The study is to differentiate the location types of IT & BT companies in Jeju agglomeration and non-concentrated areas to analyze and compare its productivity to add on the explanation of the external effect on industrial agglomeration.
In addition, ...
The study is to differentiate the location types of IT & BT companies in Jeju agglomeration and non-concentrated areas to analyze and compare its productivity to add on the explanation of the external effect on industrial agglomeration.
In addition, to find the main factor that effects productivity improvement to establish an efficient local policy for corporate productivity improvement, industry vitalization and for the development of Jeju.
The analyzed targets for this study are 78 individual IT & BT corporate within Jeju in 2009 that fits the condition that it does not have missing data on any variables etc.
Out of 78 corporate it is differentiated by 34 agglomeration corporate and 44 non-concentrated areas corporate where companies that are located within Jeju Science Park and Jeju Techno Park are agglomeration corporate and the rest are non-concentrated areas corporate.
As for the study method, by the Cobb-Douglas production function, the production function was estimated by setting up dummy variables that presents labor, capital and corporate location types that was the foundation to estimate the total factor productivity by corporate location type.
And the main factors that affect the decision of total factor productivity such as corporate age, corporate size, whether there is study development spending, whether it is relevant to IT industry etc were selected for explanatory variables to analyze the decision main factor.
StataSE 10.0 program was used for these multiple regression analysis and correlation analysis etc quantitative analysis.
From the result of the estimated production function, it was possible to anticipate that when the number of employees or tangible fixed assets increases the corporate sales will increase.
And by having similar industry companies to locate within the same area there is a sales increase effect.
By subdividing all the variables with the estimated production function and comparing total factor productivity by selecting the comparable targets for agglomeration and non-concentrated areas corporate, concentrated areas showed a higher total factor productivity.
In the end, it was analyzed that the IT & BT companies in Jeju during 2009 that were located in agglomeration has a higher productivity.
As a results of analyzing the main factors that decide the total factor productivity, small in size and old corporate are predict to have a more efficient productivity.
In this study, cross-section data was used as building time series data was inadequate and in analyzing the main factors for results, there weren’t many variables to use as the variables that are expected to effect corporate productivity were not used, there were limited individual corporate data.
In the future, on setting up the model, to analyze the total factor productivity that reflect the size of economy and overcome the study limit after building the time series data through the same study we will leave it as an assignment to gain stronger credibility.