Unemployment and Suicide Mortality

Transcription

Unemployment and Suicide Mortality:
Evidence from Regional Panel Data in
Europe
Christian Breuer
GFS Working Papers No. 2
February 2014
Unemployment and Suicide Mortality:
Evidence from Regional Panel Data in Europe
Christian Breuer
Ifo Institute for Economic Research at the University of Munich1
Abstract
This paper addresses the influence of economic activity on suicide mortality in Europe. To
this end, it employs a new panel dataset of 275 regions in 29 countries over the period 1999 2010. The results suggest that unemployment does have a significantly positive influence on
suicides. In-line with economic theory, this influence varies among gender- and age groups.
Males of working age (younger than 65 years) are particularly sensitive, while old-age suicide
mortality (older than 65 years) does not respond to unemployment. Moreover, real economic
growth negatively affects the suicide rates of working-age males. The results withstand tests
for robustness, such as sample variations, and after controlling for serial and spatial
autocorrelation.
Keywords:
Panel data, unemployment, suicide
JEL Classification: C 33 · E 24 · I 10
1
Ifo Institute, Poschinger Str. 5, 81679, Munich, Germany, Email: breuer@ifo.de. Phone: +49 (0) 89 9224 1265.
2
1. Introduction
Recent studies debate on the health consequences of the financial and economic crisis in
Europe. According to an increasing number of studies, health outcomes worsened and suicide
rates rose in the aftermath of the financial crisis.2 According to McKee et al. (2012),
Antonakakis (2013), and Karanikolos et al. (2013), European countries with large fiscal
adjustments are notably affected by increasing suicide rates.3 The situation of Greece is
therefore particularly controversial.4 The question of whether or not the recent phenomenon of
increasing suicide rates can be attributed to regional economic contractions in the Eurozone
has been controversially debated recently5.
According to economic theory, social living conditions, such as income, unemployment and
life expectancy, are rational determinants of suicide behaviour (Hamermesh and Soss, 1974).6
Empirical research, however, remains ambiguous as to whether unemployment affects
suicides.7 A number of studies find evidence supporting the hypothesis that unemployment
increases suicide rates.8 Other studies, however, do not share this view and criticise that the
2
See Stuckler et al. (2011a), Reeves et al. (2012), and Sullivan et al. (2013) on the effect of the economic
recession on suicide rates in the United States, and Stuckler et al. (2011b), for a first look at European data.
3
See also Krugman (2012) and Stiglitz (2012) on the negative consequences of fiscal adjustments in Europe.
4
Econoumou et al. (2011), Kentikelenis et al. (2011), Econoumou et al. (2012), and Karanikolos et al. (2013)
suggest that suicide rates in Greece have been causally affected by the economic downturn, while Fountoulakis
et al. (2012), Polyzos (2012) and Fountoulakis et al. (2013) do not share this view.
5
According to Eurostat (2013), unemployment rates in the Eurozone rose from 7.3 % in early 2008 to 11.7 % in
December, 2012 (seasonally adjusted), with large regional differences. While unemployment in Germany fell
from 8.1 % to 5.3 % in this period, for some countries the sharp rise in unemployment led to extraordinary high
rates of 26.7 % (Spain) and 30.4 % (Greece) at the end of 2012.
6
Di Tella et al. (2003) show how macroeconomic fluctuations affect happiness in Europe. According to their
results, unemployment does have a negative effect on happiness. Winkelmann and Winkelmann (2003) show
that the non-pecuniary effect of unemployment on life-satisfaction even exceeds the effect that stems from the
associated loss of income.
7
See Platt (1984) for a review of earlier empirical work on the connection between unemployment and suicidal
behaviour.
8
Ruhm (2000), Stuckler et al, (2011a), and Luo et al. (2011) provide evidence for the United States, Koo and
Cox (2008), Chen et al. (2010), Kuroki (2010), and Andrés et al. (2011), for Japan, Virén (1996) for Finland,
Tapia Granados (2005) for Spain, Brainerd (2001) for East European countries in the 1990s, Walsh and Walsh
(2011) for Ireland, and Stuckler et al. (2009), for a panel of European countries. Using micro-data, Gerdtham and
Johannesson (2003) as well as Browning and Heinesen (2012) find a positive relationship between
unemployment and suicide in Denmark and Sweden respectively.
3
evidence presented in the case of Europe suffers from methodological issues (Kunce and
Anderson, 2002, Andrés, 2005). Kunce and Anderson (2002), as well as Maag (2008) criticise
that an analysis of aggregated data at a national level may fail to identify the socio-economic
determinants of suicides and suggest analysing more disaggregated data. Only a few studies
have analysed the determinants of suicide at the subnational level to date. Ruhm (2000)
identifies a negative relationship between death rates and unemployment rates in the United
States, whereas suicides as an exception are positively correlated with unemployment. Chen
et al. (2010) find that unemployment has a positive effect on suicides for a panel of Japanese
prefectures. Using Japanese data at the municipal level, Kuroki (2010) finds evidence of a
positive relationship between unemployment and suicides for the male population, while the
relationship tends to be negative for females. Neumeyer (2004) examines subnational data for
Germany and confirms the finding of Ruhm (2000) that mortality rates are pro-cyclical.
