Demographers in SAS SAS in Demography...Historical demography –analysis Studied effects of various explanatory variables: Within analysis of the length of birth intervals: birth

Demographers in SAS® –SAS® in DemographyKLÁRA HULÍKOVÁ

DEPARTMENT OF DEMOGRAPHY AND GEODEMOGRAPHY

CHARLES UNIVERSITY, CZECH REPUBLIC

FACULTY OF SCIENCE

Charles University

IntroductionWhy to talk about demography in connection to SAS®?

◦ Demography studies reproduction of population – population growth, analysis of fertility, mortality, migration flows, population structure, factors of population development

◦ With increasing data availability and complexity, still more and more sophisticated analytical methods are a crucial part of the work of demographers

◦ Application of demography

◦ Study and description of past & current demographic trends

◦ Life insurance, pension & social reforms, population policy, demographic and social forecasts

◦ Historical studies, study of demographic trends in developing countries

◦ Questions about sustainable population development and its conditions, risk of social conflicts

IntroductionWhy to talk about demography in connection to SAS®?

◦ Demography studies reproduction of population – population growth, analysis of fertility, mortality, migration flows, population structure, factors of population development

◦ With increasing data availability and complexity, still more and more sophisticated analytical methods are a crucial part of the work of demographers

◦ Application of demography

◦ Study and description of past & current demographic trends

◦ Life insurance, pension & social reforms, population policy, demographic and social forecasts

◦ Historical studies, study of demographic trends in developing countries

◦ Questions about sustainable population development and its conditions, risk of social conflicts

Demography is rather small field of study, but of growing importance…

Introduction

Increasing data availability and

complexity

Need of more sophisticated and complex methods

We cannot use them

Still the same results & no new

results

We can use themNew results &

new knowledge

Introduction

Increasing data availability and

complexity

Need of more sophisticated and complex methods

We cannot use them

Still the same results & no new

results

We can use themNew results &

new knowledge

Structure of the presentationPart 1: Demographers in SAS®

◦ Situation in Faculty of Science, Charles University – ways and methods of SAS®-education

◦ Cooperation with the SAS® Institute, Czech Republic

Part 2: SAS® in Demography◦ Selected case studies

◦ Real examples of current demographic research based on SAS®

How we teach SAS®SAS® courses are part of the Master studies of Demography

◦ Demographic applications in SAS I.

◦ Demographic applications in SAS II.

◦ Demographic applications in SAS III.

◦ Elementary econometrics

◦ Demography in Life Insurance



◦ Introduction to SAS programming

◦ Work with data sets

◦ Basic procedures – SORT, MEANS, TABULATE, FREQ, etc.








◦ Demographic standardization – proc STDRATE

◦ Plots

◦ Survival analysis, Cox regression – proc LIFETEST, LIFEREG, PHREG

◦ Logistic regression – proc LOGISTIC








◦ Specific issues in SAS® - box-plots, cluster analysis

◦ Generalized linear models (GENMOD)

◦ Macros in SAS®








◦ Linear regression in SAS® - proc REG

◦ Basics of time series analysis








◦ Demographic modelling in SAS® - mortality models

◦ Our courses are traditionally focused on SAS® Base (not SAS EG)

◦ Knowledge of programming and its principles enables to switch among more statistical softwareswhen needed

How to teach the basicsDifficult start of anything new, including programming

… SAS® as a communication tool (between computer and the user)



proc univariate data=example;var residuals;histogram /normal (mu=est var=est) kernel;run;

Please, draw a histogram for me



proc univariate data=example;var residuals;histogram /normal (mu=est var=est) kernel;

---2276

ERROR 22-322: Syntax error, expecting one of the following: a numeric constant, a datetime constant, ), COLOR, CONTENTS, FILL, L, MIDPERCENTS, MU, NOPRINT, PERCENT, PERCENTS, SIGMA, W.ERROR 76-322: Syntax error, statement will be ignored.

?!Sorry, I do not

understand you fully– I do not know some word(s)



proc univariate data=example;var residuals;histogram /normal (mu=est sigma=est) kernel;run;

Sorry, do you understand it now?

SAS, please, use the procedure univariate and apply it to my data called (=) example;and please analyze the variable called residuals;I would like to see a histogram and moreover (/) in comparison to the normal distribution with characteristics estimated from the observed values (mu=est sigma=est) and also to the kernel distribution;Please, SAS, run it for me;



NOTE: PROCEDURE UNIVARIATE used (Total process time):real time 0.33 secondscpu time 0.15 seconds

Great!I did it!

