Family financial hardship across childhood and the course of criminal behaviour from adolescence to early adulthood in a UK birth cohort
Notice bibliographique
Résumé
Crime remains a pervasive challenge in society by producing substantial health, safety, and financial burdens (Office for National Statistics, 2022). In the UK, official statistics have shown that the rate of violent crimes, such as knife, firearms, and robbery offences, consistently increases from 2023 to 2024 (Office for National Statistics, 2024). Decades of research has explored the risk factors for individuals’ criminal behaviour, with one key finding that children’s early-life poverty experience is associated with later life criminal behaviour (Kipping et al., 2015; Nikulina et al., 2011; Piotrowska et al., 2015). Piotrowska et al (2015) systematically reviewed 132 studies and found that people from low socioeconomic status (particularly low income) families are more likely to engage in antisocial behaviours in general. One key reason is that lower family SES typically results in more challenging living situations, such as exposure to neighbourhood crime, more stressed parenting styles, and limited opportunities for positive social interactions, which increases the chance that they may engage in criminal behaviours in the future. Another important pathway from family financial hardship to criminal behaviour in adulthood is through childhood behavioural problems, given that these problem behaviours are often identified as early signs of aggression and defiance and can serve as precursors to more severe forms of criminal behaviours (Farrington, 2004; Murray et al., 2015). Previous quasi-experiments have shown causal relationships between family income and children’s conduct problems (Jaffee et al., 2012). For example, Milligan and Stabile (2011) exploited policy variations in benefit amounts across Canadian provinces and found that increased family benefits led to reduced levels of children’s aggressive behaviours, providing further evidence towards a causal effect of family income on children’s behaviour. Similarly, Gennetian and Miller (2002) used a randomised controlled trial to assess the differential impacts of financial incentives and employment mandates within the Minnesota Family Investment Program and observed that children within families who received benefits from the scheme showed fewer behavioural problems and better school engagement. Most existing research exploring the impact of poverty on crime has used purely objective measurements for family financial situations, such as family income, whether receiving social support, or official calculation methods for poverty (e.g., a household in the UK is considered poverty if income is below 60% of the median household income of the year (Gov.uk, 2025)). Recently, researchers have begun to consider the importance of subjective perceptions of family financial hardship in addition to objectively measured poverty. For example, one study explored the difference between subjective and objective family financial difficulty and how they are associated with children’s mental health and behaviour (Miller et al., 2024). The results indicated that youth’s perceptions of financial difficulties were associated with child externalizing problems, but caregiver-reported material deprivation was not when accounting for youth’s perceptions. Another previous study found that care-giver’s perception of family financial difficulties had an indirect effect on child aggression at middle school (grades 7-9) and high school (grades 10-12), through family conflict (Wang et al., 2022). These previous studies were either cross-sectional (Wang et al., 2022) or over a very short timeframe (Miller et al., 2024). One of the limitations of a cross-sectional design is reverse causation, i.e., it could be child’s aggression that leads to family financial problems (for example, legal expenses, loss of parents’ employment, or other costs because of children’s criminal behaviour). In addition, examining this relationship within a limited developmental period provides no insights on the long-term impacts, such as how early childhood family financial hardship might influence outcomes into adulthood. Limited research has explored the association between mother’s perception of family financial difficulty and offspring criminal behaviour in adulthood. Research exploring the association between family financial hardship and later-life outcomes has increasingly focused on different patterns of longitudinal family financial hardship experience in early-life. For example, Murray et al. (2024) specifically analysed the accumulation of poverty from early childhood to early adulthood using structured life-course analysis, demonstrating that cumulative poverty is associated with later life violence engagement. Similarly, an early study also demonstrated that children who experienced persistent poverty showed higher antisocial behaviours comparing with no poverty groups (McLeod & Shanahan, 1996). Extending the scope on general later-life health outcomes, Lai et al (2019) assessed patterns of poverty during the child’s early life and their associations with later-life health outcomes. They identified four patterns of poverty, namely never experienced poverty, poverty in early childhood, poverty in late childhood, and persistent poverty. They discovered that compared with “never in poverty”, “persistent poverty” and “poverty in late childhood” groups showed higher odds of having socioemotional behavioural problems, obesity, and longstanding illness. By contrast, another study, which used the Danish birth cohort study and identified four patterns of poverty (no poverty, intermittent poverty, perinatal poverty, and chronic poverty), demonstrated that intermittent poverty showed stronger effects on children’s later life conduct problems and stress levels compared to the “no poverty” and “chronic poverty” group (Pryor et al., 2019). Researchers suggest that families experiencing ‘intermittent poverty’ face unique challenges: 1) greater difficulties accessing relevant social supports compared to other groups, 2) stigma associated with sudden declines in socioeconomic status, and 3) having more stressful home environment and heightened anxiety because of the insecurity of family income (Pryor et al., 2019). The age-crime curve suggests that criminal behaviours rise during adolescence, peak during late adolescence, and then dramatically decline in early adulthood, with further steady decrease (Nagin & Land, 1993). The peak is thought to be because adolescence marks a period of biological, personality, and neurobiology change (Blonigen, 2010; Braams et al., 2015; Laube et al., 2020), which facilitates teenagers to engage in more risk-taking, sensation-seeking behaviours which can easily involve antisocial behaviours (Walsh, 2024). However, studies have also found evidence that a small group of individuals show crime persistence beyond childhood and into adulthood (Basto-Pereira & Farrington, 2022), and there is some evidence that childhood family financial hardship is associated with criminal behaviours in adulthood (Brown & Males, 2011; Murray et al., 2024; Sharkey et al., 2016). However, most existing studies examined criminal behaviours at a single timepoint or within a limited timeframe, and some studies only explored criminal behaviours in adolescence (Brown & Males, 2011; Kipping et al., 2015; Martins-Silva et al., 2024). Very limited studies have specifically explored the relationship between early-life family financial hardship and the rate of change, or persistence in criminal behaviour from adolescence to adulthood. This is important given evidence that the risk factors for persistence/desistance of criminal behaviour are different to risk factors for the onset (Kazemian, 2007; Laub & Sampson, 2001; Uggen & Piliavin, 1998). Understanding these patterns will provide deeper insight into whether financial hardship has a lasting impact on criminal trajectories over time, rather than just a single snapshot of criminal involvement at a specific age. Therefore, further research is needed which measures criminal behaviours across adolescence and adulthood to explore the relationship between early-life family financial hardship with persistence and rate of change of criminal behaviours into adulthood. Using repeated longitudinal data can capture dynamic relationships between exposure and outcome variables alongside addressing reverse causality and measurement error (Smith et al., 2016). However, most studies only use a single time point for exposure and outcome, and the studies that use repeated measures, tend to do so across a short time frame, or only for exposure or outcome, but not both (e.g., Shulman et al., 2013; Stevens, 2018; Murray et al., 2024). This approach misses out the evolution of variables over time, as family financial situation and people’s criminal behaviours may not be constant across time. Examining the changing configurations of family financial difficulties and the developmental patterns of criminal behaviour can offer a deeper insight into early-life poverty’s impact on criminal behaviour during adolescence and adulthood. This will shed light on identifying critical periods where interventions could be most effective in preventing future criminal outcomes.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,004 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,008 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».