The Comparison of Financing Efficiency of Small and Medium Enterprises in Economically Underdeveloped Regions in China: A Perspective Study

Abstract

Small- and medium-sized enterprises (SMEs) are important foundations to implement mass entrepreneurship and innovation and play an irreplaceable role in increasing employment, promoting economic growth, as well as scientific and technological innovations, and providing particularly social harmony and stability and imminently are strategic entities to the national economy and social development in underdeveloped regions. However, the low-efficiency financing of SMEs has gradually become a major factor that restricts the high-quality development of SMEs in the current conditions. In this paper, interest expenditure, gearing ratio, and the net debt ratio as input indicators and current asset turnover ratio, cost margin, and main business income as output indicators are used to conduct the DEA-BCC model. By utilizing the GEM-listed private enterprises between 2017 and 2020 in China, the nationwide financing efficiency of SMEs is firstly measured, and then the financing efficiencies of SMEs in economically developed regions and lagging regions are calculated separately. The comparison reveals that the financing efficiency of SMEs in economically underdeveloped regions is not only lower than the national average figure but also much lower than the financing efficiency level in economically developed regions, which is the result of the combined effect of internal and external factors that enterprises face. Further, this paper finds that unexpected public events, core technical personnel, and enterprise size have an impact on the financing efficiency of SMEs when running group testing. This paper puts forward rationalized suggestions to the institutions to improve the financing efficiency of SMEs in underdeveloped regions concerning the conducted research, which are called government, financial institutions, and enterprises.

The impact of financing efficiency in defense industry base on non‐technological innovation: Evidence from China

“…The financial data part used in this paper have some negative values, so the following function transformation is adopted for the input–output index (Chi et al, 2022; Xu et al, 2022): …”