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Mind the Gap! The effect of an increased UK State Pension Age on

expected working life of employees

DRAFT- PLEASE DO NOT CITE

Ricky Kanabar1,3 and Adriaan Kalwij2,3

June 29, 2018

Abstract

We model how individual’s retirement expectations adjust in the wake of significant reforms

to state pension age introduced in the 2011 and 2014 Pension Act in the UK. Results show

that men do not significantly adjust their expectations upward as would be consistent with the

policy objective. Instead we find that the effect of an increase in state pension age widens the

gap between an individual’s expected retirement age and his state pension age. For women,

who experienced a more rapid increase in their state pension age over the sample period, this

gap also widens but some adjustment takes place: the expected retirement age increases by

0.3 years for a one-year increase in state pension age. We control for a rich set of individual

and household economic and sociodemographic characteristics, including having an

occupational pension plan. The widening of the gap between employees expected working

life and UK State Pension Age may suggest the need for improved pension communication to

avoid employees insufficiently preparing for retirement.

JEL classification: J26

Keywords: Retirement, Expectations, United Kingdom Household Longitudinal Study

1 Corresponding author: Department for Social and Policy Sciences, University of Bath,Bath,

BA2 7JP, . United Kingdom. Email: R.Kanabar@bath.ac.uk

2 Utrecht University School of Economics, P.O.Box 80125, 3508 TC Utrecht. The

Netherlands. Email: a.s.kalwij@uu.nl

3 Network for Studies on Pensions, Ageing and Retirement (Netspar; www.netspar.nl)

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1. Introduction

One of the most important decisions an individual makes with respect to the labour market is

the decision when to retire and therefore understanding when individuals expect to do so is

crucial. Since the 1990s the retirement decision, often defined as a gradual reduction or

complete cessation of labour supply (Denton and Spencer, 2009) has gradually come to the

forefront of the policy agenda in developed countries due to the widespread recognition of

population ageing. In 2018 the UK government released it’s Industrial Strategy, a key policy

document stating the four most important issues (Grand Challenges) facing the UK; the

ageing society being one such Grand Challenge. The fiscal implications of an ageing

population have a significant impact on current and future government budgets, for example

keeping the State Pension Age (SPA) fixed at 66 in the UK (as opposed to increasing as

legislated to 68 by 2045/46) has been estimated to cost £250 billion (DWP, 2017); in

response governments have sought to introduce a range of policy reforms to reduce the

burden on the state.

One of the most widely implemented measures has been to raise the age at which individual’s

become eligible to claim their state pension (i.e. the State Pension Age, SPA); the principal

aim of this policy is to extend individual’s working lives (DWP, 2011). Alongside other

OECD countries such as Italy the UK started making reforms to the SPA during the 1990s;

the first piece of legislation enacted was to equate (and hence increase) female SPA to that of

males. Subsequent reforms were then introduced which increased the pace of equalisation

and also raised the SPA of both men and women to 66/67 depending on date of birth.

A key question therefore is how do individuals intend to respond to such policy changes?1

Expectations data is one way to analyse such a question and have been shown to play an

important role in shaping major economic decisions in the future including the decision to

retire (Disney and Tanner, 1999; Chan and Stevens, 2004). We analyse how the 2007, 2011

and 2014 State Pension Acts in the UK affected the gap between men‘s and women‘s

Expected Retirement Age (ERA) and State Pension Age (SPA) using the United Kingdom

Household Panel Study. Due to the timing of the reforms and the cohorts affected (with the

exception of certain older women see Cribb, Emmerson and Tetlow, 2016) very little is

known about how men and women have or intend to react in terms of their actual labour

supply behaviour in response to higher SPA in the UK.

Our results suggest individuals do not fully adjust their expected retirement age in response to

changes to SPA, moreover the gap between Expected Retirement Age (ERA) and SPA

widens in response to an increase in SPA. The magnitude of this widening is almost one for

one in the case of men and 0.7 for females, suggesting women make a partial adjustment. We

find the results are driven by younger sample members. We control for a rich set of

individual and household level characteristics and find occupational pension, income,

1 Another key question is how have individuals responded to changes in SPA in terms of labour supply? Cribb et al. (2016) addresses this issue (for cohorts of females born in the early 1950s only) and finds a positive impact in terms of the employment rates of affected cohorts and also additional spillovers effects in terms of partners labour supply.

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education, job occupation and sector of work are important for determining the gap between

ERA and SPA.

The findings are of policy relevance and indicate that individuals do not adjust their expected

retirement age in line with reforms to the SPA. If the intention of policymakers is to keep

individuals in employment for longer or, put another way, delaying retirement, and should

they be successful in doing so, then government need to design strategies to improve

communication of changes in SPA, for example via information campaigns, to avoid

insufficient preparations for retirement.2 The retirement decision has a wide range of

implications for both the individual in terms of consumption, savings and living standards at

older ages; and for the state from a taxation and welfare spending perspective (Manski, 2004;

Cribb et al. 2016). Therefore understanding how SPA and different economic and

sociodemographic characteristics affect retirement expectations is crucial if policymakers

wish to ensure that individual’s adequately plan for retirement.

The rest of the is paper is set out as follows. Section 2 provides an overview of the literature

regarding the role of expectations data and the small literature devoted to retirement

expectations. Section 3 summarises the main features of the UK pension system. Section 4

describes the United Kingdom Household Panel Survey . Section 5 describes the

methodology followed. Section 6 presents estimation results. Section 7 discusses the

implications of the estimation results. Section 8 concludes.

2. Literature review

The use of expectations data has been shown to play an important role in explaining major

lifecycle decisions related to consumption, savings and of relevance for this paper, retirement

decisions (Manksi, 2004; Chan and Stevens, 2004; Benitez-Silva and Dwyer, 2005; Botazzi,

Japelli and Padula, 2006). The seminal work of Bernheim (1989) using the US Retirement

History Survey (RHS) showed that the arrival of new information close to retirement leads

individuals to revise retirement age expectations in a manner which is consistent with the

standard lifecycle model. In related work, Disney and Tanner (1999) used the UK Retirement

History Survey to analyse individuals’ expected versus actual retirement ages and studied

whether expectations data plays a role in improving the accuracy of retirement models. Their

findings show that most individuals reported their expected retirement age (ERA) to be the

SPA at the time (and did subsequently retire at that age) highlighting evidence of strong cultural

norms associated with reaching SPA and the notion of retirement in the UK (Kohli, 1991).

Consistent with the findings of Bernheim (1988), Disney and Tanner (1999) also find that

individuals report their most likely retirement age as opposed to their mean (expected)

retirement age.

2 This implies that the notion of retirement in the UK is associated with a reduction in hours worked or complete exit from the labour market. Whilst the ‘default retirement age’ was scrapped in the UK in 2012; there remains a strong association between reporting being ‘retired’ and number of hours worked or exit from the labour market (Banks and Smith, 2006) .