Contrary to Ruhm (2000), however, the results of Neumeyer (2004) indicate that
unemployment rates and suicide rates in Germany are negatively correlated. In the same vein,
Andrés (2005) challenges previous results that unemployment positively affects suicides in a
panel of European countries and highlights the application of country-specific linear time
trends to account for unobserved country specific factors that are time-varying. According to
his findings, unemployment does not significantly affect suicide behaviour in Europe, after
controlling for country-specific trends.
This article contributes to the debate on how economic activity influences suicide mortality by
exploring European regional data at the NUTS-2 level. It applies a new panel dataset of 275
regions in 29 European countries over the period 1999 to 2010. The sample covers the years
of the European Monetary Union, including the economic crisis, starting in 2008. The results
suggest that unemployment does have a significant positive influence on suicides in Europe.
In-line with economic theory, this influence varies among gender- and age groups. Males of
working age (younger than 65 years) are particularly sensitive, while old-age suicide mortality
4
(older than 65 years) does not respond to unemployment. Moreover, economic growth
negatively affects the suicide behaviour of working-age males. The results withstand tests for
robustness, such as sample variations, as well as controls for serial and spatial autocorrelation.
2. Background
Since the seminal works of Wagner (1864) and Durkheim (1897), a large body of literature
has analysed the cultural, social and economic correlates of suicide. Wagner (1864) pointed to
the impact of religion, and found a different pattern of suicide behaviour in protestant and
catholic regions in Europe.9 Early contributions, however, pointed to the influence of socioeconomic factors. According to Durkheim (1897), social (dis-) integration and the individual
environment affect the incidence of suicides.
Modern economic theory suggests that living conditions, such as income, status or
unemployment, may explain suicidal behaviour. According to Hamermesh and Soss (1974),
an individual i at age a, with a permanent income Y takes his own life when the total
discounted lifetime utility Z i plus the individual’s taste for living bi reaches zero:
Z i (a, Y )  bi  0,
(1)
with

Z i (a, Y ) =  e r ( m a )U m P (m)dm ,
a
9
(2)
Becker and Wössmann (2011) find a significant difference in the suicide behaviour of Protestants and Catholics
when looking at historical data on Prussia. Koo and Cox (2008), as well as, Gearing and Lizardi (2009) argue
that societies of Christian origin regard suicide as a sin and may, thus, exhibit different suicide behaviour. Based
on this, it is possible that suicide behaviour is different in Europe, as compared to other regions. Yang and Lester
(1995) argue that unemployment only affects suicide behaviour in the US, while the effect is weak or
insignificant in other countries.
5
where  is the highest attainable age, r is the discount rate and U is the expected utility at age
m. P(m) is the probability of survival to age m given survival to age a.
The expected utility U m is negatively related to the individual’s age m and positively related
to his or her permanent income Y. The individual’s taste for living bi is supposed to be
normally distributed, so that the age-adjusted aggregate suicide rate, defined as the fraction of
individuals in the age group a for whom Z (a, Y ) reaches  b , is inversely related to
permanent income Y.
Based on this framework, Hamermesh and Soss (1974) outline testable hypotheses and argue
that an increase in income per capita or life expectancy reduces the likelihood of suicides.
They empirically demonstrate a positive relationship between unemployment and suicide
mortality in the United States. It is worthwhile to extend the simple economic framework and
to include other non-pecuniary factors that may increase lifetime utility. According to
Durkheim (1897), social integration may decrease the likelihood of suicides, arguably because
it increases expected utility. In this line, a number of factors other than income are likely to
influence utility. To control for non-pecuniary factors, empirical studies analysed the effects
of demographic, social or environmental variables on suicide mortality, such as income (Noh,
2009), fertility (Andrés, 2005, and Kuroki, 2009), divorce rates (Andrés, 2005), crime rates
(Brainerd, 2001), income distribution (Leigh and Jencks, 2007), alcohol consumption (Walsh
and Walsh, 2005), civil liberty (Jungeilges and Kirchgässner, 2002), or weather conditions
(Neumayer, 2003b).
6
3. Data and descriptive statistics
Contrary to previous research, this study relies on regional data at the NUTS-2 level and
draws on panel data over the period 1999 – 2010 from the Eurostat regional statistics
database. The cross-section includes 275 regions in 29 countries, including all EU-27 and
EFTA countries, with the exception of Denmark and Liechtenstein.10
To model suicide behaviour, (age-adjusted) suicide rates per 100,000 inhabitants are used. As
for explanatory variables, regional unemployment rates in the population aged 15 years and
above are treated as the primary proxy of a region’s (cyclical) economic condition. Eurostat
provides data on unemployment at the regional level, starting in 1999. The full sample covers
the period 1999-2010. Figure 1 shows aggregated suicide rates, as well as unemployment
rates in the EU-27, during the 1999 to 2010 sample period.
Figure 1
Unemployment and Suicide Mortality
14
12
10
8
6
4
2
Suicide rate (per 100,000 inhabitants)
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
0
Unemployment rate (%)
10
The total sample for all 31 EU-27 and EFTA countries includes 286 regions. For Denmark and Liechtenstein
data is not available at the NUTS-2 level. The panel is unbalanced, due to missing observations for various
regions and years.
7
Source: Eurostat.
