Effects of Societal Integration, Period, Region, and Culture of Suicide on Male Age-Specific Suicide Rates: 20 Developed Countries, 1955–1989

Social Science Research 29, 148–172 (2000) doi:10.1006/ssre.1999.0658, available online at http://www.idealibrary.com on

Effects of Societal Integration, Period, Region, and Culture of Suicide on Male Age-Specific Suicide Rates: 20 Developed Countries, 1955–1989 Phillips Cutright Indiana University

and Robert M. Fernquist Central Missouri State University Multiple indicators of societal integration and proxies for the culture of suicide form the model used to explain variation in male age-specific suicide rates from 1955 to 1989 in 20 developed countries. The hypothesis that certain determinants of suicide rates have changed over the period between 1955 and 1989 was rejected, as was the hypothesis that there are effects of period, net of measured predictors. The determinants of suicide rates do vary by age, with the culture of suicide playing an especially important role in the 35–64 age group. r 2000 Academic Press

AGE AND PERIOD EFFECTS ON MALE SUICIDE RATES Effects of Age Girard (1993, p. 553) observed that Durkheim’s focus on social integration as an explanation of age differences in suicide rates was ‘‘problematic’’ because male and female rates do not follow a common upward trend with age and the age pattern of suicide in economically developed countries differs from that in less developed nations.1 We accept this observation. Although efforts to examine We are grateful for the generous support of Central Missouri State University. We also thank Scott Long, Steven Stack, and Ira Wasserman for advice; Fred Pampel for the cohort/population size data; Alex Durig, Melissa Milkie, Katie Rosier, and Diane Schaefer for research assistance. We thank both anonymous referees for helpful criticisms. Address reprint requests to Phillips Cutright, Rte. 1, Box 234-C, Saluda, NC 28773. E-mail: [email protected]. 1 Girard (1993) proposed a theoretical explanation of the difference in the age structure between more and less economically developed countries. He did not examine differences in age structures 148 0049-089X/00 $35.00 Copyright r 2000 by Academic Press All rights of reproduction in any form reserved.

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change over time in the age-specific rates of younger and older men are plentiful [McCall and Land (1994, p. 58) cite 11 recent papers on adolescent suicide and 14 studies of elderly suicide], few articles actually try to explain age differences in suicide rates within a population, and we have virtually no longitudinal efforts to explain why the age-specific suicide rates differ from nation to nation. Pampel’s work is an exception, and he (1996, p. 342) has offered a perspective on age differences that presents both a theoretical framework and an empirical test using cross-national times-series data from 18 economically developed countries over the years 1953–1986. He states that: Whereas age differences in suicide once seemed the natural consequence of human development of individuals’ varying degrees of integration at different stages of the life cycle, they now also appear to stem from an age-based system of stratification that favors some age groups or generations over others. In the past, family ties and class identities bonded members of different generations in common interests, but generational sharing has shifted in recent decades from families and classes to the public arena and formal bureaucracies. . . . Thus age patterns of suicide become increasingly important as indicators of age groups’ economic and social well-being.

The degree to which an age group is or is not ‘‘favored’’ is believed to be a function of the relative size of the age group. We test this hypothesis. Period Effects Past studies of period effects can be grouped into three types. First are studies that measure suicide rates before (time 1) and then after (time 2) some event. The difference, if any, between time 1 and time 2 rates is attributed to the intervening event. When a theoretical rationale supports the hypothesis that the intervening event should have a predictable effect on suicide and when such a period effect is observed, then the cause of the difference between time 1 and time 2 rates has been ‘‘explained.’’ An example of such work is Sainsbury’s (1972, Table 5) study of suicide rates in 18 nations before (1938) and during (1944) World War II. The rates were predicted to decline due to increased societal integration in wartime, and they did. A second type of period effect is measured when a time-series analysis introduces dummy variables to indicate successive periods as a control for period effects. For example, Cutright and Smith (1986) included such measures to control ‘‘trend.’’ Their dependent variable was national birth rates in developing countries during a period of demographic transition from high to lower birth rates. Once fertility starts to decline from traditional ‘‘high’’ levels it tends to continue to decline with or without additional stimuli from socioeconomic change. Hence the need to control for trend with period dummies. Although the social psychology of such trends in suicide has not received the attention demographers have within our stratum of developed countries. It would, therefore, be premature to conclude that Durkheim’s theory would fail to account for national differences in age-specific suicide rates.

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devoted to fertility trends, very marked trends in post-World War II suicide rates are apparent in some male age groups. A third type of analysis of period effects can be illustrated by the time-series analysis of annual suicide rates in Quebec by Krull and Trovato (1994). They report only weak effects of religion, divorce, married female labor force participation, and childlessness on the suicide rates of men and women in the 1931–1956 period in Quebec—a period they characterize as one of ‘‘high integration’’ and ‘‘low individualism’’ (p. 1138). In contrast, religion, divorce, and childlessness were generally significant predictors of male and female rates in the 1961–1986 era—a period of ‘‘low integration’’ and ‘‘high individualism.’’ However, they also support the view that there was little impact of married female labor force participation on either male or female suicide rates after 1980. They claim that ‘‘by 1981, the impact of the women’s movement had changed the normative attitude of Canadian society in regards to the acceptance or married women in the labor force’’ (p. 1141). In this type of analysis the cause of the alleged period effect is not measured, but inferred. Pampel (1998, pp. 745–746) cites other researchers whose time-series analyses of suicide trends conclude that when an indicator of societal integration (e.g., divorce or female labor force participation) becomes common it loses its predictive association with suicide because norms and values ‘‘adjust’’ to past changes in societal integration indicators. Pampel (1996) also suggests that period effects—the difference in suicide rates from one year (or period) to the next—are caused by changes in the strength of predictors and by factors specific to men and women. Indicators of societal integration and demographic factors such as cohort size may also vary from period to period. These ideas are tested in Table 2. A second analysis (Tables 4 and 5) tests the hypothesis that there are period effects on suicide rates, net of measured predictors. Our method does not rely on period dummies. Rather, we substitute period-specific errors of prediction. This analysis examines 10-year age groups separately. Therefore, the number of observations is 120 or 140, depending on whether six or all seven periods are used. Age-Specific Analyses, 1955–1989 The age structure of suicide has changed in a number of countries over the years 1955–1989. Some possible causes of change in the age structure of suicide may be suggested by a detailed quantitative look at age-specific male suicide rates over the entire 1955–1989 period. Seven age groups (15–24, 25–34, . . . , 75, and older) are examined in Table 3. These analyses provide an alternative methodology to Pampel’s (1996, 1998) analyses of cohort size, age, and period effects on male suicide rates. CULTURE OF SUICIDE The empirical literature dealing with suicide has failed to develop a measure of the culture of suicide. This seems incongruous, since sociologists generally

