The 2009 Report on Manufacturing Overhead Traveling Cranes, Hoists, and Monorail Systems: World Market Segmentation by City

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The 2009 Report on Manufacturing Overhead Traveling Cranes,
Hoists, and Monorail Systems: World Market Segmentation by City

Description:
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing overhead traveling cranes, hoists, and
monorail systems across more than 2000 cities. For the year reported, estimates are given for the
latent demand, or potential industry earnings (P.I.E.), for the city in question (in millions of U.S.
dollars), the percent share the city is of the region and of the globe. These comparative
benchmarks allow the reader to quickly gauge a city 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 cities of the world). This study gives, however, my
estimates for the worldwide latent demand, or the P.I.E. for manufacturing overhead traveling
cranes, hoists, and monorail systems. It also shows how the P.I.E. is divided across the world’s
cities. 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.
What is Latent Demand and the P.I.E.?
The concept of latent demand is rather subtle. The term latent typically refers to something that is
dormant, not observable, or not yet realized. Demand is the notion of an economic quantity that a
target population or market requires under different assumptions of price, quality, and distribution,
among other factors. Latent demand, therefore, is commonly defined by economists as the industry
earnings of a market when that market becomes accessible and attractive to serve by competing
firms. It is a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not
profit) if a market is served in an efficient manner. It is typically expressed as the total revenues
potentially extracted by firms. The “market” is defined at a given level in the value chain. There can
be latent demand at the retail level, at the wholesale level, the manufacturing level, and the raw
materials level (the P.I.E. of higher levels of the value chain being always smaller than the P.I.E. of
levels at lower levels of the same value chain, assuming all levels maintain minimum profitability).
The latent demand for manufacturing overhead traveling cranes, hoists, and monorail systems is
not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be lower
either lower or higher than actual sales if a market is inefficient (i.e., not representative of
relatively competitive levels). Inefficiencies arise from a number of factors, including the lack of
international openness, cultural barriers to consumption, regulations, and cartel-like behavior on
the part of firms. In general, however, latent demand is typically larger than actual sales in a city
market.
Another reason why sales do not equate to latent demand is exchange rates. In this report, all
figures assume the long-run efficiency of currency markets. Figures, therefore, equate values based
on purchasing power parities across countries. Short-run distortions in the value of the dollar,
therefore, do not figure into the estimates. Purchasing power parity estimates of country income
were collected from official sources, and extrapolated using standard econometric models. The
report uses the dollar as the currency of comparison, but not as a measure of transaction volume.
The units used in this report are: US $ mln.
For reasons discussed later, this report does not consider the notion of “unit quantities”, only total
latent revenues (i.e., a calculation of price times quantity is never made, though one is implied).
The units used in this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate
inflationary trends) and not adjusted for future dynamics in exchange rates (i.e., the figures reflect
average exchange rates over recent history). If inflation rates or exchange rates vary in a

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substantial way compared to recent experience, actually sales can also exceed latent demand
(when expressed in U.S. dollars, not adjusted for inflation). On the other hand, latent demand can
be typically higher than actual sales as there are often distribution inefficiencies that reduce actual
sales below the level of latent demand.
As mentioned earlier, this study is strategic in nature, taking an aggregate and long-run view,
irrespective of the players or products involved. If fact, all the current products or services on the
market can cease to exist in their present form (i.e., at a brand-, R&D specification, or corporate-
image level) and all the players can be replaced by other firms (i.e., via exits, entries, mergers,
bankruptcies, etc.), and there will still be an international latent demand for manufacturing
overhead traveling cranes, hoists, and monorail systems at the aggregate level. Product and
service offering details, and the actual identity of the players involved, while important for certain
issues, are relatively unimportant for estimates of latent demand.
The Methodology
In order to estimate the latent demand for manufacturing overhead traveling cranes, hoists, and
monorail systems on a city-by-city 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 in 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.
Ignoring, for the moment, exogenous shocks and variations in utility across countries, the
aggregate relation between income and consumption has been a central theme in economics. The
figure below concisely summarizes one aspect of problem. In the 1930s, John Meynard Keynes
conjectured that as incomes rise, the average propensity to consume would fall. The average
propensity to consume is the level of consumption divided by the level of income, or the slope of
the line from the origin to the consumption function. He estimated this relationship empirically and
found it to be true in the short-run (mostly based on cross-sectional data). The higher the income,
the lower the average propensity to consume. This type of consumption function is labeled "A" in
the figure below (note the rather flat slope of the curve). In the 1940s, another macroeconomist,
Simon Kuznets, estimated long-run consumption functions which indicated that the marginal
propensity to consume was rather constant (using time series data across countries). This type of
consumption function is show as "B" in the figure below (note the higher slope and zero-zero
intercept). The average propensity to consume is constant.

