Regional Inequality in France:
The Dynamic Role of Structural Change, 1860-2010
Joan R. Rosés
Department of Economic History
London School of Economics
Email: [email protected]
Mª Teresa Sanchis Llopis
Historia e Instituciones Económicas
Universitat de València
Email: [email protected]
Alfonso Díez Minguela
Historia e Instituciones Económicas
Universitat de València
Email: [email protected]
Abstract
This study explores the evolution of regional inequality in France from 1860 to 2010.
For this purpose, we have collected a novel dataset of Gross Domestic Product (GDP)
for 22 regions, broadly corresponding to the NUTS 2 level, and three economic sectors
(agriculture, industry, services). These data correspond to the following censuses:
1860, 1896, 1911, 1921, 1931, 1954, 1962, 1975, 1982, 1990, 1999 and 2010. Then,
we examine the long run evolution of regional GDP per worker in France, and analyse
the major forces of change. In doing so, we break down regional convergence into
three main components: (a) within-industry convergence, (b) labour reallocation, and
(c) between-industry convergence. We use France and Île-de-France, richest region
over the whole period, as our benchmarks. The preliminary results indicate that
regional income inequality in France followed an upward trend during the very early
stages of industrialisation, 1860-1896. In contrast, regional GDP per worker converged
mainly in the early 1900s and during 1954-1982. These findings go in line with the
traditional view of an inverted U-shaped relationship. Nevertheless, the late decades of
the twentieth century have witnessed a considerable upswing in regional inequality.
Moreover, while labour productivity convergence was mainly the result of withinindustry convergence and labour reallocation during in the 1954-1982, this process has
been reversed in the last period with the growth of the service sector. Our empirical
analysis thus provides ground to further analyse the role played by structural change,
sectoral specific technological change and regional and European policies, in the
shaping of France’s economic cohesion.
JEL CODE:
1. Introduction
One of the major concerns in the study of French economic growth is to determine
whether the process of modern economic growth has marked a big distance between
the Paris region and the rest of the country or otherwise this country has become
throughout the last two centuries in a more egalitarian economy.
In general, the study of modern economic growth and development reveals that as a
country develops and becomes more economically integrated and industrialised, the
distribution of economic activity becomes more unequal. The manufacturing and the
high value activities tend to concentrate in a few set of more advanced regions and the
rest of the country experienced a process of retardation, concentrating its activity in
lower productivity activities such as agriculture or more traditional manufacturing. As
productivity in the modern industries progress more quickly fuelled by technological
change and economies of scale, income per capita will tend to grow faster in the
industrializing regions than in the traditional ones. Hence, in the early stages of
modern economic growth regional income per capita inequality will increase. In the
case of France, several authors confirms that modern industrialization that begun
around 1830 was followed by an increase income inequality, not only between citizens
(Morrison and Snyder, 2000) but also between regions (Bazot, forthcoming).
However, as it has been observed in several countries this upward trend in regional
inequality tends to stop and turn into a downward trend at some point in time. This
phenomenon implies that regional income per capita inequality describes an inverted
U-shaped curve as described by Williamson(1965) for the United States.
In the present study we identify that the U-shaped curve began the downward trend in
France at the end of the 19th century, and since 1896 to 2010 France’s regional
inequality measured in terms of its 22 regions dropped significantly throughout the
20th century. We have estimated GDP regional data for 1911, 1921, 1954, 1975, that
added to those benchmark estimated by other authors let us to offer a richer picture
of French regional inequality. The preliminary results indicate that regional income
inequality in France followed an upward trend during the very early stages of industrialisation,
1860-1896. In contrast, regional GDP per worker converged mainly in the early 1900s and
during 1954-1982. These findings go in line with the traditional view of an inverted U-shaped
relationship. Nevertheless, the late decades of the twentieth century have witnessed a
considerable upswing in regional inequality that has recovered levels close to 1930.
