The 2007-2012 Outlook for Educational Services in Greater China
ICON Group International, September 2006, Pages: 143
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 Greater China 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 educational services in Greater China 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 educational services 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 educational services across the regions and cites of Greater China, 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 region, 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 educational services across the regions and cities of Greater China. 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 having wealth; current income dominates the latent demand for educational services. 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 educational services in Greater China. 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 educational services in Greater China.
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 “educational services” 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 educational services 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 regions and cities in Greater China (without needing to know the specific parts that went into the whole in the first place).
Given this caveat, this study covers “educational services” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of educational services, please see below.. The NAICS code for educational services is 61. It is for this definition of educational services that the aggregate latent demand estimates are derived for the regions and cities of Greater China. “Educational services” is specifically defined as follows:
61
The Sector as a Whole
The Educational Services sector comprises establishments that provide instruction and training in a wide variety of subjects. This instruction and training is provided by specialized establishments, such as schools, colleges, universities, and training centers. These establishments may be privately owned and operated for profit or not for profit, or they may be publicly owned and operated. They may also offer food and accommodation services to their students.
Educational services are usually delivered by teachers or instructors that explain, tell, demonstrate, supervise, and direct learning. Instruction is imparted in diverse settings, such as educational institutions, the workplace, or the home through correspondence, television, or other means. It can be adapted to the particular needs of the students, for example sign language can replace verbal language for teaching students with hearing impairments. All industries in the sector share this commonality of process, namely, labor inputs of instructors with the requisite subject matter expertise and teaching ability.
611
Industries in the Educational Services subsector provide instruction and training in a wide variety of subjects. The instruction and training is provided by specialized establishments, such as schools, colleges, universities, and training centers.
The subsector is structured according to level and type of educational services. Elementary and secondary schools, junior colleges and colleges, universities, and professional schools correspond to a recognized series of formal levels of education designated by diplomas, associate degrees (including equivalent certificates), and degrees. The remaining industry groups are based more on the type of instruction or training offered and the levels are not always as formally defined. The establishments are often highly specialized, many offering instruction in a very limited subject matter, for example ski lessons or one specific computer software package. Within the sector, the level and types of training that are required of the instructors and teachers vary depending on the industry.
Establishments that manage schools and other educational establishments on a contractual basis are classified in this subsector if they both manage the operation and provide the operating staff. Such establishments are classified in the educational services subsector based on the type of facility managed and operated.
6111
Elementary and Secondary Schools
61111
See industry description for 611110 below.
611110
This industry comprises establishments primarily engaged in furnishing academic courses and associated course work that comprise a basic preparatory education. A basic preparatory education ordinarily constitutes kindergarten through 12th grade. This industry includes school boards and school districts.
6112
Junior Colleges
61121
See industry description for 611210 below.
611210
This industry comprises establishments primarily engaged in furnishing academic, or academic and technical, courses and granting associate degrees, certificates, or diplomas below the baccalaureate level. The requirement for admission to an associate or equivalent degree program is at least a high school diploma or equivalent general academic training. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
6113
Colleges, Universities, and Professional Schools
61131
See industry description for 611310 below.
611310
This industry comprises establishments primarily engaged in furnishing academic courses and granting degrees at baccalaureate or graduate levels. The requirement for admission is at least a high school diploma or equivalent general academic training. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
6114
Business Schools and Computer and Management Training
61141
See industry description for 611410 below.
611410
This industry comprises establishments primarily engaged in offering courses in office procedures and secretarial and stenographic skills and may offer courses in basic office skills, such as word processing. In addition, these establishments may offer such classes as office machine operation, reception, communications, and other skills designed for individuals pursuing a clerical or secretarial career. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
61142
See industry description for 611420 below.
611420
This industry comprises establishments primarily engaged in conducting computer training (except computer repair), such as computer programming, software packages, computerized business systems, computer electronics technology, computer operations, and local area network management. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
61143
See industry description for 611430 below.
611430
This industry comprises establishments primarily engaged in offering an array of short duration courses and seminars for management and professional development. Training for career development may be provided directly to individuals or through employers training programs; and courses may be customized or modified to meet the special needs of customers. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
6115
Technical and Trade Schools
61151
This industry comprises establishments primarily engaged in offering vocational and technical training in a variety of technical subjects and trades. The training often leads to job-specific certification. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
611511
This U.S. industry comprises establishments primarily engaged in offering training in barbering, hair styling, or the cosmetic arts, such as makeup or skin care. These schools provide job-specific certification.
611512
This U.S. industry comprises establishments primarily engaged in offering aviation and flight training. These establishments may offer vocational training, recreational training, or both.
611513
This U.S. industry comprises establishments primarily engaged in offering apprenticeship training programs. These programs involve applied training as well as course work.
611519
This U.S. industry comprises establishments primarily engaged in offering job or career vocational or technical courses (except cosmetology and barber training, aviation and flight training, and apprenticeship training). The curriculums offered by these schools are highly structured and specialized and lead to job-specific certification.
