Current poverty | poverty within lifetimes
Risk factors and trigger events
What causes poverty for families today differs from what causes poverty across generations. Causes of poverty within lifetimes can be viewed through two lenses: risk factors and trigger events:
- Risk factors are “the social characteristics or personal resources of an individual or household which mediate how resistant or vulnerable they are to poverty”;
- Trigger events are “the event[s] which actually trigger an entry into [or an exit from] poverty.”
Risk factors and trigger events are related, yet distinct. Employment as a concept, for example, can be either: the state of being unemployed is a risk factor, while the occurrence of losing a job is a trigger event. We will start with risk factors first and then transition to analysis of trigger events. We will also briefly discuss the positive role of protective factors and resilience.
Poverty risk factors are the characteristics and personal resources highly correlated with low income. Research into their degree of correlation to low income can give us the likelihood that a family that exhibits these risk factors will end up poor. Because correlation precedes causation, identifying risk factors is a necessary first step towards painting a better causal picture. Risk factors are not necessarily causal factors, however. As risk factors are about identifying correlations, they can only hint at the answers to deeper causal questions regarding why families fall into poverty—identifying trigger events or using regressions and experimental techniques are better suited for the next step in the process.
Risk factors are related to all types of poverty: transient, recurrent, and persistent. Those who find themselves in persistent poverty, however, experience more and worse outcomes that cluster together, creating a snowball effect. In other words, the relationship between risk factors tends to be more exponential than linear. Researchers have found that “a sliding scale of poverty persistence results from an accumulation and intensity of risk factors.” This is what Treasury (and much of the literature) calls multiple disadvantage or “linked and mutually reinforcing problems,” where “the impact of low income on outcomes for children is greatest when the low income is persistent over a number of years and when it is combined with a range of other risk factors.” The impact is more pronounced for Māori, with a stronger correlation between persistent low income and increased deprivation. Scholars call it a web of deprivation, consisting of a “dense network of psychological, social, historical, and economic factors that either created or perpetuated problems.”
Risk ratios and at-risk groups
One way to discern whether or not a factor is a risk factor for poverty is to calculate the risk ratio for different groups in society—particular ages, ethnicities, educational levels, and family structures. Figure 6 below illustrates the risk ratios for different groups in New Zealand society. The green dashes in the figure show the proportion of the entire population with those characteristics (e.g. about 27 percent of the population is under 18), while the gold bars show the rate of persistent low income for each group (e.g. about 29 percent of those under 18 have persistently low incomes). If persistent poverty was evenly distributed across the population, then the green dashes would be the same height as the gold bars. Green dashes above the gold bar mean there is an under-representation—these characteristics have a low association with poverty. Green dashes below gold bars mean there is an over-representation—these characteristics have a higher association with poverty.
According to this data the following characteristics are potential risk factors for persistent poverty. Overrepresentation can be measured using risk ratios— the higher the figure the greater the risk (risk ratios in parentheses below):
- Being over 65: making up about 11 percent of the population, but 26 percent of those with persistently low incomes (2.4)
- Being a sole parent: making up 11 percent of the population, but nearly 24 percent of those with persistently low income (2.2)
- Being in a family structure other than a couple or sole parent: making up 12 percent of the population, but about 21 percent of those with persistently low income (1.75)
- Having no educational qualifications: making up about 19 percent of the population, but 33 percent of those with persistently low income (1.7)
- Being Māori: making up about 13 percent of the population, but 19 percent of those with persistently low incomes (1.5)
- Being from an ethnic group other than NZ European or Māori: (making up about 11 percent of the population, but 16 percent of those with persistently low income (1.5).
