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Do Knowledge Economy Indicators Affect Economic Growth? Evidence from Developing Countries

Sustainability 2022, 14(8), 4774; https://doi.org/10.3390/su14084774

by Maha Mohamed Alsebai Mohamed^1,2

, Pingfeng Liu^1,* and Guihua Nie¹

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Reviewer 3: Anonymous

Sustainability 2022, 14(8), 4774; https://doi.org/10.3390/su14084774

Submission received: 20 February 2022 / Revised: 30 March 2022 / Accepted: 8 April 2022 / Published: 15 April 2022

(This article belongs to the Special Issue Contemporary Issues in Applied Economics and Sustainability)

Round 1

Reviewer 1 Report

Good quality.

Author Response

Response to the Editor and Reviewers

Original Manuscript ID: 1626136

Dear Respected Editor and Reviewers,

We are truly grateful for the comments on our Research Article titled: " Do knowledge economy indicators affect economic growth? Evidence from developing countries". These comments are highly insightful, which can enable us to further improve the quality of our manuscript. According to these comments, we have made careful modifications to the original manuscript. Revision portions are marked in yellow here in this note and included in the revised manuscript.

Comments and Suggestions for Authors

Good quality.

Author response: thank you so much

Author Response File: Author Response.pdf

Reviewer 2 Report

This is is very timely paper trying to investigate the main indicators in the Knowledge economy that could contribute to economic growth of some developing countries. The article is carefully written, structured and easy to follow. The main weaknesses of paper is the need to discuss in much more depth the empirical context, and the results. I detail my concerns below, and hope that they prove helpful in improving this study.

The discussion of the results of this manuscript could be improved by adding an explanation that helps paper´ readers to understand some results of estimation model, for example the not significant effect of the Expenditure on education (EDUC) variable on the Economic growth variable. A better discussion of this topic, considering research theories and the results of other researchers, I think is necessary for two reasons: - In a first time (even in the Abstract), the authors said “that the estimates of the proposed model parameters do not contradict the assumptions of economic theory, nor do they contradict the practical reality”; - Moreover, the authors formulated the research hypothesis H4 that suggest a positive relationship between the mentioned variables.

Regards to results of the study and its limitations:

-Descriptive statistics of the variables in the sample have not been provided.

While the results of both models of estimation (Fixed Effects Model and Random Effects Model) soles different relationships between the Expenditure on education and the Economic growth (negative and positive relations, respectively)”, the authors not mentioned none limitation related to chosen model. Perhaps, should be provided because the Fixed Effects Model is seen as a case where the researcher makes conditional inference to the effects that he sees in the sample (instead marginal inference about a population).

-The fixed effects model allows investigating the intertemporal and/or transversal variation by means of different independent terms. Did authors test whether or not the country or period fixed effects can be considered equal? For example, using the Redundancy Test.

-Test for Equality of variances of RESD have not been provided (i.e., Barlett, Leven, Brown-Forsythe…).

Good luck as you take this research forward!

Author Response

Response to the Editor and Reviewers

Original Manuscript ID: 1626136

Dear Respected Editor and Reviewers,

We are truly grateful for the comments on our Research Article titled: " Do knowledge economy indicators affect economic growth? Evidence from developing countries". These comments are highly insightful, which can enable us to further improve the quality of our manuscript. According to these comments, we have made careful modifications to the original manuscript. Revision portions are marked in yellow here in this note and included in the revised manuscript.

Our point-by-point response to each reviewer is listed in the following section.

Comments and Suggestions for Authors

Review Report#2

This is a very timely paper trying to investigate the main indicators in the Knowledge economy that could contribute to economic growth of some developing countries. The article is carefully written, structured and easy to follow. The main weaknesses of paper is the need to discuss in much more depth the empirical context, and the results. I detail my concerns below, and hope that they prove helpful in improving this study.

Concern1 #: The discussion of the results of this manuscript could be improved by adding an explanation that helps paper´ readers to understand some results of estimation model, for example the not significant effect of the Expenditure on education (EDUC) variable on the Economic growth variable. A better discussion of this topic, considering research theories and the results of other researchers, I think is necessary for two reasons: - In a first time (even in the Abstract), the authors said “that the estimates of the proposed model parameters do not contradict the assumptions of economic theory, nor do they contradict the practical reality”; - Moreover, the authors formulated the research hypothesis H4 that suggest a positive relationship between the mentioned variables.

Author response: The question has been addressed. Page 3,4

Author action: The relationship between spending on education and economic growth has received clear attention in the economic literature, translated in several applied studies that tried to examine the relationship between them, and these studies included many developed and developing countries, but there was no clear pattern on the empirical results of these studies, there are a group of them found Evidence that there is a strong positive relationship between the rates of spending on education at various levels (intermediate, secondary, higher) and the rates of economic growth achieved in most countries, especially countries that have given more financial allocations to this sector. Moreover, there is a causality that extends from spending on education towards the economic, and one of these studies is the (Schaltz) study, which aimed to know the impact of education on economic growth in the United States of America. education, by raising the efficiency and productivity of the workforce. Also, the study (R.M.Solow, 1957) and ٗAbiodun, 2011) found the importance of the (remaining) factors in increasing production and achieving economic development other than capital and labor factors. Education, knowledge, technological progress, and scientific research represent the largest part of them. Through his study on (the economics of agricultural production), which he conducted on the American economy, he concluded that the productivity of the individual per hour doubled, and he concluded that the remaining factors have a very big role in increasing production[12, 13].

As well as the” Denison “study that was conducted on the American economy with the aim of measuring the contribution of education to economic growth, and the study concluded that education contributes about 15% to economic growth. “Hicks” also discussed the possibility of a positive relationship between the two variables. The study confirmed that the countries that achieved the highest Economic growth rates have had the highest rates of enrollment in education, such as South Korea[14].

There are also other studies that confirmed the existence of a negative relationship between the two variables. We find Jeffrey Kouton's study aimed at analyzing the relationship between spending on education and economic growth in Côte d'Ivoire during the period from 1970 to 2015. The study used the Auto Regressive Distributed lag (ARDL) method to study the relationship between spending on education and economic growth in the short and long term. It concluded that there is a long-term negative impact of government education expenditures on economic growth during the study period, and there is a positive impact of government education expenditures on economic growth in the short term[15].

The authors believe that spending on education may have a negative impact, especially in developing countries, because these countries still need a strong infrastructure for education, especially in light of the fourth industrial wealth that depends on human power and not material capital, and therefore they need more From spending on education until it comes to fruition, as well as in most developing countries, especially a group of the study sample that cannot specialize in the proper distribution of spending on education.

Regards to results of the study and its limitations:

Concern # 2: -Descriptive statistics of the variables in the sample have not been provided.

Author response: The question has been addressed. Page 21,22

Author action: Based on the discussion on determining the knowledge economy indicators affect economic growth, this paper attempts to resolve this discussion using three different methodologies. Experimental methodologies differ in their statistical capabilities depending on the adaptation variables included in the models, and therefore the use of different methods is useful in comparing results. Below is a description of each of these experimental methods. Statistics for minimum, maximum, and standard deviations of the endogenous variable, TIC ,TF,INT ,EDUC, R&D, CC, SP, OC, FDI, and, TVI over the period (1996-2020) are presented in Table 3.

