The question to what extent corruption influences suicide remains still unanswered. This paper examines the effect of corruption on suicide using a panel data approach for 24 OECD countries over the period 1995-1999. Our results indicate suicide rates are lower in countries with lower levels of corruption. We also find evidence that this effect is approximately three times larger for males than for females. It follows that corruption has a detrimental effect on societal well-being.Corruption, Panel data, Suicide, Well- Being, OECD

Andrés, Antonio R.

Yamamura, Eiji

English

Research Papers in Economics

1 
 
Does  corruption  affect  suicide?  Empirical  evidence  from 
OECD countries 
 
Eiji Yamamura
1  and Antonio R. Andrés
2 
 
June 2011 
 
 
Abstract 
The  question  to  what  extent  corruption  influences  suicide  remains  still  unanswered.  This  paper 
examines the effect of corruption on suicide using a panel data approach for 24 OECD countries 
over the period 1995-1999. Our results indicate suicide rates are lower in countries with lower levels 
of corruption. We also find evidence that this effect is approximately three times larger for males 
than for females. It follows that corruption has a detrimental effect on societal well-being. 
 
Running title: Corruption and suicide in OECD countries 
Keywords: Corruption, Panel data, Suicide, Well- Being, OECD 
JEL classification: D73, H75, I18 
 
 
 
                                                   
1  Corresponding author: Eiji Yamamura, Department of Economics, Seinan Gakuin 
University, 6-2-92 Nishijin, Sawara-ku, Fukuoka 814-8511, Japan. Tel: +81-92-823-4543, 
Fax: +81-92-823-2506. E-mail address: cyl02111@nifty.com 
2  Aarhus University, Institute of Public Health, Bartholins Allé 2, 8000 Aarhus C, 
Denmark. E-mail address: ara@folkesundhed.au.dk 2 
 
I.  INTRODUCTION 
Ideally, governments can be expected to improve quality of life and increase well-being by 
preventing market failure. In the real world, this does not hold true. Since the seminal work of 
Mauro (1995) showing that corruption hampers economic growth, a growing number of studies have 
investigated the impact of corruption on various facets of society
3. Recently, researchers have paid 
attention to a more fundamental issue by examining the association between governance and 
well-being (Helliwell and Huang, 2008; Fischer and Rodríguez, 2008; Ott 2010, among others).   
Self-reported measures of subjective well-being are often criticized for lack of reliability and 
validity (for example, Bertrand and Mullainathan, 2001). Koivumaa et al. (2001) provided evidence 
that there is a high correlation between suicide and subjective well-being at individual and aggregate 
levels.  Unlike  self-reported  measures,  suicide  data
4  are more frequently used in cross -country 
comparisons. Self-reported data comparisons  are difficult because of problems with interpersonal 
comparisons of utility. Indeed, Daly and Wilson (2009) asserted that the determinants of well-being 
are the same determinants of suicide, using data for the United States. Thus, suicide rate is thought to 
be an appropriate proxy for well-being. Using suicide rates as an indicator of societal well-being has 
a great advantage in that they are a more reliable and objective indicator of well -being compared 
with self-reported well-being measures (Helliwell, 2007). However, few researchers have attempted 
to examine  the association between suicide and quality of governance.  In the present study, we 
investigate the effect of corruption on suicide rates. Thus, this corruption index reflects the quality of 
a country’s institutions. For that purpose, we used a simple fixed effects model to conduct estimation 
                                                   
3  For instance, it has been found that corruption has a detrimental effect on the damage 
from natural disasters (Kahn, 2005; Escaleras et al., 2007). Corruption causes traffic 
accidents (Anbarci et al., 2006). Corruption is negatively related to access to improved 
drinking water and adequate sanitation (Anbarci et al., 2009) and leads to reductions in 
public spending on education and health (Delavallade, 2006). 
4  The term suicide refers to completed suicides throughout this paper, unless noted 
otherwise. 3 
 
for 24 OECD countries. In the sections that follow, we present the data and empirical model and 
estimation results. The paper concludes with a summary of our findings. 
 
