Examination of disaggregated country-industry level dataTable 5 presents results when we estimate our empirical specification for US MNE activity in European OECD countries by individual economic sectors categorized by the BEA.Publicly available data

Examination of disaggregated country-industry level dataTable 5 presents results when we estimate our empirical specification for US MNE activity in European OECD countries by individual economic sectors categorized by the BEA.Publicly available data
Examination of disaggregated country-industry level data
Table 5 presents results when we estimate our empirical specification for US MNE activity in European OECD countries by individual economic sectors categorized by the BEA.Publicly available data on US FDI activity has limitations in how disaggregated such data can be reported and these categories represent the finest level of disaggregation for which we can get public data by country and industry on an annual basis.Our time period,sample countries,and control variables match those for the European OECD regression above.Given the importance suggested above,we also include country dummies in all specifications.The number of observations for each of the sector estimations varies due to missing data when the BEA suppresses the data point out of confidentiality concerns.As one may expect,there is substantial heterogeneity in estimates across sectors though these differences are mainly in the magnitude of estimated parameters,not their sign.Importantly,we find stronger evidence of export-platform activity in the European-OECD sample when adopting these more disaggregated sector-level data.Five of the eleven sectors show a sign pattern that is consistent with export-platform motivations for FDI—a positive coefficient on the market potential variable and a negative spatial lag.For two of these,“food and kindred products” (Column 2) and “primary and fabricated Metals“ (Column 4),both of these spatial variables are also statistically significant.The coefficient on market potential is non-negative in nine of the 11 sectors and significantly positive 4 times.The spatial lag is likewise non-positive 10 out of eleven times and significantly negative three times.The one exception to this is “Petroleum,” (Column 1).However,given that a great deal of this industry is likely tied to the geographic proximity of oil fields,such may not be surprising.
In summary,by disaggregating the data (as much as public data allow) and focusing on a fairly homogeneous group of countries distributed evenly across space,we get stronger evidence for an FDI motivation that we would expect in the European sub-sample ——export-platform FDI.This highlights how important sample selection is in estimating empirical FDI models,particularly those with spatial terms,if one wants to be able to relate such results back to FDI theory.

Examination of disaggregated country-industry level dataTable 5 presents results when we estimate our empirical specification for US MNE activity in European OECD countries by individual economic sectors categorized by the BEA.Publicly available data
表5的结果,当我们估计我们的实证规范跨国公司的活动对美国在欧洲经合组织国家的个别经济部门分类的东亚银行.公开的数据,美国的外国直接投资活动的限制,这些数据如何分类可以报告和这些类别代表了最好的水平分列的,我们可以得到公众的数据按国家和行业在年度基础上.我们的时间内,样品的国家,和控制变量符合这些欧洲经合组织回归以上.鉴于上述建议的重要性,我们还包括国家的所有规格傻瓜.的若干意见的每一个不同的部门估计,由于缺少数据时,BEA的抑制数据指出的保密问题.作为一个可以期待,有很大的异质性,跨部门的估计虽然这些差别主要是在规模估计参数,而不是他们的迹象.重要的是,我们找到更有力的证据出口平台的活动在欧洲经合组织的样本时,通过这些多列部门一级的数据.5个部门的11显示符号模式是符合出口平台动机与外国直接投资的积极系数对变量的市场潜力和空间滞后负面.为两个,“食品和同类产品” （第2栏）和“小学和金属制品” （第4栏） ,这两个空间的变数也统计学意义.系数的市场潜力是非负9的11个部门和显着的正4倍.空间滞后同样不积极的10人的11倍,并呈显着负三次.的一个例外,这是“石油” （第1栏） .然而,鉴于大量的这个行业很可能被捆在地理上接近的石油等领域可能并不奇怪.
总之,通过分解的数据（多达公共数据允许）和专注于一个相当均一的国家集团之间均匀分布的空间,我们得到更有力的证据的外国直接投资的动机,我们希望在欧洲子样-出口平台的外国直接投资.这突出多么重要样本选择是外国直接投资估算实证模型,特别是那些与空间的条件,如果一个人希望能与这样的结果回的外国直接投资的理论.