HOUSEHOLDS AND LIVING ARRANGEMENTS PROJECTIONS AT NATIONAL AND SUB-NATIONAL LEVEL -- An Extended Cohort-component Approach Yi Zeng - PowerPoint PPT Presentation

Loading...

PPT – HOUSEHOLDS AND LIVING ARRANGEMENTS PROJECTIONS AT NATIONAL AND SUB-NATIONAL LEVEL -- An Extended Cohort-component Approach Yi Zeng PowerPoint presentation | free to download - id: 6f59c1-MmRkZ



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

HOUSEHOLDS AND LIVING ARRANGEMENTS PROJECTIONS AT NATIONAL AND SUB-NATIONAL LEVEL -- An Extended Cohort-component Approach Yi Zeng

Description:

HOUSEHOLDS AND LIVING ARRANGEMENTS PROJECTIONS AT NATIONAL AND SUB-NATIONAL LEVEL -- An Extended Cohort-component Approach Yi Zeng Professor, Duke University and ... – PowerPoint PPT presentation

Number of Views:48
Avg rating:3.0/5.0
Slides: 30
Provided by: peo51
Learn more at: http://www.lse.ac.uk
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: HOUSEHOLDS AND LIVING ARRANGEMENTS PROJECTIONS AT NATIONAL AND SUB-NATIONAL LEVEL -- An Extended Cohort-component Approach Yi Zeng


1
HOUSEHOLDS AND LIVING ARRANGEMENTS PROJECTIONS AT
NATIONAL AND SUB-NATIONAL LEVEL -- An Extended
Cohort-component ApproachYi Zeng Professor,
Duke University and Peking University
2
1. THE CORE IDEAS OF THE ProFamyEXTENDED
COHORT-COMPONENT METHOD
Core idea 1 A multi-state accounting
model. ?Unlike most other macrosimulation models
which use the household as the basic unit and
require the non-conventional data on transition
probabilities among household-type statuses, ?We
use individual as the basic unit of analysis and
thus only conventionally available demographic
data are required in ProFamy model and we
forecast households and population age/sex
distributions simultaneously.
3
Demographic statuses distinguished in our ProFamy
model
Status Sym Definition and codes U.S. application
Age X 0,1,2,3,,W W is chosen by user x0,1,2,3,,100
Sex S 1. Female 2. Male s1,2
Race (optional) R To be determined by user r1,2,3,4
Marital/union status M 4 or 7 marital status model chosen by user m1,2,3,4,5,6,7
Co-residence with parent(s) K 1. With two parents 2. with one parent only 3. Not with parents. k1,2,3
Parity P p 0,1,2,, H H is chosen by user p0,12,3,4,5
co-residing children C c 0,1,2,, H (cp) c0,1,2,3,4,5
Residence (optional) U 1. Rural 2. Urban Not considered
Projection year t Single year from t1 to t2, chosen by user t12000 t22050
4
Figure 1. Seven marital statuses model
5
Core idea 2 an innovative computational strategy
in the periodic demographic accounting process
? With needed individual statuses identified, we
would have huge cross-status transition matrices
if adopting conventional computation strategy
e.g., if 7 marital/union statuses, 3 statuses of
co-residence with parents, 6 parity and 6
co-residence statuses with children are
distinguished as what was done in U.S.
applications, one has to estimate a cross-status
transition probabilities matrix with 194,481
elements at each age of each sex for each race
would require huge datasets NOT practical. ?
Thus, we adopted an innovative computational
strategy, which was originally proposed by
Bongaarts (1987) and further justified
mathematically and numerically by Zeng (1991)
6
Figure 2. Computational strategy to calculate
changes in marital/union, co-residence with
parents/children, migration and survival statuses
Changes in marital/union, co-residence with
parents/children, migration and survival statuses
occur in the middle of age interval (x,x1)
x
X1
Changes in parity and maternal statuses occur in
the 1st half of the single age interval
Changes in parity and maternal statuses occur in
the 2nd half of the single age interval
7
Core idea 3 A judicious use of stochastic
independence assumptions to face data reality
  • Also originally suggested by Bongaarts
    (1987) and adapted and generalized by Zeng (1987,
    1991) and others.
  • ? Statistical basis
  • the real-world mostly allows assumptions of
    stochastically independent
  • limited data sources force application of an
    independence assumption.
  • ? In ProFamy extended cohort-component model,
  • marital/union status transitions depend on age,
    sex, and race, but independent of other statuses
  • fertility depends on age, race, parity and
    marital status, but independent of other
    statuses
  • mortality depends on age, sex, race and marital
    status, but independent of other statuses

