i

An Analysis of the Effects of Longevity Risk on Pension

Planning in Zimbabwe

By

Elson Gonye (R9916210)

A dissertation submitted in partial fulfillment of the requirements for the degree of Master

of Business Administration

2015

Graduate School of Management

University of Zimbabwe

Supervisor: Dr. P. G. Kadenge

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DEDICATION

This research is dedicated to my wife Chenayi Svinurayi and our three children Tarumbidzwa

Bright, Anashe Brilliant and Tamiranashe Blessed.

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DECLARATION

I, Elson Gonye, do hereby declare that this dissertation is the result of my own investigation and

research, except to the extent indicated in the Acknowledgements, References and by comments

included in the body of the report, and that it has not been submitted in part or in full for any

other degree to any other university

______________ ___________

Student signature Date

______________ ___________

Supervisor signature Date

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ACKNOWLEDGEMENTS

Firstly I would like to thank our living God for giving me strength and wisdom to go through this

programme.

A special thank you goes to my supervisor Dr P. G. Kadenge for the priceless support and

supervision.

To our MBA class, especially the group that consists of Gabriel Karani, Batanai Kamunyaru,

Brian Mandimika, Abisha Mujuru, Ester Gambakwe and Anna Mugabe; you are the shining

stars!

I would also like to thank my former workmates at Quantum Consultants and Actuaries; and my

current workmates at Premier Service Medical Aid Society for their invaluable support.

Finally, I would like to thank my family and friends for all the support and encouragement.

Without them I would not have reached this far. May the Lord bless them abundantly!

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Abstract

This study is about the analysis of the effects of longevity risk on pension planning in Zimbabwe

with a particular focus on defined benefit pension schemes. A defined benefit scheme is an

arrangement whereby the pension benefits are pre-determined by a formula which is a function

of service and salary. The study adopted a quantitative methodology to achieve the following

objectives:

a) Establish how uncertainty regarding future mortality and life expectancy outcomes would

affect the funding position of defined benefit pension schemes in Zimbabwe.

b) Establish the link between mortality and life expectancy in Zimbabwe.

c) Check the adequacy of the existing assets to meet the liabilities of the pension schemes in

Zimbabwe.

d) Determine the contribution rates that would be appropriate for the future.

e) Recommend possible approaches to forecast mortality and life expectancy.

The study considered the research topical given the inability of Zimbabwean defined benefit

pension schemes to meet their liabilities. A sample of 128 participants was drawn from randomly

selected active employees who are member of pension funds. Data were gathered using a

questionnaire. The study was interested in testing the proposition that mortality is improving in

Zimbabwe. The study found that Technological Advancement, Education and Lifestyle and other

factors which include the Millennium Development Goals and Government interventions are the

major contributors to mortality improvements. Generally there are mortality improvements in

Zimbabwe. Where there is mortality improvement, there is longevity risk which has got direct

impact on defined benefit pension funds.

The research objectives were fulfilled and the following recommendations were made:

(a) Indexation of pension benefits to life expectancy in order to partially offset the impact of

longevity.

(b) Policymakers are recommended to set up a Continuous Mortality Investigations Unit

within Zimbabwe.

(c) The Regulator of pension funds is recommended to put in place mechanisms of ensuring

that pension funds and annuity providers fully account for improvements in mortality.

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Contents DEDICATION ............................................................................................................................................ ii

DECLARATION ........................................................................................................................................ iii

ACKNOWLEDGEMENTS ........................................................................................................................... iv

Abstract .................................................................................................................................................. v

LIST OF ABBREVIATIONS AND ACRONYMS ............................................................................................... x

Table of Tables ....................................................................................................................................... xi

CHAPTER 1 .............................................................................................................................................. 1

INTRODUCTION AND BACKGROUND ........................................................................................................ 1

1.1 Introduction ............................................................................................................................. 1

1.2 Background of the Study .......................................................................................................... 1

1.3 Research Problem .................................................................................................................... 2

1.4 Research Objectives ................................................................................................................. 3

1.5 Research Questions .................................................................................................................. 3

1.6 Proposition ............................................................................................................................... 3

1.7 Justification of Research .......................................................................................................... 3

1.8 Scope of Research .................................................................................................................... 5

1.9 Dissertation Outline ................................................................................................................. 5

1.10 Chapter 1 Summary ................................................................................................................. 6

CHAPTER 2 .............................................................................................................................................. 7

LITERATURE REVIEW ............................................................................................................................... 7

2.1 Introduction ............................................................................................................................. 7

2.2 Mortality, Life Expectancy and Longevity Risk ........................................................................ 7

2.3 Underlying Theories................................................................................................................. 8

2.3.1 Theories of Consumption and Savings .............................................................................. 8

2.3.2 Time Value of Money ....................................................................................................... 9

2.3.3 Pension Benefits ............................................................................................................. 10

2.3.4 Survival Models and the Force of Mortality .................................................................... 12

2.4 Factors Affecting Mortality Improvements ............................................................................. 13

2.4.1 Technological advancement ...................................................................................................... 13

2.4.2 Education and lifestyle .............................................................................................................. 14

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2.4.3 Other external factors ................................................................................................................ 14

2.5 Mortality trends...................................................................................................................... 14

2.5.1 Mortality trends in the United Kingdom .................................................................................... 14

2.5.2 Mortality improvements in the 1990s ........................................................................................ 17

2.5.3 Rates of Mortality Improvements for Pensioner and Assured lives............................................. 18

2.5.4 The Cohort Effect ..................................................................................................................... 19

2.5.5 Mortality Improvements and South Africa ................................................................................. 20

2.5.6 Mortality Improvements in Mauritius ........................................................................................ 22

2.5.6 Mortality Trends – A World Wide Picture ................................................................................. 24

2.6 Mortality Tables since the a(90) Mortality Table .................................................................... 25

2.7 Choosing the Mortality Table to use ....................................................................................... 27

2.8 The Conceptual Framework ................................................................................................... 28

2.9 Chapter 2 Summary ............................................................................................................... 29

CHAPTER 3 ............................................................................................................................................ 30

RESEARCH METHODOLOGY ................................................................................................................... 30

3.1 Introduction ........................................................................................................................... 30

3.2 Research Design .................................................................................................................... 31

3.2.1 Research Philosophy ...................................................................................................... 31

3.2.2 Research Strategy .................................................................................................................. 32

3.3 Population and Sampling Techniques ........................................................................................... 32

3.3.1 Population ............................................................................................................................. 32

3.3.2 Sampling ............................................................................................................................... 33

3.4 Sources of Data ............................................................................................................................ 34

3.5 Data Collection Procedure (Research Instrument) ........................................................................ 34

3.6 Data Analysis .............................................................................................................................. 35

3.7 The Actuarial Model .................................................................................................................... 36

3.7.1 The Actuarial Model used for the Valuation of Liabilities ......................................................... 36

3.7.2 Important Features of the Actuarial Model ................................................................................ 37

3.8 Funding Objectives and Valuation Methods ................................................................................. 38

3.9Research Ethics and Data Credibility ............................................................................................ 41

3.9.1 Research Ethics ..................................................................................................................... 41

3.9.2 Data Credibility ..................................................................................................................... 42

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Data collection instruments and justification ......................................................................................... 43

Data Analysis ......................................................................................................................................... 43

Research Gap ........................................................................................................................................ 43

3.10 Research Limitations ...................................................................................................................... 43

3.11 Chapter Summary ...................................................................................................................... 44

CHAPTER 4 ............................................................................................................................................ 45

DATA PRESENTATION AND ANALYSIS ..................................................................................................... 45

4.1 Introduction ........................................................................................................................... 45

4.2 Response rate ......................................................................................................................... 46

4.3 Demographics ........................................................................................................................ 47

4.3.1 Gender ........................................................................................................................... 47

4.3.2 Ages of Respondents ...................................................................................................... 47

4.3.3 Position at Work ............................................................................................................. 48

4.3.4 Highest Academic Qualification ..................................................................................... 49

4.3.5 Highest Professional Qualification .................................................................................. 50

4.3.6 Current Pensionable Service ........................................................................................... 50

4.4 Reliability .............................................................................................................................. 51

4.5 Normality Test ....................................................................................................................... 52

4.6 Correlation Analysis ........................................................................................................... 52

4.6.1 Technological Advancement in health delivery and Mortality Improvement.................... 53

4.6.2 Relationship between Other Factors and Mortality Improvements ................................... 53

4.6.3 Relationship between Education and Lifestyle and Mortality Improvements ................... 53

4.7 Regression Analysis ........................................................................................................... 53

4.8 Statistical Inferences .............................................................................................................. 55

4.8.1 Kruskal – Wallis Test ..................................................................................................... 55

4.9 Hypothesis Testing ................................................................................................................. 56

4.10 Actuarial Analysis of the Defined Benefit Pension Liabilities ................................................. 56

4.10.1 Actuarial Calculation and the results ................................................................................... 56

4.11 Discussion of Results ............................................................................................................. 60

4.11.1 Discussion of Results in relation to literature ...................................................................... 60

4.11.2 Other findings, discussions and suggested approaches to managing longevity risk. ............. 61

4.11.3 Approaches to managing longevity risk .............................................................................. 63

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4.12 Chapter 4 Summary ............................................................................................................... 65

CHAPTER 5 ............................................................................................................................................ 67

DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS ........................................................................ 67

5.1 Introduction ........................................................................................................................... 67

5.2 Conclusions ........................................................................................................................... 67

5.3 Validation of Research Proposition ........................................................................................ 69

5.4 Recommendations .................................................................................................................. 69

5.5 Areas for further studies ......................................................................................................... 70

REFERENCES .......................................................................................................................................... 72

APPENDIX .............................................................................................................................................. 75

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LIST OF ABBREVIATIONS AND ACRONYMS

AIDS: Acquired Immuno Deficiency Syndrome

ARV Anti-Retroviral

CMIB: Continuous Mortality Investigations Bureau

CMIR: Continuous Mortality Investigation Report

DB: Defined Benefit

DC: Defined Contribution

ELT: English Life Table

GAD: Government Actuarial Department

HIV: Human Immuno Virus

IMF: International Monetary Fund

ONS: Organization of National Statistics

PGN: Professional Guidance Notes

RBZ: Reserve Bank of Zimbabwe

SCR Standard Contribution Rate

SF Standard Fund

SPSS: Statistical Programme for Service Solutions

UK: United Kingdom of the Great Britain

USA: United States of America

WHO: World Health Organisation

Zimstat: Zimbabwe National Statistics Agency

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List of Tables

Table 2.1a : Reduction in mortality rate between 1901 and 2001, England and Wales population ........... 15

Table 1.1b: Proportion of all deaths due to infectious diseases in the period 1901 to 1910 and 2001,

England and Wales’ population ............................................................................................................. 15

Table 2.2a: Improvements in life expectancy over the 20th century, England and Wales’ population, males

.............................................................................................................................................................. 16

Table 2.2b: Improvements in life expectancy over the 20th century, England and Wales’ population,

females .................................................................................................................................................. 16

Table 2.3: Average annual rates of mortality improvement, English and Wales’s population, 1989-2001

.............................................................................................................................................................. 17

Table 2.4: Components of average annual mortality improvement rates, 1970-72 to 1991-93, England and

Wales’s population, males aged 20-64 ................................................................................................... 18

Table 2.5: Average Annual Rates of Mortality Improvements for the period 1992 to 2005 ..................... 20

Table 2.6: Population growth in intercensal periods – Island of Mauritius 1944-2010............................. 23

Table 2.7: Population and vital statistics rates – Island of Mauritius 1946-2010 ..................................... 23

Table 2.8: Demographic framework ....................................................................................................... 24

Table 2.9: Estimated World Population aged 60 or over & 65 or over (2002) ......................................... 25

Table 4.1: Gender of respondent ............................................................................................................ 47

Table 4.2: Ages of respondents .............................................................................................................. 47

Table 4.3: Position at work of respondent .............................................................................................. 48

Table4.4: Academic Qualifications of respondent .................................................................................. 49

Table 4.5: Professional Qualification of Respondent .............................................................................. 50

Table 4.6: Current Pensionable Service for the Respondent .................................................................... 50

Table 4.7: Overall Reliability Test of the Instrument .............................................................................. 51

Table 4.8: Reliability test for the transformed variables.......................................................................... 51

Table 4.9: Normality test ....................................................................................................................... 52

Table 4.10: Correlation analysis............................................................................................................. 52

Table 4.11: Regression between the independent variables and the dependant variable........................... 53

Table 4.12: Regression Anova table for factors affecting mortality improvements……………………………….53

Table 4.13: Regression coefficients table for factors affecting mortality improvements .......................... 54

Table 4.14: Kruskal – Wallis Test results ............................................................................................... 55

Table 4.15: Results of the valuation using the Projected Unit Funding Method ...................................... 57

Table 4.16: Zimbabwean year on year life expectancy (LE) at birth from 2000 to 2012………………………...60

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List of figures

Figure 3.1: Research Onion ................................................................................................................... 30

Figure 4.1: Response Rate ..................................................................................................................... 46

1

CHAPTER 1

INTRODUCTION AND BACKGROUND

1.1 Introduction

This study analyses the effects of longevity risk on pension planning in Zimbabwe. This chapter

provides the general introduction to the study by outlining the background of the study, the

research problem, the research objectives, research questions, the proposition, justification of the

research, the scope of the research and the dissertation outline.

1.2 Background of the Study

There is an increasing public and professional interest in longevity and its implications to defined

benefit pension arrangements. This is evidenced by a number of Working Papers and also

Continuous Mortality Investigations Reports which have been published by the International

Monetary Fund (IMF), some emerging economies and developed nations. The published work

has provided new insights into the development of mortality and has led to the revision of

mortality projection bases. This is what has motivated us to look at the subject with particular

reference to Zimbabwe.

During the period from end of 1999 to the beginning of 2010, the Zimbabwean pension industry

underwent one of the most turbulent phases in its history. The national economy was

characterized by hyperinflation, weakening currency, rising unemployment and shrinking

productivity. With the introduction of the multi-currency system, the bulk of the pension

balances were reduced to zero and people naturally lost confidence in the pensions industry.

According to the ZAPF, there is widespread dissatisfaction regarding the conversion of

Zimdollar pension values to the United States dollars. This problem has been further worsened

by the fact that companies are failing to remit pension contributions to pension funds. This trend

has affected the defined benefit pension schemes more than the defined contribution pension

schemes.

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When Zimdollar pension amounts were converted to United States dollars, the bulk of the

pensioners started receiving less than $10 as monthly pensions. These pensioners feel short-

changed. Pension funds have been pressurized to increase pension payouts to meaningful levels

on a paternalistic point of view. However this has left many Zimbabwean pension funds with

very poor funding levels with some pension funds having as low as 9% funding level (IPEC

Report, 2014).

The underlying trends in the Zimbabwean pension system suggest that employers are now

shifting from defined benefit schemes to defined contribution schemes. Also the number of

defined benefit schemes closing to new entrants is increasing because of increasing cost of

funding. The pension industry problem is one of Zimbabwe’s national economic problems that

require a comprehensive macro-economic framework to be in place.

The defined benefit pension schemes in Zimbabwe now have assets which do not match the

liabilities. This is not in line with the primary role of pension funds. The primary role of pension

funds is to secure the future of employees financially in order to manage longevity risk. It is with

this background that this research seeks to look at the effects of post-retirement mortality

improvements to the liability position of defined benefit pension schemes.

The last several decades have witnessed the longevity of individuals improving considerably and

consistently with the trend looking set to improve. Such demographic changes pose numerous

social and economic challenges (Haan, 2011). Notably many pension arrangements which are

typically compulsory defined benefit schemes are being strained by the greater pension demands

due to higher life expectancy.

1.3 Research Problem

Generally the defined benefit pension schemes are failing to meet their liabilities. This research

seeks to establish whether or not mortality improvements exist in Zimbabwe. If at all there are

mortality improvements, do they matter? Generally mortality improvements and life expectancy

are uncertain. When mortality improves, the present value of the capital sum or the consideration

that is required to purchase a pension at retirement also increases. The focus is therefore on

3

ensuring that insurance companies and pension fund managers are able to realize mortality

improvements and the associated longevity risk. As long as improvements in mortality are

foreseeable and they are taken into account when planning for retirement, they will have little

effect on retirement finances. There seems to be lack of education on the part of the general

public on the importance of planning for retirement life. This has been further worsened by the

Life Insurance market which has gone down due to lack of confidence as a result of what

happened during the hyper-inflation era.

