment, which may be

recommendations for future research. For future research it would also be

interesting to complement the analysis of Phillips curve models for forecasting

infl ation and infl ationary risks, such as Monte Carlo simulation.

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Appendix 1 The relationship between infl ation rate and unemployment rate in Serbia using

Bayesian linear regressions and BVAR model

The dependent variable is

The regressor us

A constant is added to X.

‚Coeff.’ ‚Post. mean’ ‚Post. std’

‚C(0)’ [ 11.9280] [ 9.9198]

‚C(1)’ [ 0.4121] [ 0.6506]

‚s^2’ [ 690.4879] [ 224.0287]

The regressor is

The regressor us

Constant

6.2953 6.3728

Posterior Phi1 coeffi cients

0.49 0.03

-0.02 0.67

Posterior covariance matrix of the VAR system

313.89 -8.57

-8.57 3.48

Appendix 2 The relationship between infl ation rate and unemployment rate in Romania using

Bayesian linear regressions and BVAR model

The dependent variable ir is

The regressor ur

A constant is added to X.

‚Coeff.’ ‚Post. mean’ ‚Post. std’

‚C(0)’ [ 4.8723] [ 9.8390]

‚C(1)’ [ 2.6743] [ 1.7099]

‚s^2’ [ 837.2592] [ 260.6157]

The regressor ir

The regressor ur

Constant

-0.0755 5.4923

Posterior Phi1 coeffi cients

0.55 1.42

-0.01 0.22

Posterior covariance matrix of the VAR system

524.54 -5.33

-5.33 0.31


Appendix 3

Unit root tests for unemployment and infl ation rate data series in Serbia and Romania

Unemployment rate and infl ation rate data series in Serbia in fi rst difference

Null Hypothesis: D(US) has a unit rootExogenous: ConstantLag Length: 2 (Automatic - based on SIC, maxlag=4)

t-Statistic Prob.*Augmented Dickey-Fuller test statistic -5.052745 0.0012Test critical values: 1% level -3.920350

5% level -3.06558510% level -2.673459

Null Hypothesis: D(US) has a unit rootExogenous: Constant, Linear TrendLag Length: 2 (Automatic - based on SIC, maxlag=4)


5% level -3.73320010% level -3.310349

Null Hypothesis: D(US) has a unit rootExogenous: NoneLag Length: 2 (Automatic - based on SIC, maxlag=4)


5% level -1.96441810% level -1.605603

Null Hypothesis: D(IS) has a unit rootExogenous: ConstantLag Length: 3 (Automatic - based on SIC, maxlag=4)


5% level -3.08100210% level -2.681330

Null Hypothesis: D(IS) has a unit rootExogenous: Constant, Linear TrendLag Length: 3 (Automatic - based on SIC, maxlag=4)


5% level -3.75974310% level -3.324976


Null Hypothesis: D(IS) has a unit rootExogenous: NoneLag Length: 1 (Automatic - based on SIC, maxlag=4)


5% level -1.96281310% level -1.606129

Unemployment rate and infl ation rate data series in Romania in fi rst difference

Null Hypothesis: D(UR) has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=4)


5% level -3.04039110% level -2.660551

Null Hypothesis: D(UR) has a unit rootExogenous: Constant, Linear TrendLag Length: 0 (Automatic - based on SIC, maxlag=4)


5% level -3.69081410% level -3.286909

Null Hypothesis: D(UR) has a unit rootExogenous: NoneLag Length: 0 (Automatic - based on SIC, maxlag=4)


5% level -1.96140910% level -1.606610

Null Hypothesis: D(IR) has a unit rootExogenous: NoneLag Length: 3 (Automatic - based on SIC, maxlag=4)


5% level -1.96627010% level -1.605026


Null Hypothesis: D(IR) has a unit rootExogenous: Constant, Linear TrendLag Length: 0 (Automatic - based on SIC, maxlag=4)


5% level -3.69081410% level -3.286909

Null Hypothesis: D(IR) has a unit rootExogenous: ConstantLag Length: 3 (Automatic - based on SIC, maxlag=4)


5% level -3.06558510% level -2.673459

Appendix 4

Granger causality for infl ation and unemployment rate in Serbia and Romania

Pairwise Granger Causality TestsSample: 1996 2015Lags: 2 Null Hypothesis: Obs F-Statistic Prob. D_IS does not Granger Cause D_US 17 0.59446 0.5673 D_US does not Granger Cause D_IS 0.07320 0.9298

Pairwise Granger Causality TestsSample: 1996 2015Lags: 2 Null Hypothesis: Obs F-Statistic Prob. D_IR does not Granger Cause D_UR 17 1.89565 0.1926 D_UR does not Granger Cause D_IR 0.70274 0.5145


Appendix 5

Johansen cointegration test for infl ation and unemployment rate in Serbia and Romania

Serbia

Included observations: 18 after adjustmentsTrend assumption: Linear deterministic trendSeries: IS US Lags interval (in fi rst differences): 1 to 1Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.707069 25.53189 15.49471 0.0011At most 1 0.173552 3.431140 3.841466 0.0640

Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.707069 22.10075 14.26460 0.0024At most 1 0.173552 3.431140 3.841466 0.0640

RomaniaIncluded observations: 18 after adjustmentsTrend assumption: Linear deterministic trendSeries: IR URLags interval (in fi rst differences): 1 to 1Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.863818 39.72968 15.49471 0.0000At most 1 * 0.192199 3.841901 3.841466 0.0500

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.863818 35.88778 14.26460 0.0000At most 1 * 0.192199 3.841901 3.841466 0.0500


Romanian Foreign Trade Dependency and StabilityElena BĂNICĂ ([email protected])

National Institute of Statistics, Bucharest, Romania,

Valentina VASILE ([email protected])

Institute of National Economy-Romanian Academy,

Cristina BOBOC ([email protected])

Bucharest University of Economics & Institute of National Economy-Romanian Academy

ABSTRACT The Romanian foreign trade was signifi cantly changed after 1990. The priva-

tization, economic restructuring and EU accession had an impact not only on the size

and foreign trade diversity but also on the structure and commerce routes. Moreover,

Romania’s Integration in the European Union on January 1st, 2007, eliminates the

trade barriers between member countries, increasing the trade relations within the

region and facilitating the expansion of intra-industry trade.

This paper aims to analyze the level and structure of foreign trade in Romania

after 1989 by analyzing the dependency and the stability indicators. The Grubel Lloyd

index is computed by type of ownership of fi rm’s capital and specifi c activity of compa-

nies – export of high-tech products, inward processing transactions, etc.

The main conclusion is that Romania should keep a right balance between

dependency on EU single market and developing trade relations with other non-EU

countries where it can have either an exceeding trade balance or comparative advan-

tages based on high-tech exports.

Keywords: foreign trade, export sustainability, FDI, high-tech products, Gru-

bel Lloyd index

1. INTRODUCTION It is well known that both export and import may play a crucial role

in economic development of a state. Any country needs exports for both

macroeconomic (smart growth) and microeconomic (employment) reasons.

Trade growth is much more volatile than GDP growth and is merely related to

companies’ turnover and profi tability in export-oriented ones. The merchandise

export is deeply correlated with the economic structure changing and innovation

at companies’ level. Exports driving companies continuously innovate and

improve their goods and services to maintain a proper level of market share.

As source of growth, exports also grant employment reshaping and

jobs structure renewing, based on global import demand. The contribution of


exports to total employment in the EU was around 10.3% on average between

2000 and 2007 (Sousa et al., 2012) and increased to 14% in 2011 (i.e. 31.2

million jobs) (Rueda-Cantuche J-M., Nuno Sousa N., 2016). Acting both on

supply and demand side each additional €1 billion of extra EU-exports supports

around 14 thousand additional jobs across the EU, mainly high-skilled and on

average better paid. Moreover, on long term, 1% increase in the openness of the

economy (ratio of imports to value added) leads to an increase of 0.6% in labor

productivity in the following year in EU (EC, 2007).

