Group Project - Economics
Summary
The Process for completing this portion of the assignment
· First, locate articles related to the subject chosen. Selected below and attached
· Cite the article using the APA style. Added below
· Write a concise summary – in your own words, no copying - of no less than 150 words for each assigned article that complies with the following:
o the authority or background of the author, including why we should trust the source of the material,
· summarize the information presented
· Possible shortcomings or biases of the work
· Remember that these articles have been peer reviewed by others in the field.
Article 1- Is recession bad for your mental health? The answer could be complex: evidence from the 2008 crisis in Spain
APA Reference:
Moncho, J., Pereyra-Zamora, P., Tamayo-Fonseca, N., Giron, M., Gomez-Beneyto, M., & Nolasco, A. (2018). Is recession bad for your mental health? The answer could be complex: evidence from the 2008 crisis in Spain. BMC Medical Research Methodology, 18(1). https://doi.org/10.1186/s12874-018-0538-2
Article 2- Effect of the economic recession on pharmaceutical policy and medicine sales in eight European countries
APA Reference:
Leopold, C., Mantel-Teeuwisse, A. K., Vogler, S., Valkova, S., de Joncheere, K., Leufkens, H. G. M., Wagner, A. K., Ross-Degnan, D., & Laing, R. (2014). Effect of the economic recession on pharmaceutical policy and medicine sales in eight European countries/Effet de la recession economique sur la politique pharmaceutique et les ventes de medicaments dans huit pays europeens/El efecto de la recesion economica sobre la politica farmaceutica y la venta de medicinas en ocho paises europeos. Bulletin of the World Health Organization, 92(9), 630. https://doi.org/10.2471/BLT.13.129114
Article 3- Psychological Well-Being During the Great Recession: Changes in Mental Health Care Utilization in an Occupational Cohort.
APA Reference:
Modrek, S., Hamad, R., & Cullen, M. R. (2015). Psychological Well-Being During the Great Recession: Changes in Mental Health Care Utilization in an Occupational Cohort. American Journal of Public Health, 105(2), 304–310. https://doi.org/10.2105/AJPH.2014.302219
Article 4- Government spending, recession, and suicide: evidence from Japan.
APA Reference:
Matsubayashi, T., Sekijima, K., & Ueda, M. (2020). Government spending, recession, and suicide: evidence from Japan. BMC Public Health, 20(1), 1–8. https://doi.org/10.1186/s12889-020-8264-1
Article 5- Influence of the Economic Crisis in 2008 on the Performance of Companies in Wood-processing Industry
APA Reference:
Sujova, A. (2015). Influence of the Economic Crisis in 2008 on the Performance of Companies in Wood-processing Industry. Procedia Economics and Finance, 34, 581–586. https://doi.org/10.1016/S2212-5671(15)01671-8
Procedia Economics and Finance 34 ( 2015 ) 581 – 586
Available online at www.sciencedirect.com
2212-5671 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the Organizing Committee of BEM2015
doi: 10.1016/S2212-5671(15)01671-8
ScienceDirect
Business Economics and Management 2015 Conference, BEM2015
Influence of the Economic Crisis in 2008 on the Performance of
Companies in Wood-processing Industry
Andrea Sujova*a
aMendel University in Brno, Zemědělská 3, Brno 613 00, Czech Republic
Abstract
The last global economic recession has broken out in 2008. Impacts of the crisis have appeared in 2009 in the European
countries. In the current globalized world of closely interdependent economies, the crisis affected almost every part of the world.
Export oriented countries noticed a stronger recession in foreign trade due to decreased demand. In the time of economic crisis
the enterprises are forced to reduce production volume, costs and number of employees in aim to keep performance or to survive.
The paper deals with influence of economic recession on the performance of companies in wood-processing industry of Slovakia
and the Czech Republic. The attention is paid to ratio indicators of performance and their indexes in the period 2006 – 2011. The
aim of the paper is to summarize performance indicators and to show their changes during economic recession in wood-
processing companies with focus on added value, labour productivity and investment effectiveness.
© 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the Organizing Committee of BEM2015.
Keywords: economic crisis; performance; ratio indicator; wood-processing companies;
1. Introduction
Performance is an ability to achieve required outputs or effects in measurable units. However, there are different
understandings for evaluation of the performance at a macroeconomic and microeconomic levels. Macroeconomic
performance is measured via indicator of value added, which reflects a value of final goods made in the economy.
(Sujová, 2005). Corporate performance is defined as an ability to reevaluate consumed sources and to create a
surplus. (Drábek, Potkány 2008). Indicators for measuring corporate performance come out from financial analysis
which are dealt by several authors of professional publications (Kislingerová and Hnilica 2005, Zalai, 2008;
* Corresponding author. Tel.: +420 545-134-075.
E-mail address: [email protected]
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the Organizing Committee of BEM2015
http://crossmark.crossref.org/dialog/?doi=10.1016/S2212-5671(15)01671-8&domain=pdf
http://crossmark.crossref.org/dialog/?doi=10.1016/S2212-5671(15)01671-8&domain=pdf
582 Andrea Sujova / Procedia Economics and Finance 34 ( 2015 ) 581 – 586
Hajduchova, 2011; Holler, 2009; Polach et al., 2012) and they can be divided into two groups: traditional and
modern. Scientific literature doesn´t contain any universally valid and generally accepted definition of the term:
performance of the branch or industry. In the case of sector performance it is going on interconnection of the macro
and micro-economic evaluation of the performance on a sectoral approach, thus there is an overlap of macro and
micro level view on performance.
The paper deals with influence of an economic recession on the performance of companies in wood-processing
industry of Slovakia and the Czech Republic. The focus of the paper on WPI has several reasons. Wood processing
industry is one of the sectors in which the Czech and Slovak economies can affect European markets by maximum
use of their own resources. The need to deal with chosen topic of the paper is mainly due to the fact that the EU puts
emphasis on the economic development based on renewable resources. The WPI is an important part of developing
economies, a new prospective direction based on biotechnology. Despite these facts, there is insufficient attention
paid to analysis of performance development of WPI and its changes, by now no study in the subject has been
published.
The aim of the paper is to summarize performance indicators and to show their changes during economic
recession in wood-processing companies with focus on added value, labour productivity and investment
effectiveness.
2. Material and methods
The required material for achievement of relevant outputs was obtained from the secondary research based on the
analysis of available scientific literature dealing with the issues of performance of enterprises and countries and on
the use of a statistics of economic results in the wood processing industry.
Analysis of knowledge in scientific literature led us to finding, that performance can be evaluated via a wide scale
of indicators, which are different at the macro a micro level. Trend of economic indicators of Czech wood
processing industry is analyzed by Kupčák (2003, 2006), Hlaváčková a Šafařík (2013, 2014). Performance of wood
processing industry from the view of investment effectiveness and investment rate in the sector is dealt by authors
Merková a Drábek (2011, 2012).
On the basis of literature dealing with the assessment of performance of national economy and enterprises, a
system of indicators suitable for evaluating the performance of the sector was set up:
The first group is represented by indicators of profitability of the sector, which are based on comparison of
economic result with volume of used sources to its achievement. Positive results of profitability are achieved plus
values and their increasing trend. According to type of used source there exist following indicators (Zalai et al. 2008;
Hajdúchová, 2011): return on equity (ROE), return on sales (ROS), return on costs (ROC), return on investment
(ROI).
(1)
(2)
The second group of indicators is based on creation of value added: value added per one employee and per one
enterprise in the sector and the rate of value added creation (RVA) expressing a percentage share of value added on
revenues, production and investment:
(3)
(4)
(5)
The third group of indicators concern evaluation of labour effectiveness:
- Labour productivity (LP) has a several modifications and presents a share of revenues (R), production (Q) and
profit (P) to one employee of the sector:
(6)
583 Andrea Sujova / Procedia Economics and Finance 34 ( 2015 ) 581 – 586
- Labour productivity rate (LPR) expresses number of employees for one monetary unit of revenues or
production:
(7)
The fourth group of indicators regards investment effectiveness:
- The investment rate (IR), which shows what proportion of the generated funds is invested. Investment rate can be
calculated as a share of investment (I) on revenues (R):
IRR = I/R (8)
- Efficiency of investments referred to as the productivity of investments is the efficiency of capital using. A
background characteristic is the average product of capital, which in microeconomic theory represents the share
of production volume fallen on one invested monetary unit. Effectiveness of investments should be monitored in
relation to production, sales and value-added. The efficiency of investment in relation to revenues (EIR) reflects
how many revenues (R) fall on one invested monetary unit:
EIR = R / I (9)
Additional indicators for evaluation of sector performance are: unit perfomance as the share of economic outputs
of the sector (value added, production, revenues, profit, investment) on one enterprise and the export performance
(EP) as the share of export on the revenues and production of the sector.
EP = EX / R (Q) (10)
Calculation of individual indicators for measuring the performance was applied in the wood processing industry
(WPI) and its individual sections in Slovakia and the Czech Republic. A characteristic feature of the WPI is
processing of raw wood and wood products production at various stage of finalisation. WPI within the classification
of business activities of the EU (NACE) consists of three sections:
- NACE 16: primary mechanical wood processing (timber industry),
- NACE 17: primary chemical wood processing (pulp and paper industry),
- NACE 31: secondary wood processing (production of furniture).
The aim was to determine an influence of economic crisis in 2008 on performance of wood-processing industry.
It is possible by identification of changes in performance indicators in the period 2006 - 2011. Changes of values of
performance indicators were identified through change indexes as follows:
- Average absolute index: (11)
- Change index: (12)
where:
pn = indicator value in period n
p0 = indicator value in basic period
The application in MS Excel was created for appropriate applications of statistical methods and for calculating
performance indicators and their indexes.
3. Results and Discussion
The study of knowledge concerning evaluation of performance ate the macro and micro levels les us to
conclusion that performance at the sector level is possible to assess on the basis of ability of the sector to create
value added, profit and on the basis of efficiency by utilization of production sources. When evaluating sector
performance it is needed to consider not only absolute indicators, but also ratio indicators. By considering an
influence of economic crisis on sector performance, a calculation of indexes of indicators and analyzing their values
is required.
584 Andrea Sujova / Procedia Economics and Finance 34 ( 2015 ) 581 – 586
The results of indicators measuring the performance of wood processing industry in Slovakia and the Czech
Republic and their indexes for a period of five years are shown in Table 1. Basic input data was economic results of
the industry. The values in the table are in millions euro.
Table 1. Performance of Wood Processing Industry (WPI) in Slovakia and the Czech Republic
Source: own calcuations
In wood processing industry of the Czech Republic the crisis had the most marked influence on the export
performance, which has fallen to one third value during 2008 and 2009 and stagnated. Its rise is noticed in 2010.
Profitability also decreased when return on equity halved and return on sales dropped by one third. The crisis
negatively affected labour productivity, which decreased by 5% and employment rate raised. The reason can be fall
of revenues and no adaption of number of employees to lower level. Investment rate decreased during crisis and two
following years. Positive trend in time of crisis was noticed by rate of value added and its rise in 2009 and also by
Indicator (mil. €)/year 2006 2007 2008 2009 2010 2011
The Czech Republic
EP (%) 55,3 56,7 57,2 57,6 61,0 62,9
Absolute index 1,4 0,5 0,4 3,4 1,9
ROE 18,4 21,0 14,6 11,3 12,2 11,8
Absolute index 2,6 -6,4 -3,3 0,9 -0,4
ROS 6,1 6,9 5,2 4,5 4,9 4,6
Absolute index 0,8 -1,7 -0,7 0,4 -0,3
RVAR 25,7 25,4 25,1 25,6 24,8 23,4
Absolute index -0,3 -0,3 0,5 -0,8 -1,4
LPR 73,4 82,8 78,7 79,1 92,4 96,2
Absolute index 9,4 -4,1 0,4 13,3 3,8
LPRR 13,62 12,08 12,71 12,64 10,83 10,39
Absolute index -1,54 0,63 -0,07 -1,81 -0,44
IRR
0,06 0,07 0,08 0,07 0,06 0,05
Absolute index 0,01 0,01 -0,01 -0,01 -0,01
EIR 17,48 15,25 12,49 14,86 16,96 18,74
Absolute index -2,23 -2,76 2,37 2,1 1,78
Slovakia
EP (%) 80,00 77,19 69,66 64,57 87,42 68,54
Absolute index -2,81 -7,53 -5,09 22,85 -18,88
ROE 22,0 22,0 23,1 3,0 18,3 9,8
Absolute index 0 1,1 -20,1 15,3 -8,5
ROS 4,9 4,3 4,7 0,8 4,9 1,9
Absolute index -0,6 0,4 -3,9 4,1 -3
RVAR 20,2 18,6 19,2 22,6 25,1 21,4
Absolute index -1,6 0,6 3,4 2,5 -3,7
LPR 97,3 101,7 102,2 92,4 95,8 113,2
Absolute index 4,4 0,5 -9,8 3,4 17,4
LPRR 10,28 9,84 9,79 10,82 10,43 8,84
Absolute index -0,44 -0,05 1,03 -0,39 -1,59
IRR 0,098 0,113 0,091 0,073 0,061 0,053
Absolute index 0,015 -0,022 -0,018 -0,012 -0,008
EIR 10,211 8,828 10,992 13,642 16,289 18,882
Absolute index -1,383 2,164 2,65 2,647 2,593
585 Andrea Sujova / Procedia Economics and Finance 34 ( 2015 ) 581 – 586
efficiency of investment which increased significantly and its increase continued in years after crisis.
In Slovak wood processing industry the crisis deepened fall of export performance by 20% except year 2010 as
the only year when it increased markedly, by 35%. Profitability was affected by crisis one year later, in 2009, when
it dropped by 84% and after recovery in 2010 there was a significant decrease again. Labour productivity declined
and productivity rate increased in 2009 when a negative influence of the crisis approved. Investment rate has fallen
gradually since 2008 to one half compared to period before crisis. In contrary, a positive trend in time of crisis can
be seen by efficiency of investment, which after its fall in time before crisis has risen gradually since 2008. The rate
of value added increased only in time of crises.
A comparison of impacts if crisis on wood processing companies in Slovakia and the Czech Republic is
presented by chain indexes of ratio performance indicators displayed in figures 1 and 2.
Fig. 1. Chain indexes of profitability and export performance in Slovak and Czech WPI
Fig. 2. Chain indexes of performance indicators in Slovak and Czech WPI
A significant negative influence of economic crisis can be seen by profitability and value added rate of Slovak
wood processing industry in 2009 which dropped very rapidly. Czech wood processing enterprises registered crisis
affect already in 2008 and the impact of crisis is noticeable in years after crisis when the performance in slight
holding decrease, however, a marked fall of performance wasn´t noticed.
