Demographics and Market Segmentation: China and India J. M. Martins, Farhat Yusuf, and Gordon Brooks Macquarie University & David A. Swanson University of California Riverside Applied Demography Conference San Antonio, Texas USA January 8-10, 2014

Overview Overview Background Data & Methods Discussion Conclusion

Overview China and India are the two most populous countries and constituted about 38 percent of the World’s population in 2005. However, they have followed different demographic courses in arriving at their current positions. Both countries also have experienced substantial expansion of their markets for a range of commodities. However, dissimilar household composition and socioeconomic paths have affected household preferences in the two countries.

Overview In this paper, we review macro demographic trends that have led to different demographic structures that have significant implications for productivity and household purchasing power and discretionary spending in the two countries. Following the review of these macro demographic trends, we turn to an examination of household expenditures based on household surveys undertaken in 2005 and assesses similarities and disparities in household preferences for broad categories of goods and services in rural and urban areas, and also for households with varying levels of income.

Overview This examination provides a basis for hypothesis building concerned with market growth for progressive commodities, in view of current demographic structures in the two countries and projected fertility and population growth.

Table 1. China and India Demographic and Socioeconomic Indicators 2005 Background Table 1. China and India Demographic and Socioeconomic Indicators 2005 The two populations have experienced substantial growth in income over the last two decades. In view of their relative importance to world markets, it is useful to gain insights into the characteristics of the two markets and how their differences in population and socioeconomic development have affected household choices and market penetration for different types of consumer goods and services. Both countries carried out household consumer expenditure surveys in 2005 that can be used to provide a preliminary examination of household preferences and market segmentation for the range of household consumer goods and services. In the context of about 2005, India’s fertility rate of 3.1 children per woman is considerably higher than China’s 1.7 that is well below the replacement level of 2.1. The difference in fertility has influenced a number of factors. It has resulted in a much higher annual rate of population growth in India (2.0%) than in China (0.75%). Ceteris paribus, this has retarded the growth in income per capita in India in comparison with China and average household purchasing power. China’s dependence ratio of 0.41 reflects a much lower proportion of dependent children and a larger proportion of its population in the more economic productive ages of 15-64 years. The lower proportion of children to educate could have facilitated greater average investment in the education of individual children and help raise literacy rates in China (91% of the adult population) in comparison to India (61%). This raised one dimension of the quality of human capital in China more than in India and its potential productivity. The larger proportion of women participating in the formal economic sector in China (69%) than India’s (34%) must also have raised overall economic productivity in China. In purchasing power parity terms using the United States as a reference, China ranks second in the World in the size of Gross Domestic Product (GDP). India ranks fifth. Together they represent about 15% of the world’s GDP (WB 2008). This is considerably less than their proportion of the World’s population (38%) and reflects a lower GDP per capita than the more developed countries. Both countries have experienced relatively large growth in GDP per capita in recent years, but China’s growth rate was twice the size of India’s in the period 1990-2005, and its GDP per capita is almost twice that of India’s. Inequality in income distribution as measured by the Gini coefficient is greater in China than India. All these factors have implications for the size and characteristics of their markets. In regard to a consideration of consumer preferences in China and India, the literature has tended to focus on the urban versus rural distinction, and demonstrated the utility of this approach for a range of purposes. However, the limited research available does not provide a comprehensive comparison between these countries utilizing the urban versus rural distinction or founded on nationally representative data. This research addresses this limitation. Purchasing Power Parities (PPPs) Comparisons of market size and purchasing power in different countries are difficult. Some comparisons use currency exchange rates from international trade. However, most goods and services consumed in domestic markets are not involved in international trade. Consequently, exchange rates from international trade do not reflect the prices in most domestic transactions. To overcome these constraints purchasing power parities (PPPs) have been devised to that allow for the conversion of the rate at which one currency would have to be used to buy the same amount of goods and services in another country. For example, how much of the currency in Country A would be required to buy a kilo of rice in comparison with how much of the currency Country B is needed to buy a kilo of the same rice in Country B. The methodology is described in: Kravis, Irving B., Alan Heston and Robert Summers. 1982. World Product and Income: International comparisons of real gross product. Baltimore: The Johns Hopkins University Press. A brief discussion of the issues involved is contained in: Cullen, Tim.2007. PPP versus the market: which weight matters? Finance & Development. March 2007, Vol.44 (1).