- Being under 18: making up about 27 percent of the population, but 29 percent of those with persistently low incomes (1.1)
According to this data the following characteristics are highly potent risk factors for persistent poverty among children:
- Living in a household in which the main source of income is a benefit
- Being part of a sole parent family, especially if that family lives on its own
- Living in a household in which no one holds a formal educational qualification
- Living in a Housing New Zealand house
In addition, this data shows that the following characteristics have a medium potential of being risk factors for persistent poverty among children:
- Living in a private rental
- Living in a household in which the highest educational qualification is a school qualification only
- Being Māori
- Being Pasifika
- Having an ethnic background other than Māori, Pasifika, or NZ European
- Living in a household with three or more children
Comparing the data from Figure 6 and Table 2, we begin to see some patterns emerging. In both datasets, we see that those with no formal educational qualifications are at significant risk, as are sole parent households. Interestingly, Table 2 tells us that the risk ratio for sole parents drops significantly if those families are living within a wider household. As well, the two datasets agree that being from an ethnic group other than NZ European elevates your risk of experiencing persistent poverty. The characteristic with the highest risk ratio according to Table 2—living in a household in which the main source of income is a benefit—is not included as a category in Figure 6, but we can assume that if it had been, it would be another risk factor that the two datasets would share in common.
Protective factors and resilience
The datasets in Figure 6 and Table 2 show us not just what characteristics or factors are strongly correlated with poverty, but also those that seem either to stop people from falling into poverty or to shorten their stay in poverty, thereby minimising the potential harm poverty may cause. These are protective factors. If risk factors represent vulnerability, protective factors represent safeguards, which can be considered “positive risk.”
Protective factors are, more often than not, the opposite side of risk factors. In Figure 6, those groups that are under-represented in poverty statistics are ones that exhibit protective factors. In Table 2, the factors with risk ratios under one could be considered protective factors. Therefore, according to the data in Figure 6 and Table 2, the following are protective (or “positive risk”) factors:
- Being of working age
- Being NZ European
- Holding at least one formal educational qualification (degree, vocational or school) or being in a household where the highest education achieved was at least some education after school
- Being part of a couple or within a two-parent or multi-adult household
- Being a member of a family with 1 or 2 children
- Being in a household where the main source of income is from the market
- Living in your own home
Educational attainment appears high on this list. While the literature shows that holding a formal educational qualification is an important factor in protecting an individual and their family from poverty (because skills and qualifications afford better employment opportunities), it does not seem to play a significant role in increasing the chances of ending a spell of persistent poverty. It would seem that when it comes to poverty, stability in the home by avoiding separations and remaining in work are key protective factors. From a life course perspective, evidence suggests that protective factors “include individual characteristics, family cohesion and warmth, good parenting and external support systems.”
In addition to characteristics that have a strong correlation with avoiding poverty or having only short experiences of poverty, there are factors that allow some at-risk families who end up in difficult situations to be able to “achieve despite the odds.” This process, whereby families draw upon protective factors to adapt to experiences of stress or adversity with relatively good outcomes, is called resilience.  That is, some families are able to suffer the same multiple life shocks and disadvantages that all those with low income face, but somehow they are able to minimise or even avoid all together the usual resulting negative long-term educational, health, and social consequences.
SuPERU recently undertook research that asked “why some low income families report their income to be adequate while others on similar incomes report their income to be inadequate.” They found that paid employment; having well-developed financial planning skills and strategies; setting aside money for future bills; ownership of assets; exhibiting a sense of being better off than others; having a belief in one’s own ability to manage; and gaining a job or partner were associated with families responding that their income was adequate. Elsewhere in the resilience literature, it has been found that having a protective, “stress-resistant” family often acts as a protection for children in otherwise high-poverty-and-deprivation-risk environments.
The risk and protective factors outlined above detail some of the characteristics that are highly correlated with falling into poverty and staying there persistently, or with avoiding poverty and experiencing only a short stay, respectively. But these are not factors that cause poverty—for those families that exhibit these risk factors to experience persistent poverty or to find their way out again, there usually has to be something referred to as a “trigger event.”
Thinking back to our discussion of income dynamics and mobility, we learned that most individuals’ and families’ incomes will fluctuate about a consistent point on the income scale as if they were attached by a rubber band. Sometimes, however, as Stephen Jenkins points out, “rubber bands will break if stretched too far by ‘shocks,’ leading to significant changes in relative income position.” These “shocks”—the things pushing families into poverty and pulling them out—are trigger events. 