Table 3. Summary statistics of innovation variables, TIC ,TF,INTE ,EDUC, R&D, CC, SP, OC, FDI, TVI, and lnGDP.

Indices	LNGDP	TIC	TF	INTE	EDUC	R&D	CC	SP	OC	FDI	TVI
Mean	8.340875	70.23456	16.04101	6.085567	1.84E+10	23634.78	11.37827	35.70098	67.44349	1.151713	1.35E+11
Median	8.341961	80.19977	14.58786	2.501567	6.15E+09	534.0000	12.00000	32.16080	59.31582	0.291274	4.96E+10
Maximum	9.725157	192.2145	38.81773	33.80113	2.55E+11	1393815.	17.00000	91.48936	168.2428	97.34762	2.59E+12
Minimum	6.139773	0.011586	1.388605	1.25E-05	3.33E+08	22.00000	2.000000	2.645503	15.63559	-42.71673	3.13E+09
Std. Dev.	0.795114	51.14502	9.370713	7.780055	3.46E+10	145778.4	3.539776	21.48950	31.59532	6.801784	3.43E+11
Skewness	-0.244721	-0.016458	0.540622	1.432313	4.029757	7.517971	-0.455273	0.475341	1.061734	8.366911	5.411967
Kurtosis	2.193319	1.654648	2.397029	4.378241	21.60376	60.94229	1.965151	2.240534	3.874142	108.4396	33.46196

Jarque-Bera	18.43637	37.50394	31.73892	209.2708	8512.277	74206.00	39.34599	30.66041	109.2001	236023.8	21642.04
Probability	0.000099	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

Sum	4145.415	34906.58	7972.380	3024.527	9.12E+12	11746487	5655.000	17743.39	33519.41	572.4016	6.73E+13
Sum Sq. Dev.	313.5743	1297443.	43553.89	30022.51	5.94E+23	1.05E+13	6214.885	229052.2	495139.2	22947.07	5.85E+25

Observations	500	500	500	500	500	500	500	500	500	500	500

Source: Prepared by the author based on EVIEWS 9 outputs.

From the previous table No. 1. We conclude the following:

The minimum value of the GDP over all the sample is 6.14 while the maximum is 9.73, with average = 8.34 and standard deviation = 0.80. • The minimum value of the TIC over all the sample is 0.012 while the maximum is 192.21, with average = 70.23 and standard deviation = 51.15. • The minimum value of the TF over all the sample is1.39 while the maximum is 38.82, with average = 16.04 and standard deviation = 9.37 .the minimum value of the INTE over all the samples is 1.25E-05 while the maximum is 33.80, with average = 6.09 and standard deviation = 7.78.
The minimum value of the EDUC over all the samples is 3.33E+08 while the maximum is 2.55E+11, with average = 1.84E+10and standard deviation = 3.46E+10. • The minimum value of the R&D overall the sample is 22.00 while the maximum is 1393815., with average = 23634.78 and standard deviation = 145778.4. • The minimum value of the CC over all the sample is 2.00 while the maximum is 17.00, with average = 11.38 and standard deviation = 3.54. • The minimum value of the SP over all the sample is 2.65 while the maximum is 91.49, with average = 35.70 and standard deviation = 21.49.
The minimum value of the OC over all the samples is 15.64 while the maximum is 168.24, with average 67.44 and standard deviation = 31.60. • The minimum value of the FDI overall the sample is -42.72 while the maximum is 97.34, with average = 1.15 and standard deviation = 6.80. • The minimum value of the TVI over all the sample is 3.43E+11 while the maximum is 2.59E+12, with average = 1.35E+11and standard deviation = 3.43E+11.

Concern # 3: While the results of both models of estimation (Fixed Effects Model and Random Effects Model) soles different relationships between the Expenditure on education and the Economic growth (negative and positive relations, respectively)”, the authors not mentioned none limitation related to chosen model. Perhaps, should be provided because the Fixed Effects Model is seen as a case where the researcher makes conditional inference to the effects that he sees in the sample (instead marginal inference about a population).

Author response: The question has been addressed. Page 18,19

Author action: 3.1.3. Methods for choosing the appropriate model

The aggregate regression model(PME) is considered one of the simplest panel data models, as this model neglects the effect of the time dimension, while the fixed effects model aims to be used to know the behavior of each set of cross-sectional data (the behavior of each country). For the purpose of estimating the parameters of this model, dummy variables of (1-N) are usually used in order to avoid the case of perfect polylinearity, and then the ordinary least squares method is used. The random effects model is appropriate for estimation in the event of a defect in the model conditions.

Fixed effects, and to estimate the random effects model, the generalized least squares (GLS) method is used.

To determine the appropriate model for the static panel data, we will present two methods, the first: the method of choosing between the cumulative regression model and the fixed effects model, and the second: the method of choosing between the fixed effects model and the random effects model.

The first method: to choose the appropriate estimation method for the study data, it is usually started by checking the presence of those unobserved effects, or through the study periods in order to apply the panel estimation methods (FEM) and (REM), and here the model is tested with a cross section for each country against a model With a common secant, the null hypothesis is the homogeneity hypothesis (common secant) H₀: μ₁ = μ_N….=μ₂) For time effects ( H0: γ1= γ2…….= γT), the null hypothesis is tested using a probability (F) According to the following formula:

Where k is the number of estimated parameters and RFEM represents the coefficient of determination when using the fixed effects model RPM represents the coefficient of determination when using the aggregative model where we compare the result of the above formula with F (N-1,NT-N-K) If the calculated F value is greater or equal to the value Tabularity, then, the fixed-effects model is the appropriate model.

The second method: We use the Hausman Test to choose between the fixed and random effect methods based on the null hypothesis that all estimation coefficients through random effect have the same efficiency as those that were estimated through fixed effect. Rejecting the null hypothesis makes the estimation coefficients by random effect more accurate. This does not mean that the estimation coefficients resulting from the fixed-effect model are ineffective. The model's assumptions are as follows:

H0: The random effects model is the appropriate model (there is no autocorrelation between variables)

H1: The fixed effects model is the appropriate model (there is an autocorrelation between variables).

The Haussmann test is expressed by the following relationship:[74, 77-79]:

Where:

The variance and covariance matrix of the regression parameters b obtained from LSDV except the constant, and the variance and covariance matrix of the random effect model Bˆ without the constant; Therefore, under the null hypothesis, the best model is a model with a random effect, which means that the individual effect is not linked to other variables, in the opposite case, the best model is a model with a fixed effect.

Concern 4# : The fixed effects model allows investigating the intertemporal and/or transversal variation by means of different independent terms. Did authors test whether or not the country or period fixed effects can be considered equal? For example, using the Redundancy Test.

