projection linx

Joint Eurostat/UNECE Work Session on Demographic Projections

with lots of links to working papers

the destiny of Russia

Yes, it is almost clear.
Below is my conclusion from the conference (+here) which took place yesterday and day before yesterday.
Since the alcohol consumption is the only determinant of Russian mortality and fertility, we could see a repetition of Gorbi period with a new leader. There are three pix here to support the thesis (which is below): two by Andreyev, and butterfly by Keenan (drawn also by Andreyev). Theirs as well as other presentations should be available somewhere on demoscope.ru.

The mortality is going down, and fertility is going up -- this combination inevitably leads to collapse of the state. The Soviet Union is already over, Russia is next. Draw your own conclusions ;)
Additional indirect evidence comes from shrinking Russian, and growing alternatives.

25 years of Vishnevski project

Almost all presentations

Whither Russia's immigration debate?

The title is Fred Weir entry in CSM (see, here), my speech here.
I had four wrong answers to their qiuz, one of them is CSM fault. It is Catherine input into Russian history.

I wrote more for the author ;)


1. Russian labor force of Russians will shrink in the near future due to two major reasons: (a) large cohorts born in 50s are leaving their working ages, and (b) smaller cohorts born in 90s are entering this age span (approximately -20-60+, i.e., years of birth around 1993 and 1953). This "substituting cohorts" are about a half of retiring ones. It was known long ago, and is absolutely not a surprise. Russian population is heavily affected by a number of catastrophes or population losses caused by social revolutions and main one was WW2.

For instance, subtracting 70 (modal age at death, age when major part of a cohort dies) from 2013 we obtain 1943, which is a peak of terrible war. It explains the recently reported decline of mortality, people who are due to die were not born 70 years ago. Because of very irregular age composition mortality will rise greatly in about 10 years.

Theoretically, a Russian woman could produce 2.1 babies on average, but they did not do it since mid 80s and mid 60s, most of the time fertility was below replacement level, and I see absolutely know reasons for fertility to jump by more than 1/3 from its current level.

2. Migration. The difference in fertility certainly does not affect migration, neither low in Russia, nor high elsewhere. Moreover nearby areas do not have really high fertility, e.g., Kirghiz women have highest fertility in the former SU, it is twice as high as of Russians, but numerically just 3 v 1.5 (number of children born by a woman). 3 is not high, 3 means it is dropping, and doing it quickly. Say, 20 yeas from now, and fertility will appear about similar. The reason to move en masse is very bad living conditions in areas of emigration, social unrest, even wars, high unemployment, etc., it is definitely not god living conditions in areas of arrival (Russia). + There is absolutely know job competition between local and arrived population. To tell about Birulevo, I have discussed it with a virtual friend http://xaxam.livejournal.com/572856.html, we both thanks Boris Yeltzin who terminated that practice of agricultural assistance from city dwellers to kolkhoz and овощным базам. I suspect no one of Birulevo pogrom-makers wishes to work at that place.

To cope with labor shortages Russian government definitely bets on migration and not on advances in technology and rising labor productivity, unlike Japan for instance. The bet was probably stimulated by the UN substitution migration project, which was very popular recently, and it had just the only opponent – Oxford Professor David Coleman [http://www.spsw.ox.ac.uk/staff/academic/profile/coleman.html], but migration generates as many problems as it solves, or more. Interestingly, that dictator Putin in this case appears more liberal than liberal Navalny, who seems to follow more xenophobic and populist ideas.

3. Improvement in population indicators, reduced mortality and rising fertility. They are real. Any government in such a case would assign appeared good events to its own activities. But what we have with life expectancy requires in depth analyses, it might be a passing away of cohorts mostly affected by war with poor health status, might be something else. First we must observe it as a sustainable trend. Fertility started to grow before so called mother capital had been introduced, plus as Khloponin reported this year some of births in some regions may be registered but not occured. To understand this we also need time, demography is a long play subject, it is not fast at all. Introduction of mother capital did not affect the shape of growing fertility trend, thus government role is disputable and suspicious. What is really true, gov-t successfully stimulated in migration.

The questions were :

.... about the underlying pressures that are
causing mass migration into cities like Moscow from nearby areas that have
very high birth rates, such as the Caucasus and Central Asia.
So, if you could tell me -- in a few short and clear phrases -- what
is the long-term outlook for Russia's labor force of Russians? That is, the
trends with birth rates and female fertility, which is to say, why Russia
can't expect to find the labor it needs in future from Russian mothers
alone.
Also, I'm told there's been some progress in solving the demographic
problem during the Putin years? Putin created programs to encourage Russian
women to have more children. There have been campaigns against drinking and
smoking. How successful have these efforts been? Do the results of these
programs change the long-term prognosis for Russian demography?
That's it. Just as briefly as you can accurately state your
conclusions. You know how journalism differs from academic work: we don't
have to prove anything, just state the arguments clearly and concisely.

Anytime over the weekend would be fine for this; I'm writing the story on Monday.

read it later

Why Are You Not Dead Yet?

5.3k

1.1k

Life expectancy doubled in the past 150 years. Here’s why.

