Book review: “Good economics for hard times”

Abhijit V. Banerjee and Esther Duflo won the 2019 Economics Nobel for their work with experiments such as randomized controlled trials (RCTs), so I was excited to read their well-reviewed book (also from 2019). RCTs are designed to tease apart correlation from causation, and I’ve always found it frustrating that economic policy (at least the way it’s presented by politicians) makes little reference to whether we have evidence that something actually works. To make good predictions about the effects of a particular policy, we need a causal understanding of the system we’re trying to improve, and experiments are the best tools we have to get that understanding.

It turns out that Good economics for hard times doesn’t focus on Banerjee and Duflo’s research, but it is about whether (and what) we know actually works in economics. It stresses that among academic economists with strong reputations, there’s a surprising amount of agreement on many issues (even on things that non-economists think are contentious, like tariffs). This consensus has come about because of a growing emphasis on experimentation rather than purely theoretical economic models.

For example, classic models of supply and demand predict that where there is high demand for labor (and high wages accordingly), workers from other places will want to come and improve their salaries. Since this would increase the supply and depress wages again, the people who already live there have an incentive to prevent anyone else from entering. (This is academic speak for “they’re coming to take our jobs.”) But this clean picture just doesn’t hold up in the real world. Most people are very reluctant to move even if it would be good for them financially. Furthermore, it’s difficult for migrants to directly replace native workers for a variety of reasons, including things like language or cultural barriers, or a lack of local connections. “Natural experiments” (where random events create a natural control group to compare against) show that migration is generally beneficial for migrants and does little if any harm to natives. Listening to politicians would give you the impression that this is a controversial topic, but there is strong agreement among most academic economists!

Other topics get similar treatment, but a major underlying theme is the “stickiness” of real markets: people and firms consistently fail to do the “optimal” thing that economic models predict they should. This isn’t necessarily because they’re irrational; rather (surprise!) people care about things other than their income: family, health, culture, etc. matter, too. Another interesting point is that we have no idea what causes macroeconomic growth, so policies that claim to do this (e.g. by lowering taxes) are essentially snake oil.

Preferences and automation

Along with what in economics is well established, the authors are also clear about what is more speculative, and in the later chapters they delve into topics where the questions are just as important but the answers are murkier. Of particular interest for me were sections on human preferences and the impact of automation on jobs and human well-being, since these are both very relevant to AI and robotics.

Preferences (chapter four) play an important role in economics models, where they’re typically assumed to be stable (not changing over time) and coherent (not contradictory). We know this assumption doesn’t hold in real life. For example, Thinking, fast and slow by Daniel Kahneman famously documents all sorts of experiments where people turn out to have changeable and/or inconsistent preferences. Banerjee and Duflo make the somewhat odd choice to focus the chapter exclusively on one particularly nasty kind of preference: prejudices against groups of people. They argue that because prejudices can be strengthened or weakened by specific factors like media diet or social contact, they don’t fit the stable-and-coherent-preference mold. Therefore, we should focus on designing interventions to reduce prejudices rather than building them into our models as unchangeable facts of life.

I was a bit disappointed with the narrow focus in this chapter. Maybe we can all agree that reducing prejudice is good, but what about other kinds of preferences? In economic models, social welfare is defined in terms of maximizing everyone’s preferences. This is all well and good when our preferences are stable, but if the models take the changeability of preferences into account, then making those preferences easier to satisfy is a perfectly valid way to increase social welfare. If most people want democracy, but an authoritarian government educates them to prefer authoritarianism, are they worse off? If this is bad, why are interventions aimed at reducing prejudice preferences okay? These kinds of questions are only becoming more urgent as social media recommendation and advertising algorithms have increasing influence over our preferences. To the extent that economics has something useful to add to the discussion, it’s not covered in this book.

Chapter seven focuses on automation. Like other things I’ve read, the authors use the economics of the past to draw lessons for a future when an increasing proportion of current jobs may be automated. But whereas most people point to the fact that jobs lost to automation in the past were replaced with new (and often better) ones enabled by technological advancements, Banerjee and Duflo focus instead on negative impacts on blue-collar workers and rising income inequality. After the industrial revolution in the UK, real blue-collar wages dropped and didn’t recover until 1820, over 60 years later. In the modern era, automation may be helping drive income inequality, where especially in the US it has been steadily rising for the past 40 years. The basic hypothesis is that when low-skilled workers lose their jobs to automation, new jobs becoming available won’t always help them: a warehouse worker can’t transition to programming the robot that replaced them overnight.1

However, there are other factors besides automation that could explain income inequality, such as tax policy and the growth of the finance industry in the US and UK. The available evidence doesn’t clearly support one explanation over the others, and academic economists are split on whether future automation will cause significant unemployment. In any case, since income inequality is already a problem, it makes sense to try to address it regardless of whether AI and robotics make things even worse in the coming years. The book’s last chapter examines policies like universal basic income and conditional cash transfers, with an emphasis on preserving people’s dignity in addition to their financial security.

Last thoughts

Overall, I found this book to be a very worthwhile read. I like that the authors are so transparent about where they are confident and where things are more speculative. They come across as genuinely wanting to help readers understand the landscape of ideas in economics that are most relevant to non-economists, rather than persuade them about any particular idea or position. I feel much better equipped to evaluate economic claims in the wild after reading this. I can’t think of a better way to conclude than Good economics for hard times itself does:

The only recourse we have against bad ideas is to be vigilant, resist the seduction of the “obvious,” be skeptical of promised miracles, question the evidence, be patient with complexity and honest about what we know and what we can know… The call to action is not just for academic economists–it is for all of us who want a better, saner, more humane world. Economics is too important to be left to economists.


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