On the other hand, imports generally refl ect the weakness of the state

in achieving its needs itself and makes them dependent of foreign countries’

growth and export potential. In some cases, the high level of dependency

on import creates higher volatility of positive effects on country well-being,

especially on long run. If we consider the overall impact of increased foreign

trade on quality of life the effects are mixed, both favorable and unfavorable,

on both short and long run (Sirgy et al., 2007; Manzella, 2013).

The Romanian foreign trade was signifi cantly changed after 1990. The

privatization, economic restructuring and EU accession impacted not only the size and

export diversity but also the structure and commerce routes in and out single market.

(Zaman & Vasile, 2003, 2005; Zaman, 2014; Chirca, 2014; Banica & Vasile, 2017).

In Romania, in 2011 for example, 1415 thousand jobs were supported

by EU exports to the rest of the world, with 63% higher than in 1995 (at EU

27 average increase was of 67%), but mainly based on low skilled jobs (84%

in 1995 and 74% in 2009). The high skilled export-supported jobs represented

only 4% and, respectively 8%. The spill over employment effect (as share of

jobs in Romania driven by the extra-EU export of the other Member States

was of only 10% in 1995 and 13% in 2011, lower than EU-28 average (17% in

2011), Poland (25%), Hungary (20%) or Czech Republic (30%, respectively)

(Rueda-Cantuche and Nuno Sousa, 2016).

In defi ning the pattern of trade, the phenomenon of migration has

also to be considered, as an export diversifi cation factor and potential export

growth of traditional products. In countries where Romanian immigrants are

established as European workers, they develop small businesses, which creates

a local demand for Romanian products. Thus, the development of commercial

routes and specialization on product groups specifi c to the country of origin

of the respective persons takes place, which induce the demand for Romanian

products abroad. Therefore, homogeneous communities of migrants generate

specifi c entrepreneurship, characterized by:

- products and services specifi c to the country of origin,

- temporal distribution of demand, dependent on the specifi c

consumption patterns of migrant workers,

- allows for the development and adaptation of the host community’s,


increasing the demand for products from the migrants’ countries of origin.

The aim of this paper is to analyze the characteristics and evolution

of foreign trade in Romania after 1989. Moreover, there are studied the

dependency and stability indicators of foreign trade in order to defi ne new

strategies and policies for future economic development of Romania.

2. MAIN COORDINATES OF ROMANIA’S FOREIGN TRADE AFTER 1989

Political changes in 1989 have infl uenced, in an irreversible manner,

the Romanian external trade, marking the starting point of the market economy

approach. The privatisation process and decreasing of company’s size through

reorganisation reduces the export potential and made diffi cult the external market

preserving, mainly in the globalised economy with no more regional market

orientation (i.e. former Comecon). After 1989, the national exports started to fall

constantly, until 1992 when it represented around 43% of the level registered

in 1989. A certain recovery has been recorded in the coming years but without

reaching the 1989 level; In 1998, Romania exported goods of 7400 million euro,

1% more than in 1997. In 1999, exports were 7977 million euro, increasing with

7,8% as against previous year. In fact, excepting 2009, exports increased year by

year, reaching in 2016 a level of 57389 million euro.

Imports also recorded a strong reduction right after 1989, while starting

with year 1992 it signifi cantly increased, exceeding the level of 1989 substantially.

In 2016, Romania imported goods of € 67344 million.

It is a remarkable evolution in terms of value of external trade relations,

as imports were 10.7 times greater in 2016, while exports were 16.4 times greater

than 1989.

Throughout the analyzed period (1989-2016), the balance of external

trade was negative. For the fi rst years of transition, this fact was explicable by

the need of modern technology in almost all economic sectors and also by the

demand for fi nal goods from the population. For the next few years the external

trade defi cit was only a refl ection of the inability to expand and conquer external

markets based on national products by Romanian capital and the continuous

increase of consumption of imported goods on national level.


Share of export and import in GDP

Figure 1Figure 1 Share of export and import in GDP

Source: NIS, TEMPO online database, http://statistici.insse.ro/shop/

0,0

5,0

10,0

15,0

20,0

25,0

30,0

35,0

40,0

45,0

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

%

Share of export in GDP Share of imports in GDP


During 1989 - 2007, the Romanian economy has undergone a

multidimensional reform, through the massive restructuring of the economy,

the transition from centralized to market economy, the privatization of state

owned enterprises, the need to ensure the functionality of the new structures.

These changes in the economic approach have taken place in parallel with the

redefi nition of external trade relations, through the increased opening of the

European single market mainly starting with pre-accession process to the EU.

Thus, the greatest share of imports in GDP was registered in 2004 (pre-accession

to the EU, opening the EU market without customs duties) while the lowest share

was recorded in 1991. About exports, the highest share in GDP was recorded

in 2014, after Romania’s completely recovery after the fi nancial and economic

crises.

After Romania’s accession in 2007, a new stage of trade relations

adjusting on the EU-single market took place. The volume of trade with EU

Member States has been signifi cantly infl uenced in a positive sense, by both

opening new markets for export and import, and intensifying the traditional

relations. Thus, if in 1989 only 45.2% of Romania’s exports were oriented to

European countries; in 2016, the export with EU member states reached over

75% of the total national export. In the case of imports, the dependency of the EU

region is even more pronounced, increasing by 77% in 2016 compared to 30% as

it was in 1989.

The degree of opening of the economy has reached the highest level in

2013, after which it has begun to stay below 75%, mainly due to the Romania’s

efforts to increase foreign trade and extra-EU diversifi ed relations. In 2009,

against the backdrop of the economic crisis, it was 60%, although gross domestic

product fell sharply, and national exports remained at the same level as a share

of GDP. This evolution shows the high dependency of the Romania’s GDP


on the export performance and EU area, respectively. Recovery of post-crisis

exports was possible based on past FDIs in the modernization and development

of production capacities. On the other hand, the recovery of imports was based

on the increase in consumption of the population as response to lower domestic

offer and bankruptcy of many domestic fi rms.

2.1. External trade balance

External trade balance is a good indicator for evaluating the stability and

growth prospects in terms of economic competitiveness on international markets.

After 1989, Romania’s external trade recorded an average annual growth as

against previous year of 109.1% for exports and 110.5% for imports.

Romania’s foreign trade

Figure 2Figure 2 Romania’s foreign trade


-30000

-20000

-10000

0

10000

20000

30000

40000

50000

60000

70000

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

mil

eu

ro

Trade balance FOB/CIF Export Import CIF


After a positive trade balance in 1989 (1536 mil euro), starting with

1990 the imports where greater than exports. The trade defi cit has increased

steadily, from € 2568 million in 1990 to a maximum of € 23515 million in

2008, the year before the economic and fi nancial crisis that affected most of

the world’s economies. Since 2009, the trade defi cit has registered a reduction

in value terms, so in 2016 it was of € 9358 million. This defi cit reduction was

primarily driven by a sharp and major drop in imports amid the crisis.

It is worth mentioning that in 2009 imports decreased by 32%

compared to the previous year, while exports decreased by only 14%, which

is why we can say that the effects of the crisis were felt more strongly in

Romania’s trading partners activity, refl ecting the export volatility and scarcity

of long-term export relations.

2.2. External trade structure

In 1989, the fi rst 10 exported goods were the products of the factories

still in operation and producing goods for export: fuels and mineral oils;


machinery and mechanical appliances; cast iron, iron, steel; automotive,

tractors and other terrestrial vehicles; furniture; articles of clothing; fertilizers.

These goods accounted for 70.0% of Romania’s exports in 1989, reaching

46% in 2016.