Sometimes, SAS® is the best teacher of SAS®…

Cooperation with the SAS® Institute, Czech RepublicCooperation between the faculty and the SAS® Institute lasts already for several years

◦ Academic licenses for researches, education and students

◦ E-learning

◦ Support for teachers – consultations, materials

◦ SAS Prize

◦ SAS Certificates for the best students

◦ Workshops

SAS® in Demography: Case study 1Latest development of fertility or mortality

◦ How to describe such a complex process as:

◦ Change of the age pattern of fertility and mortality – what is the intensity according to age

◦ Change of the overall fertility or mortality level as well as its age pattern in time

◦ Why to deal with it?

◦ Importance for demographic forecasts

◦ Detection of period or cohort effects – reflect the effects of population policies or specific behavior of some cohorts (generations)

◦ Could bring important knowledge for population policy, human resources, pension reforms, etc.

◦ What to use?

◦ Analytical methods

◦ Graphical methods – visualization of data is a contemporary trend also in demography

Hulíková Tesárková, K. 2013. Selected demographic methods of mortality analysis: Approaches focused on adults and the oldest age-groups using primarily cross-sectional data. LAP LAMBERT Academic Publishing (June 5, 2013), 404 pp. ISBN 978-3659404139.

Latest development of fertility in Italy

IT_fertility_3Dgraph.html




History of mortality in Sweden

SWE_mortality_3Dgraph.html




SAS® in Demography: Case study 2Mortality models:

◦ Express the intensity of mortality using a simple parametric function

◦ Smoothing of random variations and irregularities

◦ Possibility of mortality extrapolation to the highest ages where usually reliable data are not available

◦ Important e.g. for life insurance, pension reforms, etc.


◦ Important topic for pension agencies (preparation of pension reforms in aging populations)

◦ High importance for life insurance agencies – as precise estimation of mortality as possible is needed

◦ What we have to use?

◦ Generalized regression – estimation of parameters

◦ Generalization of the results

Hulíková, K., Burcin, B., Pachlová, T., Kašpar, D. 2016. Parametric Mortality Smoothing: Deciding on the Optimal Method. Annual Conference of Population Association of America 2016, 28th March – 2nd April, 2016, Washington D.C., USA

Analysis of mortality models

Many methods(functions) that could be used

Actual research: which function

should be used?

Application of more functions to as many real data

as possible

Circa 5 200 populations –

defined by country, sex or

year

Application of 6 the most often used functions –

weighted generalized least squares

Evaluation of all the methods

according to their suitability

Factors tied to suitability

of each particular

model

Analysis of mortality modelsCirca 5 200 populations

Estimation of parameters of 6 different models using the weighted generalized least squares

Evaluation of suitability using the AIC (Akaike Information Criterion)

Multinomial logistic regression◦ Explained variable: the most suitable model for each population

◦ Explaining variables: calendar year, sex, country or region of the country, overall level of mortality expressed by the life expectancy (life expectancy at some particular age is one of the basic demographic indicators representing the average expected number of years to be lived by a person at that age)

Generalization – which characteristics of a population could be tied to suitability of each particular model

Analysis of mortality models – examples of resultsIt was possible to confirm

◦ Systematic increase of suitability of one of the models with increasing life expectancy (i.e. with improving mortality)

MALES, FEMALES

Analysis of mortality models – examples of resultsIt was possible to confirm

◦ Systematic increase of suitability of one of the models with increasing life expectancy (i.e. with improving mortality)

◦ On the other hand, suitability of other function (in this case one of the currently used e.g. in Central European countries) is rapidly decreasing

Conclusion:◦ Calculation of the official mortality

tables could be modified

MALES, FEMALES MALES, FEMALES

Analysis of mortality models –advantages of SAS® usageCirca 5 200 x 6 (= 31 200) estimated models

◦ Macros

◦ Proc NLIN

Multinomial Logistic Regression applied to all the 31 200 cases◦ Proc Logistic

Advantages:◦ Quick

◦ Effective

◦ Complex – only one software needed for the analysis as well as summary analysis and graphs or tables

SAS® in Demography: Case study 3Historical demography:

◦ Historical demography studies reproduction of historical populations – e.g. in the 18th century

◦ Specific sources of data – parish registers

◦ Specific types of data and problems of data – family histories, missing data


◦ Aim to learn more about the past demographic trends and factors influencing it

◦ Significant relevance to demographic development in currently developing countries

◦ What we have to use?

