Measuring Innovative Capacities of the Georgia Regions

European Union (EU) experience reveal that the composite indicators are probably the most useful instruments for measuring the innovative capacities at the regional (sub-national) level. However, some gap exists in the current literature with respect to the elaboration of composite indicators for regional innovation systems (RISs) of developing countries. This article introduces the composite indicators GRIS and GCLS for measuring the regional innovative capacities (for GNUTS1 and GNUTS2 territorial classification levels, respectively). Georgia is a useful case-subject because its smallscale developing economy presents special challenges for elaborating the composite indicators for RISs. This article also includes a brief analysis using these composite indicators and indicates the significant heterogeneity among the innovative capacities of the Georgian regions.


Introduction
Regional innovative systems (RIS) are the base of national innovative system (NIS) and determine innovative potential of the country. In order to manage such a complicated multidimensional phenomenon as RIS along with following monitoring of the results obtained it is necessary to work out special quantitative instrument. From nowadays point of view such instruments are composite indicators, which make it possible to interpret multidimensional nature of RIS by means of integrated characteristic, estimate the changes which take place within regional innovation systems and positioning them.
In the recent years due to the efforts of numerous organizations and researchers huge experience is accumulated in the scope of development of the composite indicators 1 . Composite indicators were successfully utilized for estimating of the EU Lisbon strategy progress at the regional level of European Union members. Owing to this experience we come to the conclusion that within the existing theoretical and methodological frames principal difficulties related to certain composite indicators establishment are connected with the availability of qualitative initial statistical data. For example, due to the statistical data availability problem earlier European regional innovation scoreboards (2002,2003 yy) assessed innovative potential of just EU15 member countries regions. Only substantial renewal of the initial indicators composition in 2006 made possible to consider also EU new member countries regions (see Hollanders H. (2007)).
Statistical data availability problem is overall and is a burning issue especially for the countries with developing and transitional economy both at the regional and national level (see Tijssen R., Hollanders H.(2006); Bhutto, A., P.L. Rashdi, Abro, Q.M..(2012)). On the other hand, exactly in this case may turn out to be most useful to have composite indicators which reflect various features of innovative development. Lately increases interest to the issues related to the establishment of special indicators for countries with developing and transitional economy at the national level (see Archibugi D., Coco A.(2004) ;Chen D. H. C., Dahlman C. J. (2005)). However, we must notice that the modern literature insufficiently represents the problems concerning to the creating of new composite indicators related to the regional aspects of innovative development for the countries with developing and transitional economy.
In the present article we intend to show that for the countries with developing and transitional economy it is possible to elaborate composite indicator which may become an efficient tool to estimate current innovative processes at the regional level. Within our investigation we will be based on the Georgian example. The proposed composite indicator seems to be easily adapted to other countries (e.g. for the post USSR space) too because it use specially selected group of initial indicators which are quite available.
Article is arranged as follows: next paragraph contains some methodological issues and generation of GRIS-2010 indicator; the third paragraph is concerned to the capabilities of this indicator to estimate innovative level of Georgian regions and reveals its relationship with basic economic indicators of the regions; and finally we draw conclusions, submit citation and technical annexes which includes definitions of the initial indicators and results of certain calculations.

Innovative System of the Region
Concept of RIS cleared up after the intensive scientific discussion, held over last twenty years, but still it has not obtained final shape. For example, Doloreux D., Parto S. (2004) claims: "The concept of RIS has no commonly accepted definitions but usually is understood as a set of interacting private and public interests, formal institutions and other organizations that function according to organizational and institutional arrangements and relationships conducive to the generation, use and dissemination of knowledge".
To be clearer, let's discuss particular components of RIS concept. First of all, concept of RIS needs definitions of region and innovation. Following Cooke, P., Uranga, M. J., Etxebarria, G. (1997) region is: "…a territory less than its sovereign state, possessing distinctive supralocal administrative, cultural, political, or economic power and cohesiveness, differentiating it from its state and other regions". On the other hand, OECD,EUROSTAT(2005) Oslo Manual suggests following definition of the concept of innovation: "An innovation is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations". As for the essence of "innovative system", it is defined by Gregersen B., Johnson, B.(1997) in the following way: " The central idea of the concept of innovation systems is that the overall innovation performance of an economy depends not only on how specific organizations like firms and research institutes perform, but also on how they interact with each other and with the government sector in knowledge production and distribution. Innovating firms operate within a common institutional set-up and they jointly depend on, contribute to and utilize a common knowledge infrastructure. It can be thought of as a system which creates and distributes knowledge, utilizes this knowledge by introducing it into the economy in the form of innovations, diffuses it and transforms it into something valuable, for example, international competitiveness and economic growth".
Hence we can conclude that: 1. RIS is a social system which operates due to the interaction of its constituent actors (companies, research and academic organizations, regional administration, technical mediators and other formal and informal institutions); 2. In the course of functioning RIS exploits accessible resources (human, financial, infrastructural, institutional etc.) both local and national; 3. RIS ensures generation and dissemination of knowledge, as well as its utilization in innovations; 4. Results of RIS functioning call forth economic development of region.

