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We analyse the evolution of mortality rates in Spain by age and gender between 1990 and 2018. We compare municipalities, ranked by socio‐economic status (SES) and grouped into bins of similar population size, to study changes not only in levels but also in inequality in mortality across the SES spectrum. We document large decreases in mortality rates throughout the period for all age groups, including children, even after 2000, and continuing after the Great Recession. These declines are stronger for boys and men, who had higher mortality rates to begin with. We find that inequality in mortality across municipalities was low among the young by 2018, while it was higher among adult men and older women. Inequality in fact increased over the period for older men. We explore the role of different causes of death and find that this increase in inequality is driven by stronger improvements in cancer‐related mortality among men living in richer areas. These improvements are not found among women, given their increases in mortality due to lung cancer.
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FISCAL STUDIES, vol. 42, no. 1, pp. 103–121 (2021) 0143-5671
Inequality in Mortality in Spain*
Libertad Gonzálezand Ana Rodríguez-González
Universitat Pompeu Fabra; Barcelona GSE
(libertad.gonzalez@upf.edu)
Department of Economics; Lund University
(ana.rodriguez_gonzalez@nek.lu.se)
Abstract
We analyse the evolution of mortality rates in Spain by age and gender between
1990 and 2018. We compare municipalities, ranked by socio-economic status
(SES) and grouped into bins of similar population size, to study changes not
only in levels but also in inequality in mortality across the SES spectrum.
We document large decreases in mortality rates throughout the period for
all age groups, including children, even after 2000, and continuing after the
Great Recession. These declines are stronger for boys and men, who had
higher mortality rates to begin with. We find that inequality in mortality across
municipalities was low among the young by 2018, while it was higher among
adult men and older women. Inequality in fact increased over the period for
older men. We explore the role of different causes of death and find that this
increase in inequality is driven by stronger improvements in cancer-related
mortality among men living in richer areas. These improvements are not found
among women, given their increases in mortality due to lung cancer.
I. Introduction
Before the COVID-19 pandemic, Spain was projected to become the country
with the highest life expectancy in the world by 2040.1Life expectancy had
*Submitted November 2020.
We thank the participants in the Economic History seminar at Universitat Pompeu Fabra for their useful
comments.
Keywords: inequality, mortality, population health.
JEL classification numbers: I14, J10.
1Foreman et al., 2018.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License,
which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial
and no modifications or adaptations are made.
104 Fiscal Studies
been increasing steadily over the previous decades, fuelled by declines in
mortality in all age groups. Because averages hide potential variation in health
and longevity by socio-economic status, it is relevant to understand how those
gains in life expectancy are distributed.
We analyse the evolution of inequality in mortality in Spain during
the period 1990–2018. Income inequality was mostly decreasing during
the first years of this period, but it started to increase after the 2008
financial crisis, which hit the Spanish economy particularly hard.2Our
analysis focuses on age-specific mortality and considers inequality across
small geographical areas, based on municipalities, ranked by an index of
economic deprivation. By considering inequality across similar-sized areas
ranked by relative deprivation, we overcome concerns of selective migration
and of compositional changes within socio-economic groups over time.
We find substantial decreases in mortality for all age groups throughout the
period, particularly pronounced for men, and even for children. Inequality in
mortality across municipalities was low among the young and changed little
over time.
We also find substantial increases in inequality among adult and older men.
The observed increases in inequality do not appear to be related to the Great
Recession, as we observe no relevant changes in trends after 2008. They are
instead driven by larger declines in cancer-related mortality in richer (less-
deprived) areas.
We contribute to the literature by providing evidence on changes in
inequality in mortality by age and gender for a large European country.
Spain has a tax-based universal health insurance system, and most health
care is provided free of charge by public hospitals and primary care centres.
These features might mitigate the impact of income inequality on health
and mortality. However, Spain was strongly affected by the 2008 financial
crisis, and experienced a substantial increase in income inequality and sizeable
cutbacks in the health budget.3It is remarkable that we find steady declines in
mortality for all age groups, including after the Great Recession. Our results
also show essentially no inequality in the period 2016–18 for most age groups
below 50.
Several studies have documented the extent of inequality in health and
mortality rates in Spain, but they have been mainly limited to few specific
regions.4These regions have also been used in international studies comparing
socio-economic inequalities in mortality across European countries.5,6
2Anghel et al., 2018; Ferrer-i-Carbonell, Ramos and Oviedo, 2013.
3Bernal-Delgado et al., 2018.
4Borrell et al., 1999, 2008; Martínez et al., 2009; Regidor et al., 2003.
5Huisman et al., 2005; Kulhánová et al., 2014; Mackenbach et al., 2008, 2015.
6Bohácek et al. (2018) use harmonised panel data from 10 European countries, England, and the US,
and study inequalities in life expectancy at age 50 by education and gender during the period 2002–15.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
Inequality in mortality in Spain 105
Recently, Regidor et al. (2014, 2016) have used data for the whole of
Spain and their two studies are closely connected to our work. They study
the trends in mortality by socio-economic group, and changes in inequality in
mortality at the province level. However, these analyses could be confounded
by compositional changes within socio-economic groups or by selective
migration of healthier individuals from poorer to richer provinces over time.