1.4 Research Objectives

The research seeks to:

a) Establish how uncertainty regarding future mortality and life expectancy outcomes would

affect the funding position of defined benefit pension schemes in Zimbabwe.

b) Establish the link between mortality and life expectancy in Zimbabwe.

c) Check the adequacy of the existing assets to meet the liabilities of the pension schemes in

Zimbabwe.

d) Determine the contribution rates that would be appropriate for the future.

e) Recommend possible approaches to forecast mortality and life expectancy.

1.5 Research Questions

a) What is the likely impact of one year longevity shock to defined benefit pension

arrangements’ liabilities?

b) What is the link between mortality and life expectancy?

c) Are the pension scheme assets adequate to meet the liabilities?

d) What contribution rates are appropriate for the future?

e) What are the possible approaches that can be used to forecast mortality and life expectancy?

1.6 Proposition

This research proposes that mortality is improving in Zimbabwe and there are some implications

of assets and liabilities mismatch to defined benefit pension schemes’ liabilities.

1.7 Justification of Research

4

The study gives an insight to insurance and pension fund managers on the importance of tracking

mortality improvements for the purposes of putting in place measures to mitigate longevity risk.

It is the economic consequences of mortality improvements and longevity risk that are the most

important when assessing their impact. The financial implications of mispriced and under-

reserved annuity and pension portfolios are very acute due to the size of retirement funds’ assets

and the liabilities in the market. The impact of this is that pricing or reserving bases are supposed

to be continuously strengthened, and this has financial implications.

A challenge that comes out clearly in this dollarised economy is on how to deal with an ever

increasing pensioner population which has to be supported by a diminishing working population.

This is a relevant question in Zimbabwe where the government has mooted the idea of

implementing the National Health Insurance and also reforming the Social Security system.

However, should insurance companies and pension fund managers put their act right, the biggest

beneficiaries will be the Zimbabwean pensioners and the economy in general.

The study brings out clearly that mortality improvements affect any class of insurance and

pensions business where mortality rates are one of the underlying assumptions. The impact is

expected not to be in the same direction. For annuity business, higher mortality improvements

imply longer life expectancy. This means more annuity payments to be made and the annuity

rates should be higher, that is the annuity prices will be higher. For life insurance business,

mortality improvements imply longer life expectancy. This means that deaths are delayed and

there is a longer premium collection period, thus the price of life insurance should be lower.

This study can also help to increase economic activity. As disposable incomes increase through

provision of reasonable monthly pensions, demand for goods and services increases and

therefore stimulates economic activity. In his Monetary Policy Statement as at January 2015, the

Reserve Bank of Zimbabwe (RBZ) Governor states that the major causes of uncompetitiveness

of our economy are higher costs of production emanating from high mark-ups. This has been

worsened by the continued appreciation of the US dollar against major currencies; and also

cheaper imports. The Governor therefore advocates for price reductions where he sees both the

economy and the consumers benefiting from a price reduction. Lower prices induce demand

5

through the concept of price elasticity of demand. Furthermore, by resuscitating a vibrant

annuities market, financial institutions can tap into international capital and increase lines of

credit which can increase liquidity to the economy.

An important part of the Zimbabwean pension administration reform process will be to connect

or link retirement ages to advances in longevity. If undertaken now, the proposed mitigation

measures can be implemented in a gradual and sustainable manner. Any delays are likely to

increase risks to financial and fiscal stability which would then require much larger and

disruptive measures in the future.

Last but not least, mortality improvements provide a source of academic interest. A lot of

financial resources have been committed to studying mortality improvements in developed

countries and emerging economies. This is evidenced by a number of seminars held on longevity

and mortality improvements. It is therefore entirely upon us as Zimbabweans to seriously

consider working on this area as we work towards developing our country.

1.8 Scope of Research

The research focuses on the challenges being faced by defined benefit pension schemes in

Zimbabwe.

1.9 Dissertation Outline

This dissertation is outlined in such a way that Chapter 1 provides the background information

on the aspects of mortality improvements, longevity risk and the likely impact to pension

planning. Chapter 2 looks at the review of related literature in the form of theoretical and

empirical literature. It is in chapter 2 where we explore related literature around the concepts of

mortality improvements and longevity risk. This has been done through exploring some

researches by other scholars in the same field. This has helped us to understand the work that has

been carried out in this area and what Zimbabwe can learn from them.

Chapter 3 focuses on outlining the research methodology and research strategies. The

instruments used to gather data shall include interview guides and questionnaires. Pensioners’

6

data and valuation assumptions shall be obtained from pension funds. The research is

predominantly quantitative in nature.

Chapter 4 deals with results presentation and discussion. The results from questionnaires have

been analysed using SPSS. Simulation and scenario building has been done using a generic

actuarial model.

Chapter 5 provides the Conclusions and Recommendation. The chapter summarizes the drawn

conclusions and recommendations for economic and pension planning and possibly further

researches.

1.10 Chapter 1 Summary

This chapter has outlined the background information to do with mortality improvements, life

expectancy and longevity risk. The focus is on analyzing the effects of longevity risk towards

pension planning in Zimbabwe. Research objectives were outlined indicating what the research

would seek to achieve. This chapter also outlined the benefits of carrying out the research. The

next chapter focuses on reviewing related literature.

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CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter provides an overview of the literature on longevity risk, mortality trends and the

impact they have on pension planning. The chapter looks at the underlying theories before

presenting the picture on mortality trends in selected geographical areas in the world. The

chapter concludes with a discussion on the mortality tables available and the possible criterion

that can be used to choose one.

2.2 Mortality, Life Expectancy and Longevity Risk

Antolin (2007) defines longevity risk as the risk that future life expectancy outcomes turn out to

be different from what is expected. On a more specific note, Crawford et al. (2008) define

longevity risk from the perspective of an insurance company. They define longevity risk as the

risk that the company will have to face unexpected decreases in mortality. This view is supported

by Moody’s Investor Services cited in Milevsky and Promislow (2003). Moody believes that the

main risks to insurance companies, which also apply to pension funds, are the embedded equity

guarantees and inaccurate longevity assumptions due to poor mortality projections.

According to Waldron (2005), mortality projections are a result of extrapolative approaches

based on historical trends usually complemented by a mix of expert opinion and process based

methods focusing on the evolution of causes of death. Extrapolative approaches focus on past

trends and attempt to forecast future life expectancy using past information on mortality rates. A

common approach for modeling future mortality rates is based on a model which was developed

by Lee and Carter (1992) which applies time series analysis to predict future mortality rates. On

the other hand, process based approaches use biomedical assumptions to forecast death rates by

focusing on causes of death.

8

Antolin (2007) gives a numerical analysis of the impact of longevity risk on defined benefit

pension schemes. He uses a hypothetical pension fund that is closed to new entrants to compute

pension liabilities. He then takes a deterministic approach to analyse the effects of improvements

in mortality on the schemes’ pension liabilities. Antolin (2007) finds that a one-year shock in

mortality improvements per decade could increase pension liabilities by 8 to 10 percent,

depending on the age profile of the hypothetical pension scheme.

Dushi, Friedberg and Webb (2010) compute the impact of updating mortality tables, which are

used to estimate the pension liabilities, using the Lee Cater model. The results suggest that an

improvement in mortality at age 60 of about 3 years since 1980 would increase pension liabilities

by an average of 12% for a male pensioner.

2.3 Underlying Theories

2.3.1 Theories of Consumption and Savings

According to the life cycle hypothesis of savings and consumption, one would predict that

individuals would pay off debts and build savings in their working years, and then divest those

savings to support consumption in their older years (Redfoot et al., 2007). This life cycle has

been made up of mainly four phases, that is, accumulation, consolidation, spending and gifting.

At the accumulation phase, individuals are using income for their immediate needs, such as

house purchases, but are also saving for longer-term commitments such as children’s schooling.

This phase is associated with individuals’ early working years, i.e. the twenties into the thirties

years stage. At this stage, people are not afraid of taking debt.

This is then followed by the consolidation stage where earnings are now much higher than

expenses and so more chances of saving, usually for retirement. Spending happens at retirement

when individuals now enjoy the fruits of their hard work and spend. If the first two stages have

been done well then the individuals are self-sufficient and are able to lead a decent life through

dividends and interest from their investments. This leads to the final stage of gifting where, any

excess assets are used to set up charitable trusts, or to assist family and friends. However, the

culture of saving seems to be lacking in Zimbabwe. The situation has been made worse by the

9

Life Insurance industry which is currently dysfunctional due to lack of confidence in the market

as a result of what happened during the hyper-inflationary era of 2007 to 2008.

This life cycle hypothesis shows the importance of savings for one to have a decent life during

retirement. Retirement is a time of spending and appetite for debt is very low. Life into

retirement is mainly supported by a pension. One of the assumptions used when setting up a

pension arrangement is life expectancy. When pensioners live longer than expected, that poses a

challenge to pension funds because of longevity risk.

2.3.2 Time Value of Money

Pension arrangements, just like many other financial decisions (personal as well as business),

involve the concept of time value of money. According to Chandra (2012), the main objective of

a firm or that of management should be shareholder wealth maximisation, and this in part,

depends on the timing of cash flows.

Pension arrangements generally involve streams of cash flows pre-retirement (in the form of

employee and employer monthly contributions and investment income less expenses) and

streams of cash flows post-retirement (in the form of annuity payments to the pensioners or

beneficiaries). Longevity risk, which has got a direct bearing on defined benefit pension

schemes, is central to this research. Therefore, much of the development of this dissertation

depends on the clear understanding of the theoretical concept of the time value of money.

Chandra (2012) states that money has time value. A dollar today is more valuable than a dollar

received after a period of time. The realisation of the time value of money and risk is extremely

important in effective pension arrangements.

When employees pay monthly contributions towards pension during their working lifetime, they

expect to get value for money when they retire. The arrangement is similar to a firm that borrows

from a bank. The firm receives cash and commits an obligation to honour interest payment and

the repayment of the principal in future periods. In a similar manner, the pension fund receives

cash from employees and the employer on behalf of employees, and commits an obligation to

pay a pension in the form of an annuity.

10

Sound decision making requires that the cash flows that the pension fund is expected to give up

over a period (future lifetime of a pensioner) should be logically comparable. Thus in pension

arrangements we expect assets to match liabilities by nature, term and currency. Since liabilities

in a pension arrangement are long term, a financial decision has to be made every now and again

on how much should be invested now (the present value) in order to meet future obligations

(future value).

Reasons why money has time value

Chandra (2012) outlines four reasons why money has time value; and these are:

1. Risk and Uncertainty – No one holds the future and hence the future is always uncertain

and risky.

2. Inflation– In an inflationary environment, money received today has more purchasing

power as compared to the money received in future.

3. Consumption – The interest component awarded to the money invested serves as a

motivating factor for people to save for future consumption. Otherwise individuals

generally prefer current consumption to future consumption.

4. Investment Opportunities – An investor can profitably invest a dollar received today to

give him a higher value to be received after a certain period of time.

Thus we can conclude in this sub-section that time value of money is very critical to the concept

of finance in general and to the concept of pensions in particular. The concept realises that the

value of money is different at different points of time.

2.3.3 Pension Benefits

A pension plan is usually sponsored by an employer (Dickson et al., 2009). Pension

arrangements typically give employees either lump sums or annuity benefits or both on

retirement; or a deferred lump sum or annuity benefits or both on earlier withdrawal. Other

benefits include death in service benefits if an employee dies in service. This is referred to as the

Group Life Assurance benefit. Such benefits might include a lump sum, often based on salary

and sometimes service; as well as a pension benefit for the deceased member’s spouse and

children. The pension benefits therefore depend on the survival and employment status of the

11

member. They involve investment of monthly contributions long into the future to pay for future

contingent benefits.

Defined Benefit Pension Scheme

Defined Benefit (DB) pension arrangements offer retirement income which is a function of

service and salary with an employer. The benefit is pre-determined using a formula. As an

example, if an employee reaches pensionable age with n years of service and with pensionable

salary averaging S in the final three years of employment, a defined benefit annual pension at

retirement will be given by:

S*n*accrual rate, where the accrual rate is usually 1% - 2%

If the accrual rate is 2%, the annual pension will be equal to 2% of the final average salary for

each year of service. The operations of a pension scheme are defined in the rules of the scheme.

A defined benefit arrangement is funded by a stream of contributions paid by the employee with

the employer meeting the balance of cost. The capital value or the consideration for the benefit at

retirement can be calculated using a defined formula (as shall be illustrated in Chapter 3). After

calculating the capital value (future value of the benefit), it can be discounted using the valuation

rate of interest to get the present value of the retirement benefit. From the present value, a

required contribution rate can be calculated. The required contribution rate can then be split

between the employer and the employee. In most cases the employee contribution rate is known

in advance so that the employer will meet the balance of cost.

The contributions are invested and the accumulated contributions must be enough to pay the

pensions when they fall due. Since the employer meets the balance of cost, the employer is liable

to investment and longevity risk. In the event of unexpected mortality improvement, the

pensioners may outlive the accumulated funds leaving the pension fund in a huge deficit.

Therefore when setting a valuation basis to determine the required contribution rate, the

assumptions, especially mortality and investment assumptions should be as prudent as possible.

The retirement pension described above illustrates the pension payable from retirement in a

standard final salary scheme. Career average salary pension arrangements are also common in

some jurisdictions where the benefit formula is the same as the final average salary formula

12

given above. The only difference is that the average salary over the employee’s entire career is

used instead of an average for the final three or so years. The major advantage of career average

salary schemes is that they diffuse moral hazard where bosses may increase their salaries

unrealistically just before retirement.

It is common for some employees to leave their jobs before they reach retirement age. Such

members would be given a withdrawal benefit based on the same formula as the age retirement

benefit. However the pension start date may be deferred until the member reaches normal

retirement age. There may also be an option to take a lump sum with the same value as the

deferred pension, which can be invested in the pension plan of the new employer (Dickson et al,

2009).

Defined Contribution Pension Scheme

The operations of a Defined Contribution scheme are more like a savings bank account. The

employee and the employer pay a pre-determined percentage of salary as contributions into the

pension fund. The fund is expected to earn interest to justify the time value of money. However

due to the volatility of some financial markets, the returns may fluctuate up and down exposing

the employees to investment risks.

When the employee leaves or reaches retirement age, the proceeds are available to provide

retirement income. In the UK and most developing countries which are former British colonies,

including Zimbabwe, the bulk (usually two-thirds) of the proceeds must be converted into an

annuity with a pension fund or an insurance company. In other jurisdictions like the USA and

Canada, there are various other options available, for example, income draw-down without

necessarily purchasing an annuity from a pension fund or an insurance company (Dickson et al,

2009).

2.3.4 Survival Models and the Force of Mortality

After introducing the theoretical concepts of consumption and saving, time value of money and

the idea of a pension arrangement, it is necessary at this stage to briefly introduce the concept of

survival models and the force of mortality. Without necessarily going into mathematical details,

the concept of a survival model shall be demonstrated in Chapter 3.

13

The whole idea of survival models is to represent the future lifetime of an individual as a random

variable and illustrate how probabilities of death or survival can be calculated. This is a function

of the future lifetime random variable which represents the number of complete years of future

life. Every pension fund should be concerned about the future lifetime for its pensioners. To fully

understand how survival models work, we also need to bring in the concept of force of mortality

which is a fundamental concept in modeling future lifetime. The derivation of death and survival

probabilities is important especially for the calculation of capital values to determine the

liabilities of a defined benefit pension scheme. The question to be asked is whether or not the

pension fund has enough resources to take the pensioners through. Otherwise there will be

challenges of longevity risk if the pensioners outlive the available resources.

2.4 Factors Affecting Mortality Improvements

Before we look at longevity risk and mortality improvements in detail, we would like to explain

some factors which contribute to mortality improvements. According to Cooper-Williams,

Albertyn and Lewis (2012), in their research on mortality improvements in South Africa, there

are some statistics that illustrate the large potential for mortality improvements in the populations

of developing countries. They note that advancements in medicine and science are seen as the

chief contributors that drive mortality improvements. Other factors that also need to be

considered include educational, societal and lifestyle trends.

