How Fast Do Banks Adjust? A Dynamic Model of Labor-Use with an Application to Swedish Banks

This paper deals with a dynamic adjustment process in which adjustment of a key variable input (labor) towards its desired level is modeled in a panel data context. The partial adjustment type model is extended to make the adjustment parameter both firm- and time-specific by specifying it as a function of firm- and time-specific variables. Desired level of labor use is represented by a labor requirement function, which is a function of outputs and other firm-specific variables. The catch-up factor is defined as the ratio of actual to desired level of employment. Productivity growth is then defined in terms of a shift in the desired level of labor use and the change in the catch-up factor. Swedish banking data is used as an application of the above model.     

结构异质性对中国能源高质量发展的影响研究

基于结构异质性对能源高质量发展影响的理论分析,使用稀疏主成分分析方法测度中国能源高质量发展水平,构建中国和八大经济区域面板分位数回归模型,实证研究了结构异质性对中国能源高质量发展的影响。结果表明：2003—2020年中国能源高质量发展水平有所提升,但仍存在明显的区域发展不平衡问题,西部地区和北部沿海地区的能源高质量发展水平更高。产业结构与能源高质量发展水平呈负相关关系,能源消费结构、资本配置结构与能源高质量发展水平呈正相关关系。     

我国工业行业信贷资金配置效率研究

效率是资源配置的核心问题,评价资源配置效率高低的重要标准是分析资源是否流向有价值创造能力的行业.借鉴Jeffrey Wurgler(2000)及经济增加值(EVA)方法,对2002-2005年我国金融机构信贷资金在36个工业行业的配置效率进行实证分析.研究发现,信贷资金配置效率与我国工业行业价值创造能力总体上呈现出较低相关性,配置效率较差.提出了加强行业运行机制的研究,加强产业政策和信贷政策的协调配合等政策建议.     

中国东、中、西部城镇化对经济增长的贡献

中国城镇化率的地区差异较大,东部较高,西部较低,中部居中;改革开放以来全国各地区的城镇化率都有大幅度的提高,但提高幅度的地区差异也较明显,东部地区提高幅度较大,西部地区提高幅度较小,中部地区提高幅度居中.各地区城镇化推动经济增长的作用是显著的;东、中、西部三大地区中,城镇化对经济增长的贡献率依次为西部、中部和东部,同经济发展水平的顺序恰好相反.因此,继续加快中、西部地区的城镇化进程,有助于缩小地区差距.     

货币升值对不同产业就业的影响——对亚洲八国汇率与就业关系的经验分析

为了研究人民币升值可能对中国各产业就业影响,且避免单独研究中国数据所产生的局限性,本文选取八个亚洲主要国家和地区的数据,利用面板数据模型进行实证分析。实证结果显示汇率升值将对制造业的就业产生负效应,而对第三产业的效应为正,按照该结果,人民币升值将会给中国本已严峻的就业形势带来更大的压力。根据中国各产业就业形势的当前状况及发展趋势,解决中国就业问题的根本出路在于大力发展第三产业。     

产权、市场结构与中国银行业绩效

本文引入产权结构变量修正了传统的SCP(结构—行为—绩效)分析框架,并基于该框架利用截面数据模型实证分析了2000-2004年间以产权改革为主导的中国银行业绩效的影响因素。实证结果表明,产权结构单一和行业过于集中是影响银行业效率和绩效提升的两个主要因素;产权和市场结构对国有商业银行和股份制商业银行绩效影响是不同的,产权结构和市场机构对国有商业银行绩效影响显著,而对于股份制商业银行,市场结构对绩效的影响更大。因此,当前银行业改革在侧重于产权改革的同时不能忽视行业市场结构的优化。     

上海股市收益与波动的周内效应研究

股市周内效应一直是金融投资者关注的焦点问题，许多学者已做了大量研究，但多数文献将收益与波动的周内效应分开来进行研究和检验，忽视了波动与收益的共生性，其结果缺乏严密性和说服力。针对这种情况，提出平行数据GARCH模型并给出了参数的极大似然估计方法，进而对上海股市收益和波动的周内效应进行检验，既反映收益与风险存在共生关系，又避免了分别判断收益和波动的周内效应所致的缺点。     

Economic Growth and Transboundary Pollution in Europe: An Empirical Analysis

The existing empirical evidence suggests that environmental Kuznets curvesonly exist for pollutants with semi-local and medium term impacts.Ansuategi and Perrings (2000) have considered the behavioral basis for thecorrelation observed between different spatial incidence of environmentaldegradation and the relation between economic growth and environmentalquality. They show that self-interested planners following a Nash-typestrategy tend to address environmental effects sequentially: addressingthose with the most immediate costs first, and those whose costs aredisplaced in space later. This paper tests such behavioral basis in thecontext of sulphur dioxide emissions in Europe.     

An Econometric Estimation of Industrial Water Demand in France

This study investigates the structure of industrial water demand byestimating the derived demand for water on a sample of industrialestablishments located in the south-west of France. Productiontechnologies are represented by short-term variable cost functions andapproximated by a translog form. Industrial water use is modeled ashaving three components: the quantity of water bought to a waterutility, the quantity of autonomous water and the quantity of watertreated prior to use. We include in this framework water effluents,which are considered as a by-product of the production process, emittedby firms. Each of the three water components is treated as a separateinput and all are estimated as a system of simultaneous equations. Themodel is estimated on a sample of 51 industrial plants in the Girondedistrict observed from 1994 to 1996 using Seemingly Unrelated Regression(SUR) and Feasible Generalized Least Squares (FGLS). Results ofestimations show that industrial firms are sensitive to water priceinputs. Network water elasticity is estimated at –0.29. It variesfrom –0.10 to –0.79 according to the type of industryconsidered. Autonomous water price elasticity is not significant.Elasticity for treated water is evaluated at –1.42 at themean-sample and varies from –0.90 to –2.21 according to theindustry considered.