HOW FUNDING STRUCTURE AFFECTS EFFICIENCY OF R&D INVESTMENT BY LARGE- AND MEDIUM-SIZED INDUSTRIAL FIRMS IN CHINA? EVIDENCE FROM PROVINCE-LEVEL PANEL DATA

2019 ◽  
Vol 64 (04) ◽  
pp. 921-938
Author(s):  
YIXIAO ZHOU ◽  
RUNYANG ZHANG ◽  
LIGANG SONG

This study explores the efficiencies of firm’s R&D investment depending on the degree of reliance on government funding relative to firms’ private funding. Stochastic frontier analysis is applied on a sample of 30 provinces with data on R&D inputs and innovation outputs by all large- and medium-sized industrial firms in these provinces from 2000 to 2013. It is found that R&D investment financed by firms’ private funding is more efficient than that by government funding in generating new products, whereas R&D investment financed by government funding is more efficient than that by firms’ private funding in producing new patents.

2016 ◽  
Vol 17 (2) ◽  
pp. 187-200 ◽  
Author(s):  
Qi HUANG ◽  
Marshall S. JIANG ◽  
Jianjun MIAO

This study aims to gain a better understanding of how effective government subsidization is in helping foster firms’ innovation. Drawing on the exploration/exploita- tion perspective and based on data collected from Statistical Yearbook on Science and Technology Activities of Industrial Enterprises, we look into the relationship between gov- ernment subsidization and Chinese firms’ innovation efficiency by applying a stochastic frontier analysis. The results show that when government subsidies are provided in small scale, firms’ innovation efficiency decreases; only when government subsidies increase to a certain scale, does firms’ innovation efficiency start to increase. We suggest that govern- ment subsidization would generate better innovation performance should it concentrate on a smaller number of firms at one time. As existing research is still inconclusive regarding the relationship between government subsidization and firms’ technological innovation output, we shed light on the issue by revealing a “U-shaped” relationship between the two.


2018 ◽  
Vol 11 (2) ◽  
pp. 7-13
Author(s):  
Dian Ayunita N.N. Dewi ◽  
B.A. Wibowo ◽  
Iqbal Ali Husni

Tujuan dari penelitian ini adalah menghitung efiensi teknis usaha penangkapan purse seine di PPN Pekalongan secara stokastik dan deterministik dan menganalisis efisiensi teknis untuk mengetahui prospek keberlanjutan usaha penangkapan purse seine di PPN Pekalongan. Metode analisis menggunakan Stochastic Frontier Analysis (SFA) dan Deterministic Frontier Analysis (DFA). SFA dan DFA merupakan model parametrik yang digunakan untuk mengukur efisiensi teknis. Model 1: DFA dengan data cross-section akan diestimasi dengan COLS. Model 2: SFA dengan data cross-section akan diestimasi dengan ML (Maximum Likelihood). Model 3: SFA dengan data panel diestimasi dengan proses ML. Variabel independent yang digunakan pada penelitian ini adalah lama trip (fishing days atau X1), GRT (Gross Registered Tonnage atau X2) kapal, jumlah ABK (crew members atau X3) sebagai faktor determinan yang digunakan oleh penangkapan purse seine di lokasi penelitian. Dan variabel terikat (Y) adalah output yaitu produksi tangkapan per tahun dari tahun 2015-2017. Fokus penelitian pada kapal purse seine berukuran 60-90 GT. Hasil dari penelitian ini adalah efisiensi teknis dari kedua kelompok kapal menunjukkan nilai inefisiensi dalam penggunaan ketiga variabel input. Hal ini diperkuat dengan hasil nilai korelasi (R) pada metode Deterministik Frontier dan Stokastik Frontier dengan data cross section serta panel data yang rendah (0,39 dan 0,311). Variabel X1 (lama trip) memiliki hubungan negatif dengan variabel Y (produksi) sedangkan X2 dan X3 memiliki hubungan positif terhadap Y. Pada armada penangkapan purse seine ukuran 60-90 GT, ketiga variabel input tidak mempunyai pengaruh yang signifikan terhadap perubahan jumlah produksi penangkapan.


2018 ◽  
Vol 29 (1) ◽  
pp. 34-48 ◽  
Author(s):  
Jin-Li Hu ◽  
Ming-Chung Chang ◽  
Hui-Wen Tsay

Purpose The purpose of this paper is to explore Taiwan’s regional energy efficiency trend and complement the work of the total-factor energy efficiency (TFEE) index proposed by Hu and Wang (2006). It further extends panel data stochastic frontier analysis (SFA) modeling for estimating disaggregate energy efficiency. Design/methodology/approach This paper applies the panel data stochastic production frontier to estimate the TFEE scores for 20 administrative regions in Taiwan over the period 2004-2015. The SFA models include five inputs (employed population, amount of productive electricity power consumed, amount of electricity consumed for household and non-household electric lighting, amount of gasoline sales, and amount of diesel sales) and one output (total real income in the base year of 2011). Findings This research concludes with three main findings: the inefficient administrative regions of Taiwan include mostly large industrial parks and the petrochemical industry cluster; the top five administrative regions with inefficient diesel use are mostly metropolitan areas that the concern of air pollution caused by diesel system arouses the awareness to use less diesel fuel; and the average TFEE score on household and non-household electric lighting is higher than the usage efficiency of productive electricity power, gasoline, and diesel, but there is still room for efficiency improvement. Originality/value Most administrative regions in Taiwan are not efficient in almost all kinds of energy use. The results show that the efficiencies of using productive electricity power, gasoline, and diesel need to be improved a lot more.


2018 ◽  
Vol 13 (3) ◽  
pp. 773-790 ◽  
Author(s):  
Yifan Jiao ◽  
Qing Liu ◽  
Tianzhuo Liu

Purpose The relationship between exports and productivity has always been a hot topic for scholars, but no unified conclusions have been made by theory and empirical research so far. There is no denying that the relationship between the two is important though, exploring the factors affecting the relationship can bring much more reflection and inspiration. Design/methodology/approach After using stochastic frontier analysis which includes stochastic factors to calculate total factor productivity, this paper makes regressions on the panel data of Chinese manufacturing industries from 2004 to 2013. In addition, it also tests the roles of industry heterogeneity plays in analyzing the relationships between exports and productivity. Findings It turns out that in China, exports inhibit the growth of productivity overall, and scale effect is not reflected in the export sector. From the perspective of sub-sectors, exports dampen the productivity significantly in labor-intensive industries, which may be because of low learning ability. In industries with low R&D investment, exports also have significant and negative effects on industry productivity, which is because the R&D capital is not efficient and sufficient to be converted to productivity. In industries with high foreign capital rate, exports prohibit the growth of productivity because of the existence of much processing trade. In industries with low competition pressure, exports hinder the productivity because firms in such industries are possibly not competitive enough to survive in the fierce international market. Originality/value To the best of the authors’ knowledge, previous research studies did not take the industry heterogeneity into consideration when discussing the effects of exports on productivity. In this paper, cost types, R&D investment, energy consumption efficiency, export destination, foreign investment rate, state-owned ratio, competition pressure and international competitiveness are all discussed in the analysis of export and productivity. This study provides new insights to help understand the mechanism of export and productivity and the conclusions are of rich policy implications.


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