9,431 research outputs found
Red Capitalists: Political Connections and the Growth and Survival of Start-up Companies in China
This paper analyses the role of political connections in the post-entry performance of private start-up companies in China. It documents robust evidence that political affiliation enhances firmsâ survival and growth prospects, even if politically neutral start-ups enjoy faster productivity improvements. In addition, the benefits of political connections are largely confined to firms associated with local or top level governments, and they are more pronounced in capital-intensive industries.China, political connections, growth, survival
Source of Finance, Growth and Firm Size ? Evidence from China
Using a comprehensive firm-level dataset spanning the period 1998-2005, this paper provides a thorough investigation of the relationship between f$China, finance, firm size, growth
Finance and Firm Start-up Size: Quantile Regression Evidence from China
Using a unique dataset which provides information on the financial structure of start-up companies in the Chinese manufacturing industry, this paper documents robust evidence that access to formal financing channels has beneficial effects on firm size, these effects being more marked as we move up the entry size distribution. By contrast we find negative relationships between informal finance and entry size across all size quantiles. Given the well-documented positive correlations between firm size and numerous performance indicators, this paper has therefore uncovered entry size as an additional channel through which financial development promotes growth.China, finance, growth
The Effects of Foreign Acquisition on Domestic and Export Markets Dynamics in China
Using recent data from the Chinese manufacturing industry and the generalised propensity score, this paper establishes economically significant causal effects of foreign acquisition on domestic and export markets dynamics.FDI, export, finance
Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition
Handwritten mathematical expression recognition is a challenging problem due
to the complicated two-dimensional structures, ambiguous handwriting input and
variant scales of handwritten math symbols. To settle this problem, we utilize
the attention based encoder-decoder model that recognizes mathematical
expression images from two-dimensional layouts to one-dimensional LaTeX
strings. We improve the encoder by employing densely connected convolutional
networks as they can strengthen feature extraction and facilitate gradient
propagation especially on a small training set. We also present a novel
multi-scale attention model which is employed to deal with the recognition of
math symbols in different scales and save the fine-grained details that will be
dropped by pooling operations. Validated on the CROHME competition task, the
proposed method significantly outperforms the state-of-the-art methods with an
expression recognition accuracy of 52.8% on CROHME 2014 and 50.1% on CROHME
2016, by only using the official training dataset
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