II. DATA AND EMPIRICAL MODEL   
This study used a panel data set covering a 5-year period (1995–1999). As shown in the Appendix, 
Table A1, 24 OECD countries were included. The data used were extracted from several sources. 
Annual suicide deaths were extracted from the WHO Mortality Database (past update Dec 2009)
5 
which contains data for number of deaths by year, country, age group, and sex as well as cause of 
death. One important issue is that suicide can be misclassified. Hence, measurement error in  the 
suicide statistics can be an issue. Data on the number of undetermined deaths are available and we 
could also conduct robustness analysis. We used the corruption perception index (CPI) developed by 
Transparency International (TI) as a proxy for the degree of corruption
6. That is, higher values of 
CPI indicated lower corruption. This index was collected from Transparency International
7. The CPI 
has been widely used to measure cross -country corruption (for examples, see Lambsdorff 2006)
8. 
Some authors argue that indices based on perceptions reflect the quality of a country’s institutions 
(Andvig 2005). Among the set of explanatory variables included were: per capita income, economic 
inequality, unemployment rates, divorce rates, total alcohol consumption, fertility rates, and total 
population. As a measure of income, we used the per capita real gross domestic product (INCOM) in 
                                                   
5  Available at http://www.who.int/whosis/mort/download/en/index.html (accessed May 
10, 2010). 
6  An important issue is how to define corruption. There are many definitions. Most 
share a common denominator which can be expressed as follows: “the abuse of public 
authority or position for private gains.” The data are available at 
http://www.transparency.org/policy_research/surveys_indices/cpi (accessed February 2, 
2011). 
7  The SIDD adjusts the raw World Income Inequality Database (WIID) for differences 
in scope of coverage, income definition, and reference unit to a nationally representative, 
gross income, household per capita standard. 
8  Another corruption indicator is that from the International Country Risk Guide 
(ICRG). This ICRG index of corruption might be problematic as it does not measure a 
country’s level of corruption but the political risk involved in corruption. 4 
 
the year 2000 in international dollars taken from the Penn World Tables (PWT v 6.3)
9. Economic 
inequality  (GINI)  was  proxied  by  the  Gini  coefficient  which  was  taken  from  the  Standardized 
Income  Distribution  Database  (SIDD)  created  by  Babones  and  Alvarez-Rivadulla  (2007)
10. 
Harmonized unemployment rates  (UNEMP)  were taken from the OECD database to allow for 
comparisons across countries. We also employed crude divorce rates (per 1,000 people) (DIV) taken 
from the United Nations Common Database, Demographic Yearbook
11. Total recorded per capita 
alcohol consumption (ALCO) was obtained from the Global Information System on Alcohol and 
Health (GISAH) of the World Health Organization (WHO)
12. Total fertility rates (FERTIL) were 
taken from the World Development Indicators Database (World Bank 2006). Lastly, mid-year total 
population (POP) was taken from the WHO Mortality Database. 
The empirical model to explain suicide rates and analyze the impact of corruption on suicide 
takes the following form:   
 
SUICI(MSUICI, FSUICI) it =α1 CORRUPT it   + α2 ALCOit   + α3 GINI it   +α4 INCOM it   
+ α5 UNEMP it +α6 DIVit +α7 FERTILit +α8 ln(POP)it +mt+ ki +εit,    (1) 
where dependent variables in country i and year t are total suicide rates as SUICIit (male and female 
suicide rates). mt represents unobservable year specific effects such as macro-level shock at t years. 
ki and εit represent individual effects of country i (a fixed effect country vector) and the error term of 
country i and year t, respectively. The structure of the data set used in this study is a panel; mt is 
controlled by incorporating year dummies. ki holds the time invariant feature. So we can use the 
fixed effects model to capture ki (Baltagi 2005). The fixed effects allow to control for differences in 
                                                   
9  The data are available at http://pwt.econ.upenn.edu/php_site/pwt_index.php (accessed 
January 15, 2010). 
10  The data are available at http://salvatorebabones.com/data-downloads (accessed 
March 1, 2011). 
11  Available at http://data.un.org/Default.aspx (Accessed May 10, 2010). 
12  Available at http://apps.who.int/globalatlas/default.asp (Accessed May 10, 2010). 5 
 
