Productivity Paradox - Explanations

Explanations

Different authors have explained the paradox in different ways. In his original article, Brynjolfsson (1993) identified four possible explanations:

• Mismeasurement: the gains are real, but our current measures miss them;
• Redistribution: there are private gains, but they come at the expense of other firms and individuals, leaving little net gain;
• Time lags: the gains take a long time to show up; and
• Mismanagement: there are no gains because of the unusual difficulties in managing IT or information itself.

He stressed the first explanation, noting weaknesses with then-existing studies and measurement methods, and pointing out that "a shortfall of evidence is not evidence of a shortfall."

Turban, et al. (2008), mention that understanding the paradox requires an understanding of the concept of productivity. Pinsonneault et al. (1998) state that for untangling the paradox an “understanding of how IT usage is related to the nature of managerial work and the context in which it is deployed” is required.

One hypothesis to explain the productivity paradox is that computers are productive, yet their productive gains are realized only after a lag period, during which complementary capital investments must be developed to allow for the use of computers to their full potential.

Diminishing marginal returns from computers, the opposite of the time lag hypothesis, is that computers, in the form of mainframes, were used in the most productive areas, like high volume transactions of banking, accounting and airline reservations, over two decades before personal computers. Also, computers replaced a sophisticated system of data processing that used unit record equipment. Therefore the important productivity opportunities were exhausted before computers were everywhere. We were looking at the wrong time period.

Another hypothesis states that computers are simply not very productivity enhancing because they require time, a scarce complementary human input. This theory holds that although computers perform a variety of tasks, these tasks are not done in any particularly new or efficient manner, but rather they are only done faster. Current data does not confirm the validity of either hypothesis. It could very well be that increases in productivity due to computers is not captured in GDP measures, but rather in quality changes and new products.

Economists have done research in the productivity issue and concluded that there are three possible explanations for the paradox. The explanations can be divided in three categories:

• Data and analytical problems hide "productivity-revenues". The ratios for input and output are sometimes difficult to measure, especially in the service sector.
• Revenues gained by a company through productivity will be hard to notice because there might be losses in other divisions/departments of the company. So it is again hard to measure the profits made only through investments in productivity.
• There is complexity in designing, administering and maintaining IT systems. IT projects, especially software development, are notorious for cost overruns and schedule delays. Adding to cost are rapid obsolescence of equipment and software, incompatible software and network platforms and issues with security such as data theft and viruses. This causes constant spending for replacement. One time changes also occur, such as the Year 2000 problem and the changeover from Novell NetWare by many companies.

Other economists have made a more controversial charge against the utility of computers: that they pale into insignificance as a source of productivity advantage when compared to the industrial revolution, electrification, infrastructures (canals and waterways, railroads, highway system), Fordist mass production and the replacement of human and animal power with machines. High productivity growth occurred from last decades of the 19th century until the 1973, with a peak from 1929-1973, then declined to levels of the early 19th century. There was a rebound in productivity after 2000. Much of the productivity from 1985-2000 came in the computer and related industries.

A number of explanations of this have been advanced, including:

• The tendency – at least initially – of computer technology to be used for applications that have little impact on overall productivity, e.g. word processing.
• Inefficiencies arising from running manual paper-based and computer-based processes in parallel, requiring two separate sets of activities and human effort to mediate between them – usually considered a technology alignment problem
• Poor user interfaces that confuse users, prevent or slow access to time-saving facilities, are internally inconsistent both with each other and with terms used in work processes – a concern addressed in part by enterprise taxonomy
• Extremely poor hardware and related boot image control standards that forced users into endless "fixes" as operating systems and applications clashed – addressed in part by single board computers and simpler more automated re-install procedures, and the rise of software specifically to solve this problem, e.g. Norton Ghost
• Technology-driven change driven by companies such as Microsoft which profit directly from more rapid "upgrades"
• An emphasis on presentation technology and even persuasion technology such as PowerPoint, at the direct expense of core business processes and learning – addressed in some companies including IBM and Sun Microsystems by creating a PowerPoint-Free Zone
• The blind assumption that introducing new technology must be good
• The fact that computers handle office functions that, in most cases, are not related to the actual production of goods and services.
• Factories were automated decades before computers. Adding computer control to existing factories resulted in only slight productivity gains in most cases.

A paper by Triplett (1999) reviews Solow’s paradox from seven other often given explanations. They are:

• You don’t see computers “everywhere,” in a meaningful economic sense
• You only think you see computers everywhere
• You may not see computers everywhere, but in the industrial sectors where you most see them, output is poorly measured
• Whether or not you see computer everywhere, some of what they do is not counted in economic statistics
• You don’t see computers in the productivity yet, but wait a bit and you will
• You see computers everywhere but in the productivity statistics because computers are not as productive as you think
• There is no paradox: some economists are counting innovations and new products on an arithmetic scale when they should count on a logarithmic scale.