2.4.1 Technological advancement

The mortality improvements that are evident in developed countries have largely been a result of

advancements in medicine, science and engineering. There have been advancements made

towards identifying diseases, treating diseases and being able to cure and ultimately prevent

diseases. This contribution to mortality manifests itself clearly in the older ages where non-

communicable diseases have a significant impact. Non-communicable diseases are often seen as

the last hurdle in the fight against aging. Cooper-Williams, Albertyn and Lewis (2012) observe

that developed countries tend to be at the forefront in terms of medical research and development

and their populations have the means to take advantage of these advancements and access to

facilities. Whilst developed nations are at the forefront, developing nations seem to be following

the same footsteps.

14

2.4.2 Education and lifestyle

Education and lifestyle changes also play a pivotal in mortality improvements especially in

developing countries where there is lower level of education and literacy. In Zimbabwe, South

Africa and many other developing nations, there are efforts in both private and public sector to

raise the level of awareness and education with regards to HIV and AIDS. The issues to do with

causes, prevention and treatment of the condition are well articulated and publicised (Cooper-

Williams, Albertyn and Lewis, 2012).

Lifestyle changes also have a huge impact on mortality trends. Nowadays there is an observed

trend of people being obsessed with the idea of healthy living where people become cautious on

the levels of smoking, carrying out physical exercises to keep themselves fit and also the issues

to do with eating habits. However, according to WHO (2004), the impact of lifestyle changes

works in both directions with some trends worsening mortality. The prevalence of diabetes is

expected to increase at global level from 2.8% to 4.4% between the years 2000 and 2030. This is

linked to the increasing trend in physical inactivity (Cooper-Williams, Albertyn and Lewis,

2012).

2.4.3 Other external factors

There are also other factors which are driven by changes in government policy and legislation.

These include the rolling out of programmes like the antiretroviral treatment and campaign on

traffic safety.

2.5 Mortality trends

The purpose of the following sub-sections is to describe how mortality in selected parts of the

world has changed over the years. Particular attention is given to changes during the 20th century

and the beginning of the 21st century. The analysis concentrates mainly on pensioner mortality.

Past trends are analysed with particular emphasis placed on describing the changes that have

occurred during the 1990s and outlining the major factors influencing mortality changes at the

beginning of the 21st century.

2.5.1 Mortality trends in the United Kingdom

Thatcher (1999) describes the ‘explosion’ in the number of centenarians in England and Wales.

At the beginning of the 20th century there were 100 people aged 100 and over. By the end of the

15

century the figure had risen to nearly 6000. According to Thatcher (1999), this rapid growth in

the number of centenarians is due to the fall in mortality rates for people aged between 60 and

80. Thatcher goes on to mention that there is also evidence that mortality has improved in excess

of 100 years.

Willets et al. (2004) agree with Thatcher (1999). They state that over the course of the 20th

century mortality in the United Kingdom has improved to an amazing degree. The improvements

have clearly been substantial at all ages, although the youngest ages have seen the greatest

reductions in mortality. For elderly people, especially men, the improvements have been modest;

though still significant (Willets et al., 2004). The most important factor driving these mortality

improvements was the conquest of infectious diseases. As Willets et al. (2004) put it, ‘this was

the single biggest health success of the 20th century’. At the turn of the century, diseases such as

tuberculosis, typhoid, measles, scarlet fever and diphtheria exacted a terrible toll. This is

illustrated by Table 2.1a which shows the proportion of deaths, at various ages, due to infectious

diseases.

Table 2.1a: Reduction in mortality rate between 1901 and 2001, England and Wales population

Age Reduction in mortality rate Male Female

5 98% 98% 25 82% 92% 45 80% 83% 65 63% 71% 85 37% 49%

Source: ELT and GAD Interim Life Tables 2000-02 (Willets et al., 2004)

Table 1.1b: Proportion of all deaths due to infectious diseases in the period 1901 to 1910 and 2001, England and Wales’ population

Age Group Proportion of deaths due to infectious diseases 1901-10 2001

Male Female Male Female 1-14 43% 47% 6% 6% 15-44 46% 49% 2% 3% 44-64 16% 11% <1% <1%

65 and over 4% 5% <1% <1% Source: ONS. (1997a) and ONS. (2003) – (Willets et al., 2004)

16

Tables 2.2a and 2.2b show how life expectancy, calculated on the basis of life tables reflecting

mortality for each of the periods shown, with no allowance for future improvements has

increased over the 20th century. Over the first half of the century, mortality improvements were

strongest for children and young adults and generally higher for females than males.

Table 2.2a: Improvements in life expectancy over the 20th century, England and Wales’ population, males

Period

Total life expectancy on reaching the ages shown at birth at age 15 at age 45 at age 65 at age 80

1901-10 48.5 62.3 68.3 75.8 84.9 1910-12 51.5 63.6 68.9 76.0 84.9 1920-22 55.6 65.1 70.2 76.4 84.9 1930-32 58.7 66.2 70.5 76.3 84.7 1940-42 Information not available due to the 2nd Word War 1950-52 66.4 69.4 71.5 76.7 84.9 1960-62 68.1 70.3 72.1 77.0 85.2 1970-72 69.0 70.8 72.4 77.2 85.5 1980-82 71.0 72.3 73.7 78.0 85.8 1990-92 73.4 74.3 75.7 79.3 86.4 2000-02 75.9 76.6 78.0 81.0 87.7

Source: ONS. (1997a), GAD. (2003a) and ELT (Willets et al., 2004)

Table 2.2b: Improvements in life expectancy over the 20th century, England and Wales’ population, females

Period

Total life expectancy on reaching the ages shown at birth at age 15 at age 45 at age 65 at age 80

1901-10 52.4 65.1 70.5 77.0 85.4 1910-12 55.4 66.4 71.3 77.4 85.5 1920-22 59.6 68.1 72.7 77.9 85.6 1930-32 62.9 69.3 73.3 78.1 85.5 1940-42 Information not available due to the 2nd Word War 1950-52 71.5 74.0 75.8 79.3 85.8 1960-62 74.0 75.9 77.1 80.3 86.4 1970-72 75.3 76.8 77.9 81.1 87.7 1980-82 77.0 78.0 79.0 82.0 87.5 1990-92 79.0 79.7 80.5 83.1 88.4 2000-02 80.6 81.1 81.9 84.1 88.7

Source: ONS. (1997a), GAD. (2003a) and ELT (Willets et al., 2004)

Life expectancy at birth increased by 17.9 years for males and 19.1 years for females; life

expectancy on reaching age 15 increased by 7.1 years for males and 8.9 years for females. In

contrast, life expectancy on reaching age 65 only increased by only 0.9 years for men and 2.3

17

years for women, reflecting only small improvements in the mortality of elderly people (Willets

et al., 2004)

Over the second half of the century, mortality improvements have shifted along the age range. In

this period, life expectancy on reaching age 65 increased by 4.3 years for men and 4.8 years for

women, reflecting much greater rates of mortality improvements for the elderly.

2.5.2 Mortality improvements in the 1990s

The pace of the most recent improvements can be illustrated by considering how quickly

mortality has improved during the 1990s. More specifically, changes over the period from 1989

to 2001 are considered. Table 2.3 below gives annualised rates of improvement up to 2001.

Table 2.3: Average annual rates of mortality improvement, English and Wales’s population, 1989-2001

Age Group 20-29 30-39 40-49 50-59 60-69 70-79 80+

Male -0.1% -0.1% 0.8% 2.7% 3.4% 2.2% 0.9% Female 0.4% 0.7% 0.8% 2.1% 2.8% 1.4% 0.0%

Source: ONS. (2003) – Willets et al., (2004)

There has been little overall change in mortality rates for men in the 20s and 30s. Women of

these ages and both men and women in their 40s have reasonable modest improvements in

mortality rates, but very substantial improvements for the 50-79 age group, peaking at ages 60-

69 (Willets et al., 2004).

The most dramatic mortality improvements in the 1990s applied to men and women in their 50s

and 60s. There were very significant reductions in deaths from both circulatory disease and

cancer. Also for these age groups and in contrast to younger ages, there were few material causes

showing increases in mortality rates. Mortality caused by the big three killers in the UK (heart

disease, cancer and strokes) has fallen steadily. Mortality from heart disease has reduced

particularly quickly. Over the 12 year period from 1989 to 2001, the reduction at some ages has

been as much as 50% (Willets et al., 2004).

An analysis of trends in mortality by social class for causes of deaths suggests that differing

improvements in heart disease mortality have played a major role in widening differentials.

18

Table 2.4 shows the components of average annual mortality improvements for males between

the early 1970s and early 1990s split by socio-economic class.

Table 2.4: Components of average annual mortality improvement rates, 1970-72 to 1991-93, England and Wales’s population, males aged 20-64

Class Heart Disease

Lung Cancer

Stroke Accidents Suicide Other All Cause

I 1.4% 0.3% 0.3% 0.1% 0.0% 0.6% 2.7% II 1.2% 0.3% 0.3% 0.1% 0.0% 0.7% 2.6%

IIIN 1.0% 0.3% 0.2% 0.1% 0.0% 0.4% 1.9% IIIM 0.6% 0.3% 0.2% 0.1% -0.1% 0.5% 1.5% IV 0.6% 0.3% 0.2% 0.1% 0.0% 0.6% 1.8% V 0.0% 0.2% 0.1% 0.1% -0.1% 0.2% 0.5%

Total 0.8% 0.3% 0.2% 0.1% -0.1% 0.6% 1.9% Source: ONS. (1997b) – Willets et al., (2004)

According to Cooper-Williams et al (2012), the most important factor in the 1990’s largest

mortality improvements was the improved development of vaccines and antibiotics to control

infectious diseases. The largest improvements in mortality were also due to cancers and

circulatory disease improvements which stemmed from medical screening, reductions in

smoking, better awareness of diet and medical advances.

2.5.3 Rates of Mortality Improvements for Pensioner and Assured lives

Willets et al. (2004) state that, as socio-economic class mortality differentials have widened over

time, one would expect, other things being equal improvements for pensioners and assured lives

to have been greater than for general population. This statement is based on the fact that socio-

economic class mix of these groups is higher than the population average.

Whilst there are distorting factors, such as changes in underwriting practice and the changing

prevalence of life assurance or pension provision in different socio-economic groups, the

experience for males has certainly backed up the general expectation. We find the same

sentiments expressed in the CMIR21 (2004). According to CMIR21 (2004) the mortality

experience of life office pensioners, which traditionally has been the most important experience

for pension schemes, has continued to increase significantly, being about 10% lighter than in

1995-1998 and 20% lighter than the ‘92’ series tables. This means that most schemes have been

19

under-estimating mortality rates. Under-estimating the mortality rates has huge financial

implications to a pension fund.

2.5.4 The Cohort Effect

The UK is well known for experiencing a trend called the “cohort effect”. According to Willets

et al (2004), people born around the 1930s have experienced consistently higher rates of

mortality improvement than those born into earlier or later generations. This generational effect

is seen in the United Kingdom assured life experience. There is more evidence about the cohort

effect as outlined by the CMIB Working Paper (2002) which investigated the possible existence

and impact of such an effect.

The Working Paper also notes a similar cohort effect for male life office pensioners retiring at or

after the normal retirement age. Peak improvements were found occurring in the 1926 cohort. A

similar cohort effect was identified by GAD in respect of those born a few years either side of

1926 (also called the ‘golden cohort’).

These cohort trends have been incorporated into future mortality projections outlined in the

Working Paper. The ‘cohort period’ was taken as being 10, 20 and 40 years for:

1. The short cohort, assuming additional improvement until 2010

2. The medium cohort, assuming the additional improvement until 2020; and

3. The long cohort, assuming the additional improvement until 2040.

The importance of the cohort effect is not limited to the golden cohort generation since it is

generally assumed that subsequent generations’ mortality experience will be lower than their

predecessors.

In addition to the cohort effect, it has also been observed in the UK that mortality rate

improvements for elderly people have been increasing over time. The process is known as the

ageing of mortality improvement. Cooper-Williams et al. (2012) go on to say that the astounding

effect of mortality improvements can be more evident when we also consider life expectancy.

Life expectancy at birth in the UK was 45 years for males in 1901; improving to 73 years in

1991. Thus for a 45-year old male, the reduction in mortality was approximately 82%. The Table

20

below shows improvements in UK mortality based on ONS mortality rates between 1992 and

2005.

Table 2.5: Average Annual Rates of Mortality Improvements for the period 1992 to 2005

Males Females 1992-1997 1997-2001 2001-2005 1992-1997 1997-2001 2001-2005

20-24 -1.8% 2.9% 4.8% 0.6% 1.4% 2.3% 25-29 -1.3% 2.1% 3.5% 0.4% 0.5% 0.4% 30-34 -0.3% -0.7% 3.5% 0.6% 0.4% 1.5% 35-39 2.3% -0.8% 1.2% -0.4% 2.6% 2.4% 40-44 0.4% 1.7% 0.9% 0.9% 1.4% 1.2% 45-49 0.8% 0.2% 1.6% 0.4% 1.0% 1.4% 50-54 2.4% 1.3% 1.3% 1.5% 0.9% 1.4% 55-59 2.1% 2.7% 2.6% 1.8% 1.9% 1.8% 60-64 3.1% 2.7% 2.4% 2.5% 2.2% 2.7% 65-69 2.8% 3.8% 3.1% 2.1% 3.5% 1.9% 70-74 1.6% 3.6% 3.4% 0.6% 3.0% 3.1% 75-79 2.0% 1.9% 3.3% 1.3% 1.4% 2.1% 80-84 1.1% 3.0% 1.5% 0.4% 2.3% 0.8% 85-89 0.4% 1.5% 2.8% 0.1% 1.2% 1.7%

Source: CMI Working Paper 27

The Table above shows that UK has experienced the widening of social class differentials which

relate to mortality improvements. Thus the mortality of the social classes has improved more

rapidly. This implies that rapid improvements could have been experienced for insured lives and

annuitants who have select mortality as compared to the general population. Therefore the

picture portrayed by the results in the Table is expected to be different from the situation

obtaining in Zimbabwe because the country has a negligible number of insured lives. However

what comes out clearly is that in the UK there have been mortality improvements due to

improved health care. The next few sections will also look at South Africa and Mauritius as other

case studies to support the idea that mortality is generally improving.

2.5.5 Mortality Improvements and South Africa

According to Cooper-Williams et al (2012), there has been very little done in South Africa in

terms of resources being committed to mortality improvements or longevity research. This

Section of the study looks at South African mortality and mortality improvements in terms of

data and mortality tables available as well as actuarial guidance. The Section also tries to gauge

the importance and relevance of mortality improvements to the industry.

21

A paper producing South Africa’s first annuitant standard mortality tables was published in 2007

by Dorrington et al. The paper set out standard annuitant mortality tables for the period 1996-

2000 and the same paper attempts to assess mortality in age bands over a range of calendar years.

According to Cooper-Williams et al. (2012 “While mortality improvements were observed, they

were also largely disregarded due to the following reasons:

The period over which the trends were looked at was too short

The improvements seen were not consistent with other mortality studies conducted in

South Africa between 1980’s and 2000’s for those aged over 65

The implied improvements of 3% for men and 6% for women were much higher than

those observed in the United Kingdom.

The pattern of the improvements did not match the one observed in the United Kingdom

either”.

Cooper-Williams et al. (2012) state that the inconsistencies above are more than likely linked to

problems in the data, especially considering that most of the deaths had not yet been processed.

However, even if there have been some inconsistencies, Cooper-Williams et al (2012) state that

there has been a general mortality improvement in South Africa over the past few decades due to

a number of factors which include technological advancement and education and lifestyle.

Advances in screening for heart disease and stents are expected to further improve mortality for

the higher socio-economic groups in South Africa due to the availability of facilities. This

however affects the lowest socio-economic classes who may have limited access.

On the other hand, the South African government’s intention to increase access to basic

healthcare through the National Health Insurance is unlikely to affect the mortality of the higher

socio-economic groups but would have an impact on the mortality of the lowest socio-economic

groups due to improved accessibility to facilities. Pension funds should bear this in mind since it

is likely to have a huge bearing on their asset-liability position. PGN 104 (Life Offices –

Valuation of Long-Term Insurers) states that liabilities being valued using the Financial

Soundness Valuation method should take account of future experience that may be expected with

respect to mortality.