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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 manufacturing overhead traveling cranes, hoists, and
monorail systems across some 230 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 manufacturing overhead traveling cranes, hoists, and monorail systems. 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 manufacturing overhead traveling cranes, hoists, and monorail systems.
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 manufacturing overhead traveling cranes, hoists, and monorail systems.
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 or cities
are more likely to be at or near efficiency than others. These are given greater weight than others
in the estimation of latent demand compared to others 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 can not 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
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 “manufacturing overhead traveling cranes, hoists, and monorail systems” 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 product or service that might be
incorporated within manufacturing overhead traveling cranes, hoists, and monorail systems falls
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

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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 cities and the world at large (without
needing to know the specific parts that went into the whole in the first place).
Given this caveat, this study covers “manufacturing overhead traveling cranes, hoists, and monorail
systems” as defined by the North American Industrial Classification system or NAICS (pronounced
“nakes”). For a complete definition of manufacturing overhead traveling cranes, hoists, and
monorail systems, please refer to the Web site at
http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing overhead
traveling cranes, hoists, and monorail systems is 333923. It is for this definition of manufacturing
overhead traveling cranes, hoists, and monorail systems that the aggregate latent demand
estimates are derived. “Manufacturing overhead traveling cranes, hoists, and monorail systems” is
specifically defined as follows:
333923
This U.S. industry comprises establishments primarily engaged in manufacturing overhead traveling
cranes, hoists, and monorail systems.
3339231
Hoists
33392311
Complete hoists
3339231101
Chain hand hoists
3339231106
Ratchet lever hand hoists
3339231111
Wire rope puller hand hoists
3339231116
Electric (roller and link) chain hoists
3339231121
Other hoists, powered by electric motor
3339231131
Electric wire rope hoists (excluding hand, mine shaft, and slope wire rope hoists)
3339231141
Air or other nonelectric chain hoists, except hand
3339231146
Air and other nonelectric wire rope hoists (excluding hand, mine shaft, and slope wire rope hoists)
3339231151
Other hoists, not powered by electric motor
33392312
Parts and attachments for hoists (sold separately)
3339231261
Parts and attachments for hoists (sold separately)
3339233
Overhead traveling cranes and monorail systems
33392331
Complete overhead traveling cranes and monorail systems

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3339233101
Single top running bridge type overhead traveling cranes (except construction power cranes)
3339233111
Under running bridge type overhead traveling cranes (except construction power cranes)
3339233116
Gantry type overhead traveling cranes (except construction power cranes)
3339233121
Stacker_storage type overhead traveling cranes (except construction power cranes)
3339233131
Other overhead traveling cranes on fixed support
3339233136
Other overhead traveling cranes
3339233141
Buckets, grabs, and grips
3339233156
Monorail systems (manual and powered)
33392332
Double top running bridge type overhead traveling cranes (except construction power cranes)
3339233206
Double top running bridge type overhead traveling cranes (except construction power cranes)
33392333
Parts and attachments for overhead traveling cranes and monorail systems
3339233346
Other parts and attachments for overhead traveling cranes
3339233361
Parts and attachments for monorail systems (sold separately)
3339237
Winches, aerial work platforms, and automotive wrecker hoists
33392371
Complete winches, aerial work platforms, and automotive wrecker hoists
3339237110
Personnel aerial work platforms (excluding parts)
3339237111
Aerial work platforms, boom type, self_propelled
3339237113
Aerial work platforms, scissors type, self_propelled
3339237115
Aerial work platforms, not self_propelled, boom and scissors type
3339237117
Aerial work platforms, truck mounted
33392372
Winches for mounting on wheel and crawler tractors and other prime movers, complete units

