The 2019-2024 Outlook for Insurance Carriers in China

This study covers the latent demand outlook for insurance carriers across the regions of China, including provinces, autonomous regions (Guangxi, Nei Mongol, Ningxia, Xinjiang, Xizang - Tibet), municipalities (Beijing, Chongqing, Shanghai, and Tianjin), special administrative regions (Hong Kong and Macau), and Taiwan (all hereafter referred to as "regions"). Latent demand (in millions of U.S. dollars), or potential industry earnings (P.I.E.) estimates are given across some 1,100 cities in China.

For each major city in question, the percent share the city is of the region and of China is reported. Each major city is defined as an area of "economic population", as opposed to the demographic population within a legal geographic boundary.

For many cities, the economic population is much larger that the population within the city limits; this is especially true for the cities of the Western regions. For the coastal regions, cities which are close to other major cities or which represent, by themselves, a high percent of the regional population, actual city-level population is closer to the economic population (e.g. in Beijing). Based on this "economic" definition of population, comparative benchmarks allow the reader to quickly gauge a city’s marketing and distribution value vis-à-vis others. This exercise is quite useful for persons setting up distribution centers or sales force strategies. Using econometric models which project fundamental economic dynamics within each region and city of influence, latent demand estimates are created for insurance carriers. 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 cities in China). This study gives, however, my estimates for the latent demand, or potential industry earnings (P.I.E.), for insurance carriers in China. It also shows how the P.I.E. is divided and concentrated across the cities and regional markets of China. For each region, 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 geographies. Short-run distortions in the value of the dollar, therefore, do not figure into the estimates. Purchasing power parity estimates 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.

1.3 THE METHODOLOGY
In order to estimate the latent demand for insurance carriers across the regions and cities of China, 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 region, 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 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 shown as "B" 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 show as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant. For a general overview of this subject area, see Principles of Macroeconomics by N. Gregory Mankiw, South-Western College Publishing; ISBN: 0030340594; 2nd edition (February 2002).

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 insurance carriers across the regions and cities of China.

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 regions having wealth; current income dominates the latent demand for insurance carriers. 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 insurance carriers. 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 insurance carriers in China.

1.3.1 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 cannot be 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. 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 insurance carriers 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 services that might be incorporated within insurance carriers fall 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 China (without needing to know the specific parts that went into the whole in the first place).

Given this caveat, this study covers insurance carriers as defined by the North American Industrial Classification system or NAICS (pronounced "nakes").

The NAICS code for insurance carriers is 5241. It is for this definition that aggregate latent demand estimates are derived.

Insurance carriers is specifically defined as follows:
5241 This industry group comprises establishments primarily engaged in underwriting (assuming the risk, assigning premiums, and so forth) annuities and insurance policies and investing premiums to build up a portfolio of financial assets to be used against future claims. Direct insurance carriers are establishments that are primarily engaged in initially underwriting and assuming the risk of annuities and insurance policies. Reinsurance carriers are establishments that are primarily engaged in assuming all or part of the risk associated with an existing insurance policy (or set of policies) originally underwritten by another insurance carrier.
Industries are defined in terms of the type of risk being insured against, such as death, loss of employment because of age or disability, and/or property damage. Contributions and premiums are set on the basis of actuarial calculations of probable payouts based on risk factors from experience tables and expected investment returns on reserves.

52411 This industry comprises establishments primarily engaged in initially underwriting (i.e., assuming the risk and assigning premiums) annuities and life insurance policies, disability income insurance policies, accidental death and dismemberment insurance policies, and health and medical insurance policies.

524113 This U.S. industry comprises establishments primarily engaged in initially underwriting (i.e., assuming the risk and assigning premiums) annuities and life insurance policies, disability income insurance policies, and accidental death and dismemberment insurance policies.

5241131 Life insurance policies

524113101 Individual life insurance policies

52411310101 Term life insurance policies

52411310102 Whole life insurance policies

52411310103 Universal life insurance policies

52411310104 Other individual life and disability insurance policies

524113102 Group life insurance policies

5241132 Annuities

524113201 Variable deferred annuities

524113202 Fixed-rate deferred annuities

524113203 Immediate annuities

524113P Primary services

524114 This U.S. industry comprises establishments primarily engaged in initially underwriting (i.e., assuming the risk and assigning premiums) health and medical insurance policies. Group hospitalization plans and HMO establishments (except those providing health care services) that provide health and medical insurance policies without providing health care services are included in this industry.

