Pension claiming behavior after the Norwegian 2011 pension reform

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Pension claiming behavior after the Norwegian 2011 pension reform

Deisy Katherin Cabezas Pillaca

Master in Economics Department of Economics

Faculty of Social Sciences University of Oslo

May, 2021

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Preface

I want to thank to my supervisor Elin Halvorsen for her support and guidance during the writing process of this thesis and by making our weekly meetings interactive and friendly despite of being online due to Covid19-pandemic.

I want also to thank to Statistisk sentralbyr˚a for the data provided and the access to its digital environment for analysing the data.

A huge gratitude to my parents Sonia and Alipio, and my dear brother Miguel for his endless support to my academic and personal goals. In that sense, I want also to thank to all my relatives and friends who have always be giving me words of encourage to fulfill my academic goals.

Any remaining mistakes or omissions are solely my own responsability.

Deisy Katherin Cabezas Pillaca May, 2021

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Abstract

This thesis analyses the effect of a set of variables on the probability of early claiming. The set of variables reflects personal characteristics and circumstances individuals face at the time when they become eligible for early old-age pension claiming. In addition, the analysis also include simulations of expected longevity, the present value of pension benefit streams (P V B) and assumptions of discount rate to investigate their effects on the probability of early claiming. The method used is a logit regression, and the calculation of average marginal effects for the interpretation of the results. Moreover, the simulations are done by the microsimulation model MOSART. The main results of the thesis are that individual with certain characteristics such gender, achieved level of education, having a partner or children are more likely to claim early. Furthermore, special circumstances such a low labor income or a high debt amount also influence the decision of early claiming significantly. It is also found out that the level of discount rate for valuing P V B may affect the decision of early claiming, however not in the same importance as the variables on personal characteristics and circumstances.

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1 Introduction

The pension reform of the National Insurance Scheme in Norway in 2011 introduced a flexible pension claiming from age 62, which makes claiming decision disjoint from the retirement decision. In addition, the removal of an earnings test allows individuals to combine old-age pension income and labor income at the same time.

Following the 2011 pension reform, a significant fraction of individuals who were eligible for early old-age pension claiming decided to draw old-age pension benefits at an early age instead of age 67 which was the standard claiming age before 2011. One group decided to combine old-age pension income and labor income at the same time and another group started drawing old-age pension benefits and retired from the labor force.

The decision of early claiming is similar to an optimal annuity decision. By postponing claiming age, the gross annual old-age pension income increases. However, apart from the financial perspective of an optimal decision, old-age pension claiming might also depend on personal characteristics and circumstances individuals face at the time they become eligible for early claiming.

These characteristics and circumstances may be reflected on observable life cycle variables and on unobservable variables, such as subjective life expectancy and patience degree (time discounting).

This thesis will investigate whether claiming behavior is consistent with theoretical predictions, and whether different time discounting is important for the decision, analysing a data set consisted of eligible individuals for early claiming, from birth cohorts 1949-1953, who remain in the labor force. The estimations are done using the software Stata 16.1.

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2 Background

2.1 The National Insurance Scheme (NIS) and the 2011 Reform

Due to the demographic development and increasing life expectancy in Norway, the 2011 pension reform of NIS was designed to ensure a sustainable pension system from an economic and social perspective. Changes with regard to earning of pension rights and withdrawal of pension, made NIS a more flexible old-age pension scheme. The main changes introduced by the reform in 2011 were:

• The claiming age may be between 62 and 75 years old: The new NIS scheme allows individuals to start drawing old-age pension from the age of 62 instead of the standard claiming age of 67 as it was before 2011. The main requirement to be able to do this is that your accumulated pension rights allows you to receive a pension that at least corresponds the minimum pension level at age of 67. It is only until one meets this requirement that one will be eligible for early old-age pension.

Therefore, it is possible to be eligible at a later age than age 62.

• Introduction of actuarial adjustments and life-expectancy: From 2011 gross annual old-age pension income depends on the age one starts drawing old-age pension and the expected length of retirement. In practice this means that those who delay their claiming age will receive a higher gross annual pension income than those who claim old-age pension early. Furthermore, during the years one earns pension rights, pension entitlements are adjusted annually to the average wage growth and during the period of withdrawal of old-age pension, annual pension income is also adjusted annually to the average wage growth and additionally discounted by 0.75%

1 (Norwegian Labour and Welfare Administration - NAV, 2021d).

• Removal of the earnings test: Before the reform, the claiming decision was a joint decision of retirement from the labor market and withdrawal of old-age pension.

From 2011, the decision of claiming old-age pension benefits does not mean any more retirement from the labor force. It was possible to claim old-age pension benefits and work at the same time without getting reduced pension benefits.

• Flexible withdrawal of pension: Individuals can draw old age pension at 20, 40, 50, 60, 80 or 100% of their gross annual pension income which is calculated based on the accrual of pension rights at the time one decided to claim old-age pension benefits.

1This adjustment (average wage growth less 0.75%) aims to represent average wage-price growth.

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There has been also a change in the accrual of pension rights. From 2011 these are calculated based on pensionable income from all years from age 13 up until withdrawal age, instead of the best 20 years of pensionable income in the old scheme. Between ages 13 and 75, entitlements for old-age pensions in the new system are accumulated by 18.1 percent of annual labour incomes up to a ceiling of 7.1 times the basic pension unit (BA)². However, this new regulation regarding earning of pension rights applies fully from 1963 birth cohort, and gradually for the birth cohorts 1954-1962 (Norwegian Labour and Welfare Administration - NAV, 2021a).

The introduction of all these changes allows individuals to combine labor income and old-age pension income at the same time.

Changes in the private AFP

Due to the reform of NIS, some changes were also introduced to the Early Retirement Pension (AFP) in the private sector, aiming at a better integration with NIS. Similarly to NIS, earnings-tested was removed, actuarial adjustments were included and the earliest claiming age remains at 62. Moreover, getting pension from NIS and AFP at the same time was possible.

Regarding requirements for pension claiming, these were relaxed: From 2011, pensionable income has to exceed 1 BA at the time of claiming, and requirements of 10 years with a pensionable income higher than 1 BA and an average of income in the last ten years higher than 2 BA were removed.

On the public AFP, there were not introduced changes as it was in the private AFP. It was subject to earnings-test and it didn’t allow to combine AFP income with old-age pension income from NIS. For a complete description of the Norwegian pension system, see Appendix A).

To conclude, following the pension reform of 2011, NIS became a more flexible scheme and private AFP adapted to it. However, the public AFP remained unchanged.

2Norwegian Labour and Welfare Administration (NAV) uses BA to calculate social security payments of social. Annual BA is 101 351 kr. by 01.05.2020. It is adjusted on 1 May each year.

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2.2 Evolution of early old-age pension claiming

From 2011, all individuals who are at least 62 years old and who meet the requirements for old-pension claiming, are eligible for early claiming. On the left-vertical axis of figure 1, it is shown the share of private sector workers who remain in labor force that claimed old-pension benefits early, at different claiming ages in each year (bottom-horizontal axis).