Data & Methods Table 2. N of Households Sampled, China & India, 2005 The data used in the analysis come from a number of sources. The demographic and socioeconomic indicators in Table 1 for both China and India were sourced from the United Nations Development Programme (UNDP 2007) and the World Bank (WB 2008) to enhance consistency in definitions. The data on Purchasing Power Parities was sourced from the International Comparison Program for 2005 published by the World Bank (WB 2008), again, to improve consistency and comparability. Household expenditure for India was obtained from tabulations prepared by the National Sample Survey Organisation of the Ministry of Statistics and Programme Implementation from their quinquennial household survey on the subject (NSS 61st Round – July 2004-2005) (NSSO 2006 and 2007). The NSSO tabulations were prepared separately for urban and rural areas. They are presented as per capita estimates for a 30-day period. Household expenditures were estimated using average household size available. National aggregates were estimated using the number of urban and rural households as weights for the whole of India also available. Annual household expenditures for India were estimated using the equivalent of a 365-day period from the 30-day expenditures. Further household expenditure quintile estimates for urban and rural areas were grossed up, in the first instance, by using household size and then the relative weights of each 20th percentile and or decile also available. The data for China’s household expenditures was sourced from tabulations prepared by the National Bureau of Statistics of China from their Urban Household Income and Expenditure Survey conducted in 2005 and a separate household survey for rural areas in the same year. The annual estimates of household expenditure in both urban and rural areas were tabulated on a per capita basis. Household expenditures were estimated using household sizes available for both rural and urban areas. National household expenditures for China were estimated using, as weights, the number of urban and rural households in China. Household quintile expenditures were estimated using the number of households for given deciles as weights. Although the urban and rural household surveys use similar concepts, there is some lack of clarity regarding the boundaries of the household income levels in the two surveys. The urban estimates used income deciles and quintiles while the rural estimates refer to: low income, lower middle income, middle income, higher middle income, and high income, as “five equal parts”. The authors have used these estimates as representing five household income quintiles. There is also some ambiguity regarding the definition of the estimates, the authors have used tabulations relating to “Household Living Expenditure” for both urban and rural areas in China

Data & Methods Both the Chinese and Indian surveys are large national probability and stratified samples to ensure comprehensive geographical and social representation and allow for the derivation of robust estimates. The authors have made efforts to make the composition of the broad groups of commodities in the two countries close. However, it is likely that some differences between the compositions of these groups have persisted and some caution should be used in the comparison of the two countries.

