外商直接投资范文

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篇1

二、文献回顾

英国学者邓宁在海默的垄断优势理论，巴克利和卡森的内部化理论的基础上提出了国际生产折衷理论，对跨国公司及对外直接投资现象做了全新的解释。他指出：一个企业进行直接投资是有三个因素决定的，即所有权优势，内部化优势和区位优势。邓宁把区位优势看作国际投资区位选择的关键因素，并把区位因素归纳为市场因素、贸易壁垒、成本因素和投资环境，随后又补充了语言、习惯等非经济因素。

根据邓宁的理论，伍德沃和罗尔夫对影响出口导向制造业国际分配的主要因素进行了实证分析。根据他们的分析，跟投资选址呈正相关关系的因素有：GDP、汇率贬值、免税期限、自由贸易曲的规模、政治稳定因素、制造业的积聚度、土地面积等；而与选址呈负相关关系的因素有：工资、通货膨胀率、运输费用、工会组织等。

朴商天（2004）以中国市场为研究对象，对外商在华直接投资的地区性差异因素进行了实证分析，得出：对外开放度、集聚化程度、鼓励政策与直接投资之间存在着明显的正相关关系，而工资水平、研发人力则对直接投资起着反作用。基础设施对选址呈正相关关系，但对外商投资企业经营活动的影响正在减少。

这些理论都在一定程度上解释了FDI区位选择的动因，前两个是以多个国家为研究对象的，对我国具有一定的借鉴性，但不可能完全符合中国的现实情况。朴商天虽然以中国为研究对象，但他只简要的讲述了影响地区性差异的因素，对某些区域存在着哪些具体的问题方面并没有解释。本文根据这些学者提出的因素，结合中国东、中、西部的具体情况，解释对外直接投资在我国分布不均衡的原因和由之得出的一些启示。

三、影响外商对华直接投资区位选择的因素

1政策因素

张立（2002）对FDI在我国省际分布的决定因素进行了实证分析，他引入了各省执行FDI优惠政策的时间，结果显示，各省执行优惠政策的时间先后对FDI的流入有着显著的影响。我国的珠江三角洲和长江三角洲作为政策开放较早的地区，利用自己东南沿海的区位优势、政策优势和劳动力成本优势，通过建立开发区和工业园区，最先成为对外直接投资在我国的集中区。1992年中国开放内陆城市并实行也沿海地区相同的鼓励政策，推动了中国中西部地区的对外开放，近几年外商对中西部的投资有所增长，特别是长江中上游地区，如安徽、江西、湖北、云南、贵州等省份。差距不可能在短期内消除，但中西部地区已经开始了引进外资的征程。

2劳动力成本及工资水平因素

劳动力成本是影响外商直接投资区位决策的成本因素中最为主要的成本。Austin(1990)强调发展中国家吸引外资的主要原因在于低工资水平，工资高低与外商投资呈负相关关系。朴商天（2004）通过实证分析，验证了这一关系的正确性。作为人口大国，中国具有丰富而廉价的劳动力资源，这种成本竞争优势对跨国公司具有强大的吸引力。劳动力导向战略是跨国公司对华直接投资重要的区位选择战略。除了成本因素，劳动力素质也直接影响到劳动生产率的高低。特别是在一个东道国内部，低劳动力成本经常意味着低的劳动生产率，只有那些低成本并且具有较高劳动生产率的区位，才能比低成本、低劳动生产率的区位更具有吸引力，这就是外资没有因为劳动力成本低而大量流向中西部地区的原因。

但是过高的工资水平也会抑制外资的流入。以长江三角洲职工平均工资衡量的劳动力成本显示，上海、宁波、杭州、南京和苏州等地的工资水平，远高于长江以北地区城市。工资成本对FDI分布的影响就是，未来的长三角地区外资可能会更倾向于投资到工资水平较低的周边地区，甚至转移到区外。工资成本的上升对长三角的外资流入是一个不利因素。

3土地成本因素

珠三角地区开发较早，当外资聚集到一定程度后，可利用的土地越来越少，而成本不断攀升，对FDI起到了明显的抑制作用。按2002年单位土地面积的GDP衡量，深圳、汕头、广州、海口和福州等珠三角城市的土地成本在全国排在前列。自90年代以来，珠江三角洲在引进外资中所占的比重有所下降，而长江三角洲和环渤海地区的比重在持续上升。

4.基础设施质量

基础设施（公路、铁路、港口和信息通讯等）和基础工业的发展状况决定着社会生产的规模和效益，特别是具备一定投资规模的大型企业，如果生存在一个基础设施薄弱的经济环境中，将会导致投资收益递减。在我国，各地区的投资硬环境差异非常大，例如东部沿海的广东省和江苏省经过十几年的努力，目前的基础设施建设已经相当完善。根据国家统计局的统计数据显示，截至2001年，东部地区的交通线路综合密度为1597公里/平方公里，同期中部地区为680公里/平方公里，而西部仅为29公里/平方公里，与东中部地区相差甚远，成为外资进入的“瓶颈”。