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Building on the work of Bernheim (1989), Benitez-Silver and Dwyer (2004) use the US Health

and Retirement Study (HRS) to investigate the role of information in determining retirement

expectations and how expectations evolve over time.3 4 Their findings show that respondents

updated their expectations according to the newly received information, consistent with the

rational expectations hyptothesis. The authors highlight, however, that not all changes are

anticipated: in particular health and employment shocks lead to deviations from expected

retirement age consistent with the findings of Disney and Tanner (1999). Chan and Stevens

(2004) also using the HRS study the effect of pension incentives on retirement, instead of

focusing on age of actual labour market exit the authors use ERA. Their findings highlight that

ignoring individual’s preferences for retirement leads to overstating the effect of pension

incentives on retirement decisions. The authors also find a strong correlation between the

retirement decision and individual characteristics such as pension wealth, earnings, assets and

work expectations; and that expected retirement age is a strong predictor of actual retirement

age.

Recent studies have emphasized the prevalence of co-ordination in the timing of retirement and

the role of spousal effects, often referred to as joint complementarities in leisure (Lundberg

1999; Coile, 2004; Michaud and Vermeulen, 2011). Ho & Raymo (2009), using the Health and

Retirement Study (HRS), analyse joint retirement expectations and realisations among dual

earner couples. Their findings show that couples who expect/plan to retire jointly are

significantly more likely to actually do so than those who do not; moreover age, health and

spouse’s relative earnings in addition to retirement expectations play an important role in

predicting observed behaviour.

Relatively few studies have analysed how changes in SPA directly affect retirement

expectations. Notable exceptions include Botazzi, Jeppelli and Pudula (2006), De Grip,

Fouarge and Montizaan (2013) and Copolla and Wilke (2014). Botazzi, Jeppelli and Pudula

(2006) investigate what effect a series of reforms introduced in Italy during the 1990’s to raise

the SPA and reduce the replacement rate had on self-reported expected retirement ages and

pension wealth.5 Their findings suggest individuals adjusted in the expected way: males

increased their ERA by two years and females by three years.6 However, the authors also find

that there is higher offset between private wealth and perceived pension wealth among

individuals who are better informed about their pension wealth; underlining the importance of

3 For early work analysing the role of retirement expectations using HRS, specifically for the case of females see Honig (1988) who finds it is women’s own characteristics and that of their spouse’s which is important in addition to a range of sociodemographic and economic characteristics which determine whether she will work past 62. 4 In a related paper Cobb-Clark and Stillman (2006) using the Austrailian HILDA study analyse changes in

ERA over time and consistency of answers and non-random non-response. Their findings highlight the

importance of defined pension benefits and standard retirement ages (within a job), and the importance of labour

market position in order to minimize group heterogeneity. The authors advocate the use of using expected

retirement ages as opposed to subjective probabilities based on the reasoning that (i) any non-responding

individuals may in fact be facing greater uncertainty about their retirement options and (ii) asking only about the

likelihood of working at various ages suggests workers see retirement as something distinct to the complete

cessation of employment. 5 The policy also reduced the replacement rate of younger workers compared to older workers. The actual increase in SPA depended on whether an individual is working in private or public sector or alternatively self-employed. For more information see Table 1 in Botazzi et al. (2006). 6 See also Mastrogiacomo (2004) who the first of these policy reforms and whose findings are consistent with

Japelli et al. (2006) and similar to Disney and Tanner (1999) emphasizes the importance of accounting for

sample attrition and ‘don’t know’ responses.

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individual’s understanding and knowledge of the social security system (Botazzi, Jeppelli and

Pudula, 2006).

De Grip, Fouarge and Montizaan (2013) using matched survey-administrative data carry out a

similar exercise in the case of a reform introduced in the Netherlands, which increased the SPA

from 65 to 66/67 (66 from 2020 and 67 from 2025). The authors found individuals affected by

the first reform (born 1954-1959) only partially adjusted (increasing their ERA by 3.6 months)

and found stronger effects (an increase of 10.8 months) for those born from 1960 who saw their

SPA increase from 65 to 67. The study finds that the changes in ERA were driven by highly

educated females who also have higher pension wealth; suggesting policymakers should

investigate why similarly educated men and less educated individuals were not adjusting their

expectations in the manner expected. De Grip et al. (2013) also found that individual’s in

manual occupations did not revise their expectations to the same extent as individuals in

professional occupations, possibly due to health reasons. The authors suggest the reform had

an income effect (due to a change in lifetime income) and noted that individuals who had made

more years of contributions to their occupational pension were less sensitive to the announced

policy changes made to the state pension.

In closely related work, Coppola and Wilke (2014) analyse the 2007 German Pension Reform

which raised the SPA from 65 to 67, like the Dutch reform the policy affected younger cohorts

(born after 1967). Consistent with De Grip et al. (2013) the authors find that lower educated

individuals failed to revise their expectations in the manner expected, however in the German

case there was no differential response by gender. Whilst there was some evidence of

individuals adjusting their ERA, after controlling for within group cross sectional correlations,

the authors found there was lack of adjustment in ERA. Consistent with other studies these

findings highlighted the need for policymakers to promote the policy more effectively using

high quality information campaigns and marketing.

There is surprisingly little evidence for the UK on the role retirement expectations have in

shaping the retirement decision, particularly given that population ageing is a key policy

priority for government; underlined by the number and magnitude of policy reforms introduced

in recent years. The one exception we are aware of is Disney and Tanner (1999) whose data

cover to the late 1980’s/early 1990s, is prior to any kind of reform to SPA and is based on a

selected sample. We attempt to fill this knowledge gap using a large scale nationally

representative survey, the United Kingdom Household Panel Study (UKHLS) which contains

a rich set of economic and sociodemographic factors including spousal characteristics; in

addition to full DOB information and pension membership status.7 We exploit the fact our

sample period covers 2010-2016 corresponding to a time when three different State Pension

Acts were in operation allowing us to investigate the relationship between SPA and ERA.

3. Overview of Pension Policy in the UK

The pension system in the UK is principally made up of three pillars. The first of being the

(Basic) State Pension. The new State Pension in operation from April 2016 is a single tier

7 We use the secure version of the data which contains individual’s full DOB.

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flat rate basic pension based on a Pay as You Go funding structure.8 Individuals in the UK are

eligible to claim their State Pension when they reach the so-called eligibility age and not

before. The level of State Pension benefit an individual is entitled to depends on the number

of years of National Insurance Contributions made when in employment and an individual’s

DOB..9 In the 2018/19 tax year the maximum State Pension an individual can receive under

the new single tier system is £164.35 per week.10 Expenditure on state pensions represent a

significant proportion of welfare expenditures: in the tax year 2017/18 this was forecasted to

be £93.8 billion or just under 5% of GDP in GB (OBR, 2018).

In terms of its generosity the UK has the least generous public state pensions among OECD

countries (OECD, 2017) underlining the importance of additional voluntary retirement saving

through occupational and private pensions (the second and third pillars respectively). A

recent report compiled for the UK Department for Work and Pensions (DWP) highlights that

1.1 million single pensioners rely on their State Pension alone as their sole source of income

(Just, 2017).