Our study is the first analysis documenting the evolution of inequality in
mortality in Spain for all age groups, accounting for compositional changes
within socio-economic groups, while also addressing selective migration
concerns by comparing mortality rates across areas of similar population size.
II. Data and methodology
We combine information from death-certificate microdata, the local
population registry and the census, all provided by the Spanish National
Statistics Institute, for years 1990–02, 2000–02, and 2016–18. In order to
construct mortality rates, we use death counts by municipality of residence,
gender, five-year age group, and year from the death certificates, together with
population counts at the same level from the local population registry.7We
use census data in 1991, 2001 and 2011 to construct a ‘deprivation index’
for each year at the municipality level. The index combines municipality-
level information on high school dropout rates (fraction of the population
older than 16 who did not finish compulsory education),8unemployment rates
(unemployed as a fraction of the labour force), and the share of dwellings
located in buildings that are not in good condition.9
While these data sources contain information on the whole Spanish
territory, the municipality of residence is censored in death-certificate
microdata for municipalities under 10,000 inhabitants, and in the census for
municipalities under 20,000 inhabitants. For these smaller municipalities,
only the province and an indicator of municipality size is available. Thus,
we aggregate information on small localities within each province into two
groups: one for all municipalities of fewer than 10,000 inhabitants, and
another for all municipalities with a population between 10,001 and 20,000
inhabitants.
The municipality is the finest level of geographical disaggregation for
which the necessary information is available to perform this type of analysis.
The total number of municipalities in our data, including the fictitious ones
7Data from the local population registry are only available from 1996 onwards. For earlier years, we use
data from the 1991 census.
8A high school degree is equivalent to ISCED 2.
9The census classifies the state of buildings as dilapidated, bad, deficient or good. We construct this
variable as the share of houses in each municipality that are located in buildings that are not in good
condition (i.e. in a dilapidated, bad, or deficient condition).
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
106 Fiscal Studies
that group smaller towns, ranges from 380 in 1990 to 489 in 2018, an increase
due to migration from smaller to larger municipalities over time. The average
municipality population size in our sample is around 100,000 inhabitants,
while the median is 45,000 inhabitants.10
In order to construct the multiple deprivation index (MDI), we first
standardise each of the three components in each year (by subtracting its mean
and dividing over its standard deviation), and then we add up the three z-scores.
Figure A.1 in the online Appendix shows that there is strong persistence in the
value of the deprivation index for municipalities over time.
We rank municipalities from lower to higher values of the deprivation index
(from less- to more-deprived areas, or from rich to poor), and we then group
municipalities into 20 ‘bins’, each accounting for approximately 5 per cent of
the total Spanish population in each year.11 This process is done separately
for 1990 (using values of the index in 1991), 2000 (using the index in 2001)
and 2016 (using values of the index in 2011). In Section IV.1, we explore the
robustness of our findings to ranking municipalities by average income in 2016
instead (as income data at this level of geographical disaggregation are only
available from 2015 onwards). Figure A.2 in the online Appendix shows the
average characteristics of the bins in different years.
We construct mortality rates for each year, bin, gender, and each of the
following age groups: 0–4, 5–19, 20–49, 50–64, 65–79, and 80 and older. We
age-standardise mortality rates in each year using the population structure in
2016.12 Finally, we smooth mortality rates by taking the average of the three
one-year mortality rates in each period. As a result, what we call the smoothed
one-year mortality rate in 1990, for instance, is actually the average of one-
year mortality rates in 1990, 1991 and 1992.
III. Inequality in mortality in Spain, 1990–2018
Part A
Figure 1 presents the main results for our analysis of inequality in mortality.
We show mortality rates in 1990–92, 2000–02 and 2016–18 by our deprivation
index, separately by gender and for six age groups.
10We can alternatively perform the same analysis at the province level (there are 50 provinces in Spain).
Our main conclusions are robust to this alternative level of aggregation.
11In order to smooth the size of these bins, we follow Currie and Schwandt (2016) and split the two
largest municipalities (Madrid and Barcelona) into 10 smaller municipalities with identical values of the
multiple deprivation index and one-tenth of deaths and population.