national characteristics such as culture, religious concepts about death or life across nations, climate 
and  traditional  values,  and  in  periodical  characteristics  such  as  changes  in  social  acceptance  to 
suicide. The regression parameters to be estimated are α; and εit represents the classical error term. If 
CORRUPT takes 10, this indicates an absence of corruption. On the other hand, if CORRUPT takes 
0,  business  transactions  in  the  country  are  entirely  dominated  by  kickbacks  and  extortion,  for 
example. CORRUPT was included to capture the degree of governance corruption. If people are less 
likely to commit suicide in less corrupt societies, CORRUPT will take the negative sign. One of the 
reasons to employ a fixed effects model is that is a closed sample (homogenous) and we do not 
extrapolate these results to other set of countries. We also expect some correlation between the 
individual effects and some of the explanatory variables. What about the results of Hausman test? 
Following the suicide literature, we include several socioeconomic variables on the right hand 
side of our regression models (e.g. Brainerd 2001, Kunce and Anderson 2002, Andrés 2005, Chuang 
and Huang 2007, Chen et al., 2009; Noh 2009, Yamamura 2010). To begin, economic factors were 
captured by per capita income (INCOM), unemployment rate (UNEMP), and Gini index (GINI). 
Social factors were controlled for by divorce rates (DIV), total alcohol consumption (ALCO), and 
fertility rates (FERTIL). Lastly, we control for the corresponding total populations to account for 
country size
13. Table 1 provides definitions and descriptive statistics of the variables. 
 
III. ESTIMATION RESULTS.   
Results  for  the  fixed  effects  models  are  given  in  Table  2,  each  corresponding  to  another 
dependent variable (total, male, and female suicide rates). Regressions using the male suicide rates 
as dependent variable are very similar to those on total suicide rates because males account for the 
bulk of suicides. As indicated at the bottom of Table 2, the R squared values are higher in the models 
                                                   
13  Using adjusted suicide rates to control for differences in the structure of population is 
equivalent to regress the crude rate and control for age structure of population. 6 
 
in which the dependent variable is the total and male suicide rates. One might also conclude that 
male suicide behaviour could be more responsive to socioeconomic conditions as opposed to female 
behaviour.  For  the  sake  of  brevity,  we  have  focus  on  concentrated  our  focus  on  results  for 
CORRUPT and results where coefficients were statistically significant. As Table 2 shows the degree 
of corruption is consistently negatively associated with suicide rates (in all regression models).. 
Furthermore, its coefficient magnitudes are higher for total and male suicide rates than for female 
suicide rates. In particular, the    absolute value of CORRUPT coefficient was 0.65, suggesting that a 
1 point increase in CORRUPT resulted in a 0.62 point decrease in suicide rates. The absolute value 
of CORRUPT was 0.99 for male suicide rate, whereas the value was only 0.33 for female suicide 
rate. This implies that a 1 point increase in CORRUPT resulted in a 0.99 point decrease in male 
suicide rate, while a 1 point increase in CORRUPT resulted in a 0.33 point decrease in female 
suicide rate. Hence, the effect of CORRUPT on male suicide rate was approximately three times 
larger than that for female suicide rate. Regarding other control variables, our results are similar to 
many other previous empirical studies using panel data. We found significant effects of divorce rate 
on  male  suicides,  and  it  has  little  effect  on  female  suicide.  Alcohol  consumption  is  positively 
associated with suicide rates, regardless of gender. Unemployment, income inequality and fertility 
rates are found to be statistically insignificant in all regression models (for instance, Minoiu and 
Andrés, 2008). 
 
IV. CONCLUSION 
Although suicide research is a multidisciplinary subject and socioeconomic factors are well 
documented  risk  factors  for  suicide.  Past  research  has  neglected  the  role  of  quality  governance 
indicators. In particular, this study explored how corruption influences suicide rate using a panel 
dataset for 24 OECD countries. Empirical results from the fixed effects estimation revealed that 7 
 
suicide rates are lower in countries with lower levels  of corruption. Furthermore, its coefficient 
magnitude is higher for male suicide rates than for female suicide rates. This implies that corruption 
has a detrimental effect on social well-being.   8 
 