0
100
200
300
400
500
600
700
2007 2008 2009 2010 2011
Chain indexes EP, ROE, ROS of WPI SR and CR
EP SR ROE SR ROS SR EP CR ROE CR ROS CR
0
50
100
150
2007 2008 2009 2010 2011
Chain indexes LP, EIR, RVA of WPI SR and CR
LPR SR EIR SR RVA SR LPR CR EIR CR RVA CR
586 Andrea Sujova / Procedia Economics and Finance 34 ( 2015 ) 581 – 586
4. Conclusion
Results of scientific studies of internationally known professionals mention a negative influence of economic
crises especially on sales, production, investment and profit of enterprises what was approved by worsened
profitability of wood processing enterprises and by their export performance fall. Trend of ratio indicators showed
that companies in Slovakia and also in the Czech Republic didn´t reflect to fall of sales in number of employees and
their reduction wasn´t in such rate as fall of revenues was in time of crisis. The result was a decrease of labour
productivity and in contrary increase of value added rate that can be considered to be a positive effect. Interesting
finding is a rise of investment efficiency in relation to revenues that can be caused by deep decrease of investments
during the crisis. Implemented crisis actions in wood processing companies had a short term effect because of
improvement of performance only for a short time of one year after time of crisis in 2010.
References
Dobrovič, J. 2010. Development trends in management during the reform of the tax administration of the Slovak republic. Intercathedra, 36: 117-
120
Ďurisova, M., Kucharčíková, A. 2014. The Quantitative expression of Factors which Affect the Cost of Transport Enterprise. In Transport means
2014: proceedings of the 18th International conference, October 23-24, 2014, Kaunas University of Technology Lithuania, p. 190-193.
Drábek, J., Potkány, M. 2008. Ekonomika podniku, Zvolen, Technická univerzita vo Zvolene.
Hajdúchová, I. 2011. Finančná stabilita podniku. Zvolen, Technická univerzita vo Zvolene.
Hajdúchová, I., Hlaváčková, P. 2014. Vplyv globálnej ekonomiky na lesnícko-drevársky sektor v Českej a Slovenskej republike. Acta Facultatis
Xylologiae, 56(2): 135-146.
Hlaváčková, P., Šafařík, D. 2014. Problémy českých dřevozpracujících podniků a indikátory konkurenceschopnosti. Manažment podnikov, 4(1):
3-8.
Holler, A. 2009. New metrics for value based management. Wiesbaden: Springer, 2009. 217 s.
Kupčák, V. 2006. Timber industry in the Czech Republic - present situation and prospects. Intercathedra. 22, 61-64.
Merkova, M., Drabek, J., Polach, J. 2011. Impact of investment on labour productivity growth in wood processing industry in Slovak Republic.
Finance and the performance of firms in science, education and practice. Zlin, p. 324-332.
Merková, M., Drábek, J., Jelačić, D. 2012. Determinants of Effects of Foreign Direct Investment in Terms of Slovak Republic and Wood-
processing Industry of Slovakia. Drvna Industrija, 63(2):129–142
Polách, J., Drábek, J., Merková, M., Polách, J. jr. 2012. Reálné a finanční investice. Praha, C. H. Beck.
Sujová, A. 2005. Makroekonómia, Zvolen, TU Zvolen, pp. 160
Sujová, A., Hlaváčková, P., Marcineková, K. 2015. Measuring the Impact of Foreign Trade on Performance Growth of the Wood Processing
Industry. In Wood Research, 60(3): 491-502.
Šafařík, D., Badal, T. 2013. The economic efficiency of forest energy wood chip production in regional use – a case study. Acta Univ. Agric.
Silvic. Mendelianae Brunensis 61(2): 1391-1398.
Zalai, K. et al. 2008. Finančno-ekonomická analýza podniku. Bratislava, Sprint.
Czech Statistical Office. [online] Available at: http://www.czso.cz
Statistical Office of the Slovak Republic: Database Slovstat. [online] Available at: <http://slovak.statistics.sk>
RESEARCH ARTICLE Open Access
Is recession bad for your mental health?
The answer could be complex: evidence
from the 2008 crisis in Spain
Joaquín Moncho1, Pamela Pereyra-Zamora1* , Nayara Tamayo-Fonseca1, Manuel Giron1,2,
Manuel Gómez-Beneyto2,3 and Andreu Nolasco1
Abstract
Background: We explored the impact of 2008 recession on the prevalence of mental health problems in Spain.
Methods: Repeated cross-sectional survey design. Datasets from 2006 and 2011 were used, and temporal change
was examined. The study was conducted on the economically active population (16–64 years old). The two surveys
included 29,478 and 21,007 people, obtaining a 96 and 89.6% response rate, respectively.
Multiple logistic regression models were adjusted to identify poor mental health risk factors. A standardisation
analysis was performed to estimate the prevalence of people at risk of poor mental health (GHQ+).
Results: The prevalence of GHQ+ following the crisis increased in men and decreased in women. Two logistic
regression analyses identified GHQ+ risk factors. From 2006 to 2011, unemployment rose and income fell for both
men and women, and there was a decline in the prevalence of somatic illness and limitations, factors associated
with a higher prevalence of GHQ+. After controlling for age, the change in employment and income among men
prompted an increase in the prevalence of GHQ+, while the change in somatic illness and limitations tended to
mitigate this effect. After the recession, unemployed men showed a better level of somatic health. The same effects
were not detected in women.
Conclusions: The economic recession exerted a complex effect on mental health problems in men. The reduction
of prevalence in women was not associated with changes in socioeconomic factors related to the economic crisis
nor with changes in somatic health.
Keywords: Mental health, Population study, Economic recession, Unemployment, Prevalence
Background
In his seminal work “Le suicide” (1897) [1], Durkheim
argued that economic crises could increase psychiatric
pathology. There are good grounds for thinking that this
may be so, including consistent evidence that those who
become unemployed during a recession may have worse
mental health than those who do not [2]. Some studies
have documented the direct effect of the economic re-
cession on the general population’s mental health by
comparing outcomes before and after the onset of the
crisis [3–6]. However, the independent effect of changes
in the distribution of socioeconomic and health risk factors
has not been investigated. An economic recession does not
exert a homogeneous effect on the population’s health
status [7, 8], and the hypothesis of a “protective” effect of
the downturn phase of the economic cycle on mental
health has not been studied. This hypothesis is plausible
because it is known that somatic morbidity is a risk factor
for poor mental health [9] and that the population’s health
status may improve during a recession, especially during
periods of very rapid industrial contraction [10–12].
In 2008, following a period of economic growth based
largely on the property market, Spain entered a severe
and lasting economic crisis that generated a significant
decline in macroeconomic activity (GDP) and a number
* Correspondence: [email protected]
1Research Unit for the Analysis of Mortality and Health Statistics, University of
Alicante, Campus de San Vicente del Raspeig s/n, Ap. 99, 03080 Alicante,
Spain
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Moncho et al. BMC Medical Research Methodology (2018) 18:78
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http://crossmark.crossref.org/dialog/?doi=10.1186/s12874-018-0538-2&domain=pdf
http://orcid.org/0000-0001-8993-3349
mailto:[email protected]
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of other factors with a clear impact on the population,
such as an acute rise in unemployment and a deterioration
in the standard of living. A substantial and rapid rise in
unemployment can have negative effects on mental health,
but positive effects on somatic health [10], giving rise to
complex explanatory models. The Spanish National
Health Surveys (SNHS) conducted in 2006 (pre-crisis) and
2011 (crisis) by the National Statistics Institute (Ministerio
de Sanidad Servicios Sociales e Igualdad) consider a repre-
sentative sample of the Spanish population. These surveys
include Goldberg’s General Health Questionnaire (GHQ)
and items relating to demographic, socioeconomic,
support and health factors. The aim of this study was
to analyse the impact of the economic recession on
mental health, including possible pro-cyclical effects, and
specifically, to investigate variation in the prevalence of
mental health problems (GHQ+ caseness) in the general
population in relation to variations in demographic and
socioeconomic factors related to the crisis, as well as
social support and health. The study was conducted in
four phases: 1) estimation of the prevalence of GHQ+ and
identification of the risk factors in 2006 and 2011; 2) esti-
mation of changes in risk factor frequency and prevalence
between the two periods; 3) analysis of the relationship
between changes in risk factor frequency and changes
in GHQ+ prevalence; 4) exploration of the relationship
between unemployment and somatic health.
Methods
Data source
We used data from the Spanish National Health Survey
[13] for two periods: 2006 (before the start of the crisis)
and 2011 (during the crisis). National health surveys employ
a multistage, stratified-random design to identify samples of
adults. All residents in each household were screened and
one person was selected at random for the interview.
Data were collected between June 2006 and June 2007
(2006 survey), and between July 2011 and June 2012
(2011 survey). Face-to-face interviews were carried out
at home by fully trained interviewers. We restricted the
selected sample to the economically active population
(16–64 years old). A total of 29,478 people aged >
15 years old responded to the SNSH health question-
naire in 2006, and 21,007 in 2011, obtaining a response
rate of about 96 and 89.6%, respectively. Of these,
20,787 and 14,835, respectively, were aged between 16 and
64 years old, and these were the populations considered in
the present study.
Due to the complex sample design, sample subjects
were weighted to determine the number of subjects
represented by each individual in the sample [14]. The
weightings were included in the databases provided by
the National Statistics Institute.
Risk of poor mental health and explanatory variables
The risk of poor mental health was the outcome variable in
2006 and 2011 and was measured with the 12-item version
of the General Health Questionnaire GHQ-12 [15, 16].
This screening measure detects probable psychiatric disor-
ders. We used a 2-point scoring method, rating a problem
as absent (0) or present (1) according to the method
recommended by the developers of the questionnaire.
Responses were summed, and subjects obtaining scores
of 3 or above (out of 12) were classified as having poor
mental health (GHQ+).
The same explanatory variables obtained in both 2006
and 2011 were:
(a) Sociodemographic: sex (male/female), age
(16–34/35–49/50–64), marital status
(single/married/separated or divorced/widowed),
educational level (higher/other).
(b) Socioeconomic variables related to crises: occupation
(employed/unemployed/other; such as: [retired,
student, houseworker, disabled]) and household
income (euros per month, high/average/low).
(c) Social support (measured by means of the 11-item
Duke Social Support Index) [17]. Higher scores for
individual items indicate better social support.
Responses were summed, and subjects obtaining
scores of ≤32 (out of 55) were classified as having
low social support [18].
(d) Health variables: presence of a somatic illness
confirmed by a doctor (yes/no), and presence of a
limitation due exclusively to a somatic illness (yes/no).
In order to determine the presence of somatic
illness during the last year confirmed by a doctor,
the surveys posed the following questions: “I’m
going to read a list of illnesses or health problems;
do you have or have you ever had any of them? If
yes, was this in the last 12 months? Have you
consulted a doctor about it?” The list was as
follows: high blood pressure, heart attack, other
cardiovascular diseases, varicose veins in the legs,
osteoarthritis, arthritis or rheumatism, chronic
back pain (cervical), chronic back pain (lumbar),
chronic allergy, asthma, chronic bronchitis,
diabetes, stomach or duodenal ulcer, urinary
incontinence, high cholesterol, cataracts, chronic
skin problems, chronic constipation, stroke,
migraine or frequent headaches, hemorrhoids,
cancer, osteoporosis, thyroid problems, prostate
problems, menopausal problems. To analyze the
change in prevalence we considered three groups:
cardiovascular (high blood pressure, heart attack,
other cardiovascular diseases, stroke),
osteoarticular (osteoarthritis, arthritis or
rheumatism, chronic back pain-cervical, chronic
Moncho et al. BMC Medical Research Methodology (2018) 18:78 Page 2 of 10
back pain-lumbar) and other. A fair agreement
between questionnaire data and medical records
has been well established in the case of common
chronic disorders. [19, 20]. In order to determine
the presence of a limitation due exclusively to a
somatic illness, the surveys posed the following
questions: “In the last six months, to what extent
have you suffered limitations in activities of daily
living?” Response options were: not limited at all/
limited. For this question, only physical limitations
were considered.
Data analysis
Analyses were performed for men and for women. Multiple
logistic regression models were fitted to allow calculation
of adjusted odds ratios (ORs) and 95% confidence intervals
(CIs).
A standardisation analysis was performed, considering
age and variables that maintained a statistically significant
relationship with a GHQ+ in the multivariate logistic
regression analyses in the two surveys, and which showed
a significant change in frequency between 2006 and 2011
for either sex. The expected prevalences of GHQ+ in 2011
were estimated as if the population had retained the same
population structure observed in 2006. The difference
between expected and observed prevalences in 2011 could
indicate the effect of the crisis in terms of the controlled
risk variables.
Specifically, to study possible changes in GHQ+ preva-
lence in men and women for the years 2006 and 2011 ad-
justed for age, employment status, income level, presence
of a somatic illness and presence of limitations derived from
a somatic illness, the population distribution in 2006 for
each of the levels and sublevels of the previous variable
categories was projected onto the 2011 population (2
sex categories × 3 age categories × 3 employment status
categories × 3 income level categories × 2 somatic illness
categories × 2 limitation categories = 216 cells or levels) as
follows:
P2011ei ¼
P2006iP
P2006i
X
P2011i
� �
; i ¼ 1; 2; 3…::; 216
Where:
P2011ei = expected population in 2011 in level i.
P2006i = observed population in 2006 in level i.
P2011i = observed population in 2011 in level i.
Once the 2011 population had been estimated in each
of the cells, the observed prevalence in 2011 was
applied at the same level of disaggregation to obtain the
expected GHQ+ cases and the corresponding prevalence,
as described below:
ne GHQþ
2011
i ¼ P2011ei � prev
2011
i
¼ P2011ei �
nGHQþ2011i
P2011i
; i
¼ 1; 2; 3; …; 216
prev2011e;i ¼
ne GHQþ2011i
P2011ei
Where:
ne GHQþ2011i = expected GHQ+ frequency in 2011 in
level i.
prev2011i = observed GHQ+ prevalence in 2011 in level i.
nGHQþ2011i = observed GHQ+ frequency in 2011 in
level i.
Lastly, global prevalence was calculated disaggregated
by sex, adding all previous cells, as follows:
prev2011e;total ¼
P
ne GHQþ2011iP
P2011ei
In addition, expected prevalences were calculated adjust-
ing for sex, age, income level and employment status, and
for sex, age, somatic illness and limitations.
This standardisation system made it possible to distin-
guish between: (1) changes in global prevalence due to
variations in population structure between 2006 and 2011
(in terms of the variables considered), and (2) changes in
prevalence that could not be explained by changes in the
population structure. Furthermore, variation in variables
for which a significant change in the population distri-
bution was observed between the two periods under
consideration could be related to variations in global
GHQ+ prevalence.
Results
Relationship between the risk of poor mental health and
sociodemographic, socioeconomic, support and
comorbidity variables
Table 1 shows the estimations of GHQ+ prevalence
together with the adjusted odds ratios using multivariate
logistic regression, and the frequencies of the different
risk factors in 2006 and 2011. The prevalence of GHQ+
increased in men but decreased in women. For both men
and women, being a GHQ+ case presented a statistically
significant association (p < 0.05) with a low educational
level (2006 survey), being divorced/separated (2006), low
social support (2006, 2011), unemployment (2006, 2011),
low and average household income (2006, 2011), having a
somatic illness (2006, 2011) and the presence of a limita-
tion arising from a somatic illness (2006, 2011).