The trend in both rates decreases during 1999 to 2007. Both rates, however, increased
substantially after the economic crisis in 2008. This bird’s view suggests a positive
relationship between unemployment and suicide mortality for the EU-27. Figure 2 shows the
age-adjusted suicide rates of different parts of the population. I distinguish between workingage and old-age groups (< 65 years and > 65 years, respectively), as well as between genderspecific (male and female) suicide rates. With its greater exposure to negative income shocks,
theory suggests that unemployment effects on suicide behaviour are particularly strong in the
working-age population.
In comparison, economic theory predicts no direct unemployment effect on the suicide
behaviour of pensioners. This pattern is reflected in Figure 2: the suicide rates of working age
males (< 65 years) and unemployment rates exhibit a very similar development, while the
suicide rates of other groups are not related to unemployment at first view.
Figure 2
Suicide Mortality of Gender- and Age Groups
50
Unemployment rate
(%)
45
40
Male suicide rate (less
than 65 years), per
100,000
35
30
Male suicide rate (> 65
years), per 100,000
25
20
Female suicide rate
(less than 65 years),
per 100,000
15
10
Female suicide rate (>
65 years), per 100,000
5
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
0
Source: Eurostat.
8
Table 1 shows descriptive statistics. In addition to suicide and unemployment rates, it includes
fertility, heating degree days, and measures of life expectancy, as well as GDP per capita and
the regional growth rate of gross value added (GVA).
Table 1
Descriptive Statistics
Variable
Mean
Min
Max
S. D.
Obs.
Suicide rate
Male suicide rate
Female suicide rate
Suicide rate ( < 65 years)
Male suicide rate ( < 65 years)
Female suicide rate ( < 65 years)
Suicide rate ( > 65 years)
Male suicide rate ( > 65 years)
Female suicide rate ( > 65 years)
Unemployment rate
Fertility
Weather
Life expectancy
Male life expectancy
Female life expectancy
Life expectancy at age 65
Male life expectancy at age 65
Female life expectancy at age 65
GDP per capita (Euro)
Economic growth
12.59
19.70
5.81
11.02
17.12
4.92
19.69
34.75
10.01
8.29
1.54
7.54
78.74
75.72
81.70
18.43
16.50
20.03
21,175
2.40
0.70
1.10
0.30
0.40
0.70
0.30
1.00
1.80
0.30
0.80
0.84
0.06
70.40
64.70
74.10
13.70
12.10
14.70
1,100
-15.60
48.00
80.70
30.60
45.00
80.70
18.90
96.70
172.60
81.20
32.80
3.94
18.33
84.00
81.80
87.00
22.40
20.40
24.10
93,900
22.80
6.41
10.44
3.28
5.79
9.48
2.78
12.37
22.26
7.30
4.96
0.32
2.60
2.68
3.18
2.28
1.59
1.63
1.68
11,039
3.31
2,753
2,725
2,700
2,620
2,617
2,574
2,610
2,601
2,479
3,300
3,113
2,926
3,114
3,114
3,114
3,114
3,114
3,114
2,694
1,808
Source: Eurostat. The full sample with 286 regions and 12 years includes 3,432 observations. The panel is
unbalanced, due to missing observations for various regions and years.
Figure 3 depicts the regional distribution of suicide rates (per 100,000 inhabitants) in Europe
for 2010, the last year for which data is available. In this year, countries in South Europe, like
Greece and Spain, have already been affected by the economic crisis and have exhibited high
levels of unemployment. Suicide rates in this year, however, were low in South European
9
countries, as compared to countries in Central Europe. An analysis of cross-regional
differences in crude suicide rates may be inconclusive, because it is likely that unobservable
region-specific factors like climate, culture or other determinants, may influence reported
suicide rates (Andrés, 2005). It is arguable that unobserved factors may be heterogeneous
between regions, but of low volatility over time, as for example alcohol consumption or
income distribution (Leigh and Jencks, 2007). Empirical studies on the relationship between
suicide rates and socio-economic factors thus often rely on an analysis of fixed-effects panel
models to capture the effects of unobserved regional factors that are not time-varying with
(cross-section) fixed effects (Neumayer, 2003a and 2003b).
Figure 3
Regional Distribution of Suicide Mortality in Europe (2010)
Source: Eurostat.
10
4. Empirical strategy
To examine the relationship between suicide mortality and regional economic and social
determinants, this paper applies two-way fixed effects regressions of the following form:
S jt   j   t   j t  U jt   X jt    jt
(3)
where j and t index regions and years, respectively. S jt denotes the respective agestandardized suicide rate (of the total population and the male and female populations
separately) in a region j at time t.  j captures region-specific fixed-effects that cannot be
attributed to the explanatory variables.  t represents time fixed-effects to control for effects
that influence the suicide rates of all regions in one year. The application of two-way fixed
effects panel estimations is a well-established strategy and similar to the approaches of Ruhm
(2000) and Brainerd (2002). According to Andrés (2005), equation (4) contains linear trends (
 j t ) for all regions j, with t = 1, …, T to account for the influence of unobserved regional
factors that are time-varying. Following Ruhm (2000), S jt denotes either, level-, or the
natural logarithm of level values (of suicide rates). U jt denotes the unemployment rate in a
certain region and year. X jt is a vector of economic, social, and environmental controls. The
baseline equation includes fertility rates, gender specific life expectancy, and a proxy for the
influence of weather (heating degree days, divided by 365).11 In the European context, it is
conceivable that a cold climate decreases the quality of life and, thus, increases the incidence
of suicide, particularly in a cross-section analysis. This paper runs equation (3) for suicide
rates of the total population, male and female population and distinguishes between different
11
A number of studies analysed the influence of weather on suicide behaviour, with mixed results (see Breuer et
al., 1986; Deisenhammer, 2003; Dixon and Kalkstein, 2009).