3.1 State Pension and Pension Acts in the UK

The basic structure and features of the UK State Pension system were first introduced in the

Beveridge Report published in 1948. Between 1948 and 5th April 2010 the SPA for women

(men) remain fixed at 60 (65); however various other changes were enacted such as the

abolishment of high marginal tax rates on employment earnings post SPA (see Bozio et al.

,2010, for a detailed description of the history of UK State Pension until 2010 ). The first

change to SPA was made in the 1995 Pension Act, which legislated to equate the female SPA

to that of males, specifically female SPA would be increased by one month every month for

those individuals born after 6th April 1950, starting from April 2010. The full effect of the

reform when originally legislated would not be complete until March 2020.

The introduction of the 2007 Pension Act raised the SPA for both men and women to 66

between 2024 and 2026 (increasing by one month, every month), to 67 between 2034 and

2036 and to 68 between 2044 and 2046 (Bozio et al., 2010). The rational for the reform was

to rising individual life expectancy observed since the 1950s and due to advances in

healthcare (ONS, 2015), which had not been matched by an equivalent or proportional

increase in SPA thus having significant fiscal implications for the exchequer and calling into

8 For individuals who reached SPA prior to this date under the `old’ system, the State Pension system has two tiers, a flat rate basic pension and an additional pension related to earnings. For a comprehensive description of system in place prior to April 2016 see Bozio et al. (2010). Our sample is composed of individuals for whom the rules of the `old’ system determine their level of state pension whilst for younger individuals the new system is the one that matters. 9 A recent OECD report showed that gaps in employment have limited impact on retirement income in terms of state pension accrual due to welfare policies in operation in the UK (OECD, 2015). 10 In 2018-2019 the full basic state pension amounted to £125.95 per week under the old system, however the amount calculated was a function of numerous factors such as the `class’ of the contribution, see Bozio et al. (2010) for more details.

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question the fiscal sustainability of the UK state pension system going forward (Lassila and

Valkonen, 2017).

The introduction of the coalition government in 2010 saw another Pension Act being

introduced in 2011, which sought to bring forward the rise in the female SPA. The

equalisation of SPA would now be achieved by November 2018. Moreover, the increase in

the SPA from 65 to 66 would be bought forward to take place between December 2018 and

October 2020, from 2024-2026. The magnitude and far reaching extent of the 2011 reforms

were non-trivial: it has been estimated that 5 million individuals were affected (House of

Commons library, 2017).

It is important to note that the exact increase in SPA under the reform is not uniform between

men and women or within gender and depends on an individual’s date of birth. For example,

the 2011 reforms partially affected cohorts born between April 1953 and October 1954, the

magnitude was such that SPA was increased between 2-3 months (individuals born 6th April

1953-5th May 1953) and 16 months (individuals born 6th November 1953-5th December

1953). Individuals born between 6th December 1953 and 5th October 1954 saw their SPA

increase by between 16-17 months. Individuals born after 5th October 1954 would reach SPA

when they turned 66. It is therefore crucial to have information on individuals exact date of

birth to accurately analyse the effect changes in SPA have on ERA.

The final State Pension Act we consider was introduced in 2014 which bought forward the

increase in the SPA to 66; prior to this all individuals born from 6th April 1968 onwards had a

SPA of 66 this would now hold for all individuals born after 6th April 1960. For individuals

born between 6th April 1960 and 5th March 1961, SPA is 66 plus 1 month every month (for

example it is 66 years and 1 month for an individual born between 6th April 1960 and 5th May

1960). For persons born after 5th March 1961 the SPA is 67.

The 2014 Pension Act also noted that the SPA would be reviewed on a periodic basis with

the first report due by 7th May 2017. In March 2017 the Cridland Review was published. The

review made a number of recommendations in relation to state pension in the UK. In terms of

SPA it was recommended the increase in the SPA from 67 to 68 which had originally been

set to take place between 2044 and 2046 should be bought forward by 7 years to 2037- 2039;

this change affects individuals in their late 30s and early 40s (at the time it was published)

and therefore does not affect our sample respondents who are at least 45 years old.

Figure 1: UK State Pension Acts and Men’s SPA

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Figure 2: UK State Pension Acts and Women’s SPA

The changes to SPA legislated in the State Pension Acts are summarised visually in Figure 1

for men and Figure 2 for women, each figure shows the age at which an individual is eligible

to claim their SP depending on the Pension Act in place at the time of their survey interview.

Specific details of the changes made to SPA in each Act are detailed in Appendix A. The

Figures show there that has been a significant increase in SPA for individuals in the affected

cohorts over a relatively short period of time. The largest increase for men is 1 calendar year,

whereas for women it is 2 years. The changes made to SPA for women has been criticized by

the so-called Women Against State Pension Inequality (WASPI) group which argues that

females in the most affected cohorts received insufficient advance notification of the changes

they faced (House of Commons library, 2017). In terms of actual labour supply effects, Cribb

et al. (2016) analyse the increase in female SPA from 60 to 62 and found the reform

increased the employment rate of affected individuals by 6.3 percentage points. At the time of

65

66

67

1950 1955 1960 1965 1970 1975

Pension Act Reforms & Men's SPA

Pension Act 2007 Pension Act 2011 Pension Act 2014

60

61

62

63

64

65

66

67

1950 1955 1960 1965 1970 1975

Pension Act Reforms & Women's SPA

Pension Act 2007 Pension Act 2011 Pension Act 2014

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writing the reforms to men’s SPA had not yet taken effect. Indeed, the fact that many of the

changes to SPA will not take place for some time and have only been implemented gradually

underlines the importance of understanding whether individuals have made adjustments to

their ERA.

3.2 Occupational and personal pensions in the UK (Second and Third Pillars)

3.2.1 Occupational pensions.

Occupational pensions which are a voluntary form of retirement saving between an employee

and their employer and make up the second pillar of the UK pension system. These have

traditionally been Direct Contribution (DC) or Direct Benefit (DB) type pension schemes;

individuals are taxed on receipt of income from their occupational pension not at point of

contribution. Occupational pensions (and personal pensions) play an important role in

providing income in retirement, given that the level of state pension is relatively low in the

UK (OECD, 2017); for example, average net replacement rates increase from 29% (state

pension alone) to 62.2% after accounting for the presence of an occupational pension

(OECD, 2017). From April 2002 individuals have been able to claim their state pension from

the age of 55 (up from 50); highlighting the significant age gap between claiming

occupational and state pension in the UK for both men and women. A recent DWP report

highlights that only 62 % of pensioners expect to receive income from an occupational

pension therefore the state pension is an important source of income for a non-trivial

proportion of the retired population (DWP, 2015). In terms of affecting individual’s

retirement expectations, this may partly explain the high incidence of retirement transitions at

or close to SPA and the relatively low incidence of actual or expected retirement at the age of

55.

In an effort to increase occupational pension coverage and hence retirement saving (but not

retirement age per se), the UK government in April 2012 introduced a policy whereby

employees were automatically enrolled into a workplace pension (Auto-Enrollment, AE),

evidence has shown that such ‘nudge’ behaviour has been effective in terms of changing

individuals behaviour in a particular way (in this case minimising opt out) given a particular

policy objective (Cribb and Emmerson, 2016).11 Early evidence from the introduction of AE

suggests it has been largely successful in terms of coverage but issues such as increasing

contribution rates conditional on participation still remain (Cribb et al. 2016; DWP 2017).

