Clustering of health, crime and social-welfare inequality in 4 million citizens from two nations


Health and social scientists have documented the hospital revolving-door problem, the concentration of crime, and long-term welfare dependence. Have these distinct fields identified the same citizens? Using administrative databases linked to 1.7 million New Zealanders, we quantified and monetized inequality in distributions of health and social problems and tested whether they aggregate within individuals. Marked inequality was observed: Gini coefficients equalled 0.96 for criminal convictions, 0.91 for public-hospital nights, 0.86 for welfare benefits, 0.74 for prescription-drug fills and 0.54 for injury-insurance claims. Marked aggregation was uncovered: a small population segment accounted for a disproportionate share of use-events and costs across multiple sectors. These findings were replicated in 2.3 million Danes. We then integrated the New Zealand databases with the four-decade-long Dunedin Study. The high-need/high-cost population segment experienced early-life factors that reduce workforce readiness, including low education and poor mental health. In midlife they reported low life satisfaction. Investing in young people’s education and training potential could reduce health and social inequalities and enhance population wellbeing.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Nationwide data capture of poor health, crime and social-welfare dependency in 1.7 million New Zealanders.
Fig. 2: Inequality in the distributions of poor health, crime and social welfare.
Fig. 3: Impact of high-need/high-cost users.
Fig. 4: Aggregation of poor health, crime and social-welfare dependency.
Fig. 5: Replication in Danish nationwide registers linked to 2.3 million citizens.
Fig. 6: Characterizing high-need users.

Data availability

The NZIDI and Danish register data cannot be shared by the authors. Researchers who wish to use the NZIDI data must submit an application through Statistics New Zealand. Researchers who wish to use the Danish register data must request permission through the Danish Data Protection Agency. The Dunedin Study data are not publicly available as informed consent and ethical approval for public data-sharing were not obtained from participants. The data are available on request by qualified scientists. Requests require a concept paper describing the purpose of data access, ethical approval at the applicant’s institution and provision for secure data access. We offer secure access on the Duke University, Otago University and King’s College London campuses.

Code availability

Custom code that supports the findings of this study in the NZIDI is provided in the supplementary information. Custom code that supports the findings of this study in the Danish nationwide registers and the Dunedin Longitudinal Study is available from the corresponding author on request.


  1. 1.

    Alvaredo, F. et al. (eds) World Inequality Report 2018 (Belknap Press, 2018).

  2. 2.

    Fiscal Monitor: Tackling Inequality (IMF, 2017).

  3. 3.

    Income Inequality Update (OECD, 2016).

  4. 4.

    Keeley, B. Income Inequality: The Gap Between Rich and Poor (OECD Publishing, 2015).

  5. 5.

    Goodman, D., Fisher, E. & Chang, C. The Revolving Door: A Report on US Hospital Readmissions (Robert Wood Johnson Foundation, 2013).

  6. 6.

    The Concentration of Health Care Spending (NIHCM, 2012).

  7. 7.

    Farrington, D. P., Ohlin, L. & Wilson, J. Q. Understanding and Controlling Crime (Springer, 1986).

  8. 8.

    Wolfgang, M. E., Figlio, R. M. & Sellin, T. Delinquency in a Birth Cohort (Univ. of Chicago Press, 1972).

  9. 9.

    Bertrand, M., Luttmer, E. F. P. & Mullainathan, S. Network effects and welfare cultures. Q. J. Econ. 115, 1019–1055 (2000).

  10. 10.

    Dahl, G. B., Kostøl, A. R. & Mogstad, M. Family welfare cultures. Q. J. Econ. 129, 1711–1752 (2014).

  11. 11.

    Gottschalk, P. & Moffitt, R. A. Welfare dependence: concepts, measures, and trends. Am. Econ. Rev. 84, 38–42 (1994).

  12. 12.

    Milne, B. J. et al. Data resource profile: the New Zealand Integrated Data Infrastructure (IDI). Int. J. Epidemiol. 48, 677–677e (2019).

  13. 13.

    Caspi, A. et al. Childhood forecasting of a small segment of the population with large economic burden. Nat. Hum. Behav. 1, 0005 (2017).

  14. 14.

    Heckman, J. J. Skill formation and the economics of investing in disadvantaged children. Science 312, 1900–1902 (2006).

  15. 15.

    Moffitt, T. E. et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proc. Natl Acad. Sci. USA. 108, 2693–2698 (2001).

  16. 16.

    The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution (WEF, 2016).

  17. 17.

