The 2011-2016 Outlook for Ornamental
and Architectural Plaster Moldings in India
by
Professor Philip M. Parker, Ph.D.
Chaired Professor of Management Science
INSEAD (Singapore and Fontainebleau, France)
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About the Author
Dr. Philip M. Parker is the Chaired Professor of Management Science at INSEAD where he has taught courses on
global competitive strategy since 1988. He has also taught courses at MIT, Stanford University, Harvard University,
UCLA, UCSD, and the Hong Kong University of Science and Technology. Professor Parker is the author of six
books on the economic convergence of nations. These books introduce the notion of “physioeconomics” which
foresees a lack of global convergence in economic behaviors due to physiological and physiographic forces. His
latest book is "Physioeconomics: The Basis for Long-Run Economic Growth" (MIT Press 2000). He has also
published numerous articles in academic journals, including, the Rand Journal of Economics, Marketing Science, the
Journal of International Business Studies, Technological Forecasting and Social Change, the International Journal
of Forecasting, the European Management Journal, the European Journal of Operational Research, the Journal of
Marketing, the International Journal of Research in Marketing, and the Journal of Marketing Research. He is also
on the editorial boards of several academic journals.
Dr. Parker received his Ph.D. in Business Economics from the Wharton School of the University of Pennsylvania
and has Masters degrees in Finance and Banking (University of Aix-Marseille) and Managerial Economics
(Wharton). His undergraduate degrees are in mathematics, biology and economics (minor in aeronautical
engineering). He has consulted and/or taught courses in Africa, the Middle East, Asia, Latin America, North America
and Europe.
About this Series
The estimates given in this report were created using a methodology developed by and implemented under the direct
supervision of Professor Philip M. Parker, the Chaired Professor of Management Science, at INSEAD. The
methodology relies on historical figures across states or union territories. Reported figures should be seen as
estimates of past and future levels of latent demand.
Acknowledgements
Some of the methodologies and research approaches used in this report have benefited from the R&D Committee at
INSEAD, whose research support is gratefully acknowledged.
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Contents v
Table of Contents
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 19
3 ANDAMAN & NICOBAR ISLANDS 20
3.1 Latent Demand by Year - Andaman & Nicobar Islands 20
3.2 Cities Sorted by Rank - Andaman & Nicobar Islands 21
3.3 Cities Sorted By District - Andaman & Nicobar Islands 21
4 ANDHRA PRADESH 22
4.1 Latent Demand by Year - Andhra Pradesh 22
4.2 Cities Sorted by Rank - Andhra Pradesh 23
4.3 Cities Sorted By District - Andhra Pradesh 27
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 35
7 BIHAR 37
7.1 Latent Demand by Year - Bihar 37
7.2 Cities Sorted by Rank - Bihar 38
7.3 Cities Sorted By District - Bihar 40
8 CHANDIGARH 42
8.1 Latent Demand by Year - Chandigarh 42
8.2 Cities Sorted by Rank - Chandigarh 43
8.3 Cities Sorted By District - Chandigarh 43
9 CHHATTISGARH 44
9.1 Latent Demand by Year - Chhattisgarh 44
9.2 Cities Sorted by Rank - Chhattisgarh 45
9.3 Cities Sorted By District - Chhattisgarh 47
10 DADRA & NAGAR HAVELI 49
10.1 Latent Demand by Year - Dadra & Nagar Haveli 49
10.2 Cities Sorted by Rank - Dadra & Nagar Haveli 50
10.3 Cities Sorted By District - Dadra & Nagar Haveli 50
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11 DAMAN & DIU 51
11.1 Latent Demand by Year - Daman & Diu 51
11.2 Cities Sorted by Rank - Daman & Diu 52
11.3 Cities Sorted By District - Daman & Diu 52
12 DELHI 53
12.1 Latent Demand by Year - Delhi 53
12.2 Cities Sorted by Rank - Delhi 54
12.3 Cities Sorted By District - Delhi 55
13 GOA 56
13.1 Latent Demand by Year - Goa 56
13.2 Cities Sorted by Rank - Goa 57
13.3 Cities Sorted By District - Goa 57
14 GUJARAT 59
14.1 Latent Demand by Year - Gujarat 59
14.2 Cities Sorted by Rank - Gujarat 60
14.3 Cities Sorted By District - Gujarat 64
15 HARYANA 69
15.1 Latent Demand by Year - Haryana 69
15.2 Cities Sorted by Rank - Haryana 70
15.3 Cities Sorted By District - Haryana 72
16 HIMACHAL PRADESH 74
16.1 Latent Demand by Year - Himachal Pradesh 74
16.2 Cities Sorted by Rank - Himachal Pradesh 75
16.3 Cities Sorted By District - Himachal Pradesh 76
17 JAMMU & KASHMIR 77
17.1 Latent Demand by Year - Jammu & Kashmir 77
17.2 Cities Sorted by Rank - Jammu & Kashmir 78
17.3 Cities Sorted By District - Jammu & Kashmir 78
18 JHARKHAND 79
18.1 Latent Demand by Year - Jharkhand 79
18.2 Cities Sorted by Rank - Jharkhand 80
