La historia oficial y El secreto de sus ojos

I propose a model for exploring and understanding housing and homeless transitions within a multidimensional, longitudinal and ecological context. The model draws on the concept of housing pathways (Clapham 2003) and the theory of the life course (Elder et al. 1985), particularly in its applications to housing (Beer and Faulkner 2011). Different types of housing and homelessness are conceived to lie along a spectrum, with street homelessness at one end and secure housing at the other (Watson 1984). In between are graduated levels of housing deprivation that could be defined on several physical, legal, economic and psychological dimensions including dwelling type, tenure, affordability, safety and emotional well-being (Somerville 1992). For the purposes of this research, dwelling type and tenure are likely to be important differentiators between types of housing and homelessness and are readily available in many datasets. In this instance, tenure refers to the legal and social rights individuals have to occupy their current dwellings.

Individuals and families reside in forms of housing and homelessness within specific episodes of accommodation. Residential moves or transitions are made between episodes, often in pursuit of housing and life course objectives but also in response to adverse life events and cumulative disadvantage (Beer and Faulkner 2011). Drawing on the housing pathways perspective (Clapham 2003), transitions coalesce over time to create long-run housing trajectories. The literature on housing pathways as explicated by Clapham (2002, 2003) is grounded within post-modernism and social constructionism, thus placing individual experience and meaning at the centre of any analyses of housing. Nevertheless, there are valuable insights for more positivist and purely quantitative research. Most notably, the housing pathways perspective provides a framework through which to theorise and analyse the longitudinal dynamics of housing and homelessness within an ecological context (Fitzpatrick 2005), complementing earlier work by Toro et al. (1991) in recognising the potential ways in which personal characteristics and histories intersect with life events,

interpersonal connections and dynamics and the broader economic and macro-structural environment.

Through an ecological perspective, housing transitions can be seen to be comprised of two interconnected parts. These parts are: 1) exit from previous accommodation; and 2) an entry into one of several potential states along the housing and homelessness spectrum. Push factors, both positive and adverse, are theorised to drive exits. Housing, family, economic and personal shocks, including evictions, job loss and relationship breakdown, have proximal and adverse effects. Rather than treat shocks as independent factors that compete with personal deficits and vulnerabilities on the one side and macro-structural deficiencies on the other – as is implicit in many empirical models of homelessness – macro-structural changes are likely to exert their influence through the income and employment shocks they create for individuals and families. Further and similar to how neoclassical housing theory posit that the effects of income shocks depend on their relation to the size of permanent income (O’Flaherty 2009), so the effects of broader life shocks on the risk of homelessness are moderated by personal histories, vulnerabilities and access to economic, social and institutional capital. Shocks thus interact with cumulative advantage and disadvantage and more distal factors to create tipping points that push people into housing exits (Curtis et al. 2013; Wiemer 2014; Clark 2016).

On losing housing, those with the most economic capital and access to affordable housing are best placed to secure alternative housing. Individuals and families with fewer economic resources but well-formed support networks are most likely to ‘double up’ with family and friends. Those with the least resources and deepest vulnerabilities are most likely to become homeless on the streets or in improvised dwellings. Resource levels are not likely to be static, rising and falling with changes in housing and life circumstances. Continuous cycles of housing and economic disadvantage may weaken resources and deepen vulnerabilities, thereby exposing people to the most severe and entrenched forms of housing disadvantage over time (Vacha and Marin 1993; Skobba and Goetz 2015). The state has power to disrupt these patterns through the provision and rationing of shelter and refuge accommodation,

transitional, social and subsidised housing and non-accommodation support services (Wong and Piliavin 1997; Shinn et al. 1998; Metraux and Culhane 1999; Tsemberis et al. 2004). However, governments may have little or no direct impact on many households, meaning that individuals often choose or are compelled to rely on their own economic and social resources (Clapham 2002).

Data

Aspects of the model are tested using the Journeys Home study (Wooden et al. 2012; Scutella et al. 2017). Across the six waves, respondents were recorded as homeless on the streets or in improvised dwellings in 2.7 per cent of person-waves, staying in homeless accommodation such as shelters and refuges in 5.3 per cent of person-waves, staying with family or friends in 32 per cent of person-waves and staying in sub-market accommodation such as hotels, motels, hostels, boarding/lodging houses of caravans/mobile homes in 6.7 per cent of person-waves. Further descriptive statistics are provided in the Appendix. Details on the survey design are provided in Chapters 2 and 3, as well as Wooden et al. (2012) and Scutella et al. (2017).

In this chapter, I use the accommodation calendar in Journeys Home to provide information on the timing and type of housing/homelessness episodes, including those occurring between waves. As discussed in Chapter 2, respondents reported the type of accommodation they were staying in for each 10 day block since the previous interview – or the time they had spent in the current accommodation at wave one. The calendar contains 7,234 unique episodes across 14 accommodation types. Of these, 310 episodes do not have a start date. A small number of these (less than 20) are the first episode reported by respondents. These are excluded from the analysis. For the remainder, start dates were imputed by randomly assigning a date between the start of the respondent’s previous episode and the start of their next. Assuming accurate respondent recall, I have used this information to estimate the duration of accommodation episodes at each wave and the timing of transitions

to subsequent episodes. One proviso is that the accommodation calendar does not distinguish housing episodes in the social and private market sectors. This distinction is made for ongoing episodes at the time of each survey wave. However, there were 93 transitions to housing episodes that began and ended between survey waves, for which a sector was not defined. In these cases, the sector is imputed by randomly assignment based on the origin-specific probability of transitioning to social or private rental housing. Testing different approaches, for example treating all unknown episodes as private rental, suggests these do not substantially impact the results.