In the last section of this study we realize a shift-share analysis to determine to what
extent the compression of regional inequality in France between 1896 and 1982 was
caused by the process of structural, trying to distinguish among the role played by
sectoral specific productivity convergence within industries, linked to technological
change, and the role played by labour reallocation between industries in the shaping
of France’s economic cohesion.
2. A new database on French regional per capita GDPs: methods and sources
In order to analyze the long term evolution of regional inequality in France throughout
the period 1860-2010 we have estimated new regional GDP figures for the years 1911,
1921, 1954, 1962 and 1975 and 1982. For the remaining years the data have been
collected from different well known sources. We use the Toutain’s series which covers
the employment and output-levels for each French department in 1860, 1896 and
1930 for manufacturing and services. These series have been published in the
appendix of Combes, Lafourcade, Thise and Toutain (2008). For these years, the figures
for agriculture have been obtained applying the Toutain (1992) distribution of
agricultural output by department to the agriculture national output estimated by
Toutain (1987) and published by the Groningen Growth and Development Centre in
the Historical National Accounts Database. For more recent decades (1990, 1999 and
2010) we have taken the regional GDP series published by the INSEE at NUTS-2 level.
The estimation of French per capita regional GDP is mainly based on the
methodology developed by Geary and Stark (2002). This departs from the basic
principle that the national per capita GDP is equal to the sum of all regions per capita
GDP. Algebraically the total GDP of the French economy is the sum of all regions GDPs:
𝑌𝑌𝐹𝐹𝐹𝐹 = ∑𝑖𝑖 𝑌𝑌𝑖𝑖
(1)
However, given that Yi by regions and departments is unknown, it will be proxied
according to the following equation:
𝑌𝑌𝑖𝑖 = ∑𝑗𝑗 𝑦𝑦𝑖𝑖𝑖𝑖 𝐿𝐿𝑖𝑖𝑖𝑖
(2)
Where yij is the average value added per worker in the i-region at the j-sector, and Lij is
the number of workers in each i-region at the j-sector. As we do not have data for yij
this value is proxied by taking the national output per worker (yj) for each sector
(agriculture, industry and services) and assuming that regional labour productivity in
each sector is reflected by its wage relative to the French average wage in this sector
(ωij/ωj). Then, the regional GDP will be given by:
𝜔𝜔
𝑌𝑌𝑖𝑖 = ∑𝑗𝑗 �𝑦𝑦𝑗𝑗 𝛽𝛽𝑗𝑗 � 𝜔𝜔𝑖𝑖𝑖𝑖�� 𝐿𝐿𝑖𝑖𝑖𝑖
𝑗𝑗
(3)
Where ωij is the wage paid in the region i in sector j, ωj is the French wage in each
sector j and βj is a scalar which preserves the relative region differences but scales the
absolute values. This coefficient makes that the new estimations of regional GDPs sum
up the national values and hence the new series constructed yearly from a myriad of
different sources present chronological continuity in current values.
Hence, in absence of regional output figures, Geary and Stark (2002) set a model of
indirect estimation based on wage income, which allows for an estimation of GDP by
region at factor cost, in current values. The basic data involved in this estimation
procedure are national output per worker by sector, nominal wages and active
population, by each sector and region. This methodology allows us to provide new
estimates not only of regional GDPs but also figures for the different industries
(agriculture, industry and services).
The data requested are an estimation of the national output by sector and figures on
active population by sector and region jointly with nominal wages disaggregated by
sector and region. The historical series of national output by sector (agriculture,
manufacturing and services) are taken from Toutain (1987) for the years 1860, 1896,
1911, 1921 and 1930 and from the National Accounts for post-1950 years and have
been taken from the Groningen Growth and Development Centre Historical National
Accounts Database.