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believe that many vital events (e.g., birth, divorce, marriage, and homicide rates) are a manifestation of the normative order affecting these events. For example, the concept of a ‘‘culture of violence’’ is often included in models of homicide (Cutright and Briggs, 1995, Fig. 1). We believe that the omission of the culture of suicide from statistical models may result in serious specification error. The theoretical argument favoring the notion that a culture of suicide varies among nations is that societies differ in the degree to which the normative order condones or condemns suicide. Lester (1987, p. 317) notes that shared attitudes and values ‘‘. . . may be related to suicide due to a rejection of a normative order that condemns suicide.’’ Because the normative order regarding suicide varies from nation to nation (and perhaps by age and gender within nations), national suicide rates should be affected by differences among nations in their respective cultures of suicide. The more severely the normative order condemns suicide, the lower will be the suicide rate, net of other predictors. Empirical support is provided by studies that compare the suicide rates of immigrants to the United States and to Australia with suicide rates in the immigrant’s nation of origin. Sainsbury and Barraclough (1968, Table 1) report that the 1959 suicide rate of 11 different foreign-born immigrant populations in the United States correlated .87 with the suicide rates of their country of origin. One must conclude that despite residence in the United States (presumably for some years, since many immigrants did not commit suicide until they were old), these people were responding to the culture of suicide they were socialized to when still living in the ‘‘old country’’ and which may well have continued while living in ethnically homogeneous neighborhoods in the United States. Further, Lester (1972, p. 942) reports that the rank-order correlation between mid-1960s suicide rates of 16 immigrant groups in Australia and the rates in their home countries was .79 for males and also .79 for females. When these male and female rates are combined, the correlation rises to .81. The mean suicide rate of U.S. immigrants was 2.17 times the mean rate in their home countries; the Australian ratio was 2.12. Whitlock (1971, p. 843) remarks that ‘‘On the assumption that suicide is a culture-bound phenomenon, one might anticipate that methods of suicide among immigrants, would to some degree, be determined by the traditional modes of death adopted in their own countries.’’ To test this hypothesis Whitlock computed the percentage distribution of suicides by the method used in 11 immigrant populations and the native-born population in Australia. For example, only 9.8% of Australian-born male suicides died by hanging. In Italy 37.7% of men choose this method, while 27.6% of Australian Italian-immigrant male suicides also selected this method. Male immigrants from Yugoslavia, Poland, and Germany were also far more likely than Australian-born men to suicide by hanging. After statistical tests were examined, Whitlock (1971, p. 844) concluded that ‘‘. . . Australian born suicides used methods whose percentage distribution differ significantly from that found in all the countries under consideration except for

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New Zealand.’’ We conclude that this evidence also supports the view that a culture of suicide exists. A second source of empirical support for the culture of suicide concept is provided by period- and age-specific errors of prediction of male suicide rates in our 20 developed countries. Using the prediction equations in Table 2, we calculated the errors of prediction for males of a given age in each country and period. We assume that a nation with a small error has a culture of suicide that is about the average in this set of nations. A nation with a pattern of positive errors of prediction has higher suicide rate than expected and has, theoretically, a culture of suicide that does not strongly condemn suicide. A pattern of negative errors implies that the population has a stronger than average condemnation of suicide and thus lower than predicted suicide rates. To test the hypothesis that a culture of suicide exists we examined the errors of prediction from period to period to see if a clear pattern emerged. Because the normative order that maintains the culture of suicide should be relatively stable over time, the errors for each nation and age should also be relatively stable from period to period. Without such a pattern, the meaning of the errors could not be attributed to the culture of suicide. If, for example, the errors for men ages 35–44 in Denmark simply fluctuate from positive to neutral to negative, back to neutral, and so on through the seven periods we would conclude that the errors were not a function of the culture of suicide because the normative order behind the culture of suicide should be rather stable over time. If the direction of the errors does change over time, the change should show a steady pattern of rather gradual change in the same direction—e.g., from negative to a stable pattern of positive errors. The pattern taken by the errors in nearly all nations and in all male age groups conforms to these expectations. For example, for men ages 35–44 the errors of prediction are negative in all seven periods in England and Wales, West Germany, Italy, The Netherlands, Norway, Switzerland, and the United States. The errors of Austria and Greece are negative in the first three or four periods before turning positive. They are positive in all periods in Denmark, Finland, Ireland, New Zealand, and Spain. Errors in Australia, Portugal, and Sweden are positive in the first three or four periods and then negative in the remaining periods. Errors were irregular in Belgium and France before moving to positive errors in the final two periods. Canada had positive errors in five of the seven periods. The patterns for other age groups are similar to those of men ages 35–44. We interpret these results as fulfilling our expectations of what the errors should look like if they were caused, in large part, by unmeasured differences in the normative order represented by the concept of the culture of suicide. Sociopolitical Context, Region, and Suicide We also test Pampel’s measure of the sociopolitical context—a ‘‘collectivism’’ scale that is constant over the entire post-World War II period. This measure differentiates ‘‘Individualist nations with women’s movements that are indepen-

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dent from established political parties . . .’’ [from] ‘‘Collectivist nations with women’s movements that remain more closely allied to leftist parties and trade unions . . . (that) tend to emphasize state feminist strategies such as making family and work roles more compatible’’ (Pampel, 1998, p. 756). Collectivist nations are believed to be better able to moderate the effect of change in cohort size and other factors that increase suicide rates (Pampel, 1996, p. 334). One would assume that suicide rates would be lower in high- than in low-collectivism nations. Inspection of the nine nations with the highest collectivism scores shows that they are nearly all within the same regions. Thus, all three Central European, all four Scandinavian, and two of the three Western European nations comprise the high-collectivism stratum. The remaining 11 countries are low on the scale.2 Because the age structure of suicide among countries within regions tends to be similar, while often differing from the age structure and levels of nations in other regions, and because suicide rates tend to be highly correlated over time, one test of the collectivism hypothesis is to correlate historical (precollective) suicide rates and compare them with the postcollectivist era. If the rank order of suicide rates in the postcollectivist era is not much different from the precollectivist era, it would make sense to enter regional dummy variables as an alternative to the collectivism scale. Region could then be interpreted as a proxy of the culture of suicide. DATA AND METHODS Dependent Variable Annual age-specific suicide of men ages 15–24, 25–34, . . . 75 and older for the years 1955–1989 (World Health Organization, 1956–1992) are averaged over successive 5-year periods (1955–1959, 1960–1964, . . . , 1985–1989) to increase reliability (Gartner, 1990, p. 100; Cutright and Briggs, 1995, p. 239). Appendix A reports mean suicide rates by period and age. Although some gender bias in suicide rates may exist in some populations (van Poppel and Day, 1996), Pampel’s (1996, p. 344) review concludes that such bias as may exist should be fairly stable over time. Underreporting related to gender should not be a factor in an analysis restricted to men.3

2 Pampel included Japan, despite misgivings (1996, p. 344). We omitted Japan because the age and gender structure in early periods did not conform to the pattern expected of an economically developed country (Girard, 1993, Fig. 4) and no other country in our sample experienced the enormous decline in male and female suicide rates we observe in Japan—especially among persons ages 15–34. 3 It is possible that the male suicide rate in Ireland was underreported in the period of 1955–1974. Burvill et al. (1983) note that the 1962–1971 suicide rates of Irish male immigrants in Australia were much higher than expected given the very low male rates in Ireland in that period. Since 1970, however, male suicide rates in Ireland have increased in all age groups and tripled overall. Some of this increase may be due to better reporting and changes in the definition of suicide (Wasserman, 1999, personal communication). On the other hand, the small size of the Irish male Australian immigrant population suggests that the ‘‘high’’ Irish immigrant suicide rate may be unreliable.

