The 2009-2014 Outlook for Wineries in India
ICON Group International, May 2009, Pages: 321
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 wineries 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 wineries 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 wineries across the states or union territories and cites of India, 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 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 wineries across the states or union territories and cities of India. The smallest cities have few inhabitants. I 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 wineries. So, latent demand in the long-run has a zero intercept. However, I 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, I will now describe the methodology used to create the latent demand estimates for wineries in India. 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 and geographic locations, not just wineries 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, I 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, I 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 “wineries” 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 wineries 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 “wineries” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of wineries, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for wineries is 312130. It is for this definition of wineries that the aggregate latent demand estimates are derived for the states or union territories and cities of India. “Wineries” is specifically defined as follows:
312130
This industry comprises establishments primarily engaged in one or more of the following: (1) growing grapes and manufacturing wine and brandies; (2) manufacturing wine and brandies from grapes and other fruits grown elsewhere; and (3) blending wines and brandies.
3121300
WINE, BRANDY, AND BRANDY SPIRITS
31213001
White grape wines, 14 percent or less
3121300111
White grape wines, 14 percent or less
31213002
Red grape wines, 14 percent or less
3121300221
Red grape wines, 14 percent or less
31213003
Rose grape wines, 14 percent or less
3121300331
Rose grape wines, 14 percent or less
31213004
Other fruit and berry wines, 14 percent or less
3121300441
Other fruit and berry wines, 14 percent or less
31213005
Dessert wines (excluding specialties)
3121300551
Dessert wines (excluding specialties)
31213006
Effervescent wines, including sparkling wines (naturally and artificially carbonated)
3121300661
Effervescent wines, including sparkling wines (naturally and artificially carbonated)
31213007
Wine coolers
3121300771
Wine coolers
31213008
All other wines, brandy, and brandy spirits
3121300881
Vermouth
3121300891
Other specialty wines
31213008A1
Nonalcoholic wine
31213008B1
Beverage brandy, neutral fruit spirits, and neutral brandy, excluding neutral citrus residue brandy
31213008C1
Wine removed from fermenters
31213009
Brandy and spirits removed from receiving tanks
31213009D1
Brandy and spirits removed from receiving tanks
3121300A
All other wines, brandy, and brandy spirits
3121300AB1
Beverage brandy, neutral fruit spirits, and neutral brandy (excluding neutral citrus residue brandy)
3121300AD1
Applejack
3121300AE1
All other wines, brandy, and brandy spirits (including vermouth, nonalcoholic wines, and other specialty wines)
3121308
Wines, brandy, and brandy spirits
31213081
Grape wine with 14 percent or less alcohol content
312130812
White grape wine with 14 percent or less alcohol content
312130814
Red grape wine with 14 percent or less alcohol content
312130816
Rose grape wine with 14 percent or less alcohol content
312130825
Dessert wines
312130831
Effervescent wines
312130841
Non-grape fruit/berry wines, fortified wines (non-dessert), and specialty wines
312130883
Beverage brandy, neutral fruit spirits, and neutral brandy spirits
312130A
Wines
312130M
Miscellaneous receipts
312130P
Primary products
312130S
Secondary products
312130SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 312130 includes the following:
Alcoholic beverages, brandy, distilling
Applejack distilling
Beverages, wines and brandies, manufacturing
Blending brandy
Blending wines
Brandy distilling
Champagne method sparkling wine, manufacturing
Cider, alcoholic, manufacturing
Distilling brandy
Fortified wines manufacturing
Grape farming and making wine
Ice wine
Liquors, brandy, distilling and blending
Nonalcoholic wines manufacturing
Sparkling wines manufacturing
Vermouth manufacturing
Wine coolers manufacturing
Wineries
Wines manufacturing
Wines, cooking, manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of wineries 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 wineries 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 wineries 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 wineries). 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. I 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. Figures are rounded, so minor inconsistencies may exist across tables.