Definition of the Region and Initial Indicators
With the practical view of composite indicators elaboration at the regional level, definition of a region is of crucial importance. Practical definition of regions, along with conceptual aspects, should consider availability of the statistical information for initial indicators set. Various possibilities of region practical definition may exist in different countries. For example, to produce RIS composite indicator for Georgia (GRIS-2010), in light of statistical information availability, it is considerable to define region, as a second level body of state administering (see Annex A, Table A.1.). Possibility to capture information at the lower levels of country administrative division is much more restricted.
On the base of the above-stated conceptual representation (see 2.1.) and of the multilateral testing of available data we singled out assortment of initial indicators given below.
In order to reflect resource capability of RIS we picked out following indicators: Educational Level (EDL), -this indicator reflect to estimate professional skills of regional labor force. Infrastructure (INF). -this indicator reflect regional infrastructure development level and represented by the share of households, equipped with personal computers. Governmental Support (GSP) -this indicator reflect state support level and represents volume of transfers from state budget to the region.
In order to reflect of RIS social networks we have chosen following indicator: Social Network (NET). -this indicator reflect existing social nets in the region. It points out the level of participation of citizens in various voluntary organization.
In order to reflect knowledge generation and utilization we have chosen following indicators: Knowledge Generation (KNG) -this indicator reflect intellectual production in the region and represented by number of patent applications. Knowledge Intensive Production (KIP) -this indicator characterizes employment in high and medium-high technology industryes and knowledge-intensive services.
In order to reflect results of RIS functioning we have chosen indicator Competitive Capacity (CMP)-this indicator reflect value added per capita in the region.
Annex A includes thorough definitions of above-mentioned seven indicators. Certainly, this assortment of indicators needs some efforts to be retrieved from existing sources, but they seem substantially accessible for numbers of countries with developing and transitional economy. It should be noted also, that NET is the sole indicator in present assortment which uses external sourse of informationit is obtained from the World Values Survey data. This fact cannot be considered as a serious restriction because the appropriate set of WVS's questions may be involved by the state statistic offices into the regularly held General Household Survey with no effort.

Normalization and Aggregation of the Initial Data
Let us introduce following designations: R is (finite) set of regions and it's cardinality have "same direction". It means that the less value of indicator corresponds to the "worse" and greater-to the "better". Symbols  designate mean and standard deviation of the i-th indicator,1 iN  , respectively.
As far as the initial indicators are represented in different scale units, it is reasonable to normalize them. With this view we use follouing standardization procedure (z-scores): Functions, obtained as a result of the present procedure, we denominate normalized initial indicators.
Choice of aggregation procedure is a crucial moment within the process of composite indicator construction. As this problem cannot be solved unequivocally we shall use most simple and widely applied linear aggregation scheme: indicator in the composite indicator. After the decision is made, problem of aggregation procedure choice comes to the problem of weights choice. It should be noted, that despite of this important simplification weights choice issue remains non-trivial and has not unequivocal solution. In order to choose weights we use factor analysis method as in Nicoletti G., Scarpetta S., Boylaud O.(2000) (details of realization see in the Annex B).

Construction of GRIS-2010 Indicators
In this paragraph we carry out constructing procedure of the composite indicator GRIS-2010, which reflects innovative systems' current condition in the Georgian regions by 2010 status. Initial indicators values underlying GRIS-2010 calculations are given in the Annex B (see Table  B.1.). Values of normalized initial indicators presented in Table 2.1.

