Real Limits to Growth


The Limits to Growth predicted the demise of economic growth back in 1972. Though the book received much criticism since then, Graham Turner recently confirmed that current development follows the patterns predicted by the book.

But there’s one problem, which is well-known to economists in growth economics. The resource ceiling ignores technology as the capacity to switch between limited resources. The confirmatory evidences Turner found belong to the upcoming trend. Indeed, the world economy consumes more oil and food. It’s okay. Bad things are supposed to happen when the economy per capita will be unable to consume more goods and services.

And the model hasn’t yet confirmed these bad expectations. It’s unlikely to. When some resource becomes scarcer, its price increases, and humans demand more efficient technologies, like energy-saving appliances and fuel-efficient cars. The world had an oil price shock already in the 1970s. It made better air conditioning and small cars popular even in the US.

The limit of growth comes not from too little oil, but from too much oil. Everyone invests in oil technologies for more than a century because no one sees a cheaper and more abundant resource. These investments made fossil fuels very efficient and attractive. Alternative energy can hardly compete with them.

Fossil fuels impose indirect costs, affect the environment, and crowd out investments into alternative energy. They are difficult to deal with. And their prices go down, thanks to fracking and other extraction methods.

Technologies may save the world from running out of oil, but they’re themselves powerful enough to slow down development. Nuclear weapon is making troubles around for more than 60 years. Hitler nearly obtained the atomic bomb. And Germany would get it not by surprise, like a terrorist organization, but because it was one of the most developed societies in the world before the 30s. Technologies aren’t safe in the hands of most advanced and democratic countries.

So, the limits to growth are trickier than the finiteness of certain resources. And these limits are less predictable.

Macroeconomics Models and Force of Habit

The public rightly questioned macroeconomics and academic finance after the 2008 burst. Record housing prices and debt, both relative to income, look a plausible cause for concern and they are. Why, then no one prevented it?
The design of the markets discourages companies from being overly cautious. Banks didn’t quit inflated housing markets because these markets were still inflating. Profits reinforce participation.
The designers of the markets had got obvious signals too late to avoid consequences. And very few wanted to be the person who bursts balloons with a needle at a birthday party anyway. Governments and central banks waited for problems to come first.
Many more versions exist. But none of them can explain the bubble with lack of knowledge alone. People in finance see housing prices every day, and high ratios are quite telling, apart from answering the question, “When will this trend end?”
Designers and players played by the rules, and they certainly had selfish incentives. Academia was relatively free of these rules and incentives. Did macroeconomists have selfish incentives to find a bubble, instead?
Yes and no. You will barely find a major university economist who likes forecasting. Because sometimes the predictions come true. Thus, sometimes they don’t. Economists prefer discussing things that have happened already. And they do it unhurriedly. Operative policy interventions are unlikely in the environment where even publishing an academic paper takes up to several years.
More so, it’s difficult to find a serious academic paper that includes policy recommendations. Scholars explain things that have occurred. Policymakers can use these insights to forecast. By 2008, policymakers had models. Were these models good? They happened to have specific limitations. But even bankers had incentives to use the best models they might get to quit the housing market in time.
There’re no obstacles to adopting models with better predicting power. Then, maybe policymakers did use the best models they had? Rather, they used the most reliable equations: the ones that they understand and used for years. And DSGE models won over various alternatives, including those by heterodox economists, who offered equations that predicted the crisis.
A theory that predicts one-in-fifty-years events is not trusted because it can hardly earn a reputation of a reliable one. No, the theory itself may be predictive and great, but it lacks an empirical base to show its fitness. That makes this theory and underlying models an unlikely candidate for widespread use.
Macroeconomics is responsible for not knowing enough in the sense of biologists who don’t know how to cure cancer. There’re wrong turns and no malicious incentives. Right turns require outstanding efforts and time. Including time for gathering unique data, like the data that came from the terrible Great Recession. Bad theories still can be the best, until we have more evidences. Economics works when we recognize limitations of previous theories and try to build better ones.