Fecundidade abaixo da reposição, população estacionária por migração e efeitos sobre a estrutura etária

Introduction Taiwan experienced and completed demographic transition during the twentieth century. The mortality started a steady decline trend in 1920s (Barclay 1954, Wang 1986, Chen, 1979). Since then, the population grew rapidly with an accelerated speed without check of fertility decrease. The fertility level approached its peak in 1950s, reaching a TFR of 7 per woman and crude birth rates over 40 per thousand. The government initiated a national family planning program in 1965 with the intension to control the number of births. As a backing force of the family planning movement, the government promulgated in 1969 the first version of the country’s Guideline for Population Policy aiming at the goal for birth control. The crude birth rate declined to about 20 per thousand in early 1980s. However, the population growth rate remained rather high in those years, partly because of the deep-dropped low level of the crude death rate, partly owing to the increase of the number of women in reproductive ages. Allured by the seemingly growing pressure of the population growth, the government decided to reinforce the family planning program and reannounced a stronger policy in 1983 for further reduction of population growth rate, even though the TFR in Taiwan was soon reaching the belowreplacement level next year in 1984. Fertility Transition and Below Replacement Fertility in Taiwan Change in the period total fertility rate (TFR) Fertility transition, that is fertility decline from a high level to a low level, usually defined to the replacement level, occurred in Taiwan during the latter half of twentieth century. The TFR in Taiwan declined from 7.05 to 4.0 between 1951 and 1970, and continued its decline to 2.8 in 1975. Then TFR resumed its decline from 3.1 in 1976 and reached 2.0 around replacement level in 1984. After 1984, TFR stagnated around 1.75 during the period of 1986 to 1997. After around ten years’ stagnation moderately below replacement level, TFR in Taiwan resumed a significant trend of decline and touched an even lower level of 1.23 in 2003. Taiwan was enlisted into the lowest-low fertility (Kohler et al. 2002*countries in the world. TFR showed a further decline in 2004 and 2005, less than 1.2 were observed (Chang 2005). The new record of TFR, 1.1 in 2008, can be counted lower than almost all countries in the world, except two special districts of Hong Kong and Macau of China.

The AIDS epidemic has reached alarming levels in much of sub-Saharan Africa. In some countries of southern Africa, one-quarter to one-third of adults, aged 15-49 years, are infected with HIV. The AIDS epidemic is causing important changes in key demographic factors, such as increases in death to adults aged 30-49 years, reductions in life expectancy, and increases in the number of orphans. In most of sub-Saharan Africa, fertility rates are so high that even the increased death rate due to AIDS will not stop population growth. However, in a few places, low fertility coupled with increased numbers of deaths due to AIDS may lead to negative population growth. This is particularly true in such southern African countries as in Zimbabwe, Botswana, and South Africa. The article by Mekonnen et al. in this issue examines the case of Addis Ababa (1). Addis Ababa is unique in eastern Africa in that the total fertility rate is already below replacement level-- the level that would eventually produce a population with zero annual growth. The authors show that, in this setting, HIV prevalence of 10% in adults results in a negative rate of natural increase. That is, there will be more deaths than births each year. Since migration into Addis Ababa from the countryside is expected to continue, the population will continue to grow, albeit at a very low rate. Other important demographic consequences of AIDS include a tripling in the annual number of adult deaths and a reduction in life expectancy at birth by 14 years within 2004. The projections are made using the AIDS Impact Model (AIM), which is a component in the SPECTRUM system of policy models [1]. The AIM combines epidemiological calculations regarding new HIV infections, progression to AIDS-related death, and the impact of HIV on fertility with demographic projections that track the population over time by age and sex. HIV/ AIDS affects the demographic projections through increased adult and child mortality and a reduction in fertility among HIV-infected women. The demographic projection affects the HIV/AIDS calculations through changes in the age structure of the population and the underlying fertility and non-AIDS mortality rates. A different approach is used by the United Nations Population Division and the U.S. Census Bureau. They make demographic projections for every country in the world every two years. Each of them uses a separate model to project the number of AIDS-related deaths. The United Nations has used EpiModel [2] with UNAIDS estimates of HIV prevalence to project total AIDS-related deaths. These deaths are then distributed by age and sex according to a standard pattern. These AIDS-related deaths are then added to deaths due to causes other than AIDS calculated by the UN demographic model to determine total number of deaths and life expectancy. The U.S. Census Bureau uses the iwgAIDS [3] model to develop standard scenarios of AIDS-related deaths by age and sex. They then calibrate these scenarios to the estimated prevalence level in a particular country to determine AIDS-related deaths by age and sex. These deaths are added to the non-AIDS deaths calculated by their demographic model to determine total deaths. The advantage of this approach is that this allows both organizations to continue to use their standard demographic models. The disadvantage is that there is no interaction between the HIV projections and the demographic projections. For example, there is no competing risk of mortality. That is, the models ignore the possibility that people infected with HIV might die of causes other than AIDS before they die of AIDS. A third approach is the use of full-scale simulation models, such as iwgAIDS. In this case, the model uses epidemiological factors, such as number of sex partners, coital frequency, prevalence of sexually transmitted infections, and condom use, to calculate the incidence of HIV. The number of AIDS-related deaths by age and sex is determined from incidence and is fully integrated with the demographic projection processes. …

