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The 2009 Report on Bakeries and Tortilla Manufacturing: World Market Segmentation by City
ICON Group International, May 2009, Pages: 338
Market Potential Estimation Methodology
Overview
This study covers the world outlook for bakeries and tortilla manufacturing across more than 2000 cities. For the year reported, estimates are given for the latent demand, or potential industry earnings (P.I.E.), for the city in question (in millions of U.S. dollars), the percent share the city is of the region and of the globe. These comparative benchmarks allow the reader to quickly gauge a city vis-à-vis others. Using econometric models which project fundamental economic dynamics within each country and across countries, latent demand estimates are created. 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 of the world). This study gives, however, my estimates for the worldwide latent demand, or the P.I.E. for bakeries and tortilla manufacturing. It also shows how the P.I.E. is divided across the world’s cities. In order to make these estimates, a multi-stage methodology was employed that is often taught in courses on international strategic planning at graduate schools of business.
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 a market 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 bakeries and tortilla manufacturing is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be lower 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 city market.
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.
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) and not adjusted for future dynamics in exchange rates (i.e., the figures reflect average exchange rates over recent history). If inflation rates or exchange rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (when expressed in U.S. dollars, 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 earlier, this study is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. If 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 an international latent demand for bakeries and tortilla manufacturing at the aggregate level. Product and service offering details, 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 bakeries and tortilla manufacturing on a city-by-city basis, 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 country, city, state, 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 in 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 countries, 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 across countries). This type of consumption function is show 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, industries or countries with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for bakeries and tortilla manufacturing across some 230 countries. The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a 'long-run' aggregate consumption function. This long-run function applies despite some of these countries having wealth, current income dominates the latent demand for bakeries and tortilla manufacturing. So, latent demand in the long-run has a zero intercept. However, I allow firms to have 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 bakeries and tortilla manufacturing. 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, not just bakeries and tortilla manufacturing.
Step 1. Product Definition and Data Collection
Any study of latent demand across countries 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 countries or cities are more likely to be at or near efficiency than others. These 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 world’s highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e., China has 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 per capita). Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies. Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets).
The selection of countries is further reduced by the fact that not all countries in the OECD report industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom and in some cases France and Germany.
Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank). Depending on original data sources used, the definition of “bakeries and tortilla manufacturing” 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 bakeries and tortilla manufacturing 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 cities and the world at large (without needing to know the specific parts that went into the whole in the first place).
Given this caveat, this study covers “bakeries and tortilla manufacturing” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of bakeries and tortilla manufacturing, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for bakeries and tortilla manufacturing is 3118. It is for this definition of bakeries and tortilla manufacturing that the aggregate latent demand estimates are derived. “Bakeries and tortilla manufacturing” is specifically defined as follows:
3118
Bakeries and Tortilla Manufacturing
31181
This industry comprises establishments primarily engaged in manufacturing fresh and frozen bread and other bakery products.
311811
This U.S. industry comprises establishments primarily engaged in retailing bread and other bakery products not for immediate consumption made on the premises from flour, not from prepared dough.
3118110
Retail bakery products
31181101
Retail bakery products
3118110111
Breads and rolls (excluding bagels)
3118110121
Bagels
3118110131
Cakes
3118110141
Cookies
3118110151
Doughnuts
3118110161
Pies
31181101V1
Other sweet goods (sweet rolls, coffeecake, pastries, danishes, muffins, etc.)
311811M
Miscellaneous receipts
311811P
Primary products
311811S
Secondary products
311811SM
Secondary products and miscellaneous receipts
311812
This U.S. industry comprises establishments primarily engaged in manufacturing fresh and frozen bread and bread-type rolls and other fresh bakery (except cookies and crackers) products.
3118121
Bread
31181211
White pan bread
3118121111
White pan bread, except frozen
3118121121
Frozen white pan bread
31181212
White hearth bread (including French, Italian, etc.)
3118121231
White hearth bread, except frozen (including French, Italian, etc.)
3118121241
Frozen white hearth bread (including French, Italian, etc.)
31181213
Whole wheat, cracked wheat, multigrain, and other dark wheat breads
3118121351
Whole wheat, cracked wheat, multigrain, and other dark wheat breads, except frozen
3118121361
Frozen whole wheat, cracked wheat, multigrain, and other dark wheat breads
31181214
Other variety breads (rye, unleavened, muffins, bagels, and croissants)
3118121471
Rye bread (including pumpernickel), except frozen
3118121481
Frozen rye bread (including pumpernickel)
3118121491
Unleavened bread, except frozen
31181214A1
Frozen unleavened bread
31181214G1
Other variety breads (raisin, potato, self_rising, salt_free, canned, etc.), except frozen
31181214J1
Other frozen variety breads (raisin, potato, self_rising, salt_free, canned, etc.)
