one which, with diminishing marginal returns to capital, would eventually settle down at the same growth rate as before – driven by productivity. And productivity was not something that most economists felt comfortable talking about.

Instead of seeking to manipulate long-term growth rates, students of growth theory in that era were concerned instead to design policy that would ensure that the economy would select the long-term equilibrium growth path with the appropriate level of capital – not overinvesting (like Soviet Russia), and not underinvesting (like, perhaps, Britain in those days). Here’s where that manipulative use of money through the inflation tax, to ensure sufficient saving, could come in.

But for most purposes, money and finance were seen as something that could go wrong, plunging the economy into a disequilibrium of involuntary unemployment as had occurred in the 1930s, and seemed to be re-emerging in the 1970s with the collapse of the Bretton Woods system and the oil crises. Avoiding crises seemed to be the main task of financial policy.

What changed? It is an interesting vignette of intellectual history. First, as more and more data accumulated on national economic growth rates across the world, it became evident that there were large and sustained differences in average growth rates between different countries. This tended to cast some doubt on the usefulness of models that had nothing to say about what might explain such differences. Then theoretical advances emphasizing increasing returns to scale and spillovers from investment in education and technology brought it home to economists that their analytical tools could be useful in analyzing market behavior that could influence the rate of growth of economies on a permanent basis. The intellectual liberation resulting from these theoretical advances -- and the availability of cross-country data -- unleashed a tidal wave of international crosssectional growth regressions (for a survey see Easterly 2001). Each researcher had his or her favorite explanatory variables, many of them mutually correlated, and each (if advanced on its own) seeming to provide considerable explanatory power. But the theories couldn’t all be right.

In an important contribution Levine and Renelt (1992) showed that the data could not discriminate between most of the alternatives. The volume of investment and the level of education provision seemed to be the about the only economic variables robust to the inclusion of alternative candidates. Not that the other variables were necessarily irrelevant, but they were too closely correlated among each other to be able to tell which was the driving force and which was merely tagging along. A researcher coming with a strong prior belief about any of some two dozen causal variables could find confirmation in the data, but what such a researcher could not do was refute an opponent who believed that another of the collection of supposed causal variables was instead the truly significant one. Levine and Renelt seemed to put paid to the prospect that a robust causal variable would be found.

Then, unexpectedly, from the apparent wreckage of most cross-country growth studies, a neglected variable bubbled to the surface. Almost uniquely, financial depth, when tested 3