1.2. Objectives
This research is aimed be significantly prove on determinants which influence to the FDI inflows.
Also, this work is going to be able to model the relation of FDI to the above eight variables. Hence, as the result, we can estimated FDI inflows based on those variables and further develop on the area of significant to attract more investment.
2. Methodology
2.1. Data Collection
This research will use the secondary data from data.worldbank.org. Data will be collected for 230 countries and regions. But some of variables data is not applicable, hence, it will be automatically deleted during the
KEY TO VARIABLES:
- Y = Foreign direct investment, net inflows (current US$)
- X1 = Total population (in number of people)
- X2 = Total labor force (in number of people
- X3 = Percentage of population growth ( % annual)
- X4 = Liner shipping connectivity index ( 1 – 100)
- X5 = GDP per capita (in $US)
- X6 = Domestic credit to private sector (in percentage of GDP)
- X7 = Land area (sq.km)
- X8 = Cost of business startup procedure (% of GNI per capita)
2.2. Approach
Data will be analyzed based on regression of multiple linear among the 8 variables with FDI data
response in R programming.
3. Testing with R programming
3.1. Annova Table
1.2. ObjectivesThis research is aimed be significantly prove on determinants which influence to the FDI inflows. Also, this work is going to be able to model the relation of FDI to the above eight variables. Hence, as the result, we can estimated FDI inflows based on those variables and further develop on the area of significant to attract more investment.2. Methodology2.1. Data CollectionThis research will use the secondary data from data.worldbank.org. Data will be collected for 230 countries and regions. But some of variables data is not applicable, hence, it will be automatically deleted during theKEY TO VARIABLES:- Y = Foreign direct investment, net inflows (current US$)- X1 = Total population (in number of people)- X2 = Total labor force (in number of people- X3 = Percentage of population growth ( % annual)- X4 = Liner shipping connectivity index ( 1 – 100)- X5 = GDP per capita (in $US)- X6 = Domestic credit to private sector (in percentage of GDP)- X7 = Land area (sq.km)- X8 = Cost of business startup procedure (% of GNI per capita)2.2. ApproachData will be analyzed based on regression of multiple linear among the 8 variables with FDI data response in R programming. 3. Testing with R programming3.1. Annova Table
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