The 2007-2012 Outlook for Outdoor Lighting Equipment in Japan
ICON Group International, September 2006, Pages: 143
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 Japan 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 outdoor lighting equipment in Japan is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be 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 market.
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). If inflation rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (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 in the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. In 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 latent demand for outdoor lighting equipment at the aggregate level. Product and service offerings, 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 outdoor lighting equipment across the prefectures and cites of Japan, we used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, we 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 prefecture, city, 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.
Ignoring, for the moment, exogenous shocks and variations in utility across geographies, 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). This type of consumption function is shown as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant.
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 with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related is interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for outdoor lighting equipment across the prefectures and cities of Japan. The smallest cities have few inhabitants. we assume that all of these cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these prefectures having wealth; current income dominates the latent demand for outdoor lighting equipment. So, latent demand in the long-run has a zero intercept. However, we allow 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, we will now describe the methodology used to create the latent demand estimates for outdoor lighting equipment in Japan. Since this methodology has been applied to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories and geographic locations, not just outdoor lighting equipment in Japan.
Step 1. Product Definition and Data Collection
Any study of latent demand requires that some standard be established to define “efficiently served”. Having implemented various alternatives and matched these with market outcomes, we have found that the optimal approach is to assume that certain key indicators are more likely to reflect efficiency than others. These indicators 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, we have found the assumption that the highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e. some cities have 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).
Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Official Chinese Agencies, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, and the World Bank). Depending on original data sources used, the definition of “outdoor lighting equipment” 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 outdoor lighting equipment 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 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 prefectures and cities in Japan (without needing to know the specific parts that went into the whole in the first place).
Given this caveat, this study covers “outdoor lighting equipment” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of outdoor lighting equipment, please see below. The NAICS code for outdoor lighting equipment is 3351291. It is for this definition of outdoor lighting equipment that the aggregate latent demand estimates are derived for the prefectures and cities of Japan. “Outdoor lighting equipment” is specifically defined as follows:
3351291
Outdoor lighting equipment (including parts and accessories)
33512910
Outdoor lighting equipment (including parts and accessories)
3351291000
Outdoor lighting equipment (including parts and accessories)
3351291002
Incandescent (filament and quartz iodine) street and highway lighting (including bridge and tunnel lighting)
3351291004
Open high intensity discharge (including low pressure sodium and integrally mounted and remote ballasts) street and highway lighting (including bridge and tunnel lighting)
3351291006
Enclosed high intensity discharge (including low pressure sodium and integrally mounted and remote ballasts) street and highway lighting (including bridge and tunnel lighting)
3351291008
Fluorescent street and highway lighting (including bridge and tunnel lighting)
3351291011
Special purpose luminaries for highmast, sign lighting, and expressway fixtures (excluding value of poles)
3351291012
General purpose incandescent filament floodlighting
3351291014
General purpose incandescent quartz iodine floodlighting
3351291016
General purpose high intensity discharge floodlighting (including low presure sodium and integrally mounted and remote ballasts)
3351291018
General purpose fluorescent floodlighting
3351291021
High intensity discharge sportslighting
3351291022
High intensity discharge site lighting (under 20 foot mounting)
3351291024
High intensity discharge bollards
3351291026
High intensity discharge post~top lighting
3351291028
High intensity discharge large area lighting (20 to 60 foot mounting)
3351291031
High intensity discharge, incandescent, and quartz wall packs
3351291032
Outdoor PAR lampholders
3351291034
Spotlights (including indoor and stage)
3351291036
All other outdoor lighting equipment (such as underwater fountain and pool lighting)
3351291038
Runway approach lighting (including fixtures, regulators, insulating transformers, isolating lamp transformers, beacons, wind tees, and cones)
3351291041
Runway (except runway approach), taxiway, and ramp light (including fixtures, regulators, and isolating lamp transformers)
3351291042
Components and renewal parts for outdoor lighting equipment sold separately
3351291044
Steel and cast iron poles for street and highway lighting
3351291046
Aluminum poles for street and highway lighting
3351291048
Concrete poles for street and highway lighting
3351291051
All other street and highway lighting (including fiberglass and wood)
3351291052
Steel and cast iron area lighting poles for sports and other off~street use (60 foot and over)
3351291054
Steel and cast iron area lighting poles for sports and other off~street use (under 60 foot)
3351291056
Aluminum poles for area lighting for sports and other off~street use
3351291058
All other area lighting for sports and other off~street use (including concrete and wood)
33512911
Outdoor lighting equipment (including parts and accessories)
3351291100
Outdoor lighting equipment (including parts and accessories)
Step 2. Filtering and Smoothing
Based on the aggregate view of outdoor lighting equipment as defined above, data were then collected for as many geographic locations as possible for that same definition, at the same level of the value chain. This generates a convenience sample of indicators 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 geographic region, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a prefecture or city 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 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, prefecture and city-level income. Based on the overriding philosophy of a long-run consumption function (defined earlier), prefectures and cities which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that geographic entity.