For the latter 4 risk factors (income, unemployment,
somatic illness, limitation derived from somatic illness),
differences in the distribution of frequencies were ob-
served between 2006 and 2011 (Table 1). Frequencies
Moncho et al. BMC Medical Research Methodology (2018) 18:78 Page 3 of 10
Table 1 Prevalence of risk of mental health problems (GHQ+), odds ratios (OR) and 95% confidence intervals (95% CI) for the
association between GHQ+ and other explanatory variables, and distribution of population, by sex and year of survey
Men
Prevalence of Total Mental Health Problems 2006 (n = 7681), 14.5% (13.8, 15.2) 2011 (n = 5479), 16.9% (16.0, 17.7)
OR (95% CI) Distribution of population (%) (95%CI)
2006 2011 2006 2011
Age p < 0.001 p < 0.001
16–34 1 1 41.6 (40.6, 42.5) 36.6 (35.5, 37.7)
35–49 0.93 (0.79, 1.10) 1.42c (1.17,1.73) 34.9 (34.0, 35.8) 36.6 (35.5, 37.7)
50–64 0.62c (0.51,0.76) 0.82 (0.65, 1.03) 23.5 (22.7, 24.3) 26.8 (25.8, 27.8)
Educational level p = 0.011 p = 0.154
Higher/university 1 1 28.0 (27.2, 28.9) 24.5 (23.5, 25.5)
Others 1.21a (1.04, 1.41) 1.16 (0.95,1.41) 72.0 (71.1, 72.8) 75.5 (74.5, 76.5)
Marital status p < 0.001 p = 0.540
Single 1 1 43.0 (42.0, 43.9) 41.0 (39.9, 42.1)
Married 1.05 (0.89, 1.23) 1.10 (0.91, 1.32) 52.7 (51.7, 53.8) 54.3 (53.2, 55.5)
Divorced/separated 2.07c (1.54, 2.78) 1.10 (0.76, 1.59) 3.6 (3.3, 4.0) 4.1 (3.6, 4.5)
Widowed 0.78 (0.36, 1.69) 1.66 (0.75, 3.68) 0.7 (0.5, 0.9) 0.6 (0.5, 0.8)
Social support p < 0.001 p = 0.004
High 1 1 97.1 (96.8, 97.4) 97.4 (97.1, 97.8)
Low 5.05c(3.90, 6.55) 1.71b (1.19, 2.46) 2.9 (2.6, 3.2) 2.6 (2.2, 2.9)
Employment status p < 0.001 p < 0.001
Employed 1 1 74.9 (74.1, 75.7) 61.3 (60.2, 62.4)
Unemployed 2.49c(2.35, 3.06) 2.52c (2.09,3.05) 7.6 (7.1, 8.1) 20.7 (19.8, 21.6)
Other activity 1.56c (1.34, 1.86) 1.85c (1.50,2.28) 17.5 (16.8, 18.2) 18.0 (17.2, 18.9)
Household income p = 0.007 p < 0.001
High 1 1 36.6 (35.6, 37.5) 38.7 (37.6, 39.8)
Average 0.97 (0.84, 1.12) 1.33b (1.10, 1.61) 50.1 (49.2, 51.1) 35.6 (34.5, 36.7)
Low 1.28a (1.06, 1.56) 1.91c(1.55,2.36) 13.3 (12.7, 14.0) 25.7 (24.7, 26.6)
Somatic morbidity p < 0.001 p < 0.001
No 1 1 43.8 (42.8, 44.7) 47.9 (46.8, 49.1)
Yes 2.17c(1.88, 2.49) 1.79c (1.52,2.10) 56.2 (55.3, 57.2) 52.1 (50.9, 53.2)
Limitation derived from somatic morbidity p < 0.001 p < 0.001
No 1 1 86.9 (86.3, 87.6) 89.5 (88.8, 90.2)
Yes 2.50c (2.15, 2.91) 3.18c (2.61,3.88) 13.1 (12.4, 13.7) 10.5 (9.8, 11.2)
Women
Prevalence of Total Mental Health Problems 2006 (n = 11,035), 24.4% (23.6, 25.2) 2011 (n = 5522), 22.6% (21.7, 23.6)
OR (95% CI) Distribution of population (%) (95%CI)
2006 2011 2006 2011
Age p = 0.201 p = 0.069
16–34 1 1 40.1 (39.2, 41.0) 35.6 (34.5, 36.7)
35–49 1.03 (0.90, 1.18) 1.24a (1.03, 1.49) 34.8 (33.9, 35.7) 36.8 (35.8, 37.9)
50–64 0.91 (0.78, 1.07) 1.17 (0.95, 1.44) 25.1 (24.2, 25.9) 27.5 (26.5, 28.5)
Educational level p < 0.001 p = 0.568
Higher/university 1 1 26.9 (26.0, 27.7) 27.2 (26.1, 28.2)
Others 1.36c (1.20, 1.55) 1.05 (0.89, 1.25) 73.1 (72.3, 74.0) 72.8 (71.8, 73.9)
Moncho et al. BMC Medical Research Methodology (2018) 18:78 Page 4 of 10
of unemployed people and people living in low-income
households increased, and frequencies of those living in
average-income households and those with a somatic
illness and a limitation deriving from a somatic illness
decreased.
Effect of changes in the frequency of socioeconomic and
somatic health variables on the change in GHQ+
prevalence
The variables included in the standardisation analysis
were employment status, household income, presence of
a somatic illness and presence of a limitation. A slight
increase was observed in 2011 in the percentage of
people who were divorced/separated; however, the marital
status variable was not included in the analysis due to
the small number of subjects. Table 2 shows the GHQ+
prevalences observed for the different levels of explana-
tory variables. Once adjusted for age, a higher GHQ+
prevalence was observed among both men and women in
2006 and 2011 in the higher risk levels (unemployment,
low income level, somatic illness, presence of limitation).
Since the percentage distribution of risk factors varied
differently between the 2 periods (the percentage of
unemployed people and low-income households in-
creased while the percentage of people with somatic ill-
ness and limitation derived from somatic illness
decreased), its impact on the expected prevalence of
GHQ+ in 2011 also had different effects. In 2011, the
standardised (expected) GHQ+ prevalence in men by age
and socioeconomic risk variables related to the crisis (em-
ployment status and income) was 14.8%, lower than the
prevalence actually observed in 2011 (16.9%) (Table 2,
Model 1). This indicates that the rise in unemployment
and low-income households in both periods may explain
the increase in observed GHQ+ prevalence. However,
the standardised prevalence by age and variables re-
lated to somatic illness was 17.8%, higher than the
observed prevalence (Table 2, Model 2). This indi-
cates that the decrease in the frequency of having a
somatic illness and limitations between the two pe-
riods may explain a decrease in observed GHQ+
prevalence. Model 3 (Table 2) corresponds to the
prevalence of GHQ+ in 2011 standardised for all the
above variables (age, employment status, income, somatic
illness, limitation), which was 15.6%, lower than the ob-
served prevalence in 2011 (16.9%). For women, the models
adjusted for age and these two groups of risk factors did
Table 1 Prevalence of risk of mental health problems (GHQ+), odds ratios (OR) and 95% confidence intervals (95% CI) for the
association between GHQ+ and other explanatory variables, and distribution of population, by sex and year of survey (Continued)
Marital status p < 0.001 p = 0.015
Single 1 1 32.6 (31.7, 33.5) 34.9 (33.8, 36.0)
Married 0.92 (0.81, 1.05) 0.85 (0.71, 1.01) 59.3 (58.3, 60.2) 55.6 (54.4, 56.7)
Divorced/separated 1.30a (1.03, 1.64) 1.13 (0.86, 1.48) 5.5 (5.0, 5.9) 6.7 (6.2, 7.3)
Widowed 1.45a(1.07, 1.98) 1.25 (0.86, 1.82) 2.7 (2.4, 3.0) 2.8 (2.4, 3.2)
Social support p < 0.001 p < 0.001
High 1 1 97.0 (96.7, 97.3) 96.6 (96.2, 97.0)
Low 4.31c(3.34, 5.56) 3.41 (2.51, 4.63) 3.0 (2.7, 3.3) 3.4 (3.0, 3.8)
Employment status p < 0.001 p = 0.001
Employed 1 1 52.3 (51.3, 53.3) 51.2 (50.0, 52.3)
Unemployed 1.38c (1.17, 1.63) 1.44c(1.19,1.74) 10.1 (9.6, 10.7) 16.1 (15.3, 17.0)
Other activity 1.05 (0.94, 1.18) 1.12 (0.95, 1.32) 37.6 (36.6, 38.5) 32.7 (31.6, 33.7)
Household income p < 0.001 p < 0.001
High 1 1 32.2 (31.3, 33.1) 35.1 (34.0, 36.2)
Average 1.24b (1.09, 1.40) 1.30b(1.10, 1.55) 52.1 (51.1, 53.0) 35.4 (34.4, 36.5)
Low 1.83c (1.56, 2.15) 1.61c(1.33,1.94) 15.7 (15.0, 16.4) 29.5 (28.4, 30.5)
Somatic morbidity p < 0.001 p < 0.001
No 1 1 31.9 (31.0, 32.8) 37.8 (36.7, 38.9)
Yes 2.06c (1.81, 2.33) 2.18c (1.85,2.56) 68.1 (67.2, 69.0) 62.2 (61.1, 63.3)
Limitation derived from somatic morbidity p < 0.001 p < 0.001
No 1 1 84.7 (84.0, 85.4) 85.8 (85.0, 86.6)
Yes 2.07c (1.82, 2.35) 2.53c (2.14,3.00) 15.3 (14.6, 16.0) 14.2 (13.4, 15.0)
ap < 0.05, bp < 0.01, cp < 0.001
Moncho et al. BMC Medical Research Methodology (2018) 18:78 Page 5 of 10
Table 2 Percentage of population (Pop %) and prevalences observed in 2006 and 2011 (Po) and expected in 2011 (Pe) according to
different standardisation models by sex
2006 2011 2011
Pop % Po Pop % Po Model 1
a Pe Model 2
a Pe Model 3
a Pe
Men
MHP (95% CI) 14.5 (13.8;15.2) 16.9 (16.0, 17.7) 14.8 (14.0, 15.6) 17.8 (16.9, 8.7) 15.6 (14.8, 16.4)
Age
16–34 41.6 13.6 36.6 14.2 11.9 14.9 12.2
35–49 34.9 15.0 36.6 19.4 16.4 20.8 17.7
50–64 23.5 15.3 26.8 17.0 17.7 18.6 18.7
Household Income
low 11.9 23.1 18.9 28.5 24.6 26.7
average 44.7 13.6 26.3 17.1 15.6 16.1
high 32.5 12.2 28.6 11.6 10.9 11.2
no information 10.9 15.7 26.2 14.1 12.5 14.6
Occupation
Employed 74.9 12.1 61.3 12.0 12.3 12.9
Unemployed 7.6 28.1 20.7 28.4 25.9 28.0
Others 17.5 18.6 18.0 20.2 20.6 21.9
Somatic Illness
yes 56.1 19.0 52.1 21.6 22.5 19.4
no 43.9 8.7 47.9 11.7 11.8 10.8
Limitation
yes 13.1 29.7 10.5 41.2 37.9 37.8
no 86.9 12.2 89.5 13.5 14.8 12.3
Women
MHP (95% CI) 24.4 (23.6, 25.2) 22.6 (21.7, 23.6) 22.3 (21.3, 23.2) 23.4 (22.4, 24.3) 23.3 (22.4, 24.3)
Age
16–34 40.1 21.4 35.6 17.5 17.9 18.9 19.3
35–49 34.8 24.8 36.8 24.0 22.9 24.7 23.8
50–64 25.0 28.6 27.5 27.5 28.4 28.7 29.2
Household Income
low 13.6 36.4 21.1 30.9 30.8 31.9
average 45.4 24.8 25.4 23.6 23.3 24.8
high 28.0 18.6 25.2 17.7 17.6 17.9
no information 13.0 22.7 28.3 19.9 19.7 20.9
Occupation
Employed 52.3 22.1 51.2 18.8 19.0 20.1
Unemployed 10.1 30.1 16.1 30.5 27.4 29.8
Others 37.5 26.1 32.7 24.8 25.3 26.1
Somatic Illness
yes 68.1 29.0 62.2 28.6 28.2 28.4
no 31.9 14.5 37.8 12.9 13.0 12.3
Limitation
yes 15.3 40.1 14.2 48.5 42.8 43.9
no 84.7 21.5 85.8 17.3 19.9 19.6
(a) Model 1 = standardised prevalence by age, household income and occupation
Model 2 = standardised prevalence by age, somatic illness and limitation
Model 3 = standardised prevalence by age, household income, occupation, somatic illness and limitation
Moncho et al. BMC Medical Research Methodology (2018) 18:78 Page 6 of 10
not show any major differences between expected and
observed prevalence.
Employment status and somatic illness
Table 3 shows a statistically significant reduction of som-
atic diseases in unemployed men and employed women.
Using the presence of somatic illness as the response
variable, the multivariate logistic regression models built
for the periods 2006 and 2011 showed no statistically
significant association between employment status and
somatic health before the economic crisis in both sexes.
However, during the crisis, unemployed men were sig-
nificantly less likely to report poor health compared with
employed people. There was a positive and statistically
significant relationship between been a member of any
of the three groups of somatic illness and being classified
as GHQ+ case, in both 2006 and 2011. The prevalence
of osteoarticular illnesses diminished in both sexes, the
reduction being higher in the unemployed group. The
prevalence of cardiovascular and other diseases dimin-
ished significantly in employed women.
Discussion
This study has revealed that the 2008 economic recession
has exerted a complex effect on the prevalence of men at
risk of poor mental health. Compared with the pre-crisis
period, the period of recession was characterised by an
increase in unemployment and financial difficulties and by
a decrease in health problems, factors all of them related
with a higher prevalence of GHQ+ before and after the
crisis. The increase in socioeconomic factors related to the
crisis had a significant impact on the increased prevalence
of GHQ+ cases. In contrast, the decrease in somatic
illness and somatic illness derived limitations during the
recession tended to mitigate this prevalence. Overall, these
opposite effects tended to neutralise the crisis effect on
the prevalence of GHQ+ cases in both men and women,
indicating that the absolute increase in the observed
prevalence of GHQ+ cases in men and its reduction in
women must be due to factors other than those studied
here. Second, although the prevalence of somatic illnesses
decreased in all employment status categories after the
onset of the crisis, this reduction was more marked among
the unemployed men.
