By Nandita Saikia, Postdoc Research Scholar at IIASA
What matters more when it comes to preventing unexpected and tragic adult deaths, between the ages of 15 and 60, in low- or middle-income countries? Is it wealth? Or education?
With the advent of demographic and health surveys (DHS), empirical studies documented that the education level of mothers matters more than the wealth of the household when it comes to preventing deaths among children in these countries. However, the same question largely remained unanswered for adults, as such surveys rarely collect information on adult deaths and the socioeconomic status of the dead individuals. In these countries, in general, death registration systems are poor, which again hinders scientific studies addressing this issue.
One possible solution is the clever use of indirect methods or models on census and survey data, developed by demographers to derive rates from limited, deficient and defective data. These methods use indirect information collected by surveys for a different purpose. For example, by using women’s siblings’ survival status, one can estimate maternal mortality, or by using women’s widowhood status, one can estimate male adult mortality.
In our recent study on India, we used one such method, called the Orphanhood method, to document life expectancy differences in adulthood by important socioeconomic characteristics. Because of the reasons mentioned above, there is hardly any scientific evidence on life expectancy differences by education or economic status in India, a country with exceptional cultural and socioeconomic diversity. The importance of studying adult mortality disparity in India also lies in the fact that India experiences relatively higher adult mortality than some of its neighboring countries with similar level of economic development. India’s official statistics shows that adult females belonging to the northeastern state of Assam have more than two times the mortality risk of adult females belonging to the southern state of Kerala. In addition, because of drastic reduction of under five deaths in India in recent years, more and more premature deaths in India will occur in adult age in near future. We used adult parental survival data from a nationally representative large-scale survey, called the India Human Development Survey, 2015-2016, to estimate life expectancy at age 15 in 1998-1999.
We found that lower levels of education of the deceased adults or their offspring, leads to more disparity than any other socioeconomic characteristic, including income status of the offspring, caste, or religion. Literate adults of both sexes at age 15 lived about 3.5 years more than that of their illiterate counterparts. On average, parents of children educated to higher-secondary level (and above) gain an extra 3.8-4.6 years of adult life compared to parents of illiterate children. We found that disparity in adult life by caste and religion is much smaller than disparities arising from educational attainment. For example, female Hindu adults lived 1.3 years more than female non-Hindu adults and male Hindu adults lived 0.9 years more than male non-Hindu adults.
One inherent limitation of indirect demographic methods is that they cannot provide estimates in the most recent years. Despite our estimates referring to a time period about twenty years ago, they are still crucial, as this kind of disparity in adult deaths does not disappear in such a short time span. Our results suggest that investing in education can be more rewarding than anything else to prevent untimely deaths, and to prevent inequalities across population subgroups. Meanwhile, we suggest including appropriate indirect questions in surveys or censuses to track survival status by social group or small geographical area until vital registration systems in countries such as India become fully functional.
Saikia N, Bora JK and Luy M (2019). Socioeconomic disparity in adult mortality in India: Estimations using the Orphanhood method. Genus DOI: 10.1186/s41118-019-0054-1 [pure.iiasa.ac.at/id/eprint/15730/]
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.
By Sandra Ortellado, 2018 Science Communication Fellow
Science fiction depicts the future with a combination of fascination and fear. While artificial intelligence (AI) could take us beyond the limits of human error, dystopic scenes of world domination reveal our greatest fear: that humans are no match for machines, especially in the job market. But in the so-called fourth industrial revolution, often known as Industry 4.0, the line between future and fiction is a thread of reality.
Over the next 13 years, impending automation could force as many as 70 million workers in the US to find another way to make money. The role of technology is not only growing but also demanding a completely new way of thinking about the work we do and our impact on society because of it.
Rather than focusing on which jobs will disappear because of technological disruption, we could be identifying the most resilient tasks within jobs, says J. Luke Irwin, 2018 YSSP participant. His research in the IIASA World Population program uses a role- and task-based analysis to investigate professions that will be most resilient to technological disruption, with the hope of guiding workforce development policy and training programs.
“We are getting better and better at programming algorithms for machines to do things that we thought were really only in the realm of humans,” says Irwin. “The amount of disruption that’s going to happen to the work industry in the next ten years is really going to impact everyone.”