The data on working population come from the national censuses of population which
provide detailed information about active population by sectors at different territorial
levels (departments, municipalities…) for every ten years, approximately1. Figures on
wages come from different sources. French statistics provide vast information on
wages in some specific years. For example, there are two complete surveys on salaries
for 1911 and 1962 that contain detailed information of salaries for different
municipalities at the department level (for example the chêf de lieux). The 1911 survey
collects data for 34 professions and the 1962 survey for 60 economic activities2. For
the remaining years there are some specific bulletins that recorded figures on salaries
for different working categories by departments and big municipalities. Specifically we
pick up these data from the bulletins of 1929 and 1937 in order to estimate by
interpolation the relative wages for 1921 and 1954. Figures for 1975 have been
estimated by the Geary and Stark methodology taken data of employment and salaries
from several publications of the INSEE belonging to the collection of regional studies 3.
3. Long-term patterns of regional income inequality: some stylized facts
1
The population censuses are the following. 1911, 1921, 1954, 1962, 1975 and 1982.
For 1911 we have used the wages survey: "Salaires et coût de l'existence: à diverses époques, jusqu'en
1910" which contains daily nominal wages (for males) in old francs for 34 male professions. We have
found a similar survey for 1962 ,"Recensement Industriel de 1963. Resultats pour 1962". It contains
Wages by region (Nuts-2) in thousands of new francs for 60 activities. In 1921 there are no data
available for regional wages. In this case the relative wages between regions have been calculated as a
weighted average of relative wages in 1911 and relative wages in 1929. We take the relative wages and
not the wages in levels because wages in 1911 are in old francs and wages in 1929 in new francs.
Additionally wages in 1911 are daily wages and in 1929 are hour wages. The source for wages in 1929 is
the "Bulletin de la statistique générale de la France et du service d'observation des prix", tome XIX,
fascicule II (Janvier-Mars, 1930). For 1954, again there is no data available on regional nominal wages.
In this case the relative wages have been calculated as a weighted average of relative wages in 1937 and
relative wages in 1962. Wages in 1937 from "Bulletin de la statistique générale de la France et du service
d'observation des prix", tome XIX, fascicule II (Janvier-Mars, 1938).
2
3
Muet, Bolton and Cozin (1970); Chanut and Trêca (1975); Chanut and Monfort (1978); Mary and Turpin
(1981); Donnellier, Malverney and Montlouis (1987).
3.1 Stylized facts: Inequality between regions and departments
To analyze the evolution of income inequality throughout the whole period 1860-2010
we present different descriptive statistics which provide some stylized facts on
regional GDP and GDP per capita. Figure 1 draws the Gini coefficient for the French
NUTS-2 regions 4 and Figure 2 represents the same index but weighted by region
population.
Figure 1. Gini coefficient and ER(0) for GDP per capita by region (N=22; current
French francs)
Figure 2. Population weighted Gini coefficient and ER(0) for GDP per capita by
region
(N=22; current French francs)
4
Administratively, France is divided in 22 regions which corresponds to the European Union NUTS-2
level of classification: Îlle de France, Champagne-Ardenne, Picardie, Haute-Normandie, Centre, BasseNormandie, Bourgogne, Nord-Pas-de-Calais, Lorraine, Alsace, Franche-Comté, Pays de la Loire, Bretagne,
Poitou-Charentes, Aquitaine, Midi-Pyrénées, Limousin, Rhône-Alpes, Auvergne, Languedoc-Roussillon,
Provence-Alpes-Côte d’Azur, Corse. The offshores regions (Guadeloupe, Martinique and Guyane) are not
taken into account for the analysis that is centered in the European territories.
Note: The coefficient is calculated through NUT-2 regions for GDP per capita.
Source: Own calculations, see the text.
The indexes represented in Figure 1 and 2 seem to confirm that the turnaround in
the well-stablished U-shaped curve of long-run regional inequality took place in
France at the end of the nineteenth century. Since then the path of income
inequality has shown a decreasing trend until 1982, only interrupted by the
interwar period and the Second World War (1920-1950). The resumption of
economic growth afterwards let to recover around 1954 the trend towards a more
egalitarian spatial income distribution, reaching during the Golden Age period the
maximum drop in regional income inequality. This process was prolonged until
1982 when regional inequality reversed again and in the last three decades, 19822010, it is possible to observe an upturn in income regional inequality in France,
more acute when we take into account the volume of population in each region
(Figures 1 and 2).
