Population and Health- Session 10- Population Projection

8/3/2019 Population and Health- Session 10- Population Projection

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POPULATION AND HEALTH :

TECHNIQUES OF A NALYSIS AND POLICY

PERSPECTIVE

POPULATION PROJECTIONMA (SDP), 2nd Semester, 20121



PROJECTION

A projection may be defined as the numerical outcome of a particular set of assumptions regarding the future

population

Estimates or forecasts are made, in absence of census orrelated data

Some methods projects for total population

Other for sub-groups i.e., age, sex, race, other

demographic and socio-economic characteristics



POPULATION PROJECTION: FUTURE

ESTIMATES

Raise our understanding of the determinants of populationchange

y What impact would a 20% decline in birthrates have on a country·s population size and age structure in 50 years?

y How many people would move into a local area if a new f actory employing 1000 people were opened?

Projections often serve as a basis for producing other

projections (e.g., households, f amilies, school enrollment,income, and labor force)

Provides scopes for informed decision making for policy makers



A SSUMPTIONS IN PROJECTION

Every projections/estimates/forecasts are made oncertain assumption

It does not attempt to predict whether those

assumptions actually will hold true

Range of scenarios and made alternative assumption



SOME APPLICATIONS:

National population projections- Future Social Security andMedicare obligations

State projections- determine future water demands, welf are

expenditures

Local projections- new public schools, sites for fire stations,

Business enterprises- to predict demands for their products,

demand for housing

The assumptions are made based on past-trends of population

change, since ¶future is intimately tied to the past·.



POPULATION PROJECTION METHODS

Subjective methods: data, techniques, and assumptions are not clearly identified; difficult for replication

Objective methods: data, techniques, and assumptions

are clearly identified, can be replicated exactly

Smith et al. (2001) classified objective methods under-

(1) Trend extrapolation,

(2) Cohort-component methods, and

(3) Structural models

(4) Other socio-economic projections

However, these are often not ¶mutually-exclusive·



INPUT DATA -SOURCE FOR PROJECTION

Census

Population Registers

Vital Registers

Other records

Surveillance statistics

Etc.



USEFUL TERMS:

Base year: year of the earliest data used

Launch year: year of the most recent data used to make a projection

T arget year: year for which the population is projected

Base period: the number of years between the base year andlaunch year

P rojection horizon: number of years between the launchyear and target year

P rojection interval: the time increment for which projections are made (e.g., annually or every 5 years)



TREND EXTRAPOLATION



TREND EXTRAPOLATION

Involves fitting mathematical models to historical data andusing these models to project population values-

S imple extrapolation methods: requires data for only two

dates linear change, geometric change, and exponential

change;

C omplex extrapolation methods: requires data for a number

of dates to calculate, linear trend, polynomial curve, logistic

curve, and ARIMA time series

Ratio extrapolation methods: population of a smaller area is

expressed as a proportion of the population of its larger, i.e.,

constant share, shift share, and share of growth



SIMPLE EXTRAPOLATION



LINEAR PROJECTION

A ssumption: population will change by the samenumerical amount over a given period, as has registered in the past

Linear change: Pt=Po (1+r*t)

= (Pt- Po)/t, where,

y Pt=Launch year

y Po= Base year

y

t= Base period

Target year (Y)= Pt+(t* ), where t= projectionhorizon



LINEAR PROJECTION, EXAMPLE

Brazil

1960 = 70,119,071, 2000= 169,544,443

Project population for year 2010

2010 - 190,755,799 (Official statistics, Govt. of

Brazil)

r= 0.033719

2010 194,400,786



GEOMETRIC PROJECTION

A ssumption: population will change by the samepercentage rate over a given increment of time in the

future as during the base period

Geometric change: Pt=Po (1+r)t

r= (Pt/Po)1/t -1

where,

y r= annual rate of change

y

Pt=Launch yeary Po= Base year

y t= Base period

Target year (Y)= Po(1+r)t where t= projection horizon



GEOMETRIC PROJECTION, EXAMPLE

Brazil

1960 = 70,119,071, 2000= 169,544,443


r 0.022318418

2010 211,419,680

2010 - 190,755,799 (Official statistics, Govt. of Brazil)



