Artifacts in population data
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Projecting mortality based on population data is easy… or is it? Are there artifacts in the data which distort our analysis and how significant are they? By using international case studies, we look into the impact of a series of “one-off” events including wars, mass migrations, seasonality and political upheaval. How certain are we that the composition of the past population is representative of the future population and what does this mean for our projections?
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Artifacts in population data
- 1. Artifacts in Population Data Nick Owen, Chris Reynolds PartnerRe 15 April 2015
- 2. Disclaimer 15 April 2015 2 The following presentation is for general information, education and discussion purposes only. Views or opinions expressed, whether oral or in writing do not necessarily reflect those of PartnerRe nor do they constitute legal or professional advice.
- 3. 15 April 2015 3 © Fotolia.com
- 4. Artifact (or should it be Artefact?) “an unintentional pattern in data, arising from processes of collection and management” 15 April 2015 4 © Fotolia.com
- 5. Where have all the men gone? 15 April 2015 5 © Fotolia.comhttp://news.bbc.co.uk/2/hi/uk_news/magazine/3601493.stm
- 6. Discussion Topics • Projection Basics • Smoothing • Basis Risk • Migration Impact • International Comparison • External Factors and Volatility 15 April 2015 6
- 7. Mortality Projections 101 15 April 2015
- 8. The Basics 15 April 2015 8 RisktoExposure Deaths RateDeath
- 9. The Basics 15 April 2015 9 © Fotolia.com
- 10. The Basics 15 April 2015 10 Future Death Rates © Fotolia.com
- 11. Heat Map 15 April 2015 11 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 1991 2001 2011 2021 2031 2041 2051 2061 4.75%-5.25% 4.25%-4.75% 3.75%-4.25% 3.25%-3.75% 2.75%-3.25% 2.25%-2.75% 1.75%-2.25% 1.25%-1.75% 0.75%-1.25% 0.25%-0.75% -0.25%-0.25% -0.75%--0.25% -1.25%--0.75% -1.75%--1.25% ProjectedActual Source: CMI 2013
- 12. Heat Map 15 April 2015 12 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 1991 2001 2011 2021 2031 2041 2051 2061 4.75%-5.25% 4.25%-4.75% 3.75%-4.25% 3.25%-3.75% 2.75%-3.25% 2.25%-2.75% 1.75%-2.25% 1.25%-1.75% 0.75%-1.25% 0.25%-0.75% -0.25%-0.25% -0.75%--0.25% -1.25%--0.75% -1.75%--1.25% ProjectedActual Source: CMI 2013 Cohort Effect
- 13. Heat Map 15 April 2015 13 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 1991 2001 2011 2021 2031 2041 2051 2061 4.75%-5.25% 4.25%-4.75% 3.75%-4.25% 3.25%-3.75% 2.75%-3.25% 2.25%-2.75% 1.75%-2.25% 1.25%-1.75% 0.75%-1.25% 0.25%-0.75% -0.25%-0.25% -0.75%--0.25% -1.25%--0.75% -1.75%--1.25% ProjectedActual Source: CMI 2013 Cohort Effect Period Effect
- 14. )1,(),(5.0 2 1 txPtxP x, tPE(x,t) Potential Complications 15 April 2015 14
- 15. )1,(),(5.0 2 1 txPtxP x, tPE(x,t) Potential Complications 15 April 2015 15
- 16. Potential Complications 15 April 2015 16 What assumptions are we making here? )1,(),(5.0 2 1 txPtxP x, tPE(x,t)
- 17. To Smooth or not to Smooth? 15 April 2015
- 18. Potential Complications 15 April 2015 18 © Fotolia.com Too much smoothing ???
