Abstract
The purpose of this study is to assess empirically how the technical efficiency scores for 43 sub-sectors and their determinants over the period 2010 to 2017 show significant variation across the s...
Highlights
The manufacturing sector plays a crucial role in production, growth, and job creation (Naudé & Szirmai, 2012)
Results of data envelopment analysis models This section discusses the results obtained from the Data envelopment analysis (DEA) models: the CCR model with the assump tion of constant return to scale (CRS) and the BCC model under the assumption of variable return to scale (VRS)
If there is a difference between the two efficiency values, there is scale inefficiency which is computed from the ratio of technical efficiency to pure technical efficiency (TE/PTE)
Summary
The manufacturing sector plays a crucial role in production, growth, and job creation (Naudé & Szirmai, 2012). It is the most important engine of long-term growth and development, both in developed and developing countries (McKinsey, 2012) In the former, for instance, in 2015 the manufacturing sector shares 12% and 19% of the gross domestic product (GDP) of the United States and Japan, respectively (UNCTAD, 2015) and it remains a vital source of innovation & competitiveness, making enormous contributions to research & development, exports, and pro ductivity growth (McKinsey, 2012). While, in the latter, the manufacturing sector shares 27% and 16% of the GDP of China and India in 2015, respectively (UNCTAD, 2015) and it continues to provide a pathway from subsistence farming to rising incomes and living standards. Empirical evidence (Ayelign & Singh, 2019; Oqubay, 2015; Hailu & Tanaka, 2015; UNDP, 2017) has confirmed that the sector had faced several problems, such as limited access to and interrupted electricity power, a low level of export performance and competition, a shortage and irregular supply of domestic raw materials, limited access to and poor quality of internet services, and weak logistic support
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