1. EHM Theory and Structure Behavioural Labour Supply Modelling in DWP Alan Duncan, 6 th May 2009

2. 2 Motivation Limitations to tax policy evaluation in the absence of behavioural responses  Static tax microsimulation models operate on the premise that individual behaviour remains fixed when simulating the effects of tax and welfare policy reform  this approach is perfectly appropriate for evaluating the ‘next day’ impact of tax or welfare policy reform, and for looking at reforms that aren’t likely to affect behaviour (..but how do we know?)  however, static methods are limited when evaluating reforms for which economic responses are likely (or indeed, intended)  this motivates an MDU project to add behaviour to the DWP Policy Simulation Model (PSM) to simulate the effects of policy reform on households’ employment choices

3. 3 Adding Behaviour Modelling approach  empirical implementation of a structural economic model of household labour supply  uses information from static microsimulation: - for data in the estimation of the structural model - for input into the (micro)simulation of behavioural responses - find it best to use same static model for both Features of model  structural rather than reduced-form (explain rather than describe)  discrete rather than continuous (practicality, flexibility)  probabilistic (to accommodate preference heterogeneity)

4. 4 Structural economic model – “as if...”  Characterise behaviour (in the first instance, employment choices) to be driven by an economic model of household labour supply Economic foundations l Households (more accurately, tax units) are allocated a preference function that ‘ranks’ choices over working hours & income in terms of ‘utility’ or ‘happiness’ l decisions are assumed to derive from the maximisation of this preference function subject to budget constraint that is affected by taxes and welfare payments l structure of decision-making is ‘rational’ in an economic sense (choose whichever outcome yields most ‘happiness’) l basic model can be adapted to accommodate other decisions: - welfare take-up (Moffitt & Keane, IER 1999) - childcare demand (Robins, Ribar)

5. the basic model l assumes that families choose the number of hours they want to work on the basis of ‘preferences’ over hours h and net income y, as an expression of ‘utility’ or ‘happiness’ U=U(y,h) l any hours choice implies a certain net income, comprising earned and unearned income, taking full account of the tax and welfare system (the ‘budget constraint’) y[h]=w.h +  h,w,  X) The decision rule: choose hours to maximise U subject to remaining on the constraint: max h U=U(y[h],h) subject to y[h]=w.h +  h,w,  X) Structural economic model – “as if...” 5

6. yhyh h U max budget constraint chosen h Structural economic model – “as if...” 6

7. l fit model parameters to the pattern of observed choices revealed in a large and representative sample of data (FRS) l estimation process acts to rationalise observed patterns of behaviour as if they derive from choosing the ‘best’ choice among the set of alternatives presumed to exist l requires a parameterisation of preferences, and for EHM we choose a quadratic direct utility: Blundell, Duncan, McCrae and Meghir, 1999 l all parameters allowed to vary with observed factors and unobserved heterogeneity l estimate using Simulated Maximum Likelihood max h U=U(y[h],h) subject to y[h]=w.h +  h,w,  X) Estimation 7

8. yhyh h Restricting hours choices (discrete) 8

9. yhyh h 9

10. yhyh h h * =max h U= U( h, y h | X ) Restricting hours choices (discrete) 10

11. l discrete approach offers practical advantages in adding behaviour (simplifying taxes in estimation/simulation, facilitating household choices, modelling take-up, adding childcare) l also allows for general forms of random heterogeneity to enter into the preference function: U(h) = U(y[h], h | X,v) +  h l for given distributions for each v and  h, this gives rise to a modelled probability Pr( h = h j | X,v) of choosing hours h j over other hours choices... l...and a probability distribution of hours responses to tax policy reform Probabilistic model 11

12. 12 Probabilistic model Hours (reform) 010162024304048 Hours (base) 0 Pr(0,0)Pr(0,10)Pr(0,16)Pr(0,20)Pr(0,24)Pr(0,30)Pr(0,40)Pr(0,48) 10 Pr(10,0)Pr(10,10)Pr(10,16)Pr(10,20)Pr(10,24)Pr(10,30)Pr(10,40)Pr(10,48) 16 Pr(16,0)Pr(16,10)Pr(16,16)Pr(16,20)Pr(16,24)Pr(16,30)Pr(16,40)Pr(16,48) 20 Pr(20,0)Pr(20,10)Pr(20,16)Pr(20,20)Pr(20,24)Pr(20,30)Pr(20,40)Pr(20,48) 24 Pr(24,0)Pr(24,10)Pr(24,16)Pr(24,20)Pr(24,24)Pr(24,30)Pr(24,40)Pr(24,48) 30 Pr(30,0)Pr(30,10)Pr(30,16)Pr(30,20)Pr(30,24)Pr(30,30)Pr(30,40)Pr(30,48) 40 Pr(40,0)Pr(40,10)Pr(40,16)Pr(40,20)Pr(40,24)Pr(40,30)Pr(40,40)Pr(40,48) 48 Pr(48,0)Pr(48,10)Pr(48,16)Pr(48,20)Pr(48,24)Pr(48,30)Pr(48,40)Pr(48,48)