Author response: The question has been addressed. Page 29

Author action: Fixed effects of countries

Table 13. The results of Fixed effects of countries

Country		effect	Country		effect	Country		effect	Country		effect
1	Algeria	7.011929	6	China	-1.23E-06	11	Mexico	-2.94E-14	16	Romania	0.107154
2	Argentina	0.004170	7	Egypt	0.031199	12	Morocco	-0.296618	17	Sri Lanka	-0.020692
3	Brazil	0.021957	8	Hungary	0.007512	13	Peru	0.338827	18	Thailand	0.232467
4	Bulgaria	0.034605	9	Indonesia	-0.003197	14	Philippines	-0.070077	19	Tunisia	0.078507
5	Chile	6.02E-12	10	Iran	0.003616	15	Poland	0.013477	20	Turkey	-0.454377

Source: Prepared by researchers based on outputs EViews 9

From the country fixed effects table, we note that only China, Indonesia, Mexico, Morocco, Philippines, Sri Lanka, and Turkey have a negative effect ranging between 2.94E-14 for China and 0.454377 for Turkey. As for the rest of the variables, they have a positive effect, ranging between 0.003616, which belonged to Iran, and 7.011929, which belonged to Algeria.

Concern 5# : Test for Equality of variances of RESD have not been provided (i.e., Barlett, Leven, Brown-Forsythe…).

Author response: The question has been addressed. Page23-24

Author action: Variance equality tests evaluate the null hypothesis that the variances in all G subgroups are equal against the alternative that at least one subgroup has a different variance. F-test. This test statistic is reported only for tests with two subgroups (G=2). First, compute the variance for each subgroup and denote the subgroup with the larger variance as L and the subgroup with the smaller variance as S. Then the F-statistic is given by[78, 79]:

where is the variance in subgroup . This F-statistic has an F-distribution with -1) numerator degrees of freedom and -1) denominator degrees of freedom under the null hypothesis of equal variance and independent normal samples.

Bartlett test. This test compares the logarithm of the weighted average variance with the weighted sum of the logarithms of the variances. Under the joint null hypothesis that the subgroup variances are equal and that the sample is normally distributed, the test statistic is approximately distributed as a X² withG=1 degrees of freedom. Note, however, that the joint hypothesis implies that this test is sensitive to departures from normality. EViews reports the adjusted Bartlett statistic.

Levene test. This test is based on an analysis of variance (ANOVA) of the absolute difference from the mean. The F-statistic for the Levene test has an approximate F-distribution with numerator degrees of freedom and denominator degrees of freedom under the null hypothesis of equal variances in each subgroup.

Brown-Forsythe (modified Levene) test. This is a modification of the Levene test in which we replace the absolute mean difference with the absolute median difference. The Brown-Forsythe test appears to be a superior in terms of robustness and power to Levene. The following is a test for equal differences between the series in Table No5.

Test for Equality of Variances Between Series

Sample: 1996 2020

Included observations: 500

Table 5. Test for Equality of Variances Between Series



Method	df	Value	Probability


Bartlett	9	175095.1	0.0000
Levene	(9, 4987)	108.1080	0.0000
Brown-Forsythe	(9, 4987)	56.69224	0.0000

Table 6. Category Statistics



			Mean Abs.	Mean Abs.
Variable	Count	Std. Dev.	Mean Diff.	Median Diff.
LNPIB_H	500	0.792814	0.660569	0.660560
TIC	500	51.06431	45.83906	45.31824
TF	500	9.343591	7.815809	7.762982
INTE	500	7.763583	6.199492	5.602230
EDUC	500	3.45E+10	1.89E+10	1.53E+10
R_D	497	145778.4	42519.15	23417.41
CC	500	3.537827	3.116560	3.032000
SP	500	21.43747	18.17058	17.93893
OC	500	31.52767	24.53983	23.55689
TVI	500	3.42E+11	1.46E+11	1.14E+11
All	4997	1.16E+11	1.65E+10	1.29E+10


Bartlett weighted standard deviation: 1.09e+11 Source: Prepared by the authors based on EVIEWS 9 outputs

Author Response File: Author Response.pdf

Reviewer 3 Report

I read the article with great interest, a very intriguing application on the topic. The topic is close to me and related to my professional activity, allowing some collegial comments.

The authors make confident use of the data, as the chosen software allows econometric and statistical analysis, generates predictions or model simulations and prepares high-quality graphs and tables for publication.

In both Abstract and Introduction, I would expect a classic outline and structure. Please indicate clearly the purpose of this work and the target audience of the article. The current wording does not allow us to judge more than what has been done. But you didn't study the 24-year period just after research? This would allow arguments and justifications to be found for the scientific discussion and lead to well-founded proposals.

Please justify the choice of countries. The situation has changed significantly during this period. For example, both Poland and Romania have joined the European Union. The definition of a developing (or developed) country is not always clear. I suggest choosing a more tolerant wording. It can be emphasized in the title that countries were on the list of developing countries in the 1990s? Please study dictionaries and theories to select the most appropriate replacement for "developing countries", thank you for your understanding.

In the theoretical part, it is necessary to argue the justification of the chosen models and to describe more precisely the advantages and disadvantages of each.

There are many hypotheses, I do not consider it sufficient to state that they have been proved. It is important to see where we can use it next.

Tables 3, 4 and 5, Figures 6 and 7 are not described thoroughly and comprehensibly enough for the reader, please complete the description.

I am surprised by the structure of the work. I suggest calling Chapter 4 the results and the discussion, adding the necessary references to the sources in the text accordingly. I would like to ask for Chapter 5 to be redrafted. I see that the Discussion must precede the conclusions. Make it clear that the conclusions follow from the whole text and give some specific recommendations, not just wishes. Very correctly worded Limitations. It may not be necessary to separate them into a separate 5.4.chapter, as some fit in the actuality of the topic and some in the conclusions.

I would suggest a conceptual change in the presentation of the topic and the interpretation of the results. Undoubtedly, it's nice to see the opportunities that technology has in working with big data, thanks to the authors for being able to read it.

Author Response

Response to the Editor and Reviewers

Original Manuscript ID: 1626136

Dear Respected Editor and Reviewers,

Our point-by-point response to each reviewer is listed in the following section.

Review Report#3

Comments and Suggestions for Authors

I read the article with great interest, a very intriguing application on the topic. The topic is close to me and related to my professional activity, allowing some collegial comments.

Concern # 1: In both Abstract and Introduction, I would expect a classic outline and structure. Please indicate clearly the purpose of this work and the target audience of the article. The current wording does not allow us to judge more than what has been done. But you didn't study the 24-year period just after research? This would allow arguments and justifications to be found for the scientific discussion and lead to well-founded proposals.

Author response: The question has been addressed. Pages 4,32

Author action: The purpose of the current research is to analyze the knowledge economy indicators that influence economic growth in the context of developing economies. From the main objective, a set of sub-goals stem, namely:

- Highlighting the components of the knowledge economy.

- Shedding light on the theory of modern growth in the knowledge economy.

- Presentation and analysis of the most prominent effects of the knowledge economy on local production.

