It’s no secret that the iPhone is a revolutionary technology. One would expect key economic data to reflect major efficiencies gained from such technologies in our jobs and everyday lives.
Yet such an impact is hard to see in aggregate economic data, despite the apparently obvious surge in technology around us. Aggregate data show overall economic performance since the crisis has been mediocre, and even measures of productivity appear modest at best. This is puzzling.
Using a microscope, we can get a sense of how significant the impact of technology can be.
The iPhone, for instance, clearly shows up in a subset of world trade flows. The two charts below show how the September release of a new iPhone model coincides with apparent spikes in both Japanese imports of telephones from China (see the chart on the left) and Taiwanese export orders for communications equipment (see the chart on the right). The navy dots signify the first release date of each iPhone model. It’s difficult to tie the spikes conclusively to the iPhone, but it does appear that this seasonal pattern of a spike around September did not occur before the iPhone’s first introduction in 2007, our analysis shows.
Our analysis focused on export data from Taiwan and China because they are the two countries most significantly involved in iPhone production. Taiwan makes many iPhone parts; China assembles the final phone. We also focused on import data from Japan, since Japan’s data break out telephone products. Other countries’ import data, such as that of the U.S., don’t have that kind of granularity.
The iPhone constitutes only a small fraction of trade flows and can easily get lost in larger datasets. For instance, telephone equipment makes up less than 20% of Japan’s imports from China in a typical iPhone release month, and imports from China make up less than 30% of Japan’s total imports for a given iPhone release month. And seasonal adjustment of aggregate trade data may mask the blips from iPhone releases, given that these have tended to occur around September.
But the economic impact of technology is not about the number of devices traded. The real puzzle is why we don’t see the broader efficiency impact technology should bring. It has been argued that this disconnect may reflect some mismeasurement in official statistic calculations, as the ever-improving quality of technology has been difficult to capture. Keeping up with the measurement of quality improvements is challenging, but statistical agencies are making ongoing adjustments, and we expect their progress on this front will continue.
For example, the U.S. Bureau of Labor Statistics, which compiles the Consumer Price Index, implemented a methodological update this year relating to charges for unlimited wireless data. The result: a large step-change downward in (measured) prices, as the data captured the technology’s downward influence on inflation pressures. Something even more important could be at play: how we adapt to embrace technology and make more efficient use of it. This takes time and investment, and factors such as uncertainty about the future or recessions can slow down this process. A recent paper by Federal Reserve economists makes the argument that structural and cyclical headwinds, such as demographic factors and weak aggregate demand post the financial crisis, may have prevented new technologies’ influence from lifting productivity. Ultimately, accessing the impact of technology in aggregate data is not just about counting the number of iPhones sold or adjusting the data methodology to account for quality improvements. It’s also about examining ourselves.
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