When I was teaching High School, a surprising number of lessons began with needing to define our terms.
Want to figure out how to build an Artificially Intelligent system? What does it mean to be “intelligent”?
Want to make an efficient system? How do you define “efficient”?
Want to choose the best software for the job? What do you mean by “best”?
When I teach Data Science, one of the key points that always needs to be hammered home is that data cannot answer qualitative questions, only quantitative ones. It can’t tell you what is the nicest apple, but it can tell you the biggest. It can’t tell you who is the best political party, but it can tell you who got the most votes. It can’t even tell you when we have the best air quality, but it can tell you when the concentrations of specific pollutants are lowest.
One of the other most essential points is that no dataset is perfect. We rarely even have the data we want. What we have is the data we can easily measure – a proxy for the data we want. We don’t know how much NO2 is being pumped into the air by transport and manufacturing, but we do know how much NO2 we can measure at a particular place and a particular time. We don’t even know the exact population of Australia right this instant. What we do have is a combination of census counts, birth rates, and death rates, that give us what we think is a pretty good approximation, but there’s no way to take some kind of high resolution x-ray of the country and count the number of people that show up.
When I walk past an area of dense bushland, I always wonder how many different types of animals live there. Again, this isn’t a question we can answer exactly. We can use infrared cameras, DNA sampling of soil and water, and trapping over certain areas to get some idea, but there’s just no way we can know exactly.
When it comes to the Economy, though, we tend to think of it as a well defined, well understood, scientific concept with fixed attributes. To the extent that we think of it at all (aside from when we are force fed lines like “we have to do this for the sake of the economy”, where “this” can be anything from accepting brutally low unemployment benefits that are not enough to even keep people alive, to risking death so that the economy can “open up” in the presence of a wildly contagious variant of a potentially lethal disease), we think of it as measures like Gross Domestic Product (GDP), the Rate of Unemployment, and Inflation. These terms, themselves, are typically thought of as something like natural laws – fixed definitions, reliably and consistently calculated, and easily compared. And absolutely essential to the economy.
Here’s the thing, though. All of these measures were made up. They are arbitrary measures of things that aren’t necessarily important to us, or representative of what we really care about.
Consider unemployment for a moment. According to an Australian government website, “the Australian Bureau of Statistics (ABS) defines a person who is unemployed as one who, during a specified reference period, is not employed for one hour or more, is actively seeking work, and is currently available for work.” Except, according to the ABS themselves, that’s not true, or at least not the whole truth. They say:
“To be classified as unemployed a person needs to meet the following three criteria: –
- not working more than one hour in the reference week; and
- actively looking for work in previous four weeks; and
- be available to start work in the reference week.”
And this is not every country’s definition of unemployment. What we really want to know is “how many people are out of work, or want to work more than they currently are?” What we have is a rather arbitrary definition of one hour’s work (in the “reference week”, not even on average) counting as full employment. Like most measures involving people (or, indeed, real world systems), the relationship of data to the truth is… well… it’s complicated.
As for Gross Domestic Product, there are at least three different ways to calculate that, and not all countries calculate it the same way. Inflation is even more complicated. Defined by the Reserve Bank of Australia as “Inflation is an increase in the level of prices of the goods and services that households buy,” it is often measured using the Consumer Price Index, but that in itself is calculated by the ABS as the change in cost of thousands of different goods. But how, for example, do you define the cost of a litre of milk this week? Is it the price at your local supermarket? Which brand? Full cream or reduced fat? What if the price is different at the supermarket one suburb over? Which one gets used for the CPI? Oh! Look! Once again, it’s complicated.
Ok, so these measures we use to assess the economy are messier than we think they are. But here’s the thing. They are, themselves, entirely arbitrary measures that we use to define “our economy”. They don’t take into account unpaid caring work. They don’t take into account preserving wildlife and natural habitat, or pollution. They don’t, in most cases, include carbon emissions, health and fitness, or the overall wellbeing of the population.
Indeed, as NZ economist Marilyn Waring points out, if you use a typical definition of GDP, war is great for economic growth. Environmental destruction is good for the economy. Cutting down old growth forests is good for the economy.
But given that this is an arbitrary definition, we could change it. We could choose to value the things that truly matter. The things that make life better for all of us. Wellbeing. Social connectedness. Education. Health. Affordable housing. Time for recreation. Access to the natural environment. Air quality. Water quality. We can choose.
Again, we need to define our terms. How do we want to measure a “good” economy? What do we want to optimise for? How do we want to grow, and make progress? The things we most value are the three Fs. Friends, Family, and Free time. None of which are measured by GDP.
It’s a well known phenomenon that the things we measure tend to change our behaviour. The act of measuring them tells us that we value them, so we try to optimise them. I’ve written about this problem in education, as a part of Raising Heretics: Teaching Kids to Change the World, but it’s an even bigger problem for us in political and economic terms. Because if we continue to optimise our world for measures like the GDP, we will continue to fail to optimise it for liveability. For a sustainable climate. For health, and wellbeing, and justice. It’s time to choose what we care about.