Actuarial Demography (ACT-DEM)

 

Plan:

o   Scope of the Team

o   Team Members

o   Algerian Mortality Data

o   Algerian Fertility Data

o   Population Forecast

o   Organized Conferences & Workshops

o   Achieved and ongoing Research

o   References

 

The team Act-Dem is interested in quantitative demography and actuarial science issues implied by economic insurance or social security. In other words, the team aims to investigate the demographic behavior of the Algerian population and to model the different risks in the intention to define the actuarial balances.

Scope of the Team

 

The team "Actuarial Demography" focusses on studying the demographic changes and the risks supported by the population and their impact on actuarial calculations. Firstly, population dynamics needs to be measured and its future trend needs to be expected. Public planning as the orientation of public policies requires the understanding of the trend that the population dynamics will undertake in the future. Such an element returns to study the demographic phenomena as mortality, nuptiality and fertility. Then, the effect of aging and longevity on the sustainability of the social security system can be evaluated. Secondly, the social risks that people can be exposed to are numerous and need to be covered. This coverage can be considered from two points of view: Economic Insurance and Social Security. Both require the evaluation of the covered risk. When it concern social security, premium can be shared by the insured themselves and the state in the framework of its social policy, when it concerns economic insurance, it should be equivalence between the supported risk and the payed premium.

Actuarial Demography can be managed into two mean categories: Demographic modeling and Actuarial tools for life insurance.

The first part consists on the quantitative methods aiming to model the different demographic phenomena:

o Mortality & Longevity,

o Fertility,

o Population growth,

By studying the phenomena cited above, we aim to quantify the effect of population change on the social security systems in its different components:

o Direct pensions which tie directly to aging and longevity,

o Survivors benefits (spouses) which tie to the sex differential mortality and the age gap between spouses,

o Survivors benefits (orphans) which is related to the fertility behavior,

In addition, many other social risks need to be studied and quantified either from the insurance of the social security points of view:

o Life annuities / fully funded pension plans,

o Disability insurance,

o Jobless insurance,

o Illness and Health insurance,

Team Members

 

Farid FLICI (Team Leader) – Mortality & Longevity Modeling, Pensions

 

Farid is an actuary, graduated in 2006 from the National High School of Statistics and Applied Economics of Algiers. In 2011, he obtained his “Magister degree” in quantitative finance and his PhD in Statistics in 2017 from the same institution. He has dedicated his two dissertations respectively for "Provisionnement des Rentes Viagères en Algérie entre Approche Statique et Approche Prospective" and “ Longevity and Pension Plan Sustainability in Algeria: Taking the retirees mortality experience into account”. He has been teaching “Demographic analysis” and “Micro economics of insurance” at the school of Statistics between 2009 and 2012 before he joins the Center for Research in Applied Economics in the mid-2012. His task focused on Demographic modeling and Life Actuarial Science and more precisely Mortality & Longevity Modeling and Pension plan sustainability. He has some publications in peer reviewed journals and conferences proceeding of the International Association of Actuaries. He is a member of the Mortality Working Group of the IAA.

 

 

Ibrahim ZAIMEN (Junior Researcher) - Social Statistics, Fertility and Nuptiality

 

Ibrahim graduated in statistics and applied economics in 2013 from the National High School of Statistics and Applied Economics (ENSSEA). In 2016, he obtained his Magister Degree in Statistics and actually he is enrolled in PhD program at the same institution.  He dedicated his Magister dissertation to “Missing data imputation using Regression Methods”. Currently, he is a Junior Researcher at the Centre for Research in Applied Economics for Development (CREAD). His research focusses on modeling Fertility , Nuptiality and related areas. 

 

Mohamed SADOUN (Research Engineer) – Financial Modeling, Time Series

 

Sadoun_PhotoMohamed is a PhD student in Applied Mathematics more oriented to Time Series Modeling at the University of Science and Technology Houari Boumedien (USTHB). He graduated in Statistical Probability in 2010 and holds a Master's degree in Financiel Mathematics (2012) from USTHB. He integrated the Centre for Research in Applied Economics for Development (CREAD) in November 2017. His interest fields are: Probability and Statistical Inference, Modeling Financial Markets, Time series analysis and algorithmic programming. Also, he participated in several conferences on Mathematics and related fields: Journées de l’économétrie et de la finance (Maroc, Novembre 2016), The African Conference of the International Econometrics Society (Algeria, June 2017), The 30th International conference of the Jangjeon Mathematical Society (USTHB, July 2017).

