121 research outputs found
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
RouteKG: A knowledge graph-based framework for route prediction on road networks
Short-term route prediction on road networks allows us to anticipate the
future trajectories of road users, enabling a plethora of intelligent
transportation applications such as dynamic traffic control or personalized
route recommendation. Despite recent advances in this area, existing methods
focus primarily on learning sequential transition patterns, neglecting the
inherent spatial structural relations in road networks that can affect human
routing decisions. To fill this gap, this paper introduces RouteKG, a novel
Knowledge Graph-based framework for route prediction. Specifically, we
construct a Knowledge Graph on the road network, thereby learning and
leveraging spatial relations, especially moving directions, which are crucial
for human navigation. Moreover, an n-ary tree-based algorithm is introduced to
efficiently generate top-K routes in a batch mode, enhancing scalability and
computational efficiency. To further optimize the prediction performance, a
rank refinement module is incorporated to fine-tune the candidate route
rankings. The model performance is evaluated using two real-world vehicle
trajectory datasets from two Chinese cities, Chengdu and Shanghai, under
various practical scenarios. The results demonstrate a significant improvement
in accuracy over baseline methods.We further validate our model through a case
study that utilizes the pre-trained model as a simulator for real-time traffic
flow estimation at the link level. The proposed RouteKG promises wide-ranging
applications in vehicle navigation, traffic management, and other intelligent
transportation tasks
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
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