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 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 prefectures or cities). This assumption applies along the aggregate consumption function, but also over time (i.e., not all prefectures or cities in Japan are perceived to have the same income growth prospects over time). Another way of looking at this is to say that latent demand for outdoor lighting equipment is more likely to be similar across prefectures or cities that have similar characteristics in terms of economic development.
This approach is useful across geographic regions for which some notion of non-linearity exists in the aggregate cross-region consumption function. For some categories, however, the reader must realize that the numbers will reflect a prefecture’s or city’s contribution to latent demand in Japan and may never be realized in the form of local sales.
Step 5. Fixed-Parameter Linear Estimation
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because Japan consists of more than 1,000 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 prefecture has no current income, the latent demand for outdoor lighting equipment 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 outdoor lighting equipment). 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, a low-income city is assumed to have a latent demand proportional to its income, based on the cities closest to it on the aggregate consumption function.
Step 6. Aggregation and Benchmarking
Based on the models described above, latent demand figures are estimated for all major cities in Japan. These are then aggregated to get prefecture totals. This report considers a city as a part of the regional and national market. The purpose is to understand the density of demand within a prefecture and the extent to which a city might be used as a point of distribution within its prefecture. 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. we allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its prefecture. Not all cities (e.g. the smaller towns) are estimated within each prefecture as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same prefecture, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city vis-à-vis others
1 INTRODUCTION 10
1.1 Overview 10
1.2 What is Latent Demand and the P.I.E.? 10
1.3 The Methodology 11
1.3.1 Step 1. Product Definition and Data Collection 12
1.3.2 Step 2. Filtering and Smoothing 14
1.3.3 Step 3. Filling in Missing Values 14
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 15
1.3.5 Step 5. Fixed-Parameter Linear Estimation 15
1.3.6 Step 6. Aggregation and Benchmarking 15
2 SUMMARY OF FINDINGS 17
2.1 The Latent Demand in Japan 17
2.2 Top 100 Cities Sorted by Rank 19
3 AICHI 22
3.1 Latent Demand by Year - Aichi 22
3.2 Cities Sorted by Rank - Aichi 23
3.3 Cities Sorted Alphabetically - Aichi 24
4 AKITA 26
4.1 Latent Demand by Year - Akita 26
4.2 Cities Sorted by Rank - Akita 27
4.3 Cities Sorted Alphabetically - Akita 27
5 AOMORI 28
5.1 Latent Demand by Year - Aomori 28
5.2 Cities Sorted by Rank - Aomori 29
5.3 Cities Sorted Alphabetically - Aomori 29
6 CHIBA 30
6.1 Latent Demand by Year - Chiba 30
6.2 Cities Sorted by Rank - Chiba 31
