Efficiency or Competition? A Structural Econometric Analysis of Canada's AWS Auction and the Set‐Aside Provision

In 2008, Industry Canada auctioned 105 MHz of spectrum to a group of bidders that included incumbents and potential new entrants into the Canadian mobile phone market, raising $4.25 billion. In an effort to promote new entry, 40 MHz of spectrum was set‐aside for new entrants. In order to estimate the implicit cost of the set‐aside provision, we estimate the parameters of the bidders' profit function via a maximum match estimator based on the notion of pairwise stability in matches. We find that all telecommunications firms valued both geographic complementarities across auction licenses as well as absolute spectrum. Under a reasonable alternative scenario, our results indicate that the set‐aside led to a total profit loss of approximately 10%.     

农村地区食品安全监管配套制度的完善与落实

自2009年《食品安全法》颁布至今,我国农村地区的食品安全事件依旧频发,《食品安全法》在农村地区的实施效果远不如城市。造成这种状况的原因除长期以来存在的城乡二元结构和农村居民自身生活习惯等问题外,更主要的是《食品安全法》作为一部在全国范围内统一实施的法律,其明显缺乏专门针对农村食品经营主体的监管配套制度,从而使该法在广大农村地区实施的过程中监管能力明显不足。据此,提出构建食品安全监管在农村地区的配套制度,加强技术监管和信息化管理以及充分发挥社会共治等建议。     

Quasi-binomial zero-inflated regression model suitable for variables with bounded support

In recent years, a variety of regression models, including zero-inflated and hurdle versions, have been proposed to explain the case of a dependent variable with respect to exogenous covariates. Apart from the classical Poisson, negative binomial and generalised Poisson distributions, many proposals have appeared in the statistical literature, perhaps in response to the new possibilities offered by advanced software that now enables researchers to implement numerous special functions in a relatively simple way. However, we believe that a significant research gap remains, since very little attention has been paid to the quasi-binomial distribution, which was first proposed over fifty years ago. We believe this distribution might constitute a valid alternative to existing regression models, in situations in which the variable has bounded support. Therefore, in this paper we present a zero-inflated regression model based on the quasi-binomial distribution, taking into account the moments and maximum likelihood estimators, and perform a score test to compare the zero-inflated quasi-binomial distribution with the zero-inflated binomial distribution, and the zero-inflated model with the homogeneous model (the model in which covariates are not considered). This analysis is illustrated with two data sets that are well known in the statistical literature and which contain a large number of zeros.     

Adherence to Mediterranean diet and risk of prostate cancer

In Europe, countries following the traditional Mediterranean Diet (MeDi), particularly Southern European countries, have lower prostate cancer (PCa) incidence and mortality compared to other European regions. In the present study, we investigated the association between the MeDi and the relative risk of PCa and tumor aggressiveness in a Spanish population. Among individual score components, it has been found that subjects with PCa were less likely to consume olive oil as the main culinary fat, vegetables, fruits and fish than those without. However, these differences were not statistically significative. A high intake of fruit, vegetables and cooked tomato sauce Mediterranean style (sofrito) was related to less PCa aggressiveness. Results showed that there are no differences in the score of adherence to the Mediterranean dietary patterns between cases and controls, with mean values of 8.37?±?1.80 and 8.25?±?2.48, respectively. However, MeDi was associated with lower PCa agressiveness according to Gleason score. Hence, relations between Mediterranean dietary patterns and PCa are still inconclusive and merit further investigations. Further large-scale studies are required to clarify the effect of MeDi on prostate health, in order to establish the role of this diet in the prevention of PCa.     

Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.     

Poverty and educational achievement in the US: A less-biased estimate using PISA 2012 data

In the current paper, we employ the most recent Programme for International Student Assessment (PISA) data to calculate a less-biased estimate of poverty on US achievement. The PISA was specifically chosen as it is an assessment removed from a specific curriculum and instead focuses on concepts that students should know in order to participate in a global economy. Using a propensity score matching approach, our findings suggest that US students in poverty have notable educational attainment deficiencies compared to a matched group of students who are not in poverty. In other words, when we select two students who have a great deal in common but for the fact that one comes from a poverty background, the student in poverty is expected to perform nearly 28 points, or about a quarter of a standard deviation lower, on the PISA assessment. In real terms, this puts math achievement for children not in poverty on-par with the Organisation for Economic Co-operation and Development (OECD) average, while children in poverty are well below the OECD average.     

Quantifying the similarity of 2D images using edge pixels: an application to the forensic comparison of footwear impressions

We propose a novel method to quantify the similarity between an impression (Q) from an unknown source and a test impression (K) from a known source. Using the property of geometrical congruence in the impressions, the degree of correspondence is quantified using ideas from graph theory and maximum clique (MC). The algorithm uses the x and y coordinates of the edges in the images as the data. We focus on local areas in Q and the corresponding regions in K and extract features for comparison. Using pairs of images with known origin, we train a random forest to classify pairs into mates and non-mates. We collected impressions from 60 pairs of shoes of the same brand and model, worn over six months. Using a different set of very similar shoes, we evaluated the performance of the algorithm in terms of the accuracy with which it correctly classified images into source classes. Using classification error rates and ROC curves, we compare the proposed method to other algorithms in the literature and show that for these data, our method shows good classification performance relative to other methods. The algorithm can be implemented with the R package shoeprintr.     

College-bound friends and college application choices: Heterogeneous effects for Latino and White students

Friends are among the most influential social forces affecting adolescent behavior, yet little work has focused on the influence of friends on the decision to apply to college. Using data from the senior cohort of the Texas Higher Education Opportunity Project, we employed propensity score matching with sensitivity analyses to investigate links between having college-oriented friends and applying to college. We found that college-oriented friends increased the likelihood of applying to any college and to 4-year colleges, both for White and Latino students. However, Latino students benefited less from college-oriented friends compared to White students. Our findings suggest that college-choice models should more directly account for the influence of friends in applying to college, taking account of racial and ethnic differences, and future research should explore why Latino students benefit less.     

Research Design Meets Market Design: Using Centralized Assignment for Impact Evaluation

A growing number of school districts use centralized assignment mechanisms to allocate school seats in a manner that reflects student preferences and school priorities. Many of these assignment schemes use lotteries to ration seats when schools are oversubscribed. The resulting random assignment opens the door to credible quasi‐experimental research designs for the evaluation of school effectiveness. Yet the question of how best to separate the lottery‐generated randomization integral to such designs from non‐random preferences and priorities remains open. This paper develops easily‐implemented empirical strategies that fully exploit the random assignment embedded in a wide class of mechanisms, while also revealing why seats are randomized at one school but not another. We use these methods to evaluate charter schools in Denver, one of a growing number of districts that combine charter and traditional public schools in a unified assignment system. The resulting estimates show large achievement gains from charter school attendance. Our approach generates efficiency gains over ad hoc methods, such as those that focus on schools ranked first, while also identifying a more representative average causal effect. We also show how to use centralized assignment mechanisms to identify causal effects in models with multiple school sectors.