The calculation of regional GDPs per decade let us to determine quite accurately
when took place the most salient breakups in the evolution of regional income
inequality. Most of the published studies obtain similar results and place the
reversal in the bell-shaped evolution of spatial inequality in the last decades of the
nineteenth century and never later than 1930. Bazot (forthcoming), who estimates
Gross Domestic Product by department for the period 1840-1911 using the
patente, a tax on non-agricultural value-added, concludes that the modernization
of the French economy in the nineteenth century is characterized by an increasing
inequality between regions that only changed slowly towards a more egalitarian
spatial distribution in 1890 whereas wealth concentration increased in the Paris
region. Combes et al (2011) work, with data for only three cross-sections (1860,
1930 and 2000), conclude that 1860-1930 sub-period witnesses an increase in
spatial concentration (increasing inequality among regions), whereas the 19302000 sub-period is characterized by dispersion (decreasing inequality). But as these
authors recognize one of the main fragilities of their analysis is to check the validity
of the bell-shaped curve with data for only three years.
Our analysis, with regional GDP per capita estimations for eleven years across the
period 1860-2010, set the pick of regional income inequality around 1896 (not
weighted Gini). Since then the Gini index decreases every decade between 1896
and 1982. Using another kind of approach, Morrison and Snyder (2000) analyze the
evolution of income distribution in France in the eighteenth and nineteenth
centuries and find that when modern industrialization began around 1830,
inequality increased until the 1860s and since then it began to decline towards
greater equality.
Our estimations reveal that the level of regional inequality reached in 1930 (0.15)
has been recorded again in 2010 (0.15), but not as a result of a stable spatial
income distribution across the century such as Combes et al(2011) state, but after
a movement of the Gini index of going down until the level of 0.08 in 1982 and
thirty years of going up again until the nowadays level of 0.15. Only when the Paris
region is removed from the sample it is possible to observe less disparity among
the regions and even an improvement in the spatial distribution of GDP per capita
across the whole century.
An interesting question in the study of French spatial income distribution is
whether it has evolved towards an increasing polarization between a core, mainly
represented by the Paris region and a periphery that comprises the most part of
the French national territory (Paris and the “French desert”). In order to have a
cursory look into this question we have calculated the Esteban-Ray (ER) polarisation
measure which can be expressed as follows:
N
N
𝐸𝐸𝐸𝐸(K, α) = K �. � π1+α
πj �yi − yj �
i
i=1 j=1
Where 𝐾𝐾 is a constant and 𝛼𝛼 ϵ [0, 1.6] is a sensitivity parameter. When K = 1 and
α = 0 the polarization index and Gini are basically equal as can be observed in
figures 1 and 2 (ER(0)). Figure 3 illustrates the polarization index for 22 French
regions and for different α-values.
Figure 3 Population weighted ER polarisation index for GDP per capita
by region (N=22; current French francs)
As it can be expected, once regions are weighted by population size, inequality and
polarisation does not necessarily go hand-in-hand as figures 2 and 3 illustrate. In fact,
polarization has steadily increased since 1860, as figure 3 shows. Only during the
period 1954-82, polarization and inequality declined at the same time. The Golden Age
period, shows the biggest decrease in the Gini index and also the less polarized
regional income distribution. In this period, known in France as the “Trente Glorieuses”
(1945-1973), she benefited from the technological backwardness with regard to the
United States. Reconstruction after the war was highly tied to the role of central
planning which gave priority to those sectors linked to basic industries, such as
electricity, coal, iron and steel industry, cement, transport infrastructures and
machinery. The industrial policy promoted big projects for the development of basic
sectors and to provide basic goods in the less developed in order to promote their
modernization. As a consequence, the expansion went with a profound structural
change and with the increase of industry in the total output of the less developed
regions, while some of the most advanced regions such as the Paris region, Alsace,
Lorena and Nord-Pas-de-Calais begun a de-industrialization process in favor of services
and lose weight in national output.
