Publications

Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens’ decisions and outcomes1. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals2. The lack of comparability of such individual investigations limits their potential to inform policy. Here, to address this limitation and accelerate the pace of discovery, we introduce the megastudy—a massive field experiment in which the effects of many different interventions are compared in the same population on the same objectively measured outcome for the same duration. In a megastudy targeting physical exercise among 61,293 members of an American fitness chain, 30 scientists from 15 different US universities worked in small independent teams to design a total of 54 different four-week digital programmes (or interventions) encouraging exercise. We show that 45% of these interventions significantly increased weekly gym visits by 9% to 27%; the top-performing intervention offered microrewards for returning to the gym after a missed workout. Only 8% of interventions induced behaviour change that was significant and measurable after the four-week intervention. Conditioning on the 45% of interventions that increased exercise during the intervention, we detected carry-over effects that were proportionally similar to those measured in previous research3,4,5,6. Forecasts by impartial judges failed to predict which interventions would be most effective, underscoring the value of testing many ideas at once and, therefore, the potential for megastudies to improve the evidentiary value of behavioural science.

This paper proposes a framework for studying competitive mixed bundling with an arbitrary number of firms. We examine both a firm's incentive to introduce mixed bundling and equilibrium tariffs when all firms adopt the mixed-bundling strategy. In the duopoly case, relative to separate sales, mixed bundling has ambiguous impacts on prices, profit and consumer surplus; with many firms, however, mixed bundling typically lowers all prices, harms firms and benefits consumers.

This article presents an overview of methods developed for the modeling and control of local coronavirus outbreaks. The article reviews early transmission dynamics featuring exponential growth in infections, and links this to a renewal epidemic model where the current incidence of infection depends upon the expected value of incidence randomly lagged into the past. This leads directly to simple formulas for the fraction of the population infected in an unmitigated outbreak, and reveals herd immunity as the solution to an optimization problem. The model also leads to direct and easy-to-understand formulas for aligning observable epidemic indicators such as cases, hospitalizations and deaths with the unobservable incidence of infection, and as a byproduct leads to a simple first-order approach for estimating the effective reproduction number . The model also leads naturally to direct assessments of the effectiveness of isolation in preventing the spread of infection. This is illustrated with application to repeat asymptomatic screening programs of the sort utilized by universities, sports teams and businesses to prevent the spread of infection.

The Covid 19 pandemic led to a global macroeconomic shock of unprecedented magnitude.
Besides a death toll that, at the time of writing has surpassed 3.3 million, the pandemic
led to the worst peacetime decline in economic activity since the Great Depression.
Central banks reacted quickly, outpacing their responses to the 2007-2009 global financial
crisis in terms of both speed and scope. Central banks in advanced economies deployed
a vast range of tools aimed at guaranteeing that the private sector had continuous access
to credit and also cooperated with fiscal authorities with the objective of limiting the
costs of the fiscal response to the crisis. Central banks in some emerging economies were
also able to adopt expansionary policies and deployed asset purchases without serious
consequences in terms of inflation.

Some economists have argued that money-Önanced Öscal programs, sometimes called ìhe-
licopter money,î may be desirable in economies facing persistent economic weakness and low
ináation. We use a DSGE model to show that such programs could in principle provide signif-
icant stimulus (as in Gali 2020). However, we also show that these programs imply a radical
shift in the central bankís reaction function, and that the near-term stimulus to output and
ináation may be fairly small in the realistic case in which the public does not initially regard
the policy shift as credible, expectations are not very forward looking, and monetary policy
is constrained by the e§ective lower bound. We argue that monetary-Öscal cooperation that
involves a less dramatic shift in monetary policy ñ but more Öscal expansion ñ is likely to be
more e§ective under these circumstances because it would be more credible. Fiscal expansion
makes monetary policy more potent by boosting the equilibrium real interest rate path and
giving monetary policy more room to lower near-term yields, and monetary policy makes Öscal
policy more potent by keeping interest rates low and so limiting crowding out. We conclude by
discussing a number of historical examples of monetary-Öscal cooperation in light of our model.

To create a straightforward scoring procedure based on widely available, inexpensive financial data that provides an assessment of the financial health of a hospital.

This paper develops a new framework for studying multiproduct intermediaries when consumers demand multiple products and face search frictions. We show that a multiproduct intermediary is profitable even when it does not improve consumer search efficiency. The intermediary optimally stocks high-value products exclusively to attract consumers to visit and then profits by selling nonexclusive products that are relatively cheap to buy from upstream suppliers. Relative to the social optimum, the intermediary tends to be too big and stock too many products exclusively. We use the framework to study the design of shopping malls and the impact of direct-to-consumer sales by upstream suppliers on the retail market.