12That is, we apply the five-year age-group-specific mortality rate in each year to the weight that each
five-year age group has within the broader age group in 2016, separately for each bin and gender.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
Inequality in mortality in Spain 107
FIGURE 1
Age-specific mortality by deprivation rank
.5
1
1.5
2
2.5
1 year mortality (per 1,000)
0 20 40 60 80 100
Deprivation index
Females
.5
1
1.5
2
2.5
0 20 40 60 80 100
Deprivation index
Males
Age 0-4
.1
.2
.3
.4
.5
1 year mortality (per 1,000)
0 20 40 60 80 100
Deprivation index
Females
.1
.2
.3
.4
.5
0 20 40 60 80 100
Deprivation index
Males
Age 5-19
.5
1
1.5
2
2.5
3
1 year mortality (per 1,000)
0 20 40 60 80 100
Deprivation index
Females
.5
1
1.5
2
2.5
3
0 20 40 60 80 100
Deprivation index
Males
Age 20-49
2
4
6
8
10
12
1 year mortality (per 1,000)
0 20 40 60 80 100
Deprivation index
Females
2
4
6
8
10
12
0 20 40 60 80 100
Deprivation index
Males
Age 50-64
10
20
30
40
50
1 year mortality (per 1,000)
0 20 40 60 80 100
Deprivation index
Females
10
20
30
40
50
0 20 40 60 80 100
Deprivation index
Males
Age 65-79
80
100
120
140
160
1 year mortality (per 1,000)
0 20 40 60 80100
Deprivation index
Females
80
100
120
140
160
0 20 40 60 80 100
Deprivation index
Males
Age 80+
1990 2000 2016
Note: This figure shows the evolution of one-year mortality rates (smoothed over three years) for each
gender and age group by bin, with bins ranked by a multiple deprivation index combining rates of high
school dropout, unemployment and poor housing conditions in 1991, 2001 and 2011, respectively. Each
dot (‘bin’) represents average values for groups of municipalities accounting for approximately 5 per cent
of the total Spanish population in that given year. Bins are ordered from lower to higher deprivation, so
that a positive slope implies lower mortality in richer areas. Mortality rates are age-adjusted using the 2016
population.
We first focus on levels. We find large declines in mortality rates in all age
groups and in both subperiods (the 1990s and the 2000s), represented by the
drop in the level from the 1990 line to the 2016 line in all panels. This drop
is very pronounced for children, especially under the age of 5. For instance,
the mortality rate for boys under 5 was about 2 in 1990, while it was down to
0.5–0.7 in 2016.
We observe larger drops for men compared with women (especially in less-
deprived areas). For instance, in the age group 5–19, the mortality rate fell
from around 0.2 per 1,000 in 1990 to slightly below 0.1 in 2016 for girls,
while the fall for boys is much larger, from about 0.4 to about 0.1. Note also
the large fall among men aged 20–49 between 2000 and 2016.
We next focus on inequality in mortality rates between more- and less-
deprived areas, captured by the slopes of the lines. The slopes (and their
standard errors) are reported in Table 1. Inequality in mortality is very low
among children, as illustrated by the very flat slopes for age groups 0–4 and
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
108 Fiscal Studies
TABLE 1
Age-specific mortality in least- and most-deprived areas and change in inequality
Lowest MDI Highest MDI Slope of regression line p-value
1990 2000 2016 1990 2000 2016 1990 2000 2016 2000
1990 2016
2000
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Men
0–4 1.787 1.011 0.477 1.811 1.545 0.705 –0.005 0.009 0.006 0.082 0.731
(0.005) (0.006) (0.004)
5–19 0.371 0.283 0.083 0.447 0.304 0.128 0.003** 0.001 0.001*0.305 0.799
(0.001) (0.001) (0.001)
20–49 2.102 1.519 0.678 2.431 1.956 1.133 –0.004 0.015** 0.018*** 0.055 0.637
(0.006) (0.005) (0.002)
50–64 9.067 7.173 4.713 9.930 8.781 6.904 0.030 0.076*** 0.087*** 0.028 0.517
(0.019) (0.011) (0.011)
65–79 37.740 30.284 18.805 38.436 35.198 25.095 0.052 0.179*** 0.303*** 0.159 0.068
(0.122) (0.034) (0.027)
80+149.687 133.044 102.056 141.868 124.964 106.206 –0.174 –0.466*** 0.479*** 0.263 0.000
(0.238) (0.158) (0.103)
(Continued)
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
Inequality in mortality in Spain 109
TABLE 1
(Continued)
Lowest MDI Highest MDI Slope of regression line p-value
1990 2000 2016 1990 2000 2016 1990 2000 2016 2000
1990 2016
2000
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Women
0–4 1.341 0.776 0.468 1.376 1.304 0.517 –0.004 0.013** 0.009*** 0.040 0.518
(0.008) (0.006) (0.002)
5–19 0.189 0.098 0.073 0.198 0.154 0.100 –0.001 0.002*** 0.001 0.007 0.088
(0.001) (0.001) (0.000)
20–49 0.833 0.681 0.394 0.916 0.728 0.533 –0.000 0.002 0.006*** 0.454 0.061
(0.002) (0.002) (0.001)
50–64 3.635 2.887 2.488 4.471 3.449 2.819 0.031*** 0.022*** 0.020*** 0.165 0.725
(0.006) (0.004) (0.005)
65–79 19.334 13.757 9.388 22.401 18.886 12.302 0.198*** 0.184*** 0.179*** 0.762 0.884
(0.034) (0.032) (0.014)
80+122.681 102.551 83.426 123.899 105.367 87.799 0.425** –0.037 0.609*** 0.047 0.001
(0.188) (0.135) (0.116)
Note: Columns 1–6 report the means of (smoothed) one-year mortality rates for each gender and age group in 1990, 2000 and 2016, in the bin of municipalities with
lowest and highest deprivation, respectively. Columns 7–9 report the coefficient (and standard error in parentheses) of the fitted regression line in each year. Column 10
reports the p-value for the null hypothesis that the slopes are equal in 1990 and 2000, and column 11 for the null that slopes are equal in 2000 and 2016. In columns
7–9, ***,** and *indicate significance at the 1, 5 and 10 per cent levels, respectively.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
110 Fiscal Studies
5–19, and there is little change in inequality over time, with the lines being
close to parallel in the three periods.
