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TWIN PEAKS: An Analysis of the Gender Gap in Pension Income in

England

Onyinye Ezeyi (University of Bath)

Sunčica Vujić (University of Antwerp and University of Bath)

Abstract:

This paper investigates the gender gap in state and private pensions in England using

data on retirees from the English Longitudinal Study of Ageing. The analysis finds that

the female distribution of state pension income is bimodal, an idiosyncrasy that arises

because a large proportion of women have pensions derived from their spouse’s

contribution record. Though there are no gender gaps in state pension coverage, the

female-male state pension income ratio is 0.75 and Blinder-Oaxaca decompositions

indicate that only a quarter of the gap in mean state pension income is explained by

observed characteristics. The analysis is also extended to quantile regressions and

decompositions reveal “sticky floors” in state pension income. The gender pension gap

is more marked for private pensions than state pensions. Decompositions suggest that

gender differentials in characteristics matter more for private pension coverage rates

than private pension income levels. Specifically, differential characteristics account for

half of the gender gap in private pension coverage but only 28% of the gender gap in

private pension income. Quantile analysis of the gap in private pension income indicate

that though there is greater volatility, the gap remains relatively close to the mean gap.

The private pension estimates are not robust to selectivity corrections.

Key words: gender pension gap, England, decomposition methods, quantile regression

JEL: C21, C24, J31, J71

Corresponding author:

Onyinye Ezeyi

ome22@bath.ac.uk

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1. Introduction

Women have a higher risk of old age poverty than men and are more likely to claim

means-tested benefits in retirement, indicating that they do not have adequate

retirement income (Ginn, 2003; Department for Work and Pensions 2013). Women’s

higher propensity of poverty in retirement is related to their pension entitlement as

pension benefits are the main source of income for retirees in the UK. In the 2013/2014

tax year, average weekly gross income for retired households in the UK stood at £487

(in 2014 prices), of which pension benefits accounted for 67% (Department for Work

and Pensions, 2013). The pension income an individual receives in retirement is related

to their labour market outcomes and personal characteristics since individuals pay

contributions during working life to accrue pension rights and secure an income in

retirement. Despite the abundance of research into the gender wage gap, little research

has been conducted in the economic literature into the extent to which women’s lower

lifetime labour market outcomes translates into lower pension outcomes in later life.

The historical dimension of the accumulation of pension rights mean that other factors

emerge in the determination of the gender pension gap that may not be pertinent to the

gender wage gap.

An analysis of the gender gap in pensions for the UK is important for at least two

reasons. First, increasing life expectancy together with women’s greater longevity imply

that, unless women’s pension outcomes equalise with men’s, women are likely to spend

an increasing proportion of their lives at risk of poverty. Secondly, the restructuring of

the UK state pension to a one-tier system and the government’s emphasis on private

pensions in helping individuals secure adequate retirement income mean that research

on the gender pension gap can better enable the design of private pension policy in the

UK.

This paper contributes to the scant economic literature by providing a comprehensive

analysis of the gender pension gap in England. The paper first examines the factors that

determine pension coverage and pension income for state and private pensions

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separately. The paper then uses Fairlie (1999) and Blinder (1973) Oaxaca (1973)

decomposition to respectively decompose the gap in pension coverage and income into

the explained and unexplained components. The analysis conducted in this paper is

most similar to Even and Macpherson (1994) who examine the gender gap in

occupational pensions for the US, Bardasi and Jenkins (2010) who investigate the gap in

private pensions for the UK and Hänisch and Klos (2014) who examine old age income

in Germany at the mean and for the entire pension income distribution. The results

from these studies are mixed. Even and Macpherson (1994) find that among retirees,

80% of the predicted gap in occupational pension coverage and 70% of the predicted

gap in occupational pension benefit is accounted for by gender differentials in

characteristics. However, they do not employ regression-based decomposition

techniques. Conversely, Bardasi and Jenkins (2010) find that between 33%-42% of the

gap in private pension coverage is explained, while 8%-18% of the gap in benefit level is

explained. However, unlike Bardasi and Jenkins, we are able to control for earnings and

also extend our analysis to quantile regressions. Analysing German data from 2007,

Hänisch and Klos (2014) find that only 26% of the gender pension gap is explained and

that the largest share of the explained gap occurs at the bottom 25% of the pension

income distribution. To the best of our knowledge, this is the first paper to examine the

gender gap in UK state pensions using regression-based decompositions and to apply

quantile decomposition techniques for both state and private pensions.

The results from our analysis confirm the presence of gender gaps in both state and

private pensions. While state pension coverage is virtually universal for both sexes,

there are substantial gender inequalities in the level of state pension benefit received;

the raw female-male state pension income ratio is 0.75. Blinder-Oaxaca decompositions

show that only a quarter of the gap in state pension income can be attributed to gender

differentials in mean characteristics and that women’s lower state pension income

arises because they receive state pensions derived from their spouse’s National

Insurance contribution record. Results from quantile regressions provides the first

evidence of ‘sticky floors’ in state pensions. That is, the gap is largest at the bottom 30%

of the state pension income distribution than at the top of the distribution but is

decreasing as one moves up the distribution. Unlike state pensions, the analysis shows

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that there are gender gaps in both private pension coverage and income; only 63% of

women had private pension coverage compared to 90% of men and the raw female-

male pension income ratio conditional on coverage stands at 0.52. Decompositions of

private pension coverage suggest that gender differences in average characteristics

account for around half of the gap in probability of private pension receipt, while

Blinder-Oaxaca decompositions suggest that 72% of the gap in private pension income

is unexplained. The results for private pension income are not robust to selectivity

corrections.

The remainder of this paper is organised as follows. The next section discusses the

institutional context of the UK pension system for the sample of retirees used in this

analysis. It also provides explanations for why men and women may have differential

state pension outcomes. Section 3 provides economic rationale for the demand and

provision of pensions in the labour market and also examines the mechanisms through

which the gender gap in private pensions may arise. The methodology used to

decompose the gender gap in pensions in Section 4, while Section 5 describes the data

and sample used in this analysis. Model estimates and the results of the decompositions

are shown in Section 6. In Section 7, selectivity corrections are presented as robustness

checks. Section 8 summarises the findings and discusses the policy implications of the

results.

2. The Institutional Context

In the UK, pensions are provided by both the State and private sector. In order to

analyse the gender gap in pensions, it is necessary to understand the institutional

context of the UK pension system. In April 2016, a new state pension system was

introduced, however, because the sample of the sample of retirees analysed in this

study built up their pension entitlement under the pre-2016 system, this section

provides a brief overview of the state pension system until 2016 and also highlights

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features of the state pension system that may have served as possible mechanisms of

gender inequalities in state pensions. This section also describes private sector

provision of pensions in the UK.

2.1 State Pensions

For individuals retiring before April 2016, the state pension was made up a flat-rate

pension known as the Basic State Pension and an earnings-related component known as

the Additional State Pension. Individuals built entitlement to the state pension during

their working lives by paying National Insurance contributions or were given National

Insurance credits. It was possible, as it is in the post April 2016 system, to claim the

state pension and continue in paid employment. Men and women of state pension age

(65 and 63 years respectively in January 2016) with sufficient years (30 years) of

National Insurance (NI) contributions or credits received the full Basic State Pension

(BSP) of £115.95 per week in the 2015-16 tax year as well as any entitlement to the

Additional State Pension. Individuals with less than 30 years of NI contributions or

credits received a pro-rata amount. The Additional State Pension received is a

composite of the various earnings-related state pension schemes that existed i.e. the

State Earnings Related Pension Scheme and the State Second Pension. The self-

employed did not build up entitlement to the Additional State Pension (ASP) and the

ASP is not payable for the years in which employees were members of a contracted-out

private pension scheme.

For the sample of retirees under consideration, there are two main reasons why

women’s state pension outcome might be worse than men’s. First, the UK state pension

system introduced in 1948 was designed to reflect prevailing societal norms. In

particular, the state pension system reinforced the male breadwinner-female

homemaker model of familial division of labour. A man’s NI contribution record bought

two state pensions; one for himself (i.e. Category A pension) and one for his wife, valued

at 60% of his entitlement to the Basic State Pension (i.e. Category B pension). In

addition, there were strong incentives for married women to claim a state pension

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based on her husband’s NI record; until 1978, married women had the option to pay

reduced rates of NI contributions in return for forfeiting entitlement to a state pension

in their own right. This was known as the Married Woman’s Stamp and a woman that

elected to pay the lower NI contributions would receive a Category B pension on

retirement. Women that did not opt into the Married Woman’s Stamp had to pass the

Half Test which required paying sufficient NI contributions for at least half of the

number of weeks between their date of marriage and state pension age, to receive any

state pension in her own right.

Secondly, women spend a substantial period of time out of employment as due to their

caring responsibilities in the home. Thus, historically, it has been more difficult for

women to have the requisite number of years to claim entitlement to the full BSP. Since

1978, the NI system has taken into account time spent out of the labour market due to

caring responsibilities. However, individuals still needed to have paid NI contributions

for at least a quarter of their working life to receive any BSP at all and were required to

have paid 20 years of NI contributions to receive the full BSP until 2010.

2.2 Private Pensions

Private pension provision in the UK consists of occupational pension schemes and

personal pensions. Occupational pension schemes are organised by employers for their

employees, while personal pension schemes are contracts between individuals and

financial institutions. Personal pensions are the only private pension scheme available

to the self-employed. Until 2012, employees had the option to contract out of the ASP

and enrol in a private pension scheme, thereby paying a reduced rate of NI

contributions. Prior to the commencement of automatic enrolment in 2012, the

provision of an occupational pension scheme was at the discretion of the employer and

firms also had the ability to set the eligibility criteria for scheme membership.

Though there is great variety in private pension schemes, most pension schemes in

operation in the UK can be categorised as either a defined benefit pension or a defined

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contribution pension. A defined benefit pension is an occupational pension that

typically pays beneficiaries a pension according to a predetermined formula based on

salary, tenure and an accrual rate whereas defined contribution schemes involve paying

contributions into a pension fund that is invested and the accumulated contributions

and returns at retirement is used to provide a pension for beneficiaries. All personal

pensions in the UK are defined contribution pensions while occupational pension

schemes can be of the defined benefit or define contribution type. The ASP was a

defined benefit pension.

3. The Role of Pensions in the Labour Market

Several arguments have been put forward to explain the demand and supply of

pensions by individuals and firms respectively. One strand of the economic literature

views individuals’ demand for private pensions as a form of retirement income

insurance (Bodie 1990; Gustman et al. 1994). By receiving pensions as annuity income,

recipients effectively insure against the risk that assets run out before death (i.e.

longevity risk). Private pensions in particular mitigate the impact of potential

reductions to state pensions and other pensioner benefits in future and also help ensure

adequate replacement rate. Similarly, another strand of the literature explains

individuals’ demand for pensions in terms of the desire to smooth consumption over the

lifecycle. Intertemporal models of saving and retirement posit that individuals aim to

maximise expected lifetime utility subject to their lifetime budget constraint in order to

smooth consumption over the lifecycle (Modigliani and Brumberg, 1954; Feldstein

1974). In this framework, individuals demand for pension coverage stems from the

desire to have adequate income in retirement to maintain pre-retirement standard of

living.

