Giulia Brancaccio

We study information acquisition as a motive for trade in the market for municipal bonds, a primary financing source for local governments. The market is decentralized and regional dealers intermediate trades among retail investors. Dealers’ pricing behavior suggests that they learn about demand by trading. We propose and estimate a dynamic model of OTC trading that accounts for dealers’ learning and reveals that they are willing to pay 12% of the intermediation spread for the information acquired through trade. Dealers’ learning incentives benefit investors and issuers, as they strengthen trading in an illiquid market, and interact with policies on post-trade disclosure.

In this paper, we show that product design shapes search frictions and study how intermediaries may leverage this channel to increase their rents. In the US, the majority of municipal bonds are designed via negotiations between a local government and its underwriter. They are then traded in a decentralized market, where the underwriter often also acts as an intermediary. Exploiting variations in state regulations that limit government officials' conflicts of interest, we provide evidence that bond design from the government's perspective involves a trade-off between flexibility and liquidity, but the underwriter benefits from designing and trading complex bonds. Motivated by these findings, we build and estimate a model of bond origination and trades to quantify market inefficiency driven by underwriters' role in intermediating trades and discuss policy implications.

In this paper we study the role of the transportation sector in world trade. We build a spatial model that centers on the interaction of the market for (oceanic) transportation services and the market for world trade in goods. The model delivers equilibrium trade flows, as well as equilibrium trade costs (shipping prices). Using detailed data on vessel movements and shipping prices, we document novel facts about shipping patterns; we then flexibly estimate our model. We use this setup to demonstrate that the transportation sector (i) attenuates differences in the comparative advantage across countries; (ii) generates network effects in trade costs; and (iii) dampens the impact of shocks on trade flows. These three mechanisms reveal a new role for geography in international trade that was previously concealed by the frequently-used assumption of exogenous trade costs. Finally, we illustrate how our setup can be used for policy analysis by evaluating the impact of future and existing infrastructure projects (e.g. Northwest Passage, Panama Canal).

In this paper we investigate the importance of fuel oil costs in determining world trade. We use detailed data on ship movements across the globe and transaction-level shipping prices, along with a dynamic model describing the world shipping industry, to measure the elasticity of trade with respect to ship fuel costs. We find that the average estimated elasticity is 0.35, but ranges from 0.1 to about 1.2 depending on the level of the fuel cost. The pass-through of fuel costs to exporters is low, at 0.17. Strikingly, the trade elasticity features a pronounced asymmetry in low vs. high oil prices. As fuel costs decline, the elasticity plateaus. This flattening out of the elasticity is attributed to the equilibrium of the transportation sector and the changes in the relative bargaining positions of ships and exporters in particular. Finally, we use the estimated elasticity to assess the importance of ship design on trade flows: if the large fuel efficiency gains achieved in the 1980s had not been realized, trade would be 12% lower today.

We provide a guide to estimating matching functions in a spatial context. Several interactions in space take place in a decentralized fashion, such as passengers searching for taxis, ships meeting cargo, exporters meeting importers etc. A convenient modeling device to capture these meetings is the matching function, which has been used extensively in labor market settings. However in the spatial context, data availability is often limited to only one side of the market; for instance it is usually hard to find data on the number of passengers searching for a taxi. We discuss an approach to estimating matching functions that allows the researcher to recover the unobserved side of the market with relatively few assumptions. In addition, our approach obtains the matching function non-parametrically, allowing for significantly more flexibility than is commonly assumed. This additional flexibility can be key when deriving welfare and policy implications.

We explore efficiency and optimal policy in decentralized transport markets, such as taxis, trucks, and bulk shipping. We show that in these markets, search frictions distort the transportation network and the dynamic allocation of carriers over space. We derive explicit and intuitive conditions for efficiency, and show how they translate into efficient pricing rules, or optimal taxes and subsidies for the planner who cannot set prices directly. The results imply that destination-based pricing is essential to attain efficiency. Then, using data from dry bulk shipping, we demonstrate that search frictions lead to a sizeable social loss and substantial misallocation of ships over space. Optimal policy can eliminate about half of the welfare loss. Can a centralizing platform, often arising as a market-based solution to search frictions, do better? Interestingly, the answer is no; although the platform eradicates frictions, it exerts market power thus eroding the welfare gains. Finally, we use two recent interventions in the industry (China's Belt and Road Initiative and the environmental initiative IMO 2020) to demonstrate that taking into account the efficiency properties of transport markets is germane for any proposed policy.