More inequality in mortality is illustrated by steeper positive slopes.
Inequality is higher among the elderly compared with the young, especially
in the more recent periods. For example, the slope takes value of 0.5 for men
aged 80+and 0.3 for men aged 65–79 in 2016 (see Table 1), versus 0.02 for
men aged 20–49 and 0.001 for boys aged 5–19.13
Regarding changes in inequality, there is little change among adult women,
and there are low levels of inequality throughout the period for women below
65. We do find important increases in inequality in mortality among adult and
older men, with marked and significant positive slopes in 2016 in all groups
above age 19.
In the final period (2016–18), Figure 1 (and Table 1) show very low levels
of inequality among both men and women below the age of 50, but significant
and sizeable inequality for men above the age of 50 and women above the age
of 65. In Section IV, we explore the role of the Great Recession and different
causes of death in accounting for these trends.
IV. Understanding the changes in mortality over time
Part B
1. Robustness to the ranking variable
In the results presented so far (in Figure 1 and Table 1), we rank municipalities
using our composite deprivation index. We also explore the robustness of
our results to ranking municipalities by per capita income instead. Per capita
income by municipality is only available starting in 2015.
Our main analysis ranks municipalities using a deprivation index that varies
over time and combines information on local unemployment rates, educational
attainment and housing quality (see Section II). We perform the same analysis
using a time-invariant alternative ranking variable: average income in each
municipality in 2016. The data are provided by the Spanish National Statistics
Institute, and are restricted to municipalities of at least 100 inhabitants.14 We
use information on average income per capita in each municipality, and rank
localities by descending order of this variable. The results are presented in
13Note that the slopes are negative for men aged 80 or older in 1990 and 2000, indicating higher mortality
in richer places. However, this result is not robust to alternative ranking variables (see Section IV.1 and
Figure 2).
14Instituto Nacional de Estadística, 2020.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
Inequality in mortality in Spain 111
Figure 2. Our main conclusions are unchanged when using this alternative
ranking variable Instituto.15
2. The role of the Great Recession
We have documented an increase in inequality in mortality among adult men
between 1990 and 2018. In this section, we ask to what extent this increase
can be attributed to the 2008 crisis, which led to higher income inequality and
affected men’s employment particularly hard.
To this end, Figure 3 presents mortality rates for men, for several periods
from the year 2000 to 2018. The slopes and the p-values for the differences
between the slopes over time are reported in Table A.1 in the online Appendix.
First, note that the four lines are close to parallel in the first two panels in
Figure 3, corresponding to age groups 20–49 and 50–64. We find no significant
changes in inequality before or after the crisis for men below 65 (see also the
first row of Table A.1). Second, for men aged 65–79, the significant increase
in inequality takes place before the crisis (2000–05). Third, for the older
age group (80+), we observe increasing inequality before the crisis, which
continues between 2005–07 and 2010–12, but not after 2010–12. We find no
significant increases in inequality for any age groups after 2010–12.
To us, these results suggest that the observed increases in inequality in
mortality among older men cannot be attributed to the Great Regression, as
these had started before the crisis. We find that the fall in mortality rates
(in levels) continues after 2008 at a similar pace as before, and the ongoing
increases in inequality do not appear to accelerate. Thus, perhaps surprisingly,
the deep economic recession did not translate into lower life expectancy in the
following 10 years, on average, or into higher inequality in health, at least as
captured by mortality rates.
Figures 1 and 3 do show an important (and close to parallel) drop in
mortality levels among men aged 20–49 after the onset of the crisis. This may
be related to the large negative employment shock experienced by men during
the recession, especially in high-risk sectors such as construction. We explore
this possibility further in the next section.
3. The role of different causes of death
In order to understand the large declines in mortality and the increase in
inequality among men, we next analyse trends in mortality by cause of death.