3118124
Bread type rolls, muffins, bagels and croissants
31181241
Hamburger and wiener rolls
3118124111
Hamburger and wiener rolls, except frozen
3118124121
Frozen hamburger and wiener rolls
31181242
All other rolls (bread~type), including muffins, bagels, and croissants
3118124231
Brown~and~serve rolls, except frozen
3118124241
Frozen brown~and~serve rolls
3118124251
English muffins, except frozen
3118124261
Frozen english muffins
3118124271
Hearth rolls, except frozen
3118124281
Frozen hearth rolls
3118124291
Bagels, except frozen
31181242A1
Frozen bagels
31181242B1
Croissants, except frozen
31181242C1
Frozen croissants
31181242D1
Other bread~type rolls (kaiser except hearth~type, parkerhouse, etc.), except frozen
31181242E1
Other frozen bread~type rolls (kaiser except hearth~type, parkerhouse, etc.)
31181242F1
Bread stuffing, croutons, and bread crumbs (plain and seasoned)
3118125
ROLLS (BREAD_TYPE), MUFFINS, BAGELS, AND CROISSANTS
31181251
Hamburger and wiener rolls
3118125111
Hamburger and wiener rolls, except frozen
3118125121
Frozen hamburger and wiener rolls
31181252
All other rolls (bread_ type), including muffins, bagels, and croissants
3118125231
Brown_and_serve rolls, except frozen
3118125241
Frozen brown_and_serve rolls
3118125251
English muffins, except frozen
3118125261
Frozen English muffins
3118125271
Hearth rolls, except frozen
3118125281
Frozen hearth rolls
3118125291
Bagels, except frozen
31181252A1
Frozen bagels
31181252B1
Croissants, except frozen
31181252C1
Frozen croissants
31181252D1
Other bread_type rolls (kaiser except hearth_type, parkerhouse, etc.), except frozen
31181252E1
Other frozen bread_type rolls (kaiser except hearth_type, parkerhouse, etc.)
31181252F1
Bread stuffing, croutons, and bread crumbs (plain and seasoned)
31181252G1
Soft pretzels
3118127
Soft cakes, except frozen, incl fruit, pound, layer, etc.
31181271
Soft cakes, except frozen
3118127111
Snack cakes, except frozen
3118127121
Fruit cakes, holiday_type, except frozen
3118127131
All other soft cakes, except frozen (including pound, layer, sheet, cheese, etc.)
311812A
Pies, except frozen, including fruit, cream and custard
311812A1
Pies (fruit, cream, and custard), except frozen
311812A111
Snack pies (fruit, cream, and custard), except frozen
311812A121
All other pies (fruit, cream, and custard types, etc.), except frozen
311812D
Other sweet goods, except frozen, incl doughnuts, pastries, etc.
311812D1
Other sweet goods except frozen
311812D111
Yeast_raised doughnuts, except frozen
311812D131
Cake_type doughnuts, except frozen
311812D151
Pastries, except frozen (including cream puffs, eclairs, lady fingers, French pastry, puff pastry)
311812D181
All other sweet goods containing yeast, except frozen (including sweet rolls and coffeecake)
311812D191
All other sweet goods not containing yeast, except frozen (including danishes and muffins)
311812M
Miscellaneous receipts
311812P
Primary products
311812S
Secondary products
311812SM
Secondary products and miscellaneous receipts
311813
This U.S. industry comprises establishments primarily engaged in manufacturing frozen bakery products (except bread), such as cakes, pies, and doughnuts.
3118130
FROZEN BAKERY PRODUCT MANUFACTURING
31181301
Frozen soft cakes (including pound, layer, sheet, cheese, etc.)
3118130111
Frozen soft cakes (including pound, layer, sheet, cheese, etc.)
31181302
Frozen pies
3118130221
Frozen pies
31181303
All other frozen pastries
3118130331
Frozen yeast_raised doughnuts
3118130341
Frozen cake_type doughnuts
3118130351
Frozen pastries (including cream puffs, eclairs, lady fingers, French pastry, puff pastry, etc)
3118130361
All other frozen sweet goods containing yeast (including sweet rolls and coffeecake)
3118130371
All other frozen sweet goods not containing yeast (including danishes and muffins)
3118130391
Frozen cookie and cracker products
31181303V1
All other frozen bakery products
3118131
Frozen bakery products, incl. pies, cakes, sweet yeast goods, pastries, etc.
