This paper studies the optimal conduct of monetary policy in a multisector economy in which firms buy and sell intermediate goods over a production network. We first provide a necessary and sufficient condition for the monetary policy's ability to implement flexible‐price equilibria in the presence of nominal rigidities and show that, generically, no monetary policy can implement the first‐best allocation. We then characterize the optimal policy in terms of the economy's production network and the extent and nature of nominal rigidities. Our characterization result yields general principles for the optimal conduct of monetary policy in the presence of input‐output linkages: it establishes that optimal policy stabilizes a price index with greater weights assigned to larger, stickier, and more upstream industries, as well as industries with less sticky upstream suppliers but stickier downstream customers. In a calibrated version of the model, we find that implementing the optimal policy can result in quantitatively meaningful welfare gains.
We propose a class of multiple‐prior representations of preferences under ambiguity, where the belief the decision‐maker (DM) uses to evaluate an uncertain prospect is the outcome of a game played by two conflicting forces, Pessimism and Optimism. The model does not restrict the sign of the DM's ambiguity attitude, and we show that it provides a unified framework through which to characterize different degrees of ambiguity aversion, and to represent the co‐existence of negative and positive ambiguity attitudes within individuals as documented in experiments. We prove that our baseline representation, dual‐self expected utility (DSEU) , yields a novel representation of the class of invariant biseparable preferences (Ghirardato, Maccheroni, and Marinacci (2004)), which drops uncertainty aversion from maxmin expected utility (Gilboa and Schmeidler (1989)), while extensions of DSEU allow for more general departures from independence. We also provide foundations for a generalization of prior‐by‐prior belief updating to our model.
We propose and implement a new method to estimate treatment effects in settings where individuals need to be in a certain state (e.g., unemployment) to be eligible for a treatment, treatments may commence at different points in time, and the outcome of interest is realized after the individual left the initial state. An example concerns the effect of training on earnings in subsequent employment. Any evaluation needs to take into account that some of those who are not trained at a certain time in unemployment will leave unemployment before training while others will be trained later. We are interested in effects of the treatment at a certain elapsed duration compared to “no treatment at any subsequent duration.” We prove identification under unconfoundedness and propose inverse probability weighting estimators. A key feature is that weights given to outcome observations of nontreated depend on the remaining time in the initial state. We study effects of a training program for unemployed workers in Sweden. Estimates are positive and sizeable, exceeding those obtained with common static methods. This calls for a reappraisal of training as a tool to bring unemployed back to work.
This paper studies the impacts of managers in the administrative public sector using novel Italian administrative data containing an output‐based measure of productivity. Exploiting the rotation of managers across sites, I find that a one standard deviation increase in managerial talent raises office productivity by 10%. These gains are driven primarily by the exit of older workers who retire when more productive managers take over. I use these estimates to evaluate the optimal allocation of managers to offices. I find that assigning better managers to the largest and most productive offices would increase output by at least 6.9%.
This paper investigates the effect of terrorism financing and recruitment on attacks. I exploit a Sharia‐compliant institution in Pakistan, which induces unintended and quasi‐experimental variation in the funding of terrorist groups through their religious affiliation. The results indicate that higher terrorism financing, in a given location and period, generate more attacks in the same location and period. Financing exhibits a complementarity in producing attacks with terrorist recruitment, measured through data from Jihadist‐friendly online fora and machine learning. A higher supply of terror is responsible for the increase in attacks and is identified by studying groups with different affiliations operating in multiple cities. These findings are consistent with terrorist organizations facing financial frictions to their internal capital market.
We develop a tool akin to the revelation principle for dynamic mechanism‐selection games in which the designer can only commit to short‐term mechanisms. We identify a canonical class of mechanisms rich enough to replicate the outcomes of any equilibrium in a mechanism‐selection game between an uninformed designer and a privately informed agent. A cornerstone of our methodology is the idea that a mechanism should encode not only the rules that determine the allocation, but also the information the designer obtains from the interaction with the agent. Therefore, how much the designer learns, which is the key tension in design with limited commitment, becomes an explicit part of the design. Our result simplifies the search for the designer‐optimal outcome by reducing the agent's behavior to a series of participation, truth telling, and Bayes' plausibility constraints the mechanisms must satisfy.
