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Number of Robots
Number of Workers
The market for industrial robots has expanded in the last couple of decades. The reason behind this expansion can be seen from Figure 2. Figure 2 depicts that the prices of robots are continuously falling from 1990s. The fall in the prices of robots are making them attractive for the industries. The cost of production is becoming cheaper due to low prices of robots used in the production. The manufacturers are finding it profitable to replace robots in the place of labors. The opportunity cost of using robots is less as seen from the production possibility frontier. Thus, the demand of industrial robots is rising.
If Bruno uses more robots in his production function, this would lead to increase in the output at increasing rate. As the robots are cheaper and faster than humans, there will be increasing returns to scale, that is, the output increases more than the input. This is shown in the above diagram when the production function changes its shape from concave to convex. The use of more robots has made the production function convex as the use of robots has increased.
The decision of Bruno to increase the use of robots would lead to reduction in the bargaining power of Angela. According to the figure 3, the two respondents in this case are Angela and Robot. The proposer is Bruno. The figure shows that if only workers are there in the market and no robots, then the lower offers would be rejected by Angela. But in the case where the trend of automation is increasing, the probability that the lower offer is rejected by Angela is reduced. If Angela rejects a lower wage, then her payoff would be zero. Bruno would increase the use of robots. Therefore, Angela would accept the lower offer also to avoid the worse off situation. The profits of Bruno would increase as the same work would be done in less time and cost. The revenues would rise and the cost of production would fall.
No, Bruno's decision to use more robots would not lead to pareto improvement. Pareto improvement is a situation when someone is made better off without making others worse off. But the decision of using more robots would increase the profit of Bruno but also decrease the bargaining power and real wages of workers. Thus, this is not a situation of pareto improvement.
Yes, the decision of Bruno to use more robots would result in a fairer outcome. This can be said according to the substantive judgement of fairness. According to substantive judgement of fairness, the employer must have a valid reason for terminating any employee. The decision of using more robots by Bruno is valid and justified as it is resulting in more efficiency in the production. According to the procedural judgement of fairness, the decision of Bruno to use more robot is also fair. This is because procedural fairness means making a decision using fair procedures. In administrative decision making, Bruno is acting fairly. Therefore, the action of Bruno to use more robots is fair according to substantive and procedural judgement of fairness (Michie et al., 2014).
The above diagram shows the trade- off between consumption and leisure. If Bruno decides to use more robots, it would adversely affect the future levels of real wage, utility, free time and consumption. In order to increase the free time by one hour, the consumption of Angela would reduce by a larger amount. This is due to the fall in real wage of Angela.
To mitigate the potential issue of increasing automation and stop the job displacements, the government can take various policy measures. Some of them are- Providing social safety net, universal basic income and guaranteed jobs (Acemoglu and Restrepo, 2018). According to me, the most appropriate way to mitigate the issue is to create more jobs. The government can become the employer of the last resort to save the employees from dislocation. The government can enter into sectors like health, education, elderly and child care where the private sector is not performing up to the mark. The government can provide cushion to the ones who have been displaced (Fagnant and Kockelman, 2015).
Acemoglu, D. and Restrepo, P., 2018. Artificial intelligence, automation and work (No. w24196). National Bureau of Economic Research.
Fagnant, D.J. and Kockelman, K., 2015. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, pp.167-181.
Michie, S., Atkins, L. and West, R., 2014. The behaviour change wheel. A guide to designing interventions. 1st ed. Great Britain: Silverback Publishing, pp.1003-1010.
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