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The Journal of Socio-Economics 40 (2011) 723–731

Contents lists available at SciVerse ScienceDirect

The Journal of Socio-Economics

journa l homepage: www.e lsev ier .com/ locate /soceco

ocio-economic determinants of suicide in Japan

ntonio R. Andrésa,b,∗, Ferda Haliciogluc, Eiji Yamamurad

Aarhus University, Institute of Public Health, Bartholins Allé 1, 8000 Aarhus C, DenmarkAssociate researcher, Institute of Economic Analysis & Prospective Studies (IEAPS), Al Akhawayn University, Ifrane, MoroccoDepartment of Economics, Yeditepe University, 34755 Istanbul, TurkeySeinan Gakuin University, Department of Economics. Fukuokashi Sawaraku Nishijin 6-2-92, 814-8511, Japan

r t i c l e i n f o

rticle history:eceived 12 April 2011eceived in revised form 12 August 2011ccepted 30 August 2011

EL classification:22

a b s t r a c t

Japan has the highest suicide rates among the OECD countries and this public health problem seemsto be accelerating in over the recent decades. Investigating and understanding the suicidal behaviouris of crucial importance to society and health policy makers. Such an investigation could provide withuseful information for those responsible in formulating the national policies on suicide prevention. Thisstudy estimates dynamic econometric models for total, male and female suicides in Japan for the periodof 1957–2009. Using the ARDL approach to cointegration, we find that the associations of suicide with

12

eywords:ointegration

sociological factors (divorce and fertility rates) were stronger than those with economic factors (percapita GDP and unemployment) for females.

© 2011 Elsevier Inc. All rights reserved.

uicideime seriesapan

. Introduction

Suicide is a very serious public health problem. The Worldealth organization (henceforth, WHO) estimates that worldwide

here are approximately one million of deaths from suicide eachear and 20 times this number of people have attempted suicide.ccording to many medical professions, suicide is considered toe the result of depression and other psychiatric disorders (Mannt al., 2005). Although Japanese life span is the longest in the world,t has nevertheless one the world highest suicide rates with nearly3,000 people killing themselves in 2009. According to statisticalata from the WHO, Japan, in 2004, reports the highest suicide rateith 24 per 100,000 people among the OECD countries. From 1995

o 2009, the total suicide rate increased from 17 to 25 per 100,000eople.1 Suicide is also associated with substantial economic costswith particularly health care costs). In particular, Chen et al.2009a) suggested that the costs associated with suicides were

round 197 million USD in 2006 alone even if indirect costs such assychological counseling expenditure were not taken into account.

n comparison, there have been European studies highlighting

∗ Corresponding author.E-mail address: ara@folkesundhed.au.dk (A.R. Andrés).

1 Data source is as follows. Periods 1955–2004: Statistics Bureau, Ministry of Inter-al Affairs and Communications (2006). Historical Statistics of Japan Volume 1 (Newdition). Tokyo: Japan Statistical Association. Periods 2005–2009: National Policegency. http://www8.cao.go.jp/jisatsutaisaku/link/keisatsutyo.html (accessed6.06.10).

053-5357/$ – see front matter © 2011 Elsevier Inc. All rights reserved.oi:10.1016/j.socec.2011.08.002

the enormous costs of completed suicides. For instance in Ireland(Kennelly et al., 2005), the total cost has been shown to be 2.04 mil-lion Euros and in Scotland 1.88 million Euros (McDaid et al., 2007).In some Japanese media, the total costs of suicide and depressionwas reported to be about 2.7 trillion Yen in 2009 (available athttp://search.japantimes.co.jp/cgi-bin/nn20100908a2.html). Pre-vention of suicide has been integral part of the Japanese publichealth agenda. The Japanese Government aimed to reduce theannual incidence of suicide and for this purpose implemented“the Basic Act of Suicide Prevention (jisatsu taisaku kihon hou)”in 2006. In addition, to the role of government, informal socialties regarded as social capital is also thought to play an importantrole in preventing suicide in Japan (Yamamura, 2010). In fact,community based suicide prevention programs were introducedin Akita prefecture (see Motohashi et al., 2004). For making thepolicy effective, it is important to ascertain how and why suiciderate of Japan is so high based on empirical analysis.

Apart from the interest in describing and explaining suicidalbehaviour, employing rates of suicide as a societal well-being indi-cator has several advantages. First, suicide rates are a more reliableand objective indicator of well-being compared to self-reportedwell-being measures (such as life satisfaction or self-reported hap-piness). Second, suicide rates do not have the common problemsassociated with survey data on self-reported well-being. Self-

reported measures are often challenged on the basis of reliabilityand validity (see for an excellent discussion, see Bertrand andMullainathan, 2001). It has been also shown that there is a highcorrelation between suicide and subjective well-being at individual

7 f Socio-Economics 40 (2011) 723–731

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by divorce lead people to suffer from increase of distress in Japan.In Japan, over 60% of the individuals committing suicide were iden-tified as depressive (Nakao and Takeuchi, 2006).

24 A.R. Andrés et al. / The Journal o

nd aggregate level (for instance, Koivumaa-Honkanen et al., 2001).nlike as self-reported measures, suicide data is the kind of data

hat is more prone to make cross country comparisons. Using selfeported data comparisons are still difficult because of problemsith interpersonal comparisons of utility. Recently, an American

tudy concluded that the determinants of well-being are the sameeterminants of suicide (Daly and Wilson, 2009).

