Doughnut Economics

By Kate Raworth

(Penguin Books 2017)

Doughnut Economics: Seven Ways to Think Like a 21st Century Economist

Doughnut Economics is the first ever economics “page turner” I’ve ever encountered. Admittedly, I am wicked nerdy, but even I am often lulled into a lovely afternoon nap when reading economic texts. Doughnut economics gripped me from the outset, though, by addressing some of the most pressing questions that have been circulating in my own mind over the last 20 years since achieving my economics degree: how logically can GDP continue to grow indefinitely? Why do we assume that increasing GDP is commensurate with improved outcomes when data keep telling us the opposite?

I have spoken to a non-economist friend or two who have read Doughnut Economics, and I will supplement my review with their input: it can be a tricky read if you haven’t studied economics. I can absolutely see how that would be the case. If you have not been wondering for years why we have so many unanswered questions in the study and practice of economics, this book may not be the page-turner for you that it was for me. Consider yourself warned, but also, consider reading it anyway.

Limitations not Conspiracy

Don’t get me wrong, there are lots of texts that are calling for a reduced reliance on GDP growth, and I’ve tried reading some of them. What I typically find is that they are overly political and underwhelmingly economic. By that, I mean that the weakness I see in most of the “de-growth” books is that they set out to be emotionally opposed to GDP growth, and remain light on the economic understanding or research to back up the political debate.

This is where Doughnut economics is different. Rather than focusing on why economics is failing, or why capitalists are evil, Raworth looks back at economic history in a measured and balanced way, concluding that we have simply become too dependent on a small cohort of measured variables in the last 50-100 years.

The reasons we became dependent on these variables are logical: GDP was simply the easiest thing to measure back when economists started advising policymakers, and economists needed to start with simplified models to make the math work back when the math was done by hand.

Fundamentally, the revelatory element of Doughnut Economics is the voicing of a fear I’ve only ever been confident enough to whisper in economic circles: what makes us think there are “laws” of economics? I mean, I can drop an apple 1,000 times and be pretty confident it will fall all 1,000 times, personally testing Newton’s law of universal gravitation.

But I’ve never found an econometric model that lacks a residual (the difference between the predicted and the observed value). That would suggest that if there are ‘laws’ of economics – we don’t yet understand them very well. Our mathematical models cannot fully explain what happens in the economy.

Mathematical Modeling

My experience of data and economic models, both historic and predictive, is that the accuracy and the authenticity of the economic model reside primarily in the economist. There are amazing economists out there and unethical economists out there, so it is important to peer review economic output in the same way the scientific community peer reviews scientific experiments.

When several economic studies report similar findings based on historic data, we can feel more confident in the historic measurement of correlations between variables. However, I remain healthily skeptical when economists confidently claim causation between variables, and I remain even more skeptical when economists confidently predict the future. I should know; I worked in economic forecasting for over a decade.

I was never the smartest economist in the room by any stretch. I worked with incredibly talented, smart people who understood the economy far better than I. And, amongst that crowd, there was a common joke in forecasting: we were “always wrong, but never in doubt”.

The best economists I’ve worked with almost always manually adjust the output of their predictive models. Logically, this means that the best economists accept that current mathematical models are simply not able to capture all of the sophisticated relationships impacting the economy.

And this is Raworth’s fundamental point: economic models were simplified in order to quantify the economy given the tools, goals, and methods of the time. However, we now have better data, better technology and different goals. Isn’t it time we update the underlying models? Shouldn’t we start to quantify some of the non-monetary inputs that logic tells us impact our economy (like household work)?

Failing to take into account inputs and outputs that are not effectively monetized, like costs and benefits that come from the home rather than are traded in an open market, or people making socially responsible decisions, even when they cost more, means that our previous economic models are outdated. Raworth proposes some very interesting ideas for how to update them.

Her arguments throughout the book are sound, and they address some tough questions that most pragmatic and academic economists have shied away from for too long: primarily that previous economic models were built on oversimplified assumptions in order to facilitate the application of mathematical equations.

But those equations have failed dismally at predictive accuracy because economic systems are not closed monetary loops and people do not act in the way rational man theory suggests they will. Put simply, economic models need to become more complex in order to truly reflect the complexity of our economies.



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