Main exported goods by Romania in 1989 and 2016

Table 1

1989 Top export (CN2)

% in

export

1989

% in

export

2016

2016 Top export (CN2)

% in

export

2016

% in

export

1989

CN 27 - Mineral fuels 17.9 3.685 - Electrical machinery and equipment

18.4 2.4

CN84 - Machinery and mechanical appliances

12.3 11.0 87 - Vehicles 15.4 8.5

CN 72 - Iron and steel 9.2 2.284 - Machinery and mechanical appliances

11.0 12.3

CN 87 - Vehicles 8.5 15.4 94 - Furniture 4.1 4.9CN 94 - Furniture 4.9 4.1 10 - Cereals 3.7 0.5CN 62 - Clothing accessories 4.1 3.6 27 - Mineral fuels 3.6 17.9CN 31 - Fertilizers 3.9 0.1 62 - Clothing accessories 3.6 4.1CN 86 - Railway locomotives 3.7 0.3 40 - Rubber 3.5 0.9CN 73 - Articles of iron or steel 2.8 2.8 44 - Wood 2.9 2.6CN 44 - Wood 2.6 2.9 73 - Articles of iron or steel 2.8 2.8Top 10 exported goods in 1989 (% in total export)

70.0 46.0Top 10 exported goods in 2016 (% in total export)

69.0 56.9

Note: For the complete description of Combined Nomenclature chapters, see Annex 1.

Source: Authors’ computations based on TEMPO online database, NIS

Romania’s exports of goods did not change signifi cantly during the

analyzed period, but the three categories not found in the top 10 products exported

in 2016 (iron and steel, fertilizers and railway locomotives) were replaced by

goods representing mainly raw materials and semi-fi nished products with very

low added value (cereals, rubber, electrical machinery and equipment). This

sharply qualitative change in the supply of goods from average to lower added

value on export, has led to a reduction of Romania’s competitiveness towards

external partners and to the mitigation of the image created by 1989, of an

economy in full swing and development on industrial bases.

In terms of import, in 1989 the top 10 products imported were also

raw materials, used in the domestic production, such as ores, cereals, cotton,

products of chemical industry and others.


Main imported goods by Romania in 1989 and 2016

Table 2

1989 Top import (CN2)

% in

export

1989

% in

export

2016

2016 Top import (CN2)

% in

import

2016

% in

import

1989

27 - Mineral fuels 46.0 5.785 - Electrical machinery

and equipment 15.6 3.5

84 - Machinery and mechanical appliances

12.9 12.584 - Machinery and mechanical appliances

12.5 12.9

87 - Vehicles 5.7 9.5 87 - Vehicles 9.5 5.785 - Electrical machinery and equipment 3.5 15.6 27 - Mineral fuels 5.7 46.026. Ores 3.2 0.3 39 - Plastics 5.5 0.272 - Iron and steel 2.3 3.0 30 - Pharmaceutical products 4.1 1.125 - Salt 2.1 0.2 73 - Products of iron or steel 3.0 1.090 - Optical instruments 1.9 0.6 72 - Iron or steel 3.0 2.352 - Cotton 1.5 2.1 90 - Optical instruments 2.1 1.538 - Chemical products 1.3 1.4 40 - Rubber 1.8 0.6Top 10 imported goods in 1989 (% in total import)

80.4 50.9Top 10 imported goods in 2016 (% in total import)

62.7 74.9

Note: For the complete description of Combined Nomenclature chapters, see Annex 1.Source: Authors’ computations based on TEMPO online database, NIS

In 2016, compared to 1989, there was an important change in the

structure of top 10 imported products, by increasing the share of fi nished products

(pharmaceutical products, products of iron or steel) and reducing the raw materials

share in total imports. By analyzing the top of both imports and exports, it can be

concluded that in 1989 some of the raw materials where imported for production

purposes (for example, import of chemical products to produce and export

fertilizers or import of cotton to export clothes and clothing accessories); in 2016

most of imported products are semi-fi nished or fi nished goods, stimulating the

consumption and to a smaller extent, the national production.

In 2016, main defi cits were recorded in case of machinery and mechanical

appliances (€ -2121.7 mil.), pharmaceutical products (€ -2030.4 mil.) and mineral

fuels (€ -1719.5 mil.). The most important positive trade balances were recorded

in case of vehicles (€ +2460 mil.), furniture (€ +1521.0 mil.), cereals (€ +1505.1

mil.) or wood and articles of wood (€ +1012.6 mil.). Manufacture of motor

vehicles, trailers and semi-trailers is the main exporting industry in Romania.

There are many FDI companies involved in this sector; moreover, there have been

developed satellite companies, in general small and medium fi rms, which provide

materials and semi-fi nished goods for bigger producers. This is an effi cient

business model, assuring both the development of small national businesses on

regional level and comparative advantages for foreign companies.


2.3. The geography of Romania’s export

Romanian external trade has an asymmetric territorial distribution,

mainly oriented to EU single market after accession in 2007. This was not the

case in 1989, when exports to the current EU countries (EU28) accounted for

only 45.2% of total national exports. This share has grown steadily over the

26 years under review, reaching over 75% in 2016. With an average annual

growth rate of 111.4%, intra-EU exports of goods contribute with more than

25% to increasing gross domestic product, from 6% as they contributed in

1990.

Imports of goods from the EU28 Member States also followed the

export trend, their share in total national imports increasing from 30% in

1989 to 77.1% in 2016. Having almost the same annual growth rate (114.4%),

imports of goods contribute with 29% to gross domestic product, up from 8%

as in 1990.

The higher import value of goods compared with export value over

the analyzed period generated a negative infl ow of the trade balance of -4% in

2016, on gross domestic product, down from -1.6% in 1990.

The coverage of imports by exports increased for the intra-EU trade

from 79.1% in 1990 (the fi rst year with trade defi cit after 1989, when the

coverage rate was 187.1%) to 86.5% in 2016, with a peak reached in 1994,

of 97.3%. After the economic and fi nancial crisis, this indicator registered a

relative stability, varying slightly between 82.2% and 88.2%. The degree of

openness calculated as the ratio of the sum of total imports and exports to

GDP to the European market has steadily increased from 24.0% in 1990 to a

maximum of 54.8% in 2016.

Simultaneously with the development of economic relations with

the EU28, trade with states from extra-EU territory has experienced a drastic

reduction, with more than 50% as a share in the total exports and imports. In

1989 trade with non-European countries accounted for 54.8% of total exports

and 70.0% of total imports, while in 2016 the share of extra-EU trade in

Romania’s total external trade was only 25% for exports and 23% for imports.


Romania’s Intra-EU and Extra-EU trade in goods, 1989-2016

Figure 3

Source: NIS, TEMPO online database, http://statistici.insse.ro/shop/, Eurostat Comext database

However, the coverage of extra-EU imports with exports from the

same geographical area, decreased from 97.3% in 1989 to 93.0% in 2016.

During 2013-2015, the trade balance of the extra-EU trade balance of Romania

was positive. This balancing of the extra-EU trade fl ows could not compensate

the commercial defi cit registered by Romania in case of trade the European

countries. Thus, the degree of extra-EU trade openness registered constant

annual reductions, reaching only 17.2% in 2016. With an average growth rate

in the last 26 years of only 106.6% and with a reduced share in total national

export, the extra-EU exports are not able to support the national economy in

terms of competitiveness and sustainability.

2.4. Dynamics of export trade relations: traditional partners vs

new destinations

Data analyses made on trade volume by partner’s countries of Romania

have proved that, from the point of view of the intra-EU exports, there are

5 traditional markets; for over 20 years, half of the exports are oriented to

Germany, Italy, France, Hungary and the United Kingdom. In 1989 these

partners accounted for 30% of total exports, while in 2016 they represented

49.9%.

As far as the extra-EU exports are concerned, there is a relative stability

Turkey, Russian Federation, United States of America, Serbia and China being

in the list of 20 partner countries during the analyzed period. These countries

registered a falling down share in total extra-EU exports, from 36.3% in 1989

to 8.7% in 2016. During the analyzed period, the extra-EU trade decreased as

a share in total exports from 54.8% in 1989 to 24.9% in 2016.


