Intangible Assets: Computers and Organizational Capital

Martin Neil Baily:

This paper is the latest chapter in a series of important papers, by Erik Brynjolfsson, Lorin Hitt, and Shinkyu Yang and other coauthors, exploring the relationship between computers, on the one hand, and economic performance (as measured by productivity and firm market value), on the other. The basic message of this body of work is that computers are very important, but that the success of companies that use them depends on how they use them and on what other aspects of the production process have changed.

This is a good story. It may very well be the right story, and it is a very different story from the one that we heard in the 1980s, when productivity growth was weak, and everybody was wondering why computers were not contributing to the economy. For example, a student at MIT's Sloan School of Management, Gary Loveman, found that computer capital had had little or no effect on productivity.¹ In production function estimates, computer capital had a coefficient smaller than those on other kinds of capital.

Despite my sympathy with the overall story, I will raise some concerns about the results in this paper. The paper comes at a time when economists, business decisionmakers, and policymakers are reassessing the impact of information technology and its potential to fuel future economic growth and increases in equity valuation. After the computer paradox of the 1980s, there was an extravagant enthusiasm for IT in the 1990s, which in turn was followed by a technology bust, led by the collapse of the dot-coms and followed by the collapse of spending on computers and communications equipment.

The basic motivation of the paper, presented in the authors' table 1, is their earlier finding that $1 worth of computer capital appears to contribute $15 or so to a company's market value. As the authors say, however, this cannot be a structural result. Since computers are not rationed, this coefficient, if taken literally, implies that companies could boost their market value enormously just by buying computers. In the late 1990s some companies may have actually thought that buying computers would boost their market value, because indeed they bought them like there was no tomorrow. But more seriously, this is not a sensible implication and motivates attempts to explain what must be a nonstructural coefficient. Computer capital must be proxying for another variable, an unobserved characteristic of the companies.

One possibility is that the market did not provide a rational evaluation of the companies in this sample. And the market's overvaluation may have been associated with companies that were doing interesting things that involved buying a lot of computers. Computer capital may have been a proxy for irrational investor enthusiasm for the earnings growth potential of some companies. The authors argue that this cannot be the case because their data end in 1997, whereas the technology bubble mostly occurred after 1997. But the possibility of irrationality in the market was there even earlier. Federal Reserve Chairman Alan Greenspan warned of irrational exuberance when the Dow Jones Industrial Average was only at 6,000. It would be surprising if irrational valuation could explain the authors' whole set of results, but it may explain part of the puzzle. I wonder how computer capital would show up in a similar regression on company valuation today.

The authors make the case that the missing variable is organizational capital. This ties in with their overall conclusion that it is not the computers themselves but what you do with them that counts. The computer is a building block of a much larger asset of the firm, namely, the whole organizational structure that it uses to produce and market its products or services. The authors therefore construct a measure of organizational capital based on observed characteristics of the companies in their sample.

Simply adding this variable to the valuation regression lowers the coefficient on computer capital from 14.6 to 11.5 (see their table 7, columns 7-1 and 7-2). But that is not a large change. The authors argue that the key to further reducing...