EXPONENTIAL GROWTH

A ssumption: closely related to the geometric one,but it views change as occurring continuously

rather than at discrete Intervals

Exponential change Pt= Po* ert

r= {ln(Pt/Po)}/t

Where,

y r= annual rate of change

y Pt=Launch year

y Po= Base year

y t= Base period

Target year (Y)= Po*ert where t= projection

horizon



EXPONENTIAL PROJECTION, EXAMPLE

Brazil

1960 = 70,119,071, 2000= 169,544,443


r 0.022073007

2010 211,419,680

2010 - 190,755,799 (Official statistics, Govt. of Brazil)



COMPLEX EXTRAPOLATION METHOD



COMPLEX EXTRAPOLATION METHOD

Uses base-period data for more than two dates.Better deals with nonlinear population change

Three basic steps:

To assemble historical population data for differentdates during the base period

To estimate the parameters of the model selected togenerate the projection. Fit of a particular model

should be understood by studying base yearparameter data

To generate projections using the model(s) selected



EXTRAPOLATION METHOD

Linear Models: Y i = a + b( Xi ),

Where,

Y i is a set of i observations of values of a ´dependent variable,

Xi is a set of i observations of an índependent variable,µ

a= is the constant term

b= is the slope of the line describing the ́ best fitting linearµ

relationship between X and Y



LINEAR EXTRAPOLATION

P i = a + b(T i )

Where,

P i = is the population for a set of time points (e.g., years) over theperiod i

i= b to l (b = base year and l = launch year)

a and b are the estimated intercept and slope, respectivelyT i= is time over the period i = b to l

Regression Equation(y) = a + bx

P i = a + b(T i )

Slope(b)= (N Ti*Pi- (Ti)(Pi) / (NTi2 - (Ti)2)

Intercept (a)= (Pi - b(Ti)) / N

N= number of values/ elements



TO CALCULATE

Time period

(Ti)

Population

(Pi)

Ti*Pi Ti*Ti b a

1990 Y1

1995 Y2

2000 Y3

2005 Y4

(N Ti*Pi- (Ti)(Pi)

/ (NTi2 - (Ti)2)

(Pi -

b(Ti)) / N

N=5, and once all the values are known, projection can be done for

Year 2010

P i = a + b(T i )



POLYNOMIAL MODELS

Polynomial models is used for projections in which change

is not constrained to be linear

Where,

Y i = is a set of i observations of values of a dependent variable

Xi= is a set of i observations of an independent variable

a = represents the constant term

bj = the slope of the line describing the ´best-fittingµ

relationship between X ji and Y



LOGISTIC MODELS

Logistic approach explicitly allows one to place an upper

limit on the ultimate size of the population for a given area

The logistic model is consistent with Malthusian and other

theories of constrained population growth

Where,

Y is the population X is the time period

a ref lects the upper asymptote

b and c are parameters that define the shape of the curve

e is the base of the natural logarithm



ARIMA (Á UTOREGRESSI VE INTEGRATED

MO VING A VERAGEµ)

ARIMA models have occasionally been used in the

analysis and projection of populations as a whole and

of their demographic attributes.

It deals with time-series data



R ATIO EXTRAPOLATION METHOD



Ratio extrapolation methods may be used where an

area containing the population to be projected is partof a larger (´parentµ) area for which projections are

available

The ma jor types include,

(1) the constant-share,

(2) the shift-share, and

(3) the share-of growth approaches.



THE CONSTANT SHARE

Where,

P it= is the population projection for smaller area (i) in the target

year

P il= is the population of the smaller area in the launch year

P jl= is the population of the parent area ( j) in the launch year

P jt is the projection of the parent area in the target year

The constant-share method requires historical data for only

one date

Ma jor drawback- assumes all small areas will grow at same

pace



SHIFT-SHARE METHOD

Where,

The smaller area is denoted by i, the parent area by j,

z is the number of years in the projection horizon,

y is the number of years in the base period, and

b, l, and t refer to the base, launch, and target years, respectively

It can lead to substantial population losses in areas that

grew very slowly (or declined) during the base period



SHARE-OF-GROWTH METHOD

Deals with shares of population change rather than

population size



LIMITATION OF EXTRAPOLATION METHOD

Do not account for differences in demographic

composition or for differences in the components of

growth

Provide little or no information on the projected

demographic characteristics of the population

Limited usefulness for analyzing the determinants of population growth or for simulating the effects of

changes in particular variables or assumptions



COHORT COMPONENT



COHORT COMPONENT

Most widely used method for producing national-level states/provinces, and sub-county area population projections