22

The South Africa’s rollout of the antiretroviral treatment programme for HIV/AIDS is unlikely

to be of interest to anyone managing a portfolio of annuities or traditional life insurance business

since the impact is very small at retirement ages. Also the AIDS risk is underwritten out of the

traditional life insurance product. What is therefore currently obtaining in South Africa is that

mortality improvements are only covered implicitly to the extent that actuaries allow for them by

way of incorporating projected future experience or trends.

2.5.6 Mortality Improvements in Mauritius

Mauritius is facing a demographic transition with the population growth among the lowest in the

developing world. According to Suntoo (2012), this demographic transition has inevitably

brought about the problem of aging.

The results of the research show that Mauritius has completed its demographic transition in less

than four decades. The fall in mortality rates and fertility rates has led to some improvements in

life expectancy of the population and, thus, the society is aging. The paper has so much

relevance in the fast developing Mauritian society as it may help the Mauritian authorities at

reviewing the strategies regarding both formal and informal care system with a view to improve

the welfare and living conditions of the elderly population.

One of the similarities that can be drawn from the Mauritian research and this study is that the

main focus of attention is on bringing out clearly that strategies regarding the welfare of

pensioners and the elderly population should be improved. The Mauritian research is focusing on

the improvement of the welfare conditions in general.

Demographic Picture in Mauritius

What has been done for the UK mortality data in Tables 2.2a and 2.2b can also be done for

Mauritius’ demographic trends data. According to Suntoo (2012), an analysis of the mortality

trends for Mauritius since 1944 shows that the population of Mauritius has evolved over the

years in terms of both structure and size.

23

Table 2.6 below shows the demographic evolution of the period 1944-2010.

Table 2.6: Population growth in intercensal periods – Island of Mauritius 1944-2010

Census data Population enumerated at census Average Annual rate of increase (%)

11th June 1944 419 185 0.49 30th June 1952 501 415 2.26 30th June 1962 681 619 3.12 30th June 1972 826 199 1.94 2nd July 1983 966 863 1.44 1st July 1990 1 022 456 0.80 2nd July 2000 1 143 069 1.12

December 2010 1 245 289 0.40 Source: Central Statistical Office, (Compiled figures from 2005 and 2010 Reports), Port-Louis – Adapted from Suntoo (2012)

Table 2.6 shows that the population size in Mauritius was 419 185 in 1944. The annual rate of

growth, at 0.49% per annum, was well below 1% per annum. In 1952 the total population was

501 415. Looking at the figures in subsequent years, there is an indication that there was rapid

population increase. According to Professor Titmuss, cited in Suntoo (2012), when population

growth is not checked, serious repercussions can result, thereby seriously affecting the health and

social services. Thus, as a country, Zimbabwe also needs to look at population sizes across

different social groups to ensure that, among other issues, we are not affected by longevity risk.

Further analysis on the Mauritian population is shown in Table 2.7 below which outlines some

vital statistics.

Table 2.7: Population and vital statistics rates – Island of Mauritius 1946-2010

Period Population at mid-period

Crude Birth Rate

Crude Death Rate

Rate of Natural Increase

Infant Mortality

Rate 1946-50 average 483 797 44.7 20.8 23.8 119.6 1956-60 average 609 518 40.7 11.6 29.1 68.5

1970 805 489 26.8 7.8 19.0 57.0 1980 937 886 26.6 7.1 19.5 32.3 1990 1 024 571 21.3 6.7 14.6 19.9 2000 1 186 900 16.9 6.8 10.1 15.8

Dec 2010 1 245 289 11.5 7.2 4.3 12.4 Source: Central statistics Office, Port Louis (Compiled figures from 2005 & 2010 statistics) – Adapted from Suntoo (2012).

24

The demographic framework drawn, as depicted in Table 2.8 below, is based on the statistics

from Tables 2.6 and 2.7.

Table 2.8: Demographic framework

Period Crude Birth Rate

Crude Death Rate

Rates of Natural Increase

Infant Mortality Rate

1960-70 High High Very High Very High but Falling

1970-80 High but Falling Moderate High High but Falling 1980-2000 Moderate to Low Moderate to Low Moderate to Low Slightly High but

Falling 2000 onwards Low Low Low Moderate

Source: Suntoo (2012).

Tables 2.6, 2.7 and 2.8 are very useful for the purposes of describing the pattern of demographic

change registered in the evolution of the Mauritian population which has occurred over some

phases. To conclude this subject on mortality trends in this research, we would also like to give

an outline of the worldwide position.

2.5.6 Mortality Trends – A World Wide Picture

According to the report by the Population Division of the United Nations on the World Ageing

Population 1950-2050, prepared in 2002, 10% of the world populations was aged over 60 years

and above, with the population of those above 65 years standing at around 7%. In 2050, it is

estimated that the proportions for the said age groups will be 21.1% and 15.6% respectively as

shown in Table 2.9 below.

In the year 2000, 20.3% of Europe’s population belonged to the old age group of 60 years and

above while in Africa the corresponding proportion was 5.1%. Mauritius which forms part of

Africa, had the proportion for the 60 years or older standing at 9.0% and 6.2% for the age group

of 65 years and above of her total population. Mauritius’ proportions for the said age groups will

be 26.1% and 20.3% respectively.

25

Table 2.9: Estimated World Population aged 60 or over & 65 or over (2002)

Year 2000 Year 2050 Country Total

Population (millions)

Aged 60 and above as a % of

total population

Aged 60 and above as a % of

total population

Total Population (millions)

Aged 60 and above as a % of

total population

Aged 60 and above as a % of

total population

World 6 056.71 10.0 6.9 9 322.25 21.1 15.6 Europe 727.30 20.3 14.7 603.33 36.6 29.2 Africa 793.63 5.1 3.3 2 000.38 10.52 6.9 India 1 008.93 7.6 5.0 1 572.05 20.6 14.8 Malaysia 22.22 6.6 4.1 37.85 20.8 15.4 Mauritius 1.16 9 6.2 1.42 26.1 20.3 Singapore 4.02 10.6 7.2 4.62 35.0 28.6 Source: World Population Prospects, The 2002 Revision United Nations Publications

The figures in the above Table show that generally mortality is improving in the world.

2.6 Mortality Tables since the a(90) Mortality Table

Generally most Zimbabwean pension funds use the a(55) mortality table which is supposed to be

adjusted to be in line with the mortality experience for each particular pension fund. The problem

with the a(55) is that it is much heavier than all the other mortality tables which were later

developed. In fact the a(55) is way outdated. If the mortality basis for Zimbabwe is to be

adjusted to be in line with the assumed population ageing for Zimbabwe, the alternatives to be

considered will have to start from at least the a(90) mortality table which was derived from the

PA(90). This section is therefore going to look at the mortality tables that were developed since

the PA(90) mortality table. These are the tables that have been recommended by the Institute and

Faculty of Actuaries (UK) over the years.

PA (90)

Generally the PA (90) table is considered to be an outdated table which sounds relatively modern

because of the number in its name which is misleading. According to Richards and Jones (2004),

the PA (90) table was already thirty years old when its use for modern actuarial work was

questioned by Willets (1999).

The CMIR 14 (1995) quoted by Richards and Jones (2004) states that the PA (90) was a

projected table which is parallel to the PEG 1967-70 table upon which it is based. By 1979-82,

26

the mortality curve of the PA (90) was the wrong shape. If the curve was the wrong shape in

1979-82, then it is worse today. This is the reason why the PA (90) may not be relevant to use

today.

The ’80’ Series

The ‘80’ series was created from the graduation of the all-office experience of the 1979-82. This

table is also outdated. The data underlying this table is now over two decades old. However,

according to Richards and Jones (2004), there is evidence to show that the common practice of

using PMA80c2010 in some jurisdictions is still justified for industrial-type schemes that have

heavier than average mortality.

The ‘92’ Series

This series of mortality tables was the most up-to-date one available before the ‘00’ series (to be

described later). The series is still in common use by life offices in the UK. The ‘92’ series of

tables was created from the graduation of the all office experience in the 1991-94 quadrennial.

There are three potential tables (as outlined by Richards and Jones, 2004) for use in the valuation

of longevity liabilities. These are:

1. The Ixx92 tables for immediate life annuitants, for example purchased life annuities.

Here the purchase is voluntary and the qx values are lower than those for Rxx92 and

Pxx92. These tables are appropriate where there is a clear element of choice on behalf of

the policyholder to buy an annuity.

2. The Rxx92 tables for pension annuities purchased by holders of retirement annuity

contracts and personal pensions. These tables are appropriate for pension business only,

that is, where there is little element of choice on behalf of the policyholder to buy an

annuity due to compulsion in the tax regime. This mostly affects self employed people.

3. The Pxx92 tables for life office pensioners. Again these tables are appropriate only for

pension business where there is little element of choice like in a pension scheme where

pensioners are paid from the scheme. There are two noteworthy critical aspects about the

Pxx92 tables. These are:

27

(i) The mortality rates are mainly generations before the so called ‘cohort effect’

took hold. As such these rates are generally felt to have become rapidly out-of-

date. This led to the release of the interim cohort projection bases for use outside

the ‘92’ series (CMIB, 2002).

(ii) The experienced mortality rates for ages 50-60 are several times heavier than

implied by the pensioner tables. Only rates above 60 appear to be reliable for

practical use with non-impaired lives.

The ‘00’ Series

During the year 2006, the Actuarial Profession released its most recent set of mortality tables

based on the experiences over the four year period 1999-2002. These are known as the ‘00’

series and are the first base tables to be released since the ‘92’ series which covered the period

1991-94. They include new tables for the experience of life office pensioners in developed

countries and indications are that they will replace the ‘92’ series as recommended by the

Institute and Faculty of Actuaries (UK). According to Jones et al (2007), the ‘92’ series and the

new ‘00’ series are too light for the majority of retirees, although they are likely be reasonable

for the highest paid 25% of the pensioners.

2.7 Choosing the Mortality Table to use

According to the Pensions Regulator’s Consultation Document on Longevity of 2008, there have

been significant recent developments in the knowledge of current trends in mortality. Projections

which were in common use are no longer likely to be considered. Therefore when determining

mortality assumptions, there is need to demonstrate that the assumptions used for future

improvements are, overall, of sufficient strength to be justified given the level of evidence on

mortality improvements. There are two separate decisions to be made when choosing mortality

assumptions. These are:

1. The baseline table for the current rates of mortality; and

2. The allowance for future improvements.

Whilst the baseline assumption may be scheme specific, individual schemes will not normally

have evidence to make a scheme specific allowance for future improvements and will need to

base their choices on broader data.

28

From the description of mortality tables already given and also considering the mortality

projections, one would be tempted to choose the ‘00’ series 26 as best table to use in Zimbabwe.

As Jones et al (2007) put it; it appears the rates of improvement do not keep pace with historic

trends, even with the medium adjustments. In light of the uncertainty surrounding the allowance

for future mortality improvements, and the fact that the ‘92’ series tables, and indeed earlier table

projections, were quickly found to understate life expectancy improvement, no projections were

issued alongside the ‘00’ series tables. Using the ‘00’ series therefore requires scheme actuaries

to consider the appropriateness of available projection methodologies for application to

particular situations.

However, actuaries pension managers have to take a cautious approach since assumptions should

be chosen prudently. Above all, good practice requires mortality assumptions to be evidence

based and to be clearly and transparently described.

2.8 The Conceptual Framework

The conceptual framework has been specifically designed for this study

Technological

Advancement

Longevity Risk Mortality

Improvements

Education and

Lifestyle

Other External

Factors

29

The relationships that can be drawn from the above conceptual framework are as follows:

1. Technological advancement in health facilities influences mortality improvements

2. Education and lifestyle have a direct bearing on mortality improvements

3. There exist other factors that influence mortality improvements

4. Mortality improvements cause longevity risk

2.9 Chapter 2 Summary

From the given discussion on mortality, we can conclude that mortality is generally improving

and there are expectations that it will continue to improve due to technological and healthcare

improvements. Significant improvements are being witnessed in developed countries like UK.

We have also witnessed moderate improvements in developing nations. This implies that

longevity risk is increasing. So, in terms of the valuation of pension schemes, actuaries are

expected to use realistic mortality assumptions.

RESEARCH METHODOLOGY

3.1 Introduction

This chapter explains how the research was

research objectives and the problem statement outlined in Chapter 1.

a research methodology can be defined as a procedural framework within which a research is

conducted. The aspects to be covered in this c

approaches. Research strategy, research pur

techniques, primary research, secondary research, data collection

presentation. The structure of this chapter was aided by the research “o

outlined by Saunders, Lewis and Thornhill (2009) which guides a researcher o

aspects to be considered when carrying out a research.

in this research as well as research ethics and credibility of data gathered.

Figure 3.1: Research Onion adapted from

CHAPTER 3

RESEARCH METHODOLOGY

how the research was developed and conducted in order to address the

research objectives and the problem statement outlined in Chapter 1. According to Fisher (2010),

a research methodology can be defined as a procedural framework within which a research is

aspects to be covered in this chapter include research philosophies, research

approaches. Research strategy, research purpose, study population, sampling and sampling

techniques, primary research, secondary research, data collection as well as data analysis

The structure of this chapter was aided by the research “onion” shown below as

Lewis and Thornhill (2009) which guides a researcher o

when carrying out a research. This chapter also look

in this research as well as research ethics and credibility of data gathered.

adapted from Saunders et al., (2009)

30

developed and conducted in order to address the

According to Fisher (2010),

a research methodology can be defined as a procedural framework within which a research is

hapter include research philosophies, research

pose, study population, sampling and sampling

as well as data analysis and

nion” shown below as

Lewis and Thornhill (2009) which guides a researcher on those key

also looks at the limitations

31

3.2 Research Design

3.2.1 Research Philosophy

Saunders et al. (2009) state that research philosophy relates to the development of knowledge

and the nature of that knowledge. A research philosophy determines the research strategy and

methods used. Research philosophy is influenced by the view of the relationship between

knowledge (ontology) and the process to develop that knowledge (epistemology). Ontology is

concerned about what assumptions the researcher makes about the way in which the world

works, that is the nature of reality. It deals with whether reality is objective (factual) or

subjective (feelings & attitudes of people). Epistemology on the other hand deals with

assumptions on how knowledge about how a phenomenon is generated. Epistemology therefore

deals with issues of whether the researcher should be closer to the respondents or should

maintain a distance.

According to Saunders et al. (2009), there are three major research philosophies, which are, the

positivist, the interpretivist and the realist. The positivist is usually associated with natural

science research and involves empirical testing found in quantitative research. The positivist

strives to control, predict and explain by dividing things into parts and isolating them into

mechanistic processes in an external world. Saunders et al. (2009) also state that this type of

approach is objective, value free, normally uses quantitative data, deductive, and that truth has to

be confirmed with empirical evidence through hypothesis testing. Positivism is a quantitative

methodology which focuses on classified objective outcomes with causal connectivity with

respect to occurrence of events, behaviour or any aspect under investigation.

On the other hand, qualitative (phenomenological or interpretivist) approach assumes that the

social world is too complex to be assessed based on defined principles or laws as is done in

physical science as this discards other rich insights into such a complex social world (Saunders

et al., 2009). The interpretivist (phenomenology) promotes the idea that the subjective thought

and ideas are valid and is associated with qualitative research. It aims to see the study through

the eyes of the people being studied. The realist takes aspects from both positivist and

interpretivist positions. The realist is associated with mixed methods or triangulation.

32

In this study, the researcher predominantly took the positivist philosophy because this study is

based on quantitative data. The survey data was collected using a questionnaire and valuation

data for pensioners was collected directly from pension funds in Zimbabwe. Data on mortality

trends in Zimbabwe was collected from Zimstat. The variables to be included in the

questionnaire have been studied in other countries and hence are known. The researcher tested

the variables on the Zimbabwe market and drew some conclusions as outlined in chapters 4 and

5.

3.2.2 Research Strategy

There are various research strategies that can be applied in a research. A research can be an

experiment, a survey, case study, an archival research, the grounded theory and ethnography

(Saunders et al., 2009). This is part of the research which highlights the overall plan and tactics

to answer the research questions (Saunders et al., 2009). These strategies to be applied depend on

whether the purpose of the study is explanatory (establishing relationships among variables) or

exploratory (establishing new insights). This research is an explanatory one although this is a

fairly new area which has been premised in the pensions sector which is already well established

and regulated. During the course of the research study, the researcher tried to establish whether

the factors and the condition would favour mortality improvements in Zimbabwe.

