Productivity in the US?

Sometimes, by pure coincidence, I come across two articles on related subjects but which seem to be entirely contradictory. In this case, two articles discuss productivity in the US, and (in some respects) are at odds with one another.

The first article appears in the Atlantic, and says the following:

In the past decade, the flow of goods emerging from U.S. factories has risen by about a third. Factory employment has fallen by roughly the same fraction. The story of Standard Motor Products, a 92-year-old, family-run manufacturer based in Queens, sheds light on both phenomena. It’s a story of hustle, ingenuity, competitive success, and promise for America’s economy. It also illuminates why the jobs crisis will be so difficult to solve.

And later:

Yet the success of American manufacturers has come at a cost. Factories have replaced millions of workers with machines. Even if you know the rough outline of this story, looking at the Bureau of Labor Statistics data is still shocking. A historical chart of U.S. manufacturing employment shows steady growth from the end of the Depression until the early 1980s, when the number of jobs drops a little. Then things stay largely flat until about 1999. After that, the numbers simply collapse. In the 10 years ending in 2009, factories shed workers so fast that they erased almost all the gains of the previous 70 years; roughly one out of every three manufacturing jobs—about 6 million in total—disappeared. About as many people work in manufacturing now as did at the end of the Depression, even though the American population is more than twice as large today.

It is actually a very interesting article in general (the main subject is employment in manufacturing), and worth a read. In support of the statistics in productivity, it also gives some examples of how new technology is reducing the demand for labour. On the other hand, we have an article from the Washington Monthly (not a publication I have come across before), which has the following to say:

[...] both of these corporate strategies— domestic productivity improvements and global supply chain management—show up as productivity gains in U.S. economic records. When federal statisticians calculate the nation’s economic output, what they are actually measuring is domestic “value added”—the dollar value of all sales minus the dollar value of all imports. “Productivity” is then calculated by dividing the quantity of value added by the number of American workers. American workers, however, often have little to do with the gains in productivity attributed to them. For instance, if Company A saves $250,000 simply by switching from a Japanese sprocket supplier to a much cheaper Chinese sprocket supplier, that change shows up as an increase in American productivity—just as if the company had saved $250,000 by making its warehouse operation in Chicago more efficient.

The article suggests that the statistics on productivity try to account for this kind of problem, but also that there are many blind spots, such as the following:

Consider refrigerators, for example, like the ones made in the Whirlpool plant mentioned earlier. Refrigerator imports from lower-cost countries such as Mexico, Korea, and China now total almost $4 billion annually, up 19 percent since 2007. Clearly these growing imports are displacing domestic production and employment— but how much? Today the official statistics treat imported refrigerators as if they cost the same as U.S.-made refrigerators, so that $4 billion of imports is assumed to displace $4 billion of domestic production. But a more accurate measure would take into account the lower cost of Chinese- or Mexican-made appliances, so that $4 billion of imports might really displace $5 billion, $6 billion, or even $8 billion of domestic production. Unfortunately, the BLS does not track the relative price of imported and domestic fridges—or almost any other product, for that matter. Scale this problem up by a factor of a million, and you begin to see how pervasive import price bias really is. 

The interesting contrast in the articles is that it is quite likely that both articles are, in some degree, correct. There has undoubtedly been significant improvements in US productivity, as exemplified in the examples given in the Atlantic article, but the argument in the Washington Monthly that the statistics flatter the situation appears to be convincing. The Washington Monthly goes on to draw some conclusions and suggestions in light of the flattering figures, and you may or may not agree with these. However, the is one point that is made that I think is particularly important, which is that the the inaccurate figures on productivity shape the debate on economics in the US:

Indeed, our misleadingly high productivity figures help enable a whole political culture of confusion and complacency. Only after we accept that the official figures have led us astray can we hold a true political debate about what type of economy we want to have.