Work and family events
As we saw earlier, poverty is a dynamic relationship between resources and needs—both factors rise and fall over time. Changes in the work context primarily influence resources available to the family, and the family context primarily influences what the family needs. Trigger events are usually grouped into these two categories/ contexts: work and family (sometimes described in the literature as labour market changes and demographic changes). Both can happen simultaneously. Examples of trigger events include:
- Work (Resources)
- Gain or loss of a job
- Increase or decrease in income/benefit (holding workers constant)
- Family (Needs)
- Birth of a child or change in number of household members
- Marriage (or de facto relationship), repartnering and separation
Trigger events can be favourable or unfavourable, pushing a family into or pulling a family out of poverty. They can also happen at different rates across a society and can exert differing levels of strength in pushing or pulling a family into and out of poverty.
Poverty entries and exits
In the subsections below, we will look more specifically at the findings from this study to assess the relative importance of different trigger events. Two family types (couple and sole parents) were compared and contrasted because different family types have differing experiences of transitions into and out of poverty. Sole parent families are also much more likely to be persistently poor than couple families so are of particular concern. Table 4 shows the poverty rate (the number of people below the poverty threshold as a percentage of the total number of households), the exit rate (“the number of people who left poverty between one year and the next as a percentage of the total number of poor households”), and the entry rate (“the number of people who entered poverty between one year and the next as a percentage of the number of people who were non-poor”) for all children, for lone parent households, for couple households, and for all households—this information provides a base for the information to be provided in the entry and exit subsections below.
Trigger events: entries
Table 5, below, presents the relative importance of different trigger events, the probability of the event happening, and the proportion of those entering poverty following the particular events (all figures are for couple households). Think of the events as “doorways” into poverty. For comparison, the chance of any couple household entering poverty in one year is 8.2 percent. The percentages do not add up to 100 because transitions into poverty can happen when none of the identified trigger events occur and the events themselves are not mutually exclusive.
To help interpret the table, consider the entry event “joined a lone parent household”—a parental separation in other words. A small proportion of New Zealanders (1.8 percent) separate annually, although this figure is likely an underestimation due to data limitations. Approximately half of the families that experience this event (43.7 percent) will end up in poverty, accounting for around one in ten (9.6 percent) poverty entries. Given the average poverty entry rate for children in non-poor couple families is 8.2 percent annually, this means children in families who separate are over five times more likely to end up poor than those children in families that don’t separate.
Table 5 also tells us:
- The trigger event most likely to hit a couple household is a fall of 20 percent or more in labour earnings, but this event does not increase a family’s risk of falling into poverty by that much, though it does trigger about a fifth of poverty entries
- Losing one or more worker at the same time as joining a lone parent household—so losing a “breadwinning” parent—presents the highest risk to a couple household of falling into poverty, but such an event only hits a small proportion of New Zealanders and is responsible for less than ten percent of poverty entries
- About ten percent of couple households will lose a full-time worker, and this will greatly increase their risk of falling into poverty; around a quarter of poverty entries will be triggered by losing a fulltime worker
In summation, what this information suggests is that while losing a worker is a much more frequent experience than a marriage break-up, marriage break-ups are a stronger trigger for poverty entry than losing a worker. Losing a full-time worker and becoming a sole-parent family is even more likely to end in poverty.
Trigger events: exits
Tables 6 and 7, below, present the relative importance of different trigger events, the probability of the event happening, and the proportion of those exiting poverty following the particular events. As in Table 5 above, these events can be thought of as “doorways,” but this time out of, instead of into, poverty. Again, the percentages do not add up to 100 because transitions out of poverty can happen when none of the identified trigger events occur and some events are not mutually exclusive. For both couple and sole parent families, gaining a fulltime worker is by far the most common and effective pathway out of poverty. In New Zealand, sole parents find it much more difficult to translate both family and labour market changes into exits from poverty. While this trend exists internationally, it is particularly pronounced in New Zealand.