6.3 Cities Sorted Alphabetically - Chiba 32
7 EHIME 33
7.1 Latent Demand by Year - Ehime 33
7.2 Cities Sorted by Rank - Ehime 34
7.3 Cities Sorted Alphabetically - Ehime 34
8 FUKUI 35
8.1 Latent Demand by Year - Fukui 35
8.2 Cities Sorted by Rank - Fukui 36
8.3 Cities Sorted Alphabetically - Fukui 36
9 FUKUOKA 37
9.1 Latent Demand by Year - Fukuoka 37
9.2 Cities Sorted by Rank - Fukuoka 38
9.3 Cities Sorted Alphabetically - Fukuoka 39
10 FUKUSHIMA 40
10.1 Latent Demand by Year - Fukushima 40
10.2 Cities Sorted by Rank - Fukushima 41
10.3 Cities Sorted Alphabetically - Fukushima 41
11 GIFU 42
11.1 Latent Demand by Year - Gifu 42
11.2 Cities Sorted by Rank - Gifu 43
11.3 Cities Sorted Alphabetically - Gifu 44
12 GUMMA 45
12.1 Latent Demand by Year - Gumma 45
12.2 Cities Sorted by Rank - Gumma 46
12.3 Cities Sorted Alphabetically - Gumma 46
13 HIROSHIMA 48
13.1 Latent Demand by Year - Hiroshima 48
13.2 Cities Sorted by Rank - Hiroshima 49
13.3 Cities Sorted Alphabetically - Hiroshima 49
14 HOKKAIDO 51
14.1 Latent Demand by Year - Hokkaido 51
14.2 Cities Sorted by Rank - Hokkaido 52
14.3 Cities Sorted Alphabetically - Hokkaido 53
15 HYOGO 55
15.1 Latent Demand by Year - Hyogo 55
15.2 Cities Sorted by Rank - Hyogo 56
15.3 Cities Sorted Alphabetically - Hyogo 57
16 IBARAKI 58
16.1 Latent Demand by Year - Ibaraki 58
16.2 Cities Sorted by Rank - Ibaraki 59
16.3 Cities Sorted Alphabetically - Ibaraki 60
17 ISHIKAWA 61
17.1 Latent Demand by Year - Ishikawa 61
17.2 Cities Sorted by Rank - Ishikawa 62
17.3 Cities Sorted Alphabetically - Ishikawa 62
18 IWATE 63
18.1 Latent Demand by Year - Iwate 63
18.2 Cities Sorted by Rank - Iwate 64
18.3 Cities Sorted Alphabetically - Iwate 64
19 KAGAWA 66
19.1 Latent Demand by Year - Kagawa 66
19.2 Cities Sorted by Rank - Kagawa 67
19.3 Cities Sorted Alphabetically - Kagawa 67
20 KAGOSHIMA 68
20.1 Latent Demand by Year - Kagoshima 68
20.2 Cities Sorted by Rank - Kagoshima 69
20.3 Cities Sorted Alphabetically - Kagoshima 69
21 KANAGAWA 71
21.1 Latent Demand by Year - Kanagawa 71
21.2 Cities Sorted by Rank - Kanagawa 72
21.3 Cities Sorted Alphabetically - Kanagawa 73
22 KOCHI 74
22.1 Latent Demand by Year - Kochi 74
22.2 Cities Sorted by Rank - Kochi 75
22.3 Cities Sorted Alphabetically - Kochi 75
23 KUMAMOTO 76
23.1 Latent Demand by Year - Kumamoto 76
23.2 Cities Sorted by Rank - Kumamoto 77
23.3 Cities Sorted Alphabetically - Kumamoto 77
24 KYOTO 79
24.1 Latent Demand by Year - Kyoto 79
24.2 Cities Sorted by Rank - Kyoto 80
24.3 Cities Sorted Alphabetically - Kyoto 80
25 MIE 81
25.1 Latent Demand by Year - Mie 81
25.2 Cities Sorted by Rank - Mie 82
25.3 Cities Sorted Alphabetically - Mie 82
26 MIYAGI 84
26.1 Latent Demand by Year - Miyagi 84
26.2 Cities Sorted by Rank - Miyagi 85
26.3 Cities Sorted Alphabetically - Miyagi 85
27 MIYAZAKI 87
27.1 Latent Demand by Year - Miyazaki 87
27.2 Cities Sorted by Rank - Miyazaki 88
27.3 Cities Sorted Alphabetically - Miyazaki 88
28 NAGANO 89
28.1 Latent Demand by Year - Nagano 89
28.2 Cities Sorted by Rank - Nagano 90
28.3 Cities Sorted Alphabetically - Nagano 91
29 NAGASAKI 92
29.1 Latent Demand by Year - Nagasaki 92
29.2 Cities Sorted by Rank - Nagasaki 93
29.3 Cities Sorted Alphabetically - Nagasaki 93