The effect of the economic recession on the risk of
poor mental health in economically active men, exerted
through a rise in unemployment and a drop in household
income, is consistent with the model that assumes that
the negative impact on health of a contracting economy is
the result of undesirable consequences associated with
Table 3 Prevalences of somatic illness and adjusted OR (95% IC) for the association between ocupational activity and somatic illness
in economically active population
Employed Unemployed
2006 2011 diference 2006 2011 diference
Men
Cardiovascular 14.6
(13.8, 15.3)
15.8
(14.7, 16.8)
1.2 14.1
(11.7, 16.5)
15.8
(13.9, 17.6)
1.7
Osteoarticular 23.1
(22.2, 24.0)
19.2
(18.0, 20.3)
−3,9b 27.9
(24.8, 31.0)
17.4
(15.8, 18.3)
−10,5b
Other 42.1
(41.0, 43.2)
41.0
(39.6, 42.4)
−1,1 42.1
(38.7, 45.5)
37.4
(35.0, 39.8)
−4,7
Total 55.2
(54.1, 56.3)
52.9
(51.5, 54.4)
−2.3 57.8
(54.3, 61.2)
48.6
(46.1, 51.1)
−9.2b
Adjusted Odds ratio a 1 1 0.97
(0.81, 1.16)
0.85
(0.72, 0.99)
Women
Cardiovascular 12.3
(11.4, 13.2)
10.2
(9.3, 11.2)
−2,1b 12.8
(10.8, 14.9)
14.4
(12.4, 16.4)
1,6
Osteoarticular 34.4
(33.1, 35.6)
29.1
(27.6, 30.5)
−5,3b 38.4
(35.5, 41.4)
30.1
(27.5, 32.6)
−8,3b
Other 55,7
(54.3, 57.0)
51,0
(49.4, 52.5)
−4,7b 56.5
(53.5, 59.5)
52.3
(49.5, 55.2)
−4,2
Total 66.0
(64.7, 67.2)
60.5
(58.9, 62.0)
−5.5b 67.6
(64.8, 70.4)
62.6
(59.8, 65.3)
−5.0
Adjusted Odds ratio a 1 1 1.03
(0.87, 1.20)
1.09
(0.91, 1.29)
aMultivariate Logistic regression analysis controlling for age, social support, educational level, marital status, GHQ caseness, and household income
bStatistically significant change between 2006 and 2011
Moncho et al. BMC Medical Research Methodology (2018) 18:78 Page 7 of 10
job loss (anticipated or actual job loss, income reduc-
tion, difficulty in paying bills) [21–23]. Our results do
not agree with those obtained in studies conducted in
England [3, 4], where no relationship was found between a
change in employment status and an increase in poor
mental health in men. This discrepancy may be due to
differences between the increase in unemployment in
England and Spain (much lower in the former), the
periods studied, and definitions of poor mental health and
analysis methods.
This study provides evidence of the “protective” effect
of economic recession on the risk of poor mental health
among economically active men through a reduction in
somatic health problems. This decrease in health prob-
lems during an economic recession has been reported in
other studies conducting comparing outcomes before
and after the crisis [11, 12].
This study has shown that unemployment exerts a direct
effect increasing the prevalence of GHQ+ in men. How-
ever, this effect may have been mitigated by a decrease in
somatic illness among the newly unemployed. The differ-
ence between the adjusted OR in 2006 and 2011 may be
explained by the intake of previously healthy people among
the newly unemployed, a phenomenon to be expected in
periods of very rapid industrial contraction [10]; by a
reduction in negative, unexpected health consequences of
economic processes such as an increase in work, promo-
tion at work and income level as anomic processes [22], or
by a reduction in somatic problems due to the avoidance
of jobs that endanger health (for example, due to a reduc-
tion in employment and workplace accidents in construc-
tion) [24, 25]. According to the Spanish Labor Ministry,
total workplace accidents in Spain decreased between 2006
and 2011 by 68.5% in the construction sector and 34.4% in
the other sectors, a decrease that may be related to the
housing recession and the decline in global economic
activity. This is consistent with the observed decrease …
Psychological Well-Being During the Great Recession:
Changes in Mental Health Care Utilization in an
Occupational Cohort
Sepideh Modrek, PhD, Rita Hamad, MD, and Mark R. Cullen, MD
A vast literature documents a robust relation-
ship between unemployment and a variety of
mental health disorders.1,2 Although studies
show that mental health deteriorates because
of job loss,3,4 which increases during recessions,
less evidence exists on the impacts of recession
on the mental health of remaining workers.
However, the impacts of recessions on mental
health may extend beyond the direct effect of
unemployment.5,6 Increased job insecurity,6---9
feelings of powerlessness,10 increased workload,
and changes in job scope—as well as anger or
sympathy for laid-off coworkers—may affect
mental health.
It is difficult to study the mental health of
workers during recessions because measuring
changes in mental health requires sensitive
measures and panel data. We previously ex-
amined changes in new diagnoses of depres-
sion for an employed population during the
recent recession and found no significant as-
sociation, but this may be attributable to the
high specificity of our measure, which might
not have been sensitive enough to capture
subtle changes in how workers feel.11 Although
self-reported symptoms or depression scales
are thought to be sensitive to small changes
in mental health, surveys with these measures
tend to be cross-sectional and therefore cannot
account for workers’ previous mental health.1
If remaining workers are selected on mental
health, then cross-sectional estimates may un-
derestimate the true effect of recessions on the
mental health of remaining workers, similar to
the well-known healthy worker bias.12 To
estimate changes in mental health, panel data
are necessary to take into account workers’
previous mental health.
As an alternative to self-reported depression
scales, we exploited detailed claims data for
a panel of continuously employed, continu-
ously insured workers during 2007 to 2010 to
examine changes in use of mental health
services and medications during the recent
recession. We explored the yearly number of
mental health inpatient and outpatient visits
before and during the recession, as well as the
yearly supply of prescriptions filled for opiates,
antidepressants, sleep aids, and anxiolytics. We
examined these 4 drug classes because pre-
vious research suggests that job stress is related
to the onset of generalized anxiety disorder and
depression13 and that sleep disorders and drug
abuse are often precursors to depression.14 In
addition, we examined opiates because stress is
known to relate to a wide variety of psycho-
somatic illnesses, including chronic pain.15,16
We focused on discontinuous changes in the
trend for these 4 medication classes because
evidence shows an increasing trend in their use
across developed countries.17 We found de-
scriptive evidence of selection into our panel on
both physical and mental health, making the
remaining continuously employed and insured
cohort healthier. We used piecewise regres-
sion, a method used to compare trends in an
outcome variable before and after a defined
discontinuity, to examine changes in trends
after 2009. We accounted for individual-level
unobservable time-invariant characteristics
(e.g., gender, race, underlying health) with
fixed-effects regressions. We also accounted for
area-level changes in the unemployment rate.
We explored the extent to which heightened
job insecurity, caused by local plant layoffs,
may have led to an increase in mental health
services and treatments. We also examined the
medium-term patterns in the mental health
outcomes for the subset of workers who
remained employed and insured through
2012, to investigate whether any of the ob-
served changes in mental health---related utili-
zation were long lasting or dissipated with the
improvement in the economy in 2011 to 2012.
METHODS
We selected workers for our sample from
linked administrative personnel and claims
data sets from a multisite US manufacturing
firm. These data are described elsewhere.8,11,18
We focused on 2 cohorts: a panel of 11625
employees who were continuously employed
at the 25 largest plants, in 15 states, from
Objectives. We examined the mental health effects of the Great Recession of
2008 to 2009 on workers who remained continuously employed and insured.
Methods. We examined utilization trends for mental health services and medica-
tions during 2007 to 2012 among a panel of workers in the 25 largest plants, located in
15 states, of a US manufacturing firm. We used piecewise regression to compare
trends from 2007 to 2010 in service and medication use before and after 2009, the
year of mass layoffs at the firm and the peak of the recession. Our models
accounted for changes in county-level unemployment rates and individual-
level fixed effects.
Results. Mental health inpatient and outpatient visits and the yearly supply of
mental health–related medications increased among all workers after 2009. The
magnitude of the increase in medication usage was higher for workers at plants
with more layoffs.
Conclusions. The negative effects of the recession on mental health extend to
employed individuals, a group considered at lower risk of psychological distress.
(Am J Public Health. 2015;105:304–310. doi:10.2105/AJPH.2014.302219)
RESEARCH AND PRACTICE
304 | Research and Practice | Peer Reviewed | Modrek et al. American Journal of Public Health | February 2015, Vol 105, No. 2
January 2007 to December 2010, in which we
examined short-term changes in outcomes, and
a panel of 10 242 employees who were contin-
uously employed at the same 25 plants from
January 2007 to December 2012, in which we
examined medium-term changes. All employees
in the sample had insurance coverage through-
out the study period, ensuring access to mental
health services. The firm provided health in-
surance benefits with identical provider net-
works to employees and families through a local
preferred provider organization, subject to
choice in family coverage and deductible rates.
Limited changes in preferred provider coverage,
copays, and deductibles occurred during the
study period.
We used claims data to quantify utilization
of mental health services and medications.
These data provided detailed claims records
for each inpatient and outpatient medical en-
counter and for each prescription filled and
included International Classification of Diseases,
Ninth Revision, Clinical Modification (ICD-9-
CM)19 codes and Current Procedural Terminol-
ogy (CPT) codes.20 We used ICD-9-CM codes
to identify relevant diagnoses and CPT codes to
categorize face-to-face physician visits.
Measures
We used the following ICD-9-CM codes to
identify encounters with any mental health
component: 296 (bipolar disorder), 300 (anxiety
disorder), 304 (opiate dependence), 305 (alcohol
abuse), 309 (mood disorder), and 311 (major
depression). For each worker, we totaled the
number of encounters an individual had in each
year in 2 domains: outpatient physician visits
(including emergency room visits) and inpatient
hospitalizations. For outpatient visits, we only
considered encounters that had a face-to-face
visit with a doctor, as indicated by the CPT code.
We used the pharmacy claims data to
calculate use of mental health---related medi-
cations, according to the number of days’
supply that an individual filled in a year for 4
categories of medications: opiates, antidepres-
sants, sleep aids, and anxiolytics (Appendix A,
Table A, available as a supplement to the
online version of this article at http://www.
ajph.org, lists the drugs in each class). We
included opiates because they may be used for
self-medication, especially among individuals
with a history of substance abuse.21 We also
included sleep aids because insomnia often
precedes the onset of depression, and they are
often prescribed for insomnia related to de-
pression.22 We assumed that an increase in
supply was directly related to an increase in the
use of these medications, so we refer generally
to medication use here. Although many of
these medications are prescribed for multiple
diagnoses, our analysis examined deviations
from the mean use for each person across the
years. Therefore, if individuals consistently
used these drugs for other purposes, that would
not affect our results. The analysis only cap-
tured marked changes in use of these drugs.
The period of our study was marked by
a rapid change in the national unemployment
rate. We accounted for changes in regional
labor market conditions by including the yearly
county-level unemployment rate from the Bu-
reau of Labor Statistics23 as a control variable
in all of our models. We used unemployment
rates for the county in which each plant was
located.
We used the personnel data set to identify
plants experiencing high layoff rates. If a plant
had a mass termination event in which 40 or
more employees were laid off on a single day,
we considered it a high-layoff plant. We as-
sumed that surviving employees at these 7
plants experienced greater job insecurity than
did those at the 18 other plants. We used mass
termination events to categorize job instability
in previous studies.8,11
Data Analysis
We described selection on health and mental
health in particular to establish that continuously
employed workers were healthier than their
counterparts who dropped out of the sample.
We categorized workers’ baseline health status
in 2007 with a health risk score, computed with
a third-party algorithm (DxCG software, Verisk
Health Inc, Waltham, MA), which uses an in-
dividual’s historical CPT and ICD-9-CM codes
and utilization of health care services to de-
termine a risk stratification score. This score
has been used to adjust for underlying health,
particularly in health services research.24---26
A score of1indicates that the individual’s health
expenditures are likely to fall at the mean in
the following year. Each unit increase predicts
a 100% increase in expenditures above the
mean. We also compared mental health---related
inpatient and outpatient visits and use of the 4
classes of mental health---related medications for
workers who remained employed and those
who left in 2008 to 2010.
Our primary analysis involved the cohort of
continuously employed and insured workers.
We used piecewise regression, which parti-
tioned the data into 2 intervals, allowing us
to estimate a separate trend line for each
interval. The boundaries between the segments
are known as breakpoints. We modeled the
breakpoint in 2009, the peak of major layoffs
at the company and the nadir of the gross
national product nationally, comparing trends
in 2007 to 2008 with those in 2009 to 2010.
We used this breakpoint in previous studies of
the cohort,8,11 and it coincided with a company
press release in January 2009 announcing
plans for a reduction in the workforce of 13%,
citing “extraordinary times requiring extraor-
dinary actions.”27 Although layoffs at the
company ended in March 2010, hiring and
pay freezes continued at least through 2013.
We therefore investigated the medium-term
impacts by extending the postrecession seg-
ment from 2009 to 2012. We also included
county-level unemployment rates to account
for changes in the regional labor market.
Because our data included multiple obser-
vations for each individual—that is, 1 observa-
tion per person per year—we controlled for
time-invariant characteristics by including a
fixed-effects parameter at the individual level. The
inclusion of individual-level fixed effects in the
model allowed us to use only within-individual
variation to identify discontinuous changes in
utilization of mental health services and med-
ications after 2009. The inclusion of the fixed
effect precluded controlling for age directly, but
all specifications accounted for aging by in-
cluding a second- and third-degree polynomial
for age. Robust standard errors clustered at the
individual level. We assessed heterogeneity in
employees’ responses to the recession through
the use of an interaction term between the time
trend variable and whether an individual’s plant
experienced a mass termination event.
RESULTS
Table 1 presents the sample characteristics
for 3 groups: (1) 11 625 workers who were
active on January 1, 2007, and continuously
RESEARCH AND PRACTICE
February 2015, Vol 105, No. 2 | American Journal of Public Health Modrek et al. | Peer Reviewed | Research and Practice | 305
employed and insured from 2007 to 2010; (2)
10 242 workers who were active on January 1,
2007, and continuously employed and insured
from 2007 to 2012; and (3) 2946 workers
who were active on January 1, 2007, and
whose contract of employment was termi-
nated between 2008 and 2010. The second
group was a subset of the first group. The
workers in groups 1 and 2 were predomi-
nantly White men, aged 45 years on average;
56% worked at high-layoff plants. These
workers’ average risk score was 1, suggesting
that their overall health care utilization in
2007 fell at the mean of a working aged
population. By contrast, the workers who left in
2008 to 2010 (group 3) were on average 3 years
older, were more likely to be female, and had
higher risk scores in 2007 (all, P < .001).
Table A2 (available as an online supplement)
shows baseline differences in mental health utili-
zation between those who left the firm and
those who remained, confirming that the workers
who left had higher mental health inpatient and
outpatient utilization in 2007. They also used
more antidepressants, anxiolytics, and opiates.
Results presented here focus only on the contin-
uously employed and insured workforce.
Changes in Utilization of Mental Health
Services After 2009
Table 2 presents results on inpatient visits
with a mental health---related diagnosis for the
2007 to 2010 and 2007 to 2012 cohorts.
We found a marginally significant increase in
inpatient utilization in 2009 to 2010 from
2007 to 2008 (b = 0.00189; P = .078). The
magnitude of the postrecession trend line was
about 4 times as great as that of the prere-
cession trend line in the short term. In the
medium term, the prerecession and postre-
cession trends were almost identical, suggest-
ing that the increase in inpatient visits was
abrupt and short lived.