- 19. 15 April 2015 19 Russia Smoothed Source of Data: Human Mortality Database. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Available at www.mortality.org or www.humanmortality.de. PartnerRe Own Calculations
- 20. 15 April 2015 20 Russia Smoothed Source of Data: Human Mortality Database. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Available at www.mortality.org or www.humanmortality.de. PartnerRe Own Calculations
- 21. 15 April 2015 21 Russia – No Smoothing Source: HMD (own calculations)
- 22. UK - Smoothed 15 April 2015 22 Source: HMD (own calculations)
- 23. UK – No Smoothing 15 April 2015 23 Source: HMD (own calculations)
- 24. UK – No Smoothing 15 April 2015 24 See Andrew Cairns’ talk and paper for more on this Source: HMD (own calculations)
- 25. Longevity Portfolio • Over 1 million lives • Gender differentiated • Full date of birth available How are dates of birth distributed? 15 April 2015 25
- 26. Longevity Portfolio 15 April 2015 26 Source: PartnerRe
- 27. Longevity Portfolio 15 April 2015 27 Source: PartnerRe
- 28. Longevity Portfolio 15 April 2015 28 Source: PartnerRe First World War Ends Nov 11, 1918
- 29. Longevity Portfolio 15 April 2015 29 Source: PartnerRe First World War Ends Nov 11, 1918 Second World War Ends Sep 2, 1945
- 30. Longevity Portfolio 15 April 2015 30 Source: PartnerRe
- 31. Longevity Portfolio 15 April 2015 31 Source: PartnerRe First World War Ends Nov 11, 1918 Second World War Ends Sep 2, 1945
- 32. Longevity Portfolio 15 April 2015 32 Source: PartnerRe First World War Ends Nov 11, 1918 Second World War Ends Sep 2, 1945
- 33. Longevity Portfolio 15 April 2015 33 Source: PartnerRe
- 34. Basis Risk 15 April 2015
- 35. Basis Risk 15 April 2015 39 © Fotolia.com vs Population Portfolio
- 36. U.S. Example • Consider data from the Centers for Disease Control and Prevention (CDC) • Gender differentiated • Individual Age • Calendar Years 1999 – 2011 • … also includes ethnic origin 15 April 2015 40
- 37. U.S. Example 15 April 2015 41 Source: CDC - Accessed Oct 2014
- 38. U.S. Example 15 April 2015 42 Source: CDC - Accessed Oct 2014
- 39. U.S. Example 15 April 2015 43 Source: CDC - Accessed Oct 2014 ~ 2.2% per annum ~ 1.9% per annum
- 40. U.S. Example 15 April 2015 44 Source: CDC - Accessed Oct 2014
- 41. U.S. Example 15 April 2015 45 Source: CDC - Accessed Oct 2014
- 42. U.S. Example 15 April 2015 46 Source: CDC - Accessed Oct 2014 ~ 2.5% p.a ~ 2.2% p.a
- 43. U.S. Example 15 April 2015 47 Source: CDC - Accessed Oct 2014 ~ 2.5% p.a ~ 2.2% p.a~ 1.5% p.a ~ 1.1% p.a
- 45. 2 Populations 15 April 2015 49 Cat Land No mortality improvement © Fotolia.com
- 46. 2 Populations 15 April 2015 50 Cat Land No mortality improvement Dog Land No mortality improvement © Fotolia.com
- 47. 2 Populations 15 April 2015 51 Cat Land No mortality improvement Dog Land No mortality improvement q(x) is 900% of that in Cat Land © Fotolia.com
- 48. Example 1 • Residents of Dog Land migrate to Cat Land • 1% population growth – Per annum – Over 4 years – Over the age range 30 – 50 • Dogs don’t trend to local mortality experience 15 April 2015 52
- 49. Example 1 15 April 2015 53
- 50. Example 1 15 April 2015 54 Period Effect over 4 years
- 51. Example 1 15 April 2015 55 Leading edge cohort effect
- 52. Example 1 15 April 2015 56 Trailing edge cohort effect
- 53. Improvements by Calendar Year 15 April 2015 57 AnnualImprovement Calendar Years 35 45 55
- 54. Improvements by Age 15 April 2015 58
- 55. Example 2 • Residents of Dog Land migrate to Cat Land • 1% population growth – Per annum – Over 4 years – Over the age range 30 – 50 • Dogs take on cat characteristics over 10 years 15 April 2015 59
- 56. Example 2 15 April 2015 60
- 57. Example 2 15 April 2015 61
- 58. Example 2 15 April 2015 62 Fully Integrated
- 59. Real examples? 15 April 2015 63
- 60. The Polish Example 15 April 2015 64 Source: HMD (own calculations)
- 61. The Polish Example 15 April 2015 65 Source: HMD (own calculations)
- 62. The Polish Example 15 April 2015 66 Source: HMD (own calculations)
- 63. Low Immigrant Mortality or Data Artifact? • Low mortality for most immigrant groups compared to natives in the host country1 • Often attributed to beneficial health selection processes • Could it be data artifacts? • Explored in recent paper by Wallace and Kulu2 15 April 2015 67 1NZ - Hajat et al., 2010, U.S. - Abraido-Lanza et al., 1999; Palloni and Arias, 2004 2Wallace and Kulu, 2014
- 64. Potential Data Artifacts 15 April 2015 68 Misreporting of Age Misclassifying Nationality Emigrations under- registered Deaths undercounted Mobility Bias