Figure 4. Population weighted kernel densities for GDP per capita by region,
1860, 1896, and 1930
(N=22; current French francs)
Figure 5. Population weighted kernel densities for GDP per capita by region,
1954, 1982, and 2010
(N=22; current French francs)
Figure 6. Population weighted kernel densities for GDP per capita
by Department, 1860, 1896, and 1930
(N=90/95; current French francs)
Figure 7. Population weighted kernel densities for GDP per capita
by Department, 1860, 1896, and 1930
(N=90/95; current French francs)
Figures 4 and 5 illustrate distribution of GDP per capita with population-weighted
kernel densities. For this, regional GDP per capita is indexed with respect to France’s
average (=100). Then, the corresponding kernel density functions can be drawn for
each year. Having said that, these figures show how far or close are regions,
population-weighted, to France’s national average. For example, overall inequality was
reduced between 1896 and 1930, but polarization increased. This resulted from
population growth in the richest region, Île-de-France, as figure 4 shows. Both
inequality and polarization declined between 1954 and 1982, as figure 5 shows, when
the distance between the two peaks of the distribution decreased, and polarization
increased again between 1982 and 2010 when the gap between Île-de-France and
France’s national average widened again.
At the same time, the process of regional income convergence has altered the relative
position of the regions throughout the whole century (Table 1). With the exception of
the Paris region, not all the leader regions in the second half of the nineteenth century
will stay at the forefront of the income distribution in 2010. The regions with the
highest per capita income when the country reached the maximum level of regional
income inequality in 1896 were in descending order: the Paris region, HauteNormandie, Champagne-Ardenne, Nord-Pas-de-Calais, Picardie, Provence-Alps-CôteD’Azur and Rhone-Alps. With the exception of the two Alps regions, the other ones
belong to the prosperous ring around the Paris core and all of them enjoyed a per
capita income above the national average in 1896. Alsace and Lorene were also in this
heading group 5.
Throughout the 150 years analyzed the two regions that lost more positions in the
ranking of per capita income were both in the Northern borders of the Paris region
(Nord-Pas-de-Calais and Picardie). The first one ran from 1.21 of the national GDP per
capita in 1860 (3rd position) to 0.79 in 2010 (13th position) and Picardie from 1.14 (5th
position) to 0.75 (21th position). The positions in the poorer extreme of the distribution
do not present great changes. Corse remains, with the exception of 2010, as the
poorest region of France running from 0.65 of national GDP per capita in 1860 to 0.76
in 2000. However in the last benchmark this region has jumped to 0.82 of the average
national per capita income (12th position). The most successful regions are Aquitaine
and Rhone-Alpes. Aquitaine occupied the 16thposition in 1860 and reached the 6th
position in 2010, meanwhile Rhone-Alpes where Lyon is located, progressed from the
13th position in 1860 to the 2nd in 2010. However, the main characteristic throughout
the different benchmarks is a general tendency of the regions to concentrate around
75% and 100% of the national average but without surpassing it. Only the Paris region
has been over the national GDP per capita level in the 150 years analyzed and has
increased considerably its distances in the last three decades.
5
Although the region of Alsace and the department of Moselle did not belong to France in 1896 and
1911s we have decided to include them in the database in order to made the database more
homogenous throughout time.