Data & Methods Table 3. Annual Household Expenditure in China and India 2005 The substantial difference in the proportional sizes of the residual other goods and services points in that direction. Despite the efforts made using the tabulations available, it has not been possible to include telephone and postal services in the category of transport and communication, water in housing, and exclude bedding from clothing and footwear for India’s quintile household expenditures in urban and rural areas. Another constraint is the lack of standard errors of estimates. The authors used purchasing power parities in comparing the aggregated annual household expenditures in China and India. This follows the understanding that exchange rates used in international trade do not reflect the prices of most domestic transactions and that PPPs are an improved basis for such comparisons (see Notes). The arc elasticity of expenditures used in the analysis of relative proportional rates of change in expenditure as total household expenditures rise follow conventional economic practice. In 2005, China’s average household expenditure expressed in international dollars (PPPs US$) of 4,995 was 1.75 higher than India’s 2,850. China spent 39% of the average household budget on food compared with 48% in India (Table 3). This is in accordance with Engel’s Law that states that the proportion spent on food decreases as household expenditure rises. The corollary of Engel’s Law is that market opportunities for progressive commodities tend to rise as household expenditures move to higher levels. Although caution needs to be used in comparisons of the two countries because of possible differences in definitions, the higher household purchasing power in China is reflected in household allocation to the two broad categories of transport and communication and education and culture that are substantially higher in China than India (Table 3). Purchasing Power Parities (PPPs) Comparisons of market size and purchasing power in different countries are difficult. Some comparisons use currency exchange rates from international trade. However, most goods and services consumed in domestic markets are not involved in international trade. Consequently, exchange rates from international trade do not reflect the prices in most domestic transactions. To overcome these constraints purchasing power parities (PPPs) have been devised to that allow for the conversion of the rate at which one currency would have to be used to buy the same amount of goods and services in another country. For example, how much of the currency in Country A would be required to buy a kilo of rice in comparison with how much of the currency Country B is needed to buy a kilo of the same rice in Country B. The methodology is described in: Kravis, Irving B., Alan Heston and Robert Summers. 1982. World Product and Income: International comparisons of real gross product. Baltimore: The Johns Hopkins University Press. A brief discussion of the issues involved is contained in: Cullen, Tim.2007. PPP versus the market: which weight matters? Finance & Development. March 2007, Vol.44 (1).

Data & Methods Table 4. Ownership of Household Appliances and Motor Cycles in China and India 2005 The higher household purchasing power in China is reflected in the greater market penetration and household ownership of household appliances such as television sets, refrigerators, air conditioners and also of motor cycles (Table 4). There is no readily available data on motor cars from the 2005 rural survey in China to estimate motor car ownership for the whole of China.

Data & Methods Table 5. Household Expenditure in Urban Areas on Food and Durable Goods as a Proportion of Total Expenditure in China and India 2005 The analysis of the allocation of household expenditures to food and durable goods in urban areas in China and India shows the importance of relative levels of discretionary income in market penetration for durable goods. While the allocation of the household budget to food decreases as income rises, the inverse happens as allocation for durable goods rises markedly. India with a lower income per capita shows a greater decline in the proportion allocated to food and a steeper rise in the proportion spent on durable goods. This is reflected in the proportional increase allocated to durable goods for every additional unit of total household expenditure of 1.468 in China and 1.601 in India (arc elasticity of household expenditure) (Table 5). Arc Elasticity Elasticity measures the proportional change in a given item (dependent variable) divided by the proportional change in the independent variable, in this case total household expenditure. Arc elasticity measures elasticity between two points: Allen, R.G.D. 1933. The concept of arc elasticity of demand. Review of Economic Studies. 1 (3): 226-229. An equation used to measure arc elasticity is ae = [(yi – y0) / ((yi + y0)] / [(xi-x0) / (xi+x0)] where x is the independent variable (total household expenditure) and y is the dependent variable (particular expenditure item).

Data & Methods Table 6. Ownership of Household Appliances and Vehicles in Urban Areas In China and India 2005 The analysis of the ownership of household appliances by income/expenditure quintiles in urban areas both in China and India provide supporting evidence of the income segmentation of the market for durable goods (Table 6). As noted earlier, market penetration for television sets, air conditioners and refrigerators is greater in China than India. Lower incomes in India have led to lesser ownership of these domestic appliances from the lowest to the highest income groups (quintiles). In accordance with the expenditure elasticities for durable goods, previously examined, the progression is greater in India than China. In other words, for every additional unit of household total expenditure in India a higher proportion is spent on durable goods and leads to higher ownership by households with higher levels of income. The examination of the ownership of motor vehicles indicates that while market penetration of motor cars is higher in India than China, the situation is closer in the case of motorcycles. However, the progression from the lowest to the highest quintiles is much greater in India than in China for both types of vehicles (Table 6).