The theoretical literature also offers some insight into the role of occupational pensions

in the labour market. The implicit contract theory of pensions postulates that the

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matching of workers to firms in pension contracts (of the defined benefit type) induces

strong worker-firm attachments. In the implicit contract theory, pensions are viewed as

deferred compensation that impose substantial capital losses on workers that leave the

firm before reaching normal retirement age because of pension back-loading (i.e. the

disproportionate amount of pension benefit that is accrued in later years of service in

defined benefit schemes) (Kotlikoff and Wise, 1988). By offering pensions, firms create

strong incentives for workers to stay with the firm until reaching the retirement

window. Consequently, firms offer pension schemes to attract productive workers and

regulate worker turnover due to high job turnover costs.

In this framework, occupational pensions are used as a sorting device that enable firms

to identify and attract workers with low quit propensities since the workers that take

up pension jobs are likely to be stayers (Ippolito 1985; 1987). Workers with low quit

propensities will self-select into pension jobs and enter an ‘implicit contract’ with the

firm whereby workers effectively agree to long tenure with the firm, while the firm

agree to not terminate pension schemes or lay off workers as it is concerned about their

labour market reputations (Allen et al 1993; Cornwell et al. 1991). This suggests that

while all individuals demand occupational pensions, only those with low quit

propensities will take up pension jobs. However, Gustman and Steinmeier (1993) and

Even and Macpherson (1996) have shown that turnover rates in jobs with defined

contribution pensions, which are more portable, are similar to turnover rates in defined

benefit jobs casting doubt on the effects of non-portable pensions in restricting job

mobility. Instead, Gustman and Steinmeier (1993) argue that the lower turnover rates

in pension jobs is due to the higher total compensation premium of pension jobs rather

than pension back-loading.

There are numerous channels through which a gender gap in private pensions might

arise. First, the strong worker-firm attachments created by the incentive structure of

defined benefit (DB) schemes imply that women are less likely than men to have

pension coverage since women are more likely to change employers due to their

fragmented work histories. For the majority of the sample under analysis in this study,

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DB pensions would have been the predominant type of pension scheme offered by firms

during their working life as define contribution schemes were only incorporated into

national insurance system in the late 1980s. Consequently, the mobility restriction

embedded in DB pensions together with women’s discontinuous work histories would

have served as a barrier to women’s private pension coverage. Indeed, previous

research on the gender pension gap has shown that gender differentials in labour

market experience is the most important factor in the explained component of the gap

in private pension coverage and benefit (Even and Macpherson, 1994; Bardasi and

Jenkins, 2010; Hänisch and Klos 2014).

Secondly, men’s higher lifetime earnings than women will have implications for the

gender gap in private pensions. For instance, because the tax advantages of private

pensions rise with income, a larger proportion of men would demand private pension

coverage than women because the tax-efficiency of pension saving rises with income.

Nonetheless, given coverage, women’s lower lifetime earnings than men translates into

lower benefit levels from DB pension schemes and implies that women may face greater

financial constraints to contributing to defined contribution (DC) schemes despite the

increased portability of DC pensions relative to DB pensions.

Thirdly, the occupational segregation of men and women may have consequences for

the gender gap in private pensions since it was not uncommon for employers to have

different pension schemes for different classes of employees. For example, employers

operated senior-management schemes, staff schemes and works schemes that offered

different accrual rates (Blake 2003). Figures from the Office of National Statistics show

that in 2013 women accounted for 82% of workers in caring and leisure occupational

group, whereas only a third of individuals working in the highest paid category of

managers, director and senior officials were women. Thus, the over-representation of

women in low-paid occupations may worsen women’s pension outcome relative to men.

Lastly, eligibility for occupational scheme membership was often defined in terms of the

nature of employment and tenure. For instance, part-time workers were often excluded

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from scheme membership until 1995, thereby a significant proportion of women were

unable to accrue occupational pension rights and instead remained contracted in the

Additional State Pension. The earnings-related component of the state pension was

primarily aimed at individuals that did not have access to an occupational pension

scheme and was generally regarded as a less generous substitute for occupational

pensions (Blake, 2003). Indeed, Barrientos (1998) finds that 60% of individuals with

SERPS membership were women and that women were less likely to contract out of

SERPS and enrol into a private pension scheme than men.

4. Methodology

4.1 Decomposition of Pension Income

The determinants of pension income is estimated using ordinary least squares for the

subsample of individuals with state (private) pension coverage separately for each

gender:

𝑌𝑖𝑚 = 𝑋𝑖𝑚�̂�𝑚 + 𝑢𝑖𝑚

𝑌𝑖𝑓 = 𝑋𝑖𝑓�̂�𝑓 + 𝑢𝑖𝑓

where, 𝑌𝑖𝑔, is weekly net state (private) pension income for individual 𝑖 sample of

gender 𝑔 = (𝑚, 𝑓). Blinder (1973) and Oaxaca (1973) decompose the difference in the

average pension income of the male and female sample as follows:

Equation 1

�̅�𝑚 − 𝑌�̅� = (∑ 𝑋𝑖𝑚

𝑁𝑚𝑖=1

𝑁𝑚−

∑ 𝑋𝑖𝑓𝑁𝑓

𝑖=1

𝑁𝑓) �̂�𝑓 −

∑ 𝑋𝑖𝑚𝑁𝑚𝑖=1

𝑁𝑚(�̂�𝑚 − �̂�𝑓)

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where �̅�𝑔 = ∑ 𝑌𝑖𝑔

𝑁𝑔𝑖=1

𝑁𝑔. The first term on the right hand side of Equation 1 is the explained

component i.e. the portion of the gap attributed to differences in the average

characteristics of men and women. The second term is the unexplained component and

represents the portion of the gap that arises from gender differentials in the returns to

average characteristics. The unexplained component is commonly thought of as

evidence of discrimination, however, it is important to bear in mind that it captures

gender differentials in any important variables omitted from the model specification as

well as discrimination (Altonji and Blank, 1999).

4.2 Decomposition of Pension Coverage

Descriptive statistics and preliminary investigations revealed that state pension

coverage is near universal and does not significantly differ by gender. As such, the

determinants of state pension coverage are estimated for the pooled sample of men and

women. Conversely, descriptive statistics showed large discrepancies in private pension

coverage for men and women. Accordingly, the determinants of private pension

coverage is estimated separately for each sex as follows:

𝐶𝑖𝑔∗ = Z𝑖𝑔𝛾𝑔 + 𝜀𝑖𝑔 𝜀𝑖𝑔~𝑁(0, 𝜎2)

𝐶𝑖𝑔 = { 1 𝑖𝑓 𝐶𝑖𝑔

∗ > 0

0 𝑖𝑓 𝐶𝑖𝑔∗ ≤ 0

Pr(𝐶𝑖𝑔 = 1 | 𝑋𝑖𝑔 ) = Φ(Z𝑖𝑔𝛾𝑔 + 𝜀𝑖𝑔)

where 𝐶𝑖𝑔∗ is the unobserved contribution propensity for individual 𝑖 of gender 𝑔 which

is determined by observed labour market and personal characteristics plus a normally

distributed unobserved error 𝜀𝑖𝑔. 𝐶𝑖𝑔 is private pension coverage and takes the value of

1 if the individual sufficiently contributed to a private pension and 0 otherwise. Thus,

the probability that individual 𝑖 of gender 𝑔 has private pension coverage is equal to the

cumulative distribution function of the standard normal distribution of the

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determinants of private pension coverage. Decomposition analysis is conducted using

the methodology developed by Fairlie (1999) which decomposes the difference in the

average predicted probability of private pension coverage between men and women

into the component due to gender disparities in distribution of characteristics and into

the component due to gender differentials in the process determining the probability of

private pension coverage:

Equation 2

𝐶�̅� − 𝐶�̅� = [∑Φ(𝑍𝑖𝑚𝛾𝑓)

𝑁𝑚

𝑁𝑚

𝑖=1 − ∑

Φ(𝑍𝑖𝑓�̂�𝑓)

𝑁𝑓

𝑁𝑓

𝑖=1]

+ [∑Φ(𝑍𝑖𝑚𝛾𝑚)

𝑁𝑚

𝑁𝑚

𝑖=1 − ∑

Φ(𝑍𝑖𝑚𝛾𝑓)

𝑁𝑚

𝑁𝑚

𝑖=1]

where 𝑁𝑔 is the sample size for gender 𝑔 and 𝐶�̅� is the average probability private

pension receipt for gender 𝑔. In Equation 2, differences in the distribution of

characteristics are weighted with the coefficient from the female sample and differences

in the process determining private pension coverage are weighted with the male

distribution of characteristics. The first term in the first parenthesis is the

counterfactual distribution that would arise if the female sample had the same

characteristics as the male sample i.e. women’s average predicted probability of having

a private pension if they assume mean characteristics of men.

5. Data

5.1 Sample

The empirical analyses uses a sample drawn from wave 6 of the English Longitudinal

Study of Ageing which surveys individuals aged 50 and over living in private households

in England. The English Longitudinal Study of Ageing (ELSA) began in 2002 and is

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collected biennially, accordingly, there were six waves of ELSA between 2002 and 2014.

The estimating sample is restricted to the cross-section of retirees in wave 6 that also

participated in the wave 3 life history interview. The wave 3 life history interviews

contain retrospective data on marital, fertility and employment histories. Information

from the wave 3 life history interviews augmented with information from subsequent

waves form the basis of the independent variables used in the regression analysis.

This study focuses on pensioners in England and respondents that are not observed as

retired in wave 6 are excluded from the analyses. However, the pension system in the

UK is such that retirement cannot be explicitly defined as a discrete event. This is

because it is possible to claim the state pension and remain in employment. It is also

possible to receive a private pension and continue working provided it is not with the

same firm. Since there may be a gradual transition from employment to retirement for

some individuals, we define retirement in terms of three variables; respondents’ self-

reported employment status, a dummy variable indicating whether the respondent is of

above state pension age and the current working status variable deduced from survey

filtering. The age restriction is imposed because those that retire before state pension

age are not representative of the population of retirees as they are likely to be those

with substantial pension wealth or those with ill-health. The final sample comprises

1,577 women and 1,188 men at or above state pension age.1

There are four dependent variables in the regression equations: state pension coverage,

the log of net state pension benefit, private pension coverage and the log of net private

pension income. State pension benefit is the sum of income from the Basic State Pension

and the Additional State Pension deflated to 2014 prices, expressed in weekly terms. An

individual has state pension coverage if they receive a positive amount of state pension

benefit in retirement. Private pension coverage is defined in terms of the receipt of a

positive amount of private pension income and private pension income is defined as the

1 State pension age is 65 years for a man. For the majority of the female sample under analysis, female state pension age is 60 years. However, since 2010 the female state pension age has been increasing to equalise with the men’s. Consequently, some women in the sample have a state pension age greater than 60 years.

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sum of pension income received from occupational pensions and annuity income.

Pension income is inextricably linked to labour market outcomes during working life.

Therefore, the explanatory variables included in the pension coverage and benefit

equations include a set of work history variables intended to capture engagement with

the labour market; years of work experience between ages 20 and 60, years spent as

self-employed between ages 20 and 60, years spent working part-time between ages 20

and 60 and starting wage of last job before retirement. The model also includes birth

cohort, educational qualifications and marital status. The full set of explanatory

variables is shown in Table 1 in the appendix.

While the English Longitudinal Study of Ageing contains many variables that are

potentially important determinants of pension coverage and income, retrospective data

on firm characteristics such as industry, union coverage status and public-private sector

distinction is unavailable. Data on firm size is only available for those that are observed

in employment in any one wave of ELSA or those that were in employment during the

Health Survey for England. Consequently, when firm size is included in the model

specification for private pensions, it is likely to be for the subsample of pensioners that

are younger than the full sample.2 Another drawback of ELSA is that retrospective

earnings data is only available for jobs where the respondent was an employee.

