Economic Reform, Education Expansion, and Earnings Inequality for Urban Males in China, 1988-2007

In the past 20 years the average real earnings of Chinese urban male workers have increased by 350 per cent. Accompanying this unprecedented growth is a considerable increase in earnings inequality. Between 1988 and 2007 the variance of log earnings increased from 0.27 to 0.48, a 78 per cent increase. Using a unique set of repeated cross-sectional data this paper examines the causes of this increase in earnings inequality. We find that the major changes occurred in the 1990s when the labour market moved from a centrally planned system to a market oriented system. The decomposition exercise conducted in the paper identifies the factor that drives the significant increase in the earnings variance in the 1990s to be an increase in the within-education-experience cell residual variances. Such an increase may be explained mainly by the increase in the price of unobserved skills. When an economy shifts from an administratively determined wage system to a market-oriented one, rewards to both observed and unobserved skills increase. The turn of the century saw a slowing down of the reward to both the observed and unobserved skills, due largely to the college expansion program that occurred at the end of the 1990s.Earnings inequality, China

Economic Reform, Education Expansion, and Earnings Inequality for Urban Males in China, 1988-2007

In the past 20 years the average real earnings of Chinese urban male workers have increased by 350 per cent. Accompanying this unprecedented growth is a considerable increase in earnings inequality. Between 1988 and 2007 the variance of log earnings increased from 0.27 to 0.48, a 78 per cent increase. Using a unique set of repeated cross-sectional data this paper examines the causes of this increase in earnings inequality. We find that the major changes occurred in the 1990s when the labour market moved from a centrally planned system to a market oriented system. The decomposition exercise conducted in the paper identifies the factor that drives the significant increase in the earnings variance in the 1990s to be an increase in the within-education-experience cell residual variances. Such an increase may be explained mainly by the increase in the price of unobserved skills. When an economy shifts from an administratively determined wage system to a market-oriented one, rewards to both observed and unobserved skills increase. The turn of the century saw a slowing down of the reward to both the observed and unobserved skills, due largely to the college expansion program that occurred at the end of the 1990s.earnings inequality, China

One More Weight is Enough: Toward the Optimal Traffic Engineering with OSPF

Traffic Engineering (TE) leverages information of network traffic to generate a routing scheme optimizing the traffic distribution so as to advance network performance. However, optimize the link weights for OSPF to the offered traffic is an known NP-hard problem. In this paper, motivated by the fairness concept of congestion control, we firstly propose a new generic objective function, where various interests of providers can be extracted with different parameter settings. And then, we model the optimal TE as the utility maximization of multi-commodity flows with the generic objective function and theoretically show that any given set of optimal routes corresponding to a particular objective function can be converted to shortest paths with respect to a set of positive link weights. This can be directly configured on OSPF-based protocols. On these bases, we employ the Network Entropy Maximization(NEM) framework and develop a new OSPF-based routing protocol, SPEF, to realize a flexible way to split traffic over shortest paths in a distributed fashion. Actually, comparing to OSPF, SPEF only needs one more weight for each link and provably achieves optimal TE. Numerical experiments have been done to compare SPEF with the current version of OSPF, showing the effectiveness of SPEF in terms of link utilization and network load distribution

Weakly-Supervised Neural Text Classification

Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.Comment: CIKM 2018 Full Pape