Main Romania’s partner countries on export in 1989 and 2016

Table 3

1989 Top partner countries% in

export 1989

% in export 2016

2016 Top partner countries

% in export 2016

% in export 1989

Russian Federation 22.8 1.7 Germany 21.5 12.0

Germany 12.0 21.5 Italy 11.6 9.6

Italy 9.6 11.6 France 7.2 2.4

USA 5.5 1.7 Hungary 5.2 2.7

China 3.4 1.1 United Kingdom 4.3 2.4

Czech Republic 3.1 2.6 Bulgaria 3.2 1.7

Poland 3.0 2.9 Turkey 3.2 3.0

Iran 3.0 0.6 Spain 3.0 0.5

Turkey 3.0 3.2 Poland 2.9 3.0

Hungary 2.7 5.2 Czech Republic 2.6 3.1Top 10 partners in 1989 (% in total export)

68.1 52.1Top 10 partners in 2016 (% in total export)

64.8 40.4


Although trade with the Russian Federation has regressed over the

last 25 years, primarily due to the ban imposed on EU’s agriculture products,

it remains one of the important partner country for Romania’s export. Thus,

from the fi rst place held in 1989 as a trading partner (22.8% of total exports of

Romania, with € 1779.5 mil.), in 2016 we fi nd the Russian Federation on the

15th place, representing only 1.7% (€ 969.9 mil.) of the total Romania’s export.

There have been deteriorations in trade relations also with some Asian

countries such as China (from 3.4% in 1989 to 1.1% in 2016) or Iran (from

3.0% to 0.6%).

Main Romania’s partner countries on import in 1989 and 2016

Table 4

1989 Top partner countries% in

import 1989

% in import 2016

2016 Top partner countries

% in import 2016

% in import 1989

Russian Federation 33.7 2.9 Germany 20.5 10.2

Iran 12.9 0.2 Italy 10.3 0.7

Germany 10.2 10.5 Hungary 7.5 3.5

Czech Republic 4.9 2.8 France 5.5 0.6

Poland 4.3 5.1 Poland 5.1 4.3

China 4.1 5.1 China 5.1 4.1

Hungary 3.5 7.5 Netherlands 4.1 0.6

Bulgaria 2.7 3.1 Turkey 3.8 0.6USA 2.2 0.9 Austria 3.6 0.7Serbia 1.9 0.7 Bulgaria 3.1 2.7Top 10 partners in 1991 (% in total import)

80.4 38.8Top 10 partners in 2016 (% in total import)

68.6 28.0



A signifi cant deterioration in trade relations on the import fl ow was

recorded in the case of the Russian Federation, imports from this country

shrinking from 33.7% in 1989 to 2.9% in 2016. In the period under review,

Romania signifi cantly reduced imports from the countries like Iran (12.9%

of total imports in 1989) and developed external relations with partners on

European market. The trade with EU increased, especially after accession

to the EU, accounting for 77.1% of total imports, from 30.0% in 1989. The

advantages obtained by the lack of import customs duties and the on-going

process of integration into the European structures, as well as the international

political disturbances, have led to this geographic redistribution of Romania’s

trade relations. In parallel with the development of new economic relationships

and opening to new markets, trade transactions with older partners must be

preserved and strengthened to reaffi rm their competitive position on the global

property market.

Companies’ distribution to foreign trade by capital ownership is

dominated mainly by foreign capital. The number of foreign-owned fi rms

(FDI) in Romania increased constantly after Romania’s accession to the EU.

Number of Romanian and foreign companies with foreign trade activity

Figure 4Figure 4 Number of Romanian and foreign companies with foreign trade activity

Note: Foreign companies include mixt capital (foreign and Romanian) and entirely foreign capital companies.

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

2008 2009 2010 2011 2012 2013 2014 2015 2016

Nu

mb

er o

f co

mp

anie

s

Romanian companies Foreign companies

Note: Foreign companies include mixt capital (foreign and Romanian) and entirely foreign

capital companies. Romanian companies refer to entirely Romanian capital companies

Source: Authors’ computations based on NIS data

In 2016, more than 59% of the export (in terms of value) and over 53%

of the import have been carried out by companies with foreign capital, which

shows a very high dependency of the national foreign trade on capital from

other countries. The share of companies with Romanian capital is, on average,

14% of national exports throughout the analyzed period, with a decrease trend


compared to the period preceding the economic and fi nancial crisis. Mixed

capital fi rms account for about 31% of Romania’s total exports, with a slight

downward trend from year to year.

The increased role of the Romanian owned companies in sustainable

export should be a national level priority, supported by technology transfer

funding (i.e. through structural funds). But during last years, the export

dynamic shows a different picture. Except food industry, with an increased

share of domestic owned companies’ exports from 39% in 2015 to 45% in

2016 in total industry and manufacture of wood and furniture products from

64% to 66%, in all other domains Romanian companies lost theirs position on

export markets (as average, on total manufacturing, from 22% to 18%). So,

the low level of exports from Romanian companies, of only Euro 1.7 billion in

2016 cannot lead to the growth of a signifi cant part of the economy, i.e. 46%

of the total turnover at national level (ZF&PIAROM, 2018).

3.THE DEPENDENCY RATIO Our previous descriptive analysis of Romanian foreign trade reveals

a low level of both extra-EU exports and imports compared to the levels of

intra-EU exports and imports. This reduced share in total national foreign

trade should be considered with caution in terms of foreign trade overall

sustainability, of competitiveness and foreign trade market volatility generated

by the evolution of social and economic situation (crisis, political events

etc.). To determine the Romanian position compared to other EU countries

in terms of EU-dependency ratios of both exports and imports, is was made

a classifi cation of countries by intra-EU export and Intra-EU imports. Using

hierarchical and non-hierarchical classifi cation methods (Ward Method and k

Means Clustering Method) three homogenous clusters are obtained:

- Countries with very high intra-EU foreign trade relations: more

than 70% of imports are intra-UE and more than 80% of Exports

are intra-EU: Luxemburg, Austria, Portugal, Belgium, Slovenia,

Latvia, Poland, Hungary, Estonia, Czech Republic, Slovakia.

- Countries with medium intra-EU exports: between 70% and 80% of

intra-EU exports: Spain, Denmark, Netherlands, Lithuania, Romania

- Countries with lowest intra-EU foreign trade relations: less than

70% of Imports and Exports with Intra-EU trade partners: Ireland,

Germany, United Kingdom, France, Finland, Italy, Bulgaria,

Cyprus, Malta, Sweden, Croatia, Greece


EU countries by dependency ratios for import and export, 1999, 2010 and 2016

Figure 5

Source: Own computations based on Eurostat database

By representing these clusters on 2010 and 2016 data, some countries

changed their position signifi cantly in terms of partners countries for foreign

trade. Malta, United Kingdom and Greece decreased their trade relations with

EU for both Export and Import in 2010 and 2016 compared to 1999. Croatia

and Cyprus had an oscillatory evolution of trade relations. For example,

Cyprus increased the share of both intra-EU exports and imports in 2010

compared to 1999 (from 60% to 70% of Intra-EU imports and exports) and

then decreased the share of intra-EU exports in 2016 compared to 2010 (from

70% to 50%).

Romania’s foreign trade have experienced a strong orientation

towards the EU member states, both in terms of imports and exports. In 1999,

Romania had the share of both Intra-EU exports and imports of about 70%,

while in 2016 this share increased to almost 80%. This tendency can lead in


time to the fragility of the export activity. Excepting 2009, it was registered a

long-term trend of decreasing intra-EU trade dynamics, Romania, as many of

the EU countries, have tried to increase the diversifi cation of trade relations

in extra-EU area. This was mainly the response to the effects of the crisis and

the high volatility of trade relations during the crisis in the EU; the need to

rebalancing the markets (intra vs extra-EU) was also a reason for exporter’s

behaviour who target increased advantages in bilateral trade relations.

Romania’s Intra-EU exports and imports dynamics, as against previous

year (%)

Figure 6

Source: Own computations based on NIS database

From the accession to the EU in 2007, the Romanian economy did not

compete effectively on the European market and intra-EU trade contributed

to the deepening of total trade defi cit. In recent years, the coverage of imports

by exports was over 4/5 (82% in 2013). While some former communist states

registered positive trade balances in the last decade in intra-EU trade (Czech

Republic, Hungary, Poland, Slovenia, Slovakia), Romania recorded the

greatest defi cit from the European states former Comecon members. The EU

is the main export market and a source of imports equally important for most

of the EU members. Romania would need a stable fi scal framework, otherwise

exporters’ competitiveness might fail to contribute to improving the national

trade balance.

