IAREP SABE Conference 2016, Wageningen, NL

Workplace pension schemes and the effect ofprovision

Evidence from the United Kingdom

Bernardo Fonseca Nunesa

a Behavioural Science Centre, University of Stirling

10/Jul/2016, SABE-IAREP Conference - Wageningen, NL

1 / 30Workplace pension schemes and the effect of provision

Saving for retirement Analysis Further analysis Conclusion

1 Saving for retirement

2 AnalysisDataEmpirical strategyResults

3 Further analysis

4 Conclusion



Outline



3 Further analysis

4 Conclusion



Outline

3 pillars: each one requires different degrees of ability toprocess information and make informed decisions:



Saving and investing for retirementHow could saving rates be increased?

Identification and endogeneity problems



Saving and investing for retirement

A worker ends up unpensioned in two ways:



The case of unpensioned workers

1 Pensions Act 2008Automatic enrolment (nudge) to overcome behaviouralbarriers to opt-in.Incentive = matching contributions;Duty on employers = mandatory provision (2012-2018);

2 ResultsAverage opt-out rate in large companies < 10%;E.g McDonalds, Asda, and Sainsbury’s;DWP revised overall forecast from 30% to 15%;



Nationwide application of default choicesThe "Automatic enrolment" reform

1 Pensions Act 2008Automatic enrolment (nudge) to overcome behaviouralbarriers to opt-in.Incentive = matching contributions;Duty on employers = mandatory provision (2012-2018);

2 ResultsAverage opt-out rate in large companies < 10%;E.g McDonalds, Asda, and Sainsbury’s;DWP revised overall forecast from 30% to 15%;



Nationwide application of default choicesThe "Automatic enrolment" reform

1 This study used data from the period before the reform tosuggest that:

Provision is the major driver of participation;

Even in the absence of imposed regulations on incentivesand in the way workers are enrolled;



United Kingdom’s caseInformative for policy-making

Alternative policy when "one-nudge-fits-all" solutions are notfirst-best:

1 People are heterogeneous;

2 Public dislike being nudged;

3 Policy-maker prefers to preserve citizens’ decisionautonomy;



Active autonomous choices

















3 Further analysis

4 Conclusion



Outline

Relationship: Provision→ MembershipWorkplace pension scheme provision and membership;Job characteristics;Socio-demographics.



British Household Panel SurveyWaves 2-18, years 1992-2009









We use data of employed workers (N=89,760; i=15,481):No provision, not member = 25,905;Offered, not member = 16,724;Offered, member = 47,131.












Pr[pit = 1] = Φ(γ1Jit + β1Xit + ωt ) (1)

pit = an indicator variable denoting whether the employer runsa pension scheme, Jit = a vector of job characteristics, Xit = avector of socio-demographics, Φ(•) = a cumulative distributionfunction.



Empirical strategyBinary outcome model

Pr[pit = 1] = Φ(γ1Jit + β1Xit + ωt )



Empirical strategyEquation (1): pension provision

Pr[pit = 1] = Φ(γ1Jit + β1Xit + ωt )



Empirical strategyEquation (1): pension provision

Pr[pit = 1|J,X ] = sit (J,X ) (2)

p ⊥ X , J| s(J,X ) (3)

sit ∈ (0,1) = a propensity score for provision (conditional on Jand X );

Equation 3 is a balancing condition, which states that forindividuals i and j with the same s, assignment to provision isas random and identical in terms of J,X.



Empirical strategyBalancing condition



ResultsBalancing condition



ResultsExact matching on wave (2-18)



ResultsExact matching on workers (ranges 1-9)

Ait (s(J,X )) = {sjt | min ‖sit − sjt‖ < r} (4)

Ait is an identifiable set, meaning that a nearest neighbourcontrol case with estimated s falling within radius r is matchedto the i th case at time t ;



Empirical strategyCaliper distance NN 1:1 matching



Empirical strategyCommon support

δ = E [Y |p = 1]− E [Y |p = 0]︸︷︷︸= Zero

(5)

δ is the estimated effect of provision of workplace pensions onmembership; and Y is a binomial outcome denotingmembership [Yes = 1, 0 otherwise].



Empirical strategyMatching on propensity scores estimation



ResultsEquation (5): matching on propensity scores



ResultsEquation (5): MPS - private sector only

Hidden bias Γ

How strongly an omitted variable must influence the selectionprocess in order to undermine the implications of the matchinganalysis.



Sensitivity analysisMH bounds



Sensitivity analysisMH bounds - hidden bias



3 Further analysis

4 Conclusion



Outline

Pr[Yit = 1|pit = 1, pi,t−1 = 0] = Φ(α2 + γ2Jit + ρ2Fit + β2Xit )

1 Job characteristics(+) Sector: government, NHS, higher education(+) Earnings (base salary, bonus)(+) Permanent contract

2 Socio-demographics(-) Dependent children(-) Education: no degree

3 Short-run opt-in rate = 35%



Transitions in provision of a schemeWorkers who just got offered a plan [N = 3,757]

















3 Further analysis

4 Conclusion



Outline

1 Provision has a major policy effect even in the absence ofdefault options or price subsidies enforced by law;

2 Policy alternative = universal provision + active decisions;3 Preserves choice autonomy



Conclusion





Conclusion





Conclusion

=-1000pt =-1000pt


Bernardo Fonseca Nunes

Behavioural Science CentreTwitter: @_bernardofnUniversity of StirlingScotland

Email: [email protected]

www.stir.ac.uk

mailto:[email protected]

mailto:[email protected]

www.stir.ac.uk

Economy & Finance

IAREP SABE Conference 2016, Wageningen, NL