CMGPD-LNMethodological Lecture

Day 1Why Use Historical Data?Origins of the CMGPD-LN

Basic Characteristics of the CMPGD-LN

Contemporary Topics

• Family contextual effects on individual outcomes• Neighborhood and community context• Life-course processes

– Conditions in childhood– Long-term effects of socioeconomic status

• Economic, climatic and other shocks• Multigenerational processes

– Interactions with stratification and inequality

Limitations of contemporary data

• Time depth– Panel/cohort studies are recent– Prospective data only for portions of life span– Exceptions: British Cohort Studies

• Family context– Limited to parents, sometimes siblings– Typically co-resident– Exceptions: PSID, WLS

Limitations of contemporary data

• Event counts– When mortality is low, ‘degree of freedom’

problem in all but the largest datasets– Difficult to explore complex interactions

• Exogenous shocks– Rare enough that their consequences are studied

individually– Indonesian Tsunami, Hurricane Katrina etc.

Historical population databases• Individual life histories• Prospective• In some cases…

– Multigenerational– Household and community context– Kinship

• Exogenous shocks: Price spikes, climate fluctuations, disease epidemics

• High mortality levels• Examples: CMGPD-LN, HSN, PRDH, UAS, UPD

CMGPD-LN

Public release at ICPSR supported by the United States Department of Health and Human Services. National Institutes of Health. Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD057175-01A1) with funds from the American Recovery and Reinvestment Act

Recommended acknowledgement

• This research made use of the CMGPD-LN dataset. Preparation of the CMGPD-LN and documentation for public release via ICPSR DSDR was supported by United States Department of Health and Human Services National Institutes of Health Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) R01 HD057175-01A1 "Multi-Generational Family and Life History Panel Dataset" with funds from the American Recovery and Reinvestment Act.

History of the CMGPD-LN

• Early 1980s: Ju Deyuan at the First Historical Archives alerted James Lee to the existence of the registers at the Liaoning Provincial Archives (LPA) in the early

• James Lee visits LPA three times 1982-1985• Lee and Campbell visit LPA 1987• LPA provides Daoyi registers (dataset 1) that

become basis of Fate and Fortune

History of the CMGPD-LN

• Datasets 3 and 2 obtained from LPA in early nineties and coded

• 1990-1999: datasets 4-10– Datasets became available from the Genealogical

Society of Utah– Data entry carried out in the United States

• 1999-2008: datasets 11-29– Data entry carried out in China

Recommended citations

• User guide– Lee, James Z, Cameron Campbell, and Shuang Chen. 2010.

China Multi-Generational Panel Dataset, Liaoning (CMGPD-LN) 1749-1909. User Guide. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.

• Dataset– Lee, James Z., and Cameron D. Campbell. China Multi-

Generational Panel Dataset, Liaoning (CMGPD-LN), 1749-1909 [Computer file]. ICPSR27063-v5. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2011-06-27. doi:10.3886/ICPSR27063

CMGPD-LNOrganization of the Release

• Basic Dataset (DS-001)– Identifiers for data management– Basic kinship and demographic variables

• Restricted Dataset (DS-002)– Names and village locations

• Analytic Dataset (DS-003)– Richer set of socioeconomic status variables

• Kinship Dataset (DS-004)– Ancestry identifiers– Constructed kin counts

CMGPD-LNContents

• Longitudinal• Individuals and households can be linked from one register

to the next• 1.5 million observations of 260,000 people

• 1,051 paternal descent groups identified through record linkage

• 698 communities• Generational depth

• 1749-1909• 7 generations

• (Relatively) Easy to Use• Resemble longitudinally-linked Censuses• Discrete-time event history (logistic regression etc.)

CMGPD-LNContents

• Demographic outcomes• Mortality• Marriage• Reproduction (based on surviving children)• Migration• Timing of events

• Closed, can identify individuals at risk• Health and Disability

• In early registers, annotation of specific conditions for adult males.

• In later registers, indicator of whether or not disabled for adult males.

CMGPD-LNContents

• Socioeconomic characteristics• Attainment of official position for adult males• Status as an exam candidate, indicative of high

education• Given name

• Flag variables for types of name• Diminutive, indicative of low status or

aspirations• Non-Han, indicative of expressed ethnicity

• Pinyin transcriptions in restricted release

CMGPD-LNContents

• Geographic context• Villages distributed across a region the size of New

Jersey• Wide variety of economic and ecological contexts

• Basic release• Region• Unique village identifier

• Restricted release• Geocodes for villages accounting for 95% of

population

CMGPD-LNContents

• Household and family context• Household of residence• Relationship to head

• Relatives can be linked to reconstruct descent groups• Via automated record linkage based on household

relationship and longitudinal linkage of individual records

• Kin outside the household• Based kinship variables, including parent identifiers, and

counts of close kin, available now• Additional constructed kinship variables available next

year

REGION (approximate)

DISTRICT (approximate)

010

000

2000

030

000

4000

050

000

Obs

erva

tions

17501760

17701780

17901800

18101820

18301840

18501860

18701880

18901900

1910

Year

CMGPD-LNFormat

• Similar in format to a series of triennial Censuses– Individuals listed in the same order and easy to link across time

• Organizing by community, kin group, household• Detailed specification of relationship to household head• Events since the previous register are annotated

– Basis for construction of flag variables specifying occurrence of events between current register and the next

• Discrete-time event history analysis– Typically, logistic regression or complementary log-log regression– Outcome: death in the next three years

• Restricting to registers for which the immediately succeeding register is also available

CMGPDProcessing

• Images scanned from microfilm• Provided to coders in China• Coders in China transcribe contents to Excel spreadsheets

– Copy previous spreadsheet over and update based on contents of new register

• STATA programs import the contents of the spreadsheets and perform error-checking– Inconsistencies across registers

• Reports sent to coders for cleaning– Original registers coded ‘as is’, so if an inconsistency is in the original register

we leave it• STATA programs carry out automated linking of kin and generation of

variables for analysis

Pre-1789 formatFeidi Yimiangcheng, 1783

Post-1789 FormatFeidi Yimiancheng, 1792

Daoyi 1816Illegal Escape

23 sui74 sui

Dead

42 sui

Daoyi 1819

Dead

New arrival

0.2

.4.6

.81

Pro

porti

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f chi

ldre

n fo

r who

mda

ta o

n sp

ecifi

ed a

nces

tor a

re a

vaila

ble

1750 1800 1850 1900Year

Father Grandfather

Great-grandfather Great-great-grandfather

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

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