However, the fear and instability created by the potential disruption elicit chaos, and the response is hard to organize into constructive action. While the resources remain untapped, creativity and imagination are wasted on speculation instead of preparation.
“I couldn’t stand that there’s all this great evidence-based work out there about how we can improve people’s lives and no one is using it,” said Irwin, “I’m trying to align a lot of research and put it in a place where you can compare it and make it more useful and more transferable between the people who would be talking about this: educators, policymakers, employers, and anybody in the workforce.”
Using a German dataset with vocational training as well as time and task information, Irwin will break down jobs into the specific cognitive and physical skills involved and rank the durability of each skill.
Based on the identified jobs and skills, Irwin will go on to draw connections between labor-force capabilities and education policies. His goal is to scale the findings of the most resilient skills to the German labor system so that policymakers and academic institutions can retrain currently displaced workforces and reimagine the future of human work.
After all, while about half the duties workers currently handle could be automated, Mckinsey Global Institute suggests that less than 5% of occupations could be entirely taken over by computers. The future of predictable, repetitive, and purely quantitative work may be threatened, but automation could also open the door for occupations we can’t even imagine yet.
“I think people are amazing and that they have a lot more potential than we are currently capable of fulfilling,” says Irwin.
The World Economic Forum estimates that 65% of children today will end up in careers that don’t even exist yet. For now, an increasingly self-employed millennial generation works insecure, unprotected jobs. The new gig economy, characterized by temporary contracted positions, offers independence but also instability in the labor market.
Without stable work, people lose a sense of security, and that can be dangerous for a policy system that isn’t built to handle uncertainty.
The last industrial revolution caused two or three generations of people to be thrown into poverty and lose everything they had because it was all tied into their job, recalls Irwin.
“Everything gets bad when things are uncertain,” says Irwin, “And this is a very uncertain time. We need to have a better idea of what’s coming so we can actually make some change.”
Irwin, who earned his Master’s in Public Health in 2014, wants his work to have a preventative focus, trying to find those things that not enough people are talking about, but have the potential to make a huge impact on public well-being.
“Especially in the United States, where I live, we’re so tied up with our jobs—it seems like it’s over half our identity,” says Irwin, “We live to work in America.”
In a place like the US, where a job is not only a source of income, but also an identity and a health factor, Irwin’s research offers hope that technological disruption can foster opportunity instead of chaos.
Note: This article gives the views of the authors, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
By Nandita Saikia, Postdoctoral Research Scholar at IIASA
Being an author of a research article on excess female deaths in India in Lancet Global Health, one of the world’s most prestigious and high impact factor public health journals, today I questioned myself: Did I dream of reaching here when I was a little school going girl in the early nineties in a remote village in North East India?
I am the fourth daughter of five. In a country like India, where the status of women is undoubtedly poorer than men even now, and newspapers are often filled with heinous crimes against women, you may be able to imagine what it meant being a fourth daughter. Out of five sisters, three of us were born because my parents wanted a son. My mother, who barely completed her school education, did not want more than two children irrespective of sex, but was pressurized by the extended family to go for a boy after a third daughter and six years of repeated abortions.
I was told in my childhood that I was the most unwanted child in the family. I was a daughter, terribly underweight until age 11, and had much darker skin than my elder sisters and most people from our area, who have fairer skin than average in India. At my birth, my father, a college dropout farmer, was away in a relative’s house and when he heard about the arrival of another girl, he postponed his return trip.
This is a real story, but just one of those still happening in India. The fact that the girls of India are unwanted was observed from the days of early 20th century when it was written in the 1901 census:
“There is no doubt that, as a rule, she [a girl] receives less attention than would be bestowed upon a son. She is less warmly clad, … She is probably not so well fed as a boy would be, and when ill, her parents are not likely to make the same strenuous efforts to ensure her recovery.”
Regrettably, our current study shows that negligence against “India’s daughter” continues to this day.
Discrimination against the girl child can be divided in two categories: before birth and after birth. Modern techniques now allow sex-selective abortion. Despite strong laws, more than 63 million women are estimated to be ‘missing’ in India and the discrimination occurs at all levels of society.
