SJTU CMGPD 2012Methodological Lecture

Day 1 (supplemental)Strengths and Weaknesses of the

CMGPD-LN

Historical population databases

• Parish registers• Genealogies• Censuses• Household registers

Table A.1. Comparison of features of sources for historical demography

Parish registers

Vital statistics

Censuses Genealogies

Household registers

Longitudinal X X X X

Individual-level

X X X X

Detail on households

X X

Geographic specificity

X X X X

Complete community

X X X X

Population at risk

X X X X

Timing of vital events

X X X X

CMGPD-LNRelative Strengths

• Household and village of residence– Not available in genealogies, parish registers

• Longitudinal– Not available in censuses

• Complete recording of the at-risk population– Not available in parish registers

• Time-depth/Multigenerational– Not available in most household registers

• Kinship– Genealogies typically only record a single descent group

• Prospective– Genealogies are retrospective

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1750 1800 1850 1900Year

Father Grandfather

Great-grandfather Great-great-grandfather

Great-great-great-grandfather Great-great-great-great-grandfather

CMGPD-LNLimitations

• Omission of boys who died in infancy and early childhood–Can’t really do infant or early child mortality–Underestimate fertility

• Omission of daughters• No non-state occupations, or landholding

– Landholding will be able in Shuangcheng (CMGPD-SC)

010

000

2000

00

1000

020

000

0 10 20 30 40 50 60 70 80 90 100

Female

Male

Fre

quen

cy

Age in suiGraphs by SEX

0.2

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1750 1800 1850 1900Year

Boys in next 3 years Girls in next 3 years

Average numbers of boys and girls born in next 3 years to married men aged 15-50

CMGPD-LNLimitations

•Missing registers–Event-history analysis limited to registers for

which immediately following register is also available

•Unrecorded deaths–A small % of individuals who were probably

dead, were carried on alive from register to register as if they were alive

–Creates problems at advanced (80+) ages

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0 20 40 60 80 100AGE

Males Females

Using the DataRECORD_NUMBER

• RECORD_NUMBER identifies the same observation across the different datasets

• Use as the basis for one-to-one merge

local cmgpd_ln_location "..\CMGPD-LN from ICPSR\ICPSR_27063“

use "`cmgpd_ln_location'\DS0001\27063-0001-Data“

merge 1:1 RECORD_NUMBER using "`cmgpd_ln_location'\DS0003\27063-0003-Data"

Using the DataRECORD_NUMBER

• If the merged datasets won’t fit into memory, make use of options on use and merge to load specific variables

use RECORD_ID YEAR SEX using "`cmgpd_ln_location'\DS0001\27063-0001-Data“

merge 1:1 RECORD_NUMBER using "`cmgpd_ln_location'\DS0003\27063-0003-Data“, keepusing(NON_HAN_NAME)

tab YEAR if SEX == 2, sum(NON_HAN_NAME)

Using the DataMissing Values

• Following standard practice, missing values are coded as -98 or -99– -98 is structural missing– -99 is missing

• These are not the same as STATA missing, so observations will not be excluded automatically

• Especially in regressions, computations of means, etc., either manually exclude these, or recode to force exclusion– recode ZHI_SHI_REN -99 -98=. or– summ ZHI_SHI_REN if ZHI_SHI_REN != -98 & ZHI_SHI_REN != -99