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.

Documenting the Contextualization and Implementation of mhGAP-HIG in Post-earthquake Nepal

Background: The burden of mental health is increased in humanitarian settings, and needs to be addressed in emergency situations. The World Health Organization has recently released the mental health Global Action Programme Humanitarian Intervention Guide (mhGAP-HIG) in order to scale up mental health service delivery in humanitarian settings through task-shifting. This study aims to evaluate, contextualize and identify possible barriers and challenges to mhGAP-HIG manual content, training and implementation in post-earthquake Nepal.

Methods: This qualitative study was conducted in Kathmandu, Nepal. Key informant interviews were conducted with fourteen psychiatrists involved in a mhGAP-HIG Training of Trainers and Supervisors (ToTS) in order to assess the mhGAP-HIG, ToTS training, and the potential challenges and barriers to mhGAP-HIG implementation. Themes identified by informants were supplemented by process notes taken by the researcher during observed training sessions and meetings.

Results: Key themes emerging from key informant interviews include the need to take three factors into account in manual contextualization: culture, health systems and the humanitarian setting. This includes translation of the manual into the local language, adding or expanding upon conditions prevalent in Nepal, and more consideration to improving feasibility of manual use by non-specialists.

Conclusion: The mhGAP-HIG must be tailored to specific humanitarian settings for effective implementation. This study shows the importance of conducting a manual contextualization workshop prior to training in order to maximize the feasibility and success in training health care workers in mhGAP.

Gaussian Process Kernels for Cross-Spectrum Analysis in Electrophysiological Time Series

Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, the spectral mixture (SM) kernel was proposed to model the spectral density of a single task in a Gaussian process framework. This work develops a novel covariance kernel for multiple outputs, called the cross-spectral mixture (CSM) kernel. This new, flexible kernel represents both the power and phase relationship between multiple observation channels. The expressive capabilities of the CSM kernel are demonstrated through implementation of 1) a Bayesian hidden Markov model, where the emission distribution is a multi-output Gaussian process with a CSM covariance kernel, and 2) a Gaussian process factor analysis model, where factor scores represent the utilization of cross-spectral neural circuits. Results are presented for measured multi-region electrophysiological data.