高收入和低收入国家之间的经济比较

本研究分析是基于以1995 - 2008年期间由8个发达国家和8个发展中国家组成的16个国家样本的截面数据。它应该包括最高为的896个的观察值。世界银行将收入不高的国家分为不同的地区群组，我选定了欧洲和中亚地区。表1列出了被选择的国家。

然而，16个国家对某些变量在1995年和2008年期间相关的数据集是偶尔不连续的，特别是企业税率。这造成个问题的主要原因是，一些国家特别是发展中国家(例如塞尔维亚和波斯尼亚)经历了重要的事件，包括战争，分布在不同的国家和低效的政府机构。此外，数据集包括三个虚拟变量，国与国之间的距离，区域贸易协定和电话主线。然而,这个数据库提供了显著的完整评估模型。我选择的变量是关于UNCTAD和文献综述作为外国直接投资(FDI)现金流量的主要决定因素。
Economic comparison of high and lower income countries
The analysis in this study is based on a sample of cross sectional data for the 1995-2008 period on 16 countries which are comprise of 8 developed countries and 8 developing countries. It should include a maximum 896 observations. World bank classifies the non-high income countries as regional groups which I selected Europe and central Asia. Table 1 listed selected countries.
However, the data set associated with the 16 countries is occasionally discontinuous for some variables over the period of 1995 and 2008, particularly corporate tax rate. The main reason of this issue is that some countries especially developing countries (i.e. Serbia and Bosnia) experienced important events including battles, division in different countries and inefficient government agencies. Furthermore, the data set includes three dummy variables, which are distance between countries, regional trade agreements and telephone main lines. Nevertheless, this database significantly offers the complete estimation for the model. I selected variables as the main determinants of foreign direct investment (FDI) flows with respect to UNCTAD and literature review. Supposedly, other variables different than the main determinants suggested by the literature review, impact FDI, such as the values of export and imports and the consumer price index for countries. I used these variables to calculate the main determinants variables of FDI, which are:
Foreign Direct Investment (FDIIJ): it denotes net FDI outflows from developed countries into non-high-income countries. Besides, it is the dependent variable as a measure of foreign investment. Data Source: International Direct Investment Statistics, OECD (2011).
GDP per capita (GDPJ): The growth rate of GDP per capita as annual percentage is based on constant US dollar. Market size of country measured by GDP per capita or GDP. Data Source: World Development Indicators, World Bank (2011).
Distances (DISTIJ): it denotes distance between developed countries, which are investors, and non high-income countries that are investees, in kilometres. Distance basically affects transport costs and moreover, relates to efficiency of transport ways. Data Source: CEPII (2011).
Regional Trade Agreements (RTAIJ): It denotes trade agreement between developed countries and non high-income countries (=1 if there is an agreement). Data Source: World Trade Organisation (2011).
Telephone Mainlines (per 1,000 people) (TELJ): it indicates that telephone lines connect a customers’ tool to public switched telephone network. Data are showed per 1,000 people for the whole country, which is a proxy for infrastructure of country. Data Source: World Development Indicators, World Bank (2011).
Openness (OPENJ): this variable denotes a developing country’s level of openness to international finance, which calculates as the nominal import plus the nominal export divided by the nominal GDP.
Inflation, CPI deflator (INFJ): it indicates the rate of changes in consumer prices at a whole economy. Inflation is a proxy for economic stability, which is measured as follow:
Rate of Inflation = (CPIt – CPIt-1)/ CPIt-1 × 100
Real Exchange Rate (RERJ): it denotes the competiveness of country to international trade. In this paper, It calculated that it corrected value of the domestic currency against to US dollar with respect to consumer price index:
RERK : nominal exchange rateK * ( CPIUS / CPIK)*100
Question 2
The gravity model for international trade derived from the Newton’s law of gravity. Some academicians transferred the gravity equation to analyse the international trade flows between countries such as Tinbergen ‘s simple model (1962). Therefore, the gravity model examines the interactions of trade between countries by using the determinate variables. There are three types of panel estimators:
Pooled ordinary least squares (POLS): it puts the data set together which means that it doesn’t make any segregation between cross section and time series in used data.