6116
This industry group comprises establishments primarily engaged in offering or providing instruction (except academic schools, colleges, and universities; and business, computer, management, technical, or trade instruction). Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
61161
See industry description for 611610 below.
611610
This industry comprises establishments primarily engaged in offering instruction in the arts, including dance, art, drama, and music.
6116101
Establishments primarily engaged in teaching dance to children and adults.
6116102
Establishments primarily engaged in offering instruction in the arts, including art, drama, and music.
61162
See industry description for 611620 below.
611620
This industry comprises establishments, such as camps and schools, primarily engaged in offering instruction in athletic activities to groups of individuals. Overnight and day sports instruction camps are included in this industry.
61163
See industry description for 611630 below.
611630
This industry comprises establishments primarily engaged in offering foreign language instruction (including sign language). These establishments are designed to offer language instruction ranging from conversational skills for personal enrichment to intensive training courses for career or educational opportunities.
61169
This industry comprises establishments primarily engaged in offering instruction (except business, computer, management, technical, trade, fine arts, athletic, and language instruction). Also excluded from this industry are academic schools, colleges, and universities.
611691
This U.S. industry comprises establishments primarily engaged in offering preparation for standardized examinations and/or academic tutoring services.
611692
This U.S. industry comprises establishments primarily engaged in offering automobile driving instruction.
611699
This U.S. industry comprises establishments primarily engaged in offering instruction (except business, computer, management, technical, trade, fine arts, athletic, language instruction, tutoring, and automobile driving instruction). Also excluded from this industry are academic schools, colleges, and universities.
6117
Educational Support Services
61171
See industry description for 611710 below.
611710
This industry comprises establishments primarily engaged in providing noninstructional services that support educational processes or systems.
Step 2. Filtering and Smoothing
Based on the aggregate view of educational services 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 region 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, regional and city-level income. Based on the overriding philosophy of a long-run consumption function (defined earlier), regions 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 regions or cities). This assumption applies along the aggregate consumption function, but also over time (i.e., not all regions or cities in Greater China are perceived to have the same income growth prospects over time). Another way of looking at this is to say that latent demand for educational services is more likely to be similar across regions 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 region’s or city’s contribution to latent demand in Greater China 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 Greater China consists of more than 1000 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 has no current income, the latent demand for educational services 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 educational services). 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 cities in Greater China. These are then aggregated to get regional 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 region and the extent to which a city might be used as a point of distribution within its region. 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 region. Not all cities (e.g. the smaller villages) are estimated within each region as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same state, 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 16
1.3.3 Step 3. Filling in Missing Values 16
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 16
1.3.5 Step 5. Fixed-Parameter Linear Estimation 17
1.3.6 Step 6. Aggregation and Benchmarking 17
2 SUMMARY OF FINDINGS 18
2.1 The Latent Demand in Greater China 18
2.2 Top 100 Cities Sorted By Rank 19
3 ANHUI 22
3.1 Latent Demand by Year - Anhui 22
3.2 Cities Sorted by Rank - Anhui 23
3.3 Cities Sorted Alphabetically - Anhui 24
4 BEIJING 26
4.1 Latent Demand by Year - Beijing 26
4.2 Cities Sorted by Rank - Beijing 27
4.3 Cities Sorted Alphabetically - Beijing 27
5 CHONGQING 28
5.1 Latent Demand by Year - Chongqing 28