Alongside AE the government made significant changes to the way in which retirement

saving via a DC pension can be accessed and used. Until March 2015 individuals could draw

down up to 25% (tax free) as a lump sum and annuitize the remainder; however, following

the introduction of the Pensions Schemes Act 2015 in April 2015 individuals are free to use

their retirement savings as desired.

11 Auto enrollment was gradually phased in starting with larger employers before moving on to smaller employers, note that it does not cover the self-employed and we do not include such individuals in our sample for analysis purposes due to the way the UK pension system has historically treated self-employed versus employees, in addition to issues such as selection and unobserved heterogeneity.

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A priori it is not clear how this policy change may influence retirement expectations. One

possibility is that given it came into effect soon after the 2014 state pension act which

introduced further increases to SPA, the 2015 Pensions Schemes Act could have mitigated

upward revisions to ERA; for example if individuals were likely to be liquidity constrained

the policy reform meant this would no longer be the case and ceteris paribus induce

individuals to retire earlier.

3.2.2 Personal pensions.

Personal pensions are another type of voluntary retirement saving vehicle arranged between

an individual and a provider (usually an insurance firm); and make up the third tier of the UK

state pension system. Personal pensions are usually a DC type scheme and are treated in the

same way as occupational pensions from a taxation perspective.12 Individuals must be aged at

least 55 before they are eligible to claim their personal pension; the ‘Pension Freedom’

reforms introduced in 2015 affected personal pensions in the same way they did occupational

pensions.

The widespread availability and uptake of occupational pensions since the 1950s and the fact

that the UK labour market has historically been made up of employees means the proportion

of individuals who have a personal pensions is higher among the self-employed, a group of

individuals who we do not consider in this study.13 This also implies that when analysing

retirement age expectations among employees it is relatively more important to control for

the presence of an occupational pension rather than a personal pension.

3.2.3 Abolishment of the mandatory retirement age

Until October 2011 existing legislation meant that employees had relatively few employment

rights after the age of 65; for example, employees were required to request permission to

work past this age and therefore 65 became the ‘default retirement age’ (Lain et al., 2017).

This changed with the introduction of the 2011 Employment Equality Regulations (EER)

which scrapped the so default retirement age and allowed employers to retire employees only

if there as a justifiable legal basis for doing so.

The abolishment of mandatory retirement did not correspond with a sudden increase in

employment rates among older workers, however there has been a general increasing trend in

participation rates among individuals aged 65 and over since the early 2000’s (ONS, 2018). 14

12An individual can have an occupational and personal pension although certain limits exist in terms of total contributions made within a tax year. a tax year runS from 6th April each year to 5th April the following year. The annual pension contribution allowance on a personal pension is set by government; in the 2018-19 tax year it is £40,000. 13 Self-employment rates have increased in the period after the Financial Crisis; nonetheless the self-employed still only make up 15% of the labour force in the UK (ONS, 2018). 14 https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/timeseries/lfk6/lms

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On balance we do not expect the 2011 EER to affect the impact SPA has on ERA in a

systematic way, due to the longstanding relationship between reaching SPA and reducing

work hours or exiting the labour market in the UK which had been linked to strong cultural

norms and social security incentives (Kohli, 1991; Blundell, Bozio and Laroque, 2013). This

contrasts with evidence from the US which found positive effects in terms of participation

rates following the introduction of a similar policy (von Wachter, 2009).

4. Data

We use data from waves 2-6 (2010-2015) of the United Kingdom Household Panel Study

(Understanding Society) the largest annual longitudinal household survey in the UK,

providing information on around 40,000 households (Buck and McFall, 2011; Knies, 2017).

Understanding Society contains detailed information relating to a range of individual and

household economic and sociodemographic characteristics. The secure version of the study

contains individual’s day, month and year of birth which is needed to construct the exact age

at which an individual is eligible to claim their State Pension. This is important as previous

research has shown that reaching SPA in the UK is associated with an exit or significant

reduction in labour supply (Banks and Smith, 2006; Blundell, Bozio and Laroque, 2013). We

follow previous research and assume that individuals refer to retirement as a reduction in

labour supply; this could be a reduction at the intensive margin (hours worked) or at the

extensive margin (cessation of employment).

We make use of the retirement planning module available at every wave of the survey which

contains a range of questions relating to individual’s subjective expectations in relation to

events associated with retirement. The specific question of interest in relation to expected

retirement age is the following:

“There is a lot of policy interest in how people are planning for their long term future and

retirement. At what age do you expect you will retire or will consider yourself to be retired“?

Respondents provide an integer value or ‘don’t know’. The retirement planning module is

age-triggered only individuals aged 45, 50, 55, 60 and 65 who consider themselves not retired

are asked the battery of questions in the module. We exclude 65-year-old men and 60-year-

old women from our sample due to selection; it is likely that these individuals are

systematically different (in an unobservable way) from the rest of our sample as they are

working past SPA at the time the survey was administered. Appendix B describes the key

economic and sociodemographic characteristics used in the analysis.15 Appendix C provides

summary statistics on these same characteristics by gender.

15 The descriptive statistics are based on the sample used for the ‘full model’ which includes controlling for whether an individual has an occupational pension.

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When interpreting the results, it is important to note that for analysis purposes we drop

individuals whose response is ‘don’t know’. Evidence has shown that individuals who have

difficulty in answering retirement planning questions are a non-random sample; these

individuals face more uncertainty with respect to the retirement decision, retirement income,

are lower educated and less financially literate (Disney and Tanner, 1999; Lusardi, 2007;

Mastrogiacomo, 2004; van Santen, Alessie and Kalwij, 2012; and Copolla and Wilke, 2014).

We do not attempt to correct for this selection bias due to lack of available instruments,

however do compare whether such individuals differ in terms of observable characteristics

from those who gave a valid response. Descriptive statistics (not reported) based on

observable characteristics by whether an individual gave a positive response versus

answering ‘don’t know’ indicated that the latter group contained a higher proportion of

individuals with lower levels educational attainment and public-sector workers and a higher

proportion of single individuals. In addition, this group also reported on average a lower level

of individual and household income.

We restrict our sample to all individuals at each wave of the survey who are eligible to

answer the survey. We then combine wave specific observations such that we have one

observation per individual.16 Our sample consists of 2,708 males and 2,405 females for which

we have complete information. We focus only on employees given the historic differences in

terms of pension provision and retirement savings relative to the self-employed; in addition to

issues such as selection and unobserved heterogeneity. For similar reasons including the fact

the SPA is different between men and women over our sample period we analyse results

separately by gender.

Before turning to the methodology section we first document the average changes in expected

retirement age over time and by gender. We split our sample period into three separate

subperiods defined by the State Pension Act in operation at the time of the individual’s

survey interview. For each gender we split our sample into (roughly) single year age bands

based on SPA and compute the average gap between ERA and SPA for each SPA (band)-

period combination.