The point is well made, and highlights the potential dangers in statistics. It is a problem that anyone interested in economics continually confronts, which is to try to understand what we are looking at when we see the statistics that are so frequently cited by the media, policy-maker and economists. Regular readers will know that one of my concerns is the concept of GDP, which is particularly problematic. However, the problem seems to be widespread. One particularly interesting website that addresses the problems is Shadow Government Statistics (SGS), which gives alternatives to official US statistics. For example, for unemployment in the US, they have this to say about their methodology for unemployment rates:

The seasonally-adjusted SGS Alternate Unemployment Rate reflects current unemployment reporting methodology adjusted for SGS-estimated long-term discouraged workers, who were defined out of official existence in 1994. That estimate is added to the BLS estimate of U-6 unemployment, which includes short-term discouraged workers.

The U-3 unemployment rate is the monthly headline number. The U-6 unemployment rate is the Bureau of Labor Statistics’ (BLS) broadest unemployment measure, including short-term discouraged and other marginally-attached workers as well as those forced to work part-time because they cannot find full-time employment.

The methodology produces this chart of current US unemployment rates:






The contrast with official statistics that are reported so widely is quite dramatic. More differences can be found in SGS for many of the statistics that are so widely reported in the media.

The problem is that, even though we might be aware of potential problems with statistic x and statistic y (for example as I am aware of the problems with GDP) we are often unaware of the problem with other statistics that we take at face value. Up to the point that I read the Washington Monthly article, I would have accepted the statistics on productivity in the US without any particular concern. The problem that this raises is to what statistics should be readily accepted and which should be questioned. In pragmatic terms, it is not possible to investigate the source and methodology for every statistic that we read (and perhaps later quote).

That all statistics should be approached with caution is not a new insight. Although it is wrongly attributed to Disraeli, the famous quote of 'lies, damned lies and statistics' is rightly widely cited. The only way around the problem, it seems, is to continuously scan the environment for critiques of the statistics that are used. However, like all of those interested in economics, I will nevertheless use statistics that might be problematic, and do so in the belief that they are both meaningful and accurate, only to find at a later date that they are not as meaningful as they first appeared.

The real worry that arises from this is that the critiques statistics do not generally get a wide airing, and that dubious statistics remain as the accepted version of reality. If the understanding of the productivity statistics in the US are as problematic as suggested (and my view is that it is a convincing case), the statistics will indeed encourage complacency in policy formulation, and in the media, and in the electorate. More importantly if thinking of the average politician, will they be looking for alternative views on statistics, or will they simply accept what is seen as 'official'? I would suggest that only the most contrary politician might question the official statistics, and such a politician is likely to be sidelined, or seen as an outsider (or out of the mainstream).

I have been trying to think of politicians who do indeed argue against the official economic statistics, and cite the critiques of the official statistics, and have come up with a blank (at least for the UK, examples welcomed if you can think of any - there may be obvious examples I have not thought of). I suspect that it is only a very brave politician who might go against the 'official', for fear of being regarded as a 'crank'. This will, in turn influence economic policy ideas, as a policy typically requires some kind of statistical basis. If unable to cite alternatives to the 'official' statistics, or those that are widely accepted, then any policy proposed will be dead in the water from the outset. Official statistics might be compared to a super-tanker that takes miles to turn around through sheer momentum. Even where a valid critique of the statistics appears, they official statistics appear to be unstoppable through sheer momentum. 


The point is this. Assuming that the Washington Monthly critique of productivity statistics is correct, will the critique make any difference whatsoever? I have cited the critique, and I am sure some others will do so. However, the limited discussions of the article will be unlikely to halt the super-tanker of the official statistics, and might at best nudge them into a slight change of course (however, perhaps the Washington Monthly is more influential than I give it credit for?). Everyone will continue to cite the official statistics, and everyone will continue to act on the basis of the 'reality' that the statistics present, and policy will therefore be founded in fundamental errors. My worry is this; that it takes a crisis to turn these super-tankers around. Only then will the critiques that have been presented actually gain traction.

As ever, comments welcomed.

Note: Apologies for the delay in the publication of some of the comments in the last post. This was due to my own administrative error.