Our present study deals with gender discrimination after birth. We found that over 200,000 girls under the age of five died in 2005 in India as a result of negligence. We found that excess female mortality was present in more than 90% of districts, but the four largest states of North India (Uttar Pradesh, Bihar, Rajasthan, and Madhya Pradesh) accounted for two thirds of India’s total number.
I have to tell you that I was luckier than most girls. Although I was an unwanted child in our extended family, to my mother, this underweight, dark-skinned, little girl was as cute as the previous ones! She gave her best care to her daughter, and she named her “Rani” meaning “Queen” in Assamese. I am still called by this name in my family and in my village.
When I grew up, I asked her several times about her motive for calling me Rani. She always replied: “You were so ugly, the thinnest one with dark skin, I named you as “Rani” because I wanted everyone to have a positive image before seeing you! Also, it is the name of my favorite teacher in high school and she was also a very thin but bright lady!”
The positive conversations with my mother played a crucial role to my desire to have my own identity, and influenced greatly my positive image of myself and my belief that I could do something worthwhile with my life. Much later, when I started my PhD at International Institute for Population Sciences (IIPS), Mumbai, I was surprised to learn that in Maharashtra, one of the wealthiest states of India, second or third daughters are not even given a name, but instead are called ‘Nakusha’, meaning unwanted.
My parents were passionate about educating their daughters, even with their limited means. My father, who was disappointed at my birth, left no stone unturned for my education! By the time I completed secondary school, our village, as well as neighboring villages, congratulated me during the Bihu celebration (the biggest local gathering) for my good performance in school exams. My parents were proud of me by that time; yet, for some strange reason, they always felt themselves weaker than our neighbors who had sons.
Now, people from our village are proud of me not just because I teach in India’s premier university, or that I take several overseas trips in a year, but because they realize that daughters can equally bring renown to their village; daughters can be married off without a dowry; daughters can equally provide old age care to their parents; daughters too can buy property! Due to this attitude and lower fertility levels, many couples now don’t prefer sons over daughters. In a village of 200 households, there are 33 couples that have either one or two daughters, yet did not keep trying for sons. In my own extended family, no one chooses to have more than two children irrespective of their sex. The situation has changed in my village, but not everywhere.
What is the solution of this deep-rooted social menace? We cannot expect a simple solution. However, my own story convinces me that education can be a game changer, but not necessarily academic degrees. I mean a system by which girls realize their own worth and their capability that they can be economically and socially empowered and can drive their own lives. With the help of education, I made myself from an “unwanted” to a wanted daughter!
The purpose of sharing my story is neither self-promotion nor to gain sympathy, rather to inspire millions of girls, who face numerous challenges in everyday life just because of their gender, and doubt their capability, just like I did in my school days. They can make a difference if they want! Nothing can stop them!
By Dilek Yildiz, Wittgenstein Center for Demography and Global Human Capital (IIASA, VID/ÖAW and WU), Vienna Institute of Demography, Austrian Academy of Sciences, International Institute for Applied Systems Analysis
Social media offers a promising source of data for social science research that could provide insights into attitudes, behavior, social linkages and interactions between individuals. As of the third quarter of 2017, Twitter alone had on average 330 million active users per month. The magnitude and the richness of this data attract social scientists working in many different fields with topics studied ranging from extracting quantitative measures such as migration and unemployment, to more qualitative work such as looking at the footprint of second demographic transition (i.e., the shift from high to low fertility) and gender revolution. Although, the use of social media data for scientific research has increased rapidly in recent years, several questions remain unanswered. In a recent publication with Jo Munson, Agnese Vitali and Ramine Tinati from the University of Southampton, and Jennifer Holland from Erasmus University, Rotterdam, we investigated to what extent findings obtained with social media data are generalizable to broader populations, and what constitutes best practice for estimating demographic information from Twitter data.