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Independent Variables In much previous work indicators of Durkheim’s societal integration theory are the principal and often the only independent variables. While we continue to rely on these predictors, we examine several alternative predictors as well. We follow Fernquist and Cutright’s (1998) choice of multiple measures of ‘‘societal integration’’—an umbrella concept that refers to domestic, religious, and economic integration (Durkhein, 1951). Following their review of the literature on each measure of societal integration (Fernquist and Cutright, 1998, pp. 110–112), the authors tested the efficacy of each predictor on male and then female agestandardized suicide rates among 21 developed nations over the years 1955–1989. Under multivariate analysis four societal integration measures were significant. The first was female labor force participation—the percentage of the labor force that is female (International Labor Office, 1956–1991; World Bank, 1984, 1988–1989), and it was positively related to suicide. The second was the number of divorces (log ⫹ 1) per 10,000 males ages 15–64, and it too was positively related to suicide rates. Third was fertility as measured by the child:woman ratio—the number of children under age 5 per 1000 women ages 15–49. Fertility rates are lagged by 10 years for women ages 35–44, by 20 years for women ages 45–54, 30 years for women ages 55–64, and so on. Fertility rates are not lagged for women ages 15–34. These fertility rates are applied to men of comparable age. Fertility was negatively related to suicide. Data for both divorce and fertility are from the United Nations (1950–1992). The fourth measure is of religious integration (Wuthnow, 1977; Stack, 1983) and is the percentage of all books published in each nation that are defined as religious (UNESCO, 1963–1991; United Nations, 1956–1963). Religious books is negatively related to suicide rates. Annual statistics for each of these four independent variables are averaged over each 5-year period. Although the gini coefficient measuring income inequality was not significant in the analysis of male age-standardized suicide rates (Fernquist and Cutright, 1998, Table 2), we include it in our age-specific analysis. Income inequality was measured around 1970 (Hoover, 1989; Simpson, 1990) and no time-series data are available. Since inequality changes over time, the lack of longitudinal data may suppress the expected positive impact of inequality on male suicide rates. Fernquist and Cutright did not include a measure of marital status, although there is abundant evidence (Dublin, 1963; Gove, 1973; Danigelis and Pope, 1979) that marital status is an important predictor. The Gove and Hughes (1980) analysis of cross-sectional data from American cities suggests that the effect of marital status on suicide is related to whether a person is living alone. Marital status indicators can be used to indicate a status where either the person is not living alone (percentage married) or is probably living alone (percentage single or percentage widowed or divorced). Data are from United Nations (1959, 1965– 1990). Following Pampel’s discussion of cohort size effects on suicide rates (1996, pp.

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341–342), we measure this concept in a way appropriate to each age group. We follow tradition (Ahlburg and Shapiro, 1984; Easterlin, 1987) and use relative cohort size for the two younger age groups, i.e., males 15–24/males 35–64 and males 25–34/males 45–74. Because Pampel (1996, Tables 2 and 5) found that relative cohort size and population size (the percentage that an age group is of the adult male population) had similar effects on the same male age groups we used population size for males 35–44, 45–54, and 55–64. The expected negative effect of the larger cohort size on the suicide rate of older men is, theoretically, a function of the political strength of the aged as a voting bloc (Preston, 1984; Pampel, 1996, p. 341). To test this hypothesis we take the percentage that males and females ages 65 and older are of the population ages 15 and older as our measure of population size for both men ages 65–74 and those 75 and older. Collective scores are from Pampel (1998, Table 4). Greece, Portugal, and Spain were not coded by Pampel. Italy’s score was well below the mean, and we coded the other 3 Southern European nations as ‘‘low’’ as well. The 9 nations with the highest collectivism scores (Austria, Belgium, Denmark, Finland, West Germany, The Netherlands, Norway, Sweden, and Switzerland) were coded 1; the remaining 11 nations were coded 0. Means and standard deviations of independent variables and suicide rates for the years 1960–1989 are reported in Appendix B. Data that are constant for all ages and data specific to the seven age groups are reported. Female labor force participation increased by an average of 7.5 percentage points from 1955–1959 to 1985–1989. Austria, West Germany, Greece, and Ireland changed no more than 1 point, while Canada, Norway, and Portugal posted 18-point gains. The mean divorce rate rose 35.5 points; Canada, England and Wales, and the United States had gains double this average, while the divorce rates in Ireland and Spain remained at zero. Religious book production declined by nearly one-half and fertility dropped by 60 points—a decline equal to 20% of the 1955–1959 level. All these changes in societal integration should increase suicide rates. Period-specific data are available from the authors. We used the following regional codes: Southern Europe—Greece, Italy, Spain, and Portugal; Central Europe—Austria, West Germany, and Switzerland; Western Europe—Belgium, France, and The Netherlands; Scandinavia—Denmark, Finland, Norway, and Sweden; Overseas English-speaking—Australia, Canada, New Zealand, and the United States. Ireland and England/Wales are combined to form the reference category in the dummy variable analysis of regional effects. Measuring the Culture of Suicide We propose two proxy indicators of the culture of suicide. The first is regional location. If the differences among regional suicide rates are not eliminated (or greatly reduced) by including the societal integration predictors, this would indicate that an unmeasured cause of suicide related to regional location exists. We assume the ‘‘unmeasured cause’’ to be the culture of suicide (see below for discussion of Appendix C).

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CUTRIGHT AND FERNQUIST TABLE 1 Mean Male Age-Specific Suicide Rates by Region: 20 Developed Countries, 1960–1989 Age group Region

15–24

25–34

35–44

45–54

55–64

65–74

75⫹

Central Europe Western Europe Southern Europe U.S./Canada/Australia/New Zealand Scandinavia England/Wales and Ireland

23.6 9.8 4.6 16.4 30.9 6.4

31.1 17.9 7.0 20.9 38.8 11.1

36.9 22.4 8.9 22.7 47.6 12.1

47.2 29.7 13.6 26.5 46.5 13.1

50.8 37.2 18.7 28.5 44.2 16.7

55.4 46.0 24.6 30.3 48.2 16.9

75.8 74.2 37.7 36.0 30.9 16.8

All regions

13.5

20.2

24.2

30.4

33.6

36.7

48.5

Age-specific regional suicide rates are shown in Table 1. The differences in the level and the age structure among regions is very large. In Central Europe, Western Europe, and Southern Europe increasing age is accompanied by steady increases in the suicide rate, although, at the same age, the level of suicide varies sharply among the three regions. The pattern of increase with age is less severe in the four former English colonies. In Scandinavia, England/Wales, and Ireland, the rates increase to middle age and then remain relatively constant through ages 45 and older. The level of the suicide rate at the same age also varies widely among these three regions. In a later analysis we show that the bulk of these regional differences are unrelated to regional differences in societal integration. We assume that regional differences unrelated to societal integration should be related to suicide rates—a hypothesis we test. Because regional codes cannot change over time and because it is possible that the culture of suicide may shift over the years, we examined a second proxy of the culture of suicide that does allow for change. At any period the difference between a nation’s predicted and its observed suicide rate (the error of prediction) is due to measurement error in the independent and/or dependent variables and/or specification error—failure to measure one or more causes of suicide. Assuming that the bulk of the error of prediction is due to specification error, we argue that this error of prediction is due to our failure to include a measure of the culture of suicide in the prediction equation. If so, the error of prediction can itself be taken as a proxy measure of the culture of suicide at time 1. While it is also obvious that we cannot use time 1 error of prediction to account for time 1 suicide rates (if we did the explained variance would be 100%), we can use a time 1 error of prediction to predict suicide rates at time 2. Thus, the lagged error of prediction becomes our proxy for impact of the culture of suicide on time 2 suicide rates. The lagged error term allows for change in the culture of suicide over time and for differences in the pace and even direction of change in the culture of suicide among younger and older age groups. When lagged errors are used in the analysis of period effects (Tables 4 and 5)