The shares of early claimers decreases over the year after being over 25% on 2011, except for early claimers at age 62. Moreover, on the second vertical axis it is shown the evolution

Figure 1

Share of early claimers in private sector by claiming age and evolution of 62-year-old early claimers by cohort

Source: Statistics Norway, author’s own calculations

of 62-year-old early claimers which has increased after 2011 and remains over 30% in 2017.

On the top-horizontal axis, it is shown the birth cohorts to these groups of 62-year-old early claimers.

Regarding the 1949 birth cohort, Normann (2020) made a study of this cohort over the period 2012 - 2018, mainly on those who remain in the labor market. He analyses the characteristics and trends in working activity and pension withdrawal of those who receive old-age pensions and remains in the labor market (group 1) and those who remains in the

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labor market without withdrawing old-age pensions (group 2).

In group 1 men constitute almost 80% and women 20%. While in group 2, 44% are men and 56% are women. Moreover, no more than 15% of individuals in both group have basic school as the highest achieved education level. The representation of those with higher education is 26% in group 1, while 43% in group 2. Other important characteristic is the size of the household for each individual. In 2012, most of them live with someone else, and the tendency remains until 2018. The status of each individual in 2012 with respect to his working activity and withdrawal of pension may change over time. Therefore Normann (2020) follows both groups until 2018.

The share of individuals who remains in the labor market is decreasing in both groups.

However at the age of 69 in 2018, 26% of group 1 and 23% of group 2 still remains in the labor market. On pension withdrawal, all individuals from group 1 keep withdrawing old-age pension benefits during the period, except in 2015 when some stopped pension withdrawals. In case of group 2, as they get older they started claiming pension benefits and it is in 2016 when they turned 67 years old that almost 87% of group 2 were withdrawing old-age pension benefits.

In the next section, we are going to present the theoretical framework for our analysis.

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3 Literature review

Previous studies has analysed the relationship between early claiming and personal characteristics, as well as circumstances individuals face at the moment they are eligible for early claiming. Moreover, to determine if early claiming decision is optimal, previous research have focus on the effect of claiming age on the maximisation of the lifetime expected present value of pension benefits. Hence, we present two lines of research from the early claiming literature:

3.1 Characteristics of early claimers

Munnell et al. (2016) examine the previous studies and concludes that early claimers seem to be less well off than late claimers on an overall basis. However,the population of early claimers is heterogeneous, some individuals with a good health status and more financial resources and others with a poor health status and few financial resources.

In their research, Munnell et al. (2016) observe that a higher fraction of early claimers has no college education and perform blue collar jobs in comparison to late claimers. However, the fraction of individuals with fair or poor health is quite similar in both groups. The same occurs with their respective representations in the top quartile of wealth. With respect to DB pension³, early claimers are better off than late claimers.

This descriptive analysis suggests the possibility of heterogeneity in the population of early claimers⁴. In line with this, Munnell et al. (2016) identifies two groups within the population of early claimers: the disadvantaged group and the advantaged group. The former is more likely to have more physically-demanding jobs, performs blue collar jobs, no collage education and a low representation in the top quartile of wealth. On the other hand, only a small fraction of the advantaged group performs physically-demanding and blue collar jobs.

A third of this group has no college, while this represents 90% in the disadvantaged group. Almost 42% of the group is at the top quartile of wealth, while only 4.3% in the disadvantaged group. In case of health status, there is not a predominant representation in neither group.

3It is a type of pension scheme which allows claimers to be well prepared for early claiming, with respect to financial resources.

4Munnell et al. (2016) performed a Latent Class Analysis.

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Huffman et al. (2019) studied the heterogeneity of discount rates among elderly pop- ulations and if this may explain important life outcomes such as net wealth, healthy behaviors, provision for end-of-life challenges and retirement behavior with regard to claiming age. They make a regression analysis to analyse the association of claiming age and discount rate, finding that claiming ages are not significantly related to the discount rate,even with the inclusion of controls. However, claiming age is positively and significantly related to age and education years, and negatively and significantly related to poor health. However, these effects on claiming ages are marginal.

The variables used as personal characteristics are health status, financial resources (including income and net wealth), years of education, type of jobs, gender and marital status.

3.2 Claiming ages and maximization of pension benefits

Alleva (2016) remarks that the claiming decision with respect to claiming age, will depend on many factors including personal characteristics, circumstances and retirement goals.

He describes the optimal claiming age (OCA) as the claiming age that maximizes the lifetime present value of pension benefits (PVB), considering a survival function. To select the OCA for a given individual, computing the PVB requires two input criteria:

his expected longevity and a discount rate. Alleva (2016) focuses on the latter, hence analysing the sensitivity of the claiming age to discount rate.

He argues that many previous studies use a single discount rate⁵, preventing individuals to value their streams of pension benefits correctly, and choosing the OCA. Instead, he uses five discount rate categories, each of them refers to an investment allocation⁶, being the more conservative with lower rates and the more aggressive with higher rates. Low return rates refers to an investment portfolio of cash and bonds, while high return rates refers to an investment portfolio of stocks and higher risky assets.

From an ex post perspective (with a given age at death), M CA is the claiming age that would have maximized the lifetime pension benefits. He found that both exact ages 62 and 70 dominate the probabilities of being the M CA⁷.

5Previous studies of the claiming decision in USA, takes as the discount rate the yield on long-term inflation-indexed government bonds (Alleva, 2016).

6The five categories of rates (r) are: r <2.4, 2.4< r <4.6,r≈4.6, 4.6< r <6.7,r >6.7, all values in percentage.

7For men: There is 36% that the M CAwill be 62, 20% that it will be 70 and 44% that the age will be in between. For women: There is 27% that theM CAwill be 62, 30% that it will be 70 and 43% that the age will be in between.

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Furthermore, he observes that individuals with higher discount rate, has earlier claiming ages and lower risk of failing in the maximisation of the P V B. On the other hand, individuals with lower discount rate, has later claiming ages and higher risk of failing in the maximisation of the P V B. The risk of failing is associated with the survival function for a given individual. His findings are that the OCA of 62 will give the highest P V B for either gender and death age⁸, with a discount rate from 7.5%. This means that there is zero risk of failing in the maximisation of the P V B. For a discount rate higher than 3.8% for men and higher than 4.6% for women, OCA would be 62, associated with a risk that falls as the discount rate increases until it reaches 7.5%.

8The maximal age at death is 120 in the model.

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4 Theoretical Framework and Methodology

4.1 Annuity decision

This section illustrates the incentives for postponing old-age pension claiming in the re- formed pension system by presenting results from a dynamic microsimulation model called MOSART (Andreassen et al. (2020)).

