Suicides and Macroeconomic Variables: Are They Related in the Long-run?


 BackgroundThis study investigates the cross-country long run relationship between suicides and macroeconomic variables (unemployment, per capita income, and inflation). It is hypothesized that while inflation level and unemployment level stimulate suicide and intentional self-harm in a society, per capita income level alleviates suicide and intentional self-harm in a society.MethodA balanced annual data spanning the period 2000 to 2012 across 35 countries is used in the empirical analysis. We employ panel test and estimation approaches to reveal the long-run association among suicide, inflation, per capita income and unemployment series. The most conventional cross-sectional dependency tests, panel unit root tests, panel cointegration tests, and heterogeneous panel non-causality tests are implemented. ResultsWe found a statistically significant cross-country long run association between suicides and all macroeconomic variables under study. The results of the study suggest that while 1% increase in per capita income causes 0.752% decrease in suicide rate, 1% increase in inflation and unemployment rate is associated with a rise in suicide rate by 0.088% and 0.238%, respectively. In regard to causality, there is no causality is identified between inflation and suicide. On the other hand, a statistically significant unidirectional causality running from per capita income level to suicide and a unidirectional causality running from suicide to unemployment are found. A unidirectional causality running from suicide to unemployment can be stem from the fact that rises in suicides are associated with both early indicators of economic downturns and during economic downturns when unemployment increases.ConclusionHaving found that adverse economic conditions such as increase in unemployment or inflation or decrease in per capita income triggers suicides and suicides are also associated with early indicators of economic crises, this study suggest that social and economic policy measures and programs related to labor market, health safety, family support and debt relief should be implemented both prior to and during economic crises in order to prevent suicides and loss of human capital of the society. Economic policies that result in a high level of unemployment or inflation should be critically assessed from the human cost of these measures.


Abstract Background
This study investigates the cross-country long run relationship between suicides and macroeconomic variables (unemployment, per capita income, and in ation). It is hypothesized that while in ation level and unemployment level stimulate suicide and intentional self-harm in a society, per capita income level alleviates suicide and intentional self-harm in a society.

Method
A balanced annual data spanning the period 2000 to 2012 across 35 countries is used in the empirical analysis. We employ panel test and estimation approaches to reveal the long-run association among suicide, in ation, per capita income and unemployment series. The most conventional cross-sectional dependency tests, panel unit root tests, panel cointegration tests, and heterogeneous panel non-causality tests are implemented.

Results
We found a statistically signi cant cross-country long run association between suicides and all macroeconomic variables under study. The results of the study suggest that while 1% increase in per capita income causes 0.752% decrease in suicide rate, 1% increase in in ation and unemployment rate is associated with a rise in suicide rate by 0.088% and 0.238%, respectively. In regard to causality, there is no causality is identi ed between in ation and suicide. On the other hand, a statistically signi cant unidirectional causality running from per capita income level to suicide and a unidirectional causality running from suicide to unemployment are found. A unidirectional causality running from suicide to unemployment can be stem from the fact that rises in suicides are associated with both early indicators of economic downturns and during economic downturns when unemployment increases.

Conclusion
Having found that adverse economic conditions such as increase in unemployment or in ation or decrease in per capita income triggers suicides and suicides are also associated with early indicators of economic crises, this study suggest that social and economic policy measures and programs related to labor market, health safety, family support and debt relief should be implemented both prior to and during economic crises in order to prevent suicides and loss of human capital of the society. Economic policies that result in a high level of unemployment or in ation should be critically assessed from the human cost of these measures.

