How large are the benefits of transportation infrastructure projects, and what explains these benefits? This paper uses archival data from colonial India to investigate the impact of India's vast railroad network. Guided by four results from a general equilibrium trade model, I find that railroads: (1) decreased trade costs and interregional price gaps; (2) increased interregional and international trade; (3) increased real income levels; and (4) that a sufficient statistic for the effect of railroads on welfare in the model accounts well for the observed reduced-form impact of railroads on real income in the data.

This article examines the historical impact of railroads on the U.S. economy, with a focus on quantifying the aggregate impact on the agricultural sector in 1890. Expansion of the railroad network may have affected all counties directly or indirectly—an econometric challenge that arises in many empirical settings. However, the total impact on each county is captured by changes in that county’s “market access,” a reduced-form expression derived from general equilibrium trade theory. We measure counties’ market access by constructing a network database of railroads and waterways and calculating lowest-cost county-to-county freight routes. We estimate that county agricultural land values increased substantially with increases in county market access, as the railroad network expanded from 1870 to 1890. Removing all railroads in 1890 is estimated to decrease the total value of U.S. agricultural land by 60%, with limited potential for mitigating these losses through feasible extensions to the canal network or improvements to country roads.

When asked to name one proposition in the social sciences that is both true and non-trivial, Paul Samuelson famously replied: ‘Ricardo's theory of comparative advantage’. Truth, however, in Samuelson's reply refers to the fact that Ricardo's theory of comparative advantage is mathematically correct, not that it is empirically valid. In this paper we develop and implement an empirical test of Ricardo's ideas. We use novel agricultural data that describe the productivity in 17 crops of 1.6 million parcels of land in 55 countries around the world. We find that a regression of log observed output on log predicted output has a (precisely estimated) slope of 0.84 and an R-squared of 0.93. In our view, these findings offer considerable support for Ricardo's ideas.

Can Openness Mitigate the Effects of Weather Shocks? Evidence from India's Famine Era by Robin Burgess and Dave Donaldson. Published in volume 100, issue 2, pages 449-53 of American Economic Review, May 2010

The primary motivation behind quantitative work in international trade and many other fields is to shed light on the economic consequences of policy changes and other shocks. To help assess and potentially strengthen the credibility of such quantitative predictions, we introduce an IV-based goodness-of-fit measure that provides the basis for testing causal predictions in arbitrary general equilibrium environments as well as for estimating the average misspecification in these predictions. As an illustration of how to use the measure in practice, we revisit the welfare consequences of the U.S.-China trade war predicted by Fajgelbaum et al. (2020).

We develop a methodology to construct nonparametric counterfactual predictions, free of functional form restrictions on preferences and technology, in neoclassical models of international trade. First, we establish the equivalence between such models and reduced exchange models in which countries directly exchange factor services. This equivalence implies that, for an arbitrary change in trade costs, counterfactual changes in the factor content of trade, factor prices, and welfare only depend on the shape of a reduced factor demand system. Second, we provide sufficient conditions under which estimates of this system can be recovered nonparametrically. Together, these results offer a strict generalization of the parametric approach used in so-called gravity models. Finally, we use China's recent integration into the world economy to illustrate the feasibility and potential benefits of our approach.

A large agronomic literature models the implications of climate change for a variety of crops and locations around the world. The goal of the present paper is to quantify the macro-level consequences of these micro-level shocks. Using an extremely rich micro-level data set that contains information about the productivity—both before and after climate change—of each of 10 crops for each of 1.7 million fields covering the surface of the earth, we find that the impact of climate change on these agricultural markets would amount to a 0.26 percent reduction in global GDP when trade and production patterns are allowed to adjust. Since the value of output in our 10 crops is equal to 1.8 percent of world GDP, this corresponds to about one-sixth of total crop value.

The theory of comparative advantage is at the core of neoclassical trade theory. Yet we know little about its implications for how nations should conduct their trade policy. For example, should import sectors with weaker comparative advantage be protected more? Conversely, should export sectors with stronger comparative advantage be subsidized less? In this article we take a first stab at exploring these issues. Our main results imply that in the context of a canonical Ricardian model, optimal import tariffs should be uniform, whereas optimal export subsidies should be weakly decreasing with respect to comparative advantage, reflecting the fact that countries have more room to manipulate prices in their comparative-advantage sectors. Quantitative exercises suggest substantial gains from such policies relative to simpler tax schedules.

The textbook case for industrial policy is well understood: sectors with relatively large external economies of scale should be subsidized at the expense of other sectors. Little is known, however, about the magnitude of the welfare gains from such interventions. We develop an empirical strategy that leverages commonly available trade data to estimate sector-level economies of scale and, in turn, to quantify the gains from optimal industrial policy in a general equilibrium environment. Our results point toward significant economies of scale across manufacturing sectors but gains from industrial policy that are hardly transformative, even among the most open economies.

We study the gains from trade liberalization in models with monopolistic competition, firm-level heterogeneity, and variable markups. For a large class of demand functions used in the international macro and trade literature, we derive a parsimonious generalization of the welfare formula in Arkolakis et al. (2012). We then use both estimates from micro-level trade data and evidence regarding firm-level pass through to quantify the implications of this new formula.Within the class of models that we consider, our main finding is that gains from trade liberalization predicted by models with variable markups are equal to, at best, and slightly lower than, at worst, those predicted by models with constant markups. In this sense, pro-competitive effects of trade are elusive.