A key issue when using this data source is that a sample selected from a social media platform differs from a sample used in standard statistical analysis. Usually, a sample is randomly selected according to a survey design so that information gathered from this sample can be used to make inferences about a general population (e.g., people living in Austria). However, despite the huge number of users, the information gathered from Twitter and the estimates produced are subject to bias due to its non-random, non-representative nature. Consistent with previous research conducted in the United States, we found that Twitter users are more likely than the general population to be young and male, and that Twitter penetration is highest in urban areas. In addition, the demographic characteristics of users, such as age and gender, are not always readily available. Consequently, despite its potential, deriving the demographic characteristics of social media users and dealing with the non-random, non-representative populations from which they are drawn represent challenges for social scientists.
Although previous research has explored methods for conducting demographic research using non-representative internet data, few studies mention or account for the bias and measurement error inherent in social media data. To fill this gap, we investigated best practice for estimating demographic information from Twitter users, and then attempted to reduce selection bias by calibrating the non-representative sample of Twitter users with a more reliable source.
We gathered information from 979,992 geo-located Tweets sent by 22,356 unique users in South-East England and estimated their demographic characteristics using the crowd-sourcing platform CrowdFlower and the image-recognition software Face++. Our results show that CrowdFlower estimates age more accurately than Face++, while both tools are highly reliable for estimating the sex of Twitter users.
To evaluate and reduce the selection bias, we ran a series of models and calibrated the non-representative sample of Twitter users with mid-year population estimates for South-East England from the UK Office of National Statistics. We then corrected the bias in age-, sex-, and location-specific population counts. This bias correction exercise shows promise for unbiased inference when using social media data and can be used to further reduce selection bias by including other sociodemographic variables of social media users such as ethnicity. By extending the modeling framework slightly to include an additional variable, which is only available through social media data, it is also possible to make unbiased inferences for broader populations by, for example, extracting the variable of interest from Tweets via text mining. Lastly, our methodology lends itself for use in the calculation of sample weights for Twitter users or Tweets. This means that a Twitter sample can be treated as an individual-level dataset for micro-level analysis (e.g., for measuring associations between variables obtained from Twitter data).
By Valeria Bordone, University of Munich Department of Sociology and IIASA World Population Program
Everyone, consciously or unconsciously, formulates in their own mind a subjective survival probability– i.e., an estimate of how long they are going to live. This will affect decisions in different spheres of later life: retirement, investments, and healthy behaviors. Moreover, previous research has found that subjective survival probability is a good predictor of mortality. In fact, on average, people somehow know better than standard health measures the effect that their characteristics and their behavior have on life expectancy. It is however plausible not only to expect differences within the population in terms of survival, but also in the ability to predict their own survival.
(cc) roujo | Flickr
In a recent publication with Bruno Arpino from the University Pompeu Fabra and Sergei Scherbov from the Wittgenstein Centre (IIASA, VID/ÖAW, WU)., we presented for the first time joint analyses of the effect of smoking behavior and education on subjective survival probabilities and on the ability of survey respondents to predict their real survival, using longitudinal data on people aged 50-89 years old in the USA drawn from the Health and Retirement Study.
We found that, consistent with real mortality, smokers report the lowest subjective survival probabilities. Similarly, less educated people report lower subjective survival probabilities than higher education people. This is in line with the well-known positive correlation between education and life expectancy. However, despite being aware of their lower life expectancy as compared to non-smokers and past smokers, people currently smoking at the time of the survey tended to overestimate their survival probabilities. This holds especially for less educated people.
Our study suggests that in fact, education also plays an important role in shaping people’s ability to estimate their own survival probability. Whether or not they smoke, we found that more highly educated people are more likely to correctly predict their survival probabilities.
In view of the high proportion of the American population that consists of current or past smokers, a percentage that reached 77% in some male cohorts, our findings emphasize the need to disseminate more information about risks of smoking, specifically targeting people with less education.
By showing that smoking and education play together in determining how well people can assess the own survival potential, this study extends our understanding of the variability of subjective survival probabilities within a population. The fact that sub-groups within the population differently incorporate the effects of smoking into their assessment of survival probabilities may have important consequences for example on when people exit the labor market or whether they buy a life insurance, as individuals are likely to base their decisions also on their longevity expectations.