15The results are also robust to ranking areas by their values of the multiple deprivation index in 2011,
instead of re-ranking in each year as in the main specification. These results are available upon request.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
112 Fiscal Studies
FIGURE 2
Age-specific mortality by average income rank
.5
1
1.5
2
2.5
1 year mortality (per 1,000)
0 20 40 60 80 100
Mean income rank
(higher rank=poorer)
Females
.5
1
1.5
2
2.5
0 20 40 60 80 100
Mean income rank
(higher rank=poorer)
Males
Age 0-4
.1
.2
.3
.4
.5
1year mortality (per 1,000)
0 20 40 60 80 100
Mean income rank
(higher rank=poorer)
Females
.1
.2
.3
.4
.5
020 40 60 80 100
Mean income rank
(higher rank=poorer)
Males
Age 5-19
.5
1
1.5
2
2.5
3
1year mortality (per 1,000)
020 40 60 80 100
Mean income rank
(higher rank=poorer)
Females
.5
1
1.5
2
2.5
3
020 40 60 80 100
Mean income rank
(higher rank=poorer)
Males
Age 20-49
2
4
6
8
10
1year mortality (per 1,000)
0 20 40 60 80 100
Mean income rank
(higher rank=poorer)
Females
2
4
6
8
10
0 20 40 60 80 100
Mean income rank
(higher rank=poorer)
Males
Age 50-64
10
20
30
40
50
1year mortality (per 1,000)
0 20 40 60 80 100
Mean income rank
(higher rank=poorer)
Females
10
20
30
40
50
020 40 60 80 100
Mean income rank
(higher rank=poorer)
Males
Age 65-79
80
100
120
140
160
1 year mortality (per 1,000)
020 40 60 80 100
Mean income rank
(higher rank=poorer)
Females
80
100
120
140
160
020 40 60 80 100
Mean income rank
(higher rank=poorer)
Males
Age 80+
1990 2000 2016
Note: This figure shows the evolution of one-year mortality rates (smoothed over three years) for each gender and age group by bin, with bins ranked by decreasing
average income in 2016. Each dot (‘bin’) represents average values for groups of municipalities accounting for approximately 5 per cent of the total Spanish population
in that given year. Bins are ordered from higher to lower income, so that a positive slope implies lower mortality in richer areas. Mortality rates are age-adjusted using
the 2016 population.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
Inequality in mortality in Spain 113
FIGURE 3
Age-specific mortality for men before, during and after the 2008 crisis
.5
1
1.5
2
1 year mortality (per 1,000)
020 40 60 80 100
Deprivation index
Age 20-49
5
6
7
8
9
1 year mortality (per 1,000)
020 40 60 80 100
Deprivation index
Age 50-64
20
25
30
35
1 year mortality (per 1,000)
020 40 60 80 100
Deprivation index
Age 65-79
100
110
120
130
1 year mortality (per 1,000)
020 40 60 80 100
Deprivation index
Age 80+
2000 2005 2010 2015
Note: This figure shows the evolution of one-year mortality rates (smoothed over three years) for adult men
by age group and bin, with bins ranked by a multiple deprivation index combining rates of high school
dropout, unemployment and poor housing conditions in 2011. Each dot (‘bin’) represents average values
for groups of municipalities accounting for approximately 5 per cent of the total Spanish population in
that given year. Bins are ordered from lower to higher deprivation, so that a positive slope implies lower
mortality in richer areas. Mortality rates are age-adjusted using the 2016 population.
a) The role of treatable and preventable causes
We follow the classification of avoidable mortality into ‘treatable’ and
‘preventable’ causes by OECD/Eurostat (2019). Preventable mortality
includes causes of death that can be avoided mainly through public health
interventions and primary prevention (e.g. lung cancer or traffic accidents).
Treatable mortality, in turn, refers to causes of death that can be avoided with
timely and effective health care interventions, for instance with hospital care
after the onset of disease (e.g. breast cancer).16 This classification is based
on the international statistical classification of diseases and related health
problems (ICD-10) codes.
We restrict the analysis to the year 2000 onwards, as these are the years for
which causes of death are classified with ICD-10 codes in our data. Because
the classification of avoidable mortality only applies to the population younger
16See the introduction of this special issue for a more comprehensive definition.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
114 Fiscal Studies
than 75, we do not show results for the oldest group in this analysis.17 We u s e
the deprivation index in 2011 as the ranking variable.
In order to visualise the relative importance of preventable and treatable
causes, Figure 4 compares actual mortality in 2000 and 2016 with
counterfactual mortality in 2016 under different scenarios. The first scenario
(represented by green hollow triangles) keeps preventable mortality constant
at 2000 values. The second scenario (represented by blue hollow circles) keeps
both preventable and treatable mortality (i.e. all avoidable mortality) fixed at
their 2000 levels.
In order to construct counterfactual mortality in 2016 keeping preventable
(or all avoidable) mortality constant, we subtract from the total mortality rate
in 2016 the preventable (or avoidable) mortality rate in 2016, and add instead
the preventable (or avoidable) mortality rate in 2000, for each age group,
gender and bin.18
Our results in Figure 4 show that the large fall in mortality observed
for children younger than 5 can be mostly accounted for by the declines in
treatable mortality. Preventable mortality changed little in this group from
2000 to 2016, as shown by the green hollow triangles (showing 2016 mortality
if preventable mortality is kept at 2000 values) being very close to the
green solid triangles (actual 2016 mortality). In contrast, blue hollow circles
(representing 2016 mortality if both preventable and treatable mortality had
remained constant) show much higher values. The difference between the
blue hollow circles and the green hollow triangles indicates the decline in
treatable mortality. The difference between the blue solid circles, representing
mortality in 2000, and the blue hollow circles shows in turn the share of the
mortality decline that cannot be explained by preventable or treatable causes.
For children younger than 5, this difference is relatively small, which suggests
that almost all of the mortality decline can be explained by avoidable causes
(in particular, treatable causes).
