The relative labour productivity contribution of different age-skill categories for a developing economy

Abstract

Orientation: The article dealt with the estimation, computation and interpretation of the relative productivity contributions of different age-skill categories. Research purpose: The aim of the article was to estimate and compute, (1) relative productivity contributions and (2) relative productivity contribution–employee remuneration cost levels for different age-skill categories.Motivation for the study: The research was deemed necessary given the current debate on relative productivity levels and possible changes to the retirement age in the South African labour market. No real research in this regard has been published regarding the South African labour market situation.Research design, approach and method: A less restrictive production function was used, allowing for the simultaneous estimation and final computation of relative labour contribution levels of different age-skill categories.Main findings: The lower-skilled segment produced significantly smaller productivity contributions and the relative productivity contribution–employee remuneration cost ratios of the 55 years and older age group were superior in the higher-skilled segment but, at the same time, the lowest in the lower-skilled segment.Practical/managerial implications: It is recommended that human resource practitioners (given the perceived rigidity of labour legislation) implement and maintain structures that promote higher productivity levels for all age-skill categories in the workplace.Contribution/value-add: An estimation procedure, which can be applied to the measurement of the relative productivity contribution of different age-skill categories, has been established.

The impact of in-house training on the diversity dimensions of employee productivity in the South Africa workplace

“…The study employs fixed-effect panel data estimations for the in-house trained employee productivity-gender dimension, the in-house trained employee productivity-race dimension, the in-house trained-age dimension, the in-house trained employee productivity-skills dimension, the in-house trained employee remuneration-gender dimension, the in-house trained employee remuneration-race dimension, the in-house trained employee remuneration-skills dimension and the in-house trained employee remuneration-age dimension. In order to facilitate comparisons with other research and studies that deal with the diversity dimensions of employee productivity the following gender, race, age and skills level categories are used in this study:  For gender attributes two categories are used, namely a gender distribution of less than 25% female participation and a gender distribution of more than 25% female participation in the workplace (Van Zyl, 2013 andVan Zyl, 2014).  For race attributes a category in which one specific race group has more than a 60% share and a category where no particular race group has more than a 60% share in the workplace are used (Van Zyl, 2013 andVan Zyl, 2014).…”

“…In order to facilitate comparisons with other research and studies that deal with the diversity dimensions of employee productivity the following gender, race, age and skills level categories are used in this study:  For gender attributes two categories are used, namely a gender distribution of less than 25% female participation and a gender distribution of more than 25% female participation in the workplace (Van Zyl, 2013 andVan Zyl, 2014).  For race attributes a category in which one specific race group has more than a 60% share and a category where no particular race group has more than a 60% share in the workplace are used (Van Zyl, 2013 andVan Zyl, 2014).  In terms of the age attribute three groups are used, namely employees 35 years of age and younger, between 35 and 55 years of age and 55 years of age and older (Van Zyl, 2013 andVan Zyl, 2014).…”

“… For race attributes a category in which one specific race group has more than a 60% share and a category where no particular race group has more than a 60% share in the workplace are used (Van Zyl, 2013 andVan Zyl, 2014).  In terms of the age attribute three groups are used, namely employees 35 years of age and younger, between 35 and 55 years of age and 55 years of age and older (Van Zyl, 2013 andVan Zyl, 2014).  For the skills attribute the International Standard Classification of Occupations (ISCO-88) is used in order to distinguish between more skilled occupations (Category A) and less skilled occupations (Category B) in the workplace (Van Zyl, 2013).…”

“… In terms of the age attribute three groups are used, namely employees 35 years of age and younger, between 35 and 55 years of age and 55 years of age and older (Van Zyl, 2013 andVan Zyl, 2014).  For the skills attribute the International Standard Classification of Occupations (ISCO-88) is used in order to distinguish between more skilled occupations (Category A) and less skilled occupations (Category B) in the workplace (Van Zyl, 2013).…”

Talent mapping: a strategic approach toward digitalization initiatives in the banking and financial technology (FinTech) industry in Indonesia

“…The company considers talents as a very important asset in a strategy to drive the business one step ahead of their competitors. The development of employee skills will benefit the organization (Dalal and Akdere, 2018; Grant, Maxwell, and Ogden, 2014; van Zyl, 2013). By redesigning the system and model of talent development, the human capital unit can encourage companies to become dynamic ones.…”