Why investors should care about productivity

This is the first in a four-part series that explains productivity and why it matters to investors.

What if you heard there was a way for Canada to achieve improved economic growth; create more jobs; generate higher incomes; lower prices; improve standard of living; improve public services; and generally build a better economic future for Canadians. Sounds pretty good, doesn't it?

What could do all this you might ask? Well, the answer lies with something that most Canadians know little about-improving "productivity." And while improving productivity can help us achieve such benefits, there are no guarantees that all these benefits will be realized. It will depend on the decisions that are made if we are successful in improving productivity. So, at the outset, let's be clear that improved productivity brings opportunity for economic benefits-not a guarantee.

But What is Productivity?

Productivity is essentially concerned with how we combine our various resources-labour, tools, equipment, etc.-to produce goods and services. That is, it relates to the decisions we make, and the actions we take, to try to make the best use we can of all the various resources we have available.

Over the decades-indeed centuries-learning how to be more productive has been the key to our economic progress. As we shifted production activities from agricultural activity to manufacturing, our productivity improved. As we applied new technology and knowledge to our manufacturing, our productivity improved. As workers became more educated, skilled, experienced, and trained, our productivity improved.

But if productivity refers to how we use, combine, and apply our various resources, what does "improving productivity" mean? To answer that question, we need to know how productivity is measured. And that is not an easy answer.

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Measuring Productivity - It Isn't Easy

Consider a simple example to begin. Suppose you washed your car and it took an hour. But then you looked over and saw that your neighbour, in the same hour, washed two cars. You go over, check it out, and find the two cars are washed as well as the one car you washed. In a nutshell, it looks as though your neighbour's productivity was higher. But making productivity comparisons isn't easy

If you compare productivity in terms of output per worker, or hour worked, your neighbour's productivity was higher. In terms of labour used, or time invested, your neighbour washed two cars, equally well, while you washed one.

But let's consider some other factors. Did your neighbour use different equipment (e.g., a power hose while you used a cloth and pail)? Did your neighbor use a better wax that only needed one application whereas your wax needed two? Were the neighbour's two cars smaller than yours requiring less time?

So part of the problem in measuring and comparing productivity, as we saw from our simple example, lies with the fact that other factors will affect the output an employee can produce. Equipment, supplies, scale of the task, etc. all affected the person's "car washing productivity." Similarly, an employee with a power drill can likely work faster, and produce more, than a worker with a screwdriver. The same can be said for workers with a computer versus a typewriter, a car rather than a horse, a bulldozer than a shovel, and on and on.

In addition, training and experience can affect how much a worker can produce. Maybe your neighbor was a better car washer than you. So, in trying to measure and compare productivity, these kinds of factors pose complications and challenges-and that's just from washing cars. Imagine the challenge in trying to measure productivity for the whole economy.

One way statisticians try to measure productivity for the whole economy is to measure "output per worker." To do that, they take the economy's output-the Real Gross Domestic Product (GDP)-and divide that by the number of people employed.

But this brings up another challenge. All workers do not work the same hours. Some may work full-time, some part-time, some for long days, some for shorter, and so on. So measuring productivity by output per worker poses additional challenges in the fact that all workers do not work the same amount of time.

Consider a simple example to illustrate. Suppose a worker worked a 40 hour work week and produced two cars. Then, suppose the worker worked part-time and, over a 20 hour week, produced one car. If we were measuring productivity by output per worker, this would indicate that the productivity of this worker has declined. But it hasn't. The worker is as productive as before.

To try and overcome this problem, statisticians can also try to measure productivity by output per hour worked. This takes away the problem of workers working for different periods of time. They measure productivity this way by dividing real GDP by the total hours worked in the economy.

However, although calculating productivity this way takes away the problem of variations in time, it doesn't overcome the other challenges-that is, workers working with different equipment, supplies, tools, technology, and so on.

So, to try and overcome these challenges, there is yet another way that statisticians try to measure productivity-and this is referred to as "Multifactor Productivity." Measurements of Multifactor Productivity aim to look beyond just labour and time and try to consider all the various resources used production-labour, capital resources, quality of natural resources, and so on. Many people think that this is a better way to measure productivity because it gives more recognition to more of the factors that affect productivity. But measuring "multifactor productivity" is about as hard as it sounds.

In the end, regardless of which of these methods is used to measure productivity, they all face still additional challenges-for example, how do you measure the output of services-such as retail salespeople or a bank teller, or an airline flight attendant? How do you measure the output of publicly provided goods and services-such as a nurse, a road, or a school teacher? It's a pretty difficult challenge.

Setting the Record Straight

For the purposes of this article, we are going to leave the challenge of measuring productivity to others. What we are going to do here is to acknowledge:

(a) productivity is very important,

(b) improving productivity can afford us the opportunity to increase incomes and jobs and improve our standard of living,

(c) it is important to find ways to improve productivity, and

(d) that productivity in Canada is not as good as it can be-or as good as it needs to be.

In addition, as much as productivity is important to Canada's future, it is also important to investors. Productivity can affect a company's profitability, its prospects, and its potential for success. Therefore, it can affect analysts' expectations, stock prices, dividends, and overall investor returns. For that reason, later on in this series, we will include some "Investor Insights" suggesting some things investors might want to keep an eye on.

That said, in the next article, we will take a closer look at this very important "productivity challenge" for Canada.

Gary Rabbior is the president of the Canadian Foundation for Economic Education.