Zhengrui Cheng

This paper provides the first systematic analysis of who imports China’s industrial robots, why they do so, and how competitive these robots are in global markets. Using bilateral trade data from 2005–2022, we document a structural transformation in China’s position in global robotics trade: China has shifted from a net importer to a net exporter of industrial robots. Its exports are disproportionately directed toward emerging manufacturing hubs, particularly in Southeast Asia. Although Chinese robots are generally less technologically advanced than those produced by Japan, Germany, the United States, and South Korea, they offer the strongest “bang for the buck.” These “good-enough” robots are especially attractive to price-sensitive, lower-income economies that are becoming new manufacturing platforms amid global supply-chain reconfiguration. Consistent with this mechanism, we document a U-shaped relationship between robot imports and GDP per capita: high-income economies adopt frontier robots to offset high labor costs, whereas low-income economies rely on cost-effective Chinese robots to compensate for weaker labor quality rather than high wages. Finally, using a fully specified general equilibrium framework, we conduct counterfactual policy simulations to evaluate how geopolitical and industrial policy shocks affect China’s robot exports, relative wages, and global welfare. Our findings suggest that China is increasingly exporting production capability—not just products. More broadly, contrary to the view that automation substitutes for offshoring, our results show that cost-effective robots can travel with production as manufacturing relocates to lower-wage economies.

When multinational production expands, how much reaches domestically owned firms, and what distinguishes the industries in which foreign production becomes embedded in the host economy? We answer using the 2026 OECD Analytical Activities of Multinational Enterprises system, which splits production and input flows by ownership for 80 economies and 41 sectors from 2000 to 2022. Shift-share IV local projections show that, conditional on the exclusion restriction, a 10 percent current-dollar increase in MNE value added predicts 1.36 percent more domestic-owned value added on impact and 4.35 percent more after five years. The five-year response is about twice as large where MNEs source heavily from domestic-owned suppliers, although the instrumented interactions are imprecise. We then construct SupplyFit, which measures how closely domestic production capacity matches each MNE industry’s input needs. Within the same host, industries at the 90th rather than the 10th percentile of persistent SupplyFit source 5.55 percentage points more inputs from domestic firms. A one-standard-deviation annual improvement, however, predicts only 0.17 percentage points more domestic sourcing on impact. Attracting MNE production and embedding it are therefore distinct development objectives: scale creates opportunities for domestic gains, but alignment between domestic capabilities and MNE input needs predicts who captures them.

This chapter reconceptualizes the financial architecture of Global Value Chains (GVCs), arguing that Foreign Direct Investment (FDI) provides the structural foundation while Trade and Supply Chain Finance (TF/SCF) ensures operational fluidity. Using a gravity framework, we first show that trade and FDI have distinct financial underpinnings: trade is more sensitive to bilateral stock market development, while FDI responds more to private credit depth. Next, we apply network analysis to distinguish productive investment from “phantom FDI,” revealing a resilient “real economy” network where trade and FDI centrality remain highly correlated once tax havens are excluded. Finally, our econometric analysis provides evidence of “GVC assimilation,” showing that FDI fosters convergence in the use of capital inputs between source and host countries—a key channel for capital-embodied technology transfer—but not in intermediate inputs. We conclude that deep GVC integration requires a dual policy strategy that supports both long-term investment and the short-term financing that allows local firms to connect and upgrade within these global production networks.

This paper introduces a forecasting framework developed for the AI for Trade Challenge, targeting monthly bilateral trade flows at a high level of product granularity. We construct a model that predicts October 2025 trade values between the United States, China, and their top trading partners at the HS4-product level using a combination of machine learning and time-series baselines. The model incorporates over twenty macroeconomic, financial, and commodity indicators as exogenous drivers. Using LightGBM with a Tweedie loss function, the model handles the zero-inflated, heavy-tailed nature of trade data. Internal cross-validation indicates promising performance, with sMAPE of 43.22% and a total predicted trade volume of $156.4 billion. We discuss the model’s construction, performance, and broader implications for forecasting disaggregated trade flows.