8
Core idea 4 Use of the harmonic mean to ensures
consistency between the two sexes and
between parents and children in the
projection model.
We ensure the consistency between the two sexes
and between parents and children following the
harmonic mean approach, which satisfies most of
the theoretical requirements and practical
considerations (Pollard, 1977 Schoen, 1981
Keilman, 1985 Van Imholf and Keilman, 1992 Zeng
et al. 1997 1998).
9
Core idea 5. Using national model standard
schedules and summary parameters at sub-national
level to specify projected demographic rates of
the sub-national region in future years.
?The standard schedules formulate the age pattern
of demographic processes. One may take into
account anticipated changes in the age patterns,
such as delaying or advancing marriage and
fertility, changes in shape of the curve towards
more spread or more concentrated, through
adjusting the parameters (mean or median, and
interquartile range) (Zeng et al., 2000).
?The summary parameters, e.g, TFR, General rates
of marriage and divorce, etc., can be used to
tune the household and population projections
up or down for demographic scenarios.
?However, Data for estimating race-sex-age-specifi
c standard schedules of the demographic rates for
household projection may not be available at the
sub-national level. -- The core ideas 2,3,4 are
not detailed here due to time constrains
10
?The age-race-sex-specific standard schedules at
the national level can be employed as model
standard schedules for projections at the
sub-national level.
?This is similar to the widely practiced
application of model life tables (e.g., Coale,
Demeny, and Vaughn, 1983 U.N., 1982), the Brass
logit relational life table model (e.g. Murray,
2003), the Brass Relational Gompertz Fertility
Model (Brass, 1974), and other parameterized
models (e.g. Coale and Trussell, 1974 Rogers,
1986) in population projections and estimations.
?Numerous studies have demonstrated that
parameterized models consisting of a model
standard schedule and a few summary parameters
offer an efficient and realistic way to project
or estimate demographic age-sex-specific rates.
?The demographic summary parameters are most
crucial for determining changes in level and age
pattern of the age-specific rates, as long as the
model standard schedules reveal the general age
patterns. (Brass, 1978 Booth, 1984 Paget and
Timaeus, 1994 Zeng et al., 1994)
11
2. A Comparison between the ProFamy Extended
Cohort Component Model and Still-Widely-Used
Headship Rate Method
  • (1) Linkage with demographic rates
  • Headship Rate cannot link to demographic
    events, extremely hard to incorporate demographic
    assumptions of fertility, mortality,
    marriage/union formation and dissolution etc.
    (Mason and Racelis 1992 Spicer et al., 1992)
  • The ProFamy model Use demographic rates from
    conventional sources as input closely link
    projected households with demographic rates and
    summary measures on marriage/union formation and
    dissolution, fertility and mortality etc.