5集聚效应区域产业基础是吸引对外直接投资的重要因素。一方面，全球80％的FDI集中在发达工业化国家（hakrabarti，2003），外国资本在这些国家的投入也更加集中；另一方面，对外直接投资的分布呈现出比较明显的集聚效应（Figueiredoetal，2000），区域产业基础越强，外资企业越多，外商就越容易在该地区投资。Headetal（1996）对我国931家外资企业进行了研究，发现有“吸引力”的城市——那些具有良好产业基础的城市——获得了更多的投资，而集聚效应则放大了政策的直接影响。

对于集聚现象，Krugman（1991）的研究提供了一个基本的研究视角。他结合城市经济学和区域科学，认为：聚集效应的关键是规模经济，特别是外部规模经济；聚集能导致生产某一种产品的平均成本下降，进而产生递增的规模效应。聚集效应体现出一种路径依赖，进而影响后续FDI的聚集(吴丰，2001)。外商直接投资的聚集效应体现在增量FDI的区位选择受到特定区位的FDI存量的影响，即当某一地区的外商资本控制的厂商越多，新的外商就更倾向于投资该区域国家或区域(许罗丹、谭卫红，2003)。许罗丹、谭卫红(2003)、王剑、徐康宁(2004)、吴丰(2001，2002)对FDI在中国表现出的聚集效应进行了研究阐述，均认为外商投资的聚集效应明显。为了在运输成本最小化的条件下实现规模经济，制造企业倾向于在有巨大需求的市场或潜在市场区域选址，而需求本身的定位取决于制造业的分布。区域产业基础与对外直接投资的进入具有双向促进关系。资本的进入增强了该地区的产业能力，同时也强化了外资的集聚机制。以苏州为例，该地区的IT制造业目前已具备了相当完整的产业链，在开发区周围25公里内可以达到98％的产业配套率。这种配套体系在吸引跨国公司进入的过程中发挥了重要作用。苏州由此成为全球IT制造业最重要的集聚区之一。台湾十大笔记本电脑公司有九家在苏州投资，包括明基、华硕、华宇、台达在内的台湾20家最大的电子信息企业，有16家人驻苏州，随后相关配套企业相继进驻，产品包括线路板、电脑配件、主机板、扫描仪、鼠标器、及电池和笔记本电脑等等，共有1500余家IT企业，形成较为齐全的IT产业配套体系，这种产业链投资方式既使企业具备了较强的竞争优势，又增加了苏州招商引资的吸引力。

行业的地区集中可以提供一个足够大的市场使得各种各样的专业化供应商得以生存。在我国，另一个具有说服力的是广东东莞，这里集中了大量的来自海外特别是台湾地区的计算机和电子设备制造商，是公认的全国电子产品配套能力最强的地区，在此设厂，有助于厂商增强其竞争力和建立竞争优势。有了特定产业的聚集，就能吸引相关的FDI进入，而我国西部就非常缺乏这种聚集，是吸引FDI的薄弱环节。

6“核心一”体系（CPS）。在对外直接投资比较集中的地区，往往会形成“核心一”体系（CPS），在空间一上的表现即为围绕“核心”区域形成的“核心一”（CP）结构。因此，与核心区域的地理接近性，成为影响对外直接投资的重要因素。我国“核心-”体系的结构可分为两种：第一种是与投资国相邻，易于吸引投资。例如我国广东的东莞、深圳等地区，由于毗邻香港，而成为外资最先进入我国的地区之一。山东的青岛、威海等地则由于与日本、韩国接近，而成为日资和韩资集中的地区。CP结构形成后，会在该地区产生一种引力，企业在选址时将遵循引力模型中所描述的企业关系，形成集聚效应。第二种是对外直接投资在核心区域选址后，吸引了大批跨国配套企业进入，这些企业在核心区域附近选址，形成了以产业配套为特点的区域。1993年，台湾明基公司在苏州新区投资设厂，同时还召集其台湾核心配套厂商，吴江由于具有土地和区位优势成功吸引了一大批配套企业人驻，从而在以苏州为核心，以吴江为的地区形成了以产业配套协作体系为特点的CP结构。

基于对外直接投资影响因素的分析，我们可以得出促进区域经济特别是中西部经济发展的几点启示：

1.接受并推行投资自由化

加快西部对外开放的基本方向是投资自由化和贸易自由化。投资自由化主要是指那些有利于促进长期性外国直接投资的自由化政策，包括三方面内容：一是促进市场竞争原则，通过减少或消除特别针对外国投资者所采取的歧视性措施，取消市场准入限制，促进市场竞争。外国直接投资参与西部基础设施建设的潜力也非常巨大，要创造宽松的环境，鼓励外资进入能源、交通、通讯等基础设施优先发展领域，允许外资公平参与国家重大工程项目或公共项目的公开竞争招标。二是享受国民待遇的原则，即外国投资者的待遇等同于本国投资者，一方面，应取消对外资企业在税收等方面“超国民待遇”的优惠政策；另一方面，应取消对外资企业贷款、融资、投资等方面“非国民待遇”的歧视性措施，严禁对各类外资企业乱收费和变相增加非生产性负担。三是提供制度保护原则。按照市场经济原则发挥市场经济机制来促进投资自由化和吸引外资，同时创造良好的投资环境和制定相应的竞争性政策，以抑制某些私人投资和贸易的负面影响对市场竞争的破坏效应。四是尽量减少本是不必要的，繁琐的政府干预。无论是提高企业经济绩效，还是提高西部地区整个经济的效率，关键是增强市场的竞争性。在此意义上，投资自由化就是经济市场化，竞争游戏规则的公平、公开化以及监督机制的透明化、制度化。

