The 2018-2023 World Outlook for Smart City Communication Networks
- April 2017
- 296 pages
- ICON Group
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 of the world). This study gives, however, my estimates for the worldwide latent demand, or the P.I.E., for smart city communication networks. It also shows how the P.I.E. is divided across the world's regional and national markets. 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.
1.3 THE METHODOLOGY
In order to estimate the latent demand for smart city communication networks on a worldwide basis, I used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, I heavily rely on the use of certain basic economic assumptions. In particular, there is an assumption governing the shape and type of aggregate latent demand functions. Latent demand functions relate the income of a country, city, state, household, or individual to realized consumption.
Latent demand (often realized as consumption when an industry is efficient), at any level of the value chain, takes place if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and be able to serve that demand at a minimal return. The single most important variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels of the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), and or changes in utility for the product in question.
Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to consume is more constant. Similarly, in the long-run, households, industries, or countries with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for smart city communication networks across some 190 countries.
The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these countries having wealth; current income dominates the latent demand for smart city communication networks. So, latent demand in the long-run has a zero intercept. However, I allow firms to have different propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences).
Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for smart city communication networks. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories, not just smart city communication networks.
1.3.1 STEP 1. PRODUCT DEFINITION AND DATA COLLECTION
Any study of latent demand across countries requires that some standard be established to define "efficiently served". Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key countries are more likely to be at or near efficiency than others. These countries are given greater weight than others in the estimation of latent demand compared to other countries for which no known data are available.
Of the many alternatives, I have found the assumption that the world’s highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e., China has high aggregate income, but low income per capita and cannot be assumed to be efficient). Aggregate income can be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of households times average household income per capita). Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies.
Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets).The selection of countries is further reduced by the fact that not all countries in the OECD report have industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom, and in some cases France and Germany.Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank).
Depending on original data sources used, the definition of "smart city communication networks" is established. In the case of this report, the data were reported at the aggregate level, with no further breakdown or definition. In other words, any potential products that might be incorporated within smart city communication networks fall under this category. Public sources rarely report data at the disaggregated level in order to protect private information from individual firms that might dominate a specific product-market. These sources will therefore aggregate across components of a category and report only the aggregate to the public.
While private data are certainly available, this report only relies on public data at the aggregate level without reliance on the summation of various category components. In other words, this report does not aggregate a number of components to arrive at the "whole". Rather, it starts with the "whole", and estimates the whole for all countries and the world at large (without needing to know the specific parts that went into the whole in the first place).Given this caveat, in this report we define the sales of smart city communication networks as including all commonly understood products falling within this broad category, such as fiber, cellular, Wi-Fi, radio-frequency (RF) mesh, and programmable logic controller (PLC) communication technologies, irrespective of product packaging, formulation, size, or form.
Companies participating in this industry include Alcatel-Lucent, Cisco, IBM, Schneider Electric, Siemens, and Ericsson. In addition to the sources indicated below, additional information available to the public via news and/or press releases published by players in the industry was considered in defining and calibrating this category. All figures are in a common currency (U.S. dollars, millions) and are not adjusted for inflation (i.e., they are current values). Exchange rates used to convert to U.S. dollars are averages for the year in question. Future exchange rates are assumed to be constant in the future at the current level (the average of the year of this publication’s release in 2017).
This report was prepared from a variety of sources including excerpts from documents and official reports or databases published by the World Bank, the U.S. Department of Commerce, the U.S. State Department, various national agencies, the International Monetary Fund, the Central Intelligence Agency, various agencies from the United Nations (e.g. ILO, ITU, UNDP, etc.), and non-governmental sources, including ICON Group Ltd., Euromonitor, the World Resources Institute, Mintel, the U.S. Industrial Outlook, and various public sources cited in the trade press.
1.3.2 STEP 2. FILTERING AND SMOOTHING
Based on the aggregate view of smart city communication networks as defined above, data were then collected for as many similar countries as possible for that same definition, at the same level of the value chain. This generates a convenience sample of countries from which comparable figures are available. If the series in question do not reflect the same accounting period, then adjustments are made. In order to eliminate short-term effects of business cycles, the series are smoothed using a 2-year moving average weighting scheme (longer weighting schemes do not substantially change the results). If data are available for a country, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a country stricken with foot and mouth disease), these observations were dropped or "filtered" from the analysis.
