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Socioeconomic Causes of Suicide

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Economic Analysis of Suicide Prevention

Part of the book series: Economy and Social Inclusion ((ESI))

Abstract

Rich studies on suicide in Japan and other countries have looked into the issues mainly from the health and medical point of view. Many of these studies also attempted to identify effective measures against suicide. We can set three broad categories to divide these existing studies: sociological and psychological studies; epidemiological studies; and medical studies.

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Notes

  1. 1.

    Japan National Police Agency (NPA), Annual Report on Suicide (2009).

  2. 2.

    See Mann et al. (2005) as an example.

  3. 3.

    For a survey of the literature on suicide and socioeconomic variables, see Chen et al. (2012a).

  4. 4.

    Meta-regression analysis is a method that performs another regression analysis using the results of an earlier regression analysis, as the basic data.

  5. 5.

    See Choi et al. (2009) for data sources and details of the analysis.

  6. 6.

    Furthermore, in order to eliminate false correlations due to time trends and features unique to individual countries, fixed effects for countries and yearly linear trends are also added. See Chen et al. (2009) for details of the analysis and data sources.

  7. 7.

    On the other hand, divorce rates in Japan had a statistically significant relationship only with the suicide rate for men aged 22–44.

  8. 8.

    MHLW data are published in the Vital Statistics, while NPA data are published in the Annual Report on Suicide.

  9. 9.

    In general, NPA data report slightly higher suicide numbers than MHLW data.

  10. 10.

    There are limitations in examining NPA suicide data in terms of the reasons for dying by suicide. First, since 2007, the NPA has been recording multiple reasons for each suicide, which makes it impossible to decompose into individual categories of suicide data in terms of those reasons. Second, from 1999 to 2006, the reasons for suicide were not identified for those individuals who did not leave suicide notes or other writings. As a result, the sample from each year is reduced to only one-third of the total suicides in this period, perhaps incurring sample-selection problems. Until and including 1998, the NPA suicide data covered almost all suicides in terms of reasons for suicide.

  11. 11.

    For details on the decomposition formula, see Mori et al. (2012).

  12. 12.

    Note that there were some discrepancies between the actual suicide growth rate, 35.16%, and the sum of the degrees of contribution, 34.834%, due to the approximation of derivatives by first differences. We report this discrepancy as a residual, at the bottom of each table.

  13. 13.

    J = 2 (genders) × 4 (age groups) × 7 (reasons for suicide) = 56.

  14. 14.

    Tables 1 and 2 use different age-group categorizations, due to categorization differences between the MHLW and NPA datasets. The data in Table 2 exclude those of unknown age or for unidentified reasons.

  15. 15.

    The term “employment status” used throughout the chapter is somewhat of a misnomer; we followed the categorizations offered by each of the data sources.

  16. 16.

    J = 6 (employment status) × 7 (reasons for suicide) = 42.

  17. 17.

    According to the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) from Japan’s Vital Statistics, these 25 categories correspond to Categories X60–X84.

  18. 18.

    We follow the same categorization as that in Table 22 of the Vital Statistics Special Report (2004): Hanging, X70; gas poisoning, X67; drugs, X60–X66, X68, and X69; drowning, X71; jumping (others), X80; jumping (towards a moving object such as a train), X81; and others, X72–X79 and X82–X84.

  19. 19.

    In the last section, we used MHLW data from 1998 to 2007, as well as NPA data from 1999 to 2006. As mentioned, only the NPA data contain information on the number of suicides by reason and by employment status; due to changes in the NPA’s categorization system in 2007, we were limited to the use of data from 1999 to 2006.

  20. 20.

    One possible interpretation is that the unemployed include not only individuals who lost their jobs, but also individuals who were retired due to old age or for other reasons. Also, unemployed people may have already suffered from depressive illness at the same time they lost their jobs; hence, in this group, health becomes a dominant reason for suicide.

  21. 21.

    Note the difference in the growth rate, compared to that in Table 5; this is because the data in Table 5 exclude cases of unknown age or gender.

  22. 22.

    For more background on this discussion, refer to a recent survey by Chen et al. (2012a). A recent event in Japan underscores the change in the means of suicide. In early 2008, the “recipe” for creating the means of suicide by inhaling hydrogen sulfide (created by mixing an ordinary household laundry detergent with a cleanser) became widely known through internet discussion forums. On April 23, 2008, a 14-year-old girl gassed herself to death by following a method learned from the internet, releasing in the process fumes that sickened 90 people in her apartment building (April 25, 2008, Japan Times). Press reports indicated that during the first six months of 2008, more than 500 people in Japan had killed themselves by inhaling hydrogen sulfide (September 26, 2008, New York State Office of Homeland Security).

  23. 23.

    See Chen et al. (2012a) for an extensive survey of economic studies on suicide.

  24. 24.

    See, for example, Hamermesh and Soss (1974). For a recent survey of the literature of unemployment and suicide, we refer the readers to Chen et al. (2012a).

  25. 25.

    The BOJ has 37 branches. In general, there is one branch for each prefecture, but some branches serve more than one prefecture.

  26. 26.

    Respondent companies chose one of three options: “Accommodative,” “Not so severe,” or “Severe.” The lending attitude of financial institutions DI is the percentage of companies that responded “Accommodative” minus the percentage of companies that responded “Severe”.

  27. 27.

    Respondent companies chose one of three options: “Easy,” “Not so tight,” or “Tight” Financial position DI is the percentage of companies that responded “Easy” minus the percentage of companies that responded “Tight”.

  28. 28.

    The Ministry of Finance (MOF) in Japan disclosed the amount of non-performing loans, which was 8 trillion JPY in April 1992 for the first time. Since MOF changed the coverage of financial institutions and the definition of bad loans, the amount turned out to be about 40 trillion JPY in 1995.

  29. 29.

    For details about a credit crunch in 1997, see Yoshikawa (2007).

  30. 30.

    Yoshikawa (2007) found that the decline in bank loan in 1991 was accompanied by the decrease in the growth rate of the amount of the lending and interest rate. Therefore, the decline in loans in 1991 was caused by the demand factor.

  31. 31.

    In 1998, banks suddenly increased having government bonds instead of loans. It means that banks avoided risk taking and were reluctant to supply loans at that time.

  32. 32.

    Some maintain that the decline in bank loans was caused by adjustment for over-borrowing in the Bubble periods or because of a demand-side factor.

  33. 33.

    The validity of the instrumental variables was tested using Sargan’s over-identification test.

  34. 34.

    See, for example, Conwell and Brent (1995).

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Author information

Authors and Affiliations

  1. Faculty of Economics, The University of Tokyo, Bunkyō, Tokyo, Japan

    Yasuyuki Sawada

  2. Faculty of Political Science and Economics, Waseda University, Shinjuku, Tokyo, Japan

    Michiko Ueda

  3. Osaka School of International Public Policy, Osaka University, Toyonaka, Osaka, Japan

    Tetsuya Matsubayashi

Authors
  1. Yasuyuki Sawada
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  2. Michiko Ueda
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  3. Tetsuya Matsubayashi
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Correspondence to Michiko Ueda .

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Sawada, Y., Ueda, M., Matsubayashi, T. (2017). Socioeconomic Causes of Suicide. In: Economic Analysis of Suicide Prevention. Economy and Social Inclusion. Springer, Singapore. https://doi.org/10.1007/978-981-10-1500-7_3

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