Departmental Classification According to Relative Income per Adult

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The Uneven Spatial Distribution of Population According to Age

The long-term studies conducted so far could not analyze diﬀerentiated trends in the spatial distri-bution of the population by age group. The data available in this study allow this analysis. Overall, I find that the trends of population aged 0 to 19, 40 to 49 and 50 to 64 are the same as the trends observed for the whole population. On the other hand, they are significantly diﬀerent for women aged 20 to 29, 30 to 39, 65 to 79, and 80 and over. Figure 1.3.5 reveals the inequality indicators for
women aged 30 to 39 and 65 to 79.
According to women aged 30 to 39, the main diﬀerence with national trends comes from the period 1990–2014, during which the share of the first decile increased while the share of the third and fourth deciles stagnated. The evolution of the first decile was almost completely due to the increase of Seine’s share (Paris and surrondings). Consequently, France is facing a new phase of “hyper-centralization thwarted” concerning this age group. It gathers individuals at the heart of their working lives, usually with high salaries and stable work situations. This result supports Combes et al. (2011)’s paper, which show that the Gini index of the spatial distribution of tertiary value added followed an inverted U-shape from 1860 to 1982 and increased from 1982 to 2000. The parenthesis in the aggressive spatial planning policy counducted during the 1960s could explain this process. Following works on endogenous growth, economists and politicians were aware of how economically strong regions have to be supported in order to redistribute income to poorer geographic areas (see, for example, Jayet et al (2006) and Davezies (2008)).
For the older ones, changes are diﬀerent for two reasons. Overall, the Gini index increased only by 60% over the period 1851–2014, compared to 110% for the whole population. From 1851 to 1910, this index remained stable, hiding contrary evolutions: both the share of the first decile and the share of the less densely populated départements expand to the detriment of the second, third and fourth deciles. Thus, there was a deconcentration of the elderly population in France from 1851 to 1900, unique in my statistics of population by age. Between 1900 and 1968, the hyper-centralization thwarted was at work: the share of the 80% least densely populated km2 fell, while the share of the first decile increased and that of the second stagnated. Finally, between 1968 and 2014, the multipolarization appeared: the share of the most densely populated territories in old people decreased, while the one of the départements of the second, third and fourth deciles increased.
These results are quite the same for men (See Appendix 1.5.2 for the corresponding graphs). Beyond the evolution of the spatial distribution of population by age, it is interesting to know which
are the most unevenly distributed populations, and whether these relative positions have evolved over time. Figure 1.3.6 presents the age profile of the Gini index for women and several dates along the 150 years of this study.
First of all, inequalities have increased for all age groups, which is consistent with what was presented earlier. These increases are significant: if one consider the age group 0–19, the Gini index went from a value of 0.25 in 1851 to a value of 0.48 at the end of the period. With regard to the age profile of the Gini index, Figure 1.3.6 reveals that the population density inequalities in 1851 were similar for all age groups between 0 and 64-year-old, with a Gini value of about 0.25. Beyond these ages, the values were growing. In other words, women aged 65 and over were much more unequally spread than the others. This specificity of the oldest ages gradually disappeared during the end of the 19th century: the inequalities observed for this age group became the weakest from 1901 onwards. Conversely, at this date, the flat profile between age 0 to 65 disappeared too. The profile reveals an inverted U-shape, more and more pronounced over time, where the most uneven age group is 20–29.
For the period 1851–1900, this profile can be explained as follows. Spatial inequalities of population density were the same from 0 to 64-year-old because the working age groups remained in the territory Notes: Computations based on the population of women. P90-100 refers to the share of national population who lived in the 10% of km2 with the highest density values. All inequality indicators are weighted by km2 and normalized by 1851 values. “30–40” refers to age 30 to 39 (40 excluded). Sample includes 90 départements.
where they were born. Even if this observation is less true concerning the Paris region (the inverted U shape is visible since 1851 for Seine), it applies to the French territory as a whole. In this context, the strong inequalities concerning women aged 65 and over did not come from a choice of location but from spatial diﬀerences in mortality. In fact, the departmental shares were related to the mortality rates at these ages: there were fewer elderly people where their mortality rates were very high. This is the case for example in Bretagne and in the Alps, where life expectancy was very low compared to the national average. For the period 1861–1865, female life expectancy at birth was 22 in Basses-Alpes and 28 in Finistère according to Bonneuil (1997). Conversely, they were overrepresented in a broad band linking Normandie and Meuse: in Yonne, Aube, Meuse and Eure, life expectancy at birth for the same period was 55, 53.5, 52 and 50 years, respectively. Subsequently, this phenomenon progressively disappeared: spatial mortality inequalities decreased strongly from 1881 according to Bonnet and d’Albis (2018). They name this phenomenon “Centennial Convergence”. These evolutions explain the specificity of Figure 1.3.5: the share of the least densely populated territories in elderly people increased initially because of the survival of a larger number of individuals at these ages. So there was no rural counter-exodus. For these reasons, I name that diﬀerentiated spatial concentration of elderly people “undergone concentration”.
The inverted U-shaped profile that appeared in 1901 can be explained diﬀerently. From this date,
the rural exodus was powerful: the 20 to 39-year-olds moved to the most densely populated areas to find a job, leaving rural areas that no longer had to oﬀer them. This migration did not exist among older people who remained in their home territories, slowing down the unequal process for these age groups. On the opposite of the “undergone concentration” experienced by the elderly, this inverted U-shape profile results from a chosen process of territorial polarization. 20 to 39-year-old were strongly attracted by the capital and then by second-tier cities during the period 1901–2014. This was less the case for other age groups, whose location choices were less constrained. This is particularly true among older people today: their income stream is not conditioned by their location, as it is the case for the youngers. They can settle everywhere in the territory, and especially in the rural départements, less densely populated and with attractive amenities. This age profile is accentuated nowadays due to a sharp rise in inequality among 20 to 29-year-old. This age group is today the most unevenly distributed. As such, the 10% most densely populated km2 host 43% of women aged 20 to 29. For those aged 65 and over, this figure is only 32%.

