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# Population projections 2021-2070 for France – Method and assumptions - Working paper N° 2021-05

Elisabeth Algava, Nathalie Blanpain

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Elisabeth Algava, Nathalie Blanpain. Population projections 2021-2070 for France – Method and assumptions - Working paper N° 2021-05. 2021. hal-03901957

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# Population projections 2021-2070 for France – Method and assumptions

## Volume 1

### Documents de travail

N°2021-05 – November 2021

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>
Institut national de la statistique et des études économiques

2021-05

# Population projections 2021-2070
## for France - Method and assumptions

### Volume 1

Élisabeth ALGAVA* and Nathalie BLANPAIN*

November 2021

Direction des Statistiques Démographiques et Sociales - Timbre F001

88, avenue Verdier - CS 70058 - 92541 MONTROUGE CEDEX - France

Tél. : 33 (1) 87 69 62 82 - E-mail : dg75-f001@insee.fr - Insee website : http://www.insee.fr

*Ces documents de travail ne reflètent pas la position de l'Insee et n'engagent que leurs auteurs.*

*Working papers do not reflect the position of INSEE but only their author's views.*

Acknowledgements : The authors thank Isabelle Robert-Bobée, Sylvie Le Minez and Christel Colin for their proofreading and comments. We thank the 46 experts consulted for the quality of their analysis which allowed the development of the assumptions of this document: Didier BRETON, Carlo Giovanni CAMARDA, Anne GOUJON, Fanny JANSSEN, Jean-François LÉGER, Philippe LOUCHART, Gilles PISON, Grégoire REY, Jean-Marie ROBINE, Patrick SIMON, Anne SOLAZ, Laurent TOULEMON, Arkadiusz WISNIOWSKI, Didier BLANCHET, CSR Retraites ; Pascale BREUIL, Cnav ; Vianney COSTEMALLE, Drees ; Jérôme LÊ, Insee ; Virginie JOURDAN, Loreline COURT, Florian HATIER, DSED ; Gautier MAIGNE, France Stratégie ; Anthony MARINO and Amandine BRUN-SCHAMMÉ, COR ; Misha KHODABIN, Hamza ZAKRAOUI, Samuel MENARD, DG Trésor ; Sylvain PAPON, INSEE ; Gwenaël PODESTA, DGEC ; Clément ROUSSET, Drees ; Bureaux retraites, handicap et dépendance et professions de santé, Drees ; Gwenaël SOLARD, INSEE. Antonio ARGUESO, INE, Spain ; Kim DUSTAN, Statistics New Zealand ; Katarzyna GÓRAL-RADZISZEWSKA, Statistics Poland ; Helen HUGHES, ONS, United Kingdom ; Raymond KOHLI, OFS, Switzerland ; Lena LUNDKVIST, Statistics Sweden ; Dragos MONDIRU, INSSE, Romania ; Olga POETZSCH and Felix zur NIEDEN, DESTATIS, Germany ; Branislav SPROCHA, Slovakia ; Astri SYSE and Michael THOMAS, Statistics Norway ; Marie VANDRESSE, Federal Planning Bureau, Belgium ; Livia VARGA and Zoltan CSANYI, KSH, Hungary, as well as a number of anonymous persons. We are grateful to Vianney Costemalle for providing us his probabilistic projection codes and for allowing us to quote from one of his publications presenting deterministic and probabilistic projections.

<footnote>* Insee, division Enquêtes et études démographiques</footnote>
## Summary

The French National Office for Statistics, INSEE has conducted a new set of population projections for France for the period 2021-2070. Like the previous ones published in 2016, these projections cover France as a whole (including five overseas departments).

The new projections are based on population estimates by sex and age on 1 January 2021, published in March 2021. As the previous ones, these projections are made using the component method, i.e., based on assumptions about the three elements that determine population change: fertility, mortality and migration. Some methodological changes have been made in the projection of each component. In addition, the integration of new data available in the calculation of trends has led to new assumptions on the evolution of these three components. These trends were analyzed excluding the effect of the pandemic since 2020. Specific assumptions were made for 2021 and 2022.

A set of 30 scenarios was established, including a baseline scenario, 26 variants and 3 working scenarios. The variants make it possible to analyze the effects of a change in assumptions on the projected results.

The baseline fertility and mortality assumptions have been significantly revised. Fertility is lower than in the previous exercise (a total fertility rate of 1.80 compared to 1.95 previously). Life expectancy is reduced by 2.6 years for men and 3.0 years for women in 2070 (87.5 years of life expectancy at birth for men compared to 90.1 years in the previous exercise and 90.0 years for women compared to 93.0 years). Given the very high uncertainty surrounding this assumption and the recent data (an average net migration of 73,000 from 2014 to 2017, the last year for which the net migration is available), the level of net migration has been maintained at +70,000 as in the previous projection.

The results were published in an Insee Première (https://www.insee.fr/fr/statistiques/5893969) and in an Insee Résultats (https://www.insee.fr/fr/statistiques/5893969) : On 1 January 2070, according to the baseline scenario of the projection, France would have 68.1 million inhabitants, i.e. 700,000 more than in 2021.

**Key-words :** population projections, population forecast, fertility, mortality, net migration, France.
# Table of Contents

<table><tr><td>Summary</td><td>5</td></tr><tr><td>The 2021 projections incorporate recent demographic changes and update the 2016 projection</td><td>5</td></tr><tr><td>The experts who gave their opinion on future demographic developments</td><td>6</td></tr><tr><td>The 3 fertility assumptions</td><td>6</td></tr><tr><td>Total fertility rate</td><td>6</td></tr><tr><td>Mean Age at Childbirth</td><td>7</td></tr><tr><td>The 3 mortality assumptions</td><td>8</td></tr><tr><td>The 3 hypotheses of net migration</td><td>10</td></tr><tr><td>Main indicators associated with the different assumptions</td><td>11</td></tr><tr><td>Comparison of the baseline scenarios of the 2016 and 2021 exercises</td><td>11</td></tr><tr><td>Variants of the 2021 projections exercise</td><td>11</td></tr><tr><td>Number of scenarios and variants</td><td>12</td></tr><tr><td>Part 1: Projection method</td><td>13</td></tr><tr><td>1) Consultation with experts and their responses</td><td>13</td></tr><tr><td>a. Researchers</td><td>14</td></tr><tr><td>b. Official statistical system and user agencies (OSS)</td><td>15</td></tr><tr><td>c. Foreign national statistical institutes (NSI)</td><td>17</td></tr><tr><td>2) The projection method</td><td>18</td></tr><tr><td>a. The principle of the component method</td><td>18</td></tr><tr><td>Box 1 - Some definitions</td><td>20</td></tr><tr><td>b. The position of the experts on the method</td><td>21</td></tr><tr><td>Box 2 - Deterministic approach, probabilistic approach</td><td>24</td></tr><tr><td>c. The position of the experts on the scenarios</td><td>25</td></tr><tr><td>3) The horizon of the population projections</td><td>27</td></tr><tr><td>4) In the short term: how to integrate the covid-19 pandemic?</td><td>29</td></tr><tr><td>a. What do we know when we finalize these projections?</td><td>29</td></tr><tr><td>b. The position of the experts on the starting point of the projection</td><td>30</td></tr><tr><td>Box 3 - Final, provisional, or trend data: the starting point of the projection</td><td>32</td></tr><tr><td>Part 2: Three assumptions about fertility</td><td>33</td></tr><tr><td>a. Recent changes in fertility in France</td><td>33</td></tr><tr><td>b. The position of experts on the evolution of fertility</td><td>38</td></tr><tr><td>Box 4 - Setting a TFR target of 1.80 or 1.85, what difference will it make by 2070?</td><td>47</td></tr><tr><td>c. Assumptions retained</td><td>48</td></tr><tr><td>Box 5 - TFR probabilistic projection, an update of Vianney Costemalle's work</td><td>53</td></tr><tr><td>Box 6 - Estimating births and fertility in 2021</td><td>54</td></tr><tr><td>Part 3: Three assumptions about mortality</td><td>56</td></tr><tr><td>a. Recent mortality trends in France</td><td>56</td></tr><tr><td>b. The position of experts on the evolution of mortality</td><td>62</td></tr></table>
<table><tr><td>c.</td><td>Assumptions retained</td><td>72</td></tr><tr><td></td><td>Box 7 - Comparison of projected life expectancy in 2070 using different methods</td><td>85</td></tr><tr><td></td><td>Box 8 - Confidence interval with the Lee-Carter model</td><td>86</td></tr><tr><td>Part 4 :</td><td>Three assumptions about net migration</td><td>87</td></tr><tr><td>a.</td><td>Recent changes in net migration in France</td><td>87</td></tr><tr><td>b.</td><td>The position of experts on international migration</td><td>91</td></tr><tr><td>c.</td><td>Assumptions retained</td><td>101</td></tr><tr><td></td><td>Box 9 - Probabilistic projection of net migration, an update of V. Costemalle's work</td><td>107</td></tr><tr><td></td><td>Box 10 - Pandemic and net migration in France: sensitivity tests</td><td>108</td></tr><tr><td>Part 5 :</td><td>Dissemination</td><td>109</td></tr><tr><td>Bibliography</td><td></td><td>114</td></tr></table>
## THE 2021 PROJECTIONS INCORPORATE RECENT DEMOGRAPHIC CHANGES AND UPDATE THE 2016 PROJECTION

This working paper presents the methodology and assumptions for the 2021 population projections for France.

The previous population projections were published in 2016 (Blanpain, Buisson, 2016a, b and c). They were based on the age pyramid on 1 January 2013 and covered France, including the five overseas departments. Associated projections of the working population and households, and regional projections, were subsequently published.

The projections consist in projecting the population living in France (Metropolitan France, Guadeloupe, Martinique, Guyana, Reunion and Mayotte) from 2022 to 2070.

INSEE regularly updates its projections. As before, the 2021 population projections are based upon the component method. This method consists in projecting the population year by year, age by age, for men and women separately. From the age pyramid on 1 January 2021, the age pyramid on 1 January of the following year is projected by aging the population by one year, adding births, removing deaths, and adding net migration. This method therefore requires fertility, mortality and net migration assumptions. It can be described as a trend method in that the assumptions are based on what has happened in the recent past. The geographical coverage has been kept identical to that of 2016 (France including the 5 overseas departements).

The projection horizon is set at 2070, as for previous projections. Proposed for consultation, it has been validated by most of the experts who have expressed their views on the subject.

The choice of assumptions took place from February to September 2021. The procedure began with a consultation of some one hundred experts on the basis of a questionnaire (Volume 2 of this working paper) in order to obtain their opinion on potential changes in fertility, mortality and migration by 2070. The questionnaire contained an annex with a summary of demographic developments since the last projection exercise in 2016. Of the experts consulted, 46 responded. Between March and September 2021, the proposed assumptions, their justifications and their impacts were refined in the course of exchanges with certain experts and users.
## THE EXPERTS WHO GAVE THEIR OPINION ON FUTURE DEMOGRAPHIC DEVELOPMENTS

Forty-six responses to the questionnaire were received, 38 of which were accompanied by permission for detailed publication by name in this working paper. The full text of their answers is in Volume 2 of the paper.

14 responses were received from researchers specialising in demographic issues: Didier BRETON, Carlo Giovanni CAMARDA, Anne GOUJON, Fanny JANSSEN, Jean-François LÉGER, Philippe LOUCHART, Gilles PISON, Grégoire REY, Jean-Marie ROBINE, Patrick SIMON, Anne SOLAZ, Laurent TOULEMON, Arkadiusz WISNIOWSKI and one anonymous respondent.

17 responses were received from people working in a French organization using or developing projections, most often within the official statistical system: Didier BLANCHET, CSR Retraites; Pascale BREUIL, Cnav; Vianney COSTEMALLE, Drees; Jérôme LÊ, Insee; Virginie JOURDAN, Loreline COURT, Florian HATIER, DSED; Gautier MAIGNE, France Stratégie; Anthony MARINO and Amandine BRUN-SCHAMMÉ, COR; Misha KHODABIN, Hamza ZAKRAOUI, Samuel MENARD, DG Trésor; Sylvain PAPON, Insee; Gwenaël PODESTA, DGEC; Clément ROUSSET, Drees; Bureaux retraites, handicap et dépendance et professions de santé, Drees; Gwenaël SOLARD, Insee and 4 anonymous persons.

15 responses were written by persons working in a foreign national statistical institute (NSI) and responsible for the development of the projections in their country: Antonio ARGUESO, INE, Spain; Kim DUSTAN, Statistics New Zealand; Katarzyna GÓRAL-RADZISZEWSKA, Statistics Poland; Helen HUGHES, ONS, United Kingdom; Raymond KOHLI, SFO, Switzerland; Lena LUNDKVIST, Statistics Sweden; Dragos MONDIRU, INSSE, Romania; Olga POETZSCH and Felix zur NIEDEN, DESTATIS, Germany; Branislav SPROCHA, Slovakia; Astri SYSE and Michael THOMAS, Statistics Norway; Marie VANDRESSE, Federal Planning Bureau, Belgium; Livia VARGA and Zoltan CSANYI, KSH, Hungary and 3 anonymous.

## THE 3 FERTILITY ASSUMPTIONS

### Total fertility rate

Taking into account recent developments and the position of experts, a target of 1.80 for the total fertility rate (TFR) has been adopted for the baseline scenario, slightly below the level currently observed (1.83 in 2020). This target would be reached by 2023. This is a significant revision from the much higher baseline fertility assumption of the previous 2016 exercise, at 1.95.

Compared to the 2016 exercise, the gap between the baseline assumption and the high and low assumptions has been increased to +/-0.20, compared to +/-0.15 in the 2016 projections. The low assumption is at 1.60 and the high assumption at 2.00, slightly below the replacement level. The
working scenario of very low fertility has been set at 1.50, a value close to the European average in 2019. The targets of the alternative assumptions are reached in 2030.

To account for the **pandemic**, based on recent data, specific assumptions, common to all scenarios, have been made for 2021 and 2020: the **TFR decreases from 1.83 in 2020 to 1.79 in 2021 and 1.80 in 2022.**

### Observed and projected total fertility rate (TFR)

<figure><img src="image_4.png" /></figure>

Source: Insee, population estimates and vital statistics until 2021. Population projection 2021-2070 (Insee Résultats).

### Mean Age at Childbirth

In the new projection exercise, the mean age at childbirth continues to rise at a steady pace until 2052, when it reaches the level of 33 years, considered as a ceiling. In 2016, the same pattern was adopted, with a ceiling age of 32 years reached in 2040. The application of a ceiling of 33 years, reached in 2052, means that, over the last 18 years of the projection, the phenomena of postponement of childbearing diminish, and, from the 2030 generation onwards, the completed fertility eventually reaches the TFR set at 1.80.
Projected final descent of generations 1990-2030 in the baseline scenario

<figure><img src="image_5.png" /></figure>

Source: Insee, population estimates and vital statistics until 2021. Population projection 2021-2070 (Insee Résultats). For the 1990 generation, for example, fertility data are observed before age 31 and projected at 31 and after.

Interpretation: We do not know the final descent of the generations of women born after 2020 because they will not yet be 50 years old in 2070. However, by construction, the final descendants and the TFR eventually merge when the age at maternity is stable: for the generations born in 2030, the final descendants would be equal to the TFR, stabilised at 1.80 children per woman in the baseline scenario.

Coverage: France excluding Mayotte from 2000 to 2013, France from 2014 onwards.

THE 3 MORTALITY ASSUMPTIONS

Baseline scenario

As in previous projections, mortality rates are projected age by age. At each age, the downward trend in mortality observed over the last 10 years excluding the pandemic, i.e. 2010-2019, is extended. A smoothing is performed between 6 and 79 years for women and 10 and 79 years for men, in order to smooth out period and cohort effects.

As in the 2016 projections, the baseline scenario incorporates a cohort effect, observed in the past for men and women born between 1941 and 1955. While, for a given age, mortality decreases from generation to generation, this is almost not the case for these generations in adulthood: their mortality remains almost identical to that of the generations that preceded them (Blanpain, 2020a). The baseline scenario assumes that this cohort effect observed for people born between 1941 and 1955 will persist at higher ages.

Compared to the previous projections, the new projections revise life expectancy at birth in 2070 downwards for women (-3.0 years) and men (-2.6 years). In 2070, life expectancy at birth is
90.0 years for women and 87.5 years for men. This downward revision is explained by a slowdown in the increase in life expectancy since 2014.

The reduction in the gap in life expectancy at birth between men and women is slightly greater than in previous projections: the gap is 2.5 years in 2070, compared to 2.9 years in the last exercise.

## High and low life expectancy variants

Under the high life expectancy assumption, life expectancies at birth are 3.5 years higher than those retained for the baseline level in 2070. Symmetrically, in the low assumption, they are, 3.5 years lower than those retained for the baseline level. The gap between the variants has been revised upwards: in the last projections it was 3.0 years compared to the baseline assumption in 2070, the last year of the projection. This upward revision is justified by the increased uncertainty linked to the pandemic and by the recent slowdown in the rise in life expectancy, which may or may not continue in the future.

<figure><img src="image_6.png" /><figcaption>Life expectancy at birth for men and women</figcaption></figure>

Source: Insee, population estimates and vital statistics until 2021. Population projection 2021-2070 (Insee Résultats).

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013, France from 2014 onwards.
**The baseline assumption for net migration has been maintained at +70,000, as in the previous exercise.** This has many advantages: it corresponds to the average of the most recent years observed, the UN and Eurostat projections use similar assumptions, and it was also the baseline assumption of the previous projection exercise. It must be emphasised that the baseline assumption concerns the annual average of the migratory balance between now and 2070 and that each year the actual balance varies and will vary greatly. **Even more than for the other components, there is a great deal of uncertainty surrounding the evolution of net migration. This is all the more true in the context of the health crisis.**

**The 2016 assumptions have also been maintained for the variants, with a low variant at +20,000 and a high variant +120,000.** The 2016 working variant is also retained, with **zero net migration at each age**. The difference between the variants is high, reflecting the high uncertainty. As this uncertainty also weighs on the year 2021, the high and low variants are reached as early as 2021 in the scenarios that use them.

**The interpretation by the different categories of flows is instructive** because it reminds us that a migratory balance is the result of different migratory movements of entries and exits. Thus its evolution depends on that of these different flows, each influenced by different factors. On the **other hand, it is only illustrative because the evolution of the different flows is even more uncertain than that of the balance, and different situations are compatible with a balance of +70,000.** It could correspond to an immigrant balance of +170,000 and a non-immigrant balance of -100,000, a situation close to the average observed over the decade 2006-2017. But it could also correspond to larger movements that offset each other, as seems to be the trend in recent years.

The target profile by sex and age of the migratory balance that has been adopted is two-thirds female. It is negative between the ages of 18 and 26. In this age group, the inflows are not sufficient to compensate for the outflows, highly concentrated at these ages. Net migration is positive before 18 and after 26.

<figure><img src="image_7.png" /><figcaption>Observed and projected net migration from 2000 to 2070</figcaption></figure>

Coverage: France excluding Mayotte from 2000 to 2013, France from 2014 onwards.
## MAIN INDICATORS ASSOCIATED WITH THE DIFFERENT ASSUMPTIONS

### Comparison of the baseline scenarios of the 2016 and 2021 exercises

<table><thead><tr><th></th><th>Baseline scenario 2016</th><th>Baseline scenario 2021</th></tr></thead><tbody><tr><td><strong>Fertility</strong></td><td></td><td></td></tr><tr><td>Total fertility rate</td><td>1.95 from 2013 to 2070</td><td>1.80 from 2022</td></tr><tr><td>Mean age at childbirth</td><td>32.0 years from 2040</td><td>33.0 years from 2052</td></tr><tr><td><strong>Life expectancy</strong></td><td></td><td></td></tr><tr><td>Life expectancy at birth for women</td><td>93.0 years in 2070</td><td>90.0 years in 2070</td></tr><tr><td>Life expectancy at birth for men</td><td>90.1 years in 2070</td><td>87.5 years in 2070</td></tr><tr><td><strong>Migration</strong></td><td></td><td></td></tr><tr><td>Value of annual net migration</td><td>+70,000 people per year</td><td>+70,000 people per year</td></tr></tbody></table>

### Variants of the 2021 projections exercise

<table><thead><tr><th></th><th>Situation in 2020</th><th>Baseline assumption</th><th>Low assumption</th><th>High assumption</th><th>Working assumption</th></tr></thead><tbody><tr><td><strong>Fertility</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total fertility rate</td><td>1.83 children per woman</td><td>1.80 children from 2022</td><td>1.60 children from 2030</td><td>2.00 children from 2030</td><td>1.50 children from 2030</td></tr><tr><td>Mean age at childbirth</td><td>30.8 years</td><td></td><td>33.0 years from 2052</td><td></td><td></td></tr><tr><td><strong>Life expectancy</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Life expectancy at birth for women</td><td>85.1 years</td><td>90.0 years in 2070</td><td>86.5 years in 2070</td><td>93.5 years in 2070</td><td>85.6 years from 2021</td></tr><tr><td>Life expectancy at birth for men</td><td>79.1 years</td><td>87.5 years in 2070</td><td>84.0 years in 2070</td><td>91.0 years in 2070</td><td>79.7 years from 2021</td></tr><tr><td><strong>Migration</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Value of annual net migration</td><td>+ 70,000 per year</td><td>+ 70,000 per year</td><td>+ 20,000 per year</td><td>+ 120,000 per year</td><td>0 per year</td></tr></tbody></table>
NUMBER OF SCENARIOS AND VARIANTS

For each component (fertility, mortality, net migration), the projection exercise therefore uses three assumptions: a "baseline" assumption, a "low" assumption and a "high" assumption. As in the previous exercise, the low and high assumptions are symmetrical with respect to the baseline assumption for the total fertility rate, life expectancy and the level of net migration.

The combination of these assumptions leads to 27 population projection scenarios. Among these 27 scenarios, the so-called "baseline" scenario (baseline assumption for each component) is clearly identified and further detailed.

To these 27 scenarios are added 3 so-called "working" scenarios: constant mortality (maintaining the mortality rates by sex and age observed in 2019 throughout the projection period), the same number of entries and exits from the territory (zero net migration throughout the projected period) and a very low fertility, corresponding to the current European level (TFR of 1.5 children per woman from 2030 onwards).