Table 2 also shows models that included an
interaction term for working at a high-layoff
plant. We found no difference in the trend
among those with higher job insecurity. We
documented that changes in county-level un-
employment rate were related to decreases in
mental health inpatient visits.
Table 3 presents results for the number of
outpatient visits with a mental health---related
diagnosis. The results suggested a statistically
significant increase in the trends in outpatient
utilization in 2009 to 2010 over 2007 to
2008. The increase in the trend remained
elevated and significant, but the magnitude
was substantially reduced in a comparison of
the yearly increases of 2009 to 2012 and
2007 to 2008. These results were driven by
a decreasing prerecession trend and a discon-
tinuous and increasing postrecession trend.
We observed no difference between em-
ployees at high-layoff plants and those at other
plants. We also documented that changes
in county-level unemployment rates were
related to increases in mental health outpa-
tient visits. When we allowed for differences
by plant-level layoffs, the magnitude between
increases in unemployment rates and in-
creases in mental health outpatient visits was
similar across both the 2007 to 2010 and the
2007 to 2012 periods.
Changes in Utilization of Mental Health
Medications After 2009
Table 4 presents changes in the supply of
opiates, antidepressants, sleep aids, and anxio-
lytics. We examined the number of days’ supply
of each drug category separately, and we exam-
ined differential patterns among workers who
worked in high-layoff plants. Workers used more
opiates, antidepressants, sleep aids, and anxio-
lytics in 2009 to 2010 than in 2007 to 2008.
The increases in use of opiates and anxiolytics
were only marginally significant. Opiate use
increased 8.8% a year (a figure derived from the
mean initial use of 8.8 pills), or almost 5 times
the prerecession trend. We found a 13%
increase in antidepressant use (derived from the
mean initial use of 27.1 pills). The increasing
trend in the use of antidepressants in the post-
recession period was particularly notable because
of the decreasing trend in antidepressant use
before the recession.
Workers increased their use of sleep aids by
23% per year (derived from the mean initial
use of 4.4 pills). The difference in the trend for
sleep aids was driven by a small increasing
trend in the use of sleep aids in the post-
recession period and a large decreasing trend
before the recession. The 11% increase in
use of anxiolytics (derived from the mean
initial use of 7.2 pills) was almost 5 times the
prerecession trend. Use of antidepressants
was also statistically significantly higher in high-
layoff plants than in other plants. We found
no difference in trends for opiates, sleep aids,
or anxiolytics at high-layoff plants. Results
also suggested that changes in county-level
unemployment rates were robustly related to
increased use of opiates, and sleep aids.
In the medium term, our results suggested
that the increase in the trend for antidepres-
sants and sleep aids remained significant
among the 2007 to 2012 cohort, but the
magnitude of the increase was smaller. The
increase in use of antidepressants in high-layoff
plants remained significant, though the
TABLE 1—Characteristics of Workers in Continuously Employed Cohorts and Those
Employed in 2007 but Terminated Before 2010: 2007–2012
Characteristic
2007–2010 Cohort,a
No. or % or Mean 6SD
2007–2012 Cohort,a
No. or % or Mean 6SD
Workers Laid Off in 2008–2010,
No. or % or Mean 6SD
Sample size 11 625 10 242 2946
Female 20.1 19.3 25
Age in 2007, y 45.6 69.0 45.2 68.7 48.7 612.2
Race
White 82 82 80.9
Black 8.7 8.6 9.4
Hispanic 6.3 6.3 6.5
Other 3 3.1 3.2
2007 Risk score 1.0 61.0 0.98 60.96 1.42 61.95
Employed at high-layoff plant
b
55.9 56.4 52.6
aInclusion criteria were continuous employment and continuous insurance coverage during study period.
bPlant that laid off ‡ 40 employees in 1 day.
RESEARCH AND PRACTICE
306 | Research and Practice | Peer Reviewed | Modrek et al. American Journal of Public Health | February 2015, Vol 105, No. 2
magnitude was diminished. We found no
difference in trend for sleep aids, anxiolytics,
or opiates at high-layoff plants in the longer
period. However, in the models that accounted
for differences by plant-level job insecurity,
changes in county-level unemployment rates
were robustly related to increased use of
opiates and sleep aids.
DISCUSSION
We examined changes in utilization of
mental health services and medications in
a cohort of continuously employed and insured
workers employed at a firm that experienced
significant downsizing events during the 2008
to 2009 Great Recession. We documented that
remaining workers had better overall health
and significantly less utilization of mental
health services and medications in the years
before the recession.
Despite their lower use of mental health
services and medications at baseline, workers
who remained employed increased their utili-
zation of inpatient and outpatient mental health
TABLE 2—Mental Health Inpatient Utilization Among Continuously Employed and Insured Workers: 2007–2012
Yearly Mental Health Inpatient Visits
Variable 2007–2010 Cohort, b (95% CI)
2007–2012 Cohort,
b (95% CI)
2007–2010 Cohort, With
Interactions at High-Layoff
Plants,
a
b (95% CI)
2007–2012 Cohort, With
Interactions at
High-Layoff Plants,
a
b (95% CI)
Time trend before 2009 0.00056 (–0.00034, 0.00146) 0.00035 (–0.00047, 0.00117) 0.00087 (–0.00034, 0.00208) 0.00051 (–0.00065, 0.00167)
Time trend after 2009 0.00245* (0.00058, 0.00433) 0.00036 (–0.00010, 0.00083) 0.00126 (–0.00100, 0.00351) 0.00004 (–0.00061, 0.00069)
Interaction terms: job security
Before 2009 x high layoff –0.00046 (–0.00170, 0.00078) –0.00037 (–0.00153, 0.00079)
After 2009 x high layoff 0.00215 (–0.00144, 0.00575) 0.00058 (–0.00026, 0.00143)
High layoff 0.00027 (–0.00157, 0.00210) –0.00032 (–0.00193, 0.00129)
Unemployment rate –0.00029* (–0.00054, –0.00005) –0.00011 (–0.00031, 0.00010) –0.00031* (–0.00057, –0.00006) –0.00009 (–0.00028, 0.00011)
Constant 0.00423 (–0.00190, 0.01037) 0.00082 (–0.00249, 0.00413) 0.00447 (–0.00181, 0.01074) 0.00087 (–0.00267, 0.00442)
Model comparisons
Difference in trend, before vs after recession 0.00189 0.00028
Note. CI = confidence interval. Analyses were conducted with piecewise regression with a discontinuity in 2009, with fixed effects at the individual level. Models controlled for age squared and age
cubed. The 2007–2010 cohort had 46 500 observations for 11 625 individuals; the 2007–2012 cohort had 61 452 observations for 10 242 individuals.
aA high-layoff plant laid off ‡ 40 employees in 1 day (n = 7). Reference group = all other plants (n = 18).
*P < .05.
TABLE 3—Mental Health Outpatient Utilization Among Continuously Employed and Insured Workers: 2007–2012
Yearly Mental Health Outpatient Visits
Variable
2007–2010 Cohort,
b (95% CI)
2007–2012 Cohort,
b (95% CI)
2007–2010 Cohort, With Interactions
at High-Layoff Plants,
a
b (95% CI)
2007–2012 Cohort, With
Interactions at High-Layoff
Plants,
a
b (95% CI)
Time trend before 2009 –0.0107*** (–0.0171, –0.0043) –0.0051 (–0.0112, 0.0010) –0.0102** (–0.0172, –0.0032) –0.0093* (–0.0168, –0.0017)
Time trend after 2009 0.0085* (0.0006, 0.0163) 0.0060*** (0.0029, 0.0092) 0.0066 (–0.00353, 0.0166) 0.0091 (–0.0017, 0.0199)
Interaction terms: job security
Before 2009 x high layoff 0.0006 (–0.0067, 0.0079) 0.0010 (–0.0067, 0.0087)
After 2009 x high layoff 0.0037 (–0.0120, 0.0193) –0.0000 (–0.0167, 0.0167)
High layoff 0.0173** (0.0065, 0.0281) 0.0169** (0.0056, 0.0283)
Unemployment rate 0.0050*** (0.0025, 0.0076) 0.0029* (0.0005, 0.0053) 0.0046*** (0.0021, 0.0072) 0.0042** (0.0015, 0.0069)
Constant 0.0331 (–0.0059, 0.0722) 0.0631** (0.0222, 0.1041) 0.0298 (–0.0089, 0.0685) 0.0395 (–0.0029, 0.0819)
Model comparisons
Difference in trend, before vs after recession 0.0192*** 0.0112**
Note. CI = confidence interval. Analyses were conducted with piecewise regression with a discontinuity in 2009, with fixed effects at the individual level. Models controlled for age squared and age
cubed. The 2007–2010 cohort had 46 500 observations for 11 625 individuals; the 2007–2012 cohort had 61 452 observations for 10 242 individuals.
aA high-layoff plant laid off ‡ 40 employees in 1 day (n = 7). Reference group = all other plants (n = 18).
*P < .05; **P < .01; ***P < .001.
RESEARCH AND PRACTICE
February 2015, Vol 105, No. 2 | American Journal of Public Health Modrek et al. | Peer Reviewed | Research and Practice | 307
TA
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308 | Research and Practice | Peer Reviewed | Modrek et al. American Journal of Public Health | February 2015, Vol 105, No. 2
services immediately after the downturn. Con-
sistent with this evidence, we also found an
increase in the use of mental health---related
medications, specifically opiates, antidepres-
sants, sleep aids, and anxiolytics, for all
workers. When we looked at a longer period,
including data for those who remained working
in 2011 to 2012, we observed no difference in
the rate of utilization of mental health---related
inpatient visits. Use of antidepressants and
sleep aids was still higher among all workers,
and workers at high-layoff plants still used
more antidepressants, though the magnitudes
were diminished. We confirmed that beyond
the local plant …
Bull World Health Organ 2014;92:630–640D | doi: http://dx.doi.org/10.2471/BLT.13.129114
Research
630
Effect of the economic recession on pharmaceutical policy and
medicine sales in eight European countries
Christine Leopold,a Aukje K Mantel-Teeuwisse,b Sabine Vogler,a Silvia Valkova,c Kees de Joncheere,d
Hubert GM Leufkens,b Anita K Wagner,e Dennis Ross-Degnane & Richard Laingd
Introduction
European public authorities struggle to maintain a high level of
access to health care while restraining increases in expenditure
associated with an ageing population and higher demand.1–4
The recent global economic recession has put additional pres-
sure on public budgets.5,6
In 2008, Europe was affected by the financial crisis. As the
recession in Europe continued, the effect was felt especially in
southern European countries and Ireland in 2010 and 2011.
Soon the problem of financial debt for individual European
countries developed into a crisis in the Eurozone, which then
became a high priority for the European Central Bank and
the European Parliament. All countries were urged to imple-
ment cost-saving measures that affected public financing for
health care.7
Recession, which is defined as two successive quarters
of negative growth in gross domestic product (GDP), can
have a detrimental effect on the health of the population
because economic downturns are strongly associated with a
decline in health-care utilization and a deterioration in health
outcomes.8 For example, suicides and homicides increased
among working-age men and women when unemployment
rose rapidly during past recessions in Europe.9 In the current
recession, the number of uninsured non-elderly Americans
increased by 5.6 million between 2007 and 200910 and over a
quarter of Americans reported reduced routine use of medi-
cal care.11 Over the same period, insurance policy deductibles
and copayments for visits to physicians and for prescription
medicines increased, leading to a greater cost burden for
patients.12–14 Similar effects were seen in Greece. Studying the
health effects of the economic crisis in the country it was found
that patients had less access to care and preventive services
and, consequently, faced higher risks of infection with sexu-
ally transmitted diseases.15 The World Health Organization
examined the influence of the recession on expenditure on,
and the sales and prices of, medicines between 2007 and 2009
in 84 countries. It found that the economic recession had
mixed effects and that the largest declines in medicine sales
occurred in high-income countries and in Europe, particularly
in the Baltic states.16
It has been shown that countries that were seriously af-
fected by the crisis, such as the Baltic countries, Greece, Por-
tugal and Spain, abruptly implemented several pharmaceutical
policy measures between 2010 and 2011. This included price
cuts, changes in reimbursement rates and the imposition of
value-added tax on medicines.17 In other European countries,
such as Italy, in which cost-containment measures were already
in place when the crisis began, the implementation of planned
policy changes was accelerated.18
Because different countries were affected differently by the
recession and attempted to overcome budgetary constraints
in different ways, we decided to analyse systematically how
European pharmaceutical policies were affected by the reces-
Objective To identify pharmaceutical policy changes during the economic recession in eight European countries and to determine whether
policy measures resulted in lower sales of, and less expenditure on, pharmaceuticals.
Methods Information on pharmaceutical policy changes between 2008 and 2011 in eight European countries was obtained from publications
and pharmaceutical policy databases. Data on the volume and value of the quarterly sales of products between 2006 and 2011 in the 10
highest-selling therapeutic classes in each country were obtained from a pharmaceutical market research database. We compared these
indicators in economically stable countries; Austria, Estonia and Finland, to those in economically less stable countries, Greece, Ireland,
Portugal, Slovakia and Spain.
Findings Economically stable countries implemented two to seven policy changes each, whereas less stable countries implemented 10
to 22 each. Of the 88 policy changes identified, 33 occurred in 2010 and 40 in 2011. They involved changing out-of-pocket payments for
patients in 16 cases, price mark-up schemes in 13 and price cuts in 11. Sales volumes increased moderately in all countries except Greece
and Portugal, which experienced slight declines after 2009. Sales values decreased in both groups of countries, but fell more in less stable
countries.
Conclusion Less economically stable countries implemented more pharmaceutical policy changes during the recession than economically
stable countries. Unexpectedly, pharmaceutical sales volumes increased in almost all countries, whereas sales values declined, especially
in less stable countries.
a World Health Organization (WHO) Collaborating Centre for Pharmaceutical Pricing and Reimbursement Policies, Gesundheit Österreich GmbH, Stubenring 6, 1010,
Vienna, Austria.
b WHO Collaborating Centre for Pharmaceutical Policy and Regulation, Utrecht Institute for Pharmaceutical Sciences, Utrecht, Netherlands.
c IMS Institute for Healthcare Informatics, Philadelphia, United States of America (USA).
d Department of Essential Medicines and Pharmaceutical Policies, World Health Organization, Geneva, Switzerland.
e Department of Population Medicine, Harvard Medical School, Boston, USA.
Correspondence to Christine Leopold (email: [email protected]).
(Submitted: 19 August 2013 – Revised version received: 11 February 2014 – Accepted: 20 March 2014 – Published online: 16 June 2014 )
Research
Bull World Health Organ 2014;92:630–640D| doi: http://dx.doi.org/10.2471/BLT.13.129114 631
Research
Economic recession and pharmaceutical policies
sion by comparing changes in pharma-
ceutical pricing and reimbursement
policies between economically stable
and economically less stable countries.
In addition, we investigated changes in
the sale of pharmaceuticals in major
therapeutic classes before and after the
recession in these two types of countries.
