Nov 5, 2014 | Demography
By Samir K.C., IIASA World Population Program
How old are you? This is the most basic demographic question about an individual, and an easy one to answer. What is the population of the world or your country? Well, many who read the news roughly know the number, about seven billion for the world and more than a billion in China and India. But when asked more detailed questions about demography, “What percentage of people are younger than you in the world or your country?” or “What’s the remaining life expectancy for you in your country and the world?” the eyes start rolling. Such questions are important because they lead to better knowledge and awareness about the population, especially the question of life expectancy.
(Photo: UN Photo/Sebastiao Barbosa)
This is why I, with my colleagues Wolfgang Fengler (World Bank), Benedikt Gross (data visualization designer), and many others, have developed a website where people can find out their respective place in the world population or the country population: population.io. The website was launched last Saturday at the TEDxVienna.
How long will we live? Most of us in the general public do not know the answer. But demographers and actuaries can actually project the expected date of death for populations, based on factors such as place of residence, age, and sex. Demographers use data on deaths occurring during a period and the population structure to estimate death rates. These death rates are then included in the life table calculations that show, among other details, expected number of years of remaining life given one’s place of residence, age, and sex.
On population.io, you can find your own expected death date, based on population projections and details such as where you were born, where you live, and your sex. Of course, this date is just an average with a distribution. If the remaining life expectancy for a 40-year-old is 30 more years, that does not mean that all today’s 40-year-olds will die in 2044: roughly half will die earlier and half later. But we hope that exploring this tool will give people some insight into the world and their country’s population and their place within it.
How do we know how long you will live?
To answer this question, we use population projections. To make good population projections, demographers need information about the demographic structure, including current age and sex structure and assumptions about the future scenarios of mortality, fertility, and migration. A “cohort component” method is then applied to calculate the future population size and structure and to obtain number of births, deaths, and migration. This method projects each cohort born in the same one- or five-year period forward in time, to replace the older cohort occupying the age. In the process some die or migrate out (population decreases) and some migrate in (population increases), while women in reproductive age groups might give birth to children, who will then enter the population as a new cohort. All of these numbers and assumptions are needed for many purposes within and outside the discipline of population studies including for a proper answer to our question, “How long will I live?”
Here’s how the calculations behind population.io work. As an example, I’ll take myself: For a male of my age, 40 years old, on average according to the current global mortality rates, my remaining life expectancy would be about 37 years. This is bit scary for me – that means as an average “global citizen, I would die at age 77. In Nepal, where I am from, my life expectancy would be a little more than one year less. However, since I will most likely live in Austria, my remaining life expectancy increases to 43 years, an increase of 7.4 years due to migration.
On population.io, you can explore–among lots of other population data–how living in a different country would affect your life expectancy. Click to try it yourself!
Now, if I add that I belong to the highest category in terms of education, what will happen to my life expectancy? Though education is not yet included in the population.io, it turns out that that also depends to a large degree on where I live. In Portugal or Italy, a person with a university degree would have lesser advantage compared to those with lower secondary education or below (2.5 and 2.6 years more respectively) than someone living in Estonia (13.8 years more) or the Czech Republic (12.5 years), Hungary and Bulgaria (12.1 years).
What if I am a smoker? Do not exercise? These factors too play an important role in future life expectancy, and we plan to add them soon to the population.io Web site.
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
Sep 23, 2014 | Demography
By: Wolfgang Lutz, Bill Butz, Samir KC, Warren Sanderson, and Sergei Scherbov: IIASA World Population Program
Demographers from the United Nations Population Division and several universities published a paper in Science last week that argues the world population is unlikely to stop growing this century. They calculate that there is an 80% probability that world population, now 7.2 billion, will increase to between 9.6 and 12.3 billion in 2100, with the median at 10.9 billion.
Different projections for future fertility rates in countries such as China and Nigeria are one major reason for the difference in projections between IIASA and the UN. Photo Credit: Evgeni Zotov via Flickr
Next month, we will announce the results of our newest assessment at the launch of a new book entitled: “World Population and Global Human Capital in the 21st Century” (Lutz, Butz and KC, Oxford University Press 2014). Contrary to the UN projections, the IIASA medium (most likely) scenario indicates that world population will increase to 9.2 billion by 2050, peak at 9.4 billion around 2070 and start a slow decline to 9.0 billion by the end of the century.
The new UN paper uses a probabilistic approach to global population projections providing quantitative uncertainty ranges. Such an approach was first developed at IIASA. In a 1997 Nature article, IIASA used probabilistic methods to indicate that the doubling of world population was unlikely. And in a 2001 Nature article, IIASA demographers projected that there was an 85% chance that the world’s population would stop growing this century.
