Does unemployment increase suicide rates? The OECD panel evidence

Journal of Economic Psychology 30 (2009) 575–582

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Does unemployment increase suicide rates? The OECD panel evidence Yong-Hwan Noh * Department of Economics, Seoul Women’s University, 127 Gongneung 2-dong, Nowon-gu, Seoul 139-774, Republic of Korea

a r t i c l e

i n f o

Article history: Received 19 February 2008 Received in revised form 11 August 2008 Accepted 6 April 2009 Available online 17 April 2009

PsycINFO classification: 2220 JEL classification: C23 C51 J17

a b s t r a c t Previous studies of whether unemployment increases suicide rates give mixed results. None of them controlled for an interaction between unemployment and income. This paper tests the hypothesis whether the relationship between unemployment rates and suicide rates vary according to the level of real per capita GDP. We use the cross-country panel fixed effects approach to exclude cross-sectional variations but exploit time-series ones. We support that higher income is associated with higher suicide rates. In particular, the evidence shows that the implied effect of unemployment on suicide rates is positive for countries with higher income. Actually, for countries with lower-income levels, there is a negative impact of unemployment on suicides. Ó 2009 Elsevier B.V. All rights reserved.

Keywords: Suicide Unemployment Interaction effects

1. Introduction Does unemployment increase suicide rates? Economic conditions can be proxied by unemployment rates as unemployment typically involves a financial loss of an economic opportunity and hence less expected income. It also potentially involves the loss of social networks and self-confidence (Neumayer, 2004). Thus, one may expect that unemployed people translate their frustration into suicide. Income also can be interpreted in the same context as unemployment. According to the seminal work of Hamermesh and Soss (1974), the risk of suicide seems to be increased following income cut as well as unemployment because the discounted life time utility remaining to depressed people is likely to fall below a certain threshold level of taste for living. Looking at the following empirical studies, however, the direction of income and unemployment effects on suicide rates does not seem to be straightforward. Existing literatures such as Chuang and Huang (1997) and Andres (2005) report positive and significant association between unemployment and suicide by using aggregate pooled ‘ordinary least squares’ (OLS) method and panel analysis, respectively. Gerdtham and Johannesson (2003) also find that unemployment significantly increases the risk of suicides

* Tel.: +82 2 970 5529. E-mail address: [email protected] 0167-4870/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.joep.2009.04.003