Trigger events: summary
In summary, trigger events that involve changes in work and income (primarily work and hours) are much more likely than changes in family circumstances to trigger poverty transitions, both entries and exits. International research suggests this finding holds across most countries studied, and reflects the fact that labour market events occur more often than demographic ones. Research using Canadian data found that while family changes are more strongly associated with child poverty transitions, labour market events are much more frequent.  Here in New Zealand, as well as abroad, research, such as that discussed above, has found that work changes account for around four out of five poverty exits. A factor that is often overlooked but has the potential to play a significant role is changes in income for household members other than the primary “breadwinner” in the family, which account for a large number of exits according to the UK data.
Family changes, however, are relatively more likely to push families into poverty rather than pulling families out. For example, in Britain, changes in family circumstances account for at least one in five exits from poverty, and a greater proportion of entries—around two in five. Conversely, changes in work account for three out of five poverty entries, and four out of five exits. As Noel Smith & Sue Middleton, researchers at the Centre for Research in Social Policy in the UK put it, “increased household need is more likely to trigger entry into poverty than decreased household need to trigger exit from poverty.” The majority of family changes involve a “new entrant” into the family—new children or partners. Figure 7 below shows the breakdown for a subset of OECD countries.
Stephen Jenkins, reflecting on his extensive work in income dynamics and trigger events, concludes that:
To some extent, these results are a straightforward consequence of using equivalised household income as a measure of an individual’s living standards. But they are also an important reminder that individuals’ experiences of income mobility and poverty dynamics depend on their household context and changes in it—not only the changing combination of income sources from all the individuals in the household but also changes in household composition itself.
Life shocks and clustering
A similar yet related approach to trigger events involves investigating life shocks. In 2004, MSD studied the correlations between adverse life events and peoples’ living standards (not income in this case). Examples of life shocks include:
- Marriage break-up
- A mortgagee sale of home
- An unexpected and substantial drop in income
- Eviction from home/flat
- A substantial financial loss
- Being made redundant
- Becoming a sole parent
- Three months or more of being unemployed (when actively seeking employment)
- Major damage to home
- House burgled
- Victim of violence
- Receiving a non-custodial sentence
- An illness lasting three months or more
- A major injury or health problem that required substantial hospital or specialist treatment
- An unplanned pregnancy and birth of a child
The researchers found that not only is the total number of adversities faced more predictive of negative outcomes than individual factors, the findings (summarised below) also reveal a “threshold effect”:
Lower living standards tend to be associated with life shocks generally, but particularly when a person has had a large number of life shocks (eight or more). While many types of life shocks do not appear to have a significant impact when they occur in isolation, multiple shocks can combine to produce a large effect and substantially lower living standards when the overall burden of adversity reaches a certain level – the threshold effect.
This threshold effect, illustrated in Figure 8 below, is important to understand. Like the “multiple disadvantage” associated with longer spells in poverty, the cumulative and snowballing nature of life shocks underscores how problems beget more problems, tending to become too numerous and intertwined for families to overcome.
Risk factors not only tend to cluster, they also cluster in particular ways. Harnessing cross-agency “integrated” data, recent work by Treasury highlighted how statistical clustering techniques can be used to identify particular groups of at-risk populations. Similar work has been undertaken in the UK that identified sub-groups of those on low income, with the goal of seeking to “prompt more holistic and multi-agency solutions (based on an understanding of multiple factors) regarding how each group might be helped out of the distinct type of poverty they face.” Innovative work like this will form the knowledge foundation for policy that is better tailored to the complex situations families are facing.
What wasn’t found by the research on trigger events is almost as important as what was—much remains unexplained. Even work by leading international scholars with more comprehensive datasets only identified and explained around half of the entry events. Associations between events and poverty transitions can be identified, however, they “provide a potentially incomplete explanation of the underlying causes of poverty transitions.” Questions around direction of causation complicate matters: the stress caused by a spell of poverty could cause a marriage to break up, rather than the other way around, for example. Additionally, factors such as health (physical and mental) events weren’t considered in the New Zealand research, where international research suggests that around 16 percent of poverty entries were associated with someone in the household with a mental health problem; eight percent for physical health. The findings are only as good as the available data. Qualitative research could bolster and provide more texture to these findings, potentially highlighting and exploring some of the unexplained variations and difficult-to-measure issues like choice, hopes, aspirations, and expectations.
