基尼系数的含义及其不足

本文提出了用基尼系数作为衡量贫富差距的指标以及基尼系数的各种计算方法。同时指出本身的不足与统计上的难度。并认为在当今中国经济高速发展与贫富差距日益扩大的情况下，有必要不断完善这一指标体系，为政府的宏观决策提供有力的准确与科学的依据。     

Human Capital and Growth Cycles

This paper studies the dynamic interaction between human capital accumulation and economic growth. Capital market imperfections and an indivisibility in human capital investment prevent poor agents from accumulating skills, the acquisition of which positively affects technological progress. More productive technologies in turn require more sophisticated qualification and involve higher training costs. The equilibrium dynamics can be characterized by the joint evolution of productivity growth, the schooling costs, and the income distribution. Under our assumptions, individual incomes follow a non-linear Markov chain. This non-linearity generates endogenous fluctuations of schooling activities and the rate at which productivity improvements occur. We thank an anonymous referee for many helpful suggestions. Support from the German Research Foundation (DFG) under grant KA1519/2-2 is gratefully acknowledged.     

Who Gains from the Demographic Dividend? Forecasting Income by Age

Changes in the population age structure are known to influence the total income per person, but little is known about whether the changes are equally shared across the population or are concentrated on particular age groups and/or birth cohorts. The answer to this question has potentially important implications for income inequality, human capital investment, and fertility decision-making. We propose a new model of intergenerational transfers which distinguishes between the effects of changes in population structure and the effects of changes in family age structure. Using age-specific data from annual income and expenditure surveys of Taiwan between 1978 and 1998, we show that changes in age structure have had a very favorable effect on Taiwan's income growth. The gains are not equally shared by all age groups, however. Children and young adults have benefited the most, while the elderly have benefited the least. The population and family age structures have independent effects on per capita income; the effect of the population age structure is most important. Generational differences in per capita income are closely related to intergenerational differences in earnings, suggesting only a weak form of altruism. Finally, we predict that, on average, population aging will adversely influence the per capita income growth in Taiwan in the coming decades.     

新疆城市居民收入不平等现状分析

本文采用基尼系数及其分解公式对新疆2003年城市居民收入调查的2828个样本数据进行分析，根据新疆的地理特征将基尼系数按南疆与北疆进行分解计算，并按收入来源对基尼系数进行集中度分解分析，结果表明新疆的收入不平等状况并不严重，南疆的收入差距大于北疆，工资收入和转移收入是收入差距增大的主要原因。     

劳动力自由迁移为何如此重要?——基于代际收入流动的视角

未考虑人口迁移影响的代际收入弹性估计存在偏误。本文将人口迁移与区域特性纳入代际收入弹性估计方程,并运用迁移概率作为局部工具变量进行估计,发现迁移者的代际收入弹性不到未迁移者的一半。而当前我国代际收入流动的主要问题在于部分人群面临"代际低收入传承陷阱",他们可以通过迁移从而增加就业机会等方式摆脱这一陷阱,这至少可以使一代人免受代际低收入传承的困扰。进一步分析迁移对代际收入传递路径的影响,结果表明教育在可识别的代际收入传递路径中贡献最大,而迁移强化了这一影响。鉴于人口迁移对代际收入流动的重要影响,政府部门有必要进一步采取措施降低劳动力自由迁移的障碍,并合理分配教育资源,以增进社会公平程度。     

企业职工薪酬激励程度核算研究

职工薪酬激励程度和职工的努力程度均难以直接观察,这直接导致企业激励政策的盲目性.本文通过分析,提出企业薪酬激励程度核算方法,利用相关数据建构企业薪酬激励程度与企业增加值之间的回归模型,并结合有关激励模型确定企业职工最优薪酬激励程度,为企业制定薪酬激励政策提供参考依据.     

健康风险冲击对农户收入的影响

本文基于中国8个省份、1354个农户、跨度15年的微观面板数据,测算了大病冲击对于农户长期收入的影响以及健康风险冲击持续的时间。我们发现:(1)大病冲击在随后的12年里对于农户人均纯收入都有显著的负面影响;(2)大病冲击对于农户的短期与中期影响使得患病户人均纯收入平均降低5%—6%;(3)健康风险冲击的长期影响可持续大约15年,并且冲击对于中低收入农户的影响更为严重。     

关于所得税资产负债表债务法的思考

所得税“时间性差异”的概念和债务法的处理方法在国际上已有了进一步的认识和发展,在当今我国会计的国际化进程加快的情况下,引入“暂时性差异”的概念并采用资产负债表债务法来进行该差异的处理是所得税会计的发展趋势。本文通过对两种差异的深入研究及对资产负债表债务法与损益表债务法的比较分析,对资产负债表债务法在我国采用的现实基础及发展前景进行阐述。     

人力资本价值实现与政策的关系研究

人力资本理论提出后,人力资本价值特别是异质型的企业家价值逐渐受到社会的关注和重视。在我国建立现代企业制度的改革进程中,年薪制,职工持股制,股权奖励多种新的收入分配形式出现在工资制度中。企业工资制度的改革,显示了政策对人力资本收益权的支持力度,按生产要素进行分配的趋势。政策的支撑,使人力资本的收益渐趋近于人力资本的真实价值。     

Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models

We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.