Rates of Return to University Education: the Regression Discontinuity Design

Estimating the rate of return to a university degree has always been difficult due to the problem of omitted variable biases. Benefiting from a special feature of the University Admission system in China, which has clear cutoffs for university entry, combined with a unique data set with information on individual National College Entrance Examination (NCEE) scores, we estimate the Local Average Treatment Effects (LATE) of university education based on a Regression Discontinuity design. To the best of our knowledge, this is the first study to use RD design to estimate the causal effect of a university education on earnings. Our results show that the rates of return to 4-year university education relative to 3-year college education are 40 and 60 per cent for the compliers in the male and female samples, respectively, which are much larger than the simple OLS estimations revealed in previous literature. Since in our sample a large proportion of individuals are compliers (45 per cent for males and 48 per cent for females), the LATEs estimated in this paper have a relatively general implication. In addition, we find that the LATEs are likely to be larger than ATEs, suggesting that the inference drawn from average treatment effects might understate the true effects of the university expansion program introduced in China in 1999 and thereafter.Rate of return to education, Regression Discontinuity Design, China

Rates of Return to University Education: The Regression Discontinuity Design

Estimating the rate of return to a university degree has always been difficult due to the problem of omitted variable biases. Benefiting from a special feature of the University Admission system in China, which has clear cutoffs for university entry, combined with a unique data set with information on individual National College Entrance Examination (NCEE) scores, we estimate the Local Average Treatment Effects (LATE) of university education based on a Regression Discontinuity design. To the best of our knowledge, this is the first study to use RD design to estimate the causal effect of a university education on earnings. Our results show that the rates of return to 4-year university education relative to 3-year college education are 40 and 60 per cent for the compliers in the male and female samples, respectively, which are much larger than the simple OLS estimations revealed in previous literature. Since in our sample a large proportion of individuals are compliers (45 per cent for males and 48 per cent for females), the LATEs estimated in this paper have a relatively general implication. In addition, we find that the LATEs are likely to be larger than ATEs, suggesting that the inference drawn from average treatment effects might understate the true effects of the university expansion program introduced in China in 1999 and thereafter.rate of return to education, regression discontinuity design, China

A discussion on numerical shock stability of unstructured finite volume method: Riemann solvers and limiters

Numerical shock instability is a complexity which may occur in supersonic simulations. Riemann solver is usually the crucial factor that affects both the computation accuracy and numerical shock stability. In this paper, several classical Riemann solvers are discussed, and the intrinsic mechanism of shock instability is especially concerned. It can be found that the momentum perturbation traversing shock wave is a major reason that invokes instability. Furthermore, slope limiters used to depress oscillation across shock wave is also a key factor for computation stability. Several slope limiters can cause significant numerical errors near shock waves, and make the computation fail to converge. Extra dissipation of Riemann solvers and slope limiters can be helpful to eliminate instability, but reduces the computation accuracy. Therefore, to properly introduce numerical dissipation is critical for numerical computations. Here, pressure based shock indicator is used to show the position of shock wave and tunes the numerical dissipation. Overall, the presented methods are showing satisfactory results in both the accuracy and stability.Comment: Presented at 2nd International Conference in Aerospace for Young Scientists. 07-08 September 2017, Beijing, P.R.Chin

Transmission of integrin β7 transmembrane domain topology enables gut lymphoid tissue development.

Integrin activation regulates adhesion, extracellular matrix assembly, and cell migration, thereby playing an indispensable role in development and in many pathological processes. A proline mutation in the central integrin β3 transmembrane domain (TMD) creates a flexible kink that uncouples the topology of the inner half of the TMD from the outer half. In this study, using leukocyte integrin α4β7, which enables development of gut-associated lymphoid tissue (GALT), we examined the biological effect of such a proline mutation and report that it impairs agonist-induced talin-mediated activation of integrin α4β7, thereby inhibiting rolling lymphocyte arrest, a key step in transmigration. Furthermore, the α4β7(L721P) mutation blocks lymphocyte homing to and development of the GALT. These studies show that impairing the ability of an integrin β TMD to transmit talin-induced TMD topology inhibits agonist-induced physiological integrin activation and biological function in development

Optimal Transport for Treatment Effect Estimation

Estimating conditional average treatment effect from observational data is highly challenging due to the existence of treatment selection bias. Prevalent methods mitigate this issue by aligning distributions of different treatment groups in the latent space. However, there are two critical problems that these methods fail to address: (1) mini-batch sampling effects (MSE), which causes misalignment in non-ideal mini-batches with outcome imbalance and outliers; (2) unobserved confounder effects (UCE), which results in inaccurate discrepancy calculation due to the neglect of unobserved confounders. To tackle these problems, we propose a principled approach named Entire Space CounterFactual Regression (ESCFR), which is a new take on optimal transport in the context of causality. Specifically, based on the framework of stochastic optimal transport, we propose a relaxed mass-preserving regularizer to address the MSE issue and design a proximal factual outcome regularizer to handle the UCE issue. Extensive experiments demonstrate that our proposed ESCFR can successfully tackle the treatment selection bias and achieve significantly better performance than state-of-the-art methods.Comment: Accepted as NeurIPS 2023 Poste