5.2 Cities Sorted by Rank - Chongqing 29
5.3 Cities Sorted Alphabetically - Chongqing 30
6 FUJIAN 31
6.1 Latent Demand by Year - Fujian 31
6.2 Cities Sorted by Rank - Fujian 32
6.3 Cities Sorted Alphabetically - Fujian 33
7 GANSU 35
7.1 Latent Demand by Year - Gansu 35
7.2 Cities Sorted by Rank - Gansu 36
7.3 Cities Sorted Alphabetically - Gansu 37
8 GUANGDONG 38
8.1 Latent Demand by Year - Guangdong 38
8.2 Cities Sorted by Rank - Guangdong 39
8.3 Cities Sorted Alphabetically - Guangdong 41
9 GUANGXI 43
9.1 Latent Demand by Year - Guangxi 43
9.2 Cities Sorted by Rank - Guangxi 44
9.3 Cities Sorted Alphabetically - Guangxi 45
10 GUIZHOU 46
10.1 Latent Demand by Year - Guizhou 46
10.2 Cities Sorted by Rank - Guizhou 47
10.3 Cities Sorted Alphabetically - Guizhou 48
11 HAINAN 49
11.1 Latent Demand by Year - Hainan 49
11.2 Cities Sorted by Rank - Hainan 50
11.3 Cities Sorted Alphabetically - Hainan 51
12 HEBEI 52
12.1 Latent Demand by Year - Hebei 52
12.2 Cities Sorted by Rank - Hebei 53
12.3 Cities Sorted Alphabetically - Hebei 54
13 HEILONGJIANG 55
13.1 Latent Demand by Year - Heilongjiang 55
13.2 Cities Sorted by Rank - Heilongjiang 56
13.3 Cities Sorted Alphabetically - Heilongjiang 58
14 HENAN 60
14.1 Latent Demand by Year - Henan 60
14.2 Cities Sorted by Rank - Henan 61
14.3 Cities Sorted Alphabetically - Henan 63
15 HONG KONG 65
15.1 Latent Demand by Year - Hong Kong 65
15.2 Cities Sorted by Rank - Hong Kong 66
15.3 Cities Sorted Alphabetically - Hong Kong 67
16 HUBEI 68
16.1 Latent Demand by Year - Hubei 68
16.2 Cities Sorted by Rank - Hubei 69
16.3 Cities Sorted Alphabetically - Hubei 71
17 HUNAN 73
17.1 Latent Demand by Year - Hunan 73
17.2 Cities Sorted by Rank - Hunan 74
17.3 Cities Sorted Alphabetically - Hunan 76
18 JIANGSU 78
18.1 Latent Demand by Year - Jiangsu 78
18.2 Cities Sorted by Rank - Jiangsu 79
18.3 Cities Sorted Alphabetically - Jiangsu 81
19 JIANGXI 83
19.1 Latent Demand by Year - Jiangxi 83
19.2 Cities Sorted by Rank - Jiangxi 84
19.3 Cities Sorted Alphabetically - Jiangxi 85
20 JILIN 87
20.1 Latent Demand by Year - Jilin 87
20.2 Cities Sorted by Rank - Jilin 88
20.3 Cities Sorted Alphabetically - Jilin 89
21 LIAONING 91
21.1 Latent Demand by Year - Liaoning 91
21.2 Cities Sorted by Rank - Liaoning 92
21.3 Cities Sorted Alphabetically - Liaoning 93
22 MACAU 95
22.1 Latent Demand by Year - Macau 95
22.2 Cities Sorted by Rank - Macau 96
22.3 Cities Sorted Alphabetically - Macau 96
23 NEI MONGGOL 97
23.1 Latent Demand by Year - Nei Monggol 97
23.2 Cities Sorted by Rank - Nei Monggol 98
23.3 Cities Sorted Alphabetically - Nei Monggol 99
24 NINGXIA 100
24.1 Latent Demand by Year - Ningxia 100
24.2 Cities Sorted by Rank - Ningxia 101
24.3 Cities Sorted Alphabetically - Ningxia 101
25 QINGHAI 102
25.1 Latent Demand by Year - Qinghai 102
25.2 Cities Sorted by Rank - Qinghai 103
25.3 Cities Sorted Alphabetically - Qinghai 103
26 SHAANXI 104
26.1 Latent Demand by Year - Shaanxi 104
26.2 Cities Sorted by Rank - Shaanxi 105
26.3 Cities Sorted Alphabetically - Shaanxi 106
27 SHANDONG 107
27.1 Latent Demand by Year - Shandong 107
27.2 Cities Sorted by Rank - Shandong 108
27.3 Cities Sorted Alphabetically - Shandong 110
28 SHANGHAI 112
28.1 Latent Demand by Year - Shanghai 112
28.2 Cities Sorted by Rank - Shanghai 113
28.3 Cities Sorted Alphabetically - Shanghai 113
29 SHANXI 114
29.1 Latent Demand by Year - Shanxi 114
29.2 Cities Sorted by Rank - Shanxi 115
29.3 Cities Sorted Alphabetically - Shanxi 115
30 SICHUAN 117
30.1 Latent Demand by Year - Sichuan 117
30.2 Cities Sorted by Rank - Sichuan 118
30.3 Cities Sorted Alphabetically - Sichuan 120
31 TAIWAN 122
31.1 Latent Demand by Year - Taiwan 122
31.2 Cities Sorted by Rank - Taiwan 123
31.3 Cities Sorted Alphabetically - Taiwan 125
32 TIANJIN 128
32.1 Latent Demand by Year - Tianjin 128
32.2 Cities Sorted by Rank - Tianjin 129
32.3 Cities Sorted Alphabetically - Tianjin 129
33 XINJIANG UYGUR 130
33.1 Latent Demand by Year - Xinjiang Uygur 130
33.2 Cities Sorted by Rank - Xinjiang Uygur 131
33.3 Cities Sorted Alphabetically - Xinjiang Uygur 132
34 XIZANG [THIBET] 133
34.1 Latent Demand by Year - Xizang [Thibet] 133
34.2 Cities Sorted by Rank - Xizang [Thibet] 134
34.3 Cities Sorted Alphabetically - Xizang [Thibet] 134
35 YUNNAN 135
35.1 Latent Demand by Year - Yunnan 135
35.2 Cities Sorted by Rank - Yunnan 136
35.3 Cities Sorted Alphabetically - Yunnan 137
36 ZHEJIANG 138
36.1 Latent Demand by Year - Zhejiang 138
36.2 Cities Sorted by Rank - Zhejiang 139
36.3 Cities Sorted Alphabetically - Zhejiang 140
37 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 142
37.1 Disclaimers & Safe Harbor 142
37.2 User Agreement Provisions 143
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