Using Massive Online Choice Experiments to Measure Changes in Well-being

Erik Brynjolfsson, Avinash Collis and Felix Eggers

No akqhn, OSF Preprints from Center for Open Science

Abstract: GDP and derived metrics such as productivity have been central to our understanding of economic progress and well-being. In principle, changes in consumer surplus provide a superior, and more direct, measure of changes in well-being, especially for digital goods. In practice, these alternatives have been difficult to quantify. We explore the potential of massive online choice experiments to measure consumer surplus. We illustrate this technique via several empirical examples which quantify the valuations of popular digital goods and categories. Our examples include incentive compatible discrete choice experiments where online and lab participants receive monetary compensation if and only if they forgo goods for pre-defined periods. For example, the median user needed a compensation of about $48 to forgo Facebook for one month. Our overall analyses reveal that digital goods have created large gains in well-being that are not reflected in conventional measures of GDP and productivity. By periodically querying a large, representative sample of goods and services, including those which are not priced in existing markets, changes in consumer surplus and other new measures of well-being derived from these online choice experiments have the potential for providing cost-effective supplements to the existing National Income and Product Accounts.

Date: 2019-04-09
New Economics Papers: this item is included in nep-big, nep-dcm, nep-hap and nep-pay
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Citations: View citations in EconPapers (52)

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Related works:
Journal Article: Using massive online choice experiments to measure changes in well-being (2019)
Working Paper: Using Massive Online Choice Experiments to Measure Changes in Well-being (2019)
Working Paper: Using Massive Online Choice Experiments to Measure Changes in Well-being (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:akqhn

DOI: 10.31219/osf.io/akqhn

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