Policymakers can therefore draw some relevant conclusions from our study to design policies concerned with health and survivorship in later life. Despite the various anti-smoking campaigns and smoking restrictions, smokers may not be fully aware of the risks of smoking. In particular, educational groups seem to be differently exposed to the information that is disseminated to the public. Our study suggests that there is a need to target such information to less educated people, who are the most likely to underestimate the risks of smoking. Providing information on how survival probabilities vary by smoking behavior may not only reduce smoking but it may also increase individuals’ ability to assess their own survival.
(cc) Quinn Dombrowski | Flickr
Reference Arpino B, Bordone V, & Scherbov S (2017). Smoking, Education and the Ability to Predict Own Survival Probabilities: An Observational Study on US Data. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-17-012 [pure.iiasa.ac.at/14692]
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.
Faced with a sharp decline in the global fertility levels over the last few decades, many countries today are confronted with the problem of an aging population. This could translate into an economic threat: higher health-care costs for the elderly coupled with a shrinking working population will lead to lower income-tax revenues to provide for these rising costs. This can already be seen in countries like Japan, Spain, and Germany. With an increasing number of elderly dependents and not enough workers to replace them, their social support systems have become increasingly strained.
Even though in the last few decades there has been an increase in individual incomes, researchers have observed a negative correlation between the increased wealth and the number of children people choose to have. Sara Loo, as part of the 2017 Young Scientists Summer Program (YSSP), seeks to explore why people are choosing to have fewer children as their social and economic conditions change for the better.
According to a report titled World Fertility Patterns 2015, global fertility levels have gone down from just above five children in 1950 to around 2.5 children per woman in 2015. In the figure below, ‘total fertility rate’ refers to the average number of children that are born to a woman over her lifetime.
It might seem counterintuitive that better living standards would be linked to decreased fertility. One way to explain it is through the lens of cultural evolution. Loo explains that culture is constantly changing – be it beliefs, knowledge, skills, or customs. This change is reflected in people’s day-to-day behaviors and affects their choices, both professional and personal. Importantly, beliefs and customs are acquired not only from people’s parents but are largely influenced by their peers – friends and colleagues.
One of the ways in which cultural evolution has affected fertility rates is resulting from the trade-off between the number of children and the quality of life that parents desire to give each of them, says Loo. As both men and women vie for well-paying jobs to attain a higher standard of living, and as they compete for such jobs based on their education, the resources parents invest into each child’s upbringing, including education and inheritance, are crucial. Even the time parents can give to their children becomes an expensive currency.
This makes for a highly competitive environment in which everyone is trying to achieve a higher status, in order to provide better opportunities for their children. When parents have fewer children, this means giving each of them a greater chance of achieving higher status.
Loo elaborates that as everyone competes to get their children to the top of the socioeconomic ladder, this necessitates a higher investment per child, both monetarily and otherwise. The theory of cultural evolution in this case thus predicts lowered fertility as competition for well-paying jobs intensifies with a country’s development.
However, it is not that such parental strategies apply equally to all segments of a population, says Evolution and Ecology Program Director Ulf Dieckmann, who is supervising Loo’s research at the institute over the summer. He explains that it is therefore helpful to look at fertility in relation to people’s socioeconomic status, instead of just looking at a population’s average fertility rate over time.
This can give telling insights. “In many pre-industrial societies, the rich had greater numbers of children, and if anybody had less than replacement-level fertility, it was the really poor people who could not afford to raise as many children. It was over time that this correlation changed from positive to negative when richer people decided to have fewer children: if they had too many children, they could not afford to invest as much per child as was needed to secure maintaining or raising the children’s socioeconomic status. This has led to a reversal of the traditional pattern: in developed societies, fertility has been shown to drop at high socioeconomic status,” says Dieckmann.
Complementing existing research on the fertility impacts of urbanization and of women’s education and liberation, Loo plans to explore how the aforementioned mechanisms of cultural evolution can explain the observed negative correlation between socioeconomic status and fertility. Her goal is to do so using a mathematical model that can account for both economic trends and cultural trends as two key processes influencing fertility rates.
About the researcher
Sara Loo is currently a third-year PhD candidate at the University of Sydney, Australia, where her research focuses on the evolution of uniquely human behaviors. Loo is working with the Evolution and Ecology Program at IIASA over the summer, with Professor Karl Sigmund and Program Director Ulf Dieckmann as her supervisors for the project.
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.