The 2007-2012 Outlook for Wood Counters for Offices and Stores Excluding Custom Counters in India
ICON Group International, September 2006, Pages: 229
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 India 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 wood counters for offices and stores excluding custom counters in India 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 wood counters for offices and stores excluding custom counters 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 wood counters for offices and stores excluding custom counters across the states or union territories and cites of India, 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 state or union territory, 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 wood counters for offices and stores excluding custom counters across the states or union territories and cities of India. 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 states or union territories having wealth; current income dominates the latent demand for wood counters for offices and stores excluding custom counters. 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 wood counters for offices and stores excluding custom counters in India. 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 wood counters for offices and stores excluding custom counters in India.
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 “wood counters for offices and stores excluding custom counters” 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 wood counters for offices and stores excluding custom counters 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 states or union territories and cities in India (without needing to know the specific parts that went into the whole in the first place).
Given this caveat, this study covers “wood counters for offices and stores excluding custom counters” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of wood counters for offices and stores excluding custom counters, please see below. The NAICS code for wood counters for offices and stores excluding custom counters is 3372155164. It is for this definition of wood counters for offices and stores excluding custom counters that the aggregate latent demand estimates are derived for the states or union territories and cities of India.
Step 2. Filtering and Smoothing
Based on the aggregate view of wood counters for offices and stores excluding custom counters 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 state or union territory 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, state or union territory and city-level income. Based on the overriding philosophy of a long-run consumption function (defined earlier), states or union territories 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 states or union territories or cities). This assumption applies along the aggregate consumption function, but also over time (i.e., not all states or union territories or cities in India are perceived to have the same income growth prospects over time). Another way of looking at this is to say that latent demand for wood counters for offices and stores excluding custom counters is more likely to be similar across states or union territories 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 state’s, union territory’s or city’s contribution to latent demand in India 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 India has more than 5,000 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 state or union territory has no current income, the latent demand for wood counters for offices and stores excluding custom counters 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 wood counters for offices and stores excluding custom counters). 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 India. These are then aggregated to get state or union territory 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 state or union territory and the extent to which a city might be used as a point of distribution within its state or union territory. 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 state or union territory. Not all cities (e.g. the smaller towns) are estimated within each state or union territory as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same state or union territory, 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 is used.
1 INTRODUCTION 9
1.1 Overview 9
1.2 What is Latent Demand and the P.I.E.? 9
1.3 The Methodology 10
1.3.1 Step 1. Product Definition and Data Collection 11
1.3.2 Step 2. Filtering and Smoothing 12
1.3.3 Step 3. Filling in Missing Values 12
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 13
1.3.5 Step 5. Fixed-Parameter Linear Estimation 13
1.3.6 Step 6. Aggregation and Benchmarking 13
2 SUMMARY OF FINDINGS 15
2.1 The Latent Demand in India 15
2.2 Top 100 Cities Sorted By Rank 16
2.3 Latent Demand by Year in India 18
3 ANDAMAN & NICOBAR ISLANDS 19
3.1 Latent Demand by Year - Andaman & Nicobar Islands 19
3.2 Cities Sorted by Rank - Andaman & Nicobar Islands 20
3.3 Cities Sorted By District - Andaman & Nicobar Islands 20
4 ANDHRA PRADESH 21
4.1 Latent Demand by Year - Andhra Pradesh 21