The 30 scenarios obtained by combining assumptions

<table><thead><tr><th>Name of the scenario</th><th>Fertility</th><th>Life expectancy</th><th>Migration</th></tr></thead><tbody><tr><td>Baseline</td><td>Baseline</td><td>Baseline</td><td>Baseline</td></tr><tr><td colspan="4">1st group: 6 scenarios that differ from the baseline scenario by only one component</td></tr><tr><td>High fertility</td><td>High</td><td>Baseline</td><td>Baseline</td></tr><tr><td>Low fertility</td><td>Low</td><td>Baseline</td><td>Baseline</td></tr><tr><td>High life expectancy</td><td>Baseline</td><td>High</td><td>Baseline</td></tr><tr><td>Low life expectancy</td><td>Baseline</td><td>Low</td><td>Baseline</td></tr><tr><td>High migration</td><td>Baseline</td><td>Baseline</td><td>High</td></tr><tr><td>Low migration</td><td>Baseline</td><td>Baseline</td><td>Low</td></tr><tr><td colspan="4">2nd group: 4 scenarios that lead to the highest, lowest, youngest and oldest population in 2070</td></tr><tr><td>High population</td><td>High</td><td>High</td><td>High</td></tr><tr><td>Low population</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Youth population</td><td>High</td><td>Low</td><td>High</td></tr><tr><td>Elderly population</td><td>Low</td><td>High</td><td>Low</td></tr><tr><td colspan="4">3rd group: 16 other scenarios combining baseline, low and high assumptions</td></tr><tr><td colspan="4">4th group: 3 working scenarios</td></tr><tr><td>Constant mortality</td><td>Baseline</td><td>Constant=2019</td><td>Baseline</td></tr><tr><td>Very low fertility</td><td>1.50</td><td>Baseline</td><td>Baseline</td></tr><tr><td>Zero migration</td><td>Baseline</td><td>Baseline</td><td>None</td></tr></tbody></table>
This volume 1 of the working paper begins with a presentation of the expert consultation and the
general principles adopted (part I): the projection method, the starting point, the time horizon and
the scenarios. The following parts detail the construction of the fertility (part II), mortality (part III)
and migration (part IV) assumptions. Part V is devoted to dissemination.

A second volume consists of the appendices: documents from the expert consultation and their
full responses, question by question.

# PART 1 : PROJECTION METHOD

## 1) CONSULTATION WITH EXPERTS AND THEIR RESPONSES

The preparation of the new projections began with a consultation of some 100 experts on the basis
of a questionnaire (Volume 2 of this working paper), in order to gather their views on potential
changes in fertility, mortality and migration. This questionnaire contained in its annex a summary
of demographic developments since the last projection exercise published in 2016.

We received 46 responses to the questionnaire we sent out, twice as many as in 2016, probably
due to the pandemic context. The pandemic was able to stimulate more in-depth discussions on
recent trends in mortality and fertility, and to highlight the difficulties inherent in the projection
exercise. We are obviously very grateful to the experts who have been very active: relying on their
knowledge and skills is all the more crucial for this exercise, given the much greater-than-usual
uncertainty about developments, at least in the short term and perhaps in the medium term.

We distinguish three categories of respondents:

* The researchers, specialists in demographic issues, 14 respondents;

* Persons working in a French organization using or developing projections, most often within the Official Statistical System, 17 respondents, referred to as OSS;

* Persons working in a foreign national statistical institute (NSI) and responsible for the preparation of projections in their country, 15 respondents, referred to as NSI.

Eight respondents declined detailed publication by name in this working paper: 1 researcher
response, 4 responses from the OSS and 3 responses from NSIs. These will be referred to
anonymously in the remainder of the paper, leaving 13 researcher responses, 13 OSS responses
and 12 NSI responses that will be used by name.

Respondents were asked whether their response reflected their personal opinion or that of their
institution. Researchers chose the "personal opinion" response, rightly emphasizing that it was
based on their knowledge of the field(s). Respondents in the OSS and NSI categories were more
divided, although the "personal opinion" response was chosen slightly more often. The question
was probably ambiguous: their institution cannot be committed and responsible for their answers, but at the same time, the questionnaire is often completed collectively and discussed within the institution. Moreover, the answers given are closely related to the needs and practices of their institution in terms of projections.

A. RESEARCHERS

<table><tr><td>1</td><td>Didier BRETON</td><td>Professor of demography, University of Strasbourg, associate researcher at the Institut national d'études démographiques (Ined)</td></tr><tr><td>2</td><td>Carlo Giovanni CAMARDA</td><td>Researcher, Ined</td></tr><tr><td>3</td><td>Anne GOUJON</td><td>Research Director, Wittgenstein Centre for Demography and Global Human Capital, IIASA, OeAW, University of Vienna and Contract Agent, European Commission Joint Research Centre, Ispra, Italy</td></tr><tr><td>4</td><td>Fanny JANSSEN</td><td>Prof. dr., Senior researcher, Netherlands Interdisciplinary Demographic Institute, The Hague NL and Honorary Professor, Demography Department, Faculty of Spatial Sciences, University of Groningen, the Netherlands</td></tr><tr><td>5</td><td>Jean-François LÉGER</td><td>Professor and researcher, University of Paris 1 Panthéon Sorbonne, Institute of Demography (Idup)</td></tr><tr><td>6</td><td>Philippe LOUCHART</td><td>Demographer, Paris Region Institute</td></tr><tr><td>7</td><td>Gilles PISON</td><td>Professor emeritus at the Muséum national d'histoire naturelle, associate researcher at INED</td></tr><tr><td>8</td><td>Grégoire REY</td><td>Director of CépiDc-Inserm</td></tr><tr><td>9</td><td>Jean-Marie ROBINE</td><td>Professor emeritus Inserm and EPHE, scientific advisor to the management of INED.</td></tr><tr><td>10</td><td>Patrick SIMON</td><td>Research Director, Ined.</td></tr><tr><td>11</td><td>Anne SOLAZ</td><td>Research Director, Ined.</td></tr></table>
12 Laurent TOULEMON Research Director, Ined.

13 Arkadiusz WISNIOWSKI Senior Lecturer in Social Statistics, Social Statistics Department, University of Manchester

## B. OFFICIAL STATISTICAL SYSTEM AND USER AGENCIES (OSS)

14 Didier BLANCHET, CSR Pensions Pensions Monitoring Committee (CSR-Retraites)

15 Pascale BREUIL, Cnav Director of Statistics, Prospective and Research (DSPR), Caisse nationale d'assurance vieillesse (Cnav). Response: technical advice from the DSPR

16 Vianney COSTEMALLE, Drees Deputy head of the Population Health Status office, Directorate for Research, Studies, Evaluation and Statistics (Drees), Ministry of Solidarity and Health. Answer: personal opinion

17 Jérôme LÊ, Insee Head of the Immigration Unit, Insee. Answer: personal opinion

18 Virginie JOURDAN, Loreline COURT, Florian HATIER, DSED Virginie JOURDAN, Head of the Studies and Statistical Surveys Division Loreline COURT, Research Officer Florian HATIER, Head of the Division for the Valorisation of Administrative Sources Ministry of the Interior Response: advice from DSED

19 Gautier MAIGNE, France Stratégie Director of the Society and Social Policies Department, France Stratégie With Julien ROUSSELON and Mathilde VIENNOT Answer: contribution not from a specialist in these subjects, but reflecting a 'user experience', built on feedback from the department team in its use of these data over the last 5 years.

20 Anthony MARINO and Amandine BRUN-SCHAMMÉ, COR Anthony MARINO, Project Manager Amandine BRUN-SCHAMMÉ, Project Manager, Conseil d'Orientation des Retraites (COR) Answer: personal opinion These responses are not binding on the COR or the COR General Secretariat. However, they have been discussed within the Secretariat General team.
<table><tr><td>21</td><td>Misha KHODABIN, Hamza ZAKRAOUI, Samuel MENARD, DG Treasury</td><td>Misha KHODABIN, Deputy Head of Office<br>Hamza ZAKRAOUI, Deputy Head of Office<br>Samuel MENARD, Head of Office<br>Pensions and redistribution Office<br>Ministry of Economy, Finance and Recovery.<br>Answer: institution</td></tr><tr><td>22</td><td>Sylvain PAPON, Insee</td><td>Head of the Demographic Accounting Section, INSEE<br>Answer: personal opinion</td></tr><tr><td>23</td><td>Gwenaël PODESTA, DGEC</td><td>Deputy Head of the Emissions, Projections and Modelling Office, Ministry of Ecological Transition.<br>Answer: institution</td></tr><tr><td>24</td><td>Clément ROUSSET, a Drees</td><td>Head of the Microsimulation Unit, Pensions Office, Drees, Ministry of Solidarity and Health<br>Answer: institution</td></tr><tr><td>24</td><td>BRET, BHD and BPS, Drees b</td><td>Pensions, Disability and Dependency, and Health Professions offices, Drees, Ministry of Solidarity and Health</td></tr><tr><td>25</td><td>Gwenaël SOLARD, Insee</td><td>Head of the Census Methods and Processing Division, Insee.<br>Answer: personal opinion</td></tr></table>
26 Antonio ARGUESO,
INE, Spain

Director of socio-demographic statistics, INE, Spain.
Answer: The team involved in Population projections at INE
(3 people)

27 Kim DUSTAN,
Statistics New Zealand

Senior Demographer, Statistics New Zealand
Answer: personal opinion

28 Katarzyna GÓRAL-RADZISZEWSKA,
Statistics Poland

Consultant, Statistics Poland
Answer: institution

29 Helen HUGHES,
ONS, United Kingdom

Senior Research Officer, Population and Household
Projections, Centre for Ageing and Demography,
Office for National Statistics, UK
Answer: institution

30 Raymond KOHLI,
FSO, Switzerland

Scientific collaborator (population scenarios, mortality
tables, demographic methods), Demography and Migration
Section, Federal Statistical Office (FSO), Switzerland
Answer: personal opinion

31 Lena LUNDKVIST,
Statistics Sweden

The form is filled in by the group at Statistics Sweden that
works with the population projection.
Answer: The answers are based on our experiences in
Sweden in the work with the projection.

32 Dragos MONDIRU,
INSSE, Romania

Expert INSSE, Romania
Answer: institution

33 Olga POETZSCH and
Felix zur NIEDEN,
DESTATIS, Germany

Both Assistant Head of Section, DESTATIS Germany
Answer: personal and institutional opinion

34 Branislav SPROCHA, Slovakia

Head of the Demographic Research Centre in Bratislava
Answer: personal opinion

35 Astri SYSE and Michael THOMAS,
Statistics Norway

Statistics Norway, Department of Research, senior
researchers, and responsible for Norway's national
projections

36 Marie VANDRESSE,
Federal Planning Bureau, Belgium

In charge of population and household projections, Federal
Planning Bureau, Belgium
Answer: personal opinion

37 Livia VARGA and Zoltan CSANYI,
KSH, Hungary

Statistician, Census and Population Statistics Department,
KSH Hungary
Answer: personal opinion
### A. THE PRINCIPLE OF THE COMPONENT METHOD

Population projections are made using the component method. The projections consist of estimating, year by year, for men and women separately, the number of births, deaths at each age and net migration (entries minus exits from the territory) also at each age.

The starting point for the projection is the population by sex and age on 1 January 2021, the total of which is based on the population estimates and vital statistics published in March 2021 (Papon, Beaumel, 2021). The number of inhabitants is projected for each 1 January, between the starting year (2021) and the fixed horizon (2070). On 1 January n+1, the number of inhabitants is equal to the population on 1 January of the previous year n, increased by births and the balance between population entries and exits in the territory during year n, and decreased by deaths.

$$ \text{Pop}_{11.n+1} = \text{Pop}_{11.n} + \text{Births}_n - \text{Deaths}_n + \text{NetMigration}_n $$

In some years, this equation includes a fourth component, known as the adjustment component. "The introduction of an adjustment is intended to ensure consistency between, on the one hand, the change in the population of France deduced from the results of two censuses and, on the other hand, the components of this change, i.e. the natural balance and the migratory balance, estimated elsewhere. The adjustment then constitutes a third, fictitious, component of the population change, which makes it possible to adjust the population estimates to the census results. The adjustment thus reflects a lack of comparability between the figures from two censuses. It may be linked to a change in the census method but also to changes in society itself<fnref n="1" />.

Due to a change in the population census questionnaire in 2018, aimed at improving knowledge of multi-residence situations, an adjustment was introduced to estimate population changes with unchanged questioning. This questionnaire effect is visible for eight years, from 2015 to 2022, given the census method [Insee, 2021a and b].

The projection assumptions are for net in-migration and out-migration by sex and age (net migration), mortality rates by sex and age, and fertility rates by age.

The number of deaths of women (or men) of age (a) reached in the year is calculated by applying the projected mortality rates by sex and age (reached in the year, Box 1) to the 'average' population of the year. This 'average' population is estimated by the number of women (or men) present on 1 January (of age a-1), to which is added half of the net migration of women (men) of age (a). It thus takes into account variations in population numbers during the year due to exchanges with the outside world, and it is consistent with the definition of mortality rates established in the

<footnote n="1">¹ Note "Adjustment and population variations", available on the insee.fr website: https://www.insee.fr/fr/metadonnees/source/serie/s1169/documentation-methodologique</footnote>
demographic situation (Beaumel, Papon, 2021). In year n, the number of deaths of persons of sex s and age a (reached in the year) is thus calculated as follows, if a>0 :

$$
\text{Deaths}_{n,s}^{a} = \left( \text{Pop}_{11.n,s}^{a-1} + \frac{1}{2} \times \text{NetMigration}_{n,s}^{a} \right) \times \text{MortalityRate}_{n,s}^{a}
$$

The number of deaths of newborns is calculated, for boys and girls, by applying to the births of the
year the mortality rates at age 0, i.e. :

$$
\text{Deaths}_{n,s}^{0} = \text{Births}_{n,s} \times \text{MortalityRate}_{n,s}^{0}
$$

The number of births is calculated by applying the projected fertility rates by age (attained in the year) to the average population of women of fertile ages (15-50 years). The number of births to women of age (a) in year n is equal to the fertility rate of these women, multiplied by the average number of women of that age. This average number is estimated by the number of women present on 1 January (of age a-1, i.e., on 1 January), to which is added half of the net migration of women of age (a), and from which is subtracted half of the deaths of women of age (a). It thus takes into account variations in the number of women during the year due to exchanges with other countries and deaths, and it is consistent with the definition of fertility rates established by INSEE in the demographic situation (Beaumel, Papon, 2021). The annual number of births is thus calculated as follows:

$$
\text{Births} = \sum_{a=15}^{50} \left( \text{Pop}_{11.n,\text{Women}}^{a-1} + \frac{1}{2} \left( \text{NetMigration}_{n,\text{Women}}^{a} - \text{Deaths}_{n,\text{Women}}^{a} \right) \right) \times \text{FertilityRate}_{n}^{a}
$$

where a is the age reached in the year and n the year

The births are divided between boys and girls (105 boys for 100 girls).

The population on 1 January of year n+1 by sex and age reached on 1 January n+1 is then deduced
from the population by sex and age on 1 January n as follows

$$
\text{Pop}_{11.n+1,s}^{a} = \text{Pop}_{11.n,s}^{a-1} - \text{Deaths}_{n,s}^{a} + \text{NetMigration}_{n,s}^{a} + \text{Adjustment}_{n,s}^{a} \quad \text{for } a > 0
$$

$$
\text{Pop}_{11.n+1,s}^{0} = \text{Births}_{n,s}^{0} - \text{Deaths}_{n,s}^{0} + \text{NetMigration}_{n,s}^{0} + \text{Adjustment}_{n,s}^{0} \quad \text{for } a = 0
$$

where Adjustment represents the adjustment component, the years for which it was introduced.
Box 1 - Some definitions

* The **age reached in the year** is the age a person reaches in a given calendar year. It is the age on 31 December of that year. It is also the difference between a given year and the year of birth. **Age in completed years** is the number of full years that have elapsed between the person's date of birth and the reference date used.

* The **mortality rate** (or risk of death) at age (a) is obtained by dividing the number of deaths in year n of persons of the generation born in (n-a) by the number of persons of that generation on 1 January of year n, to which is added half the net migration. The mortality rates are calculated by age reached during the year.

The **life expectancy at birth** represents the mean length of life – i.e. the mean age at death – of a synthetic cohort exposed at each age to the mortality patterns of a given year.. It is a synthetic indicator of the mortality rates for the year in question.

* **Calculation of life expectancy**

In a given year n, life expectancy at birth is the average age at death of a synthetic cohort subject at each age to the age-specific risks of death observed in that year. The method of calculation is the same as that used in the publication of the demographic situation.

It is calculated as follows:

$$E_{0}^{n} = 0.5 + \frac{\sum_{i=1}^{120} S^{n}(i)}{S(0)} \quad \text{where } S(i) \text{ is the number of survivors at age } i. \text{ By convention, } S(0)=100,000.$$

The number of survivors of completed age is calculated by applying the proportion of persons who died in the year before their birthday to the number of survivors of completed age.

The number of survivors at each completed age is calculated by recurrence:

$$S^n(i) = S^n(i-1) * (1 - q^n_{i-1}) \quad \text{with } i \ge 1 \text{ and } q^n = \text{the mortality rate at age } i \text{ (completed age) observed in year } n.$$

In the same way, life expectancies can be defined at a given age x. This is the average age at death of the persons still alive at the completed age x, for a synthetic cohort.

$$E_{x}^{n} = 0.5 + \frac{\sum_{i=x+1}^{120} S^{n}(i)}{S(x)}$$
Box 1 - Some definitions (continued)

* In a given year n, the age-specific **fertility rate** (a) relates the number of births to mothers of age (a) to the average number of women of that age in that year (midpoint population).
  Fertility rates are calculated by age reached in the year.

* The **total fertility rate** is the sum of the age-specific fertility rates. This indicator is interpreted as the average number of children per woman for a synthetic cohort of women who would have the fertility rates observed at each age in year n throughout their fertile life. It thus mixes the behaviour of various generations of women. It is a synthetic indicator of fertility rates for year n.

* The **average age at maternity** is also deduced from the age-specific fertility rates. It completes the total fertility rate: it corresponds to the average age at which the fictitious generation in question gives birth to its children.

* The **completed fertility** of a given generation is the average number of children born to women of that generation.

* The **natural balance** in a given year is the difference between the number of births and the number of deaths during the year. Net migration is the difference between the number of people entering the territory and those leaving.

B. THE POSITION OF THE EXPERTS ON THE METHOD

The question was:

**Q6 We do not plan to change the projection method for this exercise: it will be based on the component method (births, deaths, migration). However, do you have any advice on changes to improve the projections in the future or examples of practical use of different methods?**

Some twenty responses emphasised their satisfaction with the method used. Jean-François Léger (5) underlines the main arguments: "À cette échelle géographique, cette méthode a tout de même l'avantage d'être simple à mettre en œuvre et elle peut s'appuyer sur une bonne documentation sur les évolutions des trois composantes de l'évolution d'une population. Cette simplicité rend cette méthode transparente et facilite l'appropriation des résultats par les utilisateurs potentiels de ce travail."

The component method in particular is unanimously supported and does not seem to have any real alternative. The experts also agree on the importance of conveying to users and the general public the uncertainty inherent in the projection exercise.
This can be done through variants further away from the baseline scenario, in order to communicate the idea that extreme values could also be reached (12, 35).

For others, such as Kim Dustan (Statistics New Zealand, 27), this means using **probabilistic or stochastic models** to project one or more components: "Conveying uncertainty in a meaningful way is an obvious area for improvement. Alternative deterministic projections convey uncertainty in a qualitative way, but stochastic projections convey uncertainty in a quantitative way. The stochastic projections are actually more intuitive for users to interpret, and also convey what trends are more certain than others. This perspective is reflected in the responses on scenarios. Thus, Giovanni Carlo Camarda (2) thinks that we should use a probabilistic approach: "travailler avec des scénarios déterministes n'est pas la meilleure façon de produire des prévisions adéquates. Je pense que l'utilisation d'une approche probabiliste est plus appropriée et la littérature récente offre une longue série d'options, notamment en ce qui concerne les prévisions de mortalité". Kim Dustan (Statistics New Zealand, 27) notes, however, that even as a complement to a probabilistic approach, working scenarios, supported by a particular hypothesis (what if?) remain of value.

Other experts suggest comparing probabilistic and deterministic approaches (Box 2) in an experimental way, including using the responses of the expert panel to calibrate the probabilistic models (13). Vianney Costemalle carried out this comparison exercise for the 2016 French projections (Costemalle, 2020).

On the contrary, several experts state that they do not believe it is necessary to use probabilistic models (6, 7, 14).

Didier Blanchet, of the CSR Retraites (14), thinks that communicating the results of these models is not easy and Philippe Louchart (6) that adequate past series are lacking for France: "L'utilisation de méthodes de **projections probabilistes** est souvent évoquée maisute encore sur l'absence de données sur longue période à l'échelle de la France entière (sauf à projeter séparément et avec des méthodes différentes la France métropolitaine et l'outre-mer) et sur la **difficulté à modéliser les migrations** faute de données détaillées sur longue période relatives aux entrées et aux sorties du territoire français. Cette limite est encore plus nette à une échelle infranationale".

Some experts stress the importance of using appropriate methods for each component (4, 16), even if it means combining deterministic and stochastic models (37).

The projection exercise is particularly difficult for migration, as Jérôme Lê of INSEE reminds us (17). To improve the quality of the projection, several experts suggest that we project inflows and outflows separately, possibly distinguishing by sex and place of birth (14, 15, 19, 26, 36). Marie Vandresse (Federal Planning Bureau, Belgium, 36) and José Argueso (INE, Spain, 26) draw on their national experiences in this regard. Gautier Maigne (France Stratégie, 19) nevertheless pointsout that the data on inflows and outflows need to be made more reliable and suggests a few ways of improving them. Astrid Syse and Michael Thomas (35) suggest communicating more about the uncertainty of migration: "We agree that volatile changes in net migration is difficult to project, and perhaps not warranted, but instead focus on communicating that there will be annual changes not accounted for in the "smooth" long-term trends?".
Virginie Jourdan, Loreline Court and Florian Hatier (DSED) suggest to take into account the link between migration and fertility: " tenir compte de l'impact des migrations (et des ICF plus élevés de femmes issues de l'immigration) sur l'ICF global selon les hypothèses retenues". In a similar vein, and even more detailed, Gautier Maigne (France Stratégie, 19) suggests making differentiated fertility and mortality projections according to the country of birth. An anonymous person also suggests that scenarios should include the interactions between components, as it seems unlikely to assume that the components move independently from each other.

Other suggestions are made in isolation:

* Philippe Louchart (6) is considering integrated national and sub-national projections: " une projection intégrée et simultanée de la population des grandes régions dans leur ensemble pour aboutir in fine à la population de France [...] constitue une autre voie qui, pour l'instant, pâtit encore d'un manque de données rétrospectives sur les migrations interrégionales";

* Anne Goujon (3) would like to add a multi-state perspective to the projections, distinguishing between urban and rural population, by education level, etc. (see also Goujon, 2020);

* Michal Thomas and Astrid Syse (35) suggest testing the use of a model to project TFR;

* Gautier Maigne (France Stratégie, 19) suggests making household projections and intermediate revisions of the projections.

