GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 367

Comparison of Social Capital’s Effect on
Consideration of Suicide between Urban and Rural Areas

Eiji Yamamura

April 2015

GCOE Secretariat
Graduate School of Economics
OSAKA UNIVERSITY
1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

ComparisonofSocialCapital’s

Effecton

Consideration of Suicide between Urban and Rural
Areas
Eiji Yamamura

An increasing number of works have addressed the socio-economic determinantsof suicide. Social capital is a key factor in preventing suicide. However, little is
known about the experience of suicide consideration using subjective values. Fromthe viewpoint of suicide prevention, it is worth examining how people think ofsuicide. This paper attempts to examine the effect of social capital on suicideconsideration based on individual-level data from Japan. Furthermore, the paper
compares the effect of social capital between urban and non-urban areas.
After controlling for various socio-economic factors, the major findings are thatboth individual-level social capital and social capital accumulated in one’s place ofresidence reduce the probability that one will consider suicide. After dividing thesample into urban and non-urban residents, the effect of social capital in one’s placeof residence for urban residents is remarkably larger than for non-urban residents.
In contrast, the effect of individual-level social capital disappears for urban
residents,

while

the

effectpersistsfornon-urban

residents.

Overall,community-level social capital plays a more important role in deterring suicide forurban residents.



Correspondence should be addressed to the author at the Department of Economics,
Seinan Gakuin University, 6-2-92 Sawaraku Fukuoka 814-8511, Fukuoka. Japan.
E-mail address: yamaei@seinan-gu.ac.jp.
Telephone: +81-92-823-4543
Fax: +81-92-823-2506

1. Introduction
The seminal work of Durkheim (1951) was the first to analyze suicide in the 19th
century from the viewpoint of social science. In the 21st century, suicide became a more
serious issue than in prior centuries, so it is crucial to implement measures to prevent
suicides.1 According to Durkheim (1951), suicide is a predictable consequence of thedegree to which one is integrated into society. Hence, the relation between individuals
and society should be analyzed when we explore how and why individuals commit
suicide. Therefore, to analyze suicide, previous works consider the extent to whichsuicide is accounted for by social factors such as social capital (e.g., Putnam 2000;
Yamamura 2010; Smith and Kawachi 2014), 2 the sex ratio (Kuroki 2014), thefertility rate (Okada and Samreth 2013), divorce, and marriage (e.g., Kunce andAnderson 2002; Neumayer 2003; Andrés et al. 2011). In addition to social
relationships, many works consider economic factors, such as public spending(Minoiu and Andrés 2008), inequality (Andrés 2005), and unemployment (e.g., Platt
1984; Yang et al. 1992; Yang and Lester 1995; Breuer 2015).
Japan experienced a remarkable increase in the suicide rate in the mid-1990s.
According to the OECD (2013), even though the rates have remained stable since then,the age-standardized rate per 100,000 population of Japan in 2011 was 20.7,
remarkably higher than that of the United States in 2011 (12.5). During the mid-1990s,
coinciding with the Asian financial crisis, economic stagnation had a detrimental
influence on the society of Japan.3 Economic researchers have provided evidence thatthe increase in the suicide rate was caused partly by the economic conditions (e.g., Koo

and Cox 2008; Chen et al. 2009; Inagaki 2010; Kuroki 2010; Sugano and Matsuki
2014, Suzuki et al. 2013; 2014).4 However, consistent with Durkheim’s view, social

factors are also significantly related to the suicide rate in Japan (e.g., Yamamura
2010, Andrés et al. 2011; Sugano and Matsuki 2014). However, the suicide rate ismore sensitive to economic factors than to social factors (Chen et al. 2009).
Even though existing works analyzing suicide have used suicide rates, there