Facing several decisions, people may consider each one in isolation or integrate them into a single optimization problem. Isolation and integration may yield different choices, for instance, if uncertainty is involved, and only one randomly selected decision is implemented. We investigate whether the random incentive system in experiments that measure ambiguity aversion provides a hedge against ambiguity, making ambiguity‐averse subjects who integrate behave as if they were ambiguity neutral. Our results suggest that about half of the ambiguity averse subjects integrated their choices in the experiment into a single problem, whereas the other half isolated. Our design further enables us to disentangle properties of the integrating subjects' preferences over compound objects induced by the random incentive system and the choice problems in the experiment.
We formulate a generalization of the traditional medium‐of‐exchange function of money in contexts where there is imperfect competition in the intermediation of credit, settlement, or payment services used to conduct transactions. We find that the option to settle transactions with money strengthens the stance of sellers of goods and services in relation to intermediaries, and show this mechanism is operative even for sellers who never exercise the option to sell for money. These latent money demand considerations imply that in general, in contrast to current conventional wisdom in policy‐oriented research in monetary economics, monetary policy can remain effective through medium‐of‐exchange transmission channels—even in highly developed credit economies where the share of monetary transactions is negligible.
AS IMMIGRATION TO OECD COUNTRIES has risen, the question of how to ensure the success of immigrant students has grown more critical. In Europe, the question is complicated by the common practice of tracking students at an early age into different types of schools. One concern is that immigrants may enroll in college preparatory tracks at lower rates than natives with similar achievement. The present study documents gaps in track choice among high-achieving immigrant students in Italy, and evaluates an intervention aimed at aligning the choices of these students with their academic potential. It thereby provides important evidence both on the education of immigrants and on educational tracking. In the United States, there are related debates about programs in which Black and Hispanic students are under-represented: for example, gifted education, advanced tracks within schools, and college prep coursework. These debates pit advocates of tracking and other policies, who extol the benefits of matching curriculum and instruction to levels of student ability, against critics who argue that tracking reinforces segregation and inequality. But the goals of the two sides are not always at odds. Segregation in tracking is often driven by factors unrelated to student ability—such as information barriers, implicit biases, or gaps in parental support and advocacy. Policies that remove these barriers and push capable students from disadvantaged groups to complete higher levels of education can improve match quality, while also promoting social integration and economic opportunity. Further, such policies may have important spillover effects by helping to change perceptions and raise expectations among parents, teachers, and future generations of students. There is mounting evidence that socioeconomic gaps in choice and representation can be reduced through policies that automate or simplify steps in the decision process. Among high-ability elementary school students, gaps in gifted program participation can be reduced by replacing a system of parent and teacher nominations with test-based, universal screening (Card and Giuliano (2016a)). Among high school students, decisions about whether and where to attend college can be influenced by polices that expand college admissions testing (Bulman (2015), Goodman (2016)), facilitate applications for financial aid (Bettinger et al. (2012)), or provide information through targeted mailings (Hoxby and Turner (2013)). One concern about such procedural and informational interventions is that they are temporary fixes and may not address underlying sources of under-representation, such as gaps in information gathering skills, parental support, and expectations. Theses gaps could make under-represented groups more likely to fail in challenging programs. For example, evidence on college mentoring suggests that light-touch “nudges” may be insufficient for producing lasting effects among disadvantaged students, especially when students lack parental support (Carrell and Sacerdote (2017), Cunha, Miller, and Weisburst (2018)). But there is also evidence that discounts such “mismatch” concerns. In a
This paper presents finite‐sample efficiency bounds for the core econometric problem of estimation of linear regression coefficients. We show that the classical Gauss–Markov theorem can be restated omitting the unnatural restriction to linear estimators, without adding any extra conditions. Our results are lower bounds on the variances of unbiased estimators. These lower bounds correspond to the variances of the the least squares estimator and the generalized least squares estimator, depending on the assumption on the error covariances. These results show that we can drop the label “linear estimator” from the pedagogy of the Gauss–Markov theorem. Instead of referring to these estimators as BLUE, they can legitimately be called BUE (best unbiased estimators).