China in Global Value Chains: Implications for Industrial Policy and Development

Rapid technological advances and liberal trade regimes permit functional reintegration of dispersed activities into new border-spanning business networks variously referred to as global value chains (GVCs). Given that the gains of a country from GVCs depend on the activities taking place in its jurisdiction and their linkages to global markets, this study starts by providing a descriptive overview of China’s economic structure and trade profile. The first two chapters of this paper demonstrate what significant role GVCs have played in China’s economic growth, evident in enhanced productivity, diversification, and sophistication of China’s exports, and how these economic benefits have propelled China’s emergence as the world’s manufacturing hub in the past two decades. However, benefits from GVC participation – in particular technological learning, knowledge building, and industrial upgrading – are not automatic. What strategies would help Chinese industries engage with GVCs in ways that are deemed sustainable in the long run? What challenges and related opportunities China would face throughout the implementation process? The last two chapters of this paper focus on implications of GVCs for China’s industrial policy and development. Chapter Three examines how China is reorienting its manufacturing sector toward the production of higher value-added goods and expanding its service sector, both domestically and internationally; while Chapter Four provides illustrative policy recommendations on dealing with the positive and negative outcomes triggered by GVCs, within China and beyond the country’s borders. To the end, this study also hopes to shed some light on the lessons and complexities that arise from GVC participation for other developing countries.

Optimal Capital Requirements in Financial Networks with Fire Sales

I explore and analyze a problem of finding the socially optimal capital requirements for financial institutions considering two distinct channels of contagion: direct exposures among the institutions, as represented by a network and fire sales externalities, which reflect the negative price impact of massive liquidation of assets.These two channels amplify shocks from individual financial institutions to the financial system as a whole and thus increase the risk of joint defaults amongst the interconnected financial institutions; this is often referred to as systemic risk. In the model, there is a trade-off between reducing systemic risk and raising the capital requirements of the financial institutions. The policymaker considers this trade-off and determines the optimal capital requirements for individual financial institutions. I provide a method for finding and analyzing the optimal capital requirements that can be applied to arbitrary network structures and arbitrary distributions of investment returns.

In particular, I first consider a network model consisting only of direct exposures and show that the optimal capital requirements can be found by solving a stochastic linear programming problem. I then extend the analysis to financial networks with default costs and show the optimal capital requirements can be found by solving a stochastic mixed integer programming problem. The computational complexity of this problem poses a challenge, and I develop an iterative algorithm that can be efficiently executed. I show that the iterative algorithm leads to solutions that are nearly optimal by comparing it with lower bounds based on a dual approach. I also show that the iterative algorithm converges to the optimal solution.

Finally, I incorporate fire sales externalities into the model. In particular, I am able to extend the analysis of systemic risk and the optimal capital requirements with a single illiquid asset to a model with multiple illiquid assets. The model with multiple illiquid assets incorporates liquidation rules used by the banks. I provide an optimization formulation whose solution provides the equilibrium payments for a given liquidation rule.

I further show that the socially optimal capital problem using the ``socially optimal liquidation" and prioritized liquidation rules can be formulated as a convex and convex mixed integer problem, respectively. Finally, I illustrate the results of the methodology on numerical examples and

discuss some implications for capital regulation policy and stress testing.