After 1989, exports have recorded a slight increase over intra-EU

imports. The trade growth, in combination with a small share of exports to

GDP, indicates the important potential of the economy to get higher profi ts


from export and to diminish the balance defi cit. Once the customs duties

have been eliminated, the annual rate of imports from intra-EU countries

has surpassed the increase of intra-EU exports. After 2008, the rate felt and

reversed, meaning that the increase rate of intra-EU exports has surpassed the

increase rate of imports, and seems to be maintain since then.

There are some important factors to consider in improving export

activity of companies in Romania, such as:

- Investment in technological transfer to improve the goods’ quality

and for products diversifi cation;

- a supportive production credit system for small and medium

enterprises for turnover growth;

- infrastructure development, to facilitate the export activity from

logistic point of view (transport infrastructure, etc.),

- a simplifi ed and stable fi scal legislation

- supporting small and medium enterprises in their efforts to expanding

export markets.

4. INTRA-INDUSTRY AND INTER-INDUSTRY, TRADE BY TYPE OF CAPITAL

Intra-industry trade may be defi ned as simultaneous export and import

of goods and services of the same sector. Inter-industry trade is defi ned as net

exports or imports of an industry. Meanwhile, intra-industry trade is defi ned as

the value of exports of the industry, which is exactly matched by the imports of

the same industry. Even if literature of economics offers many alternatives for

measurement of intra-industry trade (Balassa, 1966, Balassa & Bauwens, 1987),

Grubel-Lloyd approach is generally used. They introduced this concept in 1971

to calculate intra-industry trade values of 9 OECD-member by using the trade

data of 1968-1969 (Grubel and Llyod, 1975).

Given the export Xi and the import Mi of good I, the G-L index is

defi ned as:

= 1, there is only intra-industry trade which means that the country exports the same If GLi = 1, there is only intra-industry trade which means that the

country exports the same quantity of good I as it imports. Conversely, if GLi =

0, there is no intra-industry trade meaning that the country either only imports

or only exports the good i.


Grubel Lloyd index for Romanian foreign trade in goods, 2008-2016

Figure 7

Note: KR=Romanian capital, KM=Mixed capital, KS=Foreign capital, IP=Companies

performing inward processing activities in Romania, non-IP=Companies which do not perform

inward processing activities, HT=Companies performing foreign trade transactions with high-

tech products, non-HT=Companies which do not import/export high-tech goods.


Companies with Romanian capital are mainly oriented in exporting

goods from different industries than in importing goods. This is linked with

their need to fi nd the specifi c external markets where high processed products

can be better valuated than on domestic markets. These companies need to

import goods which cannot be found on national market in order to produce

their own goods, on a higher quality, which best meet the demands of external

benefi ciaries and provide comparative advantages on the external markets

On the contrary, mixed capital and foreign capital companies are

mainly oriented in exporting and importing goods of the same industries

which indicates a high degree of specialization of their activities in Romania.

They import semi-fi nished goods from one industry in order to obtain the fi nal

products but from the same industry as the imported ones, benefi tting from

the (still) low level paid labor force. Moreover, the national consumption of

imported/fi nal goods is stimulated by distribution of multinational enterprises

companies through local small and medium business.


Grubel Lloyd index by type of capital and activity for Romanian

Foreign trade in goods

Figure 8Figure 8 Grubel Lloyd index by type of capital and activity for Romanian Foreign trade in goods

0,3

0,5

0,7

0,9

2008 2009 2010 2011 2012 2013 2014 2015 2016

Companies with Romanian capital

Total KR IP non IP

HT non HT

0,3

0,5

0,7

0,9

2008 2009 2010 2011 2012 2013 2014 2015 2016

Companies with mixed capital

Total KM IP non IP

HT non HT

0,3

0,5

0,7

0,9

2008 2009 2010 2011 2012 2013 2014 2015 2016

Companies with foreign capital

Total KS IP non IP

HT non HT

Note: KR=Romanian capital, KM=Mixed capital, KS=Foreign capital, IP=Companies performing inward

processing activities in Romania, non-IP=Companies which do not perform inward processing activities,

HT=Companies performing foreign trade transactions with high-tech products, non-HT=Companies which

do not import/export high-tech goods.


The more competitive a product is, the higher GL index is registered.

From the analyses performed, in case of high-tech products traded on Romanian

market, the index is very close to 1 for fi rms with foreign and mixed capital.

This indicates a small added value gained by the fi nal product following

activities on Romanian market, the contribution to global chain being limited

to reduced costs for companies with employment and raw resources.

The country-specifi c determinants such as the average level of

economic development, average market size, geographic distance or economic

integration, have a direct impact on intra-industry trade. Its intensity is also

depending on similarities in economic development and market size among

partners.

Differences in economic development among trading partners,

which represent differences in capital abundance, have a positive effect on

the intensity of intra-industry trade in vertically differentiated products -


simultaneous import and export of a quality differentiated good (M. Andresen,

2003). This is highlighted in our study by the Grubel Lloyd index computed

in case of high-tech goods traded by foreign capital, whose value is very

close to 1. High-tech products traded by foreign-owned companies does not

necessarily represent development of high-intensity industry if the products

are not entirely produced in Romania, but mainly processed under inward

processing procedure and the added value is very low.

About horizontal intra-industry trade – variety of goods of the same

quality, this can have a negative relationship with product differentiation (the

number of product categories within an industry) (M. Andresen, 2003). The

Romanian markets follows the model of horizontally differentiated intra-

industry trade, characterized by monopolistic competition, which comprises

many small fi rms competing based on their variety, which represents a low

market concentration.

Inter-industry trade is to some extent more pronounced in case of

Romanian companies, for all traded goods, including high-tech. It means these

companies produce the whole product or a great part of it on national territory,

the added value in this case being important. But Romanian companies

are small and numerous, their efforts being deconcentrated, therefore their

effi ciency (productivity, openness to external markets) is low.

5. CONCLUSIONS Until 1989, Romania followed a national pattern of foreign trade,

with sustainable trade routes, having partners in ex-soviet countries, China,

America, the Arab states, and with few countries in the EU. After 1990,

Romania’s foreign trade have experienced a strong orientation towards the EU,

both in terms of imports and exports. In 2016 over three quarters of external

trade transactions were performed with partners from EU. In the absence of

a sustainable export through the domestic capital production, mainly due to

economic restructuring and fragile (new/renewed) trade relations, the main

export activity had a strong conjectural character, which over time have led to

the fragility of the export activity.

After Romania’s accession in 2007, a new stage of trade relations adjusting

on the EU-single market took place, the volume of trade with member states being

signifi cantly determined by both opening new markets for export and import and

intensifying the traditional relations. The degree of opening of the economy has

reached the highest level in 2013, after which it was below 75%, mainly due to the

Romania’s efforts to increase foreign trade and extra-UE diversifi ed relations.

Romania’s exports of goods did not change signifi cantly during the

analyzed period, but new goods representing raw materials and semi-fi nished


products with very low added value (cereals, rubber, electrical machinery and

equipment) replaced some fi nished products (i.e. fertilizers) that Romania

exported in 1989. This sharply qualitative change in the supply of goods from

average to lower added value on export, has led to a reduction of Romania’s

competitiveness towards external partners.

On the imports side, there was an important change in the structure

of top 10 imported products, by increasing the share of fi nished products

(pharmaceutical products, products of iron or steel) while reducing the raw

materials’ share in total imports.

Data analyses made on trade volume by partner’s countries of Romania

have proved that, from the point of view of the intra-EU exports, there are 5

traditional markets; for over 20 years, half of the exports was oriented to

Germany, Italy, France, Hungary and the United Kingdom.