- 65. Potential Data Artifacts 15 April 2015 69 Misreporting of Age Misclassifying Nationality Emigrations under- registered Deaths undercounted Mobility Bias “This study supports low mortality among immigrants … results are not a data artefact.” (Wallace and Kulu)
- 66. One True History? 15 April 2015
- 67. UK Population 15 April 2015 71 Source: HMD (own calculations)
- 68. France Population 15 April 2015 72 Source: Human Mortality Database
- 69. Ireland Population 15 April 2015 73 Source: HMD (own calculations)
- 70. Belgium Population 15 April 2015 74 Source: Human Mortality Database
- 71. Italy Population 15 April 2015 75 Source: HMD (own calculations)
- 72. Netherlands Population 15 April 2015 76 Source: Human Mortality Database
- 73. Denmark Population 15 April 2015 77 Source: Human Mortality Database
- 74. Portugal Population 15 April 2015 78 Source: Human Mortality Database
- 75. Sweden Population 15 April 2015 79 Source: Human Mortality Database
- 76. Austria Population 15 April 2015 80 Source: Human Mortality Database
- 77. Czech Republic Population 15 April 2015 81 Source: Human Mortality Database
- 78. Spain Population 15 April 2015 82 Source: Human Mortality Database
- 79. Norway Population 15 April 2015 83 Source: Human Mortality Database
- 80. Iceland Population 15 April 2015 84 Source: Human Mortality Database
- 81. Poland Population 15 April 2015 85 Source: Human Mortality Database
- 82. Canada Population 15 April 2015 86 Source: Human Mortality Database
- 83. Japan Population 15 April 2015 87 Source: Human Mortality Database
- 84. One true history? 88 Source: HMD (own calculations) UK IRE FR BEL ITA POR NL DMK AUT JA CAN
- 85. The Changing Seasons 15 April 2015
- 86. 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2008/9 Flu epidemic Seasonality – weekly death registrations 15 April 2015 90 Source: ONS 1999/2000 Flu epidemic
- 87. - 1,000 2,000 3,000 4,000 5,000 6,000 2010 2011 2012 2013 2014 Seasonality – Age 85+ (CMI) 15 April 2015 91 Source: CMI
- 88. - 1,000 2,000 3,000 4,000 5,000 6,000 2010 2011 2012 2013 2014 Seasonality – bank holiday effect 15 April 2015 92 Source: CMI Queen’s Jubilee 3 day week August bank holiday Christmas Royal wedding
- 89. Seasonality – weekly death registrations 15 April 2015 93 Source: ONS & MetOffice4 week moving average Deaths Temperature -40 -30 -20 -10 - 10 20 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 2000 2001 2002 2003 2004 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Deaths Temperature 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
- 90. Seasonality – conventional approach 15 April 2015 94 • Seasonality normally ignored • Deaths by calendar year • Mid-year population estimates Interval Average improvement in mortality Standard deviation in annual improvement 12 months ending December 2.3% 2.3% Ten year time series
- 91. Seasonality – choice of interval 15 April 2015 95 Source: ONS data & own calculations Conventional method Vol = 2.3% 12 months ending 30 September Vol = 1.5%
- 92. Seasonality – choice of interval 15 April 2015 96 Interval Average improvement Standard deviation in annual improvement 12 months ending December 2.3% 2.3% 12 months ending September 2.3% 1.5% • Calendar year interval overstates underlying volatility • Stochastic models - Lee Carter, CBD, RH etc Source: ONS data & own calculations
- 93. Final Thoughts 15 April 2015 97 © Fotolia.com
- 94. Standardized mortality rate 15 April 2015 98 Source: Wikipedia 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 1 10 100 1000 10000 100000 1919 1921 1923 1925 1927 1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 number of pirates SMR (UK)
- 95. Standardized mortality rate 15 April 2015 99 Source: Wikipedia 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 1 10 100 1000 10000 100000 1919 1921 1923 1925 1927 1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 number of pirates SMR (UK)
- 96. 15 April 2015 100 Expressions of individual views by members of the Institute and Faculty of Actuaries and its staff are encouraged. The views expressed in this presentation are those of the presenter. Questions Comments
- 97. Artifacts in Population Data Nick Owen nicholas.owen@partnerre.com Chris Reynolds christopher.reynolds@partnerre.com 15 April 2015
Editor's Notes
- Standard company disclaimer. Own view not necessarily reflect those of PartnerRe. Act upon anything we say today at your own risk.