Many analyses of suicide have been conducted since the 1970s (e.g., Hamermresh
1974; Yang and Lester 1995; Huang 1996; Viren 1996; Chuang and Huang 1997;
Brainerd 2001; Jungeilges and Kirchgassner 2002; Marcotte 2003).
2 It has been argued that the suicide rate may be high if the level of social capital ishigh (Kushner and Sterk 2005).
3 The increase in suicide rates also became an important issue in South Korea, so theissue has been addressed by researchers (Kim et al. 2010). Comparative works on
suicide have addressed Japan and Korea (Kim et al. 2011).
4 Even prior to the 1990s, economic factors such as unemployment rates have beensignificantly related to the suicide rate in Japan (Mothohashi 1991).
1

seems to be a large gap between committing suicide and suicide consideration. An
incident of committing suicide is regarded as an extreme case. Therefore, existing
works have dealt only with the committing of suicide and do not take into accountthe intermediate condition between committing suicide and a sound mental
condition. To prevent suicide, it is worth investigating how and why individuals
consider suicide even if they do not actually commit suicide. To analyze theintermediate situation, the current paper uses survey data from Japan that provide
informationonindividual-level

perceptionsofsuicide.

Furthermore,community-level data, such as the degree of social capital, the Gini coefficient ofincome, and the Gini coefficient of education level in a residential area, are matchedwith the individual-level data. Then, the association between socio-economic factors
in a residential area and individuals’ suicide consideration is investigated. The key
findings are as follows: social capital reduces the probability that individuals willconsider suicide, and the effect is larger for urban residents than for non-urban
residents. Further, economic and education inequality leads individuals to consider
suicide only in urban areas. These results suggest that economic and social
relationships with others in the community are more important for urban residents
than for non-urban residents.
The current paper is organized as follows: Section 2 presents a concise
explanation of the data and specifies the regression functions. In Section 3, I discuss theresults of the estimations. The final section offers concluding observations.

2. Data and Econometric Framework
2.1. Data
In the current paper, Japanese General Social Survey (JGSS) data are used.
These are individual-level data.5 The JGSS used a two-stage stratified sampling
method and was conducted throughout Japan from 2000 to 2012. The paper uses
only the data collected in 2006 because a question about suicide consideration was
included only in the 2006 survey. The JGSS was designed as a Japanesecounterpart to the General Social Survey (GSS) in the United States. The JGSS
questionnaire

includesstandard

questionsconcerninganindividual’scharacteristics via face-to-face interviews. The data cover information related toData for this secondary analysis, “Japanese General Social Surveys (JGSS), Ichiro
Tanioka,” were provided by the Social Science Japan Data Archive, Information Center
for Social Science Research on Japan, Institute of Social Science, the University ofTokyo.

5marital and demographic (age and sex) status, annual household income,6 years ofschooling, age, prefecture of current residence, and prefecture of residence at 15
years of age. A Japanese prefecture is the equivalent to a state in the United States
or a province in Canada. There are 47 prefectures in Japan.
Key variables included in the JGSS 2006 are experience of suicide considerationand social capital-related variables. With respect to perceptions about suicide, oneof the survey questions asked, “In the past 5 years, have you thought of committing
suicide at least once?” Respondents could choose one of three responses: 1 (Never), 2(Not in the past 5 years but have before that), or 3 (Yes). The responses allow me toquantify the experience of suicide consideration even if suicide has not beencommitted. Figure 1 shows that 80% of the respondents have not thought ofcommitting suicide. However, 6% of people had thought of committing suicide
within the previous five years. The suicide rate in Japan was 0.02% (OECD 2013).
Roughly, this means that the number of potential suicides was 300 times larger
than the number of those who actually committed suicide in Japan.
As a proxy for social capital, one of the survey questions asked, “Generally
speaking, would you say that most people can be trusted?” Respondents could
choose one of three responses: 3 (Yes), 2 (Depends), or 1 (No).7 According to Putnam(2000), social capital is defined as the features of a social organization, such as
networks and norms, and social trust facilitates coordination and cooperation. In
the current paper, social trust is used to measure the degree of social capital. As
argued by Uslaner (2002), trust is categorized into generalized trust (trust in mostpeople) and particularized trust (trust in members of the group to which one
belongs), which should be considered separately. Individual-level generalized trust
is measured by the response to the question indicated above.
Because information on the respondents’ residential prefecture was obtained, therate of generalized trust can be calculated for each prefecture, which is regarded asa proxy for community-level generalized trust. Further, a proxy for particularizedtrust is also captured as follows. In 1996, the Japan Broadcasting Corporation
conducted a survey on the consciousness and behaviors of prefecture residents(Japan Broadcasting Corporation 1997). One of the survey questions asked, “Do you
In the original dataset, annual earnings were grouped into 19 categories, and weassumed that everyone in each category earned the midpoint value. For the top categoryof “23 million yen and above,” I assumed that everybody earned 23 million yen. Of the
1,262 observations used in the regression estimations, there were only 18 observationsin this category. Therefore, the problem of top coding should not be an issue here.
7 Kuroki (2011) used the JGSS data to examine the relation between generalized trustand happiness levels.
6

trust community members?” Respondents could choose one of three responses: “Yes,”“Unsure,” or “No.” I calculated the rates for those who answered “yes” within aprefecture. The rate of trust in community members can be used as a proxy forcommunity-level particularized trust. It is also used to measure social capital in thecurrent research. I assume here that the rate of trust in community members wasstable over time. I obtained a proxy for each of the 47 prefectures. Individual-level
generalized trust, community-level generalized trust, and community-levelparticularized trust are used as proxies for social capital.
Gini data coefficients for prefecture-level household income were calculatedusing data from the National Survey of Family Income and Expenditure, conductedby the Ministry of Internal Affairs and Communications (1999). These surveys are
conducted every five years. To avoid endogeneity bias, the income Gini in 1999 isused as a predetermined exogenous variable. Furthermore, the Gini coefficient ofeducation level in 2000 was constructed by Hojo (2009). I also used the education
Gini to capture inequality in one’s place of residence.

2.2. Econometric Framework and Estimation Strategy
Table 1 lists the definition and basic statistics for variables used for estimationin the current paper. The estimated function of the baseline model takes thefollowing form:
SUICID im = 0 + 1 PTRUSTm+ 2GTRUSTm + 3IGTRUSTim +4GINIm +5GINI_EDUm + 6SCHOOLim + 7INCOMim + 8AGEim +9MALEm +10MARRYim +11CHILDim +128UNEMPim + uim,
where SUICID im represents the dependent variable in individual i and prefecture

m. Regression parameters are represented by . As explained previously, values for
SUICID range from 1 to 3, so the ordered probit model is used to conduct theestimations (Greene 2008). The larger the value of SUICID is, the more people arelikely to consider suicide. The error term is represented by uim. It is reasonable toassume that the observations may be spatially correlated within a prefecture, as thepreference of one agent may well relate to the preference of another in the same
prefecture. To consider such a spatial correlation in line with this assumption, I
used the Stata cluster command and calculated z-statistics using robust standard
errors. The advantage of this approach is that the magnitude of spatial correlation
can be unique to each prefecture.

To examine the association between social trust and consideration of suicide,
CTRUST,

GTRUST,and

IGTRUSTareincluded.

CTRUST

capturescommunity-level particularized trust, while GTRUST captures community-level
generalized trust because their values are common for respondents who resided in
the same prefecture. IGTRUST captures individual-level generalized trust.
Following the arguments of Yamamura (2010) and Smith and Kawachi (2014), thesigns of these coefficients are expected to be negative. Inequality is thought toincrease social isolation between people of different classes. Inevitably, people are
more likely to consider suicide in an unequal society. To examine this issue, GINI
and GINI_EDU are included, and they are predicted to have a positive sign.
Variables capturing the individual economic effects are SCHOOL, INCOM, and
UNEMP, standing for years of schooling, household income level, and an
unemployment dummy, respectively. If an improvement in economic conditions
reduces the probability that people will consider suicide, the signs of the former two
variables will become negative, while that of the latter will become positive. To
capture the age effect, marital-related influence, and the existence of children, AGE,MARRY, and CHILD are incorporated, respectively.
The values for the dependent variable, SUICID, are 1 (respondents have neverthought of committing suicide), 2 (respondents have not thought of committing
suicide in the past five years but have before that), and 3 (respondents have thoughtof committing suicide at least once). PTRUST, GTRUST, GINI, and GINI_EDU are
prefecture-level variables. Accordingly, for migrants from other prefectures,
SUICID is thought to be associated with these variables in the former residential
prefecture rather than in the current residential prefecture. Overall, migrants
cannot be reasonably included in the sample. As explained previously in this section,
I can obtain information about the respondents’ current prefecture of residence andtheir prefecture of residence at 15 years of age. If the current prefecture of residence
is different from the prefecture of residence at 15, a respondent can be defined as a
migrant. As a robustness check, estimations are conducted using not only the fullsample but also the sample excluding migrants.

3. Estimation and Discussion of Empirical Results
Tables 2, 3, and 4 list the results based on the full sample, an urban sample,
and a non-urban sample, respectively. Further, in each table, columns 1 and 2
present results based on the sample including migrants, while columns 3 and 4
present results based on the sample excluding migrants. In columns 1 and 3, all thecontrolvariables

areincludedas

independent

variables.

As

alternative

specifications, in columns 2 and 4, MARRY, CHILD, and UNEMP are not included
as a robustness check. Some respondents did not respond to the questions
associated with MARRY, CHILD, and UNEMP, so the sample size increases whenthese variables not included. In the ordered probit model, coefficients of variables
cannot simply be interpreted as marginal effects (Greene 2008, 833). Instead ofcoefficients for three probabilities, marginal effects can be calculated. For instance,
I can obtain the marginal effects of CTRUST on the probability that SUICID is 1,
the effects of CTRUST on the probability that SUICID is 2, and the effects of
CTRUST on the probability that SUICID is 3. The current paper reports themarginal effects of independent variables on the probability that SUICID is 3.8
I now proceed to examine Table 2. Looking at the social capital-related
variables CTRUST, GTRUST, and IGTRUST reveals that the coefficients were all
negative and statistically significant in columns 1–4. This implies that social capital
plays an important role in preventing suicide by leading people not to consider
suicide. That is, social capital is thought to cure the mental weakening regarded as
the cause of suicide in its early stages. Apart from this, other variables do not show
statistical significance in any columns, with the exception of AGE. That is,inconsistent with previous works (e.g., Andrés 2005; Koo and Cox 2008; Chen et al.
2009; Inagaki 2010; Kuroki 2010; Sugano and Matsuki 2014; Breuer 2015),
economics-related variables such as GINI, GINI_EDU, INCOM, and UNEMP are
not associated with suicide. Existing works examine the influence of economic
factors on actual suicide rates, while the current work investigates its influence on
potential suicide probability by using data on subjective perceptions of suicide. As
mentioned previously, in Japan, the suicide rate is only 0.02% (OECD 2013), whichis equivalent to 1 out of 300 people considering suicide within the previous five
years. That is, the number of those who have actually committed suicide is verysmall, although about 20% of people have considered suicide. Therefore, economic
factors are critical determinants for advanced cases, but not for cases in an earlystage.
I concentrate on key variables in the current paper because most of thevariables do not show statistical significance. Let me turn to the results based on
the urban sample. According to Table 3, CTRUST and GTRUST exhibit a negative
sign and statistical significance at the 1% level in all columns. However, IGTRUSTOther results (the probability that SUICID is 1 and the probability that SUICID is 2)
are available upon request from the corresponding author.