Moreover, based on the results that were reached during our study, we can offer some recommendations that officials can take into account when setting economic policies in the country, whether it is a fiscal policy or monetary policy. Investors (old-new) can also benefit from the results of that study by relying on investment in knowledge-based industries instead of traditional industries, focusing on human capital through training, and providing the necessary capabilities for workers to raise their skills. In addition, through the results of that study, researchers interested in this field can learn more about the strengths and weaknesses of the knowledge economy indicators by going deeper into the upcoming studies. Thus, the following recommendations can be made:

Encouraging creativity and innovation among individuals, especially the working group.
Creating the right environment for scientists and innovators to stop the brain drain.

- Raising the level of spending on the fields of education and scientific research.

- Updating educational programs with the requirements of the times and instilling a love of science and knowledge in young people.

- Stimulating investment in the fields of technology and modern technologies.

Concern # 2: - Please justify the choice of countries. The situation has changed significantly during this period. For example, both Poland and Romania have joined the European Union. The definition of a developing (or developed) country is not always clear. I suggest choosing a more tolerant wording. It can be emphasized in the title that countries were on the list of developing countries in the 1990s? Please study dictionaries and theories to select the most appropriate replacement for "developing countries", thank you for your understanding.

Author response: The question has been addressed. Page 16

Author action: The study sample consists of annual data for a group of 20 developing countries, during the period (1996-2020), this sample was selected on the basis of the availability of data for the study variables. The report on the global economic situation and its prospects for the year 2020 issued by the United Nations was relied upon. According to this report, countries are classified into developing countries, transitional countries, and developed countries.

Indeed, Poland and Romania have joined the European Union, but they are considered an emerging or developing countries and not developed countries, as is the case in Hungary, Bulgaria, Croatia, Turkey, and Serbia. They are European countries and not developed countries.

Concern # 3: - In the theoretical part, it is necessary to argue the justification of the chosen models and to describe more precisely the advantages and disadvantages of each.

Author response: The question has been addressed. Page 16-19

Author action: 3.1.1. Importance of panel models:,

Estimating according to these data has important advantages and gives more accurate results because it takes into account the information with the time dimension in the time series as well as the cross-sectional dimension in the different units. Efficiency and activity in econometrics, and therefore it is of great importance, which we summarize in the following points[73]:

- This type of data allows the researcher to study the differences and differences in behavior between individuals, so that the multiplier dimension enjoyed by the panel data can be translated as a multiplier dimension of the available information more than cross-sectional or temporal data.

- Panel data is distinguished from others by a greater number of degrees of freedom as well as better efficiency, and this positively affects the accuracy of the estimators. - The use of panel data will allow us to mitigate the problem of polylinearity. - The ability to identify some economic phenomena such as technical progress and economies of scale, and thus address the problem of the inability to divide economies of scale and technical progress in the analysis of production functions.

Longitudinal data gives better efficiency and an increase in degrees of freedom, as well as less linear multiplicity between variables, and more informational content. If cross-sectional or temporal data from time periods are used, the T model of cross-sectional observations measured in N let us have longitudinal data defined by the following formula[74, 75]:

(1)

whereas:

i=1,2,…………N denotes single units.

t=1,2,…………T expresses the periods of time.

yit is a vertical vector representing the dependent variable.

X_it it is an array (NTxK) representing the independent variables.

B(kx1) is a vertical vector of the parameters to be estimated where the model assumes that there are a number of k parameters in Xit below the constant limit

it is the random error term of the unit i and the period t.

Through the previous presentation, it is possible to review the pros and cons of using the panel data as shown in Table No. 1

Table 1. pros and cons of using the panel data

Positives	Negatives
1. Lots of views	1. The presence of missing views, which leads to disturbances in the quality of the estimate.
2. Taking into account heterogeneity	2. Panel data is not cylindrical, where there is a lack of either individuals or periods, or both.
3. The effects of unobserved features may be considered.	3. It is not handled by all computer programs for econometrics.
4. Low risk of polylinearity.
5. Highlight the effects of the long and short term.
6. Diminishing negligible estimation parameters.

Source: Prepared by the authors based on previous studies

3.1.2. Basic Models of Static Panel Analysis

The aggregate regression model(PME) is one of the simplest panel data models, as this model neglects the effect of the time dimension, while the fixed effects model aims to use it to know the behavior of each set of cross-sectional data, the behavior of each country for example, and for the purpose of estimating the parameters of this, the random effects model is suitable for estimating in If there is a defect in the conditions of the individual effects model, as it takes into account the changes that may occur to individuals as well as time, and to estimate the random effects model, the (Generalized Least Squares-GLS) method is used.

This model is considered one of the simplest models of longitudinal data, in which all coefficients are fixed for all time periods (ignoring any effect of time). By rewriting the model in the equation below, we get the aggregate regression model with the following formula:

J =1 ,2,3…….n t=1,2,3…..N

Where: E( εit)=0 and σ=(εit)var The Ordinary Least Squares method is used to estimate the model parameters in the above equation by the size of observations (N*T)

fixed effects model(FEM): The use of the fixed effects model takes into account the change of the slope and the section from one unit to another for the views of the cross section within the studied sample, so that it will be assumed that the parameters change in a fixed manner and on this basis they have been called the fixed effects models, so they represent both the individual and temporal dimension of the panel model so we can Estimate the model by comparing individuals against time.

In the fixed effects model, the goal is to know the behavior of each cross-sectional data set separately by making the cutoff parameter β0 vary from one group to another while keeping the slope coefficients βj constant for each cross-sectional data set (that is, we will deal with the case of heterogeneity in the variance between groups), and accordingly The fixed effects model is in the following form:

J =1 ,2,3…….n t=1,2,3…..N

Where: E( εit)=0 and σ=(εit)var The term fixed effects means that the parameter β0 for each cross-sectional data set does not change during time, but rather the change is only in the cross-sectional data sets for the purpose of estimating the parameters of the model in the above equation and allowing the parameter β0 to change between the cross-sections. Usually, dummy variables are used as much as 1-N in order to avoid multiplicity. complete linearity. Then the ordinary least squares method is used. The fixed effects model is called the least squares model for dummy variables. After adding the dummy variables D in the above equation, the final form of the model becomes as follows:

J =1 ,2,3…….n t=1,2,3…..N

random effects model(REM) The model with the random effect is represented in the fact that the constant changes randomly, and if the random effect is found in both the individual factor and the time, we call this model a model with a compound errorµ; The appropriate estimation method in this type of model is the generalized least squares (GLS) method, or by a method linking the “interpersonal” estimation (Between) to the estimation Within, which takes the following form[76]:

J =1 ,2,3…….n t=1,2,3…..N

3.1.3. Methods for choosing the appropriate model

Fixed effects, and to estimate the random effects model, the generalized least squares (GLS) method is used.

H0: The random effects model is the appropriate model (there is no autocorrelation between variables)

H1: The fixed effects model is the appropriate model (there is an autocorrelation between variables).

The Haussmann test is expressed by the following relationship:[74, 77-79]:

Where:

Concern # 4: There are many hypotheses, I do not consider it sufficient to state that they have been proved. It is important to see where we can use it next.

Author response: The question has been addressed.