Laurent Toulemon (12) insists on the positioning of the new projections in relation to the previous ones: "1. Modifier au minimum les projections par rapport aux projections précédentes, pour éviter de faire le yoyo derrière la conjoncture; 2. Justifier les changements de manière explicite et présenter leur ampleur de manière lisible".
Box 2 - Deterministic approach, probabilistic approach

Different ways of looking at the future

*This box contains an extract from Vianney Costemalle's article presenting the deterministic and probabilistic approaches [Costemalle, 2020].*

* **Deterministic approach**

"In the first instance, what we are looking to project depends, from a deterministic standpoint, on certain parameters. The selection of these parameters represents a hypothesis that is also referred to here as a scenario. A scenario is then given detailing the way in which these parameters are considered most likely to develop on the basis of accumulated knowledge, expert opinions and intuition. A given scenario corresponds to one single possible projection, and the relationship between the two is deterministic. In cases where the scenario plays out as expected, the projection will be certain. Deterministic projections answer the question: "What would happen in the future if such a scenario were to occur?". Extreme scenarios can therefore be created to see how the future would pan out if they were to come true. Deterministic projections are thus a formidable tool when it comes to exploring the future on the basis of predefined scenarios. Any uncertainty in the projection then relies on the scenario coming true. Possible scenarios are formulated, but it is impossible to know how likely they are to occur. It could even be argued that the probability of them coming true is zero (if the values are continuous) or very low (if the values are discrete). The degree of probability is estimated intuitively and is reflected in the terms used to describe these scenarios: demographers refer to the "central" scenario, which is the scenario considered the most plausible based on current knowledge, and "extreme" scenarios." To this extract from Costemalle's article, we can add that several scenarios, deviating from the baseline scenario, are presented in the deterministic approach, in order to illustrate the uncertainty inherent in the exercise.

* **Probabilistic approach**

"Conversely, probabilistic projections are based on models that attempt to take account of the uncertainty stemming from a lack of knowledge of certain aspects of the projections. These models are based on assumptions made on the basis of expert judgement and intuition. The underlying assumptions on which models for probabilistic projections are based are the equivalent of the scenarios used for deterministic projections. The advantage of probabilistic projections is that they make it possible to quantify the uncertainty based on past developments and to extrapolate it into the future to provide confidence intervals for the projections. The interpretation and use of probabilistic projections therefore differs from that of deterministic projections. By way of an example, weather forecasts have long been making use of probabilistic projections: we are not only told whether or not it will rain the next day, but also the probability that rain will fall (Raftery, 2014). Since future events are inherently uncertain, indicating the probability of their occurrence in view of current knowledge provides more information than a deterministic projection based on a scenario. In economics in particular, time series are used as a means of producing probabilistic projections: in the case of a simple random sampling method, for example, we know that the variance increases with the square root of time."

"By adding error terms to the models, it is therefore possible to create stochastic probabilistic projections. Another method for quantifying uncertainty is to use the Bayesian paradigm. Under this method, the model parameters are viewed as random variables, in the same way as error terms in stochastic models. Bayesian inference then involves estimating the *a posteriori* distribution of these parameters, i.e. after the data have been observed. This distribution gives possible values for the parameters, together with their degree of probability. It differs from the *a priori* distribution, which is the distribution given by the modeller and which is intended to reflect the knowledge of the problem before any data has been observed." In addition to Costemalle, we can say that, like deterministic projections, probabilistic projections are also based on assumptions.
The question was phrased this way:

The projections published in 2016 included 27 projection scenarios (combination of 3 fertility assumptions, 3 mortality assumptions, 3 migration assumptions), supplemented by three working scenarios (zero migration, European fertility, constant mortality). The baseline scenario was highlighted, and the other 26 scenarios were prioritized according to 3 levels, focusing on 10 of these 26 scenarios (Table 1, and

https://www.insee.fr/fr/statistiques/2496228?sommaire=2496793).

Q7. Do you think it is necessary to reproduce the same types of scenarios and keep the same order of presentation? If not, what adjustments would you suggest (deleting or adding scenarios, modifying the workings scenarios, modifying the hierarchy of presentation of the scenarios, etc.)?

<table><caption>Table 1: The 30 scenarios obtained by combining assumptions</caption><thead><tr><th>Name of the scenario</th><th>Fertility</th><th>Life expectancy</th><th>Migration</th></tr></thead><tbody><tr><td>Baseline</td><td>Baseline</td><td>Baseline</td><td>Baseline</td></tr><tr><td colspan="4"><em>1er group: 6 scenarios that differ from the baseline scenario by only one component</em></td></tr><tr><td>High fertility</td><td>High</td><td>Baseline</td><td>Baseline</td></tr><tr><td>Low fertility</td><td>Low</td><td>Baseline</td><td>Baseline</td></tr><tr><td>High life expectancy</td><td>Baseline</td><td>High</td><td>Baseline</td></tr><tr><td>Low life expectancy</td><td>Baseline</td><td>Low</td><td>Baseline</td></tr><tr><td>High migration</td><td>Baseline</td><td>Baseline</td><td>High</td></tr><tr><td>Low migration</td><td>Baseline</td><td>Baseline</td><td>Low</td></tr><tr><td colspan="4"><em>2ème groups: 4 scenarios that lead to the highest, lowest, youngest and oldest population in 2070</em></td></tr><tr><td>High population</td><td>High</td><td>High</td><td>High</td></tr><tr><td>Low population</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Youth population</td><td>High</td><td>Low</td><td>High</td></tr><tr><td>Elderly population</td><td>Low</td><td>High</td><td>Low</td></tr><tr><td colspan="4"><em>3rd group: 16 other scenarios combining baseline, low and high assumptions</em></td></tr><tr><td colspan="4"><em>4th group: 3 working scenarios</em></td></tr><tr><td>Constant mortality</td><td>Baseline</td><td>Constant</td><td>Baseline</td></tr><tr><td>European fertility</td><td>1.65</td><td>Baseline</td><td>Baseline</td></tr><tr><td>Zero migration</td><td>Baseline</td><td>Baseline</td><td>None</td></tr></tbody></table>

Sixteen experts find it useful to have a wide variety of scenarios and their adapted hierarchy. They correspond to pedagogical uses for researchers like Didier Breton (1): "Je pense que la gamme des scénarii ci-dessous est très pertinente et utile. Avoir un nombre important de scénarii est vraiment utile, même si au final, dans un cadre pédagogique ou didactique on en retient bien moins et on se concentre sur les extrêmes et le central". The variety of scenarios is also useful for people working in institutions, such as Anthony Marino and Amandine Brun-Schammé (COR, 20): "Nous
sommes d'avis à ce que vous conserviez les mêmes types de scénarios et le même ordre de présentation. Il nous semble très utile de disposer de tous les scénarios, même si l'accent peut être en effet mis davantage sur les extrema ou les changements d'une seule composante. Cela permet de faire des tests de sensibilité sur le solde du système de retraite par exemple en modifiant les seules chroniques de fécondité et/ou mortalité et/ou migration".

On the contrary, some experts find that there are too many scenarios, a potentially confusing factor (3, 34). They use only a few of them themselves (23) or propose not to publish them all on the Internet (35, anonymous).

Many of the NSI respondents describe their own scenario system without commenting on the French variants.

Two experts (2, 27) recommend abandoning scenarios and replacing them with probabilistic modelling: these responses have been included in the "method" section.

For his part, Jean-François Léger (5) proposes, from a pedagogical perspective, a different logic for constructing the various scenarios, with a basic scenario without migration that would highlight the evolution of the population through the natural balance alone, and then a scenario with the same fertility and mortality assumptions, adding the migratory component: "Selon moi, le solde migratoire devrait être intégré a posteriori, comme une composante dépendant de la dynamique naturelle et d'objectifs démographiques, et non comme une composante définie a priori".

Philippe Louchart suggests considering non-symmetrical assumptions: "réfléchir à des hypothèses non symétriques de part et d'autre du scénario central, notamment pour la fécondité, au regard des projections probabilistes existantes".

## Proposals for alternative working scenarios:

Anne Goujon (3) suggests adding a COVID-19 scenario to show the effects of the shock on the components, assuming a return to normal within a few years. This is in line with the suggestion of Helen Hughes (ONS, UK, 29), citing a paper by Charles-Edwards et al. (2020). In this paper, the authors simulate different scenarios for the consequences of the pandemic on the Australian population, with the major impact being related to the cessation of international migration, which is more or less prolonged depending on the scenario. "Results suggest that Australia's population could be 4 per cent lower by 2040 in a *Severe* scenario than in the *No Pandemic* scenario, driven by a massive reduction in international migration. Impacts on population ageing will be less severe, leading to a one percentage point increase in the population aged 65 and over by 2040."

Didier Breton (1) suggests a Southern European fertility scenario that would be even more contrasting, with lower fertility and, above all, a much later calendar.

Laurent Toulemon (12) invites to better justify the working scenarios in the publications (European fertility, no migration, no progress in the fight against death) He proposes a scenario with the average components of European projections also for mortality and migration.
Gautier Maigne (France Stratégie, 19) suggests creating new working scenarios, or creating a new category of so-called breakthrough scenarios: "scénario de baisse de l'espérance de vie, calibré par exemple sur ce qu'on observe aux États-Unis sur la période récente, [...], scénarii à solde migratoire négatif (« émigration massive ») et [...] très positif (« réfugiés climatiques »). Afin de ne pas apparaître comme des prévisions ou pouvoir être interprétées politiquement, ces 'ruptures' pourraient être situées à un horizon de moyen terme (2030 par exemple)".

### 3) THE HORIZON OF THE POPULATION PROJECTIONS

The experts were asked:

**Q4. We are considering 2070 as the horizon of the projections. Do you think we need another horizon? Do you think it would be useful to disseminate results for a longer time horizon (e.g. 2100), even if we only comment on the 2070 horizon?**

The responses are quite mixed (table).

<table><caption>Figure 1.1 - Positioning of experts on the projection horizon</caption><thead><tr><th rowspan="2"></th><th colspan="3">Do we need a horizon further away than 2070?</th></tr><tr><th>No or rather no</th><th>Yes or rather yes</th><th>Neither yes nor no</th></tr></thead><tbody><tr><th>Researchers</th><td>6</td><td>5</td><td>2</td></tr><tr><th>OSS</th><td>4</td><td>7</td><td>1</td></tr><tr><th>NSI</th><td>8</td><td>2</td><td>2</td></tr><tr><th>Anonymous</th><td>2</td><td>3</td><td>2</td></tr><tr><th>Total</th><td>20</td><td>17</td><td>7</td></tr></tbody></table>

The experts stress the greater uncertainty beyond the 50-year horizon. Jean-François Léger even suggests reducing the horizon to 2050: "2050 (soit un bond de 30 ans, l'équivalent d'une génération en démographie" (5).

Few of the NSI respondents make projections further out. Sweden comments on the projected situation in 2070 but releases a database with extended series to 2120. New Zealand similarly distinguishes between the main release and extended series for specific purposes.

Although, as Didier Blanchet - CSR Retraites (14) points out, it is in practice inexpensive to extend the series, some experts point out that the validity of these extrapolations is limited if the projection period is longer than the past period used to calculate the trends (4, 37). This could even be risky by affecting the credibility of the exercise (34).

Several experts stress the importance of highlighting the growing uncertainty that exists as the horizon moves further out (Vianney Costemalle, Drees - 16). This can be done by associating longer-term projections with a measure of uncertainty (estimated from the model's backward-looking predictive capacities) (Grégoire Rey - 8) or by proposing a set of alternative scenarios (Jean-Marie Robine - 9).
Extending the series can have several methodological interests: comparing the results of the projection with those obtained by the United Nations (13, 16, 22) (see United Nations, 2019a); observing the timing of certain phenomena, for example the crossing of life expectancies for women and men, and calculating life spans by generation (35); checking that the results obtained remain reasonable over a more distant horizon and testing the sensitivity to assumptions (4, 22). But it is above all the question of the uses of longer-term projections that is discussed, particularly by the public authorities.

Many NSI respondents cite the absence or scarcity of users of projections for a time horizon longer than 50 years (e.g. 30, 35): "a look 50 years into the future is not as abstract as 100 or 150 years and fits the period that is relevant for long-term labor market and environmental policy decisions" (Olga POETZSCH and Felix zur NIEDEN, DESTATIS, Germany, 33).

But in the French case, users involved in the evaluation of pension schemes and their past and future reforms are almost all of the opinion that projections should be extended (14, 15, 19, 20, 21, 24a), until 2080 or even 2120. Indeed, changes in legislation can have very long term effects: "modifications de la législation relative à l'acquisition des droits retraite concernant la population active peuvent avoir des effets sur très longue durée et il est utile d'avoir une vision de la dynamique de ce type de changements sur les dépenses (profil et fin de la montée en charge de la mesure...)" (Pascale Breuil, Cnav, 15). More specifically, it is a question of calculating the length of retirement by generation (20), of carrying out prospective studies: " études prospectives par génération couvrant complètement des générations nées jusqu'à la fin du dernier siècle" (Didier Blanchet, CSR Retraites, 14), of preparing during the year 2026 the Ageing 2027 report, which will have a 2080 horizon (21), or even of modelling the impact of a possible pension reform concerning newcomers, which requires an far enough horizon to include their their retirement date (24a).

For energy policies, however, a time horizon of 50 years may be sufficient (23). In the area of transport, different choices are possible and "the socio-economic evaluation of transport projects varies greatly depending on whether a period of 30, 50 or 70 years is used to evaluate them. If 70 years is chosen, it will be 2090" (Philippe Louchart, 6).
## 4) IN THE SHORT TERM: HOW TO INTEGRATE THE COVID-19 PANDEMIC?

### A. WHAT DO WE KNOW WHEN WE FINALIZE THESE PROJECTIONS?

It is important to note that the experts were consulted in March-April 2021, at the time of the 3rd wave of the Covid-19 epidemic in France, and when the vaccination rate was still low, except among the elderly (at the end of April 2021, 12% of adults were fully vaccinated<fnref n="2" /> and about three quarters of those over 70 years of age<fnref n="3" />). Only deaths in 2020 and January 2021 were known. The significant drop in births in December 2020-January 2021 was mentioned in the consultation document, but not the rebounds in March-April and summer 2021, which were not yet known.

By the time the projection exercise is finalized in September 2021, more recent data are available.

The 2020 demographic report was first published in January 2021 and a second one in March in order to take into account observed and not estimated data from November and December 2020 (Papon, Beaumel, 2021). Indeed, this period corresponded to the second wave of deaths of the Covid-19 epidemic in France, but also to part of the births of children conceived during the first containment in March-April 2020.

Provisional data on monthly births<fnref n="4" /> and deaths<fnref n="5" /> are also available until August 2021. This makes it possible to observe at least partially the effects on mortality of the fourth wave of Covid-19 in the summer of 2021. Since the August 2021 births essentially correspond to November 2020 conceptions, it is possible to observe the effects of the pandemic until the second containment, which took place from October 30 to December 15, 2020.

The results of the 2018 population census were released in June 2021⁶.

The population is thus known definitively up to 2018 and the difference between the 2017 and 2018 populations makes it possible to estimate the 2017 net migration, as shown in the demographic balance published in March 2021. More detailed data on net migration in 2017 (including a breakdown by place of birth) can be found in Insee Première 1849, published in April 2021 (Lê, 2021).

<footnote n="2">² https://solidarites-sante.gouv.fr/actualites/presse/communiques-de-presse/article/vaccination-contre-la-covid-en-france-au-29-avril-2021-pres-de-21-500-000</footnote>

<footnote n="3">³ https://covidtracker.fr/vaccintracker/, consultation of 20/10/2021</footnote>

<footnote n="4">⁴ https://www.insee.fr/fr/statistiques/5400320?sommaire=5348638</footnote>

<footnote n="5">⁵ https://www.insee.fr/fr/statistiques/4487854</footnote>

<footnote n="6">⁶ https://www.insee.fr/fr/information/2008354</footnote>
The question was phrased as follows:

Q5. In order to take account of recent developments, we plan to use the estimated population on 1 January 2021⁷ as the starting point for the projections, and not on 1 January 2018, which is the most recent final population estimates. Revisions between provisional and final data are generally modest. Do you think this choice is justified? Does it have any disadvantages in your view?

The vast majority of the experts consider that it is preferable to use the latest known data, even if they are provisional: 34 against 3 who do not approve the choice of a starting point of 1 January 2021, and 8 who provide elements without really expressing an opinion.

<table><caption>Figure 1.2 - Positioning of experts on the starting point of the projection</caption><thead><tr><th rowspan="2"></th><th colspan="3">Do you think that choosing a starting point of 1<sup>er</sup> January 2021 is justified?</th></tr><tr><th>Justified choice</th><th>The choice has some disadvantages</th><th>Other</th></tr></thead><tbody><tr><th>Researchers</th><td>12</td><td>1</td><td>0</td></tr><tr><th>OSS</th><td>8</td><td>1</td><td>2</td></tr><tr><th>NSI</th><td>7</td><td>1</td><td>4</td></tr><tr><th>Anonymous</th><td>6</td><td>0</td><td>2</td></tr><tr><th>Total</th><td>33</td><td>3</td><td>8</td></tr></tbody></table>

Many experts emphasize that this is all the more justified in the current context, in order to integrate as much as possible the impact of the health crisis, particularly the excess mortality (3, 7, 13, 15, 19, 21). But the health context also requires specific treatment to project the first few years and calculate non-crisis trends (10, 11, 12, 37): "[...]les données 2021 et 2022 peuvent être estimées spécifiquement (impact de la Covid) mais les tendances devraient être estimées sans tenir compte des effets de court terme de l'épidémie de Covid (pour se concentrer sur les variations de long terme)". (Laurent Toulemon, 12). Anne Solaz (11) reminds us of possible catch_ups: "il faudra envisager de possibles rattrapages dans la situation post-Covid : moins de morts si les morts qui auraient dû mourir à partir de 2021 sont morts du Covid en 2020, davantage de naissances en cas de report des intentions".

On the side of the arguments that could lead to not using the population of 1 January 2021 as a starting point:

- the importance of using final statistics, so as not to risk reproducing or amplifying the inaccuracy incorporated into the provisional data used as a starting point (23, 34, 35, 36);

- the symbolic preference for "round" starting points such as 2020 or 2025;

<footnote n="7">⁷ We will use the provisional data published in the "Bilan démographique 2020", exceptionally updated in March 2021 to account for the second wave of the Covid-2019 epidemic in late 2020 (Papon and Beaumel, 2021). In the detailed files of the projection results, the 2018-2020 data from the "Bilan démographique 2020", which are provisional, will be included. The latest published final data are for 1 January 2018.</footnote>
- the need to wait for the 2021 or even 2022 data in order to better take into account the health crisis (7), but this would require us to postpone the projection exercise.

Didier Blanchet, of the CSR Retraites (14), reminds us that even if we were to take a starting point in 2018, recent trends would be integrated: "Si je comprends bien l'alternative serait d'avoir un 2018-2021 projeté comme le reste mais avec les vraies évolutions de fécondité/mortalité et soldes migratoires, plutôt que de la population mesurée. L'écart doit être de 2eme ordre en effet. Autant prendre l'observé, même provisoire". Indeed, for the 2016 projections, the provisional changes in fertility and mortality for the years 2013 and 2014 had been integrated, even though the starting point was 1 January 2013. However, this was not true for net migration, and the method of calculating the 2016 fertility and mortality rates produced a lag (Box 3).

Net migration is a point of difficulty because it is usually subject to more revisions (19, 25). Gwenaël Solard (Insee, 25) also points out that only a rough estimate is available in the 2020 annual demographic report (« Bilan démographique »): "L'estimation est réalisée à partir d'une estimation du solde apparent qui est entouré d'une marge d'incertitude encore plus grande que d'habitude avec la crise sanitaire (et la fermeture des frontières) ". The estimate of the 2020 migration balance was made by taking the average of the balances observed in 2015, 2016 and 2017, i.e. a positive balance of 87,000 people.

On the other hand, starting from 1 January 2021 is rather advantageous for taking into account the adjustment linked to the change of the census questionnaire in 2018⁸(22, 25), which induces a population adjustment spread out from 2015 to 2022: the 2021 and 2022 adjustments are residual compared to those integrated in the population change from 2015 to 2020 (Insee 2021a and b).

Finally, some experts raise the issue of the starting point for territorial projections: national projections define the trends that are subsequently applied to the population in different parts of the territory. It is necessary to rely on a census in order to have definitive populations at the municipal level (and sometimes even sub-municipal level for the most populated municipalities), and thus to have coherent populations, whatever the territory chosen, to carry out the projection. The 2018-2020 populations will therefore be integrated into the data sets disseminated for the projections.

Laurent Toulemon (12) underlines the interest of having data prior to 2018, in order to be able to reconstruct series from observed years to projected years.

<footnote n="8">⁸ On the population adjustment related to the change of census questionnaire in 2018, see Insee 2021a and Insee 2021b</footnote>
Box 3 - Final, provisional, or trend data: the starting point of the projection

* In the latest INSEE projection for 2016

In 2016, the starting point for the projection was the population on 1 January 2013.

To calculate the populations on 1January 2014, 2015 and 2016, the provisional fertility and mortality rates for 2013, 2014 and 2015, published in the 2015 annual demographic report ("Bilan démographique 2015"), were applied. The net migration applied for 2013, 2014 and 2015 was the one used as the baseline assumption of the projection (+70,000) and not the net migration estimated for the "Bilan démographique 2015" (+47,000).

Since the revisions of fertility and mortality rates were very small and the average net migration in 2013-2015 was +57,000, close to +70,000, the "projected" population for 1 January 2016 was very close to the final population published in the 2018 population report.

In contrast, the fertility and mortality rates for 2016 and beyond were calculated by taking the final 2012 rates and applying the trend observed from 1995 to 2014. With this method, the rates calculated for 2016 were far removed from the rates observed for the same year 2016, because the last observed level was not taken into account. There is therefore a break in life expectancy between the projected value for 2015 which incorporated provisional data, and the higher projected value for 2016 (figure).

<figure><img src="image_8.png" /></figure>

* For the 2021 projections

The population on 1 January 2021 is that published in the "Bilan démographique 2020" in March 2021 (Papon, Beaumel, 2021). Compared to the previous projections published in 2016, this means that the net migration estimated for 2018, 2019 and 2020 in the "Bilan démographique 2020" is used, i.e. the average of the 2015-2017 balances, and not the central assumption of the projections.

For fertility and mortality, the starting points are the estimated fertility for 2020 and the estimated mortality for 2019. Then, they evolve according to the assumptions of the projection based on the trends observed in the past. This allows all available information to be taken into account. The application of this principle excludes the transitional effect of the pandemic, which is treated separately for mortality in 2021 and for fertility in 2021 and 2022.