1.3.3 STEP 3. FILLING IN MISSING VALUES
In some cases, data are available for countries on a sporadic basis. In other cases, data from a country may be available for only one year. From a Bayesian perspective, these observations should be given the greatest weight in estimating missing years. Assuming that other factors are held constant, the missing years are extrapolated using changes and growth in aggregate national income. Based on the overriding philosophy of a long-run consumption function (defined earlier), countries which have missing data for any given year are estimated based on historical dynamics of aggregate income for that country.
1.3.4 STEP 4. VARYING PARAMETER, NON-LINEAR ESTIMATION
Given the data available from the first three steps, the latent demand in additional countries is estimated using a "varying-parameter cross-sectionally pooled time series model".
The interested reader can find longer discussions of this type of modeling in Studies in Global Econometrics (Advanced Studies in Theoretical and Applied Econometrics V. 30), by Henri Theil, et al., Kluwer Academic Publishers; ISBN: 0792336607; (June 1996), and in Principles of Econometrics, by Henri Theil John Wiley & Sons; ISBN: 0471858455; (December 1971), and in Econometric Models and Economic Forecasts by Robert S. Pindyck, Daniel L. Rubinfeld McGraw Hill Text; ISBN: 0070500983; 3rd edition (December 1991). Simply stated, the effect of income on latent demand is assumed to be constant across countries unless there is empirical evidence to suggest that this effect varies (i.e., the slope of the income effect is not necessarily the same for all countries). This assumption applies across countries along the aggregate consumption function, but also over time (i.e., not all countries are perceived to have the same income growth prospects over time and this effect can vary from country to country as well).
Another way of looking at this is to say that latent demand for smart city communication networks is more likely to be similar across countries that have similar characteristics in terms of economic development (i.e., African countries will have similar latent demand structures controlling for the income variation across the pool of African countries). This approach is useful across countries for which some notion of non-linearity exists in the aggregate cross-country consumption function.
For some categories, however, the reader must realize that the numbers will reflect a country’s contribution to global latent demand and may never be realized in the form of local sales. For certain country-category combinations this will result in what at first glance will be odd results. For example, the latent demand for the category "space vehicles" will exist for Togo even though they have no space program. The assumption is that if the economies in these countries did not exist, the world aggregate for these categories would be lower. The share attributed to these countries is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers).
1.3.5 STEP 5. FIXED-PARAMETER LINEAR ESTIMATION
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the world consists of more than 200 countries, there will always be those countries, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these countries, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a country’s stock of income), but a function of current income (a country’s flow of income).
In the long run, if a country has no current income, the latent demand for smart city communication networks is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., countries which earn low levels of income will not use their savings, in the long run, to demand smart city communication networks). In a graphical sense, for low-income countries, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income countries are assumed to have a latent demand proportional to their income, based on the country closest to it on the aggregate consumption function.
1.3.6 STEP 6. AGGREGATION AND BENCHMARKING
Based on the models described in Chapter 1, latent demand figures are estimated for all countries of the world, including for the smallest economies. These are then aggregated to get world totals and regional totals. To make the numbers more meaningful, regional and global demand averages are presented. Figures are rounded, so minor inconsistencies may exist across tables.
1.3.7 STEP 7. LATENT DEMAND DENSITY: ALLOCATING ACROSS CITIES
With the advent of a "borderless world", cities become a more important criteria in prioritizing markets, as opposed to regions, continents, or countries. This report also covers the world’s top 2,000 cities. The purpose is to understand the density of demand within a country and the extent to which a city might be used as a point of distribution within its region. From an economic perspective, however, a city does not represent a population within rigid geographical boundaries. To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas. This influence varies from one industry to another, but also from one period of time to another.
