1. IMA Meeting Budapest 21-22 September 2016
The Australian Commonwealth’s labour supply microsimulation model CAPITA-B
IMA Meeting Budapest September 2016
Joseph Mercante
Australian Department of Employment
The findings and views reported are those of the CAPITA-B development team and do not necessarily reflect those of the Department of Employment or the Australian Government. The simulation results presented are preliminary and should not be quoted nor used to inform policy development.

2. Outline
Background to the project
Model
Results so far
Future work

3. Why behavioural microsimulation?
Model changes to labour supply from changes in financial incentives arising from tax, welfare & employment policies
Help develop policies to enhance labour force participation
Understand responses to increase/decrease participation (extensive margin) and increase/decrease working hours (intensive margin)
Behavioural impact may make policies less expensive

4. History of behavioural microsimulation by the Australian Government
MITTS-B (Melbourne Institute)
STINMOD-B (Hybrid model, Treasury’s Participation Modelling Project, )
Used for policy over
Need for a new model
CAPITA replacement model (Commonwealth, led by Treasury, released 2015)
CAPITA-B prototype (Productivity Commission, 2015, experimental and limited for policy applications)
Department of Employment enhancements: CAPITA-B
Comparative Analysis of Personal Income tax and Transfers of Australia – Behavioural

5. CAPITA-B Project Started end 2015 Phase 1
Basic model to replace STINMOD-B functionality
Improve Productivity Commission’s prototype
Completed July 2016 (currently undergoing QA)
Phase 2
Re-estimate wages & labour supply models
Childcare policy (non-behavioural & behavioural)
Started July 2016
completion by beginning 2017

6. Main elements of CAPITA-B model
Micro data for the simulations (SIH)
The tax/transfer model CAPITA (calculates disposable incomes)
Preference equations to estimate utilities
Wage equations to impute wage rates for non-workers
Rules for benefit take-up
A Monte-Carlo process to simulate labour supply
Calibration to observed labour supply
Modules to report results of simulations.

7. CAPITA-B design and architecture
Wage Equations
CAPITA-B
CAPITA Policy Modules
Basefiles
Output Data
Behavioural Components
Calibration
Results
Policy Parameters
Preference Equations

8. CAPITA arithmetic model
Analyses of the impact of changes to tax and transfer payment policies on individual’s tax liability, transfer payment entitlement, and their final income
Survey of Income and Housing (2011/12)
Future policy years (5 years)
Benchmarked
Broad tax and welfare system coverage
Most benefits & family payments
No superannuation
No consumption taxes

9. Labour supply modelling in CAPITA-B
Classical utility maximising labour supply assumption
Singles and single parents
Joint utility for couples
Dependents’ labour supply ignored
Discrete choice framework
11 hour choices for singles
66 combined hour choices for couples
Borrowed from MITTS
Kalb (2002), Van Soest (1995)
Imputation of wage rates for non-workers
Heckman sample selection bias correction model
Borrowed from MITTS (Kalb & Scutella 2002, Heckman 1979)

10. Modelled population Exclusion category Age Pension age 3,502,000 *
Age Pension age
3,502,000 *
Disabled, Carers & Veteran
1,361,000*
Self-employed
1,781,000*
Full-time students
2,531,000*
Other
2,227,000*
Non Private Dwellings
1,192,000
Excluded adults
8,871,000
Dependents 15 and over
1,215,000
Total excluded
10,086,000
Not excluded
8,179,000
Total persons
18,622,000
45%
* Not mutually exclusive

11. Benefit take-up Simulating people at non-observed hours choices.
At 0 hours, people don’t usually have zero income because they get transfer payments.
CAPITA-B applies a set of rules to non-beneficiaries determine what payments to put people on. Assumes 100% take-up.
Use rule to determine principal carer in couple income units.
Re-allocation of benefits for some persons
Take-up for dependents 15 years and over

12. Other features Treatment of tax deductions
Treatment of negative incomes
Parental income test

13. Testing and benchmarking
How do we know the model produces valid results?
Predicted vs observed hours distributions
Wage distributions
Impact of benefit take-up rules
Policy simulations
Do non-behavioural results make sense?
Do fiscal results (without behaviour) match expectations?
Do labour supply results make sense?
Elasticities
Comparing models
Sensitivity analysis / confidence intervals