Table 1.- Regional GDP per capita in France, 1860-2010 (France=1)
1860
1896
1911
1921
1931
1954
1962
1975
1982
1990
1999
2010
Île de France
2.05
2.07
1.74
1.69
1.64
1.70
1.60
1.48
1.46
1.52
1.54
1.65
Champagne-Ardenne
1.20
1.18
1.03
0.97
1.03
0.87
0.90
0.94
1.02
0.92
0.93
0.85
Picardie
1.15
1.10
0.92
0.87
0.97
0.83
0.89
0.96
0.89
0.85
0.81
0.76
Haute-Normandie
1.50
1.26
1.46
0.97
1.04
0.91
0.94
0.99
1.07
0.92
0.90
0.86
Centre
1.04
0.92
0.87
0.82
0.85
0.83
0.83
0.94
0.91
0.93
0.90
0.83
Basse-Normandie
0.93
0.92
0.88
0.78
0.85
0.82
0.87
0.92
0.82
0.82
0.80
0.77
Bourgogne
0.88
0.88
0.87
0.85
0.83
0.95
0.99
0.91
0.89
0.89
0.86
0.82
Nord - Pas-de-Calais
1.21
1.15
0.97
0.89
1.02
0.87
0.88
0.87
0.84
0.78
0.77
0.80
Lorraine
0.88
0.71
0.67
0.99
0.96
0.91
0.96
0.95
0.89
0.83
0.81
0.76
Alsace
0.89
n.d.
n.d.
0.99
1.10
0.97
0.94
0.97
1.03
1.02
0.99
0.91
Franche-Comté
0.72
0.76
1.09
0.98
0.77
0.93
0.99
0.90
0.87
0.86
0.87
0.78
Pays de la Loire
0.96
0.85
0.91
0.86
0.79
0.80
0.79
0.88
0.87
0.84
0.89
0.87
Bretagne
0.65
0.64
0.57
0.58
0.62
0.72
0.69
0.83
0.83
0.80
0.85
0.81
Poitou-Charentes
0.93
0.81
1.13
0.85
0.75
0.60
0.55
0.83
0.81
0.81
0.83
0.79
Aquitaine
0.80
0.91
0.99
0.85
0.84
0.86
0.83
0.88
0.93
0.87
0.88
0.87
Midi-Pyrénées
0.71
0.71
0.88
0.79
0.78
0.76
0.79
0.81
0.81
0.87
0.87
0.86
Limousin
0.77
0.69
0.88
0.79
0.70
0.73
0.75
0.89
0.76
0.82
0.81
0.74
Rhône-Alpes
0.88
0.97
1.01
0.99
0.98
1.04
1.09
0.97
0.98
1.02
1.01
0.98
Auvergne
0.73
0.78
0.75
0.76
0.80
0.84
0.87
0.85
0.78
0.85
0.82
0.79
Languedoc-Roussillon
Provence-Alpes-Côte
d'Azur
0.90
0.90
0.88
0.80
0.88
0.71
0.70
0.80
0.79
0.79
0.77
0.76
0.97
1.08
1.13
1.34
0.98
0.99
0.86
0.80
0.96
0.93 n.d.
Corse
n.d.
n.d.
0.50
0.48
n.d.
0.50
0.80
0.65
0.76
0.77
0.76
0.82
TOTAL
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.94
4. The shift-share analysis
In this section we explore some of the causes that can explain the evolution of regional
convergence in GDP per worker in France for the period 1860-2010. Up to 1982 the
spatial convergence in labour productivity was very intense, in fact this was the period
of fastest convergence across the French regions. Since 1982 to 2010 it could be
observed just the opposite trend, an increasing divergence in income per capita among
regions and greater inequality in the French regions.
In this section we carry out a type of convergence decomposition in order to separate
the sources of convergence in three different components: within-industry
convergence, labour reallocation and between industry convergence. We employ as a
benchmark of our regional GDP estimates the Paris region (Ile de France), which
remains the richest region throughout the whole period. That is to say, our results
indicates the sources of catching-out of the rest of French regions towards the Paris
region. The following equation summarizes the exercise:
Convergence in labour productivity= Within-industry convergencei,paris +
+ labour reallocationi,paris + between industry convergencei,paris
The “within-industry convergence” captures the labour productivity catch-up of each
sector with the corresponding Paris region, weighted by the average labour share in
that sector. This effect correspond to the idea present in the Neoclassical models of
economic growth of where convergence is based in the technological catch-up with
the leader. Labour reallocation quantifies the part of convergence caused by labour
force movements from one sector to another. And between industry convergence
measures the contribution to the convergence in productivities across sectors.