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3339237230
Winches for mounting on wheel and crawler tractors and other prime movers, complete
3339237231
Winches (towing, logging, and oil_field types) for mounting on tractors, trucks, and other prime
movers
3339237239
Other materials_handling machinery for mounting on tractors, trucks, and other prime movers
33392373
Electric and other winches, including marine use and automobile hoists used on tow trucks
3339237353
Electric winches, including marine use (excluding winches for tractor mounting and parts)
3339237355
Other winches, including marine use (excluding winches for tractor mounting and parts)
3339237358
Other winches, including automobile hoists used on tow trucks (excluding winches for mounting on
wheel or crawler tractors)
3339237361
Automobile hoists (used on tow trucks) (excluding parts)
33392374
Parts for winches, aerial work platforms, and automobile hoists
3339237493
Parts for winches for mounting on tractors and other prime movers (sold separately)
3339237495
Parts for winches for aerial work platforms and automobile hoists (sold separately)
333923M
Miscellaneous receipts
333923P
Primary products
333923S
Secondary products
333923SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 333923 includes the following:
Aerial work platforms manufacturing
Automobile wrecker (i.e., tow truck) hoists manufacturing
Block and tackle manufacturing
Boat lifts manufacturing
Chain hoists manufacturing
Cranes, overhead traveling, manufacturing
Davits manufacturing
Hoists (except aircraft loading) manufacturing
Locomotive cranes manufacturing
Monorail systems (except passenger-type) manufacturing
Overhead traveling cranes manufacturing
Pulleys (except power transmission), metal, manufacturing
Ship cranes and derricks manufacturing

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Winches manufacturing
Wire rope hoists manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing overhead traveling cranes, hoists, and monorail
systems as defined above, data were then collected for as many similar countries and cities as
possible for that same definition, at the same level of the value chain. This generates a
convenience sample 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 an 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.
Step 3. Filling in Missing Values
In some cases, data are available for countries or cities on a sporadic basis. In other cases, data
may be available for only one year. From a Bayesian perspective, these observations should be
given 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), cities which have
missing data for any given year, are estimated based on historical dynamics of aggregate income
for that country.
Step 4. Varying Parameter, Non-linear Estimation
Given the data available from the first three steps, the latent demand is estimated using a “varying
-parameter cross-sectionally pooled time series model”. Simply stated, the effect of income on
latent demand is assumed to be constant across cities unless there is empirical evidence to suggest
that this effect varies (i.e., the slope of the income effect is not necessarily same for all countries).
This assumption applies across cities along the aggregate consumption function, but also over time
(i.e., not all cities are perceived to have the same income growth prospects over time and this
effect can vary from city to city as well). Another way of looking at this is to say that latent demand
for manufacturing overhead traveling cranes, hoists, and monorail systems is more likely to be
similar across cities that have similar characteristics in terms of economic development (i.e.,
African cities will have similar latent demand structures controlling for the income variation across
the pool of African cities).
This approach is useful across cities for which some notion of non-linearity exists in the aggregate
consumption function. For some categories, however, the reader must realize that the numbers will
reflect a city’s contribution to global latent demand and may never be realized in the form of local
sales. For certain 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 cities in “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
cities is based on a proportion of their income (however small) being used to consume the category
in question (i.e., perhaps via resellers).
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 2000 cities, there will always be those cities,
especially toward the bottom of the consumption function, where non-linear estimation is simply
not possible. For these cities, equilibrium latent demand is assumed to be perfectly parametric and
not a function of wealth (i.e., a city’s stock of income), but a function of current income (a city’s
flow of income). In the long run, if a city has no current income, the latent demand for
manufacturing overhead traveling cranes, hoists, and monorail systems is assumed to approach
zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., cities
which earn low levels of income will not use their savings, in the long run, to demand
manufacturing overhead traveling cranes, hoists, and monorail systems). In a graphical sense, for
low income cities, latent demand approaches zero in a parametric linear fashion with a zero-zero
intercept. In this stage of the estimation procedure, low-income cities are assumed to have a latent
demand proportional to their income, based on the city closest to it on the aggregate consumption
function.

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Step 6. Aggregation and Benchmarking
Based on the models described above, latent demand figures are estimated for all cities 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.

