Figures and tables in The Retirement Decision in Dynamic Microsimulation Models: An Exploratory Review

Abstract

This article conducts an exploratory review of the decision to retire in microsimulation models (MSM) by asking two research questions: Which theoretical and methodological approaches from the scientific literature on retirement are used in MSM? What is the theoretical and methodological quality of those models? Retirement is a central topic in microsimulation. The knowledge about the concrete way in which the transition probabilities of retiring are modelled is, however, scattered across the individual MSM’s documentation. This review constitutes a first attempt at centralising the available knowledge. It has three main objectives: to link the treatment of retirement in the MSM tradition to the broader theoretical and methodological paradigms governing the study of retirement decisions, to deepen the knowledge basis on MSM, and to encourage further discussion and knowledge-sharing about how retirement decisions are modelled in MSM. Using the systematic review methodology first outlined by Moher et al. (2009), a general analysis including 32 models was conducted, after which 26 models were examined in detail. The review found a relatively uniform application of econometric methods when estimating retirement decisions, whereas some theoretical frames and variables are less well-represented.

This article contains 2 figures and 8 tables.

Figures

**Figure 1.** Flow of information through the phases of the systematic review. Source: template from Moher et al. (2009), filled and modified by the author following the example of Scharn et al. (2018).

**Graph 1.** Dynamic MSM modelling retirement by year of publication (n=32)

Tables

Table 1. Countries by number of MSM modelling retirement

Number of models	Country
1	Belgium, Denmark, Finland, Germany, Hungary, Ireland, Netherlands, Norway, Sweden, Slovenia, Spain
2	Australia, Canada, UK, Multi-Country
3	US
4	France
6	Italy

Source: own elaboration

Table 2. MSM modelling retirement by substantive components present in their retirement definition (n=32)

Substantive component of the retirement definition	Number of models
Benefit take-up	3
Transition out of employment	4
Benefit take-up Transition out of employment	24
Unknown	1
Total	32

Source: own elaboration

Table 3. MSM modelling retirement by level at which the retirement decision is made (n=32)

Level at which the retirement decision is made	Number of models
Individual	30
Household	1
Individual with possibility of analysing the household level	1
Total	32

Source: own elaboration

Table 4. MSM modelling retirement by treatment of retirement as an absorbing state (n=32)

Retirement as an absorbing state	Number of models
Yes	23
No	5
Unclear	4
Total	32

Source: own elaboration

Table 5. MSM modelling retirement by relationship with other labour market decisions (n=32)

Relationship with other labour market decisions	Number of models
Separate	26
Part of labour market supply	5
Unknown	1
Total	32

Source: own elaboration

Table 6. MSM modelling retirement by relationship with other labour market decisions and origins of transition probabilities (n=32)

Origins of transition probabilities’ distribution	Relationship with other labour market decisions			Total
Origins of transition probabilities’ distribution	Part of labour market supply	Separate	Unknown	Total
Distribution estimated from statistical equation	5	15		20
Distribution from observed data		4	1	5
Distribution from another simulation model		3		3
User-defined distribution		2		2
Unknown		2		2
Total	5	26	1	32

Source: own elaboration

Table 7. Basic features of dynamic probabilistic MSM assessing retirement as a separate labour market decision