Having projections with a starting point of 1 January 2021 is also more advantageous in terms of communication: the studies published from 2016 to 2020 were based on mortality and fertility rates with a starting point of 2012 (i.e. 8 years earlier than the publication for the article in *Économie et Statistiques* - Blanpain, 2020).
A. RECENT CHANGES IN FERTILITY IN FRANCE

The previous projections in 2016 used a total fertility rate (TFR) of 1.95 children per woman until 2070 for the baseline assumption. Two alternative assumptions of high and low fertility were retained, which deviate by 0.15 from this baseline assumption: 2.1 and 1.8. A so-called European convergence assumption explored the assumption of a fertility of 1.6 children per woman in 2070. In the different assumptions, convergence was assumed to be rapid towards the target values, reached in 2020.

The TFR in France was stable at around 2 children per woman between 2006 and 2014 (Figure 2.1), then has declined each year since 2015 to reach 1.83 in 2020, the latest data available (1.79 in metropolitan France). In view of recent fertility trends, the baseline assumption of the 2016 Insee projections therefore seems high and recent developments are in line with their lower assumption. Recent projections by institutions covering France also use a lower TFR, for example 1.84 for Eurostat in its projections published in 2020⁹ and 1.85 for the UN in 2019 (United Nations, 2019b).

<figure><img src="image_9.png" /><figcaption>Figure 2.1 - Changes in the total fertility rate</figcaption></figure>

Coverage: France excluding Mayotte until 2013 and France from 2014 onwards

Source: INSEE, population estimates and vital statistics, final data until 2017, provisional data in 2018, 2019 and 2020, INSEE population projections 2016.

<footnote n="9">⁹ Eurostat (2020) for a summary and for the detailed fertility assumptions of the projections (EUROPOP2019):
https://ec.europa.eu/eurostat/databrowse/view/proj_19naasfr/default/table?lang=fr</footnote>
Over the past decade, France has seen a greater decline in its TFR (-0.15) than the European Union as a whole (-0.03), but it remains an exception for its high level of fertility, a specificity that has been built up since the 1990's (Figure 2.2). Unlike other countries, French fertility was not affected by the economic crisis of 2008-2009 (Masson, 2015; Papon, 2021b), which could be explained in particular by the moderating role of the socio-fiscal system (Pison, 2020). The latter could also cushion the impact of the Covid-19 pandemic and the ensuing economic difficulties on fertility in the short and medium term, although the situation is rather uncertain.

At the time of the expert consultation, the first estimates of births recorded in December 2020 and January 2021 showed a particularly sharp decline in births nine months after the start of the first containment. The rectifying "Bilan démographique 2020" published in March 2021 incorporated the year-end 2020 data and revised the 2020 fertility rate to 1.83. It can be estimated that if the evolution had been similar in November-December 2020 to what it was from January to October 2020, the TFR would have been 1.84. The impact for 2020 is therefore moderate.

New elements have since<fnref n="10" /> been published by INSEE and confirm the drop in births in December 2020 and especially in January 2021 (-7% and -13% compared to December 2019 and January 2020 respectively). In February 2021, births increased compared to the previous month, and were "only" 5% lower than a year earlier (with the same number of days, February 2020 having one day more than February 2021). In March and April, births continue to increase and are slightly higher than in March and April 2020: +1% in March and +4% in April. In May and June, births decline moderately (2%) compared to the same months in 2020, returning to the pre-pandemic rate of decline. In July 2021, they are at the same level as in July 2020, while births rebound in August (+3.3%), which corresponds to conceptions during the second containment: the impact of the second containment on births is therefore different from that of the first containment. In total, over the period from January to August 2021, births finally fell by 1.5% compared to the same period a year earlier.

<footnote n="10">¹⁰ Births in 2021: https://www.insee.fr/fr/statistiques/5400320?sommaire=5348638</footnote>
<figure><img src="image_10.png" /><figcaption>Figure 2.2 - Changes in the total fertility rate in the European Union since 1980</figcaption></figure>

Source: Eurostat, 2020 Demographic Review for French data 2017-2020.

The low and high assumptions used by INSEE in 2016 appear to differ less from the baseline assumption (+/-0.15) than the assumptions used by international institutions: +/-0.5 children per woman for the UN in 2019, a low assumption at -0.37 children from the baseline assumption for Eurostat.

Insee's 2010 projections, as well as those made by Eurostat and the UN in 2019, stabilized the mean age at childbirth rapidly. The 2016 projections stood out by extending the growth of the mean age at childbirth to 32 years (assumed to be reached in 2040). In this respect, they anticipated the continuation of the trend: since 2013, the mean age has grown faster than anticipated by the 2016 projections, with no sign of slowing down (Figure 2.3). The mean age considered to be the ceiling in 2016, i.e. 32 years old, is now well over in two departments: Paris (34.0 years old in 2019) and in Hauts-de-Seine (32.6 years old). Val-de-Marne, Haute-Garonne and Rhône are close to it (31.7 years).
<figure><img src="image_11.png" /><figcaption>Figure 2.3 - Changes in the mean age at childbirth in France since 1980</figcaption></figure>

Note: 'France' excludes Mayotte until 2013 and includes Mayotte from 2014 onwards.

Source: INSEE, population estimates and vital statistics, provisional data in 2018-2019 and 2020, final before, INSEE population projections 2016.

At the departmental level, there is no obvious link between fertility intensity and mean age at childbirth (Daguet, 2021). Similarly, in the EU, some countries have an mean age at childbirth above the European average and maintain a high TFR (Ireland, Denmark, Sweden - **Figure 2.4**). Among the low fertility countries, some have a high average age (Spain, Italy) and others a low one (Poland). The Romanian TFR is similar to that of Ireland, but the mean age is 28, compared to 32.2 in Ireland. The high fertility rate in France does not therefore seem to imply a halt in the growth of the mean age at childbirth.
<figure><img src="image_12.png" /><figcaption>Figure 2.4 - Mean age at birth and TFR in European countries in 2018</figcaption></figure>

Source: EUROSTAT, https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_find&lang=fr

Over the long term, the profile of age-specific fertility rates is shifting towards greater symmetry and a greater spread of high fertility ages (Costemalle, 2020). This evolution is visible over the period 2013-2020 (Figure 2.5). However, the technique of projecting age-by-age rates used by INSEE in 2016 leads to the projection of an increasingly sharp shape. The application of a Schmertmann (2003) model, following the example of Eurostat, Italy or Belgium, would make it possible to obtain a more plausible profile with the same mean age and TFR assumptions. In this model, the profile of fertility rates is obtained from calibrated splines, according to 4 parameters: the minimum age at which the fertility rate is strictly greater than 0, the age of maximum fertility (modal age), the fertility at this modal age and the age at which the fertility reaches half of the maximum value.
<figure><img src="image_13.png" /><figcaption>Figure 2.5 - Age-specific fertility profile in 2013, 2020 and projected with the 2016 projection exercise assumptions for 2020 and 2040</figcaption></figure>

(1) Age profile corresponding to an age of 33 years and a TFR of 1.80 children per woman, obtained by applying the model described in Schmertmann (2003).

## B. THE POSITION OF EXPERTS ON THE EVOLUTION OF FERTILITY

There were six questions on fertility change:

* Q8 over the TFR's long-term horizon (baseline scenario);

* Q9 on short-term fertility trends in the pandemic context;

* Q10 on the high and low variants to be retained on the TFR;

* Q11 on the evolution of the long-term mean age at childbirth;

* Q13 on the changing age profile of fertility.

Finally, Q12 was a table to be filled in to summarize and quantify the previous answers.

### WHAT TARGET FOR THE TOTAL FERTILITY RATE IN THE BASELINE SCENARIO?

From a quantitative point of view, 24 experts gave their opinion.

<table><caption>Figure 2.6 - Experts' position on the TFR of the baseline scenario in 2070</caption><thead><tr><th></th><th>1.4</th><th>1.6</th><th>1.8</th><th>1.85</th><th>1.9</th><th>Number of responses</th><th>Average</th></tr></thead><tbody><tr><td>Researchers</td><td>1</td><td>0</td><td>2</td><td>2</td><td>0</td><td>5</td><td>1.74</td></tr><tr><td>OSS</td><td>0</td><td>1</td><td>2</td><td>5</td><td>0</td><td>8</td><td>1.81</td></tr><tr><td>NSI</td><td>0</td><td>0</td><td>2</td><td>3</td><td>2</td><td>7</td><td>1.85</td></tr><tr><td>Anonymous</td><td>0</td><td>0</td><td>1</td><td>2</td><td>1</td><td>4</td><td>1.85</td></tr><tr><td><strong>Total</strong></td><td><strong>1</strong></td><td><strong>1</strong></td><td><strong>7</strong></td><td><strong>12</strong></td><td><strong>3</strong></td><td><strong>24</strong></td><td><strong>1.81</strong></td></tr></tbody></table>
The mode is 1.85, but the average is 1.81. The 1.85 mode was cited by 12 respondents, while the 1.80 TFR assumption received 7 votes.

The answers are based mainly on an analysis in terms of completed fertility and fertility by birth rank.

A central question is to assess the role of the timing of births in the decline in the TFR in recent years. If this is the main explanation for the decline, then as long as the increase in the mean age at childbearing continues, the TFR will decline or remain at a low level. If age stops increasing and the completed fertility remains the same, it will gradually rise to the level of the completed fertility.

For Jean-François Léger (5) and an anonymous person, this implies the possibility of an increase in the TFR in the long term. Since the completed fertility is only known for generations that have reached 50 years of age, experts question the evolution by analysing the number of children already had and future intentions (20, 22). For Jean-François Léger (5), the completed fertility is not (yet) declining: "La descendance finale ne fléchit pas (encore) : Par exemple, la génération 1979, qui n'a pas encore terminé sa vie féconde, a déjà atteint les 2 enfants par femme à 40 ans, soit plus précocement que la génération 1969. Or cette génération 1979 était encore « en retard » sur la génération 1969 à 32 ans. La génération 1984 qui est en retard à 35 ans par rapport aux générations 1969 et 1979 n'a pas non plus nécessairement dit son dernier mot".

On the contrary, Didier Breton (1) believes that the current drop is not simply a shift in the calendar. He stresses the importance of fertility by birth rank to explain the French specificity, since in France infertility is low and the transition from 2 to 3 children is fairly frequent: "La probabilité d'avoir un troisième enfant parmi les femmes ayant deux enfants [a été] longtemps légèrement supérieure à 0,4. Toutes choses égales par ailleurs, un passage à 0,3 voire 0,25 (ce qui est le cas dans de nombreux pays européens) inscrirait définitivement la France dans une fécondité durable proche de 1,8 enfant par femme. C'est une r de dire cela. Or le passage de 2 à 3 enfants est lié à de très nombreux facteurs très mouvants actuellement : les histoires d'unions, la précarité et vulnérabilité économique, la politique familiale, mais aussi l'âge à l'entrée en parentalité. La question de l'infécondité me semble [...] moins centrale dans le cas français – sauf si les prises de conscience écologique et environnementale dans les générations nées après les années 2000 se traduisent par des comportements plus radicaux que les générations précédentes et notamment la diminution drastique du désir puis de la réalisation de projets de parentalité".

Sylvain Papon (Insee, 22) also discusses the evolution of infertility, which could increase if two emerging movements gain momentum: the rise of ecological considerations among the younger generations on the one hand, and the demand for a "social right" not to have children on the other: "les femmes vont de plus en plus avoir l'opportunité de choisir d'avoir des enfants ou non. Les générations du baby-boom ont acquis le droit « physique » de maîtriser leur fécondité (contraception, IVG...), la génération actuelle se bat pour en avoir le droit « social »".

Some experts believe that the role of family policies and the greater ease of reconciling family and professional life should allow the persistence of a "French exception" (22, 30).

Anne Goujon (3) stands out by anticipating, on the contrary, a very sharp decline: "Je pense que la fécondité en France va continuer de diminuer en deçà des 1,85 ou 1,80 envisagés, et que
l'exceptionnalisme français bientôt ne sera plus. La principale raison à cela : l'incertitude croissante des jeunes couples face à l'avenir – économique, environnemental, sociétal, géopolitique – renforcée par la crise sanitaire. J'envisage un ICF en dessous de 1,5 enfants à l'horizon 2050. » Comme dans les pays nordiques, « le généreux système de protection sociale n'est pas suffisant pour contrecarrer la tendance qui est non seulement de grossesses plus tardives mais aussi moindres (Hellstrand et al., 2020)".

Anne Solaz (11) is less radical, but she too believes that the drop in fertility in the Nordic countries could herald a drop in French fertility: " J'ai donc du mal imaginer un scénario à la hausse : ce serait plutôt forte baisse (comme en Norvège), moyenne baisse comme en Suède ou stabilité, d'autant que l'âge à la naissance continue d'augmenter".

Philippe Louchart (6) points out that the reasons for the decline in the TFR over the past few years have not been clearly established and that the recent decline in the fertility rate of Ile-de-France women born in France (Louchart, 2021b) raises questions about whether this "French exception" will continue over time.

Some experts also believe that the health and economic crisis could have a lasting impact. Laurent Toulemon (12), on the other hand, stresses the importance of the stability of assumptions from one projection to the next and Didier Blanchet (CSR Retraites, 14) the risk of "giving the impression of over-reacting to the recent fall".

Finally, two experts discuss the role of migration and its interaction with fertility (6, 37).

## THE HEALTH CRISIS AND THE EVOLUTION OF THE CIF IN THE SHORT AND MEDIUM TERM

Most experts imagine a return to the trend in 2022 or 2023, with no lasting impact of the pandemic on long-term fertility : a drop in fertility in 2021 and sometimes 2022, before a return to normal (5, 8, 12, 13, 14, 16, 19, 21, 22, 26, 30, 33). Sylvain Papon (Insee, 22) sums up this widely shared position well: " Nous sommes au cœur de l'ouragan, mais sur le très long-terme, l'ouragan peut n'avoir que peu de conséquences."

Astri SYSE and Michael THOMAS (35) discuss the link between the evolution of the vaccination campaign and fertility: "The answer presumably depends on the speed at which large-scale vaccination can be performed in France. Given the relatively high skepticism of vaccination, it is hard to know precisely". Misha Khodabin, Hamza Zakraoui and Samuel Ménard (21) expect a return to normal and cite foreign examples : "Certains pays ayant déjà réussi à enrayer l'épidémie Covid-19 (Nouvelle-Zélande, Australie), auraient retrouvé des niveaux de natalité comparables à ceux enregistrés en 2019". Anthony Marino and Amandine Brun-Schammé (COR, 20) cite the example of the 1993 economic crisis, whose influence was transitory.

For Didier Breton (1), if the effect already observed is due to economic uncertainty rather than health concerns, it could last until 2023. Indeed, experts who insist on economic factors envisage a slightly longer-lasting effect (23, 27, 35), as Gwenaël Podesta (DGEC, 23): " l'impact de la crise
peut être appréhendévia les anticipations sur le PIB (baisse forte en 2020, rattrapage lent jusqu'à
2022 voire 2024, puis reprise des tendances passées) ". However, a recent publication (Papon,
2021b) shows that French fertility was little affected by the 2008 economic crisis, unlike in
neighbouring countries, which raises the question of the impact on fertility of an economic crisis
accompanying the health crisis. In the short term, the impact appears to be small (Brée et al., 2021).

Marie Vandresse (Federal Planning Bureau, Belgium) reports on the assumptions made for the projections published in January 2021 in Belgium: "Cependant, suite à l'impact de l'épidémie de la COVID-19 sur la situation socio-économique, la projection table sur une nouvelle baisse de la fécondité en 2021. Les projets de naissances seraient de nouveau reportés pour certains, voire abandonnés. Dans ce contexte, la fécondité à moyen terme a par ailleurs également été revue à la baisse. Elle reste inférieure à 1,6 dans les cinq prochaines années. La crise sanitaire vient en effet ajouter un élément d'incertitude supplémentaire quant à l'évolution future de nos sociétés (Duyck et al., 2021 a and b)".

For Lena Lundkvist (Statistics Sweden, 31), the decline in fertility anticipated in Sweden for 2021-2022 is linked to the decline in immigration: "In Sweden we do specific estimations for 2021 and 2022 because of the covid-19. It is above all because lower immigration and lower fertility for foreign-born women due to that".

It is obviously difficult to quantify the decrease to be expected in 2021 or 2022. Antonio Argueso (INE, Spain) suggests a decline of around 15% in 2021 in Spain before returning to normal. Dragos Mondiru (Insse, Romania) suggests a 13% drop in births in Romania in January 2021 compared to January 2020, identical to that observed in France.

While the prospect of a transitional decline is shared by all the experts, many raise the question of a possible catch-up and therefore a transitional increase in 2021 or 2022, before the return to normal (7, 11, 12, 20, 34, 35, 37). Gilles Pison (7) is considering such a catch-up: "Les naissances qui n'auront pas eu lieu en 2021 du fait de l'épidémie de Covid-19 seront sans doute rattrapées en sortie de crise. Mais en mars 2021, il est trop tôt pour savoir si le rattrapage sera total ou partiel, et quand il aura lieu". Anne Solaz has the same opinion (11): "Il y a souvent eu rattrapage après des crises économiques. Je pense qu'il faut prévoir une estimation « d'attente » encore en 2021 tant qu'il y a incertitude sur l'avenir sanitaire et économique, ce qui peut diminuer le nombre total car pour certaines femmes ayant des enfants tard, il sera trop tard en raison de l'horloge biologique."

Several experts also point out that the decline in births due to Covid-19 is part of a downward trend that has already begun (1, 3, 6).

Jean-François Léger (5) suggests updating the projections in 2022 if the fertility trend in 2021 turns out to be too different from that anticipated.
22 experts have given numerical proposals for variants. For the high assumption the average is 2.02 and for the low assumption it is 1.58.

A very large majority of the experts (15 out of 25) agree that the range should be +/- 0.2 points, rather than +/- 0.15 points, the range chosen by only four anonymous experts. Some experts even propose a wider range, such as +/- 0.35 for Laurent Toulemon (12): " L'incertitude est gravement sous-estimée avec des écarts de +/- 0.15 enfant par femme. Je propose dans l'idéal de passer à +/- 0.35 (soit des hypothèses haute et basse de 2.2 et 1.5". For Misha Khodabin, Hamza Zakraoui and Samuel Ménard (21), the width of the interval should be defined according to past fluctuations: " la largeur de l'intervalle doit être définie en fonction de la variabilité de la natalité, si la tendance passée s'est éloignée significativement des projections qui avaient été établies, il convient de l'élargir". This argument is also cited by an anonymous expert. In the French case, if we start the period in 1975, after the very rapid fall in the early 1970's (from 2.5 in 1971 to 1.93 in 1975), we find an interval of 0.37 between a minimum of 1.66 in 1993 and a maximum of 2.03 in 2010.

Several experts propose a non-symmetrical range around the baseline assumption, with a decrease in the TFR seeming more likely than a sharp increase (6, 11, 16, 28, 31, 36). Katarzyna Góral-Radziszewska (Statistics Poland, 28) "recommends considering asymmetrical variants with low one even lower". Philippe Louchart (6) shares this idea: "Au regard des éléments précédents, je préconiserais +/- 0.2 comme vous le proposez ou plutôt un écart de 0.4 entre les deux scénarios extrêmes avec un scénario bas, plus bas et un scénario haut moins haut, l'incertitude à la baisse paraissant supérieure à celle à la hausse au regard des évolutions passées et des évolutions ailleurs dans le monde ou d'un strict point de vue probabiliste".

Livia VARGA and Zoltan CSANYI (KSH, Hungary, 37) recommend adapting the variants to different policy options: "It may be appropriate to adjust the extent of the interval according to expected government decisions. It is useful to consider the probability and durability of new family support measures and the importance of migration regarding fertility".

Sylvain Papon (INSEE, 22) suggests reaching the target TFR in the variants in 5 years, unless this implies too much variation. Jean-François Léger (5) for his part would move the scenarios towards their final value in a progressive manner: the target of 1.80 that he proposes for the baseline scenario is reached around 2025-2030, that of the high scenario at 2.0 around 2030-2035 and that of the low scenario around 2035-2040.
<caption>Figure 2.7 - Expert Positioning on TFR Variants</caption>

Difference between baseline and low and high assumptions

<table><thead><tr><th></th><th>0.15</th><th>0.2</th><th>0.25</th><th>0.3</th><th>0.35</th><th>0.4</th><th>Number of responses</th><th>Average</th></tr></thead><tbody><tr><th>Researchers</th><td>0</td><td>5</td><td>0</td><td>1</td><td>0</td><td>0</td><td>6</td><td><strong>0.22</strong></td></tr><tr><th>OSS</th><td>0</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>6</td><td><strong>0.23</strong></td></tr><tr><th>NSI</th><td>0</td><td>5</td><td>1</td><td>2</td><td>0</td><td>0</td><td>8</td><td><strong>0.23</strong></td></tr><tr><th>Anonymous</th><td>4</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>5</td><td><strong>0.20</strong></td></tr><tr><th><strong>Total</strong></th><td><strong>4</strong></td><td><strong>15</strong></td><td><strong>1</strong></td><td><strong>3</strong></td><td><strong>1</strong></td><td><strong>1</strong></td><td><strong>25</strong></td><td><strong>0.22</strong></td></tr></tbody></table>

High assumption

<table><thead><tr><th></th><th>1.59</th><th>1.8</th><th>2</th><th>2.05</th><th>2.1</th><th>2.15</th><th>2.2</th><th>Number of responses</th><th>Average</th></tr></thead><tbody><tr><th>Researchers</th><td>1</td><td>0</td><td>2</td><td>1</td><td>0</td><td>1</td><td>0</td><td>5</td><td><strong>1.96</strong></td></tr><tr><th>OSS</th><td>0</td><td>1</td><td>1</td><td>3</td><td>1</td><td>0</td><td>0</td><td>6</td><td><strong>2.01</strong></td></tr><tr><th>NSI</th><td>0</td><td>0</td><td>3</td><td>1</td><td>2</td><td>1</td><td>0</td><td>7</td><td><strong>2.06</strong></td></tr><tr><th>Anonymous</th><td>0</td><td>0</td><td>2</td><td>0</td><td>1</td><td>0</td><td>1</td><td>4</td><td><strong>2.08</strong></td></tr><tr><th><strong>Total</strong></th><td><strong>1</strong></td><td><strong>1</strong></td><td><strong>8</strong></td><td><strong>5</strong></td><td><strong>4</strong></td><td><strong>2</strong></td><td><strong>1</strong></td><td><strong>22</strong></td><td><strong>2.02</strong></td></tr></tbody></table>

Low assumption

<table><thead><tr><th></th><th>1.21</th><th>1.4</th><th>1.5</th><th>1.55</th><th>1.6</th><th>1.65</th><th>1.7</th><th>1.8</th><th>Number of responses</th><th>Average</th></tr></thead><tbody><tr><th>Researchers</th><td>1</td><td>0</td><td>0</td><td>1</td><td>2</td><td>1</td><td>0</td><td>0</td><td>5</td><td><strong>1.52</strong></td></tr><tr><th>OSS</th><td>0</td><td>2</td><td>0</td><td>0</td><td>1</td><td>3</td><td>0</td><td>0</td><td>6</td><td><strong>1.56</strong></td></tr><tr><th>NSI</th><td>0</td><td>0</td><td>2</td><td>1</td><td>1</td><td>2</td><td>1</td><td>0</td><td>7</td><td><strong>1.59</strong></td></tr><tr><th>Anonymous</th><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1</td><td>4</td><td><strong>1.65</strong></td></tr><tr><th><strong>Total</strong></th><td><strong>1</strong></td><td><strong>3</strong></td><td><strong>2</strong></td><td><strong>2</strong></td><td><strong>4</strong></td><td><strong>6</strong></td><td><strong>3</strong></td><td><strong>1</strong></td><td><strong>22</strong></td><td><strong>1.58</strong></td></tr></tbody></table>

We also questioned the experts on the usefulness of a working scenario called "European convergence" or "European fertility", which in 2016 was based on a target TFR of 1.65. We wondered what role to give to this scenario when one of the targets proposed for the low variant in the 2021 projections is 1.65.