Table 2.- Decomposition of convergence in income per worker, 1896-1930
Overall
Code_NUTS2
FR10
FR21
FR22
FR23
FR24
FR25
FR26
FR30
FR41
FR42
FR43
FR51
FR52
FR53
FR61
FR62
FR63
FR71
FR72
FR81
FR82
FR83
France
France (exc. Île de
France)
Within-industry
Agriculture
Industry
-0,0370
-0,0222
-0,0335
-0,0061
-0,0155
-0,0659
-0,0293
-0,0407
-0,0312
-0,0032
-0,0662
0,0034
-0,0092
-0,0430
-0,0248
-0,0036
Services
0,0509
0,0616
0,0485
0,0427
0,0522
0,0326
0,0342
0,0959
Labour
reallocation
0,0019
-0,0168
0,0031
-0,0014
-0,0086
0,0130
-0,0090
0,0016
Between
industry
0,0896
0,0913
0,0704
0,1114
0,1107
0,1103
0,0949
0,0800
0,0831
0,0966
0,0406
0,0827
0,1198
0,0931
0,0678
0,1491
All sectors
-0,0084
0,0221
-0,0329
-0,0273
0,0178
-0,0302
-0,0180
0,0675
0,1452
0,0961
0,0986
0,0949
0,0920
0,1486
0,0663
0,1629
0,1124
0,1300
0,1846
0,0040
-0,0299
-0,0220
-0,0174
-0,0398
0,0239
-0,0699
0,0161
-0,0343
0,0424
0,1122
-0,0280
-0,0512
-0,0696
-0,0393
-0,0502
-0,0321
-0,0868
-0,0377
-0,0561
-0,0183
0,0079
-0,0195
-0,0093
-0,0058
-0,0215
-0,0010
-0,0003
-0,0258
0,0036
-0,0233
0,0107
-0,0089
0,0515
0,0306
0,0534
0,0434
0,0114
0,0563
0,0427
0,0502
0,0450
0,0500
0,1132
0,0229
0,0048
-0,0046
-0,0070
0,0193
-0,0005
-0,0034
0,0382
0,0170
-0,0082
0,0306
0,1183
0,1212
0,1252
0,1193
0,1125
0,1252
0,1397
0,1086
0,1298
0,0958
0,0419
0,1329
0,0130
-0,0319
-0,0040
0,0489
0,0298
0,0901
0,1220
0,0016
-0,0370
-0,0124
0,0511
0,0147
0,1056
Table 3.- Decomposition of convergence in income per worker, 1954-1982
Overall
Code_NUTS2
FR10
FR21
FR22
FR23
FR24
FR25
FR26
FR30
FR41
FR42
FR43
FR51
FR52
FR53
FR61
FR62
FR63
FR71
FR72
FR81
FR82
FR83
France
France (exc. Île de
France)
0,1240
0,0285
0,1474
0,0930
0,0567
0,0086
0,0443
0,0227
0,0818
-0,0200
0,1145
0,1467
0,2064
0,1716
0,1238
0,1091
0,0142
0,0155
0,1358
0,0893
0,0336
0,0751
All sectors
0,1514
0,0355
0,1547
0,0495
-0,0214
-0,0192
0,0642
0,0452
0,0983
-0,0161
0,0246
0,0062
0,1145
0,0468
-0,0154
-0,0215
-0,0132
-0,0624
0,0250
0,0167
-0,1921
0,0179
0,0899
0,0304
Within-industry
Agriculture
Industry
0,0818
0,0842
-0,0002
0,0763
-0,0161
0,1209
-0,0167
0,0755
-0,0971
0,0658
-0,0196
0,0597
-0,0149
0,0932
-0,0094
0,0737
0,0163
0,0846
-0,0449
0,0645
-0,0681
0,0787
-0,0792
0,0695
-0,0141
0,0855
-0,0493
0,0974
-0,0684
0,0619
-0,0900
0,0674
-0,0337