Temporal Trends of Semen Quality and Fertility Rates Over the Course of a Decade: Data From King County, Washington

Management of pastoral development in the third world: by Stephen Sandford Published in association with the Overseas Development Institute, London, by John Wiley, Chichester, UK, 1983, 293 pp

SummaryBackgroundUnderstanding potential patterns in future population levels is crucial for anticipating and planning for changing age structures, resource and health-care needs, and environmental and economic landscapes. Future fertility patterns are a key input to estimation of future population size, but they are surrounded by substantial uncertainty and diverging methodologies of estimation and forecasting, leading to important differences in global population projections. Changing population size and age structure might have profound economic, social, and geopolitical impacts in many countries. In this study, we developed novel methods for forecasting mortality, fertility, migration, and population. We also assessed potential economic and geopolitical effects of future demographic shifts.MethodsWe modelled future population in reference and alternative scenarios as a function of fertility, migration, and mortality rates. We developed statistical models for completed cohort fertility at age 50 years (CCF50). Completed cohort fertility is much more stable over time than the period measure of the total fertility rate (TFR). We modelled CCF50 as a time-series random walk function of educational attainment and contraceptive met need. Age-specific fertility rates were modelled as a function of CCF50 and covariates. We modelled age-specific mortality to 2100 using underlying mortality, a risk factor scalar, and an autoregressive integrated moving average (ARIMA) model. Net migration was modelled as a function of the Socio-demographic Index, crude population growth rate, and deaths from war and natural disasters; and use of an ARIMA model. The model framework was used to develop a reference scenario and alternative scenarios based on the pace of change in educational attainment and contraceptive met need. We estimated the size of gross domestic product for each country and territory in the reference scenario. Forecast uncertainty intervals (UIs) incorporated uncertainty propagated from past data inputs, model estimation, and forecast data distributions.FindingsThe global TFR in the reference scenario was forecasted to be 1·66 (95% UI 1·33–2·08) in 2100. In the reference scenario, the global population was projected to peak in 2064 at 9·73 billion (8·84–10·9) people and decline to 8·79 billion (6·83–11·8) in 2100. The reference projections for the five largest countries in 2100 were India (1·09 billion [0·72–1·71], Nigeria (791 million [594–1056]), China (732 million [456–1499]), the USA (336 million [248–456]), and Pakistan (248 million [151–427]). Findings also suggest a shifting age structure in many parts of the world, with 2·37 billion (1·91–2·87) individuals older than 65 years and 1·70 billion (1·11–2·81) individuals younger than 20 years, forecasted globally in 2100. By 2050, 151 countries were forecasted to have a TFR lower than the replacement level (TFR <2·1), and 183 were forecasted to have a TFR lower than replacement by 2100. 23 countries in the reference scenario, including Japan, Thailand, and Spain, were forecasted to have population declines greater than 50% from 2017 to 2100; China's population was forecasted to decline by 48·0% (−6·1 to 68·4). China was forecasted to become the largest economy by 2035 but in the reference scenario, the USA was forecasted to once again become the largest economy in 2098. Our alternative scenarios suggest that meeting the Sustainable Development Goals targets for education and contraceptive met need would result in a global population of 6·29 billion (4·82–8·73) in 2100 and a population of 6·88 billion (5·27–9·51) when assuming 99th percentile rates of change in these drivers.InterpretationOur findings suggest that continued trends in female educational attainment and access to contraception will hasten declines in fertility and slow population growth. A sustained TFR lower than the replacement level in many countries, including China and India, would have economic, social, environmental, and geopolitical consequences. Policy options to adapt to continued low fertility, while sustaining and enhancing female reproductive health, will be crucial in the years to come.FundingBill & Melinda Gates Foundation.