30 NARA 94
30.1 Latent Demand by Year - Nara 94
30.2 Cities Sorted by Rank - Nara 95
30.3 Cities Sorted Alphabetically - Nara 95
31 NIIGATA 97
31.1 Latent Demand by Year - Niigata 97
31.2 Cities Sorted by Rank - Niigata 98
31.3 Cities Sorted Alphabetically - Niigata 99
32 OITA 100
32.1 Latent Demand by Year - Oita 100
32.2 Cities Sorted by Rank - Oita 101
32.3 Cities Sorted Alphabetically - Oita 101
33 OKAYAMA 102
33.1 Latent Demand by Year - Okayama 102
33.2 Cities Sorted by Rank - Okayama 103
33.3 Cities Sorted Alphabetically - Okayama 103
34 OKINAWA 104
34.1 Latent Demand by Year - Okinawa 104
34.2 Cities Sorted by Rank - Okinawa 105
34.3 Cities Sorted Alphabetically - Okinawa 105
35 OSAKA 106
35.1 Latent Demand by Year - Osaka 106
35.2 Cities Sorted by Rank - Osaka 107
35.3 Cities Sorted Alphabetically - Osaka 108
36 SAGA 109
36.1 Latent Demand by Year - Saga 109
36.2 Cities Sorted by Rank - Saga 110
36.3 Cities Sorted Alphabetically - Saga 110
37 SAITAMA 111
37.1 Latent Demand by Year - Saitama 111
37.2 Cities Sorted by Rank - Saitama 112
37.3 Cities Sorted Alphabetically - Saitama 113
38 SHIGA 115
38.1 Latent Demand by Year - Shiga 115
38.2 Cities Sorted by Rank - Shiga 116
38.3 Cities Sorted Alphabetically - Shiga 116
39 SHIMANE 117
39.1 Latent Demand by Year - Shimane 117
39.2 Cities Sorted by Rank - Shimane 118
39.3 Cities Sorted Alphabetically - Shimane 118
40 SHIZUOKA 119
40.1 Latent Demand by Year - Shizuoka 119
40.2 Cities Sorted by Rank - Shizuoka 120
40.3 Cities Sorted Alphabetically - Shizuoka 121
41 TOCHIGI 122
41.1 Latent Demand by Year - Tochigi 122
41.2 Cities Sorted by Rank - Tochigi 123
41.3 Cities Sorted Alphabetically - Tochigi 124
42 TOKUSHIMA 125
42.1 Latent Demand by Year - Tokushima 125
42.2 Cities Sorted by Rank - Tokushima 126
42.3 Cities Sorted Alphabetically - Tokushima 126
43 TOKYO 127
43.1 Latent Demand by Year - Tokyo 127
43.2 Cities Sorted by Rank - Tokyo 128
43.3 Cities Sorted Alphabetically - Tokyo 129
44 TOTTORI 130
44.1 Latent Demand by Year - Tottori 130
44.2 Cities Sorted by Rank - Tottori 131
44.3 Cities Sorted Alphabetically - Tottori 131
45 TOYAMA 132
45.1 Latent Demand by Year - Toyama 132
45.2 Cities Sorted by Rank - Toyama 133
45.3 Cities Sorted Alphabetically - Toyama 133
46 WAKAYAMA 134
46.1 Latent Demand by Year - Wakayama 134
46.2 Cities Sorted by Rank - Wakayama 135
46.3 Cities Sorted Alphabetically - Wakayama 135
47 YAMAGATA 136
47.1 Latent Demand by Year - Yamagata 136
47.2 Cities Sorted by Rank - Yamagata 137
47.3 Cities Sorted Alphabetically - Yamagata 137
48 YAMAGUCHI 138
48.1 Latent Demand by Year - Yamaguchi 138
48.2 Cities Sorted by Rank - Yamaguchi 139
48.3 Cities Sorted Alphabetically - Yamaguchi 139
49 YAMANASHI 140
49.1 Latent Demand by Year - Yamanashi 140
49.2 Cities Sorted by Rank - Yamanashi 141
49.3 Cities Sorted Alphabetically - Yamanashi 141
50 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 142
50.1 Disclaimers & Safe Harbor 142
50.2 User Agreement Provisions 143
Customers who bought this item also bought
All rights reserved. © Copyright 2013 Research and Markets WWW5
Terms and Conditions Privacy Policy Publishers Employment Opportunities Site Map Link to us Webmaster Affiliate Network