- What’s this? The Hawthorne Tower in Illinois. The Hawthorne Works was a large factory complex built by Western Electric starting in 1905. Height of its operations it had 45,000 employees. 1980s replaced with shopping center. From 1924-32 a series of experiments. Not sinister – productivity. 2 groups of workers used as guinea pigs. Lighting in work area for one group was improved, other unchanged. Productivity of more highly illuminated workers increased more than control group. Other changes made to working conditions (working hours, rest breaks, etc.), - in all cases their productivity improved when a change was made. Productivity even improved when the lights were dimmed again. When everything returned to how it was productivity at the factory was at its highest level. Absenteeism had plummeted. It was suggested that productivity gain occurred as a result of the motivational effect on the workers of the interest being shown in them. Hawthorne Effect - “people behave differently when they are being watched”. At least some part of what is observed is an artifact of the process of observation Chris/Nick. Today we‘re going to speak about artifacts in population data.
- So what is an artifact? Not the kind of thing this Indiana Jones lookalike might seek - object of cultural or historical interest. Something observed in a scientific investigation or experiment that is not naturally present but occurs as a result of the preparative or investigative procedure.
- Do we need to worry about artifacts in population data? Census – gold standard for the population – can be affected by participation bias. Late 80s early 90s – controversial ‘poll tax’ introduced in the UK. Large numbers of young men attempted to become invisible to the poll tax register – avoid paying tax. They dropped off electoral rolls. Population estimates produced by the ONS – when results of 1991 Census were known there were about a million fewer men of younger working age counted than had been estimated.
- So what will we speak about today: We’ll start with a very brief introduction on how one calculates improvement rates and projects them forward. Benefits and dangers of smoothing Basis risk The impact of inflows or outflows from a population Quick international comparison – can we learn anything from the experience seen in other countries Impact of external factors on mortality. How these factors and choices we make can impact the volatility of our experience.
- … or the beginner’s guide to mortality projections.
- The crude death rate is simply the number of deaths divided by the exposure to risk.
- Choose your preferred smoothing approach and iron out the bumps. Perhaps p-spline, Gaussian smoothing, or in my case I‘m using the Phillips EasySpeed to smooth the rates. Once these smooth rates have been determined, annualised rates of mortality imporvement can be calculated
- Now comes the actuarial judgement ... otherwise known as crystal ball gazing. Variety of projection techniques exist for forecasting future mortality rates. These could be the CMI, Lee Carter or CBD models. One thing in common ... they will all be wrong, but some less wrong then others.
- Often illustrate mortality improvements on a heat map. On the vertical axis I have age. On the horizontal axis I have calendar year. Hotter colours represent a higher level of mortality improvement. Cooler colours represent a lower level of mortality improvement (or even deterioration)
- Move along the diagonal we can follow a particular cohort of lives. Effects common to a cohort would be referred to as cohort effects.
- May have effects which are time or period dependent - not related to a particular cohort We call these period effects
- Already we start to see potential complications Denominator in our crude mortality rates is exposure to risk. Don‘t typically have this for populations.
- We approximate this by the mid year population.
- In come countries this isn‘t available so we approximate this by taking the average of the start and end year populations. What assumptions are we making here? Births uniformly distributed across the year – is this valid? Discuss more in the next section on smoothing.
- Why smooth? On the one hand smoothing removes noise and enables data features to be more easily identified. On the other hand, smoothing may smooth over real features.
- Consider this heat map for Russia. This uses data from the human mortality database and the calculations are performed using our own proprietary tools.
- Here we see some period effects. 1992 Fall of Soviet Union in 1991. 1992 - Russia experienced a serious economic crises Drop in personal income and rapid impoverishment. Adverse economic changes were followed by mortality increase. Looks like prolonged period of mortality deterioration ~ 15% per annum. August 1998 - Russia experienced another economic crisis resulting in mass impoverishment. This can’t be as clearly seen on the heat map.