The UN and IIASA population projections use very different approaches for defining the assumptions underlying future fertility and mortality trajectories. The new IIASA projections are based on the substantive input of more than 550 experts worldwide who were invited to evaluate in a peer review manner a set of alternative scientific arguments bearing directly on the future demographic trajectories. This was done through an online survey as well as a series of meetings on five continents. The resulting state of our knowledge and substantive reasoning is documented in over 500 pages in the OUP book.
Alternatively, the UN population projections have recently moved away from their earlier expert-based assumptions to the other extreme: Their new probabilistic population projections reflect expert judgment only in the design of a specific statistical model which then is applied to national time series of 60 years (1950-2010) to extrapolate 90 years (2010-2100) into the future. There is no room for country-specific expert knowledge or for substantive considerations.
IIASA population projections explicitly include education, which is one factor that leads to lower fertility rates and lower projections by IIASA compared to the UN. Source: Wittgenstein Centre Data Explorer
There are two other factors explaining the difference: One is that IIASA now systematically adds a differentiation by level of education in addition to the conventional age and sex to its population projections, as education significantly influences fertility rates (Policy Brief: Rethinking population policies). Once this important source of population heterogeneity is explicitly taken into account the future looks different. In the example of Nigeria, the UN projects an increase from 160 million in 2010 to 914 million in 2100 while IIASA projects only 576 million. The IIASA projections do consider the fact that recently Nigeria has made significant progress in girls education, such that today half of the women aged 20-24 already have secondary education, while among women aged 40-44 the percentage is only 25 percent. And since more educated women consistently have lower fertility, future fertility is likely to decline as the more educated girls enter reproductive age. Disregarding this important structural change leads to higher projections of future fertility.
Another difference lies in the reading of the current fertility levels in Africa as well as in China. The UN assumes that fertility in Nigeria has been stagnant at 6 children per woman for the past decade and for this reason their purely statistical model results in very slow future decline. However, the most recent Demographic and Health Survey (DHS 2013) for Nigeria shows that fertility has already declined to 5.5—a level the UN assumes would only be reached by 2020-25.
The same is true for other African countries such as Mali where the DHS shows fertility has already fallen to 6.1 a value that according to the UN projections would only be reached in 2025-30. For China, currently still the world’s biggest country, the UN assumes that fertility stands at 1.66 and will not decline further but rather increase in the future. Based on expert reasoning the IIASA projections assume that fertility in 2010 was around 1.5 and will decline to 1.4 in the coming decades, following the patterns of Japan, South Korea, Taiwan and Hong Kong which are currently in the 1.0 – 1.4 range.
Population pyramids for Nigeria show IIASA’s projected population and education levels for 2010 and 2050. Source: Wittgenstein Centre Data Explorer
References:
Lutz W, Butz W, and KC S, eds. 2014 World Population and Global Human Capital in the 21st Century, Oxford University Press 2014.
Patrick Gerland, Adrian E. Raftery, Hana Ševčíková, Nan Li, Danan Gu, Thomas Spoorenberg, Leontine Alkema, Bailey K. Fosdick, Jennifer Chunn, Nevena Lalic, Guiomar Bay, Thomas Buettner, Gerhard K. Heilig, and John Wilmoth. 2014. World population stabilization unlikely this century. Science 1257469 [DOI:10.1126/science.1257469]
Lutz W, Sanderson WC, Scherbov S. 1997. Doubling of world population unlikely. Nature, 387(6635):803-805 (19 June 1997) www.nature.com/nature/journal/v387/n6635/full/387803a0.html
Lutz W, Sanderson WC, Scherbov S. 2001. The end of world population growth. Nature, 412(6846):543-545 (2 August 2001) http://dx.doi.org/10.1038/35087589
Wolfgang Lutz. 2014. A Population Policy Rationale for the Twenty-First Century. Population and Development Review. doi:10.1111/j.1728-4457.2014.00696.x
Mar 17, 2014 | Alumni, Poverty & Equity, Young Scientists
Lanoi Maloiy is a PhD student at the University of South Australia in Adelaide, and a participant in the recently completed 2013-14 Southern African Young Scientists Summer Program (SA-YSSP), which IIASA co-organizes with the South African National Research Foundation and Department of Science and Technology at the University of the Free State in South Africa. In this interview Maloiy talks about her research and her experience in the program.
Lanoi Maloiy Photo Credit: Stephen Collett
Why did you apply for the SA-YSSP?
I applied for the Southern African Young Scientists Summer Program because I envisioned the program would assist my research, especially regarding ways to improve the quality of life for Africans.
