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HBR (2019), "How Should We Measure the Digital Economy?" by Brynjolfsson and Collins




GDP is often used as a proxy for how the economy is doing; however, GDP captures only the monetary value of all final goods produced in the economy. But there is another way, at least in theory, to measure consumer well-being. That measure is called consumer surplus, which is the difference between the maximum a consumer would be willing to pay for a good or service and its price.

When it comes to measuring the value of digital businesses, studies show that advertising revenues and consumer surplus are not always correlated: People can get a lot of value from content that doesn’t generate much advertising, such as Wikipedia or email. So it is a mistake to use advertising revenues as a substitute for consumer surplus. The effect of consumer surplus is even stronger in  categories of digital goods. These categories do not have comparable off-line substitutes, and many people consider them essential for work and everyday life. When Brynjolfsson and Collins asked participants how much they would need to be compensated to give up an entire category of digital goods, they found that the amount was higher than the sum of the value of individual applications in it.

Brynjolfsson and Collins worked with Erwin Diewert, Felix Eggers, and Kevin Fox to develop a method for measuring the benefits associated with the digital economy. GDP-B is an alternative metric that supplements the traditional GDP framework by quantifying contributions to consumer well-being from free goods. Policy makers, managers, and economists can estimate these contributions using the relatively inexpensive method we described earlier: Conduct large-scale surveys asking respondents how much they would need to be paid to give up a given good for a certain period of time and then validate those results by running smaller-scale studies with real monetary incentives. On a spectrum ranging from traditional macroeconomic indicators such as GDP and productivity, which tend to be very precise, to well-being indicators such as happiness, which are often coarser, the GDP-B metric lies somewhere in the middle.

The authors note that this method has two important limitations. First, GDP-B estimates are still far from comprehensive and are not as precise as the traditional GDP measure. Brynjolfsson and Collins believe they will need to include far more goods and conduct more online choice experiments for each to get a more accurate assessment of the full contribution that free goods make to the economy.

Second, like traditional GDP, this measure does not capture some of the potential negative externalities associated with goods and services, including online platforms. Several studies suggest that social media platforms can lead to addictive behavior and that internet use and smartphones may have a negative impact on happiness and mental health. Others have argued that some digital goods are damaging to social cohesion or political discourse or impose costs on consumers in the form of lost privacy. For now, the GDP-B metric captures only the personal benefits and costs associated with goods, as assessed (perhaps imperfectly) by the participants in online choice experiments, not the social costs and benefits. Brynjolfsson and Collins are working on addressing those limitations, as are others. For example, researchers have developed a range of useful methods to quantify subjective aspects of well-being, including happiness and life satisfaction. Although a survey of leading macroeconomists suggests that such metrics are not yet as precise, comparable, or reliable as “hard” metrics such as GDP are, it’s a step in the right direction.

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