Diﬀerences in Departmental Structures of Population by Age

National age structures of population have changed since 1851: at that time, the women aged 65 and over accounted for only 7% of the female population, compared with 21% today. For those under 20, these shares are 35% and 23%, respectively. These results are diﬀerent at the departmental level. Figure 1.3.7 shows that these diﬀerentiated evolutions have impacted the spatial distribution of the population for each age group.
At first I am interested in the overall dissimilarity of population age structures. I use the Kullback-Leibler divergence calculated at the national level (equation 1.2.2), namely K LDN at. The inequality indicators presented until now weighted the départements by their area. Here, the weight assigned to each département is the same. Figure 1.3.8 presents the evolution of K LDN at, for the population of women. Its evolution has not been monotonous over the period. To describe it, I define three sub-periods: 1850–1930, where the index remained broadly stable, 1930–1950 where it declined sharply, and 1950–2014 when it increased by 85% and reached a 30% level higher than the 1850s one. In order to provide an explanation for these contrary variations over the past 80 years, I use the additivity property of K LDN at. One can break down this indicator in order to get the contribution of each département in its variation between two years. This was done for the two sub-periods 1930–1950 and 1950–2014 identified in Figure 1.3.8. I map the contributions of each département in Appendix 1.5.4.
Between 1930 and 1950, France experienced a territorial homogenization. Going into more detail, the departmental age structures homogenized due to four distinct geographical areas (in red on Figure 1.5.2): Seine, Bretagne, Yonne and its neighboring départements, the South-West along the Garonne valley. To better understand what may have caused this homogenization, I represent in Figure 1.3.9 the distortion indices according to four départements, which represent these geographical areas. In order to avoid the issues of small numbers in the computations of departmental distortion indices, “65–79” and “80 and over” age groups have been gathered. Moreover, these distortion indices have been smoothed over 5 years.
Figure 1.3.9 reveals that the decline in dissimilarity between 1930 and 1950 is caused by two main factors. The first is the decrease of the overrepresentation of the oldest in Center-East and South-West (Yonne and Tarn-et-Garonne), coupled with the increase in the share of these age groups in Bretagne (Finistère for example). This phenomenon is partly explained by the process of convergence of health conditions, very powerful during the inter-war period (Bretagne quickly caught up, as shown by d’Albis and Bonnet, 2018). This process could be explained by migratory flows too. Bonnet (2018a)’s work show that migrations occured from the the whole North to the South and more specifically to the South-West. It is likely that internal migrations during the war mainly concerned active age groups, who were in the capacity to move. Consequently, the overrepresented young people in Bretagne moved to the South-West where they were underrepresented. The second reason for the decline in overall dissimilarity is specific to Seine: the distorsion index according to women aged 20 to 29 fell sharply, losing 20 points over the period. This decline began before the Second World War, which suggests that the conflict is not directly the cause.

