Edward Wiles

I am an economist studying how contracts and firm-to-firm relationships shape international trade and development. My work uses a combination of quantitative models, administrative data, and field experiments.

Currently, I am an Academy Scholar at the Harvard Academy for International and Area Studies (2025-2027).

I received a PhD in Economics from MIT in May 2025. Prior to that, I completed Bachelor's and Master's degrees at the London School of Economics.

I am British and Canadian, which has given me a lifelong interest in international football, queuing, and apologising.

Please click here for my CV.

Contact: edward [dot] m [dot] wiles [at] gmail [dot] com

Working Papers

Relational Frictions along the Supply Chain: Evidence from Senegalese Traders (with Deivy Houeix) [JMP]

We study search and trust frictions in international sourcing, and whether the rapid growth of “social commerce” in lower-income countries can alleviate them. Guided by a dynamic model of relational contracting, we run a field experiment with 1,862 garment firms in Senegal, randomly matching them to suppliers in Türkiye (search) and varying information about supplier type (adverse selection) and incentives (moral hazard). New supplier connections expand access to foreign varieties and quality, but the additional information about supplier trustworthiness is necessary for building lasting and profitable relationships. Structural estimates imply that both adverse selection and moral hazard substantially limit trade.

Quantifying the Sensitivity of Quantitative Trade Models (with Habib Ansari and Dave Donaldson)

A modern revolution in spatial economic modeling aims to answer quantitative counterfactual questions by using models that feature micro-level heterogeneity. This heterogeneity is then often assumed to come from particular parametric families---such as Frechet in Eaton and Kortum's (2002) Ricardian model. While these parametric choices greatly enhance the tractability of model simulations, it is unknown how sensitive the answers to counterfactual questions are to these assumptions of convenience because there are infinitely many alternative distributions of heterogeneity to be evaluated. We overcome this challenge by building a general trade model that leverages recent advances in the robustness literature. Our method calculates sharp bounds on the values of model counterfactuals that could obtain---while still exactly matching all aggregate trade data points, a gravity-like moment condition, and satisfying equilibrium constraints---under all possible distributions of underlying heterogeneity that lie within a given divergence from a chosen reference distribution. Applying this method to the Eaton and Kortum (2002) model, we find that the gains from trade in these models could be several times larger or smaller than they appear to be under standard benchmark distributions, even if heterogeneity is drawn from a distribution that is at least as similar to Frechet as are the types of parametric alternatives that are commonly explored in sensitivity analysis.

Selected Work in Progress

A Quantitative Model of Relational Contracts in Trade

Most international trade takes place in long-term buyer–seller relationships that expand gradually over time. A growing body of empirical evidence suggests that this pattern may reflect the importance of relational contracting: when formal enforcement is limited, continued trade itself provides incentives. Such self-enforcing relationships make trade adjustment inherently dynamic—new matches must build trust and scale from scratch—yet the quantitative trade models used to study shocks are typically static or sacrifice tractability when dynamics are introduced. I develop a dynamic multi-country general-equilibrium model that embeds optimal relational contracts within a tractable Eaton–Kortum-style framework. The model generates endogenous relationship deepening—quantities grow with relationship age—as an equilibrium outcome of self-enforcing incentives. I show that this optimal age profile scales linearly with the surplus, allowing the model to aggregate cleanly. The transition path after a shock is therefore straightforward to compute using exact hat algebra, and steady-state trade shares coincide with a static Eaton-Kortum model with endogenous origin-specific wedges. The new parameters governing the contracting friction can be estimated using increasingly available firm-to-firm trade data.

The Impact of Regional Integration on Trade and Supply Chains: Evidence from a VAT Reform in India (with Tishara Garg)

We use a landmark 2017 fiscal reform in India to quantify the gains to regional economic integration and to study how this affects the organization of supply chains. Using district-level data aggregated from firm-to-firm VAT transactions for the entire of India, we first show in the cross-section that state borders are comparable to country borders in other settings, with trade decreasing by 76% at the border. We then use event-study style regressions derived from a standard quantitative trade model to study the reform—which eliminated countrywide tax-induced interstate trading costs—and find that it increased interstate trade by 15% on average. With this estimated elasticity, the model implies that the reform increased aggregate GDP by 1% on average, with almost all districts experiencing gains. To examine how supply chains responded—including the extent to which firms shifted towards a more "hub and spokes" network, as in the classic proximity-concentration tradeoff—we are currently exploiting the micro-level firm-to-firm VAT data.

Publications

Losing Prosociality in the Quest for Talent? Sorting, Selection, and Productivity in the Delivery of Public Services (with Nava Ashraf, Oriana Bandiera, and Scott S. Lee). American Economic Review 2020, 110(5): 1355–1394

We embed a field experiment in a nationwide recruitment drive for a new health care position in Zambia to test whether career benefits attract talent at the expense of prosocial motivation. In line with common wisdom, offering career opportunities attracts less prosocial applicants. However, the trade-off exists only at low levels of talent; the marginal applicants in treatment are more talented and equally prosocial. These are hired, and perform better at every step of the causal chain: they provide more inputs, increase facility utilization, and improve health outcomes including a 25 percent decrease in child malnutrition.

Awarded the Arrow Award for the best paper published in health economics in English in 2020 by the International Health Economics Association.

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