16 In a small number cases there was more than one observation per individuals, whilst Understanding Society is an annual survey, in some cases the survey took place just after an individual’s, say, 45th birthday and just before their 46th birthday. We only keep one observation (the first) for each individual.

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Table 1: Mean difference in expected retirement age and state pension age (ERA-SPA) by

period for male respondents.

cells: mean ERA

(median)

Period

SPA (banded)

Jan1.2010-

Nov2.2011

Nov3.2011-May13

2014

May14.2014-

Dec31.2015 All

[65,65.5) -3.8 -2.9 3.7 -3.4

[65.5,66.5) -5.2 -5.7 -11.9 -6.2

[66.5, 67) No observations -6.3 -15 -14.1

All -4.6 -5.25 -13.5 -6.8

Table 2: Mean difference in expected retirement age and state pension age (ERA-SPA) by

period for female respondents.

cells: mean ERA

(median) Period

SPA (banded)

Jan1.2010-

Nov2.2011

Nov3.2011-May13

2014

May14.2014-

Dec31.2015 All

[60, 60.5) 3.8 No observations No observations 3.8

[60.5,61.5) 2.6 2.8 No observations 2.6

[61.5,62.5) 0.6 1.9 No observations 1.9

[62.5, 63.5) No observations 0.9 No observations 0.9

[63.5,64.5) -1.3 0.4 0.5 -0.5

[64.5, 65.5) -0.15 -0.1 -0.1 -1.3

[65.5, 66.5) -3.1 -2.1 -1.9 -2.4

[66.5, 67) No observations -2.7 -2.4 -2.9

All -2.1 -1.6 -2.4 -1.9

Tables 1 and 2 highlight that one common finding across genders is that younger cohorts

report (on average) a lower ERA and hence the difference between ERA and SPA is greater

for individuals with higher SPA. Turning to gender specific findings, for males we find that

within the same SPA band individuals who are surveyed later in the sample period report on

average lower ERA. For women, we find that older individuals who have a lower SPA band

(between 60 and 62.5) and are surveyed earlier in the sample period (between January 2010

and May 2014) report (on average) higher ERA and hence the gap between ERA and SPA is

positive. There is also some descriptive evidence of the ERA-SPA gap narrowing (ERA

increasing) between the first and second period for women.

14

5. Methodology

Figures 1 and 2 shows that the change to SPA is gradual and relatively small for some

cohorts; whilst for others it is much larger (up to two years), in addition the magnitude of the

changes differ by DOB in different calendar years.

This complex relationship between SPA on the one hand and calendar time and cohort on the

other, makes it difficult to use standard econometric frameworks of regression discontinuity

designs (RDD) or regression kink designs (RKD) for analysing the relationship between the

SPA and the ERA (Card et al., 2015; Card et al. 2017). Nevertheless, these studies show that

if one controls for flexible (smooth) functions of the assignment variable, then one can

nonparametrically identify the effect of SPA on ERA by exploiting the discontinuities as

shown in Figures 1 and 2 =.

One possibility is to analyse the SPA-ERA relationship around the kink and jump points

separately, however the low number of observations around these points makes this

impossible. We, therefore, take a parametric approach in which we control for polynomials in

the assignment variables, which in our case are date of birth and calendar (interview) time.

SPA varies over time and across cohorts. Our dependent variable is the difference (or gap)

between ERA and SPA (i.e., GAP=ERA-SPA). The assignment variables (determining SPA)

are date of birth and time of the interview and are denoted by, respectively, d and t. We

estimate the following equation by Least Squares for each gender:

(1) 𝐺𝐴𝑃𝑖 = 𝜔0 + 𝜔1𝑆𝑃𝐴𝑖 + 𝜔2�̃�𝑖 + 𝜔3�̃�𝑖2 + 𝜔2�̃�𝑖 + 𝜔3�̃�𝑖

2 + µ𝑿𝑖 + 𝜹𝑯𝑖 + ƺ𝑖

The individual is indexed by i, �̃�𝑖 is birth date in deviation of the sample minimum and �̃�𝑖 is

calendar time in deviation of the sample minimum. 𝑿𝑖 refers to individual characteristics

which are time invariant and finally 𝑯𝑖 refers to household characteristics (including that of

the spouse). ƺ𝑖𝑖 is an error term. In addition to individual and household characteristics the

key coefficient of interest is that associated with 𝑆𝑃𝐴𝑖. If 𝜔1 is equal to zero, ERA increases

with the same number of months as the increase in SPA, leaving the gap between ERA and

SPA unchanged.

6.Estimation results

We present results in two stages. The first stage is based on two specifications (i) without

controlling for whether an individual has an occupational state pension and (ii) controlling for

occupational state pension. We focus on (i) and discuss whether controlling for the presence

of an occupational pension changes the qualitative nature of the results. The second stage

uses the estimated coefficients to make ‘gap predictions’, or the difference between

individuals ERA and SPA at particular covariate values to highlight the main empirical

findings.

15

6.1Men

Table 3 indicates that an increase of one year in SPA reduces on average the gap between

ERA and SPA by 0.97 years, put another way, conditional on other factors controlled for this

implies that males do not adjust their expectations to an increase in SPA but actually reduce

their ERA. This latter reduction is not statistically significant. In terms of cohort effects

younger males report a lower ERA (or the effect of being younger makes the gap negative)

holding all else constant and that this effect increases at an increasing rate. There evidence of

strong positive time effects which increase at a declining rate.

Turning to economic and sociodemographic characteristics we first discuss individual and

then turn to household level factors. Individuals in poor health report on average a higher

ERA, or put another way this has a positive effect (of 1.2 years) on the gap between ERA and

SPA relative to individuals in excellent health. We find evidence education effects; relative to

individuals who hold a degree having a lower level of educational attainment has a negative

impact on the gap between ERA and SPA. The size and significance of this effect varies,

having an A-level compared to a degree lowers the gap by around 0.5 years and is even larger

for individuals with ‘other’ types of qualification.17

<Table 3 here: currently awaiting release from UKDA>

There is no significant difference between ethnic minority groups relative to the white

majority except for Black African and mixed white/Black African; being a member of these

group lowers the ERA-SPA gap by approximately 2 years. However, we interpret this result

with caution: this ethnic group represent a relatively small proportion (2%) of our sample.

Job characteristics, specifically occupation has a significant effect on the ERA-SPA gap.

Compared to individuals whose occupation is considered ‘professional’; belonging to any

other occupational groups has a negative impact on the ERA-SPA gap, the magnitude of the

belonging to a group varies however in most cases it is around one year or more. In addition

to occupation, sector of work was also found to be important. Working in the public sector

lowered the ERA-SPA gap by 1.4 years

We also control for individual and household income. In both cases the results suggest that

higher levels of household and individual income lower the ERA-SPA gap, put another way,

higher individual and household income leads individuals to report on average earlier

retirement ages.