Background
Economic theory of suicide outlined by Hamermesh and Soss [1] suggests that factors which lead to decrease in the expected lifetime utility and future income stream are associated with suicide. Especially, the suicide rate is inversely related to the permanent income while it is positively related to unemployment. When unemployment rises and permanent income decreases, the suicide rate will increase since expected lifetime utility and future expected income stream of individuals diminish under these conditions. These factors emerge especially in times of economic downturn/recession when unemployment increases and per capita income decreases. Thus, adverse macroeconomic changes can lead to increase in the suicide rate of a society.
In this framework, economic downturn causes loss of employment, reduced career progression, threat of unemployment, increased debt, and nancial strain which lead to increased stress at work, loss of status, loss of personal control, increased anxiety, negative relationships, marital breakdown, reduced social support in workplace and personal relationships, reduced social integration, decreased tolerance of mental illness, reduced access to mental health care, increased drug and alcohol misuse, increased mental illness and depression which may result in suicide [2].
Numerous country level empirical studies investigated the relationship between adverse macroeconomic changes and suicides. Country-level empirical studies suggest that adverse macroeconomic changes such as a sharp increase in unemployment and negative growth rates are associated with increase in suicide in some countries (USA, England, Ireland, Spain, Italy, Australia, Greece, Russia, Latvia, Japan, Korea, Taiwan, Singapore), while suicide mortality fell in some countries (Finland and Sweden) when economic deterioration happens. Variations in the ndings are attributed to the welfare system, social protection policies, culture and social structures of countries [22,16,23,24].
Empirical studies also indicate that the relationship between adverse economic conditions and suicide rates varies depending on the age and gender of the population. Findings indicate that the impact of adverse macro-economic conditions on suicide mortality is strongest among males and younger age groups [25,15,2,17,16,27,3].
There are also number of cross-country studies investigating the association between adverse macroeconomic changes and suicides. Andres [27] scrutinized the impact of socio-economic variables on the suicide rate in the context of 15 European countries between 1970 and 1998. No statistically signi cant impacts of unemployment rates and GDP per capita are found on suicide rates after controlling country-speci c linear trends and country and year xed effects. Stuckler et al. [28] examined the association between changes in employment and suicides in Europe by employing multivariate regression and data set including 26 European Union countries between 1970 and 2007. They found that every 1% increase in unemployment was associated with a 0.79% rise in suicides at ages younger than 65 years. Barth et al. [29] evaluated the association between socioeconomic factors (grosss domestic product, unemployment rates, labor force participation, and divorce rates) and suicide rates for 18 countries by using panel-vector error correction models. They found that socioeconomic factors are related to suicide rates although this relationship varies by sex. Increasing unemployment is signi cantly associated with increasing suicides for only women. Breuer [26] analyzed the effect of unemployment on suicide mortality in Europe by using a regional panel data set of 275 regions in 29 European countries during the period 1999 to 2010. The results suggest that 1% increase in unemployment is associated with a 1% increase in suicides among individuals aged younger than 65 years old by controlling regionspeci c trends. Gajewski and Zhukovska [24] estimated the short and long run relationship between unemployment and suicide for a panel of 10 high-income countries. Only a long-run impact of unemployment on suicides was found to be signi cant for the liberal group of countries (Canada, United States, Australia, New Zealand, and United Kingdom) while there is no signi cant association for the social-democratic countries (Norway, Sweden, Denmark, Finland, and the Netherlands). Chang et al. [25] investigated the impact of Asian economic crises in 1997-1998 on suicide in Japan, Hong Kong, South Korea, Taiwan, Singapore and Thailand. Their nding suggests an association of the Asian economic crisis with a sharp increase in suicide in some but not all East/Southeast Asian countries. These increases are most closely associated with rises in unemployment.
Different from previous studies, this study investigates the long run relationship between suicides and selected macroeconomic variables (unemployment, per capita income, and in ation) which are potentially related with suicide behavior in a panel data context by using the most conventional economic techniques and sensitive robustness tests. This study also provides causality analysis which is lack of the previous studies. Previous studies have found strong associations between adverse economic conditions and suicide rates, but they fail to provide causality analysis which reduces the policy relevance of their study. Moreover, causality analysis which is put forth in this study reconciles the con icting results of previous studies.
Next section provides research hypothesis of the study, data and methodology. Estimation results are given in Sect. 3 while Sect. 4 concludes.

Research Hypotheses
Based on the theoretical foundations outlined in the introduction and previous empirical studies on the subject, the following three hypotheses are tested in the empirical analysis section:

Hypothesis 1
In ation level stimulates suicide and intentional self-harm in a society.

Hypothesis 2
Per capita income level alleviates suicide and intentional self-harm in a society.

Hypothesis 3
Unemployment level stimulates suicide and intentional self-harm in a society.

