CONCLUSION
The objective set for this chapter is to characterize the major trends related to production and trade besides their links to different components of the knowledge economy in the context of the Arab economies.
Using descriptive methods and regression analysis, important results are attained. They concern the limited diversification of each of the Arab economies where most of the time few sectors form the basis of the economies. North African countries are mainly agricultural, natural resource and service based economies while Gulf countries are based mainly on oil and gas. This is directly expressed in their trading patterns with the rest of the world where concentration and limited diversification are also observed. These results say that the economic portfolio appears to be reduced in its composition implying that vulnerability to shocks is an element that needs to be considered.Besides, the prior, a specific focus is placed on the role of one knowledge component that is information and communication technologies
Table 9.
| Country Name/2010 | Internet Users | HEPC 2009 | GDP pc 2009 | GDP 2009 | TLEB 2009 |
| Bahrain | 13.4502406 | 6.64720009 | 9.77615575 | 23.7483093 | 4.31780579 |
| Egypt, Arab Rep. | 16.8924438 | 4.73286191 | 7.77094524 | 25.9649287 | 4.29012059 |
| Iran, Islamic Rep. | 16.0789984 | 5.65999502 | 8.41758247 | 26.5254294 | 4.28705439 |
| Iraq | 13.5820512 | 5.30038866 | 7.648192 | 24.9006133 | 4.22663006 |
| Israel | 15.421977 | 7.60306129 | 10.1697806 | 25.9982724 | 4.40066287 |
| Jordan | 14.6704135 | 5.92235 | 8.35282637 | 23.9458284 | 4.29441951 |
| Kuwait | 13.8612484 | 7.36438929 | 10.6301829 | 25.4188506 | 4.31220393 |
| Lebanon | 14.0859613 | 6.42508207 | 9.02658227 | 24.2764604 | 4.28232723 |
| Libya | 13.6986572 | 6.05733754 | 9.20608035 | 24.856197 | 4.31419098 |
| Morocco | 16.566377 | 5.02067202 | 7.94726086 | 25.2331183 | 4.27478427 |
| Oman | 14.3608012 | 6.25455796 | 9.75731067 | 24.5705596 | 4.29216497 |
| Qatar | 14.1767974 | 7.38532093 | 11.0273076 | 25.311424 | 4.35795914 |
| Syrian Arab Republic | 15.2565287 | 4.55850061 | 7.89789022 | 24.7110368 | 4.32681199 |
| Tunisia | 15.165396 | 5.49120906 | 8.33541631 | 24.4965331 | 4.31214051 |
| Turkey | 17.1817705 | 6.35522189 | 9.05412406 | 27.1441625 | 4.29995746 |
| United Arab Emirates | 15.5835097 | 7.44076882 | 10.5702859 | 26.3229275 | 4.3382525 |
| Yemen, Rep. | 14.904236 | 4.14946995 | 6.98215763 | 23.9473317 | 4.17485583 |
| Middle East & North Africa (all income levels) | 18.3863509 | 5.72469811 | 8.66963342 | 28.4131307 | 4.2834117 |
| Middle East & North Africa (developing only) | 18.0548753 | 5.24328284 | 8.09201963 | 27.6932757 | 4.27668379 |
| OECD members | 20.5541726 | 8.33941434 | bgcolor=white>10.41654831.3458743 | 4.37353794 | |
| World | 21.4355512 | 6.80247614 | 9.05020117 | 31.6927407 | 4.24315576 |
Table 10.
| Country Name/2010 | Investment in Telecoms 2010 | Health Exp PC 2009 | GDP pc 2009 | GDP 2009 | Total Life Expectancy at Birth 2009 |
| Egypt, Arab Rep. | 2113000000 | 113.620268 | 2370.71111 | 1.8898E+11 | 72.9752683 |
| Iran, Islamic Rep. | 486000000 | 287.147214 | 4525.94861 | 3.3101E+11 | 72.7518537 |
| Iraq | 456000000 | 200.414688 | 2096.85105 | 6.5193E+10 | 68.4860488 |
| Jordan | 301000000 | 373.287909 | 4242.1537 | 2.5092E+10 | 73.2896585 |
| Morocco | 1124000000 | 151.51309 | 2827.81855 | 9.0908E+10 | 71.8646341 |
| Syrian Arab Republic | 65000000 | 95.44027 | 2691.59766 | 5.3935E+10 | 75.702561 |
| Tunisia | 966000000 | 242.550288 | 4168.93675 | 4.3522E+10 | 74.6 |
| Turkey | 1682980000 | 575.490023 | 8553.74145 | 6.1455E+11 | 73.6966585 |
| Yemen, Rep. | 59000000 | 63.4003858 | 1077.24014 | 2.513E+10 | 65.0304634 |
| Middle East & North Africa (developing only) | 5854000000 | 189.290494 | 3268.28162 | 1.0642E+12 | 72.0012722 |
Table 11.