12
The ProFamy model household, elderly living
arrangement and population projection using
demographic rates as input
Headship-rate household projection
cross-sectional extrapolation of the age-specific
headship-rate, without linkage to demographic rate
13
(2) Information produced and their
adequacy for planningHeadship Rate little
information on household types and no household
sizes projection, inadequate for planning
purposes (Bell Cooper, 1990), especially most
households consumptions (e.g. home vehicles,
housing, energy use) largely depends on
household size.
Households types projected by headship rates
methods (Bureau of the Census, 1996)
Code Household type Household size
1 Married couple household Not available
2 Female-headed household,no spouse Not available
3 Male-headed household,,no spouse Not available
4 Female non-family household Not available
5 Male non-family household Not available
14
The ProFamy model needs conventionally available
data and projects much more detailed information
on households and living arrangements
Type code Household types Household sizes
One generation households One generation households One generation households
1-6 One person only by sex and marital status 1
7-12 One person other/non-relative by sex and marital status of the person 2,3,4,5,or 6
13-14 One married couple only One cohabiting couple only 2
15-16 One married couple other/non-relative One cohabiting couple other/non-relative 3,4,5,6,or 7
Two-generation households Two-generation households Two-generation households
17-18 Married couple children Cohabiting couple children 3,4,5,6,7,8,or 9
19-24 Single-parent children by sex and marital status of the single parent 2,3,4,5,6,7,8,or 9
Three-generation households Three-generation households
25-28 Married (or cohabiting) couple with children and 1 or 2 grandparents 4,5,6,7,8,or 9
29-40 Sex-marital status-specific single-parent children 1 or 2 grandparents 3,4,5,6,7,8,or 9
15
3. Data needed for household forecasting at
national and sub-national levels
(1) Base population
Contents of the data Main data resources (US applications)
A census micro data file for the state, with a few needed variables of sex, age, race (optional), marital/union status, relationship to the householder, and whether living in a private or institutional household. If a sample data set is used, 100 tabulations of age-sex distributions of the entire population and those living in group quarters, derived from the census data must be provided. Census 5 micro data or more recent and cumulative American Community Survey (ACS) data files and the published online 100 census or ACS cross- tabulations.
16
(2)-I Model standard schedules at national level
(can be used for households projections at
sub-national level)
Contents of the data Contents of the data Main data resources
(a) Age-race-sex-specific death rates (marital-status specific, if possible). Census Bureaus estimates, Schoen and Standish (2001)
(b) Age-race-sex-specific o/e rates of marriage/union formation and dissolution Pooled NSFH, NSFG, CPS, SIPP data sets, see Zeng and Land et al. (2006).
(c) Age-race-parity-specific o/e rates of marital and non-marital fertility Pooled NSFH, NSFG, CPS, SIPP data sets, see Zeng and Land et al. (2006).
(d) Age-race-sex-specific net rates of leaving the parental home, estimated based on two adjacent census micro data files and the intra-cohort iterative method (Coale1984 1985 Stupp 1988 Zeng, Coale et al., 1994). The 1990, and 2000 censuses micro data files
(e) Age-sex-specific rates of international emigration and immigration. Census 5 micro data or ACS data files
17
(2)-I I Model standard schedules at sub-national level (2)-I I Model standard schedules at sub-national level
(f) Race-sex-age-specific rates of domestic in-migration and out-migration for each state Census 5 micro data, ACS data files
(3) Demographic summary measures for the nation and sub-national regions (3) Demographic summary measures for the nation and sub-national regions
(a) Race-specific general rates of marriage and general rates of divorce Based on, census micro data, vital statistics and pooled survey data sets
(b) Race-specific general rates of cohabiting and general rates of union dissolution Based on, census micro data, vital statistics and pooled survey data sets
(c) Race-specific Total Fertility Rates (TFR) by parity Based on estimates released by the Census Bureau and the National Center for Health Statistics
(d) Race-sex-specific Life expectancies at birth Based on estimates released by the Census Bureau and the National Center for Health Statistics
(e) Race-sex-specific total numbers of male and female migrants Based on estimates released by the Census Bureau and the National Center for Health Statistics
(f) Race-sex-specific mean age at first marriage and births Based on estimates released by the Census Bureau and the National Center for Health Statistics

18
4. Validation of the extended cohort-component
method for household forecasting at sub-national
level
  • Zeng and Land et al. (2006) and Zeng et al.
    (2008) did validation tests of households
    projections for US and China at national level
    from 1990 to 2000, and then compared to the 2000
    census observations.
  • We do TWO sets of validation tests of household
    forecasts from 1990 to 2000 for each of the 50
    states and DC fo USA, all using the national
    model standard schedules.
  • Using the 1990 census data as base population
    and the summary measures estimated based on data
    before 1991, and compare the projected and the
    census-observed in 2000.
  • (2) Using the 1990 census data as base population
    and summary measures estimated based on data in
    1990s, and compares the projected and the
    census-observed in 2000.

