The impact of unemployment on heart disease and stroke mortality in European Union Countries.
Date: 2016-07-22
Institutions: Directorate-General for Employment, Social Affairs and Inclusion
Subjects: EU Member State, cardiovascular disease, economic growth, economic recession, gross domestic product, labour market, mortality, public health, report, unemployment, working population

The Impact of Unemployment 
on Heart Disease and Stroke 
Mortality in  
European Union Countries 

M. Harvey Brenner, PhD 
Professor of Behavioral and Community Health 
University of North Texas Health Science Center 

Professor of Epidemiology (Visiting) 
Hannover Medical University 

Professor of Health Policy & Management 
Johns Hopkins University, Bloomberg School of Public Health 

May – 2016 

 
 
 
 
 
 
 
 
 
 
 
 
 
"EaSI" 

This  publication  has  received  financial  support  from  the 
European  Union  Programme  for  Employment  and  Social 
Innovation 
and 
(2014-2020) 
#VT/2013/112 
#VT/2014/079.  For  further 
information  please  consult: 
http://ec.europa.eu/social/easi  .  The  information  contained 
in  this  publication  does  not  necessarily  reflect  the  official 
position of the European Commission. 

EUROPEAN COMMISSION 

Directorate-General for Employment, Social Affairs and Inclusion 
Directorate A — Employment and Social Governance 
Unit A4 — Thematic Analysis 
Contact: Filip Tanay 

E-mail:  filip.tanay@ec.europa.eu  

European Commission 
B-1049 Brussels 

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

The Impact of Unemployment 
on Heart Disease and Stroke 
Mortality in  

European Union Countries 

2016 

Directorate-General for Employment, Social Affairs and Inclusion 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The Impact of Unemployment on Heart Disease and Stroke Mortality in 
European Union Countries 

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Luxembourg: Publications Office of the European Union, 2016 

ISBN: 978-92-79-59741-1 
Doi: 10.2767/81253  

© European Union, 2016 
Reproduction is authorised provided the source is acknowledged. 

4 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Tables of Contents 

I. 

Summary 

II. 

III. 

Introduction 

Literature background 

Socioeconomic status and population health    

Impact of unemployment on population health: national versus  

Page  

6 

8 

10 

10 

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individual-level studies                                                                                                 

IV. 

Hypotheses 

Key Economic Variables 

Control Variables 

V. 

Research Method 

Random versus fixed effects 

Deductive logic of model construction 

Further deductive approach: Replication of the heart disease model 

Adding GDP while taking account of Okun’s Law 

Logic of the sequence of analysis 

VI. 

Databases and analysis 

VII. 

Findings 

Analysis of relation between unemployment and heart disease mortality 

First step: unemployment rate as the sole predictor 

Second step: adding GDP while taking account of Okun’s Law 

Third step: inclusion of additional variables in models relating 
unemployment rates to IHD 

Detailed findings 

Analysis of relation between unemployment and stroke mortality 

Stroke mortality rate model as replication of heart disease mortality 
rate model 

Tests of relations of principal economic variables to stroke mortality 
rates 

Expansion of model to include GDP per capita at 10 year’s lag 

Expansion of model to include unemployment rate at 3 year’s lag 

VIII.  Discussion and Interpretation 

IX. 

Conclusions 

The unemployment – heart disease mortality relation 

The unemployment – stroke mortality relation 

X. 

XI. 

References 

Appendix 1: Selected studies on unemployment, income and CVD 

Selected epidemiological studies on income, unemployment in 
relation to CVD 

Selected aggregate time-series studies on unemployment, income 
and CVD mortality 

Selected epidemiological studies on the effects of unemployment on 
life style 

Selected epidemiological studies on the influence of stress and 
depression on cardiovascular disease 

XII.  Statistical Tables 

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I. 

Summary 

This paper examines the relation between unemployment and CVD mortality in European 
Union  countries  during  the  first  decade  of  the  21st  century.  Two  separate  studies  are 
summarized, focussing on increased heart disease and stroke mortality rates as potential 
outcomes of the greatly extended unemployment rate during 2000 – 2010 and especially 
the Great Recession of 2007 – 2009.  

Unemployment  rates  and  mortality  from  two  cardiovascular  illnesses  are  viewed  in 
pooled  cross-sectional  analyses  over  the  range  of  EU  countries.  In  addition  to  the 
unemployment  rate,  other  economic  variables,  expressing  the  Recession,  are  studied  in 
relation to cardiovascular mortality over multiple years in a pooled cross-sectional time-
series analysis with random effects. 

The principal finding is that increases in the unemployment rate are related to increased 
heart disease and stroke mortality. Controlling for other labour market variables, such as 
labour  force  participation,  as  well  as  gross  domestic  product  (GDP)  per  capita—the 
principal indicator of recession and economic growth.  

In the first study of heart disease, the association with unemployment rates is  analysed 
contemporaneously  and  at  lags  of  1,  2,  3,  4  and  5  years.  Without  adjusting  for  any 
control variables, only the unemployment rate relationships lagged at years 1, 2, 3, 4, 5 
are  statistically  significant.  With  adjustment  for  GDPpc,  however,  the  contemporaneous 
(0  lagged  year)  for  the  unemployment  rate  also  becomes  significant  and  positive.  The 
unemployment  rate lagged  2  years shows the strongest  relation  to  IHD  mortality  rates. 
This  is  confirmed  for  age  groups  35-44,  45-54,  55-64,  65-74,  75-84  and  over  85.  The 
relations of the unemployment rate to IHD mortality rates can be seen using age-specific 
unemployment  rates  for  the  age  groups  35-44  to  65-74.  The  relations  between  the 
unemployment rate and the IHD mortality rates remain significant in the presence of as 
few  as  one  or  as  many  as  eleven  control  variables.  Variables  under  control  include 
GDPpc,  Southern  Europe  (binary  variable),  labour  force  participation,  self-employment, 
national  measures  of  poverty  and  smoking  prevalence.  Labour  force  participation  and 
self-employment  are  considerably  stronger  labour  market  predictors  (inverse)  of  IHD 
mortality rates than is the unemployment rate (positive). 

These  findings  are  consistent  with  the  epidemiological  literature  which  shows  positive 
relations  between  unemployment  and  cardiovascular  (CVD)  mortality,  and  inverse 
relations  between  income  and  CVD  mortality.  These  findings  are  also  consistent  with 
national level time-series analyses of unemployment and other macroeconomic variables 
in relation to CVD during the 20th century. The evidence is clear that the unemployment 
rate has been an important risk factor for IHD mortality since the start of the 2008-2009 
Recession in Europe. 

The second study of the impact of unemployment rates on stroke mortality replicates and 
extends the analysis of the impact of unemployment rates on heart disease mortality (for 
28  EU  countries  during  the  period  preceding  and  covering  the  Great  Recession  2000  – 
2011).  In  the  second  study,  stroke  mortality  rates  were  found  to  increase  as 
unemployment  rates  climb,  with  lags  ranging  over  2-4  years  for  both  sexes  and  age 
groups over 45 years. The peak lag of 3 years following unemployment is slightly higher 
in its prediction of stroke mortality than the initial 2-year lag.  

For males, the range of impact of the unemployment rate covers the working ages 45 – 
64, whereas for females it covers those ages as well as 65 -84! It is similarly noteworthy 
that  the  impact  of  the  labour  force  variables,  unemployment  rates  and  labour  force 
participation  rates,  have  a  greater  impact  on  stroke  mortality  in  women.  This  is 
somewhat surprising from the classic social science perspective that males are thought to 
have a greater intrinsic involvement with careers, earnings and the economy in general 
than  females.  In  the  case  of  stroke  outcomes,  this  is  not  borne  out.  Females  show  an 
effect of the labour force variables that occurs both earlier and later in life than males.  

6 

 
This  study  also  finds  that  the  labour  force  participation  rate,  a  more  comprehensive 
business  cycle  indicator  that  takes  discouraged  workers  into  account,  predicts  reduced 
stroke mortality contemporaneously with elevated labour force participation—i.e., without 
any lag (within the course of a year). The  labour force participation rate shows at  least 
twice  the  impact  on  stroke  mortality  of  the  unemployment  rate  (i.e.,  in  terms  of  effect 
size). Additionally, GDP per capita, the most prominently used measure of the presence 
of recessions (or economic recoveries) by economic specialists, is very strongly related to 
stroke  mortality  declines  and  has  around  three  times  the  impact  of  the  unemployment 
rate.  GDP  per  capita  can  be  shown 
influence  stroke  mortality  declines 
contemporaneously—i.e.,  with  zero  year’s  lag—and  with  further  lags  that  can  be 
estimated over 1 – 10 additional years (at least). This means that the impact of GDP per 
capita on stroke mortality also involves a secular trend of at least 10 years duration.  

to 

The  fact  that  unemployment  rate  increases  predict  elevated  stroke  mortality  rates 
beyond  the  usual  working  life  (i.e.,  over  age  65)  for  women  (including  ages  65  –  84), 
and that the labour force participation rate predicts increased stroke mortality from ages 
65  to  over  85  (!)  for  each  sex  demonstrate  powerful  “spread  effects”  of  these  labour 
force  variables.  This  means  that  the  labour  force  variables  influence  health  well  beyond 
the normal working life and therefore have an especially large effect on life expectation in 
older ages.  

increased  heart  disease  and  stroke  mortality 

These  findings  on  the  effects  of  labour  force  variables—especially  the  unemployment 
rate—on 
is  consistent  with  the 
epidemiological  literature  on  unemployment  in  relation  to  cardiovascular  mortality  in 
individual-level  epidemiological  studies.  This  study  raises  the  question  of  whether  it  is 
cost-effective to reduce unemployment, or its financial implications, in order to minimize 
health and health care costs.  

7 

 
 
 
 
II. 

Introduction 

Recently,  an  analysis  was  performed  for  the  European  Commission  examining  the 
relation  between  unemployment  rates  and  heart  disease  mortality  in  EU  countries  over 
the  period  leading  up  to  and  including  the  Great  Recession  (2000  –  2010).  The 
importance  of  singling  out  unemployment  during  the  Great  Recession  is  that  it  has  an 
intrinsic  meaning  for  policy  makers  as  well  as  the  general  public.  There  is  also  an 
extensive  literature  in  epidemiology  at  the  individual  level  demonstrating  this  relation 
covering the past 40 years. In this document, analysis is presented of a replication and 
extension  of  this  statistical  relation  to  encompass  stroke  mortality  among  EU  countries 
over the period 2000 – 2011.  

The  central  problem  of  this  analysis  is  to  elucidate  this  relationship  between 
unemployment  and  stroke  mortality  for  the  macro  economy,  which  makes  it  directly 
pertinent  to  national  and  international  economic  and  social  welfare  policy.  There  are 
three econometric issues to be dealt with in this analysis. The first is that it is important 
to  control  for  gross  domestic  product  per  capita  (GDP  pc),  which  involves  long-term 
economic  growth,  and  is  also  the  most  widely  used  indicator  of  the  presence  of 
recessions.  In  fact,  the  relation  between  GDP  and  the  unemployment  rate  is  so 
fundamental and causal that classic estimates have been made of the precursor status of 
GDP growth to unemployment rate reduction (Okun’s Law).  

The  second  issue  is  that  the  officially  calculated  unemployment  rate,  as  estimated  by 
EUROSTAT and the ILO, as well as the US Census, omits crucial aspects of job loss. Most 
important  is  that  the  measurement  of  unemployment  requires  that  the  officially 
unemployed  must  be  physically  capable  of,  and  actively  seeking,  work.  However,  many 
who  lose  jobs,  but  become  discouraged  about  finding  work,  stop  seeking  work  and  are 
thus no longer officially counted as unemployed. Yet, these longer-term but discouraged 
workers are most vulnerable to psychological stress and poverty and thus to subsequent 
cardiovascular reactions.  

A  third  issue  with  the  unemployment  rate  as  used  in  national  statistics,  lies  in  its 
implicitly “natural” inverse (i.e., counter-cyclical) relation to the business cycle. However, 
as  the  business  cycle  moves  into  the  upturn  phase,  the  unemployment  rate  actually 
increases, because, coming out of the recession, some people who are not in the labour 
force,  enter  the  labour  force  expecting  to  find  work  but  until  they  do  find  work  remain 
unemployed. This is one reason the unemployment rate is known as a lagging  indicator 
of  the  business  cycle.  For  this  reason  it  is  sometimes  assumed  that  the  zero  time  lag 
(i.e., exactly contemporaneous) relation of the unemployment rate to a damaging health 
outcome (such as stroke) is often unreliable.  

In  analysing  unemployment,  specifically  job  loss,  in  relation  to  stroke  mortality,  it  is 
therefore  also  logical  to  utilize  the  unemployment  rate  together  with  the  labour  force 
participation rate. In so doing, it is also important, as indicated above, to control for GDP 
per  capita.  But  clearly,  the  three  economic  indicators—GDP  per  capita,  unemployment 
rate, and labour force participation rate—are most appropriately placed at different lags, 
because  they  are  causally  related  to  each  other,  and  at  the  same  year  of  lag—e.g.  lag 
zero—induce multi-collinearity. 

Aside  from  such  econometric  issues  associated  with  the  nature  of  economic  indicators, 
there  are  equally  important  considerations  that  have  to  be  taken  into  account  when 
economic  indicators  are  used  to  predict  health  outcomes.  Principal  among  these  is  the 
need to account for other health risk or beneficial factors that bear an important relation 
to  the  outcome  in  question.  In  the  case  of  stroke  mortality,  as  in  other  types  of 
cardiovascular disease mortality, smoking is an important damaging predictor; similarly, 
vegetable fat consumption (and that of vegetable consumption generally), is a potentially 
beneficial factor.  