Including wage in the regression specification means that the analysis is restricted to

respondents whose last job of career was not in self-employment.

5.2 Descriptive Statistics

State pension coverage is near universal at approximately 99% for both the male and

female samples. Despite this, there are inequalities in state pension income.

Conditioning on coverage, average net weekly state pension income for men and

2 The distribution of birth cohort for the subsample of individuals with firm size information; born before 1934, 1934-1942 and after 1942 is 0.30, 0.35 and 0.30 for the male sample and 0.23, 0.27 and 0.50 for the female sample respectively.

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women is £147 and £110 respectively. Accordingly, the raw gender gap in average state

pension income stands at 25%.3 That is, men’s average net weekly state pension income

is 25% higher than women’s average net weekly state pension income. Men’s median

net state pension income amounts to £146 per week compared to £119 per week for

women. Thus, the raw gap in state pension income is smaller at the median than the

mean. Descriptive statistics also show that the raw gender gap in average net private

pension income is 80% for the whole sample. Specifically, average net weekly private

pension income for the entire sample of men is almost five times greater than their

female counterpart (£68 vs. £14 respectively). Amongst those with private pension

coverage, men receive nearly twice as much private pension income as women (£123

vs. £64 respectively) with the corresponding raw gap of 48%. Such large discrepancies

in the conditional and unconditional gap highlight differences in the private pension

coverage rates of men and women. Indeed, descriptive statistics indicate that less than

two-thirds of women (63%) receive any private pension income whereas 88% of men

receive income from a private pension. Moreover, the median net private pension

income without conditioning on coverage is £122 and £25 per week for men and

women respectively, while the conditional median net private pension income is £151

and £74 per week for men and women respectively. The conditional and unconditional

raw gaps in mean private pension income do not significantly differ from the associated

median.

Figure 1 shows the distribution of log average weekly net state pension income by

gender. Several key features emerge from the kernel density plot. First, it can be seen

that for both men and women, the distribution of state pension income remains flat

until approximately £33 and becomes flat again just before reaching £403. The flat left

tail is the result of the minimum number of qualifying years needed to claim any state

pension that was in force for individuals retiring before 2010, whilst the right tail

represents the fact that there is a maximum state pension that an individual can receive

made up of the full Basic State Pension and the full Additional State Pension. Second, it

can be seen that the male state pension distribution situated to the right of the female

distribution and that there is a high degree of concentration in the upper end of the

3 Defined as [1-(average female pension income/average male pension income)]*100.

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distribution, implying that men receive a higher amount of state pension income than

women. Finally, the distribution of state pension income is bimodal for the female

sample and presumably represents the idiosyncratic feature of the UK state pension

system that permitted female retirees to claim a state pension using their spouses’

national insurance contribution record that was worth 60% of their spouse’s

entitlement. Figure 2 depicts the distribution of state pension income for the female

sample by birth cohort and reveals that the proportion of women receiving close to the

full state pension has increased between the oldest and youngest cohorts.

[Place figure 1 here]

[Place figure 2 here]

Figure 3 shows the distribution of private pension income by sex. In addition to

receiving a lower amount of private pension income than men, the figure shows that the

variation in private pension income is greater for women than men. A larger proportion

of women receive a net private pension income that is less than £55 per week and most

women are clustered around the £90 mark whereas most men are clustered around the

£300 mark.

Table 1 reports the sample means of the variables used in the analysis of the gender

pension gap. The male sample have higher levels of education attainment than the

female sample, though the gap in educational qualifications diminishes when comparing

the distribution of education between the subsamples of men and women with private

pension coverage. The raw gender gap in wages from last job before retirement is 54%,

though this decreases to 49% when the sample is restricted to individuals with private

pension coverage. On average, women have 11 years less work experience than men,

with the gap reducing marginally to 10 years when comparing individuals with private

pension coverage. Women spend a longer period of time working part-time compared

to men irrespective of private pension coverage status. The number of years spent in

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self-employment is small for both men and women. Nonetheless, men spend

approximately three times as long in self-employment than women who spend an

average of one year. While only 28% of women are from the three highest occupational

classification levels, this percentage is 43% for the male sample. This proportion

increases to 35% for women and 46% for men when the sample is restricted to those

with private pension coverage.

6. Results

6.1 State Pensions

State Pension Coverage

Table 2 shows the probit estimates for the pooled sample of men and women. As

descriptive statistics indicated, the estimates suggest that the probability of state

pension coverage does not significantly differ by gender.4 The probit estimates indicate

that the probability of having a state pension does not significantly differ by level of

education, occupational classification or accumulated work experience during working

life. However, birth cohort is an important determinant of state pension coverage; older

aged individuals are more likely than those from younger cohorts to receive a state

pension. The results from the pooled regression also suggest that married individuals

are less likely than unmarried individuals to receive a state pension.5 Sample means by

gender and marital status show that married individuals in the sample are younger than

individuals not currently married and so the effect of being married may be capturing

the age effect.

4 This is confirmed by the Wald test which fails to reject the null hypothesis that the female interactions are jointly equal to zero. 5 I use a dummy variable for married in the state pension coverage equation since being single, never married and being separated perfectly predict success of state pension coverage.

18

State Pension Income

Estimates of the determinants of state pension income are presented in Table 3.

Columns 1 and 3 depict the estimates for state pension income for the male and female

sample respectively, while columns 2 and 4 introduces partner’s weekly state pension

income in the model specification.6 There are strong cohort effects in state pension

income, though the effect differs by gender. While men born before 1943 receive a

higher amount of weekly net state pension income than men born on or after 1943,

women from earlier cohorts receive a lower amount of state pension income than

women born after 1942. The heterogeneity in the effect of birth cohort is likely due to

the fact that men’s labour market attachment did not change significantly in the 20th

century whereas successive cohorts of women significantly increased their labour force

participation. In addition, policies introduced to the National Insurance (NI) system to

improve women’s state pension outcomes would have had the greatest impact on the

cohort of women born after 1942.

For both sexes, education and occupational classification have no statistical association

with state pension income. The relationship between starting income of last job before

retirement and state pension income is weak for both men and women. This is

unsurprising given that the state pension is a flat-rate benefit with a small earnings-

related component. As expected, work experience has a significant and positive impact

on state pension income, though the effect is small. It is interesting to note that the

magnitude of the effect of work experience on state pension income is similar for both

men and women which is consistent with the UK NI system whereby a year’s worth of

NI contributions provides individuals with a pro rata state pension income in

retirement. For both men and women, the longer the proportion of time spent as self-

employed, the lower the amount of state pension received. This is due to the fact that

the self-employed only accrue entitlement to the BSP and not the earnings-related

component. Similar to work experience, the size of the coefficients does not differ

significantly by gender. Years spent in part-time employment have essentially no effect

on men’s state pension income but negatively impacts the amount of state pension

6 Approximately 68% of men and 41% of women have a partner.

19

income women receive in retirement, though the effect is small. This result may be due

to the fact that men in the sample only spend an average of six months in part-time

employment compared to 8 years for women and the NI system in the UK is such that

workers only accrue entitlement to the State Pension once their weekly income from

any specific job is over a threshold known as the lower earnings limit.

Current marital status is strongly associated with the amount of state pension income

women receive and is not as important for men. Married women receive a lower

amount of weekly net state pension income than unmarried women; on average, a

single woman that has never married can expect a state pension that is 35% higher than

a married woman, while a widowed woman’s weekly net state pension is 56% higher

than a married woman. Widowed women are able to inherit their deceased spouse’s

state pension, accordingly their state pension income will be higher than those of

married women on average. Controlling for partner’s state pension income (columns 2

and 4) does not change the qualitative results. However, while the coefficient on

partner’s state pension income is positive for both sexes, the coefficient on women’s

state pension income is more significant and nearly twice the effect on men’s,

suggesting that women’s state pension income is more closely linked to their partner’s

state pension income. This indicates that married women are likely to be claiming

Category B state pensions, which are derived pensions valued at 60% of their spouse’s

entitlement.

Blinder-Oaxaca Decomposition

Error! Reference source not found. presents the Blinder-Oaxaca decomposition

shown in Equation 1. The gender gap in predicted average weekly net log state pension

income is £44 (column 1). That is, men’s average weekly net state pension income is

approximately 30% higher than women’s state pension income. Differences in the

average characteristics of men and women account for only a quarter of the gap in

average weekly net state pension income, whilst gender differentials in the returns to

average characteristics account for around three-quarters of the gap. While having the

same amount of labour market experience as men increases women’s state pension

20

income, having the same distribution of current marital status as men or spending an

identical amount of time spent in self-employment is detrimental to women’s state

pension income. Although differentials in the work history variables are significant

variables in the detailed decomposition of the explained gap, it is differences in the

returns to birth cohort, marital status and occupational classification that emerge as the

most important factors in the unexplained gap. The significance of birth cohort and

marital status in the unexplained gap is consistent with the evolution of state pension

policy which initially comprised gender-specific rules that operated through women’s

marital status but were subsequently abolished.

Columns 3 and 4 in Table 4 presents the Blinder-Oaxaca decomposition when state

pension income of respondents’ partners is included in the model specification, thus the

sample is restricted to only men and women with partners.7 The gender gap in weekly

net state pension income increases to £60 with the corresponding female-male ratio of

average weekly net state pension income amounting to nearly 0.60, providing further

evidence that married women’s state pension income are Category B pensions. Again,

differences in the returns to characteristics accounts for the majority of the gender gap

in state pension income. The detailed decompositions indicate that discrepancies in

labour market engagement is the most important factor affecting the explained

component of the gender gap in state pensions.

6.2 Private Pensions

Private Pension Coverage

Probit estimates of private pension coverage with and without firm size in the model

specification care presented in Table Error! Reference source not found.5.8 The two

specifications are presented because controlling for firm size greatly reduces the

sample size and the resultant sample comprises individuals from younger cohorts. The

7 This comprises either married or cohabiting individuals. 8 Housing financial wealth variables were included in the model specification but had zero effect on private pension coverage despite being significant only for the female sample. As such, the preferred model specification excluded housing and non-housing wealth.

21

results show that there is a cohort effect in the probability of private pension coverage

for the male sample at 10% level and none at all for the female sample. Education has

statistical associations with probability of having a private pension for both sexes,

though the effect is stronger for women. For both sexes, current marital status is

associated with the probability of private pension receipt. However, similar to the

empirical findings in the gender wage gap literature, there is heterogeneity in the effect;

being married appears to be a pension premium for men and a pension penalty for

women. The estimates show that a single woman who has never married is more likely

to have private pension coverage than a married woman, whereas there is no difference

between married and single men’s probability private coverage. This result is most

likely due related to individuals’ engagement with the labour market since women that

have never married are more likely to have stronger ties to the labour market than

married women, whereas men of differential marital status have stable work histories.

The estimate of marital status also show that widowed women are more likely to have

private pension coverage than married women, which is probably due to survivor

benefits paid out from a deceased spouse’s private pension. The negative effect of

widowhood for men may be due to the fact that they are older aged men that did not

have access to occupational pension schemes during their working life.

As expected, for both men and women, more years of work experience increases the

probability of receiving a private pension income, while time spent in self-employment

significantly reduces this probability. It is possible that the negative impact of self-

employment on private pension coverage arises because the majority of private pension

income originates from occupational pensions and the self-employed typically rely on

personal savings and assets to fund their retirement consumption.9 Starting wage of last

job before retirement is only an important determinant of women’s private pension

coverage status, consistent with the idea that women with higher wages are more likely

to contribute to a private pension scheme.

