The Rise of Artificial Intelligence and its Impact on the Economy

May 17th, 2017

Rise in utilization of robots

Last year, HBO released a new fictional TV series titled Westworld, named after the fictional theme park in which incredibly wealthy [human] visitors interact with realistic robots in a western-themed world. The first season added to the ongoing discussion regarding robotics and artificial intelligence, especially as they relate to the economy and employment. We’ve already seen how drastic of an effect it can have on the ARM industry with the innovation and implementation of interactive voice response (IVR) – which came with more cost-efficient operations at the expense of increased compliance confusion. But as robots (e.g., machines and computer/engineering programs) continue becoming the norm and optimal method in other markets such as manufacture and service industries (e.g., ATM machines replacing bank tellers), what are the impacts on the ever-changing economy and ARM industry?

According to International Federation of Robotics’ (IFR) research, in 2015, industrial robot sales grew 15% to 253,748 units and professional service robot sales ballooned by 25% to 41,060 units from 2014. The high demand for robots was driven by several industries: electronics, metal, and chemical. Among other developed countries, the US is a leader in robot utilization because of its heavy manufacturing and installation industries, which are generally employed by blue-collar workers. However, robots are increasingly replacing these workers because once they’re programmed correctly, they’re significantly more productive and efficient, as they’re able to work continuously for long hours while maintaining higher levels of accuracy. Additionally, they’re generally much cheaper and don’t require benefits (e.g., vacation leave or healthcare benefits).

Worries Regarding Income Inequality

Recently, professors Daron Acemoglu of MIT and Pascual Restrepo of Boston University conducted research regarding machines’ impact on employment and wage levels. They concluded that for every additional robot per thousand workers, the employment-to-population ratio would decrease between 0.18 to 0.34 percentage points and wages would drop by 0.25%-0.5%, which means fewer workers and, presumably, lower median incomes. Also in last year’s research paper, they thought robots could emancipate working forces and create new technology-imbedded occupations, offsetting their negative effects. However, this may be quite optimistic since the data actually showed there was a deficit of manufacturing jobs behind the wave of robot utilization between 1990 and 2007.

As alluded to previously, the primary losers are blue-collar workers since business-owners replace them with more productive and cost-efficient robots. Furthermore, these individuals, many of whom maintain low technical skills and/or lower levels of education, encounter significant difficulties in finding new jobs without further skills development or education. Proceeding forward, this process pushes many of these former workers to depend on various government benefits, such as social security subsidies and income redistributions. Ultimately, this creates even greater income inequality.

This phenomenon is best observed through the Gini Index, which measures a society’s (for a country generally) income inequality: 0 means everyone maintains the same income level while 1 represents one person holding the entire society’s wealth. In other words, as the index increases and approaches 1, there’s more income inequality since fewer people have money compared to the total society’s wealth. The figure below shows that income inequality in the US has worsened considerably over the past few decades, growing by nearly 21% overall to about 0.48 in 2015.

Effect on ARM industry

The increasing utilization of robots and its impact on the economy can create complex effects for the ARM industry, most notably:

  1. From the perspective of productivity, implementing robots (and artificial intelligence software) should shorten a creditor’s (or any other business with outstanding loans) days of sales outstanding, thus increasing its receivable turnover ratio. This efficiency boost may create more first-party collection work but would presumably reduce third-party services.
  2. After considering rising income inequality, there may be an unemployment rate increase which should lead to declines in median household incomes and consumption. This could have a dual effect:
    • Poorer individuals would be more strapped for cash, facilitating a need to borrow money and take out loans, thus accumulating debt. This debt would presumably be difficult to repay for these low-income borrowers, necessitating third-party collection services.
    • The middle class, if they aren’t too affected by the unemployment-rate spike, will be better able to repay existing debts, making collection services more useful in the short-term but not down-the-road.
  3. Additionally, as many low-skilled and low-income people realize their imminent competition with robots and artificial intelligence, they may choose to further their education and technical skills to improve their skill set. Under the current markets and all else held equal, this will surely boost the student loan market and increase revenues for collection agencies participating in this industry.
  4. Lastly, as robots utilization and artificial intelligence has become more prevalent throughout society, it’s significantly changed the ARM industry (and call centers) with regard to contacting borrowers, such as through the innovation and implementation of IVR. IVR creates a generally more cost-efficient method for businesses to interact with borrowers since they’re able to cut employee costs in favor of a more consistent system with fewer variable costs after implementation. Once businesses adopt and implement IVR into their operations, there are lower costs and risks, some of which we mentioned earlier, than employing an actual person. As robot utilization and automation become increasingly more intelligent and better able to solve complex business issues, we should expect businesses to readily adopt and implement these new means into their operations, presumably lowering costs and risks – and employment.

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