I’m from Nairobi, Kenya and from the Maasai tribe. Coming from Africa, I am passionate about improving the quality of life for all of the continent’s citizens. The Maasai are a culture that traditionally didn’t often value sending girls to school, but my parents really stressed the importance of education.
I have seen very clearly in my own life how having access to education makes a difference, and how it really presents a limitation for those who don’t have access to education. Especially for girls, not having that education really limits their options. This experience made me very passionate about education as a transformative tool. I believe that education is an important tool in eradicating poverty and eliminating oppression.
Please tell us about your project for the SA-YSSP.
My research for the SA-YSSP explores the educational experiences of Kenyan female political leaders evaluating the role of education in their leadership journey. I investigated social, cultural and historical issues regarding African women and education, including the leadership context in Africa. My doctoral work is an interdisciplinary study within the fields of gender, education, and African leadership. The study investigates the experiences of Kenyan female political leaders, and focuses on locating enablers or strategies to address the challenges women face while accessing leadership positions.
During the program I worked with IIASA population researcher Dr. Anne Goujon and my South African adviser Dr. Petronella Jonck. Working with them gave my research a new social psychology perspective which really enriched my work, because I come from an education and a leadership standpoint, it broadened my research examining it from the perspective of social psychology, evaluating the interaction and dynamics of gender within society.
I believe that this study will be beneficial to policy makers, and leadership practitioners. More studies on women leaders in Africa are essential to provide a global account of the experiences of women in leadership.
What methods did you use to conduct your study?
I did largely a qualitative study analyzing face to face interviews with 18 women political leaders in Kenya, which I had conducted in 2013. I went to where the women leaders were based, often to their constituencies or in parliament. The interviews included demographic questions, asking them about their education, qualifications, age, and marital status. Then the second half of the interview was more open ended, asking about their leadership journey, about their family background, educational background, and what factors enabled them, and factors that inhibited them, and in particular evaluating the role of education and personality. The last section of the interviews focused more on recommendations, asking their opinion on strategies that could be put into place to help women better access leadership positions. In particular, what African society could do better in terms of accommodating women, and also asking participants why it is important to have women take part in leadership, and how women leaders can enrich African society.
I will be submitting my report at the end of this month, and we plan to also submit a journal article on the work.
How has the program changed the way you think about or do research?
The SA-YSSP has informed the way in which I communicate my research, ensuring simplicity and clarity, especially to interdisciplinary audiences. It has also equipped me as an early career researcher, with knowledge and skills to locate avenues for transforming and improving the lives of Africa’s citizens through research.
What was the best thing about the SA-YSSP?
The SA-YSSP programme was an exciting and capacity building process, which provided a rich experience for me as an early career researcher. It afforded me with an invaluable learning experience. Attending lectures on writing scientific papers, systems analysis, including practical ‘hands on’ training in media communication enriched and extended my skills base. Interacting with a range of PhD students brought a new wealth of knowledge and provided a vibrant social experience. I truly appreciated the opportunity to contribute and engage in research life during the course of the summer program.
Where do you hope to go with your research career?
I have a strong desire to be part of research that transforms the lives of Africans, in particular through education and leadership development projects. I believe that attending the SA-YSSP has proved an important step towards my long-term goal of creating leadership development programs to improve the quality of life for Africans.
Lanoi Maloiy, right, with other participants in the 2013-14 Southern African Young Scientists Summer Program (SA-YSSP) Photo Credit: Rene Van Der Berg
Note: This article gives the views of the interviewee, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
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Feb 27, 2014 | Demography, Science and Policy
By Erich Striessnig, IIASA World Population Program
Is replacement level fertility really the best for society? Maybe not, say IIASA researchers. Photo Credit: Héctor Gómez Herrero via Flickr (Creative Commons License)
When asked what a desirable fertility level for populations might be, most politicians, journalists, and even social scientists would say it is around two children per woman, as this would – on the long run – prevent a population from either exploding or dying out. Other reasons for championing replacement level fertility include maintaining the size of the labor force and stabilizing the dependency ratio. But what is the evidence for this rule of thumb?
My colleague Wolfgang Lutz and I aimed to answer this question in a new study published in the journal Demographic Research. We found, not surprisingly, that the optimal fertility level strongly depends on what you mean by optimal.
The criteria for optimal fertility have often been motivated by nationalistic desires for larger and thus more powerful nations. Today our concerns run more towards the dangers of overpopulation for the environment, the climate, and the limited resources on Earth, dampening the enthusiasm for high fertility rates. But as fertility rates fall in many countries around the world, there is a growing concern about aging populations and an increasing number of elderly depending on an ever smaller number of people actively participating in the labor force.
