• Journal of Technology Innovation
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• v.19 no.3
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• pp.57-83
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• 2011

We suggested industry-specific priorities of R&D investment with R&D investment elasticity to GDP calculated from the ripple effect of 28 large-sized industry R&D investment, using a Computable General Equilibrium(CGE) Model. Priority orders apply to only 12 industries, because 16 industries with less than 1% of total investment have been excluded. First, R&D investment elasticity to GDP says that priorities are ordered as Basic metal products, Chemicals, drugs and medicines, Food, beverages and tobacco products, Electronic and electrical equipment, Transportation equipment, Precision instruments, Electrictity, gas, steam and water supply, General machinery and equipment, Communications and broadcasting, Construction, Other services, and Real estate and business services. These priorities show the status quo of Korean industry structures well. The GDP growth rate to 2030 year reference equilibrium, which is an auxiliary index, says a similar priority to results from R&D investment elasticity to GDP. In the end, two criteria of priority order can be functioned as a good index for National Science and Technology Commission deciding what industry to invest and what budget to allocate.

• Korean Management Science Review
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• v.34 no.1
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• pp.15-25
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• 2017

We evaluated industry growth and employment effects of every possible pairs of 22 manufacturing sectors and 16 regions (i.e, 352 region-sectors). We used annual data of manufacturing sectors from 2008 to 2014 for the evaluation. The evaluation comprises of two steps; We first find several region-sectors that outperform others with respect to the effects of industry growth and employment, which are measured by location quotient analysis, shift share method, employment to GDP ratio and employment elasticity. In addition, cross-efficiency analysis follows to classify region-sector pairs into two sub-categories : efficient region-sectors that deserve to hold the current level of investments and inefficient region-sectors where we should consider efficiency improvements. To examine the efficiency, R&D investment, employment size, and capital investment were used as input factors and production volume, added value, changes in employment size, changes in annual salary per capita were used as output factors. For region-sector pairs that have outstanding growth and employment effects but are inefficient, we employed a CCR DEA model and analyzed how much to adjust the values of input and output factors to improve the efficiency scores. The analysis results showed that inefficiency is mainly due to several factors such as R&D investment, changes in employment size and changes in annual salary per capita.