Data
A balanced annual data spanning the period 2000 to 2012 across 35 countries is used in the empirical analysis. The sample period and number of countries utilized in the analysis is determined by the availability of balanced data on suicide and intentional self-harm rate. Data on suicide and intentional self-harm rate are gathered from UNECE Statistical Database of UNECE while data on unemployment rate are collected from ILOSTAT made available by ILO. On the other hand, data on in ation rate and GDP per capita are obtained from the World Development Indicators (WDI) database published by the World Bank.
Suicide and intentional self-harm (SUICIDE): SUICIDE variable measures death rate per 100,000 population as a result of cause of death by suicide and intentional self-harm. This gure covers both sexes at all ages.
In ation rate (INFLATION): In ation rate is measured by the consumer price index re ects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services.
GDP per capita (GDPPERCAP): GDPPERCAP variable represents GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. It is measured in terms of current international dollars.
Unemployment rate (UNEMPLOYMENT): UNEMPLOYMENT variable re ects the unemployment rate in an economy and is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force is imputed by the sum of the number of persons employed and the number of persons unemployed. It covers 15 + ages and both sexes.
Since our variables are measured in different units, we take the natural logarithmic transformation of each variable in order to normalize the variables so that each variable is expressed in a uniform unit. Another advantage of this transformation is that the coe cient of each variable now displays the elasticity of the relevant variable.

Estimation Methodology
We employ proper panel test and estimation approaches to reveal the long-run association among suicide, in ation, per capita income and unemployment series. In accordance with this purpose, we rst test whether the series are cross-sectionally dependent. Secondly, given the existence of cross-sectional dependency, a panel unit root test accounting for cross-sectional dependency is applied to each variable.
Upon identifying that each variable are I(1), we thirdly conduct panel cointegration test explicitly counting in cross-sectional dependency in order to disclose the long-run equilibrium relationship among the variables. In addition to panel cointegration test, we apply test of parameter constancy to see if we have heterogeneous parameters across panels. Once a panel cointegration relation and heterogeneous parameters are detected for relevant series, then long-run elasticities are obtained by using a convenient panel estimation approach taking both cross-sectional dependency and heterogeneous parameters into

Results
As recently discussed in the literature, the conventional unit root tests are inappropriate to test the stationarity of series in the case of presence of cross-sectional dependence in series since they assume cross-sectional independence. Hence, instead of conventional unit root tests, it will be better to conduct second-generation unit root test, which accounts for cross-sectional dependence. Thus, we started our empirical analysis rstly by implementing various cross-sectional dependence tests. Table 2 Table 2 strongly rejects the null hypothesis of "No cross-section dependence" at the 1% signi cance level in the data. account. Lastly, heterogeneous panel non-causality test is implemented to nd out the direction of causality among the series.
In accordance with the objective of the study, we construct the following equations as the benchmark models: where µ i , i and i stands for country speci c xed effects and it subscript represents i-th country's observation at time t.
As can be seen from Table 1, based on the results of Farrar-Glauber Multicollinearity Chi-square and F tests, there is a multicollinearity problem among INFLATION, GDPPERCAP, and UNEMPLOYMENT variables. Therefore, in that sense it will be better to separately check the long-run association of each variable with SUICIDE variable.  We also conducted residual cross-section independence test for the error terms of the models in Eqs. 1, 2 and 3. As indicated by the results in Table 3, null hypothesis of cross-section independence is rejected for each equation. Given the detection of cross-sectional dependence in the data suggested by the test results in Tables 2  and 3, we apply the CIPS test for unit roots in heterogeneous panels developed by Pesaran [35] accounting for cross-sectional dependence. The CIPS test results are displayed in Table 4 below. According to the indications of the test, all series are not stationary in levels but they are stationary in rst differences at 1% signi cance level. In other words, the CIPS unit root test ndings hint that variables of SUICIDE, INFLATION, GDPPERCAP, and UNEMPLOYMENT are integrated of order one ( i.e. I (1)). Upon identifying that our series are I(1), we check the cointegration relation among variables by utilizing two different panel cointegration test paying regard to cross-sectional dependence across panels. First, we apply Persyn and Westerlund [36] error-correction-based panel cointegration tests with robust Pvalues, which is obtained through bootstrapping. The ndings are reported in Table 5 and the last column shows the robust P-values in the sense of cross-sectional dependence. Gτ and Gα, which allow error correction terms to be heterogeneous across panels, stand for group-mean test results while Pτ and Pα, which assume error correction terms to be homogeneous across panels, stand for panel test results.
Besides, group-mean test looks for cointegration in some panels whereas panel test seeks for cointegration in all panels. Robust P-values in Table 5 [34] in which cross-sectional dependence is explicitly taken into consideration. Table 6 reports the ndings of the test. The results imply that there is no cointegrating relation between SUICIDE and INFLATION variables whereas there exists a cointegrating relation between SUICIDE and GDPPERCAP variables and between SUICIDE and UNEMPLOYMENT variables at 1% signi cance level. In overall the ndings of two distinct cointegration tests in Tables 5 and 6 strongly support the presence of cointegration for the models in Eqs. 2 and 3 while weakly support the existence of cointegration for the model in Eq. 1. Even though there is a weak evidence for the existence of cointegration in the model of Eq. 1, we will assume cointegrating relation for that equation too and therefore we will estimate long-run elasticities for the model in Eq. 1 as well in addition to the models Eqs. 2 and 3. Before proceeding to estimations of long-run elasticities we implement Swamy test of parameter constancy to nd out whether parameters across panels are heterogeneous. Reported results in Table 7 show that parameters do not remain constant across panels for all three models. Therefore, the estimation model that will be chosen to estimate long-run elasticities should allow for heterogeneous slope coe cients across panel members and also account for correlation across panel members (i.e., cross-section dependence). For that reason, we preferred to use the Augmented Mean Group (AMG) estimator developed in Eberhardt and Teal [37] as an alternative to the Common Correlated Effects Mean Group (CCEMG) estimator.  Notes: Coe cient estimations are in bold faces and P-values are in italic forms. We dropped Turkmenistan from the estimation since Turkmenistan has no observation for in ation.