| Country Name/2010 | Ln(Investment in Telecoms 2010) | Ln(Health Exp PC 2009) | Ln(GDP 2009) | Ln(GDP pc 2009) | Ln(Total Life Expectancy at Birth 2009) |
| Egypt, Arab Rep. | 21.4713746 | 4.73286191 | 25.9649287 | 7.77094524 | 4.29012059 |
| Iran, Islamic Rep. | 20.0017192 | 5.65999502 | 26.5254294 | 8.41758247 | 4.28705439 |
| Iraq | 19.9380034 | 5.30038866 | 24.9006133 | 7.648192 | 4.22663006 |
| Jordan | 19.5226208 | 5.92235 | 23.9458284 | 8.35282637 | 4.29441951 |
| Morocco | 20.8401596 | 5.02067202 | 25.2331183 | 7.94726086 | 4.27478427 |
| Syrian Arab Republic | 17.9898978 | 4.55850061 | 24.7110368 | 7.89789022 | 4.32681199 |
| Tunisia | 20.6886744 | 5.49120906 | 24.4965331 | 8.33541631 | 4.31214051 |
| Turkey | 21.2438319 | 6.35522189 | 27.1441625 | 9.05412406 | 4.29995746 |
| Yemen, Rep. | 17.893048 | 4.14946995 | 23.9473317 | 6.98215763 | 4.17485583 |
| Middle East & North Africa (developing only) | 22.490391 | 5.24328284 | 27.6932757 | 8.09201963 | 4.27668379 |
Table 12.
| DETAILED REPORT | ANALYSIS OF VARIANCE | |||||||
| Statistical Regression | Degree of Freedom | Sum of Squasss | Mean Ssuares | d | Critical Value of d | |||
| Coefficient of Multiple Determination | 0.6764 | Regression | 1 | 10.072 | 10.072 | 12.653 | 0.0029 | |
| Coefficient of Determination R2 | 0.4576 | Residues | 15 | 11.941 | 0.796 | |||
| Coefficient of Determination R2 | 0.4214 | Total | 16 | 22.013 | ||||
| Error-Type | 0.8922 | |||||||
| Observations | 17 | |||||||
| Ln(Internet Users 2010) | ||||||||
| Coefficients | Error Type | Statistical t | Probability | Lower Limit for a Confidence Level = 95% | Upper Limit for a Confidence Level = 95% | |||
| Constant | -5.295 | 5.7087 | -0.928 | 0.3683 | -17.46 | 6.8725 | ||
| Ln(GDP 2009) | 0.8072 | 0.2269 | 3.5571 | 0.0029 | 0.3235 | 1.2908 | ||
Table 13.
| DETAILED REPORT | ANALYSIS OF VARIANCE | |||||||
| Statistical Regression | Degree of Freedom | Sum of Squares | bgcolor=white>Mean F | Critical Value of F | ||||
| Coefficient of Multiple Determination | 0.8373 | Regression | 2 | 15.431 | 7.7157 | 16.413 | 0.0002 | |
| Coefficient de Determination R2 | 0.701 | Residues | 14 | 6.5815 | 0.4701 | |||
| Coefficient de Determination R2 | 0.6583 | Total | 16 | 22.013 | ||||
| Error-Type | 0.6856 | |||||||
| Observations | 17 | |||||||
| Ln(Internet Users) | ||||||||
| Coefficients | Error-Type | Statistical t | Probability | Lower Limit for a Confidence Level = 95% | Upper Limit for a Confidence Level = 95% | |||
| Constant | -6.295 | 4.397 | -1.432 | 0.1742 | -15.73 | 3.1359 | ||
| Ln(GDP 2009) | 0.9827 | 0.182 | 5.4006 | 9E-05 | 0.5925 | 1.373 | ||
| Ln(HEPC 2009) | -0.567 | 0.1679 | -3.376 | 0.0045 | -0.927 | -0.207 | ||
Table 14.