9 In 2012/13, Personal pensions account for 5% of retirement income for male pensioners and 2% for female pensioners (ONS Pension Trends 2012).

22

Time spent in part-time employment and occupational classification matters more for

women’s private pension coverage than men’s; women from top-level occupations are

significantly more likely to be receiving private pension income than women from

elementary occupations. The finding aligns with the evolution of private pension policy

in the UK; at the time the sample of pensioners were still of working age, employers

were permitted to exclude part-time workers from joining their pension scheme (thus

disproportionately affecting women). In addition, UK policy enabled employers to

impose pension scheme membership as a condition of contract until 1988 and this was

particularly common for top-level occupations. Including firm size in the model

specification does not change the qualitative results; as expected, the results in columns

2 and 4 indicate that individuals that worked in smaller firms are less likely to receive a

private pension.

Decomposition of the private pension coverage

The gender gap in private pension coverage is decomposed according to Equation 2 and

the detailed decompositions of the explained component are shown in Error!

Reference source not found.6. Gender differences in the distribution of characteristics

account for 51% of the gender gap in the average predicted probability of private

pension coverage. Including firm size in the model specification marginally decreases

the explained to 50%. If women had the same distribution of characteristics as men,

their average predicted probability of private pension coverage would increase by

around 0.12. Decompositions of the private pension coverage with and without firm size

suggests that gender disparities in the distribution of years work experience and part-

time work are the two most important factors in the explained gap in private pension

coverage.

Private Pension Income

OLS estimates of private pension income for the subsample of men and women with

private pension coverage are shown in Table 7. Columns 2 and 4 includes firm size of

last job before entering retirement which imply that the resultant estimates are based

on the sample of pensioners who were observed in employment in at least one wave of

23

ELSA or HSE. Overall, it is immediately clear that labour market characteristics are more

closely linked to private pension income than state pension income. There are strong

cohort effects for the male sample; men from earlier birth cohorts receive lower private

pension income than men born after 1942. This might be due the expansion of

occupational pension schemes that occurred in the 1950s and 1960s, which meant that

men from younger cohorts would have had a longer period of time to build up pension

entitlement. For the female sample, women born before 1934 have significantly lower

private pension income than women born after 1942. Across all specifications and for

both sexes, higher levels of educational attainment are strongly associated with higher

private pension income, though there are substantial differentials in the magnitude of

the effect. The return to education is much larger for men than women; columns 1 and 3

indicate that though the average man with degree-level education receives a private

pension income that is 88% greater than a man with no qualifications, this effect is only

56% for the female counterpart. Thus, the estimates suggest that while education is

more strongly associated with men’s private pension income, education matters most

for women’s coverage status.

Marital status is an important determinant of private pension income. For both men and

women, cohabiting individuals receive a larger amount of private pension income than

those that are currently married. However, similar to finding in private pension

coverage status, there is heterogeneity in the effect of marriage. Women that have never

married receive a higher amount of private pension income than married women,

whereas men that have never married receive a lower private pension than married

men. Again, women’s marriage pension penalty may be due to their fragmented work

histories. The returns to labour market experience are more important and greater for

women than men. Specifically, on average, an extra year of work experience between

ages of 20 and 60 years of age increases female private pension income by 1.6%,

whereas there is no statistical association between men’s private pension income and

accumulated work experience or years spent working part-time. As expected, an

additional year of part-time employment reduces women’s private pension income by

2.1% but has no effect on men’s private pension income.

24

For both sexes, occupational classification is an important determinant of private

pension income though the effect is stronger for women. Women from professional

occupations receive a private pension income that is approximately twice the amount

that women from elementary occupations receive, whereas this figure is 87% for the

male counterpart. Starting wage of last job before retirement does not seem to matter

for the amount of private pension income individuals receive, which may be due to the

fact that for the sample under analysis, defined benefit pensions were the main type of

pensions available during their working life and was typically related to final salary.

Once firm size is included in the model specification, coefficient estimates increase in

absolute terms. The size of firm an individual previously worked for is an important

correlate of the amount state pension income they receive. A woman who worked in a

firm with 2 to 4 workers can expect a pension income that is half as much as a woman

that worked in a firm with more than 1000 workers, while a man that previously

worked in a firm with 2 to 4 workers can expect about 66% less. This finding is

consistent with incentive models of the wage structure in which firms wage structure

follows a steeply rising age-earnings profiles so as to discourage shirking at work

(Lazear, 1981; Akerlof and Katz, 1989). In this framework, large firms will offer

pensions because pensions can be provide a cost-efficient way of monitoring worker

effort. This is because of pension back-loading and the substantial capital losses

associated with the termination of the employment contract prior to normal retirement

age.

Blinder-Oaxaca Decomposition

Columns 1 and 2 of TableError! Reference source not found. 8 show the Blinder-

Oaxaca decomposition of private pension income of without controlling for firm size.

The estimates show that men’s average weekly net private pension income is

approximately 50% higher than women’s average weekly net private pension income.

The decomposition indicates that only 28% of the gender gap in private pension income

is attributable to differences in the mean characteristics of men and women. The most

important factor contributing to the explained gap is the number of years women spend

in part-time employment, followed by gender differentials in accumulated labour

market experience. Gender differentials in the returns to marital status emerges as the

25

most important factor in the unexplained gap. Columns 3 and 4 of Table 8 include firm

size in the regression specification for private pension income. The returns to working

in firms of varying sizes does not significantly differ by gender. However, decomposition

estimates show that gender differences in the firm size of last job before retirement is

an important source of the explained component of the gap. Controlling for the size of

firm of last job before retirement reduces the explained component of the gap to 12%.

6.3 Quantile Regression

The analysis thus far has focused on the gender pension gap at the mean of the

conditional distribution of pension income. Consequently, the estimated coefficients

presented in section 6 are assumed to be constant across the entire distribution of

pension income. Research on the gender wage gap has provided evidence of

heterogeneity in the magnitude of the gap in along the wage distribution (Albrecht et al.

2003; Arulampalam et al. 2007). Figure 4 andFigure 5 show the observed raw gap

across the whole distribution of log state pension income and log private pension

income respectively. The red line represents the mean raw gender gap.

Figure 4 demonstrates that the raw gap in state pension income is not uniform across

the entire distribution. The magnitude of the gender gap steadily decreases as one

moves up the distribution of state pension income. For instance, a woman at the 20th

percentile in the female distribution of log state pension income has a state pension that

is around 55 log points less than a man at the 20th percentile of the male distribution,

whereas a woman at the 80th percentile of the female distribution of log state pension

has a state pension that is about 18 log points less than a man at the 80th percentile of

the male distribution. That plot suggests that there are “sticky floors” in state pension

benefit; the differential is much larger for individuals at the bottom 30% of the state

pension distribution than at the top. Thus, the graph suggests that the average gender

gap in state pension is driven by the gap at the bottom of the distribution. The larger

gap at the bottom of the distribution is likely to arise because there is an effective cap on

the amount of weekly state pension income an individual can receive.

26

Figure 5 shows the raw gender gap for the entire distribution of private pension

income. Relative to state pensions, the gap is fairly constant across the pension income

distribution. The raw gap in private pension income is above the mean gap between the

12th and 53rd percentile and declines until the 80th percentile at which point it starts to

increase. The largest gap in private pension income occurs around the 25th percentile.

The gender gap in private pension income is more volatile that the gap in state pension

income and is probably due to the diverse range of private pension schemes in

operation.

The plots of the raw gap across the distribution of pension income imply that OLS

estimates may not provide an appropriate description of the gender pension gap since

OLS specifies the conditional mean function. Thus, we extend our analysis of the gender

pension gap to quantile regressions because it allows for a richer description of the

gender gap by conditioning at specific quantiles of state (pension) income distribution.

The conditional quantile function of state (private) pension income is estimated for

each sex:

𝑄𝑔𝜏(𝑌𝑔|𝑋𝑔) = 𝑋𝑔′ 𝛽𝑔(𝜏) 𝑤ℎ𝑒𝑟𝑒 0 < 𝜏 < 1

where 𝑄𝑔𝜏(𝑌𝑔|𝑋𝑔) is the conditional quantile function for gender, 𝑔 , at the 𝜏𝑡ℎ quantile

of the distribution of log state (private) pension income conditional on a set of

explanatory variables, 𝑋𝑔, and the vector of quantile-specific coefficients, 𝛽𝑔(𝜏) .

𝛽𝑔(𝜏) is estimated by minimising the weighted sum of absolute residuals (Koenker and

Basset, 1978) as follows:

min𝛽𝑔

∑ 𝜏|𝑌𝑔 − 𝑋𝑔′ 𝛽𝑔(𝜏)|

𝑛

𝑖=1: 𝑦𝑖≥𝑋𝑔′ 𝛽𝑔

+ ∑ (1 − 𝜏)|𝑌𝑔 − 𝑋𝑔′ 𝛽𝑔(𝜏)|

𝑛

𝑖=1: 𝑦𝑖<𝑋𝑔′ 𝛽𝑔

27

Table 9 andTable 10 show the quantile regression estimates of state pension income at

the 25th, 50th and 75th quantiles for the male and female sample respectively. The

estimates indicate that birth cohort and work experience are the most important

determinants of state pension income for both sexes. Education is most important for

women at the median of the state pension income distribution, while current marital

status significantly determines women’s level of state pension income irrespective of a

woman’s location in the state pension income distribution. Starting wage of last job

before retirement had no relation to men’s state pension income level but is

significantly related to the level of state pension income received by women at the

median and 75th quantile. Consistent with Barrientos’ (1998) findings, this suggest that

men contracted out of the Additional State Pension (ASP) and into a private pension

during their working life whereas women remained contracted into the ASP and only

women in the top half of the distribution state pension income receive a pension from

the ASP.

The gender gap in state (private) pension income at specific quantiles is decomposed

using the Machado and Mata (2005) technique which decomposes the gap across the

entire distribution of state (private) pension income into the explained and unexplained

components. Specifically, the Machado-Mata decomposition involves generating the

counterfactual unconditional state (private) pension income distribution that would

arise if women had the same characteristics as men but were still rewarded as women

i.e. 𝑄𝜏𝑐(𝑌𝑓) = 𝑋𝑚𝛽𝑓(𝜏). Thus, the difference in state (private) pension income can be

expressed as:

Equation 3

𝑄𝜏(𝑌𝑚) − 𝑄𝜏(𝑌𝑓) = [𝑄𝜏(𝑌𝑚) − 𝑄𝜏𝑐(𝑌𝑓)] + [𝑄𝜏

𝑐(𝑌𝑓) − 𝑄𝜏(𝑌𝑓)]

28

The first term on the right hand side is the component of the gap due to gender

differences in the distribution of coefficients, while the second term is the component of

the gap due to gender differences in the distribution of observed characteristics. Table

11 shows the estimated state pension gap and the decomposition of the observed

conditional distribution. Three main results emerge. First, the results show that the

conditional state pension income gap declines as one moves up the state pension

distribution; the estimated gap in state pension income is around 70% higher at the 10th

quantile than the gap at the conditional mean. Second, the dominance of the coefficient

effect indicates that men are better rewarded than women across all points of the

distribution. Lastly, the decompositions suggests that the share of the explained

component increases as one moves up the distribution of state pension income.

