Inequality in digital skills and the adoption of online safety behaviors

ABSTRACT

Cyber-safety behaviors are important in preventing the loss of an individual's digital assets and ensuring the safety of important daily online activities. Individuals’ cyber-safety is also critical for national cybersecurity. The issue is highly relevant for Israel, a country that relies on the digital capabilities of its workers for its major technology industries and is also often a target of cyberwarfare and cybercrime attacks. The purpose of this study is to identify the determinants of cyber-safety behavior. We investigate the role of age, gender and education in the use of safety-related digital skills and antivirus software. Using a 2014 survey of a national sample of Internet users in Israel (N?=?1850), we found that age, gender, education and quality of access are associated with the level of users’ digital security skills. In addition, these skills and the frequency of conducting financial activities online are the main determinants of antivirus behaviors. Our results expand the understanding of cyber-safety by showing that social and digital disparities are reproduced in the use of measures to prevent online threats, putting the digitally disadvantaged at greater risk of becoming victims of online threats.     

Generalized estimator of population mean by using conventional and non-conventional measures in the presence of measurement errors

In survey research, it is assumed that reported response by the individual is correct. However, given the issues of prestige bias, self-respect, respondent's reported data often produces estimated values which are highly deviated from the true values. This causes measurement error (ME) to be present in the sample estimates. In this article, the estimation of population mean in the presence of measurement error using information on a single auxiliary variable is studied. A generalized estimator of population mean is proposed. The class of estimators is obtained by using some conventional and non-conventional measures. Simulation and numerical study is also conducted to assess the performance of estimators in the presence and absence of measurement error.     

京津冀节能减排政策措施的差异与协同研究

在对京津冀1981年以来节能减排政策措施进行量化处理的基础上,建立针对节能减排政策措施有效性的计量模型,分析了京津冀节能减排政策措施的演变状况,并探究京津冀节能减排政策措施对其节能减排效果影响的差异性.研究结果表明:京津冀节能减排政策颁布经历了早期各年份相对零散、缺乏连续性到新世纪以来政策颁布数量显著增多、政策总体力度逐渐增大的过程,但三地政策总效力的增加主要是由于节能减排政策颁布数量增多引起的;京津冀三地在政策的制定过程中更多的是趋于实现短期目标,政策整体缺乏系统性和权威性;人事措施、行政措施、引导措施、财税措施、金融措施等不同节能减排政策措施对京津冀节能和减排的有效性具有明显的差异;三地对不同政策措施的使用方式、使用程度方面存在明显的差异,这对京津冀协同推进节能减排的治理工作提出挑战.论文还从京津冀完善单一节能减排政策措施的使用、加强市场手段的应用及京津冀区域协同治理等方面提出了相应政策建议.     

大数据驱动下的政府治理机制研究——基于2020年后精准扶贫领域的返贫阻断分析

大数据时代已经到来，大数据驱动下的政府治理发生何种改变，目前相关研究较少。当前政府治理的考验集中在2020年实现全面脱贫后精准扶贫领域的返贫阻断。文章以该领域为切入点，探索大数据驱动下政府治理的内在机制。研究发现：大数据背景下，政府治理能力催生出新的核心能力，即数据治理能力。数据治理能力驱动下，政府治理主体多元化有利于实现政府治理资源的宽范围、精准化动员；政府治理方式实现经验式决策向数据化决策转变，有利于实现政府资源的高效率和公平配置；政府治理客体的精准识别，有利于政府资源的精准化和最优化运用。但是客观上必须具备数据治理能力，主观上必须按照大数据驱动的要求重构政府治理体系（包括治理主体、治理方式和治理客体），才能真正实现以数据治理驱动政府治理，进而提高政府治理能力的目的。而数据治理能力客观上也加速了政府治理体系的重构，进而加快了政府治理能力的提升，并最终提高政府治理绩效。在对标大数据应用不同阶段分析常规式返贫阻断和大数据返贫阻断的不足和差距的基础上，笔者提出重构政府治理体系和提升数据治理能力等针对性建议。     

Non-tariff barrier on chicken imports into Russia: Impact on production,trade and prices

We analyze the Russian restriction on chicken imports as an example of a non-tariff barrier (NTB). Cointegration test results support the hypothesis that the NTB limited trade. We calibrate an equilibrium market model to cointegration results to assess the market impact. The tariff equivalent of the Russian chicken NTB is estimated to be 30 − 40% depending on the representation of consumer demand. Removing the NTB decreases domestic production by 4–5% and domestic price by 27%–34%, while imports increase by 326–423 thousand tons annually in 2015–2019.     

Evaluating social investment in disability policy

Disability policy in European countries is displaying a shift towards social investment: increasing human capital and access to the labour market. The reasoning that underlies this transition is that disabled persons would benefit from mainstream employment, but are impeded in traditional policy by deficiencies in labour supply and demand. However, the shift towards more activating policies in many countries is accompanied by a decline in social protection. It is unclear whether social investment may effectively promote the employment chances of disabled persons within this context. The present research examines this question through a quantitative, cross-sectional, multilevel analysis on microdata from 22 EU countries. Our findings suggest greater activation to predict lower employment chances, while reducing passive support shows mixed effects. Conversely, measures for facilitation in daily life predict greater employment chances, as do measures for sheltered work. These findings raise questions over the value of social investment for disabled persons—and underline the need to overcome broader barriers in the labour market and in society.     

运用微信平台创新高校思想政治教育的对策探析

微信具有信息资源丰富且分门别类、独特的的信息推送方式和功能多样性等特点，得到了大学生的青睐，为高校思想政治教育提供了新途径。然而，高校在应用微信开展思想政治教育中存在着观念滞后、人才短缺、管理不善等问题，对此，需要从更新观念、切入载体、提高素质以及完善管理和运行机制等方面加以改善，充分发挥微信的积极作用，提高大学生思想政治教育的有效性。     

Robust importance sampling for some typical types of utility-based shortfall risk measures using exponential twisting and kernel density techniques

A robust algorithm for utility-based shortfall risk (UBSR) measures is developed by combining the kernel density estimation with importance sampling (IS) using exponential twisting techniques. The optimal bandwidth of the kernel density is obtained by minimizing the mean square error of the estimators. Variance is reduced by IS where exponential twisting is applied to determine the optimal IS distribution. Conditions for the best distribution parameters are derived based on the piecewise polynomial loss function and the exponential loss function. The proposed method not only solves the problem of sampling from the kernel density but also reduces the variance of the UBSR estimator.     

Measuring Nonlinear Granger Causality in Mean

We propose model-free measures for Granger causality in mean between random variables. Unlike the existing measures, ours are able to detect and quantify nonlinear causal effects. The new measures are based on nonparametric regressions and defined as logarithmic functions of restricted and unrestricted mean square forecast errors. They are easily and consistently estimated by replacing the unknown mean square forecast errors by their nonparametric kernel estimates. We derive the asymptotic normality of nonparametric estimator of causality measures, which we use to build tests for their statistical significance. We establish the validity of smoothed local bootstrap that one can use in finite sample settings to perform statistical tests. Monte Carlo simulations reveal that the proposed test has good finite sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, the empirical importance of measuring nonlinear causality in mean is also illustrated. We quantify the degree of nonlinear predictability of equity risk premium using variance risk premium. Our empirical results show that the variance risk premium is a very good predictor of risk premium at horizons less than 6 months. We also find that there is a high degree of predictability at the 1-month horizon, that can be attributed to a nonlinear causal effect. Supplementary materials for this article are available online.