18.3 Cities Sorted By District - Jharkhand 81
19 KARNATAKA 83
19.1 Latent Demand by Year - Karnataka 83
19.2 Cities Sorted by Rank - Karnataka 84
19.3 Cities Sorted By District - Karnataka 88
20 KERALA 92
20.1 Latent Demand by Year - Kerala 92
20.2 Cities Sorted by Rank - Kerala 93
20.3 Cities Sorted By District - Kerala 96
21 LAKSHADWEEP 100
21.1 Latent Demand by Year - Lakshadweep 100
21.2 Cities Sorted by Rank - Lakshadweep 101
21.3 Cities Sorted By District - Lakshadweep 101
22 MADHYA PRADESH 102
22.1 Latent Demand by Year - Madhya Pradesh 102
22.2 Cities Sorted by Rank - Madhya Pradesh 103
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22.3 Cities Sorted By District - Madhya Pradesh 107
23 MAHARASHTRA 112
23.1 Latent Demand by Year - Maharashtra 112
23.2 Cities Sorted by Rank - Maharashtra 113
23.3 Cities Sorted By District - Maharashtra 118
24 MANIPUR 125
24.1 Latent Demand by Year - Manipur 125
24.2 Cities Sorted by Rank - Manipur 126
24.3 Cities Sorted By District - Manipur 126
25 MEGHALAYA 127
25.1 Latent Demand by Year - Meghalaya 127
25.2 Cities Sorted by Rank - Meghalaya 128
25.3 Cities Sorted By District - Meghalaya 128
26 MIZORAM 129
26.1 Latent Demand by Year - Mizoram 129
26.2 Cities Sorted by Rank - Mizoram 130
26.3 Cities Sorted By District - Mizoram 130
27 NAGALAND 131
27.1 Latent Demand by Year - Nagaland 131
27.2 Cities Sorted by Rank - Nagaland 132
27.3 Cities Sorted By District - Nagaland 132
28 ORISSA 133
28.1 Latent Demand by Year - Orissa 133
28.2 Cities Sorted by Rank - Orissa 134
28.3 Cities Sorted By District - Orissa 136
29 PONDICHERRY 140
29.1 Latent Demand by Year - Pondicherry 140
29.2 Cities Sorted by Rank - Pondicherry 141
29.3 Cities Sorted By District - Pondicherry 141
30 PUNJAB 142
30.1 Latent Demand by Year - Punjab 142
30.2 Cities Sorted by Rank - Punjab 143
30.3 Cities Sorted By District - Punjab 145
31 RAJASTHAN 147
31.1 Latent Demand by Year - Rajasthan 147
31.2 Cities Sorted by Rank - Rajasthan 148
31.3 Cities Sorted By District - Rajasthan 152
32 SIKKIM 156
32.1 Latent Demand by Year - Sikkim 156
32.2 Cities Sorted by Rank - Sikkim 157
32.3 Cities Sorted By District - Sikkim 157
33 TAMIL NADU 158
33.1 Latent Demand by Year - Tamil Nadu 158
33.2 Cities Sorted by Rank - Tamil Nadu 159
33.3 Cities Sorted By District - Tamil Nadu 166
34 TRIPURA 175
34.1 Latent Demand by Year - Tripura 175
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34.2 Cities Sorted by Rank - Tripura 176
34.3 Cities Sorted By District - Tripura 176
35 UTTAR PRADESH 177
35.1 Latent Demand by Year - Uttar Pradesh 177
35.2 Cities Sorted by Rank - Uttar Pradesh 178
35.3 Cities Sorted By District - Uttar Pradesh 186
36 UTTARANCHAL 194
36.1 Latent Demand by Year - Uttaranchal 194
36.2 Cities Sorted by Rank - Uttaranchal 195
36.3 Cities Sorted By District - Uttaranchal 196
37 WEST BENGAL 197
37.1 Latent Demand by Year - West Bengal 197
37.2 Cities Sorted by Rank - West Bengal 198
37.3 Cities Sorted By District - West Bengal 202
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Introduction 9
1 INTRODUCTION
1.1 OVERVIEW
This study covers the latent demand outlook for ornamental and architectural plaster moldings across the states,
union territories and cities of India. Latent demand (in millions of U.S. dollars), or potential industry earnings (P.I.E.)
estimates are given across over 2,700 cities in India. For each city in question, the percent share the city is of it’s
state or union territory and of India as a whole is reported. These comparative benchmarks allow the reader to
quickly gauge a city vis-à-vis others. This statistical approach can prove very useful to distribution and/or sales force
strategies. Using econometric models which project fundamental economic dynamics within each state or union
territory and city, latent demand estimates are created for ornamental and architectural plaster moldings. 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 in India). This study gives, however, my estimates for the latent demand, or the P.I.E., for
ornamental and architectural plaster moldings in India. It also shows how the P.I.E. is divided and concentrated
across the cities and regional markets of India. For each state or union territory, I also show my estimates of how the
P.I.E. grows over time. In order to make these estimates, a multi-stage methodology was employed that is often
taught in courses on strategic planning at graduate schools of business.
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.
1.2 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 ornamental and architectural plaster moldings 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
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Introduction 10
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, irrespect