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by using Swedish individual data. On the contrary, the fixed effects estimation of an aggregate German panel by Neumayer (2004) has provided evidence that unemployment rates are negatively related to overall male and female suicide rates.1 On the other hand, through long-term time-series analysis, Yang and Lester (1995) argue that the association between unemployment and suicide appears to be strong in the United States, but weak or non-existent in other nations. A US micro health panel analysis of Ruhm (2000) shows divergent results: recessions lower overall mortality rates, but suicides are increasing in unemployment rates.2 The increase in unemployment rates and/or the decrease in personal income are often used as proxies for the recession, instead of a technical definition based on the changes in national GDP (Ruhm, 2000). In this context, the direction of income effects on suicide rates also seems unclear. Suicide rates are expected to be higher in higher-income countries according to Jungeilges and Kirchgassner (2002). But, Hamermesh and Soss (1974) and Chuang and Huang (1997) suggest the opposite effect meaning that the income is negatively associated with aggregate suicide rates. Durkheim’s (1952) claim is about changes of income, rather than the level of income, affecting suicide rates.3 On the other hand, the recent cross-country panel analysis of Andres (2005) does not find a significant effect between income and suicide. This, in particular, is consistent with Ruhm (2000) in that personal incomes have a small and statistically insignificant effect on most cases of mortality including suicides. Significantly, the estimated effects of unemployment and income would be expected to be sensitive to the choice of countries and time periods. As these two variables are closely related to the current and expected income, it is natural to assume that there might be interactions between these two economic variables. Although it creates unbiased estimates, omitting a significant interaction term yields larger variances because this uses less information to produce coefficient estimates in the regression. But the significant information of the interaction term improves efficiency of the regression model. To address this problem, this paper examines how aggregate suicide rates are affected by unemployment rates, depending on the different development levels. In addition to the unemployment variable, therefore, a multiplicative interaction term between unemployment rates and income level is included in the estimation, by which I mean both effects would be present simultaneously. I also control for a number of socio-economic, socio-demographic and public policy variables that may affect the suicide rates. The dependence of the unemployment effect on the level of income would be theoretically underpinned with some discussion of the happiness literature. The theme to the happiness literature is that happiness is affected more by one’s sense of relative income than by absolute income (e.g., Easterlin, 1995; Easterlin, 2001; Stutzer, 2004). More income brings greater happiness, but it also generates material aspirations; if the negative effect of the latter on happiness undercuts the positive effect of the former, the positive happiness–income relation holds only in the lower part of the income range (i.e., this is supposed to be attenuated at higher absolute income levels). Systematic evidence has been found that people in industrialized countries are not becoming happier over time, while, for an individual level-data, it seems that higher income than others in their society is positively associated with higher levels of happiness (e.g., Blanchflower, Andrew, & Oswald, 2004). Given that the unhappiness and suicides are related to each other, therefore, higher income could be accompanied by lower suicide rates, but the relative income per se must matter. We estimate the fixed effects model using panel data for 24 OECD countries for the period 1980–2002. Thereby, we could control for potential omitted variable bias and unobserved time-invariant country-specific characteristics that may affect suicide rates (e.g., climate differentials). Our results show that the implied effect of unemployment on suicide rates is positive for countries with higher income, because we have a positive and significant interaction coefficient but a significantly negative unemployment coefficient. Actually, for countries with lower-income levels, there is a negative impact of unemployment on suicides. The remaining part of this paper is structured as follows. An empirical model is specified in Section 2. Section 3 presents empirical evidences from the OECD cross-country panel. Final remarks are contained in Section 4. 2. Empirical specification The purpose of this paper is to estimate the effect of unemployment on the suicide rates that is dependent on income. Evidently, suicides are not merely an indicator of mental health of population. Average suicide rates of countries could be identified as indicators of a large complex of underlying causes, including time-varying socioeconomic factors, time-constant environmental factors, and other unobserved time-specific and/or country-specific factors. This study investigates the relationship between suicide rates and a range of socioeconomic indicators in 24 OECD countries during 1980–2002. With the repeated examination of relationships between suicide rates and socioeconomic factors, the empirical test has an important strength in the sense that we could offset potential problems associated with omitted variable biases. We explicitly control 1 The argument that the unemployment affects negatively to the suicide is counter-intuitive in the perspective of Hamermesh and Soss’s (1974) suicide theory. According to Neumayer (2004), this phenomenon can be interpreted because recessions make more family ties in a psychologically stressful period. 2 According to Ruhm (2000), worsening economic conditions have negative effects on mental health, while improving physical well-being (e.g., less drinking and smoking; more time to invest in preventive medical care). 3 Durkheim’s (1897) ideas about the cause of suicides are based on his concepts of social integration and social regulation via both of which it strengthens individuals’ bonds with society. The economic environment comes into play through the business cycle. When the economy is very prosperous or depressed, it creates tremendous changes; which, in turn, give rise to diminishing social integration, thereby increasing suicides. In other words, suicides might rise not just during prosperity but also during depressions. For details, see also Lester (2001). The author thanks an anonymous referee for suggesting this possible explanation.