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 14
1.3.3 Step 3. Filling in Missing Values 15
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 16
2 SUMMARY OF FINDINGS 17
2.1 The Latent Demand in India 17
2.2 Top 100 Cities Sorted By Rank 18
2.3 Latent Demand by Year in India 21
3 ANDAMAN & NICOBAR ISLANDS 22
3.1 Latent Demand by Year - Andaman & Nicobar Islands 22
3.2 Cities Sorted by Rank - Andaman & Nicobar Islands 23
3.3 Cities Sorted By District - Andaman & Nicobar Islands 23
4 ANDHRA PRADESH 24
4.1 Latent Demand by Year - Andhra Pradesh 24
4.2 Cities Sorted by Rank - Andhra Pradesh 25
4.3 Cities Sorted By District - Andhra Pradesh 30
5 ARUNACHAL PRADESH 35
5.1 Latent Demand by Year - Arunachal Pradesh 35
5.2 Cities Sorted by Rank - Arunachal Pradesh 36
5.3 Cities Sorted By District - Arunachal Pradesh 36
6 ASSAM 37
6.1 Latent Demand by Year - Assam 37
6.2 Cities Sorted by Rank - Assam 38
6.3 Cities Sorted By District - Assam 41
7 BIHAR 44
7.1 Latent Demand by Year - Bihar 44
7.2 Cities Sorted by Rank - Bihar 45
7.3 Cities Sorted By District - Bihar 48
8 CHANDIGARH 52
8.1 Latent Demand by Year - Chandigarh 52
8.2 Cities Sorted by Rank - Chandigarh 53
8.3 Cities Sorted By District - Chandigarh 53
9 CHHATTISGARH 54
9.1 Latent Demand by Year - Chhattisgarh 54
9.2 Cities Sorted by Rank - Chhattisgarh 55
9.3 Cities Sorted By District - Chhattisgarh 57
10 DADRA & NAGAR HAVELI 60
10.1 Latent Demand by Year - Dadra & Nagar Haveli 60
10.2 Cities Sorted by Rank - Dadra & Nagar Haveli 61
10.3 Cities Sorted By District - Dadra & Nagar Haveli 61
11 DAMAN & DIU 62
11.1 Latent Demand by Year - Daman & Diu 62
11.2 Cities Sorted by Rank - Daman & Diu 63
11.3 Cities Sorted By District - Daman & Diu 63
12 DELHI 64
12.1 Latent Demand by Year - Delhi 64
12.2 Cities Sorted by Rank - Delhi 65
12.3 Cities Sorted By District - Delhi 66
13 GOA 69
13.1 Latent Demand by Year - Goa 69
13.2 Cities Sorted by Rank - Goa 70
13.3 Cities Sorted By District - Goa 71
14 GUJARAT 72
14.1 Latent Demand by Year - Gujarat 72
14.2 Cities Sorted by Rank - Gujarat 73
14.3 Cities Sorted By District - Gujarat 78
15 HARYANA 85
15.1 Latent Demand by Year - Haryana 85
15.2 Cities Sorted by Rank - Haryana 86
15.3 Cities Sorted By District - Haryana 88
16 HIMACHAL PRADESH 92
16.1 Latent Demand by Year - Himachal Pradesh 92
16.2 Cities Sorted by Rank - Himachal Pradesh 93
16.3 Cities Sorted By District - Himachal Pradesh 94
17 JAMMU & KASHMIR 96
17.1 Latent Demand by Year - Jammu & Kashmir 96
17.2 Cities Sorted by Rank - Jammu & Kashmir 97
17.3 Cities Sorted By District - Jammu & Kashmir 99
18 JHARKHAND 101
18.1 Latent Demand by Year - Jharkhand 101
18.2 Cities Sorted by Rank - Jharkhand 102
18.3 Cities Sorted By District - Jharkhand 106
19 KARNATAKA 110
19.1 Latent Demand by Year - Karnataka 110
19.2 Cities Sorted by Rank - Karnataka 111
19.3 Cities Sorted By District - Karnataka 117
20 KERALA 125