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the time 2 dependent variable is not the time 2 suicide rate. Rather, it is the time 2 error of prediction from an equation that includes the measured time 2 independent variables—the basic model predictors in Table 3. By doing this we insure that the effects of predictors associated with the time 2 period are removed before our proxy for time 1 culture of suicide of the specific male age group is introduced. This step makes the estimated effect of our proxy for the culture of suicide conservative because only the direct effects of the culture of suicide are measured. The indirect effects of culture on the other predictors are, thereby, set to zero. Statistical Procedures The ordinary least-squares (OLS) estimation is used in the period-specific analysis. Durbin–Watson tests for autocorrelation and Glejser’s (1969) tests for heteroskedasticity were done. Neither autocorrelation nor heteroskedasticity are significant in any of the equations in Table 2. For analysis of each 10-year age group over the entire time period we used a modified generalized least-squares (MGLS) approach because MGLS is appropriate for a pooled cross-sectional time-series research design (SAS, 1993, p. 179). The SHAZAM package (White et al., 1994) was used. An MGLS estimation involves a weighted transformation of the dependent variable, which corrects for both cross-sectional heteroskedasticity and first-order autocorrelation. As the rho coefficient in MGLS estimation increases in size, more efficiency is gained by using MGLS rather than OLS estimation. Traditional R 2 with OLS is not a valid measure of explained variance when MGLS techniques are used (Greene, 1990, p. 386). Buse R 2 is an appropriate measure of explained variance with MGLS (Judge, Griffiths, Hill, Lutkepohl, and Lee, 1985, Ch. 12). To control collinearity, independent variables with intercorrelations of .40 or larger were residualized (Fernquist and Cutright, 1998; Cutright and Briggs, 1995). Therefore, because female labor force shared correlations of .40 or more with divorce and fertility, female labor force was regressed on these two variables and the regression residuals (or errors of prediction) are our measure of female labor force. Similarly, because logged divorce was strongly correlated with fertility and religious book production, regression residuals of logged divorce rates are our measure of divorce. The percentage widowed and divorced in period-specific analyses was regressed on female labor force. RESULTS Period-by-Period Analysis The first goal of our period-by-period analysis is to measure the stability of effects of certain predictors on male suicide rates over the years 1955–1989. This will test the results of time-series analyses in Canada and the United States that report a decline in the impact of female labor force participation on suicide as

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TABLE 2 OLS Unstandardized Coefficients from the Regression of Male Age-Specific Suicide Rates on Collectivism, Cohort/Population Size, and Indicators of Societal Integration: 20 Developed Countries by Period, 1955–1989 Period 1955–1959 Independent variables

b

t ratio

1960–1964 b

1965–1969

t ratio

b

1970–1974

t ratio

b

t ratio

1975–1979 b

1980–1984

t ratio

b

1985–1989

t ratio

b

t ratio

Ages 15–34 Female labor force Residual collective Age 1 ⫽ older Relative cohort size Resid. widowed and divorced R2

0.95 5.08 4.31 0.05

6.4 2.5 2.7 0.7

0.98 4.70 5.47 0.12

6.4 2.9 4.6 1.8

1.17 2.69 4.96 0.23

7.7 1.8 4.7 2.8

1.26 4.38 2.79 0.41

8.7 3.6 2.8 5.7

1.32 8.08 4.60 0.03

6.8 2.0 1.9 0.1

1.21 9.80 4.36 0.08

5.7 1.15 4.8 3.7 7.50 2.3 1.5 6.90 2.6 0.8 ⫺0.01 ⫺0.1

7.16

3.2

3.11

2.5

3.20

3.1

2.23

3.6

0.57

0.8

1.33

1.5

.72

Mean suicide rate

.75 12.4

.79 12.7

.87

.78

13.0

14.8

0.27

.69 18.4

0.4

.56 20.6

21.8

Ages 35–54 Female labor force Residual collective Age 1 ⫽ older Population size Resid. widowed and divorced R2 Mean suicide rate

1.93 11.52 6.84 0.47

8.1 3.9 2.1 2.5

2.12 12.46 5.84 0.57

8.9 4.5 2.5 3.7

2.30 13.73 2.64 0.33

9.2 4.4 0.9 2.0

2.29 14.59 4.98 0.18

2.79

2.1

2.25

2.1

2.87

2.3

.80

.82 27.1

12.8 6.8 3.7 2.2

1.82 18.06 5.12 0.41

1.49

2.6 ⫺0.10 ⫺0.2

0.10

.85

.88

.79

.81 25.8

10.3 5.8 3.2 1.3

26.8

2.20 15.21 5.82 0.33

26.7

27.4

7.4 6.3 2.9 1.4

1.50 15.36 3.90 0.90

6.3 5.8 2.4 1.9

0.2 ⫺0.10 ⫺0.3 .77 29.6

28.1

Ages 55–74 Female labor force Residual collective Income inequality Population size Resid. widowed and divorced R2 Mean suicide rate

1.94 4.9 1.65 4.1 2.32 5.8 2.25 7.4 2.22 9.1 1.79 6.1 1.57 6.0 10.47 2.5 11.89 3.1 13.71 3.6 20.04 6.1 14.75 7.2 21.05 6.4 17.58 5.6 0.82 1.7 1.02 2.3 1.15 2.8 1.49 4.1 1.49 4.7 1.58 3.9 1.58 4.0 ⫺0.84 ⫺1.0 ⫺0.73 ⫺1.1 ⫺0.86 ⫺1.3 ⫺0.86 ⫺1.4 ⫺1.18 ⫺2.0 ⫺0.7 ⫺0.4 ⫺1.21 ⫺1.3 0.14

0.7

.69

0.09

0.4

.67 40.5

0.30

1.4

.73 37.0

1955–1964

0.79

3.2

.74

0.94

3.6

1.03

.82

36.5

35.4

1965–1974

3.1

1.34

.72 33.5

3.4

.69 34.9

1975–1984

33.7 1980–1989

Ages 75 and older Residual fertility Residual collective Income inequality Resid. widowed and divorced R2 Mean suicide rate

⫺0.09 18.69 2.80 1.71

⫺2.1 3.2 3.5 1.5

.52

⫺0.11 23.39 2.37 1.80

⫺3.2 4.0 3.6 1.9

.59 47.7

⫺0.06 32.23 2.77 2.71

⫺1.4 4.7 3.7 2.3

⫺0.09 32.15 2.87 2.13

.58 45.8

⫺2.0 4.9 4.1 2.1

.65 49.1

53.2

female labor force participation became widespread. A second goal is to test Pampel’s (1996) claim that the strength of the impact of cohort size is stronger in the 1950s and 1960s than in the 1970s and 1980s. To increase degrees of freedom we combine 10-year age groups—men ages 15–34, 35–54, and 55–74. For men 75 and older we combine 1955–1959 with