19
Figure 3a. Distributions of the absolute percent
errors (APE) of forecasts from 1990 to 2000, 6
main indices of households for each of the 50
states and DC, in total 306 pairs of comparisons
between ProFamy forecasted and census
observations in 2000
(A) based on data before 1991 (B)
including data in 1990s
20
Figure 3b. Distributions of the absolute percent
errors (APE) of forecasts from 1990 to 2000, 6
main indices of population for each of the 50
states and DC, in total 306 pairs of comparisons
between ProFamy forecasted and census
observations in 2000
(C) based on data before 1991 (D)
including data in 1990s
21
Table 2a. The Mean Absolute Percent Error, Mean
Algebraic Percent Error and Median Absolute
Percent Error of the main indices of household
projection between the ProFamy projections from
1990 to 2000 and the Census observations in 2000
for each of the 50 states and DC
22
Table 2b. The Mean Absolute Percent Error, Mean
Algebraic Percent Error and Median Absolute
Percent Error of the main indices of population
projection between the ProFamy projections from
1990 to 2000 and the Census observations in 2000
for each of the 50 states and DC
23
The discrepancies are within a very reasonable
range, and the ProFamy extended cohort component
approach is validated at sub-national
level. However, the ProFamy approach needs
substantially more data than does the classic
headship-rate method. Is it still worthwhile to
employ the new ProFamy approach rather than the
classic headship-rate method, if the users only
simply needs the projections of the home-based
consumption demands, such as numbers of housing
units by number of bedrooms, but do not care
about the details of the household
characteristics and the statuses of the reference
persons, such as marital/union status,
co-residence status with parents and children,
etc.? To answer this question, we project from
1990 to 2000 housing demands by of bedrooms for
each of 50 states and DC, employing headship-rate
model and ProFamy approach using data before
1990. By comparing the projected and the
census-observed of housing units by of
bedrooms in 2000, we estimated/compared the
forecasts errors, by the headship-rate method and
the ProFamy approach.
24
Table 3. Forecast errors of Mean Algebraic
Percent Error (MALPE), Mean Absolute Percent
Error (MAPE) and Median Absolute Percent Error
(MEDAPE) of housing demands projections from 1990
to 2000 (compared to the 2000 census
observations), Comparisons between the ProFamy
cohort-component approach and the constant
headship-rates
25
The constant headship-rate did much worse than
ProFamy in housing demand forecasting, but one
may argue that we could have headship rates
changing So, we did another test belowTable
4. Forecast errors of Mean Algebraic Percent
Error (MALPE), Mean Absolute Percent Error (MAPE)
and Median Absolute Percent Error (MEDAPE) of
housing demands projections from 1990 to 2000
(compared to the 2000 census observations),
Comparisons between the ProFamy cohort-component
approach and the adjusted changing
headship-rates, both approaches resulted in the
same projected total number of households as
observed in the 2000 census. ?The headship-rate
still did substantially worse.
26
-- The changing headship-rate model still did
substantially worse. Why? The censuses data
shown, as compared to 1990, the 1, 2, 3, 4?5, and
6 persons households in 2000 increased by 20.6,
16.9, 9.2, 9.3 and 15.1 percent, respectively.
American households with 1?2 persons (which more
likely need 0-1 bedroom) and 6 persons (which
more likely need 4-bedrooms) increase
substantially faster than the 3- and 4?5 person
households (which more likely need 2?3 bedrooms).
Thus, the headship-rate method, which
cannot forecast households by size, resulted in
substantially more serious forecast errors in
projecting the demands of housing units by number
of bedrooms, as compared to the ProFamy approach
whose forecasts do include detailed households
size information.
27
5. A summary of findings of households
projections from 1990 to 2000 for the 50 states
and DC
(1) the average household size would decrease
considerably in almost all states up to 2020 or
so, especially in the states with higher degree
of population aging, and remain relatively stable
afterwards
(2) of one-person households would increase
substantially in all states.
(3) Husband-wife households would decrease
moderately and cohabiting-couple households would
increase substantially, up to 2020 or so, and
remain relatively stable afterwards
(4) Directions of changes in percent of
single-parent households among the two-generation
households are diversified, increase moderately
in some states but decrease moderately or remain
unchanged in the other states. (5) Percent of
households with at least one elder aged 65 of
the total number of households, percent of
elderly aged 65 living alone and percent of
oldest-old aged 80 living alone will increase
substantially and pervasively in all states.
28
  • 6. Conclusion remarks
  • ProFamy extended cohort-component model does
    substantially better than the still-widely-used
    classic headship rates method in households
    projections.
  • In addition to the academic research, the ProFamy
    method/software can be used for home-based
    consumption and services needs/costs projections.
    For example, ProFamy method/software was employed
    for the U.S. households energy consumption
    projections in Dalton et al. (2008), for the U.S.
    housing projections at national and sub-national
    levels in Smith et al. (2008 2012), for Austrian
    and the U.S. home-based vehicles consumption
    projections in Prskawetz et al. (2004) and Feng
    et al. (2011).

29
Thank You!
About PowerShow.com