Turning to other household characteristics we find that the presence and number of dependent

children in the household raises the ERA-SPA gap, after controlling for cohort and time

effects. Housing tenure was also found to be important, relative to individuals who owned

their home outright; owning with a mortgage or renting had a positive impact on the ERA-

SPA gap. It is likely both dependent children and types of housing tenure have financial

17 Other groups refers to relatively basic skills.

16

implications in terms of staying in employment which leads to individuals reporting on

average a later retirement date. Whilst we control for a range of spousal level characteristics

(not reported) we do not find any factors which are statistically significant. Finally, we also

control for geographical region and find that living in the West Midlands, London, South East

and Scotland increases on average the ERA-SPA gap by over one year.

When we also control for whether an individual report’s being a member of an occupational

pension scheme, conditional on all other factors this has a positive effect on the ERA-SPA

gap increasing it on average by 0.79 years. Qualitatively all other findings remain unchanged.

6.2 Women

Table 4 shows that females make a partial adjustment with respect to the age which they

expect to retire in the face of a change in the SPA. Specifically, the ERA-SPA gap reduces by

0.67 years for a one-year increase in SPA. Thus, consistent with the behaviour of men,

women also report lower ERAs. In terms of cohort effects, we see a similar pattern to that

found for men, namely that younger cohorts of females are reporting lower ERAs (at an

increasing rate). We also find strong time effects (at a decreasing rate) noting that women

have seen their SPA rise more rapidly over the sample period relative to men.

We do not find evidence of education effects except for individuals who had an A-level

(equivalent to 13 years of full time education), which reduced the ERA-SPA gap by around

0.6 years compared to those with a degree. We find some evidence of differences in ERA of

women from ethnic minority groups; relative to the white majority Indian or Black African

females and mixed white and Black African females reduced the ERA-SPA gap by on

average almost and 2 and 3 years respectively. Again, we interpret this result with caution

due to relatively small sample sizes.

<Table 4 here: currently awaiting release from UKDA>

Housing tenure is also important in determining the ERA-SPA gap, like men, females who

report owning their home with a mortgage or renting report on average a higher ERA-SPA

gap relative to those who own their home outright. Table 4 shows that females living in

higher income households reported on average a lower ERA-SPA ceteris paribus.

We find some evidence that spousal characteristics such as health and job occupation are

important. Having a spouse who reported having fair health compared to excellent raised the

ERA-SPA gap by over one year. Whereas having a partner who had a skilled non-manual

occupation decreased the ERA-SPA gap by just under on year.

The presence of an occupational pension was found to be important. Individuals who reported

being a member of their employer’s scheme reported an ERA-SPA spa which was on average

0.69 years higher than those without, holding all else constant. However, unlike in the case of

men the sector of employment was not found to significant (not reported); in all other

respects the results did not change qualitatively in the extended specification.

17

6.3 Gap predictions

Using the estimates based on our preferred set of models it is possible to generate predictions

of the average gap between ERA and SPA at covariate values (of statistically significant

factors) for men and women to identify possible vulnerable groups in the sense of having a

large (negative) gap.18

We first consider the effect of SPA on the ERA-SPA gap. For men we consider the effect of

SPA being set at 65,66 and 67. Irrespective of whether we control for occupational pension

the trend remains the same: higher SPA is associated with a bigger difference between ERA

and SPA. This is consistent with Tables 1 and 2 which shows that the youngest male cohorts

in our sample (who have the highest SPA) report on average lower ERA. In the case of

women, we predict the gap based on SPA varying between 61 and 67; in this case the pattern

is even more pronounced: the gap is initially positive for SPA=61 and SPA=62 (0.77 and

0.10 years respectively) at successively higher ages after this point the gap increases, indeed

for SPA=67 the ERA-SPA gap is -3.24 years. Therefore, consistent with their male

counterparts and Table 2 women from younger cohorts report lower ERAs.

Table 5 shows that for men higher levels of individual income tends to have a negative

impact on the ERA-SPA gap, for example focusing on the specification which does not

control for occupational pension moving from the 50th to 75th percentile of the individual

income distribution increased the difference between ERA and SPA from -1.59 to -1.91

years. The magnitude of this effect hardly changes even after we control for whether an

individual is a member of their employer’s occupational pension scheme.

We find education effects only for men. Relative to degree holders those with lower levels of

educational attainment report a larger gap between ERA and SPA; this finding also holds

after we control for pension membership. Similarly, we find that job occupation (of current

job) is only important for men. Relative to an individual’s whose job is considered

professional (as defined by the Registrar General’s Social Class index) the effect of having a

job in a lower social class has a negative effect on the ERA-SPA gap, for example being in an

unskilled occupation lowers the ERA-SPA gap by 1.63 years, again the qualitative effects do

not change after controlling for pension membership. In addition to job occupation we also

find sector of work is important in determining the ERA-SPA gap; the results indicate that

working in the public sector has a relatively large negative impact on the ERA-SPA (more

than two and a half years).

Table 5: Effects of economic and sociodemographic characteristics on the ERA-SPA gap.

Specification not controlling for

occupational pension

Specification controlling for

occupational pension

Covariate Estimated gap (ageret-SPA)

measured in years

Estimated gap (ageret-SPA)

measured in years

18 We focus only on factors which are statistically significant at conventional levels for either one or

both genders.

18

Males Females Males Females

SPA

SPA=61 N/A 0.77 N/A 0.72

SPA=62 N/A 0.10 N/A 0.02

SPA=63 N/A -0.56 n/a -0.68

SPA=64 N/A -1.24 n/a -1.38

SPA=65 -0.63 N/A -0.97 N/A

SPA=66 -1.6 -2.57 -2.01 -2.77

SPA=67 -2.57 -3.24 -3.06 -3.47

Income

25th

percentile

-1.26 -2.36 -1.46 -2.48

50th

percentile

-1.59 -2.41 -1.92 -2.60

75th

percentile

-1.91 -2.46 -2.35 -2.72

Education

Degree (

min. 16 years

f/t education)

Base group Base group Base group Base group

A-level (13

f/t education)

-1.76 N/S -2.27 N/S

No

qualifications

-1.92 N/S -2.07 N/S

Social class

of current

occupation

Professional Base group Base group Base group Base group

Managerial

and technical

-1.52 N/S -1.87 N/S

Skilled non-

manual

-2.12 N/S -2.55 N/S

Skilled

manual

-1.35 N/S -2.05 N/S

Partly skilled -1.70 N/S -1.96

N/S

Unskilled

occupation

-1.63 N/S -2.16 N/S

Sector of

work

Private Base group Base group Base group Base group

Public -2.53 N/S -2.74 -2.76

19

Ethnicity

White base base base base

Indian N/S -4.23 N/S -4.16

Black

African and

mixed White

and Black

African

N/S -5.22 -5.41

Spouse’s

health

Excellent base base base Base

Fair N/S -1.68 N/S -1.76

Spouse’s

economic

status

Employed Base Base base base

Unemployed N/S N/S N/S 0.42

Spouse’s job

occupation

Professional Base Base Base base

Skilled non-

manual

n/s n/s n/s -3.38

Housing

tenure

Owned

outright

Base group Base group Base group Base group

Owned with

a mortgage

-1.34 -2.31 -1.86 -2.49

Rented or

other

-0.80 -1.00 -1.10 -0.89

N 2708 2405 2031 1830

In terms of ethnic minority belonging to the Indian or Black African (including mixed white

and Black African) groups had a significant and large negative impact on the ERA-SPA gap,

reducing it by over 4 and 5 years respectively however we interpret these results with caution

given the relatively small sample sizes of ethnic minority individuals in our sample.