READ Laboratory test of the Mach-Zehnder co-phasing technique

Conclusion

In this paper, I have linked Bonnet (2018b)’s database to census results for the period 1851–1900 in order to analyze the spatial distribution of the French population for more than 150 years. I have showed that the population was more unevenly distributed in 2014 than it was in the mid 19th century. Moreover, this increase in inequality occurred in three phases. The first, which I have named “hyper-centralization”, extends from 1851 to 1900 and was due to the increase of population share living in the 10% most densely populated km2, at the expense of all others. In particular, Seine’s share doubled in 50 years to reach 9.1%. The second phase, which I have named “hyper-centralization thwarted”, concerns the period 1901–1968. During this period, the population was increasingly concentrated in the 10% most densely populated km2, but this was no longer at the expense of the second, third and fourth decile. In these territories, the population grew at the ame pace than the national population. Finally, between 1968 and 2014, there was a phase of multipolarization: the share of both the most densely populated and the less densely populated areas decreased, while the share of départements hosting second-tier cities such as Bordeaux, Nantes or Toulouse increased. During this period, the shrinking regions of the North-East were particularly vulnerable: the share of the population living there decreased both because of migrations and birth deficit.

Table of contents :

Part 1: Essays in French Demographic and Economic History
1 Spatial Distribution of Population by Age in France, 1851–2014 21
Abstract
1.1 Introduction
1.2 Data and Methods
1.2.1 Inequality Indicators
1.2.2 Aggregation of Data Sources
1.2.3 Unification of the Geographical Framework
1.3 Results
1.3.1 Evolutions of Departmental Densities of Population since 1851
1.3.2 The Three Phases in the Evolution of Spatial Distribution of Population
1.3.3 The Uneven Spatial Distribution of Population According to Age
1.3.4 Differences in Departmental Structures of Population by Age
1.4 Conclusion
1.5 Appendices
1.5.1 Map of the 90 French Départements Used to Calculate Inequalities
1.5.2 Spatial Distribution of Men Aged 30 to 39 and 65 to 79: Inequality indices
1.5.3 Evolution of the Distribution of Population by Age Group : Decomposition Method
1.5.4 Departmental Contributions in KLDNat Evolution
1.5.5 Distorsion indices in 1856, 1896, 1946 and 2011
2 Spatial Inequalities in French Life Expectancy, 1806–2014 51
Abstract
2.1 Introduction
2.2 Data and Methods
2.2.1 Data Sources
2.2.2 Geographical Scope
2.2.3 Indicators of Inequality
2.2.4 Convergence Indicators
2.3 Results
2.3.1 The Three Phases in Reduction of Spatial Inequalities of Life Expectancy
2.3.2 Role of Infant Mortality in Shrinking Spatial Inequalities
2.3.3 Major Changes in the Geography of French Longevity
2.4 Conclusion
2.5 Appendices
3 Spatial Inequalities of Income and Welfare in France, 1922–2014 75
Abstract
3.1 Introduction
3.2 Data and Methods
3.2.1 Existing Databases Used
3.2.2 Income per Adult between 1922 and 2014: Estimation Method
3.2.3 Income and “Mortality Adjusted Income”
3.2.4 Analysis of Spatial Income Inequalities
3.3 Results
3.3.1 The Three Phases in the Decrease of Spatial Inequalities of Income per Adult 83
3.3.2 The Virtuous Convergence of Income per Adult during the Second Half of the 20th Century
3.3.3 The Evolution of Spatial Inequalities of “Mortality Adjusted Income” per Adult
3.3.4 Changes in the Geography of Development in France
3.4 Conclusion