◦ Event history analysis, Cox regression, censoring

◦ Tools of effective data organization

MarriageBirth of the 1st child

Birth of the 2nd

child

Death of the 1st child

Birth of the 3rd

child

Birth of the 4th

child

Death of the 4th

child

Death of the man =

end of marriage

Death of the

woman

Fialová, L., Hulíková, K., Kuprová, B. 2016. What stood behind the length of birth intervals in the past: a case study of Jablonec nad Nisou (Czech lands) from 17th to 19th century. Journal of Family History. [in the review process]

Historical demographyWhat are parish registers?

Historical demographyOriginal data structure:

Re-organization of the data when the aim was to study the reproduction (births of children, inter-birth intervals, child mortality)

◦ Data were no more organized according to families (ID) but each raw represented one child and data about the corresponding family

ID Date of marriage

Date of birth (woman)

Date of birth (man)

Date of death (woman)

Date of death (man)

Date of birth –1st child

Date of death –1st child

Date of birth – 2nd child

Date of death – 2nd child

1

2

Historical demographyAnalysis

◦ Instead of dates of birth and death → time duration between specific events:

◦ Time duration from marriage to birth of the 1st child

◦ Time duration between two consecutive births (birth interval)

◦ Time duration from birth to the death of the child

◦ Survival analysis, Cox regression

◦ Study of explanatory factors of observed time durations

◦ PHREG procedure

Historical demography – analysisAnalyzed locality: Jablonec – town in the northern part of the Czech Republic

Studied period: 18th century

Analyzed data: around 2 thousand families → more than 10 thousand children and data about their families

Studied behavior:◦ Frequency of births in families and birth intervals - as an indicator of the type of reproductive behavior

(tempo of family growth, level of reproductive health, or effects of infant mortality on fertility)

◦ Mortality of children within first 10 years of life – as an indicator of health condition and child care

Specific features of the study:◦ Still rather unique in the world, classical historical demographic studies were not based on modern

statistical methods

◦ Relatively big data set

Historical demography – analysisStudied effects of various explanatory variables:

◦ Within analysis of the length of birth intervals: birth parity, age of a mother at birth of a child, age of a mother at marriage, total number of births in the family, reversal birth order and survival status of a previous child

◦ Within analysis of the child mortality: age of a mother at birth of a child, age of a mother at marriage, total number of births in the family, reversal birth order, survival status of a previous child with interaction of its sex, sex-structure of previous children in a family and length of the previous birth interval

Historical demography – selected resultsAverage impact on the length of birth intervals

Prolongation

• With increasing age of mother at birth of the child

Shortening

• With increasing age of mother at marriage

• With higher number of children born in a family

• With increasing reversal birth order (longer interval before the birth of the last child in a family)

• When previous child died within 12 months after birth

Historical demography – selected resultsAverage impact on the child mortality

Increase (higher mortality)

• For higher age of mother at birth of the child (above 25)

• In families with higher total number of births

• For the last child born in a family

Decrease (lower mortality)

• When previous child survived at least first 12 months of life

• With each additional month of the length of the birth interval

Historical demography – advantages of SAS® usageAdvantages of more sophisticated methods used in the study

◦ Pure effect of all variables considered as explanatory ones was estimated

◦ Cox regression – confirmation of classical studies and more detailed results

◦ Several new and not yet fully described factors affecting the length of birth intervals and child mortality in the past were revealed

What we know from the study: reproductive behavior in the 18th century in Europe was still influenced above all by natural processes (decrease of fecundity with age, increase of child mortality with age of the mother or shortening of the birth intervals)

Currently: cooperation with colleagues from developing countries – similar results are confirmed also for the least developed populations nowadays

ConclusionUsing of the SAS® software enabled:

◦ More detailed and effective analyses

◦ Use more sophisticated methods

◦ Work effectively with large datasets

◦ Reveal some facts for the first time

Our future aims:◦ Incorporate the SAS® software more into our

research as well as education

◦ Motivate our students to use SAS® for their own research and master/doctoral theses

◦ Encourage our students to participate in the SAS Prize and SAS workshops organized for them and then use SAS in their working practice

◦ Find effective ways how to teach and explain SAS programming to the beginners

Thank you for your attention

KLARA HULIKOVA

[email protected]

mailto:[email protected]

Documents

Demographers in SAS SAS in Demography...Historical demography –analysis Studied effects of various explanatory variables: Within analysis of the length of birth intervals: birth