For most other groups, we see larger declines in preventable mortality
compared with treatable mortality. This is the case for both men and women
in age groups 5–49, although the pattern is stronger for men. For those
aged 5–19, those decreases were larger in more deprived areas, leading to a
decrease in inequality. The opposite is true for those aged 20–49, for whom
inequality increased. For men older than 50, we also see larger decreases in
preventable mortality than in treatable mortality, but the increase in inequality
that we documented in Section III arises from all types of causes: preventable,
treatable, and the remaining causes.
17We show results for the age group 65-79 as in the main analysis to ease the comparison of these results
with those in Section III.
18Note that some causes of death, particularly cardiovascular conditions, are classified as being
50 per cent preventable and 50 per cent treatable.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
Inequality in mortality in Spain 115
FIGURE 4
Actual and counterfactual mortality in 2016, with preventable and treatable mortality
constant at 2000 levels
Note: These figures compare actual mortality in 2000 and 2016 with counterfactual mortality in 2016
if preventable mortality had remained constant in 2000 values (green hollow triangles), and with
counterfactual mortality in 2016 if all avoidable mortality had remained constant in 2000 (blue hollow
circles). Bins are ranked by the multiple deprivation index in 2011.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
116 Fiscal Studies
An interesting exception to this pattern is women older than 50, for whom
we see larger declines in treatable mortality than in preventable mortality. This
is particularly the case for women aged 50–64, for whom preventable mortality
actually increased over this period, with larger increases in richer areas. As a
result, we observe a decrease in inequality in avoidable mortality.
Figure A.3 in the online Appendix shows that this increase in preventable
mortality for women aged 50–64 was due to an increase in mortality from
lung cancer, which was larger in the least-deprived (richer) areas. This figure
compares preventable mortality in 2000 with actual preventable mortality in
2016 and with counterfactual preventable mortality in 2016 if lung cancer
mortality had remained at its 2000 levels. The results of this exercise show that
the increase in mortality from lung cancer among women was large enough to
outweigh the declines in all other preventable causes.
Why did mortality from lung cancer increase for women during this period?
Figure A.4 depicts the evolution of mortality from lung cancer for men and
women aged 50–64 and 65–79 in the least- and most-deprived areas, from
1990 to 2016. Mortality from lung cancer is higher for men than for women,
but this difference declined substantially over the period, particularly in richer
areas, which experienced large declines in mortality from lung cancer among
men, but substantial increases among women.
These trends are likely linked to the patterns of adoption and cessation
of smoking observed in Spain over the past few decades.19 Until the 1970s,
smoking was much more common among men than women, with significant
convergence documented starting around 1970. Higher-educated women drove
the increase in smoking in the 1970s, while lower-educated women followed
in the 1980s.
Mortality from lung cancer thus seems to be one of the factors explaining
the smaller declines in mortality experienced by adult women, and the
increased inequality in mortality observed among older men.
b) The role of specific causes of death
Finally, we explore the role of specific causes of death in explaining the main
patterns, focusing on the most prevalent by age and gender. Figures 5 and 6
show the contribution of the two main causes of death in each group to the
observed changes in levels and inequality in mortality from 1990 to 2016, for
adult and elderly men and women.
We compare actual mortality levels in 1990 and 2016 with counterfactual
mortality in 2016, keeping mortality from the first cause of death constant
at 1990 levels (green hollow triangles), and with counterfactual mortality in
2016, keeping mortality from both the first and second cause of death constant
19Schiaffino et al., 2003.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
Inequality in mortality in Spain 117
FIGURE 5
Actual and counterfactual mortality in 2016, with first two causes of death constant
in 1990: adults
Note: These figures compare actual mortality in 1990 and 2016 with counterfactual mortality in 2016 if
mortality from the first cause of death in each group had remained constant in 1990 values (green hollow
triangles), and with counterfactual mortality in 2016 if mortality from both the first and second cause of
death had remained constant in 1990 (blue hollow circles). Bins are ranked by the multiple deprivation
index in 1991 and 2011, respectively.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
118 Fiscal Studies
FIGURE 6
Actual and counterfactual mortality in 2016, with first two causes of death constant
in 1990: elderly
Note: These figures compare actual mortality in 1990 and 2016 with counterfactual mortality in 2016 if
mortality from the first cause of death in each group had remained constant in 1990 values (green hollow
triangles), and with counterfactual mortality in 2016 if mortality from both the first and second cause of
death had remained constant in 1990 (blue hollow circles). Bins are ranked by the multiple deprivation
index in 1991 and 2011, respectively.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
Inequality in mortality in Spain 119
at their 1990 levels (blue hollow circles). We treat each chapter in the ICD
classification as one cause of death.20
Mortality from cancer and external causes were the two main reasons
for death in the 20–49 age group in 1990. We see modest improvements
in these causes among women, and more substantial declines among men,
especially from external causes. External causes include traffic accidents, an
important cause of preventable mortality in this group. They also include
workplace accidents, which may have fallen as a result of declining male
employment between 2008 and 2013, especially in the construction sector. The
declines among men were distributed fairly equally across the socio-economic
spectrum, so they do not explain the increase in inequality observed in this
group.
