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“The past is never dead. It’s not even past.” William Faulkner,

“…Healthy, Wealthy, and Wise? Physical, Economic and Cognitive Effects of Early Life Conditions on Later Life Outcomes in the U.S., 1915-2005” March 12, 2009. “The past is never dead. It’s not even past.” William Faulkner, Requiem for a Nun , Act I, Scene III (1951). Introduction.

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“The past is never dead. It’s not even past.” William Faulkner,

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  1. “…Healthy, Wealthy, and Wise?Physical, Economic and Cognitive Effects of Early Life Conditions on Later Life Outcomes in the U.S., 1915-2005”March 12, 2009

  2. “The past is never dead. It’s not even past.” William Faulkner, Requiem for a Nun, Act I, Scene III (1951)

  3. Introduction We examine effects later in life from early life circumstances (family, neighborhood, cohort)‏ like the Early Indicators Project & C2S at NU Why? Two important differences: H. Clarence Nixon described the U.S. South in the early 20th century as having “the economy of the Middle Ages without the cathedrals.” (Forty Acres and Steel Mules, 1938)‏ A national population (incl. females), with rich detail on family & local circumstances very early in life (under age 5)‏ This project could be said to be “like the Early Indicators Project without the wool uniforms and strangulated hernias.” 2. Less info on morbidity: analysis is on longevity, height & weight, and IQ ; eventually also cause of death, disability, LFP, earnings

  4. The Sources Used to Assess These Effects Are Inadequate Large, longitudinal epidemiological datasets often lack detailed information on subjects’ early lives Genealogical datasets have small numbers of observations and provide little context

  5. Our Approach Detailed information on conditions < age 5, in mid- life, at older ages, and at death – the intervals span up to 106 years Large numbers (2.5 million+)of nationally-representative observations with rich neighborhood and household context

  6. Early-life conditions: birth records, U.S. Census, maps, published info Mid-life conditions: World War Two enlistment Later-life conditions: Social Security, Medicare and VA End-of-life conditions: Social Security, State Death Records

  7. Early-life conditions (birth to age 5) birth weight, mother’s health, gestational age, delivery exact street address family structure (incl. presence of parents & birth order) parents’ SES and literacy parents’ unemployment parents’ asset ownership characteristics of neighbors local hazards/assets local mortality/disease environment [early-life conditions of parents and grandparents…]

  8. Mid-life conditions (around age 25, males only) place of residence occupation marital status family structure educational attainment height weight IQ

  9. Later-life conditions (age 65+) “income” (inferred from Social Security pension) disability (from Social Security) specific health conditions (Medicare/VA) End-of-life conditions (at death) longevity specific cause of death

  10. How? Following individuals (1) from U.S. Census samples (1900-30) into the Social Security records & State Death Records (SDR); or (2) from State Death Records back into the U.S. Census of Population and for those who served in World War II, linkage to U.S Army enlistment records (height, weight, marital status, occupation, and residence)‏ Result: 25,000 U.S.-born males followed from birth to death, with detailed info on household & neighborhood; 5,837 also linked to enlistment

  11. The Linkage Process 1965-2005 SSDI surname given name birth month birth year SSN Post-1970 SDR surname given name birth month birth year birth place SSN 1900-30 Census surname given name birth month birth year birth place 1940-43 WW Two surname given name birth year birth place (1)‏ (2) Census WW Two SDR SSDI Today’s results (2): SDR  1920 Census & WWII

  12. We use 1,537,659 Death Certificates of individuals who died in 8 states and were born in the U.S., 1915-19 From these, we randomly drew 96,099 and located 28,839 (30%) in the 1920 U.S. Census of Population

  13. Identifying information Later life outcomes Mortality information

  14. The 30% linkage rate results from individuals missed or incorrectly enumerated in the census or individuals who could be matched to more than one person in the 1920 Census But information from the individual’s original SS-% (Social Security application) on detailed birthplace and the full names of both parents will eliminate any ambiguities

  15. Social Security Form SS-5: source for NUMIDENT and Social Security Death Index

  16. The states were chosen on the basis of the easy availability of their computerized death records But they are also convenient in other respects:

  17. All 8 states are in the Death Registration Area by 1911  detailed local mortality info

  18. 9 of 20 largest cities in 1920

  19. “A Very Specific Example” Or “The Calvin C. Denning Story”

  20. Ohio Death Certificate Name: Calvin C. Denning Birth Date: 7 Apr 1916 Birth City: Hamilton Birth County: Butler Birth State: Ohio Gender: Male Race: White     Death Date: 24 Mar 1996 SSN: 275-10-7548 Father: Denning Mother: Menzer Marital Status: Married Education: 13 years Armed Forces: Yes, US Army Industry: U.S. Postal Service Occupation: Supervisor  1920 U.S. Census of Population Butler Co., OH, Hamilton Ward 3 Calvin Denning, age 3 years 9 months in 1920  Born April, 1916 in OH

  21. Hamilton, OH Ward 3

  22. factories railroad tracks public school 346 N. 11th Street church

  23. 346 N. 11th Street The Vulcan Foundry

  24. Figure 5. Sanborn Map for Part of City of Rockford, Winnebago County, Illinois, 1913.

  25. Figure 6. Plat Map for Part of Otter Creek Township, Jersey County, Illinois, 1916.

  26. U.S. Army Enlistment Records Name: Calvin C. Denning Birth Year: 1916 Race: White, citizen Nativity State or Country: Ohio Residence State: Ohio County or City: Butler   Enlistment Date: 1 Dec 1941 State: Kentucky City: Fort Thomas Newport Education: 1 year of college Civ. Occup: Multilith Operator Marital Status: Single, no dependents Height: 68 inches Weight: 131 pounds

  27. 14 children born, 10 surviving 1900 U.S. Census of Population

  28. 1900 U.S. Census of Population

  29. Able to read and write Attended school since Sept. 1, 1929 1930 U.S. Census of Population

  30. Neighborhood characteristics: - Economic and demographic info on all neighbors (incl. local 1930 unemployment rate), along with exact street addresses in city/town - Location of environmental hazards such as gas stations (source of lead after introduction of leaded gasoline c. 1926), steel mills, lead smelters, and polluted waterways - Proximity to retailers, health care, and schools We will have more than 500,000 linked 1900-1930 census  SSDI  SDR by Spring, 2009 w/education, earnings, longevity, and cause of death

  31. Shortcomings of the Data Today’s analysis uses mostly males (linkage uses name & date & place of birth, but women’s name changes at marriage prevent their linkage)‏ but Social Security is providing information on women’s names at birth to help, and state death records post-1978 provide maiden name 2. Cause of death info (along w/education) will come from 50 sets of state death records (8 now in hand: CA, CT, MA, MI, MO, MN, NC, OH)‏

  32. Social Security Form SS-5

  33. 3. The key ingredient is the SSA NUMIDENT File, which (1) includes only individuals in the Social Security system, so - someone whose entire career was in “uncovered employment” is missed - someone whose death occurred before collecting any SS benefits is missed and (2) is computerized only in the early 1970s, so - individuals who retired prior to then will lack the full set of info they provided on their SS-5 form (sent back to local office)‏

  34. Figure 1. Coverage of Deaths in SSA’s Death Master File By Age and Year, 1960-2000. Source: Hill & Rosenwaike, 2001/2002.

  35. 60-70% Figure 2. Iowa & Nebraska Males With SSN in 1940 By Age & Migration. Source: 1940 IPUMS.

  36. Figure 3. Percent in SSA NUMIDENT File With Original Application Information By Birth Year.

  37. 4. The Social Security Death Index is available only 1965-2005, so the “window” in which we can observe deaths is only 40 years of calendar time as a result, for each cohort, we will need to limit the ranges for age at death within which we examine the correlates of mortality But 70% of the 1910-1919 birth cohort died in this window

  38. example: for the sample drawn from the 1920 census (males born 1915-1919), we can look at only those who died between 50 & 85 To examine the correlates of longevity, we will run regressions of the form: E(Agedeath | Agedeathmin < Agedeath < Agedeathmax)‏ = β´Xi+γ´Yi +δ´Zi+εi where Xi are individual & household characteristics, Yi are neighborhood characteristics and Zi are economy-wide effects (e.g. GDP, pandemic, war)‏

  39. 5. State death records are only generally available 1970-2006, so the “window” there is even smaller California’s records go back to 1940, and also make it possible to include women (most states do not report birth surnames until 1979): CA birth records are also computerized from 1905: match on birth date, given name, and birthplace (CA)  25,000 males & females who died in California 1940-2004

  40. 6. The World War Two data has some oddities: -individuals are selected on the basis of physical fitness for military service, so their mortality after the war is somewhat better than the general population over 2 the decades after war -their height and weight reflect the selection criteria in place at enlistment (changes over war)‏ -military provided tobacco, leading to higher than average lung cancer & heart disease at older ages -data on height/weight only available 7/40-3/43

  41. Data Analysis Longevity, height, & weight of individuals born 1915-1919 linked from State Death Records to the 1920 U.S. Census & WWII Why 1915-1919 births & 1920 Census? -maximizes the number of links to WWII records and to State Death Records (70% of this cohort dies 1970-2006) -shows effect of conditions < age 5 -shows impact of 1918-19 influenza pandemic

  42. Maximum: 70.5 in.

  43. An additional outcome: Results of the Army General Certification Test at enlistment in World War II A standardized IQ-like test used to determine branch and task assignments These test results have never been used at the individual level on a large scale Two examples: 1. early conditions → AGCT score 2. AGCT score → longevity

  44. Where can we get these test results?: some forensic cliometrics

  45. May, 1943

  46. How can we tell when “weight” is punched and when AGCT is punched? Look at distributions: 150 110

  47. Height & Weight: AGCT:

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