Not one inch. With these words, Secretary of State James Baker proposed a hypothetical bargain to Soviet leader Mikhail Gorbachev after the fall of the Berlin Wall: if you let your part of Germany go, we will move NATO not one inch eastward. Controversy erupted almost immediately over this 1990 exchange—but more important was the decade to come, when the words took on new meaning. Gorbachev let his Germany go, but Washington rethought the bargain, not least after the Soviet Union’s own collapse in December 1991. Washington realized it could not just win big but win bigger. Not one inch of territory needed to be off limits to NATO.

On the thirtieth anniversary of the Soviet collapse, this book uses new evidence and interviews to show how, in the decade that culminated in Vladimir Putin’s rise to power, the United States and Russia undermined a potentially lasting partnership. Prize-winning historian M. E. Sarotte shows what went wrong.

For online resource allocation problems, we propose a new demand arrival model where the sequence of arrivals contains both an adversarial component and a stochastic one. Our model requires no demand forecasting; however, because of the presence of the stochastic component, we can partially predict future demand as the sequence of arrivals unfolds. Under the proposed model, we study the problem of the online allocation of a single resource to two types of customers and design online algorithms that outperform existing ones. Our algorithms are adjustable to the relative size of the stochastic component; our analysis reveals that as the portion of the stochastic component grows, the loss due to making online decisions decreases. This highlights the value of (even partial) predictability in online resource allocation. We impose no conditions on how the resource capacity scales with the maximum number of customers. However, we show that using an adaptive algorithm—which makes online decisions based on observed data—is particularly beneficial when capacity scales linearly with the number of customers. Our work serves as a first step in bridging the long-standing gap between the two well-studied approaches to the design and analysis of online algorithms based on (1) adversarial models and (2) stochastic ones. Using novel algorithm design, we demonstrate that even if the arrival sequence contains an adversarial component, we can take advantage of the limited information that the data reveal to improve allocation decisions. We also study the classical secretary problem under our proposed arrival model, and we show that randomizing over multiple stopping rules may increase the probability of success.

Careers—defined as the individually perceived sequence of attitudes and behaviors associated with work-related experiences and activities over the span of a person’s life (Hall, 2002)—inherently and inextricably involve the role of time. At the individual level, time—and particularly change over time—is essential for understanding adults’ lives and careers, as noted by both traditional adult development theories.

We develop a theoretical model of optimal growth in two-sided markets. The model posits that market output (number of transactions) is a function of the stock of supply and demand. This market output is modeled using a homogeneous production function, which can have increasing or decreasing returns to scale. The supply and demand stock levels follow a growth model in which the rate of growth at each point in time is a function of both the surplus each side of the market receives and the attrition of supply and demand (supply and demand lifetimes). The surplus can be apportioned between the two sides of the market by changing the price paid to sellers and the price charged to buyers, which we assume the platform controls. Through these price levers, the platform can pay subsidies to one or both sides of the market. We investigate the behavior of optimal market growth, including the point at which the market becomes self-sustaining and the long-run optimal size of the market. We characterize the optimal balance between supply and demand as the market size grows and determine optimal subsidy policies that maximize discounted total profit. For the case of both increasing and decreasing returns without price constraints, we show the optimal policy is to grow using an impulse of subsidy spending (a subsidy shock) to move the market immediately to its optimal long-run size. This result is consistent with the race to growth observed in many two-sided markets like ride-sharing.

We study how the popularity of an incumbent president can influence the generosity of the relief effort in response to a natural disaster as reflected in the federal SBA loan program. We document that the loan amounts relative to reported damages are higher when popularity of the incumbent president is lower. This result is consistent with the catering view of disaster relief where the president approves higher amounts when his popularity is at a low point but may hold his ground when his popularity is high. We find that this catering behavior is amplified when public sentiment regarding the disaster is high (as measured by a metric designed from Google search volume).When the public sentiment is very high even a popular president may approve high disaster aid, further supporting our catering view. We also examine the effect of other factors that increase the political importance of the disaster (e.g., swing county, campaign donations, first term, and number of presidential visits) and find evidence of increased catering. Finally, there is some evidence of interaction of catering with credit scoring rules, suggesting discrimination in loan provision in favor of richer counties and against counties with a high percentage of African Americans. Our findings point to a new venue for politics in climate change in the area of disaster relief.

Drawing upon the comprehensive survey of the American Jewish population conducted in 2013 by the Pew Research Center, we estimate the trajectory of the population and its denominational segments, 50 years into the future. Our analysis relies upon estimates of the population in 2013, fertility rates, marriage behaviors, and denominational switching patterns as inputs into a model of population demographics. We project that over the next 50 years, the demographics of U.S. Jews are likely to change dramatically, with the share of the population that is Orthodox rising from 12 percent to 29 percent, with their share of the child population increasing from 22 percent to 51 percent. Among Reform and Conservative Jews, the number of 30- to 69-year-olds is projected to drop by approximately 46 percent over this period. We also project a slight drop in the total Jewish population followed by a recovery propelled by the growing Orthodox population.