- Turn off smoothing – now we see 2 things: 1992 - Impact of 3 gradual shift @10% p.a. vs instant shift @ 33%. Maybe not significant? Either way, impact is non-zero. 1998 - impact of the 1998 banking crisis can be more clearly seen. Smoothing had smoothed over the effect.
- UK – see some strong “cohort” features. No apparent issues or concerns here
- Remove the smoothing. Now it seems we may have some data “problems”
- In particular we see this 1919 cohort Exhibits poorer experience Long thought to be the impact of Spanish Flu – development of fetus in the womb. Turns out it’s probably a distortion in the distribution of births leading to an underestimate of the exposure and subsequently higher mortality rates. See Andrew Cairns’ paper and talk for far more on this point. Points to note When you’re smoothing you’re fitting a model Before you fit a model you want to understand the underlying data After fitting a model you need to check the fit of your model, typically by performing an analysis of the residuals.
- Considered a subset of our longevity portfolio. Taken 1 million lives. Gender differentiated, but now the full data of birth is available. We ask ourselves “How are the dates of birth distributed?”
- On this chart we have the year of birth on the horizontal axis On the vertical axis we show the average day of birth from the start of the year. So if births are uniformly distributed we’d expected to see the average day of birth sit on this yellow line (182.5 days)
- This is what we see. We’ve only plotted points where we have at least 500 lives. Which is the case for all years of birth in this range
- Number of features Average day of birth for 1919 is much higher than the years either side. This coincides with the end of the first world war (Nov 1918). Not just the first world war.
- See a similar impact with the 2nd world war which ended in Sep 1945.
- Mid year population in 1919 would be the sum of births over the last 12 months. This would underestimate the true exposure for 1919.
- But there’s another observation ….
- See a downwards trend. Expected a downwards trend due to survivor bias. Namely lives born earlier in the year will be older and so it’s less likely they will survive to the start of the contract than those born later in the year. This effect will be more pronounced the further back we go … hence the downwards trend. Doesn’t explain why this side of the chart is consistently below the yellow line. Hypothesised that it may be influenced by the availability of cheap summer holidays leading to a spike in summer conceptions. Not sure as these are pre 1960 results.
- As we get closer to the portfolio date there will be fewer and fewer pensioners. This increasing downwards trend leads to a fall in the average day of birth. But it’s not a real effect, it’s a consequence of the way we’ve collected data. Once again you need to understand the nature and source of your underlying data. The data you’re using won't have been collected with your purpose in mind. Be consious that this could introduce artifacts into the data.
- Here we consider Australia from 1950s through to early 21st century. See a cohort effect Possible period effect in the 1960s?
- Remove smoothing Something strange in this left hand corner Crude data looks to be smoothed
- Go further back in time Notice that the feature persists.
- Answer is simple if you see how data in Australia is collected. Data for years before 1964 are available only in 5 year age groups. Hence the smoother nature of the crude rates But other “artifacts” Until mid 1960s the indigenous population was excluded from the federal censuses and population estimates Aboriginal births and deaths have only been included in the country’s vital statistics since 1966
- Our next section relates to basis risk.
- Why population mortality? Insurance portfolios often considered too small to discern credible trends. Population is often considered only credible sized dataset on which to derive trends. So we use population trends for insurance/pension blocks. But what implicit assumptions are we making? 1) Assuming the insured portfolio is a representative subset of the population 2) Insurance portfolio is often a “fixed” portfolio of lives. Assume the population is stable or that the insurance portfolio is subject to the same cohort effects (e.g. migration)?
- Illustrate basis risk with an example from the US. Take population data from the CDC. Gender differentiated – individual age – calendar years 1999 to 2011 But … also includes ethnic origin.
- Based on this data the US population breaks down into the following ethnic types: Just over 80% are classified as white. Next we have Black or African American Followed by Asian or Pacific Islander And then American Indian or Alaska Native.
- Calculated age standardised rate Split by Calendar Year and Gender Using the WHO standard population to standardise the mortality rate
- Very crudely we see improvement rates of 2.2% per annum for males 1.9% per annum for females. We’d then take these rates as our starting rate of improvement and project into the future.