We find spousal effects for women. Having a spouse who reported fair health relative to

excellent reduced the ERA-SPA gap by over 1.5 years. In the extended specification which

also controlled for occupational pension membership spouse’s economic state and occupation

was also important in determining the ERA-SPA gap for women. Having an unemployed

20

spouse raised the ERA-SPA gap by almost half a year whereas having a spouse who reported

being in a skilled non-manual occupation lowered the ERA-SPA gap by over three years.

7.Discussion

The estimation results and gap predictions highlighted many noteworthy findings. First, our

analyses suggest that after controlling for time and cohort effects individuals in our sample

are not adjusting their retirement expectations in a manner which is consistent with the policy

objective. Men show no response in terms of ERA to changes in the SPA, the average gap

between the ERA-SPA reduces by almost one year for a one-year increase in SPA. In the

case of women, we find partial evidence of adjustment, the ERA-SPA gap reduces by 0.7

years for a one-year increase in SPA. These findings are of policy relevance, successive State

Pension Acts have increased the SPA to extend working lives and to reduce the cost of state

pensions on the state (Barrell, 2011). The results suggest that it is younger cohorts who are

not adjusting in terms of their expected retirement age.

These individuals can of course adjust their behaviour as they approach SPA, indeed recent

evidence has shown that older women who have been affected by the State Pension Act

reforms adjust their actual labour supply when close to SPA in a manner which is consistent

with the policy objective (Cribb, Emmerson and Tetlow; 2016). It may also be the case that

younger cohorts have, on average, higher retirement savings relative to older cohorts (at the

same stage in their life) due to other policy initiatives such as auto enrollment. However, such

policies are primarily aimed at low wage workers and have initially been concerned with

pension coverage as opposed to contribution rate, moreover there has also been a general

phasing out of generous defined benefit pension plans and a higher proportion of younger

cohorts are likely to members of a defined contribution scheme. Individuals can also rely on

other forms of wealth such as private or property wealth, however evidence suggests there is

no compelling evidence to suggest that the youngest members in our sample (aged 45) are

likely to have significantly higher levels of private or property wealth than older individuals

in our sample (Cribb, Hood and Joyce, 2016). Therefore, given the analysis suggests

successively younger cohorts are reporting lower expected retirement ages it is important to

understand which factors affect the ERA-SPA gap and policies which could help to reverse

this trend.

Time effects-

The results highlight the magnitude and importance of occupational pension membership on

the ERA-SPA gap for both men and women. Unlike countries such as the Netherlands and

Germany the age at which an individual can claim an occupational pension in the UK is 55,

significantly lower than the SPA. The results indicate that membership to an employer run

occupational pension has a positive effect on the ERA-SPA gap for employees, particularly

men. More generally, we find job occupation has a significant effect on the ERA-SPA gap,

21

employees who report being in a professional occupation report on average a higher ERA

relative to lower grade occupations. Pension membership and pension type (DC or DB) has

historically varied by occupation and/or sector of work in the UK, whilst we cannot control

for sector of work or type of pension (DC or DB) we do interact pension membership with

occupation and do not find any significant interaction effects for men; for women we find

that being in a skilled non-manual occupation and not contributing to an occupational pension

has a negative effect on the ERA-SPA gap, lowering it by 1.7 years relative to professionals

who do not have an occupational pension. The results also highlight that for men there is an

education effect: relative to individuals with a degree those with lower levels of educational

attainment report on average a larger ERA-SPA gap.

We find strong income effects. For women only, household income was important whereas

for men both individual and household income were significant factors in determining the

ERA-SPA gap.19 Irrespective of gender, higher levels of income had a negative effect on the

ERA-SPA gap conditional on holding other factors at their mean. From a policy perspective

if individuals can retire early and afford to maintain their desired standard of living then this

does not negatively impact state resources, although research has shown early retirement can

be costly to state (Fenge and Pisteau, 2005). It is worth noting that wealth (including pension

wealth) is a key determinant of living standards in retirement and has been shown to be

positively correlated with working life income (Gustman, Steinmier and Tabatabai, 2012).20

8.Conclusion

Influencing the age at which individuals expect to retire is a key issue for government. The

UK government has introduced a range of policies; one of which has been to extend working

lives by increasing SPA in an attempt to mitigate the fiscal implications of an ageing

population. In this paper we consider how responsive individuals ERA is to changes in SPA.

In doing so we are able to assess whether individuals have adjusted their expectations in a

manner consistent with the policy objective; and hence our findings provide policymakers

with crucial insight into understanding the individual decision-making process with respect to

the retirement decision.

Our results suggest that individuals particularly those in younger cohorts, have not adjusted

their expected retirement age considering recent policy changes, indeed a change in SPA

tends (on average) to widen the gap between ERA and SPA conditional on controlling for

time and cohort effects and rich set of economic and sociodemographic characteristics. The

findings show that the effect is more pronounced among men and women only show partial

adjustment. If policymakers wish to use SPA as a tool to alter retirement expectations and

19Given the cohorts in our sample it is likely that for a majority of the sample it is the male who is the main breadwinner and this may explain why only household and not individual income is significant for the specifications based on the female sample. 20 Unfortunately UKHLS does not contain data related to pension wealth.

22

extend working lives among future cohorts; then these findings suggest additional initiatives

are required.

In addition to SPA we find a range of individual and household level characteristics influence

an individual’s ERA. Occupational pension membership raises the ERA-SPA gap;

highlighting the wider role occupational pensions have on shaping retirement in addition to

providing an important savings vehicle for old age. We also find higher levels of individual

and household income significantly widen the ERA-SPA gap; previous research has shown

richer individuals are also more likely to retire early in the UK (Banks and Smith, 2006).

Finally, job occupation and sector of employment were also found to be important;

individuals in lower grade occupations and working in the public sector were more likely to

report a lower ERA-SPA gap relative to professionals and those working in the private sector.

Considering our findings one route policymakers could take is to improve the effectiveness of

marketing and information campaigns related to changes to SPA that have been legislated

and retirement planning more generally. A recent initiative being considered by the DWP is

the introduction of a pensions dashboard which helps individuals improve their understanding

of their personal (forecasted) financial position in retirement (DWP, 2017). Whilst we find no

significant adjustment in terms of retirement age expectations consistent with the policy

reforms to SPA, evidence suggests cohorts of older women have adjusted their actual labour

supply and continued to stay in work (Cribb et al., 2016). Our descriptive findings also show

that older females do make some adjustment in terms of the ERA-SPA gap and that it is the

youngest cohorts of men and women who report lower ERA and underlines the need for

effective interventions for future cohorts of retirees.

