Measuring Welfare with Massive Online Choice Experiments: A Brief Introduction

GDP is a measure of production, and yet it is widely used as a proxy for well-being. It is particularly ill-suited for assessing the contributions of digital goods which are free to consumers and thus excluded from GDP measures. This underscores the need to develop new measures of well-being which can assess not only the contributions of digital goods but also welfare more generally. In Brynjolfsson, Eggers, and Gannamaneni (2017), we propose a new way of measuring consumer welfare using massive online choice experiments. This brief paper motivates the need for such an approach and introduces the method

The Efficiency of U.S. Public Space Utilization During the COVID-19 Pandemic

The COVID-19 pandemic has called for and generated massive novel government regulations to increase social distancing for the purpose of reducing disease transmission. A number of studies have attempted to guide and measure the effectiveness of these policies, but there has been less focus on the overall efficiency of these policies. Efficient social distancing requires implementing stricter restrictions during periods of high viral prevalence and rationing social contact to disproportionately preserve gatherings that produce a good ratio of benefits to transmission risk. To evaluate whether U.S. social distancing policy actually produced an efficient social distancing regime, we tracked consumer preferences for, visits to, and crowding in public locations of 26 different types. We show that the United States’ rationing of public spaces, postspring 2020, has failed to achieve efficiency along either dimension. In April 2020, the United States did achieve notable decreases in visits to public spaces and focused these reductions at locations that offer poor benefit-to-risk tradeoffs. However, this achievement was marred by an increase, from March to April, in crowding at remaining locations due to fewer locations remaining open. In December 2020, at the height of the pandemic so far, crowding in and total visits to locations were higher than in February, before the U.S. pandemic, and these increases were concentrated in locations with the worst value-to-risk tradeoff

Surfacing Norms to Increase Vaccine Acceptance

The authors show that accurate information about descriptive norms can increase intentions to accept a vaccine for COVID-19. They show that these effects are largely consistent in the 23 included countries and are concentrated among people who were otherwise uncertain about accepting a vaccine

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

How should we measure the digital economy?

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, June, 2020Cataloged from PDF version of thesis.Includes bibliographical references.Gross domestic product (GDP) measures production and is not meant to measure well-being. While many people nonetheless use GDP as a proxy for well-being, consumer surplus is a better measure of consumer well-being. This is increasingly true in the digital economy where many digital goods have zero price and as a result, the welfare gains from these goods are not reflected in GDP or productivity statistics. Chapter 1 proposes a way of directly measuring consumer's economic well-being using massive online choice experiments. It finds that digital goods generate a large amount of consumer surplus that is currently not captured in GDP. For example, the median Facebook user needed a compensation of around $48 to give it up for a month. Building up on these results, Chapter 2 extends the GDP framework to include welfare gains from new and free goods and construct a new metric called GDP-B, where B stands for benefits. It finds that including the welfare gains from Facebook would have added between 0.05 and 0.11 percentage points to GDP-B growth per year in the US. Chapter 3 proposes a way of measuring network effects on multi-sided platforms using choice experiments. It also models digital platforms allowing for heterogeneity in demand elasticity and network effects across users of different types. It then calibrates the model using an empirical application to Facebook and simulates six different taxation and regulatory policies. Chapter 4 looks at the impact of social media on subjective well-being and academic performance through a randomized controlled trial of University students. Chapter 5 summarizes the research agenda moving forward and concludes with a framework for measuring different aspects of well-being in the digital economy.by Avinash Collis.Ph. D.Ph.D. Massachusetts Institute of Technology, Sloan School of Managemen

Using massive online choice experiments to measure changes in well-being

Gross domestic product (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 wellbeing, 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 laboratory participants receive monetary compensation if and only if they forgo goods for predefined periods. For example, the median user needed a compensation of about $48 to forgo Facebook for 1 mo. 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

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

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

Rationing Social Contact During the COVID-19 Pandemic: Transmission Risk and Social Benefits of US Locations

To prevent the spread of COVID-19, some types of stores and gathering places have been shut down while others remain open. The decision to shut down one type of location and leave another open constitutes a judgement about the relative danger and benefits of those locations. Using location data from a large sample of smartphones, nationally representative consumer preference surveys, and government statistics, we measure the relative transmission risk benefit and social cost of closing about thirty different location categories in the US. Our categories include types of shops, entertainments, and public spaces. Our main analysis ranks twenty-six categories by those which should face stricter regulation via dominance across eight dimensions of risk and importance and through composite indexes. We find that from February to March, there were larger declines in visits to locations that our measures imply should be closed first. We hope this analysis will help policymakers decide how to reopen their economies