The pension reform of 2011 introduced flexible old-age pension claiming between age 62 and 75, but with an actuarial adjustment of annual pension benefits according to the expected length of retirement (called “delingstall” in Norwegian). In practice this means that those who delay their claiming age will receive a higher grossannual old-age pension income than those who claim early.

With the new system annual pension benefits for every cohort, based on entitlements from both the old and the new system, depend on claiming age (P) and remaining life- expectancy (T). Annual benefits for a person from cohortcbased on accumulated entitlements at age s starting withdrawal at age P may be calculated by dividing accumulated entitlements with divisors mainly reflecting remaining life-expectancy from ageP according to:

P B_s,c,P = P W_s

∆_c,P (1)

where P B_s,c,P is the average annual pension for a person from cohort c, based on accumulated pension wealth P W_s at age s and starting withdrawal at age P ≥ s and ∆ is the adjustment factor for a person from cohort c starting withdrawal at age P. Cohort- specific life expectancy is assumed to be gender neutral. When calculating adjustment factors it is also taken into account that payments of old-age pension benefits are indexed with wage growth minus 0.75 per cent each year after withdrawal has started (described in subsection 2.1).

However, despite the actuarial adjustment there are factors that also cause a financial benefit to total wealth from postponing pension claiming. A delay may increase total old- age pension wealth through two channels. First, accumulated pension wealth are indexed by nominal wage growth. Second, postponement may give rise to additional accumulation, depending on the level of earnings ⁹.

9As described in Section 2.1, individuals born before 1954 were under the old accrual system that was

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In order to get an estimate of the gain from postponing pension claiming on total pension a microsimulation model is useful. Here the dynamic microsimulation model MOSART is used, which includes administrative work and income history for all individuals (see Section 5 for a more detailed description of the data). Using the model, it is possible to calculate the expected present discounted value for future benefit streams under different assumptions about claiming age.

Using the microsimulation model MOSART, we can compute the present value of pension benefits streams (P V B) of an individual’s alternative choices with respect to claiming age. Note that in this case the model used disregarding the individual’s actual choice to compute hypothetical alternative choices.¹⁰ Later these counterfactual present values will be used to explain actual choices. The calculation of P V B by MOSART is similar to equation 2:

P V B =

T

X

s=P

[P r(s)( 1

1 +r)^s−P]P B_s(P) (2)

Where P B is old-age pension income at age sfor an individual with claiming age P, T is life expectancy, r is the net discount rate and P r(s) is the survival probability at ages.

In the baseline case r is assumed to be equal to 1 percent. Since r is a net discount rate (interest rate minus wage growth) this can be thought of as assuming a real interest rate of 2 percent if the real interest rate is 1 percentage point higher than the wage growth, and the long term wage growth is assumed to be 1 percent.

Following Brinch et al. (2018), we compute the Relative Money’s worth (RM W), which is the ratio of P V B at different claiming ages. To investigate the gain from postponing claiming age, this is calculated as P V B when claiming at age 63 to 67 relative to when the claiming age is 62 to see if an individual gets a gain or a loss for his or her claiming decision against the other alternative.

RM W_P,62= P V B_P

P V B₆₂ for P = 63, ...,67 (3) When the RM W is higher than one, it is a gain to claim later, whereas it is a loss when the ratio is lower than one.

based on the 20 best years of earnings.

10In practice, this means that historical data and choices are used until age 61, and simulated choices from age 62 and later.

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Table 1 shows the results of calculating of RM W_P,62 for individuals who were eligible for early claiming at age 62, born in 1949-1953. Dividing individuals into groups of men and women, we see that both groups get a gain in RM W by delaying claiming 5 years, i.e.

until age 67.

However, women gain from postponing from age 64. Thereafter, we see that the gain gap between both groups increases from age 64. Moreover, we select men and women by their life expectancy with regard to median (gender-specific) life expectancy. Men and women who have higher life expectancy than median get gain from postponing claiming age. While those with lower life expectancy get losses from postponing claiming age.

Finally, we calculateRM W_P,62at one percentage point higher and lower than the baseline net discount rate of 1 percent. The results shows that with a lower discount rate both groups get gains by delaying their claiming decision at least by one year for women and two years for men. In the case of higher discount rate, men get a loss even if they postpone claiming decision until age 67 and women get a gain only if they postpone at least by four years.

Overall, table 1 shows that women get more gains from postponing claiming age, except when they have a low life expectancy. Life expectancy refers to the expected length of life and is estimated by MOSART based on observed mortality for the entire Norwegian population and accounts for socioeconomic characteristics, more details in subsection 5.3 Variables.

4.2 Life-cycle theory

When the national insurance system is approximately actuarially fair, the decision of when to claim is based on economic theory, i.e. the choice that maximizes lifetime utility.

The main theory for understanding household inter-temporal optimization is the life-cycle model. Attributed to Franco Modigliani, the life-cycle theory states that individuals save in times when income is higher than optimal consumption and borrow when income is lower than optimal consumption, seeking to smooth consumption over their lifetimes.

Although I will not estimate a structural form life cycle model, I will use a life cycle model to guide the empirical specification and the variables included. Similar to the framework developed by Chan and Stevens (2004), we can construct a mathematical framework for the maximization of utility for the remaining lifetime from the age an individual is qualified for old-age pension claiming.

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Table 1: Relative Money’s Worth (RMW)

Postponing from age 62 until:

Age 63 Age 64 Age 65 Age 66 Age 67

Men 1.00 1.00 1.00 1.01 1.02

Women 1.00 1.01 1.02 1.03 1.04

Low life expectancy (1)

Men 0.98 0.98 0.97 0.97 0.97

Women 0.99 0.99 0.99 0.99 0.99

High life expectancy (2)

Men 1.01 1.02 1.04 1.05 1.07

Women 1.02 1.03 1.05 1.07 1.08

Lower discount rate (3)

Men 1.00 1.01 1.02 1.03 1.05

Women 1.01 1.02 1.04 1.05 1.07

Higher discount rate (4)

Men 0.99 0.99 0.99 0.99 0.99

Women 1.00 1.00 1.00 1.01 1.01

Assumptions: (1) Lower than gender median, (2) Higher than gender median, (3) One percentage point lower than baseline, (4) One percentage point higher than baseline.

Based on all individuals eligible for early claiming at age 62, born from 1949 to 1953.

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Our purpose with this is to include some extra variables that are relevant for individuals at the time of old-age pension claiming decision, instead of evaluating a lifetime utility.

