Deviant cases

Here a glance will be taken to the deviant countries from the former chapter. The analyses in Chapter 2 indicated that Estonia, Hungary, Slovenia Poland and Sweden are interesting deviant cases, because of their atypical marketization trends and levels (in timing and degrees) and because of their relatively large regression residuals. Notably, almost all of these outlier countries appear to have comparatively high suicide rates.

Looking at the correlations between several key variables in these countries, it appears that in all countries (deviant and non-deviant) the male and female suicide rates correlate highly (r > .80 in most cases). The deviant countries are not extraordinary in this regard. Remind that the correlations are typically higher in the correlation analyses for individual countries than for the total sample, which combines much more variation. Moreover, the p-value level is likely much larger for individual countries than for the entire country sample, which is then more likely to return statistically significant correlations than the individual countries. Rather interesting for the purpose of comparing deviant cases with the overall sample, is looking in which variables correlate relatively strongly and in what direction. For instance, for the general country sample, resistance is negatively correlated with the male suicide rate (r = .34, p <.001) and female suicide rate (r = 28, p <.001). In Estonia, however, the correlations are strongly positive (r = .78 and r = .74 respectively). Slovenia also turns out modest positive correlations: r = .33 and r = .30 respectively. Also in Hungary the correlation with the male suicide rate is positive, but much smaller (r = .19). Finally, in Sweden and Poland, the correlations are clearly negative (r = .58 and -.61 for Sweden and -.25 and -.22 for Poland.

Moreover, in the overall country sample, unemployment correlates modestly strong with the female suicide rate – negatively so (r = .30, p <.001), while it does weakly so with the male suicide rate (r = -.12, p <.001). All deviant cases except Hungary turn out negative correlations as well. For instance, in Sweden the correlation is relatively strong: r = -.84 and r = -.81 respectively), with a stronger correlation for the male rate. In Estonia, by contrast, the correlation is rather weak (r = .03) for the male rate, but modestly strong for the female rates (r = -.33, p <.10).

Finally, it is interesting to see some relatively strong correlations between the unemployment rate and resistance in some deviant cases, while this is not the case for the overall country sample (r = -.05, p

>.10). Especially in Estonia the two are highly positively intercorrelated (r = .99), while in Hungary they are strongly negatively correlated (r = -.79). Only Poland does not reveal a strong correlation between the two (r = -.05), looking more like the overall cross-national pattern. The other two deviant countries, Slovenia and Sweden, show negative correlations.

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Running the regression with marketization processes, unemployment growth and some control variables (see Model 5 Table 2 and 4) on the male and female suicide rate respectively, reveals again that Estonia and Slovena have strikingly large residuals. Together with Austria and Finland they have residuals larger than 3.5. These countries have unexplainably high suicide rates, considered from the control variables considered. Moreover, Austria and another high suicide country, Finland, appear as deviant cases with large residuals. With regard to the model ran for Model 5 Table 4 (female suicide rates), the residuals are somewhat smaller, but again Hungary stands out, just as Japan, with residuals clearly larger than 2.5.

Figure 1a: Cross-national scatter plot of regression residuals for men, 1960-2016

Estimation of the residuals based on the fixed effects model from Model 5, Table 2

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Japan

22.533.54Average residual of countries (male suicide rate)

0 10 20 30 40

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Figure 1b: Cross-national scatter plot of regression residuals for women, 1960-2016.

Estimation of the residuals based on Model 5, Table 4.

3.3.7 Model

Model 1 examines the effects of institutional marketization/ cultural (anti-)marketization (resistance) and unemployment, while accounting for country fixed-effects and the passing of the years (with a continuous year counter variable). Model 2 adds the interaction effect between marketization and unemployment. Model 3 then adds the economic control variables: GDP and GDP growth. Model 4 includes additional control variables about (dis)integrating factors such as the divorcerate, urbanization and social expenditures, as well as population variables such as population size and the incidence of tertiary educated people. Model 5 relaces these additional control variables by variables that describe the composition of the unemployed population: the proportion of tertiary educated, long-term unemployed and young people (15-24 years old) in total unemployment. Model 6 controls for the suicide culture (Neumayer, 2003) of a country as well as for the average suicide rate of leading economies. Model 7 substitutes these control variables for categorical dummy variables capturing transition status and regime type. Model 8 substitutes these dummy variables for the antidepressants consumption rate. Model 9 controls for the fact that some variables (some marketization indicators AustriaBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgiumBelgium

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Finland FinlandFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFranceFrance

Germany NetherlandsNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorway

Poland

.511.522.5Average residual of countries (female suicide rate)

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such as employment protection legislation strictness) have observed time series that only start in 1997, adding a dummy variable distinguishing this time period from previous years. Model 10, finally, performs a more reliable control for the fact that some variables (among which resistance), have only observed time series from 1980 on, by leaving out the years before 1980. The models were all ran on the lead value of the suicide rate, as it was thought that what has happened in the year before (regarding the unemployment rate and marketization trends) matter for the current suicide rate.

Moreover, by ensuring the correct chronological order of the correlations (x occurs first, followed by y), causality is made more probable.

The models, then look as follows:

Hypothesis 1 and 4:

𝑠𝑢𝑖𝑐𝑖𝑑𝑒𝑖𝑡+1= 𝛽0+ 𝑚𝑎𝑟𝑘𝑒𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛 𝛽1+ 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 𝛽4+ 𝑦𝑒𝑎𝑟 + 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 + 𝜀𝑖𝑡 Hypothesis 2 and 3:

𝑠𝑢𝑖𝑐𝑖𝑑𝑒_𝑖𝑡+1= 𝛽₀+ 𝑚𝑎𝑟𝑘𝑒𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛 𝛽₁+ 𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝛽₂

+ 𝑚𝑎𝑟𝑘𝑒𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛 ∗ 𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝛽₃+ 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 𝛽₄+ 𝑦𝑒𝑎𝑟 + 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 + 𝜀_𝑖𝑡

Where i is country i and t is year t, 𝛽₁ is the coefficient for marketization, 𝛽₂ is the coefficient for unemployment, and 𝛽₃ is the coefficient for the interaction effect between unemployment growth and marketization. 𝛽₄ is the coefficient for the vector of control variables.