14. Observed and predicted working hour distributions

15. Observed and predicted working hour distributions (cont.)

16. Predicted (non-workers)
Wage estimation
Observed
Predicted (workers)
Predicted (non-workers)
Partnered men
34.9
30.4
26.2
Partnered women
28.4
26.9
18.8
Single men
25.8
25.4
22.8
Single women
25.3
23.3
21.0
Single parents
25.1
24.2

17. Benefit take-up (non-dependents, at observed working hours)
Benefit type
CAPITA
CAPITA-B
Difference
Non-modelled population
7,151
7,146
-5 (0%)
Sole Parents with child ≤ 8 (PPS)
407
513
106 (26%)
Unemployment benefit (NSA)
591
1138
547 (93%)
Partner principal carer with child ≤ 6 (PPP) 85
178
67 (60%)
Youth Allowance (non-student)
189
104 (122%)
None
19,645
18,826
-819 (-5%)
Total
27,990
Unweighted sample

18. Simulated elasticities
Partnered men
Partnered women
Single men
Single women
Single parents
Own wage
0.18
0.36
0.16
0.20
% change 3 2 1
Cross wage
0.00
-0.02
Based on aggregate labour supply change to 10% increase in wage rate
Partnered women highest
Expect single parents much higher
Participation response (extensive margin) around 70% of elasticity
Partnered women higher response to their partner’s wage

19. Simulation example Increase tax threshold to deliver small tax cut
$37,000 increased to $38,000
income
rate
$18,200
$18, $37, $38,000
19%
$38, $80,000
32.5%
$80, $180,000
37%
$180,000+
45%
Notes:
Uses tax system as at 2015 (that is what model is calibrated to)

20. Non-behavioural Impact – Simulation example
Partnered men
Partnered women
Single men
Single women
Single parents
All
Impacted (%)
60.6
40.0
47.0
35.7
32.0
43.8
Winners (%)
Average win ($ pa)
$134.4
$133.8
$134.2
$134.1
Losers (%)
Average loss ($ pa)
$0.00
All persons
Overall 43% winners (partnered men largest group)
Average $134 per annum to winners

21. Behavioural Impact – Simulation example
Partnered men
Partnered women
Single men
Single women
Single parents
All
Persons responding (%)
0.13%
0.14%
0.05%
0.10%
0.04%
0.11%
Net employment change (persons)
1,374
1,476
683
526
130
4,187
Δ FTE (persons)
1,818
2,338
956
713
157
5,982
Total hours change (hours)
63,646
81,838
33,453
24,951
5,491
209,380
Participation effect (hours)
59,370
52,773
28,932
20,567
4,678
166,321
Hours effect (hours)
4,276
29,065
4,521
4,384
813
43,059
Participation component (%)
93%
64%
86%
82%
85%
79%
Low number of responders
Modest changes
Modelled persons

22. Fiscal Impact – Simulation example
Millions $
Change (non-behav.)
Change (behav.)
Revenue
-1,154
-1,093.8
Expenditure
-49.6
Net expenditure
1,154
1,044.2
All persons

23. Future work Wage and utility equations re-estimated
May experiment with improving wage imputations
Child care policy in non-behavioural & behavioural model
Confidence intervals
Policy toolkit to aid interpreting results

24. Limitations of CAPITA-B model
Some policies not currently modelled in CAPITA
Some population groups not modelled, yet may be a policy focus (eg. people close to retirement age)
Supply side (desire to work), no labour demand & business cycle considerations
100% take-up may not be realistic
Currently cannot model employment policies that involve obligations/activity tests or the impact of work-ready programs on participation
Single time period (no savings, human capital accumulation)
Exclude institutional factors / barriers to working additional hours

25. Lessons Modelling is assumption-laden.
Many factors can make results sensitive
Need extensive testing to check if the results make sense
Very low number of people responding to changes in incentives
One of a number of useful tools for policy analysis/development

26. A team effort!
Acknowledge the contribution of all team members in Quantitative Analysis Section
Other members of the team
Quoc Ngu Vu (Director)
Wei Ying Soh
Nathaniel Leonard

27. Questions