We introduce three types of variables in our model. First of all, the growth variable, a variable dependent on the model. We then orient financial development indicators. Finally, we introduce a conditional information matrix to control for variables that affect long-term economic growth.
3-1- Sample and period
a. Sample
Our region is a sample which is made up of 16 countries namely Bahrain, United Arab Emirates, Jordan, Kuwait, Qatar, Saudi Arabia, Indonesia, Malaysia, Tunisia, Turkey, Morocco, Egypt, Sudan, Iran, Algeria, Yemen. Our sample is made up of 16 countries shared over the MENA region and East Asia and the Pacific, we have compiled a database of international macroeconomic data available in “World Bank CD’’.
b. Period :
The sample of countries selected is made up of 16 MENA and East Asia and Pacific countries: 5 African countries, 8 Gulf countries, 2 East Asian and Pacific countries and 1 Mediterranean. Depending on the availability of data, our study period extends from 1990 to 2018 over a period of 29 years. The great diversity in terms of geography and in terms of country performance increases the robustness of our analyses.
3-2- Definitions and measurements of variables
a. Growth Indicator:
We chose the noted GDP Per Capita Growth Rate (GDP) (Levine et al., 2000, Beck et al., 2000, and Beck and Levine, 2004).
b. Indicators of financial development:
We propose the following indicators.
• Islamic financial development: In their 1998 study, Levine and Zervos add the measuring the development of the banking sector to cross-sectional studies of growth. According to these authors, this measure is equal to private sector bank credit divided by GDP denoted FI (Finis/GDP): Qard Hasan, Mourabahah, Ijarah, Moudarabah, Moucharakah, Salam, Istisna‘. ).
• Investment: Gross fixed capital formation, is the aggregate which measures, in national accounts, the investment (acquisition of production goods) in fixed capital of the various resident economic agents. (FDI/GDP).
• Control variables: We retained as control variables, for this work, the ratio of government expenditure to GDP (GC) as an indicator of macroeconomic stability (Easterly and Robelo (1993) and Fisher (1993)), the value ratio of trade (export + import) / GDP to capture the degree of openness (Sachs and Warver (1995)) noted (TRAD) andThe tertiary enrollment rate to control the accumulation of human capital noted (HK).
• Dummy variables: We use this nature of the variables (variable dummy: DV) because our study region is formed by countries that apply Islamic finance and others that do not. So, we note 1 for the countries that practice Islamic finance and 0 for the others.
c- Quality of governance indicator
• Governance quality index: “IQG indicator of quality of governance:The mean value of ICRG variables”. After calculating the quality of governance index, we will present descriptive statistics of this synthetic indicator..
-Political stability noted (PS): IMGs are not used by the World Bank Group to allocate resources. The impact of institutional factors namely political stability noted (PS) and realized by Kaufman D. Kraay A. and Mastruzzi M. (2003).
3-3- Financial development, FDI and economic growth in the MENA region and Asia Pacific
a- Simultaneous Equations Model
We will estimate the simultaneous equation model that we will specify later. The model to be estimated responds, in a mathematical way, to the following three equations:
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)
b- Simultaneous equation model in panel data
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There are several steps to follow, namely:
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The design, i.e. the writing or specification of the model.
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Estimation of the model equations, using appropriate techniques.
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Endogeneity problem
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Method REG3 (Three-Stage least squares)
c- Preliminary tests
The main results obtained, their interpretations and their debates compared to previous studies.
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Stationarity tests
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Collinearity study between the independent variables
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Multi-collinearity problem and model selection
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Model equations identification problem modèle
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Exclusion Restrictions
This restriction consists of assigning a zero coefficient for each endogenous or exogenous variable that does not appear in a structural equation. In our model, the variable "FDI" appears at the level of the last equation is endogenous whose exogenous variables "PS", "TRAD" and "HK" appear only at the level of the last equation and do not appear at the level of the first or second equation. There are variables that appear at the level of the first and third equations and do not appear at the level of the second (IQG).
Some model specifications require that variables be assigned an identical coefficient. This type of restriction is not present in our model. Once the restrictions on the coefficients have been made, it is essential to proceed with the identification of the system of equations. There are two identification conditions: order conditions (necessary conditions) and rank conditions (sufficient conditions).
After having selected the variables to be integrated into the model, a step prior to the step of processing simultaneous equation models is to perform model identification tests to choose the most appropriate estimation method..
In our case, we note for the model to be studied, that all the equations are over-identified. Indeed, we have three endogenous variables in the model (i.e. W = 3) "GDP", "IFD" and "FDI" and five exogenous variables: "TRAD", "INV", "GC", "PS", " VD”, “IQG” and “HK” (i.e. K = 7)
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The first equation has five exclusion restrictions and no constraint restrictions. By applying the identification conditions, the variables appearing in the human capital equation give: W' = 1, K' = 4 and r = 0 with W' being the number of endogenous variables appearing in an equation and K ' is the number of exogenous variables appearing in an equation. So let: W – W'+ K – K' = 3–1 + 6– 4 = 4 > W – 1 = 3–1 = 2, the first equation is therefore over-identified.
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The second equation has five exclusion restrictions but no constraint restrictions. We therefore have: W = 3, K = 6, W' = 1, K' = 4 and r = 0, which gives us: W – W' + K – K' = 3–1 + 6–4 = 4 > W – 1 = 2, so this equation is over-identified.
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The third equation has six exclusion restrictions but no constraint restrictions. So we have W = 3, K = 6, W'=1, K'=4 and r = 0, which implies W-W'+ K- K'= 3 − 1 + 6– 5 = 3 > W – 1 = 2, the third equation is therefore over-identified. Since in our model all the equations are over-identified, the model will therefore be over-identified.
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Sufficient conditions: Rank conditions
If the order conditions are verified, it is also necessary to verify the rank conditions (sufficient conditions)