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for the unobserved time-constant effects in suicide rates. We actually estimate the suicide rates as a function of various socio-economic variables focusing on unemployment rates and income levels. Candidate socio-economic explanatory variables are mostly chosen from what has become the standard approach of a suicide model (e.g., Andres, 2005; Chuang & Huang, 1997; Helliwell, 2004). Three determinants of suicide rates (SRi;t ), i.e., unemployment rates (UNEMPi;t ), real per capita GDP (PCGDPi;t ), and the proportion of the elderly population (OLDi;t ) are included in the model according to the economic theory of suicide developed by Hamermesh and Soss (1974). As in Ruhm (2000) and Neumayer (2004), both the unemployment rates and the real GDP growth rate (GROWTHi;t ) are introduced to control for economic fluctuations. An interaction term defined as the product of the two variables (UNEMP i;t  PCGDPi;t ) is introduced in the model to test whether countries of different income levels respond to a given change on unemployment rates in a different way. Meanwhile, according to Durkheim (1952) and Chuang and Huang (1997), suicide rates are influenced by family and social ties. From this perspective, urbanization (URBANi;t ), fertility rates (FERTILITY i;t ) and the female participation in the labor forces (LABOR F i;t ) are included as it is believed to be the indicators of family help and social bonds. Some other possible variables of various natures, which are not time-invariant, are also included. These are: public policy variables such as the size of expenditures for the unemployed (UNEMP EXPi;t ) and the old age (OLD EXPi;t );4 the size of alcohol consumption (ALCOHOLi;t ); the average public health level (health being a determinant of happiness or unhappiness, it may affect suicide significantly), proxied by carbon dioxide emissions (CO2i;t );5 the number of dependents to working-age population (DEPENDENCY i;t ), which is expected to be a determinant of the Stutzer’s (2004) sense of income aspiration in individual happiness or a stressful life. These variables may affect suicide rates, and these may not have remained constant over time and across countries, so that it may make sense to introduce these within the regressions, in order to estimate to what extent the measured correlations are robust. Otherwise, estimations may be subject to some omitted variables bias, which is problematic. We finally include a full set of year dummies (but the reference year, 1980) to control for aggregate time effects. One final comment regarding further control variables is about whether income inequality affects suicide rates. The distribution of income within a society is a different way in which income can influence suicide rates. Previous researches show no significant causal path on how inequality affects suicide (e.g., Andres, 2005; Neumayer, 2004). Also, the biggest problem when introducing income inequality in the model as an explanatory variable is the fact that the entire sample observations are limited by the availability and quality of inequality data, which of course reduces the degrees of freedom substantially. For this research, actually, the 150 income-based Gini indices of 24 OECD countries during 1995–2002 were collected from the most comprehensive cross-country Gini database of UNUWIDER (2005). However, the estimated effects of Gini on suicide rates turned out to be insignificant due to perhaps the problem of data quality and the lack of variability.6 Hence we eliminated the income inequality in the final specification. Then the baseline reduced-form equation to be estimated is as follows:

SRi;t ¼ b0 þ b1 UNEMPi;t þ b2 PCGDPi;t þ b12 ðUNEMP i;t  PCGDPi;t Þ þ Z i;t c þ ai þ dt þ ei;t

ð1Þ

where the subscript i indexes each country and t indexes time period with t ¼ 1; 2; :::; T; Z it is a vector denoting control variables; ai captures unobserved country-specific effects; dt captures unobserved time-specific effects; eit denotes the error term which is assumed to be independently and identically distributed with a zero mean and variance r2 for all i and t; and bj (j ¼ 0; 1; 2; 12) and c are, respectively, estimated coefficients and an estimated coefficient vector. Note that the interaction term attenuates or fortifies the individual effect of UNEMPi;t and PCGDPi;t , respectively. Thus, omitting a significant interaction term incorrectly leads to a specification bias. If the interaction term were to be omitted, the overall impact of a change in the unemployment rates on suicide rates would be solely measured by b1 . With the significant interaction term, however, the ‘‘net marginal effect” of UNEMP i;t on SRi;t depends on the level of PCGDPi;t as:

@E½SRi;t  ¼ b1 þ b12 PCGDPi;t @UNEMPi;t

ð2Þ

Simultaneously, the two attributes, PCGDPi;t and UNEMP i;t  PCGDPi;t , modify the individual income effect on suicide rates. While the overall impact of a change in income on suicide rates would be solely measured by b2 provided that the interaction term is to be omitted, the ‘‘net marginal effect” associated with the interaction is:

@E½SRi;t  ¼ b2 þ b12 UNEMPi;t @PCGDPi;t

ð3Þ

4 Social expenditures are the provision of benefits to individuals in order to provide support during circumstances which adversely affect their welfare. Thus, suicides, for example, following financial loss are expected to be reduced when sufficiently supported by public policies. In particular, for a given income level, the relationship between unemployment and suicide is likely to depend on unemployment expenditure, which can make unemployment less painful. 5 ‘Carbon dioxide’ (CO2) emissions are primarily a by-product of industrialization, which affect human health, and consequently the quality of life of people in the country. Emissions of CO2 are, therefore, expected to be a determinant of happiness that may affect suicide significantly. Thus we expect that larger CO2 emissions will be positively associated with suicide rates. 6 A serious problem with the Gini data is that many countries still have indices calculated by inconsistent definitions. Important differences include whether Gini index is for individuals or households and whether it is income based or expenditure based. Even in comparing Gini indices surveyed by the same criteria, there are still possibilities of different accuracy, errors of underreporting, and different survey designs across countries.