Despite of its importance, and a growing concern for the factorsriving suicide mortality, suicide in Japan has received little atten-ion. Although there have been some recent attempts, mainly usinganel data techniques, in this direction (e.g. Chen et al., 2009b;amamura, 2010). Traditional theories of suicide (Durkheim, 1951;amermesh and Soss, 1974) have been tested using time series data

or a large number of countries (e.g. Yang, 1992; Yang and Lester,990; Yang et al., 1992; Chuang and Huang, 1996; Platt and Hawton,000; Stack, 2000; Chang et al., 2010). Some researchers have inves-igated the socioeconomic determinants of suicide using time seriesata for Japan (Yamasaki et al., 2005, 2008). There are howeverew studies which employ causality or cointegration frameworko investigate the causality between suicide and its socioeconomiceterminants. A recent study is that of Inagaki (2010) who employsVector Autoregressive (VAR) model. But this methodology has

everal shortcomings. First, this methodology requires the set ofariables to be split into exogenous and endogenous variables. Sec-nd, the variables should be integrated of order 1.

This study aims at contributing to the empirical studies ofapanese suicide by applying a relatively new time series cointe-ration technique known as the Auto Regressive Distributed LagARDL) bounds testing procedure. The ARDL approach to cointegra-ion is preferable to other conventional cointegration proceduresEngle and Granger, 1987). One of the reasons for preferring theRDL approach to cointegration it is that overcome the problemf potential endogeneity of some regressors and serial correlation,hich might lead to biased estimates of the cointegrating coef-cients. Another reason is that this technique does not requirere-testing for the order of integration of the underlying time series.oreover, the results from this approach to cointegration are more

obust in presence of small samples (such as in this study) thann other cointegration techniques. Finally, as opposed to multivari-te cointegration techniques such as Johansen and Juselius (1990),t allows the cointegration relationship to be estimated by ordi-ary least squares (OLS) once the lag order of the model is chosen.

n addition to studying the total suicides, we also analysed malend female suicides separately, as the underlying determinants ofuicide could differ between the sexes (e.g. Andrés, 2005; Chuangnd Huang, 2007; Yamamura, 2010). Understanding the gender dif-erences might be also important in informing appropriate policyormulations. The remainder of this paper is organized as follows.he next section presents the socio-economic situation of Japanelating to the suicides. Section 3 describes our empirical model andethodological approach. Section 4 displays our empirical results

long with some discussions. Section 5 is the conclusion.

. Review of the socio-economic situation of Japan

Total life expectancy at birth of Japanese is 82 years old, whicheads the world in longevity (WHO, 2006; Nakao and Takeuchi,006). However, suicide rate is obviously higher than other OECDountries, which becomes the one of major problem in the modernapan society (Chen et al., 2009b). Japan’s suicide problem is veryifferent from those of other OECD countries because the impact of

he socioeconomic variables on suicide is greater in Japan than inther OECD countries (Chen et al., 2009b). To implement appropri-te suicide prevention policies, it is important to ascertain how andhy suicide rate of Japan is so high based on empirical analysis. In

Fig. 1. Changes of per capita GDP.

what follows, we begin with a simple description of the potentialsocio-economic factors affecting suicidal behaviour.

As shown in Fig. 1 illustrating changes of real per capita income,Japan has experienced the rapid economic growth in the post warperiod and became among the most developed countries. Japanesepeople enjoyed the rise in income and are thought to be satisfiedwith this life style change accompanied with economic growth.Concerning the growth rate of real per capita GDP, it drops con-stantly and to below zero several times after 1990s. We can see fromFig. 2 that the unemployment rate has been also low level until mid-1990s, however, exceeded 3% after mid-1990s. This seems to reflectthe depression period after 1992 when the prosperity of the “bub-ble economy” (from mid 1980 to the beginning of the 1990s) cameto an end in Japan. In this period, number of business bankruptciesalso steeply increased in this period because of macro level eco-nomic stagnation. In particular, it was difficult for owners of smalland medium size enterprise to run business. Economic recessionlead a lot of people to face the difficulty and suffer distress.

Transition of divorce rate in Fig. 3 shares similarity with unem-ployment rate in the point that after entering the recession perioddivorce rate remarkably increased. The increase in divorce rate canbe in part caused by the economic recession. Marriage leads coupleto be integrated into the new social network, which is expressedas “when you get married, you get married for the people aroundyou” (Brinton, 1993, p. 99). Hence, divorce seems to be more stig-matized in Japan than in the Western countries because of thegreater importance of extended family and kinship ties in marriage(Ono, 2006). That is, people who encounter the economic difficultymore likely to experience divorce and so lose the psychological sup-port from family and kinship ties. During the economic depressionperiod, not only economic difficulty but also social stigma caused

Fig. 2. Changes of unemployment rate (%).

A.R. Andrés et al. / The Journal of Socio

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Ftf

Fig. 3. Changes of divorce rate (%).