11
age groups (younger than 65 years of age and older than 65 years). To distinguish between
unemployment and the influence of other economic phenomena, the full set of controls
includes the annual growth rate of real gross value added (GVA), as a proxy for economic
growth, and regional log gross domestic product (GDP) per capita. Due to missing
observations, the additional economic controls reduce the number of observations, so all
equations are run both with and without the economic control variables, economic growth and
log GDP per capita.
Figure 3 indicates that neighbouring regions, particularly of the same country, might exhibit a
similar suicide pattern. To test whether the results are influenced by serial or spatial
autocorrelation, equation (4) includes the first lag of (regional) suicide rates, as well as a
spatial lag of suicide rates in neighbouring regions:
S jt   j   t   j t  U jt   X jt   S jt 1  WS jt    jt
(4)
, where WS jt denotes the (weighted) average suicide rate in neighboring regions of the same
country, with WS jt 
S njt * Pjtn  S jt * Pjt
P  Pjt
n
jt
, where S njt indicate the national suicide rate, Pjtn the
national population, and Pjt the regional population.12
12
This procedure is equivalent to an application of a weight matrix based on neighbouring regions, where a
neighbour is a region of the same country and neighbouring regions are weighted by their population size.
12
5. Results
a) Benchmark results
Table 2 reports the results of the benchmark regressions of equation (3). Columns 1 and 2
display the results for total population (both gender types and all age groups). Columns 3 and
4 report the results for male suicide rates, and Columns 5 and 6 show the results for female
suicide rates. Regressions in even-numbered columns treat the natural logarithm of suicide
rates as endogenous variables, while suicide rates in odd-numbered columns are in levels.
According to Ruhm (2000), this paper distinguishes between level and log level values to
investigate the functional form of a possible relationship between suicides and their
determinants. The tables show Driscoll-Kraay (1998) standard errors that allow correlations
of the error term across time and regions. As suggested by Andrés (2005), all regressions
contain cross section and year fixed effects, as well as region-specific trends to control for
unobserved factors.
The benchmark regression shows that the suicide rate of the total population is positively
related to unemployment. This relationship is particularly pronounced for males. The
coefficient for unemployment displays the estimated quantitative relationship between
unemployment and suicide mortality. Columns no. 1, 3 and 5 show the mean reaction of
suicide rates to an increase in unemployment rates. If unemployment rises by 1 percentage
point, suicide rates increase by 0.09 (per 100.000 inhabitants). Male suicides increase by 0.21
(per 100.000 male inhabitants). The relationship is positive for females too, but statistically
not significant. The interpretation of coefficient  in column no. 2, 4 and 6 differs from this
result: if unemployment rises by 1 percentage point, the suicide rate rises by 0.55 %, and male
suicides rise by approximately 1 %. Accordingly, a 1 percentage point increase in
unemployment would increase suicide cases of male population by approximately 500 per
13
year in the EU-27. The positive influence of unemployment on female suicide mortality turns
out to be insignificant. The effect is little in absolute cases, as compared to male suicides,
because the absolute number of female suicides is low on average, as compared to male
suicides.
Table 2
Fixed Effects Regressions, Benchmark
Dependent variable: Suicide mortality rate
Expl. variables
Unemployment
Fertility
Weather
Life expectancy
Total population
Men
Women
level
log
level
log
level
log
0.092***
(0.021)
0.394
(1.039)
0.188**
(0.080)
-0.751**
(0.334)
0.551*
(0.257)
0.069
(0.113)
0.015*
(0.008)
-0.063***
(0.017)
0.212***
(0.031)
2.747
(1.764)
0.359*
(0.175)
1.020***
(0.245)
0.184
(0.150)
0.016
(0.010)
0.021
(0.016)
-2.215
(1.809)
0.001
(0.053)
0.464
(0.343)
-0.170
(0.238)
0.004
(0.010)
-1.528**
(0.485)
-0.075***
(0.016)
L. e.: male
-0.500*** -0.099***
(0.098)
(0.012)
L. e.: female
Cross-section FE
Period FE
Regional trends
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
2,253
2,253
2,252
2,252
2,232
2,232
No. of regions
259
259
259
259
259
259
0.489
0.366
0.440
0.340
0.351
0.295
R²
Note: Dependent variable: age-adjusted suicide rate per 100,000 inhabitants (level and log). Explanatory
variables are unemployment rate (above 15 years old), fertility, weather conditions (heating degree days, divided
by 365 days) and life expectancy (at birth). Driscoll-Kraay (1998) standard errors in parentheses. *, **, ***
indicate significance at the 10, 5, 1% level.