We expected that some of the cost-con-
tainment policies, such as those affecting
out-of-pocket payments, would shift
the financial burden of medicines onto
patients and hypothesized that pharma-
ceutical sales would decline during this
period, especially in less economically
stable countries.
Methods
Data sources
For this longitudinal study, we used data
from two sources to derive information
on pharmaceutical policies: (i) the Phar-
maceutical Pricing and Reimbursement
Information Network (Austrian Health
Institute, Vienna, Austria), which col-
lects information from experts in na-
tional pharmaceutical pricing and from
authorities responsible for reimburse-
ment – the latter provide regular phar-
maceutical policy updates; and (ii) the
PharmaQuery database (IMS Health,
Philadelphia, United States of America),
which contains data on pharmaceuti-
cal policies. In addition, we included
information on policy changes reported
in the published literature. We grouped
policy changes into 6-month implemen-
tation periods from January 2008 until
December 2011 and we categorized
policy as relating to one of three main
areas: (i) pricing; (ii) reimbursement;
and (iii) generic drugs. Table 1 defines
the policy measures in these three areas.
Quarterly pharmaceutical sales
data for the period January 2006 to
December 2011 were obtained from
the IMS MIDAS (Multinational Inte-
grated Data Analysis System) Quantum
pharmaceutical market research service
(IMS Health, Philadelphia, USA). Data
were expressed in standard units for the
volume of sales and in constant United
States dollars (US$) for the value of
sales. A standard unit, as defined by IMS
Health, is the smallest dose of a product
– it may be one tablet or capsule for oral
preparations, one teaspoon (i.e. 5 mL)
for a syrup or one ampoule or vial for
an injectable product. The value of sales
was derived from the price deemed most
accurate for the relevant country and
was expressed in constant US$, which
were calculated by converting the local
currency into United States dollars at a
constant exchange rate. In most coun-
tries, the price used was the ex-factory
price; in Estonia, Finland, Greece and
Ireland, ex-factory prices were derived
from wholesale prices. Average stan-
dard conversion factors, which were
determined with the co-operation of
the pharmaceutical industry for each
country, were applied to estimate prices
at various points along the distribution
chain. The price calculations did not
take into account any discounts between
manufacturers, wholesalers and payers
and were not adjusted for inflation.
Our study considered only prescrip-
tion medicines, whether on or off patent,
that were available in the retail market
for the 10 highest-selling therapeutic
classes. We identified the 10 highest-
selling classes by ranking therapeutic
classes according to their sales volume
in each country. Together the combined
sales volume of products in these 10
classes accounted for at least 50% of
the total sales volume of all medicines
in each of the eight countries from 2008
to 2011 (Table 2). Data were aggregated
by therapeutic class for each country. We
had no data on individual drugs.
Country groups
We considered eight European countries
in which the majority of the population
was covered by a social security system
or national health service: Austria, Esto-
nia, Finland, Greece, Ireland, Portugal,
Slovakia and Spain. We selected these
countries because they represented a va-
riety of geographical regions and levels
of economic wealth and stability and had
been affected by the recession to differ-
ent degrees. We classified them as either
economically less stable or economi-
cally stable using categories defined
by the Organisation for Economic Co-
operation and Development (OECD)
for the level of fiscal consolidation in
2012. Fiscal consolidation was judged
according to whether the country had
adopted either concrete policies aimed
at stabilizing general government gross
debt or a long-term target for the debt-
to-GDP ratio of 60%. There were four
categories of country: (i) those that had
adopted a programme proposed by the
International Monetary Fund, the Euro-
pean Union and the European Commis-
sion (e.g. Greece, Ireland and Portugal);
(ii) those that were under clear market
pressure (e.g. Belgium, Hungary, Italy,
Slovakia and Spain); (iii) those that had
a substantial deficit or debt but which
were under less market pressure (e.g.
Austria, Denmark, Finland, France and
Germany); and (iv) those that had no
or only a marginal need for consolida-
tion (e.g. Norway, Sweden and Switzer-
land).21 In this study, we regarded eco-
nomically less stable countries as those
belonging to the first two categories (i.e.
Greece, Ireland, Portugal, Slovakia and
Spain) and economically stable coun-
tries as those belonging to the third and
fourth categories (i.e. Austria, Estonia
and Finland).
Data analysis
First, we described and analysed the
number of policy measures implement-
ed per year, per country group and per
policy category. Next, we determined the
volume and value of the sales of drugs in
each therapeutic class between 2006 and
2011 in each country and, then, we cal-
culated the combined volume and value
of the sales of drugs for all 10 therapeutic
classes for each country. Since our find-
ings for individual therapeutic classes
and for all therapeutic classes combined
were similar, we present only the results
for all therapeutic classes combined.
For this analysis, we divided the
volume and value of sales by the size of
the country’s population to control for
population growth; annual population
figures were obtained from the OECD.22
We derived the annual and average
growth rates over the study period using
both the volume and value of pharma-
ceutical sales per capita:
AGR y
y
= −
×
−
S
S 1
1 100
(1)
AAGR
AGR
= ∑
n
(2)
where AGR is the annual growth rate,
Sy is the per capital sales in a year, Sy–1 is
the per capital sales in the previous year,
AAGR is the average annual growth rate
and n is the number of years.
To compare changes in the volume
and value of sales, we calculated the dif-
Bull World Health Organ 2014;92:630–640D| doi: http://dx.doi.org/10.2471/BLT.13.129114632
Research
Economic recession and pharmaceutical policies
Table 1. National policy measures influencing pharmaceutical sales19
Policy measure Definition
Pricing
Price cut A cost-containment measure whereby the set price of a medicine is reduced by the authorities.
External price referencing External price referencing is the practice whereby the price of a medicine in one or several other
countries is used to derive a benchmark or reference price for the purpose of setting or negotiating the
medicine’s price in a given country.
Policy changes in external price referencing include the introduction or abolition of this pricing
policy and altering the methodology (e.g. changing the basket of reference countries or the way of
calculating the benchmark price).
Distribution remuneration
(i.e. mark-ups, margins and fees
for service)
Distribution remuneration is the payment of a health-care provider, whether an individual or an
organization, for the services provided. In the distribution of pharmaceuticals, wholesalers and
pharmacies are remunerated using mark-ups or regressive margin schemes or, for pharmacies alone, by
paying a “fee for service”. With mark-ups, a defined linear or percentage amount is added to the cost of
a good to ensure a profit at the wholesale or retail level or both. With regressive margin schemes, the
margin is expressed as a percentage of the selling price.
Policy changes in distribution remuneration include adjusting the mark-ups or margins used for
wholesalers or pharmacies or changing the type of distribution remuneration for a defined actor.
Changes may also be made to the types of medicines (e.g. reimbursable medicines or prescription-only
medicines) to which distribution remuneration applies.
VAT on medicine VAT is a sales tax on products that is collected in stages. It is a wide-ranging tax that is usually designed
to cover most or all goods and services, including medicines.
Policy changes in VAT include the introduction or abolition of VAT on medicines and altering the VAT
rate on medicines.
Extraordinary price review Price reviews involve reviewing the process by which the set price of a medicine was established.
Reviews may or may not be performed in combination with reimbursement reviews. Reviews can be
performed systematically (e.g. once a year) for all reimbursed medicines or for a group of medicines
(e.g. for a specific indication) or at any time.
Reimbursement
Reference price system With a reference price system, which is also referred to as internal or therapeutic reference pricing, the
third party payer determines a reference price for the reimbursement of medicines with a particular
active ingredient or in a given therapeutic class. If the price of the medicine exceeds the reference
price, the health-care consumer must pay the difference between the fixed reimbursed amount
(i.e. the reference price) and the actual pharmacy retail price in addition to any copayments (e.g.
prescription costs and percentage copayment rates).
Policy changes in the reference price system include the introduction or abolition of a reference price
system and changing the methodology by which clusters of medicines are established for determining
a reference price (e.g. by grouping identical or similar medicines).
Out-of-pocket payments Out-of-pocket payments are payments made by health-care consumers that are not reimbursed by a
third-party payer. They include cost-sharing, fixed or percentage copayments and informal payments
to health-care providers.
Delisting Delisting is the exclusion of a medicine from a reimbursement list (e.g. a positive list), which often
results in exclusion from reimbursement.
Generic drugs
INN prescribing With INN prescribing, prescribers (e.g. physicians) are required to prescribe medicines using the INN for
the pharmaceutical (i.e. the name of the active ingredient) instead of a brand name.
Policy changes in INN prescribing include its introduction or abolition, changing the way INN
prescribing is organized (e.g. by imposing or eliminating financial incentives) and changing from
indicative to obligatory INN prescribing.
Generic substitution Generic substitution is the practice of substituting a medicine, whether marketed under a trade name
or generic name (i.e. a branded or unbranded drug), by a less expensive medicine (e.g. a branded or
unbranded generic drug), which often contains the same active ingredients. Generic substitution may
be encouraged (i.e. indicative generic substitution) or required (i.e. mandatory generic substitution).
Policy changes in generic substitution include its introduction or abolition, changing the way generic
substitution is organized (i.e. imposing or eliminating financial incentives) and moving from indicative
to obligatory generic substitution.
Public campaigns Policies, regulations, measures and initiatives promoting the use of generic drugs or licensed, off-
patent medicines are typically undertaken by government authorities. Policy on generic drugs may be
targeted at prescribers, pharmacists, patients or consumers, or other stakeholders.
INN: international nonproprietary name; VAT: value-added tax.
Bull World Health Organ 2014;92:630–640D| doi: http://dx.doi.org/10.2471/BLT.13.129114 633
Research
Economic recession and pharmaceutical policies
ference between the annual growth rate
in the value of pharmaceutical sales and
the annual growth rate in the volume of
sales for each country.
Results
Changes in policy
Table 3, Table 4 and Table 5 (avail-
able at: http://www.who.int/bulletin/
volumes/92/9/13-129114) summarize
the 88 policy changes we identified in
pricing, reimbursement and generic
drugs, respectively. Economically stable
countries implemented 7 or fewer policy
changes each between 2008 and 2011;
the lowest number was 2 in Finland
(Table 6). Less economically stable
countries implemented between 10 and
22 changes each; the highest number was
22 in Portugal. The greatest number of
policy adjustments occurred in 2010
(33) and 2011 (40) and the most fre-
quently used policy measures involved
changes in out-of-pocket payments
by patients (16), changes in regula-
tions controlling the mark-up of prices
(13) and price reductions (11). Some
countries implemented several pricing
measures. For example, Spain enacted
four price cuts between 2008 and 2011.
Most changes concerned reimbursable
medicines and built on existing poli-
cies; only a few changes involved newly
implemented policies, such as the in-
troduction of internal reference pricing
in Finland.17
Changes in sales
The small increase in the volume of
pharmaceutical sales in all countries
between 2006 and 2011 is shown in
Fig. 1, Fig. 2, Fig. 3, Fig. 4 and Table 7:
the average annual per capita growth
in sales volume ranged from 0.8% in
Greece and 1.0% in Portugal to 3.7% in
Ireland, 4.0% in Slovakia and 4.6% in
Estonia. However, annual growth rates
were much more variable: from 2006 to
2007 the growth rate was over 3.7% for
all countries, with Estonia having the
highest rate at 12.2%. Between 2007
and 2009, growth remained fairly stable
in Austria and Finland but there was
a sharp decline in Estonia: the annual
growth rate was −0.5% from 2007 to
Table 2. Ten highest-sellinga therapeutic drug classes in eight European countries,b
2008–2011
Third-level code of the ATC classification20 Therapeutic class
A10C, A10H, A10J, A10K, A10L, A10M, A10N,
A10S and A10X
Antidiabetes products
A02B Antiulcer products
B01C Platelet aggregation inhibitors
C10A, C10C and C11A Lipid regulators
C09A and C09B ACE inhibitors, either as single agents or in
combination with other antihypertensives
M01A and M02A Antirheumatics
N02A Non-narcotic analgesics
N06A Antidepressants
R01A6, R01B and R06A Antiallergy drugs: systemic and nasal
preparations and topical products
R03A, R03B, R03C, R03D, R03E, R03F, R03G,
R03H, R03I, R03J and R03X
Respiratory agents
ACE: angiotensin-converting enzyme; ATC: Anatomical Therapeutic Chemical.
a Together the products in these classes accounted for at least 50% of sales by volume in each country
under investigation.
b Austria, Estonia, Finland, Greece, Ireland, Portugal, Slovakia and Spain.
Table 6. Policy measures influencing pharmaceutical sales in eight European countries, 2008–2011
Policy measure No. of measures implemented between 2008 and 2011a Total
Economically stable countriesb Economically less stable countriesb
Austria Estonia Finland Greece Ireland Portugal Slovakia Spain
Pricing
Price cuts 0 0 0 2 2 3 0 4 11
External price referencing 0 0 0 3 0 2 2 1 8
Distribution remuneration 0 1 0 3 3 3 0 3 13
VAT on medicines 1 1 0 1 1 1 0 1 6
Extraordinary price review 0 0 0 2 2 1 1 1 7
Reimbursement
Internal reference pricing 0 1 1 1 0 2 2 1 8
Out-of-pocket payments 4 1 0 0 1 5 3 2 16
Delisting 0 0 1 2 0 1 0 1 5
Generics
INN prescribing 0 1 0 0 0 1 1 1 4
Generic substitution 0 0 0 0 0 0 0 0 0
Public campaigns and other
generic policies
1 2 0 0 1 3 1 2 10
Total 6 7 2 14 10 22 10 17 88
INN: international nonproprietary name; VAT: value-added tax.
a The number of measures implemented in each year was: 4 in 2008; 11 in 2009; 33 in 2010; and 40 in 2011.
b The three economically stable countries implemented 15 measures during 2008–2011 compared with 73 in the five economically less stable countries.
Bull World Health Organ 2014;92:630–640D| doi: http://dx.doi.org/10.2471/BLT.13.129114634
Research
Economic recession and pharmaceutical policies
2008 and −9.0% from 2008 to 2009. The
growth rate declined in all economically
less stable countries but more gradually.
After the steep year-on-year decline in
Estonia in 2009, the volume of sales grew
17.1% from 2009 to 2010. In contrast,
the volume continued to decline in
economically less stable countries: for
example, from 2009 to 2010, there was
a decline of −4.1% in Greece and −0.5%
in Portugal. From 2010 to 2011, two of
the less economically stable countries
experienced a high growth in sales vol-
ume (5.5% in Spain and 7.8% in Ireland),
while the growth rate was between 1.0%
and 3.1% in most other less economi-
cally stable countries. The exception was
Portugal, which experienced a decline
of −3.7%.
The average annu a l p er c apit a
growth in the value of sales between
2006 and 2011 varied between −2.1%
in Portugal and 6.0% in Estonia. Af-
ter 2009, all countries except Austria
experienced a decrease in the value
of sales in at least one year. The larg-
est annual declines were observed in
Greece (−13.5% from 2009 to 2010) and
Portugal (−11.1% from 2010 to 2011).
Moreover, the value of sales declined
from 2010 to 2011 in all economically
less stable countries.
