What Does 7 Billion Look Like for China and India?
November 9, 2011
As world population reached 7 billion last week, stories about the implications of population growth saturated the media. While total population counts offer broad “sound bite” appeal, the underlying structure of population has far greater socio-economic, political, and environmental implications.
Population composition by sex, age, ethnicity, educational attainment, political orientation, or geography matters for everything from school planning to environmental management and even to political stability. It has been 25 years since the widespread adoption of sex-selective abortion, and pockets of developing countries around the world are now contending with a dearth of women which precludes a generation of young men from marrying and starting a family. The absence of this stabilizing social institution has already had a notable impact on crime rates, political organization, and migration patterns.
Variance in age structure has similar ramifications, and a comparison of India and China offers an illustrative example. As noted in last week’s blog piece, “7 Billion and Counting,” the population of India is expected to eclipse that of China around the year 2025. But the sheer size of the world’s two most populous countries masks stark underlying differences in population age structure. In the year 2000, China’s total fertility rate was just 1.6 births per woman, falling well below the “replacement rate” of about 2.1 needed to sustain a stationary population. So is China’s population shrinking? Not yet. This is partly because a total fertility rate of 2.1 will only maintain a constant population size under a stable mortality rate while China is making substantial gains in public health. Life expectancy at birth was already 71 in 2000 but is expected to reach 81 by 2050. In fact, China will still be growing when the population of India surpasses it. However, sometime around 2030, the population-shrinking influence of low fertility will overtake the population-growing effect of reduced mortality, and the population will begin to decline. This expectation is of course contingent on the assumption that fertility rates will not rebound and that longevity improvements fall along a similarly predictable trajectory.
The case of India is quite different. While China claims its “One Child Policy” has prevented an estimated 400 million births since its introduction in the late 1970s, a variety of factors have left a legacy of political opposition to and suspicion of fertility-limiting initiatives in India. In 2000, India’s total fertility rate was 3.1 births per average woman, nearly twice that of China. But at the same time, life expectancy was significantly lower, with the average newborn expected to live 61 years under prevailing mortality rates. Today, India is also showing promising signs of significant longevity gains, and life expectancy at birth is expected to reach 77 by 2050. Declines in mortality frequently precipitate fertility reductions in the absence of policy interventions (family size has declined rapidly from Costa Rica to Thailand with improvements in public health and general living standards), and today’s Indian women are already having fewer babies than their mothers or grandmothers did. By 2050, the total fertility rate is estimated to fall to 2.1, or replacement level.
These differences matter for socio-economic outcomes because development is driven by the labor and ingenuity of productive citizens, but a portion of these individuals’ contributions must also be allocated to provide for the youngest and oldest members of society. So how do the cases of China and India compare in terms of the ratio of the youngest and oldest groups to workers (known as the dependency ratio) over the first half of the 21st century? Let us divide the population into three crude categories: unproductive children and youth under age 20, productive workers age 20 to 64, and unproductive retirees over age 65. At the beginning of the period, China’s total dependency ratio was already quite low at .67. That is, the productive contributions of an average Chinese worker had to support both herself and two-thirds of a dependent member of society, be they youth or elderly. Despite below replacement fertility, over 80 percent of dependents were children and youth at that time due to the shorter life expectancies of earlier generations.
A low dependency ratio is good for a developing economy because it allows a society to invest in productivity inputs (infrastructure, housing, energy, etc.) rather than costly education for the young and health care for the elderly. In fact, the well-documented decline first in mortality and then in fertility characteristic of developing countries (the demographic transition) is commonly associated with the “demographic dividend” – a window of opportunity during which economies can achieve robust growth. While China has already garnered much attention for its growth to date, its dependency ratio has actually fallen further to .51 this year and will remain very low for the next decade before slowly climbing to .82 by 2050, effectively closing this “window.” But in stark contrast to 2000, 60 percent of dependents in 2050 will be elderly. In anticipation of a larger share of older citizens, China has recently considered a legal mandate for adult children to visit their parents.
The case of India follows a different trajectory. In 2000, the dependency ratio was .92, i.e., each working age Indian needed to provide for himself and nearly a whole additional person, on average. From a governance point of view, this makes economic investment quite a challenge. But over 90 percent of dependents that year were young people which could be a great boon in the nation’s future – if they are provided with adequate healthcare and education. Indeed, by 2025 the total dependency ratio will fall substantially to .69 and remain roughly constant through 2050 with ever-increasing shares of older people. If well stewarded, resources freed up by a changing population structure could support a protracted period of growth and contribute to lifting millions out of poverty.
Of course, the validity of using dependency ratios as a proxy for productivity output assumes that working age people are in fact working, and all of them in equal measure. This assumption is obviously flawed, most clearly for women. According to the World Bank, formal female labor force participation in 2009 was 67 percent in China and just 33 percent in India. Women are also the primary caregivers of both the youngest and oldest members of society, which has historically affected their ability to participate more fully in the labor market. However, lower dependency ratios in China in the present and India in the near future could help open a window of opportunity for women. They offer a windfall gain of resources to invest in women’s formal productivity while simultaneously removing a key barrier to their employment. Let’s hope both China and India are able to seize this opportunity while it lasts.
Reid Hamel is a junior associate with The Asia Foundation’s Economic Development program and a Ph.D. candidate in Demography at the University of California, Berkeley. She can be reached at [email protected]. The views and opinions expressed here are those of the individual author and not those of The Asia Foundation.
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