完善信息报备机制崔动场外衍生品市场发展

文章在回顾《场外衍生品数据报备和汇总要求报告》（CPSS—IOSCO）的基础上,介绍了交易数据报备机制和报备数据汇总机制建设取得的成果及相关要点,分析了协调各国报备信息库建设步伐存在的难点,提出了对我国建立场外衍生品标准化机制的启示和借鉴。文章认为,我国应在充分参考国际经验的基础上,研究交易报备和数据汇集的方法,参与国际相关标准体系的制定,完善我国交易信息报备机制,推动场外衍生品市场发展。     

质量管理数据分析的思考

数据分析是精确分析质量管理现状的科学方法,也是质量管理实现持续改进的有效途径。本文对如何开展质量管理的数据分析工作进行了初步探讨。     

城市森林固定CO2价值评估

本文利用我国第四次森林资源清样调查资料对上海市森林植被碳储量和碳密度的估算结果,结合植物的光合作用过程计算上海市森林固定CO2的能力,采用造林成本法对上海市森林固定CO2的价值进行了评估。笔者希望通过该研究激发人们保护城市森林以维持城市生态系统可持续发展的积极性,并为合理地进行城市森林管理和城市绿化提供参考。另外,利用系统连续的森林资源清样调查资料评估森林植被的固碳价值,能够对我国更好地履行《联合国气候变化框架公约(》UNFCCC),并为依据《京都议定书(》KyotoProtocol)进行环境外交提供帮助。     

气候变化下湖北省稻谷生长期降雨量指数保险设计

[目的]气候变化对我国粮食生产造成巨大损失,传统农业保险存在较多问题,天气指数农业保险作为金融创新工具,成为转移农业天气风险的有力路径。[方法]文章在分析降雨量指数保险合约设计思路的基础上,依照天气指数保险合约定义,选取稻谷生长期每日降水的累积降雨量作为天气指标,采用经济—气候模型和湖北省78个县市的面板数据,按风险区域分别设计了干旱指数保险合约和暴雨灾害指数保险合约。[结果]虽然湖北省全省累积降水的影响总体是负向的,但累积降水量对稻谷单产在十堰、襄阳等干旱区域的边际影响是正向的、显著的,累积降水量在暴雨集中区域江汉平原地区、咸宁市及辖内县市,有着显著的负向影响。[结论]气候变化对粮食生产的影响显著,根据面板数据的估计结果,有必要按不同的风险区域分别设计天气指数保险合约。该文的研究在天气指数保险设计方面做出了一定的探索,进一步使天气指数保险合约成为转移农业天气风险的有力创新工具。     

我国最低工资标准的就业效应——基于全国和地区的实证研究

利用1996年～2007年全国27个省、直辖市、自治区的面板数据模型,考察了最低工资标准对就业的影响。实证结果表明,最低工资标准的提高对全国总体就业有负影响,但影响力度很小。进一步的地区分析表明,最低工资标准的提高对东、西部地区的就业有负影响,对中部地区的就业则无显著性影响。最后给出了相关的政策建议。     

全球价值链下中国的对外贸易

全球价值链(GVC)作为一种组织和治理力量,已使当今的国际贸易发生了重大变化,也对中国的对外贸易产生了深刻影响。本文首先总结在GVC的影响下,当前国际贸易呈现的6个特征性事实;其次揭示了在中国贸易急剧增长的表象下,隐藏着GVC这样一种组织和治理的力量,并从结构、地区、主体和地位等方面,分析了GVC对中国对外贸易的影响;最后,指出中国外贸在GVC下面临的挑战及相关的对策。     

Firm heterogeneity and immigrant wage assimilation

This article investigates the role of job mobility in immigrant wage assimilation. I use longitudinal linked employer–employee data for Portugal to estimate the immigrant wage catch-up in log wage regressions with both individual and firm fixed effects. I show that moving to firms with higher wage premiums accounts for approximately 30% of the immigrant wage catch-up in the first years.     

Financial components of FDI and choice of capital structure: an analysis for 30 OECD countries

Even though the choice of capital structure depends on the three different financial components of foreign direct investment (FDI), previous research has regarded FDI as unidimensional rather than multidimensional. This study addresses new findings in the FDI area and investigates the relevant determinants of capital structure in 30 OECD countries from 2006 to 2014 within the framework of a simultaneous equation model. Our primary findings reveal that each component has its own deterministic features driven by relevant policy variables and risks in the market. While an increase or decrease in equity capital shows the ability of the host country to attract new investments, the subsequent components are mostly used to adjust the equity capital investment exposure.     

Do stock market returns predict changes to output? Evidence from a nonlinear panel data model

Recent empirical work suggests a predictive relationship between stock returns and output growth. We employ quarterly data from a panel of 27 countries to test whether stock returns as useful in predicting growth. Unlike previous research, our approach allows for the possible non-linear effect of recessions on the growth-return relationship. There is strong evidence to suggest that a linear model would be misspecified and provide potentially misleading inference. Using a switching regression approach, we find evidence that returns are most useful in predicting growth when the economy is in recession.First version received: November 2002/Final version received: April 2003This paper benefited greatly from discussions with Kalvinder Shields, Mark Harris, Pete Summers, and Vance Martin. Two anonymous referees provided useful comments on an earlier version of the paper for which we are grateful. The usual disclaimer applies to any errors and omissions. Funding from The University of Melbourne greatly assisted in the completion of this paper.     

Using paradata to collect better survey data: Evidence from a household survey in Tanzania

Data are a key component in the design, implementation, and evaluation of economic and social policies. Monitoring data quality is an essential part of any serious, large‐scale data collection process. The purpose of this article is to show how paradata should be used before, during, and after data collection to monitor and improve data quality. To do this we use timestamps, global positioning system (GPS) coordinates, and other paradata collected from an 800‐household survey conducted in Tanzania in 2016. We demonstrate how key paradata can be used during each phase of a research project to identify and prevent issues in the data and the methods used to collect it. Our results corroborate the importance of collecting and analyzing paradata to monitor fieldwork and ensuring data quality for micro data collection in developing countries. Based on these findings we also make recommendations as to how researchers can make better use of paradata in the future to manage and improve data quality. We argue for an expansion in the understanding and use of varied paradata among researchers, and a greater focus on its use for improving data quality.