In all groups older than 50, cancer and circulatory conditions are the first
two causes of death. While declines in mortality from cancer are responsible
for a significant share of the decline in mortality for younger groups, mortality
from circulatory disease is more important for the older groups. For those older
than 80, in particular, virtually all of the decline in mortality can be accounted
for by declines in mortality from circulatory problems.21
For women, we observe little improvement in mortality from cancer. The
improvements in mortality from some frequent types of cancer, such as breast
cancer, are shadowed by the increased mortality from lung cancer. For men, in
turn, we see substantial declines in mortality from cancer, particularly for ages
50–64, which were more pronounced in richer areas.
Mortality from cancer is thus responsible for most of the increase in
inequality in mortality among older men. This can be seen in the fact that the
gradient in mortality in 1990 is virtually parallel to counterfactual mortality
in 2016 if mortality from cancer had remained at 1990 values. That is,
inequality would not have increased if mortality from cancer had not changed.
Improvements in mortality from circulatory disease, however, seem to explain
little of the increases in inequality.
V. Conclusions
We analyse mortality rates in Spain by age and gender between 1990 and 2018.
We rank municipalities by an index of average economic deprivation, and we
group them into bins of similar size in terms of population. This allows us to
20For example, mortality from cancer represents mortality from causes C00-D49 (Neoplasms, chapter 2
of ICD-10 codes). Causes of death are classified in our microdata with ICD-9 codes before 1999, and with
ICD-10 codes thereafter.
21We also explore the role of different causes of death in accounting for the negative slopes observed for
menaged80+in Figure 1. We find that these are not driven by cardiovascular problems, but by mortality
from cancer, in particular lung and prostate cancer, which were more prevalent in richer areas.
© 2021 The Authors. Fiscal Studies published by John Wiley & Sons Ltd. on behalf of Institute for Fiscal Studies
120 Fiscal Studies
study changes in mortality rates in levels, but also changes in inequality in
mortality between poorer and richer areas.
We document large decreases in mortality rates throughout the period for
all age groups, including children, even after 2000 (and even after the Great
Recession). These declines are stronger for boys and men, who had higher
mortality rates to begin with.
We find that inequality in mortality across municipalities is low among the
young by 2016–18, but higher among adult men and older women. Inequality
in fact increased between 1990 and 2016 for older men. We explore the role of
different causes of death and find that this increase in inequality is explained
by larger improvements in cancer-related deaths among men living in richer
areas. These improvements are not found among women, who instead suffered
increases in their mortality rates due to lung cancer.
Supporting Information
Additional supporting information may be found online in the Supporting
Information section at the end of the article.
Appendix
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... This paper focuses on Spain, a country for which there continues to be very little evidence available concerning the link between longevity and income in older age groups [37][38][39][40][41] (see the literature review section for details). None of the cited papers for Spain look at the specific case of retirement pensioners; most focus on adults aged 50 and over from the general population due to the lack of information on retired individuals. ...
... However, they also provided new evidence of a possibly lower gradient in mortality and health in Spain. González and Rodríguez-González [41] analyze the evolution of inequality in mortality in Spain during the period 1990-2018. They focused on age-specific mortality and considered inequality across narrowly defined geographical areas, ranked by average SE status. ...
... Our findings also reveal that, in a European context, LE inequality among retired Spanish men is relatively small. This is in line with previous findings for Spain involving older adults and using very different methodologies and/or databases [37][38][39][40][41]. Regidor et al. [39], for example, conclude that mortality inequalities in older Spanish adults are small. ...
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Background: While educational inequalities in mortality are substantial in most European countries, they are relatively small in Spain. A better understanding of the causes of these smaller inequalities in Spain may help to develop policies to reduce inequalities in mortality elsewhere. The aim of the present study was therefore to identify the specific causes of death and determinants contributing to these smaller inequalities. Methods: Data on mortality by education were obtained from longitudinal mortality studies in three Spanish populations (Barcelona, Madrid, the Basque Country), and six other Western European populations. Data on determinants by education were obtained from health interview surveys. Results: The Spanish populations have considerably smaller absolute inequalities in mortality than other Western European populations. This is due mainly to smaller inequalities in mortality from cardiovascular disease (men) and cancer (women). Inequalities in mortality from most other causes are not smaller in Spain than elsewhere. Spain also has smaller inequalities in smoking and sedentary lifestyle and this is due to more smoking and physical inactivity in higher educated groups. Conclusion: Overall, the situation with regard to health inequalities does not appear to be more favourable in Spain than in other Western European populations. Smaller inequalities in mortality from cardiovascular disease and cancer in Spain are likely to be related to its later socio-economic modernization. Although these smaller inequalities in mortality seem to be a historical coincidence rather than the outcome of deliberate policies, the Spanish example does suggest that large inequalities in total mortality are not inevitable.
