A Note on the Implications of Automation for Economic Growth and the Labor Share

Prettner, Klaus

doi:10.1017/s1365100517000098

Cited by 87 publications

(82 citation statements)

References 16 publications

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“…For this case the standard Solow (1956) framework predicts, under certain circumstances, perpetual growth even without technological progress and a declining labor income share (Prettner, 2017). In our contribution we show that countries with a lower population growth rate have a stronger incentive to invest in the adoption of automation.…”

Section: Discussionmentioning

confidence: 62%

“…While the effects of K t and L t on wages and on the rate of return on traditional physical capital are straightforward, we have a non-standard effect of the accumulation of automation capital: As P t increases, the wage rate decreases because workers compete with automation capital, whereas the rate of return on traditional physical capital increases because automation capital substitutes for workers and therefore raises the productivity of traditional physical capital. Together with the fact that the income stream earned by automation capital flows to the capital owners this mechanism has the potential to explain the decrease in the labor income share that we have observed over the last few decades (Steigum, 2011;Prettner, 2017). It is important to note at this point, that, while automation reduces the marginal value product of labor and thereby the wage rate, labor productivity as measured by output per worker increases in the wake of automation.…”

Section: Production and Automationmentioning

confidence: 79%

“…We follow Prettner (2017) and assume that the production function has a Cobb-Douglas structure with respect to human labor and traditional physical capital. However, the additional non-standard production factor "automation capital" is a perfect substitute for labor such that aggregate output is given by…”

Section: Production and Automationmentioning

confidence: 99%

“…Instead, the production function has the potential to lead to long-run growth if the savings rate is large enough such that a positive accumulation rate of automation capital can be sustained (cf. Steigum, 2011;Prettner, 2017). Note that Equation (5) resembles the properties of an AK type of production structure.…”

Section: Production and Automationmentioning

confidence: 99%

“…The intuitive explanation for this is clear. For a given population size, automation overcomes the diminishing marginal product of traditional physical capital that acts as a boundary for long-run economic growth in the standard Solow (1956) model (see Prettner, 2017, for the analysis of the implications of automation for long-run economic growth in such a setting). Once the tasks which could previously only be carried out by human labor are automated, the stock of labor becomes, essentially, a reproducible production factor.…”

Section: Production and Automationmentioning

confidence: 99%

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Automation and Demographic Change

Abeliansky

Prettner²

2017

SSRN Journal

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We analyze the effects of declining population growth on the adoption of automation technology. A standard theoretical framework of the accumulation of traditional physical capital and of automation capital predicts that countries with a lower population growth rate are the ones that innovate and/or adopt new automation technologies faster. We test the theoretical prediction by means of panel data for 60 countries over the time span from 1993 to 2013. Regression estimates provide empirical support for the theoretical prediction and suggest that a 1% increase in population growth is associated with approximately a 2% reduction in the growth rate of robot density. Our results are robust to the inclusion of standard control variables, the use of different estimation methods, the consideration of a dynamic framework with the lagged dependent variable as regressor, and changing the measurement of the stock of robots.JEL classification: J11, O14, O33, O40.

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Section: Discussionmentioning

confidence: 62%

Section: Production and Automationmentioning

confidence: 79%

Section: Production and Automationmentioning

confidence: 99%

Section: Production and Automationmentioning

confidence: 99%

Section: Production and Automationmentioning

confidence: 99%

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Automation and Demographic Change

Abeliansky

Prettner²

2017

SSRN Journal

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Examining the role of artificial intelligence in advancing pollution reduction strategies

Liu,

Liang

2024

Manage Decis Econ

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This study is based on the panel data from 2006 to 2020 to explore the relationship between artificial intelligence (AI) and pollution emission. The study demonstrates a notable and statistically significant reduction in pollution emission intensity attributable to AI. This finding is robust, holding up under various sensitivity analyses and addressing endogeneity using instrumental variable techniques. The mechanism analysis reveals that AI's role in decreasing emissions primarily arises from three factors: the promotion of technological innovation, increased investment in emission reduction technology, and the replacement of low‐skilled labor with more efficient alternatives. We also find more substantial environmental benefits in areas with stricter environmental regulations and higher initial levels of pollution. Additionally, AI is shown to facilitate a symbiotic relationship between economic growth and environmental management. This research provides both empirical evidence and theoretical insights that reinforce the essential role of AI in driving green transformations, process upgrades, and high‐quality development. It situates these advancements within the broader context of the interplay between artificial intelligence, environmental sustainability, and economic progression.

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Artificial intelligence and income inequality: Do technological changes and worker's position matter?

Goyal

Aneja

2020

J Public Affairs

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The technological progress in artificial intelligence (AI) has led to mass unemployment and various types of inequalities in the recent years. This study is an attempt to see how income inequality has increased with the changes in technology. The study also investigated how technology such as AI has affected (directly or indirectly) to working position and working process of workers. Analysis of the study is based on the data of automation and Gini‐coefficients. The study finds that the relationship between AI and income distribution has always been considered negative and this is what has been observed in this study and it directly affects the distribution of income and jobs. Due to the automation, low and medium skill jobs are declining, and unemployment is increasing, further the income gap between middle and high skill labor is increasing. Gini‐coefficients of developing nations are higher than in developed nations, indicating that income inequality in developing nations is higher than the developed nations.

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A Note on the Implications of Automation for Economic Growth and the Labor Share

Cited by 87 publications

References 16 publications

Automation and Demographic Change

Automation and Demographic Change

Examining the role of artificial intelligence in advancing pollution reduction strategies

Artificial intelligence and income inequality: Do technological changes and worker's position matter?

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