2.积极开发人力资源

就西部而言，开发人力资源具有尤其重要的特殊意义。因为西部地区最大的资源是人力资源，也是中国目前就业压力最大的主要地区之一，由于政府投资本身创造不了多少就业，因此政府的作用主要是通过有效的人力资源开发政策，吸引外国投资创造更多的就业岗位，这对扩大就业、缓解失业压力具有重要作用。这就要求政府不仅要实行直接影响FDI的投资自由化和贸易自由化的政策，更重要的是要将人力资源开发放在极其重要的位置上，包括大力发展教育，积极培育劳动力市场和人才市场，鼓励外资企业对其员工进行人力资源开发以提供更多的培训机会，增加中央对西部地区的转移支付，鼓励少数民族控制人口增长，在逐步解决“收入贫困”的过程中逐步解决“人类贫困”、“知识贫困”和“文化贫困”问题等。

3.实行跨国公司友好型政策，加快基础设施建设，积极吸引跨国公司直接投资

篇2

一、引言

随着山东省经济的快速发展和国际经济环境的不断改善，山东省在对外贸易和利用外资方面取得了很大的进步。据山东省统计年鉴资料显示，截至2004年底，累计已有113家世界500强在山东省兴办企业262家。2004年，新批合同外商直接投资214.5亿美元，比上年增长53.7%，实际外商直接投资87.0亿美元，增长22.7%；新签外商直接投资项目5891个，增长11.1%。与此同时，山东省的进出口贸易也得到了迅猛发展，年出口额由1985年的23.4652亿美元增加到2004年的358.7286亿美元；年进口额由1985年的17.9796亿美元增加到2004年的249.0850亿美元。

对于国际直接投资东道国而言，外商直接投资与进口或出口的关系表现为二者的互补性、替代性或是相互关系的不确定性。本文通过实证分析来探讨山东省FDI与进、出口贸易的关系。

二、实证分析

（一）数据来源和研究方法

为了从定量角度考察山东省外商直接投资与进出口贸易的相关性，本文选取山东省1980年至2004年的年度经济数据，运用协整方法进行分析，建立误差纠正模型描述变量之间的长短期关系，并对变量进行Granger因果关系检验。其中，FDI是各年度的实际利用外商直接投资金额，EX代表各年度的出口贸易额，IM代表各年度的进口贸易额。本文为了研究方便，并且考虑到在分析中取各变量的自然对数后不会改变变量之间的关系，在这里对各序列进行自然对数变换，变换后各变量序列分别取LNFDI、LNEX、LNIM。

二）平稳性检验

所谓时间序列的平稳性，是指时间序列的统计规律不随时间的位移而发生改变，也就是说，生成变量时间序列数据的随机过程的特征（数学期望、方差及协方差）不随时间变化而变化。在对时间序列进行计量分析时，首先要对各变量进行平稳性检验。在现实经济中，许多经济变量的时间序列是非平稳的，对非平稳的时间序列进行回归可能会出现谬误回归（spuriousregression）的现象，导致标准的t和F检验无效。本文采用ADF检验法对变量LNFDI、LNEX、LNIM进行单位根检验，考察序列是否平稳。检验结果见表2：

注：（C,T,K）分别代表所设定的检验方程含有截距、时间趋势及滞后阶数，N指不含C或T，K的选择标准是以和值最小为准则。

以上对时间序列LNFDI、LNEX、LNIM的平稳性检验表明，在10%的显著水平下，不能拒绝三个变量存在单位根的假设，LNFDI、LNEX、LNIM均为非平稳序列，而它们的一阶差分LNFDI、LNEX、LNIM均为平稳序列。由此可知，LNFDI、LNEX、LNIM均为I(1)序列。

（三）协整检验

为了分析外商直接投资于山东省进出口贸易的关系，本文分别对LNFDI与LNEX、LNFDI与LNIM的关系进行协整检验。协整分析技术是20世纪80年展起来的一种分析方法。协整分析是由若干服从单位根过程的变量组成的系统，若这些变量的某一线性组合式平稳的，则称这一稳定线性组合为协整关系。协整分析描述了这些变量之间的长期稳定关系。

关于协整检验的方法主要有以下两种:一是Engle和Granger提出的基于协整回归残差的ADF检验的EG两步法；二是Johansen提出的基于VAR模型对协整向量系数进行极大似然估计和检验。本文采用的世恩格尔——格兰杰（Engle-Granger）两步法分别对LNFDI与LNEX、LNFDI与LNIM之间的关系进行协整检验。