Other factors that ideally should be brought under statistical control in order to focus on 
the  GDP  per  capita  and  employment  related  variables,  as  indicators  of  recession,  are 

8 

 
measures  of  chronic  poverty.  These  are  especially  interesting  when  one  examines  the 
economic  situation  of  European  populations,  since,  in  principle,  they  are  generally 
politically  well-protected  by  highly  developed  social  welfare  systems.  Nevertheless,  an 
indicator  of  a  chronically  very  low  living  standard  is  the  absence  of  complete  access  to 
potable  water  and  sanitation.  Further,  an  important  measure  of  the  extent  of 
intergenerational poverty, and difficulty of social mobility, is the adolescent fertility rate. 
In the latter case, both adolescent mothers and their offspring are frequently subject to 
low  educational  attainment,  low-wage  employment  and  life-long  weak  earning  capacity 
as well as high unemployment rates.  

In the present analysis, the project began in an attempt to replicate the findings on the 
relation  of  unemployment  to  heart  disease  mortality  to  observe  whether  they  apply  to 
mortality due to stroke (or cerebrovascular disease). It is assumed in this paper that it is 
logical  to  try  a  nearly  identical  modelling  process  in  the  analysis  of  stroke  mortality, 
compared  to  that  which  has  been  successful  in  the  models  prepared  for  heart  disease 
mortality.  The  rationale  behind  this  approach  regarding  the  similarities  of  models 
predicting the two causes of death, rests on the assumption that the two phenomena are 
closely related. These assumptions are based on three points of similarity: (1) similarity 
of  epidemiological  predictors;  (2)  similarity  of  biological  mechanisms;  and  (3)  similarity 
of dietary and environmental risk factors.  

9 

 
 
 
 
III.  Literature background 

Socioeconomic status and population health 

European  and  United  States’  epidemiologic  studies  have  shown  since  the  1970s  that 
socioeconomic  status  (SES)  is  a  stable  risk  factor  for  illness  and  mortality  in  individual 
persons (Galobardes, Smith and Lynch, 2006; Frankel, Smith, and Gunnell, 1991; Kaplan 
and Keil, 1999). Since heart disease, more specifically IHD, also referred to as Coronary 
Heart  Disease,  is  the  largest  segment  of  mortality  in  industrialized  countries,  it  is  a 
central component of the more general, and nearly universal, relation between SES and 
overall  mortality  (and  severe  morbidity)  –  also  referred  to  as  the  “social  gradient”  in 
disease. The higher the SES the lower the morbidity and mortality rates. 

The significance of SES involves its components – income, education, occupational skill-
level and their causal interrelations. Thus, parental SES tends to predict the educational 
level  of  children.  Children’s  education,  in  turn,  predicts  their  occupational  skill-level, 
which then gives rise to their initial and permanent income levels. These epidemiological 
studies,  also  showing  a  positive  relation  between  unemployment  and  CVD  illness  has 
been confirmed since the 1970s through time-series analysis of CVD mortality in the US 
and nine additional European countries (Brenner and Mooney, 1982; Gallo, Teng, Falba, 
Kasl  et  al.,  2006;  Fillate,  Johansen,  et  al.,  2003;  Hendriksson,  Lindblad,  Agren  et  al., 
2003;  Morris,  Cook,  Shaper,  1994;  Sorlie  and  Rogot,  1990;  Crombie,  Kenicer,  et  al. 
1989; Moser, Fox, et al., 1986). 

At the national (or macroeconomic) level, recession influences overall employment (and 
unemployment)  and  the  creation  of  new  employment  via  entrepreneurship,  especially 
self-employment. Further, under conditions of unemployment lasting more than  e.g. six 
months,  it  is  common  for  the  unemployed  to  leave  the  labour  force  altogether,  as 
“discouraged  workers,”  never  to  return  to  the  labour  force,  or  to  subsequently  accept 
positions  at  much  lower  skill  levels,  wages  or  hours  (i.e.,  underemployment).  Thus, 
longer-term unemployment has implications for decreased labour force participation and 
underemployment.  Further,  national  level  unemployment  rates  can  be  mitigated  by 
heightened self-employment, but only if family or other financing is available. Thus, the 
impact of unemployment on health should ideally be estimated in the context of GDP per 
capita,  labour  force  participation  and  self-employment  (Brenner,  1971;  1987b;  1997; 
2005; 2012). 

Impact of unemployment on population health: national versus individual-level 
studies 

Unemployment  is  an  important  risk  factor  for  mortality  and  morbidity—especially  if  the 
unemployment  is  of  long  duration  (Tausky et  al.,  1967/8;  Hallsten  et  al.,  1999;  Kasl  et 
al., 2000). Brenner (2011) reviewed European Commission studies dated 1998-2004 and 
examined  the  time-series  relations  between  mortality  and  economic  growth  and 
unemployment. These studies covered the 15 original European Union countries and the 
US over the period 1960-2000 (Brenner, 1979; 1984; 2000; 2002) and are extensions of 
original  work  done  in  the  1970s  and  early  1980s  (Brenner  1984).  Short-term  positive 
relationships between unemployment and mortality are also seen within a year following 
increased  unemployment  rates.  In  a  pooled  cross-sectional  study  of  metropolitan  areas 
of  the  US,  with  a  lag  of  two  years  following  increased  unemployment,  and  inclusion  of 
poverty  and  income  inequality  as  control  variables,  the  standard  positive  relation  was 
found  for  heart  disease,  stroke  and  homicide  (Merva  and  Fowles,  1999),  which  is 
consistent  with  the  findings  of  virtually  all  epidemiological  studies  in  Europe  and  North 
America  over the last  thirty years (Kasl, 2000). In another pooled  cross-sectional study 
of  the  US,  based  on  US  states,  without  any  lags  and  without  the  usual  epidemiological 
controls, these inverse relations were not replicated (Ruhm, 2000). Most recently, studies 
based on the Health and Retirement Survey have shown strong effects of unemployment 
on heart disease mortality (Gallo et al, 2004, 2006; Dupre et al., 2012) as well as overall 

10 

 
 
mortality  (Noelke  et  al.,  2014).  Over  the  past  decade,  several  European  studies  have 
been concentrating on the impact of firm restructuring with downsizing (i.e., job losses). 
The  studies  have  uniformly  found  increased  unemployment  to  be  related  to  increased 
morbidity  and  mortality.  In  a  technical  report  to  the  European  Commission  (Brenner, 
2009;  2011)  reviewed  the  empirical  literature  and  identified  unemployment  as  a  key 
socioeconomic  determinant  of  health,  particularly  for  men.  Within  the  last  year  two 
studies  have  shown  marked  increases  in  depression  in  multi-country  studies  of  firm 
downsizing in Europe (Brenner et al., 2014) and industrialized countries (Reeves, McKee, 
Stuckler, 2014). 

IV.  Hypotheses 

Key Economic Variables 

  A  higher  unemployment  rate  predicts  higher  levels  and  increases  in  the 
cardiovascular  mortality  rate.  Levels  refer  to  cross-sectional  heart  disease  and 
stroke  mortality  rate  differentials  among  EU  countries.  Changes  refer  to  trends 
and fluctuations over 2000 – 2010 in the case of heart disease and 2000 – 2011 
for  stroke.  Unemployment  refers  to  persons  experiencing  job  loss  and  new 
entrants into the labour market who are seeking, and are capable of, work.  

  A higher labour force participation rate predicts lower levels and decreases in the 
cardiovascular  mortality  rate.  The  “labour  force”  includes  both  employed  and 
unemployed individuals. Unemployed persons who stop seeking work (and usually 
do  not  receive  unemployment  insurance  benefits)  because  they  do  not  believe 
they  could  find  (suitable)  employment  are  referred  to  as  “discouraged”  workers 
and  are  not  counted 
force.  The  emotional  situation  of 
“discouragement” in job losers who no longer seek work (and  the related loss of 
subsidized  income),  as  well  as  the  fact  that  discouraged  workers  tend  to  be 
among  the  uncounted  long-term  jobless  would  predict  a  larger  and  more 
immediate effect on stroke mortality.  

in  the 

labour 

  GDP per capita predicts lower cardiovascular mortality rates in the short, medium 
and  long-term.  GDP  per  capita  is  the  basic  indicator  of  national  income  and 
wealth,  as  well  as  the  level  of  economic  development.  Economic  growth  is  a 
function of the development of physical and human capital and thus of innovation, 
education, and knowledge development. European societies are heavily dominated 
by  service  industries.  GDP  per  capita  is  the  basis  of  expenditure  on  health, 
education and welfare, social insurance and investments in new technologies that 
elevate worker and environmental safety. Thus, GDP per capita is a fundamental 
source  of  increased  population  health  and  life  expectancy.  It  is  also  the  primary 
(positive)  indicator  of  business  cycles  and  thus,  inversely,  of  recession.  It  is 
therefore  also  a  principal  predictor  of  (reduced)  unemployment  rates  and 
therefore of economic instability.  

Control variables 

  Chronic poverty 

Chronic  poverty  should  predict  higher  cardiovascular  mortality  rates.  Persons  in 
chronic poverty are epidemiologically at higher risk for cardiovascular disease and 
its outcomes. This is assumed to result from stress which could directly influence 
psychophysiological reactions, or contribute to smoking, poor diets and exposure 
to noxious environments.  

In this study, an indicator of regions with extraordinarily low levels of income are 
those with limited access to potable water and sanitation facilities. This condition 

11 

 
 
 
 
 
 
 
is relatively unusual but is nevertheless measurable to some degree in several EU 
countries.  

A  second  and,  it  is  assumed,  more  prevalent  indicator  of  chronic  poverty  is  the 
adolescent fertility rate. Both mothers and children in this circumstance are known 
to  have  relatively  low  family  incomes  and  education  levels.  Thus,  initial  family 
poverty  rates  are  often  maintained  through  the  subsequent  generation  involving 
low-skilled  jobs  and  relatively  high  long-term  unemployment  rates  as  well  as 
unstable low-wage employment.  

  Smoking and diet 

Smoking prevalence is predicted to increase cardiovascular mortality, but the time 
lag  is  uncertain.  It  is  possible  that  reduced  smoking  prevalence  would  predict 
decreases in heart disease and stroke mortality in highly vulnerable age groups—
in  the  short  term.  However,  there  is  greater  consensus  in  the  literature  on  the 
impact  of  smoking  in  cohorts,  ranging  as  long  as  20  years  in  duration.  In  this 
study smoking prevalence is used as a control only in short-term time lags, given 
data availability and the requirement to focus on unemployment and labour force 
participation as key predictor variables.  

Dietary  factors  potentially  influencing  cardiovascular  mortality  have  traditionally 
tended  to  be  focused  on  fat  consumption  and  vegetable  consumption.  Saturated 
fat  consumption  has  been  identified  as  a  harmful  risk  factor,  whereas  vegetable 
fat  consumption  (especially  as  identified  in  the  Mediterranean  diet  and  including 
olive  oil)  has  been  found  to  reduce  cardiovascular  illness,  including  stroke.  Fruit 
and vegetable consumption in general has also been found epidemiologically to be 
an  important  source  of  reduction  of  chronic  disease  mortality.  It  is  therefore 
predicted that vegetable fat consumption, even at short-term lags, will be seen to 
reduce heart disease and stroke mortality rates.  

V. 

Research Method 

This  study  uses  pooled  cross-sectional  time-series  analysis  (often  referred  to  as  panel 
analysis)  as  the  basis  for  modelling  the  effect  of  unemployment  rates  on  heart  disease 
and stroke mortality rates in European countries. This method combines traditional cross-
sectional  analysis  and  temporal  analysis  over  multiple  years—in  this  case  2000  –  2010 
for heart disease and 2000 – 2011 for stroke. The analyses thus include space and time 
components  simultaneously.  The  cross-sectional  aspect  of  this  method  allows  a 
comparison,  at  a  point  in  time,  among  countries,  of  how  different  levels  of 
unemployment  rates,  under  different  national  conditions  of  severity,  duration,  welfare 
compensation,  Active  Labour  Market  Policies  and  health  care  availability,  can  influence 
cardiovascular  mortality.  In  fact,  it  is  often  assumed,  that,  given  the  extensive  social 
safety  net  and  welfare  state  systems  of  European  countries,  sufficient  protection  is 
available to fundamentally prevent unemployment rates from influencing mortality rates 
in a significant manner. 

Multiple years (of cross-sectional analysis) are included for several reasons: (1) to assure 
that  the  results  of  any  specific  year’s  analysis  are  not  unusual  (i.e.,  idiosyncratic)  with 
respect  to  the  relation  of  unemployment  to  heart  disease  and  stroke  mortality.  (2)  The 
analysis  also  includes  a  time-series  analytic  component—as  well  as  time  trends,  cycles, 
and  other  fluctuations  in  unemployment  and  heart  disease/  stroke  mortality  over  11 
years.  This  especially  allows  us  to  observe  much  of  the  impact  of  the  Great  Recession 
prior to its onset and during its occurrence—in terms of its effects on unemployment and 
its  health  implications.  (3)  Several  control  variables  are  included  in  the  model.  These 
take account of overall GDP—the most powerful trend affecting mortality rates—and most 
frequently  used  measure  by  economists  to  indicate  the  starting  and  ending  dates  and 
severity of recessions.  

force  participation,  which  supplements  the 
Another  control  variable 
unemployment rate by taking account of persons who have lost employment, but may no 

labour 

is 

12 

 
 
 
 
longer be counted among the unemployed because they have stopped seeking work due 
to  discouragement  and  have,  thus,  through  standard  counting  methods,  left  the  labour 
force.  Also  under  control  is  a  measure  of  chronic,  intergenerational  poverty  such  as 
exhibited  by  the  adolescent  fertility  rate.  And  finally,  smoking  prevalence  is  controlled 
for—as  the  single  most  important  behavioural  risk  factor  influencing  cardiovascular 
mortality. These variables are placed in the model to assure that the measured effects of 
unemployment  are  really  not  due  to  other  factors  that  are  associated  both  with 
unemployment and, independently, with cardiovascular mortality.  

