1. CMGPD-LN Methodological Lecture
Day 2
Strengths and Weaknesses of the CMGPD-LN

2. Historical population databases
Parish registers
Genealogies
Censuses
Household registers

3. Table A.1. Comparison of features of sources for historical demography
Parish registers
Vital statistics
Censuses
Genealogies
Household registers
Longitudinal X
Individual- level
Detail on households
Geographic specificity
Complete community
Population at risk
Timing of vital events

4. CMGPD-LN Relative 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

10. Omission of boys who died in infancy and early childhood
CMGPD-LN Limitations
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)
Fate and Fortune in Rural China. Emphasize that only males are studied 10

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

13. CMGPD-LN Limitations 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

15. Using the Data RECORD_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\ Data“ merge 1:1 RECORD_NUMBER using "`cmgpd_ln_location'\DS0003\ Data"

16. Using the Data RECORD_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\ Data“ merge 1:1 RECORD_NUMBER using "`cmgpd_ln_location'\DS0003\ Data“, keepusing(NON_HAN_NAME) tab YEAR if SEX == 2, sum(NON_HAN_NAME)

17. Using the Data Missing 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 =. or
summ ZHI_SHI_REN if ZHI_SHI_REN != -98 & ZHI_SHI_REN != -99