Showing papers by "Yu Xie published in 2018"
University of Southern California1, Duke University2, Stockholm School of Economics3, University of Virginia4, Center for Open Science5, University of Amsterdam6, University of Pennsylvania7, University of North Carolina at Chapel Hill8, University of Regensburg9, California Institute of Technology10, New York University11, Research Institute of Industrial Economics12, Cardiff University13, Mathematica Policy Research14, Northwestern University15, Ohio State University16, University of Sussex17, Texas A&M University18, Royal Holloway, University of London19, University of Zurich20, University of Melbourne21, University of Wisconsin-Madison22, University of Michigan23, Stanford University24, Rutgers University25, Columbia University26, University of Washington27, University of Edinburgh28, National University of Singapore29, Utrecht University30, Arizona State University31, Princeton University32, University of California, Los Angeles33, Imperial College London34, University of Innsbruck35, Harvard University36, University of Chicago37, University of Pittsburgh38, University of Notre Dame39, University of California, Berkeley40, Johns Hopkins University41, University of Bristol42, University of New South Wales43, Dartmouth College44, Whitman College45, University of Puerto Rico46, University of Milan47, University of California, Irvine48, Paris Dauphine University49, University of British Columbia50, Ludwig Maximilian University of Munich51, Purdue University52, Washington University in St. Louis53, University of California, Davis54, Microsoft55
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.
1,586 citations
TL;DR: The wage gap between mothers and non-mothers is examined in both nuclear and multi-generational families in the context of contemporary China, which has a long tradition of patriarchal families and shows that each additional child lowers hourly wages by about 12 percent.
Abstract: Past research on the "motherhood wage penalty" has all been based on data from nuclear families, leaving open the possibility that the motherhood wage penalty may be lower or even absent in multi-generational families. In this paper, the wage gap between mothers and non-mothers is examined in both nuclear and multi-generational families in the context of contemporary China, which has a long tradition of patriarchal families. Using 1993-2006 China Health and Nutrition Survey data, the magnitude and variation of motherhood penalty is explored with fixed effects models among 1,058 women. It is found that each additional child lowers hourly wages by about 12 percent. In addition, the results show that the motherhood penalty is largest for women living with husband's parents, smaller for women not living with parents, and nil for women living with their own parents.
35 citations
TL;DR: Li et al. as discussed by the authors examined the long-term effects of birth month on socioeconomic attainment and the compliance rate regarding the cutoff date and found a significant variation in socioeconomic achievement by birth month in adulthood in ways that differ from those reported by Liu and Li.
Abstract: Compulsory education systems usually specify a cutoff date regulating the precise age for entry into primary school. Existing literature from the USA and Europe has demonstrated that children born just before the cutoff date are disadvantaged in academic performance and has formalized this phenomenon as the “age position effect.” A recent study by Liu and Li (Sociol Stud 6:169–245, 2015) reported similar findings for China. Our study, however, challenges Liu and Li’s conclusion by examining the long-term effects of birth month on socioeconomic attainment and the compliance rate regarding the cutoff date. Using the 2005 1% Mini-census Survey and the 1992 National Sample Survey of the Living Situation of Chinese Children, we found a significant variation in socioeconomic achievement by birth month in adulthood in ways that differ from those reported by Liu and Li. In addition, we found compliance to be affected by both birth month and parental characteristics. In conclusion, our article proposes parental self-selection as an alternative explanation for the cutoff date effect.
12 citations
TL;DR: This paper defined the marginal treatment effect (MTE) as the expected treatment effect conditional on the propensity score (instead of all observed covariates) as well as a latent variable representing unobserved resistance to treatment.
Abstract: An essential feature common to all empirical social research is variability across units of analysis. Individuals differ not only in background characteristics, but also in how they respond to a particular treatment, intervention, or stimulation. Moreover, individuals may self-select into treatment on the basis of their anticipated treatment effects. To study heterogeneous treatment effects in the presence of self-selection, Heckman and Vytlacil (1999, 2001a, 2005, 2007b) have developed a structural approach that builds on the marginal treatment effect (MTE). In this paper, we extend the MTE-based approach through a redefinition of MTE. Specifically, we redefine MTE as the expected treatment effect conditional on the propensity score (instead of all observed covariates) as well as a latent variable representing unobserved resistance to treatment. The redefined MTE improves upon the original MTE in a number of aspects. First, while it is conditional on a unidimensional summary of covariates, it is sufficient to capture all of the treatment effect heterogeneity that is consequential for selection bias. Second, the new MTE is a bivariate function, and thus is easier to visualize than the original MTE. Third, as with the original MTE, the new MTE can also be used as a building block for evaluating standard causal estimands such as ATE and TT. However, the weights associated with the new MTE are simpler, more intuitive, and easier to compute. Finally, the redefined MTE immediately reveals treatment effect heterogeneity among individuals who are at the margin of treatment. As a result, it can be used to evaluate a wide range of policy changes with little analytical twist, and to design policy interventions that optimize the marginal benefits of treatment.
1 citations