13. l allows behavioural simulations to be compared with static benchmark l process guarantees that, wherever possible, model predictions line up with observed choices under the base (benchmark) policy regime l requires unobserved heterogeneity terms to be drawn from a conditionaI distribution to guarantee that simulations under the base system are aligned to observed patterns of data l Need to be careful - check calibration draws Calibration (‘alignment’) 13

14. 14 Probabilistic model Hours (reform) 010162024304048 Hours (base) 0 Pr(0,0)Pr(0,10)Pr(0,16)Pr(0,20)Pr(0,24)Pr(0,30)Pr(0,40)Pr(0,48) 10 Pr(10,0)Pr(10,10)Pr(10,16)Pr(10,20)Pr(10,24)Pr(10,30)Pr(10,40)Pr(10,48) 16 Pr(16,0)Pr(16,10)Pr(16,16)Pr(16,20)Pr(16,24)Pr(16,30)Pr(16,40)Pr(16,48) 20 Pr(20,0)Pr(20,10)Pr(20,16)Pr(20,20)Pr(20,24)Pr(20,30)Pr(20,40)Pr(20,48) 24 Pr(24,0)Pr(24,10)Pr(24,16)Pr(24,20)Pr(24,24)Pr(24,30)Pr(24,40)Pr(24,48) 30 Pr(30,0)Pr(30,10)Pr(30,16)Pr(30,20)Pr(30,24)Pr(30,30)Pr(30,40)Pr(30,48) 40 Pr(40,0)Pr(40,10)Pr(40,16)Pr(40,20)Pr(40,24)Pr(40,30)Pr(40,40)Pr(40,48) 48 Pr(48,0)Pr(48,10)Pr(48,16)Pr(48,20)Pr(48,24)Pr(48,30)Pr(48,40)Pr(48,48)

15. 15 Probabilistic model (calibrated) Hours (reform) 010162024304048 Hours (base) 0 00000000 10 00000000 16 Pr(16,0)Pr(16,10)Pr(16,16)Pr(16,20)Pr(16,24)Pr(16,30)Pr(16,40)Pr(16,48) 20 00000000 24 00000000 30 00000000 40 00000000 48 00000000

16. l Validate the model - compare model simulations with ‘known’ evaluation evidence - estimate model over periods of tax policy reform l Be sensitive to model choice - ceteris paribus (‘as if...’) - ‘rational’ model better for some groups than others l Recognise limitations - model not configured to accommodate unemployment - wages and prices taken as exogenous - no interactions with demand side of labour market To Adam... Issues 16

17. EHM Practicalities Behavioural Labour Supply Modelling in DWP Adam Richardson, 6 th May 2009

18. 18 Policy Simulation Model Static Microsimulation Model ­Models GB tax and benefit system ­Based on FRS With additional info drawn from admin data ­Uprated to current year Financial amounts draw-down of old benefits grossing / calibration ­Written in SAS, with graphical interface ­Takes less than a minute to run ­Used by analysts across DWP

19. 19 Incorporating Behaviour Budget constraints (several hours) ­Requires entry wages for the unemployed Preference functions ­Calibration (30 – 40 minutes) ­Simulation (10 minutes) Probabilistic Results ­Validation Compare to known reforms Other indicators of incentives

20. 20 Example Results Change: increase level of out-of-work benefits

21. 21 Example Results Change in Total Employment

22. 22 Example Results Change in Spending on Benefits

23. 23 Example Results Employment Transitions (Lone Parents) Reform Hours 010162024304048 Base Hours 0521,7781,038173 99444237 1031249,06617158 00 164,72645798,68400000 205,68061818090,5690000 242,292265944456,216000 304,779444295127117110,57800 406,245674311243163135166,9370 482,32713313210134363375,804

24. 24 Way Forward Validation Roll out to DWP analysts ­Stress-testing Expand scope of modelling

25. 25 Questions?