REFERENCES 
Anbarci, N., Escaleras, M., & Register, C.A. (2006.) Traffic fatalities and public sector corruption. 
Kyklos, 59(3), 327-344. 
Anbarci,N., Escaleras,M. & Register,C.A. (2009). The ill effects of public sector corruption in the 
water and sanitation sector, Land Economics, 85(2), 363-377. 
Andrés, A.R., (2005). Income inequality, unemployment, and suicide: a panel data analysis of 15 
European countries. Applied Economics, 3, 439-451. 
Andvig, J.C. (2005). A House of Straw, Sticks or Bricks. Some Notes on Corruption Empirics, Paper 
presented at the IV Global Forum on Fighting Corruption and Safeguarding Integrity, June 
2005. 
Babones, S.J., & Alvarez-Rivadulla, M.J. (2007). Standardized income inequality data for use in 
cross-national research. Sociological Inquiry, 77, 3-22. 
Baltagi, B. (2005). Econometric Analysis of Panel Data, John Wiley and Sons.   
Bertrand  M,  &  Mullainathan  S.,  (2001).    Do  people  mean  what  they  say?  implications  for 
subjective survey data. American Economic Review, 91(2), 67–72. 
Brainerd, E. (2001). Economic reform and mortality in the former Soviet Union: A study of the 
suicide epidemic in the 1990s. European Economic Review, 45, 1007-1019. 
Chen, J., Yun Jeong Choi, and Yasuyuki Sawada (2009). ‘How Is Suicide Different in Japan?’ Japan 
and the World Econom,y 21(2), 140-50. 
Chuagn, H.L,  &  Huang,  W.C.  (2007).  A  Re-examination  of  the  suicide  rates  in Taiwan.  Social 
Indicators Research, 83(3), 465-485. 
Delavallade,  C.  (2006).  Corruption  and  distribution  of  public  spending  in  developing  countries. 
Journal of Economic Finance, 30, 222-239. 
Daly M, & Wilson DJ. (2009). Happiness, unhappines and suicide: an empirical assessment. Journal 9 
 
of European Economic Association, 7, 539-549.   
Escaleras,M., Anbarci,N., & Register,C.A. (2007). Public sector corruption and major earthquakes: A 
potentially deadly interaction. Public Choice, 132(1), 209-230. 
Fischer, J.A.V., & Rodríguez, A. (2008). Political institutions and suicide: A regional analysis of 
Switzerland. TWI Research Paper. Universitat Konstanz. 
Helliwell, J.F., & Huang, H. (2008). How’s your government? International evidence linking good 
government and well-being. British Journal of Political Science, 38, 595-619. 
Helliwell, J.F. (2007). Well-being and social capital. Does suicide post a puzzle? Social Indicators 
Research, 455-496. 
Kahn,  M.E.  (2005).  The  death  toll  from  natural  disasters:  the  role  of  income,  geography,  and 
institutions. Review of Economics and Statistics, 87, 271-284. 
Koivumaa-Honkanen H, Honkanen R, Viinamki H, Heikkil J, Kaprio J, & Koskenvuo M . (2001). 
Life satisfaction and suicide: a 20-year follow-up study. The American Journal of Psychiatry, 
158, 433-459. 
Kunce, M., Anderson, A.L. (2002). The impact of socio-economic factors on state suicide rates: A 
methodological note. Urban Studies, 39, 155-162. 
Lambsdorff,  J.G.,  (2006).  Causes  and  Consequences  of  Corruption:  What  do  we  know  from  a 
Cross-Section  of  Countries?  in  S.  Rose-Ackerman  (ed.),  International  Handbook  on  the 
Economics of Corruption, Cheltenham, UK: Edward Elgar, 2006, 3-51. 
Mauro, P. (1995). Corruption and growth. The Quarterly Journal of Economics, 110(3), 681-712. 
Minoiu, C., & Andres, A.R. (2008). The effect of public spending on suicide: Evidence from US 
state data. Journal of Socio-Economics, 37(1), 237-261.   
Noh, Y.H., (2009). Does unemployment increase suicide rates? The OECD panel evidence. Journal 
of Economic Psychology, 30, 575-582.   10 
 
Ott, J.C. (2010). Good governance and happiness in nations: Technical quality precedes democracy 
and quality beats size. Journal of Happiness Studies, 11,353-368. 
World Bank. (2006). World Development Indicators, CD-ROM, Washington, DC: World Bank. 
Yamamura, E. (2010). The different impacts of socio-economic factors on suicide between males 
and females. Applied Economics Letters, 17, 1009–1012. 
  