Fig. 4 shows that rates of suicide has obviously decreased fromid 1950s to 1970, and then slightly increased until mid-1990s

or males and females. For example, in Korea with the similarocio-cultural background, the remarkable increase of suicide raterom 1997 to 1998 under the economic recession period (Khangt al., 2005). However, in case of Korea, not only male but alsoemale suicide rate increased. It is surprising to observe that inhe end of 1990s suicide rate of male has drastically increasedhereas that of female was stable. According to Fig. 4, there wasmarked rise in female suicide rates from 1997 to 1998 although

ts magnitude was smaller than for male suicide rates. Indeed, inate 1997, Hokkaido Takushoku Bank (one of major commercialanks) and Yamaichi Securities (one of Major securities) becameankrupt. Further 1998, The Long Term Credit Bank of Japan andhe Nippon Credit Bank were nationalized, implying the old eco-omic regime’s failure (Cargill, 2006). Taken together, these results

mply that the problem of committing suicide became remarkablyerious, especially for males. The question arises “why is there dif-erence of suicide rate between male and female?”. An imbalancef increases in suicide appears to come from the different impactf various factors between males and females. As pointed out byakao and Takeuchi (2006), the most drastic increase has involvediddle-aged males partly because most middle-aged males may

e too busy to visit a clinic when they feel mental distress. There-ore, the case of Japan is suitable for examining how committinguicide depends upon gender and differences in the impact of socio-conomic factors.

. Literature review

.1. International experience

Sociologists have played an important role in providing theheory of suicide. Durkheim (1951) viewed the suicide as a soci-

ig. 4. Changes of rate of suicides. Note: Number of total suicides per total popula-ion (100,000), number of male suicides per male population (100,000), number ofemale suicides per female population (100,000).

-Economics 40 (2011) 723–731 725

ological phenomenon. He argues that suicide is related to bothsocial integration and social regulation. Economists claim that sui-cide involves rational economic decision making. Hamermesh andSoss (1974) were the first to provide an economic theory of suicide.According to their economic model an individual decides to commitsuicide when the discounted expected lifetime utility remaining tohim falls below some threshold level. This model also predicts thatsuicide rates would increase with age, unemployment and decreasewith income (Hamermesh and Soss, 1974). Recently, Suzuki (2008)incorporates the concept of income uncertainty within the modelof Hamermesh and Soss (1974). These approaches (sociological andeconomics) motivate many of the control variables included in avariety of econometric studies of macro level determinants of sui-cide.

According to the Hamermesh and Soss’s model, the higher futureexpected income is, the higher is the expected utility; thus, livingis relatively more attractive than committing suicide, and a higherincome should lower suicide rates. However, Durkheim postulatesthat higher income levels increase independence (the opposite ofsocial integration) and might lead to a higher suicide rate. Along thisline, Lester (1996) and Unnithan et al. (1994) state that economicdevelopment increases rates of suicide. Both the existing economicand sociological theories are inconsistent, and they do not permita determination of whether income or economic growth may havea positive or negative effect on suicide. Durkheim (1951) suggeststhat changes in income are more likely to be relevant for suicidethan the absolute level of income. The empirical evidence for theeffect of income on suicide is mixed, however. Though some empir-ical studies indicate that suicide rates have a positive associationwith income (e.g. Hamermesh, 1974; Jungeilges and Kirchgässner,2002; Viren, 1999), there are many others suggesting the oppositeeffect (e.g. Andrés, 2005; Brainerd, 2001; Neumayer, 2003; Chuangand Huang, 1997, 2007; Minoiu and Rodríguez, 2008; Altinanahtarand Halicioglu, 2009; Andrés and Halicioglu, 2010). Others havereported an insignificant effect of income on suicide (Ruhm, 2000;Cuellar and Markowitz, 2006). The significant negative correlationeffect seems to be stronger for men than for women Qin et al.(2003).

Another economic variable that has received a lot of attentionis the unemployment rate. Unemployment implies less economicopportunity, lowering an individual’s expected income and there-fore increasing the likelihood of a person’s committing suicide.The unemployment rate is often used as a proxy variable foreconomic hardships and lifetime earnings, because measuring anagent’s lifetime income is not easy in practice (Koo and Cox, 2008).But unemployment might be also associated with factors suchas depressive episodes, anxiety, and loss of self-confidence thatmight lead directly to suicide. Much of the empirical literaturereports a positive relationship, associating higher unemploymentwith higher suicide rates (for example, Brainerd, 2001; Ruhm, 2000;Chuang and Huang, 1997, 2007; Lin, 2006; Andrés, 2005; Koo andCox, 2008; Minoiu and Rodríguez, 2008). Furthermore, the impactof unemployment might also differ across gender. In particular,male suicide rates are significantly affected by unemployment, butfemale suicide rates are not (Chuang and Huang, 1997).