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1960–1964, 1965–1969 with 1970–1974, 1975–1979 with 1980–1984, and 1980– 1984 with 1985–1989 to increase degrees of freedom. For younger ages the older age group is coded 1, the younger, 0. Female labor force is specific to period, but not to age, while collective and income inequality are a constant through all periods. Collectivism is residualized to eliminate its strong correlations with female labor force, age, and cohort/population size. This variable controls the impact of the culture of suicide on period-specific suicide rates. The percentage widowed and divorced is also residualized, as discussed above. In this period-specific analysis the definition of cohort or population size varies by age. For men ages 15–34 relative cohort size is coded as the ratio of men ages 15–34 to those ages 35–64. This coding is similar to Ahlbury and Schapiro’s (1984, p. 100) ratio of males ages 16–29 to males ages 30–64. For older men we follow Pampel’s (1996) measure of population size. Thus, for men ages 35–54 the measure is the percentage that men in this age group are of all adult men; for men ages 55–74 population size is the proportion that men ages 55–74 are of the total adult male population, and for men 75 and older it is the percentage that men ages 65 and older are of the total adult male population. The unstandardized regression coefficients, their T ratios, explained variance (R 2), and the mean suicide rate for each age group and period are reported in Table 2. T ratios of 2.0 are significant at the .05 level. The mean suicide rate for men ages 15–34 exhibits a steady increase from period to period—nearly doubling from years 1955–1959 to 1985–1989. The dominant predictor of suicide rates of males ages 15–34 is female labor force participation and there is little indication that the strength of this predictor weakens over time. Nations high on collectivism have higher suicide rates than do low-collectivism countries, and this difference ranges from 2.69 to 9.8. The suicide rate difference between the two age groups conforms with expectations and averages about 4.8. Relative cohort size is positive in six periods, but significant in only two. Percentage widowed and divorced takes the expected positive coefficient in all periods but loses significance in the final three periods. The R 2 peaks at .87 in 1970–1974 before declining to .56 in 1985–1989. For men ages 35–54 mean suicide rates show no clear trend. Female labor force participation is again the most powerful predictor in all periods. Collectivism retains its positive and significant coefficients. The older age group again has higher rates in all years. In four periods large population size is positive and significantly related to higher suicide rates and takes borderline significance in the 1985–1989 period. Widowed and divorced is again positive and significant only in the years 1955–1974. Explained variance is somewhat higher in this age group than it was among the younger men and also shows a similar pattern of increase and decline over the years. Among men ages 55–74 suicide rates declined over the 1955–1989 period. Although the suicide rates of men ages 65–74 average 4 to 5 points higher than those of men ages 55–64 (Appendix A), we omit the age dummy variable because it correlates .95 with the percentage widowed and divorced. Since the percentage

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widowed and divorced is much higher among men ages 65–74 than those ages 55–64, including this variable may help explain why suicide rates are typically higher in the older age group. Female labor force maintains its role of dominant predictor, while collectivism continues its strong positive relationship with suicide rates. For the first time income inequality enters the equations as a significant predictor. Inequality is positive in all periods and significant beginning in 1965. Population size is, as expected by Preston (1984) and Pampel (1996), negatively related to suicide in all seven periods, but is significant only in years 1975–1979. The percentage widowed and divorced is positive in all years and is significant in 1970–1974 and in the remaining three periods. The trend of increasing and then declining explained variance emerges once again. For men ages 75 and older mean suicide rates decline into the years 1965–1974 and then rise through the 1980s. Residualized lagged fertility is substituted for female labor force participation because we assumed that having or not having sons and daughters (and grandchildren) would be more relevant to this age group than whether daughters (or granddaughters) were in the labor force. Fertility is negative in all periods and significant in three of four periods. Collectivism continues to have positive and significant effects. Income inequality has a positive and significant impact in all years, while the percentage widowed and divorced is positive in all years and gains strength with time. The measure of population size is trimmed because it is never significant. Explained variance remains in the .52 to .59 range in the first three periods before rising to .65 in the 1980s. Testing for Spurious Effects of Collectivism Although we found a very stable and highly significant pattern of positive effects of collectivism on nearly all male age-specific suicide rates, the lack of a theoretical explanation of why positive effects exist prompts us to test for spuriousness. Assume that nations that introduced collectivist social policies after World War II happened to be nations with a history of high suicide rates. Because suicide rates tend to be highly correlated over time,4 a nation with a high rate at time 1 (pre-WWII) will be expected to have a high suicide rate at time 2 (post-WWII). To test whether collectivism had an effect on 1955–1989 male suicide rates, net of prewar rates and current societal integration levels (indicated here by 1955–1959, 1965–1969 . . . 1985–1989 female labor force participation), we regressed 1938 male suicide rates (Sainsbury, 1972, Table 5) and current female labor force participation on 1955–1959, 1965–1969, 1975–1979, and 1985–1989 suicide rates. Because Sainsbury’s rates include suicide at all ages we use age-standardized male suicide rates rather than specific age groups as the dependent variable. The 1971 adult male population of England and Wales is the standard population [see Newell (1988, pp. 66–67) and Shryock and Siegel (1976, Ch. 8)]. The errors of prediction from each of the four equations become the new 4 For example, the correlation of 1910–1914 and 1960 suicide rates in 20 countries (Dublin, 1963, Table III) was .85. The 1926–1930 rates correlated .87 with 1960 rates.

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dependent variable and collectivism is regressed on them. If collective is related to the variance that is not explained by 1938 suicide and current female labor force participation, it will be correlated with the errors of prediction. We found that collectivism did not show a single significant regression coefficient with the errors of any one of the four periods. Therefore, we conclude that the large positive regression coefficients taken by collectivism in Table 2 are spurious. What meaning should we attach to these coefficients? We believe that the high correlations of suicide rates over time support the view that a culture of suicide specific to each nation—and perhaps to subgroups within each nation—exists. Collectivism is a crude measure of national differences in the culture of suicide. Nations high in collectivism were all located within the same three regions and, with only one exception, all nations in each region were high on collectivism. Since collectivism is strongly related to high suicide rates it follows that Central and Western European and Scandinavia countries must also tend to be high male suicide nations. Therefore, if the various regions were separately coded as dummy variables, these three regions would take large positive regression coefficients with male suicide rates even if several indicators of societal integration were included in a multivariate analysis. This hypotheses is tested. AGE-SPECIFIC TIME-SERIES ANALYSIS Basic Model The top panel of Table 3 displays the regression coefficients, T ratios, estimated rho coefficients, and explained variance for each 10-year age group. Rho coefficients are high, indicating that MGLS, rather than OLS, is the preferred statistical model. Residualized female labor force is significant in all but the 55–64 age group—the age stratum with the smallest explained variance. The unstandardized female labor force coefficient is about the same for the first four age groups. Residualized divorce has strong positive regression coefficients in all age groups and is even significant at ages 55–64. The divorce coefficient is as strong in the two oldest as in the 35–54 age groups. Fertility is negative, significant, and at about the same strength in all seven ages. Religious books are negative in all and fails significance only in the 55–64 age group. This regression coefficient tends to increase with age. The regression coefficient for relative cohort size is positive and has a p value of .059 at ages 15–24 and is positive and highly significant at ages 25–34. All older ages use the percentage that each age group is of the adult male population except ages 65 and older. For ages 65 and older we test the voting bloc hypothesis of Preston (1984) and Pampel (1996)—population size is the percentage that persons 65 and older are of the total adult population. The population size coefficient is positive and significant at ages 35–44, fails significance at ages 45–64, and is significant but positive for ages 65–74. It is positive but insignificant at ages 75 and older and is, therefore, omitted from the model. Percentage of single males is positive and significant for ages 15–24, but

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TABLE 3 MGLS Unstandardized Coefficients from the Regression of Male Age-Specific Suicide Rates on Measures of Societal Integration Plus Two Proxies for the Culture of Suicide: 20 Developed Countries, 1955–1989 Age groups

15–24 Independent variables

b

25–34

t ratio

Res. female labor force 0.59 6.9 Res. divorce 2.29 8.3 Fertility ⫺0.05 ⫺13.4 Religious books ⫺0.67 ⫺8.7 Cohort/pop. size 0.09 1.9 Marital status 0.23 3.4 Income inequality — — Estimated rho R2 Region Central Europe Scandinavia Western Europe Aus./N.Z./ Can./U.S. Southern Europe Estimated rho R2 Lagged errors Lagged errors Estimated rho R2

