Determinants of FDIs for the EU in 2018


Price is what you pay.

Value is what you get.

Warren Buffet


The research is based on econometric analysis in order to identify the factors of the element that represents the accelerator of economic growth, which are Foreign Direct Investment (hereinafter: FDI). We present the correlation between FDI and Doing Business sub-indices for 2018, such as: starting a business, electricity supply, the possibility of obtaining a loan, the amount of taxes, execution of contracts, obtaining building permits, registration of property, protection of minority investors, international trade, resolving insolvency. This analysis is based on a large sample of all 46 eurozone countries (member states and EU candidate countries, with the exception of Morocco), a study that confirms the factors that condition the motives for the transfer of foreign capital to a selected economy.

Direct investments abroad include the investments of the direct investor (legal or natural person), ie. resident, in the company in which the investment is made, ie. non-resident. Direct investments in the country include the investments of the direct investor (legal or natural person), ie. non-resident, in the company in which the investment is made, ie. resident.

Business regulation can radiate life for new ideas. When a software engineer ie. developer identifies that there is potential for developing a better and cheaper product, business regulation can be crucial to starting your own startup business. The entrepreneur will direct his own capital to an economy that transparently and predictably regulates business activities. In contrast, in economies where business regulation is inconvenient or ambiguous, there is a demotivation to start a business. In this case, the economy suffers a loss from a potentially new entrepreneur who is accompanied by investment capital and job creation. Consequently, consumers on the consumer side have a smaller offer, which is usually of poor quality and high price.

The ten indicators that the World Bank annually publishes in the Doing Business report postulate the usefulness and quality of business regulation of businesses. They illustrate the improvement of business regulation.

Namely, the high initial costs for establishing a company lead to low total productivity. Existing companies will continue to operate independently of low productivity because there is little competition. The lack of effective business regulation ultimately leads to the direction of potential companies towards the informal sector.

The Doing Business report provides credit information from credit agencies and bureaus. When functioning well, these institutions are a key element of economic and financial infrastructure by strengthening access to financial services, especially lending. By collecting and sharing credit information, such agencies reduce asymmetry in information, increase access to credit for small companies, provide lower interest rates, improve borrower discipline, strengthen banking supervision and credit risk monitoring.

Doing Business focuses on the quality of legal institutions. For companies investing in capital, legal mechanisms are needed to prevent corporate misuse of corporate insiders for personal gain (especially during a financial crisis or market shock).

Indicators of business operations to address insolvency point to evidence of a strong correlation between quality regulation and effective results.

In cross-border trade, Doing Business measures the effectiveness of trade logistics. The automation of ports is a trade facilitation and engine for regional economic development.

Namely, the ten sub-indices from the Doing Business report for 2018 that are included in this research as independent (explanatory) variables, ie. regressors that are necessary to explain the dependent (explained) variable i.e. the FDI regression index along with the 2018 Transparency International Corruption Perceptions Index (CPI) and the results of the 2015 OECD PISA Testing are as follows: starting a business, access to electricity, access to credit, tax rate rates, execution of contracts, building permits, property registration, protection of minority investors, cross-border trade, resolving insolvency., , , , , , , , ,

The regression model we take as the basis for statistical decision making and economic interpretation is with excluded explanatory variables that are statistically insignificant but not economically insignificant in practical terms (tax rates, contract execution, building permits, starting a business, protection of minority investors, resolving insolvency, cross-border trade, corruption), a model that will contain only property registration, the possibility of obtaining credit, access to electricity and PISA results as significant explanatory variables can be expressed mathematically as follows :

SDI – FDI inflows in 46 countries for 2018, ((dependent variable);

- constant member (section coefficient);

property registration (explanatory variable)

possibility for obtaining a loan (explanatory variable);

possibility for obtaining a loan (explanatory variable);

 - students' ability through reading, math and science to face life's challenges (explanatory variable).

In this way, the specified reduced regression model can be evaluated by the least squares method, and below in the table we get the coefficient estimates.


Table: Evaluated reduced regression model with least squares method in econometric software Eviews

In the final reduced regression model, we can conclude that at all three levels of significance ,  and  statistically significantcoefficient of slope is only access to electricity (struja), while the explanatory variable property and credit are significant only with a confidence level of 90% (α=0,1and 95% (α=0,05). A statistically insignificant slope coefficient with both confidence levels of 99% (α=0,01and 95% (α=0,05) is PISA which is only significant with a confidence level of 90% (α=0,1).


Forecasts may tell you a

great deal about the forecaster;

They tell you nothing about the future.

Warren Buffet


The relevance of the solidly specified regression model that includes four significant explanatory variables (, , and property registration, credit opportunity, access to electricity, and PISA results) that condition the explanation of the explained variable FDI we have demonstrated by completing the assumptions of the classical linear regression model through which we received the best linear, impartial and efficient estimates of the parameters. The distinction between this research and other analysis on this topic is in the included explanatory variables that originate from the sub-indexes of the Doing Business report for 2018 that we use vis-à-vis other scientists in the other analysis. Finally, we owe an economic explanation to the obtained regression coefficients from the conducted evaluation of the regression model.

Namely, if the sub-index of property registration increases by one point, on average FDI will decrease by -1757289154.51 billion dollars. The distorted picture of the practice that reflects the interpretation of this result from the regression analysis can be corrected through the two-way ie. the inverse proportional correlation between FDI and the sub-index property, in the sense that FDI also has an impact on the level of expenditures in property and legal relations and the quality of the land management system in national economies which are determined by the level of economic development of the country expressed through GDP growth per capita.

In the event that the value of the sub-index increases the ability to obtain a loan by one point, the FDI will increase by an average of 1302223825.27 billion dollars, a result that falls within the domain of theoretical economic resonance of the relationship between differentiated benefits by the state foreign investors in the spirit of creating a favorable business climate.

If the value of the sub-index access to electricity increases by 1 point, on average FDI will increase by 1956290630.65 billion dollars. Again, this result, like the previous one, coincides with the common sense economic interpretation of the relationship between the various determinants and FDI.

If the value of the independent variable PISA results increases by one point, on average FDI will decrease by -27818184160.43 billion dollars. The distorted picture of the reality that reflects the interpretation of this result from the regression analysis can be corrected through the two-way ie. the inverse proportional relationship between FDI and the PISA indicator, in that FDI also has an impact on living standards ie. quality of life (measured by GDP per capita) which in turn greatly affects the quality of housing, food, dress code, motives and the overall set of features that defines the lifestyle of potential professionals and academics.



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