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

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

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

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

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

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

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

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%

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%

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%

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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