The research was also punctuated by some desktop analysis using data obtained from pension

funds and Zimstat.

3.3 Population and Sampling Techniques

3.3.1 Population

Target population refers to the entire group of individuals or objects to which researchers are

interested in generalizing the conclusions (Saunders et al., 2009). In statistics, a sampling frame

is the source material or device from which a sample is drawn. Sampling frame is a list of all

those within a population who can be sampled, and may include individuals, households or

institutions. In this study, the population is all the pension funds in Zimbabwe. The full list of

pension funds was obtained from the Insurance and Pensions Commission (IPEC) and also the

33

Zimbabwe Association of Pension Funds (ZAPF). In order to ensure a good representation of

each institution as well as to ensure consistency in responses, at least 15 questionnaires were

administered at each institution.

3.3.2 Sampling

Various sampling techniques are available for use depending on the nature of the research and

the characteristics of the population under study. These techniques can be divided into two,

namely probability and non-probability sampling techniques. According to Saunders et al

(2009), with probability samples the chance, or probability, of each case being selected from the

population is known and is usually equal for all cases. This means that it is possible to answer

research questions and achieve objectives that require estimating statistically the characteristics

of the entire population from sample statistics. For non-probability samples, the probability of

each case being selected from the total population is not known and it is impossible to answer

research questions or to address objectives that require you to make statistical inferences about

the characteristics of the population. Probability sampling techniques include simple random

sampling, systematic sampling, cluster sampling and stratified random sampling. Some of the

non-probability sampling techniques are quota sampling, purposive or judgmental sampling, and

convenience sampling.

In this study, the researcher used stratified random sampling method. Random sampling is a way

of selecting a member at random from a sampling frame by using random number tables, a

computer or an online number generator and Saunders et al. (2009) goes further to distinguish

this from stratified sampling in that stratified sampling is a modification of random sampling that

ensures that sample is put in strata (layers) so that there is an equal representation of the different

groups in the variable under study. Thus the 15 expected respondents were put in the following

strata:

Executive Management 3

Management 4

Non-Managerial 4

Pensioners 4

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3.4 Sources of Data

Data was collected from employees who belong to pension funds, pensioners and Zimstat. The

other 5 questionnaires were distributed to employees at the Zimbabwe Association of Pension

Funds (ZAPF).

3.5 Data Collection Procedure (Research Instrument)

Data can be collected through a combination of interviews and questionnaires. The study used a

questionnaire as the research instrument and at least 15 questionnaires were distributed to each of

the pension funds in Zimbabwe with the target of bringing the total number of questionnaires to

at least 225. Additional questionnaire were distributed to ZAPF. Stratified sampling was used to

ensure that there was representation from executive management; management and non-

managerial staff and pensioners. Some of the questionnaires were delivered by hand while others

were distributed by email. The researcher expected to achieve a response rate of between 50%

and 75%. However, the actual response rate was 44%, which by any standard is still fine

according to Saunders (2009). The distribution of the questionnaires and collection of responses

took two weeks. Follow-ups were done through telephone calls and email so as to ensure a good

number of responses.

Justification of the research instrument

Questionnaires are typically used in survey situations, where the purpose is to collect data from a

relatively large number of people, say between 100 and 1,000 (Rowley, 2014). This research

involved capturing responses from 230 respondents and the questionnaire becomes an ideal

instrument. Rowley (2014) further argues that a questionnaire is necessary since the study is

quantitative where the researcher is concerned with the frequencies on issues to do with

opinions, attitudes, experiences, processes, behaviors, or predictions. In this study the researcher

was mainly concerned with possibility of pensioner mortality improvements whose existence

depends on technological improvements in healthcare among other issues. The researcher should

be able to infer his findings on the population and then make conclusions on whether there is

mortality improvement or not. If there is mortality improvement, the study also needs to establish

the magnitude of the effect of longevity risk on pensioner liability and recommend possible ways

of ensuring that there is matching of assets and liabilities.

35

In summary then, questionnaires are useful when:

The research objectives centre on surveying and profiling a situation, to develop overall

patterns.

Sufficient information is already known about the situation under study that it is possible

to formulate meaningful questions to include in the questionnaire.

Willing respondents can be identified, who are in a position to provide meaningful data

about a topic. Questionnaires should not only suit the research and the researcher, but

also the respondents.

3.6 Data Analysis

Data obtained through questionnaires was processed by use of a statistical package, SPSS

applying the following procedure:

a) Coding: Data was coded and input in SPSS

b) Demographics: Frequency tables for the demographics were presented and the researcher

explained the relevance of including any of the variables in the demographics.

c) Reliability: The reliability of the instrument was measured using the Cronbach’s Alpha.

Generally a good instrument should have a Cronbach’s Alpha of greater than 0.7. The

overall reliability of the instrument was 0.842

d) Normality tests: The test for normality was done to determine whether the researcher

would proceed by using parametric tests or non-parametric tests. Since the number of

respondents was 128, which is less than 2,000, a Shakiro-Wilk test was done.

e) Correlations

Correlation measures the extent to which two continuous variables are related, in other

words, it shows how one variable changes with respect to changes in the other variable

(Rowley, 2014). The most common tests are the Pearson’s correlation coefficient and

Spearman’s rank correlation coefficient. Pearson’s is used for interval or continuous data

while Spearman’s is used for ordinal variables.

f) Regression analysis

Regression goes much further than correlation. Not only does it look at the relationship

between the variables but it also goes on to develop a line of best fit that best describes

36

the relationship between the variables (Rowley, 2014). The coefficient of determination

enables a researcher to assess the strength of relationship between a dependent variable

and one or more independent variables (Saunders, 2009). The coefficient of

determination is given by R-squared and it takes any values between 0 and 1. This

coefficient is a measure of the degree of the independent variables’ explanatory power.

Thus, a perfect predictor would have a coefficient of 1.

g) Statistical inference: This was done to ensure that these results can be adequately used to

represent the whole population.

Assets and liabilities shall be analysed using an Actuarial Model

3.7 The Actuarial Model

Here we describe the Actuarial Valuation Model used in the valuation of Defined Benefit

Schemes. The researcher also gives an outline of the two methods of determining the standard

contribution rate, that is, the Projected Unit Method and the Attained Age Method. These are the

methods used in this study. The main focus of attention in this research was to assess the effects

of changing mortality bases of a pension scheme. The major concern was to see how post-

retirement mortality affects the liability position of the pension schemes. The funding methods

set out in this dissertation apply to defined benefit schemes where pensions are based on final

earnings.

3.7.1 The Actuarial Model used for the Valuation of Liabilities

The aim of this research was to assess the impact of changes in mortality to Defined Benefit

pension schemes. We do this by observing the changes to the Standard Contribution Rate (SCR)

and the Standard Fund (SF) as we make changes to the mortality assumptions. To achieve this

we needed a valuation model which enabled us to calculate the SCR and the SF. Thus we needed

a valuation model which enables us to achieve actuarial valuation objectives, that is:

to investigate the adequacy of the existing assets to meet the liabilities of the scheme

accrued to the valuation date.

to review the contribution rate that would be appropriate for the future.

37

The valuation model used in this research is an Excel workbook built by the researcher which

uses commutation functions. With this model, it is easy for us to calculate the SCR and the SF.

With the workbook in place, the task basically was to change mortality tables in the model and

observe the changes to the SCR and the SF. We are mainly concerned about mortality after

retirement. The Model should enable us to calculate the amount of money required:

now to afford the benefits promised in respect of past service, that is the reserve.

to pay in the future to honour the promise on the benefits in respect of future service, that

is the contributions

The above two bullet points illustrate how we embrace the concept of Time Value of Money.

3.7.2 Important Features of the Actuarial Model

Service Table

This is a multiple decrement table with the decrements for death in service (d), withdrawal from

service (w), age retirement (r) and ill-health retirement (i). The table has values for lx, rx, ix, dx

and wx, where la is the radix of the table associated with the entry age a. We then used the

following:

lx/la =Pr [a member is in service at age x | in service at age a]

dx/la =Pr [a member dies in service between x and x + 1 | in service at age a]

wx/la =Pr [a member withdraws from service between x and x + 1| in service at age a]

rx/la =Pr [a member retires on age grounds between ages x and x + 1| in service at age a]

The Service Table provides us with decrement probalilities.

Withdrawal: probability is initially high, but decreases with age.

Death: probability is initially low, but increases with age.

Ill-health: probability increases with age but is very low.

These decrements are very important in the calculation of Expected Present Values (EPVs) of

future benefits.

38

Promotional Salary Scale

There is also need for a Salary Scale. We define a scale sx such that, ignoring general inflation,

Salary escalation:

sx+t/sx = E

salary earned between ages x + t - 1/2 and x + t + 1/2

in service throughout

salary earned between ages x - 1/2 and x + 1/2 The salary scale uses the same numbers as in the previous edition of the Formulae and Tables for

Actuarial Examinations. However, in the spreadsheet to be used for the calculations, sx was

defined as shown above.

Importance of the Service Table and the Promotional Salary Scale to this research

The aim of this study was to come out with results which are as realistic as possible. We

therefore used realistic data from pension funds so that the whole process can be used to

generalise what happens in all pension funds in Zimbabwe. This differs from the approach used

by Cooper-Williams et al (2012) who used hypothetical data in their study.

Salary and final salary calculations

In order to project expected benefits and contributions we have to project salaries forward. If S is

the salary earned by a member in the year of age x – ½ to x + ½, the salary expected to be earned

in the year of age x + t -1/2 to x + t +1/2 is:

S.sx+t.(1+e)t/sx

We define sex = (1+e)x.sx, so that the salary expected to be earned in the year of age x + t -1/2 to

x + t +1/2 is:

S.sex+t/s

ex, allowing for inflationary and promotional increases

This is appropriate for this research since we are able to project salaries.

3.8 Funding Objectives and Valuation Methods

In determining the rates of contributions to be made to a pension scheme, the main objective is

that sufficient assets should be built up during the working lifetimes of employees to provide

their pensions and other benefits. It is also desirable that, as far as possible, the costs of the

benefits accruing for future service should be expressed as a percentage of pensionable earnings

that can be expected to remain reasonably stable. If a scheme is closed to new entrants, the

39

Attained Age Method would be appropriate, as explained in Section 3.8.2. However, if a scheme

is ongoing, then the Projected Unit Method would be appropriate, as explained in Section 3.8.1.

As such, the valuation methods to be used in the calculations for this study are the Projected Unit

and the Attained Age Methods. There is also a third valuation method, the Entry Age method.

This method is not applicable here because the data to be used comes from mature pension

schemes.

3.8.1The Projected Unit Method

Under the Projected Unit Method:

1. The value of all benefits which will accrue during the year following the valuation date,

based on projected pensionable earnings at retirement or death, together with risk benefits

payable on death or ill health retirement during that year is calculated. The result is then

divided by the pensionable payroll to produce a standard contribution rate.

2. To enable the level of funding in relation to benefits accrued for service to the valuation

date to be assessed, the value of the liabilities in respect of such service again based on

projected pensionable earnings at retirement or death is calculated. The value is then

compared with the value placed on the assets.

3. The recommended contribution rate is usually determined as the sum of the standard

contribution rate and an adjustment to reflect any shortfall or surplus of assets over

liabilities in respect of past service.

Reasons for using the Projected Unit Method

This method is widely used in Zimbabwe. It has the major advantage that the pension is paid for

as it accrues, not in advance which is unpopular with employers. It is also not paid for in arrears,

which is unpopular with employees, whose benefits may not be secure.

The method is suitable based on the assumption that the rate at which new members join the

scheme is the same as that of members exiting the scheme. The standard contribution rate

calculated by this method depends on the composition of the membership by age, salary and

40

status. If the average age should increase, the contribution rate would tend to rise. An example of

this situation is when a scheme is closed to new entrants; and the Projected Unit method would

not be appropriate. However, in the normal course the average age would be expected to be fairly

stable, but it must be borne in mind that no approach to financing a scheme can avoid

fluctuations in the required rate of contribution. This will always be sensitive to the

characteristics of the future membership and their experience.

3.8.1The Attained Age Method

Under the Attained Age Method:

1. The value of all benefits, which will accrue after the valuation date during the remaining

working lifetimes of the members, based on projected pensionable earnings at retirement

or death, is calculated. The result is then divided by the present value of the pensionable

earnings payable during the remaining lifetimes to produce a projected standard

contribution rate. We shall, however, value the insured death in service benefits on a

current cost basis by taking the expected cost in the year after the valuation date.

2. To enable the level of funding in relation to benefits accrued for service to the valuation

date to be assessed, the value of the liabilities in respect of such service again based on

projected pensionable earnings at retirement or death is calculated. This value is then

compared with the value placed on the assets.

3. The recommended contribution rate is usually determined as the sum of the standard

contribution rate and an adjustment to reflect any shortfall or surplus of assets over

liabilities in respect of past service.

Reasons for using the Attained Age Method

There was also an option to use the Attained Age Method especially considering what is

happening in the market where most Defined Benefit (DB) schemes are now closed. However

the schemes that participated in this research are not closed to new entrants.

With the Attained Age Method, future contributions are calculated to be level over the whole of

the projected future period of the member’s service compared with only one year (or the control

41

period) used in the Projected Unit Method. Since the contributions will be greater than those

required to pay for the accrued benefits, as defined by the standard fund, there will be a ‘surplus’

arising. This surplus should not be available for distribution, but should be a future service

reserve, to be held for when the workforce has aged to the point when contributions are not

sufficient to cover accruals – which may never happen. Therefore there is an advantage that if

the scheme were to close, contributions should remain level.

The Attained Age Method of valuation leads to a recommended contribution rate on the

assumptions made which will remain stable as members increase in age. No allowance is made

for new entrants. However, it must be borne in mind that no approach to financing a scheme will

avoid fluctuations in the required rate of contribution. This will always be sensitive to the

characteristics of future membership and experience of the scheme.

In this research study, the Attained Age Method was only used to test the robustness of the

model since all the valuation data that came from the 15 pension funds showed that the schemes

are not closed to new entrants.

3.9Research Ethics and Data Credibility

3.9.1 Research Ethics

Greener (2008) defines ethics as moral choices that determine decisions, standards and behavior.

The researcher faces a number of moral choices and dilemmas as he addresses research

procedures for example how and where to meet people, how to gather the data and how to deal

with people who are not cooperative.

a) Access: The researcher addressed this by providing full information about the purpose of

the study and the researcher’s objectives. Where necessary, a student Identification Card

was displayed by the researcher for identification purpose.

b) Researcher’s identity: The researcher’s identity had the potential to affect the respondents

who viewed his study as a way of gaining market intelligence within the pension

industry. The researcher fully explained to the respondents how the research would be

based on the respondent’s opinion rather than market sensitive issues.

c) Time: People generally do not respond in time. In this study, the researcher put a lot of

effort towards following up with the respondents. This was done using phone call and

42

emails. The researcher was working with a deadline and naturally he expected to have a

high response rate but it was not possible to force those who were not prepared to

participate. Hence the 56% response rate.

d) Confidentiality: the purpose of ethical considerations in a research is to ensure that no

one is harmed or suffers adverse consequences from the research activities (Cooper and

Schindler, 2003). The researcher ensured that the rights of the respondents were protected

by not writing their names and also the names of the pension funds whose data we used

in this study.

3.9.2 Data Credibility

The credibility of a research is measured by its validity and reliability. Greener (2008)

characterised validity in three different ways: face validity, construct validity and internal

validity. Face validity is the ability to recognise that the instrument and the method being used

are proper just from the face of it. Even a lay person would see the sense of the research. This is

important to encourage participation. Construct validity of the instrument is when the instrument

and method are able to measure what the researcher intends to measure. Sometimes results can

be invalidated because the respondent does not understand the questions and goes on to answer

in a way that they think is intended. Internal validity relates to causality, the ability to identify

independent factors; and how the factors cause effect to the dependent variable. To address the

issue of validity, the researcher made use of a large sample of 230 participants after carrying out

a pilot study. The pilot study helped the researcher to adjust some questions.