14
Column 3 indicates that male suicides respond to weather conditions. Accordingly, the suicide
rates increase in particularly cold years. An increase in the number of heating degree days per
year of 1 on average increases the suicide rate of males by 0.36. Moreover, the results indicate
that male and female suicides are negatively related to life expectancy. This latter result
confirms earlier results in literature on this topic (e.g. by Brainerd, 2001).
b) Additional controls
Table 3 reports the results of equation (3) after additionally including economic controls. The
results show that real economic growth (gross value added) is negatively correlated with male
suicide rates, while GDP per capita only has a statistically significant negative effect on
suicide mortality in row no. 1 and 2. An increase in real GVA of 1 percent would decrease
male suicide mortality by 0.5 percent. This result does not, however, diminish the relationship
between unemployment and suicide mortality, which remains statistically significant for
males in both specifications. Accordingly, two separate factors positively affect male suicides
in an economic downturn: firstly, a decrease in growth, and secondly, an increase in
unemployment. This finding underlines the economic effects on suicides in the recent
economic downturn in Southern Europe.
Life expectancy is also negatively correlated with suicide for both gender groups and
statistically significant, while the effect of weather and fertility turns out to be insignificant.
15
Table 3
Fixed Effects Regressions, Full Set of Controls
Expl. variables
Unemployment
Fertility
Weather
Life expectancy
Total population
Men
Women
level
log
level
log
level
log
0.132**
(0.046)
-1.574
(1.744)
-0.182
(0.259)
-0.653
(0.418)
0.773
(0.452)
-0.214
(0.208)
-0.022
(0.019)
-0.057**
(0.019)
0.310***
(0.081)
-2.423
(1.935)
-0.116
(0.350)
1.315***
(0.376)
-0.266*
(0.140)
-0.021
(0.017)
0.038
(0.033)
-1.457
(2.689)
-0.180
(0.203)
1.082*
(0.565)
0.038
(0.455)
-0.010
(0.029)
-1.568*
(0.693)
-0.077**
(0.025)
-0.087***
(0.021)
0.135
(0.212)
-0.004
(0.184)
L. e.: male
-2.192
(2.175)
-0.101**
(0.031)
-0.034
(0.076)
-0.495***
(0.108)
-0.629*
(0.279)
-2.087
(1.659)
-0.014
(0.026)
L. e.: female
GDP per capita
Economic growth
Cross-section FE
Period FE
Regional trends
Observations
No. of regions
R²
-2.965* -0.145**
(1.585)
(0.051)
-0.042*** -0.281**
(0.013)
(0.091)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1,429
212
0.502
1,429
212
0.363
1,429
212
0.449
1,429
212
0.337
1,412
212
0.416
1,412
212
0.313
by 365 days), life expectancy (at birth), GDP per capita (log), and economic growth (annual growth rate of gross
value added). Driscoll-Kraay (1998) standard errors in parentheses. *, **, *** indicate significance at the 10, 5,
1% level.
16
c) Different age groups
Tables 4 and 5 distinguish between age-specific suicide rates. Table 4 reports results for the
working-age population (< 65 years), while Table 5 includes results for the old-age population
(> 65 years). Note that unemployment has an effect on both, male and female suicide rates at
ages below 65, but has no influence on suicide at ages above 65, a result very much in line
with the economic theory. If unemployment rises by 1 percentage point, both, male and
female suicides (of the working age population) rise by approximately 1 percent.
Accordingly, unemployment increases suicides of working-age population (with an elasticity
of approximately one for both gender groups), but has no statistically significant effect on
suicide behavior in the age above 65. Beside unemployment, the effect of weather does have a
statistically significant positive effect on the suicides of working-age males. Furthermore, life
expectancy continues to negatively impact suicides in all age and gender groups.
d) Serial and spatial autocorrelation
To test whether the results are influenced by serial or spatial autocorrelation, equation (4)
includes the first lag of the regional suicide rate, as well as a spatial lag, where the suicide rate
in neighbouring regions is the population-weighted suicide rate in all regions of the same
country. Table 6 shows the results of equation (4). The lagged suicide rate, as well as the
suicide rate in neighbouring regions, turns out to be statistically significant. The positive sign
of the spatial lag indicates that, beyond regional characteristics, there may be determinants at
the national level that simultaneously influence suicide rates in all regions of a country. This
influence, however, does not diminish the positive effect of regional unemployment on
regional suicide mortality, which turns out to be statistically significant in most of the
17
specifications. The effect of regional weather on suicides now turns out to be negative. It is
conceivable that the influence of spatial autocorrelation in weather might influence the results.
The negative effect of life expectancy on suicides, however, remains robust.
