AI and Trade: Why Europe Cannot Afford to Lag on Adoption

Artificial intelligence (AI), and generative AI in particular, is poised to transform productivity across a broad range of activities, with the strongest effects concentrated in knowledge-intensive services such as finance, professional services, and ICT. Its economic impact will nevertheless depend on how quickly countries adopt and integrate it into their economies. Evidence points to substantial cross-country differences in adoption, particularly within Europe. Yet AI is not only a domestic transformation; it is also a productivity shock transmitted through international trade. Productivity gains abroad lower import prices and reshape competitiveness across countries and sectors. Our analysis shows that these forces interact: countries can benefit from foreign AI progress through cheaper imports, but without sufficient domestic adoption, they risk losing competitiveness in AI-exposed sectors. The global diffusion of AI therefore makes domestic adoption capacity and openness to trade complementary determinants of future growth.

Mapping Economic Opportunities in Global Clean Energy Supply Chains

The energy transition offers countries that can manufacture clean energy technologies substantial opportunities for sustainable economic growth. This paper provides a framework for context-aware industrial policy by applying economic complexity theory to a newly constructed dataset of twelve key clean energy supply chains (CESCs). We find that CESCs are diverse but highly interdependent; they are also growing faster and are more concentrated than other industries. CESCs exhibit substantial entry, exit and competitive churn, and countries are more likely to enter CESC industries that are related to their existing productive capabilities. We also explore changing global competitiveness and country positioning in these industries, and draw out implications of these patterns for industrial policymakers.

Tackling Discrepancies in Trade Data: The Harvard Growth Lab International Trade Datasets

Bilateral trade data informs foreign and domestic policy decisions, serves as a growth indicator, determines tariffs, and is the basis for financial and investment decisions for corporations. Accurate trade data translates into better decision-making. However, the raw bilateral trade data reported by UN Comtrade suffer from two structural problems: reporting differences between country partners and countries reporting in different product classification systems, which require product-level harmonization to compare data across countries. In this paper, we address these challenges by combining a mirroring technique and a data-driven concordance method. Mirroring reconciles importer and exporter differences by imputing country reliability scores and applying a weighted country-pair average to calculate the estimated trade value. We harmonize product classifications across vintages by calculating conversion weights that reflect a product’s market share. The resulting publicly available datasets mitigate issues in raw trade statistics, reducing reporting inconsistencies while maintaining product-level granularity across six decades.

Global Networks, Monetary Policy and Trade

We develop a novel framework to study the interaction between monetary policy and trade. Our New Keynesian open economy model incorporates international production networks, sectoral heterogeneity in price rigidities, and trade distortions. We decompose the general equilibrium response to trade shocks into distinct channels that account for demand shifts, policy effects, exchange rate adjustments, expectations, price stickiness, and input–output linkages. Tariffs act simultaneously as demand and supply shocks, leading to endogenous fragmentation through changes in trade and production network linkages. We show that the net impact of tariffs on domestic inflation, output, employment, and the dollar depends on the endogenous monetary policy response in both the tariff-imposing and tariff-exposed countries, within a global general equilibrium framework. Our quantitative exercise replicates the observed effects of the 2018 tariffs on the U.S. economy and predicts a 1.6 pp decline in U.S. output, a 0.8 pp rise in inflation, and a 4.8% appreciation of the dollar in response to a retaliatory trade war linked to tariffs announced on “Liberation Day.” Tariff threats, even in the absence of actual implementation, are self-defeating— leading to a 4.1% appreciation of the dollar, 0.6% deflation, and a 0.7 pp decline in output, as agents re-optimize in anticipation of future distortions. Dollar appreciates less or even can depreciate under retaliation, tariff threats, and increased global uncertainty.

Publisher’s Version

Tackling Discrepancies in Trade Data: The Harvard Growth Lab International Trade Datasets

De Facto Openness to Immigration

Various factors influence why some countries are more open to immigration than others. Policy is only one of them. We design country-specifc measures of openness to immigration that aim to capture de facto levels of openness to immigration, complementing existing de jure measures of immigration, based on enacted immigration laws and policy measures. We estimate these for 148 countries and three years (2000, 2010, and 2020). For a subset of countries, we also distinguish between openness towards tertiary-educated migrants and less than tertiary-educated migrants. Using the measures, we show that most places in the World today are closed to immigration, and a few regions are very open. The World became more open in the first decade of the millennium, an opening mainly driven by the Western World and the Gulf countries. Moreover, we show that other factors equal, countries that increased their openness to immigration, reduced their old-age dependency ratios, and experienced slower real wage growth, arguably a sign of relaxing labor and skill shortages.

Explore the country rankings in our interactive visualization website and learn more about the project, Leveraging the Global Talent Pool to Jumpstart Prosperity in Emerging Economies.