Reliability of an instrument is measured by the Cronbach’s alpha which is a single test estimate

to measure how reliable an instrument is. Gliem and Gliem (2003) define the Cronbach’s Alpha

as the average value of the reliability coefficients one would obtain for all possible combinations

of items when split into two half-tests. This coefficient ranges from 0 to 1 and the closer it is to 1

the more consistent or reliable the instrument is. A good instrument should have a Cronbach’s

Alpha of at least 0.7.

43

Data collection instruments and justification

The survey data was collected through the use of questionnaires. Structured and semi structured

questions were administered. A check list was provided to the pension scheme managers for

them to provide the data that was required for the actuarial valuation.

Data Analysis

Data obtained from the questionnaires was analysed using SPSS where the following was put to

test:

(a) Reliability tests were done by measuring validity and credibility of the research instrument.

Credibility was measured using the Cronbach’s Alpha.

(b) Test for normality. There are two tests for normality: the Kolmogorov-Smirnov test and the

Shapiro-Wilk test. Since the sample is between 3 and 2,000, we used the Shapiro-Wilk test.

(c) Transformation of data was carried out for the purposes of loading all variables into the

factors that were under consideration.

(d) Significance and influence of each of the three factors was determined using R, R-squared

and the adjusted R-Squared values for the variables.

Research Gap

There has not been a similar study in Zimbabwe which is in the public domain.

Pensioner assets do not match the liabilities and longevity risk is likely to worsen the gap.

The research adds to the body of knowledge. It seems very little attention has been paid to

mortality improvements and longevity risk

This area has attracted a lot of attention in developed countries as witnessed by an increasing

number of working papers.

3.10 Research Limitations

The study was limited to Harare only because of time and cost constraints. Time permitting;

researches on mortality issues should be longitudinal. To cater for this limitation, we used the

44

actual valuation data from 15 pension defined benefit pension funds to carry out actuarial

valuations.

3.11 Chapter Summary

This chapter outlined how we went about carrying out the research, firstly by looking at the

research philosophy that influences the researcher’s decision and then the identification of the

population and sampling frame as well as the sampling method used. The Chapter has also

outlined the Actuarial Valuation methods used and these are the Projected Unit Method and the

Attained Age Method. The Projected Unit Method is used to give conclusions on schemes which

are in force whilst the Attained Age Method is used to give conclusions on schemes which are

closed to new entrants. This Chapter also outlined the reasons for the use of the research

instruments chosen, the actuarial valuation methods and how data was analyzed before

concluding by looking at the limitations of the research and the ethical issues involved in the

research.

45

CHAPTER 4

DATA PRESENTATION AND ANALYSIS

4.1 Introduction

This chapter is structured in such a way that it commences with the analysis of the response rate.

Then we proceed with the analysis of the research findings, presenting results and discussions in

seriatim of the research objectives identified in Chapter 1. The process involves comparing

information obtained through the review of literature as discussed in chapter two against

information obtained from the research findings. The testing of the research proposition, to do

with mortality improvements in Zimbabwe, is based on the data obtained using a questionnaire.

The effects of mortality improvements on pension planning have been done using an actuarial

model outlined in Chapter 3. The data used here came from pension funds as outlined in the

valuation data requirements checklist. This chapter ends with concluding remarks and

recommendations.

46

4.2 Response rate

Figure 4.1: Response Rate

Figure 4.1 above summarises the response rate. A response rate can be defined as a measure of

the extent to which the final data set includes all sampled members. It is calculated as the number

of research subjects who responded divided by the total number of subjects in the study sample

including those who refused to participate. A total of 230 questionnaires were administered

personally and through e-mails by the researcher and out of these, 128 questionnaires were

successfully returned. With n = 128 and N = 230, the overall response rate was 56%. This

response rate was aided by the physical distribution and the vigorous follow up on emails which

were adopted by the researcher. This gave a response rate that is consistent with Grays (2009)

and Dillman (2007) who say that questionnaires distributed physically or by email yield a good

response rate.

56%

44%

Responded Unresponded

47

4.3 Demographics

4.3.1 Gender

Table 4.1: Gender of respondent

Gender Frequency Percentage

Male 79 61.7%

Female 49 38.3%

Total 128 100%

Out of 128 respondents, 62% were males and 38% were females and this sample is

representative of the current situation in the pensions industry where there are more males than

females. However, the results on gender go against the results of the Labour Force Survey of

2014 done by Zimstat which reveal that in Zimbabwe, of all the people employed formally, 51%

are females and 49% are males. The analysis of the gender distribution is important for this study

because it is necessary to get the opinions of both males and females in terms of pension

planning in Zimbabwe. The issue of pension affects everyone including children. However in

this research we have not considered children to be part of the respondents to the questionnaire

because generally children have better mortality compared to adults.

4.3.2 Ages of Respondents

Table 4.2: Ages of respondents

Age (X years) Frequency Percentage

X < 30 8 6.2%

30 = <X< 50 56 43.8%

50 =<X< 70 46 35.9%

X >= 70 18 14.1%

Total 128 100%

Table 4.2 above gives clear evidence that the 30 – 50 age group with n=56, constituting a

proportion of 44%, is the largest group of formally employed people. This is supported by the

Zimstat’s Labour Force Survey of 2014 which states that people are spending more time in

colleges and universities before they get employed. More and more people have seen the

importance of attaining higher education in their lives. This is a critical point in our research in

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the sense that education and lifestyle have been considered as a contributor to mortality

improvements. Another significantly big group is the 50 – 70 age-group n=46 which constitutes

about 36% of the sample. This group consists of some people who are still employed and others

who are already pensioners. We also have about 14% n=18 of people who are aged above 70

years. About 6% n=8 of the respondents are aged less than 30 years. This is not a good result

because it does not promote the intergenerational cross subsidisation which is necessary in

pension planning. Intergenerational cross subsidisation helps to strike a balance between the

contributions coming from active employees and liability obligation towards the benefits of the

pensioners and their beneficiaries.

4.3.3 Position at Work

Table 4.3: Position at work of respondent

Position at work Frequency Percentage

Senior Management 16 12.5%

Middle Management 16 12.5%

Junior Management 20 15.6%

Non-Managerial 20 15.6%

Pensioner 56 43.8%

Total 128 100%

The results in table 4.3 above show that 44% n=56 of the respondents were pensioners, about

16% n=20 were non-managerial staff, about 16% n=20 were junior managers, about 13% n=16

middle management and 13% n=16 were senior managers including executives. The results are

more skewed to the pensioners group. The pensioners have responded overwhelmingly most

probably because they are the most affected group if pension funds fold. Hence the need to

ensure that pension funds are well managed to ensure that they operate with healthy funding

levels.

49

4.3.4 Highest Academic Qualification

Table 4.4: Academic Qualifications of Respondent

Academic Qualification Frequency Percentage

Masters 52 40.6%

First Degree 54 42.2%

A’ Level 5 3.9%

O’ Level 8 6.2%

Primary Education 9 7.0%

Total 128 100%

The bulk of the respondents are degreed as evidenced by the 41% (n=52) of respondent having

masters degrees as the highest qualifications and 42% (n=54) having first degrees as the highest

qualifications. We also have about 4% (n=5) of the respondents who have A’ Level, about 6%

have O’ Level and 7% have primary education as the highest qualification. Those with primary

level of education as the highest level of education are mainly employed by mines and most of

them are in their retirement or are about to retire. Whilst there was no respondent who is a

professor or a holder of a Doctorate, the statistics show that the participants had enough

academic qualifications to understand issues to do with mortality. One’s level of education can

also determine that person’s life expectancy. This seems to agree with the results in section 4.1.2

where we have highlighted that most of the employees are aged between 30 and 50 years as a

result of them having spent more time seeking higher levels of education.

Whilst attaining higher levels of education has a positive contribution towards mortality

improvements, there is a danger that the same people may have a shorter working life if

retirement ages are not adjusted upwards. Shorter working life may imply contributing less

towards retirement benefits if salary levels and investment income do not compensate the time

spent in school.

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4.3.5 Highest Professional Qualification

Table 4.5: Professional Qualification of Respondent

Professional Qualification Frequency Percentage

Associate 21 16.4%

Diploma 48 37.5%

Certificate 50 39.1%

Other 9 7.0%

Total 128 100%

The results in Table 4.5 show that it is becoming a trend for people to support their academic

qualifications with professional qualifications. The statistics show that none of the respondents is

a Fellow or a Chartered Secretary in their respective professions. However about 16% (n=21) are

Associates in various fields, about 38% (n=48) have Diplomas in their respective professions

and, about 39% (n=50) have Certificates in their professional areas. There is also the remaining

7% (n=9) where the respondents have any other professional qualification. This included those

with driver’s licenses and also those who have not yet attained any professional qualification.

4.3.6 Current Pensionable Service

Table 4.6: Current Pensionable Service for the Respondent

Service in years (X) Frequency Percentage Cumulative %

2=<X<5 24 18.8% 18.8%

5=<X<10 16 12.5% 31.3%

10=<X<15 20 15.6% 46.9%

15=<X<20 33 25.8% 72.7%

X>=20 35 27.3% 100%

Total 128 100%

Table 4.6 above shows that all the respondents have current pensionable service of more than 2

years. About 53% (n=68) of the respondents have current pensionable service of more than 15

years which means that their defined benefit liability is substantial. Those with pensionable

service of less than 15 years (47%, n=60) are the ones with a higher probability of changing

benefits and take their withdrawal benefits. Withdrawal benefits have a negative effect on

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Asset/Liability modeling for the pension fund in the sense that the pension funds will be forced

to disinvest prematurely.

4.4 Reliability

Table 4.7: Overall Reliability Test of the Instrument

Reliability Statistics

Cronbach's Alpha Number of Items

0.842 33

The data collected was tested for reliability in order to check if there was internal consistency

between the constructs. Table 4.7 shows that the research instrument scored a high score on the

reliability test as measured by the Cronbach’s alpha of 0.842. A good instrument should have a

Cronbach’s Alpha which is greater than 0.6 as recommended by Saunders et al. (2009).

Therefore the results from data collected using the research instrument can be relied upon as

there is a high probability of validity and consistency in the data.

Table 4.8: Reliability test for the transformed variables

Factor Number of Items Cronbach's Alpha

Technological Advancement to Health Delivery 7 0.853

Education and Life Style 14 0.848

Other Factors 11 0.843 Mortality 1 0.825

OverallCronbach's Alpha 33 0.842

Table 4.8 above illustrates the reliability test results for the transformed variables as measured by

the Cronbach’s Alpha. The number of items column shows the number of questions under each

construct, for example, there were 7 questions under the Technological Advancement to health

delivery variable and this variable gave a Cronbach’s Alpha of 0.853. All variables had high

reliability levels thereby implying that the respondents gave consistent and valid responses.

Other variables yielded Cronbach’s Alpha values as follow: Education and Lifestyle (0.848),

Other Factors (0.843) and Mortality (0.825).

Before the actual survey, the researcher carried out a pilot study with 20 questionnaires to check

on face and content validity of the instrument. The results of the pilot study helped us to adjust

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some questions after getting expert advice from an academic (Dr Kadenge) who supervised this

study.

4.5 Normality Test

Table 4.9: Normality test

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic Df Sig. Statistic df Sig.

Mortality .080 128 .031 .953 128 .000

a. Lilliefors Significance Correction

Table 4.9 above illustrates the results of a normality test carried out on the data to check whether

or not the data was normally distributed. The test statistic of 0.953 which was achieved with a

significance of 0.000 (p<0.05) shows that the data was not normally distributed therefore non-

parametric tests were conducted. Since the sample is less than 2,000 we have used the Shapiro-

Wilk test and data is normally distributed if test statistic is 1.

4.6 Correlation Analysis

Table 4.10: Correlation analysis/ Matrix

Tech_Advance Other_Factors Educ_Life Style Mortality

Tech_Advance 1

Other_Factors .704** 1

Educ_Life Style .789** .592** 1

Mortality .944** .831** .889** 1

**. Correlation is significant at the level 0.01 (2-tailed)

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4.6.1 Technological Advancement in health delivery and Mortality Improvement

Basing on the results in Table 4.10 above, there is a strong positive relationship (r=0.944**,

p<0.01) between technological advancement in health delivery and mortality improvements.

This means that the more a country advances technologically in health delivery, the more the

mortality of the population in that country improves. In other words life expectancy at all ages is

expected to increase. This is positive news to everyone. However policy makers then need to

realise that mortality improvements are closely linked to longevity which affects pension funds.

The results on the relationship between technological advancement and mortality are in

agreement with the reviewed literature as discussed in chapter two of this research.

4.6.2 Relationship between Other Factors and Mortality Improvements

The results in Table 4.10 above show a positive and strong relationship (r=0.831**, p<0.01)

between other factors and mortality improvements. The other factors that have been considered

in this research are the Millennium Development Goals, Government intervention (for example

the supply of ARVs and cancer drugs which are not easily accessible), Legislation and the

Traffic Safety campaigns. The results show that the above listed factors have a significant

towards mortality improvements.

4.6.3 Relationship between Education and Lifestyle and Mortality Improvements

The relationship between education and lifestyle and mortality improvements is characterized by

r=0.889 with p<0.01. This implies a strong positive relationship. In other words, the higher the

education level the more improved the lifestyle and the more improved is the mortality rate.

4.7 Regression Analysis

Table 4.11: Regression between the independent variables and the dependant variable

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .821 .674 .665 .0458

Predictors: (Constant), Educ_Life, Other_Factors, Tech_Advance

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The mortality factors outlined in this study have a strong positive relationship with mortality

improvements as evidenced by a correlation coefficient of 0.821. Three broad factors were used

in this study and these are technological advancement in health delivery, education and lifestyle

and other factors as explained in Chapter 2. This then means that if Zimbabwe or any country

pays more attention to these factors this would result in significant mortality improvements.

These factors have an explanatory power of 67.4% of all the factors that contribute towards

mortality improvements of human beings.

Table 4.12: Regression Anova table for factors affecting mortality improvements

ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 12.237 3 4.079 80.259 .003a

Residual 6.302 124 .051

Total 18.539 127

Predictors: (Constant), Educ_Life, Other_Factors, Tech_Advance

Dependent Variable: Mortality

Table 4.13: Regression coefficients table for factors affecting mortality improvements

Coefficientsa

Model

Unstandardized Coefficients Standardized Coefficients

t Sig. β Std. Error βeta

1 (Constant) .034 .178 0.934 .000

Tech_Advance .421 .064 .431 2.429 .016

Other_Factors .311 .222 .332 0.207 .021

Educ_Life 0.234 .029 .350 1.524 .009

Dependent Variable: Mortality

R=0.821; R Square = 0.674; Adjusted R Square = 0.665; F = 80.259. *significance at p<0.05

Tables 4.12 and 4.13 outline the results of the regression analysis. The results show that the

goodness of fit is quite satisfactory as evidenced by the Adjusted R squared which is equal to

0.665. This implies that the independent variables have 66.5% explanatory power towards

mortality improvements in Zimbabwe. Thus this research has not been able to include factors

that explain the remaining 34.5% thereby giving room for further research in future.

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Table 4.13 shows the coefficients of the regression that yield the regression line as shown below:

Mortality Improvements = 0.034 + 0.421*(Technological Advancement in health delivery) +

0.311*(Other Factors) + 0.234*(Education and Lifestyle)

The βeta values show that Technological Advancement in health delivery has more explanatory

power as shown by a βeta of 0.421 with p=0.016 as compared to Other Factors (β = 0.311,

p=0.021) and Education and Lifestyle (β = 0.234, p=0.009). All the p-values are less than 0.05

which signifies that the results for all variables in the regression analysis model were statistically

significant. This depicts a positive relationship between all the factors used in this study and

mortality improvements in Zimbabwe.

4.8 Statistical Inferences

4.8.1 Kruskal – Wallis Test

This test is done to make a comparison of two or more variables when the data is not normally

distributed. The researcher conducted the Kruskal – Wallis Test for the three broad factors that

affect mortality improvement. The factors are Technological Advancement in Health Delivery,

Education and Lifestyle. The results of the test are shown in the table below:

Table 4.14: Kruskal – Wallis Test results

Test Statisticsa,b

Tech_Advance Other_Factors Educ_Life Mortality

Chi-Square 20.598 7.941 32.128 20.974

Df 4 4 4 4

Asymp. Sig. .434 .095 .334 .168

a. Kruskal Wallis Test

b. Grouping Variable: Position

Table 4.14 shows that the Chi-Square test results are statistically insignificant. With the degrees

of freedom equal to 4 across the board, the levels of significance are Technological

Advancement Health Delivery (0.434), Education and Lifestyle (0.095), Other Factors (0.334)

and Mortality (0.168). Since all the p-values are greater than 0.05 we conclude that the factors

56

have varying explanatory power to mortality improvements. This result is in agreement with the

regression model outlined in section 4.7.