Source	Country	Name of model	Sample size (approximate)	Population categories	Data source	Theoretical framework	Method of calculation of transition probabilities	Source of parameters for the calculation of transition probabilities	Number of statistical equations	Validation	External alignment
Harding et al., 2009	Australia	APPSIM	188000 individuals	Australian population	Survey	No information provided	Logit	Historical survey data	2 M/F)	Validation strategy, comparison with existing data	Optional, for total labour force participation
Debrand et al., 2012	France	ARTEMIS	1420598	French private-sector employees	Administrative and survey	No information provided	Probabilities assigned from empirical distribution	Distribution of 1935 cohort	No equations estimated	No information provided	No information provided
Mazzaferro and Morciano (2012)	Italy	CAPP-DYN	52772 individuals	Italian population	Survey	Option-Value model	Optimisation equation	No information provided	1	No information provided	Not for retirement, only for demographic variables
Belloni and Alessie, 2009; Borella and Moscarola, 2010	Italy	CERPSIM3	No information provided	Italian population	Administrative and survey	Reduced-form econometric model	Probit	Historical administrative data	2(M/F)	Cross-sectional validation with available data	No information provided
Bachelet et al., 2014; Blanchet et al., 2011; Buffeteau et al., 2011	France	DESTINIE II	65000 individuals	French population	Survey	Reduced-form model (baseline)	Logit (baseline) Optimization equation (application)	Historical administrative data	2(M/F)	No specific information on validation of retirement decision	Not for retirement, only for demographic variables (alignment by adjustment of probabilities)
Ando and Nicoletti Altimari (2004)	Italy	DYNAMITE	25000 individuals per wave, 67000 households in total	Italian households	Survey	Reduced-form econometric model	Ordinary Least Squares Continuous hazard model as an option	Historical survey data	2 (planned age for everyone and revised decision for older workers)	No information provided	Not for retirement, only for demographic variables
Nataša Kump, personal communication, 2019, 2022	Slovenia	DyPenSI	391425 individuals	Slovenian population	Administrative and survey	Reduced-form econometric model	Logit	Starting population	2 (M/F)	Comparison with alignment tables	Optional alignment for labour market
Patxot et al., 2018	Spain	DyPeS	Unknown	Spanish population	Administrative	Reduced-form econometric model	Hazard model	Historical administrative data	2(M/F)	No information provided	No information provided
Tikanmäki et al. (2014)	Finland	ELSI	250000	Adult Finnish population	Administrative	No information provided	Probabilities assigned from another model	PTS macro model	No information provided	Sample-testing and model calibration with register data from 2009-2013	All transition probabilities are updated yearly according to projections from a semi-aggregate model
Brewer et al., 2007	UK	IFS Model	12100 individuals	British population over 50	Survey	No information provided	Logit	Starting population	4 (M/F, full/part-time)	Retroactive simulations are compared to existing data.	For total labour force participation; alignment with observed data by adjusting probabilities
O’Donoghue et al. (2009)	Ireland	Irish Dynamic Cohort Microsimulation Model	1000	Synthetic cohort	Simulated	No information provided	Probabilities assigned from empirical distribution	Cross-sectional Irish survey data	No equations estimated	Comparison with survey and administrative data	No information provided
Maitino et al. (2020)	Italy	IrpetDin	No information provided	Italian citizens	Survey	No information provided	Optimisation equation	Assumption	1	Retroactive simulations are compared to existing data	For total labour force participation
Richiardi and Richardson, 2017	Multi-country	JAS-mine Labour Force Participation	No information provided	Citizens from Italy, Spain, Ireland, Hungary and Greece	Survey	No information provided	Probabilities assigned from empirical distribution	Starting population	No equations estimated	No information provided	No information provided
Leombruni and Richiardi, 2006	Italy	LABORSim	50000	Italian population	Survey	No information provided	User-defined	User-defined	User-defined	No information provided	Not for retirement, only for demographic variables
Richiardi and Richardson, 2015	UK, Italy	LABSim	Unknown (112196 observations in regression analysis for IT, 84028 for UK)	UK and IT population	Survey	No information provided	Logit	Unclear	2 per country (partnered/single)	No information provided	Not for retirement, only for demographic variables
Federaal Planbureau (2017)	Belgium	MIDAS	305019	Belgian population	Administrative	No information provided	Probabilities assigned from another model	MALTESE meso model	No equations estimated	Stylised validation through modelling (Dekkers)	Retirement is aligned with MALTESE projections
Smith et al., 2007; Smith and Favreault, 2013	US	MINT7	82782 individuals	US population	Survey	Reduced-form econometric model	Probit	Historical survey and administrative data	2 (married/unmarried)	Retroactive simulations are compared to existing data	Not for retirement, only for other variables
Fredriksen and Stølen, 2015; Fredriksen (1998)	Norway	MOSART	40000	Norwegian population	Administrative	No information provided	No information provided	Past retirement patterns	No information provided	Retroactive simulations are compared to existing data	No external alignment
van de Ven, 2011	UK	NIBAX	No information provided	Synthetic cohort	Simulated	Structural model	Utility maximisation functions	Assumption calibrated by survey data	No information provided	Retroactive simulations are compared to existing data	Structural parameter calibration using survey data
Gal et al., 2009	Hungary	NYIKA	6000000	Hungarian pension contributors	Administrative	No information provided	No information provided	Past retirement patterns	No equations estimated	No information provided	No information provided
Holmer et al., 2016	US	PENSIM	Not fixed	Synthetic cohort	Simulated	Reduced-form econometric model	User-defined	User-defined	User-defined	Retroactive simulations are compared to existing data, and cross-sectional validation with other models	No information provided
Berteau-Rapin et al., 2015	France	PRISME	5000000 individuals	Persons affiliated with French social security	Administrative	No information provided	Logit	Starting population	46[2] (M/F, age - by trimester)	Retroactive simulations are compared to existing data.	No information provided
van Sonsbeek, 2011	Netherlands	SADNAP	Unknown (1% of the Dutch population)	Dutch population	Administrative	Option-Value model	Optimisation equation	Assumption based on theoretical literature	1	Cross-sectional validation with available data and other models (not necessarily MS)	Not for retirement, only for demographic variables
Flood et al., 2012	Sweden	SESIM III	300000 individuals	Swedish population	Administrative and survey	Reduced-form econometric model	Logit	Starting population	1	No information provided	Not for retirement, only for other variables
Caretta et al., 2013	Italy	TDYMM	43388	Italian population	Administrative	No information provided	Probabilities assigned from empirical distribution	Starting population	No equations estimated	Retroactive simulations are compared to existing data	Not for retirement, only for labour market modules
Duc et al., 2015	France	TRAJECTOIRE	350000	French pension contributors	Administrative	No information provided	Probabilities assigned from another model	PROMESS cell-based model	At least 12 (by sex, country of birth, generation, insurance duration, and contributing regime)	Retroactive simulations are compared to existing data	In some cases, the results of the transition probabilities are aligned with the PROMESS meso-model

