An empirical approach to assessing the impact of the 2008 alcohol bans in indigenous communities in Queensland, Australia. We use the State Government's own policy goals to assess whether the bans can be shown to have had a significant effect.

1. Measuring the effects of alcohol-supply restrictions in indigenous communities in Queensland, Australia Gabrielle Blumberg, Jonathon Flegg, Troy Gill, Sarah Hauser, Jared Kreutzer, Faith Rose, and Steven Paling

2. Background Indigenous communities in Australia do not enjoy the same standard of living, opportunities, and outcomes as the rest of the Australian population. Availability of alcohol and its widespread misuse has been argued to be one of the major causes of social malfunction in indigenous communities. The State of Queensland, NE Australia, has a large indigenous population (around 3.1%) compared with national average (2.5%) While subject to state and federal law, ‘discrete’ indigenous communities are self-governing and have a considerable amount of autonomy.

3. 3 The Communities In blue: 19 remote and self-governing indigenous communities with high levels of alcohol abuse. Map Source: http://www.atsip.qld.gov.au/people/communities/community-map/

5. Alcohol may not be transported into or through restricted areas, except on designated highways;

6. Drinking in public places will be banned; and

9. Increase overall health for everyone living in the community;

10. Create safer communities, especially for women and children; and

11. Provide opportunities to rebuild social norms and healthy families.*ATSIS 2009a

13. Reported offences against a person;

14. Breaches of alcohol restrictions;

15. New substantiated notifications of harm;

16. New finalized child protection orders; and

17. School attendance.(ATSIS 2009a)

18. Research Question Research Question: What has been the effect of introducing alcohol-supply restrictions in ‘discrete’ indigenous communities on key indicators of social dysfunction? Policy Implication: Have the alcohol-supply measures been successful in contributing to the Queensland Government’s welfare goals for indigenous communities?

21. Methodology Assess available datasets; Find an acceptable control group using propensity matching scores; Difference-in-differences (DD) estimation; Difference-in-difference-in-differences (DDD) estimation; and Robustness checks using a placebo test and sequential removal of communities.

22. Data Available for Analysis For data on community specific policy interventions: http://www.atsip.qld.gov.au/ . All data are available monthly (education and hospital data) or quarterly from 1998 onwards.

23. Propensity Matching: Finding our Control Group Queensland has many other ‘discrete’ indigenous communities. However, by themselves they are a poor control. The Queensland Government did not view alcohol abuse to be sufficiently high enough within these communities to warrant treatment. Margolis et al (2008) failed to find a valid control group within Far Northern Queensland.

24. Propensity Matching: Finding our Control Group In the Northern Territory there are a large number of similar ‘discrete’ indigenous communities that have not received policy intervention. A valid control group would be communities that are matched with treated Queensland communities

26. Reported offences against a person;

27. New substantiated notifications of harm;

28. New finalized child protection orders; and

30. Income per capita;

31. Average educational attainment;

32. Average alcohol consumption per capita;

33. Number of FTE police officers;

34. The existence of a licensed alcohol outlet within the community; and

36. But there is a better way … “A more robust analysis … can be obtained by using both a different state and a control group within the treatment state” - Imbens and Wooldridge, 2007

37. Specification: Triple Diff (DDD) Estimation Difference-in-Difference #1 – Queensland Where: Q = Queensland, NT = Northern Territory, D = Dysfunctional communities (treatment or matched), O = Other (non-treated and non-matched) This diff-in-diff allows for The permanent difference in outcomes between the two groups The change that would have occurred in all Queensland communities, in the absence of the treatment because of common factors (ex: state-based policies and spending) The resulting difference isolated represents the effect of: (i) the treatment, and (ii) the change that would have occurred in these “dysfunctional” communities anyway, such as mean reversion (2)

38. Specification: Triple Diff (DDD) Estimation Difference-in-Difference #2 – Northern Territory Where: Q = Queensland, NT = Northern Territory, D = Dysfunctional communities (treatment or matched), O = Other (non-treated and non-matched) Like the previous one, this diff-in-diff that allows for permanent differences in outcomes between the two groups, and a change (or trend) in the outcome variable that is common to both groups. Because there is no actual treatment for the matched communities, this diff-in-diff isolates the change in the matched groups due to being a dysfunctional community (such as mean reversion). (3)

39. Specification: Triple Diff (DDD) Estimation Difference-in-Difference-in-Differences Subtracting (2) from (1) clearly gives us the change in the outcome variable in the Queensland treatment group that is due to the treatment. Identifying assumption - Outcome variable in dysfunctional communities would have changed in the same way in Northern Territory and Queensland in the absence of treatment (and after stripping out other aggregate factors which likely vary across the two states, which is what the first two diff-in-diffs do). (4) DD1 DD2

40. Specification: Triple Diff (DDD) Estimation In an approach analogous to Imbens and Wooldridge (2007) with the addition of matching control group: DDD estimation performed for all communities (i) by quarter (t). Q is a dummy equal to 1 if community is in Queensland. D is a dummy equal to 1 if it the community is matched. P is a post-dummy for all observations after the 2008 policy implementation. Y is a vector containing our 5 outcome variables, using logs. Using robust standard errors for heteroskedasticity and autocorrelation. (5)

41. Specification: Triple Diff (DDD) Estimation QLD, Matched, Post β0+ β1+ β2+ β3 + δ0 + δ1 + δ2 +δ3 QLD, Matched, Pre β0+ β1+ β2+ β3 QLD, Non-matched, Post β0+ β1+ δ0 + δ1 QLD, Non-matched, Pre β0+ β1 NT, Matched, Post β0+ β2 + δ0 + δ2 NT, Matched, Pre β0+ β2 NT, Non-matched, Post β0 + δ0 NT, Non-matched, Pre β0 Diff DD DDD Diff Diff DD Diff

42. Specification: Triple Diff (DDD) Estimation Dif-in-Dif: Queensland Dif-in-Dif: Northern Territory YQ,M,Pre YNT,M,Pre δ2 δ2 δ0 + δ2 δ0 + δ1 + δ2 + δ3 δ2 + δ3 δ2 + δ3 YNT,M,Post YQ,N,Pre YQ,M,Post YNT,N,Pre δ0 δ0 δ0 + δ1 δ0 + δ1 YNT,N,Post YQ,N,Post 2008 Policy Implementation 2008 Policy Implementation Pre Post Pre Post

49. Aboriginal and Torres Strait Islander Services. 2009b. Fact Sheet 2: Legislative Changes. [Online] (Updated 21 Jan 2009) Available at: http://www.atsip.qld.gov.au/government/programs-initiatives/alcohol-reforms/documents/fact-sheet-02-legislative-changes.pdf [Accessed 30 March 2010]

50. Imbens, G. W. and J. M. Wooldridge. 2007. “What’s New in Econometrics?” NBER Lecture Notes Series. Available at: http://www.nber.org/WNE/lect_10_diffindiffs.pdf [Accessed 26 April 2010].

51. Margolis, S. A., V. A. Ypinazar, and R. Muller. 2008. “The Impact of Supply Reduction Through Alcohol Management Plans on Serious Injury in Remote Indigenous Communities in Remotre Australia: A Ten-Year Analysis Using Data from the Royal Flying Doctor Service”. Alcohol and Alcoholism 43(1): 104-10.

53. Appendix 1: Intervention Communities

54. Appendix 2: Proximity of Matching Communities NT QLD Map Source: http://www.ag.gov.au/