.85

b

35–44

t ratio

0.67 6.2 2.53 7.4 ⫺0.05 ⫺13.6 ⫺0.68 ⫺7.1 0.12 3.6 — — —

—

.87 .82

b

45–54

t ratio

0.50 4.3 3.29 8.4 ⫺0.03 ⫺7.0 ⫺0.86 ⫺7.8 0.56 3.8 — — —

—

.88 .76

55–64

t ratio

b

0.67 4.2 3.21 4.9 ⫺0.04 ⫺5.4 ⫺0.74 ⫺3.8 0.51 1.7 — — 0.83

3.7

.87

t ratio

b

0.19 1.1 1.86 2.6 ⫺0.02 ⫺2.8 ⫺0.17 ⫺0.7 0.53 1.4 — — 1.22

4.3

.88

.55

.46

75 and older

65–74

b

t ratio

— — 4.24 7.2 ⫺0.02 ⫺3.4 ⫺1.29 ⫺7.1 1.70 6.2 2.38 8.6 1.46

8.1

.81 .27

b

t ratio

— — 5.36 7.2 ⫺0.02 ⫺2.3 ⫺1.82 ⫺8.3 2.51 5.4 1.20 5.5 2.67

8.6

.85 .72

.68

13.2 8.8

7.1 7.4

16.6 14.4

8.7 13.3

21.8 23.5

27.5 8.3

23.0 39.1

7.8 10.7

14.8 34.5

3.3 6.6

16.5 31.8

4.1 7.6

29.5 30.9

4.6 5.2

2.1

1.2

5.9

2.2

13.6

6.0

17.8

7.4

14.0

3.7

20.6

5.7

48.7

6.5

9.1

7.8

7.7

7.5

8.9

9.6

11.3

4.9

6.6

1.7

6.3

1.6

12.7

2.1

⫺3.5

⫺2.6

⫺8.6

⫺7.9

⫺3.6

⫺3.5

⫺7.3

⫺3.1

⫺16.3

⫺4.1

⫺4.6

⫺1.4

8.3

1.7

.69

.76 .88

0.90 0.03

19.4 .95

.81 .96

0.85 0.18

20.1 .95

.74 .92

.80

.79

.91

.85

0.98 38.2 0.90 32.9 0.28 ⫺0.0 .96 .95

0.86 24.9 0.0 .93

.82 .86

.81

0.78 15.9 0.96 26.0 0.0 ⫺0.0 .91 .96

no marital status indicator is significant until we test the percentage widowed and divorced males at ages 65 and older. This marital status indicator is positive and significant in both older age groups. Income inequality is positive and highly significant only at ages 45 and older, and the impact of this predictor clearly increases with age. Perhaps the most startling statistic from our basic model is the extreme variation in explained variance. It declines from a high of .82 at ages 15–24 to a low of .27 at ages 55–64 before sharply rebounding to .72 and .68 at ages 65–74 and 75 and older. We believe that the trend in explained variance across ages is largely a function of the strength of the culture of suicide relative to the impact of the structural integration measures. To test this hypothesis we now introduce the regression coefficients for each region (with England/Wales and Ireland being the reference category).

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Impact of Region on Suicide The middle panel of Table 3 examines the impact of region on suicide. One measure of the net effect of region on suicide is the difference between explained variance in the basic model and explained variance when the regional codes are added to the basic model equation. Comparing explained variance from the basic model with explained variance in the model that includes regional dummies we find only a small gain of 6 percentage points for ages 15–24, but this gain increases to 20 points at ages 25–35, goes to 37 points for ages 35–44, to 49 for ages 45–54, and increases a whopping 58 points at ages 55–64. The impact of region then decreases sharply to only 14 and 13 points in the two older age groups. The unstandardized regression coefficients for the regional codes are the difference between the suicide rate of England/Wales and Ireland and each of the other regions, net of the predictors in the basic model. For example, the mean suicide rate of Central European men ages 15–24 was 23.6 (Table 1). This is 17.2 points higher than the 6.4 rate of men of the same age in England/Wales and Ireland—the reference category. The regression coefficient of 13.2 for Central European men ages 15–24 means that this rate was 13.2 points greater than the reference category rate after adjustment for differences in the basic model predictors. Thus, 76.7% (13.2/17.2) of this regional difference remains, and this is one estimate of the net effect of Central European culture on suicide of men ages 15–24 over the 1960–1989 period. Similar data for the remaining ages and regions is shown in Appendix C. These data show that regional differences in societal integration do not explain differences in suicide among regions. We note, however, that unmeasured structural indicators of status integration (Gibbs, 1994) and/or the density of social networks (Pescosolido and Georgianna, 1989; Pescosolido, 1994) might explain some of the net gain in explained variance we attribute to regional differences in the cultural of suicide. Future research should examine this possibility. Lagged Errors as a Predictor The bottom panel of Table 3 reports the regression coefficient for the lagged error term in equations that include only predictors from the basic model for each age group. Regional dummies are excluded. The regression coefficient of the lagged error term is always positive, significant, and ranges from a low of .78 to a high of .98. Estimated rho is near zero, indicating that both the MGLS and OLS regression coefficient will be about equal—as they in fact are. Explained variance is somewhat higher with the lagged errors than with regional codes. This is expected because the lagged errors allow for change in the culture of suicide across periods, while the regional codes are static. Also, although lagged errors include variation in the culture of suicide by age, period, and nation, this predictor can be effected by its possible control-of-measurement error as well as still-unknown specification errors. But the impact of lagged errors on boosting explained variance over that from the basic model alone is quite

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TABLE 4 Estimated Period Effects on Ages 15–54 Male Suicide Rates with and without Actual and Proxy Measures of the Culture of Suicide: 20 Developed Countries, 1960–1989 Period Age group

Statistic

1960–1964 1965–1969 1970–1974 1975–1979 1980–1984 1985–1989

15–24 Dev. mean Basic only Add region

⫺4.4 1.4 0.4

⫺4.1 ⫺0.5 ⫺1.2

⫺1.3 0.2 ⫺0.1

2.1 0.7 0.8

3.2 ⫺1.5 ⫺0.6

4.5 ⫺0.3 0.7

25–34 Dev. mean Basic only Add region

⫺4.0 2.8 0.9

⫺3.7 0.8 ⫺0.1

⫺2.7 ⫺0.7 ⫺0.5

0.9 ⫺1.0 ⫺0.4

4.3 ⫺1.2 ⫺0.2

5.3 ⫺0.7 0.3

35–44 Dev. mean Basic only Add region

⫺3.1 3.1 1.1

⫺1.2 1.8 0.8

⫺0.5 1.0 0.9

0.4 ⫺0.6 ⫺0.0

2.3 ⫺1.6 ⫺0.4

2.1 ⫺4.4 ⫺2.4

45–54 Dev. mean Basic only Add region

0.1 4.6 1.4

0.2 6.0 2.9

⫺0.7 2.1 1.3

⫺0.2 ⫺2.8 ⫺1.1

1.0 ⫺4.0 ⫺1.2

⫺0.5 ⫺6.0 ⫺3.3

similar to the impact of region on explained variance in the different age groups. Either proxy of the culture of suicide has a similar impact on each age group. IMPACT OF PERIOD Inspection of Appendix A reveals clear evidence of trend in most age groups. The two exceptions to this generalization are the stable rates among men ages 45–54 and 65–74. Only men ages 55–64 show a pattern of declining rates. Among 15- to 24-year-olds the rates show steady gains—rising from 9.1 to 18.0 between 1960–1964 and 1985–1989, while a gain of 9.6 can be seen for men ages 25–34 over the same years. A smaller gain of some 5 points is present in the 35–44 age group. The question we now ask is do our predictors explain the change in suicide rates over time? Is there an effect of period, net of the independent variables, in our models? To test the hypothesis that there is no net effect of period on suicide rates we first compute the deviation of each period’s suicide rate from the mean rate for all six periods. (The period of 1955–1959 is excluded to allow the use of the lagged errors of prediction in Table 5.) For men ages 15–24 the mean was 13.5 and the 1960–1964 rate was 9.1. This is 4.4 points below the mean, and the deviation from the mean reported in the first row and column 1 of Table 4 is, therefore, ⫺4.4. The sum of negative deviations is always equal to the sum of positive deviations, net of rounding error. To measure period effects net of the predictors in our basic model for each age group (i.e., the predictors in the upper panel of Table 3) we compute the predicted suicide rate for each period and take the difference (the error of prediction) between the observed and the predicted suicide rate of each