Contents:
1INTRODUCTION & METHODOLOGY11
1.1Overview and Definitions11
1.2Market Potential Estimation Methodology11
1.2.1Overview11
1.2.2What is Latent Demand and the P.I.E.?12
1.2.3The Methodology12
1.2.3.1Step 1. Product Definition and Data Collection14
1.2.3.2Step 2. Filtering and Smoothing18
1.2.3.3Step 3. Filling in Missing Values18
1.2.3.4Step 4. Varying Parameter, Non-linear Estimation18
1.2.3.5Step 5. Fixed-Parameter Linear Estimation19
1.2.3.6Step 6. Aggregation and Benchmarking19
2USING THE DATA20
3CITY SEGMENTS RANKED BY MARKET SIZE21
3.1Top 15 Markets21
3.2Markets 16 to 3022
3.3Remaining Cities by Market Rank23
4CITY SEGMENTS IN ALPHABETICAL ORDER126
4.1A: from Aalborg to Az Zawiyah126
4.2B: from Bacolod to Bydgoszcz133
4.3C: from Caaguazu to Cyangugu141
4.4D: from Da Nang to Dzhizak149
4.5E: from East London to Esteli153
4.6F: from Fagatogo to Funchal155
4.7G: from Gabes to Gyumri158
4.8H: from Hachinohe to Hyderabad162
4.9I: from Iasi to Izmir166
4.10J: from Jaboatao to Jyvaskyla169
4.11K: from Kabul to Kzyl-Orda171
4.12L: from La Ceiba to Lyon179
4.13M: from Macae to Mzuzu185
4.14N: from Nacala to Nzerekore195
4.15O: from Oaklahoma City to Oyem200
4.16Ö: from Örebro to Örebro202
4.17P: from Pago Pago to Pyuthan203
4.18Q: from Qandahar to Quito210
4.19R: from Rabat to Rustavi211
4.20S: from S. Luis Potosi to Szombathely214
4.21T: from Tabligbo to Tyre226
4.22U: from Uberaba to Utulei233
4.23V: from Vacoas-Phoenix to Vukovar235
4.24W: from Wadi Medani to Wuhan238
4.25X: from Xalapa to Xian239
4.26Y: from Yamagata to Yungkang240
4.27Z: from Zadar to Zvishavane241
5CITY SEGMENTS RANKED BY COUNTRY242
5.1Afghanistan242
5.2Albania242
5.3Algeria243
5.4American Samoa243
5.5Andorra243
5.6Angola244
5.7Antigua and Barbuda244
5.8Argentina245
5.9Armenia246

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5.10Aruba246
5.11Australia247
5.12Austria247
5.13Azerbaijan248
5.14Bahrain248
5.15Bangladesh249
5.16Barbados249
5.17Belarus250
5.18Belgium250
5.19Belize251
5.20Benin251
5.21Bermuda251
5.22Bhutan252
5.23Bolivia252
5.24Bosnia and Herzegovina252
5.25Botswana253
5.26Brazil254
5.27Brunei259
5.28Bulgaria259
5.29Burkina Faso260
5.30Burma260
5.31Burundi260
5.32Cambodia261
5.33Cameroon261
5.34Canada262
5.35Cape Verde262
5.36Central African Republic263
5.37Chad263
5.38Chile264
5.39China264
5.40Christmas Island265
5.41Colombia265
5.42Comoros265
5.43Congo (formerly Zaire)266
5.44Cook Islands266
5.45Costa Rica266
5.46Cote dIvoire267
5.47Croatia267
5.48Cuba268
5.49Cyprus268
5.50Czech Republic269
5.51Denmark269
5.52Djibouti270
5.53Dominica270
5.54Dominican Republic270
5.55Ecuador271
5.56Egypt271
5.57El Salvador272
5.58Equatorial Guinea272
5.59Estonia272
5.60Ethiopia273
5.61Fiji273
5.62Finland274
5.63France274
5.64French Guiana275
5.65French Polynesia275
5.66Gabon275
5.67Georgia276
5.68Germany276
5.69Ghana277
5.70Greece277
5.71Greenland278
5.72Grenada278

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5.73Guadeloupe279
5.74Guam279
5.75Guatemala279
5.76Guinea280
5.77Guinea-Bissau280
5.78Guyana280
5.79Haiti281
5.80Honduras281
5.81Hong Kong281
5.82Hungary282
5.83Iceland282
5.84India283
5.85Indonesia284
5.86Iran285
5.87Iraq285
5.88Ireland286
5.89Israel286
5.90Italy287
5.91Jamaica287
5.92Japan288
5.93Jordan291
5.94Kazakhstan291
5.95Kenya292
5.96Kiribati292
5.97Kuwait292
5.98Kyrgyzstan293
5.99Laos293
5.100Latvia293
5.101Lebanon294
5.102Lesotho294
5.103Liberia294
5.104Libya295
5.105Liechtenstein295
5.106Lithuania295
5.107Luxembourg296
5.108Macau296
5.109Madagascar296
5.110Malawi297
5.111Malaysia297
5.112Maldives298
5.113Mali298
5.114Malta298
5.115Marshall Islands299
5.116Martinique299
5.117Mauritania299
5.118Mauritius300
5.119Mexico301
5.120Micronesia Federation302
5.121Moldova302
5.122Monaco302
5.123Mongolia303
5.124Morocco303
5.125Mozambique304
5.126Namibia304
5.127Nauru304
5.128Nepal305
5.129New Caledonia305
5.130New Zealand306
5.131Nicaragua306
5.132Niger307
5.133Nigeria307
5.134Niue308
5.135Norfolk Island308

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