4.2 Cities Sorted by Rank - Andhra Pradesh 22
4.3 Cities Sorted By District - Andhra Pradesh 26
5 ARUNACHAL PRADESH 31
5.1 Latent Demand by Year - Arunachal Pradesh 31
5.2 Cities Sorted by Rank - Arunachal Pradesh 32
5.3 Cities Sorted By District - Arunachal Pradesh 32
6 ASSAM 33
6.1 Latent Demand by Year - Assam 33
6.2 Cities Sorted by Rank - Assam 34
6.3 Cities Sorted By District - Assam 36
7 BIHAR 38
7.1 Latent Demand by Year - Bihar 38
7.2 Cities Sorted by Rank - Bihar 39
7.3 Cities Sorted By District - Bihar 41
8 CHANDIGARH 44
8.1 Latent Demand by Year - Chandigarh 44
8.2 Cities Sorted by Rank - Chandigarh 45
8.3 Cities Sorted By District - Chandigarh 45
9 CHHATTISGARH 46
9.1 Latent Demand by Year - Chhattisgarh 46
9.2 Cities Sorted by Rank - Chhattisgarh 47
9.3 Cities Sorted By District - Chhattisgarh 49
10 DADRA & NAGAR HAVELI 51
10.1 Latent Demand by Year - Dadra & Nagar Haveli 51
10.2 Cities Sorted by Rank - Dadra & Nagar Haveli 52
10.3 Cities Sorted By District - Dadra & Nagar Haveli 52
11 DAMAN & DIU 53
11.1 Latent Demand by Year - Daman & Diu 53
11.2 Cities Sorted by Rank - Daman & Diu 54
11.3 Cities Sorted By District - Daman & Diu 54
12 DELHI 55
12.1 Latent Demand by Year - Delhi 55
12.2 Cities Sorted by Rank - Delhi 56
12.3 Cities Sorted By District - Delhi 57
13 GOA 59
13.1 Latent Demand by Year - Goa 59
13.2 Cities Sorted by Rank - Goa 60
13.3 Cities Sorted By District - Goa 61
14 GUJARAT 62
14.1 Latent Demand by Year - Gujarat 62
14.2 Cities Sorted by Rank - Gujarat 63
14.3 Cities Sorted By District - Gujarat 68
15 HARYANA 73
15.1 Latent Demand by Year - Haryana 73
15.2 Cities Sorted by Rank - Haryana 74
15.3 Cities Sorted By District - Haryana 76
16 HIMACHAL PRADESH 79
16.1 Latent Demand by Year - Himachal Pradesh 79
16.2 Cities Sorted by Rank - Himachal Pradesh 80
16.3 Cities Sorted By District - Himachal Pradesh 81
17 JAMMU & KASHMIR 83
17.1 Latent Demand by Year - Jammu & Kashmir 83
17.2 Cities Sorted by Rank - Jammu & Kashmir 84
17.3 Cities Sorted By District - Jammu & Kashmir 85
18 JHARKHAND 86
18.1 Latent Demand by Year - Jharkhand 86
18.2 Cities Sorted by Rank - Jharkhand 87
18.3 Cities Sorted By District - Jharkhand 89
19 KARNATAKA 91
19.1 Latent Demand by Year - Karnataka 91
19.2 Cities Sorted by Rank - Karnataka 92
19.3 Cities Sorted By District - Karnataka 96
20 KERALA 101
20.1 Latent Demand by Year - Kerala 101
20.2 Cities Sorted by Rank - Kerala 102
20.3 Cities Sorted By District - Kerala 105
21 LAKSHADWEEP 109
21.1 Latent Demand by Year - Lakshadweep 109
21.2 Cities Sorted by Rank - Lakshadweep 110
21.3 Cities Sorted By District - Lakshadweep 110
22 MADHYA PRADESH 111
22.1 Latent Demand by Year - Madhya Pradesh 111
22.2 Cities Sorted by Rank - Madhya Pradesh 112
22.3 Cities Sorted By District - Madhya Pradesh 117
23 MAHARASHTRA 123
23.1 Latent Demand by Year - Maharashtra 123
23.2 Cities Sorted by Rank - Maharashtra 124
23.3 Cities Sorted By District - Maharashtra 130
24 MANIPUR 136
24.1 Latent Demand by Year - Manipur 136
24.2 Cities Sorted by Rank - Manipur 137
24.3 Cities Sorted By District - Manipur 137
25 MEGHALAYA 138
25.1 Latent Demand by Year - Meghalaya 138
25.2 Cities Sorted by Rank - Meghalaya 139
25.3 Cities Sorted By District - Meghalaya 140
26 MIZORAM 141
26.1 Latent Demand by Year - Mizoram 141
26.2 Cities Sorted by Rank - Mizoram 142
26.3 Cities Sorted By District - Mizoram 142
27 NAGALAND 143
27.1 Latent Demand by Year - Nagaland 143
27.2 Cities Sorted by Rank - Nagaland 144
27.3 Cities Sorted By District - Nagaland 144
28 ORISSA 145
28.1 Latent Demand by Year - Orissa 145
28.2 Cities Sorted by Rank - Orissa 146
28.3 Cities Sorted By District - Orissa 149
29 PONDICHERRY 152
29.1 Latent Demand by Year - Pondicherry 152
29.2 Cities Sorted by Rank - Pondicherry 153
29.3 Cities Sorted By District - Pondicherry 153
30 PUNJAB 154
30.1 Latent Demand by Year - Punjab 154
30.2 Cities Sorted by Rank - Punjab 155
30.3 Cities Sorted By District - Punjab 158
31 RAJASTHAN 161
31.1 Latent Demand by Year - Rajasthan 161
31.2 Cities Sorted by Rank - Rajasthan 162
31.3 Cities Sorted By District - Rajasthan 166
32 SIKKIM 171
32.1 Latent Demand by Year - Sikkim 171
32.2 Cities Sorted by Rank - Sikkim 172
32.3 Cities Sorted By District - Sikkim 172
33 TAMIL NADU 173
33.1 Latent Demand by Year - Tamil Nadu 173
33.2 Cities Sorted by Rank - Tamil Nadu 174
33.3 Cities Sorted By District - Tamil Nadu 183
34 TRIPURA 192
34.1 Latent Demand by Year - Tripura 192
34.2 Cities Sorted by Rank - Tripura 193
34.3 Cities Sorted By District - Tripura 194
35 UTTAR PRADESH 195
35.1 Latent Demand by Year - Uttar Pradesh 195
35.2 Cities Sorted by Rank - Uttar Pradesh 196
35.3 Cities Sorted By District - Uttar Pradesh 205
36 UTTARANCHAL 214
36.1 Latent Demand by Year - Uttaranchal 214
36.2 Cities Sorted by Rank - Uttaranchal 215
36.3 Cities Sorted By District - Uttaranchal 216
37 WEST BENGAL 218
37.1 Latent Demand by Year - West Bengal 218
37.2 Cities Sorted by Rank - West Bengal 219
37.3 Cities Sorted By District - West Bengal 223
38 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 228
38.1 Disclaimers & Safe Harbor 228
38.2 User Agreement Provisions 229
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