1、对LNFDI与LNEX的协整检验

首先用LNEX对LNFDI做普通最小二乘回归，然后对回归残差做单位根检验。单位根检验的方法采用ADF检验法，ADF检验采用带有趋势项带有常数项的形式，滞后阶数选为6。检验结果根据残差的ADF检验结果知，残差不存在单位根，即残差是平稳序列。这说明LNFDI与LNEX之间存在协整关系。协整方程为：

LNEX=3.607857+0.35751LNFDI(1)

(30.26889)(7.415309)

R^2=0.763846AD.R^2=0.749954F=54.98681DW=0.405013

从方程(1)可以看出，变量LNFDI的系数为0.35751,说明FDI对EX的弹性系数为0.35751，即FDI每增长1%，EX将增长0.35752%。

用LNEX对LNFDI做普通最小二乘回归，得到协整方程为：

LNFDI=-8.304089+2.419141LNEX(2)

(-5.994780)(7.415309)

R^2=0.763846AD.R^2=0.749954F=54.98681DW=0.423218

方程(2)说明，LNEX对LNFEI的弹性系数为2.419141,即EX每增长1%,FDI将增长2.419141%。

2、LNFDI与LNIM的协整检验在线

首先用LNIM对LNFDI做普通最小二乘回归，然后对回归残差做单位根检验。仍采用ADF检验法，检验结果如

根据表4的检验结果知，残差存在单位根，使非平稳序列。这说明LNFDI与LNIM之间不存在长期的均衡关系，即二者之间不存在协整关系。

（五）因果关系检验

协整检验的结果表明，山东省外商直接投资与出口之间存在长期稳定的均衡关系，但是这种均衡关系是否构成因果关系，还需进一步验证，本文采用Granger因果关系检验法验证。Granger曾指出，因果关系检验只有在两个变量协整的情况下才是有效的。由于前面已经验证出山东省外商直接投资与出口之间存在显著的协整关系；而山东省外商直接投资与进口之间不存在协整关系，因此，此处只须进一步对山东省外商直接投资与出口这两个变量序列进行Granger因果关系检验。在Granger因果关系检验过程中，滞后阶数取5，检验结果见表5在线

从表5的检验结果中可以看出，山东省外商直接投资与出口之间存在着单向的因果关系。在10%的显著性水平下，外商直接投资是对外出口的格兰杰原因，而出口不是外商直接投资的格兰杰原因。

三、结论与建议

本文通过运用协整检验和Granger因果关系检验来研究山东省外商直接投资与进出口贸易的关系，结果表明：

篇3

二、文献回顾

英国学者邓宁在海默的垄断优势理论，巴克利和卡森的内部化理论的基础上提出了国际生产折衷理论，对跨国公司及对外直接投资现象做了全新的解释。他指出：一个企业进行直接投资是有三个因素决定的，即所有权优势，内部化优势和区位优势。邓宁把区位优势看作国际投资区位选择的关键因素，并把区位因素归纳为市场因素、贸易壁垒、成本因素和投资环境，随后又补充了语言、习惯等非经济因素。

根据邓宁的理论，伍德沃和罗尔夫对影响出口导向制造业国际分配的主要因素进行了实证分析。根据他们的分析，跟投资选址呈正相关关系的因素有：GDP、汇率贬值、免税期限、自由贸易曲的规模、政治稳定因素、制造业的积聚度、土地面积等；而与选址呈负相关关系的因素有：工资、通货膨胀率、运输费用、工会组织等。

朴商天（2004）以中国市场为研究对象，对外商在华直接投资的地区性差异因素进行了实证分析，得出：对外开放度、集聚化程度、鼓励政策与直接投资之间存在着明显的正相关关系，而工资水平、研发人力则对直接投资起着反作用。基础设施对选址呈正相关关系，但对外商投资企业经营活动的影响正在减少。

这些理论都在一定程度上解释了FDI区位选择的动因，前两个是以多个国家为研究对象的，对我国具有一定的借鉴性，但不可能完全符合中国的现实情况。朴商天虽然以中国为研究对象，但他只简要的讲述了影响地区性差异的因素，对某些区域存在着哪些具体的问题方面并没有解释。本文根据这些学者提出的因素，结合中国东、中、西部的具体情况，解释对外直接投资在我国分布不均衡的原因和由之得出的一些启示。

三、影响外商对华直接投资区位选择的因素

1政策因素

张立（2002）对FDI在我国省际分布的决定因素进行了实证分析，他引入了各省执行FDI优惠政策的时间，结果显示，各省执行优惠政策的时间先后对FDI的流入有着显著的影响。我国的珠江三角洲和长江三角洲作为政策开放较早的地区，利用自己东南沿海的区位优势、政策优势和劳动力成本优势，通过建立开发区和工业园区，最先成为对外直接投资在我国的集中区。1992年中国开放内陆城市并实行也沿海地区相同的鼓励政策，推动了中国中西部地区的对外开放，近几年外商对中西部的投资有所增长，特别是长江中上游地区，如安徽、江西、湖北、云南、贵州等省份。差距不可能在短期内消除，但中西部地区已经开始了引进外资的征程。