| DETAILED REPORT | ANALYSIS OF VARIANCE | |||||||
| Statistical Regression | Degree of Freedom | Sum of Ssuares | nean Ssuares | F | Critical Value of F | |||
| Coefficient of Multiple Determination | 0.76967 | Regression | 2 | 11.4764 | 5.73822 | 5.08658 | 0.04324 | |
| Coefficient de Determination R2 | 0.59239 | Residues | 7 | 7.89678 | 1.12811 | |||
| Coefficient de Determination R2 | 0.47593 | Total | 9 | 19.3732 | ||||
| Error-Type | 1.06213 | |||||||
| Observations | 10 | |||||||
| Ln(Investment in Telecoms) | ||||||||
| Coefficients | Error Type | Statistical t | Probability | Lower Limit for a Confidence Level = 95% | Upper Limit for a Confidence Level = 95% | |||
| Constant | -1.1267 | 6.84712 | -0.1646 | 0.87394 | -17.318 | 15.0641 | ||
| Ln(HEPC 2009) | 0.44691 | 0.58076 | 0.76951 | 0.46676 | -0.9264 | 1.82019 | ||
| Ln(GDP 2009) | 0.74604 | 0.29035 | 2.56942 | 0.03704 | 0.05946 | 1.43262 | ||
Table 15.
| DETAILED REPORT | ANALYSIS OF VARIANCE | |||||||||
| Statistical Regression | Degree of Freedom | Sum of Squares | Mean Squares | F | Critical Value of F | |||||
| Coefficient of Multiple Determination | 0.62631 | Regression | 1 | 5.3289 | 5.3289 | 4.51818 | 0.07113 | |||
| Coefficient of Determination R2 | 0.39226 | Residues | 7 | 8.25606 | 1.17944 | |||||
| Coefficient of Determination R2 | 0.30545 | Total | 8 | 13.585 | ||||||
| Error-Type | 1.08602 | |||||||||
| Observations | 9 | |||||||||
| Ln(Investment in Telecoms) | ||||||||||
| Coefficients | Error-Type | Statistical t | Probability | Lower Limit for a Confidence Level = 95% | Upper Limitfor a Confidence Level = 95% | |||||
| Constant | 1.63331 | 8.62684 | 0.18933 | 0.85521 | -18.766 | 22.0325 | ||||
| Ln(GDP pc 2009) | 0.72681 | 0.34193 | 2.1256 | 0.07113 | -0.0817 | 1.53534 | ||||
(ICTs). The analysis conducted in the second part of this chapter shows clearly how important improvements could be ICT based and how the different ICT components are enhancing the levels of production and services. This confirms again
the roles of ICTs in enhancing not just the production and trading processes but also the other welfare components such as health and education. These effects are again translated into impacts on production and trade.
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KEY TERMS AND DEFINITIONS
Economic Sectors: Mainly traditional levels of aggregation used to show how each economy is producing and trading different goods and services.
Dominant Sectors: Those aggregates that ensure the highest economic outcomes as measured through different economic variables as they appear in the chapter.
ICTs: Information and Communication Technologies refer to the large array of means and technologies used in telecommunication, information, and media but that are increasingly mobilized in different economic and social sectors.
Production and Trading Systems: Used to express the major trends occurring in Arab countries where oil and gas play an important role in the trading patterns of some Middle Eastern countries.
Trade Concentration: In terms of the number of commodities and services, a measure of concentration refers to how trade is based on a small or larger number of goods and services. The concept and the measure of concentration are generally used to show how an economy could be vulnerable to international market shocks in commodities and services. An economy with a limited number of export goods is generally vulnerable to any sudden price decrease for these goods.
Trade Diversification: Represents and measures the level of multiplicity of commodities and services that are included in both imports and exports. By opposition to concentration, a larger diversification of trade is economically preferred.
Value Added per Sector: As a measure of production, this is used in the context of this chapter to better show the contribution of the most important sectors and aggregates in each economy.
This work was previously published in Knowledge-Based Economic Policy Development in the Arab World, edited by Ahmed Driouchi, pages 147-171, copyright 2014 by Business Science Reference (an imprint of IGI Global).
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