Good for the Common Good: Sociotropic Concern and Double Standards toward High- and Low-Skilled Immigrants in Six Wealthy Countries

Immigration policy has conventionally implied a double standard, in which high-skilled immigrants are more acceptable due to their potential contribution to the national economy, little welfare burden, and better cultural adaption, while low-skilled ones are not favored, because of a belief in their limited contribution to the common good. In contrast to the egocentric interest explanation, we emphasize the importance of such sociotropic concerns and suggest that acceptance of immigrants with different skill levels is an outcome of perceived growth and distributional impacts or threatened cultural boundaries. Drawing data from the 2011 Transatlantic Trends: Immigration survey, we performed seemingly unrelated regression modelling to compare natives’ attitudinal responses in six wealthy countries. We found that in addition to the evidence that high-skilled immigrants are favored over low-skilled ones, the worry about welfare burden to the nation is one of the main factors causing locals to dislike low-skilled immigrants. The public who perceive immigrants’ threats to the national economy in terms of taking jobs away in general are also likely to disfavor high-skilled immigrants. Expectations of cultural assimilation are somewhat detached from acceptance of high-skilled immigrants. As the research results imply clear limitation of the double-standard perspective, we propose a new scheme for understanding both double- and single-standard views and incorporate these variations into the sociotropic theory and future research design.

     

Semiparametric estimation for seasonal long-memory time series using generalized exponential models

We consider a generalized exponential (GEXP) model in the frequency domain for modeling seasonal long-memory time series. This model generalizes the fractional exponential (FEXP) model [Beran, J., 1993. Fitting long-memory models by generalized linear regression. Biometrika 80, 817–822] to allow the singularity in the spectral density occurring at an arbitrary frequency for modeling persistent seasonality and business cycles. Moreover, the short-memory structure of this model is characterized by the Bloomfield [1973. An exponential model for the spectrum of a scalar time series. Biometrika 60, 217–226] model, which has a fairly flexible semiparametric form. The proposed model includes fractionally integrated processes, Bloomfield models, FEXP models as well as GARMA models [Gray, H.L., Zhang, N.-F., Woodward, W.A., 1989. On generalized fractional processes. J. Time Ser. Anal. 10, 233–257] as special cases. We develop a simple regression method for estimating the seasonal long-memory parameter. The asymptotic bias and variance of the corresponding long-memory estimator are derived. Our methodology is applied to a sunspot data set and an Internet traffic data set for illustration.     

THE STATE'S INTEREST SEEKING AND ECONOMIC STAGNATION IN THE THIRD WORLD:

In the development literature, the state's interest seeking is considered a crucial determinant of economic stagnation in the Third World. The internality inefficiency, opportunity costs, and structural disincentives generated by such a state constitute the key mechanisms responsible for slow growth. This study provides a major quantitative test of this hypothesis. A sectoral approach is proposed to measure the interest seeking of the state by using the central government expenditures for state employees' wages and salaries as an indicator. As the analysis of pooled data from fifty-five less-developed countries reveals, high expenditures on salaries impeded the GDP growth rate in 1970–1990. In response to the argument that the African economy is more vulnerable to state predation, this regional effect hypothesis is further tested with an interaction model. The results reveal that the harmful growth effect of the state's interest seeking cuts across varied regions and can be generalized.     

Analytical expression for the integrated squared density partial derivative of a multivariate normal mixture distribution

This paper describes the derivation of the analytical expression for the integrated squared density partial derivative (ISDPD) in a multivariate normal mixture model. The analytical expression of the ISDPD is derived for arbitrary dimensions with partial derivative orders up to four. Although the value of the ISDPD can be obtained by using the common numerical integration via mathematical software (such as Maple, Mathematica, Matlab, etc), it suffers from the limitation of computation time when the dimension or the number of mixture components of the considered multivariate normal mixture model are large. Moreover, numerical comparison indicates the benefits of speed offered by our proposed analytical expression are far superior to the numerical integration performed by Maple. With this analytical expression, the ISDPD can apace be calculated with no assistance of numerical integration.     

Characterizing the reconstruction and enumerating the patterns of DNA sequences with repeats

A repeat in a DNA sequence is a substring that appears more than once. In DNA sequencing, the occurrence of repeats may hinder the unique reconstruction. In addition, the number of possible reconstructions depends on the pattern of repeats in a DNA sequence. Arratia et al. studied the patterns of DNA sequences with twofold repeats that result in k-way reconstructions. In this paper, multiple-fold repeats, including twofold repeats, are considered. For each pattern of DNA repeats, the possible reconstructions of the DNA sequence are enumerated by its reduced digraph. Then the reconstructions of DNA sequences with repeats are characterized using the pattern graphs. Finally, for DNA sequences with n repeats, the patterns of DNA repeats resulting in k-way reconstruction are enumerated. Dedicated to Professor Frank K. Hwang on the occasion of his 65th birthday.     

External technology acquisition and product innovativeness: The moderating roles of R&D investment and configurational context

An increasing number of studies in technology/innovation management contribute to the understanding of the relationship between external technology acquisition and product innovation performance. On the other hand, the meta-analytic reviews of new product development literature indicate a positive impact of product innovativeness on product innovation performance. However, existing research has not examined the link between external technology acquisition and product innovativeness. This study, therefore, aims to fill this gap of knowledge by investigating the external technology acquisition—product innovativeness relationship and examining the moderating roles of R&D investment and configurational context on this link. Based on a panel sample of 105 high-technology firms over a six-year period, a least squared dummy variable s model reveals interesting results. First, external technology acquisition has a positive impact on product innovativeness. Second, R&D investment increases the effect of external technology acquisition on product innovativeness. Lastly, firm size exhibits a negative effect on the contribution of external technology acquisition to product innovativeness. However, firm age positively impacts the relationship between external technology acquisition and product innovativeness. In addition, this study reveals a positive effect of product innovativeness on firm growth.     

Fitting Distributions with the Polyhazard Model with Dependence

The polyhazard model with dependent causes, first introduced to fit lifetime data, generalized the traditional polyhazard model by allowing the latent causes of failure to be dependent by using copula functions. When modeling lifetime data, marginal distributions are supported on the positive reals. Dropping this restriction, the method generates a rich family of univariate distributions with asymmetries and multiple modes. We show that this new family of distributions is able to approximate other distributions proposed in the literature, such as the generalized beta-generated distributions. These distributions are fitted to three real data sets.     

An extensive power evaluation of a novel two-sample density-based empirical likelihood ratio test for paired data with an application to a treatment study of attention-deficit/hyperactivity disorder and severe mood dysregulation

In many case-control studies, it is common to utilize paired data when treatments are being evaluated. In this article, we propose and examine an efficient distribution-free test to compare two independent samples, where each is based on paired observations. We extend and modify the density-based empirical likelihood ratio test presented by Gurevich and Vexler [7] to formulate an appropriate parametric likelihood ratio test statistic corresponding to the hypothesis of our interest and then to approximate the test statistic nonparametrically. We conduct an extensive Monte Carlo study to evaluate the proposed test. The results of the performed simulation study demonstrate the robustness of the proposed test with respect to values of test parameters. Furthermore, an extensive power analysis via Monte Carlo simulations confirms that the proposed method outperforms the classical and general procedures in most cases related to a wide class of alternatives. An application to a real paired data study illustrates that the proposed test can be efficiently implemented in practice.     

Multinomial Selection for Comparison with a Standard

The multinomial selection problem is considered under the formulation of comparison with a standard, where each system is required to be compared to a single system, referred to as a “standard,” as well as to other alternative systems. The goal is to identify systems that are better than the standard, or to retain the standard when it is equal to or better than the other alternatives in terms of the probability to generate the largest or smallest performance measure. We derive new multinomial selection procedures for comparison with a standard to be applied in different scenarios, including exact small-sample procedure and approximate large-sample procedure. Empirical results and the proof are presented to demonstrate the statistical validity of our procedures. The tables of the procedure parameters and the corresponding exact probability of correct selection are also provided.