Table 12 and Table 13 present the quantile estimates of private pension income for the

male and female sample respectively. Overall, the results are qualitatively similar to the

OLS estimates of private pension income presented in the previous sub-section, though

two patterns appear that are worth emphasising. First, the effect of current marital

status is greatest for women in the bottom quartile than women at the median or top

25% of the private pension income distribution. That is, the richest female pensioners

are not as affected by their marital status as female pensioners at or below the median.

Second, unlike OLS estimates, the quantile estimates show that starting wage of last job

before retirement is significantly related to the amount of private pension income

received by men in the top 25% and women in the bottom 25% of the distribution. This

might suggest that defined benefit pensions, which are a function of wages, delivered

better private pension incomes for male pensioners but resulted in a lower private

pension income for women. This result is consistent with issues surrounding the

portability of private pensions and women’s fragmented work histories.

Table 14 shows the estimated gap in private pension income for the 10th, 25th, 50th, 75th

and 90th percentiles. The estimated gap is largest at the 25th percentile. Similar to the

Oaxaca-Blinder decompositions, the decomposition estimates of the conditional

quantile functions at specific quantiles show that the majority of the gap is due to the

gender differences in coefficients. However, identical to Hänisch and Klos (2014), the

29

results indicate that share of gender differentials in the distribution of observed

characteristics is largest at the bottom of the distribution quantile.

7. Selection Bias in Private Pensions

Decomposition estimates of the gap in private pension income tentatively showed that

most of the gap is attributed to gender differentials in the returns to characteristics.

However, descriptive statistics have shown that private pension coverage is not

widespread; nearly than two-thirds of women and 88% of men were in receipt of an

income from private pensions. The theoretical literature discussed in section 3

suggested that workers do not randomly assign themselves with firms offering

occupational pensions but instead self-select into firms whose compensation pack align

with their own preferences, implying that those with private pension income are not

representative of the entire population of retirees.

We thus estimate the determinants of private pension income using the Heckman

selection model (1979) that explicitly models the selection equation for private pension

coverage to take into account the probability of private pension receipt in the

estimation of private pension income. The model estimated is set out as follows:

𝐶𝑖𝑔∗ = Z𝑖𝑔𝛾𝑔 + 𝜀𝑖𝑔 𝜀𝑖𝑔~𝑁(0,1)

𝐶𝑖𝑔 = { 1 𝑖𝑓 𝐶𝑖𝑔

∗ > 0

0 𝑖𝑓 𝐶𝑖𝑔∗ ≤ 0

𝑌𝑖𝑔 = 𝑋𝑖𝑔𝛽𝑔+ 𝑢𝑖𝑔 𝑢𝑖𝑔~(0, 𝜎2)

Equation 4

𝑌𝑖𝑔 = 𝑋𝑖𝑔𝛽𝑔 + 𝜌𝜀𝑢𝜎𝑢𝜆𝑖𝑔(𝑍𝑖𝑔𝛾𝑔)

30

where, 𝑍𝑖𝑔 is a vector of labour market and personal characteristics determining private

pension coverage for individual 𝑖 from sample 𝑔, 𝐶𝑖𝑔 is private pension coverage, 𝑌𝑖𝑔 is

private pension income and 𝑋𝑖𝑔 is a vector of labour market and personal

characteristics determining private pension income. The model assumes that the error

terms, 𝜀𝑖𝑔 and 𝑢𝑖𝑔, are correlated with each other (𝜌𝜀𝑢) and are normally distributed

with mean 0 and variance 𝜎2. 𝜆𝑖𝑔 is the ratio of the probability density function to the

cumulative distribution function which takes account of the selection bias. Exclusion

restrictions require that 𝑋𝑖𝑔 is a subset of Z𝑖𝑔, consequently, I exclude starting wage of

last job before retirement since this variable had weak statistical associations with

private pension income.

Robustness Checks

Table 15 presents the estimates of private pension income after correcting for selection.

The positive sign on rho indicates that unobservables in the private pension coverage

equation are positively associated with private pension income. The Blinder-Oaxaca

decomposition estimates of the gap in private pension income after correcting for

selection are shown in Table 16Table 16. The results indicate that 80% of the gap in

private pension income is explained by gender differentials in average characteristics,

with discrepancies in work experience accounting for most of the explained gap.

8. Discussion

Using data from the English Longitudinal Study of Ageing, this paper finds that despite

universal state pension coverage, there are large gender gaps in state pension income.

The conditional average weekly net state pension income for men is £147,

approximately 25% higher than women’s state pension income. The gender gap in

private pension outcomes is substantially larger; less than two-thirds of retired women

in England receive an income from a private pension, yet 88% of men have private

31

pension coverage. Given coverage, women’s average weekly net private pension income

amounts to £64, around half the amount than men (£123).

For both sexes, the analysis of state pensions income indicate that, apart from years of

work experience, personal characteristics such as birth cohort and marital status are

more important in determining the amount of state pension income individuals receive

than labour market characteristics. The results show that subsequent cohorts of women

have better state pension outcomes than earlier cohorts. This is consistent with the

abolition of gender-specific policies embedded in the state pension system and the

introduction of policies acknowledging women’s caring responsibilities within the

National Insurance system. The descriptive statistics and regression results based on

samples restricted to individuals with partners suggest that women’s state pension

income is closely linked to their spouse’s state pension income. Specifically, the analysis

provides evidence that a large proportion of women have derived pensions i.e. 60% of

their husband’s entitlement known as Category B pensions. It would have been

interesting to further distinguish state pension coverage into those with only Basic State

Pension coverage and those with entitlement to both the BSP and the Additional State

Pension. However, it is not possible to identify those with Additional State Pension

coverage because the basic state pension and the earnings-related component are

reported one payment.

Blinder-Oaxaca decompositions of state pension suggests that the unexplained

component accounts for the majority of the gender gap in state pension income; around

three-quarters of the gap in state pension income is attributable to differential effects of

coefficients. However, as mentioned before, it is important to bear in mind that any

omitted variables from the state pension income equations are subsumed into the

unexplained component of the gender pension gap. Yet, even when respondents’

partner’s state pension income is controlled for, the unexplained component reduces

marginally to only 74% and the gap in state pensions widens. Gender discrepancies in

the average number of years of work experience account for the majority of the

explained portion of the gap, while the differential effect of marital status is the most

32

important factor in the unexplained gap. Estimates of the gap in state pension income at

specific quantiles provide evidence of sticky floors in state pension income. That is, the

gap in state pension income is wider at the bottom of the distribution of income than at

the top. The conditional gap estimated at various quantiles suggest that the majority of

the gap is accounted for the by the coefficient effect, though similar to Hanisch and Klos

(2014), the share of the unexplained gap decreases as one moves up the distribution of

state pension income.

The gender gap in private pension income is 48%, marginally less than the 50%

estimated by Bardasi and Jenkins (2010) for the UK. Labour market characteristics are

more closely related to the private pension outcomes of men and women than state

pension outcomes. Education, work history and occupational classification are

significant determinants of private pension coverage. As expected, firm size had strong

statistical associations with private pension coverage; those who worked in larger firms

are more likely to be receiving a private pension in retirement. Decompositions of the

gap in the average predicted probability of private pension receipt suggest that gender

differentials in the distribution of characteristics account for half of the gap in coverage

rates. While there were little cohort effects in the probability of private pension

coverage for both sexes, birth cohort is an important determinant of the amount of

private pension men and women receive. Similar to the result in private pension

coverage, marital status is significantly associated with private pension income though

there is heterogeneity in the effect; married men experience a pension premium,

whereas there is a pension penalty for being a married woman.

Blinder-Oaxaca decompositions indicate that the explained component accounts for

only 12%-28% of the gap in private pension income. Of the unexplained component,

gender differentials in the returns to marital status is the most important factor. As was

the case in the analyses conducted by Even and Macpherson (1994) and Bardasi and

Jenkins (2010), this analysis also finds that characteristics matter most in explaining

private pension coverage than private pension income. However, the Blinder-Oaxaca

decompositions are not robust to sample selection; when selection corrections are

33

accounted for 80% of the gap is attributed to gender differentials in average

characteristics. Similar to quantile decompositions of state pension income, quantile

estimates of the gap in private pension income suggest that only a small proportion of

the gap is due to gender differentials in the distributions of characteristics.

The analysis undertaken in this paper has shown that historic inequalities in the labour

market outcomes of men and women manifest in the present. From its inception in

1948, the state pension system had embedded within it deeply gender-biased rules that

created disparities in men and women’s state pension outcomes. Nonetheless, the

analysis has shown that the state pension outcomes of successive cohorts of women is

the ability to build up entitlement when taking time out of the labour market for caring

will mean that future female pensioners will have better state pension outcomes than

the current cohort of female retirees. However, the gradual retrenchment of state

pensions means individuals will have to rely on private pensions to secure a retirement

income level that is in excess of the minimum standard of living maintained by the state

pension. This shift in focus implies that while private pension coverage rates among

female workers is likely to increase over time, more needs to be done in the workplace

to address the factors restricting women’s ability to secure adequate private pension

income. In particular, employers that best accommodate for family commitments by

providing childcare vouchers, job share options are likely to deliver better pension

outcomes for their female employees since gaps are driven by differentials in labour

market experience and length of working life spent in part-time employment.

34

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38

Appendix

Descriptive Statistics

Table 1: Sample means by sex and pension status

Men Women

Full

sample

With state

pension

coverage

With private

pension

coverage

Full

sample

With state

pension

coverage

With private

pension

coverage

With pension coverage - 0.98 0.877 - 0.985 0.634

Log weekly net state/private pension

income - 4.99 4.81 - 4.702 4.16

Birth cohort

Born before 1934 0.38 0.37 0.37 0.32 0.32 0.32

Born 1934-1942 0.38 0.40 0.38 0.30 0.31 0.31

Born after 1942 0.24 0.23 0.25 0.37 0.37 0.38

Highest educational qualification

No qualifications 0.40 0.40 0.36 0.51 0.51 0.42

O-level(s) 0.16 0.16 0.16 0.21 0.21 0.23

A-level(s) 0.25 0.25 0.27 0.19 0.19 0.23

Degree 0.20 0.19 0.21 0.9 0.9 0.13

Marital status

Married (including civil partnership) 0.73 0.73 0.75 0.51 0.51 0.43

Cohabiting 0.02 0.02 0.02 0.03 0.03 0.04

Single, never married 0.05 0.05 0.04 0.04 0.04 0.06

Widowed 0.14 0.14 0.13 0.32 0.32 0.37

Divorced 0.05 0.05 0.05 0.09 0.09 0.08

Separated 0.01 0.01 0.01 0.01 0.01 0.01

Weekly net state pension income of

partner given coverage £82 £82 - £145 £145 -

Work history

Years of work experience between

ages 20-60 32.9 32.9 33.2 21.6 21.6 23.5

Years worked as part-time ages 20-

60 0.48 0.48 0.46 8 8 7.4

Years worked in self-employment

ages 20-60 3.3 3.3 3 1.3 1.3 1

Log weekly net starting wage of last

job before retirement 5.2 5.2 5.2 4.6 4.6 4.8

Occupational classification

Managers and senior officials 0.17 0.17 0.18 0.7 0.7 0.8

Professional occupations 0.14 0.14 0.15 0.11 0.11 0.15

Associate professional and technical

occupations 0.12 0.12 0.13 0.10 0.10 0.12

Administrative and secretarial

occupations 0.7 0.7 0.8 0.29 0.29 0.32

Skilled trades occupations 0.19 0.19 0.18 - - -

39

Personal service occupations 0.3 0.3 0.2 0.11 0.11 0.10

Sales and customer service

occupations 0.3 0.3 0.3 0.11 0.11 0.8

Process, plant and machine

operatives 0.14 0.13 0.12 0.5 0.5 0.3

Elementary occupations 0.11 0.11 0.11 0.17 0.17 0.12

Number of observations 1,188 1,175 1,042 1,577 1,554 1,000

40

Figure 1: Kernel density estimates of the state pension income distribution

Figure 2: Kernel density estimates of the female state pension income distribution by birth cohort