As mentioned above, Durkheim (1951) indicates that suicide isinfluenced by other factors. These factors relate to the way in whichindividuals are integrated into a social group that is regulated bynorms and conventions. This sociological approach predicts thatlower levels of social integration and regulation are associated withhigher societal suicide rates. From this perspective divorce and fer-tility rates can be viewed as indicators of social integration. Divorce

can be also a traumatic event for the individuals involved as wellas for other affected parties, and it might lead individuals towardisolation and reduced poor psychological well-being. Thus, higherdivorce rates might be expected to have a positive correlation with

7 f Socio

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26 A.R. Andrés et al. / The Journal o

uicide rates. Another explanation is that a divorced individual hasower utility than a married one because marriage has a merce-ary value (Becker, 1974). Koo and Cox (2008) also suggest thativorced people have less utility than married people and there-ore they are more likely to commit suicide. Several studies haveeported a positive association between divorce and suicide (e.g.ndrés, 2005; Chuang and Huang, 1997, 2007; Kunce and Anderson,002; Lester, 1996; Neumayer, 2003). Also, some papers show thathe male suicide rate is more sensitive to divorce than the femaleuicide rate (e.g. Koo and Cox, 2008; Andrés, 2005; Yamamura,010; Neumayer, 2003). Again, endogeneity concerns are relevantere, as divorce might be also related to mental health problems. Ithould be also noted that this variable might capture the influencef diverse societal problems. Durkheimian arguments of social inte-ration suggest that increased fertility rates should be associatedith lower levels of suicide, as the presence of children promotes

ocial and family ties. By increasing social integration, these factorsower the likelihood of a person’s committing suicide. Empiricalesearch has documented the existence of a protective effect of fer-ility against suicide (e.g. Andrés, 2005; Neumayer, 2003; Chuangnd Huang, 2007). However, some studies like Chen et al. (2009b)nd Lester (1995) show that the birth rate has either a positivempact or no impact on suicide rates. One possible explanation forhe latter result is that childcare may put excessive strain on a par-nt or be too much of an economic burden, thus leading to suicidalehaviour (Chen et al., 2009b). Endogeneity issues might be rel-vant here, as better functioning people are more likely to havehildren.

Lastly, the gender differences in suicide represent a double puz-le: Whilst rates of suicide are far higher among males, femalesave higher rates of non-fatal attempts. This suggests there may beifferent responses by males and females to the control variablessed in the formal analysis. In light of the gender differential in sui-idal behaviour (e.g. Minoiu and Rodríguez, 2008; Altinanahtar andalicioglu, 2009; Andrés, 2005; Yamamura, 2010), we run separateodels for males and females. Although the cause of these differ-

nces has not been sufficiently investigated (Yamamura, 2010).In sum, the formal literature provides ambiguous results on the

ays socioeconomic factors relate to male and female suicide rates.he existing literature has not come to a firm conclusion about theorrelates of suicide. This is due to different countries employed inhe empirical analysis, more points of the time, and the statisticalechniques employed (time series/cross-section analysis). Never-heless, of all the variables considered, the results corresponding toocial factors such as divorce and fertility seem to be more robusthan those related to economic factors such as unemployment andncome. Nonetheless, the socio-economic control variables used inhis paper appear to be among the relatively important determi-ants.

. Japanese experience

Although, the epidemiological literature has explored the riskactors of suicide in Japan (e.g. Yamasaki et al., 2008; Motohashit al., 2004), there are a few studies exploring the determinants ofuicide in Japan from an economic perspective (Watanabe et al.,006; Koo and Cox, 2008; Akechi et al., 2006; Chen et al., 2009b;amamura, 2010; Inagaki, 2010). Watanabe et al. (2006) usingrefecture level data find that unemployment rate and personalankruptcy are positively associated with suicide rates. Koo andox (2008) using time series data find that the relationship between

nemployment and suicide is significantly positive for males andemales. Akechi et al. (2006) shows that there is an inverted Uhape between alcohol consumption and suicide employing pre-ecture level data between 1953 and 1986. Chen et al. (2009b)

-Economics 40 (2011) 723–731

employing a panel data approach by using Japanese data andOECD data analyse to what extent suicide in Japan is differentfrom suicides in other countries. Inagaki (2010) using time seriesfocuses on the link between income inequality and suicide. Hefinds a positive relationship between income inequality proxiedby the Gini index and suicide rates. Kuroki (2010) is the mostrecent paper using Japanese data at municipality level. He providesevidence that unemployment has a positive significant effect onmale suicide rates and that this effect differs across age groups,in particular, the largest effect is found in the 55–64 age group.He also finds a negative effect of unemployment on female sui-cide rates. That is, higher unemployment is associated with lowerfemale suicide rates. They conclude that the impact of socioeco-nomic factors on suicide in Japan is greater than in OECD countries.Lastly, Yamamura (2010) using panel data at prefectural levelsuggests that social capital and divorce have an impact on sui-cide rates and that these effects are different between males andfemales. This leads us to anticipate that sociological factors playsmore critical role on determining on suicide rates than othercountries.

Unlike previous studies of suicide in Japan, this work employsa new recently methodological approach using time series data toexamine how suicide is related to socio-economic factors in Japanas in the short as well as in the long run. This approach is morerobust in presence of small samples, and allows us to account forpotential endogeneity of the variables included in the empiricalmodel. Endogenity issues might lead to misleading results in pastempirical studies.

5. Model and methodology

Following the empirical literature on suicide (for an extensivereview of the literature, see Lester and Yang, 1997), we form the fol-lowing long-run relationship between suicide, per capita income,unemployment rate, divorce rate, and fertility variables in linearform as:

stj = a0 + a1yt + a2utj + a3dt + a4ft + εt (1)

where the subscript t indexes time period with t = 1957, . . ., 2009;j indexes each suicide with j = 0 (total), 1 (male), and 2 (female);st is suicide rate; yt is per capita real income; ut is the unemploy-ment rate; dt is the divorce rate; ft is the fertility rate; and εt isthe classical error term. All variables are in their natural logarithmswhich allow us interpreting the estimated coefficients as constantelasticities.

