Who Is a Criminal? – Definitions, Data, and Implications

Central inquiry: Who warrants the label “criminal”?
- Does the term apply only to those who commit legally defined crimes?
- Does detection or conviction matter, or merely the act itself?
- Are you a criminal when engaging in minor illegality that is rarely prosecuted?
Significance: The definition influences social stigma, policy design, resource allocation, and self-identity.

Consumer disputes
- Being over-charged at a store and how one responds (walk away? demand correction? exploit cashier error?).
Tax minimisation through deception
- Fabricating or exaggerating expenses to reduce liability.
Insurance claims
- Inflating value, misreporting age/condition, or claiming non-existent loss.
Workplace “souvenirs”
- Taking office supplies or property without permission.
Traffic violations
- Running red lights, speeding, rolling stops.
Ethical dilemma: Large share of public commits such acts, yet rarely self-identifies as criminal; shows disjunction between legal and social definitions.

Integrated Data Infrastructure (IDI)
- A linked, anonymised database maintained by Statistics New Zealand.
- Collates administrative records (justice, health, education, tax, etc.).
Presentation created for Minister Amy Adams (former Minister of Social Development, National-led government).
- Purpose: Identify points of intervention to reduce criminal justice involvement.

Detection caveat: “Most crime is never detected,” yet convictions offer insight.
Prevalence of conviction by age 28
- Overall: $\frac{1}{4}$ (25 %) of the cohort.
- Men: $\frac{1}{3}$ (≈33 %).
- Māori or Pasifika men: $\frac{1}{2}$ (50 %).
Offence seriousness
- Many convictions are for minor offences (shoplifting, careless driving). Despite low severity, official contact still recorded.

Visual from slide: Classic hump-shaped “age–crime” curve.
- Peak offending in youth/late teens.
- Half of all detected crime completed by age 22 (i.e.
  $50\%$ of total).
Chronic vs. occasional offenders
- $\frac{4}{5}$ (80 %) of convictions are attributable to individuals who began offending before age 20.
- Indicates small “prolific” subgroup within larger cohort.
Implication: Early prevention/intervention (childhood → late teens) likely yields the largest reduction in aggregate crime.

Potential definitions considered:
1. “Someone who commits crime.” Too broad; would include large portions of population engaging in trivial illegality.
2. “Someone with any recorded conviction.” Still captures many; dilutes meaning when convictions are for minor acts.
3. “Someone currently processed by the system.” Time-bound, ignores life-course change.
4. “Undetected habitual offender.” Difficult to observe; relies on self-report data.
Problem of the “dark figure”: substantial volume of crime unrecorded by police, leading to under-representation in official data.
Question of threshold: How many convictions (or what offence severity) justifies permanent labeling? No consensus.

Stigma vs. rehabilitation
- Labeling may entrench deviance; cautious use advised.
Disproportionate impact on Māori and Pasifika males
- Raises issues of systemic bias, social inequality, colonial legacies.
Resource prioritisation
- Evidence advocates focusing on early childhood/youth interventions to curb the trajectory of prolific offenders.
Public perception gap
- Society often tolerates “everyday” illegality yet harshly condemns detected street crime; indicates moral selectivity.

Life-course criminology: age-graded pathways, turning points, desistance.
Labeling theory: societal reaction creates the “criminal” identity.
Routine activities theory: opportunity structures that enable minor offences (e.g.
workplace theft).
Social inequality and crime: overrepresentation of certain groups in system suggests structural factors.

Memorise key ratios: $\frac{1}{4}$ overall conviction, $\frac{1}{3}$ men, $\frac{1}{2}$ Māori/Pasifika men.
Understand the policy relevance of the age–crime curve (early intervention).
Prepare to critique definitions of “criminal” with examples of undetected/common offences.
Reflect on how data linkage (IDI) enhances research yet still underestimates total offending due to detection limits.