By Laura Helmuth

estimating GAR from RLMS

Unfortunately, I do not have access to RLMS forum to discuss some appearing but probably elementary issues.
It might not exist ;(
Here are 2 pix, with differently counted denominator.


However, SPSS report incorrect denominator. Why? Correct denominator moves estimates below reported line, but a shape (trend) remains somewhat similar.

Multidimensional Life Table Estimation of the Total Fertility Rate and Its Components

Using discrete-time survival models of parity progression and illustrative data from the Philippines, this article develops a multivariate multidimensional life table of nuptiality and fertility, the dimensions of which are age, parity, and duration in parity. The measures calculated from this life table include total fertility rate (TRF), total marital fertility rate (TMFR), parity progression ratios (PPR), age-specific fertility rates, mean and median ages at first marriage, mean and median closed birth intervals, and mean and median ages at childbearing by child’s birth order and for all birth orders combined. These measures are referred to collectively as “TFR and its components.” Because the multidimensional life table is multivariate, all measures derived from it are also multivariate in the sense that they can be tabulated by categories or selected values of one socioeconomic variable while controlling for other socioeconomic variables. The methodology is applied to birth history data, in the form of actual birth histories from a fertility survey or reconstructed birth histories derived from a census or household survey. The methodology yields period estimates as well as cohort estimates of the aforementioned measures.

the paper, published in the last Demography, there are also papers about money transfers to Georgia, and mortality projections of (non)smoking

How Many People Have Ever Lived on Earth?

it seems like a question from N. Keyfitz Applied Math Demography, isn't it?
Below Carl Haub answers the question, and have a look at the table with his estimates. It seems like he calculated total births.
  
Let me reformulate: How Many person-years People Have Ever Lived on Earth?
probably that proportion would be about 1/4 ?

pdf or what?

Have found an interesting thing, in RLMS questionaries for rounds 1 and 4 the search in (pd)file does not work, albeit it works in 2 and 3.
What does it mean?
Different program to convert word2pdf? Supposedly, they used word in 1992.
Unfortunately, the very first round does not have an abortion question. The next three has but with condition since our last meeting, which makes a denominator (women-years) less clear.

Russian mortality for cohorts with 50+ registered age points

the first cohort, 1899 - has 50 points, the last - also 50, in between --- 51 points.
These are the longest available series for Russia, albeit at advanced ages not really reliable

discussing reproductive health at Rosstat, May 2013

more linx2 PLOSEONE must read papers

In April 2009, the most recent pandemic of influenza A began. We present the first estimates of pandemic mortality based on the newly-released final data on deaths in 2009 and 2010 in the United States.

We obtained data on influenza and pneumonia deaths from the National Center for Health Statistics (NCHS). Age- and sex-specific death rates, and age-standardized death rates, were calculated. Using negative binomial Serfling-type methods, excess mortality was calculated separately by sex and age groups.

In many age groups, observed pneumonia and influenza cause-specific mortality rates in October and November 2009 broke month-specific records since 1959 when the current series of detailed US mortality data began. Compared to the typical pattern of seasonal flu deaths, the 2009 pandemic age-specific mortality, as well as influenza-attributable (excess) mortality, skewed much younger. We estimate 2,634 excess pneumonia and influenza deaths in 2009–10; the excess death rate in 2009 was 0.79 per 100,000.

Pandemic influenza mortality skews younger than seasonal influenza. This can be explained by a protective effect due to antigenic cycling. When older cohorts have been previously exposed to a similar antigen, immune memory results in lower death rates at older ages. Age-targeted vaccination of younger people should be considered in future pandemics.

Nguyen AM, Noymer A (2013)

Influenza Mortality in the United States, 2009 Pandemic: Burden, Timing and Age Distribution

PLoS ONE 8(5): e64198. doi:10.1371/journal.pone.0064198

***

We construct a stochastic SIR model for influenza spreading on a D-dimensional lattice, which represents the dynamic contact network of individuals. An age distributed population is placed on the lattice and moves on it. The displacement from a site to a nearest neighbor empty site, allows individuals to change the number and identities of their contacts. The dynamics on the lattice is governed by an attractive interaction between individuals belonging to the same age-class. The parameters, which regulate the pattern dynamics, are fixed fitting the data on the age-dependent daily contact numbers, furnished by the Polymod survey. A simple SIR transmission model with a nearest neighbors interaction and some very basic adaptive mobility restrictions complete the model. The model is validated against the age-distributed Italian epidemiological data for the influenza A(H1N1) during the  season, with sensible predictions for the epidemiological parameters. For an appropriate topology of the lattice, we find that, whenever the accordance between the contact patterns of the model and the Polymod data is satisfactory, there is a good agreement between the numerical and the experimental epidemiological data. This result shows how rich is the information encoded in the average contact patterns of individuals, with respect to the analysis of the epidemic spreading of an infectious disease.