Recent advances in econometric literature dictate that the long-run relation in Eq. (1) should incorporate the short-run dynamicadjustment process. It is possible to achieve this aim by expressingEq. (1) in an error-correction model, known as the Engle–Granger’s(1987) approach.

�st,j = b0 +m1∑

i=1

b1i,j�st−i,j +m2∑

i=0

b2i�yt−i +m3∑

i=0

b3i�ut−i,j

+m4∑

i=0

b4i�dt−i +m5∑

i=0

b5i�ft−i + �εt−1 + �t (2)

where � represents change, � is the speed of adjustment param-eter, and εt−1 is the lagged error term, which is estimated fromthe residuals of Eq. (1). The Engle–Granger method requires thatall variables in Eq. (1) are integrated of order one, I(1), and that

the lagged error term is integrated order of zero, I(0), in order toestablish a cointegration relationship. If some variables in Eq. (1)are non-stationary, we may use a new cointegration method. Thisprocedure is known as ARDL approach to cointegration of Pesaran

f Socio-Economics 40 (2011) 723–731 727

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Table 1Unit root results.

Variables ADF PP ERS

st,0 2.66 2.21 1.46st,1 2.85 2.43 1.43st,2 2.35 1.98 1.78yt 1.50 1.23 0.31ut,0 2.58 2.94 1.53ut,1 2.51 3.05* 1.54ut.2 2.63 2.62 1.52dt 3.14 2.59 2.56*

ft 1.89 2.28 1.67�st,0 4.07* 6.24* 4.11*

�st,1 3.97* 5.96* 3.71*

�st,2 4.15* 6.67* 3.81*

�yt 2.79 3.08* 2.52*

�ut,0 4.33* 5.16* 3.73*

�ut,1 4.54* 5.65* 3.66*

�ut,2 4.19* 5.12* 4.20*

�dt 3.29* 3.98* 2.28�ft 6.64* 12.3* 5.44*

Notes: Sample levels are 1958–2009 and differences are 1959–2009. The criticalvalues for ADF and PP with a constant and without a trend at the 5% level of signifi-cance are 2.91. The critical value for ERS with a constant and without a trend at the5% level of significance is 2.29. All test statistics and critical values are expressed in

Unit root tests results are displayed in Table 1. The conditions forapplying the ARDL bounds testing approach are satisfied. In otherwords, all variables included in the model are either I(0) or I(1).

Table 2The results of F and W tests for cointegration.

95% LB 95% UB 90% LB 90% UB

Panel A: The assumed long-run relationship: F/W(s0

∣∣y, u0, d, f )

F-statistic5.30 3.10 4.35 2.60 3.74

W-statistic26.51 15.52 21.75 13.01 18.70

Panel B: The assumed long-run relationship: F/W(s1

∣∣y, u1, d, f )

F-statistic6.54 3.10 4.35 2.60 3.74

W-statistic32.72 15.52 21.75 13.01 18.70

Panel C: The assumed long-run relationship: F/W(s2

∣∣y, u2, d, f )

F-statistic3.46 3.10 4.35 2.60 3.74

W-statistic

A.R. Andrés et al. / The Journal o

t al. (2001) that combines Engle–Granger two steps procedure intone by replacing εt−1 in Eq. (2) with its equivalent from Eq. (1). εt−1s substituted by linear combination of the lagged variables as inq. (3):

st,j = c0 +n1∑

i=1

c1i,j�st−i,j +n2∑

i=0

c2i�yt−i +n3∑

i=0

c3i�ut−i,j

+n4∑

i=0

c4i�dt−i +n5∑

i=0

c5i�ft−i + c6st−1,j + c7yt−1

+ c8ut−1,j + c9dt−1 + c10ft−1 + vt (3)

To obtain Eq. (3), one has to solve Eq. (1) for εt and lag the solu-ion equation by one period. Then, this solution is substituted fort−1 in Eq. (2) to arrive at Eq. (3). Eq. (3) is a representation of theRDL approach to cointegration.

Pesaran et al. (2001) approach to cointegration has someethodological advantages in comparison to other single cointe-

ration procedures. They are as follows: (i) endogeneity problemsnd inability to test hypotheses on the estimated coefficients inhe long-run associated with the Engle–Granger (1987) methodre avoided; (ii) the long and short-run parameters of the modeln question are estimated simultaneously; (iii) the ARDL approacho testing for the existence of a long-run relationship between theariables in levels is applicable irrespective of whether the under-ying regressors are purely I(0), purely I(1), or a combination ofhe two; (iv) the small sample properties of the bounds testingpproach are far superior to that of multivariate cointegration, asrgued in Narayan (2005).