0.5

11

.52

Den

sity

2 3 4 5 6 7Log Weekly Net State Pension Income

Women Men

Conditional Distribution of State Pension Income by Sex0

.51

1.5

Den

sity

2 3 4 5 6Log Weekly Net State Pension Income

Born before 1934 Born 1934-42

Born after 1942

Conditional Distribution of Female State Pension Income by Cohort

41

Figure 3: Kernel density estimates of the private pension income distribution

0.1

.2.3

.4

Den

sity

0 2 4 6 8Log Weekly Net Private Pension Income

Women Men

Conditional Distribution of Private Pension Income by Sex

42

Regression Estimates

Table 2: Probit estimates of state pension coverage for pooled sample

Pooled sample VARIABLES

Female -0.187 (0.203) Birth cohort Born before 1934 0.269* (0.155) Born 1934-1942 0.422** (0.154) Highest educational qualification O-level(s) 0.113 (0.194) A-levels(s) 0.109 (0.179) Degree -0.055 (0.220) Married -0.309** (0.149) Work history Years of work experience between ages 20-60 0.004 (0.008) Years worked as part-time ages 20-60 0.007 (0.010) Years worked in self-employment ages 20-60 -0.010 (0.008) Log weekly net starting wage of last job before retirement 0.012 (0.077) Occupational classification Managers and senior officials -0.227 (0.293) Professional occupations -0.162 (0.297) Associate professional and technical occupations -0.423 (0.262) Administrative and secretarial occupations -0.060 (0.250) Skilled trades occupations -0.017 (0.370) Personal service occupations 0.018 (0.331) Sales and customer service occupations 0.029 (0.330) Process, plant and machine operatives 0.297 (0.403) Constant 2.267*** (0.526) Pseudo R-squared 0.046 Log-likelihood -183.171 Observations 2,765

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses.

43

Table 3: OLS regression estimates of state pension income

Men Women

VARIABLES (1) (2) (3) (4)

Birth cohort

Born before 1934 0.090*** 0.123*** -0.150*** -0.217***

(0.032) (0.042) (0.027) (0.046)

Born 1934-1942 0.094*** 0.132*** -0.095*** -0.112***

(0.031) (0.043) (0.023) (0.033)

Highest educational qualification

O-level(s) 0.027 0.035 -0.000 0.028

(0.027) (0.036) (0.027) (0.041)

A-level(s) 0.015 0.028 0.023 0.008

(0.029) (0.035) (0.031) (0.041)

Degree 0.029 0.063 0.070* 0.033

(0.038) (0.049) (0.038) (0.060)

Marital status

Cohabiting 0.052 -0.030 0.341*** 0.407***

(0.053) (0.056) (0.039) (0.054)

Single, never married 0.014 - 0.345*** -

(0.061) - (0.046) -

Widowed 0.065** - 0.558*** -

(0.028) - (0.026) -

Divorced 0.027 - 0.393*** -

(0.038) - (0.033) -

Separated 0.050 - 0.251*** -

(0.061) - (0.095) -

Work history

Years of work experience between ages 20-60 0.005*** 0.004* 0.007*** 0.009***

(0.002) (0.003) (0.001) (0.002)

Years worked as part-time between ages 20-60 0.000 0.003 -0.005*** -0.009***

(0.003) (0.003) (0.001) (0.002)

Years worked in self-employment ages 20-60 -0.006*** -0.006*** -0.008*** -0.008**

(0.001) (0.001) (0.002) (0.003)

Log weekly net starting wage of last job before retirement

-0.002 -0.005 0.020* 0.025

44

(0.010) (0.012) (0.012) (0.016)

Occupational classification

Managers and senior officials 0.015 0.056 -0.006 -0.082

(0.035) (0.044) (0.044) (0.066)

Professional occupations -0.009 -0.013 -0.021 -0.026

(0.039) (0.052) (0.043) (0.065)

Associate professional and technical occupations 0.004 -0.005 0.060 0.028

(0.041) (0.050) (0.043) (0.065)

Administrative and secretarial occupations -0.066 0.010 0.030 0.007

(0.041) (0.051) (0.034) (0.051)

Skilled trades occupations 0.018 0.027 - -

(0.032) (0.040) - -

Personal service occupations -0.058 -0.039 -0.044 -0.116*

(0.050) (0.068) (0.040) (0.061)

Sales and customer service occupations 0.030 0.086 0.019 -0.041

(0.114) (0.196) (0.039) (0.060)

Process, plant and machine operatives -0.046 -0.022 0.068 0.042

(0.047) (0.070) (0.048) (0.064)

Log weekly net state pension income of partner given coverage

- 0.075* 0.130**

- (0.044) (0.061)

Constant 4.755*** 4.414*** 4.309*** 3.658***

(0.092) (0.221) (0.063) (0.321)

R-squared 0.047 0.052 0.314 0.180

Observations 1,175 794 1,554 632

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses.

45

Table 4: Blinder-Oaxaca decomposition of the gender gap in state pension income

Gap % of gap Gap % of gap VARIABLES (1) (2) (3) (4)

Predicted average log state pension income: men 4.987*** 4.976***

(0.012) (0.016)

Predicted average log state pension income: women 4.632*** 4.438***

(0.016) (0.016)

Gap 0.355*** 100 0.538*** 100

(0.019) (0.023)

Characteristics 0.087*** 25 0.140*** 26

(0.019) (0.033)

Coefficients 0.268*** 75 0.399*** 74

(0.025) (0.040)

Detailed: Characteristics

Birth cohort -0.003 -4 -0.005 -4

(0.002) (0.004)

Education 0.002 2 0.001 1

(0.003) (0.004)

Marital status -0.059*** -68 -0.002 -1

(0.006) (0.002)

Years of work experience ages 20-60 0.093*** 107 0.108*** 77

(0.012) (0.019)

Years worked as part-time ages 20-60 0.046*** 53 0.070*** 50

(0.009) (0.014)

Years worked as self-employed ages 20-60 -0.012*** -14 -0.013*** -9

(0.003) (0.003)

Log weekly net starting income of last job before

retirement

0.006 7 0.006 4

(0.004) (0.005)

Occupational classification 0.014 16 0.010 7

(0.009) (0.013)

Log weekly net state pension income of partner - - -0.036* -26

Detailed: Coefficients

46

Birth cohort 0.007*** 3 0.004 1

(0.002) (0.005)

Education -0.005 -2 0.002 1

(0.005) (0.007)

Marital status -0.103*** -38 0.061*** 15

(0.012) (0.011)

Years of work experience ages 20-60 -0.072 -27 -0.162* -41

(0.068) (0.098)

Years worked as part-time ages 20-60 -0.007 -3 0.013* 3

(0.005) (0.008)

Years worked as self-employed ages 20-60 0.001 0 0.002 1

(0.004) (0.006)

Log weekly net starting income of last job before

retirement

-0.112 -42 -0.149 -37

(0.074) (0.098)

Occupational classification 0.102*** 38 -0.009 -2

(0.019) (0.012)

Log weekly net state pension income of partner - - -0.279 -70

Constant 0.456*** 170 0.916** 230

(0.101) (0.385)

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses; Decompositions presented use women’s coefficients to weight the differential in characteristics.

47

Table 5: Probit estimates of private pension coverage

Men Women

VARIABLES (1) (2) (3) (4)

Birth cohort

Born before 1934 -0.262* 0.026 -0.114 -0.016

(0.142) (0.278) (0.097) (0.191)

Born 1934-1942 -0.035 0.008 -0.126 0.023

(0.136) (0.217) (0.088) (0.152)

Highest educational qualification

O-level(s) 0.189 0.458** 0.343*** 0.109

(0.155) (0.223) (0.092) (0.131)

A-level(s) 0.526*** 0.622*** 0.438*** 0.541***

(0.139) (0.209) (0.112) (0.171)

Degree 0.325* 0.205 0.597*** 0.197

(0.189) (0.269) (0.164) (0.226)

Marital status

Cohabiting -0.611** -0.758* 0.583*** 0.172

(0.307) (0.443) (0.222) (0.261)

Single, never married -0.001 -0.191 0.616*** 1.561***

(0.233) (0.340) (0.228) (0.451)

Widowed -0.229 -0.768*** 0.711*** 0.753***

(0.143) (0.244) (0.091) (0.150)

Divorced -0.399** -0.537* -0.127 -0.149

(0.199) (0.314) (0.125) (0.179)

Separated -0.276 0.046 -0.046 -0.057

(0.377) (0.499) (0.368) (0.472)

Work history

Years of work experience between ages 20-60 0.049*** 0.059*** 0.042*** 0.031***

(0.010) (0.017) (0.004) (0.007)

Years worked as part-time ages 20-60 -0.026 -0.070* -0.018*** -0.017***

(0.019) (0.038) (0.005) (0.006)

Years worked in self-employment ages 20-60 -0.018*** -0.019 -0.044*** -0.029**

(0.006) (0.012) (0.008) (0.014)

Log weekly net starting wage of last job before retirement

-0.037 0.017 0.103** 0.148**

(0.047) (0.068) (0.040) (0.058)

48

Occupational classification

Managers and senior officials 0.226 0.031 0.595*** 1.006***

(0.202) (0.296) (0.161) (0.241)

Professional occupations 0.273 0.416 0.914*** 1.114***

(0.241) (0.363) (0.179) (0.269)

Associate professional and technical occupations 0.449* 0.328 0.611*** 0.971***

(0.235) (0.351) (0.156) (0.222)

Administrative and secretarial occupations 0.420 0.749 0.451*** 0.893***

(0.287) (0.514) (0.109) (0.163)

Skilled trades occupations -0.162 -0.150 - -

(0.174) (0.273) - -

Personal service occupations -0.127 -0.638 0.273** 0.595***

(0.318) (0.446) (0.137) (0.211)

Sales and customer service occupations -0.694*** -0.782** -0.053 0.198

(0.259) (0.383) (0.129) (0.187)

Process, plant and machine operatives -0.298* -0.483* -0.512*** 0.054

(0.178) (0.266) (0.197) (0.267)

Firm size

2-4 - -0.444 - -0.978***

- (0.434) - (0.269)

5-19 - -0.501* - -0.602***

- (0.283) - (0.185)

20-99 - -0.773*** - -0.838***

- (0.274) - (0.220)

100-499 - -0.120 - -0.618***

- (0.404) - (0.230)

500-999 - -0.442* - -0.513***

- (0.265) - (0.179)

Constant -0.150 -0.389 -1.464*** -1.269***

(0.433) (0.752) (0.217) (0.326)

Observations 1,188 555 1,577 805

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses.

