Authors: Godfred Anakpo & Umakrishnan Kollamparambil
Affiliated organization: Development Southern Africa
Type of publication: Article – Research paper
Date of publication: September 7th, 2021
Introduction
The implication of technological innovation to wage structure has sparked significant interest in the field of research and politics. Prior studies have sought to empirically explore the theory of technological change with wage effect and Hicks’s theory of wage linking technological change to wage distribution in industrial sphere. According to Keynes’s widely cited postulation, technological change not only induces technological unemployment due to mismatch of skills, but high technology also requires specialised cognitive skills which could raise average wage in that skills category. Relatedly, at the core of Hicks’s theory of wage are three mechanisms by which technological change alters wage structure. First, when technological advancement is designed for routine tasks operated by a low-skilled labourer, it raises the productivity and income of the workers. Second, the change in the factor prices (including labour) stimulates the search for new methods which use more of the cheaper factor and less of the more expensive one (induced inventions). Third, as the technological innovation requires more labour in areas that cannot be automated, the wage1 rises and vice versa.
Hardly any studies, however, have sought to investigate if AI has any effect on average wages in Southern Africa. Hence, this study is critical in investigating the effect of AI on average wages in Southern Africa. Following the existing studies in the area, guided by operational expression of AI, machine automation is a major feature of AI that substitutes for human intelligence in operating certain tasks, hence the study used this as a proxy for the measurement and estimation of AI impact.
A unit increase of artificial intelligence (automated machine) per 100 000 adults significantly decreases average wage by 0.5%
Materials and methods
This analysis used panel data of 10 Southern African countries from 2004 to 2017. Countries covered in this region are South Africa, Zimbabwe, Mozambique, Botswana, Malawi, Angola, Lesotho, Zambia, Swaziland and Namibia. The main variables of interest are average wages and artificial intelligence, which are expected to have a positive or negative relationship in the technological change and wage theory.
Results
The highest logarithmic mean value of wage was recorded by Mozambique (30.8), followed by Zambia (29.4), Angola (27.3) and then South Africa (26.6), with Zimbabwe recording the lowest (20.4). The average values for artificial intelligence (measured as the number of automated machines per every 100 000 adults that substitute human labour) is highest for South Africa (50.2), followed by Namibia (39.7) and Botswana (26.3), with Malawi recording the lowest (3.1). The highest mean value of unemployment rate was recorded by Lesotho (26.9%), followed by Swaziland (25.9%), South Africa (25.9%) and then Namibia (20.9%), with Zimbabwe recording the lowest (5.0%). Inflation is highest for Angola (15.5%) and Malawi (15.3%). The statistics for logarithmic gross domestic product per capita (GDPC) ranges from a minimum of 6.8 to a maximum of 9.5. Botswana and South Africa recorded the highest level of GDPC at 9.5 and 9.3, respectively. This is followed by Namibia (9.1) and Swaziland (9.0), with Mozambique being the lowest (6.8).
Based on the study findings, a policy direction focusing on wage stabilisation, redistribution of income, advance learning and skill development training that promotes competitiveness to computerisation is recommended
Report in the table shows a significant negative association between artificial intelligence and average wages. Thus, a unit increase of artificial intelligence (automated machine) per 100 000 adults significantly decreases average wage by 0.5%. Additionally, a unit increase of artificial intelligence (automated machine) per 100 000 adults increases GDP per capita by 0.11%. Furthermore, a one percentage increase in inflation significantly increases average wage rate by 0.21%, whereas a 100% change in per capita income increases average wage by 38.4%. The table also shows significant positive relationship between artificial intelligence and inflation; thus, a unit change in artificial intelligence significantly increases inflation rate by 0.12%. Similarly, a 100% increase in per capita income increases AI by 3.7 units. The table shows a significant positive relationship between the artificial intelligence and unemployment rate; thus, an increase of one unit of automated machine per 100 000 adults significantly increases unemployment rate by 0.031%, while a percentage rise in inflation is accompanied by 0.014% increase in unemployment. It also documents that a 100% increase in per capita income leads to 1.70% decrease in unemployment rate.
A unit increase of artificial intelligence (automated machine) per 100 000 adults increases GDP per capita by 0.11%
Conclusion
Changes in average wage are usually attributed to technological change in the past industrial revolutions. Past studies have established that there is a relationship between artificial intelligence and wage structured in the context of developed economies, but with very limited work in the developing economies. Findings from the study shows that artificial intelligence has a significant negative relationship with average wages but is positively associated with GDPC, unemployment and inflation. The study also finds inflation and GDPC to be positively associated with average wage. Based on the study findings, a policy direction focusing on wage stabilisation, redistribution of income, advance learning and skill development training that promotes competitiveness to computerisation is recommended.
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