Some experts consider that it is not useful to make a different scenario (5, 6, 14, 20, 2 anonymous). Philippe Louchart (6) considers that a TFR at the generation replacement level could be the subject of a working scenario since he proposes scenarios with lower TFRs.

Some experts contest the very idea of European convergence (3, 26, 35). Anne Goujon, for instance, relies on a recent article for this (Castiglioni et al., 2021). Marie Vandresse (Federal Planning Bureau, Belgium, 36) is close to this position since, in her view, such a scenario is in contradiction with our projection approach 'if recent demographic trends were to continue'.

Several experts find it useful to add a very low fertility scenario. Anne Solaz (11) speaks of a "sharp decline in the Norwegian style, to 1.56", Gilles Pison (7) suggests 1.50 or 1.55, Sylvain Papon, from INSEE, 1.5. For Laurent Toulemon, "the European convergence scenario should converge towards the value projected by Eurostat for 2070, not towards the current value of the TFR for European countries". Jérôme Lê (Insee, 17) proposes to take the current average for the EU excluding France. The average TFR in the EU (weighted by the total population) is 1.52 in 2019 and 1.65 in 2070 in
the Eurostat projections. The average TFR excluding France is 1.46 in 2019 and projected by Eurostat to be 1.62 in 2070.

MEAN AGE AT CHILDBIRTH

25 researchers commented on the value of the mean age at childbirth in 2070. Most (17) agree with our proposal of 33 years, and some propose a higher age, bringing the mean proposal to 33.2 years.

All the experts believe that the mean age will increase, and they put forward various reasons for this. Anthony Marino and Amandine Brun-Schammé (COR, 20), for example, insist on the uninterrupted growth of the mean age, which is more dynamic for the first child and faster than the evolution of the age of entry into working life. For some experts, the 33-year ceiling is likely to be exceeded: "Quant au palier proposé à 33 ans dans 30 ans, je peux juste signaler que ce palier reste inférieur à celui observé à Paris en 2018 (33.8 ans en 2018)" (Philippe Louchart, 6), or "la PMA devient déjà de plus en plus fréquente. Les progrès techniques peuvent tout à fait permettre de dépasser ce « plafond »" - Sylvain Papon, Insee, 22.

Several experts (16, 24b, 27, 32) even consider that past trends and future prospects do not justify the setting of a ceiling, beyond which the age would stop increasing, as Vianney Costemalle of the DREES (16): "Je ne vois pas pourquoi plafonner l'âge à 32 ans. La tendance d'avoir des enfants de plus en plus tard dans la vie va selon moi se poursuivre, et les progrès médicaux vont faire en sorte de rendre cette tendance possible (par exemple congélation des ovocytes, réparation du génome des embryons avec Crispr-Cas9 [indépendamment des questions éthiques et politiques que cela peut soulever])".

Some experts (3, 5, 6, 22) find it difficult to retain the same mean age for the three scenarios, especially if the TFR assumptions are more differentiated than in 2016 (3, 6, 24b). For example, Anne Goujon (3) says: "Je comprends l'idée de fixer l'âge à la maternité pour des raisons de simplicité, toutefois, si vous adoptez des intervalles plus larges au niveau de l'ICF, cela parait moins bien défendable. Peut-être envisager un plafond à 33 ans pour l'hypothèse centrale, 32 pour la haute et 34 pour la basse ?". Branislav Sprocha (Slovakia, 34) believes "that setting the same aging of fertility age-profile for all scenarios is not the best solution. Not only different intensities of fertility should be reflected in different scenarios, but also different developments in the aging process of the fertility profile should be included".

The link between fertility intensity and age at childbirth seems to be more important in other European countries such as Germany, as pointed out by Olga Poetsch and Felix zur Nieden (DESTATIS, Germany, 33): "In this respect, the situation in Germany is apparently not comparable. Postponing of births resulted so far in lower fertility. Therefore, the stronger the mean age at birth will increase the lower the assumption for the TFR will be". This explains why foreign experts are more sensitive to this link, like Astri Syse and Michael Thomas (Statistics Norway, 35): "[A mean age at 33] seems reasonable, however, perhaps in the low fertility scenario, the cap could be extended to 35 years? With medical advances in the area of infertility, it may be possible for
women to extend childbearing ages (something to consider if you are thinking so far into the future, e.g. 2040-2070)."

It is true that the assumption of independence between the mean age and the level of the TFR is unlikely, but it is practical for comparing scenarios, and has relatively little influence on the results of the projections, as Gilles Pison (7) reminds us: "Quant à appliquer la même évolution de l'âge à la maternité quelle que soit l'hypothèse sur l'ICF, c'est la solution la plus simple. On pourrait raffiner en introduisant une relation entre tendance de l'ICF et tendance de l'âge à la maternité - au cours des dernières décennies, les périodes de hausse de l'ICF ont souvent été des périodes de ralentissement du retard des maternités, et à l'inverse, celles de baisse de l'ICF, des périodes d'accélération du retard des maternités. Mais ce type de raffinement ne me parait pas avoir d'intérêt pour les projections de long terme que vous préparez". Laurent Toulemon shares the same opinion: "La variation de l'âge moyen n'a que très peu d'impact sur le nombre des naissances, à ICF fixé. Vous avez raison de faire la même variation pour tous les scénarios".

"A specific effort on mean ages is justified if you want to look at the completed fertility of generations and if you have assumptions about that."

Didier Breton (1) insists on the importance of the age at first child : "[C'est l'indicateur] le plus important et pertinent sur lequel il faudrait élaborer les scenarii. En effet, de lui dépend fortement l'infécondité d'une part, mais aussi l'agrandissement des familles".

<table><caption>Figure 2.8 - Experts' views on the mean age at childbirth in 2070</caption><thead><tr><th></th><th>32-33</th><th>33</th><th>33-34</th><th>34</th><th>35</th></tr></thead><tbody><tr><th>Researchers</th><td>1</td><td>3</td><td>1</td><td>1</td><td></td></tr><tr><th>OSS</th><td></td><td>5</td><td>1</td><td>1</td><td>1</td></tr><tr><th>NSI</th><td>1</td><td>7</td><td></td><td></td><td></td></tr><tr><th>Anonymous</th><td></td><td>2</td><td></td><td></td><td>1</td></tr><tr><th>Total</th><td>2</td><td>17</td><td>2</td><td>2</td><td>2</td></tr></tbody></table>

THE EVOLUTION OF AGE-SPECIFIC FERTILITY RATES

In the 2016 projections, fertility was modeled age by age, assuming a linear change in the rate at each age. Some adjustments were then made to obtain a plausible age profile, with fertility rising to an age of maximum fertility corresponding roughly to the mean age at childbirth, and then declining. This method led to a concentration of fertility at a few key ages that became more and more pronounced as the years passed. For the 2021 projection, the experts were asked their opinion on the modelling of age-specific rates using the method described by Schmertmann (2003), which deduces a likely fertility profile from a few assumptions and leads to a greater spread of fertility, with identical TFR and mean age at childbirth (Figure 2.5).

Several experts are in favour of it (1, 6, 12, 20, 21, 22, 28, 37, one anonymous), for various reasons. For Didier Breton (1), it would correspond to the idea of a 'reproductive divide': [Cela] irait dans le sens d'une « fracture reproductive » c'est-à-dire de tenir compte du développement de deux
modèles de calendrier de fécondité, très lié à la trajectoire scolaire". For Philippe Louchart (6), the change in the age profile could also be explained by differences according to the country of birth: "l'analyse séparée de la fécondité par âge des femmes nées en France (ou non immigrées) et des femmes nées à l'étranger (ou immigrées) depuis une vingtaine d'années ". Anthony Marino and Amandine Brun-Schammé, from the COR, support the change in method.

Some experts (11, 35, 36, two anonymous) dispute the idea of a spread, such as Anne Solaz (11): « Je ne crois pas trop à un étalement. Je ne sais pas si l'âge à la première maternité retarde encore, on risque plutôt d'avoir plus de concentration pour les âges centraux » Olga Poetzsch and Felix zur Nieden (DESTATIS, Germany, 33) share this idea: "From our point of view, postponing births to a higher age leads to a greater concentration of fertility in the fourth decade of life. We tested Schmermann's model for deriving assumptions and decided against it".

Anne Goujon (3) suggests considering an alternative approach, the one proposed by Joop de Beer in 2011, called TOPALS (de Beer, 2011). Indeed interesting, this approach gives slightly better results in the case of France in 2008 than those of Schmertmann (2003). However, the difference is small and the main advantage of TOPALS is that it makes it possible to assume that the projected age profile converges towards a standard profile (for example, the average European profile or the profile of a country considered to be a precursor). In the French case, it seems to us that the Schmertmann model prolongs the phenomenon observed in the past of spreading out, which moreover corresponds to a convergence towards the European average, since Joop de Beer clearly shows the French singularity of a more concentrated profile around the age of 30 in 2008.

Jean-François Léger links the age profile to the intensity of fertility: "Un ICF faible (en dessous de 1.7) [...] pourrait éventuellement correspondre à une fécondité concentrée autour de 30 ou 35 ans comme c'est le cas aux Pays-Bas par exemple".

Several experts (7, 12, 14) point out that the age profile of fertility has little influence on the projection results. Consequently, Gilles Pison (7) argues for the adoption of a simple age-specific fertility rate curve model: "un âge moyen à la maternité correspondant au mode de la courbe des taux de fécondité par âge, et une courbe symétrique de part et d'autre".
Box 4 - Setting a TFR target of 1.80 or 1.85, what difference will it make by 2070?

It may seem that a baseline assumption of 1.80 or 1.85 does not fundamentally change the projection, but it actually affects the results quite significantly. In both cases, given the mortality assumptions of the baseline scenario presented in Part III, the natural balance becomes negative fairly quickly (2035 and 2037), even if births are higher around 2035-2045, when the children of the larger generations of the 2000-2010 period are born. The positive migratory balance (+70,000 in the central scenario, see part IV)) is sufficient to compensate for the negative natural balance until 2049 with a TFR of 1.85; but only until 2044 with a TFR of 1.80. By 2070, the population would be 1.1 million higher with a TFR of 1.85 than with a TFR of 1.80. The impact on the completed fertility (CF) is quite quickly visible: the 1997 generation would have a CF of 1.96 children per woman with a target TFR of 1.80 and 2.00 children per woman with a target TFR of 1.85; the 2028 generation would have CFs equal to the target TFRs of 1.80 and 1.85.

<table><thead><tr><th></th><th>TFR 1.80</th><th>TFR 1.85</th></tr></thead><tbody><tr><td>Negative natural balance from ...</td><td>2035</td><td>2037</td></tr><tr><td>The population decreases from ...</td><td>2044</td><td>2049</td></tr><tr><td colspan="3"><strong>In 2070</strong></td></tr><tr><td>Births (in thousands)</td><td>660</td><td>697</td></tr><tr><td>Total population (in thousands)</td><td>68,104</td><td>69,252</td></tr><tr><td>Share of under-20s (%)</td><td>20.2</td><td>20.8</td></tr><tr><td>Ratio of over 65s to 20-64s (%)</td><td>56.8</td><td>56.0</td></tr></tbody></table>

Births and Deaths

<figure><img src="image_14.png" /></figure>

Population

<figure><img src="image_15.png" /></figure>

Source: Insee, population estimates and population projections 2021-2070.
TOTAL FERTILITY RATE: BASELINE ASSUMPTION AT 1.80, VARIANTS AT +/-0.2

It was difficult to decide between a TFR of 1.80 and 1.85 on the basis of expert responses. The difference seems small, but a total fertility indicator that is 0.05 children per woman higher in the long term nevertheless changes the results of the projection (Box 4). **For the baseline scenario, a TFR assumption of 1.80 was finally retained,** slightly below the current value of 1.83 and close to the average of the experts' responses, at 1.81. The target, very close to the current level, would be reached by 2022.

Experts have mostly urged against over-reacting to the **pandemic**, and the data have so far proved them right: the period of declining births in December 2020, January 2021 and February 2021 coincides with a decline in conceptions restricted to the period of the first containment, but a partial catch-up was observed in March-April 2021, and births rebounded in July and August 2021. The evolution of births is since then less easy to relate to the sanitary conditions 9 months before. Different assumptions can be made about the evolution of the remaining 4 months of 2021 and 2022. The assumption adopted for 2021 is that the trend between September and December 2021 will be similar to that observed between May and August, before returning in 2022 to the pre-crisis trend path(Box 6). This would lead to a **TFR that would decrease from 1.83 in 2020 to 1.79 in 2021 and 1.80 in 2022.** These values should be compared with those that would have been observed if the downward trends from 2017 to 2019 had continued, i.e. 1.84 in 2020, 1.82 in 2021 and 1.80 in 2022.

The vast majority of experts recommended increasing the interval between variants. The update of the probabilistic work of Vianney Costemalle (Box 5) also encourages increasing the interval between variants<fnref n="11" />: With an average of the *a priori* law set at 1.80, the long-term TFR would be 1.81, and the TFR would stay between 1.55 and 2.06 children per woman in 2070 with a 95% probability." These two arguments lead us to retain a **difference of +/-0.20 between the baseline assumption and the two variants,** compared with +/-0.15 in the 2016 projections. **The low variant will be at 1.60 and the high variant at 2.00,** slightly below the generation replacement level<fnref n="12" />. **The working scenario of very low fertility will be set at 1.50,** a value close to the European average in 2019 (the total population-weighted average of the TFRs of the EU countries is 1.52 in 2019).

In 2016 projections, convergence towards the TFR target was rapid in the different variants. The baseline assumption of 1.80 corresponds almost to the current level, and for 2022 the assumptions used lead to a total fertility rate of 1.80 (Box 6). It is therefore logical to assume in the baseline scenario that the TFR will stabilise at its target level of 1.80 by 2022. On the other hand, reaching 2.0, 1.6 or even 1.5 in two or three years' time is tantamount to assuming a very rapid reversal of

<footnote n="11">¹¹ This update does not help much in the choice of the target TFR in the chosen probabilistic modelling, as the projected median TFR in 2070 is highly dependent on the long-term assumption set a priori.</footnote>

<footnote n="12">¹² The "current" renewal threshold (for a fictitious generation consistent with the TFR measure in 2020) is estimated at 2.06 children. Its theoretical minimum, in the absence of any mortality of women before and during their fertile period, would be 2.045.</footnote>
the trend. Since the interval between the variants and the baseline scenario has been increased to +/-0.20, this raises the question of the rate at which the assumptions of the variants are reached. In the past, the drop in the TFR in the 1970's was very steep, almost 1 child per woman lower between 1966 and 1976 (Figure 2.9). But since the mid-1980's, the 5-year variations do not exceed +/- 0.15-0.16 points (+0.16 between 1995 and 2000, -0.15 between 2014 and 2019). On the other hand, the 10-year variations reach +/- 0.20 to 0.25 points during certain periods (for example +0.26 between 1996 and 2016, -0.22 between 2010 and 2020). This is why, for the variants, a convergence towards the target TFR by 2030, which is slower than in the 2016 projections, is retained (Figure 2.10).

<figure><img src="image_16.png" /><figcaption>Figure 2.9 - Changes in TFR from 5 years ago (yellow) or 10 years ago (blue) since 1972</figcaption></figure>

Coverage: Metropolitan France

Source: Insee, population estimates.
<figure><img src="image_17.png" /><figcaption>Figure 2.10 - The TFR from 1994 to 2070</figcaption></figure>

Source: Insee, population estimates and population projections 2021-2070.

Coverage: France excluding Mayotte until 2013, France from 2014 onwards.

THE ASSUMPTION ON MEAN AGE AT CHILDBIRTH : AN INCREASE TO 33 YEARS AND THEN A STABILISATION

Concerning age at chilbirth, the idea of a **ceiling of 33 years in 30 years** is maintained, even if some experts would have let the age increase beyond that. A continuation of the trend would lead to a mean age of 34.4 years in 2070 (Figure 2.11) and would limit the decline in completed fertility. The application of a ceiling of 33 years, reached in 2052, means that, over the last 18 years of the projection, the phenomena of postponement of childbearing diminish, and the completed fertility reaches the TFR target level (Figure 2.13). The impact of a continued increase in the mean age at childbirth until 2070 would be fairly modest on the results of the projection: changes in the level of the population or in the proportion of elderly people would be insignificant; on the other hand, the completed fertility would remain above 1.90 until the generations of the 2030's.
<figure><img src="image_18.png" /><figcaption>Figure 2.11 - Changes in the mean age at childbirth from 1994 to 2070</figcaption></figure>

Source: Insee, population estimates and population projections 2021-2070.

Coverage: France excluding Mayotte until 2013, France from 2014 onwards.

On the profile of the age-specific fertility rate, several experts rightly pointed out that it does not have a decisive influence on the results of the projection and it was chosen to apply the Schmertmann (2003) model, which gives a fairly plausible profile for age-specific fertility rates (Figure 2.12).

<figure><img src="image_19.png" /><figcaption>Figure 2.12 - Age-specific fertility rates in 2020 and 2070, 2016 and 2021 projection years</figcaption></figure>

Source: Insee, population estimates and central scenario of the 2016-2070 and 2021-2070 population projections.

Coverage: France.

Some experts believe that the increase in the mean age will be achieved through a concentration of fertility at certain ages, rather than a spread, as biological constraints limit fertility at high ages. The spread is more a result of the dispersion of calendars due to the "reproductive divide"
mentioned by Didier Breton and the pursuit of medical progress that makes it possible to increase fertility at advanced ages. Since 1994, there has been more of a spread than a concentration: in 1994, the peak fertility age was 28 years and represented 8.4% of the TFR, and the five peak years were 26-30 years and represented 38.9% of the TFR. In 2020, the modal age is 31 years and represents 7.3% of the TFR, the maximum 5 years are 29-33 years and represent 35.5%.

COMPLETED FERTILITY: 2 CHILDREN PER WOMAN BORN IN 1990, 1.9 AND 1.8 FOR THOSE BORN IN 2015 AND 2030

With the new baseline assumptions, completed fertility would be significantly lower than that projected in 2016. The drop is rapid between the baseline scenarios of the two exercises since, in the previous projections, the completed fertility remained above the generation replacement level until the generations of the early 2000s, and above 2 until the 2012 generation. In the new projections, it falls below 2 as of the 1991 generation. In both exercises, the completed fertility of the 1980-1990 generations is clearly above 2 children per woman, as they had the most children during the high fertility period of the 2000's (Papon, 2021a). The difference in projected completed fertility for the 1990's generations is very largely due to the fact that the 2016 projections overestimated fertility for the years 2013-2020, as can be seen when we replace the projected rates in 2016 with the observed rates for those years (dark yellow dotted line).

<figure><img src="image_20.png" /><figcaption>Figure 2.13 -Completed fertility of the 1980 to 2030 generations: comparison of the different 2021 scenarios and the baseline scenario of the 2016 projections</figcaption></figure>

Source: Insee, population estimates and population projections 2016-2070 and 2021-2070.

**Coverage:** The age-specific fertility rates, from which the completed fertility is calculated, are for metropolitan France until 1993, France excluding Mayotte from 1994 to 2013 and France from 2014 onwards. The field is therefore composite for a single generation.
Box 5 - TFR probabilistic projection, an update of Vianney Costemalle's work

Vianney Costemalle has made Bayesian probabilistic projections for France using the component method (Costemalle, 2020). For fertility, he proceeds in three steps: projection of the TFR, then independent projection of age-specific rates, then reconciliation of the two by recalibration. For the TFR, he uses "first-order autoregressive model. The UN uses the same method for its third stage of fertility change, on the assumption that the TFR tends towards 2.1 in all countries (Alkema et al., 2010)". Compared to the UN method, V. Costemalle had "chosen to estimate the parameters of the model by Bayesian inference, rather than by maximum likelihood". He found a median long-term TFR of 1.93, "slightly below the mean of the a priori distribution, which is set at 1.95. According to the model used, the TFR will be between 1.63 and 2.26 children per woman in 2070 at a probability of 95%." We reproduced his model, thanks to his programs, for which we thank him very much. We just updated the results by taking into account the 2014-2020 data (the 2018-2020 years being provisional), and also varying the long-term TFR. Taking the latest data into account reduces the value of the projected TFR in 2070, but the size of the confidence interval increases slightly. By setting the long-term TFR at 1.85, which corresponds to the average over the reference period 1975-2020 (1.86), the confidence interval is significantly reduced: depending on the modelling chosen, the TFR will be between 1.58 and 2.07 children per woman in 2070 with a 95% probability. With a long-term TFR set at 1.80, the results change little: the TFR will be between 1.55 and 2.06 children per woman in 2070 with a 95% probability. A long-term TFR of 1.70 also increases the size of the confidence interval.

Projected TFR for 2070 :

2.5% and 97.5% order quantiles and median of the a posteriori distributions

<figure><img src="image_21.png" /></figure>

Coverage: Metropolitan France

Long term TFR set at 1.95
Costemalle (2020)
Ref. 1975-2013

Update
Ref 1975-2020
Box 6 - Estimating births and fertility in 2021

At the time of freezing the assumptions for the projections, the data available for the year 2021 are the number of births from January to August. To measure changes, we chose to relate them to the same months in 2016. For example, in March 2021, there were 4,443 births to mothers aged 40 and over, 23% more than in March 2016 (3,600): the index is 123. The month-by-month comparison compensates for the monthly variations in births. Indeed, as age is measured in age reached in the year, the number of monthly births is structurally higher at the beginning of the year for the oldest mothers and at the end of the year for the youngest. Taking 2016 as a reference year allows us to compare the variations observed following the pandemic with those that have been in place for several years: since 2014, fertility has been falling every year. The trend decline, estimated here over the period January 2017-October 2020, is more marked among the youngest mothers, while the rise in fertility after age 35 continues (Figure 1, dotted lines). With reference to this trend, the decline was strongest in January for all age groups (figure, solid lines) while a catch-up was observed in March-April 2021. Between May and August 2021, the developments are contrasted but in total, compared to May-August 2016, the overall level of births is only 0.1% lower than it would be if it were on the 2017-2020 trend. The deficit remains most noticeable for mothers under 25 (table), as it has since December 2020 (Papon, 2021c).

