In the context of the modernization within China's vast population, this paper compares the conditions of populations as they relate to economic growth in both China and India. These two nations are developing countries notable for their huge populations. The results show that the current development level in India is equivalent to that of China in the late 1990s. When compared from a similar start economic level, India has not yet demonstrated a higher or longer demographic dividend than China. Due to a significantly lower labor force participation rate in the working-age population, the size of the Indian labor force will not surpass that of China in the first half of the 21st century, enabling China to maintain its position as having the world's largest labor force. China has huge advantages in terms of human capital stock and the population urbanization. After entering the phase of “double second” in population and economic size, China, in particular, needs to transform its huge advantages in human capital into labor productivity and increased labor participation.

China's negative population growth is an objective law of population development, with a unique transition process and development trend. In the future, long-term and rapid negative population growth may pose challenges to the economy. It is necessary to cope with negative population growth actively by establishing a fertility support policy system, improving population quality, optimizing the spatial layout of the population, raising per capita consumption, and enhancing labor productivity.

Drawing upon data from 2000, 2010 and 2020 population censuses, this study examines trends and patterns of the empty-nest elderly households in China. The level of empty-nest elderly households has been increasing both in urban and rural areas over the last 20 years, and has increased in all China's provinces over the past decade. In 2020, empty-nest elderly population is estimated approximately to be 0.15 billion, of which 7.7 million are the oldest old living alone. The oldest old and female elderly are more likely to live alone. Nearly 70% of the living-alone elderly are widowed, and nearly 10% are still unmarried. Empty-nest elderly live mainly on family support, social security, and labor income. Their health conditions are generally good, but a small proportion are disabled. Few living-alone elderly have caregivers to provide care for them, and even for those disabled only 19.86% receive such care. Policy implications are discussed.

The key to accurately assessing China's fertility level lies in high-quality data. The 7th population census has obtained data of very high quality mainly due to the newly added ID number registration and application of information technology, providing a good opportunity for reassessing China's fertility level. This study shows that the total fertility rate (TFR) of China maintained above 1.6 from 2006 to 2017, exceeded 1.7 in most of the years, but dropped sharply from 2017 to 2020. The TFR was fluctuating considerably, ranging from a low of just 1.3 in 2020 to a high of 1.89 in 2012 and 1.88 in 2017, with a 15-year average of 1.7. Fertility preferences, fertility policy adjustments, and COVID-19 had marked influence on fertility level. The average number of children ever born suggests that there is still potential for improvements in China's fertility level, and the key measure is to fully eliminate the emerging fertility inhibiting factors and build a fertility/family-friendly society to tap this potential.

Different from the negative population growth caused by exogenous events in the past, the endogenous negative population growth driven by prolonged life expectancy and long term low fertility will become a new important population phenomenon in the future. Demographically speaking, compared with exogenous negative population growth, the endogenous one tends to accumulate long term negative population growth momentum. Once it happens, it lasts longer, is accompanied by population ageing, and is more difficult to convert back to positive growth. For the economic impacts, both of these two kinds of negative population growth have aggregate effect, but the endogenous negative population growth shows a clearer structural effect, a more stable expectation effect, and a long-swing effect which differs from the exogenous one. The looming endogenous negative population growth is not necessarily negative, but may have positive effects. The focus is how to seize the response window period and give full play to the positive effects on the basis of eliminating the negative effects of negative population growth.

Using the WPP2019 data, this article investigates negative population growth across the world during 1950-2018, and compares negative population growth between China and typical countries with identical origin point model. 107 countries (regions) in the world experienced negative population growth in 1950-2018, of which 20 were caused by the inherent mechanism of population. These 20 countries are all from Europe except Japan, and have experienced 19-year longest negative growth duration on average, and confront low fertility and population ageing. Compared to Germany, Hungary, and Russia, negative population growth occurs later in Japan and China, exhibiting a pattern of rapid development, long-term acceleration and weak resilience. In addition, the working-age population decrease earlier than the total population in Japan and China, ageing is severer, and the proportions of children aged 0-14 are lower. It is of growing significance to explore the new rules of population development, policy responses and long-term planning as soon as possible in the negative population growth era.

This study explores the relationship between China??s provincial total fertility rate(TFR) calculated from census data and adjusted by scholars in 1982,1990,2000 and 2010 and the provincial human development index (HDI). China experienced rapid increase in the HDI and continuous decrease in the TFR at all provinces and shrinking regional disparities in both of them. The two variables are negatively correlated while the inhibition effect of HDI on TFR is gradually decreased. After dividing regions by different policy types,we find that the two still have negative correlation and without Showing a J-shape relation in different category of regions,although Shanghai,Beijing and Tianjin have reached the very high human development level which exceeds 0. 788 in 2010. Unlike some western developed countries,China??s fertility level does not turn to rise with the socioeconomic development. Without adjusting fertility policy,the fertility level of all the provinces would continue declining with the socioeconomic development. A timely releasing of fertility policy can effectively restrain further decline of TFR