8

shows statistical significance only in column 1. Further, the coefficient’s sign for
IGTRUST is negative in columns 1–3 and positive in column 4. Therefore, theresults for IGTRUST are not robust, so IGTRUST does not have an influence on
SUICID. Considering the social capital-related variables jointly leads me to argue
that, in urban areas, community-level social capital contributes to the prevention ofsuicides, but individual-level social capital does not. As for variables used to capture
inequality, it is interesting to observe that GINI and GINI_EDU have a positive
sign and are statistically significant, with the exception of column 2. This implies
that inequality in society provides a motive to consider suicide in urban areas.
Furthermore, the absolute values of GINI_EDU are about four times larger thanthose of GINI. I interpret this as suggesting that inequality in education plays agreater role than income inequality in dividing society and thus isolates people tothe extent that they may consider suicide. That is, non-economic factors are morecritical in providing motives to consider suicide in an early stage than economic
factors.
As shown in Table 4, the social capital-related variables CTRUST, GTRUST,and IGTRUST all show a negative sign and are statistically significant in columns
1–4. However, the z-values of CTRUST and GTRUST are smaller than those in
Table 3. Furthermore, the absolute values of their coefficients are about 0.002 or
0.003, remarkably smaller than those in Table 3. Comparing the results for
CTRUST and GTRUST in Table 4 with those in Table 3 indicates that
community-level social capital is more important in preventing suicide in urban
areas than in non-urban areas. In contrast, individual-level social capital(IGTRUST) is more important in non-urban areas than in urban areas. Regarding
inequality, GINI and GINI_EDU are not statistically significant in any columns,
although they show a positive sign. Therefore, in non-urban areas, inequality
cannot be considered a key factor that causes suicide.
From the discussion thus far, I suggest that one’s relationships with others playa greater role in preventing suicide in urban areas than in non-urban areas. The
reason for this might be that social relations are weaker and thus more valuable forurban residents. Furthermore, economic factors are not associated with theprobability that people have considered suicide. This differs from the results ofexisting works that have investigated the rare and extreme cases in which people
have actually committed suicide.

4. Concluding Remarks
Many works have attempted to examine the relationship between
socio-economic factors and suicide. However, these works analyzing suicide aremainly based on suicide rates in administrative districts, such as states, prefectures,and municipalities. Cases of suicide are very rare compared with cases in whichpeople consider suicide. Most people who have considered suicide do not commit it.
From the viewpoint of preventing suicide, it is valuable to investigate how and why
individuals consider suicide even if they do not actually commit suicide. Therefore,
the current paper uses survey data from Japan to analyze the issue.
The main findings are that both individual-level social capital and social capital
accumulated in one’s place of residence are negatively associated with potential
suicides. After dividing the sample into urban and non-urban residents,
community-level social capital was found to make a greater contribution to suicide
prevention in urban areas than in non-urban areas. However, the effect ofindividual-level social capital disappears for urban residents but persists for
non-urban residents. I interpreted this as implying that social capital is scarcer andthus valuable in reducing social isolation in urban areas. Further, economic andeducation inequality leads individuals to consider suicide only in urban areas.
Therefore, economic and social relations with others is a more important remedy forsuicide consideration in urban residents than in non-urban residents.
Community-based suicide prevention programs have been started in some model
project communities that are financially supported by a local government of Japan(Motohashi et al. 2004). The results of the current paper show that such
community-based programs are a more useful measure to prevent suicide in urban
areas than in rural areas. Therefore, community-based suicide prevention programs
should be implemented in urban areas.

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Figure 1. Respondents’ self-reported thoughts of suicide.

Note: Question: In the past 5 years, have you thought of committing suicide at leastonce?
The numbers are the respondent’s choices: 1 (Never), 2 (Not in the past 5 years but have
before that), or 3 (Yes).