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TABLE 5 Actual and Estimated Period Effects on Ages 55–75 and Older Male Suicide Rates with and without Proxy Measures of the Culture of Suicide: 20 Developed Countries, 1960–1989 Period Age group

Statistic

1960–1964 1965–1969 1970–1974 1975–1979 1980–1984 1985–1989

55–64

Dev. mean Basic only Add region Lag errors

3.3 8.5 4.8 0.7

2.3 3.9 1.7 ⫺0.4

0.1 1.9 1.4 0.9

⫺2.4 ⫺2.4 ⫺1.7 ⫺1.8

⫺0.9 ⫺4.0 ⫺1.6 1.3

⫺2.3 ⫺7.9 ⫺4.6 ⫺0.6

65–74

Dev. mean Basic only Add region Lag errors

0.4 ⫺3.6 ⫺3.8 ⫺1.9

0.4 0.3 ⫺0.8 3.0

0.4 ⫺0.1 ⫺0.3 ⫺0.8

⫺1.0 0.6 0.6 0.3

0.0 2.0 2.0 0.8

⫺0.5 0.8 1.4 ⫺1.5

75 and over Dev. mean Basic only Add region Lag errors

⫺2.4 ⫺0.9 ⫺0.0 ⫺1.1

⫺3.1 ⫺4.1 ⫺4.0 ⫺1.4

⫺2.3 ⫺5.9 ⫺4.7 ⫺2.4

⫺1.2 ⫺2.9 ⫺3.3 0.5

2.4 3.5 3.4 2.0

6.7 10.4 8.6 2.5

period as our initial measure of the net effect of period on the suicide rate. A second estimate is found by adding region to the regression equation and again computing the errors of prediction for each period. Including a proxy for the culture of suicide should reduce specification error and thus yield superior estimates of period effects. Table 4 displays the results of these calculations. Data for each of the four younger age groups are in successive panels. The first row of each panel shows the deviations from the mean 1960–1989 suicide rate for each period. The second row shows the mean error of prediction from the basic model equations in Table 3, while the third row displays mean errors when region is included in the prediction equation. For men ages 15–24 and those ages 25–34 the basic model provides rather solid control over expected period effects. Changes in the predictors in the basic model reduce and/or eliminate period effects. The addition of region provides near total control of period effects in both age groups. At ages 35–44 and 45–54 we must introduce region as a predictor to better control period effects. Most of the upward trend among men ages 35–44 is captured by change in the basic equation, and the regional proxy for the culture of suicide further reduces our indicator of period effects. Among men 45–54 the stable rates persist despite changes in the independent variables that would be expected to increase suicide rates. Therefore the mean errors move from slightly positive to a negative peak of ⫺3.3 in the final period—about 10% of the mean suicide rate in this age group. Table 5 follows the format in Table 4, but now also includes the mean errors of prediction from equations with the basic model and the lagged errors—the second proxy of the culture of suicide. Among the three oldest age strata the errors of

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prediction from the basic model alone are actually larger than the mean deviations in 13 of the 18 comparisons. Including the region proxy also fails to move the period effects much closer to zero. But when the lagged error term is introduced a very substantial reduction in period specific errors occurs. Among men ages 55–64—whose rates declined over time—the lagged errors no longer fit the pattern of the deviations from the mean. At ages 65–74 there was no evidence of trend in the deviations from the mean and the lagged errors also fail to reveal any evidence of a coherent pattern of period effects. Suicide rates of men ages 75 and older were steady before increasing sharply in the 1980s. The lagged errors estimate of period effects moderates these gains, especially for the period of 1985–1989. The error of prediction of 2.5 in this period is only 4.5% of the suicide rate of this period. DISCUSSION The hypothesis that the impact of societal integration measures such as female labor force participation will weaken as populations adjust to new sex roles was not supported. The unstandardized regression coefficients of female labor force participation (Table 2) show no pattern of decline as we move from the 1955–1959 through the 1985–1989 periods. This holds for men ages 15–34, 35–54, and 55–74. In the same period-by-period analysis relative cohort size was only significant for men ages 15–34 in the 1970–1979 period. At ages 35–54 population size did not achieve a strong and positive impact on suicide until the years 1980–1989. Population size was negative and significant for men ages 55–74 only in the period of 1975–1979. For men ages 75 and older population size was insignificant in all periods. These data do not support Pampel’s (1996, p. 354) view that the effects of cohort/population size are stronger before than after 1970. Nor do they confirm his view that these effects will be positive and significant among men under age 35 and negative and significant for men ages 65 and older. Income inequality was not significant for men under age 55. For men ages 55–74 income inequality was positive in all periods and significant during 1960–1964 and all later periods. Among men ages 75 and older, income inequality was positive and significant in all periods. Percentage widowed and divorced was significant for men ages 55–74 in 1970 and all later periods. For men ages 75 and older percentage widowed and divorced was positive in all periods and significant in 1965 and later periods. The percentage of widowed men ages 55–74 declined from 10.6 in 1955–1959 to 6.7 in 1985–1989, a change one would not ordinarily associate with an increase in the impact of widowhood on male suicide. It may be that prior to the 1970s many widowed men lived with kinfold and a trend toward living alone resulted in a substantial increase in widowed men living alone—a change that could explain the emergence of this predictor as significant. We were unable to find longitudinal cross-national data on living arrangements to test this hypothesis. The surprisingly large and positive effect of collectivism on suicide rates of

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men of all ages and periods was found to be the spurious result of the tradition of high suicide rates in countries that adopted collectivist social policies after World War II. Controls for prewar suicide levels nullified the impact of collectivism. These findings led to an analysis of regional differences in suicide rates. The sharp differences in the level and the age structure of suicide among regions was taken as one piece of evidence that a culture of suicide exists. Empirical support for this concept was also found in investigations that found that the suicide rate of immigrants’ country of origin well predicts the suicide rates of immigrants in their new homeland—whether the United States or Australia. Further, analysis of the errors of prediction of age and period-specific suicide rates provided additional support for the concept of the culture of suicide. The first proxy measure for the culture of suicide was regional location. The second was the lagged error of prediction. In our MGLS analysis (Table 3) of age-specific suicide rates covering the years 1960–1989, we found that introducing either region or the lagged errors of prediction with the basic equation of societal integration measures resulted in marked gains in explained variance, especially among men ages 35–64. This suggests that the strength of the culture of suicide compared to that of societal integration is stronger among men in the middle years—why this is the case should be the subject of further inquiry. The impact of indicators of societal integration, as measured with the basic model alone, varied somewhat by age. Female labor force was no longer significant at ages 55–64 and older, but divorce had a positive and significant impact on all ages. Fertility depressed suicide most strongly among men ages 34 and under, although it had significant negative effects at all ages. Religious books was negative and significant in all but the 55–64 age group, but took the largest unstandardized regression coefficients among men ages 65 and older. Marital status (the percentage married) had no significant net impact on ages 25–64. The percentage single increased suicide rates among men ages 15–24, while the percentage widowed and divorced increased suicide among men ages 65 and older. Income inequality had positive and significant impact on suicide of men ages 45 and older, and these effects increased with each older age stratum. Cohort/population size was positive and significant among men ages 15–24 and 25–34, as expected. It was also positive and significant among men ages 35–44. These findings support those of Ahlburg and Schapiro (1984, Table 1). Pampel (1996, Table 5) reports significant positive impacts on suicide rates of men 34 and under, but none for men ages 35–44. Our population size coefficients for men ages 65–74 and 75 and older are large, positive, and significant—in contrast to the negative and significant coefficients in Pampel’s model. His relatively low level of explained variance, use of multiple age groups in the same regressions, and probable specification errors may explain this difference. Our results from the basic equation alone generally continue when region is added. For example, population size continues to be positive and significant among men ages 75 and older and is positive, with borderline significance for those ages 65–74. In general, the largest positive impact of region on suicide was in Central