This is an extract from Kieran’s research series “The Heart of Poverty | Uncovering Pathways into and out of Disadvantage in New Zealand” Discussion Paper. (Released 2016)
 Smith and Middleton, A review of poverty dynamics research in the UK, 5.
 Jonathan Bradshaw et al., “The drivers of social exclusion,” (UK Social Exclusion Unit, 2004). See Jenkins, Changing fortunes, chapters 10 and 11 for multivariate analysis on at-risk groups.
 For an overview on clustering and disadvantage, see Jonathan Wolff and Avner De-Shalit, Disadvantage (Oxford University Press, 2013). See also Susan Morton et al., Growing Up in New Zealand – Vulnerability Report 1: Exploring the definition of vulnerability for children in their first 1000 days (2014), 61. The risk factors that the authors refer to are for “vulnerability,” but the relationship holds for poverty and poor outcomes as well.
 Smith and Middleton, A review of poverty dynamics research in the UK, 66.
 New Zealand Treasury, Improving outcomes for children, 3, 10. Conversely, short spells of low income are unlikely to lead to hardship. On Multiple disadvantage, see John Jensen, Sathi Sathiyandra, and Morna Matangi-Want, “The 2004 New Zealand Living Standards Survey: What does it signal about the importance of multiple disadvantage?” Social Policy Journal of New Zealand 30 (2007): 132-134
 New Zealand Treasury, A descriptive analysis of income and deprivation in New Zealand, 26.
 “In a similar way, Coffield and colleagues also rejected the idea of a ‘cycle of deprivation’ preferring … the metaphor of a ‘web of deprivation’ – which, they argued, better characterised the ‘dense network of psychological, social, historical and economic factors that either created or perpetuated problems’ for the families they studied.” Frank Coffield et al., A cycle of deprivation? a case study of four families, 1980. See also Tom MacInnes et al., Monitoring poverty and social exclusion (Joseph Rowntree Foundation, 2015) 16-17.
 Low income here refers to: “income that is less than 60% of median pre-tax equivalised household income in that year.” New Zealand Treasury, A descriptive analysis of income and deprivation in New Zealand, 6.
 Over the past twenty years, the risk ratio for sole parents has risen while it has dropped for couple parent households and other family types with children. Perry, Household incomes in New Zealand, 118. Sole parents also tend to have lower mobility than other families. New Zealand Treasury, A descriptive analysis of income and deprivation in New Zealand,16.
 Laura Adelman and Andreas Cebulla, The dynamics of poverty and deprivation in the UK (2003), 142; Smith and Middleton, A review of poverty dynamics research in the UK, 78, 86.
 Smith and Middleton, A review of poverty dynamics research in the UK.
 Matthew Gibbons, Income and Occupational Intergenerational Mobility in New Zealand: Treasury Working Paper 10/06 (2010), 8.
 Superu, Family Resilience: In Focus (2015), 9-10.
 For a good summary of family resilience as a concept, see Superu, Family Resilience (2015), Michael Rutter, “Resilience as a dynamic concept,” Development and psychopathology 24, no. 02 (2012): 336 and Michael Rutter, “Implications of resilience concepts for scientific understanding.” Annals of the New York Academy of Sciences 1094, no. 1 (2006): 1-12.
 Joyce Arditti, “Introduction and conceptual overview” in Joyce Arditti ed., Family Problems: Stress, Risk and Resilience (Wiley-Blackwell, 2015), 1–14.
 Superu, Perceptions of income adequacy by low income families (2015), 3.
 Superu, Perceptions of income adequacy by low income families, 26. Being solely reliant on a benefit, losing a job or partner were negatively associated with income adequacy. The authors note that their research was tentative as “identified factors associated with reported income adequacy, but these are not necessarily causal factors that lead to better (or worse) economic resilience.” Additional research is required to support stronger claims.
 Alfred Baldwin, Clara Baldwin and Robert Cole, “12 Stress-resistant families and stress-resistant children,” in Dante Cicchetti et al. eds. Risk and protective factors in the development of psychopathology (Cambridge University Press, 1992).
 Jenkins, Changing Fortunes, 361.