This thesis studies two topics in the macro-labour literature: investment in human capital and fertility decisions. The thesis comprises of three chapters, the first studying the effects of private tutoring in Korea and its resemblance to a human capital rat race and the second and third investigating the rapid decline in fertility rates experienced in developing countries over the past few decades. With many countries having reached universal primary and secondary education, parental spending on education for supplementary and enrichment purposes has begun to resemble a rat race. In many Asian countries, it is normal for students to receive some or several forms of private tutoring alongside formal schooling. However, unlike the returns to schooling or the effects of school quality on student achievement which have been widely studied, the effects of private tutoring have received limited attention. In the first substantive chapter of this thesis, I exploit exogenous variation in spending on private tutoring caused by the imposition of a curfew on the operating hours of tutoring institutes in Korea, to estimate the impact of spending on tutoring on long-term educational and labour market outcomes. The first stage estimates highlight the severity of the rat race, with curfews imposed as late as 10pm still constraining tutoring expenditure. While I do not find any significant effects of tutoring expenditure on entering college, when I interact tutoring expenditure with parental education, I find a significant, positive effect of tutoring on attending any college for children of less educated parents, while the effect for children of more educated parents is not significantly different from zero. Given that the less educated parents spend much less on tutoring, these results indicate diminishing marginal effects of tutoring, while the lack of an effect in the specification linear in tutoring expenditure points to the average impact (local to those constrained by the curfew) being close to zero. I also find that tutoring expenditure has a positive effect on both completing a four year degree and on being employed. I place these empirical findings within an asymmetric information framework to explain how the use of test scores as a signal for ability leads to inefficiently high investments in tutoring, leading to a rat-race equilibrium. The second paper highlights the trends in fertility rates observed in developing countries, pointing out that cross-country differences in fertility rates have fallen very rapidly over the past four decades, with most countries converging to a rate just above two children per woman. In the second substantive chapter in the thesis, my co-author and I argue that the convergence in fertility rates has taken place despite the limited (or absent) absolute convergence in other economic variables and propose an alternative explanation for the decline in fertility rates: the population-control programmes started in the 1960s which aimed to increase information about and availability of contraceptive methods, and establish a new small-family norm using public campaigns. Using several different measures of family planning programme intensity across countries, we show a strong positive association between programme intensity and subsequent reductions in fertility, after controlling for other potential explanatory variables, such as GDP, schooling, urbanisation, and mortality rates. We conclude that concerted population control policies implemented in developing countries are likely to have played a central role in accelerating the global decline in fertility rates and can explain some patterns of that fertility decline that are not well accounted for by other socioeconomic factors. In the third main chapter of the thesis, we build on the findings presented in the previous chapter by studying a quantitative model of endogenous human capital and fertility choice, augmented to portray a role for social norms over the number of children. The model allows us to gauge the role of human capital accumulation on the decline in fertility and to simulate the implementation of population-control policies aimed at affecting social norms on family size. We also consider extensions of the model in which we allow a role for the decline in infant and child mortality and for improvements in contraceptive technologies (the second main component of the population-control programmes). Using data on several socio-economic variables as well as information on funding for family planning programmes to parametrise the model, we find that, as argued in the previous chapter, policies aimed at altering family-size norms provided a significant impulse to accelerate and strengthen the decline in fertility that would have otherwise gradually taken place as economies move to higher levels of human capital and lower levels of mortality.

Demography is usually analysed as a derivative of economic and political developments. Further, public health as a sector is influenced by population statics and dynamics. Where is the key to sustainable healthcare policies in a post-communist country with an ancient history, restrictive population pyramid and rich cultural traditions? The universal key to future progress is education. The overall aim of this article is to make a short but sharp overview of demographic developments in Bulgaria and to relate them to the possible educational strategies for future public health reinforcement. Bulgaria is a native country for 7 050 034 people. Since 2007 the country is a European Union (EU) Member State. In 2017, the Crude Birth rate (CBR) in Bulgaria was 9.0 ‰; Total Fertility rate (TFR) remains below the replacement level – 1.6. The processes of population change due to emigration have a significant negative impact on the population growth. The emigrants for 2017 are 31 586 (mainly ages 25 to 44). By 2020, people aged over 60 will increase by one third and will exceed 28% of the Bulgarians; and more than 50% of the population will live in the cities. Bulgaria is among the five “oldest” countries in EU. However, these are global trends. Therefore, low CBR, ageing and urbanization are natural processes, which need public health awareness and adequate strategies. Herewith, some misleading interpretations of the demographic facts are refuted: 1) The main reasons for the demographic crisis in Bulgaria are the low CBR and TFR – Not true! In 2017, CBR and TFR in Bulgaria are the same as in many EU countries. The real problem is that Bulgaria has been with a negative natural growth for 29 years because of sustainably high brain drain process. The Demographic replacement rate is around 60% for 2017. The young people go to study abroad and don't come back. 2) Ageing is a demographic tsunami and a fast-on-going social crisis - Not true! Demographic aging is not a sudden event, and many countries are already able to cope with it successfully. How can these processes be managed in Bulgaria? The key strategies are education developments at all levels. The social need for attractive forms of continuous training aimed at transferring skills for active aging is growing. 3) People stop learning when they become 60. Old people do not understand new technologies- Not true! Worldwide, people over 60 are one of the fastest growing Internet communities. In Bulgaria these processes are slower due to language and economic barriers. However, more and more adults are using computer technologies. The sustainable strategy is the lifelong education. The lifelong learning framework emphasizes that learning occurs during the whole course of a person’s life and it has the social potential to support and to empower older adults. Thereafter, the seniors can be a valuable intellectual and economic support for their community if an adequate education is provided. Conclusion: Demography is also a derivative of education. The presented demographic challenges could be managed and the key strategy is education. Public health training should also adapt. Moreover, jobs that provide care and cure will exist forever. A researcher could be substituted by a computer, but a healthcare worker partially. Public health professions, which provide social and psychological support, will be among the future professions. Lifelong educational programs have to be adapted to these trends.