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Therefore, on average and ceteris paribus, the marginal effect of unemployment is not constant with respect to the income level but varies with different real per capita GDP levels. Likewise, other things being equal, the marginal effect of income varies with different unemployment rates. 3. Estimation 3.1. Estimation method Pooled OLS as well as cross-sectional models are often criticized because they do not shed light on intertemporal dependence of events. But, we can accommodate dynamics better via panel data analysis. Admittedly, large proportion of panel analysis involves the fixed effects model and the random effects model, ultimately to get consistent and efficient estimators. Whether the effect is fixed or not is associated with whether an individual effect (ai ) is correlated with explanatory variables. However, economists are usually agnostic of random effects models because of the strong assumption that unobserved individual heterogeneity is distributed independently of the regressors. Therefore, the finally chosen specification is a country fixed effects with T  1 time dummy variables.7 The fixed effects estimate uses within-country variations in socio-economic variables and have the potential to improve on aggregate time-series analyses. In a fixed effects sense, by using ai and dt in Eq. (1), we can control for unobserved country- and time-specific effects that may be correlated with the explanatory variables and produce biased coefficient if omitted. Specifically, fixed effects estimation makes it possible to eliminate a potential source of omitted-variable bias and captures the unobserved heterogeneity that causes the inconsistency in the OLS regression. Therefore, we can take advantage of panel estimation to control for time-invariant country-specific unobservable effects of suicide. This advantage appears to be essential in the estimation of suicide equation, considering the survey results of Helliwell (2004) where various country-specific factors that may affect suicide rates are verified (e.g., climate, sunshine, latitude, religion, geographical locations, cultural differentials, and so on). Also, time dummies are often included to control aggregate time-specific effects that may affect suicide rates in any period but are not captured by other individual explanatory variables. This, of course, eliminates the information that varies across time periods but not across countries. 3.2. Data The annual suicide rates of 24 OECD countries over the period 1980-2002 are to be regressed on a set of explanatory variables in order to assess their relative contribution to aggregate suicide rates. A panel data set is from the two data sources, OECD Health Data 2005 and the World Bank World Development Indicators 2006. For an international organization, the quality of cross-country panel data depends on two dimensions: the quality of national statistics and the quality of its internal process for collection. By excluding non-OECD countries in the analysis, we expect increased data transparency. In particular, the quality of OECD Health Data has been improved since the mid-1980s. For many years, the OECD has identified any discrepancy between national data and the standard definitions via the consultation of sources and methods attached to the variables. It has changed definitions, sources and discrepancies with each successive release for the comparability and consistency among OECD member countries. The World Bank has also been revised and updated for decades to improve the quality of indicators, especially, through consultation with and substantial contributions from its thematic networks that are connected to the international and government agencies. These two sources of data are believed to be sufficiently consistent to enable cross-national comparisons than any others alternatives. Table 1 shows the annual average gender differences of national suicide rates. Without any exception, even the countries with the very low rates, in all countries the male suicide rates are higher than the female suicide rates. Average suicide rates for males are 3.14 times higher for females. Other variable definitions and their descriptive statistics are displayed in Table 2 and are largely self-explanatory. Meanwhile, Fig. 1 shows that the patterns of suicide rates vary markedly between countries. Finland and Austria have been the country with the higher suicide rates, while Southern parts of Europe (e.g., Greece, Spain and Italy) have comparatively low suicide rates. Striking rise in suicide rates occurred in Ireland and Korea, while suicide rates of other countries have been decreased or stationary over the period considered. For countries such as Iceland and Luxembourg, suicide rates fluctuate substantially over the period considered. In most countries, female suicide rates are stationary at the lower level relative to the male rates. As we see from the raw profiles on suicide rates, the variation in suicide rates across time and countries is marked. The selected econometric estimation method (i.e., a fixed effects model) is of particular value in this regard. To check the variation in the data, we may want to know the information on the percentage of country-specific time variation in the data. The R-squared of the fixed effects estimation using time-demeaned suicide rates of country i at time t, ðSRi;t  SRi Þ, is interpreted as the proportion of time variation in the data, SRi;t , that is explained by the time variation in the explanatory variables (see for example, Chrinko, Fazzari, & Meyer, 1999; Wooldridge, 2003). Since the data are mean differenced, time-constant cross-

7 Although it is a weak test because the null hypothesis is that the random effects estimator is correct, we can use a Hausman specification test to suggest whether fixed effects estimator is necessary. The null hypothesis is rejected in the Hausman test. Therefore, this paper reports only the result of the fixed-effects model.