Author action: Author action: Our study contains 10 (independent) explanatory variables that affect economic growth (the dependent variable), so in order to know the effect of each of the indicators defined by the independent variable on the dependent variable expressed in economic growth, 10 hypotheses were formulated for the study. Through the study, the validity or incorrectness of these assumptions was proven.

Concern # 5: Tables 3, 4 and 5, Figures 6 and 7 are not described thoroughly and comprehensibly enough for the reader, please complete the description.

Author response: The question has been addressed. Page 21-22,24,25,26,27,28,29

Table 3. Summary statistics of innovation variables, TIC ,TF,INTE ,EDUC, R&D, CC, SP, OC, FDI, TVI, and lnGDP.

Indices	LNGDP	TIC	TF	INTE	EDUC	R&D	CC	SP	OC	FDI	TVI
Mean	8.340875	70.23456	16.04101	6.085567	1.84E+10	23634.78	11.37827	35.70098	67.44349	1.151713	1.35E+11
Median	8.341961	80.19977	14.58786	2.501567	6.15E+09	534.0000	12.00000	32.16080	59.31582	0.291274	4.96E+10
Maximum	9.725157	192.2145	38.81773	33.80113	2.55E+11	1393815.	17.00000	91.48936	168.2428	97.34762	2.59E+12
Minimum	6.139773	0.011586	1.388605	1.25E-05	3.33E+08	22.00000	2.000000	2.645503	15.63559	-42.71673	3.13E+09
Std. Dev.	0.795114	51.14502	9.370713	7.780055	3.46E+10	145778.4	3.539776	21.48950	31.59532	6.801784	3.43E+11
Skewness	-0.244721	-0.016458	0.540622	1.432313	4.029757	7.517971	-0.455273	0.475341	1.061734	8.366911	5.411967
Kurtosis	2.193319	1.654648	2.397029	4.378241	21.60376	60.94229	1.965151	2.240534	3.874142	108.4396	33.46196

Jarque-Bera	18.43637	37.50394	31.73892	209.2708	8512.277	74206.00	39.34599	30.66041	109.2001	236023.8	21642.04
Probability	0.000099	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

Sum	4145.415	34906.58	7972.380	3024.527	9.12E+12	11746487	5655.000	17743.39	33519.41	572.4016	6.73E+13
Sum Sq. Dev.	313.5743	1297443.	43553.89	30022.51	5.94E+23	1.05E+13	6214.885	229052.2	495139.2	22947.07	5.85E+25

Observations	500	500	500	500	500	500	500	500	500	500	500

Source: Prepared by the author based on EVIEWS 9 outputs.

From the previous table No. 1. We conclude the following:

The minimum value of the GDP over all the sample is 6.14 while the maximum is 9.73, with average = 8.34 and standard deviation = 0.80. • The minimum value of the TIC over all the sample is 0.012 while the maximum is 192.21, with average = 70.23 and standard deviation = 51.15. • The minimum value of the TF over all the sample is1.39 while the maximum is 38.82, with average = 16.04 and standard deviation = 9.37 .the minimum value of the INTE over all the samples is 1.25E-05 while the maximum is 33.80, with average = 6.09 and standard deviation = 7.78.
The minimum value of the EDUC over all the samples is 3.33E+08 while the maximum is 2.55E+11, with average = 1.84E+10and standard deviation = 3.46E+10. • The minimum value of the R&D overall the sample is 22.00 while the maximum is 1393815., with average = 23634.78 and standard deviation = 145778.4. • The minimum value of the CC over all the sample is 2.00 while the maximum is 17.00, with average = 11.38 and standard deviation = 3.54. • The minimum value of the SP over all the sample is 2.65 while the maximum is 91.49, with average = 35.70 and standard deviation = 21.49.
The minimum value of the OC over all the samples is 15.64 while the maximum is 168.24, with average 67.44 and standard deviation = 31.60. • The minimum value of the FDI overall the sample is -42.72 while the maximum is 97.34, with average = 1.15 and standard deviation = 6.80. • The minimum value of the TVI over all the sample is 3.43E+11 while the maximum is 2.59E+12, with average = 1.35E+11and standard deviation = 3.43E+11.

Table 7. Variance inflation coefficient values (VIF).

Variable	TIC	TF	INTE	EDUC	R&D	CC	SP	OC	FDI	TVI
VIF	4.26251	1.37619	2.87187	5.29095	6.76466	3.54109	1.55845	1.72611	1.09769	9.67663

Source: Prepared by the authors based on EVIEWS 9 outputs

Thus, Variance Inflation Factors Test is used to ensure that there is no collinearity between the independent variables and to test the allowed variance for each of the variables of the estimated model, taking into account that the variance inflation factor does not exceed the value (VIF<10). Table 3. Shows that the test value did not exceed 10, which indicates that the absence of linear collinearity between the independent variables (price) of the estimated model for the indicators of the knowledge economy in the group of developing countries under study. We find that TIC=4.26<10, TF=1.38<10, INTE=2.87<10, EDUC=5.291<10, R&D = 6.29<10, CC=3.54<10, SP=1.56<10, OC=1.73<10, FDI=1.10< 10 and finally the independent variable TVI = 9.68 < 10.

4.1.1. Measurement Model

Although the results of Table.8 indicate that the fixed effects are more suitable for cross-sectional data across countries, due to the higher coefficient of determination (0.93) than the random (0.85) and aggregate regression model (0.76), but it is preferable to continue the analysis and use the Hausman test to compare between the fixed effects model and the effects model.

Table 8. Model estimation by cumulative regression, fixed effects, and random effects.

Dependent variable: LNGDP (per capita GDP)

Period: (1996- 2020), number of panel views: 497

explanatory variables

aggregate regression model) PRM (

Fixed Effects Model

) FEM)

random effects model

) REM (

TIC

0.004035

(0.0000)

0.004458

(0.0000)

0.004413

(0.0000)

0.022180

(0.0000)

0.000842

(0.7371)

0.004335

(0.0678)

INTE

0.034424

(0.0000)

0.018249

(0.0000)

0.020021

(0.0000)

EDUC

6.16E-12

(0.0000)

-3.28E-13

(0.7525)

2.11E-13

(0.8361)

R&D

-9.91E-07

(0.0018)

-4.29E-07

(0.0372)

-3.96E-07

(0.0507)

0.033482

(0.0004)

0.050686

(0.0000)

0.048199

(0.0000)

0.007571

(0.0000)

0.005173

(0.0000)

0.005948

(0.0000)

-0.002991

(0.0001)

-0.005129

(0.0000)

-0.004500

(0.0000)

FDI

0.003213

(0.2381)

0.001901

(0.1988)

0.001962

(0.1844)

TVI

-1.50E-13

(0.3494)

7.88E-13

(0.0000)

6.79E-13

(0.0000)

R-squared

0.760221

0.936837

0.850547

Adjusted R-squared

0.755288

0.932770

0.847472

F-statistic

154.0869

230.3899

276.5857

Prob(F-statistic)

0.000000

A- Results of the pooled regression model (PRM):

The above-estimated model shows the significance of the variable coefficient (TIC) at the significant level of 0.05 This is because the test probabilistic value of the TIC coefficient of (0.000) is less than 0.05 Therefore, we reject the null hypothesis and accept the alternative hypothesis that states that there is a significant effect of the variable Number of mobile phone users.