2劳动力成本及工资水平因素

劳动力成本是影响外商直接投资区位决策的成本因素中最为主要的成本。Austin(1990)强调发展中国家吸引外资的主要原因在于低工资水平，工资高低与外商投资呈负相关关系。朴商天（2004）通过实证分析，验证了这一关系的正确性。作为人口大国，中国具有丰富而廉价的劳动力资源，这种成本竞争优势对跨国公司具有强大的吸引力。劳动力导向战略是跨国公司对华直接投资重要的区位选择战略。除了成本因素，劳动力素质也直接影响到劳动生产率的高低。特别是在一个东道国内部，低劳动力成本经常意味着低的劳动生产率，只有那些低成本并且具有较高劳动生产率的区位，才能比低成本、低劳动生产率的区位更具有吸引力，这就是外资没有因为劳动力成本低而大量流向中西部地区的原因。

但是过高的工资水平也会抑制外资的流入。以长江三角洲职工平均工资衡量的劳动力成本显示，上海、宁波、杭州、南京和苏州等地的工资水平，远高于长江以北地区城市。工资成本对FDI分布的影响就是，未来的长三角地区外资可能会更倾向于投资到工资水平较低的周边地区，甚至转移到区外。工资成本的上升对长三角的外资流入是一个不利因素。

3土地成本因素

珠三角地区开发较早，当外资聚集到一定程度后，可利用的土地越来越少，而成本不断攀升，对FDI起到了明显的抑制作用。按2002年单位土地面积的GDP衡量，深圳、汕头、广州、海口和福州等珠三角城市的土地成本在全国排在前列。自90年代以来，珠江三角洲在引进外资中所占的比重有所下降，而长江三角洲和环渤海地区的比重在持续上升。

4.基础设施质量

篇4

Abstract：Foreign direct investment is now perceived as a classic form of business across the world. By transferring foreign capitals， technologies or managerial expertise， FDI has the potential to be a catalyst of host countries’ economic development. Meanwhile， foreign firms and their home countries could also benefit from FDI by enjoying profits， low-cost products， etc. As the result， there is no wonder why FDI has been increasingly recognized as a win-win choice of both home and host countries. However， in making or attracting overseas investment， foreign firms and host countries both face a same issue： how to minimize risks. Indeed， various risks， such as political， economic， exchange rate and legal risks， all may affect the direction of FDI because its adverse impact on firms’ profitability. By using panel data estimation based on the dataset of flows of U.S. FDI to 43 developing countries during the period of 1984 to 2007， the empirical results of this paper statistically confirmed the negative relationship between risk and flows of FDI.

Key words：Risk FDI Panel dataset

Introduction

Although tracing back to its exact origin is practically impossible， foreign direct investment （FDI） is now perceived as a classic form of business across the world. By definition， FDI refers to “an investment made to acquire lasting interest in enterprises operating outside of the economy of the investor” （International Monetary Fund， IMF， 1993）.

By receiving capital， technologies or managerial expertise， developing countries like China and India have benefited from FDI greatly， while at the same time low-cost products have entered advanced economies from developing economies， profiting both Western enterprises and consumers. Therefore， it is widely accepted that FDI is a win-win choice of both home and host countries and “a major catalyst to development” （Organization for Economic Co-operation and Development， OECD， 2002）.

Considering the following two questions， the first being how nations attract FDI and the second being how MNEs decide where to invest， there exists a short answer for both questions： to minimize risks. Indeed， given their influences in affecting firms’ profitability， various risks， such as a sudden political upheaval， a huge fluctuation in exchange rate， or an unfavourable amendment of legal provisions， etc.， are primary concerns for investors. Those risks， in particular， are more striking and common in developing countries， as those countries typically have weak and fragile political， economic and legal frameworks. Thus， minimizing risks is a key to success for both nations seeking capitals and MNEs creating FDI.

Section 1 Literature Review

1.1 A Brief Review of the History and Phenomenon of FDI

Foreign direct investment is a major form of international capital flows. It involves a physical investment by a company from one country to another （Sullivan and Sheffrin， 2003）. Foreign direct investment distinguishes itself from international portfolio investment by the degree of control of a foreign affiliate （IMF， 1993）. According to the IMF’s definition， when an investment into a foreign company is worth more than 10% of the voting power of the company， the investment is defined as a foreign direct investment. In calculation， FDI is commonly divided into three parts： equity capital， reinvested earnings and intra-company loans. However， UNCTAD （2009） pointed out that countries do not always compile with those three categories when collecting and allocating data.

To understand the incentives behind the decisions of firms in undertaking investments abroad， Seyf （2001） identified the following motivations： to explore new markets； to acquire new technologies； to overcome tariff and other protective restrictions； to enhance efficiency of servicing a foreign market by localizing production； to reduce risk by diversifying market as well as product； to be able to combat the threat of rivals in the international marketplace and so on.