20.1 Latent Demand by Year - Kerala 125
20.2 Cities Sorted by Rank - Kerala 126
20.3 Cities Sorted By District - Kerala 130
21 LAKSHADWEEP 134
21.1 Latent Demand by Year - Lakshadweep 134
21.2 Cities Sorted by Rank - Lakshadweep 135
21.3 Cities Sorted By District - Lakshadweep 135
22 MADHYA PRADESH 136
22.1 Latent Demand by Year - Madhya Pradesh 136
22.2 Cities Sorted by Rank - Madhya Pradesh 137
22.3 Cities Sorted By District - Madhya Pradesh 146
23 MAHARASHTRA 156
23.1 Latent Demand by Year - Maharashtra 156
23.2 Cities Sorted by Rank - Maharashtra 157
23.3 Cities Sorted By District - Maharashtra 166
24 MANIPUR 175
24.1 Latent Demand by Year - Manipur 175
24.2 Cities Sorted by Rank - Manipur 176
24.3 Cities Sorted By District - Manipur 177
25 MEGHALAYA 178
25.1 Latent Demand by Year - Meghalaya 178
25.2 Cities Sorted by Rank - Meghalaya 179
25.3 Cities Sorted By District - Meghalaya 179
26 MIZORAM 180
26.1 Latent Demand by Year - Mizoram 180
26.2 Cities Sorted by Rank - Mizoram 181
26.3 Cities Sorted By District - Mizoram 181
27 NAGALAND 183
27.1 Latent Demand by Year - Nagaland 183
27.2 Cities Sorted by Rank - Nagaland 184
27.3 Cities Sorted By District - Nagaland 184
28 ORISSA 185
28.1 Latent Demand by Year - Orissa 185
28.2 Cities Sorted by Rank - Orissa 186
28.3 Cities Sorted By District - Orissa 189
29 PONDICHERRY 193
29.1 Latent Demand by Year - Pondicherry 193
29.2 Cities Sorted by Rank - Pondicherry 194
29.3 Cities Sorted By District - Pondicherry 194
30 PUNJAB 195
30.1 Latent Demand by Year - Punjab 195
30.2 Cities Sorted by Rank - Punjab 196
30.3 Cities Sorted By District - Punjab 200
31 RAJASTHAN 204
31.1 Latent Demand by Year - Rajasthan 204
31.2 Cities Sorted by Rank - Rajasthan 205
31.3 Cities Sorted By District - Rajasthan 210
32 SIKKIM 216
32.1 Latent Demand by Year - Sikkim 216
32.2 Cities Sorted by Rank - Sikkim 217
32.3 Cities Sorted By District - Sikkim 217
33 TAMIL NADU 218
33.1 Latent Demand by Year - Tamil Nadu 218
33.2 Cities Sorted by Rank - Tamil Nadu 219
33.3 Cities Sorted By District - Tamil Nadu 238
34 TRIPURA 259
34.1 Latent Demand by Year - Tripura 259
34.2 Cities Sorted by Rank - Tripura 260
34.3 Cities Sorted By District - Tripura 260
35 UTTAR PRADESH 262
35.1 Latent Demand by Year - Uttar Pradesh 262
35.2 Cities Sorted by Rank - Uttar Pradesh 263
35.3 Cities Sorted By District - Uttar Pradesh 279
36 UTTARANCHAL 296
36.1 Latent Demand by Year - Uttaranchal 296
36.2 Cities Sorted by Rank - Uttaranchal 297
36.3 Cities Sorted By District - Uttaranchal 299
37 WEST BENGAL 301
37.1 Latent Demand by Year - West Bengal 301
37.2 Cities Sorted by Rank - West Bengal 302
37.3 Cities Sorted By District - West Bengal 311
38 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 320
38.1 Disclaimers & Safe Harbor 320
38.2 ICON Group International, Inc. User Agreement Provisions 321
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