Table 4
F. E. Regressions, Age Group < 65 Years
Expl. variables
Unemployment
Fertility
Weather
Life expectancy
Total population
Men
Women
level
log
level
log
level
log
0.117***
(0.021)
1.991
(1.403)
0.216**
(0.092)
-0.796*
(0.362)
0.866**
(0.286)
0.243*
(0.129)
0.019**
(0.007)
-0.064**
(0.021)
0.235***
(0.036)
4.250*
(2.287)
0.400**
(0.171)
1.184***
(0.281)
0.311**
(0.135)
0.019**
(0.008)
0.038
(0.022)
-0.793
(1.283)
0.003
(0.052)
0.980*
(0.523)
0.102
(0.285)
0.001
(0.009)
-1.379**
(0.447)
-0.065***
(0.013)
-0.624***
(0.121)
-0.130***
(0.032)
L. e.: male
L. e.: female
Cross-section FE
Period FE
Regional trends
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
No. of regions
2,252
259
2,252
259
2,251
259
2,251
259
2,216
259
2,216
259
R²
0.455
0.324
0.428
0.303
0.264
0.237
18
Table 5
F. E. Regressions, Age Group > 65 Years
Expl. variables
Unemployment
Fertility
Weather
L. e. at age 65
Total population
Men
Women
level
log
level
log
level
log
-0.016
(0.071)
-4.612
(5.661)
0.082
(0.153)
-1.131
(0.668)
-0.022
(0.314)
0.040
(0.221)
0.023
(0.020)
-0.063
(0.050)
0.007
(0.088)
-0.801
(3.524)
0.126
(0.417)
0.137
(0.270)
0.147
(0.187)
0.025
(0.024)
0.040
(0.041)
-4.317
(5.345)
-0.227
(0.193)
0.566
(0.601)
-0.094
(0.268)
-0.008
(0.028)
-3.505**
(1.214)
-0.145**
(0.050)
L. e.: male
-1.604** -0.099***
(0.628)
(0.025)
L. e.: female
Cross-section FE
Period FE
Regional trends
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
No. of regions
2,242
259
2,242
259
2,235
259
2,235
259
2,155
259
2,155
259
R²
0.382
0.253
0.265
0.190
0.345
0.244
19
Table 6
Fixed Effects Regressions, Controlling for Autocorrelation
Expl. variables
Total population
level
Unemployment
Fertility
Weather
Life expectancy
log
0.072***
0.407
(0.019)
(0.275)
-0.140
-0.089
(0.652)
(0.095)
-0.284*** -0.024***
(0.057)
(0.004)
-1.087***
-0.083***
(0.248)
(0.015)
L. e.: male
L. e.: female
Lagged suicide rate
Spatial lag
Cross-section FE
Period FE
Regional trends
Observations
No. of regions
R²
-0.150**
(0.048)
0.704***
(0.042)
Men
Women
level
log
level
log
0.135***
(0.018)
0.757
(0.676)
-0.368***
(0.098)
0.886**
(0.335)
0.010
(0.109)
-0.023***
(0.005)
0.033***
(0.010)
-0.559
(0.888)
-0.152**
(0.048)
0.443
(0.394)
-0.133
(0.245)
-0.025*
(0.013)
-1.591***
(0.247)
-0.064***
(0.016)
-0.468*** -0.090***
(0.067)
(0.024)
-0.194** -0.204*** -0.232** -0.163*** -0.120***
(0.062)
(0.052)
(0.073)
(0.027)
(0.029)
0.730***
0.646***
0.650*** 0.470*** 0.472***
(0.044)
(0.062)
(0.087)
(0.111)
(0.090)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1,996
250
0.511
1,996
250
0.400
1,973
250
0.442
1,973
250
0.349
1,944
250
0.420
1,944
250
0.321
by 365 days), life expectancy (at birth), lagged suicide rate, and the spatial lag of the suicide rate. Driscoll-Kraay
(1998) standard errors in parentheses. *, **, *** indicate significance at the 10, 5, 1% level.
20
e) Robustness
To ensure that the results are robust, the sample size is varied and all regions of one country
are consecutively excluded in the benchmark regression. Table 7 contains the results of these
sample variations (total population, suicide rates in levels), after excluding country by
country. The main finding, namely that unemployment affects suicide mortality, remains
robust for all sample adjustments. Furthermore, an increase in life expectancy decreases
suicide mortality for every sample.
The results are robust to further tests, particularly after including economic controls in agespecific regressions for both young and old-age groups. Moreover, exogenous variables, with
the exception of unemployment, are excluded in the benchmark, as well as in age- and
genderspecific regressions. Controls for serial and spatial autocorrelation are also included
in all regressions for both age groups. The finding that unemployment affects suicide
mortality, particularly among young males, remains statistically significant.
21
Table 7
Sensitivity Analysis
Country excluded
Belgium
Bulgaria
Czech Republic
Germany
Estonia
Ireland
Greece
Spain
France
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Iceland
Norway
Switzerland
Full Sample
unemployment
0.093***
0.099***
0.093***
0.106***
0.089***
0.093***
0.100***
0.117***
0.091***
0.098***
0.093***
0.088***
0.082***
0.091***
0.082***
0.093***
0.094***
0.096***
0.094***
0.085***
0.092***
0.092***
0.094***
0.088***
0.085***
0.091***
0.103***
0.094***
0.088***
0.092***
S. E.
0.020
0.021
0.020
0.027
0.018
0.021
0.020
0.027
0.019
0.023
0.021
0.020
0.016
0.019
0.023
0.021
0.020
0.021
0.035
0.022
0.021
0.021
0.021
0.025
0.023
0.022
0.022
0.019
0.019
0.021
Obs.
2,209
2,217
2,173
1,979
2,242
2,231
2,123
2,078
2,011
2,100
2,247
2,245
2,242
2,242
2,183
2,243
2,157
2,163
2,109
2,203
2,205
2,253
2,225
2,209
2,173
2,005
2,242
2,185
2,190
2,253
Regions
248
253
251
223
258
257
246
241
237
242
258
258
258
258
252
258
247
250
243
254
251
259
255
254
251
228
258
252
252
259
R² within
0.481
0.493
0.487
0.486
0.474
0.489
0.497
0.494
0.498
0.492
0.489
0.488
0.477
0.491
0.451
0.491
0.494
0.493
0.499
0.498
0.489
0.489
0.487
0.491
0.496
0.495
0.49
0.496
0.494
0.489
Note: Dependent variable: age-adjusted suicide rate per 100,000 inhabitants (level). Explanatory variables are
unemployment rate (above 15 years old), fertility, weather conditions (heating degree days, divided by 365 days)
and life expectancy (at birth). Driscoll-Kraay (1998) standard errors in parentheses. *, **, *** indicate
significance at the 10, 5, 1% level.