1.2 Theoretical Analysis of Risks Associated with FDI

As noted at the beginning of this paper， given MNEs’ ultimate goal of maximizing profits， various risks which directly affect profitability are the primary concerns of firms who seek offshore investments. Many scholars pointed out that investing aboard carries additional risks compared with investing domestically （Lessard， 1996； Musonera， 2008）. Those additional risks which were frequently mentioned in previous studies include political risk， economic risk， exchange rate risk， legal risk， sovereign risk， and so on.

Political Risk

One common concept given to political risk is known as “the actions of national governments which interfere with or prevent business transactions， or change the terms of agreements， or cause the confiscation of wholly or partially foreign owned business property” （Weston and Sorge， 1972）. In this context， political risk partially represents the concept of legal risk. Especially， in many developing countries， administrative authorities hold tremendous influences over the operation of the judicial systems， which means that some judicial actions against foreign firms may be reflections of political decisions. However， Robock （1971） criticized the opinion by pointing out that “political fluctuations which do not change the business environment significantly do not represent risk for international business”.

Economic Risk

Economic risk of host countries ranks high in the list of risks associated with FDI， as Musonera （2008） claimed that “the level of economic activity of a country sets the stage for business operations.” Meldrum （2000） defined economic risk as a significant change in the economic structure or growth rate that produces a major change in the expected return of an investment. This definition makes it clear that FDI are subject to the economic factors of host countries， as it affects the profitability of MNEs.

Exchange Rate Risk

Exchange rate risk has become an increasingly highlighted risk since the collapse of the Bretton Woods system in the early 1970s which marked the end of fixed exchange rates regime among world’s major economies （Schmidt and Broll， 2008）. In general， exchange rate risk refers to “the effect that unanticipated exchange rate changes have on the value of the firm” （Giddy and Dufey， 1997）. More specifically， it is a risk stemming from changes in the exchange rates for different currencies and affecting investments and business operations of companies undertaking international business （BNET， 2007）.

Legal risk

A sound legal environment is considered to be crucial to investors， especially foreign investors， in operating their businesses. Perry-Kessaris （2003） pointed out that it is common to argue that the direction of FDI to some extent depends on the effectiveness of destination countries’ legal systems. Contrary to such a sound legal environment which possesses characteristics including transparency， certainty， accountability， and consistency， etc.， legal risk arises from uncertainty due to “legal actions or uncertainty in the applicability or interpretation of contracts， laws or regulations” （， 2004）.

1.3 Previous Empirical Studies on the Relationship between Risks and FDI

Ramcharran （1999） pointed out that there are two types of empirical studies “survey method” and “statistical analysis” in analyzing the correlation between country risk and FDI. In using the survey method， researchers typically request a large number of executives from different multinational firms to rank or list risks which may affect their overseas investment decisions. By employing this method， Basi （1963）， Aharoni （1966）， and Agodo （1978） among others found that the risk mangers concern the most is political risk. Obviously， a common criticism regarding the survey method is solely dependent on respondents’ subjectivity （Kobrin， 1979； Brewer， 1981）.

The statistical analysis requires the use of econometric techniques. Ordinary least square estimation is a fundamental technique applied by researchers when studying the relationship between country risk and FDI. Also， panel data is often employed and fixed or random effect can be applied depending on the current circumstances.

2 Empirical Studies

2.1 Methodology

This paper studies the relationship between risks and FDI focusing on the U.S. outward FDI flows in 43 developing countries during the period of 1984 to 2007. The choice of this period allows the empirical analysis of this paper to be based on a more recent time spam than most of previous studies. In analyzing the impact of various risks on FDI flows， panel dataset is employed in this paper. As Hsiao （2003） and Baltagi （2005） identified， applying panel data analysis has the following advantages. Firstly， panel data analysis controls individual heterogeneity in the way illustrated later. Secondly， effects that are not detectable in cross-sectional or time-series dataset will be captured by panel data analysis. Thirdly， more complicated behavioural models are able to be constructed and tested in panel data analysis. Finally， panel data analysis offers more variability， reduces collinearity among variables， and enhances degrees of freedom and efficiency.

The regression model applied in the estimation is stated as follow：

（1）

where ， and .

In the model， is the amount of U.S. outward FDI to country at time ， is a vector of variables including controls as well as key variables that are under primary interest. is the time invariant country specific characteristics and is the error term that varies both across time and countries.

By using panel dataset， this paper applies “fixed effect” in estimating the regression equation above. It is assumed the country specific effect is correlated with the explanatory variables. One of the major advantages of using fixed effect is that this econometric technique eliminates the country specific characteristics before estimation. If such characteristics exist， the regression results are likely to be biased. For example， a MNE’s decision to invest into a particular country may be based on that country’s specific location and culture. However， those country specific features may well correlate with variables that are included in the regression equation， making the estimation results biased because some of the effects of time invariant variables that are not included in the equation have been absorbed by variables that are included in the equation. The following steps illustrate how the within estimation solves this potential problem. Firstly， by taking the equation above and average across time we obtain

（2）

where .

