戶籍限制型人口流動的社會、經濟、政治影響及對策研究LSE課件

1、HOUSEHOLDS AND LIVING ARRANGEMENTS PROJECTIONS AT NATIONAL AND SUB-NATIONAL LEVEL - An Extended Cohort-component ApproachYi Zeng Professor, Duke University and Peking University 1. THE CORE IDEAS OF THE ProFamyEXTENDED COHORT-COMPONENT METHODCore idea 1 : A multi-state accounting model.Unlike most o

2、ther 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

3、and we forecast households and population age/sex distributions simultaneously. Demographic statuses distinguished in our ProFamy model StatusSymDefinition and codes U.S. application AgeX0,1,2,3,W; W is chosen by user x=0,1,2,3,100 SexS1. Female; 2. Male s=1,2 Race (optional)RTo be determined by use

4、rr=1,2,3,4Marital/union statusM4 or 7 marital status model chosen by user m=1,2,3,4,5,6,7 Co-residence with parent(s)K1. With two parents; 2. with one parent only; 3. Not with parents. k=1,2,3 ParityPp = 0,1,2, H; H is chosen by user p=0,12,3,4,5+ # co-residing childrenCc = 0,1,2, H (cp) c=0,1,2,3,4

5、,5+ Residence (optional)U1. Rural; 2. Urban Not considered Projection yeartSingle year from t1 to t2, chosen by user t1=2000; t2=2050 Figure 1. Seven marital statuses model Core idea 2: an innovative computational strategy in the periodic demographic accounting process With needed individual statuse

6、s 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

7、 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 mathe

8、matically and numerically by Zeng (1991)Figure 2. Computational strategy to calculate changes in marital/union, co-residence with parents/children, migration and survival statusesChanges in marital/union, co-residence with parents/children, migration and survival statuses occur in the middle of age

9、interval (x,x+1)xX+1Changes 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 intervalCore idea 3: A judicious use of stochastic independence assumptions to face data reality Also originall

10、y 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, mar

11、ital/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; Core idea 4: Use of

12、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 prac

13、tical considerations (Pollard, 1977; Schoen, 1981; Keilman, 1985; Van Imholf and Keilman, 1992; Zeng et al. 1997; 1998). 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 marriag

14、e 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). Core idea 5. Using national model standard schedules and summary parameters at sub-national level to specify projec

15、ted demographic rates of the sub-national region in future years. 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-specific stan

16、dard 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 constrainsThe age-race-sex-specific standard schedules at the national level can be employed as model standard schedules for proje

17、ctions 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 para

18、meterized 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 demo

19、graphic 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., 19

20、94)2. A Comparison between the ProFamy Extended Cohort Component Model and Still-Widely-Used Headship Rate Method(1) Linkage with demographic ratesHeadship Rate: cannot link to demographic events, extremely hard to incorporate demographic assumptions of fertility, mortality, marriage/union formation

21、 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. The

22、ProFamy model household, elderly living arrangement and population projection: using demographic rates as inputHeadship-rate household projection: cross-sectional extrapolation of the age-specific headship-rate, without linkage to demographic rate(2) Information produced and their adequacy for plann

23、ingHeadship 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 headsh

24、ip rates methods (Bureau of the Census, 1996) CodeHousehold typeHousehold size1Married couple householdNot available2Female-headed household,no spouseNot available3Male-headed household,no spouseNot available4Female non-family householdNot available5Male non-family householdNot availableThe ProFamy

25、model needs conventionally available data and projects much more detailed information on households and living arrangementsType code Household types Household sizes One generation households1-6 One person only by sex and marital status 1 7-12 One person & other/non-relative by sex and marital status

26、 of the person 2,3,4,5,or 6+ 13-14 One married couple only; One cohabiting couple only 215-16 One married couple & other/non-relative; One cohabiting couple & other/non-relative 3,4,5,6,or 7+ Two-generation households 17-18Married couple & children; Cohabiting couple & children 3,4,5,6,7,8,or 9+ 19-

27、24Single-parent & children by sex and marital status of the single parent 2,3,4,5,6,7,8,or 9+ 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,

28、6,7,8,or 9+ 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 t

29、o 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 c

30、umulative American Community Survey (ACS) data files and the published online 100% census or ACS cross- tabulations.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-s

31、ex-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 (d) Age-race-sex-specific net rates of leaving the parental home, estimated based on t

32、wo 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(2)-I Model stan

33、dard schedules at national level (can be used for households projections 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 summar

34、y measures for the nation and sub-national regions (a) Race-specific general rates of marriage and general rates of divorceBased on, census micro data, vital statistics and pooled survey data sets (b) Race-specific general rates of cohabiting and general rates of union dissolution (c) Race-specific

35、Total Fertility Rates (TFR) by parityBased on estimates released by the Census Bureau and the National Center for Health Statistics(d) Race-sex-specific Life expectancies at birth (e) Race-sex-specific total numbers of male and female migrants (f) Race-sex-specific mean age at first marriage and bir

36、ths 4. Validation of the extended cohort-component method for household forecasting at sub-national levelZeng 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 observ

37、ations. 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 pr

38、ojected 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. Figure 3a. Distributions of the absolute percent errors (APE) of forecasts from 1990 to 2000,

39、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 1990sFigure 3b. Distributions of the absolute percent errors (APE) of forecasts from 1990 to

40、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 1990sTable 2a. The Mean Absolute Percent Error, Mean Algebraic Percent Error and Median

41、 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 DCTable 2b. The Mean Absolute Percent Error, Mean Algebraic Percent Error and Median Absolute Percent Error of the m

42、ain 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 DCThe discrepancies are within a very reasonable range, and the ProFamy extended cohort component approach is validated at sub-national level.Howev

43、er, 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 num

44、bers 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

45、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 an

46、d the ProFamy approach. 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-

47、component approach and the constant headship-ratesThe 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 below:Table 4. Forecast errors of Mean Algebraic Percent Error (MALPE), Mean Ab

48、solute 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

49、 projected total number of households as observed in the 2000 census. The headship-rate still did substantially worse. - The changing headship-rate model still did substantially worse. Why? The censuses data shown, as compared to 1990, the 1, 2, 3, 45, and 6+ persons households in 2000 increased by

50、20.6, 16.9, 9.2, 9.3 and 15.1 percent, respectively. American households with 12 persons (which more likely need 0-1 bedroom) and 6+ persons (which more likely need 4-bedrooms) increase substantially faster than the 3- and 45 person households (which more likely need 23 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 si