- Here we’ve broken down mortality by ethnic type At this point we’ve not standardised … watch how sensitive the results are to standardisation
- Standardised rates have moved considerably.
- For male Black or African Americans we see an improvement rate of 2.5% per annum For females it’s 2.2% p.a.
- Considering those classified as white we see 1.5% and 1.1% p.a. respectively. These trends are different from the total population level. Intended to illustrate that we could materially misestimate trend if the composition of your portfolio is different from that of the population.
- My part of the talk has a common thread which seems to be related to temperature… First, migrations – that’s a politically hot topic. Also comparison of heat maps by country Finally a piece on seasonality and temperature So why migration? 1)basis risk. May be features which aren’t relevant to your needs 2)artifacts caused by presence of migrations in the past.
- Projection of evolving population structure 1st population – cat land. [NEXT] 2nd population – dog land
- Projection of evolving population structure 1st population – cat land. [NEXT] 2nd population – dog land
- Projection of evolving population structure 1st population – cat land. [NEXT] 2nd population – dog land
- Projection of evolving population structure -some of the dogs move to Cat Land -leading to 1% growth -over four years, and it’s limited to certain ages. Project the two groups, cats and dogs, separately, counting their exposures and deaths and measuring aggregated impact of Cat Land’s population statistics. We show the change in mortality using a heat map…
- The migration creates a signature pattern: Initial age period effect: for 4 years mortality gets worse by ca. 10% per annum.
- The migration creates a signature pattern: Initial age period effect: for 4 years mortality gets worse by ca. 10% per annum.
- The migration creates a signature pattern: Initial age period effect: for 4 years mortality gets worse by ca. 10% per annum. Leading cohort effect: As dogs age, the combined population suffers from worse mortality than the cat-only population that went before Leading cohort effect: As dogs age, the combined population suffers from worse mortality than the cat-only population that went before
- The migration creates a signature pattern: Initial age period effect: for 4 years mortality gets worse by ca. 10% per annum. Leading edge cohort effect: As dogs age, the combined population suffers from worse mortality than the cat-only population that went before Trailing edge cohort effect: Population returns to cat-only leading to heavy improvements.
- Cross section by calendar year for various ages -oscillation
- Cross section by age for years -initially see a hump (during the period effect) -then see an oscillation (after migration has finished)
- This is quite an extreme example… …mortality difference nine times. …4% population growth ….and mortality improvement artefact is around +/-10% In practice far less extreme, and swamped by other features in the data.
- Struggled to find real examples of immigration signature -Too much noise -Not enough difference in mortality
- Poland Some interesting features here – first we see the signature migration signature.
- Poland – emmigration, not immigration. Some interesting features here – first we see the signature migration signature. 1975: Following some changes in the law regarding German citizenship, “Aussiedlers” left the country for Germany
- 1975: Following some changes in the law regarding German citizenship, “Aussiedlers” left the country for Germany 1980’s: Because of “Martial law”, communist repressions and poverty, people moved mainly to the US, Canada and Germany 2004: The labor market opened in the UK, followed by other EU countries
- Low mortality for most immigrant groups compared to natives in the host country1 (NZ - Hajat et al., 2010, U.S. - Abraido-Lanza et al., 1999; Palloni and Arias, 2004) Often attributed to beneficial health selection processes Could it be data artifacts?
- 1. Misreporting of Age – especially at advanced ages. For example in the US. Possibly due for access to state benefits, easy when records can’t be checked. Depress mortality rates at older ages and affect the age distribution of deaths. 2. Misclassifying nationality The misclassification of ethnicity on death certificates may also occur. Causes a biased numerator. 3. Emigrations under-registered. Could refer to immigrants not registering an exit . Could be innocent. Could be incentive to stay on a certain country’s register. -become PHANTOMS in the data, statistically immortal, age with country’s official statistics 4. Deaths undercounted Return migration for workers in deteriorating health Causes a selection bias in the deaths that are counted. missing deaths when for the most part these lives counted towards exposure 5. In Europe, ‘mobility bias’ Migrants may frequently return to their origin country for short or long periods (independent of health status) given the geographic proximity of many host and origin countries. If the host country is unaware of these frequent departures, individuals will continue to contribute to denominators even though they are not permanently resident in the host country.
