Federaal Planbureau Economische analyses en vooruitzichten Integrating a random utility random...

Federaal PlanbureauEconomische analyses en vooruitzichten

Integrating a random utility random opportunity labour supply model in

MIDAS Belgium: presentation of on-going work

Gijs Dekkers, Federal Planning Bureau

CESO, KU LeuvenCEPS/INSTEAD

André DecosterCES, KU Leuven

Bart CapéauCES, KU Leuven

European Meeting Of The INTERNATIONAL MICROSIMULATION ASSOCIATION, October 23-24th, 2014, MAASTRICHT

Economische analyses en vooruitzichtenFederaal Planbureau

Integrating a random utility random opportunity labour supply model in MIDAS Belgium

– Current versions of MIDAS include simple, reduced-form behavioural equations– Not ideal for reform analysis– complicating factor: MIDAS is dynamic– Another complicating factor: alignment– This presentation reports on on-going work to introduce the “random utility–random

opportunity model” (a.k.a. RURO) in the dynamic-ageing microsimulation model MIDAS of Belgium.

– Brief overview of this presentation• A birds-eye view on RURO• Simulation in LIAM2: a simple example of code• Oh, static is static, and dynamic is dynamic, and never the twain shall meet.

Wage thriftStabilityAlignment

• Some preliminary results


standard model– choice of discrete h– h: uniform distr.– gross wage given– tax-benefit system– functional form U(.)– assumptions about stochastic part– => prob (h)

RuRo-model

Oslo model choice of j: (h,w,k) h: non uniform gross wage distrib. tax-benefit system functional form U(.) assumptions about stochastic part => prob (h,w)


• probability:

• standard multinomial logit-model(relative attractiviness of the choice)

• RuRo

• weighted by measure of ‘availability’

RuRo-model


• Structural => empirical specifications– preferences– opportunities (job availability)

RuRo-model


• preferences: Box-Cox

RuRo-model


• job availability– market versus non-market

– market subset

RuRo-model


• coefficients for utility function• coefficients for opportunities

– market versus non market (q0)– hours (peaks): g2(h)– wage distribution: g1(w)

RuRo-model


RuRo-model

males femalesCoeff SE t-value Coeff SE t-value

Leisure coefficients M/F in couplesexponent -7.178 0.543 -13.23 -1.845 0.451 -4.09constant 35.345 11.778 3.00 205.964 51.100 4.03ln(age) -19.054 6.464 -2.95 -115.314 28.543 -4.04ln(age)^2 2.686 0.898 2.99 17.345 4.030 4.30# children between 0 and 3 -0.059 0.084 -0.70 1.232 0.516 2.39# children between 4 and 6 0.047 0.089 0.52 1.646 0.546 3.02# children between 7 and 9 -0.100 0.088 -1.13 1.219 0.552 2.21region WAL 0.255 0.104 2.45 2.131 0.708 3.01region BXL 0.207 0.163 1.27 0.545 1.019 0.53Educ LOW -0.294 0.128 -2.30 2.334 1.184 1.97Educ HIGH -0.055 0.093 -0.59 -3.085 0.708 -4.36Leisure coefficients single M/Fexponent -3.118 0.705 -4.42 -1.113 0.611 -1.82constant 70.790 43.123 1.64 323.745 78.769 4.11ln(age) -38.329 23.933 -1.60 -177.290 43.301 -4.09ln(age)^2 5.610 3.334 1.68 25.346 6.014 4.21# children between 0 and 3 0.000 0.000 0.00 3.706 1.609 2.30# children between 4 and 6 -1.001 2.263 -0.44 0.914 1.184 0.77# children between 7 and 9 -2.742 1.318 -2.08 -1.377 1.055 -1.30region WAL 2.509 0.774 3.24 2.853 1.047 2.72region BXL 0.765 0.740 1.03 -2.365 1.127 -2.10Educ LOW -0.692 0.736 -0.94 1.811 1.430 1.27Educ HIGH -0.881 0.645 -1.37 -2.682 1.046 -2.56Wage equation M/FSigma (RMSE) 0.253 0.004 63.73 0.256 0.004 59.47constant 2.037 0.027 76.25 2.010 0.026 77.29potential experience 2.420 0.225 10.77 2.275 0.228 9.98potential experience^2 -3.666 0.500 -7.33 -3.449 0.565 -6.10Educ LOW -0.146 0.017 -8.36 -0.097 0.022 -4.32Educ HIGH 0.242 0.014 17.38 0.280 0.015 18.35

Some quite very extremely preliminary estimation results


RURO in MIDAS BE: A simple example of LIAM2 code ad_earnings: args: gender, age code: [...] return: [...] ad_welfare: args: income code: [...] return: [...]

ad_unemployment: args: entitlement conditions code: [...] return: [...] utility_optimisation: - i: 1 - max_u: 0 - utility_rndm: normal(0.0, 1.0) * 100 - while: cond: (i < 200) code: - joboffer: [make a MC simulation] - hours: if(joboffer, [make a random draw of discrete hours], 0) - hourly_wage: if(joboffer, ad_earnings(gender, age), 0) - incomeW: if(joboffer, hourly_wage * hours, ad_unemployment(...)) - welfare: ad_welfare(incomeW) - leisure: 1 - hours / (168 * 52) - utility: function of (incomeW + welfare, leisure, utility_rndm) - max_u: max(max_u, utility) - opt_hours: if(i == 0, hours, if(max_u == utility, hours, opt_hours)) - i: i + 1

Function: generate earnings

Function: generate unemployment benefit

Function: generate welfare benefit

200 iterations

Take max(utility)

utility

Draw a number of hours (or not)Does the individual gets a job offer?

Optimal choice after i iterations


RURO in MIDAS BE: MIDAS is dynamic

• Wages increase with productivity• Social and fiscal parameters increase, but at a lower rate in the short and

middle run

• This will cause the RURO model to keel over as simulated time goes by


Complicating factor: MIDAS is dynamic

Starting dataset ± 2.2K2 individuals in 2002

DEMOGRAPHICMODULE t

LABOUR MARKETMODULE t

PENSION & BENEFITS MODULE t

CONTRIBUTIONS AND TAXATION MODULE t

REDISTRIBUTION, POVERTY, INEQUALITY OTHER OUTPUT

Simulate earnings i, t=A*

Simulate alternative incomes i, t=A

Derive net income i, t=A

Select hours where U(i)=Max, t

Simulate job-offers i, t

Draw hours i

t=20

02 to

206

0

i= 1

to 2

00

A = year of estimation – currently 2007* Stochastic components are constant over t (exception is ‘joboffer’ and only the random component of earnings changes with labour market transitions).

Derive utility i, t=A*

RURO

MID

AS


Complicating factor: alignment

• It is of course sad, but MIDAS is being used in a policy-assessment environment.

• Therefore, we use alignment by sorting to be able to assess policy measures in conjunction with a semi-aggregate model (see Dekkers, Inagaki and Desmet, 2012)• Alignment includes:

– Who works and who does not– Unemployment– Early retirement/CELS– Private and public sector employment– …– And all this to age, gender and period

• Hence, heterogeneity in choice sets needs to be included in an alignment procedure in simulation.

• Who receives a job-offer at period t?– ‘risk’ based on individual characteristics, using estimation results of RURO– Aligned to gender, age and period


Complicating factor: alignment

Foreach t = 2002 to 2060

Foreach i = 1 to 200

MC simulation of inversion at i

Logit simulation of ‘risk’ joboffer J(i) at i, given working(t – 1)

Joboffer(i)=inverse(joboffer(i-1)) Joboffer(i) = ALIGNMENT(age, gender, t)

If inversion at i

simulation of hours h

simulation of earnings at A

If Joboffer(i)

Unemployment benefit if eligible at t

Apply means-test for welfare at AAdd family benefitsDerive net total income at ADerive utitlity(i)

MAX=max(MAX, utility(i)


Some extremely preliminary simulation results

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

joboffer MALE joboffer FEMALEMALE FEMALE



2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

0.4

0.45

0.5

0.55

0.6

0.65

0.7

variant FEMALE basis FEMALE

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 390.6

0.62

0.64

0.66

0.68

0.7

0.72

0.74

variant 90% MALE basisvariant MALE



1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 392829303132333435363738

optimal hours

base variant BRUSS base variant FLANDbase variant WALL

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 3920

25

30

35

40

45

optimal hours

base variant MALE base variant FEMALEvariant MALE variant FEMALE



1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 3938

38.539

39.540

40.541

41.542

42.5

optimal hours men

base variant edu LOW M base variant edu M Mbase variant edu H M

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 3905

10152025303540

optimal hours women

base variant edu L F base variant edu M Fbase variant edu H F


Integrating a random utility random opportunity labour supply model in MIDAS Belgium

Thank you


Assumptions and hypotheses of the Study Committee on Ageing

Key demographic hypotheses 2007 2030 2050 2060Fertility 1.81 1.76 1.76 1.77Life expectancy at birth

Men 77.3 81.2 84.0 85.3women 83.3 87.0 89.7 90.9

Key macro hypotheses

Up to 2011

2011-2014 ≥ 2015

Yearly productivity 0.01% 1.28% 1.50%Unemployment rate 14.75 in 2014 Decreasing towards 8%

Social policy hypotheses 2009-2010 ≥ 2015Wage ceiling Current legislation 1.25%Minimum right per working year 1.25%Welfare adjustment non-lump-sum benefits Employed and self-employed

0.50%

Welfare adjustment of lump-sum benefits 1.00%

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