文章首次从教育选择的角度出发,探讨了人力资本投资与收入差距之间的关系,作者首先建立了教育回报的代际交迭模型,推断出富裕家庭教育回报率高这个事实会拉大社会的贫富差距.作者使用1992年至2009年的中国城镇住户调查数据对理论推论进行了论证,实证结果表明出身“寒门”的子女期望教育回报率更低,这拉大了与富裕家庭群体的收入差距,也是“寒门难出贵子”的原因.自2001年来,家庭因素带来的教育回报差异能够解释19.1％的城镇居民收入差距,引入工具变量的结果表明,真实的情况可能更加严重.作者进一步模拟了“新常态”下的贫富差距状况,结果表明,经济潜在增速下降会使得基尼系数扩大6.1％.

首先通过调整的菲利普斯曲线来估计国际油价变动对我国通货膨胀的影响.研究发现,国际油价对我国通胀的影响具有时变特征:2000年之前影响不显著,之后呈明显的上升态势,其中,2008年之前和之后我国CPI对国际石油价格的当季弹性分别大致稳定在0.01和0.019左右,即国际油价10%的变动会引起我国当季CPI同向变动0.1和0.19个百分点.进而通过一个两阶段方法来分析不同类型结构性冲击对我国通货膨胀的影响,发现相比于石油供给冲击,总需求冲击和石油个体性需求冲击对我国一般物价的影响更为持续.前者的影响仅在2个季度内显著,而后两者的影响则持续到5-6个季度.

2008年,中国政府针对金融危机推出了总量4万亿的经济刺激政策,这是政策干预经济最典型的一个案例。本文通过一个理论模型提出,在金融危机之前政策扶持对受扶持企业发展有利,中央及地方政府也能够从行业优先发展中获益。但金融危机带来的市场环境恶化,使行业与政府之间存在利益目标不一致的可能,政府基于自身利益最大化的理性决策可能对企业发展不利,进而加重企业盈利困境。实证结果显示,2001-2007年,扶持政策使得企业的ROA大约提高了0.9%~1.2%。金融危机时期,扶持政策给企业的ROA带来了1.0%~1.7%的损害,这种损害程度在随后的时期进一步加强,扩大到1.8%~2.3%。金融危机与政策扶持的共同作用也加剧了产能过剩,使得被扶持行业的产能过剩程度提高了4.6%,但这种影响滞后了3年才被观察到。

本文通过向量误差修正模型(VECM)研究汇率变动对中国贸易顺差的影响.我们发现,人民币汇率变动会经由两条路径来影响进出口贸易:第一是通过进口价格和出口价格的改变以及进出口贸易弹性来影响进口和出口,本文称之为“直接效应”;第二是汇率变动还会通过出口量的变动来间接影响进口量,本文称之为“联动效应”.实证研究还发现,金融危机前,由于加工贸易在我国对外贸易中所占份额较大,因而联动效应在现实中占据主导地位,人民币汇率变动对我国贸易顺差的影响并不明显.金融危机后,本币贬值带来的“J曲线”效应在短期增强,汇率调整引发的波动幅度增大了两倍,但恢复均衡所需要的调整周期变短.本文的研究在一定程度上解释了已有文献中对“J曲线”效应是否存在的分歧,并建议以国际收支为目标的汇率政策调整做出更为审慎的决策.

PurposeThe purpose of this paper is to estimate the value of a statistical life (VSL) in China using the hedonic wage model, and to explore the regional difference in VSL within the country.Design/methodology/approachUsing the hedonic wage regression, this paper estimates the compensating wage differential for incremental job mortality risk among Chinese workers. The implied VSL is derived for China and its different regions. The data is from the 2005 China inter‐census population survey, consisting of 1.3 million urban and rural workers. The authors also made important improvement in the model specification to explicitly address the missing variable issue in the previous studies.FindingsThe paper results indicate that the industry mortality risk has a significant impact on the wage rate. The implied VSL is 1.81 million RMB, a value substantially higher than previous estimates. The results also suggest a sizable urban‐rural difference, with the urban VSL being 4.3 times higher than the rural estimate. The strong urban‐rural inequality of income could be attributed to the segregation between the urban and rural labor markets.Practical implicationsThe paper findings indicate the importance of reforming the current workers' compensation standard and improving the institutional environment, as well as enhancing the labor protection in the rural labor market.Originality/valueThis paper is the first attempt to estimate the value of life in China using the census based data. The paper results contribute to the growing literature in obtaining comparable VSL estimates in the developing countries.

This paper aims to estimate the Value of a Statistical Life(VSL) in China using the 2005 Inter-Census 1% Population Survey data based on the standard hedonic wage method,and to explore the systematic regional difference in VSL within the country.Our results indicate that the industry mortality risk has a significant impact on the wage rate.The implied value of life is estimated to be 1.81 million RMB(approximately 0.19 million 2000 US dollars),which is substantially higher than the previous estimates seen in international literature.The results also suggest that sizable difference exists in the value of life between the urban and rural population,with the urban VSL about 4.3 times higher than the rural estimate.It reflects the serious urban-rural inequality in income and a division between the two sectors’ labor markets.As this is the first attempt in the literature to estimate the value of life in China using the national population survey data,and several important improvements have been made on the regression model compared with the previous studies,our paper provides important policy implication on reforming the fatal accident compensation laws and strengthening the rural labor market system.