Research Process and Terminology Paper Essay
Research Process and Terminology Paper
The aim of this paper is to address the linkage between foreign direct investment (FDI) flows and the number of natural disasters. By using the data of 94 countries in the period of 1984 to 2004 and applying a variety of empirical tests, the result appears that natural hazards have significantly negative effects on FDI of countries.
A. Economic Effects of Natural Disasters and The Determinants of Foreign Direct Investment
Economic Effects of Natural Disasters
There are three patterns that concern with the economic effects of natural hazard. The first two strands concentrates on the primary or short-term effects and long-term effects of hazards on economy. While the short-term effect strand achieves abundant evidences of negative disasters’ impacts on GDP, the long-term effect strand cannot reach a clear conclusion. The third strand focuses on the capacity to mitigate the destructive effects of natural risks. A brief conclusion is that the negative impacts of risks can be diminished by country’s institutions.
Determinant of Foreign Direct Investment
There are three types of foreign direct investment, namely:
(1) Operating new
(2) Moving an existing
(3) Moving a part of existing
The first type is considered as location decision and categorized in pull factor, the latter two types are relocation decision and belong to push factor. Following this logic, propositional pull factors to put in models are the level of openness and the size of the economy. Obviously, the push factor in models is natural risks. Other determinants which are mainly focused are institutions, such as government infrastructure, political freedom, corruption, etc.
B. Data and Methods
The data for analyzing impacts of natural disasters on FDI flows are taken from the EMDAT, which provides by the institution Center for Research on the Epidemiology of Disasters (CRED) and World Bank. Some observations were dropped because of missing data, the data which is used in this research contains an unbalance panel with 1,822 country-year observations from 94 countries (29 in Africa, 17 in Asia, 22 in Europe and 26 in Americas) in the period 1984-2004. Table 2 presents descriptions of dependent and independent variables.
At this point, it is important to look again at two primary variables which devoted to results of empirical tests. The first key variable is FDI, which is measured by the total net inflows of foreign direct investment as a percentage of GDP. FDI is the dependent variable in all models. The second key variable relates to natural hazards. Since both recent and longerterm risks have its impacts on investors, the authors deliver four variables that are concerned with the number of natural risks happening in four time period: Total events in the prior year, total events in the prior 5 years, total events in the prior 10 years, total events in the prior 25 years. Table 3 shows the correlations between FDI/GDP and each of four variables referring to the measures of natural risks.
It is undoubtedly true that both the counted measure as number of natural hazards and the monetary measure as the estimation of “dollar value of damages” affect decision makers. While it can be argue that result as the dollar amount of damages may have substantial influence on investors’ decisions, it is obvious that estimating the consequence of natural disasters is complex and not as accurate as “counts of disasters”. For this reason, models will mainly focus on counts of disasters. Moreover, the research emphasizes on five types of natural hazards that severely devastate infrastructures, physical capital and labor forces. As such, these five types are earthquakes, floods, volcanoes, landslide and windstorms (include hurricanes).
The following two variables which refer to the degree of openness and incentive in trade and investment are Trade and Investment. The former is taken from World Bank’s 2008 World Development Indicators and the latter is provided by Political Risk Services Group, assembled by the IRIS Center at the University of Maryland. Regarding to a country’s reliability for trade and investment, the investment variable is the estimation of three factors: contract viability/risk of exportation, repatriation of profits and delay in payments. These three factors are rank from 0 to 12 and the higher value illustrates the higher risk in investment.
The final three variables in the base model are Inflation, Gov. stability and Rule of law. The Inflation variable is the inflation level of each country in a particular year and taken from 2008 World Development Indicators. The other two variables are collected from the International Country Risk Guide, with reflecting the level of stability of government and adhesion to the rule of law. The higher value implies the better environment for investors. Those variables contribute to the base model as this form:
FDIit = α0 + α1Total events in the prior # yearsit + α2GDP per capitait + α3GDP growthit + α4Tradeit + α5Investmentit + α6Inflation + α7Gov. stabilityit + α8Rule of lawit + γi + γt + εit
This research also employs γi as country fixed effects over time and γt as year fixed effects for all countries.
C. Results and Their Implications
The below table indicates the linkage between foreign direct investment and natural disasters by applying the base model.
It can be seen from Table 4 that all four natural hazard variables have significantly negative effects on FDI in each of models. Moreover, there is a decline trend in coefficients of disaster variables when measuring in Total events in the prior 1 year to Total events in the prior 25 years, which suggests that relatively recent risks have more significant influence than long term risks on investors’ decisions. The next two variables, which are GDP per capita and GDP growth, are positive as expected and significant. However, although both Trade and Investment variables have positive effects on FDI, only Trade is significant. The Inflation variable is negative and significant in all four models. Only Gov. stability variable has unexpected side and both Gov. stability and Rule of law are not significant in all models. The authors also employ the empirical tests to find out different effects of five particular types of disasters. The result is presented in Table 5.
The outcome demonstrates that all other non-disaster variables have the same reaction and all damage variables are negative in side. However, Windstorms is significant in all three cases, Volcanoes is significant in two cases while Landslides, Earthquake and Floods are significant in only one case. Hence, there is evidence to support the view that each type of hazards has its effects on FDI, the clearest evidence is found on Windstorms. Regardless the inaccurate in estimation of dollar value of damages, the research generates the final test by using the base model with “dollar value of damages” in place of “counts of disasters”. The result is displayed in Table 6.
Similarly with the above case, all non-disaster variables have the same result as the base model case. Though disaster variables are negative and significant in all case, they do not decline from recent to older events. A draw conclusion may be policy makers equally focus on relative recent and longer-term risks or maybe there is error in data.
To sum up briefly, there are four important conclusions. First and foremost, natural disasters have significant and negative effect on foreign direct investment. Second, there are some evidences to support the view that decisions of foreign investors are deeper affected by relative recent events in comparing to longer-term events. Third, different types of natural hazards are considered to have different impacts on foreign direct investment, the most severe impact is found on windstorms. Finally, regardless the intricacy and inaccuracy in monetary measuring the value of damages, the model which focuses on dollar value of damages also addresses the same result with the base model: natural disasters discourage foreign direct investment.
University/College: University of California
Type of paper: Thesis/Dissertation Chapter
Date: 25 October 2016
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