Research

So-called “connector” countries have been argued to benefit from the US-China trade tensions, given their rising share in US imports. This paper draws an important distinction between trade reallocation—countries increase domestic production to substitute for declining Chinese exports to the US—and trade rerouting—countries serve as one-stop place for transshipment of Chinese exports to the US. Leveraging granular data on trade and FDI flows and global input-output linkages, focusing on six Asian countries, we first document that the connector role of these countries may reflect their growing domestic markets and Chinese supply chain reconfiguration, beyond trade rerouting from China to the US. We then zoom in on value-added components and deploy a synthetic control approach to disentangle trade reallocation from trade rerouting. While the evidence remains elusive for five of the six countries, Vietnam appears to have benefited from trade reallocation, with increased domestic content in its exports to the US in strategic sectors, instead of facilitating significant transshipment of Chinese exports to the US. Such domestic production expansion also helped increase domestic content in Vietnam’s exports to the rest of the world, and may be partly due to Chinese firms relocating to Vietnam through greenfield FDI. Despite potential short-term gains, trade reallocation increases connector countries’ vulnerability to geoeconomic fragmentation with losses to all countries in the long run.

This paper investigates the role of cross-border investments, including by Sovereign Wealth Funds (SWFs), in driving economic growth and diversification of Gulf Cooperation Council (GCC) countries. Utilizing novel deal-level datasets, we analyze GCC cross-border investment portfolios across various dimensions such as time, geography, and industry. We show that recent years have witnessed an increase in GCC cross-border investments. While the geographic distribution of these investments remains diverse and balanced across different regions, both inward and outward investments are increasingly directed towards the services sector. The empirical results demonstrate a significant positive relationship between both cross-border inward and domestic investments on GCC real non-hydrocarbon GDP. Notably, the medium-term increase in real non-hydrocarbon GDP resulting from inward investments is three times larger than that from domestic investments. This amplification could be explained by increased inward investments in high-growth services sectors. Although domestic investments, including by SWFs, are contributing to the GCC economic transformation, their efficiency could be further enhanced by focusing on strategic partnerships with international investors and fostering a more transparent and competitive business environment. Recent shift in GCC investments in renewable and clean energy projects will further support diversification efforts.

The Middle East (ME) is often perceived as region with rentier economies and uncompetitive markets. Evidence of market power in the region however is scant. In this paper, we ask the following three questions: Is the ME uniquely uncompetitive? Has the evolution of market power in the region traced the global rise in market power? What government policies and actions influenced the market power in the region and can taxes be a way to even the playing field? To answer these questions, we utilize comprehensive firm-level data from Compustat between 2004 and 2022 and employ two methods for estimating markups (production function and cost-share approach). We document that market power among listed firms in the ME is higher than in the US, but on a downward trend. We find that the value-added tax (VAT) reforms introduced by some Gulf states from 2018 to 2022 resulted in a reduction of market power, an additional benefit beyond increasing fiscal space. While policymakers should continue to use available regulatory levers to achieve economic efficiency and a level playing field, VAT could be considered as an alternative instrument.

This paper explores Asia-Pacific's medium-term growth prospects using two approaches. First, growth accounting analysis and machine-learning estimation reveal how demographics, capital deepening, productivity, and human capital shaped Asia's growth. Second, an innovative algorithm forecasts growth by matching countries' current conditions with historically analogous periods using Dynamic Time Warping (DTW). Comparing pattern-based forecasts with traditional projections highlights economic convergence and demographic headwinds. Results show that without ambitious reforms, Asia's growth will likely moderate, though remaining the world's fastest growing region. The paper offers data-driven tools for policymakers to identify growth drivers and generate robust forecasts.

​The paper explores the drivers of political fragility by focusing on coups d’état as symptomatic of such fragility. It uses event studies to identify factors that exhibit significantly different dynamics in the runup to coups, and machine learning to identify these stressors and more structural determinants of fragility—as well as their nonlinear interactions—that create an environment propitious to coups. The paper finds that the destabilization of a country’s economic, political or security environment—such as low growth, high inflation, weak external positions, political instability and conflict—set the stage for a higher likelihood of coups, with overlapping stressors amplifying each other. These stressors are more likely to lead to breakdowns in political systems when demographic pressures and underlying structural weaknesses (especially poverty, exclusion, and weak governance) are present or when policies are weaker, through complex interactions. Conversely, strengthened fundamentals and macropolicies have higher returns in structurally fragile environments in terms of staving off political breakdowns, suggesting that continued engagement by multilateral institutions and donors in fragile situations is likely to yield particularly high dividends. The model performs well in predicting coups out of sample, having predicted a high probability of most 2020-23 coups, including in the Sahel region. 

We empirically examine U.S. monetary policy spillovers to the Middle East and Central Asia (ME & CA) region by decomposing U.S. interest rates changes into two orthogonal shocks: the pure monetary policy shock and the information news shock. Using a sample of 16 ME & CA countries, we find that when interest rates increase, the two shocks have opposite spillovers on the region. Tightening driven by contractionary monetary policy shocks hinders growth, while tightening driven by positive information news shocks boosts growth despite higher interest rates. Countries with weaker fundamentals face more negative spillovers from contractionary monetary policy shocks but may sometimes benefit more from positive information news shocks. Moreover, high oil prices mitigate both spillovers for oil exporters while global risk appetite amplifies both spillovers. Finally, we estimate a large degree of heterogeneity in the impact of the 2022 U.S. tightening cycle on ME & CA countries, with oil exporters with stronger fundamentals withstanding well the shock and oil importers with weaker fundamentals being hit the most. 

This paper investigates the link between crisis risk estimation and crisis prevention policy in the context of sudden stops with growth impacts. We propose a two-stage framework that embeds an early-warning problem into a policy-making problem, and conduct a welfare analysis of crisis risk estimation based on subsequent policy responses. Building upon this two-stage framework, we show that there is welfare cost asymmetry between two types of errors: the welfare-maximizing weight on the missed crises is greater than that on the  false alarms. This paper introduces a constrained optimization problem under the Neyman-Pearson paradigm to solve this error-asymmetry problem, and conducts backtesting with multiple algorithms under this paradigm with asymmetry-warranted evaluation metric. Bringing this welfare-based two-stage framework to emerging market countries' data, we uncover the time-varying risk tolerance of policymakers. 

As external crises can be economically disruptive events for advanced economies, emerging markets, and low-income economies alike, predicting such crises is an important task, yet a somewhat inglorious one. In this paper, we generate crisis lists for two types of external crises — sudden stops with growth impact, and exchange market pressure events — for more than 150 countries over 28 years. We then evaluate the performance of signal extraction approach and machine learning techniques for the prediction of such crises. Bearing in mind the potentially sharp divergence between in-sample and out-of-sample performance, we design a rigorous testing procedure attuned to the temporal dependence of macro data and the manner in which the models would be used in practice. We find that for sudden stops with growth impact, the performance of signal extraction approach and machine learning techniques is comparable, and signal extraction approach sometimes performs better. For exchange market pressure events, which are more heterogeneous than sudden stops, we find that tree-based machine learning techniques do appear to perform better than signal extraction approach and regularized linear regression models. We use the output of the models to shed light on variable importance rankings and some of the critical non-monotonicities and interactions that machine learning uncovers from the historical data.