curriculum vitae

We propose a novel identification strategy to isolate exogenous immigration shocks across US counties, by interacting quasi-random variations in the composition of ancestry across counties with the contemporaneous inflow of migrants from different countries. We show a positive causal impact of immigration on local innovation and wages at the five-year horizon. The positive dynamic impact of immigration on innovation and wages dominates the short-run negative impact of increased labor supply. A structural estimation of a model of endogenous growth and migrations suggests the increased immigration to the United States since 1965 may have increased innovation and wages by 5 percent.

We study how decades-long exposure to individuals of a given foreign descent shapes natives' attitudes and behavior toward that group. Using individualized donations data, we show that long-term exposure to a given foreign ancestry leads to more generous behavior specifically toward that group's ancestral country. Focusing on exposure to Arab Muslims to examine mechanisms, we show that long-term exposure (i) decreases explicit and implicit prejudice against Arab Muslims, (ii) reduces support for policies and political candidates hostile toward Arab Muslims, (iii) increases charitable donations to Arab countries, (iv) leads to more personal contact with Arab Muslims, and (v) increases knowledge of Arab Muslims and Islam.

This paper quantifies the aggregate effects of financing constraints. We start from a standard dynamic investment model with collateral constraints. In contrast to the existing quantitative literature, our estimation does not target the mean leverage ratio to identify the scope of financing frictions. Instead, we use a reduced-form coefficient from the recent corporate finance literature that connects exogenous debt capacity shocks to corporate investment. Relative to a frictionless benchmark, collateral constraints induce losses of 7.1% for output and 1.4% for total factor productivity (TFP) (misallocation). We show these estimated losses tend to be more robust to misspecification than estimates obtained by targeting leverage.

We analyze a large data set of commercial records produced by Assyrian merchants in the nineteenth century BCE. Using the information from these records, we estimate a structural gravity model of long-distance trade in the Bronze Age. We use our structural gravity model to locate lost ancient cities. In many cases, our estimates confirm the conjectures of historians who follow different methodologies. In some instances, our estimates confirm one conjecture against others. We also structurally estimate ancient city sizes and offer evidence in support of the hypothesis that large cities tend to emerge at the intersections of natural transport routes, as dictated by topography. Finally, we document persistent patterns in the distribution of city sizes across four millennia, find a distance elasticity of trade in the Bronze Age close to modern estimates, and show suggestive evidence that the distribution of ancient city sizes, inferred from trade data, is well approximated by Zipf's law.

We use 130 years of data on historical migrations to the U.S. to show a causal effect of the ancestry composition of U.S. counties on foreign direct investment (FDI) sent and received by local firms. To isolate the causal effect of ancestry on FDI, we build a simple reduced-form model of migrations: Migrations from a foreign country to a U.S. county at a given time depend on (1) a push factor, causing emigration from that foreign country to the entire U.S., and (2) a pull factor, causing immigration from all origins into that U.S. county. The interaction between time-series variation in origin-specific push factors and destination-specific pull factors generates quasi-random variation in the allocation of migrants across U.S. counties. We find that doubling the number of residents with ancestry from a given foreign country relative to the mean increases the probability that at least one local firm engages in FDI with that country by 4 percentage points. We present evidence that this effect is primarily driven by a reduction in information frictions, and not by better contract enforcement, taste similarities, or a convergence in factor endowments.

The gravity equation in international trade states that bilateral exports are proportional to economic size and inversely proportional to geographic distance. While the role of size is well understood, that of distance remains mysterious. I offer an explanation for the role of distance: If (i) the distribution of firm sizes is Pareto, (ii) the average squared distance of a firm's exports is an increasing power function of its size, and (iii) a parameter restriction holds, then the distance elasticity of trade is constant for long distances. When the firm size distribution follows Zipf's law, trade is inversely proportional to distance.

This paper analyzes firm's pricing-to-market decisions in vertically differentiated industries. We first present a model featuring firms that sell goods of heterogeneous quality levels to consumers who are heterogeneous in their income and thus their marginal willingness to pay for quality increments. We derive closed-form solutions for the unique pricing game under costly international trade. The comparative statics highlight how firms' pricing-to-market decisions are shaped by the interaction of consumer income and good quality. We derive two testable predictions. First, the relative price of high qualities compared to low qualities increases with the income of the destination market. Second, the rate of cost pass-through into consumer prices falls with quality if destination market income is sufficiently high. We present evidence in support of these two predictions based on a dataset of prices, sales, and product attributes in the European car industry.

I propose a model of international trade with liquidity constraints. If firms must pay a fixed entry cost in order to access foreign markets, and if they face liquidity constraints to finance these costs, only those firms that have sufficient liquidity are able to export. A set of firms could profitably export, but are prevented from doing so because they lack sufficient liquidity. More productive firms that generate large liquidity from their domestic sales, and wealthier firms that inherit a large amount of liquidity, are more likely to export. This model offers a potential explanation for the apparent lack of sensitivity of exports to exchange rate fluctuations. When the exchange rate appreciates, existing exporters lose competitiveness abroad, and are forced to reduce their exports. At the same time, the value of domestic assets owned by potential exporters increases. Some liquidity constrained exporters start exporting. This dampens the anti-competitiveness impact of a currency appreciation. Under some conditions, it may reverse it altogether and increase aggregate exports. In this sense, the model is able to rationalize the co-existence of competitive devaluations and competitive revaluations.