Individuals might decide their claiming age based on the current value of variables and the expectation of some other variables. Equation 4 represents the utility function:

U_t=

T

X

s=a

β^s−au(C_s;θ) (4)

It depends on consumption C, which depends on having a living partner (θ), the time preference rateβ, and expected longevityT. Moreover,sis the time period that is referred to as ages and a is the age of eligibility for old-age pension that is the start point for our mathematical framework. β is the discount factor, where β = 1/(1 + ρ) and ρ is the time preference rate that reflects the degree of impatience for consuming today. Higher impatience (↑ ρ) will result in lower β (β < 1), implying that the marginal utility of consumption today is higher than the marginal utility of consumption tomorrow, shown by the intertemporal Euler equation:

u⁰(C_a) =β(1 +r)u⁰(C_a+1) (5)

With the introduction of actuarial adjustments, those who claim later will receive higher old-age pension income than those who claim early. Following Brinch et al. (2018), the decision of delaying pension claiming is equivalent to buying an annuity since future old- age pension income will be higher. In this context, we can construct an similar Euler equation, comparing the marginal utility of consumption by drawing old-age pension at the age of eligibility a with an older claiming age a+ 1.

The real interest rate (r) is a given parameter for individuals when they face the decision of claiming, however the discount factor (β) is subjective factor in the equation. The degree of impatience for getting old-age pension benefits may vary across individuals.

The utility function is subject to a budget constraint at age s:

T

X

s=a

( 1

1 +r)^s−aC_s=A_a+

T

X

s=a

( 1

1 +r)^s−a[Y L_s+P B_s(P)] (6) where A is wealth, Y L annual labour income, and P B is annual pension benefits that

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depends on the age of claiming P.

The left-hand side of the budget constraint is the sum of the present value of consumption at each age, valued at the age of eligibilitya. The right-hand side includes the accumulation of net assets (assets minus debts) until the age of eligibility (A_a) and present value of the sum of labor income (Y L), old-age pension income (P B) that depends on the timing of pension claiming (P) (Actuarial adjustments explained in Subsection 2.1).

Then, inserting equation 6 in equation 4 for C_s and R = _1+r¹ , we get the maximization problem of utility:

maxU_a(P) =

T

X

s=a

β^s−au_C(A_a+R^s−a[Y L_s+P B_s(P)]

R^s−a ;θ) (7)

The maximization of utility depends on the accumulation of net assets (A), labor income (Y L), old-age pension income (P B), and having a living partner (θ). Due to actuarial adjustments, old-age pension incomeP B_sis a function of the timing of claiming, henceforth, claiming age P. In the empirical model, we are going to analyse the relationship of a set of explanatory variables such as the ones above mentioned and personal characteristics on the decision of early claiming.

This model, mainly based on equations 1, 2 and 7 has some theoretical implications that we are going to investigate with an empirical analysis. Equation 1 and 2 shows that higher subjective survival probability increases expected RM W_P,62 which may lead to postponement of claiming decision. Since on average women have higher life expectancy, they are more likely to postpone than men due to gains in RM W_P,62, as we have seen in table 1.

Equation 7 shows that financial resources for an individual at his age of eligibility may come from accumulation of net assets, labor income and old-pension income. An individual who have negative net assets and even low labor income is more likely to start drawing old-age pension benefits at an early age.

We have assumed that the utility of consumption is dependent on having a living partner for individuals in their 60s. It is likely that for example expenditures on travels and other recreations has a higher utility when there is someone to share it with. In that sense, those with a partner are more likely to claim early.

Moreover, those with a high time preference rate (lowβ) will be more likely to claim early

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because they have a preference for higher consumption today than tomorrow according to the Euler equation, however, we are not going to estimate the effect of time preference rate.

Although not shown in the model above, the framework could have been extended to include an altruistic bequest motive, where consumption of offspring is also included in the utility function. In this case the existence of children would also led to a preference for early claiming, since parents might wish to transfer resources to their children also during retirement. Therefore, apart from including variables on financial status, marital status and gender in the empirical model, we are going to include some other variables such as having children.

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5 Methodology, Data, and Variables

5.1 Methodology

For a better understanding of the methodology and the rest of sections in this thesis, we define some concepts according to the characteristics of the pension system in Norway at present and how our empirical analysis will be structured:

• Age of eligibility: The age at which individuals become eligible for old-age pension claiming.

• Claiming age: The age at which individuals start drawing old-age pension from NIS.

• Early claimers: Individuals who start drawing old-age pension at an early age (between 62 and 66).

• Late claimers: Individuals who start drawing old-age pension from the standard age of 67 and older.

Following the life-cycle model and its adaptation in the previous subsection 4.2, individuals would select a claiming age that maximizes the expected discounted lifetime pension benefits. In a pension system that allows to choose the claiming age, individuals face an important decision problem that has effect on their economic status for the rest of their life.

On a first stage, we identify early claimers and late claimers in our sample to do a counterfactual analysis. The objective is to identify which variables are statistically significant on the probability of being or not an early claimer. Therefore, we try to find the relationship and effect of a set of explanatory variables on the probability of being an early claimer.

Using a Linear OLS RegressionY =β₀+β₁X_1i+· · ·+β_kX_ki+u_i will make the interpretation of coefficient estimates easy but the predicted probability can be below 0 or above 1. To address this problem, we will use a logit regression, using the cumulative standard logistic distribution function (Stock & Watson, 2015):

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P r(Y_i = 1) =F(Z) = 1 1 +e^−z with Z =β0 +β1X1i+· · ·+βkXki+ui

and 0≤F(Z)≤1

Then, the predicted probabilities are between 0 and 1. To estimate the coefficients, logit model uses the method Maximum Likelihood Estimation (MLE). By this method, the estimators ( ˆβ₀,βˆ₁, . . . ,βˆ_k) are the value of the coefficients that maximize the likelihood function which is the joint probability of the data. Moreover, the interpretation of these coefficients is not the same as the ones in the linear regression model. In the logit model, the predicted change in the probability of being an early claimer (Y_i = 1) will depend on the value the variable X_i takes.

To interpret the results of the logit regression model, we’ll calculate the average marginal effect (AME) of each explanatory variable on the probability of being an early claimer.

Moreover, due to actuarial adjustment in the calculation of pension benefits, the annual gross payment of pension benefits increases by delaying the claiming age. However the period of years is unknown due to the uncertainty of the age at death. Brinch et al. (2018) explains that “a delay in claiming is equivalent to buying an annuity: current pension benefits are given up in return for a higher future income stream”. Therefore, estimating expected longevity and the expected present value of pension benefits (P V B) at different claiming ages let us calculateRM W_P,a at claiming ageP with respect to age of eligibility a.

Brinch et al. (2018) shows that the correlation between RM W and expected longevity is 0.96, where most of values of RM W are around 1.0. In that sense, we assume values of RM W lower than 0.8 as outliers and remove them from our data set.

Following this, we find the correlation between RM W_67,62 and expected longevity in our data set at each net discount rate and then start running the logit regression model.

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Figure 2: RMW and expected longevity

Source: Reprinted from Brinch et al. (2018, Figure 2(a), p.992).

5.2 Data description

The data set used in our analysis is based on register data from Statistics Norway and simulated data by the MOSART model. The data set consist of actual observations until year 2018 and simulated observations at the year the individual is expected to die, according to his expected longevity.