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Europe and Scandinavia, followed by Western Europe and then the four large former English colonies. Southern Europe typically had lower rates than those in the reference category (England/Wales and Ireland). This suggests that the normative order condemning suicide is weakest in Central Europe and Scandinavia and strongest in Southern Europe, Ireland, and England/Wales. The failure of national differences in societal integration to account for most of the regional differences in age-specific male suicide rates also suggests that the age structure of suicide may be partially determined by the culture of suicide. For example, the normative order condemning suicide among the young may be very strong in Southern Europe, while in the same countries the normative order may be much less restrictive for older men. Future research on the age structure of suicide should test this hypothesis. Including the lagged errors of prediction as a second proxy for the culture of suicide should have improved the measurement of this concept because it is specific to each age group and allows for change over time. When it replaces the regional codes the lagged error term is positive and significant in all age groups but, with the exception of men ages 75 and older, the gain in explained variance over that of the regional proxy is no more than 8%. Our final analysis tested the hypothesis that effects of time, net of measured predictors, exist. Pampel (1996, Table 5) reports that time squared had positive and significant effects on male suicide, net of a number of control variables. Since men of all ages are included in his data set we assume that the effect of time is on men in most or all age groups. In general, our analysis of the effects of time (Tables 4 and 5) found that among men ages 15–24, 25–34, 35–44, and 45–54 the basic equation and the regional codes virtually eliminated the period effects one might expect from the observed trend (or lack of trend) in suicide rates. Although men in the three oldest age categories exhibited different patterns of change over time, the basic equation with the lagged errors generally reduced period effects to near zero. We conclude that specification error may be the cause of the significant impact of time squared on male suicide rates in the Pampel model. This study used a large cross-national database to directly examine period-byperiod and age-specific male suicide rates. Because suicide rates specific to age and period are used in place of a single database combining all ages and periods, we believe that differences in conclusions regarding the effects of various predictors on male suicide rates should probably be resolved in our favor. Our analysis provides little support for the argument advanced by Pampel (quoted at the beginning of this article) that age differences in suicide are increasingly ‘‘. . . indicators of age groups’ economic and social well-being.’’ Our data strongly support the view that age differences exist, in part, because societal integration variables have differential impacts on different age groups. But our analyses also provided evidence that the culture of suicide, whether measured with regional codes or the lagged errors, is also a powerful determinant of national differences in male age-specific suicide rates.

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APPENDIX A Means and Standard Deviations of Age-Specific Male Suicide Rates by Period: 20 Developed Countries, 1955–1989 Period

Age

Statistics

1955– 1959

1960– 1964

1965– 1969

1970– 1974

1975– 1979

1980– 1984

1985– 1989

15–24

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

8.8 6.4 15.9 10.9 21.0 13.0 33.2 18.5 39.9 19.6 44.1 17.0 49.3 25.2

9.1 6.1 16.2 9.6 21.1 12.3 30.5 17.0 36.9 17.2 37.1 15.9 46.1 23.6

9.4 5.9 16.5 10.3 23.0 13.8 30.6 16.9 35.9 17.8 37.1 17.6 45.4 23.6

12.2 8.0 17.5 10.9 23.7 14.3 29.7 17.1 33.7 17.6 37.1 17.8 46.2 25.4

15.6 10.0 21.1 11.1 24.6 14.5 30.2 16.8 31.2 15.7 35.7 17.4 47.3 26.3

16.7 9.6 24.5 12.7 26.5 14.6 31.4 17.7 32.7 15.2 37.1 18.0 51.0 28.3

18.0 9.6 25.5 12.2 26.3 14.3 29.9 15.4 31.3 13.6 36.2 16.2 55.3 29.1

25–34 35–44 45–54 55–64 65–74 75⫹

APPENDIX B Means and Standard Deviations of Predictors of Suicide by Age: 20 Developed Countries, 1960–1989 Statistics Independent variables

Mean

Residual female labor force Residual divorce rate Religious books Income inequality

0.0 0.0 5.1 33.7

SD All ages 4.1 1.1 3.0 4.4

Fertility Relative cohort size Percentage single Percentage widowed and divorced Suicide rate

Ages 15–24 330.0 76.5 48.8 7.3 89.0 4.5 0.1 0.5 13.5 8.9

Fertility Relative cohort size Percentage widowed and divorced Suicide rate

Ages 25–34 330.0 76.5 52.9 8.5 1.5 1.7 20.2 11.7

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Mean

SD

Fertility Population size Percentage widowed and divorced Suicide rate

Ages 35–44 350.0 75.4 17.2 1.6 2.9 2.9 24.2 13.8

Fertility Population size Percentage widowed and divorced Suicide rate

Ages 45–54 359.4 77.1 15.4 1.5 4.2 2.7 30.4 16.5

Fertility Population size Percentage widowed and divorced Suicide rate

Ages 55–64 367.8 74.2 13.0 1.4 6.7 2.1 33.6 16.0

Fertility Population size Percentage widowed and divorced Suicide rate

Ages 65–74 369.8 76.3 11.5 2.3 13.8 2.3 36.7 16.7

Fertility Population size Percentage widowed and divorced Suicide rate

Ages 75 and older 379.8 83.4 11.5 2.3 33.7 4.8 48.5 25.8

APPENDIX C Raw and Adjusted Differences in Male Suicide Rates of Five Regions from the Mean Male Suicide Rates of England/Wales, and Ireland by Age: 20 Developed Countries, 1960–1989 Age group 15–24

25–34

35–44

45–54

55–64

65–74

75⫹

Region

Raw

Adj.

Raw

Adj.

Raw

Adj.

Raw

Adj.

Raw

Adj.

Raw

Adj.

Raw

Adj.

Central Europe Scandinavia Western Europe Aus./Can./N.Z./U.S. Southern Europe

17.2 11.9 3.3 10.6 ⫺1.8

13.2 8.8 2.1 9.1 ⫺3.5

20.0 19.8 6.8 9.8 ⫺4.1

16.6 14.1 5.9 7.7 ⫺8.6

24.8 26.7 10.3 10.6 ⫺3.2

21.8 23.5 13.6 8.9 ⫺3.6

34.1 21.1 16.6 13.4 0.5

23.0 39.1 17.8 11.3 ⫺7.3

34.1 29.8 20.5 11.8 2.0

14.8 34.5 14.0 6.6 ⫺16.3

38.5 27.3 29.1 13.4 7.7

16.5 31.8 20.6 6.3 ⫺4.6

59.0 31.4 57.4 19.2 20.9

29.5 30.9 48.7 12.7 8.3

Eng./Wales & Ireland

6.4

11.1

12.1

13.1

16.7

16.9

16.8

Note. The ‘‘raw’’ rate is the observed suicide rate (Table 1) minus the observed rate of England/Wales and Ireland. The ‘‘adjusted’’ rate is the unstandardized regional dummy variable coefficient (Table 3)—the difference between the regional rate, net of the basic equation predictors, and the mean rate of England/Wales and Ireland.

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