ehm theory and structure

EHM Theory and StructureBehavioural Labour Supply Modelling in DWP

Alan Duncan, 6th May 2009

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Motivation

Limitations to tax policy evaluation in the absence of behavioural responsesStatic tax microsimulation models operate on the premise that individual behaviour remains fixed when simulating the effects of tax and welfare policy reformthis 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

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Adding Behaviour

Modelling approachempirical implementation of a structural economic model of household labour supplyuses 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 modelstructural rather than reduced-form (explain rather than describe)discrete rather than continuous (practicality, flexibility)probabilistic (to accommodate preference heterogeneity)

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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 Households (more accurately, tax units) are allocated a preference

function that ‘ranks’ choices over working hours & income in terms of ‘utility’ or ‘happiness’

decisions are assumed to derive from the maximisation of this preference function subject to budget constraint that is affected by taxes and welfare payments

structure of decision-making is ‘rational’ in an economic sense (choose whichever outcome yields most ‘happiness’)

basic model can be adapted to accommodate other decisions: - welfare take-up (Moffitt & Keane, IER 1999)

- childcare demand (Robins, Ribar)

the basic model

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)

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:

maxh U=U(y[h],h) subject to y[h]=w.h+h,w, X)


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yh

h

Umax

budget constraint

chosen h


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fit model parameters to the pattern of observed choices revealed in a large and representative sample of data (FRS)

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

requires a parameterisation of preferences, and for EHM we choose a quadratic direct utility:Blundell, Duncan, McCrae and Meghir, 1999

all parameters allowed to vary with observed factors and unobserved heterogeneity

estimate using Simulated Maximum Likelihood

maxh U=U(y[h],h) subject to y[h]=w.h+h,w, X)

Estimation

2 2( , ) yy hh yh y hU y h y h yh y h

* '0

* '0

y y y y

h h h h

X v

X v

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yh

h

Restricting hours choices (discrete)Restricting hours choices (discrete)

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yh

h


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yh

h

h*=maxh U= U( h, yh | X )


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discrete approach offers practical advantages in adding behaviour (simplifying taxes in estimation/simulation, facilitating household choices, modelling take-up, adding childcare)

also allows for general forms of random heterogeneity to enter into the preference function: U(h) = U(y[h], h | X ,v) + h

for given distributions for each v and h , this gives rise to a modelled probability Pr(h = hj | X ,v) of choosing hours hj over other hours choices...

...and a probability distribution of hours responses to tax policy reform

Probabilistic model

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exp[ ( , ; , )]Pr( | , )

exp[ ( , ; , )]

jjj

kk

hK

hk

U h y X vh h X v

U h y X v

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Probabilistic model

Hours (reform)

0 10 16 20 24 30 40 48

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)


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)






allows behavioural simulations to be compared with static benchmark

process guarantees that, wherever possible, model predictions line up with observed choices under the base (benchmark) policy regime

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

Need to be careful - check calibration draws

Calibration (‘alignment’)

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Probabilistic model

Hours (reform)

0 10 16 20 24 30 40 48

Hours(base)



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)






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Probabilistic model (calibrated)

Hours (reform)

0 10 16 20 24 30 40 48

Hours(base)

0 0 0 0 0 0 0 0 0

10 0 0 0 0 0 0 0 0

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 0 0 0 0 0 0 0 0

24 0 0 0 0 0 0 0 0

30 0 0 0 0 0 0 0 0

40 0 0 0 0 0 0 0 0

48 0 0 0 0 0 0 0 0

Validate the model- compare model simulations with ‘known’ evaluation evidence- estimate model over periods of tax policy reform

Be sensitive to model choice- ceteris paribus (‘as if...’)- ‘rational’ model better for some groups than others

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

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EHM PracticalitiesBehavioural Labour Supply Modelling in DWP

Adam Richardson, 6th May 2009

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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

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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

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Example Results

Change: increase level of out-of-work benefits

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Example Results

Change in Total Employment

-50,000

-45,000

-40,000

-35,000

-30,000

-25,000

-20,000

-15,000

-10,000

-5,000

0

Lone parent Single man Single womanCouples with

childrenCouples without

children

Demographic Group

Ch

ang

e in

Em

plo

ymen

t

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Example Results

Change in Spending on Benefits

-£0.40

-£0.20

£0.00

£0.20

£0.40

£0.60

£0.80

£1.00

£1.20

IS HB CTB WTC CTC Total

Ch

ang

e in

£b

illio

n s

pen

t b

y G

ove

rnm

ent,

per

yea

r

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Example Results

Employment Transitions (Lone Parents)

Reform Hours

0 10 16 20 24 30 40 48

Base Hours

0 521,778 1,038 173 173 99 44 42 37

10 312 49,066 17 15 8 15 0 0

16 4,726 457 98,684 0 0 0 0 0

20 5,680 618 180 90,569 0 0 0 0

24 2,292 265 94 44 56,216 0 0 0

30 4,779 444 295 127 117 110,578 0 0

40 6,245 674 311 243 163 135 166,937 0

48 2,327 133 132 101 34 36 33 75,804

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Way Forward

Validation

Roll out to DWP analysts Stress-testing

Expand scope of modelling

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Questions?

ehm theory and structure

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