Determinants of labor productivity among SMEs and large‐sized private service firms in Kenya

Abstract

Using the 2018 World Bank enterprise data, this study sought to establish whether the determinants of labor productivity in the Kenyan private service sector varied with the size of the firm. Unlike previous studies that just focus on the determinants of labor productivity among manufacturing firms; this study employed the two-stage switching regressions model to correct for the firm-size effect. The findings revealed that capital intensity, employee wage, high school education, and managers' experience impacted positively and significantly on labor productivity while tax burden and power outages significantly decreased labor productivity across all firms. The differences in the determinants of labor productivity across both firms were, however, found to be negligible and insignificant. With the political uncertainty and tax burden | AMUTABI AND WAMBUGU How to cite this article: Amutabi C, Wambugu A. Determinants of labor productivity among SMEs and large-sized private service firms in Kenya.

Productivity determinants in the service industry: differences between high and low knowledge intensive sectors

“…In general, taxes discourage investment in projects that can increase the efficiency of firms. Taxes, then, constitute a cost for the company (Romero-Jordán et al ., 2020), and thus, firm productivity responds negatively to increases in taxes (Amutabi and Wambugu, 2020; Galindo and Pombo, 2011; Schwellnus and Arnold, 2008). In this line, Castro and Jorrat (2013) states that tax incentives are key elements to promote productivity.…”

“…Therefore, since small firms record low levels of profitability, the tax base is lower, and therefore, taxes are less harmful. In the case of developing countries (Amutabi and Wambugu, 2020; Galindo and Pombo, 2011), larger firms would be the most affected due to the high levels of taxes and the complex procedures involved in the payment.…”

“…Baumol (1967) argued that economies with a growing share of services would experience less aggregate productivity; recent works indicate the important role of service industries as suppliers of intermediate products to other industries, thereby as an economic booster and as providers to final consumers. In this context, recent conducted works analyze the firm productivity in the service industry, identifying productivity determinants related to management, size and age (Martins et al ., 2018) of firms, research & development (R&D) (Harris and Moffat, 2015), innovation (Martins et al ., 2018; Tello, 2017) and trade involvement (Amiti and Wei, 2009; Love et al ., 2010; Nasir, 2012) as well as external factors such as the market concentration (De Jorge and Suárez, 2014; Wolfmayr et al ., 2013) and regulation (including taxes) (Amutabi and Wambugu, 2020; Castro and Jorrat, 2013). The external factors are especially important since they influence on the firms' productivity, but they are beyond their control.…”

“…In addition, in the Ecuadorian regulatory framework, new microenterprises are exempt from the income tax during the first three years since the first fiscal year of generation of operational income as long as they create net employment and incorporate national added value to their production processes (SRI (Servicio de Rentas), 2004). Given this fiscal framework, one could think that microenterprises would be less affected in terms of productivity by taxes than bigger firms as in the case presented by Amutabi and Wambugu (2020) in which large companies would be the most affected because they pay higher amounts on taxes. Another aim of this research is to determine the influence of taxes on the productivity of firms by size.…”

Identifying Factors Influencing Productivity of Older Workers in Service Sector: A Case Study in Pilot Companies in Thailand

“…Looking into the role and impact of education on productivity, some evidence confirms that differences in educational levels could lead to a variation in productivity in the service sector [ 30 , 34 , 35 , 36 ]. For example, according to Van Dalen, Henkens [ 30 ], there is no significant effect of middle education level (aged between 10–15 years old) on productivity between younger and older workers.…”