 

Meryem CHINOUNE (Research Engineer) – Survival Analysis, Disability

 

Meryem is a  PhD student (2nd year) in Applied Statistics at the National High School of Statistics and Applied Economics (ENSSEA), her research focuses on  “Survival Analysis” She’s currently occupied as an engineer for research support at the Centre for Research in Applied Economics for Development (CREAD). She obtained a master degree in 2016 in Applied Statistics and Econometrics, Her Master's dissertation was entitled: Analytical Study and Short-Term Forecast of air passenger’s traffic (2006-2015). Her skills revolve mostly around Statistics, Data Analysis (XLSTAT, SPSS) and Time Series (EVIEWS).

 

Soumia BOUCHOUK (Research Engineer) – Fertility, Nuptiality

 

Soumia holds the diploma of engineer in statistics and applied Economics / option: management and decision models from the High National School of Statistics and Applied Economics (ENSSEA) in 2011. Since 2014, she has been working as research engineer at the Centre for Research in Applied Economics for Development (CREAD). Actually, she’s preparing a Master degree in Demography, option: “Health & Population” and she is preparing her dissertation on “the regional differences in fertility behaviour of the Algerian Population”.

 

 

 

Algerian Mortality Data

Usually, the practice of mortality modeling distinguishes mainly two types of life tables: Demographic life tables and Actuarial Life tables. The first type is concerned with the mortality of the global population while the second focusses on the mortality of the insured population. Here, data can be provided by the social security system (including retirement) or from the private insurance companies (internal models or experience life tables) or from the whole insurance market (Market’s life tables). In some situations, when the data of the insured population is lacked, the global population life tables can be used for insurance products pricing and reserving.

Implementing Mortality forecasting Models requires to make available a continuous mortality surface as long and as consistent as possible. Mortality data in Algeria are mainly provided by the Office for National Statistics (ONS: www.ons.dz)  in the framework of its annual publications. The first life table based on the civil registration data has been published in 1977. The ONS has tried to publish this kind of statistics with an annual frequency, but it become possible only starting from 1998. Accordingly, some years of the period [1977-1997] are still with no data. Similarly, some life tables of the same period were closed out at early ages (below the age 80).

The Mortality Rates as published by the ONS can be downloaded through the following links:

nQx_Males

nQx_Females

nQx_Both sexes

 

(Data Source : ONS annual publications

 

Period (1977- 2011): OFFICE NATIONAL DES STATISTIQUES ONS. (2012-a). Rétrospective de données Statistiques     1962 - 2011 : Démographie. www.ons.dz/IMG/pdf/CH1-DEMOGRAPHIE.pdf

2012: OFFICE NATIONAL DES STATISTIQUES ONS. (2013). Démographie Algérienne  2012. http://www.ons.dz/IMG/pdf/demographie_algerienne2012.pdf

2013: OFFICE NATIONAL DES STATISTIQUES ONS. (2014, n° 658). Démographie Algérienne  2013. http://www.ons.dz/img/pdf/demographie_algerienne2013.pdf

2014: OFFICE NATIONAL DES STATISTIQUES ONS. (2015, n° 690). Démographie Algérienne  2014. http://www.ons.dz/IMG/pdf/Demographie_algerienne_2014.pdf

2015: OFFICE NATIONAL DES STATISTIQUES ONS. (2016, n° 740). Démographie Algérienne  2015. http://www.ons.dz/IMG/pdf/Demographie_algerienne_2015.pdf

2016: OFFICE NATIONAL DES STATISTIQUES ONS. (2017, n° 779). Démographie Algérienne  2016. http://www.ons.dz/IMG/pdf/Demographie_algerienne_2016.pdf )

 

National life tables are published following five ages description. Actuarial calculations (longevity pricing among others) need to make available continuous historical mortality surfaces under a single age description. Also, mortality rates need to be extended until the survival age limit or nearly. In a previous work (Flici, 2014), we have tried to estimate the missing data in the Algerian mortality surface.

