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This econometric study covers the outlook for aircraft maintenance, repair, and overhaul (mro) services in Africa. For each year reported, estimates are given for the latent demand, or potential industry earnings (P.I.E.), for the country in question (in millions of U.S. dollars), the percent share the country is of the region and of the globe. These comparative benchmarks allow the reader to quickly gauge a country vis-à-vis others. Using econometric models which project fundamental economic dynamics within each country and across countries, latent demand estimates are created. This report does not discuss the specific players in the market serving the latent demand, nor specific details at the product level. The study also does not consider short-term cyclicalities that might affect realized sales. The study, therefore, is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved.
This study does not report actual sales data (which are simply unavailable, in a comparable or consistent manner in virtually all of the countries in Africa). This study gives, however, my estimates for the latent demand, or the P.I.E. for aircraft maintenance, repair, and overhaul (mro) services in Africa. It also shows how the P.I.E. is divided across the national markets of Africa. For each country, I also show my estimates of how the P.I.E. grows over time (positive or negative growth). In order to make these estimates, a multi-stage methodology was employed that is often taught in courses on international strategic planning at graduate schools of business.
Table Of Contents:
1 INTRODUCTION 81.1 Overview 81.2 What is Latent Demand and the P.I.E.? 81.3 The Methodology 91.3.1 Step 1. Product Definition and Data Collection 111.3.2 Step 2. Filtering and Smoothing 121.3.3 Step 3. Filling in Missing Values 131.3.4 Step 4. Varying Parameter, Non-linear Estimation 131.3.5 Step 5. Fixed-Parameter Linear Estimation 141.3.6 Step 6. Aggregation and Benchmarking 141.3.7 Step 7. Latent Demand Density: Allocating Across Cities 142 AFRICA 162.1 Executive Summary 162.2 Algeria 172.3 Angola 182.4 Benin 182.5 Botswana 202.6 Burkina Faso 212.7 Burundi 222.8 Cameroon 222.9 Cape Verde 232.10 Central African Republic 242.11 Chad 252.12 Comoros 262.13 Congo (formerly Zaire) 262.14 Cote d'Ivoire 272.15 Djibouti 282.16 Egypt 292.17 Equatorial Guinea 302.18 Ethiopia 302.19 Gabon 312.20 Ghana 322.21 Guinea 332.22 Guinea-Bissau 332.23 Kenya 342.24 Lesotho 352.25 Liberia 362.26 Libya 362.27 Madagascar 372.28 Malawi 382.29 Mali 392.30 Mauritania 402.31 Mauritius 412.32 Morocco 412.33 Mozambique 422.34 Namibia 432.35 Niger 442.36 Nigeria 452.37 Republic of Congo 462.38 Rwanda 472.39 Sao Tome E Principe 482.40 Senegal 482.41 Sierra Leone 492.42 Somalia 502.43 South Africa 512.44 Swaziland 522.45 Tanzania 532.46 The Gambia 542.47 Togo 552.48 Tunisia 562.49 Uganda 572.50 Western Sahara 582.51 Zambia 582.52 Zimbabwe 592.53 Frequently Asked Questions (FAQ) 602.53.1 Category Definition 602.53.2 Units 602.53.3 Methodology 613 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 633.1 Disclaimers & Safe Harbor 633.2 Icon Group International, Inc. User Agreement Provisions 64