Discussion
In summary, our estimation results suggest that %1 increase in per capita income causes 0.752% decrease in suicide rate while 1% increase in in ation and unemployment rate is associated with a rise in suicide rate by 0.088% and 0.238%, respectively. As the estimation results reveal that, among three macro-economic variables, per capita income has the largest impact on suicide.
Causality analysis provided in this study con rms the results of previous studies on the impact of GDP per capita on suicides. Meanwhile, a unidirectional causality running from suicide to unemployment is identi ed opposite to the expectations of previous studies on the effect of unemployment on suicides.
However, this result is consistent with observations of Stuckler [22] that suicides rises both before and during times of increases in unemployment. In other words, it is observed that suicides increase with the emergence of early indicators of economic crises when unemployment rate is still low as well as with economic recession when unemployment rate is high. Thus, this result also reconciles the con icting results of previous studies.
Consistent with country-speci c studies, our ndings also indicate that estimation results for Finland regarding the impact of unemployment and GDP per capita on suicides is opposite to the group speci c estimations of other countries in the sample. Besides Finland, it is found that estimation results of Czech This study suggests that there is a statistically signi cant cross-country long run association between suicides and macroeconomic variables of in ation, unemployment rate and per capita income. The results of the study indicate that while %1 increase in per capita income causes 0.752% decrease in suicide rate, 1% increase in in ation and unemployment rate is associated with a rise in suicide rate by 0.088% and 0.238%, respectively.
In regard to causality, there is no causality is identi ed between in ation and suicide. On the other hand, a unidirectional causality running from per capita income level to suicide and a unidirectional causality running from suicide to unemployment are found. A unidirectional causality running from suicide to unemployment can be stem from the fact that rise in suicides is associated with both early indicators of economic downturns and during economic downturns when unemployment increases.
Having found that adverse economic conditions such as increase in unemployment or in ation or decrease in per capita income triggers suicides and suicides are also associated with early indicators of economic crises, this study suggest that social and economic policy measures and programs related to labor market, health safety, family support and debt relief should be implemented both prior to and during economic crises in order to prevent suicides and loss of human capital of the society. Economic policies that result in a high level of unemployment or in ation should be critically assessed from the human cost of these measures.

Declarations
Ethics approval and consent to participate: Not applicable