8 
 
Table 1 
Variable definitions, means, and standard deviations (Observations = 102). 
Variables  Definition  Mean  Standard 
Deviation 
SUICI  Suicide rate (per 100,000)  14.5  6.4 
MSUICI  Male suicide rate (per 100,000)  21.4  9.4 
FSUIC  Female suicide rate (per 100,000)  7.8  4.1 
CORRUPT  Degree of corruption  7.6  1.7 
ALCO  Recorded adult per capita alcohol consumption (in 
liters) 
10.7    3.0 
GINI  Gini coefficient  0.42  0.11 
INCOM  Per capita income ($1000 US)  23.2  7.0 
UNEMP  Unemployment rate (%)  7.4  3.6 
DIV  Crude divorce rate (per 1,000; %)  2.0  0.9 
FERTIL  Fertility rate, total (births per woman)          1.6            0.2 
POP  Mid-year population (millions)     37.2          58.9 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
9 
 
Table 2 
Panel data regression models. Fixed effects approach.   
Explanatory 
variables 
(1) 
Dependent variable: 
Total suicide rate 
(2) 
Dependent variable: 
Male suicide rate 
(3) 
Dependent variable: 
Female suicide rate 
CORRUPT  -0.65** 
(-2.00) 
-0.99* 
(-1.95) 
-0.33* 
(-1.70) 
ALCO  2.05*** 
(4.65) 
3.26*** 
(4.79) 
0.89*** 
(3.38) 
GINI  5.83 
  (0.83) 
13.6 
  (1.27) 
-1.69 
  (-0.41) 
INCOM  -0.49** 
              (-2.23) 
-0.86** 
              (-2.55) 
-0.14 
              (-1.06) 
UNEMP    0.21 
(1.21) 
  0.25 
(0.97) 
  0.16 
(1.59) 
DIV  2.33** 
(2.45) 
4.33*** 
(2.95) 
0.38 
(0.67) 
FERTIL  -0.84 
(-0.24) 
-2.97 
(-0.55) 
1.22 
(0.59) 
Ln(POP) 
 
0.41 
(1.58) 
0.57 
(1.41) 
0.27* 
(1.74) 
R-squared (Within)  0.39  0.41  0.32 
No. of observations  102  102  102 
Note: Numbers in parentheses are t-statistics. *, ** and *** indicate significance at 10, 5 and 1 
percent levels, respectively. Year and country dummies are included in all estimations, but to save 
space are not reported.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
10 
 
APPENDIX.   
Table A1. OECD countries in the regression analysis     
 
 
 
 
 
 
 
 
 
 
 
Australia  Japan 
Austria  Luxembourg 
Belgium  Netherlands 
Canada  New Zealand 
Denmark  Norway 
Finland  Portugal 
France  South Korea 
Germany  Spain 
Greece  Sweden 
Iceland  Switzerland 
Ireland  United Kingdom 
Italy  United States 

A House of Straw, Sticks or Bricks. Some Notes on Corruption Empirics, Paper presented at the IV Global Forum on Fighting Corruption and Safeguarding Integrity,

A Re-examination of the suicide rates in Taiwan.

Causes and Consequences of Corruption: What do we know from a Cross-Section of Countries?

Corruption and distribution of public spending in developing countries.

Corruption and growth.

Do people mean what they say? implications for subjective survey data.

Does unemployment increase suicide rates? The OECD panel evidence.

Econometric Analysis of Panel Data,

Economic reform and mortality in the former Soviet Union: A study of the suicide epidemic in the 1990s.

Happiness, unhappines and suicide: an empirical assessment.

How’s your government? International evidence linking good government and well-being.

Income inequality, unemployment, and suicide: a panel data analysis of 15 European countries.

Life satisfaction and suicide: a 20-year follow-up study.

Political institutions and suicide: A regional analysis of Switzerland.

Public sector corruption and major earthquakes: A potentially deadly interaction.

Standardized income inequality data for use in cross-national research.

The death toll from natural disasters: the role of income, geography, and institutions.

The different impacts of socio-economic factors on suicide between males and females.

The effect of public spending on suicide: Evidence from US state data.

The ill effects of public sector corruption in the water and sanitation sector,

The impact of socio-economic factors on state suicide rates: A methodological note.

Traffic fatalities and public sector corruption.

Well-being and social capital. Does suicide post a puzzle? Social Indicators Research,

World Development Indicators,

Does corruption affect suicide? Empirical evidence from OECD countries

Does corruption affect suicide? Empirical evidence from OECD countries

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics