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“…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,

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Presentation on theme: "“…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,"— Presentation transcript:

1 “…Healthy, Wealthy, and Wise? Physical, Economic and Cognitive Effects of Early Life Conditions on Later Life Outcomes in the U.S., ” 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 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: 1.A national population (incl. females), with rich detail on family & local circumstances very early in life (under age 5) ‏ 2. Less info on morbidity: analysis is on longevity, height & weight, and IQ; eventually also cause of death, disability, LFP, earnings Introduction H. Clarence Nixon described the U.S. South in the early 20 th century as having “the economy of the Middle Ages without the cathedrals.” (Forty Acres and Steel Mules, 1938) ‏ This project could be said to be “like the Early Indicators Project without the wool uniforms and strangulated hernias.”

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

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

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 ( ) 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 Census surname given name birth month birth year birth place WW Two surname given name birth year birth place SSDI surname given name birth month birth year SSN Post-1970 SDR surname given name birth month birth year birth place SSN (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., From these, we randomly drew 96,099 and located 28,839 (30%) in the 1920 U.S. Census of Population

13 Identifying information Mortality information Later life outcomes

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: 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

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

23 346 N. 11 th 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.

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27 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

28 14 children born, 10 surviving 1900 U.S. Census of Population

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

31 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 census  SSDI  SDR by Spring, 2009 w/education, earnings, longevity, and cause of death

32 Shortcomings of the Data 1.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) ‏

33 Social Security Form SS-5

34 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) ‏

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

36 Figure 2. Iowa & Nebraska Males With SSN in 1940 By Age & Migration. Source: 1940 IPUMS %

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

38 4. The Social Security Death Index is available only , so the “window” in which we can observe deaths is only 40 years of calendar time But 70% of the birth cohort died in this window 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

39 example: for the sample drawn from the 1920 census (males born ), 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(Age death | Age death min < Age death < Age death max ) ‏ = β´X i +γ´Y i +δ´Z i +ε i where X i are individual & household characteristics, Y i are neighborhood characteristics and Z i are economy-wide effects (e.g. GDP, pandemic, war) ‏

40 5. State death records are only generally available , 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

41 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

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

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44 Maximum: 70.5 in.

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46 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

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

48 May, 1943

49 How can we tell when “weight” is punched and when AGCT is punched? Look at distributions:

50 Height & Weight: AGCT:

51 March through May 1943: mean “weight” falls to ∼ 100 standard deviation rises

52 The peak shifts from 150 to 110

53 Particularly for the period March through May 1943, the distribution in the “weight” field mimics the Army’s published AGCT tabulations

54 What can we do with this information 1. Examine the set of early-life influences already used 2. Add the influence of growing up in a place with lead water pipes 3. Examine AGCT as a predictor of later life outcomes For a city or town with lead pipes hard water → more calcification, less lead → IQ↑ ↑ acidic water → more corrosion, more lead → IQ↓

55 Hardness ↑ → AGCT ↑ Acidity ↑ → AGCT ↓

56 How large are these effects? Moving from the 5 th to the 95 th percentiles in hardness and from the 95 th to the 5 th percentiles in acidity (i.e. to harder, less acidic water) and using the coefficients in Column 5: Reyes (2007) reports that the scientific consensus is that with a fall in atmospheric lead exposure of 15 μg/dL (as actually occurred ) → IQ ↑ 7.5 points AGCT ↑ by 6.0 points

57 What’s the mechanism? The impact of lead exposure on the brain: Result: lower IQ, more impulsivity and criminality

58 Omaha, Nebraska American Smelting and Refining Co. consolidated several plants in By 1924, the plant at the corner of 5th and Douglas streets was the largest lead refinery in the world.

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60 The plant was closed in July, 1997 & cleanup began

61 IQ and Longevity: very few existing studies 1. Holsinger, Helms, & Plassman (2007): 492 twin pairs from the National Research Council Twins Registry of WWII veterans → no effect once genetic component is removed 2. Whalley & Deary (2008): 2,972 children born in Aberdeen and followed to age 76 → relative survival probability to age 76 was only 0.79 if IQ was 15 pts. ↓ We have 500,000 males w/IQ linkable to death

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63 The California data (births in CA linked to deaths in CA , using only given name, exact date of birth, and place of birth/death – CA) makes possible two additional comparisons: 1. males vs. females: does the impact of early life conditions differ by sex? 2. how much of the impact for males is missed because of the “window” for the State Death Records outside CA? Sensitivity Analysis

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66 Conclusions and Future Directions 1. Month of birth matters, effect varies by age/sex 2. Home environment (presence of parents, household size, birth order) matters 3. The effects of early environment are stronger for males than for females (greater ♂ frailty?) ‏ 4. Absent father  shorter life, lower weight & BMI but absent mother  shorter stature; mechanism? 5. Strongest impact of influenza pandemic is on weight & BMI at enlistment in WWII 6. Height ≈ age 25 has a non-linear effect on longevity (optimum=70.5 in.) ‏

67 7. At least in California, early life conditions (except for month of birth) have a stronger effect on longevity at younger ages  the effects we find using data from other states at older ages is probably a lower bound 8. The future -Adding more women (outside California) ‏ -Disaggregating by cause of death -Adding local controls (weather, local mortality, physical features of the neighborhood) ‏ -Adding macroeconomic data (Davis’s IP series) ‏ -Earnings, LFP, Disability, Medicare/VA health


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