Impact of Investment Climate on Total Factor Productivity of Manufacturing Firms in Vietnam

Abstract

Investment climate has been acknowledged as a key factor that significantly influences economic performance. Improving the investment climate may foster the development of the private sector by creating sustainable jobs and opportunities for entrepreneurs, which contributes to sustained poverty reduction in developing countries. This research examined the relationship between the investment climate and firm productivity by exploring a unique panel dataset of 1310 enterprises operating in the manufacturing sector in Vietnam. Productivity was measured as the total factor productivity (TFP) obtained by production function estimation using Levinsohn and Petrin’s approach. Investment climate factors included infrastructure, labor skills, regulatory governance and institutions, and access to finance. It was shown that restrictions on the investment climate were harmful to firm productivity. The lack of Internet and financial accessibility, low educational level of employees, administrative burden and the cost of bribery were negatively associated with firm TFP. The results indicate that access to Internet and finance, and quality of labor should be further enhanced while administrative burden and corruption should be significantly reduced to strengthen the TFP. The findings of this study may provide insights for policymakers who aim to improve the investment climate and firm productivity and thereby contribute to the sustainable growth of the country.

The Causal Effect of Access to Finance on Productivity of Small and Medium Enterprises in Vietnam

“…Therefore, the latter two approaches are frequently applied. In this paper, following the study of Giang [30], productivity was measured as the TFP obtained from the production function estimated by Levisohn and Petrin [21] approach. The main idea behind this approach is that an intermediate input could be used as a proxy for the unobserved firm productivity and unbiased estimates of the production function.…”

“…The survey provided detailed information on enterprise history, owner background characteristics, firm performance, employment, investment and access to finance. It also covers firms in in 18 manufacturing sectors and can be divided into six broad categories: (1) Food and beverages, (2) Non-metal and plastic materials, (3) Metal and machinery products, (4) Wood products and furniture, (5) Textiles, and (6) Others [30]. Each firm in the survey was provided with a code that remained unchanged over years, which allowed us to generate a balanced panel dataset following individual firms.…”

“…In all estimations, we also included the firm-specific factors (characteristics of the firm itself) and external environment factors that affected firms' economic performance. The benchmark for selecting these firm specific and investment climate variables follows the studies of Giang et al [30], Bastos and Nasir [55], Escribano and Guasch [56], Dollar et al [57], and Kinda et al [58]. To be more specific, we imposed a set of firm-specific variables: firm size, age, and export.…”

Measurement and Spatial Variation of Green Total Factor Productivity of the Tourism Industry in China

“…Compared with the Malmquist index, this index can measure positive outputs and reduce negative outputs, and it can better reflect green development demands [33]. Drawing on the research results of the GTFP from iron and steel, fossil energy, manufacturing, and other industries [6][7][8], we therefore adopted the DEA model based on the function of directional distance and the ML index to calculate tourism GTFP in China.…”

“…For example, the GTFP of energy-intensive and highly polluting enterprises on the micro-level have been examined, with Morfeldt, Long, and Zhang [5][6][7] using a modified data envelopment analysis (DEA) model encompassing the directional distance function to measure the GTFP of steel works, cement plants, and oil refineries. Studies have also been undertaken examining the GTFP of different industries using a meso perspective, such as investigations by Giang, Han, and Liu [8][9][10], who used the Super-SBM model (Slack Based Model) and the Malmquist index to measure the GTFP of manufacturing, agriculture, and logistics, as well as analyzed why the GTFP of these industries changed. By using a variety of mathematical models, the GTFP of micro-enterprises and meso-industries has been examined using an elaboration of research methods and the application of econometric models, thereby realizing a more realistic measurement of industrial economy and reversing the distortion of economic performance evaluation caused by ignoring resource consumption and environmental expense.…”