US economy

A Deserved Nobel Economics Prize Reminds Us Facts Matter


Nobel Memorial Prizes in economics are given for long-term research, not for economists’ role in current debates, so they don’t necessarily have much bearing on the political moment. You might expect the disconnect to be especially strong when the prize is given mainly for the development of new research methods.

And that’s the case for the latest prize, awarded Monday to David Card, Joshua D. Angrist and Guido W. Imbens, leaders in the “credibility revolution” — a change in the way economists use data to assess theories — that has swept through economics over the past generation.

It turns out, however, that the credibility revolution is extremely relevant to current debates. For studies using the new approach have, in many though not all cases, strengthened the argument for a more active government role in addressing inequality.

As I’ll explain, that’s not an accident. But first, what’s this revolution all about?

Economists generally can’t do controlled experiments — all we can do is observe. And the trouble with trying to draw conclusions from economic observations is that at any given time and place lots of things are happening. For example, the economy boomed after Bill Clinton raised taxes on high incomes and reduced the budget deficit. But did these fiscal policies cause prosperity, or was Clinton just lucky in presiding over a tech boom?

Before the credibility revolution, economists basically tried to isolate the effects of particular policies or other changes by using elaborate statistical methods to control for other factors. In many cases that’s still all we can do. But any such attempt is only as good as the controls, and there is typically endless room for dispute about the results.

In the 1990s, however, some economists realized there was an alternative approach, that of exploiting “natural experiments” — situations in which the vagaries of history deliver something close to the kind of controlled trial researchers might want to conduct but can’t.

The most famous example is the research that Card conducted along with Alan Krueger on the effects of minimum wages. Most economists used to believe that raising the minimum wage reduces employment. But is this true? In 1992 the state of New Jersey increased its minimum wage while neighboring Pennsylvania didn’t. Card and Krueger realized that they could assess the effect of this policy change by comparing employment growth in the two states after the wage hike, essentially using Pennsylvania as the control for New Jersey’s experiment.

What they found was that the increased minimum wage had very little if any negative effect on the number of jobs, a result since confirmed by looking at many other instances. These results make the case not just for higher minimum wages, but for more aggressive attempts to reduce inequality in general.

Another example: How can we assess the effects of safety net programs that aid children? Researchers have taken advantage of natural experiments created by, among other examples, the gradual rollout of food stamps in the 1960s and 1970s and several discrete jumps in Medicaid’s availability in the 1980s. These studies show that children who received aid became much healthier, more productive adults than nonrecipients.

And such studies make a strong case for the Biden administration’s Build Back Better initiative, which emphasizes investment in children as well as in conventional infrastructure.

Finally, big changes in unemployment insurance over the course of the pandemic — a huge increase in generosity, then a sudden cutoff, then a partial restoration, then another cutoff, with some states cutting benefits sooner than others — provide several natural experiments letting us test whether, as conservatives always insist, unemployment insurance deters the unemployed from seeking new jobs.

Well, the data provide a clear answer: While there may be some disincentive effects from unemployment benefits, they’re small.

Overall, then, modern data-driven economics tends to support more activist economic policies: Raising wages, helping children and aiding the unemployed are all better ideas than many politicians seem to believe. But why do the facts seem to support a progressive agenda?

The main answer, I’d argue, is that in the past many influential people seized on economic arguments that could be used to justify high inequality. We can’t raise the minimum wage, because that would kill jobs; we can’t help the unemployed, because that would hurt their incentives to work; and so on. In other words, the political use of economic theory has tended to have a right-wing bias.

But now we have evidence that can be used to check these arguments, and some don’t hold up. So the empirical revolution in economics undermines the right-leaning conventional wisdom that had dominated discourse. In that sense, evidence turns out to have a liberal bias.

Again, the research honored by this Nobel isn’t political, but it has important political implications. And most of those implications favor a policy move to the left.





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