311813M
Miscellaneous receipts
311813P
Primary products
311813S
Secondary products
311813SM
Secondary products and miscellaneous receipts
31182
This industry comprises establishments primarily engaged in one of the following: (1) manufacturing cookies and crackers; (2) preparing flour and dough mixes and dough from flour ground elsewhere; and (3) manufacturing dry pasta. The establishments in this industry may package the dry pasta they manufacture with other ingredients.
311821
This U.S. industry comprises establishments primarily engaged in manufacturing cookies, crackers, and other products, such as ice cream cones.
3118211
Crackers, soft pretzels, biscuits, and related products
31182111
Saltine crackers
3118211111
Saltine crackers
31182112
Cracker sandwiches made from crackers produced in this plant
3118211221
Cracker sandwiches made from crackers produced in this plant
31182113
All other crackers, soft pretzels, biscuits, and related products
3118211331
Graham crackers
3118211341
Cracker meal and crumbs
3118211351
Soft pretzels
3118211391
Other crackers and related products (sponge, sprayed, low~sugar biscuits, melba toast, unsalted soda crackers, taco shells, etc.)
3118212
CRACKERS, BISCUITS, AND RELATED PRODUCTS
31182121
Saltine crackers
3118212111
Saltine crackers
31182122
Cracker sandwiches made from crackers produced in same plant
3118212221
Cracker sandwiches made from crackers produced in same plant
31182123
All other crackers, cracker meal and crumbs, biscuits, and related products
3118212331
Graham crackers
3118212341
Cracker meal and crumbs
3118212391
Other crackers and related products (sponge, sprayed, low_sugar biscuits, melba toast, unsalted soda crackers, taco shells, etc.)
3118214
Cookies, wafers, and ice cream cones and cups, except frozen
31182141
Sandwich cookies (except frozen), made from cookies made in same plant
3118214111
Sandwich cookies (except frozen), made from cookies made in same plant
31182142
Chocolate chip cookies (except frozen)
3118214221
Chocolate chip cookies (except frozen)
31182143
Marshmallow, crcme_filled, and oatmeal cookies, wafers, toaster pastries, ice cream cones and cups (except frozen)
3118214331
Marshmallow cookies (except frozen)
3118214341
Creme_filled cookies (except frozen)
3118214351
Oatmeal cookies (except frozen)
3118214361
Other cookies and wafers (except frozen), excluding wafers for making ice cream sandwiches
3118214371
Toaster pastries (except frozen)
3118214381
Wafers for making ice cream sandwiches (except frozen)
3118214391
Ice cream cones and cups (except frozen)
311821M
Miscellaneous receipts
311821P
Primary products
311821S
Secondary products
311821SM
Secondary products and miscellaneous receipts
311822
This U.S. industry comprises establishments primarily engaged in manufacturing prepared flour mixes or dough mixes from flour ground elsewhere.
3118220
PREPARED FLOUR MIXES (INCLUDING REFRIGERATED AND FROZEN DOUGHS AND BATTERS), MADE FROM PURCHASED FLOUR
31182201
Cake mixes, including gingerbread, made from purchased flour
3118220121
Cake mixes, including gingerbread, made from purchased flour
31182202
Flour mixes (including refrigerated and frozen doughs and batters), except cake mixes, made from purchased flour
3118220211
Pancake and waffle mixes, made from purchased flour
3118220231
Biscuit mixes, made from purchased flour
3118220241
Other prepared flour mixes (including cookie, piecrust, doughnut, and other sweet yeast goods mixes), made from purchased flour
3118220251
Bread and bread_type roll mixes, made from purchased flour
3118220261
Refrigerated doughs and batters (cookie, biscuit, bread and bread_type roll, pasta, pizza, coffeecake, pancake, etc.), made from purchased flour
3118220271
Frozen doughs and batters (cookie, biscuit, bread and bread_type roll, pasta, pizza, coffeecake, pancake, etc.), made from purchased flour
3118226
Flour mixes and refrigerated and frozen doughs and batters, made from purchased
31182261
Flour mixes
311822611
Cake mixes, including gingerbread
311822613
All other flour based mixes, except cake mixes
31182264
Refrigerated doughs and batters incl bread, bread-type rolls, and biscuit dough
31182266
Frozen doughs and batters
311822661
Frozen bread and bread-type-roll doughs, all sizes
311822662
All other frozen doughs and batters, incl. cookie, pizza, coffee cake, etc.