The bounds-testing procedure is based on the F- or Wald-tatistics, and this is the first stage of the ARDL cointegrationethod. Accordingly, a joint significance test that implies no

ointegration hypothesis, (H0: c6 = ...... = c10 = 0), against thelternative hypothesis, (H1: at least one of c6 to c10 /= 0), shoulde performed for Eq. (3). The F-test used for this procedure has aon-standard distribution. Thus, Pesaran et al. compute two sets ofritical values for a given significance level with and without a timerend. One set assumes that all variables are I(0), and the other setssumes that they are all I(1). If the computed F-statistic exceedshe upper critical bounds value, then the H0 is rejected. If the F-tatistic falls into the bounds, then the test becomes inconclusive.astly, if the F-statistic is below the lower critical bounds value, itmplies no cointegration.

Once a long-run relationship has been established, Eq. (3) is esti-ated using an appropriate lag-selection criterion. At the second

tage of the ARDL cointegration procedure, it is also possible tobtain the ARDL representation of the error-correction (EC) model.o estimate the speed with which the dependent variable adjustso independent variables within the bounds-testing approach, fol-owing Pesaran et al. (2001), the lagged-level variables in Eq. (3) areeplaced by ECt−1 as in Eq. (4):

st,j = ˛0 +k1∑

i=1

˛1i,j�st−i,j +k2∑

i=0

˛2i�yt−i +k3∑

i=0

˛3i�ut−i,j

+k4∑

i=0

˛4i�dt−i +k5∑

i=0

˛5i�ft−i + �ECt−1 + �t (4)

A negative and statistically significant estimation of � not onlyepresents the speed of adjustment but also provides an alternativeeans of supporting cointegration between the variables.

absolute terms for convenience. Rejection of unit root hypothesis is indicated withan asterisk. � stands for first difference.

6. Results

Annual data over the period 1957–2009 were used to estimateEq. (3) by the ARDL cointegration procedure of Pesaran et al. (2001).Variable definitions and sources of data are provided in Appendix.

To implement the Pesaran et al. (2001) cointegration procedure,one has to ensure that none of the explanatory variables in Eq.(1) is above I(1). In the presence of I(2) or higher variables, thecomputed statistics provided by Pesaran et al. (2001) are not valid.Consequently, the implementation of unit root tests in the ARDLapproach is necessary to ensure that none of the variables includedin the model is integrated of order 2 or beyond. Three tests wereused to test unit roots in the variables: Augmented Dickey–Fuller(henceforth, ADF) (1979, 1981), Phillips–Perron (henceforth, PP)(1988), and Elliott–Rothenberg–Stock (henceforth, ERS) (1996).

17.31 15.52 21.75 13.01 18.70

If the test statistic lies between the bounds, the test is inconclusive. If it is above theupper bound (UB), the null hypothesis of no level effect is rejected. If it is the belowthe lower bound (LB), the null hypothesis of no level effect cannot be rejected.

728 A.R. Andrés et al. / The Journal of Socio-Economics 40 (2011) 723–731

Table 3ARDL cointegration results.

Regressor Coefficient Standard error T-ratio

Panel A: Estimated long-run coefficients using the ARDL approach for aggregate suicide model: ARDL (1,1,1,0,1) selected based on the Akaike Information Criterion,1957–2009

Dependent variable st,0

yt −0.4106* 0.1397 2.9385ut,0 0.2024 0.1922 1.0533dt 0.8832* 0.3990 2.2134ft −0.7003** 0.3352 1.7134Constant 0.3437 1.4435 0.8130

Panel B: Error correction representation resultsDependent variable �st,0

�yt −0.9085* 0.3787 2.3988�ut,0 0.0586 0.0095 0.6116�dt 0.3709* 0.1301 2.8501�ft −0.0575 0.1222 0.4712ECt−1 −0.4199* 0.1057 3.9714

Diagnostic testsR̄2 0.41 F-statistic 8.89* �2

SC(1) 0.06 �2

FF(1) 0.73

RSS 0.12 DW-statistic 1.94 �2N

(2) 19.26 �2H

(1) 2.49

RSS stands for residual sum of squares. T-ratios are in absolute values. �2SC

, �2FF

, �2N

, and �2H

are Lagrange multiplier statistics for tests of residual correlation, functional formmis-specification, non-normal errors and heteroskedasticity, respectively. These statistics are distributed as chi-squared variates with degrees of freedom in parentheses.The critical values for �2(1) = 3.84 and �2(2) = 5.99 are at 5% significance level. ***Significance at 10% level.

Vb

pidVtsnaiobvc

TA

RmT

* Significance at 1% level.** Significance at 5% level.

isual inspections of the variables in logarithm show no structuralreaks.

Eq. (3) is estimated in two stages. In the first stage of the ARDLrocedure, the long-run relationship of Eq. (1) was established

n two steps. First, the selection of the lag length on the first-ifferenced variables for Eq. (3) was obtained from unrestrictedector Autoregression (VAR) by means of Akaike Information cri-

eria (AIC) and the Schwarz Bayesian Criterion (SBC). The resultsuggest the optimal lag length as 2, but this stage of the results isot presented here to conserve space. Second, a bound F-test waspplied to Eq. (3) in order to determine whether the dependent andndependent variables are cointegrated in each model. The resultsf the bounds F-testing are reported in Table 2. From Table 2, it can

ee seen that the computed F statistics are above the upper boundalues in the cases of total and male suicides models’ thus, implyingointegration relations.

able 4RDL cointegration results.