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Background: Studies of the effect of macroeconomic fluctuations on mortality in different socioeconomic groups are scarce and have yielded mixed findings. We analyse mortality trends in Spain before and during the Great Recession in different socioeconomic groups, quantifying the change within each group. Methods: We did a nationwide prospective study, in which we took data from the 2001 Census. All people living in Spain on Nov 1, 2001, were followed up until Dec 31, 2011. We included 35 951 354 people alive in 2001 who were aged between 10 and 74 years in each one of the four calendar years before the economic crisis (from 2004 to 2007) and in each one of the first four calendar years of the crisis (from 2008 to 2011), and analysed all-cause and cause-specific mortality in those people. We classified individuals by socioeconomic status (low, medium, or high) using two indicators of household wealth: household floor space (<72 m(2), 72-104 m(2), and >104 m(2)) and number of cars owned by the residents of the household (none, one, and two or more). We used Poisson regression to calculate the annual percentage reduction (APR) in mortality rates during 2004-07 (pre-crisis) and 2008-11 (crisis) in each socioeconomic group, as well as the effect size, measured by the APR difference between the pre-crisis and crisis period. Findings: The annual decline in all-cause mortality in the three socioeconomic groups was 1·7% (95% CI 1·2 to 2·1) for the low group, 1·7% (1·3 to 2·1) for the medium group, and 2·0% (1·4 to 2·5) for the high group in 2004-07, and 3·0% (2·5 to 3·5) for the low group, 2·8% (2·5 to 3·2) for the medium group, and 2·1% (1·6 to 2·7) for the high group in 2008-11 when individuals were classified by household floor space. The annual decline in all-cause mortality when people were classified by number of cars owned by the household was 0·3% (-0·1 to 0·8) for the low group, 1·6% (1·2 to 2·0) for the medium group, and 2·2% (1·6 to 2·8) for the high group in 2004-07, and 2·3% (1·8 to 2·8) for the low group, 2·4% (2·0 to 2·7) for the medium group and 2·5% (1·9 to 3·0) for the high group in 2008-11. The low socioeconomic group showed the largest effect size for both wealth indicators. Interpretation: In Spain, probably due to the decrease in exposure to risk factors, all-cause mortality decreased more during the economic crisis than before the economic crisis, especially in low socioeconomic groups. Funding: None.
Article
In this essay, we ask whether the distributions of life expectancy and mortality have become generally more unequal, as many seem to believe, and we report some good news. Focusing on groups of counties ranked by their poverty rates, we show that gains in life expectancy at birth have actually been relatively equally distributed between rich and poor areas. Analysts who have concluded that inequality in life expectancy is increasing have generally focused on life expectancy at age 40 to 50. This observation suggests that it is important to examine trends in mortality for younger and older ages separately. Turning to an analysis of age-specific mortality rates, we show that among adults age 50 and over, mortality has declined more quickly in richer areas than in poorer ones, resulting in increased inequality in mortality. This finding is consistent with previous research on the subject. However, among children, mortality has been falling more quickly in poorer areas with the result that inequality in mortality has fallen substantially over time. We also show that there have been stunning declines in mortality rates for African Americans between 1990 and 2010, especially for black men. Finally we offer some hypotheses about causes for the results we see, including a discussion of differential smoking patterns by age and socioeconomic status.
Article
This study evaluates the relationship between income and mortality in Spain over a long period of declining in income inequality. The ratio between income in the richest and poorest provinces was 2.74 in 1970 and 2.10 in 2010. Pearson correlation coefficients for the association between provincial income and the measures of mortality were estimated, as well as absolute and relative differences between the mortality rates of the poorest and richest provinces. The correlation coefficient between income and infant mortality decreased from −0.59 in 1970 to −0.17 in 2010, and lost significance from 1995 onwards. The coefficient for premature all-cause mortality increased from −0.04 in 1970 to −0.40 in 2010, and acquired significance beginning in 2005. The coefficient also increased in mortality from cardiovascular, respiratory and digestive diseases. No association was found between provincial income and cancer mortality or mortality from injuries. The findings on premature mortality do not support the theory that decreasing income inequality will lead to reduced inequalities in mortality.
Article
Within Europe, women in the southern regions have the lowest inequalities in mortality. This study evaluates inequalities in mortality from different causes by educational level and their contribution to total mortality inequalities in adult women in one of these regions. The 2001 population census in the Region of Madrid was linked with deaths in the following 20 months according to the mortality registry. The population of women was stratified into three age groups, and the mortality rate ratio and mortality rate difference by educational level were estimated in each age group. The contribution of each cause of death to total mortality inequality was estimated based on the absolute index of inequality. In women aged 45-64 years, no significant relation was observed between educational level and mortality from the leading causes of death. In women aged 25-44 years and in those aged 65 and over, the mortality rate ratios and differences from the leading causes of death gradually increased from the highest to the lowest educational level. AIDS, respiratory diseases and digestive diseases, in young adult women, and cardiovascular diseases, in older women, were the causes of death that contributed most to inequality in mortality. At the beginning of the twenty-first century, mortality inequalities by educational level were not seen in middle-aged adult women in the Region of Madrid. In contrast, mortality inequalities were found in young women and in older women, although the main causes of death that contributed to these inequalities were different in each group.