Fig. 5 depicts the difference be-
tween the annual growth rate in the
value of pharmaceutical sales and the
annual growth rate in the volume of sales
in each country between 2006 and 2011.
In general, between 2006 and 2008, the
annual value of pharmaceutical sales
increased more than the annual volume
of sales in both economically stable and
less stable countries, which indicates
that the average price per unit increased.
From 2009 onwards, during the period
when most policy changes were imple-
mented, the growth in the annual value
of sales was less than the growth in the
annual volume, which indicates a de-
crease in average price per unit.
Discussion
Although countries adjust their phar-
maceutical policy frameworks continu-
ously, a surge of policy changes seems to
have taken place during the economic
recession, particularly in 2010 and 2011.
Unexpectedly, both economically stable
and economically less stable countries
Fig. 1. Volume of pharmaceutical sales, quarterly, in three economically stable
European countries, 2006–2011
Vo
lu
m
e
of
sa
le
s
(s
ta
nd
ar
d
un
its
p
er
ca
pi
ta
)a
90
80
70
60
50
40
30
20
10
0
Year
Austria EstoniaFinland
2006 2007 2008 2009 2010 2011
a The volume of pharmaceutical sales is for the 10 highest-selling therapeutic classes in each country.
Fig. 2. Volume of pharmaceutical sales, quarterly, in five economically less stable
European countries, 2006–2011
Vo
lu
m
e
of
sa
le
s
(s
ta
nd
ar
d
un
its
p
er
ca
pi
ta
)a
120
100
80
60
40
20
0
Year
Greece SlovakiaPortugalIrelandSpain
2006 2007 2008 2009 2010 2011
a The volume of pharmaceutical sales is for the 10 highest-selling therapeutic classes in each country.
Fig. 3. Value of pharmaceutical sales, quarterly, in three economically stable European
countries, 2006–2011
Va
lu
e
of
sa
le
s
(c
on
st
an
t U
S$
p
er
ca
pi
ta
)a
30
25
20
15
10
5
0
Year
Austria EstoniaFinland
2006 2007 2008 2009 2010 2011
US$: United States dollars.
a The value of pharmaceutical sales is for the 10 highest-selling therapeutic classes in each country.
Bull World Health Organ 2014;92:630–640D| doi: http://dx.doi.org/10.2471/BLT.13.129114 635
Research
Economic recession and pharmaceutical policies
experienced a slight increase in the
consumption of pharmaceuticals in the
10 highest-selling therapeutic classes,
as measured in standard units per
capita. As expected, the annual growth
in the per capita value of medicine sales
decreased in economically less stable
countries in 2010 and 2011.
Our study shows that economi-
cally stable countries implemented fewer
policy measures between 2008 and 2011
than economically less stable countries.
The most f re quent ly i mplemente d
policy changes targeted out-of-pocket
payments for patients. Previous studies
have shown that increases in copay-
ments, such as prescription fees, tend
to lead to lower medicine utilization,
especially in times of economic reces-
sion and increased unemployment.23–30
Policy measures such as the medicine
price cuts (also applied in the form of
discounts) that were implemented in
Greece, Portugal and Spain could have
had a negative effect on the availability of
medicines if they caused pharmaceutical
companies to withdraw their products
from national reimbursement lists.31
Contrary to our expectations, however,
we did not observe a major decline in
the consumption of pharmaceuticals
during the recession in the therapeutic
categories studied as most countries
continued to experience a moderate
positive annual growth in sales volume.
However, in line with media reports of
drug shortages in Greece and Portugal,
our data showed that the sales volumes
of important medicines for chronic
diseases, such as angiotensin-converting
enzyme inhibitors and antidepressants,
dropped drastically in these two coun-
tries in 2010.31 Hence, although the over-
all growth in sales volume was positive,
the rate of growth appears to have fallen
to below the prerecession level, which
ranged from 5% to 12%.
In contrast, the rate of growth in
the value of pharmaceutical sales de-
clined, especially in economically less
stable countries. This decrease may have
been due partly to inflation: the average
inflation rate in 2010 and 2011 gener-
ally ranged between 2.0% and 3.4%,
although it was as low as −1.6% in 2010
in Greece and as high as 5.1% in 2011
in Estonia.32 Our analysis did not take
inflation into account. The decrease
may also have occurred because the
Fig. 4. Value of pharmaceutical sales, quarterly, in five economically less stable
European countries, 2006–2011
Va
lu
e
of
sa
le
s
(c
on
st
an
t U
S$
p
er
ca
pi
ta
)a
50
45
40
35
30
25
20
15
10
5
0
Year
2006 2007 2008 2009 2010 2011
Greece SlovakiaPortugalIreland Spain
US$: United States dollars.
a The value of pharmaceutical sales is for the 10 highest-selling therapeutic classes in each country.
Table 7. Per capita growth in pharmaceutical sales for the 10 highest-selling
therapeutic classes in eight European countries, by volume and value,
2006–2011
Country Per capita annual sales growth (%)
2006–
2007
2007–
2008
2008–
2009
2009–
2010
2010–
2011
Average for
2006–2011
Volume of salesa
Economically stable
countries
Austria 4.6 4.0 2.7 1.5 1.1 2.8
Estonia 12.2 −0.5 −9.0 17.1 3.1 4.6
Finland 3.7 3.8 2.3 3.4 1.0 2.8
Economically less
stable countries
Greece 5.6 0.3 0.7 −4.1 1.5 0.8
Ireland 4.1 1.4 4.3 0.8 7.8 3.7
Portugal 6.1 1.8 1.1 −0.5 −3.7 1.0
Slovakia 6.1 7.1 1.7 4.1 1.0 4.0
Spain 6.4 0.2 1.5 0.7 5.5 2.9
Value of salesb
Economically stable
countries
Austria 7.3 6.3 2.2 0.4 1.5 3.5
Estonia 20.5 5.2 0.3 7.0 −3.2 6.0
Finland 3.1 6.3 −2.2 −2.6 0.7 1.1
Economically less
stable countries
Greece 13.3 7.0 6.8 −13.5 −2.4 2.2
Ireland 7.6 7.2 3.6 −1.9 …
RESEARCH ARTICLE Open Access
Government spending, recession, and
suicide: evidence from Japan
Tetsuya Matsubayashi1* , Kozue Sekijima2 and Michiko Ueda3
Abstract
Backgrounds: Austerity has been shown to have an adverse influence on people’s mental health and suicide rates.
Most existing studies have focused on the governments’ reactions to a single event, for example, the Great
Recession of 2008.
Methods: This study focused on significant changes in fiscal policy between 2001 and 2014 in Japan. The size of
expenditures by national and local governments decreased dramatically between 2001 and 2006 under the
neoliberal reform and then increased after the global economic crisis and the Great East Japan Earthquake. Using
the data from 47 prefectures between 2001 and 2014, we tested whether more spending by the local governments
was associated with a lower suicide rate in their jurisdiction. We also investigated whether this relationship was
particularly salient during a more severe recession.
Results: Our analysis revealed that an increase of 1% in the per capita local government expenditures was
associated with a decrease of 0.2% in the suicide rates among males and females aged between 40 and 64 and
that this correlation was strengthened as the unemployment rate increased, particularly among males.
Conclusions: Government’s reaction to economic crises can either exacerbate or mitigate the negative impact of
the economic recession on people’s mental health and suicide rates.
Keywords: Suicide, Government spending, Recession, Austerity, Japan
Background
The relationship between an economic downturn and
people’s health has been extensively studied in a variety
of disciplines. This topic has drawn increasing attention
from scholars after the global economic crisis in 2008
[1–3]. Evidence on whether economic downturn im-
proves or worsens people’s health is mixed, depending
on the measures of health, demographic groups, levels of
economic development, and the degree of downturns
[1–4]. The results of previous studies are more consist-
ent when we focus on mental health and suicidal risks as
a measure of health: mental health worsens, and suicidal
risks increase during the recession [5].
The adverse influence of the economic downturn on
mental health and suicidal risks could be exacerbated or
mitigated by government actions. This possibility be-
came particularly evident in the aftermath of the 2008
Great Recession when many nations adopted fiscal auster-
ity as a political reaction to the massive economic crisis
[6–8]. Austerity in the period of the economic downturn
can worsen people’s mental health in two major ways: by
increasing economic insecurity among vulnerable individ-
uals and by reducing healthcare services [9, 10]. Indeed,
suicide rates increased after the Great Recession in coun-
tries where the austerity measures had been taken, includ-
ing Greece, Ireland, Portugal, and Spain [11–17].
At the same time, the government can mitigate the
effect of adverse economic shock by taking proper
actions. For example, the amount of New Deal relief
spending allocated to the US cities after the Great De-
pression between 1929 and 1940 was negatively corre-
lated with the suicide rates of the area [18]. Similarly,
the negative effect of the recessions on suicide rates was
shown to be weakened in countries with relatively larger
social welfare spending [19–21], though others reported
no such relationship [22].
© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
* Correspondence: [email protected]
1Osaka School of International Public Policy, Osaka University, 1-31
Machikaneyama, Toyonaka, Osaka 560-0043, Japan
Full list of author information is available at the end of the article
Matsubayashi et al. BMC Public Health (2020) 20:243
https://doi.org/10.1186/s12889-020-8264-1
http://crossmark.crossref.org/dialog/?doi=10.1186/s12889-020-8264-1&domain=pdf
http://orcid.org/0000-0001-7922-9630
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/publicdomain/zero/1.0/
mailto:[email protected]
This study offers additional evidence on the role of the
government’s actions to prevent suicide in the period of
recession using longitudinal data from Japan. The study
period is from 2001 to 2014, during which Japan experi-
enced both the reduction and expansion of government
expenditures under different administrations and polit-
ical climates. More precisely, government expenditures
significantly decreased under the neoliberal reform be-
tween 2001 and 2006 and then increased after the global
economic crisis in 2008 and the Great East Japan Earth-
quake in 2011. In response to a severe recession that
Japan experienced after the Asian financial crisis in the
late 1990s, Junichiro Koizumi, who became the prime
minister of Japan in 2001, adopted several major neo-
liberal reforms that downplayed the economic role of
the government. Koizumi’s administration downsized
the amount of government expenditures by up to 10%
between 2001 and 2006, as compared to 2000. Such aus-
terity measures, however, did not continue after Koizumi
stepped down in 2006. The several administrations after
Koizumi increased the amount of government expendi-
tures mainly to stimulate economic activities after the
Great Recession in 2008. In particular, Abe’s second ad-
ministration (2012-) initiated various aggressive economic
measures to recover from the long recession, including in-
terventions that increased government spending on public
infrastructure. In addition, the amount of public spending
also expanded after the Great East Japan Earthquake in
2011 to mitigate its impact and to accelerate recovery
from the disaster.
Thus, Japan has experienced both austerity and expansion
as government policies over the last two decades. These pol-
icy changes at the national level also fundamentally affected
the financial situation of subnational governments that
relied heavily on fiscal transfers from the national
government as a source of revenue. Prefectures and
municipalities in Japan are administrative units that
have independent sources of revenues, but about 30%
of their total expenditures rely on a transfer from the
national government. Thus, the amount of spending by
local government crucially depends on the economic
policies of the national government.
In particular, when Koizumi’s administration lowered
the number of transfers from the central government to
subnational governments as part of his neoliberal reform,
the volume of spending by subnational government
declined, as shown in Fig. 1. The solid line in Fig. 1 depicts
the change in the total amount of expenditure by the
national government between 2001 and 2014 using the
amount in 2000 as a baseline [23]. The dashed line in
Fig. 1 depicts the total amount of spending by local
governments [24]. The figure indicates that local gov-
ernment expenditures were highly correlated with national
government expenditures and that local government
expenditures decreased until 2007 and then increased.
Notably, Fig. 1 also shows that the overall crude sui-
cide rate shown as a solid gray line declined rapidly
just after the amount of national and local govern-
ment spending increased.
This study used these changes in the amount of local
government spending associated with the policy
changes at the national level in Japan to understand
how the level of government expenditures affects sui-
cide rates. Using data from 47 prefectures between
2001 and 2014, we tested two hypotheses: (1) Higher
spending by the local governments was correlated with
the lower suicide rates in their jurisdictions, and (2) the
negative relationship between local government spend-
ing and the suicide rates was particularly strong during
a more severe recession.
Fig. 1 The change in the expenditures of the national and local governments and the suicide rate per 100,000 in Japan between 2001 and 2014,
as the year of 2000 as a baseline (=100). Note: Data on the amount of expenditures were adjusted for inflation
Matsubayashi et al. BMC Public Health (2020) 20:243 Page 2 of 8
Our study improves upon previous studies on the role
of government expenditures on suicide by focusing on
different time horizons and types and levels of policy-
making [25]. First, the evidence presented here does not
concern only the government reaction to a single event,
such as the studies focusing on the Great Recession of
2008. We examined the impact of various government
actions over a period of 14 years. Second, this is the first
study on the association between government spending
and suicide in a non-European country, whereas the
existing literature focused primarily on European coun-
tries and the United States. Third, our study is not a
cross-country analysis, which often faces a challenge to
isolate the effect of government spending from other
national-level policy changes. Because our study used
subnational variations over time, we were able to use
unit-specific and year-specific fixed effects and thus con-
trol for unit-specific time-invariant and country-specific
characteristics.
Methods
We created panel data for 47 prefectures between 2001
and 2014. The number of observations was 658. We lim-
ited our data coverage until 2014 because of data availabil-
ity. Using the panel data, we tested the first hypothesis
that the larger amount of spending by subnational govern-
ments was correlated with the lower suicide rates in their
jurisdictions by estimating the following model:
½S� jt ¼ α½E� jt þ β½U� jt þ λw jt þ μ jT þ φt þ ρ j þ ε jt
ð1Þ
where the outcome variable [S]jt is a natural log of the
suicide rate per 100,000 individuals in the year t in pre-
fecture j. Suicide includes all deaths classified as X60-
X84 under ICD-10. Considering the possibility that the
effects of government expenditures varied by age and
sex, we generated the suicide rates for six subpopulation
groups: (1) males aged 20–39, (2) males aged 40–64, (3)
males aged 65 and over, (4) females aged 20–39, (5) fe-
males aged 40–64, and (6) females aged 65 and over.
The suicide data were calculated by using data from the
Vital Statistics [26].
Our primary explanatory variable is [E]jt, which de-
notes the per capita government expenditures in prefec-
ture j in the year t. We used the sum of expenditures
of the prefectural government in j and all municipal
governments in j from the Annual Report of Local Pub-
lic Finance [24]. Japan is divided into 47 prefectures,
and the number of municipalities in each prefecture
ranges from 15 to 179. Both prefectural and municipal
governments can use their expenditures on social wel-
fare, public health, employment-related issues, public
works, education, and disaster relief. We used the total
amount of spending, rather than spending specifically
for social welfare and public health because other types
of local government spending can affect people’s well-
being. For example, spending on infrastructure would
produce job opportunities for the unemployed and may
improve their economic and mental well-being. We
transformed the per capita amount, adjusted for infla-
tion, into a natural log for estimation.