49

Table 6: Fairlie decomposition of private pension coverage

Gap % of gap Gap % of gap

VARIABLES (1) (2) (3)

Probability of private pension receipt: men 0.879 0.879 Probability of private pension receipt: women 0.637 0.637 Gap 0.242 0.242 Total explained 0.124 51 0.120 50 Birth cohort -0.004 -3 -0.005* -4 (0.003) (0.003) Highest educational qualification 0.015*** 12 0.015*** 13 (0.004) (0.004) Marital status -0.029*** -23 -0.029*** -24 (0.005) (0.005) Years of work experience between ages 20-60 0.137*** 111 0.136*** 113 (0.012) (0.012) Years worked as part-time ages 20-60 0.039*** 32 0.038*** 32 (0.010) (0.010) Years worked in self-employment ages 20-60 -0.023*** -19 -0.023*** -19 (0.004) (0.004) Log weekly net starting wage of last job before retirement 0.015*** 12 0.014** 12 (0.006) (0.006) Occupational classification -0.026*** -21 -0.026*** -22 (0.009) (0.009) Firm size - -0.000 0 - (0.001) Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses. Decompositions presented use women’s coefficients to weight the differential in the distribution of characteristics.

50

Table 7: OLS estimates of private pension income

(1) (2) VARIABLES Men Men firm

size Women Women

firm size

Birth cohort Born before 1934 -0.282*** -0.013 -0.186** -0.206 (0.078) (0.246) (0.093) (0.230) Born 1934-1942 -0.164** -0.066 0.006 -0.071 (0.075) (0.186) (0.082) (0.173) Highest educational qualification O-level(s) 0.274*** 0.539** 0.181** 0.224 (0.097) (0.214) (0.091) (0.161) A-level(s) 0.190** 0.552*** 0.311*** 0.813*** (0.089) (0.182) (0.092) (0.182) Degree 0.878*** 0.875*** 0.564*** 0.762*** (0.101) (0.249) (0.116) (0.250) Marital status Cohabiting 0.498*** -0.213 0.255* 0.376 (0.146) (0.530) (0.138) (0.278) Single, never married -0.414** -0.560 0.422*** 1.256*** (0.177) (0.400) (0.124) (0.223) Widowed -0.196** -0.880*** 0.602*** 1.175*** (0.097) (0.288) (0.088) (0.169) Divorced -0.183 -0.770** -0.037 -0.240 (0.170) (0.376) (0.135) (0.214) Separated -0.132 -0.311 -0.318 -0.584 (0.221) (0.484) (0.331) (0.568) Work history Years of work experience between ages 20-60

0.007 0.071*** 0.016*** 0.047***

(0.008) (0.020) (0.004) (0.008) Years worked as part-time ages 20-60 0.000 -0.027 -0.021*** -0.030*** (0.020) (0.047) (0.004) (0.008) Years worked in self-employment ages 20-60

-0.024*** -0.033** -0.036*** -0.052***

(0.005) (0.015) (0.010) (0.019) Log weekly net starting wage of last job before retirement

0.017 0.043 0.051 0.169**

(0.035) (0.072) (0.038) (0.068) Occupational classification Managers and senior officials 0.721*** 0.708*** 0.480*** 1.361*** (0.141) (0.267) (0.162) (0.278) Professional occupations 0.874*** 1.077*** 1.052*** 1.863*** (0.137) (0.269) (0.148) (0.286) Associate professional and technical occupations

0.848*** 0.996*** 0.761*** 1.543***

(0.139) (0.274) (0.152) (0.263) Administrative and secretarial occupations 0.725*** 1.102*** 0.444*** 1.341*** (0.158) (0.290) (0.120) (0.203) Skilled trades occupations 0.174 0.052 - - (0.135) (0.242) - - Personal service occupations 0.338 -0.238 0.151 0.708*** (0.206) (0.619) (0.149) (0.271) Sales and customer service occupations 0.359** -0.276 0.231 0.277 (0.177) (0.500) (0.166) (0.250) Process, plant and machine operatives 0.312** -0.206 0.128 0.156 (0.135) (0.275) (0.268) (0.394) Firm size

51

2-4 -0.655 -1.146*** (0.413) (0.303) 5-19 -0.415 -0.678*** (0.265) (0.219) 20-99 -0.911*** -1.140*** (0.309) (0.278) 100-499 -0.465 -0.924*** (0.295) (0.290) 500-999 -0.456** -0.509** (0.222) (0.214) Constant 3.985*** 1.491* 2.884*** 0.124 (0.350) (0.864) (0.220) (0.379) Observations 1,042 555 1,000 805 R-squared 0.298 0.269 0.254 0.378

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses.

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Table 8: Blinder-Oaxaca decomposition of the gender gap in private pension income

Gap % of gap Gap % of gap VARIABLES (1) (2) (3) (4)

Predicted average log state pension income: men 4.687*** 4.755*** (0.042) (0.085) Predicted average log state pension income: women 3.987*** 3.709*** (0.053) (0.172) Gap 0.700*** 100 1.046*** 100 (0.068) (0.192) Characteristics 0.199*** 28 0.130* 12 (0.059) (0.076) Coefficients 0.501*** 72 0.917*** 88 (0.082) (0.187) Detailed: Characteristics Birth cohort -0.012* -6 -0.014* -11 (0.007) (0.007) Highest educational qualification 0.019* 10 0.019* 15 (0.010) (0.010) Marital status 0.019 10 -0.021 -16 (0.020) (0.052) Years of work experience between ages 20-60 0.134*** 67 0.135*** 104 (0.035) (0.035) Years worked as part-time ages 20-60 0.144*** 72 0.142*** 109 (0.029) (0.029) Years worked in self-employment ages 20-60 -0.052*** -26 -0.052*** -40 (0.011) (0.011) Log weekly net starting wage of last job before retirement 0.013 7 0.012 9 (0.011) (0.011) Occupational classification -0.065** -33 -0.068** -52 (0.031) (0.030) Firm size - - -0.023*** -18 - - (0.008) Detailed: Coefficients Birth cohort -0.008*** -2 -0.009*** -1 (0.003) (0.003) Highest educational qualification -0.003 -1 -0.004 0 (0.019) (0.019) Marital status -0.219*** -44 0.366 40 (0.046) (0.231) Years of work experience between ages 20-60 -0.281 -56 -0.232 -25 (0.279) (0.282) Years worked as part-time ages 20-60 0.009 2 0.004 0 (0.019) (0.020) Years worked in self-employment ages 20-60 0.015 4 0.014 2 (0.015) (0.015) Log weekly net starting wage of last job before retirement -0.168 -34 -0.161 -18 (0.250) (0.251) Occupational classification 0.127* 25 -0.015 -2 (0.069) (0.030) Firm size - - -0.016 -2 - - (0.037) Constant 1.029*** 205 0.969** 106 (0.381) (0.391) Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses; Decompositions presented use women’s coefficients to weight

53

the differential in characteristics.

Quantile Estimates

Figure 4: Raw gender differential in state pension income by quantile conditional on coverage

Figure 5: Raw gender differential in private pension income by quantile conditional on coverage

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Table 9: Quantile estimates of state pension income for male sample without partner’s pension income

25th Median 75th VARIABLES: (1) (2) (3)

Birth cohort Born before 1934 0.072*** 0.082*** 0.111*** (0.019) (0.017) (0.021) Born 1934-1942 0.064*** 0.057*** 0.133*** (0.015) (0.017) (0.021) Highest educational qualification O-level(s) 0.010 -0.000 -0.006 (0.016) (0.022) (0.019) A-levels(s) -0.016 -0.014 0.014 (0.018) (0.019) (0.030) Degree -0.014 -0.013 -0.030 (0.027) (0.024) (0.027) Marital status Cohabiting -0.013 -0.010 0.038 (0.019) (0.025) (0.073) Single, never married 0.001 -0.017 -0.009 (0.052) (0.059) (0.024) Widowed 0.031* 0.029 0.029 (0.016) (0.024) (0.028) Divorced -0.013 0.011 -0.009 (0.031) (0.020) (0.050) Separated 0.043 0.052 0.091 (0.069) (0.042) (0.125) Work history Years of work experience between ages 20-60 0.005*** 0.004*** 0.002*** (0.002) (0.002) (0.000) Years worked as part-time ages 20-60 0.004 0.001 -0.000 (0.003) (0.002) (0.003) Years worked in self-employment ages 20-60 -0.007*** -0.008*** -0.007*** (0.000) (0.001) (0.001) Log weekly net starting wage of last job before retirement

0.003 0.004 0.007

(0.007) (0.007) (0.000) Occupational classification Managers and senior officials -0.017 0.020 0.020 (0.025) (0.025) (0.044) Professional occupations 0.008 0.035 -0.019 (0.035) (0.028) (0.043) Associate professional and technical occupations

-0.014 0.006 -0.010

(0.024) (0.025) (0.025) Administrative and secretarial occupations -0.059 -0.021 -0.076*** (0.041) (0.034) (0.024) Skilled trades occupations -0.008 0.014 -0.004 (0.025) (0.025) (0.025) Personal service occupations -0.039 -0.023 -0.087 (0.107) (0.040) (0.000) Sales and customer service occupations -0.004 0.018 0.011 (0.048) (0.061) (0.021) Process, plant and machine operatives -0.017 0.007 -0.000 (0.024) (0.021) (0.032) Constant 4.652*** 4.786*** 4.982 (0.079) (0.066) (0.000) Pseudo R-squared 0.056 0.049 0.044

55

Observations 1175 1175 1175 Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses.

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Table 10: Quantile estimates of state pension income for female sample without partner’s state pension income

25th Median 75th VARIABLES: (1) (2) (3)

Birth cohort Born before 1934 -0.132*** -0.177*** -0.121*** (0.020) (0.022) (0.027) Born 1934-1942 -0.074*** -0.145*** -0.092*** (0.015) (0.023) (0.022) Highest educational qualification

O-level(s) -0.016 -0.007 -0.008 (0.014) (0.023) (0.028) A-levels(s) 0.031* 0.090*** 0.041* (0.019) (0.029) (0.023) Degree 0.040* 0.101*** 0.031 (0.023) (0.038) (0.037) Marital status Cohabiting 0.458*** 0.359*** 0.200*** (0.045) (0.027) (0.027) Single, never married 0.470*** 0.363*** 0.271*** (0.066) (0.028) (0.039) Widowed 0.645*** 0.614*** 0.520*** (0.019) (0.023) (0.025) Divorced 0.497*** 0.411*** 0.300*** (0.012) (0.032) (0.024) Separated 0.076 0.284** 0.352*** (0.089) (0.122) (0.037) Work history Years of work experience between ages 20-60

0.005*** 0.008*** 0.007***

(0.001) (0.001) (0.001) Years worked as part-time ages 20-60

-0.003*** -0.006*** -0.005***

(0.001) (0.001) (0.001) Years worked in self-employment ages 20-60

-0.005*** -0.009*** -0.007***

(0.001) (0.001) (0.003) Log weekly net starting wage of last job before retirement

0.002 0.020** 0.024**

(0.005) (0.009) (0.010) Occupational Classification Managers and senior officials -0.033 -0.025 -0.019 (0.032) (0.047) (0.040) Professional occupations -0.017 -0.015 -0.043 (0.031) (0.039) (0.043) Associate professional and technical occupations

-0.002 -0.034 -0.008

(0.018) (0.037) (0.048) Administrative and secretarial occupations

0.038** 0.009 0.011

(0.017) (0.028) (0.036) Skilled trades occupations - - - - - - Personal service occupations -0.023 -0.043 -0.087* (0.021) (0.034) (0.046) Sales and customer service occupations

0.028 -0.015 -0.042

(0.020) (0.026) (0.048)

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Process, plant and machine operatives

0.031 0.049 -0.007

(0.026) (0.032) (0.045) Constant 4.214*** 4.332*** 4.565*** (0.030) (0.053) (0.061) Pseudo R-squared 0.294 0.257 0.202 Observations 1,554 1,554 1,554

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses.

