ORIGINAL PAPER

The influence of the economic crisis on the associationbetween unemployment and health: an empirical analysisfor Spain

Rosa M. Urbanos-Garrido •

Beatriz G. Lopez-Valcarcel

Received: 22 July 2013 / Accepted: 8 January 2014

� Springer-Verlag Berlin Heidelberg 2014

Abstract

Objectives To estimate the impact of (particularly long-

term) unemployment on the overall and mental health of

the Spanish working-age population and to check whether

the effects of unemployment on health have increased or

been tempered as a consequence of the economic crisis.

Methods We apply a matching technique to cross-sec-

tional microdata from the Spanish Health Survey for the

years 2006 and 2011–2012 to estimate the average treat-

ment effect of unemployment on self-assessed health

(SAH) in the last year, mental problems in the last year and

on the mental health risk in the short term. We also use a

differences-in-differences estimation method between the

two periods to check if the impact of unemployment on

health depends on the economic context.

Results Unemployment has a significant negative impact

on both SAH and mental health. This impact is particularly

high for the long-term unemployed. With respect to the

impact on mental health, negative effects significantly

worsen with the economic crisis. For the full model, the

changes in effects of long-term unemployment on mental

problems and mental health risk are, respectively, 0.35 (CI

0.19–0.50) and 0.20 (CI 0.07–0.34).

Conclusions Anxiety and stress about the future associ-

ated with unemployment could have a large impact on

individuals’ health. It may be necessary to prevent health

deterioration in vulnerable groups such as the unemployed,

and also to monitor specific health risks that arise in

recessions, such as psychological problems.

Keywords Economic crisis � Unemployment �Self-assessed health � Mental health �Matching techniques � Spain

JEL Classification J64 � I12 � I18

Introduction

The impact of economic recessions on health has been

previously addressed. Researchers mainly focused on the

role played by unemployment as a mediator agent [1–3],

because unemployment and working conditions constitute

major social determinants of health [4]. Beyond the influ-

ence of the institutional context of the labour market and

social protection, most attention has been paid to the study

of the risk factors linking labour status and health. Several

health economics papers conclude that economic down-

turns have a counter-cyclical role in terms of health, and

that short-term unemployment improves population health

and reduces mortality in developed countries [5–9].

Moreover, public health literature provides evidence that

being employed protects and promotes health [10–13].

Previous studies show that unemployment and fall in

income may lead to obesogenic diets [14] or be associated

with health risk behaviours such as excessive alcohol

consumption [15], more smoking [16] or decreased phys-

ical activity [17]. Furthermore, a reduction in the level of

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10198-014-0563-y) contains supplementarymaterial, which is available to authorized users.

R. M. Urbanos-Garrido

Complutense University of Madrid, Madrid, Spain

B. G. Lopez-Valcarcel (&)

Departamento de Metodos Cuantitativos en Economıa y Gestion,

University of Las Palmas de Gran Canaria, Campus de Tafira,

35017 Las Palmas de Gran Canaria, Spain

e-mail: bvalcarcel@dmc.ulpgc.es

123

Eur J Health Econ

DOI 10.1007/s10198-014-0563-y

income may discourage seeking medical attention to avoid

treatment costs [18]. This effect is particularly strong in

those countries where health coverage is linked to labour

status and/or the amount of copayments is significant [19].

Unemployment can also impair mental health by various

psychological mechanisms, loss of self-esteem, pessimism

about the future, etc. [20–25]. At the same time, however,

as the unemployed have a lower opportunity cost of their

time, they may choose to invest in health through healthy

lifestyles [26], or improve their mental health by doing

volunteer work, although the psychological benefits of

volunteering depend on factors such as reciprocity and the

time devoted to volunteer [27]. It is also expected that

work-related diseases will be reduced when unemployment

increases [28, 29].