The completed mortality surfaces (nQx) can be downloaded here:

nQx_Males_completed

nQx_Females_completed

nQx_Both sexes_completed

 

Data Source:

Annual publications of the Office for National Statistics ONS (Five-ages mortality rates) ; missing data has been estimated by Flici (2014).

 

Then, we have interpolated the detailed ages mortality rates and extrapolated them to the age beyond 80 in Flici (2016). Thus, we obtained a detailed continuous mortality surface extended till the age 120.

Figure 1 shows the obtained mortality surface in logarithmic scale. Figure 2 show the same surface in a 3D scale.

Figure 1: 2D mortality surface (log mortality rates), male population

Figure 2: Algerian Mortality Surface, 1977-2014, detailed ages 0 to 120.

Source: Annual publications of the Office for National Statistics ONS (Five-ages mortality rates) ; missing data has been estimated by Flici (2014), Single ages mortality rates interpolated and old age mortality rates have been extrapolated by Flici (2016-b).

The Data used to draw plots 1 and 2 can be downloaded following the links:

qx_Males

qx_Females

qx_Both_sexes

 

Data Source:

Annual publications of the Office for National Statistics ONS (Five-ages mortality rates) ; missing data has been estimated by Flici (2014), Single ages mortality rates interpolated and old age mortality rates have been extrapolated by Flici (2016-b).

 

Mortality forecasting:

Mortality forecasting is an essential exercise since it allows to consider the future improvement in mortality. This last, can be used as a Population Health Index when more sophisticated tools are lacked (Health Life Expectancy, Disability Free Life Expectancy). Also, Population projections require projecting the age specific mortality rates in the future. In insurance, mortality forecasting allows to take the future improvement of mortality well-known as “Longevity Risk” when pricing and reserving life annuities and pensions.

 

Algerian Fertility Data

Fertility data in Algeria are mainly provided by the Office for National Statistics on the basis of the civil registration data. This principal source is completed by census data and specific surveys data. Our database was constituted by the five ages fertility rates published by the Office for National Statistics (ONS) over the periods [1964-1969], [1976-1991], [1994-2000] and [2008-2014]. In concern of population censuses, only the last three ones (1987, 1998 and 2008) gave the five ages fertility rates of the female population ages [15-49].

This data allowed us to constitute a discontinuous fertility surface in the sense that some years of the observation periods are still with no data. Missing data were estimated in FLICI(2016-d), and the Age Specific Fertility Rates ASFR’s have been interpolated by the same work. Detailed data are presented in Figure 3.

 

Figure 3: Algerian Fertility Surface, detailed ages (15 – 49 years old), period: 1964 – 2014

Source: Annual publications of the Office for National Statistics ONS (Five-ages Fertility rates): civil registration and census data. missing data has been estimated by Flici (2016-d).

 

The initial database containing the five ages fertility rates as provided by civil registration or census data can be downloaded from the link:

Five_Ages_Fertility_Rates

 

The completed Fertility surface containing the five-ages fertility rates for the period 1964 to 2016 can be downloaded from the link:

Five_Ages_Fertility_Rates_Completed

The database containing the ASFR’s for ages going from 15 to 49 and the period from 1964 to 2014 can be downloaded from the link:

Age_Specific_Fertility_Rates

 

Data Source:

Annual publications of the Office for National Statistics ONS (Five-ages fertility rates) ; missing data has been estimated and Age specific Fertility rates have been interpolated by Flici (2016-d).

 

Population Forecast

One of the most important exercises in the field of quantitative demography is population forecasting. Public planning, social policies designing and actuarial calculations need to project the population structure by age and sex in the future. In this sense, and in order to evaluate the financial sustainability of the Algerian retirement system, the population of Algeria has been projected to the horizon of 2070. Further details about the used technics and methodology can be found in Flici (2017).

To project the Age Specific Mortality Rates to the future, we proceeded by a coherent mortality forecasting methodology. Throughout the application process, it turned out that the use of the Lee Carter model (Lee and Carter, 1992) to project mortality rates independently for males and females leads to some incoherence regarding the sex mortality ratio by the horizon of the forecast. The use of the coherent mortality forecasting approach allows to avoid this kind of incoherence.

In another work (Flici, 2016-c), we compared two coherent mortality models: The product Ratio method proposed by Hyndman et al. (2013) and the Lee-Carter model with an additive common component for males and females proposed by Li and Lee (2005). It turned out that the first model leads to better results regarding the Goodness of fit and the coherence.

