1. SJTU CMGPD 2012 Methodological Lecture Recommended Acknowledgments Contemporary Applications of Historical Data Origins of the CMGPD-LN Key Features

2. 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

3. Acknowledging the CMGPD Please include acknowledge and cite the CMGPD in your publications This will allow us to document use of the CMGPD Will facilitate future applications for support to release additional databases by providing evidence of demand Please also send us copies of any papers that results from use of the CMGPD

4. Recommended acknowledgement Please include in ALL publications 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.

5. Recommended citations Please include in ALL Publications 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

6. 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

7. 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

8. 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.

9. 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

10. 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

11. 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

12. CMGPD-LN Organization of the Release Basic Dataset (DS-001) – Identifiers for data management, basic 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 Additional files with

13. CMGPD-LN Contents 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.)

14. CMGPD-LN Contents 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.

15. CMGPD-LN Contents 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

16. CMGPD-LN Contents 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

17. CMGPD-LN Contents 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

20. REGION (approximate)

21. DISTRICT (approximate)

23. CMGPD-LN Format 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

24. CMGPD Processing 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

25. Pre-1789 format Feidi Yimiangcheng, 1783

26. Post-1789 Format Feidi Yimiancheng, 1792

27. Daoyi 1816 Illegal Escape 23 sui 74 sui Dead 42 sui

28. Daoyi 1819 Dead New arrival

33. 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\27063-0001-Data“ merge 1:1 RECORD_NUMBER using "`cmgpd_ln_location'\DS0003\27063-0003-Data"

34. 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\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)

35. 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 -99 -98=. or – summ ZHI_SHI_REN if ZHI_SHI_REN != -98 & ZHI_SHI_REN != -99