Foreign trade in Romania is mainly performed by foreign-owned

fi rms. The GL index, computed for the whole foreign trade with all member

countries, by type of ownership, revealed that Romanian capital is mainly

oriented to exports of goods from different industries than in importing goods.

On the contrary, foreign-owned companies are mainly oriented in exporting

and importing goods of the same industries which indicates a high degree of

specialization of their activities in Romania.

The consumers’ satisfaction is mainly based on goods quality, and

this is directly linked with technology included or used in production of the

goods. Without considering the nature or the usefulness of products, meaning

food, beverages, intermediate industrial products, goods from IT sector, etc.,

technology is extremely important in all economic sector’s development.

Without technology, Romania’s domestic sector became an exporter of raw

materials while foreign capital produce, with cheaper qualifi ed labor force

as compared with other countries, goods for export with cost-comparative

advantage. To overcome this disadvantage of the domestic capital, either a

reorientation of exports to new markets or a diversifi cation of national supply

would be necessary, which drives the need to stimulate domestic fi rms to create

and produce sustainable goods for the domestic and international markets. As

innovation and research activity is more specifi c to other EU countries, more

developed on this sector, imports of technology from these countries would be

better to be continued. For exports, cooperation activities have to be initiated

with more partners from countries outside the EU.

Romanian foreign trade performance on EU single market is rather

limited by comparative disadvantages that are continuing to adjust our export

with EU member states. Therefore, the main coordinates for an improved

national export strategy would consist of:


- preserving trade relations supported by traditional advantages;

- a minimum threshold in balance trade for indispensable imports;

- reconsidering national technological potential for diversifying

exports of goods and services, with a higher trade effi ciency - over average

value-added rate than the actual one at national level;

- supporting domestic capital development for high tech through

structural funds – based on technological transfer to businesses.

Romania’s economic relations must not remain highly dependent on

the EU market. Given the Brexit phenomenon, stability needs to be ensured

for the future on traditional trade networks with the UK. We stress that Brexit

must serve as a catalyst for renewing the Romanian foreign trade strategy

and policies by keeping a right balance between dependency on EU single

market as member of EU and developing trade relations on medium and long

term with other countries, where Romania can have either an exceeding trade

balance or comparative advantages based on high tech exports.

The comparative advantages of foreign companies based on reduced

cost of labor force start to diminish in importance, and FDI companies’

relocation perspective should be considered. The national investments efforts

should be mainly oriented in technological transfer, infrastructure development

and education. Because of massive migration of qualifi ed personnel, currently

the employees are the most important production factor of companies, which

are forced to increase the salaries and improve working condition in order

to prevent their leaving abroad, and/or employment to other competitors on

the national market. Despite the reduced cost of labor force compared with

other European countries, Romania misses high and medium-high qualifi ed

workers, and on a short term this will make the difference in foreign investors

decision when choosing the country to invest.

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Annex 1

Detailed description of Chapters from Combined Nomenclature mentioned in the study

Chapter 10 - Cereals

Chapter 26 - Ores, slag and ash

Chapter 27 - Mineral fuels, mineral oils and products of their distillation; bituminous

substances; mineral waxes

Chapter 29 - Organic chemicals

Chapter 30 - Pharmaceutical products

Chapter 31 - Fertilisers

Chapter 38 - Miscellaneous chemical products

Chapter 39 - Plastics and articles thereof

Chapter 40 - Rubber and articles thereof

Chapter 44 - Wood and articles of wood; wood charcoal

Chapter 52 - Cotton

Chapter 62 - Articles of apparel and clothing accessories, not knitted or crocheted

Chapter 72 - Iron and steel

Chapter 73 - Articles of iron or steel

Chapter 76 - Aluminium and articles thereof

Chapter 84 - Nuclear reactors, boilers, machinery and mechanical appliances; parts thereof

Chapter 85 - Electrical machinery and equipment and parts thereof; sound recorders

and reproducers, television image and sound recorders and reproducers, and parts and

accessories of such articles

Chapter 86 - Railway or tramway locomotives, rolling stock and parts thereof;

railway or tramway track fi xtures and fi ttings and parts thereof; mechanical (including

electromechanical) traffi c signalling equipment of all kinds

Chapter 87 - Vehicles other than railway or tramway rolling stock, and parts and accessories thereof

Chapter 90 - Optical, photographic, cinematographic, measuring, checking, precision,

medical or surgical instruments and apparatus; parts and accessories thereof

Chapter 94 - Furniture; bedding, mattresses, mattress supports, cushions and similar stuffed

furnishings; lamps and lighting fi ttings, not elsewhere specifi ed or included; illuminated

signs, illuminated nameplates and the like; prefabricated buildings


Statistical Model for Prediction of Future Trend in Hypertensive Disease in Adult Poplation of RomaniaOana-Florentina GHEORGHE-FRONEACarol Davila”University of Medicine and Pharmacy,

Bogdan DOROBANTU ([email protected])

Carol Davila”University of Medicine and Pharmacy,

Corina ILINCAFaculty of Sociology and Social Work, University of Bucharest,

Stefania MATEIDivision of Social Sciences, Research Institute of the University of Bucharest,

Marian PREDAFaculty of Sociology and Social Work, University of Bucharest,

Maria DOROBANTUCarol Davila”University of Medicine and Pharmacy

ABSTRACT Purpose: The present study aims to analyse the past and future trend in

HT’s prevalence, awarness, treatment and control in adult Romanian population using

statistical models based on the results of the three national-representative surveys.

Methodology: using the data from the three national-representative surveys:

SEPHAR I, II and III conducted between 2005 and 2016, by means of Simple exponen-

tial smoothing and Brown linear smoothing analysis using IBM SPSS 20.0 software

we evaluated the past in future trend (up to year 2020) of hypertension’s prevalence,

awareness, treatment and control in our adult population.

Main fi ndings: The evolution of HT’s prevalence is characterized by signifi -

cant oscillations in the analysed period (2005-2016) and in 2020, is estimated to be

44%. Awareness of hypertension followed a steady trend of growth from 2005 to 2016.

If in 2005 the percentage of hypertensive aware of their condition was 44.3%, in 2016

their percentage reached 80.9%, being expected to increase up to 96.2 % in 2020. The

percentage of treated hypertensives increased to 59.2% in 2012 and 75.2% in 2016,

and is expected to reach 91.2% in 2020 unless there are major events at the level of

risk factor changes. In 2005, BP control rate was 19.9%, percentage which rose to

30.8% in 2016 and is expected to increase up to 36.6% by year 2020.

Conclusions: Based on the results of our study, in Romania, hypertension’s

prevalence has increased in the last 11 years and will continue on an upward trend,


if no preventive strategies at population level will be implemented in the near future.

Although being on a positive trend for HT’s awareness, treatment and control, hyper-

tension management will remain suboptimal in Romania in the future, if all the infl uenc-

ing conditions remain, on average, similar to previous years, leading to a continuous

up-ward trend in cardiovascular mortality in our country.

Keywords: statistical model, prediction, trend, hypertension, national, repre-

sentative

1. INTRODUCTION Hypertension (HT) through cardiovascular diseases (CVD) is the

most common cause of mortality in developed countries. Out of an estimated

55 million annually total deaths across the globe, about 30% are from

cardiovascular causes [1-5].

In Europe, cardiovascular mortality has seen in recent decades a

divergence trend between Central and Eastern Europe Cuontries, where it

achieved very high rates, and Northern and Western Europe countries where

cardiovascular mortality is on a steadily declining trend [1-5].

At the current stage, where the genetic condition of CVD is only

deciphering, the most effective therapeutic approach is the intervention on

major modifi able cardiovascular risk factors.

Among these, HT has the highest prevalence and one of the most

important effects on cardiovascular morbidity and mortality, being recognized

as a independent risk factor for the development of all manifestations of

atherosclerosis. More, in the last 20 years, HT has risen in the ranking of top

20 leading cardiovascular risk factors on the fi rst position [1-5].