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Y.-H. Noh / Journal of Economic Psychology 30 (2009) 575–582 Table 1 Average suicide rates of 24 OECD countries (1980–2002).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

a

Country

Male suicide rates (MSRi;t )a

Female suicide rates (FSRi;t )a

Sex ratio (MSRi;t /FSRi;t )

Australia Austria Belgium Canada Switzerland Germany Denmark Spain Finland France UK Greece Ireland Iceland Italy Japan Korea Luxembourg Netherlands Norway New Zealand Portugal Sweden USA

19.45 32.74 27.49 20.15 29.18 22.24 27.69 10.32 38.72 27.65 11.06 5.07 15.32 19.54 10.39 23.88 17.41 23.70 12.63 19.27 20.69 12.05 21.01 19.05

5.14 10.25 11.01 5.45 10.53 8.12 13.41 3.07 9.70 9.50 3.75 1.49 4.19 5.87 3.35 10.82 6.68 8.39 6.56 6.65 5.89 3.61 8.53 4.57

3.81 3.24 2.54 3.72 2.80 2.82 2.20 3.36 3.99 2.92 3.14 3.72 3.73 3.68 3.14 2.23 2.61 2.96 1.96 2.91 3.58 3.47 2.47 4.21

Average Standard deviation

20.22 8.51

6.89 3.41

3.14 0.83

Deaths per 100,000 people, due to intentional self-harm Source: Calculations based on OECD (2005).

Table 2 Variable definitions and summary statistics. Variable SRi;t

Total Male Female

UNEMP i;t PCGDP i;t GROWTHi;t FERTILITY i;t LABOR F i;t OLDi;t DEPENDENCY i;t ALCOHOLi;t CO2i;t UNEMP EXP i;t OLD EXPi;t URBAN i;t

Definition

Obs.

Mean

Std.

Min.

Max.

Intentional self-harm (deaths per 100,000 people)

528 528 528 527 552 552 529 552 552 552 530 531 479 494 552

13.24 20.22 6.89 7.07 20,092 2.81 1.69 40.73 13.40 0.5055 10.60 9.74 250.1 1,317.3 76.00

5.58 8.51 3.41 4.24 7,913 2.50 0.28 4.45 2.70 0.0460 3.06 4.70 201.0 735.7 12.54

2.40 4.00 0.70 0.20 3,223 -6.85 1.15 27.90 3.80 0.3933 4.30 2.60 0.0 51.0 29.44

29.20 46.10 20.20 23.90 45,565 11.28 3.23 48.20 18.70 0.7043 20.60 30.01 1,074.0 3,740.0 97.45

Unemployment (% of total labor force) Per capita GDP (constant 2000 US $) Growth rate of per capita GDP (%) Fertility rate (births per woman) Female labor forces (% of total) Population ages 65 and above (% of total) Dependency ratio (dependents to working-age population) Per capita alcohol consumption (liters, age 15+) CO2 emissions (metric tons per capita) public expenditures for the unemployed (per capita, US$ PPP) public expenditures for the old age (per capita, US$ PPP) Urban population (% of total)

Sources: OECD (2005), World Bank (2006).

section variation is eliminated. Then we know that the ð1  R2 Þ from the dummy variable regression ðSRi;t  SRi Þ ¼ Dt c þ ei;t , (where Dt is the 1981–2002 time dummies that is one for period t and zero otherwise, c is an estimated coefficient vector, and ei;t is an error term), indicates the proportion of time variation in the data that cannot be explained by aggregate time effects. In other words, the variance of ei;t relative to the variance of ðSRi;t  SRi Þ provides the information on the percentage of country-specific time variation in the data. If this statistic equals to zero, country-specific variation is completely absent. For the overall suicide rates, 92.9% of time-series variation turned out to be country specific. This statistic is higher for the male (94.6%) relative to the female (87.5%). Because the data we construct from the OECD and World Bank have substantial country-level variation, we can say that a fixed effects analysis using panel data is adequate for the present suicide analysis. 3.3. Estimation results Cross-country panel regressions of suicide rates on unemployment and real per capita GDP are presented in Table 3. Socioeconomic variables are relevant to suicide rates. Most socio-economic variables except urbanization seem to have a significant impact on overall suicide rates. Although the estimation results for male suicide rates are not substantially different

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Y.-H. Noh / Journal of Economic Psychology 30 (2009) 575–582 Austria

Belgium

Canada

Denmark

Finland

France

Germany

Greece

Iceland

Ireland

Italy

Japan

Korea

Luxembourg

Norway

Portugal

50 0

New Zealand

Spain

50

Netherlands

0

suicide rates

0

50

0

50

Australia

1980 1985 1990 1995 2000

Switzerland

U.K.