To fix the future scenarios of the fertility rates evolution in Algeria during the upcoming half century, we consider the views of experts rather than a probabilistic forecast. We aim to keep the population forecast results obtained in the present work within the framework of the National official projection methodology. Currently, the final report has not been published yet. But the framework of the projection was discussed and fixed in the meeting of the population forecasts subcommittee of the National Committee for Population (NCP) which depends from the National Health Ministry.

Discussions have mainly focused on a comparison between the probabilistic modeling approach and the view of experts approach. The first approach works well in the case of mortality, but not as well for fertility. The use of the Lee-Carter model as it was adapted to forecast fertility rates (Lee, 1993) led approximately to a constant Total Fertility Rate (TFR) at around 3 births / women by the horizon of the forecast. Experts of the NCP judged that this scenario can be used as a High Scenario rather than a middle one. Fertility rates are expected to keep slightly decreasing in the coming years. The low scenario was defined in order to respect the minimum TFR required for the population regeneration (2.1). In final, a level of 2.5 was kept as a medium scenario by 2030. Since the projection results was greatly depending of these underlying hypothesis which present a kind of weakness over time, the projection's horizon was limited to only 2030 for the National Population forecast. In order to keep working within these hypothesis while extending the projection's horizon until 2070 to suit the objective of the present work, we keep the same level to be expected at 2070 rather than 2030 as in the national projection.

Figure 4: Forecasting Life expectancy at birth and Total Fertility Rate, and Population structure , Algeria: 2018-2070

The projection results as the projected life expectancy and total fertility rates can be downloaded from the links:

Fertility_Projection

Life_Expectancy_Projection

Population_projection_5_ages

 

Data Source:

Mortality forecast (FLICI, 2016-c)

Fertility forecast (FLICI, 2017)

Population Projection (FLICI, 2017)

First, we have forecasted two components: Mortality and Fertility. Because immigration's data are missed or not available in the required format, we suppose that the immigration flow is equal to 0. The combination of the age specific mortality and fertility rates applied to the population structure given by the population census of 2008, we obtained the global population evolution and its age and sex structure for the whole period 2015-2075. When we observe the transformation of the population pyramid from 2020 to 2070, we can deduce that the top of the pyramid is expected to enlarge, compared to the basis of the pyramid. That reflects the aging process of the Algerian population during the 50 upcoming years. The part of the population aged 60 and over is expected to increase from around 8% in 2015 to keep around 20% starting from 2050.

Figure 7.22 lets appear the expected decrease in the report of the population at working age, on the population at retirement age. In 2015, we have more than 7 individuals at working age corresponding to 1 individual at retirement age. This report is expected to fall to 4.5 in 2030 and to stabilize around 2.7 for the period [2050-2070]. This funding means that, if it is difficult to keep equilibrium between incomes and outcomes of the pension plan under a theoretical report of 7, it will be more difficult to keep it under a value of 2.7. The main challenge will be to make a great part of the population at working age in occupation and then within the social security system. In the following parts, we will try to consider these two last elements in order to define the long term sustainability of the pension plan.

 

Organized Conferences

Even before the creation of the team of “Actuarial Demography” properly saying, Farid FLICI initiated in January 2016 a series of conferences on actuarial methods applied to life insurance known as JAVA (Journée d’Actuariat Vie Algérie). Until December 2017, 5 editions of JAVA have been organized.

JAVA I:  January 27th,2016 , CREAD

·        Les Tables de mortalité Comme outils de tarification et de provisionnement en assurance vie– Farid FLICI (CREAD) 

·        Construction Tables de mortalité d’expérience  pour une compagnie d’assurance – Moussa MEHIRECHE (Ministère des Finances) 

·        Tables de mortalité Adaptée à l’expérience de mortalité des retraité – Farid FLICI (CREAD) ;

JAVA II:  March 7th, 2016, Univ. Bouzareah.

·        Construction Tables de mortalité d’expérience  pour une compagnie d’assurance – Moussa MEHIRECHE (Ministère des Finances) ;

·        Longevity and life annuities reserving in Algeria : comparison of mortality models – Farid FLICI (CREAD) ;

JAVA III: 7 December 7th, 2016, ENSSEA, Kolea.

·        Santé / prévoyance collective et individuelle Garantie/règles de souscriptions/tarification (Hamza Rabehi, Institut des Actuaires FRANCE) 

·        Financial Sustainability of the Algerian Pension Plan (Farid FLICI, CREAD)

JAVA IV :  May 25th,  2017, CREAD

·        On the heterogeneity of human population as reected by the mortality dynamics. (Séverine ARNOLD, UNIL, Switzerland, Via Skype)

·        Closing-out the Algerian Life tables: For more Accuracy and Adequacy at old ages (Farid FLICI, CREAD)

JAVA V, 26 Novembre 2017, CREAD.

·        Tables de mortalité en assurance vie en Afrique Subsaharienne  (Aymric Kamega, PDG d’ACAM-Vie, Cameroun, Via Skype

·        Tables de mortalité d’expérience incorporant une échelle de projection : l’expérience des retraités algériens (Khadidja SENOUCI, Doctorante, ENSSEA)

 

 Achieved and ongoing research

FLICI, F. and HAMMOUDA, NE. 2014. “Analysis of half-century of mortality evolution in Algeria: 1992 -2012”. Conference of the Middle East Economics Association MEEA. June 2014. Tlemcen, Algeria.

Abstract:

 Since the independence, the Algerian population has earned almost 30 years in life expectancy at birth. However, this improvement was accompanied by changes in the estimation methodology and improvement in vital statistics quality. Thus, we can not simply analyze mortality evolution in Algeria without addressing all the necessary caution in regards to the methodological changes effects. Our objective is to give a summary presentation of these methodological changes and data imperfections sources before to achieve a relative analysis of the life expectancy evolution. This analysis considers the data published by the Office of National Statistics (ONS) from 1962 to 2012.

Key-words: mortality, life expectancy, change-points, civil records, Algeria.

 

 

 

FLICI, F. 2014. "Estimation of the missing life tables in the Algerian mortality Surface (1977- 1999) by using the Lee-Carter formula”. SMTDA-2014. June 2014, Lisbon. Portugal.

 

kappa 12Abstract:

Mortality forecasting needs usually the availability of a continuous mortality surfaces. In Algeria, the first life table based on the civil registration data has been constructed in 1977. At that time, the life tables could not be constructed with an annual frequency because of the lack of data and experience. It became possible only starting from 1998. No one has tried to estimate the missing life tables of the period [1977-1998]. Today, it is necessary to make this data available and that’s the main objective of the present paper. The idea is to use the Lee-Carter model (Lee and Carter, 1992) to estimate this missing data. For this, the model parameters were firstly estimated on the available data. Then, the time component will be fitted; the fitting function will be used to estimate the missing values. Combined with the age parameters, that leads to estimate the missing mortality rates for males, females and both sex populations while improving the adequacy compared to the original data.

Key-words: Mortality Rates, Missing data, polynomial fit, Lee Carter, Algeria.

 

 

 

FLICI, F. 2016. “Provisionnement des rentes viagères en Algérie entre approche statique et approche prospective”, Bulletin Français d’Actuariat, 16 (31) : 5-40.

Résumé :

L’espérance de vie à la naissance de la population Algérienne ne cesse de réaliser des améliorations considérables. Cette longévité combinée avec les nouvelles contraintes du marché algérien des rentes viagères ne fait que réduire l’utilité des tables de mortalité statiques et accentuent la nécessité d’utiliser les tables de mortalité prospectives pour la tarification et le provisionnement. En l’absence de données de mortalité spécifiques au marché des rentes viagères, nous proposons, dans le présent travail, de nous baser directement sur les données de la population globale afin de construire une table de mortalité prospective pour la tarification et le provisionnement des rentes viagères en Algérie. Les résultats obtenus sous l’approche prospective sont comparés à ceux obtenus sous l’approche statique des tables de mortalité.

Mots-clefs : Provisionnement, rentes viagères, longévité, table de mortalité statiques, table de mortalité prospectives, Algérie.

 

FLICI, F. 2016. “Closing out the Algerian life tables: for more accuracy and adequacy at old ages’’, Proceeding of the International Association of Actuaries – ASTIN Colloquium, 2016, Lisbon-Portugal.

