Regional Competitiveness in Turkey
Emine Demet Ekinci hamamcıThe aim of this study is to determine the competitiveness indices of the sub regions according to NUTS2 level in Turkey and to estimate efficiency of these indices for creating GDP per capita. In this study, it has been taken into account four basic factors -economic structure, innovation, human capital, infrastructure and accessibility- that affect both the high competition and the level of GDP per capita. In this study, the methods have been followed Exploratory Factor Analysis (EFA) and Data Envelopment Analysis (DEA). Inputs of study are seventeen items related to four factors, whereas output item is per capita GDP. In the study, regional competition indices are firstly obtained with EFA. Then, by using DEA, it has been estimated efficiency of these indices for creating GDP per capita. According to the indices’ results of economic and innovative infrastructure, skilled labor infrastructure and regional basic infrastructure, TR10 İstanbul, TR31 İzmir, TR42 Kocaeli and TR51 Ankara are most competitive sub regions of Turkey. In the CCR Model, only three sub regions - TR10 Istanbul, TR42 Kocaeli and TR51 Ankara- have become efficient in generating per capita income, whereas eight sub regions -TR10 İstanbul, TR21 Tekirdağ, TR42 Kocaeli, TR51 Ankara, TR82 Kastamonu, TRA2 Ağrı, TRB2 Van and TRC2 Şanlıurfa- are efficient in the BCC Model.
Türkiye’de Bölgesel Rekabet Edebilirlik
Emine Demet Ekinci hamamcıBu çalışmanın amacı Türkiye’de İBBS Düzey-2’ye göre alt bölgelerinin rekabet gücü endekslerini belirlemek ve söz konusu endekslerin kişi başına düşen Gayri Safi Yurt İçi Hasıla (GSYH) yaratmadaki etkinliklerini tespit etmektir. Çalışmada hem yüksek eksenli rekabeti oluşturan hem de kişi başına düşen GSYH’yi etkileyen dört temel unsur -iktisadi yapı, yenilikçilik, beşeri sermaye ile altyapı ve ulaşılabilirlik- dikkate alınmaktadır. Çalışmada Açımlayıcı Faktör Analizi (AFA) ve Veri Zarflama Analizi (VZA) yöntemleri takip edilmektedir. Girdi değişkeni yukarıda belirtilen dört faktöre ait 17 değişkenden oluşmaktadır. Çıktı değişkeni ise kişi başına düşen GSYH’dir. Çalışmada öncelikle AFA ile bölgesel rekabet endeksleri elde edilmektedir. Daha sonra VZA ile bu endekslerin girdi olarak kişi başına düşen GSYH yaratmadaki etkinlikleri tahmin edilmektedir. İktisadi ve yenilikçi altyapı, kalifiye işgücü altyapısı ve bölgesel temel altyapı endeks sonuçlarına göre TR10 İstanbul, TR31 İzmir, TR42 Kocaeli ve TR51 Ankara, Türkiye’nin en rekabetçi alt bölgeleridir. CCR modelinde kişi başına GSYH yaratmada sadece üç alt bölge -TR10 İstanbul, TR42 Kocaeli ve TR51 Ankara- etkin olurken BCC modelinde etkin olan bölgeler TR10 İstanbul, TR21 Tekirdağ, TR42 Kocaeli, TR51 Ankara, TR82 Kastamonu, TRA2 Ağrı, TRB2 Van ve TRC2 Şanlıurfa’dır.
Today, improvement of regions’ competitiveness is among the primary agenda items of governments. Accordingly, it is desired to determine the factors influencing regional competitiveness and to find solution ways by specifying the strong and weak aspects of the region. But there is no consensus regarding the definition and measurement of regional competitiveness. Therefore, studies being conducted about competitiveness aim to reach a conclusion by focusing on different factors. However, whether the regions have attained high level of incomes and employment, is accepted as an important indicator of competitiveness of regions in literature. Namely, the higher the income or employment a region can generate, the more its competitive power will be defined as proportionally. As a result of the studies, it has found out that there is a close relationship between regional differences of GDP per capita and the four basic factors - structure of economic activity, innovative capacity, accessibility of the region, and accumulation of knowledge and skills relating with the labor force-. Even though these four basic factors listed above are important particulars influencing GDP per capita, they are also among the essential aspects of high scale competitiveness.
In this study, the regional competitiveness has been examined by evaluating four basic factors that affect both the high scale competitiveness and the GDP per capita. In this regard, it is aimed to determine the competitiveness level of the regions and to determine their effectiveness in generating per capita income.
The study has covered 26 sub regions according to NUTS2 within current regional classification. In this study, the methods have been followed Exploratory Factor Analysis (EFA) and Data Envelopment Analysis (DEA). In the study, the input variables have consisted of 17 items representing the four basic factors mentioned above. The output variable is the GDP per capita. In the study, regional competition indices are firstly obtained with EFA. Then, by using DEA, it has been estimated efficiency of these indices for creating GDP per capita.
According to the results of EFA, principal components have consisted of three sub-dimensions and they have accounted for approximately 81% of the total variance in the original data matrices. After the principal components (factors) were obtained, they have been named according to the features. The first factor has been named as economic and innovative infrastructure owing to include all the economic variables and the patent rate representing innovation. The second factor is skilled labor infrastructure, including resources that create human capital. In the third component, regional qualifications have presented as proximity to the port, total highway length and average bank credit. So this factor has defined as regional basic infrastructure.
Then, the composite indices have been calculated taking into account the weights of the variables in each principal components. According to the results; the most competitive regions are TR10, TR51 and TR31 sub regions at the economic and innovative infrastructure; TR51, TR10 and TR31 sub regions at the skilled labor infrastructure; TR10, TR31 and TR42 sub regions at the regional basic infrastructure, whereas the lowest competitive regions are TRA2, TRB2 and TRC2 sub regions at the economic and innovative infrastructure; TRB2, TRC2 and TRA2 sub regions at the skilled labor infrastructure; TRC2, TRB2 and TRC3 sub regions at the regional basic infrastructure, respectively.
In the study, DEA has been performed by using regional competitive indices as inputs. At this point, the aim of the study is to investigate efficiency in generating per capita GDP with the competitive potentials of sub regions. In the CCR Model results, only three DMUs - TR10 Istanbul, TR42 Kocaeli and TR51 Ankara- have become efficient. Eight sub regions -TR10 İstanbul, TR21 Tekirdağ, TR42 Kocaeli, TR51 Ankara, TR82 Kastamonu, TRA2 Ağrı, TRB2 Van and TRC2 Şanlıurfa- are efficiency in the BCC Model. According to the result of the slacks movements, in Turkey, inefficient DMUs couldn’t convert the skilled labor input to output at most.