4.9 Hypothesis Testing

In chapter two a conceptual framework was presented which is backed by the literature

reviewed. The model was applied in order to confirm whether or not mortality is improving in

Zimbabwe. This research proposed that mortality is improving in Zimbabwe and there are

serious implications to defined benefit pension schemes’ liabilities. This proposition was

accepted at 95% confidence interval since all the factors under consideration had a strong

positive relationship with mortality improvements.

4.10 Actuarial Analysis of the Defined Benefit Pension Liabilities

The proposition on mortality improvements tested positive at 95% confidence interval. As a

result, we took a step further to assess the impact of mortality improvements on Defined Benefit

pension liabilities. This is actually the main objective of this study. So the analysis has been done

through an actuarial valuation. In the valuation we varied the mortality bases, keeping the

economic assumptions constant. The mortality bases used are taken from the mortality tables

outlined in chapter two. There is mortality improvement imbedded from one mortality basis to a

later mortality basis.

4.10.1 Actuarial Calculation and the results

The set of data that was used in the calculations comes from the 15 Defined Benefit pension

funds which have been submitting annual returns to the Insurance and Pension Commission

since the dollarization of the Zimbabwean economy. Deliberately we decided not to include the

NSSA and the Public Service Pension Funds in this research because of their big sizes and also

that they have a strong backing from the government. So they have been considered to be

outliers.

The data from the pension funds has been combined to form a single pension fund. We used the

Projected Unit Funding Valuation method based on the actuarial model outlined in chapter three.

This method is applied to schemes which are expected to continue in force. With the available

data, we calculated the Standard Contribution Rate (SCR) and the Standard Fund (SF) for each

mortality basis. The economic assumptions used are:

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Interest rate, i = 7%; Salary escalation rate, e = 5%; Pension escalation rate, j = 3% and; Rate of

revaluation in deferment, rev = 3%

Table 4.15: Results of the valuation using the Projected Unit Funding Method

Mortality basis SCR SF ($ million) PA(90) 14.34% 424.95 PMA80 Base 14.60% 445.80 PMA92 Base 16.14% 460.91 PMA92c2010 17.44% 485.26 PMA92mc2010 18.67% 509.39 PMA92mc2028 19.55% 529.33 PMA92(mc)diag 19.96% 553.71 PMA00 Base 17.10% 484.16 PMA00mc2028 19.38% 501.77 PMA00(mc)diag 19.77% 521.10

The mortality base tables used were obtained from the published CMIB tables. We generated the

other mortality bases by projecting the PMA92 and the PMA00 Base tables using the “92

factors” with the medium cohort (1% minimum) effect. From Table 4.15 above, note that the

SCR and the SF figures for the PMA92mc2010 are higher than those for PMA92c2010 which is

also a projection of the PMA92 Base with no cohort effect.

The “92 factors” used for projections are only suitable for projecting the PMA92 Base tables.

There are no published projection tables for PMA00 so we used the “92 factors” as the best

available estimate. The actual projection methodology for the PMA00 is still under debate at the

international actuarial professional level and is beyond the scope of this study. In Zimbabwe we

do not carry out continuous mortality investigations. The latest mortality investigation that was

done by Zimstat was as in 2006. We used the diagonal projection factors to get the

PMA92(mc)diag and the PMA00(mc)diag. For the other projections, we used calendar year

factors.

The following points apply to mortality projections:

(i) The CMI library contains a two-way table of cumulative mortality reduction factors, by

age and calendar year.

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(ii) The cumulative reduction factors can be defined as RF(x,t) = qx,t/qx,0 where x is the age

and t is the elapsed time from the base year. This is the approach we used to generate the

PMA92mc2010 rates from the PMA92 Base table.

(iii)Each sheet starts from 100% in 1992 and subsequent columns show the cumulative

reduction factors to the years in question.

According to the CMIR (2002), the following are the key points underlying the approach to

future improvements in mortality projections:

(i) It is assumed that the current rates of improvements converge by age and tend to a

long term target rate of improvement to around the year 2029.

(ii) For the principal projections, this long term target is 1% per annum applicable to rates

of death for all ages, for both genders and the different countries in the world; broadly

equivalent to the average annual rate of improvement over the whole of the 20th

century.

(iii) The transition from the assumed rates of mortality improvement by age and gender

for the first year of projection to the target rate is more rapid at first for males and less

rapid for females.

We arranged the mortality bases in the order in which they were established. As we move from

the PA(90) to the PMA00, there is a general pattern of an increase in the SCR and the SF figures.

This shows that as we move from the earliest mortality basis to the latest, there is a reflection of

mortality improvement. Thus, as mortality improves, the liability position of a defined pension

scheme goes up and the SCR is expected to increase. This means that there is an increase in

longevity risk. The projections on the PMA00 have been done using the “92 factors” which is

not a ‘proper’ methodology for projecting the PMA00. This is the likely explanation as to why

its SCR and SF figures are less than those for the PMA92 projected bases.

There would not be a problem of longevity risk if the mortality basis used assumes reality and

the contributions to the scheme are as prescribed by the valuation results. In chapter 2 we

mentioned that the PMA92 mortality basis with medium cohort effect appears to be more

realistic. We also mentioned that most of the schemes are using the a(55) which is derived from

the A(90). As expected, this is not commendable because such schemes have started facing

59

longevity risk. Examples of such schemes whose problems are already in the public domain are

the Local Authorities Pension Fund and the ZESA Pension Fund. These two schemes are among

the schemes that were analysed in this study.

We would expect a scheme to face longevity risk problems if the mortality basis used is not

realistic. For example if, instead of using the PMA92mc2010, the scheme decides to use the

PMA92c2010, we will face the following situation to the combined scheme used by the

researcher in the calculation:

SCR = 17.44% instead of SCR = 18.67%

SF = $485.26 million instead of SF = $509.39 million

Thus the future service liability will be under-funded by 1.23% of the payroll every year

(18.67% - 17.44% = 1.23%), assuming the value of the assets is also $485.26 million.

Basing on the combined scheme used in the valuation analysis, the total annual payroll is

$192.63 million. So the future service liability will be under-funded by 1.23% * $192.63 million

= $2.37 million every year. This amount of money can have a huge bearing on the sponsoring

companies’ cash flows:

Looking at the SF figures, the scheme will have a deficit of $509.39 m - $485.26 m = $24.13 m.

The deficit has a bearing on past service liability. If the deficit continues unchecked, some

pension schemes will not be able to meet their promises on pensions. This amount of money

should be paid into the respective pension funds proportionately from the sponsoring companies’

profits. This is a huge sum of money considering that the bulk of the companies are performing

below their capacity utilisation levels due to the economic downturn.

Therefore pension fund managers should take the issue of longevity risk seriously in pension

planning. The sponsoring companies are the most affected because they will be forced to use

money from their ‘profits’ to meet pension scheme liabilities since they are expected to meet the

balance of cost. This reduces the sponsoring companies’ available free funds if any, for other

projects. Dividends will be reduced or may not be declared at all. This may cause the share

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prices to fall and the market to lose confidence in the companies. Some companies have already

shut down. When companies close, it is the members of the pension fund who suffer.

Having presented our results, the next section discusses these results as we try to derive

managerial implications to pension scheme managers and policy makers. Already we have

highlighted the need to use realistic mortality basis so that the liabilities are not understated.

Later on we shall also present the negative effects of over-funding. But our major challenge at

the moment in Zimbabwe is under-funding.

4.11 Discussion of Results

4.11.1 Discussion of Results in relation to literature

This section of the study focuses on discussing the research findings in relation to the literature

reviewed. The main focus of attention for this study was to analyse the effects of longevity risk

to pension planning in Zimbabwe and this was done in two parts. The first part was based on

testing the hypothesis that mortality is improving in Zimbabwe. The second part was to then say,

if mortality is improving what is the effect to pension planning in light of the longevity risk that

is associated with mortality improvements.

In discussing longevity risk, it must be realised that measures of life expectancy are based on

large groups of averages. According to Cowell and Rappaport (2005), in the case of personal life

expectancy for an individual, it is an imponderable concept. In other words the precise value is

not known until that person dies. This is a critical point since it reveals the very weakness of

relying on average life expectancies for planning purposes. So in our discussion here, we tried to

expand on this point. The analysis and interpretation has been done already so we now focus on

the major finding and discussion thereof.

According to the data obtained from Zimstat, there are not age specific life expectancies. We

only managed to get life expectancies at birth. Basing on life expectancies at birth, the trend

shows that mortality is improving in Zimbabwe, as shown below:

Table 4.16: Zimbabwean year on year Life Expectancy (LE) at Birth from 2000 to 2012

Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

LE at birth 37.78 37.13 36.5 39.01 37.82 39.13 39.29 39.5 44.28 45.77 47.55 49.64 58.05

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The approach that we took to forecast mortality and life expectancy for those ages whose details

could not be obtained from Zimstat was to use extrapolative models. These models are the type

of models mostly used by actuaries. They express age-specific mortality rates as a function of

calendar times using past data. In this study we used a stochastic approach.

Literature has it that there are several approaches to project mortality rates (CMI, 2004, 2005;

Wong-Fupuy and Haberman, 2004). What comes out clearly out of this research is that pension

funds providing defined benefit pensions need population projections to assess the number of

members potentially entitled to a pension at any given point in time.

The main assumptions to produce population projections are to do with fertility, mortality and

net migration flows. The finer details to do with mortality and life expectancy projections are

beyond the scope of this study. That can always be pursued in future studies where we can

include issues like process-based methods and explanatory based approaches. What has been

critical to this research was to demonstrate that mortality is improving in Zimbabwe and

analyzing how, in light of mortality improvements, longevity risk affects defined benefit pension

funds.

4.11.2 Other findings, discussions and suggested approaches to managing longevity

risk.

Findings

1. At least 65% underestimate average life expectancy (65% pensioners and 69% active

employees) based on the respondent’s current age. Only one pensioner in four (25%) and

30% of active employees gave an estimate of life expectancy which is closer to the actual

figures obtaining, according to the actuarial model used.

2. Among both pensioners and active employees, at least 70% predict that they will live to

at least 80 years.

Discussion

One of the study’s surprising findings is that over 30% of the active employees underestimate

population average life expectancy by six or more years. The two research findings point to the

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respondents’ lack of understanding of the likelihood of living until older ages and the need to

plan for long living. Improvements in mortality increase the chance of living to extreme old ages.

This brings the risk of serious and costly illness in addition to the risk of outliving their

resources. Some pensioners grossly do not appreciate the implications of longevity to their

financial needs, especially if they are to survive to their nineties. The model calculates the

probability of living to at least 90 years to be about 20% for males and 32% for females.

Finding

3. Comparing the respondents’ estimates of their life expectancy with population life

expectancy shows that more than half of them do not think they will live longer than the

average person of the same age and gender.

Discussion

The third finding reinforces the findings of poor understanding of longevity risk. This has so

many implications to retirement planning and pensions policy issues. Perhaps this helps to

explain why many people under-save. Given a choice, they may prefer a lumpsum over an

annuity.

Finding

4. The substantial deterioration in pension funds’ budgetary position created by the increase

in life expectancy calls for the Insurance and Pensions Commission to review their

policies to address the costs created by increasing longevity. Overall, the calculations

done by the researcher show that the pensionable age should be increased from 55 years

or 60 years to 65 years. Thus, a 6.35 year increase in age 60 life expectancy calls for the

pensionable age to be increased by 4.55 years in order to restore the pension fund’s

budgetary position by increasing the net transfer to the pension fund from the active

employees.

The main focus of attention for this research has been mortality and life expectancy in terms of

the assumptions used in managing pension funds. The other assumptions are the economic

assumptions which have been held constant for the purposes of analysing the longevity risk. In

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terms of the overall management of pension funds, it is important to also consider sensitivity

analysis of the economic assumptions as a guide to firm decision making.

If all the economic assumptions change by the same magnitude, the changes to the SCR and the

SF will be very small. This is because what is important in the calculation is the quantity i-e. So

if the economic assumptions change by the same magnitude, the quantity i-e will remain the

same hence the insignificant changes. However if only one assumption changes, the impact will

be big especially if only the interest rate changes.

It is also important to highlight that well managed pension funds can experience over-funding.

For example if the assumptions used are too prudent, the scheme may become over-funded. This

may cause unnecessary surpluses to emerge. The money that could have been invested in

productive activities of the company will be tied to the pension fund. A certain amount of over-

funding is appropriate as a contingency margin to cushion against adverse movements in the

financial markets.

Some of the studied pension schemes choose to use the prior year’s surplus to de-risk through

buyouts, swaps and reducing exposure to equities. This demonstrates the importance of assessing

and managing pension risks and being able to take opportunities when they arise. Thus, the next

sub-section of this chapter discusses the various approaches to managing longevity risk.

4.11.3 Approaches to managing longevity risk

One of the major highlights of this research is that longevity risk has serious implications to

pension funds. Of particular concern is the fact that the pension scheme may be under-funded if

it is not well managed. Therefore it is imperative to look at approaches that can be used to

manage this type of risk. Some of the approaches are discussed here.

1. Closing the scheme to new entrants

This involves limiting membership of a defined benefit pension scheme to existing

employees. New employees join a different pension scheme, usually on a money

purchase (defined contribution) basis. However, it should be noted that closing the

scheme to new entrants does not stop longevity risk from growing. It slows the scheme’s

exposure to the risk.

2. Changing the definition of final salary

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A defined benefit pension is a function of annual salary. To limit exposure to longevity

risk, a relatively uncommon approach is to change the definition of final salary away

from the actual salary earned in the final year of employment since this is open to abuse.

Top executives tend to escalate their salaries just before retirement so that they can earn a

bigger pension. An example of a new definition would be ‘career revaluation’ where

revaluation might be in terms of an inflation index rather than a higher earnings index.

This may however be hard to implement in practice due to lack of data and technological

expertise.

3. Modifying pay rises

This is way of differentiating pay rises in light of future pensions. Members of a closed

defined benefit pension scheme may receive an explicitly lower pay rise compared to

employees who are not members.

4. Increasing employee contributions

This does not modify the total longevity risk. The option redistributes the cost of funding

the risk.

5. Reducing the rate of future accruals

A scheme offering 1/40th of the final salary for each year served might declare that future

accruals will take place at a lower rate, for example 1/45 or 1/55. This again does not

help to get rid of the longevity risk; rather it reduces the rate at which the problem grows.

6. Increasing the retirement age

The option does not only reduce the period of pension payment, it also increases the

period of accrual. Alternatively the retirement age may not be fixed. Instead, the

retirement age may be determined by a formula which is a function of percentage

increase in life expectancy. Willets et al (2004) says that if final salary schemes are to

continue to play a significant role in pension provision in the 21st century, more flexible

definitions of retirement age may become an essential component of scheme design.

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7. Closing the scheme to future accruals

This involves closing the scheme to new entrants and also stopping future benefit accrual

for the existing members. This option helps to stop the longevity risk from growing but it

is very unpopular with existing members of the scheme.

A major obstacle to closing the scheme to future accruals is that the Trust Deeds of some

pension funds specify that this automatically triggers winding-up or a buy-out. With so

many pension schemes not funded to discontinuance levels, this would crystallise the

need for cash injections into pension funds. So this option may be taken as a last resort.

8. Winding-up and buy-out

The only way for employers to completely get rid of longevity risk is to wind up the

scheme and secure benefits with an insurance company. This is known as a buy-out,

which is the purchase of annuities en masse in lieu of the pension fund’s benefits.

4.12 Chapter 4 Summary

This chapter focused on the presentation and analysis of the research findings. The critical

elements dealt with include the response rate, demographic properties, frequency tables,

reliability test, normality test, and correlation and regression analyses. SPSS was used to

analyses the data gathered using questionnaires. A total of 128 out of 230 distributed

questionnaires took part in the research.

