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In recent years, female participation in TMT has become a hot issue in corporate governance domain. Do female executives bring a better corporate performance? This paper analyzes the correlation between the proportion of female executives and corporate performance by multiple regression analysis, furthermore, this paper adopts the propensity score matching to check whether other confusing factors have interferenced the results of multiple regression. The results of multiple regression and propensity score matching both shows that the proportion of female executives has significantly improved the value of the enterprise Tobin’Q. Based on the above empirical results, this paper give some advices, such as the government should perfect the laws and regulations, enterprises need to improve the talent selection and so on. Introduction Influenced by traditional culture, women tend to be ignored in work, and even worse, women are often blocked by glass ceiling. However, much evidences show that women could be the key to improve corporate performance. This phenomenon attracts many experts and scholars to study on it. Whether female could really improve corporate performance? How can we eliminate the impact of other factors on performance? The significance of studying these issues lies in keenly discovering management talents and cultivating them, which is one of the key ways to enhance the competitive advantage. In order to explore this topic, this paper firstly analyzes the correlation between female participation in TMT and corporate performance by multiple regression analysis, furthermore, this paper adopt propensity score matching(PSM) confirm the point of view. Literature Review At the end of the 20th century, foreign scholars began to explore the impact of women's participation in TMT on corporate performance. Most scholars' research supports a positive correlation between the two. Lickerish-Rovers M[1]studied 99 Dutch listed companies, the results showed that companies with high performance are often those with female directors. B.J. Chen [2] thought that selected corporate innovation performance will improve with the increase of female executives. H.Y. Yan and X.W. Liu [3],T. Ren[4] ,N. Zhang[5]confirmed the same results based on a study of the listed companies. However, some other scholars believe that female executives have no impact on business performance, and even undermine performance. Ahern KR[6] considered that female directors harmed the company's performance because of the young female directors and poor experience. Darmadi S[7] studied the relationship between the management's gender and Tobin’Q, return on total assets in 2007,showing that female executives with the two are both negatively correlated. Mirza HH[8] hold the view that the fewer women in the company, the higher the performance. In some studies, scholars believe that women's participation in TMT is also affected by factors such as the industry and female talent quality. Gallego-Álvarez I[9] found gender diversity does not make a difference in corporate performance, but there is a positive impact between the proportion of 408 female directors and corporate performance in some male-dominated industries such as construction and manufacturing. Experts and scholars use regression analysis, questionnaires and many other methods to study the correlation between female executives and corporate performance, getting many different results. As for the more convincing results, we need continue to do more studies. Model and Variables Assignment Propensity Score Matching In 1983, Rosenbaum and Rubin formally proposed the concept of propensity scores. The practical significance is that PSM could reduce the confusion caused by many other related factors. The propensity score is calculated by using the Logit or Probit model, and then matching in a certain way. In this paper, we adopted a commonly used way--Nearest-Neighbor Matching, which is based on the principle of comparing the propensity scores of each sample in the control group and the test group, matching the smallest gap one to one. This paper refers to Catalyst's approach, assigning a female executive ratio greater than 40% to 1 and the rest to 0. According to the difference in the proportion of female executives, the observation group was divided into an experimental group with a female executive ratio of 1 and a control group with a ratio of 0. Then we choose logit model to estimate the probability that each observation sample may contain female executives in the test group and the sample group, that is, the p value. The logit model is as follows: ls age lev numexe ) p 1 p ln( P i log y 5 4 3 2 1 β β β β β α ) ( + + + + + = = = bs t (1) In this equation, y is assigned 1 if the ratio of female executives is above 40% and the rest to 0. P value is propensity score.

The Study of Female Executives and Corporate Performance– Based on Propensity Score Matching Model