North America

Robots & Ontario’s Minimum Wage

Economists have long tried to identify “goldilocks wages”: ideal compromises in the tradeoff between higher minimum wages and higher rates of un(der)employment. This is, of course, far more than merely a theoretical pursuit. With an election coming up in Ontario next year, it is also one of the main issues likely to spill over from economics into politics. The province plans on raising its minimum wage, from $11.40 today to $14 in 2018 and $15 in 2019. Inevitably, this has raised questions as to whether or not it will lead to more jobs being outsourced or automated, if employers decide they cannot afford to pay the higher wages.

Thus far, most of the minimum wage studies that have been conducted have tended to ask questions such as:

  • How many jobs within the jurisdiction that is planning on raising its minimum wage are susceptible to outsourcing or automation?
  • How many workers within the jurisdiction that is planning on raising its minimum wage earn less than what the minimum wage will become?
  • How does the planned minimum wage compare to that of other nearby jurisdictions?
  • How migration-elastic are the jurisdiction’s labour markets (in other words, how likely is there to be an exodus of workers to other jurisdictions, if domestic minimum wages are not raised)?

One of the complicating factors these studies generally reveal is that conditions vary from place to place even within the same jurisdiction. In Ontario, for instance, there are obvious differences between Toronto and most of the other smaller cities and towns in the province. A smaller share of Toronto’s labour force earns less than $14 dollars per hour. A smaller share of Toronto’s labour force may have jobs susceptible to automation. Toronto’s labour force might also be more migration-elastic, given that the population of Toronto is relatively young. Young workers may be somewhat more willing to move to faraway markets like Western Canada or foreign markets like the US (or, linguistically, Quebec) if wages at home are too low.

The Night Moves 

There are many other variables that one could analyze as well when attempting to determine whether a given minimum wage is suitable. Due to current technological trends, two in particular may be worth discussing:

— the disparity between an economy’s manual labour costs and energy prices
— the disparity between an economy’s daytime energy prices and overnight energy prices

The former variable will help decide how likely an economy is to employ sophisticated machines—robots—to substitute for human labour. Robots tend to be energy-intensive, so an economy in which energy is cheap but labour is expensive will, generally speaking, be ripe for roboticization. Arguably, an example of such an economy is Quebec. Its manual labour costs are high because its population is older than the Canadian average, and much older than the US or global averages. Yet its electricity prices are among the lowest in North America. Ontario’s other neighbour, Manitoba, also has some of the cheapest electricity in North America.

The latter variable has the same implications. Because robots which replace manual labour generally consume a lot of energy, and because one of the main advantages of robots relative to human workers is that machines do not need to rest or sleep overnight, an economy in which the cost of energy overnight is cheap compared to the cost of daytime energy might be one in which roboticization will be likelier to occur.

Obviously this conversation remains a speculative one at the moment, since widespread roboticization has not yet occured. Still, it may be important to have it anyway, as it appears to have a special relevance for Ontario:

1) Energy/Labour

Ontario’s energy prices are very high by Canadian standards. They are more than double those of Quebec and Manitoba, for example. Yet Ontario’s energy remains roughly middle-of-the-pack when compared to prices in US states, and is even extremely cheap when compared to many wealthy countries in Europe and East Asia. Electricity in Ontario is only about half as expensive as in Europe’s largest economy, Germany. These lower energy costs, when combined with Canada’s relatively high labour costs, is why some have predicted that Canadian firms will experience among the highest savings from roboticization (see graph below).

For Ontario, there is therefore a risk that jobs will be lost not merely to robots working within Ontario, but also to those working within other nearby Canadian markets where energy prices are far lower than in Ontario.

Labour Cost Savings

Source: Boston Consulting Group 

2) Daytime/Overnight

While Canada in general has a high disparity between energy costs (which are relatively cheap) and labour costs (which are relatively expensive), it is Ontario in particular that has a high disparity between daytime energy costs (which are relatively expensive) and overnight energy costs (which are relatively cheap). This is because Ontario is a world leader in nuclear power generation (see graph below). Nuclear power plants, unlike natural gas or hydroelectric plants, cannot be shut off at night without wasting fuel. Ontario has such a large surplus of overnight electricity that it often has to pay its producers to turn off their power plants at night, and often sells overnight power at prices that are well below the cost of production.

Nuclear Generation

Source: US Energy Information Administration 

At the moment, this is not a situation that is unique to Ontario. Like nuclear plants, coal power plants also cannot easily be shut off at night. Economies which rely on coal therefore often have surplus overnight power as well. In recent years, however, there has begun a major shift from coal-based power to gas or renewables. The Dow Jones U.S. Coal Index has lost more than 95 percent of its value since 2011, for example.

As economies rely less on coal and more on gas plants (which can be shut off at night) and solar power (which cannot help but be shut off at night), nuclear economies like Ontario are becoming far more unique in their disparity between daytime and overnight energy prices. This is true also of Ontario’s wider region: in the US, the two largest nuclear producers by far are Pennsylvania and Illinois, both fellow Great Lake states. Ontario’s immediate neighbours, New York and Michigan, are the fourth and tenth largest producers, respectively.

Moreover, because of its geographic size, Ontario is a burgeoning player in the wind-power industry. Yet  because of its geographic location, Ontario does not produce much solar power. Wind turbines cannot be shut off overnight either without wasting “fuel” (i.e. without wasting wind), whereas solar plants only produce power in the daytime. This too is driving Ontario’s disparity between its daytime and overnight costs.

Because humans rest at night, but robots do not have to, the disparity between an economy’s daytime and overnight power costs could become a major determinant in the susceptibility of its labour force to automation.

Conclusion

These inquiries into the question of roboticization, though preliminary (and perhaps still quite premature), suggest that Ontario should be especially careful when carrying out minimum wage increases. Given the disparity between daytime and overnight energy costs in Ontario, as well as the disparity between energy and labour costs within Canada in general, it may be that employment in the region will face a high level of competition from robots. If Ontario wants to improve the standard of living of its minimum wage workers, it might be wiser to pursue alternative policies, such as reducing income taxes on its lowest tax brackets.

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