3.5 Appendices
3.5.1 Publications Used to Compute Departmental Incomes
3.5.2 Map of the 90 French Départements in 1967
3.5.3 Methodology Used to Compute “Mortality Adjusted Incomes”
3.5.4 Spatial Inequalities of Income Densities
3.5.5 Shrinking Regions: A Classification
3.5.6 Departmental Classification According to Relative Income per Adult
4 Beyond the Exodus of May-June 1940: Internal Flows of Refugees in France
Abstract
4.1 Introduction
4.2 Data and Methods
4.2.1 Departmental data
4.2.2 Framework of the Study
4.2.3 Estimation of Annual Departmental Populations and Internal Migrations
4.3 Results
4.3.1 The Global Consequences of the War
4.3.2 Annual Monitoring of Internal Population Migrations
4.4 Conclusion
4.5 Appendices
4.5.1 Map of the 86 French Départements
4.5.2 Cause of Death Classification
4.5.3 Evolutions of Departmental Populations by Component, 1939–1946
4.5.4 Yearly Variations of Population due to Migratory Movement
Part II: Data and Methods
5 Computations of French Lifetables by Département, 1901–2014 127
Abstract
5.1 Introduction
5.2 Sources
5.2.1 Deaths
5.2.2 Births
5.2.3 Censuses
5.3 Methods
5.3.1 HMD Protocol Methods
5.3.1.1 Raw Data Adjustments
5.3.1.2 Splitting Deaths into Lexis Triangles
5.3.1.3 Computations of Populations by Age at 1st January of each Year
5.3.1.4 Adjustment of Computed Mortality Rates
5.3.2 Specific Departmental Methods
5.3.2.1 Specific Methods Due to Data Quality
5.3.2.2 Specific Methods Due to the Two World Wars
5.3.2.3 Specific Methods Due to Territorial Changes
5.3.2.4 Specific Methods Due to Missing Data
5.3.3 Reliability of the Data and Comparison with Other Studies
5.4 Available Results and Discussion
5.4.1 Available Results
5.4.2 Discussion
5.4.2.1 Census Reliability
5.4.2.2 Interdepartmental Migrations
5.4.2.3 Domiciliation of Deaths during the Two World Wars
5.4.2.4 Small Département Figures
5.5 Conclusion
5.6 Appendices
5.6.1 Computations of Population on 1st January
5.6.1.1 Intercensal Survival
5.6.1.2 Precensal Survival Method
5.6.1.3 Extinct Cohorts Method
5.6.1.4 Survivor Ratio Method
5.6.2 Set of Different Lifetables
5.6.3 Census Adjustments
5.6.3.1 Distribution of Deaths of Unknown Age in 1901 Census
5.6.3.2 Addition of Age Group for Pre-1946 Censuses
5.6.3.3 Adjustment of Censuses by Cubic Splines
5.6.4 Estimates of Military Deaths during the Two World Wars
5.6.5 Estimates of Deportees
5.6.5.1 Born-abroad Deportees
5.6.5.2 French Deportees
5.6.6 Missing Data During the Two World Wars
5.6.6.1 Births and stillbirths
5.6.6.2 Deaths
5.6.7 Reorganization of Ile-de-France in 1968
5.6.8 Computations of 1st January Populations by Class of Départements
5.6.9 Sources of Raw Data
6 Computations of French Income Distributions by Département, 1960–2014 169
Abstract
6.1 Introduction
6.2 Data and Spatial Framework
6.2.1 Fiscal Data at the Departmental Level
6.2.2 Departmental Demographic Data
6.2.3 National Data
6.2.4 Spatial Framework
6.3 Methods
6.3.1 Period 1986–2014
6.3.1.1 Raw Fiscal Statistics Available
6.3.1.2 Taxable Income and Fiscal Income Distributions
6.3.2 Period 1960–1969
6.3.2.1 Computations of Total Tax Units by Département
6.3.2.2 Computations of Fiscal Income by Département
6.3.2.3 Computations of Fiscal Income Distributions for all Tax Units
6.3.3 Template of Fiscal Income Distributions by Département
6.4 Results
6.4.1 Fiscal Income per Adult
6.4.2 Intra-departemental Inequalities
6.4.3 Spatial Distribution of Tax Units Belonging to each Fractile
6.5 Conclusion
6.6 Appendices
6.6.1 Computations of Departmental Fiscal Incomes Using Regional Accounting
6.6.2 Supplementary Materials