52412 This industry comprises establishments primarily engaged in initially underwriting (i.e., assuming the risk and assigning premiums) various types of insurance policies (except life, disability income, accidental death and dismemberment, and health and medical insurance policies).

524126 This U.S. industry comprises establishments primarily engaged in initially underwriting (i.e., assuming the risk and assigning premiums) insurance policies that protect policyholders against losses that may occur as a result of property damage or liability.

5241261 Private passenger auto insurance

524126101 Private passenger auto insurance, California

524126102 Private passenger auto insurance, New York

524126103 Private passenger auto insurance, Texas

524126104 Private passenger auto insurance, Florida

524126105 Private passenger auto insurance, Pennsylvania

524126106 Private passenger auto insurance, Illinois

524126107 Private passenger auto insurance, Ohio

524126108 Private passenger auto insurance, Michigan

524126109 Private passenger auto insurance, New Jersey

524126111 Private passenger auto insurance, North Carolina

524126112 Private passenger auto insurance, all other areas

5241262 Homeowner’s insurance

524126201 Homeowner’s insurance, California

524126202 Homeowner’s insurance, New York

524126203 Homeowner’s insurance, Texas

524126204 Homeowner’s insurance, Florida

524126205 Homeowner’s insurance, Pennsylvania

524126206 Homeowner’s insurance, all other areas

5241263 Commercial auto insurance

5241264 Non-auto liability insurance

524126401 Product liability insurance

524126402 Medical malpractice insurance

524126403 Other non-auto liability insurance

5241265 Commercial multiple peril insurance

5241266 Worker’s compensation insurance

5241267 Other property and casualty insurance

524126P Primary services

524126SM Other receipts

524127 This U.S. industry comprises establishments primarily engaged in initially underwriting (i.e., assuming the risk and assigning premiums) insurance policies to protect the owners of real estate or real estate creditors against loss sustained by reason of any title defect to real property.

524128 This U.S. industry comprises establishments primarily engaged in initially underwriting (e.g., assuming the risk, assigning premiums) insurance policies (except life, disability income, accidental death and dismemberment, health and medical, property and casualty, and title insurance policies).

52413 See industry description for 524130.

524130 This industry comprises establishments primarily engaged in assuming all or part of the risk associated with existing insurance policies originally underwritten by other insurance carriers.

This report was prepared from a variety of sources including excerpts from documents and official reports or databases published by the World Bank, the U.S. Department of Commerce, the U.S. State Department, various national agencies, the International Monetary Fund, the Central Intelligence Agency, various agencies from the United Nations (e.g. ILO, ITU, UNDP, etc.), and non-governmental sources, including ICON Group Ltd., Euromonitor, the World Resources Institute, Mintel, the U.S. Industrial Outlook, and various public sources cited in the trade press.

1.3.2 STEP 2. FILTERING AND SMOOTHING
Based on the aggregate view of insurance carriers 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 a 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.

1.3.3 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.

1.3.4 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 crosssectionally pooled time series model". The interested reader can find longer discussions of this type of modeling in Studies in Global Econometrics (Advanced Studies in Theoretical and Applied Econometrics V. 30) , by Henri Theil, et al., Kluwer Academic Publishers; ISBN: 0792336607; (June 1996), and in Principles of Econometrics, by Henri Theil John Wiley & Sons; ISBN: 0471858455; (December 1971), and in Econometric Models and Economic Forecasts by Robert S. Pindyck, Daniel L. Rubinfeld McGraw Hill Text; ISBN: 0070500983; 3rd edition (December 1991). 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 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 insurance carriers 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 China and may never be realized in the form of local sales.

1.3.5 STEP 5. FIXED-PARAMETER LINEAR ESTIMATION
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the China consists of more than 1,000 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 region has no current income, the latent demand for insurance carriers 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 insurance carriers). 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.

1.3.6 STEP 6. AGGREGATION AND BENCHMARKING
Based on the models described above, latent demand figures are estimated for all major cities in China. These are then aggregated to get region 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. I 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 towns) 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 region, 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.