The register data from Statistics Norway are assembled based on annual tax records as well as other registers, such as the one administered by the Norwegian Labor and Welfare Administration (NAV) and it covers the entire population in Norway. These data are of high quality because most information is third-party reported and very little is self- reported.

The simulated data refers to the present value of pension benefits streams (P V B) at different claiming ages and expected longevities. These simulations are made by the MOSART model. MOSART is a dynamic microsimulation model for education, labor supply and pensions, see Andreassen et al. (2020). It utilizes administrative data registers available in Statistics Norway, covering the entire population back to the 1960s.

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Its main use is for calculating pension accrual rights and future pension benefits, based on labor income history and other relevant characteristics according to the structure of the pension system. At present MOSART includes old-age pensions, disability pensions and surviving spouse pensions in the National Insurance Scheme (NIS), early retirement benefits in both the private and the public sector, and occupational pensions in the public sector.

We start with a big panel data set with year observations for each individual registered in the National Population Register (NPR) in Norway until 2018, then we filter it according to the methodology and empirical model of the analysis in this thesis.

First, we select which birth cohorts will be included in our analysis. Following Brinch et al. (2018), our analysis is focused on the cohorts who were only exposed to early claiming decision under the new regulations, those who turned 62 years old from 2011, henceforth, birth cohorts from 1949 and later¹¹. However, we need to limit the number of birth cohorts used in our analysis since we are going to identify early claimers at different claiming ages between 62 and 66, and late claimers those who claim old-age pension at age 67.

According to Figure 1 in Subsection 2.2, individuals who claim old-age pension early take this decision at different claiming ages. In that sense we take in birth cohorts that might have individuals with claiming age between 62 and 65. The 1949 - 1951 birth cohorts turn 67 in 2018, so we can identify early claimers at different claiming ages andlate claimers, those who claim at age 67.

Moreover, we have also decided to include 1952 and 1953 birth cohorts although they turn 66 and 65 respectively in 2018, aiming for a bigger data set. In that sense the group of late claimers will be based on cohorts 1949 - 1951, while the group of early claimers will be based on cohorts 1949 - 1953. Table 2 shows the birth cohort in our data set and the highest age individuals turn in year 2018.

The data set only includes individuals who were resident in Norway at the year they become eligible for claiming of NIS old-age pension since individuals who are resident abroad face additional regulations (Norwegian Labour and Welfare Administration - NAV, 2021b).

As it is explained in Section 2, Private AFP was adapted to the new NIS and become more flexible, while Public AFP remained unchanged. Public AFP pensions were earnings tested and couldn’t been claimed together with pension from NIS.

11Cohorts 1945-1948, those who turned 65-62 years old before 2011, were exposed first to the AFP scheme under the old regulation.

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Table 2: Birth cohorts and claiming ages by 2018 Birth cohorts

1949 1950 1951 1952 1953

2011 62

2012 63 62

2013 64 63 62

2014 65 64 63 62

2015 66 65 64 63 62

2016 67 66 65 64 63

2017 68 67 66 65 64

2018 69 68 67 66 65

In that sense, for public sector workers, it was more beneficial to withdraw pensions benefits from the AFP between 62 and 67 years old and retire from labor force. Therefore, following the criteria on decoupling claiming decision and retirement, the data set include only private sector workers.

Moreover, individuals with a permanently reduced earning capacity due to illness or injury, are also excluded since they face additional regulations with regard to disability benefits and old-age pension (Norwegian Labour and Welfare Administration - NAV, 2021c).

In the new flexible NIS and private AFP scheme in Norway, pension claiming and retirement decisions aren’t joint any more. Therefore, our sample consists of individuals who remained in the labor force, excluding those who claimed pension early and retired from the labor force since they face an additional decision with regard to retirement.

Finally, our sample includes individuals from cohorts 1949 - 1953, who were eligible for NIS old-pension claiming, were registered as working in the private sector, remained in the labor force and were not receiving disability pension at their ages of eligibility, in total 68,348 individuals.

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5.3 Variables

The list of selected variables for our empirical analysis is:

• Early claimer:

It is a dummy variable to identify individuals who claim old-age pension before the standard retirement age of 67 and who remain in the labor force. The other individuals who become eligible for old-age pension at any age between 62 and 66 but start drawing old-pension at age 67 or older are defined as late claimers.

• Age of eligibility:

It is the age when an individual become eligible for claiming old-age pension from NIS. The eligibility is given by NAV, according to the requirements for early claiming (Subsection 2.1). The age of eligibility goes from 62 to 66 in the data set.

• Claiming age:

It is the age when an individual starts drawing old-age pension from NIS. The age of claiming goes from 62 to 69 in our data set.

• Birth year:

It shown the selected birth cohorts, from 1949 to 1953.

• Female:

It is a dummy variable for gender.

• Couple:

It is a dummy variable for whether an individual has a living partner. It is gener- ated from the cohabitation status of individuals. It includes both married couples, cohabitant couples and same-sex couples.

• Children:

It is a dummy variable for whether an individual has children.

• Level of education:

It is a categorical variable based on the highest completed education level for each individual.

• AFP affiliation:

It is a dummy for whether an individual work in a firm from the private sector that has an AFP scheme. For almost 15% of individuals, there wasn’t registration on AFP affiliation at their age of eligibility. We decided to refer to them as private sector workers without AFP affiliation.

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• Labor income:

This variable includes wages and social benefits such as paid sick leave, unemploy- ment benefits, parental leave benefits for a given year. These social benefits also count as pensionable income by NAV. This variable is measured in Norwegian kroner (NOK) and has been transformed into four categories using quartiles, being Q1 the group with lowest labor income and Q4 the group with highest labor income.

The transformation has been done according to values on the year before the age of eligibility.

• Household financial wealth:

It is based on savings in bank accounts for the household the individual is registered in. This information comes from the tax records registered by The Norwegian Tax Administration (Skatteetaten). It is measured in Norwegian kroner (NOK) and has been transformed into four categories using quartiles. The transformation has been done according to values on the year before the age of eligibility.

• Housing wealth:

It is the tax value for the household’s housing the individual is registered in. This information is registered by Skatteetaten. It is measured in Norwegian kroner (NOK) and has been transformed into four categories using quartiles. The transformation has been done according to values on the year before the age of eligibility.

• Household debt:

It is the amount of debt, measured in Norwegian kroner (NOK), the individual’s household has. This information comes from the tax records. It has been transformed into four categories using quartiles. The transformation has been done according to values on the year before the age of eligibility.

• Expected longevity: It the simulated length of life from MOSART. The estimates of expected longevity is based on observed mortality for the entire Norwegian population over the years 2001 - 2010 and account for socioeconomic characteristics such gender, age, residence place (county), achieved education level, disability history, family status (single, married/cohabiting, with or without children) and also longevity of parents, making it a more sophisticated modelling of survival probabilities than official life tables. Therefore, the basis for expected longevity is a logit model for mortality probabilities. See Brinch et al. (2018) for more details.