While all of these fears relate to the same problem – an unbalanced population age-structure – the resulting assessments of what level of fertility would be desirable completely ignore the heterogeneity of the population with regard to important demographic characteristics, especially the population’s education structure.
In our study, we wanted to account for the fact that more education not only has higher economic costs, including later entry to the labor market and higher life expectancy, which can hardly been seen as a negative effect. But education also leads to higher productivity, less unemployment, and a healthier workforce that would on average retire later. To include these factors in our assessment, we ran thousands of simulations using varying constant rates of fertility.
What we found is that when we factor in education, the level of fertility that on the long run would lead to the lowest level of dependency is well below the supposedly magical level of two children per woman.
We also tried to link the effects of different fertility rates to the resulting environmental burden by factoring in expected carbon emissions. Not surprisingly, higher rates of fertility lead to faster population growth and more emissions. That suggests that an environmentally aware society should aim for even lower fertility levels.
While our research is not intended to prescribe fertility levels for individuals and countries, the conclusions drawn from this thought experiment suggest that the widespread popular notions that current fertility levels–for example in France or the US are just right because they are around replacement level, whereas they are too low in countries like Germany or Austria–may be wrong. According to our new study, the opposite is true.
Reference
Striessnig, E, Lutz W. (2014) How does education change the relationship between fertility and age-dependency under environmental constraints? A long-term simulation exercise Demographic Research, 30(16):465-492 http://www.demographic-research.org/volumes/vol30/16/
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
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Dec 2, 2013 | Demography, Science and Policy
By Raya Muttarak, Research Scholar, IIASA World Population Program
For many years social scientists have observed a connection between economic downturns and a reduction in both unhealthy behaviors and mortality—a paradigm known as “healthy living in hard times.” One possible explanation for such counterintuitive findings was that people when people lose their jobs, have more spare time to dedicate to physical activities. Moreover, under an increased threat of unemployment, those who were still employed might limit their smoking and alcohol consumption in order to reduce their chances of being laid off. So the general agreement among social scientists has been that we should not worry too much about the impact of an economic crisis on health and mortality.
Our current global economic recession is the worst economic recession in contemporary history, however. And in a new study we found out that in fact, its consequences for health are very different from previous economic downturns. Since 2008, the number of smokers has increased substantially along with the increase in unemployed people.
In the new study, published in the journal Tobacco Control, we analyzed the effects of the current economic crisis on smoking in the United States, using data for around 2 million people from the Center for Disease Control’s Behavioral Risk Factor Surveillance System (BRFSS) survey for 2005 to 2010. In contrast to previous economic literature, we found an estimated increase of around 600,000 smokers due to the economic crisis.
Our study took into account the increase of the US population, the pre-crisis trends in smoking prevalence and the change in the distribution of population by socio-demographic characteristics.
We did find that “healthy living in hard times” still holds true for the employed individuals. Perhaps for fear of being laid off due to insurance reasons, these people maintain their healthy lifestyle. On the other hand, once without a job, people either started smoking or relapsed, possibly because of stress related to their new economic situation.
In contrast to previous economic literature, the new study finds an estimated increase of around 600,000 smokers due to the economic crisis. © Alexander Babich | Dreamstime.com
Since smoking prevalence by employment status remained more or less the same as before the crisis, a sharp increase in the number of unemployed individuals consequently led to a massive rise in the number of smokers. This has counterbalanced the trend in declining smoking rate among the employed.
Two main lessons can be drawn from our findings. First of all, YES…. this time it is different. The magnitude of the crisis has substantially changed the share of those with and without a job, with the latter being much more likely to engage in risky health behaviors. This in turns reversed most of the conclusions drawn by the previous literature. Second, we should not underestimate the impact of job-related stress factors on healthy behaviors. The idea that joblessness could be seen as a holiday where someone can engage in self-empowering activities is nice and reassuring, but it is meant to fail when unemployment is essentially unexpected and unwanted.
Reference
Gallus S, Ghislandi S, Muttarak R. Effects of the economic crisis on smoking prevalence and number of smokers in the USA. Tob Control. 2013 Aug 16. doi: 10.1136/tobaccocontrol-2012-050856. [Epub ahead of print] PubMed PMID: 23956058.
About the author
Raya Muttarak is a Research Scholar with the World Population (POP) Program. She came to IIASA in September 2011. Her current research covers three broad themes: 1) educational inequality and vulnerability and adaptation to climate change, 2) immigrant integration and ethnic inequality in education, the labor market and climate change adaptation, and 3) socioeconomic determinants of health risks and behaviors. More>>
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
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