Does innovation matter for Chinese hightech exports? a firm-level analysis

This paper analyzes the role of innovation activities in the export participation of Chinese high-tech firms during the period of 2005-2007. Using a parametric, instrumental variable approach and a non-parametric matching method, we find that firm-level innovation efforts, measured by R&D and product innovation dummies, play only a minor role for domestic exporters. Foreign-invested firms dominate the high-tech exports but do not rely on indigenous innovation activities. These results thus confirm prior findings that the success of Chinese high-tech exports does not result from heavy R&D expenditure and technological progress. Moreover, different types of innovation measures show different impacts on the likelihood of exporting. The impacts of innovation on exporting vary widely across industries and provinces.Exporting, Innovation, R&D, High technology, China

Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network

Human mobility prediction is a fundamental task essential for various applications, including urban planning, location-based services and intelligent transportation systems. Existing methods often ignore activity information crucial for reasoning human preferences and routines, or adopt a simplified representation of the dependencies between time, activities and locations. To address these issues, we present Hierarchical Graph Attention Recurrent Network (HGARN) for human mobility prediction. Specifically, we construct a hierarchical graph based on all users' history mobility records and employ a Hierarchical Graph Attention Module to capture complex time-activity-location dependencies. This way, HGARN can learn representations with rich human travel semantics to model user preferences at the global level. We also propose a model-agnostic history-enhanced confidence (MAHEC) label to focus our model on each user's individual-level preferences. Finally, we introduce a Temporal Module, which employs recurrent structures to jointly predict users' next activities (as an auxiliary task) and their associated locations. By leveraging the predicted future user activity features through a hierarchical and residual design, the accuracy of the location predictions can be further enhanced. For model evaluation, we test the performances of our HGARN against existing SOTAs in both the recurring and explorative settings. The recurring setting focuses on assessing models' capabilities to capture users' individual-level preferences, while the results in the explorative setting tend to reflect the power of different models to learn users' global-level preferences. Overall, our model outperforms other baselines significantly in all settings based on two real-world human mobility data benchmarks. Source codes of HGARN are available at https://github.com/YihongT/HGARN.Comment: 11 page

Does innovation matter for Chinese hightech exports? a firm-level analysis

This paper analyzes the role of innovation activities in the export participation of Chinese high-tech firms during the period of 2005-2007. Using a parametric, instrumental variable approach and a non-parametric matching method, we find that firm-level innovation efforts, measured by R&D and product innovation dummies, play only a minor role for domestic exporters. Foreign-invested firms dominate the high-tech exports but do not rely on indigenous innovation activities. These results thus confirm prior findings that the success of Chinese high-tech exports does not result from heavy R&D expenditure and technological progress. Moreover, different types of innovation measures show different impacts on the likelihood of exporting. The impacts of innovation on exporting vary widely across industries and provinces

Does innovation matter for Chinese hightech exports? a firm-level analysis

This paper analyzes the role of innovation activities in the export participation of Chinese high-tech firms during the period of 2005-2007. Using a parametric, instrumental variable approach and a non-parametric matching method, we find that firm-level innovation efforts, measured by R&D and product innovation dummies, play only a minor role for domestic exporters. Foreign-invested firms dominate the high-tech exports but do not rely on indigenous innovation activities. These results thus confirm prior findings that the success of Chinese high-tech exports does not result from heavy R&D expenditure and technological progress. Moreover, different types of innovation measures show different impacts on the likelihood of exporting. The impacts of innovation on exporting vary widely across industries and provinces

LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion of the information of multi-hop neighbors to include more nodes with homophily. However, the heterophily might differ among nodes, which requires to consider the local topology. Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module. For better fusion, we propose a novel and efficient Initial Residual Difference Connection (IRDC) to extract more informative multi-hop information. Moreover, we provide theoretical analysis on the effectiveness of LocalSim representing node homophily on synthetic graphs. Extensive evaluations over real benchmark datasets show that our proposed method, namely Local Similarity Graph Neural Network (LSGNN), can offer comparable or superior state-of-the-art performance on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model can significantly boost the performance of existing GNNs. Our code is provided at https://github.com/draym28/LSGNN.Comment: The first two authors contributed equally to this work; IJCAI2

How Exporting Firms Respond to Technical Barriers to Trade?

This paper investigates how Technical Barriers to Trade (TBT) affect firm export performance. The implementation of the “Children-Resistance” act (CR act) in the EU offers an ideal quasi-natural experiment to identify the causal effect of TBTs on firm performance. Using data on Chinese firms that export cigarette lighters between 2004 and 2008, empirical results show that firms that export to the EU not only adjust their product quality to meet the requirements in the CR act, but also upgrade their product quality in other dimensions. However, both the export value and export volume to the EU decline. At the same time, less productive exporters are forced to exit from the EU market. In addition, while the effect of the CR act on export quality is significant only in the implementation year, its impact on firm-level export scale last longer even after its implementation, which is referred to as a dynamic impact. Lastly, Heterogeneous effect of TBT is also documented