For  example,  a  population  subject  to  chronic  poverty  is  likely  to  be  vulnerable  to 
unemployment as well as to heart disease and stroke. This would raise the question as to 
whether  the  effects  attributed  to  unemployment  are  actually  partly,  or  entirely,  due  to 
chronic  poverty.    Similarly,  smoking  is  more  frequent  among  the  unemployed  and  is  a 
principal risk factor for heart disease and stroke. Thus, one would want to assure that the 
cardiovascular  effects  attributed  to  unemployment  are  really  not  a  complete  or  partial 
by-product of smoking.  

In order to include these control variables in the predictive models for heart disease and 
stroke  mortality,  we  require  sufficient  degrees  of  freedom  to  perform  adequate  tests  of 
significance. Clearly, there are important restrictions in the degrees of freedom available 
from one year of cross-sectional analysis with a sample size of 28 units (i.e., countries). 
Using 11 years of cross-sections provides the expanded degrees of freedom.  

Random versus fixed effects 

Two standard methods of modelling with a pooled cross-sectional time-series designs are 
commonly  used.  The  choice  of  method  depends  largely  on  the  nature  of  the  variables 
used with respect to their time-series properties. In brief, if the model contains variables 
that  do  not  vary  at  all  (e.g.  dichotomous)  or  are  “slow  moving”  in  the  sense  that  they 
vary  little  from  one  time  period  to  another  or  have  very  strong  time  trends,  then  the 
random effects method is required (Wooldrige, 2002).  

Deductive logic of model construction 

We  start  with  the  most  ubiquitous  and  pervasive  finding  in  all  of  modern  epidemiology, 
referred to as the “social gradient” in health. This finding is that increased socioeconomic 
status  (SES)  measured  by  income,  educational  level,  occupational  skill  level  (in 
graduated  scales)—and  most  recently  by  unemployment  rates  (inverse  relation)—is 
closely related to lower levels of illness and mortality, regardless of diagnosis, age, sex, 
and nationality (Galobardes, Smith and Lynch, 2006; Frankel, Smith, and Gunnell, 1991; 
Kaplan and Keil,1993; Brenner and Mooney, 1982; Gallo, Teng, Falba, Kasl et al., 2006; 
Fillate, Johansen, et al., 2003; Hendriksson, Lindblad, Agren et al., 2003; Morris,  Cook, 
Shaper, 1994; Sorlie and Rogot, 1990; Crombie, Kenicer, et al. 1989; Moser, Fox, et al., 
1986).  

These  findings  are  at  micro—or  individual—level.  The  position  taken  in  this  paper  that 
findings  at  the  macro-level—if  they  are  to  represent,  and  elaborate,  well-established 
causal  relations,  cannot  contradict  findings  at  the  micro  (individual)  level,  at  which  the 
effects of unemployment and mortality are thought to occur.  

Continuing with this logic, if declines in income, or increases in unemployment, are found 
to  occur  at  the  individual  level,  or  aggregated  to  the  level  of  the  population,  then  it 
follows  that  declines  in  population  health  should  similarly  occur.  Such  declines  in 
population health should involve the major diagnostic categories of illness and mortality. 
Heart  disease  and  stroke  are  the  first  and  third  most  important  cause  of  illness  and 
mortality in industrialized societies, including all EU member countries. 

13 

 
 
 
 
In  summary,  given  the  extensive  individual-level  epidemiologic  evidence  of  the  inverse 
relation of income and employment to cardiovascular illness and mortality, it follows that 
national increases in unemployment and the declines in income are plausibly followed by 
increased cardiovascular illness and mortality rates at the national level.  

In order to construct a comprehensive model that follows the logic of the universal SES-
health  relation,  it  would  be  important  to  include  as  many  socioeconomic  variables  as 
practical on economic and statistical grounds. These would involve, at the national level, 
unemployment rates, labour force participation rates and GDP per capita as having short- 
to medium-term impacts on cardiovascular mortality.  

Labour force participation rates would be included (in addition to unemployment rates) to 
assure  that  the  potential  findings  based  on  unemployment  rates  would  not  be  distorted 
as  a  result  of  the  fact  that  measured  unemployment  does  not  include  longer-term  job 
losers  who,  through  discouragement  at  not  finding  new  employment,  would  leave  the 
labour force rather than remain (technically) unemployed. 

In  the  overall  heart  disease  and  stroke  models,  we  also  wish  to  control  for  population 
trends  that  influence  poverty  rates,  and  the  extent  of  long-term  trends  of  low-income 
employment,  and  unemployment,  due  to  such  factors  as  family  instability.  A  prominent 
factor  of  this  type  is  adolescent  fertility  (Hulme,  D,  Moore,  K  and  Shepherd,  A,  2001; 
OECD Social Policy Division, 2010).  

Further  controls  to  be  tested  in  the  heart  disease  and  stroke  models  would  involve 
income  inequality—which  could  also  reflect  poverty  (Sen,  1986)—and  which  has  been 
argued  to  negatively  influence  health  (De  Maio,  2007)  although  this  remains 
controversial.  

A final type of control for potential confounding would be the most important behavioural 
risk  factor  associated  with  cardiovascular  mortality,  namely  smoking  prevalence.  It 
should  also  be  mentioned  that  there  are  other  less  well-established  behavioural  risk 
factors  for  cardiovascular  illness  such  as  high  alcohol  consumption,  poor  nutrition,  and 
overweight.  However,  the  major  socioeconomic  control  variables  (GDP  per  capita, 
adolescent fertility, Gini), should in principle adjust for these potential behavioural risks, 
since they are associated with low SES.  

Further deductive approach: Replication of the heart disease model 

The  deductive  approach  outlined  above,  in  which  a  macro  (national-level)  model  was 
developed  from  a  large  literature  on  health  risks,  based  on  micro  (individual-level 
evidence), was used in an initial report to the EU based on a study investigating the link 
between  unemployment  and  heart  disease  mortality  rates  in  EU  countries  (Brenner, 
2013). 

Extending  this  deductive  logic  further,  it  was  assumed  that  stroke  mortality  would  be 
predicted by a similar model to that which successfully predicted heart disease mortality. 
The  basis  of  this  assumption  is  that  stroke  and  ischemic  heart  disease  have  essentially 
the  same  behavioural  risk  factors  (e.g.  psycho-social  stress,  diet,  hypertension, 
arteriosclerosis,  aging)  since  they  are  part  of  the  same  cardiovascular  disease  process, 
which also includes kidney disease. (WHO, Media Centre Cardiovascular diseases, 2015) 

Thus,  in  the  present  study,  the  heart  disease  model  was  replicated  using  the  same 
independent  variables  of  unemployment,  labour  force  participation  and  GDP  per  capita 
and  controlling  for similar  potential confounders.  The  stroke  model  also  took  account  of 
the  fact  that  GDP  per  capita  needed  to  show  a  lag  (to  stroke  mortality)  that  involves  a 
minimum of five years, in order that multi-collinearity not render insignificant the relation 
to  unemployment  (at  lags  0  –  3)  due  to  Okun’s  law  by  which  GDP  is  strongly  inversely 
related  to  unemployment  rates  (especially  in  recessions).  It  had  been  found  that  the 

14 

 
 
optimal  heart  disease  mortality  model  involved  the  unemployment  rate  at  two  year  lag 
and GDP per capita at a seven year lag.  

Adding GDP while taking account of Okun’s Law 

A minimal model for heart disease or stroke mortality in industrialized countries would at 
least  include  one  major  proxy  variable  for  aggregate  population  SES  in  addition  to  the 
unemployment  rate.  At  the  national  level  GDP  per  capita  is  the  closest  we  come  to  an 
aggregate variable that represents the national standard of living as well as the sources 
of national investment. As an aggregate measure, the GDP per capita does considerably 
more  than  measure  the  “average”  of  SES  for  a  national  population.  It  also  signifies  the 
potential  for  investment  in  national  health  services,  education  and  social  welfare, 
including pensions and support of the disabled.  

The  great  problem,  however,  in  adding  GDP  per  capita  to  a  model  of  mortality  already 
involving the unemployment rate is that, like the unemployment rate itself, the GDP is a 
fundamental  indicator  of  business  cycle  fluctuations.  Thus,  concurrently  –  i.e.,  at  zero 
years'  lag  –  GDP  per  capita  and  the  unemployment  rate  will  be  directly  and  robustly 
inversely  related  to  each  other.  The  principal  reason  is  that  even  decline  in  the  growth 
rate  of  GDP  per  capita  has  a  causal  relation  to  increased  unemployment  due  to  the 
decline  in  demand  for  goods  and  services.  Declines  in  consumer  demand  signal  to 
employers  that  fewer  employees  will  be  needed  for  firms  to  remain  profitable  at  their 
previous rate. 

So regular is the relation between GDP per capita and the unemployment rate that a rule 
of  thumb  stating  the  extent  of  GDP  growth  that  is  needed  to  produce  a  one  percent 
decrease in the unemployment rate has been in use since the 1960s. It is referred to as 
Okun’s  Law,  offered  by  Arthur  Okun  who  proposed  the  relationship  in  1962.  The 
relationship varies depending on the country and the time period under consideration. In 
Okun’s  original  statement  of his law,  a two  percent  increase  in  output  corresponds  to  a 
one  percent  decline  in  the  rate  of  cyclical  unemployment;  a  0.5  percent  increase  in 
labour  force  participation;  a  0.5  percent  increase  in  hours  worked  per  employee;  and  a 
one percent increase in output per hours worked (labour productivity). 

We therefore have the situation that GDP per capita declines can have a damaging effect 
on health through their impact on the unemployment rate. In that case, GDP per capita is 
the more distal causal variable while unemployment is the more proximal cause of health 
decline.  In  this  scenario,  however,  unemployment  is  an  intermediate  component  of  the 
more  general  relation  of  GDP  to  health.  This  common  cyclical  concurrence  produces 
difficulty for adding GDP per capita to the unemployment rate in order to produce a more 
complete and plausible model for e.g. IHD. 

Our solution is based on the fact that the relationship of GDP per capita to cardiovascular 
mortality occurs over multiple years – i.e., there are strong lengthy, medium- and short-
term  relations  of  GDP  per  capita  to  cardiovascular  mortality.  This  is  because  GDP  per 
capita is a foundational variable which includes aggregate SES at the national level. This 
relation  involves  implications  for  long-term  investments,  such  as  in  education,  health 
care and infrastructure, as well as short-term consequences for personal household and 
government expenditures.  

On  the  other  hand,  the  most  important  effects  of  unemployment  on  health  are 
empirically  observed  at  2-4  year’s  lag,  and  especially  at  a  two-year  lag.  Therefore,  we 
observe  empirically  that  if  we  wish  to  examine  a  short-medium  term  relation  of 
unemployment  rates  to  stroke  mortality–i.e.,  at  2-4  year’s  lag    –  and  include  the 
medium-long  term  impact  of  GDP  per  capita  in  the  model,  then  we  usually  require  at 
least a five-year lag of the GDP to mortality. If we place both the GDP per capita and the 

15 

 
 
 
unemployment  rate  at  2-4  year’s  lag,  the  presence  of  the  GDP  will  force  the 
unemployment rate into insignificance (due to multi-collinearity).  

Logic of the sequence of analysis 

As mentioned above, the first full modelling exercise for stroke was to replicate the initial 
heart  disease  mortality  model,  for  EU  countries,  repeating  the  two  year  lag  of 
unemployment to stroke mortality and the GDP per capita lag of seven years.  

The  second  type  of  model  aimed  to  reproduce  aspects  of  the  initial  replication,  but 
removed  the  labour  force  participation  rate  in  order  to  observe  the  completely 
independent impact of the unemployment rate alone at different lags.  

The third model type sought to reproduce aspects of the initial replication, but removed 
the  unemployment  rate,  in  order  to  examine  the  completely  independent  impact  of  the 
labour force participation rate alone at zero lag.  

With the forth model type, the issue was to determine the complete lag structure of GDP 
per capita in relation to stroke mortality in the absence of both the unemployment rate 
and the labour force participation rate.  

The fifth model repeated the initial replication and expanded the GDP lag to 10 years, in 
order  to  accommodate  the  potential  for  investment  in  pharmaceutical  development  and 
other mechanisms of physical and human capital formation.  

In  the  sixth  and  final  model,  the  unemployment  rate  is  tested  at  a  lag  of  three  years, 
with the remainder of the control  variables included and the GDP per capita maintained 
at a lag of ten years.  

VI.  Databases and analysis 

The  databases  from  which  the  variables  are  extracted  include,  for  the  labour  force  and 
other  economic  variables,  EUROSTAT  supplemented  by  the  International  Labour 
Organization,  World  Bank,  and  the  International  Monetary  Fund.  Data  on  heart  disease 
and stroke mortality for the EU countries are drawn from EUROSTAT with verification of 
reliability  from  the  World  Health  Organization  (WHO,  Geneva).  Data  on  international 
smoking prevalence, access to water and sanitation facilities, and adolescent fertility, are 
from the WHO. Vegetable food supply per capita is drawn from the Food and Agriculture 
Organization  (FAO)  databases.  Statistical  analysis  is  performed  under  the  pooled  cross-
sectional  time  series  method,  with  random  effects  (Wooldridge,  2002,  p.  286),  using 
STATA/SE for large databases.   

16 

 
 
 
 
 
VII.  Findings 

  Analysis of relation between unemployment and heart disease mortality 

The  overall  problem  is  to  construct  a  minimally  plausible  model  of  Ischemic  Heart 
Disease  (IHD)  –  on  epidemiological  grounds  –  that  will  also  permit  us  to  assess  the 
contribution of unemployment rates. As an absolutely minimal IHD model for Europe, we 
can rely on a variable (or variables) that will serve as a marker for socioeconomic status 
(SES).  The  rationale  is  that,  as  discussed  below,  SES  is  epidemiologically  the  most 
pervasive  and  ubiquitous  –  i.e.,  generalizable  –  predictor  of  mortality  at  all  ages,  both 
sexes,  nationalities  and  ethnic  groups.  In  industrialized  countries,  SES  is  an  especially 
sensitive risk factor in cardiovascular diseases.  

As  we  shall  see,  prediction  of  total  age-adjusted  IHD  mortality  rate  across  European 
countries  by  unemployment  rates  alone  will  yield  between  9-13  percent  of  variance 
explained. This percentage is probably insufficient to create a reasonable epidemiological 
IHD  model,  even  though,  for  some  middle-aged  groups,  variance  explained  by 
unemployment rates will rise to 20-30 percent.  

First step: unemployment rate as the sole predictor 

Nevertheless, for illustrative purposes, we begin by exploring the simplest relation of the 
overall unemployment rate, as is  normally taken as a marker of economic recession, to 
variation  in  the  age-adjusted  IHD  mortality  rate  across  all  countries  of  the  European 
Union and across time over the period 2000-2010. 

We observe that at 0 lag, (i.e., concurrently), the adjusted R² (i.e., variance explained) 
is  .07  –  but  the  relation  is  not  statistically  significant,  yet  the  significant  relation  at  2 
years lag shows a coefficient of 3.79. See Table 1. 

Table 1:  

Relation of unemployment rate to age-adjusted IHD mortality rate, by 
lagged years of unemployment 0-5 

Moving to lags of 1-5 years the relation is now always significant, with explained variance 
of 9-11 percent. The peak of this relation is at a lag of 2 years with an R² of 50 percent, 
coefficient  of  2.04  and  standardized  beta  .24  (i.e.,  comparative  estimate  of  impact  of 
unemployment). See Table 2.  

17 

 
 
 
 
 
 
 
 
 
Table 2:  

Relation  of  unemployment  rate  to  age-adjusted  IHD  mortality  rate, 
adjusted  for  GDP  per  capita,  by  lagged  years  of  unemployment  0-5  and 
lagged years of GDPpc 5-10 

Predicting  of  ischaemic  heart  diseases  (ICD-10  codes  I20-I25)  death  rate  per  100,000  of 
total population (age standardized) 

Model 03b.1l5.2l0 
over. R² 
Coefficients/Betas 
5 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
Unemployment rate as % of total labour force 15+ years old 

Model 03b.1l6.2l1 
over. R² 
Coefficients/Betas 
6 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
1 year lag of Unemployment rate as % of total labour force 15+ years old 

Model 03b.1l7.2l2 
over. R² 
Coefficients/Betas 
7 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 

Model 03b.1l8.2l3 
over. R² 
Coefficients/Betas 
8 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
3 years lag of Unemployment rate as % of total labour force 15+ years old 

Model 03b.1l9.2l4 
over. R² 
Coefficients/Betas 
9 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
4 years lag of Unemployment rate as % of total labour force 15+ years old 

Model 03b.1l10.2l5 
over. R² 
Coefficients/Betas 
10 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
5 years lag of Unemployment rate as % of total labour force 15+ years old 

Legend:  

Red = Significance <.05 
Bold Black = Significance <.10 
Light Black = NS 

asdr 

0.51 

AR(1)  Robust 
0.51 

0.51 

Fixed 

0.51 

Coef.  Betas  Coefficients 
-6.00 
0.77 

-0.76 
0.11 

-5.34 
-0.03 

-6.00 
0.77 

-6.00 
0.78 

asdr 

0.51 

AR(1)  Robust 
0.51 

0.52 

Fixed 

0.51 

Coef.  Betas  Coefficients 
-5.42 
1.36 

-0.71 
0.17 

-5.21 
0.25 

-5.42 
1.36 

-5.38 
1.37 

asdr 

0.50 

AR(1)  Robust 
0.50 

0.52 

Fixed 

0.50 

Coef.  Betas  Coefficients 
-4.79 
2.04 

-0.66 
0.24 

-5.00 
0.96 

-4.79 
2.04 

-4.71 
2.08 

asdr 

0.51 

AR(1)  Robust 
0.51 

0.52 

Fixed 

0.51 

Coef.  Betas  Coefficients 
-4.81 
1.70 

-0.67 
0.20 

-4.90 
1.07 

-4.81 
1.70 

-4.72 
1.74 

asdr 

0.51 

AR(1)  Robust 
0.51 

0.51 

Fixed 

0.51 

Coef.  Betas  Coefficients 
-4.89 
1.17 

-0.67 
0.14 

-4.46 
1.01 

-4.89 
1.17 

-4.79 
1.21 

asdr 

0.51 

AR(1)  Robust 
0.51 

0.50 

Fixed 

0.51 

Coef.  Betas  Coefficients 
-4.81 
0.58 

-0.62 
0.07 

-3.74 
0.59 

-4.81 
0.58 

-4.67 
0.62 

Second step: adding GDP while taking account of Okun’s Law 

A  minimal  model  for  IHD  in  industrialized  countries  would  at  least  include  one  major 
proxy variable for aggregate population SES in addition to the unemployment rate. At the 
national  level  GDP  per  capita  is  the  closest  we  come  to  an  aggregate  variable  that 
represents the national standard of living as well as the sources of national investment. 
As an aggregate measure, the GDP per capita does considerably more than measure the 
“average” of SES for a national population. It also signifies the potential for investment in 
national health services, education and social welfare, including pensions and support of 
the disabled.  

18 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The  great  problem,  however,  in  adding  GDP  per  capita  to  a  model  of  mortality  already 
involving the unemployment rate is that, like the unemployment rate itself, the GDP is a 
fundamental  indicator  of  business  cycle  fluctuations.  Thus,  concurrently  –  i.e.,  at  zero 
year’s  lag  –  GDP  per  capita  and  the  unemployment  rate  will  be  directly  and  robustly 
inversely  related  to  each  other.  The  principal  reason  is  that  even  decline  in  the  growth 
rate  of  GDP  per  capita  has  a  causal  relation  to  increased  unemployment  due  to  the 
decline  in  demand  for  goods  and  services.  Declines  in  consumer  demand  signal  to 
employers  that  fewer  employees  will  be  needed  for  firms  to  remain  profitable  at  their 
previous rate. 

So regular is the relation between GDP per capita and the unemployment rate that a rule 
of  thumb  stating  the  extent  of  GDP  growth  that  is  needed  to  produce  a  one  percent 
decrease in the unemployment rate has been in use since the 1960s. It is referred to as 
Okun’s  Law,  offered  by  Arthur  Okun  who  proposed  the  relationship  in  1962.  The 
relationship varies depending on the country and the time period under consideration. In 
Okun’s  original  statement  of his law,  a two  percent  increase  in  output  corresponds  to  a 
one  percent  decline  in  the  rate  of  cyclical  unemployment;  a  0.5  percent  increase  in 
labour  force  participation;  a  0.5  percent  increase  in  hours  worked  per  employee;  and  a 
one percent increase in output per hours worked (labour productivity). 

We therefore have the situation that GDP per capita declines can have a damaging effect 
on health through their impact on the unemployment rate. In that case, GDP per capita is 
the more distal causal variable while unemployment is the more proximal cause of health 
decline.  In  this  scenario,  however,  unemployment  is  an  intermediate  component  of  the 
more  general  relation  of  GDP  to  health.  This  common  cyclical  concurrence  produces 
difficulty  for  adding  GDP  per  capita  to  the  unemployment  rate  (with  both  variables  at 
zero lag) in order to produce a more complete and plausible model for e.g. IHD. 

Our  solution  is  based  on  the  fact  that  the  relationship  of  GDP  per  capita  to  IHD  occurs 
over multiple years – i.e., there are strong lengthy, medium- and short-term relations of 
GDP  per  capita  to  IHD.  This  is  because  GDP  per  capita  is  a  foundational  variable  which 
includes aggregate SES at the national level. This relation involves implications for long-
term investments, such as in education, health care and infrastructure, as well as short-
term consequences for personal household and government expenditures.  

On  the  other  hand,  the  most  important  effects  of  unemployment  on  health  are 
empirically  observed  at  0-3  year’s  lag,  and  especially  at  a  two-year  lag.  Therefore,  we 
observe  empirically  that  if  we  wish  to  examine  a  very  short-term  relation  of 
unemployment rates to IHD – i.e., at lag zero – and include the medium-term impact of 
GDP per capita in the model, then we usually require approximately a five-year lag of the 
GDP  to  mortality.  Similarly,  optimally,  tests  of  the  one-year  lag  of  unemployment  rates 
to  IHD  should  include  a  six-year  lagged  GDP  per  capita,  assuming  that  we  want  to 
examine  the  health  effects  of  unemployment  rate  as  thoroughly  as  possible  while 
observing  the  medium-term  effects  of  GDP  per  capita.  If  we  place  both  the  GDP  per 
capita  and  the  unemployment  rate  at  lag  zero,  the  presence  of  the  GDP  will  force  the 
unemployment rate into insignificance (due to the fundamental relation of GDP per capita 
to the unemployment rate).  

We  now  examine  the  relation  of  unemployment  rates  to  IHD  covering  lags  0-5,  holding 
constant GDP per capita at a five-year temporal distance. Thus, GDP per capita at lag 5 is 
added to unemployment lag zero, GDP per capita lag 6 is combined with unemployment 
rate lag 1, GDP per capita lag 7 with unemployment rate lag 2 and so on (see Table 2). 
Multi-collinearity due to the intrinsic relation between GDP per capita and unemployment 
rates, at the same lag, will not allow unbiased analysis of the relation of unemployment 
rates to IHD.  Models with the type of lag separation discussed above allow us to see the 
effect  of  unemployment  rates  at  0-5  year  lags,  while  including  substantial  influence  of 
the GDP per capita variable. 

19 

 
 
Now,  if  we  fix  the  unemployment  rate  at  2  years  lag,  we  can  observe  the  effects  of 
GDPpc  with  a  very  short  negative  impact  on  IHD  mortality,  –  at  lag  0  –  a  negative 
medium-term impact – at lags 5-6 or 7 – and a negative longer-term impact at lag 10. 
We find that even at all three lag relations of the GDPpc, the relation between GDPpc and 
IHD  mortality  is  negative,  statistically  significant  and  robust.  Nevertheless,  in  this 
experiment,  the  relation  between  the  unemployment  rate  at  2  years  lag,  and  the  IHD 
mortality  rate  remains  significant.  Thus,  there  is  no  necessary  arbitrariness  in  selecting 
the  appropriate  lag  for  GDPpc  in  order  to  estimate  the  relations  of  the  unemployment 
rate to IHD mortality (Table 3). 

Yet  if  we  wish  to  optimize  the  relation  of  the  unemployment  rate  to  IHD  mortality, 
considering the presence of GDPpc in the model, we will select the unemployment rate at 
2 years lag and the GDPpc perhaps at 7 years lag. The reason is that (1) at GDPpc lag 5 
the  model  R²  is  maximized  at  .70,  but  the  coefficient  for  unemployment  rate  lagged  2 
years  is  1.22;  (2)  on  the  other  hand,  the  coefficient  for  unemployment  rate  lagged  2 
years is highest, i.e., 2.6, at GDPpc lagged 10 years; (3) thus, taking into account both 
the  R²  for the  model  and  the  magnitude  of the  unemployment  rate coefficient  lagged  2 
years, the optimum middle ground is around GDPpc lagged 7 years, where the model R² 
is .69 and the unemployment rate lagged 2 years coefficient is 2.03 (Table 3). 

Table 3:  

Relation  of  unemployment  rate  lagged  2  years  to  age-adjusted  IHD 
mortality rate, adjusted for GDPpc lagged 0-10 years and Southern Europe 

Predicting of ishaemic  heart diseases (ICD-10 codes I20-I25) death rate per 100,000 of 
total population (age standardized) 

Coefficients/Betas 
Model 03b.1l0.2l2 
0 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l1.2l2 
1 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l2.2l2 
2 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l3.2l2 
3 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l4.2l2 
4 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l5.2l2 
5 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l6.2l2 
6 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l7.2l2 
7 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 

20 

Coef. 

Betas 
over. R²=0.66 
-0.6 
-5.6 
0.17 
1.52 
-81.7 
-0.17 
over. R²=0.67 
-0.7 
-5.94 
0.06 
0.57 
-80.4 
-0.16 
over. R²=0.68 
-0.8 
-6.22 
-0.03 
-0.26 
-79.3 
-0.14 
over. R²=0.69 
-0.78 
-5.99 
0.01 
0.11 
-78.2 
-0.14 
over. R²=0.69 
-0.74 
-5.82 
0.07 
0.65 
-77.2 
-0.15 
over. R²=0.70 
-0.7 
-5.56 
0.13 
1.22 
-76.5 
-0.15 
over. R²=0.69 
-0.67 
-5.09 
0.2 
1.78 
-75.7 
-0.15 
over. R²=0.69 
-0.66 
-4.82 

 
 
 
 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l8.2l2 
8 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l9.2l2 
9 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Model 03b.1l10.2l2 
10 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 
Legend: Red = Significance <.05;   Bold Black = Significance <.10; Light Black = NS 

2.03 
0.24 
-0.15 
-74.95 
over. R²=0.69 
-0.66 
-4.76 
2.12 
0.25 
-0.15 
-74.2 
over. R²=0.68 
-4.7 
-0.64 
2.3 
0.28 
-0.15 
-73.8 
over. R²=0.66 
-0.58 
-4.53 
0.31 
2.6 
-0.16 
-73.7 

Third  step:  inclusion  of  additional  variables  in  models  relating  unemployment 
rates to IHD 

We  now  have  a  basis  for  including  additional  variables  in  the  IHD  model  to  observe 
whether the basic relation between unemployment rate and IHD remains significant if we 
control  for  other  factors  that  are  related  to  unemployment  or  are  thought  to  influence 
IHD  as  a  result  of  epidemiological  risk  factor  status.  We  begin  with  the  well-known 
observation  that  Mediterranean  countries  tend  to  show  lower  rates  of  IHD  than  other 
European geographic areas, other things equal. This phenomenon is usually attributed to 
especially healthy Mediterranean diets – high in fruits and vegetables, fish, olive oil, and 
red wine and low in saturated fats (Estruch, et al, 2006).  

Other  hypotheses  offered  to  explain  the  relatively  low  IHD  death  rates  in  Southern 
European  countries  include  an  absence  of  cold  winter  temperatures,  such  as  prevail  in 
Northern  Europe,  and  more  closely  knit  family  ties  –  offering  greater  social  cohesion  or 
“social capital” (McGregor, Watkin and Cox, 2004). Our analysis shows that this “dummy” 
variable – i.e., 0/1 binary variable – for Southern European countries indeed contributes 
substantially to the explained variance of our IHD model (see Table 4). 

21 

 
 
 
 
 
 
Table 4:     Relation of unemployment rate to age-adjusted IHD mortality rate, adjusted 

for GDPpc and 

                   Southern Europe by lagged years of unemployment 0-5 and lagged years of 

GDPpc 5-10 

Predicting of ischaemic  heart diseases (ICD-10 codes I20-I25) death rate per 100,000 of 
total population (age standardized) 

Model 03b.1l5.2l0 
over. R² 
Coefficients/Betas 
5 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 

Model 03b.1l6.2l1 
over. R² 
Coefficients/Betas 
6 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
1 year lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 

Model 03b.1l7.2l2 
over. R² 
Coefficients/Betas 
7 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
2 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 

Model 03b.1l8.2l3 
over. R² 
Coefficients/Betas 
8 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
3 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 

Model 03b.1l9.2l4 
over. R² 
Coefficients/Betas 
9 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
4 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 

Model 03b.1l10.2l5 
over. R² 
Coefficients/Betas 
10 years lag of GDP per capita at PPP in thousands of constant 2005 int. $ 
5 years lag of Unemployment rate as % of total labour force 15+ years old 
Dummy (1=Southern Europe, 0=other states) 

asdr 

AR(1)  Robust  Fixed 
0.70  0.51 

0.70 

0.70 

Coef. 

-6.00 
0.77 
-76.25 

Betas  Coefficients 
-0.76 
0.10 
-0.15 

-5.41 
-0.01 
-75.06 

-6.00 
-6.00 
0.77  0.78 

-76.25 

asdr 

AR(1)  Robust  Fixed 
0.70  0.51 

0.70 

0.70 

Coef. 

-5.44 
1.36 
-75.35 

Betas  Coefficients 
-0.71 
0.17 
-0.15 

-5.28 
0.27 
-74.20 

-5.44 
-5.38 
1.36  1.37 

-75.35 

asdr 

AR(1)  Robust  Fixed 
0.69  0.50 

0.70 

0.69 

Coef. 

-4.82 
2.03 
-74.95 

Betas  Coefficients 
-0.66 
0.24 
-0.15 

-5.07 
0.96 
-73.88 

-4.82 
-4.71 
2.03  2.08 

-74.95 

asdr 

AR(1)  Robust  Fixed 
0.69  0.51 

0.70 

0.69 

Coef. 

-4.84 
1.69 
-74.02 

Betas  Coefficients 
-0.67 
0.19 
-0.15 

-4.98 
1.07 
-73.37 

-4.72 
-4.84 
1.69  1.74 

-74.02 

asdr 

AR(1)  Robust  Fixed 
0.69  0.51 

0.68 

0.69 

Coef. 

-4.93 
1.17 
-73.15 

Betas  Coefficients 
-0.67 
0.13 
-0.16 

-4.57 
1.01 
-72.93 

-4.93 
-4.79 
1.17  1.21 

-73.15 

asdr 

AR(1)  Robust  Fixed 
0.68  0.51 

0.65 

0.66 

Coef. 

-6.09 
1.15 
-81.87 

Betas  Coefficients 
-0.65 
0.12 
-0.17 

-3.91 
0.59 
-72.90 

-4.88 
-4.67 
0.59  0.62 

-72.68 

Legend:  

Red = Significance <.05; Bold Black = Significance <.10; Light Black = NS 

22 

 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
The  next  set  of  variables  for  testing  as  potential  confounders  includes  other  labour 
market measures – i.e., labour force participation and self-employment.  

The  next  most  important  variables  are  those  indicating  high  rates  of  poverty,  relatively 
low  income  and/or  the  potential  for  low  life-time  earnings.    These  variables  include  the 
percentage  of  a  population  with  access  to  water  and  sanitation  (inverse  indicator  of  a 
high poverty rate), adolescent fertility rates and proportion of the  labour force engaged 
in agriculture.  

Finally,  potentially  the  most  important  source  of  IHD  mortality,  namely  smoking 
prevalence, is included for epidemiological completeness.  

Detailed findings 

 

If  unemployment  is  lagged  over zero  –  5  years  in  relation  to  age-adjusted  heart 
disease mortality, without any control variables, then unemployment at lags 1 – 5 
will  be  statistically  significant  and  the  highest  coefficient  for  unemployment  is  at 
lag 2. The overall R square is approximately 0.10 (See table 1).  

  When unemployment is related to age-adjusted heart disease mortality at zero – 
5 years lag, with a control for GDP per capita  at lags ranging from 5  – 10 years 
(in  order  to  avoid  multicolinearity  with  unemployment  and  maintain  medium- 
long-term  relations  of  GDP  per  capita  to  age-adjusted  heart  disease  mortality), 
the 2-year lag for unemployment has the highest coefficient, with GDP per capita 
lagged  7  years.  This will  be  the  case  regardless  of  the  method  of  calculating  the 
regression  coefficient—i.e.,  random  effects,  random  effects  with  AR  (1) 
adjustment, robust random effects, or fixed effects (See table 2). 

  The  optimum  lag  for  unemployment  in  its  relation  to  age-adjusted  heart  disease 
mortality is at 2 years, and that for GDP per capita is 7 years, where an additional 
control variable—dummy for Southern Europe—is added to the model. The overall 
R square now rises to approximately 0.70 (See table 4).  

  However,  if  the  unemployment  relations’  lag  to  age-adjusted  heart  disease 
mortality  is  fixed  at  2  years,  and  GDP  per  capita  as  well  as  the  dummy  for 
Southern Europe are in the model, then the optimum lag for GDP per capita is 5 
years since the highest overall R square is 0.70 for the model (See table 3).  

  The relation between unemployment and age-adjusted heart disease mortality at 
2 years’ lag for unemployment controlling for GDP per capita and Southern Europe 
(dummy) can be seen for ages 35 – 44, 44 – 54, 55 -64, 65 – 74, 85 and over, 
but not for under age 35 (See table 5).  

23 

 
 
 
 
 
 
 
 
 
Table 5:  
by age, adjusted for economic and life style variables 

Relation of unemployment rate lagged 0 or 2 years to IHD mortality rates, 

Legend:  

Red = Significance <.05 
Bold Black = Significance <.10 
Light Black = NS 

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  But  if  the  relation  between  unemployment  and  age-adjusted  heart  disease 
mortality  controls  for  11  economic  and  life-style  variables,  then  a  significant 
coefficient can be seen for all age groups from 15 – 24 to 85 +. For age groups 15 
– 24 and 25 – 34, the lag for unemployment is at zero, whereas for all age groups 
over  35,  the  unemployment  lag  is  at  2  years.  The  control  variables  are  GDP  per 
capita,  total  population  served  with  improved  water  and  sanitation,  labour  force 
participation as per cent of total  population 15+, employment in agriculture as a 
proportion  of  total  labour  force  15+,  adolescent  fertility  rate  (births  per  1,000 
women  ages  15  –  19),  Gini  index  of  inequality  in  household  net  disposable 
income,  inflation  (consumer  prices  annual  per  cent),  proportion  of  regular  daily 
smokers in the male population ages 15+, Dummy 1 (1 = Southern Europe, 0 = 
other  states),  Dummy  2  (1  =  Poland,  Bulgaria,  and  Romania,  0  =  other  states). 
(See table 5).  

  As can be seen in table 5, the coefficients for unemployment continually increase 
as population's age: 15 – 24 = 0.01, 25 – 34 = 0.04, 35 – 44 = 0.20, 45 – 54 = 
1.17,  55  –  64  =  2.54,  65  –  74  =  3.78,  75  –  84  =  7.41,  85  and  over  =  43.59. 
These  rising  coefficients  by  increasing  age  are  consistent  with  higher  levels  of 
heart  disease  prevalence  by  age  in  the  European  population.  In  general,  the  R 
square  and  fit  of  the  overall  model,  R  square,  also  tends  to  improve  with  age, 
except for the oldest age groups. Thus, 15 – 24 = 0.3602, 25 – 34 = 0.6402, 35 
– 44 = 0.7629, 45 – 54 = 0.8848, 55 – 64 = 0.9888, 65 – 74 = 0.9162, 75 – 84 
= 0.8018, 85 and over = 0.7129.  

  Finally,  we  now  compare  the  impact  of  unemployment  on  age-adjusted  heart 
disease  mortality  with  lags  for  unemployment  at  0,  1  and  2  years  in  models 
controlling  for  the  11  economic  and  life  style  variables.  In  this  comparison  all 
three  lags  for  unemployment  are  statistically  significant,  but  the  highest 
coefficient  and  smallest  confidence  interval  is  at  a  lag  of  2  years.  However,  the 
overall  R  square  for  the  model  is  highest  when  unemployment  is  at  lag  0  = 
0.8884, somewhat lower at lag 1 = 0.8774 and lower still at lag 2 = 0.8687. See 
tables 6, 7, 8 and accompanying figures. 

25 

 
 
 
 
 
 
Table 6:  
mortality rate, adjusted for economic and life style variables. 

Relation  of  unemployment  rate  lagged  0  years  to  age-adjusted  IHD 

Predicting of ischaemic heart diseases (ICD-10 codes I20-I25) death rate per 100,000 of total population. Pooled analysis 
for 11 years (2000-2010), 28 EU countries. 

Random-effects GLS regression 
Group variable:  
R-sq:    within   

between   
overall  
Wald chi2(10)      =  737.62 
sigma_u   26.861826 
to u_i) 

numcod   
= 0.6609   
= 0.8982   
= 0.8884   

Number of obs         
Number of groups    
Obs per group:   min  
avg  
max  

= 
= 
= 
= 
= 

307 
28 
10 
11.0 
11 

corr(u_i, X)   = 0 (assumed) 
sigma_e   10.389635 

Prob > chi2        =  0.0000 
rho      .86986833      (fraction  of  variance  due 

Predictors 

Coef.  Std.Err 

t 

-

5 years lag of GDP/cap. at PPP in thousands of cons. 2005 int. $ 

-3.95 

0.42 

Total pop. served with Improved water & Sanitation (%) 

-2.49 

0.75 

Labour force participation as % of total population 15+ 

-2.25 

0.55 

Self-employment as a proportion (%) of total employment 15+ 

-1.98 

0.56 

0 years lag of Unemployment rate as % of total labour force 15+ 

Employment in agriculture as a proportion (%) of total LF 15+ 

Adolescent fertility rate (births per 1,000 women ages 15-19) 

Proportion of regular daily smokers in the male pop. age 15+ 

0.74 

3.69 

1.12 

0.67 

Dummy 1 (1=Southern Europe, 0=other states) 

Dummy 2 (1=Poland, Bulgaria & Romania, 0=other states) 

-75.09  12.41 

-

143.76  19.88 

P>t  Beta 
-
0.49 
-
0.14 
-
0.16 
-
0.18 

9.40  0.00 

3.31  0.00 

4.05  0.00 

3.54  0.00 

-

-

-

0.25  2.90  0.00  0.09 

0.81  4.57  0.00  0.29 

0.49  2.28  0.02  0.10 

-

0.26  2.57  0.01  0.11 
-
0.21 
-
0.27 

6.05  0.00 

7.23  0.00 

-

Constant 

594.82  81.88  7.26  0.00  0.03 

Legend:   Red = Significance <.05; Bold Black = Significance <.10; Light Black = NS 

26 

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Table 7:  Relation  of  unemployment  rate  lagged  1  year  to  age-adjusted  IHD  mortality 

rate, adjusted for economic and life style variables 

Predicting of ischaemic  heart diseases (ICD-10 codes I20-I25) death rate per 100,000 of total population. Pooled analysis 
for 11 years (2000-2010), 28 EU countries. 

Random-effects GLS regression 
Group variable:  
R-sq:    within   

between   
overall  
Wald chi2(10)      =  728.75 
sigma_u   26.295798 
to u_i) 

numcod   
= 0.6623   
= 0.8870   
= 0.8774   

Number of obs         
Number of groups    
Obs per group:   min  
avg  
max  

= 
= 
= 
= 
= 

304 
28 
10 
10.9 
11 

corr(u_i, X)   = 0 (assumed) 
sigma_e   10.059353 

Prob > chi2        =  0.0000 
rho      .87234043      (fraction  of  variance  due 

Predictors 

6 years lag of GDP/cap. at PPP in thousands of cons. 2005 int. $ 
Total pop. served with Improved water & Sanitation (%) 
Labour force participation as % of total population 15+ 
Self-employment as a proportion (%) of total employment 15+ 
1 year lag of Unemployment rate as % of total labour force 15+ 
Employment in agriculture as a proportion (%) of total LF 15+ 
Adolescent fertility rate (births per 1,000 women ages 15-19) 
Proportion of regular daily smokers in the male pop. age 15+ 
Dummy 1 (1=Southern Europe, 0=other states) 
Dummy 2 (1=Poland, Bulgaria & Romania, 0=other states) 
Constant 

Coef. 

-3.54 
-2.83 
-2.15 
-1.94 
0.84 
3.54 
1.04 
0.80 
-72.32 
-138.71 
605.84 

Std.Err 
0.39 
0.75 
0.54 
0.55 
0.30 
0.82 
0.48 
0.25 
12.25 
19.65 
81.43 

t 
-8.98 
-3.76 
-3.95 
-3.52 
2.84 
4.33 
2.15 
3.18 
-5.90 
-7.06 
7.44 

P>t 
0.00 
0.00 
0.00 
0.00 
0.01 
0.00 
0.03 
0.00 
0.00 
0.00 
0.00 

Beta 
-0.46 
-0.16 
-0.15 
-0.18 
0.10 
0.28 
0.09 
0.13 
-0.21 
-0.26 
0.06 

Legend:  

Red = Significance <.05; Bold Black = Significance <.10; Light Black = NS 

27 

atatatatatatatatatatatbebebebebebebebebebebebgbgbgbgbgbgbgbgbgbgbghrhrhrhrhrhrhrhrhrhrcycycycycycycycycycyczczczczczczczczczczczdkdkdkdkdkdkdkdkdkdkdkeeeeeeeeeeeeeeeeeeeefifififififififififififrfrfrfrfrfrfrfrfrfrfrdededededededededededegrgrgrgrgrgrgrgrgrgrgrhuhuhuhuhuhuhuhuhuhuhuieieieieieieieieieieieitititititititititititlvlvlvlvlvlvlvlvlvlvlvltltltltltltltltltltltlululululululululululumtmtmtmtmtmtmtmtmtmtmtnlnlnlnlnlnlnlnlnlnlnlplplplplplplplplplplplptptptptptptptptptptrororororororororororosksksksksksksksksksksksisisisisisisisisisisiesesesesesesesesesesesseseseseseseseseseseseukukukukukukukukukukuk0100200300400observed values0100200300400predicted values 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8:  
mortality rate, adjusted for economic and life style variables 

Relation  of  unemployment  rate  lagged  2  years  to  age-adjusted  IHD 

Predicting of ischaemic heart diseases (ICD-10 codes I20-I25) death rate per 100,000 of total population. Pooled analysis 
for 11 years (2000-2010), 28 EU countries. 

Random-effects GLS regression 
Group variable:  
R-sq:    within   

between   
overall  
Wald chi2(10)      =  745.17 
sigma_u   25.974699 
to u_i) 

numcod   
= 0.6746   
= 0.8781   
= 0.8687   

Number of obs         
Number of groups    
Obs per group:   min  
avg  
max  

= 
= 
= 
= 
= 

301 
28 
9 
10.8 
11 

corr(u_i, X)   = 0 (assumed) 
sigma_e   9.5995131 

Prob > chi2        =  0.0000 
rho      .87982999      (fraction  of  variance  due 

Predictors 

7 years lag of GDP/cap. at PPP in thousands of cons. 2005 int. $ 
Total pop. served with Improved water & Sanitation (%) 
Labour force participation as % of total population 15+ 
Self-employment as a proportion (%) of total employment 15+ 
2 years lag of Unemployment rate as % of total labour force 15+ 
Employment in agriculture as a proportion (%) of total LF 15+ 
Adolescent fertility rate (births per 1,000 women ages 15-19) 
Proportion of regular daily smokers in the total pop. age 15+ 
Dummy 1 (1=Southern Europe, 0=other states) 
Dummy 2 (1=Poland, Bulgaria & Romania, 0=other states) 
Constant 

Coef. 

-3.27 
-3.14 
-1.87 
-1.61 
1.14 
3.02 
0.99 
0.88 
-71.35 
-134.44 
604.39 

Std.Err 
0.36 
0.76 
0.54 
0.54 
0.35 
0.83 
0.47 
0.24 
12.12 
19.49 
80.26 

t 
-9.01 
-4.15 
-3.49 
-2.97 
3.25 
3.63 
2.11 
3.70 
-5.89 
-6.90 
7.53 

P>t 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.04 
0.00 
0.00 
0.00 
0.00 

Beta 
-0.43 
-0.19 
-0.14 
-0.16 
0.12 
0.25 
0.09 
0.15 
-0.21 
-0.26 
0.11 

Legend:  

Red = Significance <.05; Bold Black = Significance <.10; Light Black = NS 

  Analysis of relation between unemployment and stroke mortality 

Stroke mortality rate model as replication of heart disease mortality rate model 

A stroke mortality rate model is developed that can essentially reproduce the findings for 
heart  disease  mortality  as  originally  created.  In  this  original  heart  disease  mortality 
model the principal lag of the unemployment rate is at 2 years, that for the labour force 

28 

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participation  rate  is  at  zero,  and  the  lag  for  GDP  per  capita  is  at  an  optimal  7  years. 
Smoking  and  dietary  controls  are  included  at  lag  zero.  Smoking  prevalence  is  utilized 
only for males because prevalence for females was not a statistically significant predictor 
of  stroke  mortality.  Food  supply  from  vegetable  sources  is  the  dietary  control  and  is 
statistically  significant.  Controls  for  chronic  poverty  include  (lack  of)  availability  of 
potable  water  and  sanitation,  and  the  adolescent  fertility  rate  at  lag  zero  for  both 
variables  were  significant.  The  Gini  index  is  also  used  as  a  control  for  enlarged  income 
inequality.  

For  total  stroke  mortality,  the  unemployment  rate,  LFPR  and  GDP  per  capita  are 
statistically significant. The R squared is at .80 (i.e., .7975). Examining the impact of the 
unemployment rate and the other economic variables among the age groups 35 – 44 to 
85  +:  (1)  the  unemployment  rate  is  significant  from  35  –  44  through  65  –  74;  (2)  the 
labour force participation rate is significant (negative) for ages 35 through 85 + (!); (3) 
GDP per capita is also significant for 35 – 85 +. (Male) smoking prevalence is significant 
for 35 – 44 and for 65 – 85 +; vegetable food supply is (negatively) significant for 35 – 
85 +; potable water and sanitation availability is (negatively) significant for 35  – 85 +; 
and  adolescent  fertility  is  also  significant  for  ages  35  –  85+.  Finally,  the  Gini  index  is 
significant (with a positive sign) for ages 35 – 44, 55 – 64, 75 – 84 and 85 +. 

Clearly  stroke  mortality  follows  the  pattern  of  heart  disease  mortality  in  EU  countries 
over  2000  –  2010  in  the  sense  that  it  is  partially  predicted  by  the  unemployment  rate, 
labour  force  participation  rate,  GDP  per  capita  and  the  control  variables  used  in  the 
original heart disease mortality model. See Table 9. 

Table 9:     Stroke mortality rate model as replication of heart disease mortality 

Tests of relations of principal economic variables to stroke mortality rates 

Unemployment rate without LFPR, but including other control variables 

The  next  step  was  to  examine  the  two  major  labour  force  indicators  to  determine 
whether  they  show  predictable  patterns  in  relation  to  stroke  mortality  while  taking 
account of the other control variables.  

The unemployment rate was examined for lag relations to stroke mortality at 0, 1, 2, 3, 4 
and 5 years of lag, adjusting for the control variables described earlier. In this analysis 0, 
1 and 5 years of lag were not statistically significant, while lags 2 – 4 showed statistical 
significance.  This  confirms  that  the  most  important  initial  lag  of  impact  of  the 
unemployment rate to stroke mortality is at 2 years. See Table 10. 

29 

 
 
 
 
 
Table 10:    Unemployment rate without LFPR, but including other control variables 

Labour  force  participation  without  unemployment  rate,  but  including  other  control 
variables 

The LFPR is a far broader measure of labour market involvement of the population while 
the  unemployment  rate,  as  a  measure  of  joblessness,  has  the  disadvantage  of  not 
including jobless individuals who have given up seeking work.  

It is clear that the labour force participation rate is a much more powerful labour market 
predictor (inverse) of stroke mortality than the unemployment rate (positive). First, the 
impact of the LFPR extends, for males, over the age groups 35 – 85 + while for females 
it  extends  over  25  –  85+.  Second,  the  impact  (i.e.,  effect  size)  of  the  LFPR  on  stroke 
mortality is often twice as great as that of the unemployment rate. This is the case even 
after  adjusting  for  the  control  variables  cited,  and  in  the  absence  of  the  unemployment 
rate in the model predicting stroke mortality. See also Table 11.  

30 

 
 
 
 
 
 
 
 
Table 11:  Labour force participation rate without unemployment rate, but including other 

control variables 

GDP  per  capita  without  the  unemployment  rate  and  LFPR,  but  including  other  control 
variables 

The  third  important  recessional  indicator  is  the  most  accepted  as  a  business  cycle  and 
economic  trend  variable,  namely,  the  GDP  per  capita.  In  this  paper  it  can  be  observed 
that  GDP  per  capita  has  a  range  of  impact  on  stroke  mortality  starting  at  lag  zero  and 
extending  over  at  least  10  years.  This  can  be  seen  with  adjustment  for  the  control 
variables  cited  and  in  the  absence  of  the  labour  market  variables.  It  is  especially 
important  to  omit  the  unemployment  rate  in  this  analysis,  since  GDP  per  capita  is  a 
major cyclical predictor of unemployment. See Table 12. 

31 

 
 
 
 
 
 
 
 
Table  12:          GDP  per  capita  without  the  Unemployment  rate  and  LFPR,  but  including 
other control variables 

      Expansion of model to include GDP per capita at 10 year’s lag 

In replicating the original heart disease mortality model to examine whether it applies 
to stroke mortality, the 7-year lag for GDP per capita was taken as the optimum lag 
originally  found  for  heart  disease  mortality.  However,  it  had  subsequently  been 
determined  (see  above)  that  the  impact  of  GDP  per  capita  on  stroke  mortality 
extends  over  at  least  a  decade.  Thus,  the  labour  force  variables  predicting  stroke 
mortality  were  retested  in  a  model  that  contained  GDP  per  capita  at  lag  10,  the 
unemployment  rate  at  lag  2,  and  LFPR  at  lag  zero.  All  other  control  variables  were 
included. Results were virtually the same as found when replicating the original heart 
disease model and GDP was estimated earlier at a lag of seven years. See Table 13.  

32 

 
 
 
 
 
 
 
Table 13:     Expansion of model to include GDP per capita at 10 year’s lag 

      Expansion of model to include unemployment rate at 3 year’s lag 

It was found that there is a somewhat stronger relation between the unemployment 
rate and stroke mortality when the unemployment rate is at a lag of 3 rather than 2 
years.  This  would  mean  that  the  unemployment  rate  lag  effect  for  stroke  occurs 
slightly  later  than  for  heart  disease  mortality.  The  stroke  mortality  model  was  then 
tested  thoroughly  for  total  mortality  as  well  as  by  sex  and  age.  The  unemployment 
rate was included at lag 3, the LFPR at lag zero and the GDP per capita at lag 10. The 
unemployment  rate  was  significant  in  the  age  groups  45  –  84  for  total  stroke 
mortality. For males, it was significant for the working ages 45 – 64 and for females 
45  –  84.  At  the  same  time,  for  males  the  labour  force  participation  rate  influences 
stroke  mortality  (inversely)  in  the  ages  35  –  85  +,  while  for  females  it  influences 
stroke mortality over 25  – 85+. Clearly, the  influence of  labour market variables on 
stroke mortality is greater for females than for males. See Table 14.  

33 

 
 
 
 
 
 
 
 
Table 14:    Expansion of model to include unemployment rate at 3 year’s lag 

34 

 
 
 
 
VIII.  Discussion and Interpretation 

The  primary  observation  is  that  the  unemployment  rate  has  a  positive  relationship  to 
stroke mortality that is similar to that found earlier for heart disease mortality in the EU. 
The only notable difference is that the unemployment rate can be seen to influence heart 
disease mortality within the first few months of an increase in unemployment (i.e., within 
the same contemporaneous year), while there is a lag of  approximately two years after 
which increased stroke mortality follows a heightened unemployment rate. However, for 
both  stroke  and  heart  disease,  increased  labour  force  participation  rates  predict 
decreased mortality contemporaneously—i.e., as soon as the LFPR increases.  

This raises interesting questions about the interpretation of the unemployment rate—as a 
measure of joblessness—versus that of the LFPR as measuring population involvement in 
economic  activity.  Clearly,  these  measures  are  not  the  inverse  of  each  other.  For 
example,  as  the  EU  unemployment  rate  continued  to  increase  after  2009,  the  LFPR 
continued to increase, largely for women over 45.  

The  LFPR  is  certainly  a  more  far-reaching  indicator  of  labour  market  activity,  compared 
with  the  unemployment  rate,  since  the  unemployment  rate,  as  a  measure  of  job  loss, 
nevertheless  excludes  many  of  the  long-term  jobless  who  have  left  the  labour  force, 
being  discouraged  in  their  efforts  to  find  employment.  At  the  same  time,  the  LFPR 
includes  new  and  older  entrants  into  the  labour  force  who  then  comprise  a  part  of  the 
employment-to-population ratio. It is possible that being at work—as a health predictor—
is not exactly the opposite of either losing work (or being jobless and seeking work)—as 
a health or illness predictor.  

As to the mechanism(s) whereby job loss predicts heart disease and stroke mortality, the 
traditional  perspective  would  point  to  the  concept  of  “stress.”  This  mechanism  could  be 
manifested in psychophysiological  responses to shock and loss, such as increased blood 
pressure, assuming the inability to cope over a sustained period of time (Matthews et al., 
2001;  Esch  2002).  Similarly,  the  mental  stress  could  result  in  behavioural  responses 
including inability to stop smoking, excessive use of alcohol, weight gain or consumption 
of unhealthful diets (Montgomery et al. 1998). Additionally, major stress could engender 
depression,  a  risk  factor  for  cardiovascular  disease,  withdrawal  or  hostile  emotional 
responses  which  strain  the  stability  of  social  relations  among  friends  and  family.  Such 
potential  damages  to  social  ties  are  an  important  source  of  increased  morbidity  and 
mortality  in  the  epidemiological  literature  (Seeman,  1996).  Finally,  it  should  simply  be 
stated that impoverished conditions, often brought on by unemployment are well known 
to  be  associated  with  cardiovascular  diseases,  including  stroke  (Galobardes  and  Lynch, 
2006).  

The  issues  of  economic  life  stress  and  emotional  and  behavioural  responses  to  it  are 
relevant to the question of why the unemployment rate, and the LFPR, should apparently 
influence heart disease and stroke mortality beyond the usual ages of working life—i.e., 
after 65, in EU countries with extensive pensions, government financed health care and 
other  social  welfare  systems.  This  paper  refers  to  these  overarching  influences  of  the 
labour  force  variables  as  “spread  effects.”  An  interpretation  would  be  that  loss  of 
employment  for  the  principal  (or  any)  family  breadwinner  affects  the  entire  family  (or 
extended  family  members).  This  extension  refers  to  spouses  or  partners  as  well  as  to 
children and the elderly. Of course, cardiovascular illness, as a cause of death, is a health 
outcome  that  largely  involves  the  elderly  who  are  not  frequently  in  the  labour  force. 
Thus, the possible mechanisms of disturbed financial support, or damage to social ties—
and lack of social support (Uchino, 2006)—may plausibly be the main linkages of national 
patterns  of  employment  loss  to  cardiovascular  mortality.  Also,  it  is  possible  that  under 
significant conditions of stress, some persons do not seek appropriate health care, or do 
not  comply  with  medical  recommendations,  even  where  such  health  care  is  otherwise 
accessible.  

35 

 
 
 
 
IX.  Conclusions 

The unemployment – heart disease mortality relation 

The  finding  that  the  unemployment  rate  at  lags  of  0-5  years  is  positively  related  to 
Ischemic Heart Disease (IHD) mortality is consistent with epidemiological literature at the 
individual  level  of  analysis.  This  substantial  literature  indicates  that  unemployment  is  a 
risk  to  heart  disease  morbidity  and  mortality.  A  modifying  factor  is  that  the 
unemployment rate at lag 0 is positively but not significantly related to IHD mortality  – 
without  control  for  other  variables.  However,  with  adjustment  even  for  GDPpc,  the 
unemployment rate at lag 0 is positively and significantly related to IHD mortality rates 
(with GDPpc lagged, e.g. 5 years).  

The  finding  in  this  study  that  the  unemployment  rate  lag  0  is  positively  related  to  IHD 
mortality is somewhat different from the results of 20th century time-series studies which 
show  non-significant  relations  to  IHD  mortality  of  unemployment  rates  lagged  0  and  1 
year,  but  strong  positive  relations  of  the  unemployment  rate  at  lags  of  2  and  3  years. 
This  finding  is  consistent  with  20th  century  time-series  studies  showing  the  relation  at 
unemployment rate lag 2, 3 and longer years, especially since the current work on IHD 
shows the peak relationship of unemployment rates to be at a lag of 2 years.  

Somewhat different findings cited in this study on the relation of unemployment over 0-5 
years  to  a  major  cause  of  mortality  could  be  due  to:  (a)  the  usually  large  and  intense 
effect  of  the  current  Great  Recession,  (b)  the  sample  consisting  of  European  Union 
countries only and (c) IHD mortality being used as the health outcome (as compared to 
other health measures).  

With adjustment for GDPpc, Southern Europe and other economic and life style variables, 
the unemployment rate at lags 0, 1, 2 remains significantly related to IHD mortality and 
the  unemployment  rate  lag  2  is  shown  to  have  the  strongest  relationship.  However, 
GDPpc  shows  the  strongest  overall  relation  to  IHD  mortality  (compared  with  any  other 
variable  in  this  study)  with  the  highest  R²,  coefficient  and  standardized  data  for  GDPpc 
lagged 0-10 years.  

Labour market variables other than the unemployment rate show strong predictability to 
IHD mortality. Indeed, they are stronger than the assessed impact of the unemployment 
rate.  These include labour force participation and self-employment at lag 0 with negative 
signs,  and  employment  in  agriculture  as  a  percent  of  total  employment  with  a  positive 
sign at lag 0.  

Poverty  indices  are  positively  related  to  IHD  mortality.  These  include  percent  of  the 
population (not) served with improved water and sanitation, the adolescent fertility rate 
and employment in agriculture as proportion of total employment.  Southern Europe (as 
a dummy or binary variable) shows a negative relation to IHD mortality, supporting the 
hypotheses  of  healthy  Mediterranean  diet,  relatively  mild  winter  temperatures,  and 
possibly stronger social (family and community) ties. 

In summary, the unemployment rate at lags of 0-2 years show strong positive relations 
to  IHD  mortality  over  2000-2010  in  European  countries.  These  relations  hold  over  at 
least 35-74 years of age and even over 75-84 and 85+ years of age. These relations hold 
regardless of whether less than 3 or more than 10 variables are used as controls.  

36 

 
 
The unemployment – stroke mortality relation 

The  study  on  stroke  mortality  rates  similarly  indicates  that  they  have  been  significantly 
influenced  by  unemployment  rates  during  the  period  preceding  and  including  the  Great 
Recession,  over  2000  –  2011  in  European  Union  countries.  These  results,  also  obtained 
via pooled cross-sectional time-series multiple regression analysis, are found even under 
controls  for  (1)  other  major  economic  indicators  of  recession—the  labour  force 
participation rate and GDP per capita—as well as (2) smoking and dietary risks to stroke, 
and (3) measures of severe chronic poverty such as (lack of) access to potable water and 
sanitation, and  the  adolescent  fertility  rate.  It  is  again  noteworthy  that  the  labour  force 
participation  rate  has  a  considerably  greater  impact  on  stroke  mortality  than  the 
unemployment rate, which is the principal subject of this analysis. From this perspective, 
it is remarkable that the previous literature, both in epidemiology and in macroeconomic 
research,  hardly  ever  considers  the  relation  of  labour  force  participation  to  health, 
although the relation of unemployment to damaged health is well attested in individual-
level epidemiologic studies. 

The macroeconomic relation of unemployment rates to stroke mortality rates is similar to 
that of unemployment rates to heart disease mortality rates, except that the lag between 
unemployment rates and stroke mortality rates appears to be longer. That the relations 
between  unemployment  rates  and  stroke  should  resemble  that  shown  in  heart  disease 
mortality  is  not  surprising  in  that  both  are  cardiovascular  illnesses  and  similar 
psychological, biological and medical mechanisms are probably involved in both types of 
illnesses.  

It can now be seen that relations between unemployment and cardiovascular illness, that 
have been observed in epidemiological studies of individual persons, can be observed as 
well at the national level with macroeconomic variables, such as the unemployment rate, 
in contemporary times. This means that national and regional policies that could prevent 
or  reduce  unemployment  rates,  or  mitigate  the  related  economic  losses,  and  finance 
active labour market policies, have a much broader function. Such policies could enhance 
population health and life expectancy, and probably reduce high levels of national health 
care expenditures.   

37 

 
 
 
 
X. 

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cardiovascular risk factor study in Southern Sweden (CRISS).  Scand J Public Health 31, 
305-311, 2003. 

Hulme,  D,  Moore,  K  and  Shepherd,  A,  Chronic  Poverty:  Meanings  and  Analytical 
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McGregor  GR,  Watkin  HA  and  Cox  M.  Relationships  between  the  seasonality  of 
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McLoyd  VC,  Jayaratne  TE,  Ceballo  R,  and  Borquez  J.  Unemployment  and  work 
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41 

 
 
 
 
XI.  Appendix 1: Selected studies on unemployment, income and CVD 

Selected epidemiological studies on income, unemployment in relation to CVD 

Income and CVD 

The  review  of  the  literature  by  Galobardes,  Smith  and  Lynch  (2006)  found  that  the 
current  evidence  from  individual-level  studies  shows  that  those  who  experienced  less 
favourable childhood socioeconomic circumstances are at greater risk for developing and 
dying  of  CVD  in  adulthood.  Thus,  if  the  social  patterning  of  CVD  outcomes  observed  in 
adulthood  is  already  determined  by  socioeconomic  circumstances  experienced  in 
childhood,  interventions  that  aim  at  reducing  economic,  political,  social,  and  cultural 
disadvantages should start early in life. 

Frankel,  Smith,  and  Gunnell  (1999)  state  that  the  relation  between  childhood 
socioeconomic  position  and  adult  cardiovascular  mortality  is  examined  in  3,750 
individuals  whose  families  took  part  in  the  Carnegie  survey  of  family  diet  and  health  in 
England and Scotland between 1937 and 1939. 

The study by Kaplan and Kei l (1993) says researchers  are still  learning about different 
modifiable factors that may influence cardiovascular diseases. Socioeconomic status may 
provide  a  new  focus.  During  40  years  of  study  there  has  been  a  consistent  inverse 
relation  between  cardiovascular  disease,  primarily  coronary  heart  disease,  and  many  of 
the  indicators  of  SES.  There  is  some  evidence  that  areas  with  the  poorest  socio-
environmental  conditions  experience  later  onset  in  the  decline  in  cardiovascular 
mortality. 

Brenner  and  Mooney  (1982)  developed  a  multivariate  model  of  the  impact  of  more 
fundamental changes in the socioeconomic and bio-physical environments and applied it 
to  cardiovascular  disease  mortality  rates,  by  sex,  in  England  and  Wales  and  Scotland 
during  1955-1976.  The  predictive  model  includes  factors  associated  with  (1)  long-term 
growth  in  the  economy,  (2)  deleterious  behavioural  risk  factors  loosely  associated  with 
economic growth—especially cigarette consumption per capita, (3) economic instability—
especially  recession  as  indicated  by  factors  related  to  unemployment,  income  loss,  and 
recessional  declines  in  average  weekly  hours  worked  in  manufacturing  industries,  (4) 
health  care,  and  (5)  physical  environmental  disturbances-especially  very  cold 
temperatures. 

Unemployment and CVD 

Gallo,  Teng,  Falba,  Kasl,  Krumholz  and  Bradley  (2006)  state  that  after  controlling  for 
established  predictors  of  the  outcomes,  displaced  workers  had  a  more  than  twofold 
increase in the risk of subsequent MI and stroke relative to working persons. Physicians 
who  treat  individuals  who  lose  jobs  as  they  near  retirement  should  consider  the  loss  of 
employment a potential risk factor for adverse vascular health changes. 

Filate, Johansen, Kennedy and Tu (2003) observed significant regional variations in CVD 
mortality  rates  per  100,000  populations.  Newfoundland  and  Labrador  had  the  highest 
CVD and IHD mortality rates, while Nunavut and the Northwest Territories had the lowest 
CVD  and  IHD  mortality  rates.  Health  region  smoking  and  unemployment  rates  were 
identified  as  the  most  important  factors  associated  with  CVD  and  IHD  mortality  at  the 
health region level. 

The  study  by  Henriksson,  Lindblad,  Agren,  Nilsson-Ehle  and  RastamIn  (2003)  was  a 
prospective  study  of  men,  all  the  same  age  and  without  obvious  individual  threats  of 
future  job  loss.  It  was  found  that  cross-sectional  associations  between  unemployment 
and  CVD  risk  factors  were  lost  in  times  of  lower  employment  rates.  Thus  it  is  vital  to 
consider  general  unemployment  rates  in  society  when  the  attributable  risk  of 
unemployment  is  estimated  in  health  promotion  strategies  aimed  at  preventing 
cardiovascular disease. 

42 

 
Morris,  Cook,  Shaper  (1994)  indicate  that  after  adjustment  for  a  wide  range  of 
background  variables  including  social  class,  health  behaviour,  and  health  status  before 
loss  of  employment  the  mortality  still  remained  significantly  raised.  This  suggests  a 
causal  effect,  but  set  against  this  is  the  non-specific  nature  of  the  effect,  with  the 
increased mortality involving both cancer and cardiovascular disease. 

The  study  by  Sorlie  and  Rogot    (1990)  found  that  employed  persons  aged  25-64  years 
were  found  to  have  standardized  mortality  ratios  from  61%  to  74%  of  the  average, 
depending  upon  their  sex  and  race.  Unemployed  men  had  standardized  mortality  ratios 
slightly  above  100,  but  these  values  were  1.6  and  2.2  times  higher  than  those  for 
employed white men and black men, respectively. 

Crombie,  Kenicer,  Smith  and  Tunstall-Pedoe  (1989)  indicate  strong  associations  with 
several  measures  of  social  disadvantage,  the  strongest  being  with  percentage  of  male 
unemployment.  A  fitted  multiple  regression  model  with  mortality  from  coronary  heart 
disease  in  men  found  independent  effects  of  two  social  variables  (percentage  male 
unemployment and percentage social class III-V) and one climatic factor (rainfall). 

The  study  by  Moser,  Fox,  Goldblatt  and  Jones (1986)  provides  evidence  which  could  be 
seen as supporting hypotheses about relationships between stress and overall mortality, 
with a marked excess for suicides. The evidence with respect to ischaemic heart disease 
is positive but less convincing with excess mortality from this cause principally occurring 
among younger unemployed men and among the wives of men who were seeking work 
in  1971.  Given  the  sharp  contrasts  in  the  pattern  and  levels  of  unemployment  between 
1971 and 1981 it is difficult to extrapolate from these findings to the present day. 

Selected  aggregate  time-series  studies  on  unemployment,  income  and  CVD 
mortality 

The time-series analysis work by Brenner (2012) shows that health expenditures, labour 
force  participation,  and  GDP  per  capita,  tend  to  decrease  CVD  mortality,  controlling  for 
consumption risk factors.  

The study by Brenner (2005) shows that economic growth, cumulatively over at least a 
decade,  is  the  central  factor  in  mortality  rate  decline  in  the  US  over  the  20th  century. 
The  net  effect  of  increased  unemployment  is  a  substantial  increase  in  mortality.  This  is 
also  reflected  in  the  entirely  negative  relation  between  the  cumulative  effects  of  the 
employment to population ratio and mortality rates over a decade. 

Time-series  regression  analysis  work  by  Brenner  (1997)  showed  that,  holding  constant 
the  effects  of  tobacco,  animal  fats  and  alcohol,  increased  income  and  social  welfare 
expenditures  are  related  to  heart  disease  mortality  rate  declines,  whereas  increased 
unemployment  and  business  failure  rates  are  related  to  heart  disease  mortality  rate 
increases over more than a decade. 

Brenner’s (1987b) data for nine countries in the Post World War II era were investigated: 
Australia, Canada, England and Wales, Denmark, Federal Republic of Germany, Finland, 
France, Sweden and the United States. In all nine countries unemployment and business 
failures are positively related to heart disease mortality, and in eight countries the trend 
of economic growth shows an inverse relationship. 

The predictive model by Brenner and Mooney (1982) includes factors associated with (1) 
long-term  growth  in  the  economy,  (2)  deleterious  behavioural  risk  factors  loosely 
associated  with  economic  growth—especially  cigarette  consumption  per  capita,  (3) 
economic 
to 
unemployment, income loss, and recessional declines in average weekly hours worked in 

instability—especially 

recession  as 

indicated  by 

related 

factors 

43 

 
 
 
manufacturing industries, (4) health care, and (5) physical environmental disturbances—
especially very cold temperatures. 

The findings of the study by Brenner (1971) clearly indicate that economic downturns are 
associated  with  increased  mortality  from  heart  disease  and  that,  conversely,  heart 
disease mortality decreases during economic upturns. 

Selected epidemiological studies on the effects of unemployment on life style 

Unemployment and Alcohol 

Study  results  by  Popovici  and  French  (2013)  show  a  positive  and  significant  effect  of 
unemployment  on  drinking  behaviours,  and  the  findings  are  robust  to  numerous 
sensitivity  tests.  Perhaps,  macroeconomic  policy  decisions  intended  to  stimulate  the 
economy  during  economic  downturns  should  also  consider  the  avoided  personal  costs 
and externalities associated with alcohol misuse. 

The study by Montgomery, Cook, Bartley and Wadsworth (1998) compared men who had 
never been unemployed. The adjusted relative odds amongst men with over three years 
of accumulated unemployment (after adjustment for possible confounding socioeconomic 
and  behavioural  factors  measured  prior  to  unemployment)  were  2.15  for  problem 
drinkers.  Men  who  had  experienced  unemployment  in  the  year  prior  to  the  interview, 
compared to those who had not, after adjustment, were significantly more likely to drink 
heavily  and  to  have  a  drinking  problem.  Unemployment  may  play  a  significant  part  in 
establishing life-long patterns of hazardous behaviour in young men. 

Unemployment and Diet 

The  American  Dietetic  Association  (2010)  indicates  that  food  insecurity  rates  in  the 
United  States  parallel  poverty  rates,  and  food  insecurity  worsens  in  recessionary  times. 
With  the  poor  economic  conditions  in  2009  and  2010  in  the  United  States,  it  is 
anticipated  that  food  security  will  increase,  further  emphasizing  the  urgency  of  this 
health issue in the United States. 

Dave  and  Kelly  (2010)  showed  that  a  higher  risk  of  unemployment  is  associated  with 
reduced consumption of fruits and vegetables and increased consumption of “unhealthy” 
foods such as snacks and fast food. Among individuals predicted to be at highest risk of 
being unemployed, a one percentage point increase in the resident state’s unemployment 
rate is associated with a 2-8% reduction in the consumption of fruits and vegetables.  

Unemployment and Smoking 

Novo, Hammarstrom and Janlert (2000) found that a low level of education, and among 
women  also  financial  problems  and  motherhood,  were  associated  with  more  frequent 
smoking.  Unemployment  was  associated  with  tobacco  consumption,  especially  among 
women and during the boom. Thus, smoking habits were found to be a question of both 
unemployment and tobacco trends in society. 

Hammarstrom  and  Janlert  (1994)  showed  that  irrespective  of  early  smoking,  smoking 
habits developed more unfavourably among unemployed young people than among those 
with no unemployment during the period studied. The odds ratio of being a smoker at the 
age  of  21  years  when  unemployed  more  than  20  weeks  during  the  observation  period, 
compared  with  those  without  or  with  short  unemployment,  was  2.44  for  men  and  3.45 
for  women.  It  seems  that  unemployment  is  a  risk  factor  for  development  of  tobacco 
smoking in young people, especially among women. 

44 

 
 
 
 
Unemployment and Social Relations 

McKee-Ryan, Song, Wanberg and Kinicki (2005) showed that unemployed individuals had 
lower psychological and physical well-being than did their employed counterparts. Within 
unemployed  samples,  work-role  centrality,  coping  resources  (personal,  social,  financial, 
and  time  structure),  cognitive  appraisals,  and  coping  strategies  displayed  stronger 
relationships with mental health than did human capital or demographic variables. 

The  study  by  McLoyd,  Jayaratne,  Ceballo,  and  Borquez  (1994)  showed  that  maternal 
unemployment  and  work  interruption,  influences  adolescent  socioemotional  functioning. 
Current unemployment, but not past work interruption, had a direct effect on depressive 
symptomatology  in  mothers.  As  expected,  depressive  symptomatology  in  mothers 
predicted more frequent maternal punishment of adolescents, and this relation was fully 
mediated by mothers' negative perceptions of the maternal role. More frequent maternal 
punishment was associated with increased cognitive distress and depressive symptoms in 
adolescents,  and  consistent  with  predictions,  these  relations  were  partially  mediated  by 
adolescents'  perceptions  of  the  quality  of  relations  with  their  mothers.  Adolescents  who 
perceived their families as experiencing more severe economic hardship reported higher 
anxiety, more cognitive distress, and lower self-esteem. 

Selected  epidemiological  studies  on  the  influence  of  stress  and  depression  on 
cardiovascular disease 

Whooley  and  Wong  (2013)  showed  that  depression  is  a  robust  risk  factor  for  the 
development  of  cardiovascular  disease  in  healthy  populations,  and  is  predictive  of 
adverse  outcomes  (such  as  myocardial  infarction  and  death)  among  populations  with 
pre-existing  cardiovascular  disease.  An  important  direction  critical  toward  meaningful 
progress in the field of depression and cardiovascular disease is better understanding of 
how  depression  is  related  to  cardiovascular  disorders  other  than  CAD,  including 
cerebrovascular  disease,  peripheral  arterial  disease,  atrial  fibrillation,  and  other 
arrhythmias. 

The review by Peterson and Kim (2011) identifies several unresolved issues that require 
answers,  answers  that  can  be  found  with  further  research.  One  crucial  but  unexplored 
issue  is  whether  randomized  controlled  trials  that  increase  psychological  health  assets 
protect against cardiovascular events. The field of behavioural cardiology should expand 
its  focus  and  make  room  for  psychological  health  assets  when  conceptualizing  how 
psychological factors impact cardiovascular health. 

The work by Goldston and Baillie (2008) showed  the volume, strength and consistency 
of  the  association  in  depression  and  CHD,  the  dose-relationship  of  the  risk  and  the 
plausibility  of  the  potential  pathophysiological  mechanisms  have  led  to  the  conclusion 
that  depression  is  an  independent  etiological  and  prognostic  risk  factor  in  CHD. 
Furthermore, the magnitude of increased risk incurred by depression on both the etiology 
and  prognosis  of  CHD  is  equal  to  many  of  the  conventional  cardiac  risk  factors  such  as 
smoking, high cholesterol and hypertension. It has been proposed that depression exerts 
its  adverse  impact  on  the  cardiovascular system directly  through  biological  mechanisms 
including  increases  in  heart  rate,  blood  pressure,  cardiac  arrhythmias,  platelet 
aggregation and inflammation, and indirectly through its association with behavioural and 
lifestyle  factors  such  as  smoking,  physical  inactivity,  lack  of  adherence  to  medical 
regimes and social isolation. 

The European Heart Network (2006) study discusses some of the biological mechanisms 
of  stress,  including  regulation  of  cortisol  levels,  heart  rate  variability,  blood  pressure, 
plasma  fibrinogen  levels,  inflammatory  responses  and  other  immune  system  reactions, 
and  testosterone/oestrogen  levels.  Regeneration  after  stress  is  crucial,  and  sufficient 
high-quality deep sleep is essential for regeneration. 

45 

 
 
 
The  work  by  Zellweger,  Osterwalder,  Langewitz  and  Pfisterer  (2004)  indicates  that 
evidence  is  growing  that  depression  per  se  is  an  independent  risk  factor  for  cardiac 
events  in  a  patient  population  without  known  CAD.  There  are  several  behavioural  and 
pathophysiologic factors that seem to link depression with development of CAD. One may 
hypothesize that patients treated for their depression might better adhere to risk factor 
modifications, prescribed medications and rehabilitation programs. 

Esch, Stefano, Fricchione and Benson (2002) found that stress has a major impact upon 
the circulatory system. It plays a significant role in susceptibility, progress, and outcome 
of  cardiovascular  diseases.  In  particular,  stress  may  cause  or  exacerbate  disease 
processes depending on the type of stressor involved (e.g. physical, chemical, biological, 
mental,  psychosocial  etc.)  and/or  the  duration  of  its  influence  on  an  organism.  Mental 
and psychosocial stressors have a profound impact, and in this regard, the sympathetic 
nervous  system  represents  a  generally  important  effector  of  the  stress  response  with 
potentially deleterious influences on the cardiovascular system. 

Findings by Matthews, Gump and Owens (2001) tested the influence of chronic stress on 
cardiovascular  and  neuroendocrine  responses  to  and  recovery  from  acute  stressors  and 
whether  the  effects  are  gender  specific.  Men's  basal  levels  and  acute  stress  responses 
may be more affected by environmental factors than are women's. Relative to women in 
midlife,  men  may  be  at  heightened  risk  for  CVD  because  of  enhanced  acute-stress 
responses as well as delayed recovery after cessation of acute stress. 

Wheatley (1984) found that mental stress may directly influence coronary heart disease 
(CHD) and also a number of its etiologic risk factors. Out of a total of 77 patients treated 
with a placebo, there were five cases of myocardial infarction during the trial periods as 
compared to no such cases among 81 patients treated with the antianxiety drugs. 

46 

 
 
 
XII.  Statistical Tables 

Table of Contents 

Table 1. 

Relation of unemployment rate to age-adjusted IHD mortality rate, by 
lagged years unemployment 0-5 

Table 2. 

Relation of unemployment rate to age-adjusted IHD mortality rate, 
adjusted for GDP per capita, by lagged years of unemployment 0-5 and 
lagged years of GDPpc 5-10 

Table 3. 

Relation of unemployment rate lagged 2 years to age-adjusted IHD 
mortality rate, adjusted for GDPpc lagged 0-10 years and Southern Europe 

Table 4. 

Relation of unemployment rate to age-adjusted IHD mortality rate, 
adjusted for GDPpc and Southern Europe by lagged years of unemployment 
0-5 and lagged years of GDPpc 5-10 

Table 5. 

Relation of unemployment rate lagged 0 or 2 years to IHD mortality rates, 
by age, adjusted for economic and life style variables 

Table 6. 

Relation of unemployment rate lagged 0 years to age-adjusted IHD 
mortality rate, adjusted for economic and life style variables 

Table 7. 

Relation of unemployment rate lagged 1 year to age-adjusted IHD mortality 
rate, adjusted for economic and life style variables 

Table 8. 

Relation of unemployment rate lagged 2 years to age-adjusted IHD 
mortality rate, adjusted for economic and life style variables 

Table 9. 

Stroke mortality rate model as replication of heart disease mortality 

Table 10.  Unemployment rate without LFPR, but including other control variables 

Table 11.  Labour force participation rate without unemployment rate, but including 

other control variables 

Table 12.  GDP per capita without the Unemployment rate and LFPR, but including 

other control variables 

Table 13.  Expansion of model to include GDP per capita at 10 year’s lag 

Table 14.  Expansion of model to include unemployment rate at 3 year’s lag 

Page 

17 

18 

20 

22 

24 

26 

27 

28 

29 

30 

31 

32 

33 

34 

47 

 
 
 
 
 
 
 
 
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doi: 10.2767/81253