We also conclude the significance of the variable coefficient (TF) below the level of significance (0.05)because the test probability value (TF) which is (0.000) is less than 05.0. Therefore, we reject the null hypothesis and accept the alternative that states that there is a significant effect of the number of fixed telephone users on economic growth. We also conclude the significance of Internet users (INTE), Expenditure on education (EDUC), Patent applications for residents (R&D), control corruption (CC) and political stability (OC), because the probability value of the variables, which is INTE (0.000) , (EDUC) (0.000) , R&D (0.0018) ,CC(0.0004),SP(0.0000)OC(0.0001) respectively, is less than 05.0. The null hypothesis and we accept the alternative, which states that there is a significant effect of the previous variables and economic growth.

We also conclude that the coefficient of the two variables, Foreign direct investment (FDI) and Total value of international trade (TVI) is not significant below the significant level of (0.05)because the probabilistic value of the coefficient (FDI) which is (0.2381) is greater than 0.05 and the probability value of the factor (TVI) which is greater than (0.3494 ) is greater than 0.05 Therefore, we accept the null hypothesis which states that there is no significant effect of two variables (FDI, TVI) on economic growth.

We also note the significance of the calculated F value below the level of significance (0.05), because its probabilistic value has reached (000.0), which is less than (0.05), and this means that the estimated model as a whole is significant, and the value of the coefficient of determination R2 reached (76), which means that the estimated model as a whole is significant. The independent variables explain (76%) of the changes in economic growth, while the remaining (24%) are due to factors found within the random error.

B- Fixed Effects Model (FEM):

It is evident from the model estimated in the above in the previous table No. 3 that the significance of the variable coefficient (TIC) is below the level of significance of 05.0, because the probability value of the factor TIC of (0.0000) is greater than (0.05), so we accept the null hypothesis that states that there is a significant effect of the variable Number of mobile phone users in economic growth.

We also conclude that the coefficient of variables Number of fixed telephone users (TF) and Expenditure on education (present in US dollars (EDUC) and Foreign direct investment (FDI) is not significant under the level of significance of 05.0 because the probabilistic value of the test coefficient (TF ,EDUC,FDI) of (0.7371),(0.7525),(0.1988) respectively is greater than (0.05), so we accept the null hypothesis which states that there is no significant effect of the previous variables in GDP per capita.

On the other hand, we find the significance of Internet users (INTE) and Patent applications for residents (R&D), control corruption (CC), political stability (SP), trade openness (OC), and the total value of international trade (TVI) under a significant level of 05.0, because the value of The test probability of the coefficient (INTE, R&D,CC,SP,OC,TVI) of (0.0000),(0.0372),(0.0000),(0.0000),(0.0000) ,(0.0000)in the same previous order is less than (0.05), so we accept the null hypothesis which states that there is a significant effect of the previous variables on the rate of economic growth.

We also note the significance of the calculated F value below the level of significance (0.05), because the probability value for it has reached (000.0), which is less than (0.05), and this means that the estimated model as a whole is significant, and the value of the coefficient of determination R² reached(93) and this means that the estimated model as a whole is significant. The independent variables explain (93%) of the changes in economic growth, while the remaining (7%) are due to factors found within the random error.

C- Random Effects Model (REM):

It is clear from the model estimated above in Table 3 the significance of the coefficient of the variables Number of mobile phone users (TIC) and Internet users (INTE), control corruption (CC) , political stability (SP)and the total value of international trade (TVI) under the level of significance of (0.05) because the probabilistic value of the factor TIC,INTE,CC,TVI, which is of (0.0000),(0.0000),(0.0000),(0.0000),(0.0000) ,(0.0000) (respectively greater than 05.0), so we accept the null hypothesis which states that there is no significant effect of the previous variables on economic growth.While

We also conclude that the coefficient of variables Number of fixed telephone users (TF) and Expenditure on education (EDUC), Patent applications for residents (R&D)and Foreign direct investment (FDI) is not significant under the level of significance of 05.0 because the probabilistic value of the test coefficient (TF ,EDUC, R&D FDI) of (0.0678) ,(0.8361),(0.0507),(0.1844) respectively is greater than (0.05), so we accept the null hypothesis which states that there is no significant effect of the previous variables in GDP per capita.

We also note the significance of the calculated F value below the level of significance (05.0), because its probabilistic value has reached (000.0), which is less than (0.05), and this means that the estimated model as a whole is significant, and the value of the coefficient of determination R² reached (0.84), which means that the estimated model as a whole is significant. The independent variables explain (84%) of the changes in economic growth, while the remaining (16%) are due to factors found within the random error.

Table 10. Hausman Test.

Correlated Random Effects - Hausman Test
Test Summary	Chi-Sq. Statistic	Chi-Sq. d.f.	Prob.
Period random	25.391195	10	0.0047

Through the previous table No. 10 we note that the probability corresponding to the Hausman test is (0.0047) less than 5%, from which we accept the alternative hypothesis H1 and reject the null hypothesis H0. When observing the fixed effects model, it turns out that the model is significant and that the explanatory power of the model is high through the value of the coefficient of determination R².

Table 11. Fixed Effects Model.

Fixed Effects Model) FEM (
Variable	Coefficient		Prob.
TIC	0.004458		(0.0000)
TF	0.000842		(0.7371)
INTE	0.018249		(0.0000)
EDUC	-3.28E-13		(0.7525)
R&D	-4.29E-07		(0.0372)
CC	0.050686		(0.0000)
SP	0.005173		(0.0000)
OC	-0.005129		(0.0000)
FDI	0.001901		(0.1988)
TVI	7.88E-13		(0.0000)
C	7.395045	(0.0000)
R-squared	0.936837
Adjusted R-squared	0.932770
F-statistic	230.3899
Prob(F-statistic)	0.000000

It is clear from the model estimated in the above in Table 11 that the value the limit is fixed c of the model is significant below the level of significance (0.05), because the probability value t-test for the fixed limit of (0.0000) is less than the level of significance (0.05), that is, we reject the null hypothesis which states that there is no significant The fixed term and we accept the alternative hypothesis that states the significance of The limit is fixed.

