This paper explores the nature of fluctuations in world bulk shipping by quantifying the impact of time to build and demand uncertainty on investment and prices. We examine the impact of both construction lags and their lengthening in periods of high investment activity, by constructing a dynamic model of ship entry and exit. A rich dataset of secondhand ship sales allows for a new estimation strategy: resale prices provide direct information on value functions and allow their nonparametric estimation. We find that moving from time-varying to constant to no time to build reduces prices, while significantly increasing both the level and volatility of investment. (JEL G31, L11, L62, L92) Adjustment costs and irreversibilities figure prominently in both the modern theory of firm-level investment under uncertainty as well as in aggregate business cycle models. 1 Yet there is little empirical evidence of their quantitative importance in specific settings. It is the goal of this paper to fill this gap by focusing on a particular industry, that of oceanic bulk shipping. This industry lends itself to this analysis, 2 as it provides an outstanding example of the interaction between uncertainty and adjustment costs, which take the form of time to build: shipping firms-conducting 70 percent of world seaborne trade (in tons)-face long lags between the order and delivery of a new vessel, while the uncertain demand for sea transport may substantially alter conditions during this wait. For instance, the recent growth of raw material imports, particularly in China, led to sustained increases in freight rates and a sevenfold surge in the new ship backlog between 2003 and 2008. The crisis of 2008 led to an idling of the existing fleet, at the same time that another 70 percent of that fleet was still scheduled for delivery by 2012.To study this issue we construct a dynamic model of ship entry and exit. We structurally estimate this model employing a novel estimation strategy based on the observation that resale prices reflect value functions. In counterfactual experiments 1 In the context of firm-level investment, see, for example, Abel (1983); Pindyck (1991); Dixit (1992); Caballero (1999). In the context of business cycle models, see, for example, Kydland and Prescott (1982) who emphasized time to build as a key element in matching the properties of aggregate investment, or the recent business cycle models such as Christiano, Eichenbaum, and Evans (2005); Smets and Wouters (2007).2 Since the very beginnings of mathematical economics, Tinbergen (1931) viewed shipping cycles as interesting illustrations of business cycles more generally.

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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 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) implies that net exporters (importers) face higher (lower) trade costs leading to misallocation of productive activities across countries; (ii) creates 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 common 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).

Tamer, Ali Yurukoglu, and seminar participants at various universities for many helpful comments. We also thank the Editor and the anonymous referees. Charis Katsiardis and Adrian Torchiana provided excellent research assistance. The research leading to these results has received funding from the European Research Council under the European Community's Seventh Framework Programme Grant Agreement no. 230589 and Agence Nationale de la Recherche Projet ANR-12-CHEX-0012-01. All remaining errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

In structural dynamic discrete choice models, unobserved or mis-measured state variables may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address such measurement problems when they relate to state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in terms of conditional choice probabilities (ECCP estimators). These estimators do not require observing or modeling the agent's entire information set, nor solving or simulating a dynamic program. As such, they are simple to implement and computationally light. We provide constructive arguments for the identification of model primitives, and establish the estimator's consistency and asymptotic normality. Four applied examples serve to illustrate the ECCP approach's implementation, advantages, and limitations: dynamic demand for durable goods, agricultural land use change, technology adoption, and dynamic labor supply. We illustrate the estimator's good finite-sample performance in a Monte Carlo study, and we estimate a labor supply model empirically for taxi drivers in New York City.