Knowledge that Transforms

Many e-commerce platforms use buyers’ personal data to intermediate their transactions with sellers. How much value do such intermediaries derive from the data record of each single individual? We characterize this value and find that one of its key components is a novel externality between records, which arises when the intermediary pools some records to withhold the information they contain. Our analysis has several implications about compensating individuals for the use of their data, guiding companies’ investments in data acquisition, and more broadly studying the demand side of data markets. Our methods combine modern information design with classic duality theory and apply to a large class of principal-agent problems.

The decisions of judges, lenders, journal editors, and other gatekeepers often lead to significant disparities across affected groups. An important question is whether, and to what extent, these group-level disparities are driven by relevant differences in underlying individual characteristics or by biased decision makers. Becker (1957, 1993) proposed an outcome test of bias based on differences in post-decision outcomes across groups, inspiring a large and growing empirical literature. The goal of our paper is to offer a methodological blueprint for empirical work that seeks to use outcome tests to detect bias. We show that models of decision making underpinning outcome tests can be usefully recast as Roy models, since heterogeneous potential outcomes enter directly into the decision maker’s choice equation. Different members of the Roy model family, however, are distinguished by the tightness of the link between potential outcomes and decisions. We show that these distinctions have important implications for defining bias, deriving logically valid outcome tests of such bias, and identifying the marginal outcomes that the test requires.

Many real-life settings of individual choice involve social interactions, causing targeted policies to have spillover effects. This article develops novel empirical tools for analysing demand and welfare effects of policy interventions in binary choice settings with social interactions. Examples include subsidies for health-product adoption and vouchers for attending a high-achieving school. We show that even with fully parametric specifications and unique equilibrium, choice data, that are sufficient for counterfactual demand prediction under interactions, are insufficient for welfare calculations. This is because distinct underlying mechanisms producing the same interaction coefficient can imply different welfare effects and deadweight-loss from a policy intervention. Standard index restrictions imply distribution-free bounds on welfare. We propose ways to identify and consistently estimate the structural parameters and welfare bounds allowing for unobserved group effects that are potentially correlated with observables and are possibly unbounded. We illustrate our results using experimental data on mosquito-net adoption in rural Kenya.

This paper analyses how limits to the complexity of statistical models used by market participants can shape asset prices. We consider an economy in which the stochastic process that governs the evolution of economic variables may not have a simple representation, and yet, agents are only capable of entertaining statistical models with a certain level of complexity. As a result, they may end up with a lower-dimensional approximation that does not fully capture the intertemporal complexity of the true data-generating process. We first characterize the implications of the resulting departure from rational expectations and relate the extent of return and forecast-error predictability at various horizons to the complexity of agents’ models and the statistical properties of the underlying process. We then apply our framework to study violations of uncovered interest rate parity in foreign exchange markets. We find that constraints on the complexity of agents’ models can generate return predictability patterns that are simultaneously consistent with the well-known forward discount and predictability reversal puzzles.

This paper studies the optimal design of a macro-prudential instrument, a loan-to-value (LTV) limit, and its implications for monetary policy in a model with nominal rigidities and financial frictions. The analysis accounts for both an effective lower bound on the nominal interest rate and an upper bound on the ability of LTV limits to stimulate credit demand. The welfare-based loss function features a role for macro-prudential policy to enhance risk-sharing. Optimal LTV limits are strongly countercyclical. In a house price boom-bust episode, the active use of LTV limits alleviates debt-deleveraging dynamics and prevents the economy from falling into a liquidity trap.

This paper incorporates diagnostic expectations into a general equilibrium macroeconomic model with a financial intermediary sector. Diagnostic expectations are a forward-looking model of extrapolative expectations that overreact to recent news. Frictions in financial intermediation produce non-linear spikes in risk premia and slumps in investment during periods of financial distress. The interaction of sentiment with financial frictions generates a short-run amplification effect followed by a long-run reversal effect, termed the feedback from behavioural frictions to financial frictions. The model features sentiment-driven financial crises characterized by low pre-crisis risk premia and neglected risk. The conflicting short-run and long-run effect of sentiment produces boom–bust investment cycles. The model also identifies a stabilizing role for diagnostic expectations. Under the baseline calibration, financial crises are less likely to occur when expectations are diagnostic than when they are rational.

Fears of immigrants as a threat to public health have a long and sordid history. At the turn of the 20th century, when immigrants made up one-third of the population in crowded American cities, contemporaries blamed high urban mortality rates on the newest arrivals. We evaluate how the implementation of country-specific immigration quotas in the 1920s affected urban health. Cities with larger quota-induced reductions in immigration experienced a persistent decline in mortality rates, driven by a reduction in deaths from infectious diseases. The unfavourable living conditions immigrants endured explains the majority of the effect as quotas reduced residential crowding and mortality declines were largest in cities where immigrants resided in more crowded conditions and where public health resources were stretched thinnest.

The strategic importance of commitment in bargaining is widely acknowledged. Yet disentangling its role from key features of canonical models, such as proposal power and reputational concerns, is difficult. This paper introduces a model of bargaining with strategic commitment at its core. Following Schelling (1956, The American Economic Review, vol. 46, 281–306), commitment ability stems from the costly nature of concession and is endogenously determined by players’ demands. Agreement is immediate for familiar bargainers, modelled via renegotiation-proofness. The unique prediction at the high concession cost limit provides a strategic foundation for the Kalai bargaining solution. Equilibria with delay feature a form of gradualism in demands.

In the late nineteenth century, the North American bison was brought to the brink of extinction in less than two decades. We demonstrate that the loss of the bison had immediate, negative consequences for the Native Americans who relied on them and ultimately resulted in a persistent reversal of fortunes. Once amongst the tallest people in the world, the generations of bison-reliant people born after the slaughter lost their entire height advantage. By the early twentieth century, child mortality was 16 percentage points higher and the probability of reporting an occupation 19 percentage points lower in bison nations compared with nations that were never reliant on the bison. Throughout the latter half of the twentieth century and into the present, income per capita has remained 25% lower, on average, for bison nations. This persistent gap cannot be explained by differences in agricultural productivity, self-governance, or application of the Dawes Act. We provide evidence that this historical shock altered the dynamic path of development for formerly bison-reliant nations. We demonstrate that limited access to credit constrained the ability of bison nations to adjust through re-specialization and migration.

Libor is an estimate of interbank borrowing costs computed daily from rates reported by a fixed panel of banks. Evidence suggests that banks have manipulated Libor in recent years by misreporting their borrowing costs. I estimate a strategic reporting model that identifies banks’ borrowing costs as well as their motives for misreporting. The estimation places a lower bound on the value that Libor would have had if banks had truthfully reported their borrowing costs. The model is identified even when unobserved heterogeneity exists in the form of a common cost component that is known by all banks but unobservable to the econometrician and is allowed to follow a non-stationary process. The only data used for identification are banks’ Libor quotes. Overall, I find that the estimated lower bound for the unmanipulated Libor is always above the published Libor, with an average deviation of 25 basis points at the worst of the financial crisis of 2007–2008. The estimated bound displays a pattern similar to two other measures of interbank borrowing costs that have been used previously to assess the extent of manipulation. The model is also used to determine the extent to which misreporting was motivated by signalling or banks’ net exposure to Libor. The estimation results indicate that sending creditworthiness signals was the main driver of systematic misreporting from 2007 to 2010.