Introduced by Cannan (1895), subsequently used by Bowley(1924), and later rediscovered independently by Whelpton(1928)

Cohort-component method divides the launch-year populationinto age-sex groups (i.e., birth cohorts) and accounts separately for the fertility, mortality, and migration that the cohort goes through in projection horizon

Provide in-depth knowledge on population dynamics

Cohort-component models typically use either single years or5-year groups- important methodological advancement



STEPS IN COHORT COMPONENT METHOD

Step 1: Mortality Step 2: Migration (Out or net)

Step 3: Fertility

Step 4: Population (M/F)



PROJECTING MORTALITY

Age-specific mortality will remain constant (short-

horizon)- Simple A ssumption

Long-horizon needs adjustment for changing rates

with changing socio-economic scenario

Various extrapolation techniques are used, based on

assumption on various socio-economic determinants

shaping mortality trends

A ssumption- future will mirror the past in important

ways



CONTINUED«

Number of techniques ties mortality rate in one population to

those in another

¶Targeting approach·- is based on assumption realistic forpopulation to be projected

Choice is based on similarities in SE, cultural, behavioral characteristics, medical technology and primary causes of death

Targeting approach- ¶cause-delay·, considering younger cohort inthe same population, instead of same cohort in anotherpopulation

Mortality probability gets reduced due to advancement in medical technology

Mortality probability can also be taken from life-tables



PROJECTING FERTILITY

Cohort perspective

Period perspective (more commonly used)

y Current A SFRs- assumed will remain constant through

projection horizon. Data based on recent year data or

average data for several years

y Non-linear models used for projecting A SFRs for a wider

horizon, and also for countries that completed demographic

transition

¶Targeting approach·- checking for realistic assumption

for convergence



PROJECTING MIGRATION

Importance in population growth at national and sub-national level

Gross & Net migration approaches- both used widely

Gross migration approach-

y Out-migration rate:

y Applying out-migration rate to the launch year of population to

provide a projection of the total ¶pool· of inter-state out-migrants forall states.

y This ¶pool· is then allocated to each state based on proportion of in-migrant population received in the base period

Age-sex data (5year before the census)

Age-sex data for decennial census



CONTINUED«.

Net Migration Approach:

y Top down: 1) Projection on net-migration

2) Projection on age-sex specific migration

y Bottom-up: 1) Separate age-sex group projection

2) Projection of net-migration based on age-sexratio

Projecting migration need careful considerations, based

on contemporary and historical trends

Local and national level projections needs separate focus



STRUCTURAL MODELS FOR PROJECTION



STRUCTURAL MODELS FOR PROJECTION

Adjusting for f actors beyond demography.

Economic-demographic models: projection of

population for larger geographic region andeconomic activity; i.e., labour-market, states,

nations

Urban-Systems model: small geographic areas forland-use, transportation pattern etc.



OTHER SOCIO-ECONOMIC PROJECTION



OTHER SOCIO-ECONOMIC PROJECTION

Most useful for feeding policy-requirement anddirectly affected by policy decision

Two ma jor approaches:

´Participation ratio methodµ (also known as the´participation rate methodµ, ´prevalence ratio

method,µ and íncidence rate methodµ)

Cohort progression method



P ARTICIPATION RATIO METHOD

Current and historical data are used to construct

participation ratios

These ratios are projected into the future

Projected ratios are then applied to population

projections (stratified by age, sex, and other

characteristics) for the geographic area(s) underconsideration to obtain a set of socioeconomic projections

Must have sufficient demographic detail



COHORT PROGRESSION R ATIO

Numbers with the socioeconomic characteristic or

the corresponding participation ratios are

projected on a cohort basis

Using information on changes in the numbers or

participation ratios between two previous dates.



REFERENCE

Methods and Materials of Demography, Chapter

21

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Population and Health- Session 10- Population Projection

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