In general, MOSART use the entire population or a large sample to obtain expectation values. However, Brinch et al. (2018) used the model in a different way to obtain expectation values at individual level, simulating expected longevity with repeated draws for each individual. This is computationally demanding with regard

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to time. Therefore, we are going to run one draw for each individual to estimates expected longevities for each individual.

• Net discount rate: The discount rate used to calculate present value of future pension benefits. The discount rate can be chosen freely in MOSART model simulations, but since pensions are indexed by wage growth (or the growth in BA to be precise) it is only necessary to determine the net discount rate. A net discount rate is equal to interest rate minus wage growth. In the baseline case a net discount rate of 1 percent is chosen. This can be thought of as equal to assuming a real interest rate of 2 percent if the real interest rate is 1 percentage point higher than the real wage growth, and the long term real wage growth is assumed to be 1 percent. Two alternatives are considered: a net discount rate equal to 0 (i.e. one percentage point lower than baseline) and a net discount rate equal to 2 (i.e. one percentage point higher than baseline).

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6 Descriptive Statistics

Table 3 shows that the data set consists of 83,173 individuals in the private sector by age of eligibility for NIS old-age pension. Our analysis is focus on individuals who had the option of being an early claimer or a late claimer, those with age of eligibility between 62 and 66. Most of individuals in the selected cohorts become eligible at age 62 (87.3%).

Table 3: Eligible individuals for NIS old-age pension Age of eligibility Nr. of individuals Percentage

62 72,637 87.33

63 4,505 5.42

64 3,004 3.61

65 1,959 2.36

66 1,068 1.28

Total 83,173 100.00

As we mentioned these individuals work in the private sector, where not all firms have an AFP scheme. Table 4 shows that almost half of eligible individuals in a firm that has an AFP scheme.

Table 4: AFP affiliation status of eligible individuals Nr. of individuals Percentage

With AFP affiliation 38,594 46.40

Without AFP affiliation 44,579 53.60

Total 83,173 100.00

Note: The status of AFP affiliation is at the age of eligibility for early pension claiming.

Since we are considering five birth cohorts and we have observations until year 2018, we can present the distribution of early claimers by different ages of eligibility and claiming ages in table 5.

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Table 5: Distribution of early claimers by age of eligibility and claiming age Age og eligibility

Claiming age 62 63 64 65 66 Total

62 35,912 0 0 0 0 35,912 (59.80)

63 10,908 1,702 0 0 0 12,610 (21.00)

64 3,996 402 1,100 0 0 5,498 (9.16)

65 2,763 326 252 757 0 4,098 (6.82)

66 1,252 145 107 78 349 1,931 (3.22)

Total 54,831 2,575 1,459 835 349 60,049 (91.31) (4.29) (2.43) (1.39) (0.58)

Note: The percentages of the total number of early claimers are given in parenthesis.

It is shown that almost 60% of early claimers took the decision at the age of 62, however fractions of early claimers for older claiming ages fall significantly. This is consistent with table 3 where we can see that around 87% of individuals in the data set become eligible at the age of 62. Moreover, 54,831 early claimers with age of eligibility 62 represent almost 75% from the number of eligible individuals at age 62 from table 3.

Table 6 shows the number of late claimers, those who claim old-age pension from age 67 and older. These groups consist of individuals born between 1949 and 1951, since we have observations until year 2018.

Table 6: Distribution of late claimers by age of eligibility Age of eligibility

62 63 64 65 66 Total

6,494 798 698 508 490 8,988

(72.25) (8.88) (7.77) (5.65) (5.45)

Note: The percentages of the total number of late claimers are given in parenthesis.

Finally table 7 shows the summary statistics for the samples we are going to use in the empirical analysis.

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Table 7: Summary statistics

Variables Sample 1 Sample 2 Sample 3

Early claimer 0.49 0.85 0.38

Female 0.19 0.17 0.69

Couple 0.80 0.81 0.74

Children 0.90 0.91 0.91

Birth cohort

1949 0.20 0.18 0.19

1950 0.20 0.22 0.20

1951 0.20 0.22 0.19

1952 0.20 0.19 0.20

1953 0.20 0.19 0.22

Education level

Primary and lower secondary 0.44 0.47 0.62

Upper secondary 0.32 0.33 0.23

Higher educ. - short 0.14 0.12 0.11

Higher educ. - long 0.10 0.07 0.04

AFP affiliation 0.49 0.53 0.29

N 72,637 42,406 9,468

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7 Empirical Model and Results

Table 8 shows a strong positive association between RM W_67,62 and expected longevity in our data set at each net discount rate. For higher net discount rate, there is higher correlation, although the increase is not large.

Table 8: Correlation between RMW and longevity Net discount rate Longevity

RM W 67 62 0% 0.9211

RM W 67 62 1% 0.9262

RM W 67 62 2% 0.9266

Notes: 1)RM W 67 62 refers to the ratio ofP V Bat the claiming age of 67 and P V B at the claiming age of 62.

2) The sample consists of individuals who are eligible at age 62, born from 1949 to 1953.

Furthermore, since expected longevity is simulated and determined by many of the same explanatory variables that are in the empirical model for the early claiming decision, expected longevity is highly correlated with other variables (See Expected longevity in subsection 5.3). This is easily seen by first running a simple logit regression model:

Y_i =β₀+β₁T_i+u_i (8) Where Y is a dummy variable for early claiming andT is expected longevity. The sample used in this simple regression includes individuals who are eligible at age 62, born from 1949 to 1953. The coefficient of the regression is -0.0056 which is highly statistically significant at 0.1%, showing a negative relationship. Next, we compare results with a logit regression model that includes both expected longevity and all potentially correlated explanatory variables:

Y_i =β₀+β₁T_i+β₂X_i⁰+u_i (9) Here X⁰ is a vector of observable characteristics, including dummies for gender, having a partner, having children, AFP affiliation, and categorical variables like achieved education level and quartile level for labor income, savings, housing’s tax value and debt amount.

After running the logit regression, we get that the coefficient is -0.0021, still showing a negative relationship but less statistically significant at 5%. These results are shown in Table B in Appendix B.

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This thesis does not aim to estimate the causal effect of longevity on retirement (which is complicated by the correlation between economic status and mortality). For this we refer to the study by Brinch et al (2018) who use parental longevity as a proxy for subjective individual independent mortality risk, and find a causal and negative effect of subjective expected longevity on early claiming.

Instead, we take the negative relationship between expected longevity andRM W as given, and seek to control for the part of RM W that is driven by longevity. Based on figure 2 and table 8, this relationship is assumed to be linear so that we run an OLS regression of RM W 67 62 and expected longevityT and instead of calculating predicted RM W 67 62, we use the residuals from this regression in the following analysis.

