This article aims to estimate the effects of ICT intensity on labor productivity, employment and output of agro-processing industries. To achieve this, the ICT intensity index is applied to rank industries into 'more ICT-intensive' and 'less ICT-intensive' groups. Thereafter, the annual growth rates of labor productivity, employment and output were calculated. Ultimately, the effects of ICT intensity were examined using Pooled Mean Group estimation, the Toda and Yamamoto Granger Non-Causality Test, and the Impulse Response Function and Variance Decomposition analyses. The findings suggest that ICT intensity yields higher positive and significant effects on the growth of the more ICT-intensive industries. Evidence of a causal relationship was detected for the more ICT-intensive industries. The findings further proved that ICT intensity contributed more to the forecast error variance in the growth of the more ICT-intensive industries. Overall, this article provides evidence of ICT-led growth for industries that use ICT most intensively.
The paper serves to examine whether the growth in labour productivity (LP) in the manufacturing sector following policy reforms after democracy can be attributed to ICT. To achieve this, we examine the link
This study examined the relative technical efficiencies in input use by credit and non-credit user farmers in Maruleng Municipality of Limpopo Province, South Africa. The differentials in the technical efficiency levels of maize and green beans farmers were examined. The study used primary data collected from a stratified random sample of 72 emerging farmers of which 32 were credit users and the remaining 40 were noncredit users. Data were analyzed using Cobb-Douglas Production function model. All the variable inputs examined were statistically significant for both credit and non-credit users except pesticides and irrigated land inputs for maize farmers. The results of the study revealed that technical efficiency levels between credit users and non-credit users is too wide being 9.843 for credit users and 2.892 for non-credit users. The implication for this is that, the odds of being efficient is related to credit use and thus credit is a necessary tool to improve farmers' technical efficiency levels. The study, therefore, recommends that the existing farm credit system including other agricultural programmers' should be reviewed, refocused, and be more accessible to emerging farmers in order to improve efficiency in the input use by emerging farmers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.