U.S.A.

0

50

Sweden

1980 1985 1990 1995 2000

1980 1985 1990 1995 2000

1980 1985 1990 1995 2000

1980 1985 1990 1995 2000

year total

male

female Graphs by country Fig. 1. Suicide rates profiles for 24 OECD countries (1980–2002).

from those for the overall and female suicide rates, CO2 emissions do not explain significantly male suicide rates. Estimates can be summarized as follows. First, unemployment rates are positively and significantly related to overall, male and female suicide rates if we do not include an interaction term in the estimation. However, if we run the regression with the interaction, the estimated coeffi^1 ) on overall and male suicide rates are negative and statistically significant, cients associated with the unemployment (b ^12 ) is significantly positive.8 Second, we find a positive and significant relationship while the coefficient of interaction term (b between per capita GDP and suicide rates (i.e., rich countries, on average and ceteris paribus, tend to have higher suicide rates than poor countries). Third, the suicide rates are inversely related to the economic growth. Fourth, we found significantly negative relationships between fertility rates and suicide rates. Also, female labor participation is estimated with positive and significant coefficients for overall, male and female suicide rates. Thus we support the aforementioned expectation that the higher fertility rate and lower female economic participation are promoting factors of family and social ties which reduce suicide incentives. Fifth, there is a strong association between the proportion of the elderly population and suicide rates. Also, we found a significant and positive association between the dependency ratio and suicide rates. Sixth, alcohol consumption affects suicide significantly. Seventh, suicide rates are positively related to CO2 emissions, although its coefficient is insignificant for males. Eighth, public policy variables are relevant to suicide. That is, social support during periods of unemployment and for the aged is associated with less suicide rates. Ninth, urbanization has an insignificant effect on overall and male suicide rates, while it has a significantly positive effect on female suicide rates. Finally, although estimated coefficients of time dummies are not reported here, their F-test results are significant at the 1% level. The coefficients on the year dummy variables get smaller for more recent years. In other words, suicide rates are getting smaller over time than in the reference year, 1980. Given the year dummies are individually quite significant, it is not surprising that as a group the year dummies are jointly very significant. Since the coefficient of a multiplicative interaction term is significantly positive, it is important to note that the presence of two attributes, UNEMPi;t and UNEMPi;t  PCGDPi;t , modifies the individual unemployment effect on growth. That is, since ^1 < 0 and b ^12 > 0, the negative impact of unemployment on suicide rate is reduced by income.9 Thus, the greater unemployb 8 ^12 –0 significantly), statistically significant b ^1 captures the impact of an increase in UNEMPi,t on SRi,t when As long as there is an interaction effect (i.e., b ^2 captures the impact of PCGDPi,t on SRi,t when UNEMPi,t. Since the impact (slope) of UNEMPi,t on SRi,t varies PCGDPi,t = 0, while statistically significant b ^ 1 tells us distorted information when PCGDP i;t –0. Likewise, in the presence of an depending on the level of PCGDPi,t which is non-zero values, a significant b ^2 literally in evaluating a hypothesis relating UNEMPi,t and PCGDPi,t on SRi,t. interaction term, it is distorted to interpret b 9 Notice that the unrestricted regression involves the multiplicative interaction term and a joint test of whether this is zero, in addition to testing for the significance of each coefficient separately. In a joint test of significance one tests the null hypothesis of no impact H0:b1 = b12 = 0 and H0:b2 = b12 = 0 against the alternative that at least one of the coefficients is not zero. The F-statistics for this joint test actually rejected the null in the 1-2 % significance level, although I do not report them here.

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Y.-H. Noh / Journal of Economic Psychology 30 (2009) 575–582 Table 3 Regression results of fixed country effects: 24 OECD countries (1980–2002). Explanatory variables

UNEMP i;t PCGDP i;t UNEMP i;t  PCGDP i;t GROWTHi;t FERTILITY i;t LABOR F i;t OLDi;t DEPENDENCY i;t ALCOHOLi;t CO2i;t UNEMP EXP i;t OLD EXPi;t URBAN i;t Constant Within-R2 Observations

Dependent variables Overall suicide rates (SRi;t )

Male suicide rates (SR M i;t )

Female suicide rates (SR F i;t )

(1)

(3)

(5)