Secondly， by subtracting （2） from （1） we have

（3）

It can be seen from （3） that the country specific characteristic is eliminated. Therefore， （3） is the final equation to be estimated and the technique is referred as fixed， or within estimation. Furthermore， robust standard error is applied through the Fixed Effect estimations.

Based on the above model and the availability of data， the general form of the estimation equation can be written as below：

FDI = FDI （economic risk， political risk， control variables）

where economic risk is measured by inflation rates of host countries， political risk is measured by the indices of bureaucracy quality and democratic accountability of host countries， both issued in the International Country Risk Guide （ICRG）. Control variables include gross domestic product （GDP） per capita and exchange rates of host countries.

It is important and necessary to include variables which also affect flows of U.S. outward FDI along with various risk factors in the regression estimation. This is because if those variables are excluded， their effects on the flows of U.S. outward FDI will be captured by the error term. Given the potential correlation between those variables and various types of risks， the error term in the regression equation will be correlated with the key risk variables that are under primary interest. As a result， it leads to the violation of Gauss-Markov conditions which biases the estimation results.

The reason why exchange rates act as a control variable here rather than a measure of exchange rates risk results from the unavailability of daily， monthly or quarterly exchange rates between the U.S. and host countries， which restricts the calculation of volatilities of exchange rates used to measure exchange rates risk.

According to above analysis， an appropriate empirical model for analyzing the relationship between flows of the U.S. outward FDI and political and economic risks can be given in the following linear form.

where ， and .

In the equation， denotes flows of U.S. outward FDI into country i in year t ； denotes inflation rate of country i in year t ； denotes bureaucracy quality of country i in year t ； denotes democratic accountability of country i in year t ； denotes average official exchange rate in a year between the U.S. and country i in year t ； country i in year t ； country i in year t ； denotes GDP per capita of country i in year t . Besides， β0 is a constant； βs are estimated coefficients of each variable； εis an error term with a zero conditional mean.

The justification of all independent variables is shown as follows：

1. Inflation rate. Acting as a proxy of economic risk， inflation rate is expected to have a negative correlation with flows of U.S. outward FDI. That is， other things being equal， when a country’s inflation rate increases， especially when it increases sharply， U.S. outward FDI to that country is expect to decrease， because a higher inflation rate indicates lower macroeconomic stability and higher risk， vice versa.

2. Bureaucracy quality. Bureaucracy quality is an indicator of political risk. According to the PRS Group （2009） who is responsible for the conducting of ICRG， bureaucracy quality measures “the institutional strength and quality of the bureaucracy”. It is rated between 0 and 4 with 0 indicating the lowest bureaucracy quality and the highest political risk because of drastic changes in policy or interruptions in government services （ICRG， 2009） and 4 indicating the highest bureaucracy quality and the lowest political risk. Thus， it is expected to have a negative correlation between bureaucracy quality and FDI. That is， other things being equal， when a country is assigned a low rating of bureaucracy quality， suggesting the country has high political risk， flows of U.S. outward FDI to that country is expected to be low， vice versa.

3. Democratic accountability. Democratic accountability is another proxy of political risk. It measures the responsiveness of a government to its citizens such that the irresponsiveness may cause the failure of the government （ICRG， 2009）. Countries are rated between 0 and 6 in terms of their democratic accountabilities. The higher a country is rated， the lower the political risk of that country possesses and vice versa. Hence， a negative correlation between democratic accountability and flows of U.S. outward FDI is expected.

4. GDP per capita. GDP per capita represents the size of a country’s economy. One of the main incentives for MNEs to invest abroad directly is to gain access to foreign market. As a result， countries with higher GDP per capita are likely to attract more FDI from the U.S.， ceteris paribus. To analyse the effects of different risks on U.S. FDI outflow， it is necessary to control for GDP per capita of the host countries， otherwise， estimated coefficients may be biased as different forms of risks of a country is likely to be correlated with its level of GDP per capita.

5. Exchange rate. Exchange rate is measured in terms of local currency units per U.S. dollar， so that an increase in exchange rate indicates a depreciation of a host country’s currency. As explained earlier， exchange rate is a control variable in the regression， which means that it acts as a general determinant of the flows of U.S. outward FDI. Thus， exchange rate is expected to positively correlate with the flows of U.S. outward FDI， ceteris paribus. The rationale behind this expected relationship is that when a country’s currency depreciates， foreign firms homed in another country with a relatively stronger currency would have their production costs reduced and profitability increased.

2.2 Data Sources

The flows of U.S. outward FDI are obtained from the website of the Bureau of Economic Analysis （BEA） of the U.S. Department of Commerce. The data have been adjusted into constant 2000 U.S. dollars in the unit of million.

