The 2010-2015 Outlook for Online Health and Pharmacy-Related Products in India

  • ID: 1065233
  • July 2009
  • Region: India
  • 319 Pages
  • ICON Group International
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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 online health and pharmacy-related products in India is not actual READ MORE >

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1.1 Overview

1.2 What is Latent Demand and the P.I.E.?

1.3 The Methodology

1.3.1 Step 1. Product Definition and Data Collection

1.3.2 Step 2. Filtering and Smoothing

1.3.3 Step 3. Filling in Missing Values

1.3.4 Step 4. Varying Parameter, Non-linear Estimation

1.3.5 Step 5. Fixed-Parameter Linear Estimation

1.3.6 Step 6. Aggregation and Benchmarking


2.1 The Latent Demand in India

2.2 Top 100 Cities Sorted By Rank

2.3 Latent Demand by Year in India


3.1 Latent Demand by Year - Andaman & Nicobar Islands

3.2 Cities Sorted by Rank - Andaman & Nicobar Islands

3.3 Cities Sorted By District - Andaman & Nicobar Islands


4.1 Latent Demand by Year - Andhra Pradesh

4.2 Cities Sorted by Rank - Andhra Pradesh

4.3 Cities Sorted By District - Andhra Pradesh


5.1 Latent Demand by Year - Arunachal Pradesh

5.2 Cities Sorted by Rank - Arunachal Pradesh

5.3 Cities Sorted By District - Arunachal Pradesh


6.1 Latent Demand by Year - Assam

6.2 Cities Sorted by Rank - Assam

6.3 Cities Sorted By District - Assam


7.1 Latent Demand by Year - Bihar

7.2 Cities Sorted by Rank - Bihar

7.3 Cities Sorted By District - Bihar


8.1 Latent Demand by Year - Chandigarh

8.2 Cities Sorted by Rank - Chandigarh

8.3 Cities Sorted By District - Chandigarh


9.1 Latent Demand by Year - Chhattisgarh

9.2 Cities Sorted by Rank - Chhattisgarh

9.3 Cities Sorted By District - Chhattisgarh


10.1 Latent Demand by Year - Dadra & Nagar Haveli

10.2 Cities Sorted by Rank - Dadra & Nagar Haveli

10.3 Cities Sorted By District - Dadra & Nagar Haveli


11.1 Latent Demand by Year - Daman & Diu

11.2 Cities Sorted by Rank - Daman & Diu

11.3 Cities Sorted By District - Daman & Diu


12.1 Latent Demand by Year - Delhi

12.2 Cities Sorted by Rank - Delhi

12.3 Cities Sorted By District - Delhi

13 GOA

13.1 Latent Demand by Year - Goa

13.2 Cities Sorted by Rank - Goa

13.3 Cities Sorted By District - Goa


14.1 Latent Demand by Year - Gujarat

14.2 Cities Sorted by Rank - Gujarat

14.3 Cities Sorted By District - Gujarat


15.1 Latent Demand by Year - Haryana

15.2 Cities Sorted by Rank - Haryana

15.3 Cities Sorted By District - Haryana


16.1 Latent Demand by Year - Himachal Pradesh

16.2 Cities Sorted by Rank - Himachal Pradesh

16.3 Cities Sorted By District - Himachal Pradesh


17.1 Latent Demand by Year - Jammu & Kashmir

17.2 Cities Sorted by Rank - Jammu & Kashmir

17.3 Cities Sorted By District - Jammu & Kashmir


18.1 Latent Demand by Year - Jharkhand

18.2 Cities Sorted by Rank - Jharkhand

18.3 Cities Sorted By District - Jharkhand


19.1 Latent Demand by Year - Karnataka

19.2 Cities Sorted by Rank - Karnataka

19.3 Cities Sorted By District - Karnataka


20.1 Latent Demand by Year - Kerala

20.2 Cities Sorted by Rank - Kerala

20.3 Cities Sorted By District - Kerala


21.1 Latent Demand by Year - Lakshadweep

21.2 Cities Sorted by Rank - Lakshadweep

21.3 Cities Sorted By District - Lakshadweep


22.1 Latent Demand by Year - Madhya Pradesh

22.2 Cities Sorted by Rank - Madhya Pradesh

22.3 Cities Sorted By District - Madhya Pradesh


23.1 Latent Demand by Year - Maharashtra

23.2 Cities Sorted by Rank - Maharashtra

23.3 Cities Sorted By District - Maharashtra


24.1 Latent Demand by Year - Manipur

24.2 Cities Sorted by Rank - Manipur

24.3 Cities Sorted By District - Manipur


25.1 Latent Demand by Year - Meghalaya

25.2 Cities Sorted by Rank - Meghalaya

25.3 Cities Sorted By District - Meghalaya


26.1 Latent Demand by Year - Mizoram