<caption>Figure 1 - Change in births between November 2020 and August 2021 by age of mother</caption>

*In index base 100 for the same month of 2016*

<figure><img src="image_22.png" /></figure>

Coverage: France
Estimating 2021 births and fertility (continued)

Therefore, the following assumptions were made:

- Fertility rates move with the same differential to their trend as births between January and August 2021;

- From September to December 2021, both fertility rates and births move with the same differential to their trend as births between May and August 2021;

- Fertility rates return to their 2017-2020 trend over the whole of 2022: the TFR rises compared to 2021, but remains lower than in 2019.

This leads to the assumption of a TFR of 1.79 children per woman in 2021 and 1.80 in 2022, then a stabilisation at this level, corresponding to the targetTFR of the central scenario, from 2023. From 753,000 births in 2019 and 736,000 in 2020, this would rise to 725,000 in 2021 and 717,000 in 2022.

<table><caption>Figure 2 - Change in births in 2021</caption><thead><tr><th rowspan="2">Mother's age</th><th colspan="5">Deviation from trend Jan. 2017-Oct. 2020, in %.</th></tr><tr><th>Jan-Feb</th><th>March-April</th><th>May-August</th><th>Sept-Dec (assumption)</th><th>2021 set (assumption)</th></tr></thead><tbody><tr><td>Under 20 years old</td><td>-24.4</td><td>-4.6</td><td>-7.4</td><td>-7.4</td><td>-9.3</td></tr><tr><td>20-24 years old</td><td>-15.0</td><td>-4.3</td><td>-5.0</td><td>-5.0</td><td>-6.4</td></tr><tr><td>25-29 years old</td><td>-10.3</td><td>+3.8</td><td>-1.1</td><td>-1.1</td><td>-1.8</td></tr><tr><td>30-34 years old</td><td>-8.2</td><td>+5.0</td><td>+0.8</td><td>+0.8</td><td>+0.0</td></tr><tr><td>35-39 years old</td><td>-9.3</td><td>+5.7</td><td>+2.7</td><td>+2.7</td><td>+1.1</td></tr><tr><td>40 years and older</td><td>-15.5</td><td>+9.1</td><td>-1.4</td><td>-1.4</td><td>-2.3</td></tr><tr><td>All</td><td>-10.3</td><td>+4.1</td><td>-0.1</td><td>-0.1</td><td>-1.1</td></tr></tbody></table>

Coverage: France
# PART 3 : THREE ASSUMPTIONS ABOUT MORTALITY

## A. RECENT MORTALITY TRENDS IN FRANCE

The previous projections retained a baseline assumption of life expectancy at birth of 93.0 years for women and 90.1 years for men in 2070 (Figure 4.1). Two alternative assumptions of high and low life expectancy were retained at + or - 3 years from this baseline assumption: 90 years and 96 years for women and 87.1 years and 93.1 years for men. An assumption of constant life expectancy equal to its 2014 level (at 85.3 years for women and 79.1 years for men) had also been used. In addition, the previous projections had extended a cohort effect, visible for the generations born from 1941 to 1955, the so-called "plateau generations". For these generations, mortality stagnates in adulthood while it decreases for the previous and following generations (Blanpain, 2020).

From 2014 to 2019, life expectancy progress slowed in France. In 2020, the year of the health crisis, life expectancy at birth even fell to its 2014 level (85.1 years for women and 79.1 years for men). The cohort effect also continued from 2016 to 2020 and contributed to the slowdown in life expectancy gains. In 2019, life expectancy is equal to the level of the low assumption of the 2016 Insee projections (0 year difference for women and men). In 2020, it is below the level of the low assumption of 2016 (-0.6 years for women and -0.8 years for men). In view of the recent evolution of life expectancy at birth, the baseline assumption of the 2016 Insee projections therefore appears high. Recent projections by institutions covering France also assume a lower life expectancy in 2070, for example 90.9 years for women and 86.2 years for men for Eurostat in its projections published in 2020¹³, and 91.5 years for women and 86.1 years for men for the United Nations published in 2019 (United Nations, 2019c). Both of these exercises were conducted before the Covid-2019 pandemic.

<footnote n="13">¹³ Eurostat, 2020 and EUROPOP2019 data online: https://ec.europa.eu/eurostat/datab browser/view/proj_19nalexp/default/table?lang=fr</footnote>
<figure><img src="image_23.png" /><figcaption>Figure 4.1 - The evolution of life expectancy at birth and its projection by INSEE in 2016</figcaption></figure>

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013 and France from 2014 onwards

Sources: INSEE, population estimates and vital statistics, provisional data in 2018, 2019 and 2020 and INSEE 2016 population projections.

In France, the slowdown in the increase in life expectancy since 2014 concerns all ages: the average annual slope of mortality rates between 2014 and 2019 shows almost at every age a smaller decrease in mortality than in previous periods (Figure 4.4).

This lower decrease in mortality affected several causes of death. The decline in mortality is thus less over the period 2008-2016 compared to the period 2000-2007 for the two main categories of
causes of death, tumours and cardiovascular diseases (Boulat et al., 2019 and data online <fnref n="14" />). This suggests that this slowdown is not only due to short-term causes.

<figure><img src="image_24.png" /><figcaption>Figure 4.4 - Annual change in age-specific mortality rates for different periods</figcaption></figure>

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013 and France from 2014 onwards

Sources: INSEE, population estimates and vital statistics.

2020 is a high mortality year due to the Covid-19 epidemic. This is a conjunctural phenomenon and not a long-term trend. It does not seem advisable to include the year 2020 in the reference period for the past. The mortality of the year 2020 is taken into account in the age pyramid on 1 January 2021, but the Covid-19 epidemic will also have consequences on the evolution of mortality in 2021, 2022 or even after. Beyond the deaths resulting from a Covid-19 infection, these consequences could be both positive and negative.

**Possible positive consequences** on mortality: harvest effect (the most fragile people have already died, which leads to a drop in mortality in the following years, as in 2004 following the 2003 heat wave), drop in diseases transmissible by contact thanks to the development of physical distance (and therefore lower influenza lethality, for example), drop in certain causes of death due to the development of teleworking (drop in transport accidents, etc.).

**Possible negative consequences:** increase in deaths linked to the decrease in screening, consultations, the postponement of operations, increase in certain causes of death due to the economic crisis (deterioration in mental health, difficulties in accessing care, etc.) or the development of teleworking (deaths linked to sedentary lifestyle, isolation, etc.), increase in deaths linked to acquired fragility following infection with Covid-19, etc.

## France's position in Europe

In Europe (Figure 4.2), France is in 2019 at :

- 5th position for life expectancy of the population, 1.7 years above the EU-27

- 8th position for male life expectancy, 1.4 years above the EU-27.

- 2nd position for female life expectancy, 1.9 years above the EU-27.

<footnote n="14">¹⁴ Cause of death data: https://www.cepidc.inserm.fr/causes-medicales-de-deces/interroger-les-donnees-de-mortalite)</footnote>
In Europe, female life expectancy converged somewhat from 2009 to 2019: in general, countries with low life expectancy made more progress (Figure 4.3). For France, female life expectancy was high in 2009 and increased less than the EU-27 from 2009 to 2019 (+0.9 years, compared to +0.4 years for French women).

In Europe, male life expectancy has converged very little from 2009 to 2019 (Figure 4.3). For France, male life expectancy was slightly above the EU average in 2009 and increased by 1.9 years, slightly less than the EU-27 from 2009 to 2019 (+2.1 years).

The impact of the health crisis on the differences in life expectancy between European countries is currently difficult to assess (Ourliac, 2021).
<table><caption>Figure 4.2 - Life expectancy at birth by country in 2019</caption><thead><tr><th colspan="2">total</th></tr></thead><tbody><tr><td>Spain</td><td>84.0</td></tr><tr><td>Switzerland</td><td>84.0</td></tr><tr><td>Italy</td><td>83.6</td></tr><tr><td>Sweden</td><td>83.2</td></tr><tr><td><strong>France</strong></td><td><strong>83.0</strong></td></tr><tr><td>Norway</td><td>83.0</td></tr><tr><td>Ireland</td><td>82.8</td></tr><tr><td>Netherlands</td><td>82.2</td></tr><tr><td>Belgium</td><td>82.1</td></tr><tr><td>Finland</td><td>82.1</td></tr><tr><td>Austria</td><td>82.0</td></tr><tr><td>Portugal</td><td>81.9</td></tr><tr><td>Greece</td><td>81.7</td></tr><tr><td>Slovenia</td><td>81.6</td></tr><tr><td>Denmark</td><td>81.5</td></tr><tr><td><strong>EU27</strong></td><td><strong>81.3</strong></td></tr><tr><td>Germany</td><td>81.3</td></tr><tr><td>Czech Republic</td><td>79.3</td></tr><tr><td>Albania</td><td>79.1</td></tr><tr><td>Turkey</td><td>79.1</td></tr><tr><td>Estonia</td><td>79.0</td></tr><tr><td>Croatia</td><td>78.6</td></tr><tr><td>Poland</td><td>78.0</td></tr><tr><td>Slovakia</td><td>77.8</td></tr><tr><td>Northern Macedonia</td><td>76.6</td></tr><tr><td>Armenia</td><td>76.6</td></tr><tr><td>Azerbaijan</td><td>76.6</td></tr><tr><td>Lithuania</td><td>76.5</td></tr><tr><td>Hungary</td><td>76.5</td></tr><tr><td>Serbia</td><td>76.0</td></tr><tr><td>Latvia</td><td>75.7</td></tr><tr><td>Romania</td><td>75.6</td></tr><tr><td>Bulgaria</td><td>75.1</td></tr><tr><td>Georgia</td><td>74.1</td></tr><tr><td>Ukraine</td><td>73.4</td></tr></tbody></table>

<table><thead><tr><th colspan="2">male</th></tr></thead><tbody><tr><td>Switzerland</td><td>82.1</td></tr><tr><td>Sweden</td><td>81.5</td></tr><tr><td>Italy</td><td>81.4</td></tr><tr><td>Norway</td><td>81.3</td></tr><tr><td>Spain</td><td>81.1</td></tr><tr><td>Ireland</td><td>80.8</td></tr><tr><td>Netherlands</td><td>80.6</td></tr><tr><td><strong>France</strong></td><td><strong>79.9</strong></td></tr><tr><td>Belgium</td><td>79.8</td></tr><tr><td>Austria</td><td>79.7</td></tr><tr><td>Denmark</td><td>79.5</td></tr><tr><td>Finland</td><td>79.3</td></tr><tr><td>Greece</td><td>79.2</td></tr><tr><td>Germany</td><td>79.0</td></tr><tr><td>Portugal</td><td>78.7</td></tr><tr><td>Slovenia</td><td>78.7</td></tr><tr><td><strong>EU27</strong></td><td><strong>78.5</strong></td></tr><tr><td>Albania</td><td>77.6</td></tr><tr><td>Czech Republic</td><td>76.4</td></tr><tr><td>Turkey</td><td>76.4</td></tr><tr><td>Croatia</td><td>75.5</td></tr><tr><td>Northern Macedonia</td><td>74.7</td></tr><tr><td>Estonia</td><td>74.5</td></tr><tr><td>Slovakia</td><td>74.3</td></tr><tr><td>Azerbaijan</td><td>74.3</td></tr><tr><td>Poland</td><td>74.1</td></tr><tr><td>Serbia</td><td>73.4</td></tr><tr><td>Hungary</td><td>73.1</td></tr><tr><td>Armenia</td><td>73.1</td></tr><tr><td>Romania</td><td>71.9</td></tr><tr><td>Bulgaria</td><td>71.6</td></tr><tr><td>Lithuania</td><td>71.6</td></tr><tr><td>Latvia</td><td>70.9</td></tr><tr><td>Georgia</td><td>69.8</td></tr><tr><td>Ukraine</td><td>68.4</td></tr></tbody></table>

<table><thead><tr><th colspan="2">female</th></tr></thead><tbody><tr><td>Spain</td><td>86.7</td></tr><tr><td><strong>France</strong></td><td><strong>85.9</strong></td></tr><tr><td>Switzerland</td><td>85.8</td></tr><tr><td>Italy</td><td>85.7</td></tr><tr><td>Portugal</td><td>84.8</td></tr><tr><td>Finland</td><td>84.8</td></tr><tr><td>Sweden</td><td>84.8</td></tr><tr><td>Ireland</td><td>84.7</td></tr><tr><td>Norway</td><td>84.7</td></tr><tr><td>Slovenia</td><td>84.5</td></tr><tr><td>Belgium</td><td>84.3</td></tr><tr><td>Greece</td><td>84.2</td></tr><tr><td>Austria</td><td>84.2</td></tr><tr><td><strong>EU27</strong></td><td><strong>84.0</strong></td></tr><tr><td>Germany</td><td>83.7</td></tr><tr><td>Netherlands</td><td>83.7</td></tr><tr><td>Denmark</td><td>83.5</td></tr><tr><td>Estonia</td><td>83.0</td></tr><tr><td>Czech Republic</td><td>82.2</td></tr><tr><td>Poland</td><td>81.9</td></tr><tr><td>Turkey</td><td>81.8</td></tr><tr><td>Croatia</td><td>81.6</td></tr><tr><td>Lithuania</td><td>81.2</td></tr><tr><td>Slovakia</td><td>81.2</td></tr><tr><td>Albania</td><td>80.7</td></tr><tr><td>Latvia</td><td>80.1</td></tr><tr><td>Hungary</td><td>79.7</td></tr><tr><td>Armenia</td><td>79.7</td></tr><tr><td>Romania</td><td>79.5</td></tr><tr><td>Azerbaijan</td><td>78.9</td></tr><tr><td>Bulgaria</td><td>78.8</td></tr><tr><td>Northern Macedonia</td><td>78.6</td></tr><tr><td>Serbia</td><td>78.6</td></tr><tr><td>Georgia</td><td>78.4</td></tr><tr><td>Ukraine</td><td>78.3</td></tr></tbody></table>

Source: Eurostat, extracted on 15 July 2021,
https://ec.europa.eu/eurostat/fr/web/main/data/database
<caption>Figure 4.3 - Life expectancy at birth in 2019 and life expectancy gain 2009-2019 by country</caption>

<figure><img src="image_25.png" /><figcaption>Female</figcaption></figure>

<figure><img src="image_26.png" /></figure>

Source: Eurostat.
Six questions addressed mortality trends:

* Q14 on the long-term horizon of life expectancy (baseline scenario);

* Q15 on the low and high variants to be retained on life expectancy;

* Q16 on the evolution of short-term life expectancy in the pandemic context;

* Q17 on the gender gap in life expectancy;

* Q18 on the cohort effect for the 1941-1955 generations.

Finally, Q19 was a table to be filled in to summarize and quantify the previous answers.

## WHAT TARGET FOR LIFE EXPECTANCY IN THE BASELINE SCENARIO?

From a quantitative point of view, 14 experts gave their opinion.

<table><caption>Figure 4.4 - Experts' views on life expectancy at birth in 2070</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">2070</th></tr><tr><th>female</th><th>male</th></tr></thead><tbody><tr><td rowspan="4">researchers</td><td>5</td><td>Jean-Francois LÉGER</td><td>88.1</td><td>83.5</td></tr><tr><td>7</td><td>Gilles PISON</td><td>87.0</td><td>84.0</td></tr><tr><td>12</td><td>Laurent TOULEMON</td><td>92.0</td><td>89.0</td></tr><tr><td>13</td><td>Arkadiusz WISNIOWSKI</td><td>90.5</td><td></td></tr><tr><td rowspan="3">OSS</td><td>14</td><td>Didier BLANCHET, CSR Pensions</td><td>90.5</td><td>86.5</td></tr><tr><td>20</td><td>Anthony MARINO and Amandine BRUN-SCHAMMÉ, COR</td><td>90.0</td><td>87.1</td></tr><tr><td>22</td><td>Sylvain PAPON, Insee</td><td>90.0</td><td>86.0</td></tr><tr><td rowspan="5">NSI</td><td>28</td><td>Katarzyna GÓRAL-RADZISZEWSKA, Statistics Poland</td><td>89.0</td><td>85.0</td></tr><tr><td>30</td><td>Raymond KOHLI, FSO, Switzerland</td><td>90.5</td><td>87.5</td></tr><tr><td>31</td><td>Lena LUNDKVIST, Statistics Sweden</td><td>89.7</td><td>87.6</td></tr><tr><td>35</td><td>Astri SYSE and Michael THOMAS, Statistics Norway</td><td>92.0</td><td>90.0</td></tr><tr><td>37</td><td>Livia VARGA and Zoltan CSANYI, KSH, Hungary</td><td>90.5</td><td>87.5</td></tr><tr><td rowspan="2">anonymous</td><td></td><td>anonymous 1</td><td>90.5</td><td>87.5</td></tr><tr><td></td><td>anonymous 2</td><td>88.9</td><td>82.9</td></tr><tr><td></td><td></td><td>average</td><td>89.9</td><td>86.5</td></tr><tr><td></td><td></td><td>minimum</td><td>87.0</td><td>82.9</td></tr><tr><td></td><td></td><td>maximum</td><td>92.0</td><td>90.0</td></tr></tbody></table>
<table><caption>Figure 4.5 - Experts' position on the reference period for extending mortality rates</caption><thead><tr><th></th><th>rather 2000-2019</th><th>rather 2010-2019</th><th>rather 2015-2019</th><th>other</th></tr></thead><tbody><tr><th>researchers</th><td>2</td><td>1</td><td>1</td><td>3</td></tr><tr><th>OSS</th><td>1</td><td>7</td><td>0</td><td>1</td></tr><tr><th>NSI</th><td>4</td><td>3</td><td>0</td><td>4</td></tr><tr><th>anonymous</th><td>1</td><td>2</td><td>0</td><td>1</td></tr><tr><th>total</th><td>8</td><td>13</td><td>1</td><td>9</td></tr></tbody></table>

Among the 14 experts who spoke quantitatively, the average life expectancy in 2070 is 89.9 years for women and 86.5 years for men. The minimum is 87.0 years and the maximum 92.0 years for women and 82.9 years and 90.0 years for men.

Thirteen experts, a majority of them, favour a 10-year period (2010-2019) to extend the trends in mortality rates (8, 14, 19, 20, 21, 22, 23, 24b, 30, 32, 34, 2 anonymous). Eight experts favour a longer period (20 years, from 2000 to 2019). One expert (5) prefers a shorter period.

The arguments in favour of a 10-year period are as follows:

* Sylvain Papon of INSEE (22) thinks that the increase in life expectancy will not regain the pace observed in the second half of the 20th century: « Tant qu'une nouvelle innovation médicale ne viendra pas bouleverser l'état des choses, il est à craindre que la hausse de l'espérance de vie ne retrouve pas le rythme observé lors de la seconde moitié du XXème siècle. Je pense ainsi qu'utiliser la moyenne des 10 dernières années est plus prudent. »

* Grégoire Rey (8) believes that there is no major trend identified that would allow us to think that life expectancy will resume its rise: « Sur les 60 dernières années, la situation a rarement été aussi incertaine qu'aujourd'hui. Il n'y a pas de tendance majeure identifiée qui permettrait de penser que l'espérance de vie va reprendre sa hausse. Je privilégierais une tendance sur 10 ans »

* For Anthony Marino and Amandine Brun-Schammé, COR (20), the decline in mortality has become, at almost all ages, less rapid during the recent period 2013-2018 than during the previous period 1998-2013. The only exception concerns the 50-65 age group, the "plateau generations": « La baisse de la mortalité est devenue, à presque tout âge, moins rapide durant la période récente 2013-2018 que durant la période antérieure 1998-2013. La seule exception concerne la tranche d'âge de 50 à 65 ans, « générations palier », qui s'est d'abord traduit par une baisse ralentie de la mortalité vers 55-60 ans durant la période 1998-2013 puis par une baisse ralentie - voire une hausse - de la mortalité vers 65 ans durant la période 2013-2018. Ainsi le ralentissement récent de la progression de l'espérance de vie correspond à un effet de période, qui affecte la plupart des générations au cours de la période récente. Une période relativement récente, 2010-2019, pourrait ainsi être retenue. Cette tendance semblerait conduire au scénario bas actuel, qui deviendrait de fait le scénario central
et conduirait donc en 2070 à réviser à la baisse l'espérance de vie à la naissance de 3 ans pour les femmes comme pour les hommes. »

The arguments in favour of a 20-year period are as follows:

* Gilles Pison (7) suggests considering the longest period, which makes it possible to smooth out the effect of "accidents: « Comme période de référence pour la tendance, je suggère de considérer la période la plus longue, ce qui permet de lisser l'effet des « accidents » - canicule de 2003, grippes saisonnières meurtrières lors de quatre des six derniers hivers. »

* Laurent Toulemon (12) would be conservative: « Je serais conservateur : l'Insee a fait un effort pour remonter ses hypothèses après des décennies de pessimisme, autant ne pas baisser trop les projections pour l'espérance de vie. »

* Vianney Costemalle of the Drees (16) wishes to avoid prolonging conjunctural effect: « On peut penser qu'elle va continuer à augmenter et que la santé sera une préoccupation de plus en plus majeure de la société. Pour prolonger mieux vaut prendre une longue période sur le passé (20 ans) pour éviter de prolonger des effets conjoncturels. »

Jean-François Léger (5), who advocates a short recent period, believes that as we approach the limit values of human life span, annual gains should therefore a priori continue to decrease, unless spectacular discoveries are made : « La croissance de l'espérance de vie a ralenti ces dernières années, ce qui est normal. À mesure que l'on approche des valeurs limites de la durée de vie humaine, les gains annuels devraient donc a priori continuer de diminuer, sauf découvertes spectaculaires. »

Carlo Giovanni Camarda (2) would prefer "to use probabilistic and data-driven methods". "À moins que quelqu'un ne soit vraiment spécialisé sur le cas français, il est difficile de « deviner » les valeurs de l'espérance de vie future, et tout choix peut toujours être considéré comme subjectif. »

For Jean-Marie Robine (9) thinks that it becomes difficult to read the trends, especially over short periods such as the last ten years : « de nombreux prévisionnistes prévoient pour l'avenir une accélération des vagues de chaleur avec le réchauffement climatique ou des épidémies avec la globalisation des échanges. Entre une tendance dont la pente est moins forte pour la croissance de l'espérance de vie féminine et des fluctuations annuelles de mortalité plus fortes et plus fréquentes, il devient difficile de lire les tendances surtout sur des courtes périodes comme par exemple les dix dernières années. Le point clef pour la poursuite de la croissance de l'espérance de vie (surtout féminine) repose sur notre capacité à gérer la fragilité les personnes très âgées (+ de 85 ans). L'avis des biologistes et des gériatres me
semble donc essentiel pour pouvoir argumenter les différentes hypothèses que l'on peut faire pour le futur. »

Olga Poetzsch and Felix zur Nieden, Destatis, Germany (33) use different reference periods, one that reflects the long-term trend and one that reflects the short-term trend as well as a combination of both: "We consider different reference periods to derive different assumptions - one that reflects the long-term trend (e.g. 50 years) and one that reflects the short-term trend (e.g. years 10) as well as a combination of both".