From the previous table No11 we note that the probability corresponding to Fisher’s statistic is equal to (0.000000 ) which is less than 5%, and the model is significant. and the coefficient of determination R² is estimated by 0.936837 that is, the independent variables explain the dependent variable with 0.94% and the rest are explained by other variables that were not included in The model, as the probability corresponding to Fisher's statistic for variables shows that it is less than 5%, meaning that all variables are significant, except for (TF, EDUC,FDI ) . Number of fixed telephone users (per 100 people), Expenditure on education (present in US dollars),and Foreign direct investment (% of GDP) It is Not significant. In addition, The Model can be written as follows:

Fixed effects of countries

Table 13. The results of Fixed effects of countries

Country		effect	Country		effect	Country		effect	Country		effect
1	Algeria	7.011929	6	China	-1.23E-06	11	Mexico	-2.94E-14	16	Romania	0.107154
2	Argentina	0.004170	7	Egypt	0.031199	12	Morocco	-0.296618	17	Sri Lanka	-0.020692
3	Brazil	0.021957	8	Hungary	0.007512	13	Peru	0.338827	18	Thailand	0.232467
4	Bulgaria	0.034605	9	Indonesia	-0.003197	14	Philippines	-0.070077	19	Tunisia	0.078507
5	Chile	6.02E-12	10	Iran	0.003616	15	Poland	0.013477	20	Turkey	-0.454377

Source: Prepared by researchers based on outputs EViews 9

Concern # 6: I am surprised by the structure of the work. I suggest calling Chapter 4 the results and the discussion, adding the necessary references to the sources in the text accordingly. I would like to ask for Chapter 5 to be redrafted. I see that the Discussion must precede the conclusions. Make it clear that the conclusions follow from the whole text and give some specific recommendations, not just wishes. Very correctly worded Limitations. It may not be necessary to separate them into a separate 5.4.chapter, as some fit in the actuality of the topic and some in the conclusions.

Author response: The question has been addressed. Page 16-27

Author action: 4. Results and Discussion

4.1. The role of the (KE) in the developing countries under study

4.1.1. Measurement Model

4.1.2 Selection the appropriate Model

4.1.2.1 Lagrange Multiplier Test

4.1.2.2 Hausman Test

4.2. Discussion

This study aimed to measure the impact of the knowledge economy (Number of mobile phone users, Number of fixed telephone users, Internet users, Expenditure on education, Patent applications, for residents, control corruption, political stability, Trade openness (exports + imports, Foreign direct investment, and Total value of international trade) on economic growth in 20 developing countries, for the period (1996-2020). Using the economic measurement of the panel data, the study concluded that the economic growth of the countries under study is linked to a positive relationship with some knowledge indicators, which are the number of mobile phone users, the number of fixed telephone users, expenditure on education, the control corruption, political stability, foreign direct investment, and the total value of international trade. This is in line with economic theory, which is the result recorded for the improvement of knowledge economy indicators and its impact on raising economic growth rates.

While some knowledge economy indicators (Expenditure on education, Patent applications, for residents, Trade openness (exports + imports) / gross domestic product) have an inverse relationship with economic growth rates, and this result contradicts the economic theory, and the study hypotheses can be verified.

Moreover, we found that the hypotheses (H1, H2,H3,H6,H7,H9,H10 ) were proven correct,

Hypothesis 1 (H1). The Number of mobile phone users positively affects GNP per capita. And Hypothesis 2 (H2). The number of fixed telephone users positively affects the per capita GDP. Hypothesis 3 (H3). Internet users positively affect per capita GNP. Hypothesis 6 (H6). The control corruption positively affects the per capita share of the gross national product. Hypothesis 7 (H7). Political stability positively affects the per capita GNP. Hypothesis 9 (H9). Foreign direct investment positively affects the per capita gross national product. Hypothesis 10 (H10). The total value of international trade positively affects the per capita gross national product.

Proving the validity of this hypothesis, there is a positive relationship between the number of mobile phone users and economic growth, Where the increase of one unit of the number of mobile phone users leads to an increase in growth by 0.0045 units. p<05%, p= (0.000) Perhaps the most important characteristic of developing countries in this field is the increase in the number of mobile phone users, especially at the time of the spread of crises. new corona virus. The mobile phone is now the most widely used device in the world, and the companies that produce this device are now the first in the world, this reflects the position of this technology in global markets, and those developing countries, such as China, have the largest share of it. This result is consistent with the study of (Irena Paličková (2014), Mehrara, M. & Rezaei, A. (2015), Hoh Yan Chun (2017), Mahboub, A Salman, M. (2007), Holtan, C. (2013), Madesen, J. B (2011)). While differing with the results of studies (Nour, S,S,OM (2015), Hassan, M. (2004), Nour, S,SOM (2002)) Jeffrey Kouton's(2018). while in our study we found that the hypotheses ( H4,H5,H8) were incorrect.

While these hypotheses are not validated, Hypothesis 4 (H4). Expenditure on education positively affects the per capita GNP., Hypothesis 5 (H5). Patent applications, for residents, affect positively the per capita GNP., Hypothesis 8 (H8). Trade openness (exports + imports) / gross domestic product positively affects the per capita GNP. The results of the studies are inconsistent with (Crisculio & Martin (2004). Barro & Lee (2010) Amin, M. & Matto, A. (2008), Manjinder Kaur and Lakhwinder Singh(2016))

Conclusions

5.1. Summary of Findings

The model we obtained (Equation No.2) shows the following results:

a positive relationship between the number of mobile phone users and economic growth, Where the increase of one unit of the number of mobile phone users leads to an increase in growth by 0.0045 units. Perhaps the most important characteristic of developing countries in this field is the increase in the number of mobile phone users, especially at the time of the spread of crises. new corona virus. The mobile phone is now the most widely used device in the world, and the companies that produce this device are now the first in the world, such as Apple, Samsung, Huawei, and others. This reflects the position of this technology in global markets, and those developing countries, such as China, have the largest share of it.

also, a positive relationship between the number of fixed telephone users and economic growth; Where the increase of one unit of the number of fixed telephone users, leads to an increase in growth by 0.00084 units, this indicator over time witnessed a decline due to the monopoly of the mobile phone during daily transactions, whether in work or social life. There is also a positive relationship between the percentage of Internet users and economic growth; where the increase in Internet users by one unit leads to a rise in growth by 0.01825 units, and this is due to the role that the use of the Internet plays in economic, financial, and commercial transactions. The time and distances were shortened and thus the cost was reduced. While there is a negative relationship between spending on education and economic growth; Where an increase of one unit of spending on education leads to a decline in economic growth by 3.28E-13 alone, and perhaps the most important characteristic of developing countries is their strong need for more spending and their interest in education, and of course to have strong education; You have to spend to provide the appropriate atmosphere for that by building schools, universities, and infrastructure, and providing all the requirements and equipment as well as good framing. The decline in economic growth here is explained because the fruits of education come in the long term, not in the short term.

There is a negative relationship between patent applications for residents and economic growth; Where an increase in patents by a unit leads to a decrease in growth by 4.29E-07 units, which is the opposite of what economic theory says. This can be explained by Schumpeter's idea, which he called creative destruction, any invention that cancels an invention. This new technology can monopolize the market for a period, until a new product is invented, during which time the institution can be affected. On the one hand, some inventions are not used in the current period, but rather take time until they have these needs in the market, so their impact will be in the long run. Some of them never see the light and are destroyed by newer technology, especially with the great acceleration in the world, and the intense competition between companies, which has a negative impact. There is a positive relationship between the control corruption index and economic growth; Increasing the anti-corruption index by one unit leads to an increase in economic growth by 0.051. This relationship proves the extent to which these countries rely in establishing principles of governance and combating corruption.