Source: own elaboration

Table 8. Explanatory variables in MSM assessing the retirement decision using an equation

Variable	n	Models
Birth cohort
Birth cohort	3	Dynamite, CERPSIM3, MINT7
Demographics
Age	6	APPSIM, DESTINIE II, IFS, DyPeS, MINT7, LabSIM
Education	10	APPSIM, PRISME, DESTINIE II, DYNAMITE, IFS, DyPeS, SESIM III, MINT7, DyPenSI, LabSIM
Gender	4	SADNAP, SESIM III, MINT7, LabSIM
Marital status	3	APPSIM, DYNAMITE, IFS
Origins	2	PRISME, MINT7
Race of spouse	1	MINT7
Employment history
Career length	1	DESTINIE II
Past illness, inactivity, part-time employment or unemployment	3	APPSIM, PRISME, DESTINIE II
Employment-related variables
Employment status (employed, unemployed, sick leave, fragmented employment, disability, self-employed)	5	PRISME, DyPeS, MINT7, DyPenSI, LabSIM
Unemployment benefits	1	DyPeS
Economic incentives
Accrual	1	SESIM III
Expected Social Security Wealth	1	DYNAMITE
Internal Rate of Return	1	DYNAMITE
Net Present Value	1	SESIM III
Option Value	3	DESTINIE II, SADNAP, CAPP-DYN
Peak Value	2	CERPSIM3, DyPeS
Potential benefits	1	DyPeS
Replacement rate	3	SADNAP, DyPeS, MINT7
Social Security Wealth (SSW)	1	CAPP-DYN
Value of benefits	1	CERPSIM3
Geographic factors
Region	3	DYNAMITE, CERPSIM3, LabSIM
Health
Health status	3	APPSIM, IFS, MINT7
Disability status	1	LabSIM
Household-related variables
Age difference with spouse	1	MINT7
Age of spouse	1	MINT7
Contribution history of spouse	1	MINT7
Dependents	2	DYNAMITE, MINT7
Employment status of spouse	4	SESIM III, MINT7, DyPenSI, LabSIM
Spouse reached pension age	1	LabSIM
Spouse disability status	1	LabSIM
Head of household	1	DYNAMITE
Household composition	1	SESIM III
Income earners in the household	1	DYNAMITE
Income of spouse	1	MINT7
Macro variables
Unemployment rate	1	DyPeS
Pension-related variables
Age at maximum pension	1	DyPeS
Coverage	1	MINT7
Duration of coverage or contributions	2	PRISME, MINT7
Eligibility conditions	2	DyPeS, MINT7
Insurance in more than one regime	1	PRISME
Time to maximum pension	1	DyPeS
Reached pension age	1	LabSIM
Wealth and income
Current income	4	DyPeS, SESIM III, IFS, LabSIM
Family wealth	1	DYNAMITE
Homeownership	1	MINT7
Past income	2	DESTINIE II, MINT7
Ratio financial wealth/past income	1	MINT7
Wealth	1	MINT7
Work characteristics
Occupation	2	DYNAMITE, CERPSIM3
Sector	3	DYNAMITE, CERPSIM3, DyPenSI
Time dimension
Year	2	DYNAMITE, IFS
Continuous time	1	DYNAMITE
Other
Inverse Mill’s ratio from probit estimate of being in the labour force	1	DYNAMITE

Source: own elaboration