David Brooks has written a great column arguing that technocratic management of the economy leaves something to be desired. His particular focus is on the growing disillusion with attempts to manage our response to the economic crisis. My favorite part is this:

The liberal technicians have an impressive certainty about them. They have amputated those things that can’t be contained in models, like emotional contagions, cultural particularities and webs of relationships. As a result, everything is explainable and predictable. They can stand on the platform of science and dismiss the poor souls down below.

Yet over the past 21 months, it has been harder to groove to their certainty.

In practice, the problem of excessive abstraction that Brooks identifies worsens as we try to evaluate the effects of proposed interventions and programs over years and decades, rather than months and quarters. 

Consider the role of very low interest rates in stimulating economic growth in the software industry, where I work. Easy monetary policy, along with various other forms of stimulus, has likely worked as advertised, at least in part, stimulating some extremely difficult to quantify general economic growth, which has in turn created demand for enterprise software, among many other things. And low interest rates probably resulted in certain additional development projects within large companies being greenlighted, thanks to a lower discount rate. Many traditional large enterprise software companies have large cash hoards, but they mostly use them to finance acquisitions, not to expand capacity and increase aggregate output. Why this is so turns out to be important for understanding the potential effects of this policy on the industry.

A series of technical/business-model innovations — most prominently, Software-as-a-Service (SaaS) and open-source — is transforming the software industry, but the rational incentive of the incumbent managers is to suppress the innovations or, at best, slow-walk them and channel them in directions consistent with their current business models. So, right now, entrepreneurs, incumbent company management teams, and the capital markets are jockeying to seize the potential value that these innovations are unleashing. 

Large company growth will disproportionately come from adding not just more of the current “capacity” (mostly people) but the different kinds of capacity that are required for these new business models. For example, more software engineers trained in traditional languages and accustomed to working on large, structured projects are less useful for growth than engineers with experience in web-focused technologies and used to working in a so-called agile development environment. And it’s not as simple as incumbent companies simply changing their hiring specs; it’s difficult to transform settled company expertise, systems, compensation plans, culture, and so forth to operate in this new environment. 

Large software companies do not have plans on the drawing boards for the moral equivalent of a new ball-bearing factory if only demand were higher. Their primary strategic problem, is this regard, is that they don’t know how to build the new capacity. But the existence of the competitive threat forces their hand, and so they buy the new kind of capacity in the form of corporate acquisitions.

One major effect of a Fed policy of easy money, then, is that large software companies can borrow lots of money cheaply and use it to acquire entrepreneurial companies that usually require more equity financing than debt financing. This does not add capacity to the world, it simply transfers management control over some very important assets from entrepreneurs to incumbents. 

Will this lead to higher or lower economic output in 2015, 2020, and 2030? I don’t know. Then again, neither does anybody else.

The example I’ve highlighted focuses on the complications in trying to forecast the effect of lower interest rates on the software industry, given the emergence of new technologies and business models. Of course, there are many other complicated effects impacting everything from the feasibility of leveraged buy-outs to the re-opening of the IPO window. Each will advantage or disadvantage some parts of the industry at the expense of others. And stimulus can be anything from low interest rates to running deficits to quantitative easing, and the software industry is one small part of the overall economy — this is an example of one complication for one type of stimulus in one industry.

Where is any of this complexity captured in the econometric models that purport to explain how fiscal deficits, interest rates, and quantitative easing are driving everything from car dealerships to television broadcasters to consumers of dog food, all of whom face their own unique dynamics? Without it, I doubt the ability of any model to forecast the long-run impacts of a multi-trillion-dollar program to intervene in the economy in the name of creating self-sustaining growth in the long term. All I can say with confidence is that if you believe, as I do, that a good rule of thumb is “Over any sustained period, markets supported by an appropriate culture will do a better job than politicians of allocating resources to generate high economic growth,” then at some point the distortions created by such a policy would likely outweigh any benefits.

In an emergency, the idea of stimulus is not an inherently bad one; in fact, I have advocated it in certain circumstances. But it is inherently dangerous. Its effects are, at best, only loosely predictable in the short run, it is addictive, and it is likely pernicious if sustained. 

From at least the time of J. S. Mill, the fundamental methodology of economics has been to use introspection to develop theories about human behavior, systematize them into theories, and then try to compare the predictions of these theories to the real world. For reasons I have gone into at boring length, it is very difficult to conduct reliable tests of useful, non-obvious rules that predict the effects of our proposed interventions in economics and other social sciences. The big problem with most economic theories that claim to be able to guide our interventions with confidence is not usually that the causal pathway they propose is incorrect, but that it’s radically incomplete. It is typically one of an all-but-innumerable array of interconnected causes in a maze of causation that produces highly unpredictable outcomes.  Despite the confident assertions of academicians, the Law of Unintended Consequences remains in force.