 See Stephen P. Jenkins, Christian Schluter and Gert Wagner, Child poverty in Britain and Germany (2001) for early work on trigger events pulling families out of poverty. See also DiPrete & McManus (2006) and Ballantyne et al., Movements Into and Out of Child Poverty in New Zealand, 9; and Bane and Ellwood, Slipping into and out of poverty: The dynamics of spells, 4 cited in Jenkins, Changing Fortunes, 239 for early work in the area of poverty dynamics and trigger events.
 Smith and Middleton, A review of poverty dynamics research in the UK, 37.
 Peter Kemp et al., Routes out of poverty (Joseph Rowntree Foundation, 2004).
 EAG, Working Paper no.3: Life course Effects on Childhood Poverty (Office of the Children’s Commisioner, 2012), 5; Jenkins, Changing Fortunes, 361.
 Greg Duncan et al., “Poverty dynamics in eight countries.” Journal of Population Economics 6, no. 3 (1993): 215-234.
 Jenkins, Changing Fortunes, 364, chapters 10 and 11.
 Jenkins, Rigg, and Devicienti, The dynamics of poverty in Britain cited in Kemp et al., Routes out of poverty, 11-12.
 Kristie Carter and Fiona Imlach Gunasekara, Dynamics of Income and Deprivation in New Zealand, 2002-2009,18.
 Suzie Ballantyne et al., “Triggering movements into and out of child poverty: A comparative study of New Zealand, Britain and West Germany,” Social Policy Journal of New Zealand (2004): 93.
 Ballantyne et al., “Triggering movements into and out of child poverty: A comparative study of New Zealand, Britain and West Germany.”
 Only couple households’ entries are considered here because there wasn’t a large enough sample of sole parent households who were not poor to produce statistically significant results.
 Ballantyne et al., “Triggering movements into and out of child poverty: A comparative study of New Zealand, Britain and West Germany,” 93.
 Smith and Middleton, A review of poverty dynamics research in the UK, 39. 75. Jenkins, Changing Fortunes, 361.
 Jenkins, Changing Fortunes, 364; Ballantyne et al., Movements Into and Out of Child Poverty in New Zealand, 9.
 Miles Zyblock, Garnett Picot, and Wendy Pyper, Why do children move into and out of low income: changing labour market conditions or marriage and divorce? (Statistics Canada, 1999).
 Jenkins, Rigg, and Devicienti, The dynamics of poverty in Britain.
 Jenkins, Changing Fortunes, 258, 361.
 Jenkins, Changing Fortunes, 261, 361
 Smith and Middleton, A review of poverty dynamics research in the UK, 39.
 Jenkins, Changing Fortunes, 361.
 Jensen et al, New Zealand Living Standards 2004 – An Overview (MSD, 2006), 22.
 Jensen et al, New Zealand Living Standards 2004, 8-9. These effects remain even when other related factors are controlled for. See also Jensen et al., The 2004 New Zealand Living Standards Survey: What does it signal about the importance of multiple disadvantage? Appendix for the regression model data. Gary Evans, Dongping Li and Sara Sepanski Whipple, “Cumulative risk and child development,” Psychological Bulletin 139, no. 6 (2013): 1342.
 McLeod et al., Using Integrated Administrative Data to Identify Youth Who Are at Risk of Poor Outcomes as Adults, Analytical Paper 15/02 (New Zealand Treasury, 2015). Because the work published in this space so far focuses on broader disadvantage rather than poverty, the findings are more indicative than instructive for poverty research.
 Claudia Wood et al., Poverty in Perspective (Demos, 2012), 14.
 OECD, Growing Unequal, 168.
 Jenkins, Schluter and Wagner, Child poverty in Britain and Germany, 11.
 Jenkins, Changing Fortunes, 243.
 The same complication arises with living standards and life shocks. Jensen et al, New Zealand Living Standards 2004, 22.
 Jenkins, Rigg, and Devicienti, The dynamics of poverty in Britain. For New Zealand health data and life shocks. See Jensen et al, New Zealand Living Standards 2004, 21.
 Smith and Middleton, A review of poverty dynamics research in the UK, 43 1