It is known that among the classification of developing countries is their appropriate climate for investment, and that many developing countries were the reason for hindering progress is corruption. For example, Egypt. Positive relationship between the indicator of political stability and economic growth; Where an increase of one unit of the political stability index leads to an increase in economic growth by 0.0052 units, and this is an important factor that explains the importance of political stability in economic progress and growth.

While there is a negative relationship between trade openness and economic growth; Where the increase in trade openness by one unit leads to a decline in economic growth by 0.00513 units, and this is an indication that developing countries are still in need of more openness to the outside, so that those developing countries can provide the appropriate climate for investment and trade, and thus exchange new technologies between countries and increase the per capita share of local produce. This is a result contrary to the economic theory, but it is a possible result, considering that the liberalization of global trade and the openness of the world’s economies to each other is a trend that is difficult to stand against, and such openness has risks for small economies with few resources, and therefore the liberalization may not bring the expected results from it. Therefore, the study recommends economic decision-makers in developing countries to set policies that stimulate the development of production and increase its efficiency to the extent that it can compete with goods coming from other countries, with interest in the export of knowledge-based goods.

The existence of a positive relationship between foreign direct investment and economic growth; Where an increase of one unit of foreign investment leads to an increase in economic growth by 0.00190 units, and foreign direct investment helps to transfer technology, and this is what the developing countries have benefited from in supporting their economies. Finally, there is a positive relationship between the value of total international trade and economic growth; increasing international trade by one unit contributes to raising economic growth by 7.88E-13, as international trade plays a major role in developing economies and contributes significantly to GDP.

5.2. Conclusions

After conducting the standard study using panel models for a sample of 20 developing countries, the results indicated that the knowledge economy has a significant positive impact on economic growth represented by the per capita GDP. All indicators used in the study had a positive impact, except for the education indicator, which had a weak negative impact, and it is known that these countries still need more from interest in education and keenness to own a good educational system, and to invest in human capital to be the actual engine of its economic growth. In addition, research and development represented by patents and the trade openness index had a negative impact. The most influential was the foreign direct investment, the value of total international trade, the percentage of internet users, the number of mobile phone users and the value of the total international trade, and this reflects the great role of these indicators in bringing technology, benefiting from foreign capital, as well as providing the appropriate environment for investment, which is shown by the anti-combat indicator. Corruption, which also had an impact on economic growth. In addition to all this, the equally important indicator is information and communication technology, which is the economic artery of every country; this indicator plays a major role in the economy in the recent period due to the transformation of the social and economic lifestyle with the knowledge economy. Sales of service sectors increased, the largest international companies became in this sector such as Amazon, and sectors related to technology such as Microsoft, Apple, and Samsung. In general, the indicators of the knowledge economy contributed positively to the increase in the per capita GDP, and thus played a major role in the achievement of developing countries in maintaining high growth rates during the past decades.

Through this study, we can say that it is time for reform experiences in developing countries to emerge

From the prison of focus on traditional economic policies to the vastness of built structural policies

Based on recent trends in the field of economy and development, and focus on high sectors

The added value, and perhaps the most important of these sectors is the knowledge economy sector, for this and based on the results

In this study, the following recommendations can be made:

Encouraging creativity and innovation among individuals, especially the working group.

Creating the right environment for scientists and innovators to stop the brain drain.

- Raising the level of spending on the fields of education and scientific research.

- Updating educational programs with the requirements of the times and instilling a love of science and knowledge in young people.

- Stimulating investment in the fields of technology and modern technologies.

Finally, as with any research, this contribution comes with some limitations. First, given the method of data collection chosen, most developing countries imply a lack of data and a fallacy in that data. This might be considered a limitation of the study. Secondly, most developing countries are still importing technology from developed countries that may not suit their environment, and this leads to not reaping the fruits of knowledge expected to be obtained.

Hence, our study aimed that the economic problem today is based on the abundance of information and not the traditional scarce resources, because the knowledge economy has become the decisive element in all aspects of economic activity, and knowledge has become the basis for any economic or social growth, and through that, the world has shifted from research and collision in order to Sources of scarce resources to search and clash in order to control as much knowledge sources as possible.

Future research should look at how to direct economic resources towards knowledge industries in a manner equivalent to the volume of resources directed towards investments in the sectors of construction, tourism, sports, and entertainment, and the need to search for ways to support scientific research and researchers in the field of knowledge technologies and increase the volume of spending on scientific research so that it constitutes a good percentage of Gross National Product, which has a positive impact on the country's national economy.

Concern # 7: I would suggest a conceptual change in the presentation of the topic and the interpretation of the results. Undoubtedly, it's nice to see the opportunities that technology has in working with big data, thanks to the authors for being able to read it.

Author response: The question has been addressed. Page2,3

Author action: Online interconnection and the constant exchange of information between different systems have created vast amounts of data available for analysis. And that this “big data” is constantly increasing in size and needs sophisticated tools called data analytics to try to produce relevant information. With the rapid development of information technologies, such as cloud computing, mobile Internet, and Internet of Things, and the enhancement of IT applications, all types of data are quickly created and aggregated in ways different. Big data is seen as a game-changing tool capable of revolutionizing the way countries work in many industries, and therefore this will certainly affect economic science as one of the social sciences highly sensitive to any changes in the surrounding environment, and therefore policy makers in the country should be expected These changes that result from technological development, which necessitate new standards and methods for data collection and analysis.

The country, especially the developing countries, is facing unprecedented opportunities and challenges, according to the nature and advantages of big data, so that ways must be explored to deal with the challenges of big data, which represents a growing market estimated at $67 billion by 2021, with the Analytics Data program being the market leader, more than 98 % of the information stored around the world is electronic, and the business community has accepted this new type of information because it may be useful. The traditional methods of data analysis are no longer useful, and new and advanced tools are needed, and data analysis programs provide these tools[4]. Also, investing in big data can support and enhance the ability of investors to make decisions by improving the quality of the data that is obtained[5]. Since the advent of the term big data, researchers have not agreed on its definition. Some see them as large-volume, high-speed, and diversified information assets that require innovative forms of information processing in order to support decision-making[6, 7]. Some have defined it as data that cannot be stored or analyzed by traditional hardware and software[8]. Some see them as data sets whose size exceeds the ability of typical database programs to capture, store, manage, and analyze[9]. As defined by the International Organization for Standardization (ISO) as “a set or sets of data that have properties such as size, speed, variance, variance, validity of the data, etc. that cannot be efficiently processed using current and traditional technology to benefit from.” Smeda“ sees huge amounts of data At a high speed, complex and ever-changing, requiring advanced technologies that enable information capture, storage, distribution, management, and analysis[10]. Therefore, researchers can conclude that big data is a huge amount of complex data that is characterized by high levels of diversity, magnitude, and speed. It may be in the form of notes, regular numbers, letters, words, analog signs, pictures, percentages, shapes Engineering, or symbols. It may be in the form of a visual or an audio clip, and it is recorded and therefore its storage media differ from the usual, and it can only be benefited from after being processed by high-tech information systems capable of converting these different types of data into useful information that can be used in making decisions.

Author Response File: Author Response.pdf

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