Our findings add to a very small literature concerning the role of expectations data in shaping

retirement decisions in the UK and make an important contribution to understanding how

SPA policy can be used to examine changes in ERA. The results suggest further action is

needed if policymakers wish to use SPA to influence future retirement age expectations, for

example via more effective information campaigns. The decision to retire is dynamic and can

change over time, one implication is to analyse how retirement age expectations change over

time considering the significant policy changes in the UK concerning SPA, and how

forthcoming changes to men’s SPA due to come into effect in late 2018 affect actual labour

supply, we leave this for future research.

23

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https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/lifeexpect

ancies/articles/howhaslifeexpectancychangedovertime/2015-09-09

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(2017). Returns to work after retirement: a prospective study of unretirement in the United

Kingdom. Ageing & Society, 1-26.

Van Santen, P., Alessie, R., & Kalwij, A. (2012). Probabilistic survey questions and incorrect

answers: Retirement income replacement rates. Journal of Economic Behavior &

Organization, 82(1), 267-280.

26

Appendix

Appendix A: The UK State Pension Acts.

Men

2007 Pension Act: until and including November 2, 2011

Main changes made to SPA:

• If a man is born before 6th April 1959: SPA=65

• If a man is born after 6th April 1959: SPA=65+1 month (1 month if born on 6th

April, less if born after 6th April), born between 6th May and 5th June 1959:

SPA=65+2 months (if born on 6th May less if born after),. Born between 6th

February and 6th March 1960: SPA=65+11 (11 months if born on 6th February

1960, less if born after)

• If a man is born after 6th March 1960 but before 6th April 1968: SPA=66

2011 Pension Act: November 3, 2011 – May 13, 2014

Main changes made to SPA:

• If man is born before 6th December 1953: SPA=65

• If a man is born after 6th December 1953 and before 5th January 1954: SPA=65+3

months, born between 6th January and 5th February 1954: SPA=65+3+1, …., born

between 6th September 1954 and 5th October 1954: SPA=65+3+8 (for the youngest

i.e. born 5th October 1954, it will be 66 for those born on 6th September 1954).

• If a man is born October 1954 – March 1968: SPA=66

• If a man is born between 6th April-5th May 1968: SPA=66+1, …, between 6th

February 1969 and 5th March 1969: SPA=66+11 (if born on 6th February 1969)

• If a man is born from 6th March 1969 onwards: SPA=67

• If a man is bornafter 5th April 1969 but before 6th April 1977 attains pensionable age

when the person attains the age of 67.Born 6th April 1977 to 5th May 1977 then SPA

is 67+1 month (if born on 6th April 1977)

• If a man is born 6th May 1977 to 5th June 1977 then SPA is 67+2 month (if born on

6th May 1977) until Until 6th March 1978 to 5th April 1978 after which SPA: 68

• A man born after 5th April 1978 attains pensionable age when the person attains the

age of 68.

Period 3: 2014 Pension Act: May 14, 2014 – end of sample period

• Cohorts up to and including 5th April 1960: as in period 2.

• If a man is born on or after 6th April 1960-5th May 1960: SPA=66+1 month, …,6th

February 1961-5th March 1961:SPA+66+11

• If a man is born in March 1961 or later: SPA=67

• The increase in SPA going from 67 to 68 is identical to that described in period 2.

27

Women

Period 1: 2007 Pension Act : until and including November 2, 2011

• A woman born before 6th April 1950 attains pensionable age when she attains the age

of 60.

• -A women born between 6th April 1950 and 5th May 1950 reaches SPA when she is

60+1 month (at most if she is born on 6th April 1950), If 6th May1950≤born<5th

June 1950 then SPA: 60+2 months... and so on...until 6th March 1955≤born<5th

April 1955: SPA is 64 years and 11 months.

• A womn born after 5th April 1955 but before 6th April 1959 she attains pensionable

age when she is 65

• -A woman after 6th April 1959- same rules as detailed for men above.

Period 2: 2011 Pension Act: November 3, 2011 – May 13, 2014

• -A woman who is born 6th April 1953 -5th May 1953: SPA is 63 years + 3

months…… (note that under the 1995/2007 act her SPA would have been 63 years+1

months), so an increase of two months.

• -A woman born 6th May 1953-5th June 1953: SPA is 63 years + 6 months (note that

under the 1995/2007 act her SPA would have been 63 years+2 months), so an

increase of four months.

• …and so… until:

• A woman born 6th November 1953-5th December 1953: SPA is 65.

• Then for those born after 6th December 1953 the spa for females is same as that for

males above, note however that the increase in state pension age is larger for those

individuals born between December 1953 and March 1955, compared to individuals

born after 1955.

Period 3: 2014 Pension Act: May 14, 2014 – end of sample period

• -Same rules as for men.

28

Appendix B

Table 1: variable definitions.

Variable Definition

Dependent variable

ERA (Expected retirement age) Individuals expected age of retirement

Individual level covariates

SPA Age at which individual is eligible to claim

their state pension according to State

Pension Act in operation at time of

respondent’s survey interview.

Marital status 1 if in group, 0 otherwise.

- Single (base group)

- Married/living as a couple

- Divorced

- Widowed

Self-reported health 1 if in group, 0 otherwise.

-Excellent (base group)

-Very good

-Good

-Fair

-Poor

Government office region of residence 1 if in group, 0 otherwise.

-North East (base group)

-North West

-Yorkshire and Humberside

-East Midlands

-West Midlands

-East of England

-London

-South East

-South West

-Wales

-Scotland

-Northern Ireland

Highest educational qualification 1 if in group, 0 otherwise. In descending

order.

-University degree (base group)

-Other higher degree (for example nurse)

-A-level (13 years full time education)

-GCSE (11 years full time education)

-Other qualification

-No qualification

Long term health condition 1 if in group, 0 otherwise.

29

-Yes (base group)

-No

A long-standing physical or mental

impairment, illness or disability. By 'long-

standing' refers to anything that has troubled

you over a period of at least 12 months or

that is likely to trouble you over a period of

at least 12 months.

Ethnicity 1 if in group, 0 otherwise.

- White majority (base group)

- Other white groups

- Indian

- Pakistani

- Bangladeshi

- Black Caribbean or mixed W&BC

- Black African or mixed W&BA

- Chinese

- Arab, other Asian, mixed W&As or other

ethnic groups

Occupational classification Professional

Managerial and technical

Skilled non-manual

Skilled manual

Partly skilled

Unskilled

Log total net income Total net income adjusted for inflation.

Total net (of tax and NICS) income is the

sum of:

labour income, miscellaneous income,

private benefit income, investment income,

pension income and social benefit income.

Spousal level characteristics Economic status

Health status

Job occupation

Household level covariates

Housing tenure Housing tenure of household individual

resides in at the time of survey interview:

Household log income Total post –tax equivalised household net

income (of all household members) which

has been adjusted for inflation (2015 prices)

and household size using the OECD scale.

Number of individuals not in paid

employment in household

1 if true, 0 otherwise.

0 (base group)

1

30

2

3

Number of dependent children in household 1 if true, 0 otherwise.

0 (base group)

1

2

3

Member of employers pension scheme 1 if true, 0 otherwise.

0 yes (base group)

1 no

31

Appendix C: Descriptive characteristics by gender.

<Awaiting release from the UKDA>