311822M
Miscellaneous receipts
311822P
Primary products
311822S
Secondary products
311822SM
Secondary products and miscellaneous receipts
311823
This U.S. industry comprises establishments primarily engaged in manufacturing dry pasta. The establishments in this industry may package the dry pasta they manufacture with other ingredients.
3118230
DRY MACARONI, SPAGHETTI AND EGG NOODLE PRODUCTS, MITSE (EXCEPT CANNED OR FROZEN)
31182301
Dry macaroni, spaghetti, vermicelli, and other pasta products (water content less than 14 percent), mitse
3118230111
Dry macaroni, spaghetti, vermicelli, and other pasta products, except noodles, (water content less than 14 percent), mitse
3118230121
Dry noodle products of all shapes, sizes, and types, mitse, except Chinese noodles (water content of less than 14 percent)
31182302
Dry macaroni and noodle products packaged with other purchased ingredients, not canned or frozen
3118230211
Dry (water content less than 14 percent) macaroni, spaghetti, vermicelli, and other macaroni products, mitse, packaged with other purchased ingredients, not canned or frozen
3118230221
Wet macaroni, spaghetti, vermicelli, and other pasta products, except noodles (water content 14 percent or more) except refrigerated
3118230231
Dry (water content less than 14 percent) noodle products of all shapes, sizes, and types (except Chinese), mitse, packaged with other purchased ingredients, not canned or frozen
31182303
Noodle products of all shapes, sizes, and types, except Chinese noodles, dry, wet and refrigerated, and refrigerated macaroni, spaghetti, vermicelli, and other pasta products
3118230331
Refrigerated macaroni, spaghetti, vermicelli, and other pasta products, except noodles
3118230341
Dry noodle products of all shapes, sizes, and types, except Chinese noodles (water content less than 14 percent)
3118230351
Wet noodle products of all shapes, sizes, and types, except Chinese noodles (water content 14 percent or more), except refrigerated
3118230361
Refrigerated noodle products of all shapes, sizes, and types, except Chinese noodles
3118233
Macaroni, spaghetti, vermicelli, and noodles
311823M
Miscellaneous receipts
311823P
Primary products
311823S
Secondary products
311823SM
Secondary products and miscellaneous receipts
31183
This industry comprises establishments primarily engaged in manufacturing tortillas.
311830
This industry comprises establishments primarily engaged in manufacturing tortillas.
3118300
TORTILLAS SOLD IN BULK OR PACKAGES, NOT FROZEN OR CANNED
31183001
Tortillas sold in bulk or packages, not frozen or canned
3118300100
Tortillas sold in bulk or packages, not frozen or canned
3118301
Tortillas, not frozen
311830M
Miscellaneous receipts
311830P
Primary products
311830S
Secondary products
311830SM
Secondary products and miscellaneous receipts
Step 2. Filtering and Smoothing
Based on the aggregate view of bakeries and tortilla manufacturing as defined above, data were then collected for as many similar countries and cities as possible for that same definition, at the same level of the value chain. This generates a convenience sample 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 country, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a country 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 for countries or cities 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 income. Based on the overriding philosophy of a long-run consumption function (defined earlier), cities which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that country.
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 across cities 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 countries). This assumption applies across cities along the aggregate consumption function, but also over time (i.e., not all cities are perceived to have the same income growth prospects over time and this effect can vary from city to city as well). Another way of looking at this is to say that latent demand for bakeries and tortilla manufacturing is more likely to be similar across cities that have similar characteristics in terms of economic development (i.e., African cities will have similar latent demand structures controlling for the income variation across the pool of African cities).
This approach is useful across cities for which some notion of non-linearity exists in the aggregate consumption function. For some categories, however, the reader must realize that the numbers will reflect a city’s contribution to global latent demand and may never be realized in the form of local sales. For certain category combinations this will result in what at first glance will be odd results. For example, the latent demand for the category “space vehicles” will exist for cities in “Togo” even though they have no space program. The assumption is that if the economies in these countries did not exist, the world aggregate for these categories would be lower. The share attributed to these cities is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers).
Step 5. Fixed-Parameter Linear Estimation
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the world consists of more than 2000 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 city has no current income, the latent demand for bakeries and tortilla manufacturing 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 bakeries and tortilla manufacturing). 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, low-income cities are assumed to have a latent demand proportional to their income, based on the city closest to it on the aggregate consumption function.
Step 6. Aggregation and Benchmarking
Based on the models described a
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