RM W_i =β₀+β₁T_i+u_i (10) We decided to use RM W 67 62 since the decision of postponing claiming by five years that give more gain with respect to RM W, as it is shown in table 1.

Then, we run the logit regression model using the residuals, assuming a net discount rate of 1%. In addition, we decided also to run the logit regression model with a more restricted sample to see if the results differs significantly. Sample 2 includes individuals who were eligible at age 62 and who claimed at age 62 (early claimers) and those who claimed from age 67 or older (late claimers). The coefficients of the logit regression model for both samples are shown in table A-1 in Appendix B.

To interpret the results we have calculated the average marginal effect (AME) of our selected variables on early claiming decision from the results of the logit regression. The average marginal effects for both samples are shown in table 9.

First, we will see marginal effects for sample 1. On average, women’s probability of being an early claimer is lower than men’s probability by 11.4 percentage points and it is highly statistically significant. Having a partner and children increase the probability in 4.1 and 5.2 percentage points respectively.

With respect to birth cohorts, younger cohorts have higher probability of early claiming than the baseline-1949 cohort. The difference in percentage points increases as birth cohort are younger, being 1953 birth cohort the one that has 17.3 percentage points more than 1949 birth cohort on the probability of early claiming. This is in line with the statistics shown in figure 1. The interpretation of the results for RM W_67,62 will be explained later with table 11.

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Table 9

Average marginal effect of the logit model. Eligible individuals at age 62

Sample 1 Sample 2

Residual of RMW at 1% -0.423^∗∗∗ (0.086) -0.289^∗∗ (0.111)

Female -0.114^∗∗∗ (0.005) -0.103^∗∗∗ (0.007)

Couple 0.041^∗∗∗ (0.006) 0.042^∗∗∗ (0.007)

Children 0.052^∗∗∗ (0.007) 0.041^∗∗∗ (0.009)

Birth cohort

1950 0.096^∗∗∗ (0.006) 0.032^∗∗∗ (0.007)

1951 0.127^∗∗∗ (0.006) 0.065^∗∗∗ (0.007)

1952 0.157^∗∗∗ (0.006)

1953 0.173^∗∗∗ (0.006)

Education level

Upper secondary -0.032^∗∗∗ (0.005) -0.033^∗∗∗ (0.006) Higher educ. - short -0.114^∗∗∗ (0.006) -0.123^∗∗∗ (0.009) Higher educ. -long -0.224^∗∗∗ (0.008) -0.252^∗∗∗ (0.012) AFP affiliation 0.172^∗∗∗ (0.004) 0.240^∗∗∗ (0.005) Labor income in quartiles

Q2 0.005 (0.006) -0.008 (0.007)

Q3 -0.014^∗ (0.006) -0.022^∗∗ (0.008)

Q4 -0.082^∗∗∗ (0.007) -0.083^∗∗∗ (0.009)

Savings in quartiles

Q2 -0.025^∗∗∗ (0.006) -0.024^∗∗ (0.007)

Q3 -0.040^∗∗∗ (0.006) -0.040^∗∗∗ (0.008)

Q4 -0.055^∗∗∗ (0.006) -0.055^∗∗∗ (0.008)

Housing tax value in quartiles

Q2 0.004 (0.006) 0.013 (0.007)

Q3 -0.019^∗∗∗ (0.006) -0.011 (0.008)

Q4 -0.051^∗∗∗ (0.006) -0.032^∗∗∗ (0.009)

Debt amount in quartiles

Q2 0.016^∗∗ (0.006) 0.029^∗∗∗ (0.008)

Q3 0.035^∗∗∗ (0.006) 0.059^∗∗∗ (0.008)

Q4 0.062^∗∗∗ (0.006) 0.084^∗∗∗ (0.008)

N 58,109 21,270

Standard errors in parentheses

∗ p <0.05,^∗∗ p <0.01,^∗∗∗ p <0.001

Note: Sample 1 includes individuals who claimed at 62 (early claimers) and who didn’t (late claimers). Sample 2 includes those who claimed at 62 (early claimers) and those who claimed after the age of 66 (late claimers).

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Moreover, the level of education has a negative relationship with the probability of being an early claimer and marginal effects increases by higher level of education. The following marginal effects are with respect to individuals with primary or lower secondary education (baseline level of education). Individuals with an upper secondary education have 3.2 percentage points lower probability of being early claimer, individuals with a short higher education have 11.4 percentage point lower probability and individuals with a long higher education have 22.4 percentage points lower probability.

Individuals who work in a firm that has an AFP scheme are more likely to claim early than those who don’t have an AFP affiliation. The difference in the probability is 17.2 percentage points. All marginal effects of these variables are statistically significant at a 0.1% significant level.

Analysing the marginal effects of variables on financial status and taking first quartile as baseline, we find a negative and significant relationship between the probability of early claiming and labor income at the fourth quartile. On savings the relationship is negative and statistically significant at all quartiles, with a significant level of 0.1%. The same happens with housing’s tax value, although marginal effects are significant only on third and fourth quartile.

On the contrary, the probability of early claiming decreases with higher debt amount and it is statistically significant at a 0.1% significant level for third and fourth quartiles.

Regarding sample 2, the relationships between observable variable and the probability of early claiming are the same as in sample 1. The marginal effects of younger cohorts are smaller in sample 2. Besides, AFP affiliation increase even more the probability of early claiming by 24 percentage points in sample 2 than in sample 1. For variables on income and wealth, the marginal effects remain almost the same.

After describing marginal effects of sample 1, we want to compare the results of sample 1, those who become eligible at age 62 and the group of eligible individuals at age between 63 and 65, henceforth sample 3. As shown in table 5, the majority of early claimers start claiming at 62, so those who start claiming between 63 and 65 is a much smaller group. Table 10 shows marginal effects for sample 3. As in sample 1, on average women’s probability of early claiming is lower than for men in 7.3 percentage points.

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Table 10

Average marginal effect of the logit model. Eligible individuals from age 63 and age 65 Sample 3

Residual of RMW at 1% -0.004 (0.055)

Female -0.073^∗∗∗ (0.013)

Couple 0.019 (0.015)

Children 0.019 (0.021)

Birth cohort

1950 0.048^∗∗ (0.017)

1951 0.077^∗∗∗ (0.017)

1952 0.117^∗∗∗ (0.018)

1953 0.123^∗∗∗ (0.017)

Education level

Upper secondary -0.038^∗∗ (0.014) Higher educ. - short -0.132^∗∗∗ (0.018) Higher educ. - long -0.200^∗∗∗ (0.026)

AFP affiliation -0.025 (0.013)

Labor income in quartiles

Q2 0.016 (0.016)

Q3 -0.013 (0.016)

Q4 -0.092^∗∗∗ (0.017)

Savings in quartiles

Q2 0.027 (0.016)

Q3 0.015 (0.017)

Q4 -0.006 (0.018)

Housing tax value in quartiles

Q2 0.026 (0.016)

Q3 -0.020 (0.017)

Q4 -0.049^∗∗ (0.017)

Debt amount in quartiles

Q2 -0.004 (0.016)

Q3 0.017 (0.017)

Q4 0.077^∗∗∗ (0.018)

N 7,376

∗ p <0.05,^∗∗ p <0.01,^∗∗∗ p <0.001

Note: Sample 3 consists of individuals who are eligible at age between 63 and 65. Those who claimed before age 67 are referred to as early claimers and as late claimers those who didn’t.