Regressor Coefficient Standard error T-ratio

Panel A. Estimated long-run coefficients using the ARDL approach for male suicide modDependent variable st,1

yt −0.5420* 0.1297 4.178ut,1 0.0133 0.1755 0.758dt 1.1635* 0.4030 2.887ft 0.0456 0.1716 0.266Constant 2.3613** 1.2274 1.923

Panel B. Error correction representation resultsDependent variable �st,1

�yt −1.0050* 0.3704 2.712�ut,1 0.0560 0.0812 0.689�dt 0.4896* 0.1382 3.542�ft 0.0192 0.0712 0.269ECt−1 −0.4208* 0.0944 4.455

Diagnostic testsR̄2 0.47 F-statistic 10.6*

RSS 0.15 DW-statistic 2.11

SS stands for residual sum of squares. T-ratios are in absolute values. �2SC

, �2FF

, �2N

, and �is-specification, non-normal errors and heteroskedasticity, respectively. These statistic

he critical values for �2(1) = 3.84 and �2(2) = 5.99 are at 5% significance level. ***Signifi* Significance at 1% level.

** Significance at 5% level.

The ARDL cointegration procedure was implemented to esti-mate the parameters of Eq. (3) with maximum lag-order set to 2,which is selected on the basis of AIC, SBC and R̄2 selection criteria.This stage involves estimating the long-run and short-run coeffi-cients of Eqs. (1) and (2).

The summary ARDL results with some diagnostic tests fortotal suicides, male suicides, and female suicides are presented inTables 3–5, respectively. The overall empirical results appear to berather satisfactory. First, income enters negatively in the regres-sions for overall, male, and female suicides. The long-run elasticityof suicide with respect to income is highest in the case of male sui-cides. This is −0.54, suggesting that one per cent increase in percapita income will decrease the number of male suicides by 0.54%

whilst other factors remain constant. The long-run income elastic-ities with respect to total and female suicides are −0.41 and −0.36,respectively. This finding implies that males are more vulnerable

el: ARDL (1,1,0,0,0) selected based on the Schwarz Bayesian Criterion, 1957–2009

51019

80871

�2SC

(1) 0.34 �2FF

(1) 1.31�2

N(2) 18.10 �2

H(1) 2.41

2H

are Lagrange multiplier statistics for tests of residual correlation, functional forms are distributed as chi-squared variates with degrees of freedom in parentheses.cance at 10% level.

A.R. Andrés et al. / The Journal of Socio-Economics 40 (2011) 723–731 729

Table 5ARDL cointegration results.

Regressor Coefficient Standard error T-ratio

Panel A. Estimated long-run coefficients using the ARDL approach for female suicide model: ARDL (1,0,0,0,1) selected based on the R-Bar Squared Criterion, 1957–2009Dependent variable st,2

yt −0.3677* 0.2491 18.0085ut,2 0.0197** 0.3431 1.7628dt 0.8844 0.6168 0.2570ft 0.4974* 0.4241 2.0300Constant −0.6752 2.8321 0.2384

Panel B. Error correction representation resultsDependent variable �st,2

�yt −0.0869*** 0.0565 1.5367�ut,2 0.0046 0.0818 0.0570�dt 0.2091** 0.1127 1.8552�ft −0.1197 0.1251 0.9564ECt−1 −0.2364* 0.0899 2.6297

Diagnostic testsR̄2 0.21 F-statistic 3.98* �2

SC(1) 0.48 �2

FF(1) 1.10

RSS 0.13 DW-statistic 1.84 �2N

(2) 22.41 �2H

(1) 0.16

RSS stands for residual sum of squares. T-ratios are in absolute values. �2SC

, �2FF

, �2N

, and �2H

are Lagrange multiplier statistics for tests of residual correlation, functional formmis-specification, non-normal errors and heteroskedasticity, respectively. These statistics are distributed as chi-squared variates with degrees of freedom in parentheses.The critical values for �2(1) = 3.84 and �2(2) = 5.99 are at 5% significance level.

tiseiibttisaisdawsbcncirtfa

ttmgmroaidtif

* Significance at 1% level.** Significance at 5% level.

*** Significance at 10% level.

o income loss than females. However, the magnitude of this effects rather minimal. Second, unemployment rates are positively andignificantly associated with female suicides. The long-run partiallasticity of suicides with respect to unemployment rates is 0.01,ndicating that a 1% rise in unemployment rates will trigger anncrease in female suicides by about 0.01%. Although there seems toe the almost same impact exists in the case of the male suicides buthat is not statistically significant. Hence one argues broadly thathe impact of male and female unemployment rates on suicides isdentical. Gender seems to have no special effect on a suicide deci-ion, when an individual becomes unemployed. Third, divorce ratesre positively correlated with suicides but are statistically insignif-cant in the case of female suicides. Male population appears to beuffering more as a result divorce since the long-run elasticity ofivorce rate with respect to male suicides is 1.16, suggesting that1% increase in divorce rates will rise the male suicides by 1.16%hich is the stronger determinant of suicide in the entire analy-

is. Finally, we find a statistically significant negative associationetween fertility rates and suicides only in the case of the total sui-ides. Thus, a 1% rise in the total fertility rates will drop the totalumber of suicides by 0.70 whilst the other explanatory factors areonstant. The long-run elasticities of suicides in respect to fertil-ty for male and female suicides appears to be in wrong signs. Inegards to the relative magnitude of the explanatory variables inhis study, the fertility rate seems to be the second most importantactor in explaining suicides, followed by real per capita incomend unemployment rates.