Data & Methods Table 7. Average household size and average annual household expenditure in urban and rural areas in China and India 2005 Average household expenditures in rural areas are about half those in urban areas both in China and India. The larger number of people in rural households, especially in China, further erodes their per capita household purchasing power and their allocation of expenditures on non-food items (Table 7).

Data & Methods Table 8. Household Expenditure Allocation in Urban and Rural Areas: China and India 2005 The Engel index (expenditure on food as a proportion total household expenditure) is substantially higher for rural than urban areas in the two countries. The higher discretionary household expenditure in urban areas allows higher proportional allocations in urban areas to progressive categories such as household appliances and service, transport and communication and education and culture in the two countries. The difference in household allocations between rural and urban areas is particularly marked in India for transport and communication and education and culture. The characteristics of the housing markets in China and India must be substantially different and influence different patterns in household allocations. The same applies to clothing and medical services (Table 8).

Data & Methods Table 9. Expenditure Patterns of Urban households by Income Quintiles in China and India 2005 An examination of the household consumption patterns as income rises clearly indicates that the allocation to food declines substantially in rural as well as urban areas. The noted growing allocations to progressive commodities such as household appliances, transport and communication and education and culture are more substantial in rural than urban areas in both countries (Table 9). This is consistent with Engel’s Law, which states that the proportion spent on food decreases as household expenditure rises. The corollary of Engel’s Law is that market opportunities for progressive commodities tend to rise as household expenditures move to higher levels. Recall that Engels index = expenditure on food as a proportion total household expenditure. The arc elasticities are usually larger in rural with lower household discretionary income than in urban areas. However, the pattern of progression for medical services that is substantial in India both in rural and urban areas is not marked in China with arc elasticities of about unit. Although clothing and footwear receive a larger proportion of the household budget in China than India. The rate of progression in urban and rural areas in China is relatively small and regressive in India (Tables 9 and 10). Arc Elasticity Elasticity measures the proportional change in a given item (dependent variable) divided by the proportional change in the independent variable, in this case total household expenditure. Arc elasticity measures elasticity between two points: Allen, R.G.D. 1933. The concept of arc elasticity of demand. Review of Economic Studies. 1 (3): 226-229. An equation used to measure arc elasticity is ae = [(yi – y0) / ((yi + y0)] / [(xi-x0) / (xi+x0)] where x is the independent variable (total household expenditure) and y is the dependent variable (particular expenditure item).

Data & Methods Table 10. Expenditure Patterns of Rural Households by Income Quintiles in China and India 2005 The arc elasticities are usually larger in rural with lower household discretionary income than in urban areas. However, the pattern of progression for medical services that is substantial in India both in rural and urban areas is not marked in China with arc elasticities of about unit. Although clothing and footwear receive a larger proportion of the household budget in China than India. The rate of progression in urban and rural areas in China is relatively small and regressive in India (Tables 9 and 10). Arc Elasticity Elasticity measures the proportional change in a given item (dependent variable) divided by the proportional change in the independent variable, in this case total household expenditure. Arc elasticity measures elasticity between two points: Allen, R.G.D. 1933. The concept of arc elasticity of demand. Review of Economic Studies. 1 (3): 226-229. An equation used to measure arc elasticity is ae = [(yi – y0) / ((yi + y0)] / [(xi-x0) / (xi+x0)] where x is the independent variable (total household expenditure) and y is the dependent variable (particular expenditure item).

Data & Methods Table 11. Ownership of Household Appliances and Vehicles in Urban and Rural Areas, China and India 2005 Market penetration of television sets, refrigerators and air conditioners is substantially higher in urban than rural areas in both countries. As mentioned previously, household ownership of these appliances is greater in China than India, and the rural/urban ownership ratio is lower in India than China for TV sets and refrigerators and the inverse in the case of air conditioners. Household ownership of motor cycles is higher in rural than urban areas in China but the ownership of motor vehicles is not available for rural China, while in India the rural/urban ratio for both motor cars and motor cycles is rather low (Table 11).