This paper brings a new perspective to the population growth implications of the low fertility levels of European countries. For years between 2009 and 2018, whether constant fertility, mortality, and net migration would generate long‐run population growth or population decrease is indicated simply and visually by comparison of the total fertility rate (TFR) to a migration‐adjusted replacement level. The results show that, when considered in combination with concurrent net migration and mortality, the population growth implication of low fertility varies between countries and over time. For Sweden, Luxembourg, Belgium, and the United Kingdom for all the years considered the fertility–mortality–migration combination is coherent with long‐run population growth. For the former two countries, long‐run population growth would be sustained by net migration at current levels even if fertility were to fall to very low levels. In contrast, for every Eastern European country and year considered, unchanged fertility–mortality–migration combination would lead to population decrease. The need for an alternative view of low fertility in terms of whether the TFR is above or below a migration‐adjusted replacement level and the need for a migration context‐specific view, as distinct from a universal best view, of the desirability of fertility level are discussed.

Following the traditional order of life course events in MENA countries, this chapter discusses the timing and determinants of motherhood. In Chapter 3, we highlighted the striking decline in fertility, maternal mortality, and infant mortality rates in MENA countries during the last two decades. The decline in fertility rates is largely due to delay of childbirth. In line with the increasing age at first marriage, the average age of transition to motherhood has increased in MENA countries more than in other developing regions of the world (Lloyd et al., 2005). However, the previous chapters showed that early childbirth is still experienced by a non-negligible proportion of women at younger ages, even before leaving education and before entering the labor market. Thus, it can be expected that the general trend towards delayed childbirth, in combination with the non-negligible incidence of teenage childbirth, has been accompanied by an increasing social differentiation in patterns of first childbirth within MENA societies.

In the present report, we continue to look how fertility rates and infant mortality rates may help explain global population trends. In summary, fertility rates in Asia, and Latin America and the Caribbean have been declining by quite a lot, which explains the decline in births in those regions. On the other hand, fertility rates increased recently in North America and Europe, which explains the increases in births there. Finally, Sub-Saharan Africa has the highest fertility rate, and consequently the highest number of births. However, the fertility rates in Sub-Saharan Africa have been declining. So while the number of births continue to increase, it is increasing at a slower rate. Infant mortality rates (IMR) declined in all regions but the rate of decline varied considerably. IMR was very high in Sub-Saharan Africa, declined the least, and by 2005-2010 was the highest of anywhere. IMR was the lowest in Europe and North America, declined substantially, and was still the lowest in 2005-2010. IMR also declined substantially in Asia, Latin America and the Caribbean, and North Africa, and, by 2005-2010, were generally at levels comparable to Europe and North America in the 1950s. Because IMR was high and didn't decline by much in Sub-Saharan Africa, the fertility rate didn't contribute as much to population as it would have if the IMR had been lower. That is, the highest world population growth is in Sub-Saharan Africa (see the first report), but if IMR in Sub-Saharan Africa had been lower, the population growth would have been even higher. Fertility rates are declining in Sub-Saharan Africa, but so are infant mortality rates, and so if the IMR declines faster than does the fertility rate then declining fertility rates will not result in lower numbers of infants and children.