In this paper we provide new evidence about the impact

of unemployment (particularly long-term unemployment)

on overall health and on mental health by using microdata

for the Spanish adult population. But, beyond the mere

effect of unemployment on health, our main interest is to

analyse the differential association between both variables

in both pre-crisis and current-crisis periods. A priori, we

cannot expect an unambiguous effect. On the one hand,

with the crisis the situation of the unemployed becomes

‘normal’, so the stigma that could harm mental health

disappears, whereas if unemployment is rare, the percep-

tion of low self-esteem and isolation may be amplified

[30]. This effect is supported by some studies showing

unemployment as a stronger risk factor when it is rare, for

all-cause mortality [31], hospital-treated non-fatal suicidal

behaviour [32, 33] or, more recently, suicides [30]. How-

ever, the higher the unemployment, the worse the per-

spectives of getting a job and the more precarious is the

future as a worker. In this sense, we would expect further

deterioration of jobless people’s health during the eco-

nomic recession compared to previous times of economic

upturn. This effect was confirmed by Preti and Miotto for

Italy. They find that a rise in suicide rates is accompanied

by a concurrent rise in unemployment rate percentage [34].

Finally, we can also find in previous literature that the level

of unemployment seems to have no major influence on the

mortality risk [35].

Spain is experiencing a lasting and severe economic

crisis. The fall of GDP, the rise of public debt and the high

public deficit highlight the gravity of the Spanish economic

situation. But the most significant feature in the Spanish

crisis is the increasing unemployment rate, which rose from

8.5 % in early 2007 to 27.2 % in the first quarter of 2013

[36], exceeding the rate of any other country in the Euro-

pean Union. In the same period, the percentage of unem-

ployed who have been looking for a job for over a year

(long-term unemployed) rose from 21.2 to 56.3 % [36].

These data widely justify addressing the relationship

between unemployment, particularly long-term jobless-

ness, and health. Besides, there is little relevant literature

on this subject for the Spanish case, singularly from the

beginning of the current economic crisis. In the 1990s,

Benavides et al. [37], by using data from health surveys,

showed a positive association between unemployment, ill

health and more use of health services, but found that this

association is less clear where high unemployment rates

can be considered a long-standing phenomenon. Further-

more, Tapia [38] found a positive relationship between

unemployment and mortality and showed how mortality

rates increase when unemployment decreases during eco-

nomic expansion periods. More recently, Pascual and

Rodrıguez [39] showed that being unemployed during the

crisis tends to improve the self-assessed health (SAH) for

people living in Catalonia, whereas the effect of unem-

ployment on mental health seems to be unrelated to the

economic crisis.

Materials and methods

We use microdata from the Spanish national health survey

(SNHS) for two periods: 2006 (before the start of the crisis)

and 2011–2012 (during the crisis) [40, 41]. Both surveys,

which are comparable to other European health databases,

include very similar questions. National health surveys

employ a multistage, stratified-random design to identify

samples of adults. We have restricted the selected sample

to the working-age population (16–65 years old). As our

main interest focuses on the relationship between long-

term unemployment (over 1 year) and health, we use a

restricted subsample only composed of employed and long-

term unemployed (n = 13,663 for 2006, and n = 9,495 for

2011–2012). However, as a complementary analysis we

also analyse the impact of unemployment on health from a

wider perspective, thus using the sample including

employed and all unemployed workers (n = 15,324 for

2006 and n = 10,855 for 2011–2012).

With the aim of checking the impact of unemployment

on overall and mental health, we employ matching meth-

ods. Once this issue is addressed, we will test if there is an

incremental effect of unemployment on health as derived

from the economic recession. In order to test this effect, we

will use difference-in-difference (DiD) techniques. These

methods have been previously used to disentangle effects

of unemployment on health [42].

Estimation of the impact of unemployment on health

in 2006 and 2011–2012: matching methods

We use matching methods based on propensity score [43]

separately for 2006 and 2011–2012. Probit regressions are

R. M. Urbanos-Garrido, B. G. Lopez-Valcarcel

123

used to estimate the probability of being unemployed for

more than 12 months (‘treated’) as a function of the

observable covariates vector X associated with unemploy-

ment for each year. The parameter of interest to estimate is

the average treatment effect (ATT) of unemployment on

unemployed. It is defined as

ATT ¼ EðY1 � Y0jD ¼ 1Þ ¼ EðY1jD ¼ 1Þ � EðY0jD ¼ 1Þ;ð1Þ

where Y represents health, subscripts 1 and 0 mean

unemployed and employed, respectively, and D = 1 means

unemployed. The second term on the right side of Eq. (1) is

the counterfactual: what the health level of an unemployed

person would be if he/she had a job. Several assumptions

need to be made in order to identify average unobserved

counterfactuals. It is assumed that all the relevant differ-

ences between treated and non-treated are captured in the

X vector. A common support condition is also imposed on

the treated units. Treated units whose probability of being

treated is larger than the largest p in the non-treated pool

would be left unmatched. We use different matching

methods (k-nearest neighbours, with k from 1 to 4—

approximately the sample ratio between non-treated and

treated—within calipers equal to 0.05, and a kernel with a

normal distribution) to check for robustness. We report the

results for the kernel with a normal distribution. As a

complementary analysis we also estimate the matching

models for the full sample of employed and unemployed

(both short- and long-term).

Estimates of the incremental crisis effect: DiD

An estimate of the effect of the economic crisis on the

health impact of long-term unemployment may be obtained

by using a DiD technique. We estimate a regression model

with the pooled data of both health surveys. Controlling by

X covariates, the model includes two main fixed effects,

one for the crisis (k) and another for the employment status

(d), as well as the interaction between them (c):

Yidt ¼ aþ dUnempit þ kt þ cðUnempit � tÞ þ X0itbt þ eidt;

ð2Þ

where t = 0 means 2006, t = 1 means 2011–2012, and

subscript d stands for the employment status. The effect of

X variables is assumed to be different in both years. The

unbiasedness of the structural estimators depends on the

parallel paths assumption. In order to make that assumption

as plausible as possible, we included in X all the covariates

that could have an influence on health and could be related

to the employment status before the crisis and during the

crisis. Under the usual hypothesis on the stochastic e term

(mean zero, independent of the regressors), the parameters

d and c provide information on the effects of unemploy-

ment on health before (d) and during (d ? c) the economic

crisis.

Alternatively, we estimate the model including all

unemployed, assuming that the impact on health of dif-

ferent lengths of unemployment may be different:

Yidt ¼ aþX4

k¼1

dkUnempkit þ kt þX4

k¼1

ckðUnempkit � tÞ

þ X0itbt þ eidt

ð3Þ

where k = 1, 2, 3 and 4 stand, respectively, for unem-

ployed who never worked, those who have been unem-

ployed for less than 6 months, those who have been

unemployed for a period between 6 and 12 months and,

finally, for long-term unemployed.

Definition of variables

Overall health is proxied by self-assessed health (SAH).

The SAH question is formulated as follows in the SNHSs:

‘During the last 12 months, would you say that your health

status has been very good, good, fair, poor, very poor?’.

Our variable which will take the value one if the individual

declares his/her health as fair, poor or very poor, and zero if

health is perceived as good or very good. This categori-

zation has been used in previous studies [44, 45].

We consider that mental health risks linked to unem-

ployment may operate in both the short and long term. As

shown by Lucas et al. [46], the effect of unemployment on

life satisfaction lasts for some time, but the unemployed

quickly seem to be mentally adapted to their new status.

Furthermore, as was mentioned above, in the context of

economic crisis the social stigma of unemployment that

could harm mental health tends to fade away. Thus, mental

health is represented by two variables: first, a dummy

variable which indicates the presence of chronic depres-

sion, anxiety or other mental problems during the previous

year, which is used as a proxy of permanent mental health

(Pmhealth); second, we use the Goldberg index [47], which

represents short-term mental health risk and is frequently

used in clinical medicine. This variable (Rmhealth) is

computed by using the answers to a 12-item set of ques-

tions (see Table A1, Supplementary Material). Each

question has four possible answers, which are recoded as

0 = ‘no problem’ or 1 = ‘with problems’. The final

dummy takes the value 1 if the person has three or more

positive answers to the Goldberg 12-item scale question-

naire (which is shown in Table A1, Supplementary Mate-

rial). This categorization has been previously used in

related literature [21].

An empirical analysis for Spain

123

In both models (matching and DiD regression) the

X vector of covariates includes age, sex, education and

region. The variable Female, taking the value one for

women and zero for men, represents sex. Educational level

is categorized by means of five dummies: primary educa-

tion or below (Ed1, reference category), compulsory

Table 1 Definition of variables and descriptive statistics

Variable Definition 2006

(n = 15,324)

2011–2012

(n = 10,855)

Meana Meana

Labour status Employed 1 if the person declares to be employed 88.4 % 75.9 %

Unem_never 1 if the person declares to be unemployed and he/she has never

worked

0.6 % 1.0 %

Unem_6 1 if the person declares to be unemployed for 6 months or less 5.1 % 7.0 %

Unem_6_12 1 if the person declares to be unemployed for a period between

6 months and 1 year

1.7 % 3.8 %

Unem_12 1 if the person declares to have been unemployed for 1 year or

more

4.2 % 12.1 %

Overall health SAH Self-assessed health: 1 if fair, poor or very poor; 0 if good or

very good

24.9 % 19.2 %

Mental health Pmhealth 1 if the person declares chronic depression, anxiety or other

mental problems during the previous year

11.4 % 7.6 %

Short-term mental health risk

(Goldberg index)

Rmhealth 1 if the person has three or more positive answers to the

Goldberg 12-item scale questionnaire

18.3 % 19.3 %

Age Age in years 40.49 (10.91) 42.02 (10.78)

Female 1 if female 52.3 % 45.5 %

Education Ed1b Primary education or below (reference category) 29.0 % 10.0 %

Ed2b Compulsory secondary education 22.0 % 45.0 %

Ed3b Non-compulsory and pre-university secondary education 16.1 % 13.7 %

Ed4b Specific labour training 9.3 % 9.0 %

Ed5b University graduate 23.0 % 22.3 %

Region (autonomous

communities)

Reg1 Andalucıa (reference category) 7.9 % 12.5 %

Reg2 Aragon 9.0 % 4.0 %

Reg3 Asturias 2.8 % 3.5 %

Reg4 Baleares 6.9 % 3.9 %

Reg5 Canarias 4.2 % 5.5 %

Reg6 Cantabria 5.5 % 3.1 %

Reg7 Castilla y Leon 3.8 % 5.6 %

Reg8 Castilla-La Mancha 3.3 % 3.8 %

Reg9 Cataluna 9.2 % 10.7 %

Reg10 Comunidad Valenciana 6.3 % 8.8 %

Reg11 Extremadura 2.8 % 4.4 %

Reg12 Galicia 10.2 % 5.4 %

Reg13 Madrid 8.0 % 10.2 %

Reg14 Murcia 6.3 % 4.1 %

Reg15 Navarra 5.9 % 3.0 %

Reg16 Paıs Vasco 4.0 % 5.9 %

Reg17 La Rioja 2.5 % 3.4 %

Reg18 Ceuta and Melilla 1.7 % 2.2 %

Chronic conditions Chronic 1 if the person suffers from any chronic illness from a list of 12

conditions

56.4 % 48.2 %

Sample of employed and unemployed people aged 16–65a For the categorical variables, data are % in the category; for continuous variables, data include standard deviation in bracketsb The categories do not add up to one because there are some persons with missing education level

R. M. Urbanos-Garrido, B. G. Lopez-Valcarcel

123

secondary education (Ed2), non-compulsory and pre-uni-

versity secondary education (Ed3), specific labour training

requiring non-compulsory secondary education (Ed4) and

university graduate (Ed5). Household income could not be

considered as a regressor because it is not available for the

SNHS 2011–2012. We also include a set of dummies

representing the region of residence, with Andalusia acting

as the reference category. Regional dummies may act as a

proxy of the availability of re-employment opportunities in

the geographical area [48]. Furthermore, the regional factor

may be relevant as regional public authorities can imple-

ment social policies aimed at moderating adverse effects of

unemployment and precarious work on health [49].

Finally, in the matching probit equation for propensity

of unemployment we control for ‘permanent’ health-rela-

ted conditions. As has been discussed in previous studies

[42, 50, 51], the causal relationship between unemploy-

ment and health is, a priori, bidirectional, as remaining

jobless may increase the risk of illness, but also some

conditions may affect the probability of being unemployed.

Therefore, we include a dummy equal to one if the person

declares that he/she suffers at least one of twelve chronic

diseases: osteoarthritis, arthritis or rheumatism, chronic

allergy, asthma, thyroid problems, heart disease, cervical

hernia, lower back hernia, stomach ulcer, skin diseases,

constipation, headache and haemorrhoids. As a robustness

check, we also estimate the models excluding chronic

conditions.

Definitions of all the variables are shown in Table 1.

The results are detailed in the following section. All cal-

culations were made with Stata12 software [52].

Results

Table 1 shows descriptive statistics for all the variables for

the whole sample of people aged 16–65, employed and

unemployed, for 2006 and 2011–2012. It is worth noting

that the composition of both samples by education level

and geographical location differs. As the samples are

truncated at 65 years of age, a substantially higher pro-

portion had attained the compulsory educational level in

2011–2012 than in 2006. Besides that, a number of people

with very low education, who had been working in

unskilled jobs in the building sector during the economic

boom, might have left the labour market during the crisis.

These changes in the composition of the active population

after the crisis aftermath would induce some changes in the

regional composition of the sample, too.

Table 2 shows basic descriptive statistics of health

indicators by labour status before and during the crisis. The

unemployed are classified in five categories according to

the duration of unemployment. As may be observed, SAH

of Spaniards participating in the labour market, employed

or unemployed, is better in 2011–2012 than in 2006,

despite the severity of the Spanish economic recession. The

percentage of people declaring bad health drops from 24.9

to 19.2 %. Also the percentage of people declaring to have

suffered depression, anxiety or mental problems in the last

12 months is lower in 2011–2012 than in 2006. These

results, which may seem paradoxical, have also been

observed with survey data from Catalonia [39]. They may

reflect that health is being assessed in relative terms. Thus,

in the context of economic problems and high unemploy-

ment, health would rank lower among individuals’ con-

cerns. For the short-term mental health risk variable,

however, the total percentage of individuals at risk in

2011–2012 is slightly higher than in 2006, mostly due to

deterioration of this health proxy for workers who have

recently lost their job (unemployment duration shorter than

6 months), and for unemployed persons who have been

looking for a job for more than 1 year, as suggested by

Table 2.

According to Table 2, SAH and mental health seem to

be worse among unemployed people (except for those who

have never worked) than among employed people. The

longer the unemployment period, the wider the gap.

However, descriptive results shown in Table 2 could be

biased estimators of group differences because of compo-

sitional effects. The groups of employed and unemployed

by duration differ significantly by sex, age, educational

level, region of residence and health conditions.

Table 3 reports the ATT estimates for 2006 and

2011–2012 by using kernel estimates with a Gaussian

kernel, for the subsample of employed people (‘untreated’)

and unemployed people (‘treated’). No individuals are

excluded because of common support requirements in

2006 and only one is excluded in 2011–2012. We firstly

show the results for the impact of long-term unemploy-

ment on health. The estimated probit equations for pro-

pensity score are displayed in Table A2 (Supplementary

Material). The results obtained from alternative matching

approaches are shown in Table A3 (Supplementary

Material). All results are very robust to the matching

method. The second column of Table 3 contains the

sample data corresponding to the unemployed. The third

column shows the estimated health of the unemployed if

they had been working (counterfactual). The fourth col-

umn is the difference between the two previous columns

and it estimates the impact of unemployment on health.

This estimate is called the average treatment effect (ATT)

as it measures the loss in health that may be attributable to

unemployment. Finally, the fifth column shows the sta-

tistical significance of the ATT estimates. The left and

right sides of the table show, respectively, results for 2006

and 2011–2012.

An empirical analysis for Spain

123

The estimates show that one or more years of unem-

ployment tend to significantly deteriorate the overall and

mental health before the economic recession and also

during the crisis. Once we account for the X covariates,

long-term unemployment increases the probability of

showing mental health risk by 10.4 percentage points (pp)

before the crisis, and by 16 pp for the period 2011–2012.

The ATT for mental health problems in the last year rises

Table 2 Health indicators by labour status before and during the economic crisis

Health outcome Bad health (%)a Mental problems in last 12 months (%)b Mental health risk in the short-term (%)c

Year 2006 2011–2012 2006 2011–2012 2006 2011–2012

Employed 23.8 17.2 10.7 5.9 17.1 15.9

Unem_never 17.7 15.8 7.4 3.5 19.2 12.3

Unem_6 29.0 18.2 17.1 8.5 25.9 28.1

Unem_6_12 34.1 21.3 17.3 12.7 32.3 25.6

Unem_12 42.1 31.8 19.4 16.8 30.0 33.4

Total 24.9 19.2 11.4 7.7 18.3 19.3

All the reductions of the percentages between 2006 and 2011–2012 are statistically significanta Percentage declaring that their self-assessed health in the last 12 months is fair, bad or very badb Percentage declaring that they have had chronic depression, anxiety or other mental problems in the last 12 monthsc Percentage declaring three or more positive answers to the Goldberg items

Table 3 Impact estimates of unemployment on health 2006 and 2011–2012

Long-term unemployment

Dependent variablea Pre-crisis (2006) Crisis (2011–2012)

Unemployed

E(Y1|D = 1)

(%)b

Counterfactual

E(Y0|D = 1)

(%)c

Impact

(ATT)

(pp)d

te Unemployed

E(Y1|D = 1)

(%)

Counterfactual

E(Y0|D = 1)

(%)

Impact

(ATT)

(pp)

t

(SAH) % bad health 41.7 29.9 11.8 5.8*** 31.7 21.0 10.7 7.8***

(Pmhealth) % mental health

problems

19.5 13.9 5.6 3.4*** 16.7 7.0 9.7 9.0***

(Rmhealth) % mental health risk in

the short term (Goldberg)

30.0 19.6 10.4 5.5*** 33.4 17.4 16.0 11.5***

Total unemployment (short- and long-term)

Dependent variable Pre-crisis (2006) Crisis (2011–2012)

Unemployed

E(Y1|D = 1)

(%)

Counterfactual

E(Y0|D = 1)

(%)

Impact

(ATT)

(pp)

t Unemployed

E(Y1|D = 1)

(%)

Counterfactual

E(Y0|D = 1)

(%)

Impact

(ATT)

t

(SAH) % bad health 33.6 26.7 6.9 5.6*** 25.3 18.6 6.7 6.8***

(Pmhealth) % mental

health problems

17.2 12.5 4.7 4.9*** 13.0 6.3 6.7 9.2***

(Rmhealth) % mental health risk

in the short term (Goldberg)

27.9 19.2 8.7 7.6*** 29.7 16.6 13.1 12.9***

Matching methods. Propensity score with Gaussian kernel

*** p \ 0.01. Control variables are age, sex, education, region and chronic conditionsa Matching models to estimate the effect of long-term unemployment on overall health (SAH), on mental health problems (Pmhealth) and on

mental health risk in the short term (Rmhealth)b Sample data corresponding to unemployedc Estimated data for unemployed if they had been working (counterfactual)d Average treatment effect (ATT) = column 2 - column 3. It measures the loss in health attributable to unemployment; as it is a difference

between 2 %, it is expressed as percentage pointse Ratio to determine statistical significance of the ATT estimates

R. M. Urbanos-Garrido, B. G. Lopez-Valcarcel

123

from 5.5 pp in 2006 to 9.7 pp in 2011–2012. The ATT

when SAH is considered was 11.8 in 2006 and 10.7 in

2011–2012. Thus, the effects of long-term unemployment

on mental health seem to be larger in times of economic

downturn, whereas this association is not found in SAH. It

is plausible that self-reported health is not capturing so

much real changes in health but changes in the perceived

level of health, which could be affected by the fact men-

tioned above that health is being assessed in relative terms.

As mentioned in the previous section, we initially con-

sider in the X vector of covariates the presence of chronic

conditions, as poor health may increase the risk of

becoming unemployed. However, this variable could be

endogenous as some chronic diseases could also worsen

when a worker loses his/her job. To deal with this problem

we did some robustness checks by excluding the dummy

chronic from the model. The results, which are fully

reported in Table A4 (Supplementary Material), which are

similar to those shown in Table 3.

The bottom part of Table 3 collects the results from

matching models estimated for the full sample of

employees and unemployed, both short- and long-term (the

corresponding probit estimates are reported in Table A5 of

the Supplementary Material). It shows also the significant

effects of unemployment on SAH and both dimensions of

mental health, although these are much lower with regard

to those linked to long-term joblessness. Furthermore, the

results again suggest that the negative impact of unem-

ployment on mental health may be higher during economic

recessions.

The DiD estimates mostly confirm these results. Table 4

shows the estimates corresponding to the health impact of

long-term unemployment. Therefore, the estimated effects

correspond to Eq. (2) in the ‘‘Materials and methods’’

Table 4 DiD estimates of the health impact of long-term unemployment before the economic crisis and changes during the economic crisis

Dependent variable Effect Coefficients (95 % CI)a Pseudo

(R2)bPseudo

(R2)cn

Model without controlsb Full modelc

(SAH) % bad health (k) Change in SAH after the

crisis

-0.28 (-0.45; -0.12) -0.19 (-0.42; ?0.04) 0.044 0.070 23,754

(d) Effect of unemployment

in the base year (2006)

0.40 (0.30; 0.50)*** 0.32 (0.22; 0.42)***

(c) Change in the effect of

unemployment on SAH

after the crisis aftermath

0.06 (-0.07; ?0.18) 0.04 (-0.09; ?0.17)

(Pmhealth) % mental health

problems

(k) Change in Pmhealth

after the crisis

-0.20 (-0.42; ?0.01) -0.22 (-0.52; ?0.08) 0.06 0.079 23,711

(d) Effect of unemployment

in the base year (2006)

0.25 (0.14; 0.37)*** 0.21 (0.09; 0.32)***

(c) Change in the effect of

unemployment on

Pmhealth after the crisis

aftermath

0.34 (0.19; 0.49)*** 0.35 (0.19; 0.50)***

(Rmhealth) % mental health risk

in the short term (Goldberg)

(k) Change in Rmhealth

after the crisis

-0.02 (-0.18; -0.15) 0.0073 (-0.23; ?0.25) 0.025 0.054 23,162

(d) Effect of unemployment

in 2006

0.36 (0.25; 0.47)*** 0.3476 (0.24; 0.46)***

(c) Change in the effect of

unemployment on

Rmhealth after the crisis

aftermath

0.20 (0.07; 0.34)*** 0.2027 (0.07; 0.34)***

Differences-in-differences model to estimate the effect of long-term unemployment on overall health (SAH), on mental health (Pmhealth)

problems and on mental health risk in the short term (Rmhealth). The three dependent variables are defined in the ‘‘Materials and methods’’

section

*** Significant at 1 % (p \ 0.01)a Estimated effects correspond to Eq. (2). The parameter of highest interest is c. It measures the change in the effect of unemployment on health

after the crisis compared to the effect in 2006. Point estimates and 95 % CIb Model controlling only for age and sexc Full model that adjusts for age, sex, education and region allowing different effects in each year (2006 and 2011)

An empirical analysis for Spain

123

section. The parameter k shows the change in each of the

health measures that occurred during the crisis. The

parameter d accounts for the effects of long-term unem-

ployment on health. Finally, the parameter of highest

interest is c, which measures the change in the effect of

unemployment on health after the crisis compared to the

effect in 2006. We estimate two alternative models in order

to check how the X vector of covariates may alter the

results.

As shown in Table 4, long-term unemployment has a

significant impact on both overall and mental health.

Moreover, the interaction term c is positive and significant

for mental health models, suggesting that negative effects

of unemployment on people’s psychological health are

intensified because of the economic crisis. That intensifi-

cation is higher for mental problems—e.g. depression and

anxiety—in the last year than for the short-term mental

health risk (Goldberg index). However, SAH does not seem

to worsen more with unemployment in times of economic

crisis than before the crisis aftermath. It may also be ver-

ified that estimates barely depend on the vector of covari-

ates. Our results are consistent with the hypothesis

suggested by Karasek and Theorell [53], in the sense that

economic recessions may encourage individuals to antici-

pate stressful situations, including job loss and difficulty in

dealing with financial obligations.

We have also estimated alternative DiD models that

include all unemployed and the corresponding dummies for

different periods of unemployment (Table A7, Supple-

mentary Material). The obtained results are similar to those

shown in Table 4. Except for those who have never

worked, unemployment negatively influences overall

health and mental health. The impact on overall health

increases with the length of unemployment. Like in our

base model, which was restricted to long-term unemploy-

ment, the impact on overall health does not seem to change

in times of crisis. Moreover, the impact on mental condi-

tions is larger after the crisis, as in the base model, only for

the long-term unemployed. The effects on the Goldberg

index become more serious after the crisis for those who

are unemployed for less than 6 months and also for those

who are unemployed for more than 12 months.

Discussion

Our results are in line with previous work showing a

positive relationship between unemployment rates and

mental health risks [34], and are also consistent with those

found by Gili et al. [54], who show how the economic

crisis has significantly increased the frequency of mental

health disorders among primary care users in Spain, par-

ticularly among families experiencing unemployment.

However, the results here reported differ from those found

by previous Spanish studies that check how the impact of

unemployment on health varies depending on the economic

context [37–39], which could be partially explained by

differences in the definition of health variables.

Our study has a number of limitations. First, cross-sec-

tional data do not allow for exploration of causal relation-

ships between unemployment and health as longitudinal

databases do. Previous research with panel data from the

Spanish sample of the EU-SILC did not confirm the sig-

nificant effect of unemployment on SAH for the period

2007–2010 [55]. A similar result is found by Bockerman

and Ilmakunnas [42], who use panel data from the European

Community Household Panel for Finland. They show that

the event of unemployment does not matter as such for SAH

and conclude that the cross-sectional negative relationship

between unemployment and SAH is related to the fact that

persons who have poor SAH are being selected for the pool

of the unemployed. Nevertheless, the EU-SILC waves do

not include specific information about mental health—

although it may be assumed that SAH also includes the

individuals’ rating of their mental health—so the impact of

unemployment on Spaniards’ psychological health with

longitudinal microdata cannot be verified.

Second, it has to be noted that some relevant determi-

nants of unemployment may be excluded from the X vector

of covariates, such as the occupational sector or the eligi-

bility for public subsidies, and thus our estimates may be

biased. The omission of other relevant variables may also

bias the estimates. This is the case of household income,

which is not available in the SNHS for 2001–2012. The

effects of unemployment on health could in fact be

reflecting the impact of the lack of income. However, this

problem will be mitigated as long as omitted variables

operate similarly in both periods. In addition, the dummy

for chronic conditions included in the X vector could be

endogenous, and the results consequently would be biased.

To deal with this problem we did some robustness checks,

with satisfactory results.

Third, the proxies for overall health and permanent mental

health are constructed from survey questions, which refer to

the last 12 months. Therefore, when we use the full sample of

unemployed (including short- and long-term jobless people),

we are searching for associations between variables which are

defined for different reference periods. However, this prob-

lem disappears when the analysis focuses on the impact of

long-term unemployment on health.

Fourth, the self-reported definition of unemployed could

bias the estimation results owing to self-selection, if those

who have been unemployed for a long time tend to classify

themselves as inactive.

Fifth, although in the DiD estimation we adjusted for all

the measured covariates that might be correlated to labour

R. M. Urbanos-Garrido, B. G. Lopez-Valcarcel

123

status before the crisis and/or during the crisis, we cannot

ensure that the parallel paths assumption is satisfied.

Finally, we cannot reach conclusions about the overall

impact of the economic crisis on Spaniards’ health, as the

recession has not yet finished.

Conclusions

We provide new and robust evidence about the significant

impact of (particularly long-term) unemployment on

overall health and mental health with individual-level data

for Spain. We also investigate whether the effects of

unemployment on health have increased or been tempered

as a consequence of the economic crisis, confirming that

psychological effects of unemployment are more serious in

times of recession. Our results may lead one to conclude

that anxiety and stress about the future associated with

unemployment could have greater impact on individuals’

health than the palliative effects of social protection pro-

vided during the economic recession. Although economic

effects of job loss may be softened by the safety net of the

welfare state, the maximum duration of unemployment

benefits is 2 years, far less than the duration of the eco-

nomic recession. After the maximum period of unem-

ployment benefits, many households are forced to take part

into the minimum income programs offered by the regional

administrations. In this sense, recent research shows how

physical and mental health problems were better for those

individuals benefiting from those programs who had taken

part in work-related activities, thus suggesting that welfare-

to-work policies may have positive unintended health

effects [56].

It also has to be noted that Spain has adopted strict

austerity measures in recent years, which include signifi-

cant cuts in health spending and some reductions of the

unemployment benefits. Furthermore, it is likely that

additional cuts will occur in the near future. Therefore, the

incremental effect on health shown here could be amplified

when the recession comes to an end.

The results could also point to the need for preventing

health deterioration in vulnerable groups such as the

unemployed, and also for monitoring specific health risks

that arise in recessions, such as psychological problems.

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