26.2 Cities Sorted by Rank - Mizoram

26.3 Cities Sorted By District - Mizoram


27.1 Latent Demand by Year - Nagaland

27.2 Cities Sorted by Rank - Nagaland

27.3 Cities Sorted By District - Nagaland


28.1 Latent Demand by Year - Orissa

28.2 Cities Sorted by Rank - Orissa

28.3 Cities Sorted By District - Orissa


29.1 Latent Demand by Year - Pondicherry

29.2 Cities Sorted by Rank - Pondicherry

29.3 Cities Sorted By District - Pondicherry


30.1 Latent Demand by Year - Punjab

30.2 Cities Sorted by Rank - Punjab

30.3 Cities Sorted By District - Punjab


31.1 Latent Demand by Year - Rajasthan

31.2 Cities Sorted by Rank - Rajasthan

31.3 Cities Sorted By District - Rajasthan


32.1 Latent Demand by Year - Sikkim

32.2 Cities Sorted by Rank - Sikkim

32.3 Cities Sorted By District - Sikkim


33.1 Latent Demand by Year - Tamil Nadu

33.2 Cities Sorted by Rank - Tamil Nadu

33.3 Cities Sorted By District - Tamil Nadu


34.1 Latent Demand by Year - Tripura

34.2 Cities Sorted by Rank - Tripura

34.3 Cities Sorted By District - Tripura


35.1 Latent Demand by Year - Uttar Pradesh

35.2 Cities Sorted by Rank - Uttar Pradesh

35.3 Cities Sorted By District - Uttar Pradesh


36.1 Latent Demand by Year - Uttaranchal

36.2 Cities Sorted by Rank - Uttaranchal

36.3 Cities Sorted By District - Uttaranchal


37.1 Latent Demand by Year - West Bengal

37.2 Cities Sorted by Rank - West Bengal

37.3 Cities Sorted By District - West Bengal


38.1 Disclaimers & Safe Harbor

38.2 ICON Group International, Inc. User Agreement Provisions

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In order to estimate the latent demand for online health and pharmacy-related products 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 online health and pharmacy-related products 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 online health and pharmacy-related products. 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 online health and pharmacy-related products 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 online health and pharmacy-related products 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 “online health and pharmacy-related products” 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 online health and pharmacy-related products 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, in this report we define online health and pharmacy-related products as including all commonly understood products falling within this broad category, irrespective of product packaging, formulation, size, or form. Companies participating in this industry include PlanetRx, Google, Express Scripts, CVS, and Rite Aid. In addition to the sources indicated below, additional information available to the public via news and/or press releases published by players in the industry (including reports from AMR Research, Global Industry Analysts, Forrester Research, Frost & Sullivan, Gartner, IDC, and MarketResearch.com) was considered in defining and calibrating this category All figures are in a common currency (U.S. dollars, millions) and are not adjusted for inflation (i.e., they are current values). Exchange rates used to convert to U.S. dollars are averages for the year in question. Future exchange rates are assumed to be constant in the future at the current level (the average of the year of this publication’s release in 2009).

Step 2. Filtering and Smoothing

Based on the aggregate view of online health and pharmacy-related products 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 online health and pharmacy-related products 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 online health and pharmacy-related products 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 online health and pharmacy-related products). 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.

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