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

Non unital generalized tracially approximated C*-algebras

Let $\Omega$ be a class of ${\rm C^*}$-algebras. In this paper, we study a class of not necessarily unital generalized tracial approximation ${\rm C^*}$-algebras, and the class of simple ${\rm C^*}$-algebras which can be generally tracially approximated by ${\rm C^*}$-algebras in $\Omega$, denoted by ${\rm gTA}\Omega$. Let $\Omega$ be a class of unital ${\rm C^*}$-algebras and let $A$ be a simple unital ${\rm C^*}$-algebra. Then $A\in {\rm gTA}\Omega$, if, and only if, $A\in {\rm WTA}\Omega$ (where ${\rm TA}\Omega$ is the class of weakly tracially approximable unital ${\rm C^*}$-algebras introduced by Elliott, Fan, and Fang).Consider the class of ${\rm C^*}$-algebras which are tracially $\mathcal{Z}$-absorbing (or are of tracial nuclear dimension at most $n$, or are $m$-almost divisible, or have the property $\rm SP$). Then $A$ is tracially $\mathcal{Z}$-absorbing (respectively, has tracial nuclear dimension at most $n$, is weakly ($n, m$)-almost divisible, has the property $\rm SP$) for any simple ${\rm C^*}$-algebra $A$ in the corresponding class of generalized tracial approximation ${\rm C^*}$-algebras.Comment: 21 pages. arXiv admin note: text overlap with arXiv:2309.08900, arXiv:2203.05700, arXiv:2206.0503

Uterus at a price: Disability insurance and hysterectomy

Taiwanese Labor, Government Employee, and Farmer Insurance programs provide 5 to 6 months of salary to enrollees who undergo hysterectomies or oophorectomies before their 45th birthday. These programs create incentives for more and earlier treatments, referred to as inducement and timing effects. Using National Health Insurance data between 1997 and 2011, we estimate these effects on surgery hazards by difference-in-difference and bunching-smoothing polynomial methods. For Government Employee and Labor Insurance, inducement is 11-12% of all hysterectomies, and timing 20% of inducement. For oophorectomies, both effects are insignificant. Enrollees' behaviors are consistent with rational choices. Each surgery qualifies an enrollee for the same benefit, but oophorectomy has more adverse health consequences than hysterectomy. Induced hysterectomies increase benefit payments and surgical costs, at about the cost of a mammogram and 5 pap smears per enrollee.Accepted manuscrip

A genomic signature for accurate classification and prediction of clinical outcomes in cancer patients treated with immune checkpoint blockade immunotherapy

Tumor mutational burden (TMB) is associated with clinical response to immunotherapy, but application has been limited to a subset of cancer patients. We hypothesized that advanced machine-learning and proper modeling could identify mutations that classify patients most likely to derive clinical benefits. Training data: Two sets of public whole-exome sequencing (WES) data for metastatic melanoma. Validation data: One set of public non-small cell lung cancer (NSCLC) data. Least Absolute Shrinkage and Selection Operator (LASSO) machine-learning and proper modeling were used to identify a set of mutations (biomarker) with maximum predictive accuracy (measured by AUROC). Kaplan-Meier and log-rank methods were used to test prediction of overall survival. The initial model considered 2139 mutations. After pruning, 161 mutations (11%) were retained. An optimal threshold of 0.41 divided patients into high-weight (HW) or low-weight (LW) TMB groups. Classification for HW-TMB was 100% (AUROC = 1.0) on melanoma learning/testing data; HW-TMB was a prognostic marker for longer overall survival. In validation data, HW-TMB was associated with survival (p = 0.0057) and predicted 6-month clinical benefit (AUROC = 0.83) in NSCLC. In conclusion, we developed and validated a 161-mutation genomic signature with outstanding 100% accuracy to classify melanoma patients by likelihood of response to immunotherapy. This biomarker can be adapted for clinical practice to improve cancer treatment and care

Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional Data

Non-probability samples become increasingly popular in survey statistics but may suffer from selection biases that limit the generalizability of results to the target population. We consider integrating a non-probability sample with a probability sample which provides high-dimensional representative covariate information of the target population. We propose a two-step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded-concave penalties to select important variables for the sampling score of selection into the non-probability sample and the outcome model. We show that the penalized estimating equation approach enjoys the selection consistency property for general probability samples. The major technical hurdle is due to the possible dependence of the sample under the finite population framework. To overcome this challenge, we construct martingales which enable us to apply Bernstein concentration inequality for martingales. In the second step, we focus on a doubly robust estimator of the finite population mean and re-estimate the nuisance model parameters by minimizing the asymptotic squared bias of the doubly robust estimator. This estimating strategy mitigates the possible first-step selection error and renders the doubly robust estimator root-n consistent if either the sampling probability or the outcome model is correctly specified

Analysis of the Host Transcriptome from Demyelinating Spinal Cord of Murine Coronavirus-Infected Mice

Persistent infection of the mouse central nervous system (CNS) with mouse hepatitis virus (MHV) induces a demyelinating disease pathologically similar to multiple sclerosis and is therefore used as a model system. There is little information regarding the host factors that correlate with and contribute to MHV-induced demyelination. Here, we detail the genes and pathways associated with MHV-induced demyelinating disease in the spinal cord. High-throughput sequencing of the host transcriptome revealed that demyelination is accompanied by numerous transcriptional changes indicative of immune infiltration as well as changes in the cytokine milieu and lipid metabolism. We found evidence that a Th1-biased cytokine/chemokine response and eicosanoid-derived inflammation accompany persistent MHV infection and that antigen presentation is ongoing. Interestingly, increased expression of genes involved in lipid transport, processing, and catabolism, including some with known roles in neurodegenerative diseases, coincided with demyelination. Lastly, expression of several genes involved in osteoclast or bone-resident macrophage function, most notably TREM2 and DAP12, was upregulated in persistently infected mouse spinal cord. This study highlights the complexity of the host antiviral response, which accompany MHV-induced demyelination, and further supports previous findings that MHV-induced demyelination is immune-mediated. Interestingly, these data suggest a parallel between bone reabsorption by osteoclasts and myelin debris clearance by microglia in the bone and the CNS, respectively. To our knowledge, this is the first report of using an RNA-seq approach to study the host CNS response to persistent viral infection

The burden of chronic ureteral stenting in cervical cancer survivors

ABSTRACT ARTICLE INFO ______________________________________________________________ ______________________ Purpose: Ureteral obstruction in cervical cancer occurs in up to 11% of patients, many of whom undergo ureteral stenting. Our aim was to describe the patient burden of chronic ureteral stenting in a population-based cohort by detailing two objectives: (1) the frequency of repeat procedures for ureteral obstruction; and, (2) the frequency of urinary adverse effects (UAEs) (e.g., lower urinary tract symptoms, flank pain). Materials and Methods: From SEER-Medicare, we identified 202 women who underwent ureteral stent placement prior to or following cervical cancer treatment. The frequency of repeat procedures and rate ratios were compared between treatment modalities. The rates and rate ratios of UAEs were compared between our primary cohort (stent + cervical cancer) and the following groups: no stent + cervical cancer, stent + no cancer, and no stent + no cancer. The "no cancer" group was drawn from the 5% Medicare sample. Results: 117/202 women (58%) underwent >1 stent procedure. The frequency of additional procedures was significantly higher in patients who received radiation as part of their treatment. UAEs were very common in women with stent + cancer. The rate of UTI was 190 (per 100 person-years), 67 for LUTS, 42 for stones, and 6 for flank pain. These rates were 3-10 fold higher than in the no stent + no cancer control group; rates were also higher than in the no stent + cancer and the stent + no cancer women. Conclusions: The burden of disease associated with ureteral stents is higher than expected and urologists should be actively involved in stent management, screening for associated symptoms and offering definitive reconstruction when appropriate