Abstract:

Giving data unavailability or irregularity beyond a certain age, particularly in developing countries, the model life tables are an unavoidable solution to estimate the old ages mortality. As an international standard, this tool provides approximate estimations which are not specified for a particular country, thus the obtained results are not always satisfactory especially when these model life tables are not used in the right way. Thereby, estimating the old ages mortality by extrapolating the observed trend at the younger ages is assumed to provide more coherent results. In the present paper, we evaluate and compare some proposed old ages mortality models to extend mortality rates beyond the age of 80 for the Algerian population. The comparison will be based on various criteria: Goodness-of-fit, predictive capacity, sex differential coherence, predicted age limit and single sexes vs both-sexes adequacy. The final results will be used to correct the historical series of life expectancy at birth for Algeria between 1977 and 2014.

Key-words: mortality rates, goodness of fit, predictive capacity, BIC, MSE, extrapolation, life expectancy, Algeria.

 

 

 

FLICI, F. 2015. “Life annuities calculation in Algeria: Continuous time approach”. Journal of Statistical Science and Application, April 2015, Vol. 3, No. 5-6, 85-100.

figure 7Abstract:

The present paper aims to show the impact of continuous time calculation on life insurance pricing and reserving in the Algerian context. The discrete time approach allows insurance companies to facilitate calculation process but with less accuracy. This approach implies constancy of death quotients during a year. However, the death risk is a continuous function in time. For more accuracy and equity in pricing, calculation needs to consider the exact dates of different payments and also a continuous capitalization process. This gives more adequate premium with fewer hypotheses. This work shows how insurers can propose more adequate pricing using the same actuarial life table.

Keywords: Life annuities, life table, continuous time, fitting, extrapolating, Algeria.

 

FLICI, F. 2015. "Mortality forecasting for the Algerian population with considering cohort effect”. Proceeding of the International Association of Actuaries. IAAC 2015. Oslo-Norway.

2.SED-RHAbstract:

Mortality forecasting becomes a challenge not only for demographers but also for actuaries. Different models have been proposed for this issue ensuring both efficiency and simplicity. In the beginning, most of the proposed models were based on the time and age dimensions. The observation of the error’s distribution in the case of some populations revealed that some cohorts are not well represented by the Age-Period approach. The idea was to introduce a cohort component in order to improve the goodness-of-fit and the forecasting capacity. In the present paper, we propose to forecast the specific-age mortality rates for the Algerian population by comparing the Age-Period Approach (Lee-Carter model) and the Age-Period-Cohort approach (Renshaw and Haberman, 2006; Currie, 2006).

Key-words: Mortality forecasting, Cohort, fitting, Algeria, life annuities.

 

 

FLICI, F. 2016. “Longevity and Life Annuities Reserving in Algeria: Comparison of mortality models”. International Actuarial Association – Life Section Conference, Hong Kong, April 2016. (Abstract)

Abstract: pro mortality surfaces

Using prospective life tables for pension plan managing and life annuities reserving is now more than essential especially for developing countries for which life expectancy keeps improving. Algeria takes part of these countries and it is more than important to provide the life insurance sector a dynamic life table to be used for actuarial calculations. Here, we aim to achieve this objective by comparing a set of mortality models, principally Lee-Carter and Cairns-Blake-Dows models with variants. To do this, we will use the Algerian mortality surfaces; for males and females, for the period [1977-2013] and for the age range [50 - 79]. Models evaluation and selection will be principally based on the goodness-of-fit, and performed by introducing complementary criteria: the predictive capacity, the male female coherence, and also the expected gap in life expectancy between males and females.

Key-words: Prospective mortality models, goodness-of-fit, coherence, forecasting.

 

FLICI, F. 2016. Coherent Mortality Forecasting for the Algerian Population”. SAMOS Conference in Actuarial Sciences and Finance ASF2016. Samos, Greece, May 2016.

Figure_14Abstract:

Mortality forecasting is much needed for population projections and actuarial prospective calculations. The independent single sex’s mortality forecasting leads in almost cases to some incoherence regarding the expected male female mortality evolution. To avoid any unrealistic convergence or divergence in this sense, a coherent mortality forecasting is needed. In the present paper, we do a coherent mortality forecasting for the male female Algerian populations by comparing the two main proposed approaches: Li and Lee (2005) and Hyndman and al. (2013).

Key-words: Life expectancy, mortality, coherent forecast, sex ratio, Algeria.