Table 1. Descriptive Statistics
Variables

Definition

Mean

Std. Dev

Max

Min

SUICID

Considering suicide:

1.22

0.53

3

1

46.6

4.3

59.3

40.1

21.1

2.7

28.8

14.3

2.09

0.56

3

1

29.5

1.2

35.3

27.5

Question: In the past 5 years, have you thought of committing
suicide at least once?
Choices for respondents are as follows:
1 (Never), 2 (Not in the past 5 years but have before that), or 3 (Yes).
PTRUST

Rate of those who trust community members within a residential
prefecture: rate of those whose reply to the question (exhibited indefinition of GTRUST) is 3 (%)

GTRUST

Rate of those who generally trust others within a residential
prefecture (%).

IGTRUST

Respondent’s individual level of generalized trust.
Question: Generally speaking, would you say that most people canbe trusted?
Choices for respondents are as follows:
3 (yes), 2 (depends), 1 (no).

GINI

Gini coefficient of household income within a residential prefecture
in 1999

GINI_EDU

Gini coefficient of education within a residential prefecture in 2000

9.9

0.2

11.1

9.2

SCHOOLYears of schooling

12.1

2.62

18

6

INCOM

Household income a

611.4

417.0

2,300

0

AGE

Age

52.3

16.6

89

20

MALE

Male dummy: 1 if respondent is male; otherwise 0

0.45

---

1

0

MARRY

Dummy for a married person: 1 if respondent is married; otherwise

0.78

---

1

0

0
CHILD

Number of children

1.71

1.16

8

0

UNEMP

Dummy for unemployment. 1 if respondent is unemployed;

0.02

---

1

0

otherwise 0

Notes:a In tens of thousands of yen.

Source: Ministry of Health, Labor and Welfare (various years) Jinko Dotai Tokei Tokushu Hokoku.
Asahi

Shinbunsha

Asahi-Shinbunsha.

(various

years).

Minryoku:

TODOFUKEN-BETSUMINRYOKU

SOKUTEISHIRYOSHU.

Tokyo:

Table 2. Full sample: regression results on considering suicide (ordered probit model)
PTRUST
GTRUST
IGTRUSTGINI
GINI_EDUSCHOOL
INCOM
AGE
MALE
MARRY
CHILD
UNEMP

(1)

(2)

(3)

(4)

−0.003***−0.002**−0.003**−0.003**(−2.61)(−2.41)(−2.29)(−2.23)−0.003**−0.003**−0.003*−0.003*(−2.30)(−2.21)(−1.85)(−1.97)−0.019***−0.018***−0.022**−0.020**(−3.21)(−2.89)(−2.50)(−2.18)

0.002

0.002

0.002

0.003

(0.59)(0.75)(0.42)(0.83)

0.005

0.009

0.008

0.014

(0.28)(0.57)(0.48)(0.86)−0.001−0.0010.001

0.0004(−0.06)(−0.42)(0.59)(0.02)

0.005−0.003

0.014

0.011

(0.45)(−0.24)(1.10)(0.90)−0.002***−0.002***−0.002***−0.002***(−7.30)(−7.72)(−5.91)(−5.87)−0.005−0.001−0.001−0.003(−0.63)(−0.99)(−0.11)(−0.25)−0.032*−0.024(−1.81)(−1.33)

0.001

0.004

(0.22)(0.92)

0.024−0.020(0.41)(−0.51)

Migrants

Including

IncludingExcludingExcludingLog−666.7−769.2−480.6−549.1

1262

1413

903

1013

pseudolikelihood
Observations

Notes: Values without parentheses are marginal effects on the probability that a
respondent will choose 3 for a question about a desire for suicide. In other words, thevalues are the probabilities that respondents have thought of committing suicide atleast once in the past 5 years. The reported values of INCOM (SCHOOL) are multipliedby 1000 (10) for convenience of interpretation. Values in parentheses are z-statistics
obtained by the robust standard error clustered on residential prefecture. *, **, and ***indicate significance at the 10, 5, and 1 percent level, respectively. In all estimations,dummies for size of residential place are included, but the results are not reported.
16