From the perspective of public health policies, it is very important

to know the prevalence of CV risk factors and the evolutionary trend of their

prevalence in the general population, which would allow anticipation of

the evolution of the CV mortality curve and the evaluation of the benefi t of

different CV prevention strategies.

Representative data for Romanian adult population regarding the

prevalence of HT and other CV risk factors are availlable through the results

of the three national representative surveys SEPHAR I, SEPHAR II and

SEPHAR III conducted between 2005 and 2016 [1,2,6].

The present study aims to analyse the past and future trend in HT’s prevalence,

awarness, treatment and control in adult Romanian population using statistical

models based on the results of the three national-representative surveys.

2. METHODOLOGY Detailed description of SEPHAR I, II and III surveys’ methodology

has been previously published elsewhere, therefore below will detail only


those parameters that are object of the current paper, with an emphasis on the

statistical methodology used [1,2,6].

Hypertension’s defi nition

At each study visit, 3 consecutive BP measurements were taken

at time interval of at least 1 minute, using an automatic oscillometric BP

measuring device according to current guidelines for HT management [5].

Hypertension was defi ned as systolic blood pressure (SBP) ≥ 140mmHg

and/or diastolic blood pressure (DBP) ≥ 90mmHg at both study visits, using

the arithmetic mean of the second and third BP measurement of each study

visit (without taking into consideration the fi rst BP measurement from each

visit), or previously diagnosed HT under treatment during the last two weeks,

regardless of BP values.

The prevalence of HT is calculated by the ratio between the number of

subjects identifi ed as being hypertensive and the total number of the subjects

enrolled in the survey.

Hypertension’s awareness defi nition

Awareness of HT was defi ned by the percent of hypertensive subjects

who declared being previously diagnosed with HT by a doctor.

Hypertension’s control defi nition

Controlled BP values was defi ned by SBP < 140mmHg and DBP <

90mmHg in hypertensive subjects who were treated for at least 2 weeks before

(current treatment) [5], taking into account the maximum value between the

two SBP/DBP values from each visit, in t

The therapeutic control rate was defi ned by the ratio between treated

hypertensive subjects with controlled BP values and the total number of

hypertensive subjects under current treatment.

Statistical analysis

Statistical analysis was performed with IBM SPSS Statistics 20.0

software at a signifi cance level of p ≤ 0,05. The statistical methods used for

each trend analysis in our study are detailed in Table 1. Since all the SEPHAR

surveys used in this study were conducted on representative samples for

Romanian adult populations selected by a multi-stratifi ed sampling procedure

that had age categories, genders, place of residence and territorial regions as

sampling strata, all trend analysis were adjusted for these parameters.


Statistical methods used for each analysis. HT: hypertension

Table 1Analysis Statistical Method

HT’s prevalence trend Simple Exponential SmoothingHT’s awarness trend Brown Linear Smoothing

HT’s treatment rate trend Brown Linear Smoothing HT’s control rate trend Brown Linear Smoothing

Description of the “Simple exponential smoothing” analysis

The “Simple Exponential Smoothing” method [7-9] is a method that

predicts the value of a variable based on the trend observed in the evolution of

some variables in the past. In practice, we can see how a variable has evolved in

the past and, on the basis of these observations, it is stated how it could evolve

in the future if the conditions in the last year under analysis remain constant.

As a working principle, the method starts from the observation

of how the values evolved from the fi rst to the last observation. Then, with

multiple iterations, an optimal value of a generically defi ned factor “alpha” (or

smoothing factor) that manages to predict the fewest errors the values already

observed is identifi ed. That alpha factor that worked best to identify past values

is then used to predict future values, with the specifi cation that this alpha factor

is calculated at different levels for more recent values than for older values.

This prediction method is used predominantly to predict data for which

there is no constant pattern of evolution over time of values (steady or steady

decrease) or seasonal evolution patterns. Its advantage over other methods of

predicting is that more recent observations are attributed to a higher weight in

establishing the trend than the older observations. As such, it is most effective

in making predictions based on a limited number of observations. Also, this

method has the ability to be less sensitive to values that are inconsistent with

a central trend and to eliminate the effect generated by them.

The main limitation of the “Simple Exponential Smoothing” method

is that it does not take into account the infl uence of explanatory variables.

Models that also account for the effect of some other parameters on the

predicted value require a higher number of observed values. In the case of

three observations, the value predicted by the models that take into account

the effect of additional variables (eg ARIMA) is only a statistical artefact

(being, in fact, equal to the average of the three observations).

Description of the “Brown linear smoothing” analysis

The “Brown linear smoothing” method [7-9] is similar to that

described in Appendix 3 as a working principle, except that instead of being

used for variables that exhibit irregularities in time oscillation follows a clear

linear trend over time (either just increase or decrease).


For each trend prediction model we calculated the following parameters:

• Root Mean Square Error (RMSE) that is a Measure of how connected

the data are around the best model fi t

• Mean absolute percentage error (MAPE) that is a measure of

prediction accuracy

• Mean absolute error (MAE) that is a measure of forecast error

• Normalized Bayesian Information Criterion (NBIC) that measures

How effi cient the model is in terms of predicting the data

3. RESULTS AND DISCUSSIONS 3.1 Past and future trend in hypertension’s prevalence

As depicted in Figure 1, the evolution of HT’s prevalence is

characterized by signifi cant oscillations in the analysed period (2005-2016).

Although a slight decrease in HTA prevalence is observed between 2005 and

2012, it is not sustained for a longer period, so that in 2016 a higher HT’s

prevalence is achieved than in the case of the initial measure.

Following the eleven-year evolution that has been the subject of the

study, it is expected that in 2020 the HT’s prevalence will be 44%. This value is

slightly lower than in 2016, but not enough to mark a lasting downward trend.

However, the projected value should be interpreted with caution as the prevalence

of HTA is sensitive to any change in traditional risk factors of this disease such

as salt-intake, smoking, obesity and diabetes mellitus. The predicted value is if

the future conditions remain similar to those prevailing in the previous 11 years.

The trend in hypertension’s prevalence

Figure 1Figure 1. The trend in hypertension’s prevalence

Trend model parameters: RMSE = 3,055; MAPE = 4,699; MAE = 1,96; NBIC = 2,6

44,9

40,4

45,1

44

38

39

40

41

42

43

44

45

46

2005 (SEPHAR I) 2012 (SEPHAR II) 2015 (SEPHAR III) 2020 (predicted

value)

Trend model parameters: RMSE = 3,055; MAPE = 4,699; MAE = 1,96; NBIC = 2,6


Analysing the prevalence of HT by genders, it can be seen that, except

in 2012, HT’s prevalence was higher among men than women. This situation is

also expected to persist in 2020 when the prevalence among women is estimated

to be 42% and among men in whom the HT’s prevalence is estimated to be

47.2%. At the same time, based on the above data, the following situation can

be noticed: for women, the general trend is the stagnation in HT’s prevalence,

while for men there is a more signifi cant decrease than in 2012. This fact

makes the differences between women and men in terms of HT’s prevalence

diminish as time passes.

The trend in hypertension’s prevalence by gender

Figure 2Figure 2. The trend in hypertension’s prevalence by gender

41,1

50,1

42,2

38,4

42,2

48,4

42

47,2

0

10

20

30

40

50

60

WOMEN MEN

2005 (SEPHAR I) 2012 (SEPHAR II) 2015 (SEPHAR III) 2020 (predicted value)

Taking this time in analysis the age-related HT’s prevalence, it can be

seen that the prevalence of HT increases in general with age, with the highest

percentages of hypertensive individuals being found in subjects over the age

of 65. What appears to be remarked is that while for people over 45 years of

age, HT’s prevalence has either a stagnation trend or a downward trend (albeit

with some fl uctuations), for people under 44, the values indicate an up-ward

trend in HT’s prevalence, more evident among people aged 18-24 years.