Liccardo A, Fierro A (2013)

A Lattice Model for Influenza Spreading

PLoS ONE 8(5): e63935. doi:10.1371/journal.pone.0063935

***

Reducing health inequalities is a key objective for many governments and public health organizations. Whether inequalities are measured on the absolute (difference) or relative (ratio) scale can have a significant impact on judgments about whether health inequalities are increasing or decreasing, but both of these measures are not often presented in empirical studies. In this study we investigated the impact of selective presentation of health inequality measures on judgments of health inequality trends among 40 university undergraduates. We randomized participants to see either a difference or ratio measure of health inequality alongside raw mortality rates in 5 different scenarios. At baseline there were no differences between treatment groups in assessments of inequality trends, but selective exposure to the same raw data augmented with ratio versus difference inequality graphs altered participants’ assessments of inequality change. When absolute inequality decreased and relative inequality increased, exposure to ratio measures increased the probability of concluding that inequality had increased from 32.5% to 70%, but exposure to difference measures did not (35% vs. 25%). Selective exposure to ratio versus difference inequality graphs thus increased the difference between groups in concluding that inequality had increased from 2.5% (95% CI −9.5% to 14.5%) to 45% (95% CI 29.4 to 60.6). A similar pattern was evident for other scenarios where absolute and relative inequality trends gave conflicting results. In cases where measures of absolute and relative inequality both increased or both decreased, we did not find any evidence that assignment to ratio vs. difference graphs had an impact on assessments of inequality change. Selective reporting of measures of health inequality has the potential to create biased judgments of progress in ameliorating health inequalities.

Harper S, King NB, Young ME (2013)

Impact of Selective Evidence Presentation on Judgments of Health Inequality Trends: An Experimental Study

PLoS ONE 8(5): e63362. doi:10.1371/journal.pone.0063362

***

A criminal career can be either general, with the criminal committing different types of crimes, or specialized, with the criminal committing a specific type of crime. A central problem in the study of crime specialization is to determine, from the perspective of the criminal, which crimes should be considered similar and which crimes should be considered distinct. We study a large set of Swedish suspects to empirically investigate generalist and specialist behavior in crime. We show that there is a large group of suspects who can be described as generalists. At the same time, we observe a non-trivial pattern of specialization across age and gender of suspects. Women are less prone to commit crimes of certain types, and, for instance, are more prone to specialize in crimes related to fraud. We also find evidence of temporal specialization of suspects. Older persons are more specialized than younger ones, and some crime types are preferentially committed by suspects of different ages.

Tumminello M, Edling C, Liljeros F, Mantegna RN, Sarnecki J (2013)

The Phenomenology of Specialization of Criminal Suspects

PLoS ONE 8(5): e64703. doi:10.1371/journal.pone.0064703

***

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air).

Citation: Huang Z, Wu X, Garcia AJ, Fik TJ, Tatem AJ (2013)

An Open-Access Modeled Passenger Flow Matrix for the Global Air Network in 2010

PLoS ONE 8(5): e64317. doi:10.1371/journal.pone.0064317

***

There is believed to be a ‘beauty premium’ in key life outcomes: it is thought that people perceived to be more physically attractive have better educational outcomes, higher-status jobs, higher wages, and are more likely to marry. Evidence for these beliefs, however, is generally based on photographs in hypothetical experiments or studies of very specific population subgroups (such as college students). The extent to which physical attractiveness might have a lasting effect on such outcomes in ‘real life’ situations across the whole population is less well known. Using longitudinal data from a general population cohort of people in the West of Scotland, this paper investigated the association between physical attractiveness at age 15 and key socioeconomic outcomes approximately 20 years later. People assessed as more physically attractive at age 15 had higher socioeconomic positions at age 36– in terms of their employment status, housing tenure and income - and they were more likely to be married; even after adjusting for parental socioeconomic background, their own intelligence, health and self esteem, education and other adult socioeconomic outcomes. For education the association was significant for women but not for men. Understanding why attractiveness is strongly associated with long-term socioeconomic outcomes, after such extensive confounders have been considered, is important.

Benzeval M, Green MJ, Macintyre S (2013)

Does Perceived Physical Attractiveness in Adolescence Predict Better Socioeconomic Position in Adulthood? Evidence from 20 Years of Follow Up in a Population Cohort Study

PLoS ONE 8(5): e63975. doi:10.1371/journal.pone.0063975

***

Can online behaviour be used as a proxy for studying urban mobility? The increasing availability of digital mobility traces has provided new insights into collective human behaviour. Mobility datasets have been shown to be an accurate proxy for daily behaviour and social patterns, and behavioural data from Twitter has been used to predict real world phenomena such as cinema ticket sale volumes, stock prices, and disease outbreaks. In this paper we correlate city-scale urban traffic patterns with online search trends to uncover keywords describing the pedestrian traffic location. By analysing a 3-year mobility dataset we show that our approach, called Location Archetype Keyword Extraction (LAKE), is capable of uncovering semantically relevant keywords for describing a location. Our findings demonstrate an overarching relationship between online and offline collective behaviour, and allow for advancing analysis of community-level behaviour by using online search keywords as a practical behaviour proxy.

Kostakos V, Juntunen T, Goncalves J, Hosio S, Ojala T (2013)

Where Am I? Location Archetype Keyword Extraction from Urban Mobility Patterns

PLoS ONE 8(5): e63980. doi:10.1371/journal.pone.0063980