Uni in the brewery - Peter Siminski
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Transcript of Uni in the brewery - Peter Siminski
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UNI IN THE BREWERYAugust 2012
I Was Only Nineteen, 45 Years Ago:
What Can we Learn from Australia’s
Conscription Lotteries?
Associate Professor Peter Siminski
2012 ARC DECRA Recipient
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Outline
• Australia’s conscription lottery
• Mechanisms and research motivations
• Methods and data
• Employment Effect
• Crime Effects (work in progress)
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Australia’s National Service Lotteries (1965-1972)
• Two lotteries per year = 16 lotteries• 20 y.o. men required to register for lottery• By date of birth
• National Service = Army for two years
• 804,286 registered, 237,048 balloted-in, 63,375 enlisted, 18,654 served in Vietnam.
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Depiction of first stage – prob of Army service
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Depiction of first stage – prob of Army service in Vietnam
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Mechanisms by which ballot outcome may affect outcomes
• Draft avoidance behaviour: education, marriage, health
• Army Service in Australia: army training, removal from civilian life e.g. labour market & marriage market
• Service in Vietnam: stress, combat, chemical exposure, cultural exposure, hostile reception on return to Australia
• Vets’ compensation and programs: cash benefits (direct and indirect effects), health insurance, education
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Research Motivations
• Full costs of conflict
• Reform of military practice
• Acknowledgment
• Appropriate health interventions for veterans
• Assess adequacy and design of compensation
• Long run effects of experiences in early adulthood
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Our contributions
• Aus conscription lotteries solve selection problem
• Compared to US, cleaner assignment and less concern over confounders (e.g. education).
• Between-cohort variation: operational vs non-operational service
• Vets’ compensation system differs from US in important ways
• Several sources of quality data; many relevant outcome variables
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} {
Intuition of Methodology
All men in a 6-month birth cohort
Balloted in Balloted out
LATE =
ˆ ˆ
I O
I O
y y
p p
y = a given health outcome p = proportion enlisted
Reduced Form =
Compliers Compliers
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Main Model
Where y is employment (binary)r is Vietnam-era army service, v is army service in VietnamC is a vector of 16 (6-month) cohort dummiesz is a binary binary ballot outcome instruments, which is interacted with
C to give 16 IVs
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'i r i v i i i
y r v C
1 2' '
i r i r i rir zC C
1 2' '
i v i v i viv zC C
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Approach and Data
• Two Sample 2SLS (Atsushi & Solon, 2010)
– Use cross-fitted values from the 1st stage regressions in 2nd
stage regression
– Treating 1st stage results as known
• First stage data
– Unit records from 2 military personnel databases
– Combined with published resident population of 20 year old men (at time of each ballot)
– N = 868,606
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2nd Stage Data
• Census 2006 (N = 675,832)
• Criminal Court data (NSW, QLD, VIC) (1994-2010) (179,363 cases with guilty verdicts)
• Vets’ Disability Pension data (1990-2009)
• ATO data (1992-2009) (N ~ 1,000,000)
• AIHW National Mortality Database (1994-2007)
• 45 & UP (sample survey)
• Australian Cancer Database (1982-2011)
• ED NMDS
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2SLS effects on economic outcomes
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Violent Crime
• Why might military service affect violent crime perpetration?
– Combat exposure (Rohlfs, 2010, JHR): desensitisation
– Threat of combat exposure: stress / mental health
– Training, e.g. weapons + ‘dehuminisation of the enemy’ + rapid respons
– Removal from civilian life / social ties / replaced with masculine culture
• Military service potentially decreases crime due to training in discipline, health, vocational skills
• Existing evidence mostly correlational, exceptions are
– Rohlfs (2010, JHR) finds effects of combat intensity on violence in U.S.
– Galliani et al. (2011, AEJ: Applied) find effects on crime, but not violent crime in Argentina
– Lindo et al. (2012) find effects on violent crime in US.16
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2SLS Approach - Crime
• Preferred Strategy to identify effects of training: limit sample to cohorts that remained in Australia. Identify r directly in 2nd stage regression:
• Strategy 2: use all cohorts; assume service in Vietnam did not decrease crime, to get an upper bound estimate for r.
• Strategy 3: identify effect of r and v, exploiting cross-cohort variation in treatment effects:
• Short story: nothing significant and point estimates negative. Key question: are estimates precise enough?
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ˆ ˆ 'd r cz v cz d d
y r v C
ˆ 'd r cz d d
y r C
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Visual Reduced Form – Violent Cases (guilty)
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0.00
0.02
0.04
0.06
0.08
1945.1 1945.2 1946.1 1946.2 1947.1 1947.2 1948.1 1948.2 1949.1 1949.2 1950.1 1950.2 1951.1 1951.2 1952.1 1952.2
Ballot In Ballot Out
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2SLS effect of army training on crime (youngest 4 cohorts)
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mean of
dependent variable
Estimated 2SLS effects
Endogenous variable Point estimates (robust S.E.s)
Point estimate as % of mean
Upper bound as % of mean
A. Dependent Variable: All crimes
Army service (r) .329 -.039 (.053) -12% 19%
B. Dependent Variable: Violent crimes
Army service (r) .065 -.023 (.013) -36% 3.6%
C. Dependent Variable: Non-violent crimes
Army service (r) .265 -.016 (.048) -6% 29%
D. Dependent Variable: Property crimes
Army service (r) .045 -.013 (.013) -28% 29%
E. Drink Driving Crimes
Army service (r) .081 -.015 (.014) -19% 15%
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2SLS effect of army training on crime (upper bounds: all 16 cohorts)
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mean of
dependent variable
Estimated 2SLS effects
Endogenous variable Point estimates (robust S.E.s)
Point estimate as % of mean
Upper bound as % of mean
A. Dependent Variable: All crimes
Army service (r) .269 .001 (.014) 0.4% 11%
B. Dependent Variable: Violent crimes
Army service (r) .054 .000 (.004) -0.1% 15%
C. Dependent Variable: Non-violent crimes
Army service (r) .215 .001 (.013) 0.5% 12%
D. Dependent Variable: Property crimes
Army service (r) .036 .000 (.004) 0.6% 22%
E. Drink Driving Crimes
Army service (r) .069 .002 (.005) 3.4% 17%
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2SLS effects on crime (including r and v together)
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mean of
dependent variable
Estimated 2SLS effects
Endogenous variables point estimates (robust S.E.s)
point estimate as % of mean
Upper bound as % of mean
A. Dependent Variable: All crimes
Army service (r) .269
-.005 (.036) -2% 24%
Army service in Vietnam (v) .020 (.095) 7% 76%
B. Dependent Variable: Violent crimes
Army service (r) .054
-.008 (.010) -14% 20%
Army service in Vietnam (v) .024 (.025) 44% 137%
C. Dependent Variable: Non-violent crimes
Army service (r) .215
.002 (.033) 1% 31%
Army service in Vietnam (v) -.004 (.084) -2% 75%
D. Dependent Variable: Property crimes
Army service (r) .036
-.007 (.010) -19% 36%
Army service in Vietnam (v) .021 (.025) 59% 196%
E. Drink Driving Crimes
Army service (r) .069
.001 (.010) 2% 32%
Army service in Vietnam (v) .003 (.028) 4% 83%
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Conclusions: Crime
• No significant effect on crimes of any type
• In the preferred specifications, we consider effects of army training:
– the point estimates are all negative
– we can rule out violent crime effects larger than 3.6%
– Under reasonable assumptions, can rule out effects on crime overall larger than 11%
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2SLS effect on family outcomes
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2SLS effect on health measures
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