Similar to country-level data, the reader needs to realize that latent demand allocated to a city may or may not represent real sales. For many items, latent demand is clearly observable in sales, as in the case for food or housing items. Consider, again, the category "satellite launch vehicles." Clearly, there are no launch pads in most cities of the world. However, the core benefit of the vehicles (e.g. telecommunications, etc.) is "consumed" by residents or industries within the world's cities. Without certain cities, in other words, the world market for satellite launch vehicles would be lower for the world in general. One needs to allocate, therefore, a portion of the worldwide economic demand for launch vehicles to regions, countries, and cities.
This report takes the broader definition and considers, therefore, a city as a part of the global market. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its home country, within its region, and across the world total. Not all cities are estimated within each country as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same country, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city is used vis-à-vis others.
1.2 WHAT IS LATENT DEMAND AND THE P.I.E.?
1.3 THE METHODOLOGY
1.4 FREQUENTLY ASKED QUESTIONS (FAQ)
2 SUMMARY OF FINDINGS
2.1 THE WORLDWIDE MARKET POTENTIAL
3.1 EXECUTIVE SUMMARY
3.6 BURKINA FASO
3.9 CAPE VERDE
3.10 CENTRAL AFRICAN REPUBLIC
3.13 REPUBLIC OF CONGO
3.14 COTE D'IVOIRE
3.17 EQUATORIAL GUINEA
3.20 THE GAMBIA
3.39 SAO TOME E PRINCIPE
3.41 SIERRA LEONE
3.43 SOUTH AFRICA
3.49 CONGO (FORMERLY ZAIRE)
3.53 WESTERN SAHARA
3.54 ST. HELENA
3.57 SOUTH SUDAN
4.1 EXECUTIVE SUMMARY
4.8 HONG KONG
4.18 NORTH KOREA
4.19 PAPUA NEW GUINEA
4.23 SOUTH KOREA
4.24 SRI LANKA
4.28 TIMOR - LESTE, DEMOCRATIC REPUBLIC OF
5.1 EXECUTIVE SUMMARY
5.21 THE NETHERLANDS
5.29 THE UNITED KINGDOM
5.31 THE FAROE ISLANDS
5.33 ISLE OF MAN
5.35 SAN MARINO
5.37 BOSNIA AND HERZEGOVINA
5.50 CZECH REPUBLIC
6 LATIN AMERICA
6.1 EXECUTIVE SUMMARY
6.8 COSTA RICA
6.10 EL SALVADOR
6.23 THE FALKLAND ISLANDS
7 NORTH AMERICA & THE CARIBBEAN
7.1 EXECUTIVE SUMMARY
7.3 ANTIGUA AND BARBUDA
7.5 THE BAHAMAS
7.8 THE BRITISH VIRGIN ISLANDS
7.10 THE CAYMAN ISLANDS
7.13 DOMINICAN REPUBLIC
7.19 PUERTO RICO
7.20 ST. KITTS AND NEVIS
7.21 ST. LUCIA
7.22 ST. VINCENT AND THE GRENADINES
7.23 TRINIDAD AND TOBAGO
7.24 THE UNITED STATES
7.27 ST PIERRE AND MIQUELON
7.28 TURKS AND CAICOS ISLANDS
7.29 THE U.S. VIRGIN ISLANDS
7.31 SINT MAARTEN
8.1 EXECUTIVE SUMMARY
8.2 AMERICAN SAMOA
8.5 FRENCH POLYNESIA
8.9 NEW CALEDONIA
8.10 NEW ZEALAND
8.11 SOLOMON ISLANDS
8.15 WESTERN SAMOA
8.16 COOK ISLANDS
8.18 WALLIS AND FUTUNA
8.19 MARSHALL ISLANDS
8.20 MICRONESIA FEDERATION
8.21 CHRISTMAS ISLAND
8.23 NORFOLK ISLAND
8.24 THE NORTHERN MARIANA ISLAND
9 THE MIDDLE EAST
9.1 EXECUTIVE SUMMARY
9.13 SAUDI ARABIA
9.14 SYRIAN ARAB REPUBLIC
9.16 THE UNITED ARAB EMIRATES
10 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS
10.1 DISCLAIMERS & SAFE HARBOR
10.2 ICON GROUP INTERNATIONAL, INC. USER AGREEMENT PROVISIONS