 

FLICI, F. and PLANCHET, F. 2016.Construction of a dynamic life table based on the Algerian retired population mortality experience”. Mathematical and Statistical Methods for Actuarial Sciences and Finance conference MAF2016, Paris, France, April 2016.

Abstract

The aim of the present paper is to construct a prospective life table adapted to the mortality experience of the Algerian retired. Mortality data for the retired population is only available for the age range [45-95[ and for the period [2004-2013]. The use of the conventional prospective mortality models is not supposed to lead to a robust mortality forecast given the shortness of the lenght of the historical data. To ensure a robust forecasting, we use the global population mortality as an external reference. The positionning of the experience mortality on the reference will allow to project the Age Specific Death Rates calculated on the basis of the experience of the retired population.

key-words: Prospective life-tables, Experience life-tables, reference mortality, retired population, Algeria.

 

FLICI, F. and PLANCHET, F. 2016. Financial Sustainability of the Algerian Pension System”. Working paper, 2016.

 

Abstract:

Maintaining retirement systems stability becomes a big challenge for all countries. Such sustainability is widely related to a set of elements changing over time as population structure, longevity, employment and affiliation to social security. For the case of Algeria, public retirement works according to the pay-as-you-go principle and equilibrium is maintained by public subsidy. But, population is now aging, and longevity is improving. The retired population is supposed to grow faster than the population at working age. Consequently, it will be hard to keep equality between retirement incomes and outcomes in such conditions especially if we consider the weakness of the insured population in respect to the global population. The part of this last depends largely of public employment; the private sector is still less covered. In the other hand, a set of elements are still defined by the social and economic context as wages, pension benefit amount and contribution rates. In the present paper, we present a general review of the different variables affecting the stability of the Algerian system and then we do a simulation about the future evolution. In final, we give a set of recommendations in order to strengthen the stability of the Algerian Retirement System during the 50 coming years.

Key-words: Pension plan, pay-as-you-go, experience mortality, contribution rates, aging, longevity.         

 

References

FLICI, F. 2017. FLICI, F. (2017). “Longevity and pension plan sustainability in Algeria : Taking the retirees mortality experience into account”, Doctoral thesis, Ecole Nationale Supérieure de Statistique et d'Economie Appliquée (ENSSEA), Algiers, Algeria.

FLICI, F. 2016-a. “Provisionnement des rentes viagères en Algérie entre approche statique et approche prospective”, Bulletin Français d’Actuariat, 16 (31) : 5-40.

FLICI, F. 2016-b. “Closing out the Algerian life tables: for more accuracy and adequacy at old ages’’, Proceeding of the International Association of Actuaries – ASTIN Colloquium, 2016, Lisbon-Portugal.

FLICI, F. 2016-c. “Coherent Mortality Forecasting for the Algerian Population”. SAMOS Conference in Actuarial Sciences and Finance ASF2016. Samos, Greece, May 2016.

FLICI, F. 2016-d. “Projection des taux de fécondité par âge de la population Algériennes”. Working paper. Cread.

FLICI, F. and PLANCHET, F. 2016. Financial Sustainability of the Algerian Pension System”. 3° Journée d’Actuariat Vie Algérie – JAVA3, Kolea, Algeria, December 2016.

FLICI, F. 2014. "Estimation of the missing life tables in the Algerian mortality Surface (1977- 1999) by using the Lee-Carter formula”. SMTDA-2014. June 2014, Lisbon. Portugal.

FLICI, F. and HAMMOUDA, NE. 2014. “Analysis of half-century of mortality evolution in Algeria: 1992 -2012”. Conference of the Middle East Economics Association MEEA. June 2014. Tlemcen, Algeria.

HYNDMAN, R. BOOTH, H. and YASMEEN, F. (2013). Coherent mortality forecasting:  the product-ratio method with functional time series models. Demography, 50(1) : 261- 283.

LEE, R. (1993). Modeling and forecasting the time series of US fertility: Age distribution, ranges, and ultimate level. International Journal of Forecasting, 9: 187-202.

LEE, R. and CARTER, L. (1992). Modeling and Forecasting U. S. Mortality. Journal of the American Statistical Association, 87 (419) : 659-671.

LI, N. and LEE, R.D. (2005). Coherent mortality forecasts for a group of populations:  an extension of the Lee Carter method. Demography, 42 (3): 575-594.