Table 3. Urban sample: regression results considering suicide (ordered probit model)
PTRUST
GTRUST
IGTRUSTGINI
GINI_EDUSCHOOL
INCOM
AGE
MALE
MARRY
CHILD

(1)

(2)

(3)

(4)

−0.009***−0.007***−0.016***−0.013***(−4.51)(−3.47)(−3.16)(−3.59)−0.009***−0.008***−0.023***−0.018***(−3.80)(−2.91)(−3.52)(−3.17)−0.033**−0.019−0.0010.013(−2.50)(−1.42)(−0.05)(0.51)

0.010**

0.006

0.025***

0.016*(2.18)(1.50)(3.05)(1.79)

0.044**

0.023

0.112***

0.070*(2.20)(0.65)(2.61)(1.97)

0.001

0.0004

0.002

0.003

(0.17)(0.14)(0.41)(0.56)

0.024

0.003

0.041

0.018

(0.63)(0.08)(1.23)(0.43)−0.001***−0.002***−0.001−0.001(−3.82)(−2.66)(−0.92)(−1.07)−0.009−0.0210.010−0.002(−0.72)(−1.41)(0.46)(−0.10)−0.018−0.022(−0.36)(−0.87)0.013*

0.014**(1.88)(2.43)

UNEMP
Migrants

Including

IncludingExcludingExcludingLog−135.5−185.2−80.8−111.6

267

330

161

199

pseudolikelihood
Observations

Notes: Values without parentheses are marginal effects on the probability that a
respondent will choose 3 for a question about a desire for suicide. In other words, thevalues are the probabilities that respondents have thought of committing suicide atleast once in the past 5 years. The reported values of INCOM are multiplied by 1000 for
convenience of interpretation. Values in parentheses are z-statistics obtained by therobust standard error clustered on residential prefecture. Because of the small sample,
there is no observation of unemployed persons, so the results for UNEMP cannot be
obtained. *, **, and *** indicate significance at the 10, 5, and 1 percent levels,
respectively.
17

Table 4. Non-urban sample: regression results on considering suicide (ordered probit
model)PTRUST
GTRUST
IGTRUSTGINI
GINI_EDUSCHOOL
INCOM
AGE
MALE
MARRY
CHILD
UNEMP

(1)

(2)

(3)

(4)

−0.002*−0.002*−0.002*−0.002*(−1.88)(−1.86)(−1.76)(−1.92)−0.003**−0.003**−0.003*−0.003**(−2.05)(−1.85)(−1.68)(−1.76)−0.015**−0.018***−0.025***−0.026***(−2.29)(−2.78)(−3.13)(−3.33)

0.001

0.002

−0.0010.001(0.26)(0.45)(−0.37)(0.22)

0.007

0.010

0.015

0.020

(0.36)(0.64)(0.91)(1.31)−0.001−0.001−0.0001−0.001(−0.47)(−0.60)(−0.06)(−0.32)0.002−0.004

0.009

0.009

(0.10)(−0.37)(0.67)(0.73)−0.002***−0.002***−0.002***−0.002***(−6.63)(−6.59)(−5.69)(−5.74)−0.002−0.002−0.002−0.003(−0.19)(−0.30)(−0.22)(−0.26)−0.033*−0.015(−1.76)(−0.73)−0.0030.0004(−0.78)(0.07)

0.042−0.005(0.62)(−0.10)Migrants

Including

IncludingExcludingExcludingLog−525.8−583.7−392.6−435.9

995

1083

742

814

pseudolikelihood
Observations

Notes: Values without parentheses are marginal effects on the probability that
respondent will choose 3 for a question about a desire for suicide. In other words, thevalues are probabilities that respondents have thought of committing suicide at least
once in the past 5 years. The reported values of INCOM are multiplied by 1000 for
convenience of interpretation. Values in parentheses are z-statistics obtained by therobust standard error clustered on residential prefecture. *, **, and *** indicate
significance at the 10, 5, and 1 percent levels, respectively.
18