[U]jt in eq. (1) refers to the percentage of unemployed
people in prefecture j in year t. Building on recent re-
search on the same topic [21, 22, 27, 28], we used the
unemployment rate as a measure of recession. We used
the unemployment rate for the total population, though
this data is limited in that it is not age-specific. The data
were obtained from Statistics Japan [29].
Further, wjt refers to the socioeconomic characteristics
of each prefecture in each year, all of which were likely
to affect both the suicide rate and the government’s ex-
penditures. Specifically, included in wjt are income per
capita, fiscal strength index, population size, and per-
centages of the dependent population aged under 14 and
65 and over, in each prefecture and year. Income per
capita, obtained from the System of National Accounts,
was defined as the total amount of income in prefecture
j in the year t divided by the population size [30]. The fi-
nancial strength index measures the fiscal conditions of
each prefecture each year. The index exceeds 1 if the
amount of revenue coming from the prefecture’s finan-
cial sources exceeds the amount of fiscal demand and
falls below 1 otherwise. This index is used to determine
the amount of money transferred from the national to
the local government. Because there were considerable
year-to-year fluctuations, the values were averaged over
the past 3 years. The data were obtained from the An-
nual Report of Local Public Finance [24]. The population
size and the percentages of the dependent population
were obtained from the Annual Resident Registers [31].
We used natural logs of the total population and income
per capita in our regression analysis.
Finally, φt in eq. (1) represents the year fixed effects,
while ρi represents the prefecture fixed effects unique to
each prefecture. The year fixed effects allowed us to con-
trol for the effects of annual socioeconomic and political
changes at the national level, such as the effects of
macroeconomic policies and business cycle that might
affect the entire country. It also controls for the effects
of natural disasters such as the Great East Japan Earth-
quake in 2011. The prefecture fixed effects allowed us to
control for the effects of time-invariant characteristics of
the prefectures, such as the effects of culture related to
suicide, and climate and geographic conditions. The in-
clusion of the prefecture fixed effects in eq. (1) means
that the model used variations in the level of
Matsubayashi et al. BMC Public Health (2020) 20:243 Page 3 of 8
government expenditures over time within each local
government. We also added the prefecture-specific lin-
ear time trends, μjT, to the model to control for the ef-
fects of linear trends in the suicide rates unique to each
prefecture.
To test the second hypothesis that the negative rela-
tionship between local government spending and the
suicide rates was particularly robust during a more se-
vere recession, we included the interaction term between
[E]jt and [U]jt in the model as:
½S� jt ¼ α½E� jt þ β½U� jt þ γ½E� jt�½U� jt þ λw jt
þ μ jT þ φt þ ρ j þ ε jt ð2Þ
Because the marginal effect of local government ex-
penditures was hypothesized to change as the unemploy-
ment rate increased, we calculated and plotted the
marginal effect of [E]jt and its confidence interval at the
different values of the unemployment rate.
Results
The summary statistics of all variables used in our esti-
mation are presented in Table 1. The suicide rates were
higher among males than females. Males aged 40–64
showed the highest rate among all subgroups.
Table 2 reports the estimation results. Columns (1) to
(3) report the results using the male suicide rates by three
age groups, while columns (4) to (6) report the results
using the female suicide rates. The prefecture-specific and
year-specific fixed effects and the prefecture-specific linear
time trend were always included in the models, but the
Table 1 Summary Statistics
Mean SD Min Max
Log of suicide rate: male 20–39 3.419 0.205 2.674 4.056
Log of suicide rate: male 40–64 3.926 0.257 3.287 4.684
Log of suicide rate: male 65- 3.811 0.214 3.132 4.453
Log of suicide rate: female 20–39 2.399 0.287 1.105 3.070
Log of suicide rate: female 40–64 2.638 0.213 1.733 3.183
Log of suicide rate: female 65- 3.000 0.296 1.749 3.926
Log of government expenditures
per capita
13.658 0.212 13.102 14.393
Unemployment rate 4.257 1.029 2.100 8.400
Log of income per capita 14.848 0.153 14.506 15.541
Fiscal strength index 0.457 0.196 0.197 1.406
Log of population size 14.495 0.742 13.283 16.396
Percent under 14 13.848 1.111 10.786 19.879
Percent over 65 22.745 3.357 13.150 31.141
N of observations 658
Note: Data covered the period from 2001 to 2014 in 47 prefectures of Japan
Table 2 Estimated influences of government expenditures on suicide rates by sex and age
(1) Male 20–39 (2) Male 40–64 (3) Male 65- (4) Female 20–39 (5) Female 40–64 (6) Female 65-
Log of government
expenditures per capita
−0.017 − 0.246 0.069 0.188 −0.207 − 0.043
(−0.183, 0.150) (− 0.428, − 0.063) (− 0.080, 0.219) (− 0.292, 0.669) (− 0.383, − 0.032) (− 0.322, 0.237)
Unemployment rate 0.014 0.023 0.062 −0.057 − 0.007 − 0.008
(− 0.039, 0.067) (− 0.017, 0.063) (0.020, 0.105) (− 0.110, − 0.004) (− 0.078, 0.064) (− 0.080, 0.064)
Log of income per capita −0.266 − 0.173 −0.109 − 0.183 − 0.236 0.604
(−0.988, 0.457) (− 0.673, 0.327) (− 0.630, 0.413) (− 1.245, 0.879) (− 1.143, 0.671) (− 0.068, 1.276)
Fiscal strength index − 0.026 0.038 0.107 − 0.518 − 0.055 − 0.397
(− 0.323, 0.271) (− 0.298, 0.375) (− 0.146, 0.359) (−1.245, 0.208) (− 0.494, 0.383) (− 1.078, 0.283)
Log of population size 1.396 3.446 1.881 10.457 0.976 2.042
(−3.959, 6.750) (−0.267, 7.160) (−2.597, 6.360) (−0.311, 21.225) (−5.407, 7.359) (−2.602, 6.686)
Percent under 14 0.149 −0.045 −0.095 0.170 −0.005 − 0.233
(−0.061, 0.360) (− 0.185, 0.095) (− 0.289, 0.100) (−0.160, 0.500) (− 0.244, 0.233) (− 0.436, − 0.029)
Percent over 65 0.154 0.044 0.017 0.049 −0.023 0.046
(−0.012, 0.319) (−0.085, 0.174) (− 0.106, 0.139) (− 0.176, 0.275) (−0.215, 0.170) (− 0.096, 0.189)
Prefecture fixed effect Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
Prefecture-specific liner trend Yes Yes Yes Yes Yes Yes
N of observations 658 658 658 658 658 658
R squared 0.342 0.825 0.405 0.262 0.269 0.448
Note: Table entries are regression coefficients with 95% confidence intervals in parentheses. Standard errors are clustered by prefectures. The dependent variable
is a log of the suicide rate per 100,000 by sex and age. Data covered the period from 2001 to 2014 in 47 prefectures of Japan
Matsubayashi et al. BMC Public Health (2020) 20:243 Page 4 of 8
estimates are not reported in the table. In order to address
the potential heterogeneity and autocorrelation in the
error terms within each prefecture, standard errors were
clustered by prefectures.
Table 2 shows that the log of government expenditures
per capita was negatively correlated with the log of suicide
rates by males and females aged 40–64. More specifically,
as the per capita expenditures decreased by 1%, the suicide
rate among males aged 40–64 increased by 0.25% (95%
CI: − 0.43, − 0.06) and the suicide rate among females
aged 40–64 increased by 0.21% (95% CI: − 0.38, − 0.03).
We found that the estimates were small or the confidence
intervals were large in the other columns, suggesting that
the local government’s expenditures had little relationship
with the suicide rates by other sex and age subgroups.
Because the local government spending was shown to
have a strong relationship only with the suicide rates of
middle-aged males and females in Table 2, we focused
on these two subgroups and estimated eq. (2). Using the
estimated results reported in Table 3, we plotted the
marginal effect of local government expenditures in a
solid line and its confidence interval in a dashed line at
the different values of the unemployment rate in Fig. 2.
The vertical solid line in Fig. 2 denotes the average
amount of government expenditures. The top panel
shows that the negative relationship between local gov-
ernment expenditures and the log of suicide rate by
males aged 40–64 was particularly relevant when the un-
employment rate was modestly to extremely high. The
confidence intervals overlap the horizontal line of zero
when the unemployment was relatively low, suggesting
that local government spending had a negligible relation-
ship with the middle-aged male suicide rates when eco-
nomic conditions are good. The bottom panel also
indicates that the marginal effect of local government
expenditures on the suicide rate of middle-aged females
became larger as the unemployment rate was higher, but
the change in the marginal effect over the scale of the
unemployment rate was small.
Discussions
This study investigated the relationship between govern-
ment expenditures and suicide rates by using the varia-
tions in the amount of local government spending
associated with the national-level economic policy
change. Our estimation models controlled for the effects
of other relevant factors, such as the unemployment rate
and per-capita income, as well as the prefecture- and
year-specific factors and time trends. We found that the
suicide rates of middle-aged males and females tended
to increase when prefectural governments decreased
their spending level. This negative association was stron-
ger among males when the unemployment rate in-
creased. These findings suggest that local suicide rates
could be reduced when local governments increase their
spending. The magnitude of the effect is not trivial. Dur-
ing our study period, the average male suicide rate for
the middle-aged was 31.19, and a one-percent increase
in government spending would translate to a reduction
of the suicide rate per 100,000 by 0.078 cases, which
translates to 0.36 cases of suicide in each prefecture and
17 suicides across the whole of Japan per year.
However, we also found that the amount of spending by
subnational governments had little relationship with the
suicide rates of the younger generation and those of the
elderly population. The null-finding for the elderly group
is not surprising, as they are less likely to be working and,
hence, less likely to benefit from any increased economic
activities associated with an expansion in government
spending. However, they are also more likely to be benefi-
ciaries of welfare-related spending, and thus the finding is
somewhat counterintuitive at the same time. As for the
younger generation, it is possible that their suicide rates
are determined by an entirely different set of factors. Ac-
cording to the data compiled by the National Police
Agency, among those whose motives and reasons behind
Table 3 Estimated relationships between government expenditures
and suicide rates conditional on the unemployment rate
(1) Male 40–64 (2) Female 40–64
Log of government
expenditures per capita
0.173 − 0.026
(− 0.343, 0.688) (− 0.458, 0.406)
Expenditures × unemployment − 0.090 − 0.039
(− 0.182, 0.002) (− 0.126, 0.047)
Unemployment rate 1.256 0.528
(−0.003, 2.515) (−0.648, 1.703)
Log of income per capita −0.153 − 0.228
(−0.600, 0.295) (−1.129, 0.674)
Fiscal strength index −0.014 −0.078
(−0.301, 0.272) (−0.511, 0.354)
Log of population size 3.823 1.140
(0.190, 7.457) (−5.393, 7.672)
Percent under 14 −0.036 − 0.002
(− 0.178, 0.105) (− 0.243, 0.240)
Percent over 65 0.029 −0.029
(−0.100, 0.158) (−0.222, 0.163)
Prefecture fixed effect Yes Yes
Year fixed effect Yes Yes
Prefecture-specific liner trend Yes Yes
N of observations 658 658
R squared 0.827 0.269
Note: Table entries are regression coefficients with 95% confidence intervals in
parentheses. Standard errors are clustered by prefectures. The dependent
variable is a log of the suicide rate per 100,000 by men and women aged 40
to 64. Data covered the period from 2001 to 2014 in 47 prefectures of Japan
Matsubayashi et al. BMC Public Health (2020) 20:243 Page 5 of 8
their suicide are known, 3 and 14% of suicide deaths by
those aged less than 20 and aged 20–29 are related to eco-
nomic hardship, respectively, whereas 19 and 22% of
deaths by those aged 30–39 and 40–49 are attributable to
economic conditions, respectively [32]. Among the elderly
population (ages 70 and higher), a majority of their sui-
cides is due to health-related reasons, and only 6% of their
suicides are due to economic factors.
We conducted robustness checks to ensure that our re-
sults are not sensitive to the specific model and data that
were chosen. The above results were based on the total
amount of government spending of both prefectural and
municipal governments combined. We evaluated the same
models with the total amount of government expenditures
only by prefecture governments. The main results did not
change. In order to check if the expenditures in the
previous year matter for the well-being of the population,
we also took a one-year lag of local government’s expendi-
tures and added the lagged value as an additional regres-
sor. We found that the amount of government spending
in the previous year does not affect the suicide rate; the es-
timated results suggest that only the government expendi-
tures in the current year affect the suicide rate for that
year. Both results are available upon request.
The present study contributes to the existing literature
by providing further evidence on the importance of gov-
ernment actions on suicide rates. Depending on how the
government reacts to economic shocks, it can either ex-
acerbate or mitigate the negative impact of the economic
recession on the population. The findings of this study
are mostly consistent with those of other studies that
showed that austerity measures during an economic
Fig. 2 The marginal effect of government spending conditional on the unemployment rate in 47 prefectures of Japan. Note: These graphs are
based on the estimation results in Table 3
Matsubayashi et al. BMC Public Health (2020) 20:243 Page 6 of 8
crisis could have a detrimental effect on people’s mental
health and health in general.
The major strength of this study lies in the fact that we
examined the effect of different government reactions to
multiple economic crises over an extended period within
a single country. Most previous studies have examined the
impact on people’s health of government reactions to a
single major financial crisis, such as the 2008 Great Reces-
sion, by using a cross-national comparison. Our analysis
of the sub-national data in Japan allowed us to isolate the
effect of government spending from other major national-
level policy changes, which thus provided more robust evi-
dence for the relationship between austerity and suicide.
Moreover, to our knowledge, this is the first study on the
association between government spending and suicide in
a non-European country.
This study has several limitations. First, our study can-
not provide answers as to why increased government
spending can decrease the number of suicides as our study
was ecological in nature. Second, our analysis did not
explicitly consider the potential effect of various suicide
prevention measures on suicide rates. The Japanese
government introduced its first national suicide preven-
tion program in the mid-2000s, and the suicide rates have
decreased since around 2012. The prefecture fixed effects
and the prefecture linear trends that we included in the
model should capture most of the effects of suicide pre-
vention measures. However, in order to check this possi-
bility more explicitly, we also added the per capita amount
of transfers from the national government earmarked for
suicide prevention activities to model (1) to control for
the effect of local suicide prevention activities. We found
that our main results did not change by this modification
and also that the amount spent on suicide prevention ac-
tivities does not seem to affect suicide rates in a meaning-
ful way. The results of this supplementary analysis are
reported in Table 4.
Conclusion
In conclusion, our findings suggest that government actions
can significantly affect the health of the general public, even
if the actions are not directly related to spending for public
health and social security. Decreasing the level of overall
government spending can be detrimental to people’s health.
Table 4 Estimated influences of total government expenditures and expenditures for suicide prevention programs on suicide rates
by sex and age between 2001 and 2014 in 47 prefectures of Japan
(1) Male 20–39 (2) Male 40–64 (3) Male 65- (4) Female 20–39 (5) Female 40–64 (6) Female 65-
Log of government expenditures
per …
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