(2) ***

***

(4) *

***

(6) ***

0.1470 (0.0491) 0.0003*** (0.0001) – 0.1521*** (0.0503) 2.9419*** (0.7632) 1.0513*** (0.1791) 0.6003*** (0.1432) 21.191*** (6.6679) 0.4924*** (0.1111) 0.2843** (0.1259) 0.0002 (0.0013) 0.0012*** (0.0004) 0.0063 (0.0333) 51.194*** (10.624)

0.2421 (0.0971) 0.00026*** (0.0001) 0.0000299*** (0.00000) 0.1721*** (0.0512) 2.4855*** (0.7421) 1.1625*** (0.1755) 0.4908*** (0.1344) 26.110*** (6.2814) 0.4859*** (0.1081) 0.2990** (0.1251) 0.0031** (0.0015) 0.0015*** (0.0004) 0.0148 (0.0323) 57.850*** (10.171)

0.1168 (0.0707) 0.0004*** (0.0001) – 0.2285*** (0.0814) 3.4328*** (1.1287) 1.5144*** (0.2547) 0.8884*** (0.2229) 21.541** (9.6250) 0.7426*** (0.1717) 0.2771 (0.2828) 0.0002 (0.0021) 0.0015** (0.0007) 0.0446 (0.0521) 65.880*** (15.238)

0.3960 (0.1501) 0.00036*** (0.0001) 0.0000394*** (0.00001) 0.2549*** (0.0826) 2.8314*** (1.1132) 1.6609*** (0.2529) 0.7440*** (0.2066) 28.024*** (9.0735) 0.7341*** (0.1673) 0.2965 (0.2821) 0.0040* (0.0023) 0.0018*** (0.0007) 0.0168 (0.0504) 74.651*** (14.820)

0.1787 (0.0389) 0.0002*** (0.0000) – 0.0732** (0.0325) 2.3736*** (0.5908) 0.6568*** (0.1393) 0.3082*** (0.1105) 20.033*** (4.9421) 0.2562*** (0.0744) 0.2794*** (0.1063) 0.0005 (0.0009) 0.0007** (0.0004) 0.0283 (0.0225) 38.118*** (8.866)

0.0962 (0.0727) 0.00018*** (0.0000) 0.0000211*** (0.00000) 0.0874*** (0.0339) 2.0512*** (0.5786) 0.7353*** (0.1380) 0.2308** (0.1073) 23.509*** (4.908) 0.2516*** (0.0727) 0.2898*** (0.1066) 0.0025** (0.0012) 0.0009** (0.0004) 0.0431** (0.0223) 42.820*** (8.7483)

0.5343 427

0.5631 427

0.4467 427

0.4710 427

0.5833 427

0.6075 427

Note: Specifications (1), (3), and (5) are run without an interaction term as baselines, while the other specifications accommodate interactions between unemployment and income. Numbers in parentheses are White-style heteroskedasticity-consistent robust standard errors. Estimated coefficients of time dummies are not reported to save space, but their F-test results are significant at the 1% level. Due to missing observations for some countries, we have reached the size of data to be 427 instead of 552 observations that covers the years 1980–2002 (i.e., 23 years) of 24 countries. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

ment rate increases suicide rates with the higher level of income. When an interaction effect of unemployment and income is included in the regression, for example, overall suicide rates are negatively related to unemployment for per capita incomes below about $8,097 and positively related above that income. For male suicide rates the break-even income is about $10,050. The results were less robust for the impact of unemployment on female suicide rates. However, the two attributes, PCGDPi;t and UNEMPi;t  PCGDPi;t , do not modify the direction of the individual income effect ^12 > 0). Therefore, we support that higher ^2 > 0 and b on suicide rates since both have significantly positive coefficients (i.e., b income is associated with higher suicide rates, but higher unemployment is associated with lower suicide rates provided that income levels are low enough.10 The intuition here is as follows. If everybody is poor, loosing a job for a while is not as much of a stigma as loosing a job where everybody else is very successful. This seems to fit the results of the happiness literature, where happiness is affected more by one’s sense of relative income than by absolute income (e.g., Easterlin, 1995; Easterlin, 2001; Stutzer, 2004). 4. Concluding remarks The most interesting aspect of aggregated suicide is not its overall level. Rather, we are more interested in finding out causes of the variance of suicide rates across time and space. An obvious explanation for this variance is that economic and social conditions also vary over time and space. Previous studies of whether unemployment increases suicide rates

10 The control variables say a lot about the estimation results because their collective presence determines the size and sign of the estimated coefficients. For example, for those countries with low income level, that a higher unemployment is associated with a lower overall suicide rate could be because any one of more of the following phenomena: (a) a higher growth rate, (b) a higher fertility rate, (c) a higher per capita public expenditure for the elderly, and (d) a higher per capita public expenditure for the unemployed.