DISCUSSION The discussion of findings must be guarded because of the constraints arising from the nature of the data used and should be viewed as preliminary findings. The authors are concerned with the possible inconsistencies in definitions in the two countries. The lack of standard errors of the estimates is another concern in assessing the significance of differences. Nevertheless, the large stratified probability samples used and the consistency of most findings with empirical evidence from other countries and generic theoretical frameworks are indications of the usefulness of these preliminary findings.

DISCUSSION China and India are two large markets by any standards, if for no other reason than their large populations. However, their development has taken place against different demographic trends that have influenced their demographic structures. China has been favored in terms of lower population growth (with implications for growth in income per capita) and an age structure with a lower proportion of dependent children and a higher proportion in the more economic productive age of 15-64 years. It is also apparent that productivity in China could also have benefited from a higher literacy rate of its adult population and female participation in the formal economic sector.

DISCUSSION The comparison of the two countries shows consistent findings that support the tenet of the importance of rising income per capita in the growth of markets for non-food items, especially in relation to more progressive commodities such education services, transport and communication, and consumer durables.

DISCUSSION Chinese households with a higher income spend proportionally less on food and more on these progressive commodities. Within each of the two countries, urban households also spend a lower proportion of their expenditures on food and a higher proportion on these items. Ownership of household appliances in the two countries supports the notion of considerable segmentation of markets for progressive commodities between urban and rural areas and between different income groups.

DISCUSSION In China, the large market penetration of television sets both in urban and rural areas might have been affected by government policies that favored access to these appliances as a means of providing information. Similarly, the high market penetration of motor cycles and low penetration of motor cars might also reflect government priorities. Household preferences in the two countries show substantial similarities regarding progressive commodities but China’s propensity to spend more on education is striking. The differences in literacy rates in the two countries could be partly affected by government policies but could also suggest relative household concern with education.

DISCUSSION The higher proportion of expenditure on clothing and footwear in China could be partly due to the larger proportion of China’s population living in colder climates.

CONCLUSION Different paths of demographic and socioeconomic development have led to greater household purchasing power in China than India. This has affected the nature of their markets for the range of consumer goods and services. These markets reflect household preferences for progressive goods and services as their discretionary income rises and spend a lower proportion of the household budgets on basic commodities such as food.

CONCLUSION The Engel indices (expenditure on food as a proportion total household expenditure) indicate that households in China have greater discretionary purchasing power than India’s and households in rural areas in both countries with higher Engel indices also have lower discretionary spending on progressive commodities. Although this is a preliminary examination guarded by the constraints in the data used, it is clear that in both countries the markets are highly segmented in terms of income groups, and there also are substantial differences between urban and rural segments, partly because of differences in household income. The Engel index (expenditure on food as a proportion total household expenditure) Engel’s Law that states that the proportion spent on food decreases as household expenditure rises. The corollary of Engel’s Law is that market opportunities for progressive commodities tend to rise as household expenditures move to higher levels.

CONCLUSION In both countries, household discretionary spending on appliances, transport and communications and education and culture (e.g., recreation) reflect this segmentation. The segmentation of markets for these progressive commodities is supported by market penetration in terms of ownership of household appliances such as television sets, refrigerators and air conditioners, and also motor cars and motor cycles, which is usually greater in urban than rural areas and households in the higher income quintiles.

CONCLUSION The relatively high Engel indices (expenditure on food as a proportion total household expenditure) in the two countries in comparison with those of more developed countries indicate the potential for future growth in the markets for progressive commodities in China and India.

CONCLUSION This may especially the case with India, which is expected to slow down its population growth, reduce the proportion of dependent children in its population, and raise the proportion of people in more economically productive ages and possibly in women participation in the formal productive sector. This should enhance growth in productivity and income per capita and lead to higher household discretionary spending on progressive commodities.

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