On the Design of Effective Sanctions: The Case of Bans on Exports to Russia

We build on Baqaee and Farhi (2019, 2021) and derive a theoretically-grounded criterion that allows targeting bans on exports to a sanctioned country at the level of ∼5000 6-digit HS products. The criterion implies that the costs to the sanctioned country are highly convex in the market share of the sanctioning parties. Hence, there are large benefits from coordinating export bans among a broad coalition of countries. Applying our results to Russia reveals that sanctions imposed by the EU and the US in response to Russia’s invasion of Ukraine are not systematically related to our arguments once we condition on Russia’s total imports of a product from participating countries. We discuss drivers of these differences, and then provide a quantitative evaluation of the export bans to show that (i) they are very effective with the welfare loss typically ∼100 times larger for Russia than for the sanctioners; (ii) improved coordination of the sanctions and targeting sanctions based on our criterion allows to increase the costs to Russia by about 80% with little to no extra cost to the sanctioners; and (iii) there is scope for increasing the cost to Russia further by expanding the set of sanctioned products.

Pandemic-era Inflation Drivers and Global Spillovers

We estimate a multi-country multi-sector New Keynesian model to quantify the drivers of domestic inflation during 2020–2023 in several countries, including the United States. The model matches observed inflation together with sector-level prices and wages. We further measure the relative importance of different types of shocks on inflation across countries over time. The key mechanism, the international transmission of demand, supply and energy shocks through global linkages helps us to match the behavior of the USD/Euro exchange rate. The quantification exercise yields four key findings. First, negative supply shocks to factors of production, labor and intermediate inputs, initially sparked inflation in 2020–2021. Global supply chains and complementarities in production played an amplification role in this initial phase. Second, positive aggregate demand shocks, due to stimulative policies, widened demand-supply imbalances, amplifying inflation further during 2021–2022. Third, the reallocation of consumption between goods and service sectors, a relative sector-level demand shock, played a role in transmitting these imbalances across countries through the global trade and production network. Fourth, global energy shocks have differential impacts on the US relative to other countries’ inflation rates. Further, complementarities between energy and other inputs to production play a particularly important role in the quantitative impact of these shocks on inflation.

COVID-19 and emerging markets: A SIR model, demand shocks and capital flows

We quantify the macroeconomic effects of COVID-19 for a small open economy. We use a two-country framework combined with a sectoral SIR model to estimate the effects of collapses in foreign demand and supply. The small open economy (country one) suffers from domestic demand and supply shocks due to its own pandemic. In addition, there are external shocks coming from the rest of the world (country two). Aggregate exports of the small open economy decline when foreign demand goes down, and aggregate imports suffer from lockdowns in the rest of the world. We calibrate the model to Turkey. Our results show that the optimal policy, which yields the lowest output loss and saves the maximum number of lives, for the small open economy, is an early and globally coordinated full lockdown of 39 days.

Economic Costs of Friend-shoring

The nature of international trade has changed significantly since the early 1990s: the liberalisation of cross-border transactions, advances in information and communication technology, reductions in transport costs, and innovations in logistics have given firms greater incentives to break up the production process and locate its various stages across many countries. As a result, global supply chains have become very common, accounting for around a half of global trade in 2020 (World Bank 2020).

The prevalence of global value chains has been underpinned by the well-functioning international trade rule enshrined in the General Agreement on Tariffs and Trade (GATT) and later the WTO, as well as regional agreements. However, geopolitical tensions and disruptions to global value chains – ranging from cyber-threats, the US-China trade war (Fajgelbaum et al. 2022), and the Russian invasion of Ukraine to systemic issues such as the Covid-19 pandemic and the climate crisis – have led policymakers to re-evaluate their approach to globalisation. Many countries are considering ‘friend-shoring’ – trading primarily with countries sharing similar values (such as democratic institutions or maintaining peace) – as a way of minimising exposure to weaponisation of trade and securing access to critical inputs, particularly those required for green transition (Arjona et al. 2023, Attinasi et al. 2023).

In contrast to optimisation under free trade, friend-shoring – by imposing constraints – is likely to be less efficient. But how high is the price that needs to be paid for the alleged insurance benefits brought about by friend-shoring? To shed some light on this question, this chapter assesses the economic costs of friend-shoring, with a focus on broadly defined emerging Europe and European neighbourhood economies. We make three main points. First, we show that, in the medium run, friend-shoring is bad for most economies and generally leads to real output losses globally. Second, only countries that manage to remain non-aligned may see real output gains, but these gains are much smaller than the losses incurred by other countries and not guaranteed. Third, economic costs of friend-shoring are higher than the economic costs of sanctions imposed on Russia after its invasion of Ukraine.