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The same happens with achieved level of education, it reduce the probability of early claiming and its negative effect is bigger as individuals have a higher level of education.

Individuals with a long higher education have 20 percentage points less in the probability of being an early claimers than those who have primary or lower secondary education.

However, although we found out that having a partner, children and work in a company that has an AFP scheme increase the probability of early claiming in sample 3, marginal effects are not statistically significant as they were in sample 1.

Regarding variables on financial status in table 10, marginal effects in sample are not statistically significant with the exception of some few variables. The most remarkable is that labor income at fourth quartile reduces the probability of early claiming and debt amount at fourth quartile increases the probability. Both are statistically significant at a 0.1% significant level.

So far, we have assumed a net discount of 1 percent for calculating RM W_67,62 in table 9 and 10. Taking sample 1, we are going to show the marginal effects for our logit model at each net discount rate of 0, 1 and 2 percent. Table 11 shows that excluding the driving effect of expected longevity on RM W67,62, the probability of early claiming has a negative relationship with RM W67,62. It means that if individuals expect to get a gain by postponing claiming age they are more likely to postpone it. This negative relationship is significant statistically at a 0.1% significant level. Without interpreting the marginal effects, it increases with higher net discount rate which implies that with higher discounting there is a smaller fraction that gain from postponing (as also seen in table 1) but the relationship between gaining and early claiming becomes stronger.

Therefore, it is likely that the negative relationship is driven by those who have a lot to gain from postponing.

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Table 11: Average marginal effect of the logit model, at different interest rate.

Sample 1

0% 1% 2%

Residual of RMW -0.280^∗∗∗ (0.076) -0.423^∗∗∗ (0.086) -0.497^∗∗∗ (0.094) Female -0.114^∗∗∗ (0.005) -0.114^∗∗∗ (0.005) -0.113^∗∗∗ (0.005) Couple 0.041^∗∗∗ (0.006) 0.041^∗∗∗ (0.006) 0.041^∗∗∗ (0.006) Children 0.052^∗∗∗ (0.007) 0.052^∗∗∗ (0.007) 0.053^∗∗∗ (0.007) Birth cohort

1950 0.097^∗∗∗ (0.006) 0.096^∗∗∗ (0.006) 0.096^∗∗∗ (0.006) 1951 0.128^∗∗∗ (0.006) 0.127^∗∗∗ (0.006) 0.127^∗∗∗ (0.006) 1952 0.158^∗∗∗ (0.006) 0.157^∗∗∗ (0.006) 0.156^∗∗∗ (0.006) 1953 0.175^∗∗∗ (0.006) 0.173^∗∗∗ (0.006) 0.172^∗∗∗ (0.006) Education level

Upper secondary -0.031^∗∗∗ (0.005) -0.032^∗∗∗ (0.005) -0.031^∗∗∗ (0.005) Higher educ. - short -0.114^∗∗∗ (0.006) -0.114^∗∗∗ (0.006) -0.113^∗∗∗ (0.006) Higher educ -long -0.223^∗∗∗ (0.008) -0.224^∗∗∗ (0.008) -0.223^∗∗∗ (0.008) AFP affiliation 0.172^∗∗∗ (0.004) 0.172^∗∗∗ (0.004) 0.172^∗∗∗ (0.004) Labor income in quartiles

Q2 0.006 (0.006) 0.005 (0.006) 0.004 (0.006)

Q3 -0.013^∗ (0.006) -0.014^∗ (0.006) -0.013^∗ (0.006)

Q4 -0.082^∗∗∗ (0.007) -0.082^∗∗∗ (0.007) -0.082^∗∗∗ (0.007) Savings in quartiles

Q2 -0.026^∗∗∗ (0.006) -0.025^∗∗∗ (0.006) -0.025^∗∗∗ (0.006) Q3 -0.041^∗∗∗ (0.006) -0.040^∗∗∗ (0.006) -0.040^∗∗∗ (0.006) Q4 -0.056^∗∗∗ (0.006) -0.055^∗∗∗ (0.006) -0.054^∗∗∗ (0.006) Housing tax value in quartiles

Q2 0.004 (0.006) 0.004 (0.006) 0.004 (0.006)

Q3 -0.020^∗∗∗ (0.006) -0.019^∗∗∗ (0.006) -0.019^∗∗ (0.006) Q4 -0.051^∗∗∗ (0.006) -0.051^∗∗∗ (0.006) -0.051^∗∗∗ (0.006) Debt amount in quartiles

Q2 0.017^∗∗ (0.006) 0.016^∗∗ (0.006) 0.017^∗∗ (0.006)

Q3 0.035^∗∗∗ (0.006) 0.035^∗∗∗ (0.006) 0.035^∗∗∗ (0.006)

Q4 0.062^∗∗∗ (0.006) 0.062^∗∗∗ (0.006) 0.063^∗∗∗ (0.006)

N 58730 58109 57456

∗ p <0.05,^∗∗ p <0.01,^∗∗∗ p <0.001

Note: The sample used here is sample 1 that includes individuals who claimed at 62 (early claimers) and who didn’t (late claimers).

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However, it is difficult to determine from table 11 how much higher discounting - or impatience - may contribute to explaining the fraction of early claimers. An alternative approach is to check if assuming a higher discount rate can predict a higher fraction of early claimer than a lower discount rate does. To do this, we estimate the three regressions in table 11 on a 50% random part of the sample and use the estimated coefficients to predict the fraction of early claimers using the other half of the sample (out-of-sample).

For comparison, we also compare the predictions with a version of the empirical model that excludes the residual RM W as an explanatory variable.

Table 12: Predicting the fraction of early claimers out-of-sample Net discount rate Fraction St.dev.

No RM W - 0.48919 0.1617

RM W_67,62 0% 0.48929 0.1619

RM W_67,62 1% 0.48930 0.1620

RM W_67,62 2% 0.48931 0.1621

Actual fraction 0.49864

Notes: 1)RM W67,62refers to the ratio ofP V Bat the claiming age of 67 and P V B at the claiming age of 62. 2) The sample used is 50% of sample 1 not used for estimation, i.e. number of observations is 28,594. Empirical model as in table 11.

Table 12 shows that including residualRM W as a measure of the gain/loss from postponing the claiming decision improves the prediction, but not by very much. Furthermore, higher discounting does increase the predicted fraction and brings it closer to the actual value, but only to a very small degree.

Pension claiming behavior after the Norwegian 2011 pension reform