International trade offers a unique environment for the study of large-scale networks for two reasons. First, the structure of international trade intrinsically resembles that of a network: countries are connected to each other by trade linkages; individual exporting firms are connected to other importing firms in foreign countries; migrants personally know people from various countries. Second, most international transactions and transnational migrations are recorded, by customs or immigration authorities, so detailed data are readily available for the empirical study of those large-scale networks. This chapter reviews recent advances in international trade and the economic networks and suggests promising avenues for research on the role networks play in international trade. The author describes both important empirical studies of networks in trade, and powerful theoretical tools that can be used to analyze the micro and aggregate properties of large-scale networks.

Motivated by empirical evidence I uncover on the dynamics of French firms' exports, I offer a novel theory of trade frictions. Firms export only into markets where they have a contact. They search directly for new trading partners, but also use their existing network of contacts to search remotely for new partners. I characterize the dynamic formation of an international network of exporters in this model. Structurally, I estimate this model on French data and confirm its predictions regarding the distribution of the number of foreign markets accessed by exporters and the geographic distribution of exports.

We generalize Krugman's (1979) new trade model by allowing for an explicit production chain in which a range of tasks is performed sequentially by a number of specialized teams. We demonstrate that an increase in market size induces a deeper division of labor among these teams which leads to an increase in firm productivity. The paper can be thought of as a formalization of Smith's (1776) famous theorem that the division of labor is limited by the extent of the market. It also sheds light on how market size differences can limit the scope for international technology transfers.

What is the impact of real estate prices on corporate investment? In the presence of financing frictions, firms use pledgeable assets as collateral to finance new projects. Through this collateral channel, shocks to the value of real estate can have a large impact on aggregate investment. To compute the sensitivity of investment to collateral value, we use local variations in real estate prices as shocks to the collateral value of firms that own real estate. Over the 1993-2007 period, the representative US corporation invests $0.06 out of each $1 of collateral.

This paper extends the Mussa and Rosen (1978) model of quality pricing under perfect competition. Exporters sell goods of different qualities to consumers who have heterogeneous preferences for quality. Production is subject to decreasing returns to scale and, therefore, supply and the toughness of competition react to cost changes brought about by exchange rate fluctuations. First, we predict that exchange rate shocks are imperfectly passed through into prices. Second, prices of low-quality goods are more sensitive to exchange rate shocks than prices of high-quality goods. Third, in response to an exchange rate appreciation, the composition of exports shifts toward higher quality and more expensive goods. We test these predictions using highly disaggregated price and quantity U.S. import data and find only weak empirical evidence in support of our theory.

By considering a model with identical firms, Paul Krugman (1980) predicts that a higher elasticity of substitution between goods magnifies the impact of trade barriers on trade flows. In this paper, I introduce firm heterogeneity in a simple model of international trade. When the distribution of productivity across firms is Pareto, which is close to the observed size distribution of US firms, the predictions of the Krugman model with representative firms are overturned: the impact of trade barriers on trade flows is dampened by the elasticity of substitution, and not magnified.

We explore the mechanics of empathy. We show that information about an outgroup can activate and magnify empathy when presented in conjunction with an experience simulating their struggles. This response increases the willingness to help the struggling group. We provide evidence for this effect in an immersive virtual reality experiment where participants ("witnesses") experience a simulation of the struggle of unauthorized migrants ("protagonists"), then replicate these results in a series of controlled lab experiments. We show that information enhances the witnesses' empathetic response and drives them to engage in more prosocial behavior when it increases their perceived interpersonal similarity, or relatability to the protagonist - an effect we trace to attention: eye-tracking data reveals that information provision concentrates witnesses' gaze on the struggles of the protagonist instead of searching through peripheral elements of the scene. Conversely, only information packages that strengthen perceived relatability - an effect that can vary across subgroups with heterogeneous attributes - magnify empathy. Together, our evidence suggests that the ability to put oneself in the shoes of another person or group can be enhanced by activating empathy through simple, targeted, information provision.

What was the role of trade, and how did economic activity evolve at the End of Antiquity, when political power shifts away from the Mediterranean towards northern Europe and the Middle East? To answer those questions, we assemble a database of hundreds of thousands of ancient coins from the fourth to the tenth century, estimate a dynamic model of trade and money where coins gradually diffuse along trade routes, and recover granular regional trade and real consumption time series. Our estimates suggest that Mediterranean trade was disrupted by the newly formed border between Islam and Christianity; that economic activity shifts away from the Mediterranean starting in the fifth century; that real consumption peaks in the Middle East in the eighth century; and that by the end of the ninth century, Atlantic regions from Islamic Andalusia to Frankish northwestern Europe have become the wealthiest regions of the ancient western world.

To study social, economic, and historical questions, researchers in the social sciences and humanities have started to use increasingly large unstructured textual datasets. While recent advances in NLP provide many tools to efficiently process such data, most existing approaches rely on generic solutions whose performance and suitability for domain-specific tasks is not well understood. This work presents an attempt to bridge this domain gap by exploring the use of modern Entity Linking approaches for the enrichment of museum collection data. We collect a dataset comprising of more than 1700 texts annotated with 7,510 mention-entity pairs, evaluate some off-the-shelf solutions in detail using this dataset and finally fine-tune a recent end-to-end EL model on this data. We show that our fine-tuned model significantly outperforms other approaches currently available in this domain and present a proof-of-concept use case of this model. We release our dataset and our best model.