22
6. Conclusion
It is a well-established finding in the empirical literature on the determinants of health and
mortality that unemployment affects suicides in developed countries. Evidence from the
United States (Ruhm, 2000), Japan (Kuroki, 2010), and selected European countries
(Brainerd, 2001) shows that unemployment increases suicide mortality. Stuckler et al. (2009),
find a positive correlation between unemployment and suicides in a panel of 24 European
countries. Other authors, however, challenge these findings. According to Andrés (2005), the
correlation of unemployment and suicide is biased and disappears after controlling for timevarying local factors with region-specific trends.
This paper addresses the influence of unemployment on suicide mortality with a focus on
Europe. To that end, it employs a new regional panel dataset of 275 European regions in 29
countries over the period 1999 to 2010. The sample covers the years of European Monetary
Union, as well as the economic crisis starting in 2008. In contrast to Andrés (2005), the results
suggest that unemployment does have a significant positive influence on suicide mortality.
This influence varies among gender and age groups. Males of working age (younger than 65
years) are particularly sensitive, while old-age suicide mortality (older than 65 years) does not
respond to fluctuations in unemployment. These results confirm the theoretical prediction that
unemployment increases suicide mortality (of the working-age population), but has no effect
on suicides in the old-age population. This finding highlights the theoretical assumption that
unemployment implies a negative shock in expected income for the (unemployed share of the)
working-age population, while the old-age population does not suffer (directly) from
unemployment. Moreover, my findings indicate that real economic growth negatively affects
suicide rates of working-age males. According to this, two separate factors negatively affect
23
male suicides in an economic downturn: firstly, a decrease in growth, and secondly, an
increase in unemployment.
The results hold after robustness tests, such as sample variations and after including regionspecific trends. Moreover, suicide mortality responds to life expectancy, a result that supports
the economic theory (Hamermesh and Soss, 1974).
These results are in line with those presented by a large body of empirical literature on the
determinants of suicide mortality, particularly for non-European countries (Ruhm, 2000; Koo
and Cox, 2008; Kuroki, 2010; Luo et al., 2011; Stuckler et al., 2011), but contrary to previous
findings for Europe (Neumeyer, 2004, Andrés, 2005). The different finding may reflect the
application of new regional data and different samples. While Andrés (2005) explores crossnational data for 27 European countries over the period 1970 to 1998, this paper investigates a
panel dataset of 275 European regions over the period 1999 to 2010. It is conceivable that
previous studies fail to significantly identify the relationship between unemployment and
suicide because of a relatively large level of aggregation (Kunce and Anderson, 2004, and
Maag, 2008). The new evidence presented in this paper, may, thus, reflect the application of
more disaggregated data and a considerable improvement in the sample size.
The conflicting results of previous studies of European data at the national level, as presented
in Andrés (2005) and Stuckler et al. (2009), remain a challenge for future research,
particularly because both studies apply national panel data, provided by the WHO, starting in
1970. It is conceivable that the different findings reflect differing empirical strategies or
sample sizes.
In-line with Stuckler et al. (2009) and based on a new panel dataset at the regional level, my
findings suggest that a one percentage point increase in unemployment is associated with an
(approximately) one percent increase in suicides among individuals aged younger than 65
years.
24
The results strengthen the assumption that the recent economic crisis will be accompanied by
an increase in suicide mortality, particularly in South Europe. Sullivan et al. (2013) highlight
the need for suicide prevention strategies in the light of increasing suicide rates in the United
States. The results presented in this paper suggest taking into account the effects of economic
fluctuations on mental health and developing a viable suicide prevention strategy against the
background of on-going fiscal and economic adjustments in Europe.
25
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30
Appendix
Table A 1
Fixed Effects Regressions, Full Set of Controls, Age Group < 65 Years
Explanatory variables
Unemployment
Fertility
Weather
Life expectancy
Total population
Men
Women
level
log
level
log
level
log
0.201***
(0.056)
0.027
(1.677)
-0.084
(0.178)
-0.539
(0.386)
1.857**
(0.584)
-0.107
(0.209)
-0.015
(0.019)
-0.055**
(0.023)
0.397***
(0.091)
0.002
(2.242)
0.015
(0.294)
2.221***
(0.509)
-0.152
(0.134)
-0.015
(0.020)
0.076**
(0.033)
-0.413
(2.111)
-0.173
(0.114)
2.065**
(0.797)
0.162
(0.490)
-0.023
(0.026)
-1.365**
(0.597)
-0.064***
(0.019)
Life expectancy: male
Life expectancy: female
GDP per capita
Economic growth
Cross-section fixed effects
Period fixed effects
Region-specific trends
Number of observations
Number of regions
R²
-0.485*** -0.087**
(0.146)
(0.030)
-0.197
0.135
(1.289)
(0.299)
-0.017
-0.162
(0.019)
(0.474)
-1.578
(1.256)
-0.046***
(0.014)
-0.160
(0.133)
-0.271**
(0.106)
-1.633
(1.805)
-0.087**
(0.034)
-0.085
(0.086)
-0.350**
(0.112)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1,428
212
0.477
1,428
212
0.346
1,428
212
0.454
1,428
212
0.331
1,399
212
0.295
1,399
212
0.262
1% level.