Basing on the proposition on mortality improvements in Zimbabwe, which was tested and

accepted at 95% confidence interval, we went on to carry out actuarial valuations using the data

obtained from 15 pension funds. The data sets were combined to form a single pension scheme

for simplicity. The results of the actuarial valuations show that as mortality improves, the

liability position of the pension funds grows. As the liability position of a scheme grows, the

contribution rate should also increase. The analysis demonstrated that it is important to use the

right mortality basis when carrying out the valuation of a pension scheme so that results may not

be misleading.

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The results suggest that since mortality improving, longevity risk is increasing to most schemes.

Because of the economic recession and expectations of tightening liquidity crunch, most

schemes’ investment vehicles have not been performing well. As a result most companies are

finding it difficult to fund ever increasing contributions to their pension schemes. This could be

the reason why there is an increasing number of schemes closing to new entrants as a way of

longevity risk. Chapter 4 ended with a discussion on the various ways of managing longevity

risk.

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CHAPTER 5

DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

This study investigated the effects of longevity risk on defined benefit pension schemes.

Literature reviewed has shown that little research has been done on mortality improvements and

the effects of longevity risk in developing countries. Cooper-Williams et al (2012) share the

same sentiments when they say little has been done in South Africa in terms of research on

mortality improvements.

It is with this background that the research sought to give an insight into the explanation of the

effects of longevity risk in Zimbabwe and how the risk can be mitigated as outlined in Chapter 4.

In chapter 2 we looked at mortality trends. Various authorities cited in this study have concluded

that mortality is improving. Research data was collected using the methodology outlined in

chapter 3. Results presented in chapter 4 illustrate that as mortality improves the value of the

liabilities increases. With most schemes operating in deficits, employers find it difficult to cope

with ever increasing required contribution. The situation can be made worse if pension scheme

managers fail to realise the implications of longevity risk. Conclusions have been drawn based

on the results of the analysis done to the data collected.

5.2 Conclusions

5.2.1 Uncertainty regarding mortality and life expectancy outcome

Data obtained from Zimstat show that little or no information can be drawn for life expectancies

at various age groups except for the life expectancy at birth. Due to the lack of enough mortality

data from Zimstat, estimating and forecasting life expectancy and mortality rates for ages greater

than seventy years can be challenging. Data for old at old ages may not be accurate because of

small sample problems. In our sample we only had 18 out of 128 participants aged 70 years or

more. Zimstat has official population statistics which are not sufficiently accurate to produce

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reliable estimates of mortality rates at higher ages. The latest mortality investigation was done in

2006 using 2006 data only. Accurate results on mortality should be based on longitudinal studies.

To go around this challenge, in this study we had to extrapolate historical trends using a

stochastic model informed by the Gombertz model to connect the life expectancies to the

mortality tables outlined in chapter 2.

5.2.2 Checking the adequacy of the existing assets to meet the liabilities of the pension

schemes accrued to the valuation dates.

It is rather hard to assess the overall impact of longevity risk in defined benefit pension schemes

when there is little by way of disclosure of mortality assumptions. It is highly likely that some

pension schemes use more optimistic mortality assumptions than others. A white-collar scheme

valued for convenience on the old minimum funding requirement mortality basis of PA(90)

would be grossly under-funded.

It is impossible for this study to consider every aspect of the topic under study. However we can

make a general conclusion that one of the major risks faced by defined benefits pension schemes

is longevity risk. Without greater transparency and disclosure of mortality bases by schemes, it is

difficult to assess the effects of mortality improvements to pension funds. It is therefore

imperative that pension scheme management move with the changing times and apply prudential

mortality bases. That way it will be possible to accurately demonstrate the adequacy of existing

assets to meet the liabilities of pension schemes.

This study tends to agree with authors like Richards and Jones (2004), O’Brien (2003) and

Willets et al (2004) in calling for the use of modern mortality rates and also the disclosure of life

expectancy assumptions in scheme reports. It is disheartening to note that all the pension

schemes whose data we used in this research are using outdated mortality bases. More so, some

of them are operating in deficits. Obviously the deficit position will be worse if the right

mortality basis is used.

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5.2.3 Review the contribution rates that would be appropriate for the future

The model used in the valuation process has been described in chapter 3. The researcher

considered this model to be suitable after exposing it to various model checks. Using the service

table and the promotional salary scales combined with economic assumptions and the mortality

bases, the researcher was able to calculate the Standard Contribution Rate and the Standard

Fund.

5.2.4 Recommended approach to forecasting mortality and life expectancy

As outlined in chapter 4, there are several approaches that can be used to project mortality rates.

In light of the uncertainty surrounding future mortality and life expectancy outcomes, we

recommend the use of a stochastic approach since it attaches probabilities to different outcomes.

We therefore recommend the use of this approach as a common methodology to forecast

mortality rates and life expectancy. This makes it easier to assess the risks and uncertainties

adequately.

5.3 Validation of Research Proposition

The research proposition was that mortality is improving in Zimbabwe with serious implications

to defined benefit pension planning. We uphold the proposition and conclude that, basing on the

factors considered to contribute to mortality improvements, there are mortality improvements in

Zimbabwe at 95% confidence interval. Due to mortality improvements, pension funds are faced

with longevity risk. This is one of the critical aspects pension scheme managers should seriously

take into consideration when planning for their members’ retirement benefits.

5.4 Recommendations

(a) Management

This study recommends that management of pension funds should work closely with

actuaries and make sure that the trustees of pension funds recommend the use of recently

developed mortality rates so that at any given point they are able to match liabilities with

assets. On the same note, we recommend the indexation of pension benefits to life

expectancy in order to partially offset the impact of longevity risk. In other words, there

is need for managers of pension funds to take risk based approaches.

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(b) Policymakers

We recommend that policymakers, in their quest to reform the pension system, should

realise that defined benefit pension schemes are being strained by the greater pension

demands concurrent higher life expectancy. Such a demographic change presents

numerous social and economic challenges which will end up having a direct impact on

the fiscus. In order to deal with the consequences of increasing life expectancy on

government finances, we recommend that the Government of Zimbabwe should set up a

continuous mortality investigations unit within Zimstat. This may go a long way in

assisting many small and medium size pension funds who are facing challenges to

provide the necessary financial resources and technical capacity to produce forecasts

using a common stochastic methodology mentioned above.

(c)Regulator

We also recommend the Insurance and Pensions Commission (IPEC) to be actively

involved in this subject of longevity risk. There may be need to change the regulatory

framework so that pension funds and annuity providers should fully account for

improvements in mortality and life expectancy. In liaison with Zimstat, IPEC is

recommended to provide guidance to pension funds and annuity providers regarding the

type of approaches suitable in forecasting mortality improvements and also in assessing

the associated impact.

5.5 Areas for further studies

(a) Researches on mortality cannot be exhaustive unless a longitudinal study is carried out.

This however requires a lot of financial resources and expertise. There is therefore a

wider room for institutions like the University of Zimbabwe or Zimstat to consider

starting continuous mortality investigations. Such investigations can stretch to more than

100 years such that processes are passed from one generation to the other. Mortality

projection methodologies can be updated as we go until we build our country’s mortality

rates table which captures the average limiting age.

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(b) The SPSS analysis gave R Square = 0.674. This implies that the identified factors only

have an explanatory power of 67.4%. Therefore further researches can also be done in

order to identify the other factors that also affect mortality improvements but have not

been considered in this research.

(c) Actuarial reports obtained from the pension funds that provided us with membership data

that was used in the calculations show that the assumptions used are being derived

deterministically. A further research can be conducted in order to analyse the

performance of pension funds if assumptions are to be determined stochastically.

As part of the concluding remarks, it is worth mentioning at this stage that the data collected was

suitable and enough for a study that is carried out within half a year. The model applied was able

to predict that retirement life increases with longevity. It was however difficult to measure the

real impact. There may be some mismatches because the simulations were done with parameters

calibrated from recent data and at the same time applying long-run averages. If our country is

serious about improving people’s standard of living into retirement, the mortality investigation

unit should be established as soon as possible.

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UK

2. Antolin, P. (2010) Longevity Risk and Private Pensions, No. 3, OECD Publishing.

3. Chandra, P. (2012) Financial Management – Theory and Practice, Fifth Edition, TMH

Publishing Company LTD, New Delhi.

4. Continuous Mortality Investigation Bureau (2002) Working Paper 1: An interim basis for

adjusting the 92 series mortality projections for cohort effects. CMIB: London

5. Continuous Mortality Investigation Bureau (2004) Working Paper 14.

6. Continuous Mortality Investigation Bureau (2004) Working Paper 21.

7. Continuous Mortality Investigation Bureau (2005) Working Paper 24

8. Cooper, D.R. and Schindler, P.S. (2003) Business Research Methods, McGraw-Hill

9. Cooper-Williams, J., Albertyn, L. and Lewis, P. (2012) Mortality Improvements in South

Africa, Actuarial Society of South Africa, Cape Town.

10. Crawford, E. D., Grubb, R.L. and O’Brien, B. (2008) Mortality results from a randomised

prostate-cancer screening trial, Massachusetts Medical Society.

11. Dickson, D. C. M., Hardy, M. R., and Waters, H. R. (2009) Actuarial Mathematics for Life

Contingent Risks, Cambridge University Press, New York

12. Dushi, I., Friedberg, L. and Webb, A. (2010) Mortality Heterogeneity and the Distributional

Consequences of Mandatory Annuitisation, New York, USA.

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13. Ferreira, P. C. and Pessoa, S.A. (2005) The Effects of Longevity and Distortions on

Education and Retirement, No. 590, Botafogo, Brazil.

14. Fisher, C.D. (2010) International Journal of Management Reviews, Vol 12, No. 4, British

Academy, British Publishing Ltd

15. Greener, S. (2008) Business research Methods, Ventus Publishing

16. Haan, P. (2011) Longevity Life-Cycle Behaviour and Pension Reform, No. 5858, Institute of

the Study of Labour, Victoria Prowse.

17. Jones, A. (2007) Our Changing Future Discussion Series and the Faculty of Actuaries

Students Society.

18. Labour Force Survey Report – Zimbabwe Statistics Agency (2004)

19. Lee, R. D. and Carter, L. R. (1992), “Modelling and forecasting U.S. mortality”, Journal of

the American Statistical Association 87(14), 659-671.

20. Milevsky, M. A. and Promislow, D. (2003) Mortality Derivatives and the Option to

Annuitize. York University Finance Working Paper No. MM08-1

22. Redfoot, D., Scholen, K. and Brown, K. (2007) Reverse mortgages, niche product or

mainstream solution, Washington

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Review,Vol. 37 Iss 3 pp. 308 – 330

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Mortality Assumptions used in the calculation of company pension liabilities in the EU.

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25. Saunders, M., Lewis, P. AND Thornhill, A. (2009), Research Methods for business students

Fifth Edition, Prentice Hall, London

26. Thatcher, A. R. (1999) The Long term pattern of adult mortality and highest attained age,

Journal of the Royal Statistics Society, Series A 162, pp 5-43.

27. Waldron, H. (2005) Mortality Differentials by Lifetime Earnings Decile: Implications for

Evaluations of proposed Social Security Law Changes. Social Security Bulletin, Vol 73, No.

1

28. Willets, R. C. (1999) Mortality in the next millennium. Paper presented to Staple Inn:

Actuarial Society.

29. Willets, R. C. (2004) Longevity in the 21st Century, British Actuarial Journal 10, part IV pp

685-898

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Developments in the United Kingdom and United States. North American Actuarial Journal,

No. 8 Vol 2

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APPENDIX

University of Zimbabwe

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Graduate School of Management

University of Zimbabwe

My name is Elson Gonye, a Graduate School of Management student at the University

of Zimbabwe and currently working towards attaining a Master’s Degree in Business

Administration (MBA). I am carrying out a research with the following topic:

An Analysis of the Effects of Longevity Risk on Pension Planning in Zimbabwe

I would like to request for your time in completing this questionnaire. I have endeavored

to keep the questionnaire as simple as possible in order to limit your time involvement. I

greatly appreciate your input and all responses will be treated with a great sense of

confidentiality. I would like to thank you for taking time to complete this questionnaire.

Your support towards this noble cause is highly appreciated

Kindly tick in the boxes provided or write in the spaces provided

If you have any problem understanding the questions please feel free to contact me on

0772802910 or elsongonye@yahoo.com.

Yours faithfully

77

Elson Gonye

Student Number: R9916210

SECTION A: DEMOGRAPHICS 1. Please indicate your gender

Male 1 Female 2

2. Please indicate your age in years <30 years 30 to <50 years 50 to <70 years >=70 1 2 3 4

3. Indicate your current position Senior Management 1 Middle Management 2 Junior Management 3 Non Managerial 4 Pensioner 5

4. Please indicate your highest academic qualification Doctorate 1 Masters 2 First Degree 3 A’ Level 4 O’ Level 5 Primary Education 6 None 7

5. Please indicate your highest professional qualification Chartered/Fellow 1 Associate 2 Diploma 3 Certificate 4 Other (specify) 5

6. For how long have you been with the pension fund pre-retirement?

Less than 2 years 1 2 – 5 years 2 6 – 10 years 3 11 – 15 years 4 16 – 20 years 5 Above 20 years 6

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SECTION B: Technological Advancement in health delivery

B1. How do you rate your understanding of the use of the internet? (Use a

scale of 1 to 5 where 1 is the worst and 5 is the best) 1 2 3 4 5

B2. To what extent do you think hospitals and other care providers in

Zimbabwe have integrated medical technology into their practices? (Use a scale of 1 to 5 where 1 is the worst and 5 is the best)

1 2 3 4 5

B3 – B6. How do you rate the advantage of using the advantages of using the following towards mortality improvements? (Use a scale of 1 to 5 where 1 is the worst and 5 is the best)

1 2 3 4 5 Electronic Medical Records (EMR) Telehealth Services X- Rays CT Scans

B7. From 1980 to where we are today, how do you rate Zimbabwe’s efforts

towards embracing innovation in to medicine? Very Bad Bad No difference Good Very Good 1 2 3 4 5

SECTION C: Education and Lifestyle C1. Are you conscious about exercising?

Yes No Neutral 1 2 3

C2. Are you conscious about eating health?

Yes No Neutral 1 2 3

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C3. Do you think wellness and vitality programmes help someone to live

longer? Yes No Neutral 1 2 3

C4. How do you rate the level of awareness and education with regards to HIV

and AIDS in Zimbabwe Very Bad Bad No difference Good Very Good 1 2 3 4 5

SECTION D: Other External Factors

D1 – D4 What is the influence of each of the following factors towards

mortality improvement in Zimbabwe? (1. Very weak 2. Weak 3. Neutral 4. Strong 5. Very Strong)

1 2 3 4 5

Millennium Development Goals Government Intervention (eg supply of ARV’s)

Legislation Traffic Safety campaigns

7. What do you think is the Zimbabwean average life expectancy for someone at your age?

Life expectancy at age ……………………..

8. Until what age do you think that you, yourself, can expect to live to? Age at death

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9. Why do you think you will live until that age?

1. Family history 1 2. Personal health 2 3. Guessing 3 4. Average Life Expectancy 4 5. Health habits (i.e., exercise, eat

right) 5

6. Positive attitude (i.e., no stress, determination, enjoy myself)

6

10. Do you think that the life expectancy of people today is longer, shorter

or about the same as life expectancy 20 years ago?

1. Longer 1 2. About the same 2 3. Shorter 3

E1 – E5. Please indicate whether you (and your spouse) should do the

following: (1. Strongly disagree, 2. Disagree, 3. Neutral, 4. Agree, 5 Strongly Agree)

1 2 3 4 5

1. Eliminate all your consumer debt by paying off loans

2. Completely pay off your mortgage if you are a homeowner

3. Try to save as much as you can 4. Cut back on spending 5. Buy an insurance product or

choose a plan option that will provide you with guaranteed income for life

F1 – F4. If you were to live five years longer than expected, are you likely to

do the following? (Strongly disagree, 2. Disagree, 3. Neutral, 4. Agree, 5 Strongly Agree)

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1 2 3 4 5

1.Reduce expenditures significantly 2. Dip into money that you might

have otherwise left to heirs

3.Deplete all your savings and be left only with Social Security

4.Use the value of your home to help fund your remaining retirement years

End of Questionnaire Thank you!