Gillian Tett of the FT published a Comment today: ‘A puzzle Yellen cannot solve with a rate rise’. The column reports on the long standing data that suggests American productivity has been muted for some time, and for some reason economists and other mandarins have no idea why. The good news, as reported by Gillian, is that recent data from the Bureau of Labor and Statistics (BLS) suggest that productivity grew 2.2% on an annualized basis in the third quarter of this year. This data point may be one of several that helps Janet Yellen, Fed Chair, decide to raise interest rates at next week’s Fed meeting, the first raise since 2006. But what is wrong with productivity? The column explores the possible reasons for the apparent loss of productivity – and they have been explored before. There are three of them.
First, the data might be wrong. The economy no longer looks like it used to – with a bunch of farmers farming or factory workers producing widgets. The economy is as much a service economy, and increasingly now a digital, information or thought-based economy. The number-crunchers at the BLS (and everywhere else, for that matter) have had great difficulty tracking data that explains how work changes and how input vary with outputs. I have analyzed this issue recently (See US Productivity Grew by more than 16% in the last quarter, up from 11% in the previous – Newspaper headline, April 28th, 2024) and it is likely that changes in how the BLS calculates productivity might make this issue even more complex.
Second, the data might be right but something fundamental has changed in the economy to limit growth. This is somewhat tied to the idea that poor skills, exhausted infrastructures, and/or government regulation all conspire to prevent or limit changes needed to drive productivity. This has an intuitive ring to it but it is hard to demonstrate.
The third idea is that there is a long-delayed build up of innovative changes that have yet to feed through to productivity improvements and any data the BLS collect. This idea happens to align nicely with some thinking I have been doing on the topic. I suspect that some technologies, for example, offer short-term improvement to productivity, while others provide more long-term productivity opportunities that are not seen in the data until much, much later. I looked at this idea recently in How IT-based productivity might evolve in the next few years.
I believe all three reasons have some truth to them, but I think the third is the ultimate source that bleeds into the other two. Being close to technology and being an IT analysts I can see, first hand, many of the different technologies adopted by organizations and the different ways they are used. For example email is no longer a driver of productivity. It offered an initial opportunity to drive increased output (communication) for a given amount of input (hours worked). But email has not changed in years; all we do is do more of the same stuff, over the dinner table, in bed, while sitting on the train. So growth increases but without any change productivity.
Likewise, a lot of innovations in and around the smart phone are not actually addressing how work is done. Many innovations just move work around (the fabled self service economy which does, alone, not drive productivity) or tackle non-core work.
Some other innovations require investment up front but spin-out other innovations much later. Look at software. Some software is like email (what I might call a dead-end innovation) yet other software creates an innovation platform, like big data or IOT, that offers the opportunity for subsequent innovation that drives even more productivity – but some time, even years, later. I don’t mean that firms acquire a platform – I mean firms acquire some technology for a specific reason and in so doing, almost unknown to them, that technology or method ends up creating a new line of work or thinking that others leverage for a subsequent innovation.
This is key (for me) to the productivity paradox for it is this that supports the second and third reasons above: something has fundamentally changed – the nature of innovation and its potential, in the digital economy, for yielding economic value over time. I also feel there are some gaps in how the BLS perceives how IT innovation works, and so they don’t collect the right data. The BLS treats software is like IP and so it is measured accordingly. There is no recognition how some software helps process work faster (cuts costs) and how some software drives new business models (new revenue) and both create vastly different kinds of potential future innovation.
The bottom line is that all three reasons are valid – and it is the nature of IT innovation and the different ways in which IT innovation yields economic value – that is at the heart of the current productivity paradox. For more on this, see my last blog: How IT-based productivity might evolve in the next few years.
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