The trend in hypertension’s prevalence by age category

Figure 3Figure 3. The trend in hypertension’s prevalence by age category

8,8

15

28,1

51,4

65,5

75

11,17,8

23,1

49,7

65,8

81

15,4

21,6

34,6

50,3

62,666,9

15,4 16,2

29,8

50,3

64,2

72,9

0

10

20

30

40

50

60

70

80

90

18 24yers 25 34 years 35 44 years 45 54 years 55 64 years 65 years


If in 2005 the prevalence of HT was signifi cantly higher in the rural

area compared to the urban environment, in the years to come the differentiation

seems to diminish and the values found in the two residence environments are

similar. However, predictions for the year 2020 point to a moderate increase

in the differences between rural and urban areas, with higher HTA prevalence

in rural areas than in urban areas.

The trend in hypertension’s prevalence residence

Figure 4Figure 4. The trend in hypertension’s prevalence residence

49,5

41,641,439,8

46,244,5

46,2

42,5

0

10

20

30

40

50

60

Rural Urban


The analysis carried out at the level of the development regions

highlights a tendency to increase the prevalence of HTA, especially in the

South-East, Center and Bucharest regions. It is worth noting from Figure 5,

that the year 2016 was characterized by a marked increase in HT’s prevalence

in several development regions, including South-East, South-West, West,


North-West, Center and Bucharest-Ilfov. In contrast, the South region shows

a signifi cant decrease in the percentage of hypertensives, which, however, due

to the oscillatory effects is not expected to be maintained in 2020 as well.

The trend in hypertension’s prevalence by regions

Figure 5

Figure 5. The trend in hypertension’s prevalence by regions

45,3

32,4

43,446

42,9 41,5

34,9 32,9

40,2 39,1

45,8

40,536,7 38,4 39,2 4142,5

44,7

38,9

49,3 49,7 49,745,7

42,442,544,7

4246,1 44,4 44,4 45,7

42,4

0

10

20

30

40

50

60


3.2 Past and future trend in awareness of hypertension

Awareness of hypertension followed a steady trend of growth from

2005 to 2016. If in 2005 the percentage of hypertensive aware of their

condition was 44.3%, in 2016 their percentage reached 80.9%, being expected

to increase up to 96.2 % in 2020 if the conditions remain, on average, similar.

The trend in hypertension’s awareness

Figure 6Figure 6. The trend in hypertension’s awareness

Trend model parameters: RMSE = 4.234; MAPE = 2.478; MAE = 2.003; NBIC = 3.257

44,3

65,6

80,9

96,2

0

20

40

60

80

100

120

2005 (SEPHAR I) 2012 (SEPHAR II) 2015 (SEPHAR

III)

2020 (predicted

value)



Evolution of known hypertension follows an upward trend in both

genders. In all the years undergoing the analysis (2005, 2012, 2016), the rate

of HT’s awareness has been shown to be higher among hypertensive women

than hypertensive men, a situation that is expected to persist in 2020 as well.

The trend in hypertension’s awareness by gender

Figure 7

34,6

52,8

62,2

75,675,1

86,988

98,2

0

20

40

60

80

100

120

Awear (Men) Awear (Women)


3.3 Past and future trend in hypertension treatment

In 2005, 38.9% of people with hypertension were treated. Their

percentage increased to 59.2% in 2012 and 75.2% in 2016, and is expected to

reach 91.2% in 2020 unless there are major events at the level of risk factor

changes.


The trend in hypertension’s treatment

Figure 8Figure 8. The trend in hypertension’s treatment

Trend model parameters: RMSE = 3.041; MAPE = 1.91; MAE = 1.453; NBIC = 2,59

38,9

59,2

75,2

91,2

0

10

20

30

40

50

60

70

80

90

100


value)

Trend model parameters: RMSE = 3.041; MAPE = 1.91; MAE = 1.453; NBIC = 2,59

As with awareness, the rate of treatment for high blood pressure is

higher for women than for men. If in 2005 30.1% of men with high blood

pressure were treated, in 2016 their percentage reached 66.7%, and it is

expected to increase up to 83.7%. For women, in 2005, 45.6% of those with

hypertension were treated, the percentage rising to 78% in 2016, and expected

to reach 89.1% if all conditions remain similar.

The trend in hypertension’s treatment by gender

Figure 9

30,1

45,649,7

66,966,7

7883,7

89,1

0

10

20

30

40

50

60

70

80

90

100

Rat de tratament HTA (b rba i) Rat de tratament HTA (femei)



3.4 Past and future trend in hypertension’s control

The therapeutic control rate of high blood pressure follows an upward

trend. In 2005, the proportion of treated hypertensive patients with optimal

BP control was 19.9%, percentage which rose to 30.8% in 2016. In 2020, the

therapeutic control rate is expected to increase up to 36.6%.

The trend in hypertension’s control

Figure 10


19,9

25

30,8

36,6

0

5

10

15

20

25

30

35

40


value)


As with previous variables, optimal blood pressure control rate is

higher among women than in men. However, an ascending trend can only be

predicted for hypertensive women whereas in hypertensive males, the rate of

BP control is expected to be rather stagnant.


The trend in hypertension’s control by gender

Figure 11

21,1 19,9

27,4

23,624,2

36,8

25,6

48,4

0

10

20

30

40

50

60

Control BP (Men) Control BP (Women)


CONCLUSIONS Based on the results of our study, in Romania, hypertension’s

prevalence has increased in the last 11 years. According to this past trend, HT’s

prevalence is expected to continue on an upward trend, increasing up to 44%

by 2020, if no preventive strategies at population level will be implemented in

the near future.

Although, our results point out that HT’s awareness and treatment is

continuously improving in our country (by 2020 awarness rate increasing up

to 96.2% and treatment rate of HT up to 91.2%) current BP control rate, is farr

for what is considered optimal (30.1%), and will remains so in 2020 (36.6%),

if the conditions remain, on average, similar.

In the current picture Romania is and will remain a very high

cardiovascular risk country if all the infl uencing conditions on HT’s

prevalence, treatment and control, will remain, on average, similar to previous

years, leading to a continuous up-ward trend in cardiovascular mortality in our

country.

A special emphasis should be addressed to young population between

18-24 years in which the up-ward trend in HT’s prevalence is the most

prevalent, and they should be the target of future preventive programs.

Study limits

The trend models provided by this analysis should be interpreted

with caution due to inherent study limitations. The major limitation arise

from the fact that the trend models are not sensitive to dependent variables

change due to the limited number of time series observations (three for an


11-years period of observation). For a well specifi ed forecasting model, the

number of observed cases should exceed the number of predictive parameters.

This specifi cation could not have been achieved based on available data. As

a consequence, the models could not handle the variety and complexity of

the predicted phenomena, but they could refl ect a situation that could be

prognosticated if the current values of other factors that lead to hypertension

remain constant in time.

Extensions of the research

SEPHAR survey project continuation with the conduction of a new

epidemiological survey, SEPHAR IV, represent a necessary step in HT’s

management in our country, where, due to its cardiovascular complications,

HT is responsible for over 62% of total deaths. Having an improved estimation

of the real trend in HT’s prevalence, treatment, and control, by increasing the

number of time series observations, SEPHAR IV will serve serve as a more

solid base for future prevention strategies, which are urgently needed in our

very high CV risk country.

The infl uence of different socio-demographic characteristic of our

adult population (such as level of education, area of residence, dietary habits)

may have important infl uence upon HT prevalence, awareness, treatment and

control and future studies addressing in depth factors associated with poor

blood pressure control in Romania should conducted.

Aknowlegment

SEPHAR II and SEPHAR III was realized with fi nancial support from

Romanian Society of Hypertension. All the authors have equal contribution to

the current paper.

References

1. Dorobantu M, Tautu O, Dimulescu D et al., 2018, Perspectives on hypertension’s

prevalence, treatment and control in a high cardiovascular risk East European

country: data from the SEPHAR III survey. J Hypertens. 2018 Mar;36(3):690-700.

2. Dorobantu M, Darabont R, Ghiorghe S et al., 2014, Hypertension prevalence

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Marian Preda · 2020. 2. 19. · Marian Preda Faculty of Sociology and Social Work, University of...

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