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Table 11: Decomposition of the gender gap in state pension income using quantile regression

Total gap Effects of: Characteristics Coefficients

10th 0.606 [100%] -0.036 [-6%] 0.642 [106%] (0.032) (0.059) (0.062) 25th 0.645 [100%] -0.016 [-3%] 0.660 [103%] (0.013) (0.043) (0.042) Median 0.610 [100%] -0.015 [-3%] 0.626 [103%] (0.021) (0.044) (0.047) 75th 0.460 [100%] 0.024 [5%] 0.437 [95%] (0.024) (0.053) (0.053) 90th 0.406 [100%] 0.119 [29%] 0.286 [71%] (0.027) (0.068) (0.067)

Note: Bootstrap standard errors with 100 replications in parentheses. All pension gaps are significant at the 1% level.

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Table 12: Quantile estimates private pension income male sample without firm size

(1) (2) (3) VARIABLES: 25th Median 75th

Birth cohort Born before 1934 -0.390*** -0.177** -0.193*** (0.089) (0.069) (0.062) Born 1934-1942 -0.176** -0.100* -0.105** (0.079) (0.057) (0.053) Highest educational qualification

O-level(s) 0.218* 0.232** 0.204** (0.122) (0.094) (0.082) A-levels(s) 0.238** 0.239*** 0.208*** (0.120) (0.076) (0.048) Degree 1.034*** 0.791*** 0.628*** (0.126) (0.079) (0.085) Marital status Cohabiting 0.452** 0.322** 0.219 (0.188) (0.140) (0.258) Single, never married -0.458*** -0.358*** -0.331* (0.164) (0.125) (0.180) Widowed -0.279** -0.226** -0.160** (0.132) (0.109) (0.077) Divorced -0.287** -0.092 -0.284*** (0.139) (0.136) (0.052) Separated -0.005 -0.204** -0.489*** (0.212) (0.083) (0.069) Work history Years of work experience between ages 20-60

0.018*** 0.014** 0.005

(0.007) (0.007) (0.007) Years worked as part-time ages 20-60

-0.020** 0.002 0.028***

(0.010) (0.032) (0.010) Years worked in self-employment ages 20-60

-0.030*** -0.025*** -0.020***

(0.003) (0.006) (0.003) Log weekly net starting wage of last job before retirement

0.009 0.041 0.062***

(0.036) (0.026) (0.022) Occupational classification Managers and senior officials 0.905*** 0.684*** 0.724*** (0.221) (0.143) (0.085) Professional occupations 0.949*** 0.611*** 0.544*** (0.208) (0.140) (0.097) Associate professional and technical occupations

0.966*** 0.645*** 0.628***

(0.209) (0.146) (0.100) Administrative and secretarial occupations

0.710*** 0.545*** 0.523***

(0.245) (0.136) (0.138) Skilled trades occupations 0.105 0.058 0.073 (0.213) (0.144) (0.078) Personal service occupations 0.699 0.257 -0.008 (0.599) (0.175) (0.227) Sales and customer service occupations

0.475** 0.136 0.012

(0.207) (0.189) (0.120)

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Process, plant and machine operatives

0.370* 0.132 0.139*

(0.216) (0.148) (0.076) Constant 3.184*** 3.888*** 4.580*** (0.370) (0.299) (0.279) Pseudo R-squared 0.195 0.204 0.217 Observations 1,042 1,042 1,042

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses.

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Table 13: Quantile estimates private pension income female sample without firm size

(1) (2) (3) VARIABLES: 25th Median 75th

Birth cohort Born before 1934 -0.243* -0.251*** -0.154 (0.133) (0.093) (0.000) Born 1934-1942 -0.096 -0.053 0.115** (0.092) (0.079) (0.049) Highest educational qualification

O-level(s) 0.136 0.158 0.231*** (0.112) (0.109) (0.080) A-levels(s) 0.420*** 0.260** 0.290*** (0.117) (0.101) (0.054) Degree 0.837*** 0.418*** 0.456*** (0.148) (0.106) (0.085) Marital status Cohabiting 0.441*** 0.139* 0.082 (0.096) (0.082) (0.123) Single, never married 0.625*** 0.265** 0.247*** (0.184) (0.129) (0.063) Widowed 0.732*** 0.536*** 0.554*** (0.117) (0.088) (0.070) Divorced 0.135 0.000 -0.000 (0.192) (0.155) (0.063) Separated -0.597 -0.703 -0.337 (0.673) (0.637) (0.000) Work history Years of work experience between ages 20-60

0.016*** 0.019*** 0.017***

(0.006) (0.003) (0.000) Years worked as part-time ages 20-60

-0.022*** -0.028*** -0.025***

(0.004) (0.004) (0.004) Years worked in self-employment ages 20-60

-0.048** -0.050*** -0.036**

(0.024) (0.010) (0.015) Log weekly net starting wage of last job before retirement

0.105** 0.038 0.063

(0.045) (0.036) (0.000) Occupational classification Managers and senior officials 0.444** 0.540** 0.642*** (0.190) (0.229) (0.132) Professional occupations 1.209*** 1.177*** 0.828*** (0.126) (0.148) (0.063) Associate professional and technical occupations

0.809*** 0.900*** 0.720***

(0.152) (0.157) (0.087) Administrative and secretarial occupations

0.568*** 0.448*** 0.483***

(0.130) (0.139) (0.073) Skilled trades occupations - - - - - - Personal service occupations 0.100 0.160 0.131 (0.145) (0.187) (0.122) Sales and customer service occupations

0.119 0.485** 0.349***

(0.203) (0.224) (0.083)

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Process, plant and machine operatives

-0.009 0.318 0.169

(0.230) (0.315) (0.242) Constant 1.960*** 3.133*** 3.533 (0.297) (0.183) (0.000) Pseudo R-squared 0.166 0.179 0.157 Observations 1,000 1,000 1,000

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses.

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Table 14: Decomposition of the gender gap in private pension income (without firm size) using quantile regression

Total gap Effects of: Characteristics Coefficients

10th 0.731 [100%] 0.018 [3%] 0.713 [97%] (0.135) (0.337) (0.383) 25th 0.831 [100%] 0.130 [16%] 0.701 [84%] (0.062) (0.255) (0.269) Median 0.701 [100%] 0.022 [3%] 0.679 [97%] (0.062) (0.140) (0.151) 75th 0.583 [100%] -0.055 [-9%] 0.638 [109%] (0.054) (0.104) (0.089) 90th 0.545 [100%] -0.094 [-117%] 0.639 [117%] (0.054) (0.115) (0.081)

Note: Bootstrap standard errors with 100 replications in parentheses. All pension gaps are significant at the 1% level.

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Robustness Checks

Table 15: Estimates of Private Pension Income with Selectivity Corrections

Men Women

Log PPI Log PPI

VARIABLES

Birth cohort

Born before 1934 -0.301*** -0.167*

(0.085) (0.095)

Born 1934-1942 -0.160** 0.014

(0.080) (0.083)

Highest educational qualification

O-level(s) 0.298*** 0.170*

(0.096) (0.094)

A-level(s) 0.110*** 0.167***

(0.042) (0.046)

Degree 0.304*** 0.189***

(0.035) (0.038)

Marital status

Cohabiting 0.494** 0.285**

(0.238) (0.137)

Single, never married -0.415*** 0.420***

(0.156) (0.122)

Widowed -0.181* 0.590***

(0.096) (0.090)

Divorced -0.183 -0.076

(0.150) (0.146)

Separated -0.135 -0.289

(0.303) (0.335)

Work history

Years of work experience between ages 20-60 0.006 0.018***

(0.008) (0.004)

Years worked as part-time ages 20-60 0.001 -0.021***

65

(0.015) (0.004)

Years worked in self-employment ages 20-60 -0.026*** -0.036***

(0.004) (0.010)

Socio-economic classification

Managers and senior officials 0.702*** 0.542***

(0.127) (0.164)

Professional occupations 0.868*** 1.100***

(0.140) (0.149)

Associate professional and technical occupations 0.848*** 0.811***

(0.134) (0.154)

Administrative and secretarial occupations 0.722*** 0.505***

(0.149) (0.123)

Skilled trades occupations 0.163 -

(0.120) -

Personal service occupations 0.341 0.120

(0.221) (0.158)

Sales and customer service occupations 0.350 0.218

(0.219) (0.172)

Process, plant and machine operatives 0.317** 0.169

(0.128) (0.267)

Constant 4.073*** 3.004***

(0.317) (0.178)

ρ 0.029 0.031

(0.178) (0.056)

σ 0.988 1.044

(0.021) (0.030)

Average mills value 0.223 0.605

Log likelihood -1862.765 -2327.335

Observations 1188 1577

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses.

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Table 16: Blinder-Oaxaca + Heckman Private Pension

Gap % of gap VARIABLES (1) (2)

Predicted average log private pension income: men 4.739*** (0.038) Predicted average log private pension income: women

4.113***

(0.048) Gap 0.625*** 100 (0.062) Characteristics 0.497*** 80 (0.092) Coefficients 0.128 20 (0.103) Detailed: Characteristics Birth cohort -0.022** -4 (0.011) Highest educational qualification 0.006 1 (0.013) Marital status -0.154*** -31 (0.028) Years of work experience between ages 20-60 0.605*** 122 (0.051) Years worked as part-time ages 20-60 0.272*** 55 (0.040) Years worked in self-employment ages 20-60 -0.093*** -19 (0.017) Socio-economic classification -0.117** -24 (0.047) Size - - Detailed: Coefficients Birth cohort 0.001 1 (0.009) Highest educational qualification 0.009 7 (0.022) Marital status 0.094 73 (0.081) Years of work experience between ages 20-60 -0.818*** -639 (0.284) Years worked part-time ages 20-60 -0.117*** -91 (0.039) Years worked in self-employment ages 20-60 0.048*** 38 (0.019) Socio-economic classification 0.074 59 (0.048) Constant 0.835*** 652 (0.288)

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors in parentheses. Decompositions presented use women’s coefficients to weight the differential in characteristics.

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Table of the Existing Literature on the Gender Pension Gap

Study Country of

Analysis Methodological Approach Main Results

Even and Macpherson (1994)

United States Probit OLS Estimates from the above models are used to predict occpational pension coverage rates and benefit income levels when men and women assume the characteristics of one another.

Gender differences in observed characteristics explain 80% of the gap in occupational pension income. Around 70% of the gap in occupational pension benefit level is explained by gender differentials in characteristics.

Bardasi and Jenkins (2010)

United Kingdom

Heckman selection model. Blinder-Oaxaca decomposition of private pension income. Gomulka-Stern decomposition of private pension income receipt probabilities.

Differences in returns account for at least 80% of gap in private pension income. Differences in characteristics account for between 33% to 42% of the gap in private pension income receipt probabilities.

Hänisch and Klos (2014)

Germany OLS Blinder-Oaxaca Quantile regression Firpo decomposition

Blinder-Oaxaca decompositions show that the explained component accounts for 26% of the gap in mean pension income, with employment and education contributing most to the explained gap. The magnitude of the gender pension gap declines for increasing quantiles of the pension income distribution. Across the entire distribution, the unexplained gap is larger than the explained gap in pension income. The proportion of the gap attributed to the explained component is largest at the bottom of the distribution.