Tables 3–5 also report the coefficients of coefficients of ECt−1he error correction model. All coefficients of ECt−1 are statis-ically significant and have the negative expected sign in all

odels. This situation provides further confirmation for cointe-ration relationships between variables of total and male suicidesodels as well as suggesting an alternative means of long-run

elationship in the case of female suicide model. The magnitudef the speed of equilibrium is relatively low, since their valuesre less than 0.5. The lowest error correction coefficient appearedn the female regression model, which means that about 25% of

isequilibrium is corrected every year. As the suicide is a long-erm phenomenon, the short-run elasticities will have no realmpact in policy designing therefore we are not evaluating themurther.

7. Summary and conclusions

This paper, from a socioeconomic point of view, investigates thedeterminants of suicides in Japan for the time span between 1957and 2009. Unlike earlier studies, this paper employs a relativelyrecent econometric procedure, the ARDL approach to cointegra-tion, which has been utilized to obtain the long-run elasticities ofthe suicides with respect to the total, male and female suicides.To our knowledge, this paper is the first paper to apply an ARDLapproach to examine the determinants of suicide in Japan. Thisapproach seems to have several potential advantages as it needs noa large number of observations to guarantee the robustness of theestimators and performance of the statistical tests. Furthermore,the choice of the suicide static model could influence the analysis.Individuals might respond with some delayed to changes in socioe-conomic factors. In this case, suicides are explained by current andlagged differenced values of real per capita income, unemployment,and divorce rates.

We show that in the long run, the divorce is the highest suicidecause and the Japanese men seem to be suffering particularly fromthis situation. The second most important determinant of the sui-cides in Japan is also a sociological factor, fertility rates. As expected,the female population are more affected with the decreasing levelof fertility rates. Combining these two suicide causes, one mayargue that sociological factors are more dominant than economicfactors in the case of Japanese suicides, which is inconsistent withChen et al. (2009b). This might be partly because that Chen et al.(2009b) uses the panel data of OECD countries to make a compari-son between Japan and other OECD countries. For robustness checkof this paper by comparing Japan and other OECD countries, it isrequired to use time series data of other OECD countries to conductARDL estimation in the future studies. Furthermore, the economicdeterminants of suicides in Japan appear to be moderate in mag-nitude and similar in both sexes indicating that male and femaleparticipation to work and sharing the burden of economic diffi-culties are almost the same. Our results support the existence of along run relationship between socio-economic factors and suicides,

regardless of gender.

Finally, recommendations for suicide prevention are generally acombination of strategies targeting high-risk groups and strategiestargeting a whole population. The findings of this study reveal that

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30 A.R. Andrés et al. / The Journal o

overnment policies should promote family cohesion and provideconomic incentives to raise birth rates, as these policies will beffective in lowering suicide rates.

cknowledgements

The authors are grateful to an anonymous referee for his/herseful comments and suggestions on an earlier version of this work.

ppendix A. Appendix

Data Definitions and SourcesAll data were collected online with the provided internet links

elow:st,j are crude suicide rates for total, male and females per 100,000

n logarithm.Source: Period 1955–2004: Statistics Bureau, Ministry of Inter-

al Affairs and Communications (2006). Historical Statisticsf Japan Volume 1 (New Edition). Tokyo: Japan Statisti-al Association. Period 2005-2009: National Police Agency.ttp://www8.cao.go.jp/jisatsutaisaku/link/keisatsutyo.htmlaccessed 16.06.10).

yt is per real capita income in logarithm. Base year is 1990.Source: Period 1955–2003: Statistics Bureau, Ministry of Inter-

al Affairs and Communications (2006). Historical Statistics ofapan Volume 1 (New Edition). Tokyo: Japan Statistical Association.eriod 2004–2009: Cabinet office of Government of Japan.ttp://www.esri.cao.go.jp/jp/sna/qe101-2/gdemenu ja.htmlaccessed 16.06.10).

ut,j are unemployment rates for total, male and females in loga-ithm.

Source: Period 1955–2009: Statistics Bureau, Ministry ofnternal Affairs and Communications. http://www.stat.go.jp/ata/roudou/longtime/03roudou.htm#hyo 1 (accessed 16.06.10).

dt is divorce rate per 1000 in logarithm.Source: Period 1955–2003: Statistics Bureau, Ministry of Inter-

al Affairs and Communications (2006). Historical Statistics of Japanolume 1 (New Edition). Tokyo: Japan Statistical Association. Period004–2009: Ministry of Health, Labour, Welfare.

http://www.mhlw.go.jp/toukei/saikin/hw/jinkou/suikei09/ndex.html (accessed 16.06.10).

ft is fertility rate per 1000 in logarithm.Source: Period 1955–2003: Statistics Bureau, Ministry of

nternal Affairs and Communications (2006). Historical Statis-ics of Japan Volume 1 (New Edition). Tokyo: Japan Statisticalssociation. Period 2004–2009: Ministry of Health, Labour, Wel-

are. http://www.mhlw.go.jp/toukei/saikin/hw/jinkou/suikei09/ndex.html (accessed 16.06.10).

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