Knowledge that Transforms

Despite the increasing importance of multiple priors in various domains of economics, choice-based incentive-compatible multiple-prior elicitation remains an open problem. This paper develops a solution, comprising a preference-based identification of a subject’s probability interval for an event, and a method for eliciting it. The method applies under weak decision-theoretic assumptions, with no need for probabilistic sophistication. To demonstrate its feasibility, we implement it in three incentivized experiments on artificial and natural sources of uncertainty. Intervals elicited by our method are sensitive to the direction and amount of information and are typically consistent with “objective” probabilities where available. We find a predominance of non-degenerate probability intervals, with intervals being wider when there is less information or predictability. The probability intervals elicited with our method are similar to those stated by subjects on aggregate, suggesting that the method can provide behavioural foundations for the use of stated probability-interval techniques in the field.

We propose a new model of internal migration, based on persistent and spatially correlated idiosyncratic utility. The model is motivated by a new fact in the data that simple moving cost models struggle to match: the t-year interstate migration rate is proportional to the square root of t. The new model maintains the tractability and flexibility of standard migration models, but better matches the dynamics of migration, including the new fact. It has substantially different welfare implications and makes different counterfactual predictions, especially in terms of dynamic adjustment and long-run responses.

We apply classical statistical decision theory to a large class of treatment choice problems with partial identification. We show that, in a general class of problems with Gaussian likelihood, all decision rules are admissible; it is maximin-welfare optimal to ignore all data; and, for severe enough partial identification, there are infinitely many minimax-regret optimal decision rules, all of which sometimes randomize the policy recommendation. We uniquely characterize the minimax-regret optimal rule that least frequently randomizes, and show that, in some cases, it can outperform other minimax-regret optimal rules in terms of what we call profiled regret. We analyse the implications of our results in the aggregation of experimental estimates for policy adoption, extrapolation of Local Average Treatment Effects, and policy making in the presence of omitted variable bias.

We analyse earnings responses to six large payroll tax and income tax reforms in France. Our findings indicate full pass-through to workers when there is a strong and transparent link between contributions and expected benefits. In contrast, employer payroll taxes with no tax-benefit linkage exhibit limited pass-through to workers, while income tax nominally borne by employees show nearly full pass-through. Together with a meta-analysis of the literature, we interpret these results as empirical support for the long-standing hypothesis that tax-benefit linkage matters for the incidence of payroll taxes. In the absence of such linkage, our findings suggest that the individual-level incidence of payroll taxes aligns with their statutory incidence.

Trade wars and financial sanctions are again becoming an increasingly common part of the international economic landscape, and the dynamics of the exchange rate are often used in real time to evaluate the effectiveness of sanctions and policy responses. We show that sanctions limiting a country’s exports or freezing its assets depreciate the exchange rate, while sanctions limiting imports appreciate it, even when both types of policies have exactly the same effect on real allocations, including household welfare and government fiscal revenues. Beyond the direct effect from sanctions, increased precautionary savings in foreign currency also depreciate the exchange rate when they are not offset by the sale of official reserves or financial repression of foreign-currency savings. We show that the dynamics of the ruble exchange rate following Russia’s invasion of Ukraine in February 2022 are quantitatively consistent with the combined effects of these forces calibrated to the observed sanctions and government policies. We evaluate the associated welfare, fiscal and inflationary consequences for both Russia and the coalition of Western countries.

Time-inconsistency may play a central role in explaining inter-temporal behaviour, particularly among poor households. However, little is known about the distribution of time-inconsistent agents, and time-preference parameters are typically not identified in standard dynamic choice models. We formulate a dynamic discrete choice model in an unobservedly heterogeneous population of possibly time-inconsistent agents. We provide conditions under which all population type probabilities and preferences for both time-consistent and sophisticated agents are point-identified and sharp set-identification results for naïve and partially sophisticated agents. Estimating the model using data from a health intervention providing insecticide-treated nets (ITNs) in rural Odisha, India, we find that about two-thirds of our sample comprises time-inconsistent agents and that both sophisticated and naïve agents are considerably present-biased. Counterfactuals show that the under-investment in ITNs attributable to present-bias leads to substantial costs that are about four times the price of an ITN.

Using sales and leasing data, this paper finds three novel effects of a higher property transaction tax: higher buy-to-rent transactions alongside lower buy-to-own transactions despite both being taxed, a lower sales-to-leases ratio, and a lower price-to-rent ratio. This paper explains these facts by developing a search model with entry of investors and households, households choosing to own or rent in the presence of credit frictions, and homeowners deciding when to move house. A higher transaction tax reduces homeowners’ mobility and increases demand for rental properties, which explains the empirical facts and leads to a lower homeownership rate. The deadweight loss is large at 111% of tax revenue, with more than half of this due to distorting decisions to own or rent.

Politicians rely on political brokers to buy votes throughout much of the developing world. We investigate how social networks facilitate these vote-buying exchanges. Our conceptual framework suggests brokers should be particularly well-placed within the network to learn about non-copartisans’ reciprocity in order to target transfers effectively. As a result, parties should recruit brokers who are central among non-copartisans. We combine village network data from brokers and citizens with broker reports of vote buying, allowing us to use broker and citizen fixed effects. We show that networks diffuse information about citizens to brokers who leverage it to target transfers. In particular, among those citizens who are not registered to their party, brokers target reciprocal citizens about whom they can learn more through their network, and these citizens are more likely to support the brokers’ party. Moreover, recruited brokers are significantly more central than other citizens among non-copartisans, but not among copartisans. These results highlight the importance of information diffusion through social networks for vote buying, broker recruitment, and ultimately for political outcomes.

We provide a macroeconomic theory where demand for goods has a productive role. A search friction prevents perfect matching between producers and potential customers. Larger demand induces more search, which, in turn, increases GDP and measured total factor productivity (TFP). We embed the product-market friction in a standard neoclassical model and estimate it using Bayesian techniques. Business cycles are driven by preference shocks, true technology shocks, and investment-specific shocks. Preference shocks have qualitatively similar effects as true productivity shocks. These shocks account for a large share of the fluctuations in consumption, GDP, and measured TFP and can be identified using shopping time data.

Many countries base college admissions on a centrally administered test. Students invest a great deal of resources to improve their performance on the test, and there is growing concern about the high costs associated with these activities. We consider modifying the test by introducing performance-disclosure policies that pool intervals of performance rankings. Pooling affects the equilibrium allocation of students to colleges, which hurts some students and benefits others, but also affects students’ effort. We investigate how such policies can improve students’ welfare in a Pareto sense, study the Pareto frontier of pooling policies, and identify improvements that are robust to the distribution of college seats. We illustrate the potential applicability of our results with an empirical estimation that uses data on college admissions in Turkey. We find that a policy that pools a large fraction of the lowest-performing students leads to a Pareto improvement in a contest based on the estimated parameters. A laboratory experiment based on the estimated parameters generally supports our theoretical predictions.