Indonesia, the largest country in Southeast Asia, has as its national motto “Unity in Diversity.” In 2010, Indonesia stood as the world's fourth most populous country after China, India and the United States, with 237.6 million people. This archipelagic country contributed 3.5 per cent to the world's population in the same year. Its relative contribution to the world population will be stable at around 3.4–3.5 per cent until 2050. According to the median variant of the United Nations estimate (2013), Indonesia's population will continue to grow, reaching 300 million in 2033. The future demographics of Indonesia are likely to be very different from today's pattern, just as the current situation varies markedly from the past. Indonesians are increasingly living longer and having fewer children. Benefiting from the ease and advancement of transportation and information technology, Indonesians are increasingly more mobile, venturing into a wider labour market both within and outside Indonesia. Indonesia has nearly completed its first demographic transition, from both high fertility and mortality to low fertility and mortality rates. The end of the first demographic transition is marked by the “replacement” level of fertility, which is the number of children a couple has that are needed to replace themselves. Population experts believe that the replacement level is reached when the fertility rate is about 2.1. However, Espenshade, Guzman and Westoff (2003) argue that the replacement rate does not occur at that rate, but relates instead to the mortality rate. The onset of the replacement level of fertility has further implications for the ethnic composition of a population. If the replacement level can be maintained for about forty years, the population will stabilize with zero growth. However, in many cases, this is not a reality. Some regions can easily fall into below replacement fertility for so long as to threaten the population with extinction. For instance, Japan and Germany are depopulating in this way. Some provinces in Indonesia such as Jakarta, Yogyakarta and Bali have already completed their first demographic and are now in the second demographic transition, where the fertility rate is below the replacement level. As Lesthaeghe (1991) argues, under the regime of the second demographic transition, marriage is no longer universal, occurs at older ages and can be childless. There are higher-order needs on individualization, self-actualization and a rising awareness of human rights.

Two Stage Sampling procedure has been found to be much more acceptable for demographic surveys owing to the lower cost involved and also due to the fact that it gives rise to a good representative sample. .As such, it has been found to be much acceptable for estimating total fertility rate (TFR), an important measure of fertility. The objective of this paper is to estimate the TFR in the slum pockets of Guwahati city in Assam under two stage sampling technique and to bring out a comparative analysis of the TFR calculated under the proposed sampling design and under the Simple Random Sampling (SRS) method.The study reveals that the overall fertility scenario in the slum areas of Guwahati, as far as TFR is concerned, when calculated under two stage sampling technique is above the replacement level. However, the TFR calculated by SRS method is found to be almost equal to the replacement level, implying under estimation of TFR when calculated by this method. Moreover, it has been seen that the adjusted TFRs derived after adjusting the biases marginally increases in case of SRS technique and decreases in case of two stage sampling technique. The MSE s are found to be higher in case of conventional technique, implying that the sampling error involved in the conventional method is more as compared to two stage sampling technique. We may thus consider two stage sampling method as a better technique for estimating TFR.

Prospects for Fertility Decline in Africa

We describe a Bayesian projection model to produce country-specific projections of the total fertility rate (TFR) for all countries. The model decomposes the evolution of TFR into three phases: pre-transition high fertility, the fertility transition, and post-transition low fertility. The model for the fertility decline builds on the United Nations Population Division's current deterministic projection methodology, which assumes that fertility will eventually fall below replacement level. It models the decline in TFR as the sum of two logistic functions that depend on the current TFR level, and a random term. A Bayesian hierarchical model is used to project future TFR based on both the country's TFR history and the pattern of all countries. It is estimated from United Nations estimates of past TFR in all countries using a Markov chain Monte Carlo algorithm. The post-transition low fertility phase is modeled using an autoregressive model, in which long-term TFR projections converge toward and oscillate around replacement level. The method is evaluated using out-of-sample projections for the period since 1980 and the period since 1995, and is found to be well calibrated.

Japan's total fertility rate declined to 1.29 in 2003, the lowest in its modern era. Such a drastic decline in fertility rate is extremely unusual. The decreased birthrate creates an unbalanced demographic composition between the productive and dependent populations. This development may result in agreater burden per person regarding social security and have a negative effect on Japan's long-term economic performance. This article surveys the literature on the decline in Japan's fertility rate. It emphasizes the policy implications with respect to supporting households that want to have children. In the first section of the article, we describe a long-term trend in Japan's fertility rate and show that the decline after the 1970s was primarily attributed to a decline in marriage rates, and partly due to a decline in the number of households with three or more children. We then present a survey empirical studies to examine the relationship between birthrates and several factors: an increase in the opportunity costs of having children caused by increases in female wages and labor participation, the growing costs of child care, the shortage of child care services, poor company support for child care leave, and direct public compensation for having children. We conclude that all policies supporting female workers and child care are especially important.