We investigate the degree to which corporate governance and ownership affect the innovation performance of firms in China with a particular focus on privately owned small and medium enterprises (SMEs). Using the appropriate theoretical frameworks, we derive hypotheses regarding the impact of ownership concentration, board size and composition, and the background of the CEO on innovative activity. These hypotheses are tested using a unique sample of 370 mostly private and relatively small Chinese firms in Zhejiang province, for the period 2004 to 2006. Using two measures of innovation, invention patents and new product sales, and a variety of estimation methods appropriate to each measure, we find limited evidence that corporate governance affects innovation performance, but the results do depend on the measure of innovation. In general, the results suggest that for this sample, corporate governance and ownership affect innovation activity more strongly when innovation is measured by patenting activity, rather than new product sales. We conclude with a discussion as to why this might be.

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Minutiae extraction is of critical importance in automated fingerprint recognition.Previous works on rolled/slap fingerprints failed on latent fingerprints due to noisy ridge patterns and complex background noises. In this paper, we propose a new way to design deep convolutional network combining domain knowledge and the representation ability of deep learning. In terms of orientation estimation, segmentation, enhancement and minutiae extraction, several typical traditional methods performed well on rolled/slap fingerprints are transformed into convolutional manners and integrated as an unified plain network. We demonstrate that this pipeline is equivalent to a shallow network with fixed weights. The network is then expanded to enhance its representation ability and the weights are released to learn complex background variance from data, while preserving end-to-end differentiability. Experimental results on NIST SD27 latent database and FVC 2004 slap database demonstrate that the proposed algorithm outperforms the state-of-the-art minutiae extraction algorithms. Code is made publicly available at: https://github.com/felixTY/FingerNet.

We address the institutional voids hypothesis, which suggests affiliation with a business group will improve a firm's performance in circumstances of poor‐quality institutions and extensive market failures. We hypothesize that initial positive effects of group affiliation should decline as the quality of market institutions improves. Further, we hypothesize that differences in state and private ownership will influence the value and persistence of firm affiliation. Using data on 476 publicly listed firms in 1999 and 467 matched firms in 2004, we find support for a temporal hypothesis that affiliation with a business group improves performance, but the value of group affiliation declines over time. We also find support for a state ‘helping hand’ hypothesis that suggests firms with high levels of state ownership initially experienced an amplified value effect from their group affiliation, which disappeared by 2004. The results suggest that China's policy makers are beginning to establish an institutional and market infrastructure that is conducive to entry by unaffiliated, freestanding firms.

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