Gwenaël Podesta, DGEC (23) would appreciate if INSEE could provide some information on the impact of climate change on mortality: « il serait appréciable que l'INSEE apporte quelques éléments sur l'impact du changement climatique sur la mortalité. A minima, une hypothèse d'un réchauffement de +2°C (borne supérieure de l'accord de Paris sur le climat, et hypothèse du Plan national d'adaptation) ou +3°C (trajectoire découlant des objectifs actuels des pays) pourrait être faite pour estimer l'impact sur la mortalité »

THE HEALTH CRISIS AND THE EVOLUTION OF LIFE EXPECTANCY IN THE SHORT AND MEDIUM TERM

<table><caption>Figure 4-6 Expert opinion on the level of short-term life expectancy</caption><thead><tr><th></th><th>2022=2019</th><th>2021, 2022 or 2023 = return to recent trend</th><th>Return to 2019 level after 2022</th><th>2022= faster than recent trend</th><th>Other or don't know</th></tr></thead><tbody><tr><th>researchers</th><td>1</td><td>5</td><td>0</td><td>1</td><td>3</td></tr><tr><th>OSS</th><td>0</td><td>4</td><td>1</td><td>0</td><td>3</td></tr><tr><th>NSI</th><td>1</td><td>4</td><td>1</td><td>0</td><td>4</td></tr><tr><th>anonymous</th><td>2</td><td>3</td><td>0</td><td>0</td><td>1</td></tr><tr><th>total</th><td>4</td><td>16</td><td>2</td><td>1</td><td>11</td></tr></tbody></table>

Sixteen experts, i.e. a very large majority are in favour of a return of life expectancy in 2022 (sometimes in 2021 or 2023) to the level obtained by extending the mortality rates according to the recent trend before 2020 (4, 5, 6, 7, 12, 15, 20, 22, 23, 28, 30, 31, 36, 3 anonymous). One expert (9) thinks that the 2022 level will be higher than that obtained by extending the recent trend.

Four experts (8, 26, 2 anonymous) believe that life expectancy in 2022 will only reach its 2019 level. Two experts (21, 32) think that the 2019 level will only be reached again after 2022.

Why might life expectancy in 2022 return to the level obtained by extending mortality rates according to the recent trend?
* Because of the harvest effect (6, 15, 20, 22). Thus for Pascale Breuil (Cnav, 15): « Si l'on suppose que la crise sanitaire s'est accompagnée d'un effet moisson important (personnes particulièrement fragiles), et sous réserve que la crise prenne fin, on peut supposer qu'il y aurait un effet rebond puis que les effets de la crise s'estomperaient sauf si vous avez des éléments objectifs indiquant un impact durable sur l'ensemble de la population. »

* Better vaccination coverage (7, 36): « Une hypothèse à prendre en compte éventuellement aussi : suite à l'épidémie de Covid-19, une adhésion plus importante en France aux vaccinations de manière générale. Il pourrait en résulter que lors des futurs épisodes de grippe saisonnière, la surmortalité serait plus faible qu'au cours des derniers épisodes, du fait d'une meilleure couverture vaccinale » according to Gilles Pison (7),

* Less transmission of other diseases with the wearing of masks according to Anthony Marino and Amandine Brun-Schammé of the COR (20)

Why life expectancy in 2022 could be at its 2019 level ?

* Continuation of degraded care during the peaks of Covid-19, economic crisis : « Il peut y avoir des conséquences à la hausse (un renforcement des soins, un effet moisson qui s'étale, une faible circulation des autres maladies infectieuses), et à la baisse (suite de soins dégradés pendant les pics de Covid-19, crise économique). Très difficile de savoir lesquelles prendront le dessus, je mettrai le niveau de 2019. » according to Grégoire Rey (8)

Why would the 2019 level not be reached until after 2022?

* For the experts from DG Trésor (21), we should consider a pessimistic scenario that would foresee a slow rise in vaccination, and the potential ineffectiveness against certain variants: « Pour 2022, il paraît encore optimiste de fixer un retour au niveau 2019. Sous réserve des données encore à intégrer, jusqu'à l'échéance la plus tardive qui précède la fixation des hypothèses, il nous paraît plus central de prolonger légèrement les tendances sur 2022 voire 2023. Ces scénarii pourront évoluer en fonction des éléments à venir et en fonction du recul acquis au moment de la fixation des hypothèses, mais il parait sain d'envisager un scénario pessimiste qui prévoirait une montée en charge lente de la vaccination, et la potentielle inefficacité contre certains variants. »

Why might life expectancy in 2022 be higher than the level obtained by extending mortality rates according to the recent trend ? :

* For Jean-Marie Robine (9), we can think by analogy with the year 2004 (very strong gains), which followed the 2003 heat wave: « On peut réfléchir par analogie avec
l'année 2004 qui suivit la canicule de 2003. Les très forts gains de longévité observés en 2004 dépassent largement la somme des récupérations sur 2003 (effet de moisson ou autres) et des gains attendus pour 2004. Un saut de longévité s'est produit en 2004 qui peut expliquer un certain ralentissement des années suivantes. Ce saut de longévité s'explique sûrement par un changement dans l'attention portée aux personnes très âgées et à leurs besoins, surtout dans les EHPAD, après la canicule de 2003. Verra-t-on en 2022 et les années suivantes un nouveau changement dans notre attention portée aux personnes très âgées et aux personnes fragiles entrainant un saut similaire de longévité ? C'est très probable, surtout et à nouveau dans les EHPAD. »

VARIANTS: HIGH AND LOW LIFE EXPECTANCY TARGETS

<table><caption>Figure 4-7 Expert opinion on the gap in life expectancy at birth in 2070 compared to the baseline assumption</caption><thead><tr><th></th><th>rather 3.0 years</th><th>rather 3.5 years</th><th>no preference</th><th>based on the past</th><th>rather confidence interval</th><th>other</th></tr></thead><tbody><tr><th>researchers</th><td>0</td><td>2</td><td>0</td><td>2</td><td>2</td><td>3</td></tr><tr><th>OSS</th><td>1</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>NSI</th><td>1</td><td>2</td><td>3</td><td>2</td><td>0</td><td>0</td></tr><tr><th>anonymous</th><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td><td>0</td></tr><tr><th>total</th><td>2</td><td>11</td><td>3</td><td>6</td><td>3</td><td>3</td></tr></tbody></table>

Eleven experts, i.e. the majority (7, 12, 14, 20, 21, 22, 24b, 30, 35, 2 anonymous) prefer a range of +/-3.5 years for the low and high variants of life expectancy in 2070, which is justified by an increase in uncertainty. Thus, for Didier Blanchet (14), of the CSR-Retraites is mostly symbolic, but may be a way of indicating that we think that uncertainty has increased: « passer à +/- 3,5 est surtout symbolique à vrai dire, mais peut constituer une façon de marquer qu'on pense que l'incertitude s'est accrue ». For DG Trésor (21), the Covid-19 health crisis, or the increase in heat waves argue for an expansion « à long terme, on note des événements probables pouvant avoir un impact significatif sur cette valeur, la crise sanitaire Covid-19 par exemple, ou encore l'augmentation des épisodes de canicule à prévoir en lien avec le réchauffement climatique. Ces éléments plaident pour un élargissement. »

Two experts (25, 26) prefer an interval of +/- 3 years, in order to facilitate comparisons with previous projections (26). For Pascale Breuil of the Cnav (15), in general, a certain inertia in the assumptions is to be preferred: « De manière générale, en cas d'incertitude, une certaine inertie dans les hypothèses est à privilégier, afin que les ajustements se fassent progressivement tous les cinq ans et d'éviter tout sur-ajustement, susceptible d'avoir des conséquences fortes (en matière de pilotage des retraites par exemple). »

Six experts (4, 19, 31, 33, and 2 anonymous) suggest the idea of using the past to define the low and high variants, rather than retaining a predefined interval, which is summarised by
Fanny Janssen (4): "for example for the high variant it could consist of an extrapolation of the modal age at dying. For the low variant it could consist of an extrapolation of the more recent trends in age-specific mortality". In Sweden (31), the new projections are based on the average of the 25 highest and lowest variations in life expectancy over the period 1970-2020, "we use the mean value of the 25 observations of highest and lowest annual change in life expectancy in the 51-year period 2070-2020."

Gautier Maigne of France Stratégie (19) also suggests planning for two work/breakdown scenarios: stable life expectancy (at the 2019 level), falling life expectancy: « prévoir deux scénarios de travail / rupture : stabilité de l'espérance de vie (au niveau 2019), baisse de l'espérance de vie. »Three experts (2, 13, 27) were in favour of a confidence interval rather than a predefined interval.

For Jean-François Leger (5), the low scenario would be the maintenance of life expectancies at their current level or almost" with a high scenario of +2.5 years for women and +3.7 years for men : « le scénario bas serait le maintien des espérances de vie à leur niveau actuel ou presque » avec un scénario haut à + 2,5 ans pour les femmes et +3,7 ans pour les hommes. »

Vianney Costemalle of the Drees (16) pleads for round figures +/-3 years or +/- 4 years to facilitate communication.

THE GENDER GAP IN LIFE EXPECTANCY

From a quantitative point of view, 13 experts gave their opinion.

<table><caption>Figure 4-8 Expert opinion on the gender gap in life expectancy in 2070</caption><tbody><tr><td rowspan="3">researcher</td><td>5</td><td>Jean-Francois LÉGER</td><td>4.6</td></tr><tr><td>7</td><td>Gilles PISON</td><td>3.0</td></tr><tr><td>12</td><td>Laurent TOULEMON</td><td>3.0</td></tr><tr><td rowspan="3">OSS</td><td>14</td><td>Didier BLANCHET, CSR Pensions</td><td>4.0</td></tr><tr><td>20</td><td>Anthony MARINO and Amandine BRUN-SCHAMMÉ, COR</td><td>2.9</td></tr><tr><td>22</td><td>Sylvain PAPON, Insee</td><td>4.0</td></tr><tr><td rowspan="5">NSI</td><td>28</td><td>Katarzyna GÓRAL-RADZISZEWSKA, Statistics Poland</td><td>4.0</td></tr><tr><td>30</td><td>Raymond KOHLI, FSO, Switzerland</td><td>3.0</td></tr><tr><td>31</td><td>Lena LUNDKVIST, Statistics Sweden</td><td>2.1</td></tr><tr><td>35</td><td>Astri SYSE and Michael THOMAS, Statistics Norway</td><td>2.0</td></tr><tr><td>37</td><td>Livia VARGA and Zoltan CSANYI, KSH, Hungary</td><td>3.0</td></tr><tr><td rowspan="3">anonymous</td><td></td><td>anonymous 1</td><td>3.0</td></tr><tr><td></td><td>anonymous 2</td><td>6.0</td></tr><tr><td></td><td>average</td><td>3.4</td></tr><tr><td></td><td></td><td>minimum</td><td>2.0</td></tr><tr><td></td><td></td><td>maximum</td><td>6.0</td></tr></tbody></table>
<table><caption>**Figure 4-9 Expert opinion on the** decrease of the gender gap in life expectancy</caption><thead><tr><th></th><th>Same pace as before 2021</th><th>Slower pace than in the past</th><th>other</th></tr></thead><tbody><tr><th>researchers</th><td>7</td><td>0</td><td>1</td></tr><tr><th>OSS</th><td>6</td><td>1</td><td>0</td></tr><tr><th>NSI</th><td>10</td><td>0</td><td>1</td></tr><tr><th>anonymous</th><td>2</td><td>0</td><td>0</td></tr><tr><th>total</th><td>25</td><td>1</td><td>2</td></tr></tbody></table>

Among the 13 experts who expressed themselves quantitatively, the average F/M gap in life expectancy at birth in 2070 is 3.4 years, with a minimum of 2 years and a maximum of 6 years.

Almost all the experts (4, 5, 6, 7, 8, 9, 12, 14, 19, 20, 21, 22, 24b, 26, 27, 28, 30, 32, 33, 34, 35, 36, 37) are in favour of bringing life expectancy for women and men closer together at the same pace as in the past, despite the stagnation of the F/M gap observed from 2017 to 2020.

One of the arguments is the high level of the life expectancy gap in France compared to other OECD or Western European countries. Thus, for Gautier Maigne, France Stratégie (19) « On peut aussi noter que l'écart français d'espérance de vie reste relativement important par rapport à celui constaté dans beaucoup d'économies avancées. ». Similarly, for Kim DUSTAN, Statistics New Zealand (27), "a gap of 6 years is relatively high by OECD standards."

Another argument concerns smoking-related deaths, which Fanny Janssen (4) believes should decrease more rapidly among men than among women: "A further reduction of the sex gap in life expectancy would be logical, especially given that smoking-attributable mortality among French men is expected to continue to decline at a fast rate, whereas smoking-attributable mortality among French women - which was still increasing till recently - will likely decline at a smaller rate (Janssen et al., 2020). Also, within Western Europe, the sex gap in life expectancy in France is currently higher than in other countries, and for a large part affected by sex differences in smoking-attributable mortality (Janssen, 2020)."

On the other hand, for Gautier Maigne, France Stratégie (19), this continued narrowing seems relevant because of social factors (the differences in participation in the labour market, and even in the type of jobs held, are likely to narrow further) and societal and cultural factors: « Ce prolongement du rapprochement semble pertinent sous l'effet :

De facteurs sociaux, notamment si l'on considère que dans un temps long, les écarts de participation au marché du travail, voire de typologie d'emplois occupés, ont bien vocation à se réduire encore.

De facteurs sociétaux et culturels, une partie de la surmortalité masculine étant liée à des conduites à risque (addictions, accidents) parfois associées, soit à une certaine représentation
« viriliste » de la masculinité, soit d'ailleurs au mal être que celle-ci peut induire (fort écart de taux de suicide entre les sexes). Le gradient dans le recours au système de soins et à la prévention semble partiellement relever des mêmes causes. Or les « codes » et l'inconscient collectif semblent quand même plutôt tendre à une forme d'homogénéisation progressive. »

Carlo Giovanni Camarda (2) advises work on what is commonly called consistent mortality prediction : « les travaux sur ce que l'on appelle communément la prévision cohérente de la mortalité. À partir de (Li & Lee, 2005), une pléthore d'articles ont été publiés à ce sujet. J'essaierais d'utiliser ces approches pour, au moins, comparer les résultats data-driven avec ceux que vous proposez. »

THE GENERATIONS 1941-1955

Throughout their adult life, the evolution of the mortality of the so-called "plateau" generations, born from 1941 to 1955, is less favourable than that of the previous or following generations. Over the recent period, we observe for these generations a quasi-stagnation of mortality for men, and a slight increase in mortality for women. The cohort effect will be taken into account in this population exercise. The question was: "It is assumed that female mortality will be stable in the future for the "plateau" generations. What do you think?"

<table><caption>Figure 4-10 Expert opinion on mortality trends for females belonging to "plateau" generations</caption><thead><tr><th></th><th>No cohort<br>effect<br>taken into<br>account</th><th>Rather stable<br>mortality for<br>women</th><th>Mortality change<br>less favourable<br>for women than<br>for men</th><th>Rather lower<br>mortality for<br>women</th><th>No<br>preference</th><th>other</th></tr></thead><tbody><tr><th>researchers</th><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>2</td></tr><tr><th>OSS</th><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><th>NSI</th><td>0</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><th>anonymous</th><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>total</th><td>2</td><td>9</td><td>2</td><td>1</td><td>1</td><td>4</td></tr></tbody></table>

Nine experts, i.e. a majority (8, 14, 15, 16, 21, 28, 34, 35), are in favour of a stable mortality for women of the "plateau" generations.

Two experts (5,7) think that the inclusion of the generation effect is not necessary, for simplification reasons (5,7) and because the effect does not play a long-term role in the projection results (5).
If the mortality of women of the "plateau" generations stagnates, this amounts to taking the same assumption (a stagnation) for women and men of these generations. For this reason, Gautier Maigne of France Stratégie (19) prefers a less favourable evolution of mortality for women than for men for these generations: « Cette question 18 sous-entend qu'on suppose en fait une stagnation pour chaque sexe, et qu'il n'y aurait pour ces générations aucune convergence hommes-femmes, ce qui ne semble pas avoir été le cas jusqu'ici, pas plus que pour les autres générations. ». Similarly, for Livia VARGA and Zoltan CSANYI, KSH, Hungary (37), "the mortality of men and women may vary, so there is no need to make the same assumptions".

On the contrary, for Raymond Kohli, SFO, Switzerland, mortality could also decrease for these generations: « Il ne me paraît pas impossible que la mortalité diminue passablement à nouveau aussi pour ces générations. ».

Carlo Giovanni Camarda (2) suggests that INSEE could propose an age + time approach in which cohort effects could be incorporated (see Renshaw & Haberman, EMI 2006).

Laurent Toulemon (12) would not envisage a stagnation of mortality which continues until the very old ages for these cohorts: « A priori je ferai une projection tous sexes confondus, puis une projection de la différence entre hommes et femmes et je verrais ce que ça donne pour les femmes, mais je n'envisagerais pas une stagnation de la mortalité qui se prolonge jusqu'aux très grands âges pour ces cohortes »
BASELINE ASSUMPTION: DECLINE IN MORTALITY RATES AT THE SAME PACE AS OVER THE LAST DECADE, WITH CONSIDERATION/INTEGRATION OF A COHORT EFFECT

The baseline assumption is that mortality will continue to decline at the same pace as in the past until the projection horizon. This requires defining a reference period for the past. A majority of experts (13) have chosen a 10-year period, from 2010 to 2019, in order to take into account the slowdown in life expectancy observed since 2014. A significant number of experts (8) favour a longer period of 20 years, i.e. 2000-2019, in order to smooth out conjunctural and cohort effects, to avoid major changes in assumptions compared to the previous projection exercise and to take into account the fact that health will be a major concern for society.

**We use a 10-year period for the past, 2010-2019.** This significantly changes the baseline assumption compared to the previous exercise, but it is closer to the baseline assumption of Eurostat and the UN. We also propose to **smooth out the age-specific evolutions in order to avoid conjunctural and cohort effects.**

Other methods have been tested in order to compare the level of projected life expectancy in 2070. They all lead to a downward revision of the level of life expectancy in 2070 compared to previous INSEE projections (Box 7). However, life expectancy in 2070 would be at a slightly higher level if the 2000-2019 slope were used to extend the mortality rates, instead of the 2010-2019 slope.
<figure><img src="image_27.png" /><figcaption>**Figure 4.11** - Annual change in mortality rates by age and period</figcaption></figure>

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013, France from 2014 onwards.

Sources: INSEE, population estimates and vital statistics.
<figure><img src="image_28.png" /><figcaption>Figure 4.12 - Mortality rates in 1950, 2019 and 2070</figcaption></figure>

Note: Mortality rates are trend logistically adjusted beyond 105 years for women and 101 years for men.

Coverage: Metropolitan France in 1950, France in 2019 and 1970.

Sources: INSEE, population estimates and vital statistics in 1950 and 2019, Population projection 2021-2070 in 2070.
The application without smoothing of the rate of decline of mortality rates over the period 2010-2019 would lead to a distortion of the curve of mortality rates by age in 2070 (Figure 4.12): female mortality would be durably the same at age 65 and at age 75, which is unlikely. This is due to the long-term repercussion of the stagnation of mortality, characteristic of the “plateau” generations (1941-1955) which is prolonged. It is therefore necessary to smooth the rates in order to avoid such distortions.

Over a very long period, for example from 1950 to 2019, the annual change in mortality rates is almost stable from 10 to 80 years. For men, a constant annual change in mortality rates from age 10 to 80 of -2.0% by 2070 is therefore assumed. This corresponds to the average annual change observed at all these ages from 2010 to 2019. For women, we assume an annual change in mortality rates from age 6 to 80 of -1.2% by 2070, which corresponds to the average annual change observed at all these ages from 2010 to 2019. Before 10 years for men and 6 years for women, as well as after 80 years for all, we retain the annual change by age between 2010 and 2019 (Figure 4.11).

Mortality stagnates in adulthood for the "plateau" generations, born from 1941 to 1955 (Blanpain, 2020). We retain as a baseline assumption that the cohort effect will continue until the very high ages. The mortality rates for women increase slightly around age 65 over the 2010-2019 period. A majority of experts validated the assumption of a stable mortality rate for women in the future for the "plateau" generations. However, two experts indicated that a greater decline in the rates for men than for women should be taken, in order to continue the narrowing of the gender gap in life expectancy for the "plateau" generations.

**For the "plateau" generations, we use the average annual change 2010-2019 at age 69,** the last age at which the "plateau" generations are fully observed over the period. This makes it possible to retain a lower slope for women than for men (Figure 4.11) and to have a quasi-stagnation of mortality rates for women (-0.1% per year).
<table><caption>Figure 4.12 - Annual change in mortality rates used for the projections (baseline assumption)</caption><thead><tr><th></th><th>Age 0-5 years</th><th>Age 6-80 years old</th><th>Age 81 or older</th></tr></thead><tbody><tr><td><strong>Female</strong><br>'Plateau' generations<br>(1941-1955)</td><td></td><td>-0.2%, slope at age 69 between 2010 and 2019</td><td></td></tr><tr><td>Other generations</td><td>from -0.6 to -1.3% / year<br>annual slope of the rate<br>at each age between<br>2010 and 2019</td><td>-1.3%<br>average slope at all<br>ages from 6 to 80<br>between 2010 and 2019</td><td>from -1.3% to -0.3% / year<br>annual slope of the rate at<br>each age between 2010<br>and 2019</td></tr><tr><td><strong>Male</strong><br>'Plateau' generations<br>(1941-1955)</td><td>Age 0-9</td><td>Age 10-80</td><td>Age 81 or older</td></tr><tr><td>Other generations</td><td>from -0.4 to -2.0% / year<br>annual slope of the rate at<br>each age between 2010<br>and 2019</td><td>-2.0%<br>average slope at all<br>ages from 10 to 80<br>between 2010 and 2019</td><td>from -2.0% to -0.1% / year<br>annual slope of the rate at<br>each age between 2010<br>and 2019</td></tr></tbody></table>

Taking into account the cohort effect slows down life expectancy slightly, while smoothing the annual evolution of mortality rates increases it slightly. This leads to similar life expectancies before and after smoothing and applying the generation effect (Figure 4.13).
<figure><img src="image_29.png" /><figcaption>Figure 4.13 – Female and male life expectancies at birth and at age 65</figcaption></figure>

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013, France from 2014 onwards.

Sources: INSEE, population estimates and vital statistics and population projection 2021-2070.
The gender gap in life expectancy decreased from 1994 to 2017 and remained stable from 2017 to 2020 at around 6.0 years. Almost all the experts recommended extending the convergence of life expectancy between women and men. The chosen assumptions lead to this convergence at birth and at age 65 (Figure 4.14).

<figure><img src="image_30.png" /><figcaption>Figure 4.14 - Gender gap in life expectancies at birth and at age 65</figcaption></figure>

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013, France from 2014 onwards.

Sources: Eurostat, Europop2019; UN, World Population Prospects 2019; INSEE, population estimates, vital statistics and population projection 2021-2070.

BASELINE ASSUMPTION: IN 2022, LIFE EXPECTANCY RETURNS TO ITS 2010-2019 GROWTH TRAJECTORY

For the short-term trend, a large majority of experts have indicated that life expectancy should return to the level it would have had without the Covid-19 epidemic around 2022. The arguments are better vaccination coverage, the harvest effect (fragile people died during the epidemic and this reduces deaths in the following years), and the development of physical distance.

**We propose a return of life expectancy in 2022 to its 2010-2019 growth path,** i.e. +0.25 years of life expectancy for women and +0.45 years for men between 2019 and 2022 (Figure 4.9). The rates in 2021 are estimated from observed deaths from January to August 2021 by sex and an estimate of deaths for the months of September to December 2021 by sex. For the end of 2021, an increase in the number of deaths compared to what would be expected in the absence of an epidemic<fnref n="15" /> has been estimated with the help of DREES (Ministry of Health).

<footnote n="15">¹⁵ Expected deaths in the absence of an epidemic are calculated according to the method detailed in the INSEE blog [Blanpain, 2021]</footnote>
Indeed, since the beginning of the Covid-19 epidemic, the differences between observed and expected deaths are correlated with the volumes of hospital deaths of patients with Covid-19. Therefore, modelling Covid-19 deaths until the end of the year allowed us to estimate the excess of deaths over the expected based on demographic trends. To do this, from the Covid-19 deaths observed on 15 September, an initial projection to the end of September could be deduced from the observed evolution of the incidence rate of Covid-19 cases. This rate is in fact a good leading indicator of deaths approximately two weeks ahead. Next, for the period from October to December, a baseline scenario for the evolution of Covid-19 deaths was based on assumptions of a continued decline in deaths in October before a stabilisation of their level by the end of 2021. In this scenario, considered the most likely, the number of deaths in 2021 is thus estimated at 648,000 deaths, including 322,000 female deaths and 326,000 male deaths, i.e. a life expectancy at birth of 85.54 years for women and 79.48 years for men (compared with 85.1 for women and 79.1 for men in 2021).

At the same time, alternative scenarios were explored, without being considered as the most probable: a scenario leading to slightly lower numbers of deaths, in the event of a continuation of the decline in deaths until the end of the year; a scenario leading to significantly higher deaths, if in particular a variant appeared that largely escaped the protective effect of vaccination.
<figure><img src="image_31.png" /><figcaption>Figure 4.15 - Life expectancies at birth and at age 65</figcaption></figure>

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013, France from 2014 onwards.

Sources: INSEE, population estimates and vital statistics and population projection 2021-2070.

LIFE EXPECTANCY IN 2070: 90.0 YEARS FOR WOMEN, 87.5 YEARS FOR MEN AND VARIANTS AT +/- 3.5 YEARS.

For the variants, a majority of experts (11) retained +/- 3.5 years of life expectancy at birth in 2070 compared to the baseline assumption. A significant number of experts (6) suggest using the past as a basis for defining the variants rather than retaining a pre-determined gap.

**We retain +/- 3.5 years of life expectancy at birth in 2070 compared to the central assumption.** For comparison, the 95% confidence interval of the Lee-Carter model is -4.0 years and + 2.8 years for women and -2.9 years and + 2.3 years for men in 2070 (Box 7).
For the low and high assumptions, the annual evolution of the mortality rates by age is slowed down or accelerated at each age. This makes it possible to have mortality rates that are symmetrical with respect to the baseline assumption at all ages. For the low assumption, an annual evolution of mortality rates continuing the trend taken over a shorter and less favourable period, 2014-2019, was tested. But this leads to more chaotic evolution of mortality rates, inconsistent at certain ages compared to those of the baseline assumption.

In order to take into account the uncertainty related to the Covid-19 epidemic, a deviation of +/- 0.5 years in 2022 is retained from the baseline assumption. This corresponds to a slightly faster divergence than in the 2016 projections, where the divergence was +/-0.3 years for women and +/- 0.4 years for men in 2016. For comparison, the confidence interval of the Lee-Carter model is +/- 1.1 year for women and -1.0 year and + 0.9 year for men in 2022 (Box 7).

As a working scenario, a projection with a constant life expectancy assumption, equal to its 2019 level, is also carried out.
<figure><img src="image_32.png" /><figcaption>Figure 4.16 Life expectancies at birth and at age 65</figcaption></figure>

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013, France from 2014 onwards.

Sources: Eurostat, Europop2019; UN, World Population Prospects 2019; INSEE, population estimates and vital statistics, population projection 2013-2070 and population projection 2021-2070.
<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013, France from 2014 onwards.

Sources: Eurostat, Europop2019; UN, World Population Prospects 2019; INSEE, population estimates and vital statistics, population projection 2013-2070 and population projection 2021-2070.
The baseline assumption leads to a downward revision of life expectancy at birth compared with the previous projections (Figure 4.16): -3.0 years for women and -2.6 years for men in 2070, i.e. the level of the low assumption of the previous projections.

The assumptions of the new projections are relatively close to those of Eurostat and the UN (slightly lower for women and slightly higher for men). The projections of these organizations were made in 2019 and 2020 and were able to take into account the recent slowdown in life expectancy.

<table><caption>Figure 4.17 Projected life expectancy in 2070</caption><thead><tr><th rowspan="2"></th><th colspan="2">At birth</th><th colspan="2">At 65</th></tr><tr><th>female</th><th>male</th><th>female</th><th>male</th></tr></thead><tbody><tr><th>INSEE 2016</th><td>93.0</td><td>90.1</td><td>28.8</td><td>26.1</td></tr><tr><th>UN 2019</th><td>91.5</td><td>86.1</td><td>27.7</td><td>23.6</td></tr><tr><th>EUROSTAT 2020</th><td>90.9</td><td>86.2</td><td>27.0</td><td>23.7</td></tr><tr><th>INSEE 2021</th><td>90.0</td><td>87.5</td><td>26.6</td><td>24.6</td></tr></tbody></table>
Box 7 - Comparison of projected life expectancy in 2070 using different methods

Three methods were tested.

A Lee-Carter model (Lee-Carter, Carter, 1992):

$$ \ln \mu_{xt} = \alpha x + \beta x * kt, $$

- μxt represents the instantaneous death rate at year t and age x,

- αx is the age-specific component x

- βx reflects the sensitivity of instantaneous mortality at age x to the general evolution of kt

- kt describes the evolution of mortality in year t,

The kt were extended using a "Multivariate Random Walk with Drift" time series model over the period 2010-2019.

An age, period, cohort model (Cairns et al., 2009):

$$ \ln \mu_{xt} = \alpha x + kt + \gamma(t-x), $$

- μxt represents the instantaneous death rate at year t and age x,

- αx is the age-specific component x

- kt describes the evolution of mortality in year t,

- γ(t-x) is the cohort-specific component (t-x)

The kt were extended using a "Multivariate Random Walk with Drift" time series model over the period 2010-2019.

The γ(t-x) were extended using an ARIMA(1,1,0) model with drift

An alternative method:

- Extension of mortality rates by sex and age at the rate observed over the 20-year period, 2000-2019.

These three methods are compared to the **previous INSEE projections** published in 2016, to the **Eurostat projections** published in 2020, to the **UN projections** published in 2019, and lastly to the method retained for the **new 2021 projections**.

Methods for projecting mortality

<table><thead><tr><th rowspan="2"></th><th colspan="2">Life expectancy at birth</th><th rowspan="2">Population in 2070<br>(in millions)</th><th rowspan="2">Ageing ratio<br>65+/20-64 (%)</th></tr><tr><th>female</th><th>male</th></tr></thead><tbody><tr><td>INSEE 2016</td><td>93.0</td><td>90.1</td><td>76.4</td><td>57.4</td></tr><tr><td>UN 2019</td><td>91.5</td><td>86.1</td><td>66.6<br>(Metropolitan France)</td><td>57.6<br>(Metropolitan France)</td></tr><tr><td>EUROSTAT 2020</td><td>90.9</td><td>86.2</td><td>69.4</td><td>56.8</td></tr><tr><td>Lee-Carter</td><td>90.1</td><td>87.0</td><td>68.2</td><td>57.2</td></tr><tr><td>Age, period, cohort</td><td>90.5</td><td>89.0</td><td>69.0</td><td>59.1</td></tr><tr><td>Slope 2000-2019</td><td>91.7</td><td>88.5</td><td>68.6</td><td>57.8</td></tr><tr><td><strong>INSEE 2021</strong></td><td><strong>90.0</strong></td><td><strong>87.5</strong></td><td><strong>68.1</strong></td><td><strong>56.8</strong></td></tr></tbody></table>

The different methods all lead to a downward revision compared to previous INSEE projections published in 2016. Depending on the method, female life expectancy in 2070 is between 90.0 and 91.7 years, and male life expectancy is between 86.1 and 88.5 years.
Box 8 - Confidence interval with the Lee-Carter model

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

Coverage: Metropolitan France until 1993, France excluding Mayotte from 1994 to 2013, France from 2014 onwards.

Sources: INSEE, population estimates and vital statistics and population projections 2021-2070.

The 95% confidence intervals obtained with the Lee-Carter method on life expectancy at birth in 2070 are :

- 86.1 to 92.9 years for women, i.e. -4.0 years and + 2.8 years compared to the median hypothesis

- 84.1 to 89.3 years for men, i.e. -2.9 years and + 2.3 years compared to the median hypothesis

The 95% confidence intervals obtained with the Lee-Carter method on life expectancy at birth in 2022 are :

- 84.7 to 86.9 years for women, i.e. -1.1 years and +1.1 years compared to the median hypothesis

- 79.2 to 81.1 years for men, i.e. -1.0 years and +0.9 years compared to the median hypothesis
# PART 4: THREE ASSUMPTIONS ABOUT NET MIGRATION

## A. RECENT CHANGES IN NET MIGRATION IN FRANCE

The baseline assumption for the 2016 projections was a positive annual net migration of 70,000 people from 2013 to 2070. The average over the last five years is slightly higher, but it is slightly lower over ten years (Figure 4.1). Above all, net migration appears to be increasingly volatile: high in 2013 and especially in 2017, it was low in 2009-2011, 2014 and 2015 (Figure 4.2).

<table><caption>Figure 4.1 - Average balance over different periods</caption><thead><tr><th></th><th></th><th>France <sup>1</sup></th><th>Metropolitan France</th></tr></thead><tbody><tr><td>1 year</td><td>2017</td><td>+ 155</td><td>+ 167</td></tr><tr><td>5 years</td><td>average 2013-2017</td><td>+ 78</td><td>+ 91</td></tr><tr><td>10 years</td><td>average 2008-2017</td><td>+ 62</td><td>+ 75</td></tr><tr><td>30 years old</td><td>average 1988-2017</td><td>+ 71</td><td>+ 74</td></tr><tr><td>50 years old</td><td>average 1968-2017</td><td>-</td><td>+ 72</td></tr></tbody></table>

1: France excluding Mayotte until 2013 and France from 2014 onwards.

Source: INSEE, population estimates and vital statistics.

<figure><img src="image_37.png" /><figcaption>Figure 4.2 - Net migration since 1982</figcaption></figure>

Note: 2018 to 2020 points are provisional, estimated by default as the average of the years 2015-2017.

Coverage: France excluding Mayotte until 2013 and France from 2014 onwards

Source: INSEE, population estimates and vital statistics.
In previous projections, a breakdown of net migration into three population categories was proposed: persons born in France, persons born French abroad and immigrants. Insee now publishes the inflows and outflows each year, distinguishing between two categories: immigrants and non-immigrants (Insee, 2021c). Immigrant inflows and non-immigrant outflows increased from 2006 to 2016 at roughly the same rate. But **non-immigrant outflows seem to have become quite volatile** and would explain, for example, the high level of net migration in 2013 and moreover in 2017 (Figure 4.3). There were an estimated 275,000 non-immigrant outflows in 2016 and 152,000 in 2017. Therefore, while non-immigrant net migration was around -150,000 in 2014, 2015 and 2016, it is around -50,000 in 2017. In such a context, it is even more difficult to predict the evolution and the fluctuations of net migration are likely to be more important because outflows and inflows will compensate each other in some years, and in others not.

<caption>Figure 4.3 – Inflows, outflows and net migration, immigrants and non-immigrants</caption>

<figure><img src="image_38.png" /><figcaption>(b) Net migration</figcaption></figure>

<figure><img src="image_39.png" /></figure>

Coverage: France excluding Mayotte until 2013 and France from 2014.

Source: Insee, population, inflows and outflows estimates.

Another notable recent change is that female net migration has been significantly higher than male net migration in recent years. Before 1975, net migration was predominantly male. The change in migration policy in 1975 led to a reduction in labour migration and an increase in migration for family reunification. This explains the strong increase in the share of women: 78% of net migration over the period 1975-1989. The following period saw a rebalancing: on average, 56% of net migration was made up of women between 1990 and 2005, and 55% between 2006 and 2009. The share of women then increased over the period 2010-2013, and even more so over the latest known years (2014-2017): this new imbalance seems to be linked more to outflows than inflows, and to the fact that the increase in outflows concerns men
more than women. Extending recent trends over 4 years (2014-2017) would lead to a **positive net migration, female at 83% and 17% male**, i.e. out of 70,000 people, 12,000 men and 58,000 women (**Figure 4.4**). This represents a difference of 46,000 per year, i.e. after 50 years there will be more than 2 million more women than men, compared with 500,000 in the previous projections, since the annual migration surplus of 70,000 people (baseline assumption) was made up of 40,000 women and 30,000 men.

By age, the structure has changed significantly compared to the 1975-2005 period: the slightly negative net migration between 18 and 24 years of age has become much more marked over the 2006-2017 period, with a continued strong gap between men and women (**Figure 4.5**). Between the ages of 25 and 29, the increase in male exits is also apparent, as net migration becomes negative. In the 30-44 age group, on the other hand, the net migration increases and is more balanced between men and women. This does not mean that there is a massive influx of immigrants between the ages of 30 and 44 (most of them enter at younger ages), but that there are few outflows at these ages.

<figure><img src="image_40.png" /><figcaption>Figure 4.4 - Share of women in the average annual net migration</figcaption></figure>

Coverage: Metropolitan France until 1998, France excluding Mayotte from 1999 to 2013, France from 2014 onwards.

Source: INSEE, population estimates and vital statistics.
<figure><img src="image_41.png" /><figcaption>Figure 4.5 - Net migration by sex and age</figcaption></figure>

Coverage: Metropolitan France until 1998, France excluding Mayotte from 1999 to 2013, France from 2014 onwards.

Source: INSEE, population estimates and civil status statistics.

All in all, it seems even more difficult than usual to formulate a hypothesis on **future variations given the significant uncertainties on the recent past and the near future** linked to the at least apparent volatility of outflows, the particularly high net migration for 2017 and the difficulty of anticipating the influence of the health crisis linked to covid-19 on international migration in the short and medium term.

In 2020 and 2021, or even 2022, the components of net migration have all been and will undoubtedly be affected: both inflows and outflows from the national territory have been significantly slowed down, or even made almost impossible at certain times. As an indication of the disruption to immigrant inflows, fewer first residence permits were issued in 2020 (Figure 4.6). However, the drop in inflows could be offset by a drop in outflows, which makes it very difficult to estimate net migration for 2020 and 2021. It should be remembered that net migration estimate for the year 2020 published in the 2020 demographic report by INSEE (Papon, Beaumel, 2021) is obtained by arithmetical average of the last three known figures on net migration (2015, 2016, 2017), without taking into account any possible impact of the pandemic.
<figure><img src="image_42.png" /><figcaption>Figure 4.6 - Issuance of first residence permits by type of reasons</figcaption></figure>

Source: MI- DSED data as of 15 June 2021 <fnref n="16" />

Coverage: Non-EEA countries, Metropolitan France.

B. THE POSITION OF EXPERTS ON INTERNATIONAL MIGRATION

The questionnaire consisted of 4 questions and a table to be filled in on the evolution of migration:

* One question asked about the baseline assumption for the average annual net migration (Q20)

* One on high and low variants (Q21)

* One was on the interpretation of the net migration by distinguishing flows by category (immigrants and non-immigrants)

* One on the evolution of female and male net migration, female net migration being increasingly higher than male one.

Finally, the table proposed to give values to be retained as baseline, high and low assumptions for the net migration at some key dates between 2021 and 2070.

<footnote n="16">¹⁶ https://www.immigration.interieur.gouv.fr/Info-ressources/Etudes-et-statistiques/Statistiques/Chiffres-cles-sejour-visas-eloignements-asile-acces-a-la-nationalite/Derniers-chiffres-cles</footnote>
The experts agree on the **high level of uncertainty regarding migration**. Several experts mentioned it earlier in the questionnaire in response to questions on the method or variants.

The experts are therefore wondering how to communicate this uncertainty, so as not to let
users think that the net migration could be permanently fixed at the value set as a baseline
assumption. A first, fairly simple way of doing this is to present the values as averages or sums
over several years (21, 33): Misha Khodabin, Hamza Zakraoui and Samuel Ménard (DG Trésor,
21) suggest presenting data over five years with a positive cumulative net migration of
350,000 persons. Kim Dunstan (27) thinks that this uncertainty is more visible when using
stochastic models. Antonio Argueso (INE, Spain, 26), citing the example of Spain, suggests that
we insist on the fact that projections are not forecasts: "Migratory movements in Spain have
also undergone major changes<fnref n="17" /> that are difficult to foresee. For this reason, we have
emphasized in the dissemination of the results that the projections are a statistical simulation
of what would happen under certain assumptions, instead of a forecast of the future".

For Jean-François Léger, this justifies treating the net migration separately from the other
components, in particular in order to emphasise the main result of the projection in his view:
a natural balance that is becoming negative, a situation that is unprecedented in France: " les
hypothèses relatives aux migrations ne devraient pas être intégrées a priori dans l'exercice de
projection démographique mais devraient découler des résultats de la simulation
« naturelle » de la population française et « d'objectifs » démographiques. De ce fait, la prise
en compte des migrations répondrait à la question suivante : compte tenu des résultats
auxquelles conduisent les hypothèses en matière de mortalité et fécondité, quel devrait être
le solde migratoire annuel moyen pour que l'accroissement de la population française soit de
x personnes ou de y %.”

LONG-TERM EVOLUTION: CENTRAL HYPOTHESIS AND VARIANTS

This uncertainty is explained by the **diversity of factors** likely to influence migration, as Misha Khodabin, Hamza Zakraoui and Samuel Ménard (DG Trésor, 21) point out: " Bien que ce chiffre [70 000] puisse paraître conservateur, les tendances migratoires sont particulièrement difficiles à prédire du fait de la multiplicité des dynamiques en jeu (démographiques, économiques, environnementales, politiques). [...] Il nous semble cependant, autant que l'on puisse en présager, que les déterminants structurels des migrations à destination des pays avancés ne devraient pas être fondamentalement bouleversés."

<footnote n="17">¹⁷ The graph provided by Antonio Argueso (INE, Spain, 26) in support of his answer shows that the volatility of the Spanish net migration is extremely high and linked to the economic situation: the net migration was strongly negative following the 2009 crisis (around 250,000 in 2013) but it is estimated to be +450,000 recently.</footnote>
Experts point out the importance of demographic balances (ageing in Europe, population growth in emigration countries) which suggest that inflows could continue to increase (5, 22, 30). This demographic situation is also likely to affect migration policy decisions (5, 30). For example, Raymond Kohli (OFS, Switzerland, 30) states that in Switzerland, projections assume a higher net migration rate until 2030-2040 to compensate for the retirement of the baby boomers.

The experts stress the importance of analysing long-term trends (34, 31), particularly in terms of migration policy (31, 35). Lena Lundkvist (Statistics Sweden, 31) cites the Swedish example of a massive intake of refugees in 2016, followed by a restrictive immigration policy.

The context of global warming is also cited as a potentially important factor in changing migration flows (5, 13, 22).

**Most of the experts who express themselves quantitatively on the baseline assumption support the proposed assumption of 70,000 (10 experts out of 14), which is a prudent assumption, in line with previous projections (6, 14), in line with past observations (18, 20) and similar to that made in international projection exercises (21).**

Three experts support the assumption of annual net migration of 80,000 people (12, 13, 19). Jérôme Lê of Insee (17) stands out by proposing a balance of 130,000: "[70 000] me semble trop faible. Même s'il est très fluctuant, depuis 15 ans, le solde migratoire présente quand même des tendances bien marquées. [...] Une hypothèse centrale a minima de +100 000 me semble plus réaliste".

7 experts take up the **variants we proposed at 20,000 and 120,000**, i.e. +/-50,000 compared to the baseline assumption. This fairly wide range is considered useful in view of the uncertainty (18, 35). Laurent Toulemon suggests widening it (0-160), as does Jérôme Lê of Insee (20-200). On the other hand, one expert questions the usefulness of such a wide range (37).

Several experts suggest abandoning the **symmetry of the variants** because, in their view, the uncertainty on the upside is greater (13, 17, 22). For Sylvain Papon (Insee, 22), the low assumption should be raised: "Les « pressions » vont plutôt dans le sens d'une hausse que d'une baisse. L'hypothèse basse me paraît peu crédible. Elle peut évidemment être vraie de façon conjoncturelle (on l'a vu à deux reprises dans les années 50 et à deux reprises dans les années 70 – on l'observera peut-être suite à la fermeture des frontières de 2020) mais maintenir ce niveau pendant 50 ans est une hypothèse irréaliste. Un plancher à 30 000 ou 40 000 serait plus plausible. L'hypothèse haute, bien que vraiment élevée, paraît plus atteignable." Arkadiusz Wisniowski (13) suggests instead raising the high assumption: "The high one, though, I believe could be set higher, e.g. at the values observed in the recent past, such as 150,000. This would demonstrate the potential effect of increased attractiveness of France as a destination country."