Variables including inflation rate， GDP per capita and exchange rate come from the World Development Indicators （WDI） in the version of June， 2009. WDI is conducted by the World Bank and published on the Bank’s website. In the empirical studies， inflation rates of individual countries are given in decimal. Data of GDP per capita are in constant 2000 U.S. dollars. Exchange rates are real exchange rates derived from official ones. The reason of using official exchange rates is that although they may undervalue or overvalue domestic currencies， given MNEs have to transfer funds through legal channels in most cases， it is these rates concerning MNEs.

As noted previously， two risk measures of bureaucracy quality and democratic accountability are conducted by the International Country Risk Guide， a product of the PRS Group.

2.3 Data Description

Table 1 illustrates the statistics of variables contained in the regression which shows numbers of observations， means， standard deviations， minimums and maximums of both dependent variable and independent variables. While the dependent variable of the flows of U.S. outward FDI and three independent variables of GDP per capita， inflation rate， and exchange rate has substantial variation， the other two independent variables， namely bureaucracy quality and democratic accountability， only have very small variations because of their rating-based characteristic.

Table 2 presents the correlation matrix of independent variables included in the regression equation. The correlation coefficients among those independent variables are in the range of -0.0815 to 0.3337， indicating it is unlikely to have the problem of severe multicollinearity.

2.4 Empirical Results

Table 3 Regression estimates of each independent variables

Independent Variables Coefficients Robust Standard Errors P-values

GDPPC 3.015874 .6543028 0.000***

INFL -1.124499 .6546437 0.086*

EXRATE .0483234 .0288662 0.094*

BQ 859.2279 283.2691 0.002***

DA 399.4627 161.8561 0.014**

*** Statistical significance at 1% level； ** Statistical significance at 5% level； * Statistical significance at 10% level

Table 3 displays the parameter estimates of both risk factors and control variables by using fixed-effect. Satisfactorily， coefficients of all variables have the signs as theories suggest and are statistically significant 10% significant level with bureaucracy quality and GDP per capita being significant at 5% and 1% levels respectively.

Looking separately， inflation rate， which acts as a measure of economic risk， indicates that higher economic risk of a country tends to reduce flows of FDI from the U.S. to that country， ceteris paribus. As two political risk indicators， the coefficients of bureaucracy quality and democratic accountability suggest that U.S. investors are very concerned by a foreign country’s political risk. In other words， when a country’s political risk level is lifted by a professional institution， such as the PGS group in this case， flows of U.S. outward FDI to that country tend to decrease substantially. This finding is consistent with the results of recent empirical studies that political risk and flows of U.S. outward FDI are positively correlated with each other. In terms of control variables， GDP per capita indicates that when a country’s market gets bigger， U.S. investors will make more investment to that country， in part to serve the purpose of exploring a new market or better serving a foreign market. The coefficient of exchange rate demonstrates that exchange rate does not affect flows of U.S. outward FDI strongly. One reason may be U.S. firms’ capacities to use hedging instruments against some unfavourable exchange rate movements.

3. Conclusion

3.1 Evaluation

Admittedly， although the results of empirical studies are rather reasonable and satisfactory， there exist several drawbacks in the paper. One major shortcoming， as mentioned in the section of introduction， is the severe data limitation which prevents the paper from studying more risks together and comprehensively. Exchange rate risk and legal risk were not included in the empirical studies， instead， they were analyzed theoretically in the section of literature review.

Moreover， as shown in table 2， the independent variables of bureaucracy quality and democratic accountability are measured at a limited scale and thus have very small variations as a result of their rating-based characteristic， therefore the variables are not as informative as otherwise they would be， causing the regression results less precise. However， if measurements of the two variables were rated at a larger scale， they would be more informative and the preciseness of the regression’s estimates can be improved.

3.2 End Remarks

It has been widely recognized that FDI benefits both enterprises who operating business abroad and countries who receive foreign capitals. However， a common issue that draws attentions from firms and countries is the various risks associated with FDI. Indeed， empirical results of this paper demonstrate that when risks of a country undermine the profitability of foreign firms， it also means that the attractiveness of the country to foreign firms falls. Furthermore， the results show that political risk has substantial influence on the direction of a foreign firm’s overseas investment， regardless how political risk is measured. Besides， although economic risk is measured by inflation rate only， the result still highlights the adverse impact of economic risk on the flows of U.S. outward FDI.

There is no doubt that political risk and economic risk are not the only risks concerning a foreign investor. Subject to the severe limitation of data， this paper fails to empirically assess other risks associated with FDI. However， the paper does not arbitrarily lead to the conclusion that other risks have no effect on FDI. To the contrary， given the feature that political risk overlaps with legal risk to some extent， together with the fact that this overlapping is more striking in developing countries who are also the subject of this paper， it can be argued that the high coefficients of two political risk factors hint the possible existence of the effect of legal risk on FDI.

References：

[1]Busse， Matthias and Carsten Hefeker， 2005. “Political Risk， Institutions and Foreign Direct Investment”， HWWA Discussion Paper 315.

[2]Fitzpatrick， Mark， 1983. “The Definition and Assessment of Political Risk in International Business： A Review of the Literature”， Academy of Management Review， 1983， Vol. 8， No.2， pp. 249-254.