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Decoding the AI Future: Further reading—the productivity paradox

August 9, 2023
a woman in a business suit sitting at a table with laptops

Economists worry a lot about productivity because it is one of the key drivers of economic growth.

The relationship between technology and productivity over the past few decades, particularly in developed economies, is often referred to as the "productivity paradox". The term was first coined by Erik Brynjolfsson in 1993 to explain the disconnect between the noticeable advances in the IT revolution and the lack of productivity growth, particularly in the US.

To give an overview, digital technologies have grown exponentially over the past 30 years. We've seen a rise in personal computing, the advent of the internet, the ubiquity of mobile devices, and more recently, advancements in AI and machine learning. Despite these significant technological advancements, productivity growth in many developed economies has been slow, a phenomenon often called "secular stagnation".

Several hypotheses have been proposed to explain this productivity paradox:

  • Measurement issues: Traditional productivity measures focus on economic output per hour worked. These measures may not fully capture productivity improvements from technology, especially in service sectors and in areas like quality enhancements. For example, how do we measure the productivity gain from having almost all human knowledge accessible via a smartphone?
  • Lag Effect: The benefits of technological change may not be immediately apparent. There could be a significant time lag as businesses learn to integrate new technologies, restructure their operations, and retrain their workforce. This is often called the "gestation period" for technology.
  • Distribution of Gains: Technology may lead to increased productivity at a micro (firm or industry) level but this may not translate to macro (economy-wide) productivity gains. This could be due to a variety of reasons such as technology increasing income inequality or only benefitting certain sectors.
  • Skills Gap: Rapid technological change can lead to a "skills gap" where the workforce doesn't have the necessary skills to effectively use the new technology, thus limiting productivity gains.
  • Decreasing Technological Returns: Some scholars argue that recent technological innovations are less impactful than previous ones. For example, indoor plumbing had a bigger impact on productivity and wellbeing than the latest smartphone.

While many economists and policymakers expect that advancements in areas like AI and machine learning will eventually lead to a significant increase in productivity, the question remains as to when we will start to see these impacts reflected in productivity statistics.

A key point to make in discussions about productivity is that it’s not just productivity we care about: we actually care about doing more with less. This is where a different measure is important: total factor productivity. Productivity in its simplest form is about how much output you get from a certain amount of input. For example, if you work at a pizza shop and you make 10 pizzas in an hour, that's your productivity. If you figure out a way to make 15 pizzas in an hour instead, your productivity has increased.

But that's a pretty simple example, right? In the real world, making a product or offering a service usually involves many different types of inputs. In the case of our pizza shop, you don't just need one pizza maker. You need ingredients like dough, sauce, and cheese, and you also need equipment like ovens, pizza cutters, and maybe even a delivery car.

Total Factor Productivity (TFP) is a way to measure productivity that takes into account all these different inputs, not just one of them. So, when we talk about TFP, we're not just talking about how many pizzas one pizza maker can make in an hour. We're talking about how efficiently the entire pizza shop is using all of its resources (workers, ingredients, equipment) to produce pizzas.

Why is TFP important? Well, imagine if the pizza shop just hired more pizza makers to increase the number of pizzas produced per hour. That might look like a productivity increase at first, but it doesn't necessarily mean the shop is using its resources more efficiently. It's just using more resources.

But if the pizza shop can produce more pizzas without using any more workers, ingredients, or equipment – for example, by rearranging the kitchen to reduce the time pizza makers spend walking back and forth, or by tweaking the recipe to get more pizzas from the same amount of dough – that's an increase in TFP. It means the shop is getting better at turning its inputs into outputs. And in the long run, being able to make more with less is a key driver of economic growth and prosperity.

So while basic productivity measures can be helpful, TFP gives a more complete picture of how efficiently an economy is using all its resources, not just labor.

There’s another important metric: Marginal Productivity and this is what companies care about.

Remember that TFP is about how efficiently all inputs (or factors) of production—workers, ingredients, and equipment in our pizza shop—are used to produce output (pizzas). If we can make more pizzas without using any more of our inputs, that's an increase in TFP. It's a measure of our productivity considering all factors.

On the other hand, Marginal Productivity is about the additional output (extra pizzas) you get from adding just one more unit of a specific input (like one more worker), while keeping all other inputs constant.

For example, let's say our pizza shop currently has 3 workers and makes 90 pizzas an hour. That's 30 pizzas per worker per hour. If we hire one more worker and now we can make 125 pizzas an hour, the marginal productivity of the worker is 35 pizzas per hour (125 pizzas minus 90 pizzas). That's because we're looking at the extra output we got from adding that one additional worker, assuming nothing else changed.

The difference between TFP and Marginal Productivity is in what they're measuring. TFP is about overall productivity considering all inputs, while Marginal Productivity is about the additional output you get from increasing a single type of input. Economists use both to understand productivity and efficiency in different ways and contexts.

Understanding marginal productivity is especially important when a business is deciding whether to hire more workers, buy more machines, or invest in more materials. If the additional output (marginal productivity) from hiring an additional worker is more than the cost of hiring that worker, then it makes sense to hire. Otherwise, it doesn't.

Read the full Decoding the AI Future series: