Using State Child Labor Laws to Identify the Causal Effect of Youth Employment on Deviant Behavior and Academic Achievement

On the basis of prior research findings that employed youth, and especially intensively employed youth, have higher rates of delinquent behavior and lower academic achievement, scholars have called for limits on the maximum number of hours per week that teenagers are allowed to work. We use the National Longitudinal Survey of Youth 1997 to assess the claim that employment and work hours are causally related to adolescent problem behavior. We utilize a change model with age-graded child labor laws governing the number of hours per week allowed during the school year as instrumental variables. We find that these work laws lead to additional number of hours worked by youth, which then lead to increased high school dropout but decreased delinquency. Although counterintuitive, this result is consistent with existing evidence about the effect of employment on crime for adults and the impact of dropout on youth crime.

Assessing the (eco)macroeconomic performance index of India: A data envelopment analysis approach

The prime objective of this article is to construct a robust macroeconomic performance (MEP) index of the Indian economy using the data envelopment analysis (DEA) approach. Seven major macro indicators, namely, economic growth, employment rate, terms of trade, inflation rate, fiscal deficit, pollution, and climate change are used to compute MEP and Eco‐MEP indices of the Indian economy from 1980–1981 to 2018–2019. Overall, both the MEP and Eco‐MEP index scores have quite similar best performing years worst performing years, and have also captured the major events that affected adversely the Indian economy during the past decades. The trend in the overall performance of the Indian economy was better in the 1980s and the 1990s but has deteriorated since 2000. The autoregressive distributed lag bounds testing approaches to cointegration methods are used to test the robustness/utility of these indices. The estimated results show that MEP and Eco‐MEP have a positive impact on private investment, foreign investment inflows, foreign direct investment, and a negative effect on the current account deficit. Hence, the suggested composite MEP index is stable, robust and truly captures the economic performance of India. The constructed MEP composite index may be used by foreign investors, rating agencies, private investors, and policymakers for their planning and decision‐making processes.     

计量经济研究实践中的数据运用误差

计量经济学方法已被广泛地应用于经济研究的各个领域,理论、方法、数据是计量经济研究的三大基本要素。综观国内大量的计量经济研究成果,我们发现不尽严密和科学之处并不少见。本文从数据运用角度,列举了计量经济研究实践中的三类偏误现象,以呼吁人们关注计量经济学研究的客观性     

数据要素对制造业高质量发展的影响——来自制造业上市公司微观视角的经验证据

随着数字经济与实体经济的不断融合发展,数据已经成为经济发展进程中的关键生产要素。基于手工整理的2011—2020年我国A股制造业上市公司年报数据的研究表明：制造业高质量发展是企业研发创新、生产制造、市场匹配三个环节整体联动实现更高效率发展的一种高级状态。数据要素能够显著促进我国制造业高质量发展,且主要是通过关键性技术突破与知识创新的研发创新效应、生产流程优化与协同的生产协同效应产生影响,但市场匹配效应尚未完全显现;数据要素对我国制造业高质量发展的推动作用在非国有企业、高技术密集度行业与东部地区更加显著。研究结论为数字经济时代下我国如何激活数据要素潜能、推进传统产业转型升级提供了政策启示。     

健康医疗大数据的积极利用主义

健康医疗大数据是国家重要的基础性战略资源。为促进我国健康医疗大数据产业发展，应该明确健康医疗大数据积极利用主义规制模式，解决积极利用与知情同意原则之间的冲突。我国立法应鼓励健康医疗大数据的积极利用，通过知情同意原则的灵活运用及替代性或补充性保护机制的引入，解决健康医疗数据利用和保护之间的冲突，按照数据类型区分原则，对健康医疗大数据进行系统、适当的分类，明确原则性规定和例外情形。     

Municipal population size and the benefits of inter-municipal cooperation: panel data evidence from Japan

ABSTRACT

Intermunicipal cooperation (IMC) facilitates efficient public service delivery. This study measures how the benefits municipalities gain through IMC differ according to municipal population size and service provision type. Focusing on public health and fire protection services, this study employs panel data regression for 1,706 Japanese municipalities over a five-year period, 2012–2017. This study has several major findings. First, an increase in the IMC spending ratio reduces public spending in both the studied services. Second, the marginal effect of IMC varies depending on the size of the municipal population. More specifically, the marginal effect of IMC on public health has a narrow confidence interval with a wide ranging population scale, while the marginal effect of IMC on fire protection produces negative values for only 151 municipalities due to extensive standard error. These findings can help municipalities take advantage of IMC to improve public service delivery.     

统计学方法在微量物证数据处理中的应用

统计学方法在微量物证理化检验工作中受到越来越多的重视,采用有效的统计学方法对检验数据进行分析,不仅能够深度挖掘复杂数据背后信息,还能不断提高数据的应用价值和证据本身的证据价值。本文对目前微量物证领域常用统计学方法进行归纳总结,简要介绍了常用统计方法的应用,对各类方法优缺点进行说明。最后,对法庭科学工作者都比较感兴趣的贝叶斯统计与似然比方法在理化检验数据统计分析中的应用进行了介绍。     

国内外舆情治理研究热点及趋势分析

以CNKI和Web of science核心数据库收录2002-1-1至2019-12-31文献为研究样本,运用Cite Space软件及文献计量方法,通过国内外文献样本对比研究,分析舆情治理领域研究起源、演化路径、热点及研究趋势。发现:已有的国内外研究热点主要集中在网络舆情演化机理的探讨和分析,使用大数据技术对网络舆情分析、研判、预测和预警,网络舆情监管和治理这三个方面;国外舆情治理的研究趋势是舆情演化影响因素分析、社交媒体情感行为分析和大数据网络舆情技术研究;国内舆情治理研究趋势是舆情信息分析研究、舆情的管理及公共政策制定研究、大数据驱动的隐私保护机制、利益相关者及信息生态研究。研究结论对舆情治理领域科学研究和实践应用具有一定的参考价值。     

On the Legal Regulation of Algorithms

Increasingly, algorithms challenge legal regulations, and also challenge the right to explanation, personal privacy and freedom, and individual equal protection. As decision-making mechanisms for human-machine interaction, algorithms are not value-neutral and should be legally regulated. Algorithm disclosure, personal data empowerment, and anti-algorithmic discrimination are traditional regulatory methods relating to algorithms, but mechanically using these methods presents difficulties in feasibility and desirability. Algorithm disclosure faces difficulties such as technical infeasibility, meaningless disclosure, user gaming and intellectual property right infringement. And personal data empowerment faces difficulties such as personal difficulty in exercising data rights and excessive personal data empowerment, making it difficult for big data and algorithms to operate effectively. Anti-algorithmic discrimination faces difficulties such as non-machine algorithmic discrimination, impossible status neutrality, and difficult realization of social equality. Taking scenarios of algorithms lightly is the root cause of the traditional algorithm regulation path dilemma. Algorithms may differ in attributes due to specific algorithmic subjects, objects and domains involved. Therefore, algorithm regulation should be developed and employed based on a case-by-case approach to the development of accountable algorithms. Following these development principles, specific rules can be enacted to regulate algorithm disclosure, data empowerment, and anti-algorithmic discrimination.