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 firms’ 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

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

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

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