Georgia's Regions Innovative Capacities Estimation by GRIS-2010
The ranking of Georgia's Regions by Indicator GRIS-2010 and its sub indicators is given in the Table 3 GRIS-2010 and its sub indicators analysis shows that there is considerable difference between the RIS of the Georgia's regions by their innovative capacities (see Fig.3.1. Panels A,B). We should notice that inner resources of almost every region of Georgia except Tbilisi (TB) and Ajara (AC) are below the country average. We also should take notice of a fact that links to NIS of the regions, contained in the clusters CL4= (IM, SQ, MM, SJ), CL5= (GU,SS,RL,KA) are below the country average.
More detailed analysis shows (see Fig.3.1. Panels C,D) that differences between the regions RIS are deeply rooted in the inequalities by such the factors, as: education, infrastructure, governmental support, knowledge generation capacities etc. Problem of elimination of abovementioned inequalities in the regions is one of the most serious challenges Georgia confronts. Suppose production process in the region is being described by Cobb-Douglas production function: Romer D.(1996)) and ( (  Fig. 3

Conclusion
Regional innovative systems (RIS), as the components of national innovative system, determine innovative potency of the country. This circumstance makes very important to work out specific quantitative instrument with a view of analysis and monitoring processes within the RIS-es of the countries with developing and transitional economy. Considerably RIS composite indicators should serve as such an instrument. Unfortunately the modern literature insufficiently represents the problems concerning to the creating of composite indicators related to the regional aspects of innovative development for the countries with developing and transitional economy.
The goal of present article is to make up for above-mentioned deficiency at least in part. On the basis of Georgian example we showed that for the countries with developing and transitional economy it is possible to elaborate composite indicator which may become an efficient tool to estimate current innovative processes at the regional level. For the construction of the composite indicator introduced here we used specially selected quite available set of initial indicators and applied widely practiced factor analysis technique. Testing of this composite indicator based upon the Georgian regions' data, revealed its satisfactory capacity both for regions ranking, classification and regarding links with main economic indicators of the regions.
Present composite indicator seems to be easily adapted in the case of other countries. However, we apprehend its narrowness we consider it necessary further detailed investigations to elaborate composite indicators which will serve as an index of innovative processes regional aspects for the countries with developing and transitional economy.

Regions
With the view of research goals Georgian regions are represented by the second level administration units (see Table A.1).

EDL -Educational Level:
Indicator defined as a share 25 -64 age population having tertiary education in the region. Demension: percent Information source: Households general survey (Geostat)

INF -Infrastructure:
Indicator defined as a share of households equipped by PC in the region. Demension: percent Information source: Households general survey (Geostat)

GSP -Governmental Support
Indicator defined as per capita transfers from state budget to the region. Demension: GEL per capita Information source: State budget (Ministry of Finance of Georgia) and Demographic statistics (Geostat)

NET -Social Nets
Indicator defined as share of region residents which have stated that they are active members of at least one voluntary organization contained in the following list: sports and fitness, religious, arts and education, labor union, political parties, environmental, charity-humanitarian, any other. Information is captured trough the answers on questions V24-V33 of World Values Survey (). Demension: percent Information source: World Values Survey

KNG -Knowledge Generation
Indicator is defined as a number of patent applications per 1000 labor force representatives. In case of co-authors application the index is to be divided proportionally co-author's regions number. Demension: Number of patents per 1000 labor force representatives Information source: Patent applications (SAKPATENTI) and Labor statistics (Geostat)

KIP -Knowledge-intensive Production and Services
Indicator is defined as share of employed in High-and Medium-high technology Industries and Knowledge-intensive Services in the region. High-and Medium-high technology Industries and Knowledge-intensive Services are to be determined according to standart classification (see Table A.2).

2
In slightly different way, we use also additional information obtained from the normalized initial indicators cluster analysis (see Fig.B.2. Panel B) which makes practicaly unequivocal procedure of initial indicators arrangement within sub indicators (seeTable B.3.)

Panel A -normalized initial indicators clustering.
Panel B -Regions' clustering by GRIS-2010 indicator.