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  • br Data considerations and methodology

    2018-11-07


    Data considerations and methodology We assembled yearly counts of suicides that occurred from the fall of the monarchy in 1910–2013. During this period, Portuguese population increased from less than 6 million to more than 10.5 million. To take this fact into consideration, we computed suicides per 100.000 inhabitants. Data was obtained from death certificates of Portuguese residents who died in the country over the study period, as provided by the Portuguese Statistical Authority (INE), the WHO Mortality Database, and the EUROSTAT. Since significant differences between men and women have been demonstrated in prior studies of broad economic trends and suicide in Portugal, we run different analyzes for both male and female suicides. Thus, our dependent variables comprise three different time series: total, male, and female suicide per 100.000 inhabitants (suicide rates here after), represented in Fig. 2. These variables range from 1.9 to 20.2 suicides by 100.000 females in the first case and 100.000 males in the second. We concluded that our dependent variables are generated by autoregressive processes, which verify the stationarity requirement. However, these processes are remarkably persistent. Table A2 presents the results of the DF tests. The second requirement is necessary in order to make valid inference, as it influences the estimated variance of the coefficients. This can be tested by a Breusch–Godfrey test and, given its violation, correlation was solved by the introduction of a lag of the dependent variable in the model. The p-value for this test can be found in the regression tables. Additionally, Fig. 2 suggests that overall suicide rates have a positive trend until 1939 – the p-Cresyl sulfate in which it reaches its maximum. However, the linear trend loses all significance when the autoregressive term is included. As far as the main regressor is concerned – Gross Domestic Product (GDP) – we decided to use the difference of its logarithm, since the Portuguese GDP is not covariance-stationary. This transformation is a proxy for its growth rate and satisfies the previous requirement. Fig. 3 illustrates GDP growth evolution and reveals tremendous volatility in the 1920s and 1930s, followed by a period of stable growth from 1950s until late 1990s, as explored in the Introduction. The last decade in our study is however characterized by anemic growth and a sharp GDP contraction concomitant with the European sovereign debt crisis. To test whether GDP fluctuations affected in a systematic and significant way the number of suicides in Portugal, we decided to control for political and social variables, bearing in mind that, due to the period studied, we suffered from important data constraints that will be discussed in Section 5. Social welfare may also be influenced by other conditions, and for this reason, the following controls were added and graphs depicting their evolution are shown in Fig. A2: Relevant descriptive statistics are displayed in Table 1, where all variables are presented. All detailed data sources per year are accessible in Table A3. Lastly, there are two methodological changes in GDP and Infant Mortality Rate (IMR) series – as it can be seen in Table A2. The adjustment in GDP has to do with the fact that the first part is in constant prices of 1958 until 1952 and, the second, is in constant prices of 2001. On the other hand, the IMR methodology changes because, from 1980 onwards, the National Institute of Statistics (henceforth, INE) ignores data from newborns whose mothers are non-resident in Portugal. To deal with these changes, three dummies were created: change_imr, which takes value 1 after 1981; change_gdp that takes value 1 after 1952; and change_gdp_growth, taking value 1 only in 1953. The last accounts for the change in GDP growth and the previous one in the cycle. These dummies are interacted with the corresponding regressor as we expect that p-Cresyl sulfate a change in the way these are measured may lead to a change in the impact on the dependent variable, rather than a change in its conditional expectation.