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are suggestive, but not conclusive. None of them controlled for an interaction between unemployment and income. Crosscountry panel suicide regressions have been estimated by fixed effects to investigate the relationship between unemployment and suicide rates. Thereby, we could avoid estimation biases due to the uncontrolled influence of time-invariant country-specific omitted variables. To allow for the possibility that the net effect of unemployment on suicide rates varies with the income level, we added a multiplicative interaction term between unemployment and the income level under the fixed effects model. The unemployment does significantly affect suicide rates, but in a way that varies with income: in a positive manner for high-income countries, but in a negative manner for low-income countries. Thus we support that economic downturns represented by unemployment rates are not necessarily associated with higher suicide rates. Actually, it has been shown that higher income is associated with higher suicide rates.11 For other control variables, we have found that a higher share of old people increases aggregate suicide rates. It also seems that higher female labor forces are associated with higher suicide rates, while higher fertility rates and social support are associated with lower suicide rates. However, it remains further studies, which social-level interventions will have a specific effect on suicide rates. For the robustness of the results, it is also worth pursuing further the suicide rates of particular age groups, such as the young and the old. Those age-specific suicide rates may be caused by different factors, so that this may not leave the unemployment–suicide relationship unchanged. Acknowledgement This work was supported by a special research grant from Seoul Women’s University (2009). References Andres, Antonio Rodriguez (2005). Income inequality, unemployment, and suicide: A panel data analysis of 15 European countries. Applied Economics, 37, 439–451. Blanchflower, David, Andrew, G., & Oswald, J. (2004). Well-being over time in Britain and the USA. Journal of Public Economics, 88, 1359–1386. Chrinko, Robert S., Fazzari, Steven M., & Meyer, Andrew P. (1999). How responsive is business capital formation to its user cost? An exploration with micro data. Journal of Public Economics, 74, 53–80. Chuang, Hwei-Lin, & Huang, Wei-Chiao (1997). Economic and social correlates of regional suicide rates: A pooled cross-section and time-series analysis. Journal of Socio-Economics, 26, 277–289. Durkheim, E. (1952). Le suicide, Paris (English edition: Durkheim, Emile, suicide: A study in sociology, translated by J. A. Spaulding and G. Simpson, London, Routledge and Kegan Paul). Easterlin, Richard A. (1995). Will raising the incomes of all increase the happiness of all? Journal of Economic Behavior and Organization, 27, 35–47. Easterlin, Richard A. (2001). Income and happiness: Towards a unified theory. Economic Journal, 111, 465–484. Gerdtham, Ulf-G, & Johannesson, Magnus (2003). A note on the effect of unemployment on mortality. Journal of Health Economics, 22, 505–518. Hamermesh, Daniel S., & Soss, Neal M. (1974). An economic theory of suicide. Journal of Political Economy, 82, 83–98. Helliwell, J. F. (2004). Well-being and social capital: Does suicide pose a puzzle. National Bureau of Economic Research, working paper 10896. Jungeilges, Jochen, & Kirchgassner, Gebhard (2002). Economic welfare, civil liberty, and suicide: An empirical investigation. Journal of Socio-Economics, 31, 215–231. Lester, Bijou Yang (2001). Learnings from Durkheim and beyond: The economy and suicide. Suicide and Life-Threatening Behavior, 31, 15–31. Neumayer, Eric (2004). Recessions lower (some) mortality rates: Evidence from Germany. Social Science and Medicine, 58, 1037–1047. OECD (2005). OECD Health data 2005. Ruhm, Christopher J. (2000). Are recessions good for your health? Quarterly Journal of Economics, 115, 617–650. Stutzer, Alois (2004). The role of income aspirations in individual happiness. Journal of Economic Behavior and Organization, 54, 89–109. UNUWIDER (2005). United Nations University – World Institute for Development Economics Research, World income inequality database (WIID), Version 2.0a, Helsinki. Wooldridge, J. M. (2003). Introductory econometrics: A modern approach (2nd ed.), USA: Thomson. World Bank (2006). World development indicators CD-ROM, Washington, DC Yang, Bijou, & Lester, David (1995). Suicide, homicide, and unemployment. Applied Economics Letters, 2, 278–279.

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Perhaps, what happens is that fast growth takes people ahead of their aspirations, and that is enjoyable for human beings, until aspirations steadily catch