Unconditional quantile regressions, earnings disparity and gender discrimination in post-transformation of urban China

Market-oriented economic reform has gone through several key stages to bring substantial changes to current Chinese economy. It has accelerated after 1992, and meets the pattern transformation of economic development in 2002. During this dramatic and complicated economic transitional process, some issues caused people’s attention included the questions as: how does the earnings distribution change between genders from early market economy to post market economy; how do education, work experience, marriage and other factors affect gender earnings and what is the difference in internal group of women. In this paper, it will be used the data of the Chinese household income projects in 2002 and 2007 to analyse earnings disparity between genders and inner woman group. The unconditional quantile regression finds that comparing with past, the negative effect on earnings of marriage and taking care of child has much decreased, especially to women. However, high return rate to education of female workers is not as significant as before, the rate of work experience even fall faster. Along with the gender earnings gap increasing, the unexplained gap (discrimination gap) also increased over time, and is particularly pronounced for the lower and higher earnings group of women

Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen

Unconditional quantile regressions, earnings disparity and gender discrimination in post-transformation of urban China

Market-oriented economic reform has gone through several key stages to bring substantial changes to current Chinese economy. It has accelerated after 1992, and meets the pattern transformation of economic development in 2002. During this dramatic and complicated economic transitional process, some issues caused people’s attention included the questions as: how does the earnings distribution change between genders from early market economy to post market economy; how do education, work experience, marriage and other factors affect gender earnings and what is the difference in internal group of women. In this paper, it will be used the data of the Chinese household income projects in 2002 and 2007 to analyse earnings disparity between genders and inner woman group. The unconditional quantile regression finds that comparing with past, the negative effect on earnings of marriage and taking care of child has much decreased, especially to women. However, high return rate to education of female workers is not as significant as before, the rate of work experience even fall faster. Along with the gender earnings gap increasing, the unexplained gap (discrimination gap) also increased over time, and is particularly pronounced for the lower and higher earnings group of women

Designing the Game to Play: Optimizing Payoff Structure in Security Games

Effective game-theoretic modeling of defender-attacker behavior is becoming increasingly important. In many domains, the defender functions not only as a player but also the designer of the game's payoff structure. We study Stackelberg Security Games where the defender, in addition to allocating defensive resources to protect targets from the attacker, can strategically manipulate the attacker's payoff under budget constraints in weighted L^p-norm form regarding the amount of change. Focusing on problems with weighted L^1-norm form constraint, we present (i) a mixed integer linear program-based algorithm with approximation guarantee; (ii) a branch-and-bound based algorithm with improved efficiency achieved by effective pruning; (iii) a polynomial time approximation scheme for a special but practical class of problems. In addition, we show that problems under budget constraints in L^0-norm form and weighted L^\infty-norm form can be solved in polynomial time. We provide an extensive experimental evaluation of our proposed algorithms