According to the gravity model, I decided to run the following regression and the outcomes of which are presented in Appendix 1:
Ln fdiij = β0 + β1 ln gdpi + β2 ln gdpj + β3 ln gdppci + β4 ln gdppcj + β5 ln openj + β6 ln reri + β7 ln rerj + β8 ln telj + β9 ln distij + β10 rtaij
Fixed effects (FE): it is using F and likelihood linear tests and Quantitative Micro Software (2007, p. 498) states “The fixed effects portions of specifications are handled using orthogonal projections.” Furthermore, dummy variables exclude from model, so that the regression is;
Ln fdiij = β0 + β1 ln gdpi + β2 ln gdpj + β3 ln gdppci + β4 ln gdppcj + β5 ln openj + β6 ln reri + β7 ln rerj + β8 ln inf
Random effects (RE): it is interested in the realizations of independent variable with mean zero and limited variance. It has the similar regression with FE.
Ln fdiij = β0 + β1 ln gdpi + β2 ln gdpj + β3 ln gdppci + β4 ln gdppcj + β5 ln openj + β6 ln reri + β7 ln rerj + β8 ln inf
According to the results of POLS, The appropriate model seems to be high with an adjusted R2 of 0.76 and the independent variables in the regression explain the variation at the approximately 76% level in FDI outflow. Furthermore, FE estimator is the highest with adjusted R2 and the independent variables elucidate the model at about 87% level in dependent variable, whilst RE has the lowest explanation with adjusted R2 0.58. By excluding the dummy variables (rtaij, distij, telj), FE estimator is more significant with respect to other estimators.
It is generally accepted that depreciation in home country currency increases FDI flows into that country. For this reason, I expect that a deprecation in the real exchange rate (hereafter RER) of our non high-income countries or a rise in developed countries currency directs and increases FDI flows into these countries. In the other word, rerj has positive effect; whilst reri is inversely correlated with FDI flows. Because, an appreciation of the developing countries’ RER leads to depreciation their competiveness associated with attracting foreign investments. Goldberg and Kolstad (2000, p.21) rightly points out that “… a depreciation of the domestic currency does make foreign facilities more expensive, and probably leads to a reduction in demand for physical investment abroad.”
FE and RE estimators confirmed our expectations while POLS only rejected our expectation in related to RER of non high-income countries. In the other words, rerj have positive but not great impact on FDI flow into developing countries in the outcomes of FE, RE and POLS. However, reri has linear and insignificant correlated with FDI flow in the results of POLS, despite it affects negatively dependent variable in results of FE and RE.
The coefficients of GDP and per capita GDP showed significant impacts based on the results of each estimator. GDP and per capita GDP are a proxy for Market Size and increasing market size leads to efficiently utilize the resources and avoid exploitation of them. Therefore, market size has positive effects on FDI. When I use per capita GDP is a proxy for market size, gdppci affects positively and significantly FDI flows into developing countries, but surprisingly, a rise in per capita GDP of developing countries has negative impact on FDI in the outcomes of POLS and FE except RE. The different result occurs when GDP is used as a proxy for market size. In the POLS’s result, GDP positively and significantly affect FDI flow. However other estimators found that it has negative and great impacts on FDI. However, if I make a decision about selecting GDP or per capita GDP as an indicator of market size, my answer will be per capita GDP, because GDP reflects the bulk of GDP more so than income.
Surprisingly, the impact of inflation in developing countries on FDI is fairly small but positive in all estimators. It points out that when Inflation is used as a proxy for economic stability, increasing the ambient of stability leads to move FDI to unstable countries. However, the opposite of this situation should be valid. Moreover, the coefficient of telephone main lines as a proxy for infrastructure is significant and positive. It denotes better infrastructure attracts FDI to developing countries as a significant determinant.