- Wallace & Kulu - controlled for known/likely artefacts, found that low immigrant was probably a real feature of the data. FOR THE UK. -Implications for mortality projections . In particular , migration features are not relevant for projection a closed portfolio of annuitants/pensioners and should be removed. -Migration-related artifacts (phantom populations) might affect your statistics in general.
- UK actuary setting assumptions... 1) Assured lives experience => Interim cohort projections SC, MC, LC 2010 / 2020 / 2040 2) Population data showing the same thing. 3) same question: how long will it last 4) in 2008, looks like it is finishing? Often say there is only one history. Not true – there are lots of countries we can learn from. Question … is the cohort effect ending???
- France Has a cohort effect Maybe it’s finished already
- France Has a cohort effect Maybe it’s finished already – hard to say, swamped by effect of smoking ban.
- France Has a cohort effect Looks like it’s finished already
- Italy Very similar
- Netherlands Very similar, same problem as Ireland with smoking ban
- Netherlands Also has a cohort effect Probably diminishing
- Portugal Same again?
- Sweden Weak cohort effect
- Often say there is only one history. Not true – there are lots of countries we can learn from. Question … is the cohort effect ending???
- Often say there is only one history. Not true – there are lots of countries we can learn from. Question … is the cohort effect ending???
- Finish trip around europe, some countries with no visible cohort effect…. Spain No cohort effect?
- Often say there is only one history. Not true – there are lots of countries we can learn from. Question … is the cohort effect ending???
- Often say there is only one history. Not true – there are lots of countries we can learn from. Question … is the cohort effect ending???
- Often say there is only one history. Not true – there are lots of countries we can learn from. Question … is the cohort effect ending??? 1975: Following some changes in the law regarding German citizenship, “Aussiedlers” left the country for Germany 1980’s: Because of “Martial law”, communist repressions and poverty, people moved mainly to the US, Canada and Germany 2004: The labor market opened in the UK, followed by other EU countries
- Canada Same cohort effect again? Probably finished
- Japan Very strong cohort effect, probably finished N.B. May not be the same reasons – but similarity is striking
- Selection of countries with cohort effect UK Ireland France Belgium Italy Portugal Netherlands Denmark Austria Cohort effects all start in the mid sixties, around the same age group.
- -over-dispersion, impact on stochastic models (volatility), impact on a projection (last data point) -summer-summer vs winter-winter timing (Samoplig frequency/Nyquist therorem?) -correlation to temperature records? => climate change impact
- -obvious seasonality effect -peak is very close to the year end – so timing of peaks will lead to volatility of YoY figures -typical peak-to-trough is around 20%.
- -much more seasonality amongst the elderly -typical peak to trough ca. 50% -can also identify some features…. 1. Christmas 2. August Bank Holidays 3. 3 Day week in 2012 - record low number of deaths registered. Office was closed. 4. Royal Wedding
- - N.B. bank holiday effect. - Some of this might be real – e.g. elderly hanging on for Christmas. …..most likely to be the office hours of the registrar office. - i.e Date of REGISTRATION vs Date of DEATH -CMI data by age only availible for short time series. Return to ONS data (not split by age).
- -obvious seasonality effect -2011, 2012 => late winter - where gradient is steep, then deaths in year may be sensitive to timing of the winter. - late winter & early winter => heavy calendar year - early winter & late winter => light calendar year
- -Sept – Sept model has 60% volatility - September also has most cosistent temperature
- -Over-dispersion models and/or autoregressive models go some way to fixing this, -But that seems to be a round-about way of fixing an underlying problem in your data treatment
- Be careful so that you don’t slip up. The data you’re using won't have been collected with your purpose in mind – any polishing / manipulation you’re not aware of? What artifacts are in the data. How clear are you about what’s in your numerator & denominator. If you’re smoothing your data – you’re fitting a model. Understand the raw data first and then check the fit of the fitted model. Basis risk. Is the population representative of your portfolio? How is the pool of lives changing over time and is it material? … and going right back to the Hawthorne effect could the observation process … such as the definition of investigation year … create artifacts in your results. Today we’ve spoken about the known unknowns … unfortunately we’ll need hindsight to consider the unknown unknowns.
- So let me leave you with one final thought on correlation and causation. We saw earlier that if you could predict the weather you may be able to predict how mortality will change. But what about this example? Any thoughts on what these might be?
- Number of pirates … may be correlated but unlikely to be causation. Thanks