Theory of suicide

Suicide can be explained by a wide variety of factors. Moreover, it is attempted to be explained by different fields of science. On one hand, medicine (namely psychiatry) and psychology try to explain the phenomenon from physiological point of view. On the other hand, social sciences as sociology and economics try to analyse human behaviour and factors affecting it. Sometimes these two strands of science go against each other. In this paper I attempt to link major biological factors established in the medical literature with existing economic theories of suicide.

3.1.1 Biology of suicide

Despite often being a striking event for families and friends, suicides do not happen com-pletely out of the blue. Typically, victims of suicide indicate their need for help in some ways even though that might be not noticed by surrounding people (WHO, 2014). Given this fact I can suppose that individuals committing suicide are likely to be affected by a particular condition that does not kill them immediately. Medical literature suggests that one of the major factors developing suicidal behaviour is depression. There exist numerous studies demonstrating positive link between depression and suicide (see Beskow (1990) for a detailed summary of the literature on the topic). People affected by depression are at a high risk of committing suicide (Lynch and Duval, 2011). As a result, I can consider suicide as a complication of depression. Even though there is no evidence for existence of one major cause of depression, there are several factors associated with it. Once we identify these potential causes of depression, we can analyse if the same drivers can be used to predict suicide rates.

First of all, depression is normally provoked by some stressful event (Burrows, 1977; Risch et al., 2009). However, individuals can react to these occasions differently: not everyone becomes depressed after loosing a job or having a divorce. Besides individual preferences, the likelihood of a certain stressful experience initiating depression might be affected by genetics. All events in our life provoke an influx of serotonin into the space between neu-rons to activate serotonin (5-hydroxytryptamine, 5-HT) receptors (Beliveau et al., 2017).

Some of the 5-HT receptors are responsible for such functions as regulation of anxiety or mood(Tatarczynska et al., 2004; Young, 2007). Different combinations of these two func-tions can yield emofunc-tions that people describe as “happiness” or “unhappiness”. However, a functional polymorphism (basically, difference in forms) of the serotonin transporter (5-HTT) can regulate the sensitivity of individuals to different stressful events (Caspi et al., 2003; Kendler et al., 2005; Goldman et al., 2010; Karg et al., 2011). In other words, the

3.1 Theory of suicide

other and the reason for it is individual’s genetics. As a result, we can expect a particular (genetically determined) form of 5-HTT to be suicide-promoting (Lee et al., 2015).

Besides genetics, there exist other factors that make people to be more prone to depression.

Some studies argue that lower availability of serotonin may be a crucial factor in depres-sion(Meltzer, 1989; Kambeitz and Howes, 2015). However, currently there is a big debate in the literature if this is truly the case (Cowen and Browning, 2015; Healy, 2015). I do not aim to contribute to this discussion, but rather use potential theoretical mechanism that has been successfully applied for therapy. A range of studies suggests that serotonin is positively associated with light. By “light” we can mean sun light in general (Petridou et al., 2002; Lambert et al., 2002; Sansone and Sansone, 2013) or more precisely defined ultra-violet radiation (UVR) (Iyengar, 1994; Zawilska et al., 2007). If we believe serotonin to be efficient for depression prevention, we can expect exposure to solar radiation to be negatively correlated with depression. Moreover, the existing literature suggests that even if serotonin levels play no role in depression occurrence, there must be other ways for light to affect people’s mood. Currently, bright light therapy (BLT) is an accepted treatment op-tion for depression (Mårtensson et al., 2015). Both exposure to BLT (Eastman et al., 1998;

Young, 2007; Al-Karawia and Jubair, 2016) or solar radiation (Eastman, 1990; Gambichler et al., 2002) can be efficient for treating various types of depression. Similar beneficial ef-fects of the sun light are observed if suicide rates are considered (Lambert et al., 2003). As a result, regardless of the particular channel, we can expect higher levels of UVR exposure to be negatively associated with suicide rates².

To sum this section up, the existing studies of depression and suicide claim that various stressful life events can provoke depression. Acute forms of depression can lead to suicide.

However, individual vulnerability depends on a set of various biological factors. In this paper I concentrate on the two potential drivers, that I can, at least, try to measure at the population level: genetics and environment (namely, UVR). Firstly, some people (or populations) might be genetically predetermined to be more stressed, hence, more vulnerable to depression. Secondly, lack of UVR can contribute to higher depression rates that, in turn, result into more suicides.

3.1.2 Economics of suicide

Even though biology plays crucial role in the mechanisms of suicide, we have to consider other factors that can be potential drivers. Different economic and social conditions can be considered as stress factors that trigger depression or suicide. Since Durkheim economists

2It is important to remember that excessive exposure to solar radiation can be harmful for general health:

skin cancer or cataract are the most common threats (Lucas et al., 2006). Even though these diseases do not necessarily cause immediate depression, they can raise stress levels that, in turn, cause depression.

This issue will be addressed in the Section 3.3.1.

and sociologists tend to explain suicides with various models of human behaviour. These theories do not necessarily oppose the biological mechanisms, moreover, they can be in-tegrated into physiological models of suicide. Existing economic literature often lists low incomes (Helliwell, 2007; Andres and Halicioglu, 2010; Daly et al., 2013) and unemployment (Andres, 2005; Chang et al., 2013; Jalles and Andresen, 2015) among the drivers of suicide.

From biological point of view, these can be seen as stressful life events. If we talk about stress, we normally imagine some sudden and unexpected changes in life, however, persis-tent states (e.g., long-term poverty) can also raise stress levels (Baum, 1990). Furthermore, Minkoff et al. (1973); Kovags et al. (1975); Beck et al. (1985) identified hopelessness (more precisely defined as the cognitive element of negative expectations), which is a component of the depression syndrome, as a stronger indicator of suicidal intent than depression itself.

In other words, when individuals do not see perspective in their life, they are more likely to commit suicide.

Of course, hopelessness is the state that should be described individually. However, there are some factors that might be quite universal for the majority of the people. For example, the World Values Survey indicates that more than 62 percent of the total surveyed across the world are worried about losing their job and more than 87 percent find that work plays an important role in their lives (Inglehart et al., 2014). Assuming that to an extent generalisation of preferences is reasonable, we can recall the seminal paper by Hamermesh and Soss (1974) that presents a model of suicide behaviour. In this paper I slightly modify the original model to adjust it for the purpose of this study.

First, let us assume that the utility an individual derives at timemis a function of income Y:

U_m=αY_m, (3.1)

where α ∈ (0,1) is a biological parameter. I plug income into the utility function, as it allows consumption of more and wider variety of goods that should lead to a higher utility level. However, by Y we can also consider other things that derive us positive utility, like family, friends or achievements of the favourite football team, so it should be rather considered as a vector of variables. However, individuals also incur costs of maintaining themselves alive at particular agea: K(a). As people become older, the age-related disutility becomes larger: medical expenditures, physical or emotional pain rise with age (^∂K_∂a >0). Summing it up, the individual’s lifetime utility at a certain agealooks