0,0601
-0,0819
0,0459
-0,0010
0,0645
-0,0100
0,0524
-0,1214
0,0198
-0,0260
0,0547
-0,0322
0,0727
Services
-0,0146
-0,0406
0,0498
-0,0093
0,0099
-0,0594
-0,0140
-0,0191
-0,0026
-0,0358
0,0139
0,0159
0,0431
-0,0012
-0,0089
0,0012
-0,0395
-0,0263
-0,0386
-0,0256
-0,0905
-0,0109
Labour
reallocation
-0,0186
0,0074
0,0079
0,0270
0,0369
0,0134
0,0374
0,0327
0,0181
0,0370
0,0633
0,0779
0,0479
0,0800
0,0964
0,0856
0,0558
0,0612
0,0522
0,0273
0,1193
0,0509
Between
industry
-0,0087
-0,0144
-0,0153
0,0165
0,0411
0,0145
-0,0574
-0,0552
-0,0347
-0,0409
0,0266
0,0626
0,0439
0,0447
0,0427
0,0450
-0,0285
0,0166
0,0586
0,0453
0,1064
0,0063
-0,0101
0,0517
0,0078
Table 4. Decomposition of convergence in income per worker, 1982-2010
Overall
Code_NUTS2
All sectors
Within-industry
Agriculture Industry
Labour
Services reallocation
Between
industry
FR10
FR21
FR22
FR23
FR24
FR25
FR26
FR30
FR41
FR42
FR43
FR51
FR52
FR53
FR61
FR62
FR63
FR71
FR72
FR81
FR82
FR83
-0,1424
-0,1293
-0,1856
-0,0818
-0,0479
-0,0893
-0,1131
-0,1549
0,0569
-0,0777
-0,0550
-0,0670
-0,0666
-0,1254
-0,0545
-0,0232
-0,0387
-0,0208
-0,1164
-0,1221
-0,0273
-0,1419
-0,1464
-0,2313
-0,0993
-0,1302
-0,1107
-0,1559
-0,2051
1,2958
-0,1723
-0,1268
-0,0934
-0,0818
-0,1150
-0,0612
-0,0638
-0,0804
-0,0909
-0,0450
-0,0435
0,0382
-0,0089
0,0062
0,0076
0,0176
0,0109
-0,0048
0,0049
0,0006
-0,0020
0,0093
0,0055
0,0099
0,0150
0,0048
0,0080
0,0051
0,0002
-0,0049
-0,0116
0,0067
0,0099
-0,0950
-0,1129
-0,1212
-0,0932
-0,1009
-0,1015
-0,1098
-0,1335
1,3637
-0,1509
-0,1120
-0,0934
-0,0909
-0,1051
-0,0757
-0,0812
-0,0733
-0,0875
-0,0728
-0,0537
-0,0513
-0,0380
-0,0397
-0,1178
-0,0238
-0,0402
-0,0043
-0,0510
-0,0723
-0,0660
-0,0307
-0,0204
-0,0099
-0,0060
-0,0146
0,0065
0,0123
-0,0073
0,0015
0,0394
0,0034
0,0796
-0,0984
-0,0758
-0,0538
-0,0701
-0,0060
-0,0646
-0,0411
-0,0466
-1,2508
-0,0443
-0,0207
-0,0294
-0,0534
-0,0557
-0,0409
-0,0237
-0,0469
-0,0227
-0,0791
-0,0762
-0,0531
0,0979
0,0929
0,0995
0,0877
0,0883
0,0859
0,0839
0,0968
0,0119
0,1389
0,0925
0,0559
0,0686
0,0453
0,0476
0,0643
0,0886
0,0927
0,0077
-0,0024
-0,0123
France
France (exc. Île de
France)
-0,0623
-0,0902
0,0042
-0,0737
-0,0207
-0,0273
0,0553
-0,0825
-0,1019
0,0053
-0,0860
-0,0213
-0,0501
0,0695
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