31
Table A 2
Fixed Effects Regressions, Full Set of Controls, Age Group > 65 Years
Total population
level
Unemployment
Fertility
Weather
Life expectancy
-0.101
(0.071)
-9.345
(8.962)
-0.790
(0.506)
-0.549
(0.333)
log
Men
level
Women
log
-0.060
-0.059
0.049
(0.362)
(0.228)
(0.312)
-0.199
-8.087
-0.133
(0.287)
(4.697)
(0.224)
-0.055*** -1.514*** -0.059***
(0.015)
(0.441)
(0.011)
-0.033
(0.030)
-2.097*
-0.071
(1.122)
(0.048)
Economic growth
Number of regions
R²
log
0.056
(0.052)
-4.897
(9.134)
-0.386
(0.572)
1.778*
(0.882)
-0.029
(0.520)
0.002
(0.042)
-0.053
(0.049)
0.049
(0.392)
0.779
(0.897)
-7.125
(4.861)
-0.052
(0.072)
0.054
(0.221)
-0.273
(0.293)
-4.489
(7.438)
-0.050
(0.172)
0.020
(0.257)
-0.178
(0.391)
-1.056
(1.033)
-9.372
(5.758)
0.044
(0.059)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1,421
212
0.396
1,421
212
0.283
1,417
212
0.281
1,417
212
0.235
1,361
212
0.345
1,361
212
0.234
GDP per capita
level
1% level.
32
Table A 3
Fixed Effects Regressions, Controlling for Autocorrelation, Age Group < 65 Years
Total population
level
Unemployment
Fertility
Weather
Life expectancy
0.090***
(0.021)
1.149
(0.850)
-0.156***
(0.043)
-0.948***
(0.196)
Spatial lag
Number of regions
R²
log
0.579***
(0.157)
0.042
(0.077)
-0.008
(0.008)
-0.077***
(0.017)
level
0.191***
(0.037)
2.675
(1.488)
-0.184
(0.101)
Women
log
1.100***
(0.274)
0.131
(0.105)
-0.008
(0.005)
level
0.023
(0.018)
-0.821
(1.298)
-0.092
(0.083)
log
0.777
(0.593)
0.025
(0.266)
-0.015
(0.015)
-1.377***
(0.214)
Lagged suicide rate
Men
-0.186***
(0.048)
0.610***
(0.059)
-0.054***
(0.012)
-0.551***
-0.107**
(0.133)
(0.041)
-0.240*** -0.238*** -0.253** -0.155*** -0.187***
(0.062)
(0.051)
(0.078)
(0.032)
(0.021)
0.758*** 0.567*** 0.674*** 0.291*** 0.471***
(0.068)
(0.051)
(0.091)
(0.049)
(0.065)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1,994
250
0.465
1,994
250
0.386
1,993
250
0.437
1,993
250
0.351
1,947
249
0.317
1,947
249
0.301
by 365 days), life expectancy (at birth), GDP per capita (log), lagged suicide rate, and the spatial lag of the
suicide rate. Driscoll-Kraay (1998) standard errors in parentheses. *, **, *** indicate significance at the 10, 5,
1% level.
33
Table A 4
Fixed Effects Regressions, Controlling for Autocorrelation, Age Group > 65 Years
Total population
level
Unemployment
Fertility
Weather
Life expectancy
0.031
(0.037)
-1.493
(2.602)
-0.706***
(0.211)
-0.871***
(0.159)
0.407
(0.250)
0.131
(0.158)
-0.063***
(0.017)
-0.067**
(0.022)
Lagged suicide rate
Spatial lag
Number of regions
R²
-0.189***
(0.039)
0.623***
(0.100)
Men
log
level
Women
log
0.124
(0.080)
-2.519
(2.832)
-1.193***
(0.300)
0.480*
(0.223)
0.264
(0.175)
-0.042*
(0.023)
-2.463***
(0.720)
-0.067
(0.039)
level
0.058**
(0.025)
-0.148
(3.474)
-0.132
(0.304)
log
1.395
(0.937)
0.034
(0.319)
-0.007
(0.041)
-0.047
-0.038
(0.129)
(0.022)
-0.201*** -0.179*** -0.209*** -0.196*** -0.205***
(0.025)
(0.050)
(0.022)
(0.045)
(0.034)
0.353***
0.333*
0.045
0.478**
0.152
(0.086)
(0.176)
(0.080)
(0.187)
(0.121)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1,982
250
0.425
1,982
250
0.264
1,972
249
0.299
1,972
249
0.225
1,874
248
0.382
1,874
248
0.284
by 365 days), life expectancy (at birth), GDP per capita (log), lagged suicide rate, and the spatial lag of the
suicide rate. Driscoll-Kraay (1998) standard errors in parentheses. *, **, *** indicate significance at the 10, 5,
1% level.
34

Unemployment and Suicide Mortality

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