    OECD Statistics. Income Distribution Database (2019).

  18. 18.

    Jahan, S. Human Development Report 2016: Human Development for Everyone (United Nations Development Program, 2016).

  19. 19.

    Moffitt, T. E. et al. How common are common mental disorders? Evidence that lifetime prevalence rates are doubled by prospective versus retrospective ascertainment. Psychol. Med. 40, 899–909 (2010).

  20. 20.

    United States Government Accountability Office. Costs of Crime: Experts Report Challenges Estimating Costs and Suggest Improvements to Better Inform Policy Decisions (2017).

  21. 21.

    Hidden in Plain Sight: What Cost-of-Crime Research Can Tell Us About Investing in Police (RAND Center on Quality Policing, 2010).

  22. 22.

    Heckman, J. J. & LaFontaine, P. A. The American high school graduation rate: trends and levels. Rev. Econ. Stat. 92, 244–262 (2010).

  23. 23.

    Hale, D. R., Bevilacqua, L. & Viner, R. M. Adolescent health and adult education and employment: a systematic review. Pediatrics 136, 128–140 (2015).

  24. 24.

    Rodwell, L. et al. Adolescent mental health and behavioural predictors of being NEET: a prospective study of young adults not in employment, education, or training. Psychol. Med. 48, 861–871 (2018).

  25. 25.

    Elango, S., García, J. L., Heckman, J. J. & Hojman, A. in Economics of Means-Tested Transfer Programs in the United States Vol. 2 (ed. Moffitt, R.) 235–297 (Univ. of Chicago Press, 2016).

  26. 26.

    Heckman, J., Moon, S. H., Pinto, R., Savelyev, P. & Yavitz, A. Analyzing social experiments as implemented: a reexamination of the evidence from the HighScope Perry Preschool Program. Quant. Econom. 1, 1–46 (2010).

  27. 27.

    Deaton, A. & Cartwright, N. Understanding and misunderstanding randomized controlled trials. Soc. Sci. Med. 210, 2–21 (2018).

  28. 28.

    Nagin, D. S. & Sampson, R. J. The real gold standard: measuring counterfactual worlds that matter most to social science and policy. Ann. Rev. Criminol. 2, 4.1–4.23 (2019).

  29. 29.

    American Academy of Pediatrics. Policy statement: school-based mental health services. Pediatrics 113, 1839–1845 (2004).

  30. 30.

    Heckman, J., Moon, S. H., Pinto, R., Savelyev, P. & Yavitz, A. The rate of return to the HighScope Perry Preschool Program. J. Pub. Econ. 94, 114–128 (2010).

  31. 31.

    Kieling, C. et al. Child and adolescent mental health worldwide: evidence for action. Lancet 378, 1515–1525 (2011).

  32. 32.

    Shonkoff, J. P., Phillips, D. & Keilty, B. Early Childhood Intervention: Views from the Field: Report of a Workshop (National Academy Press, 2000).

  33. 33.

    Heller, N. Who really stands to win from universal basic income? The New Yorker (2018).

  34. 34.

    Schwab, K. The Fourth Industrial Revolution (World Economic Forum, 2018).

  35. 35.

    The World Bank. World Bank Group President Jim Yong Kim Opening Remarks at the 2018 Annual Meetings Press Conference (2018).

  36. 36.

    Cutler, D. M. & Lleras-Muney, A. Education and Health: Evaluating Theories and Evidence Working Paper No. 12352 (NBER, 2006).

  37. 37.

    Olesen, S. C., Butterworth, P., Leach, L. S., Kelaher, M. & Pirkis, J. Mental health affects future employment as job loss affects mental health: findings from a longitudinal population study. BMC Psychiatry 13, 144 (2013).

  38. 38.

    Fukkink, R., Jilink, L. & Oostdam, R. A meta-analysis of the impact of early childhood interventions on the development of children in the Netherlands: an inconvenient truth? Eur. Early Child Educ. 25, 656–666 (2017).

  39. 39.

    Gardner, F. et al. The earlier the better? Individual participant data and traditional meta-analysis of age effects of parenting interventions. Child Dev. 90, 7–19 (2019).

  40. 40.

    Paul, K. I. & Moser, K. Unemployment impairs mental health: meta-analyses. J. Vocat. Behav. 74, 264–282 (2009).

  41. 41.

    Patton, G. C. et al. Our future: a Lancet commission on adolescent health and wellbeing. Lancet 387, 2423–2478 (2016).

  42. 42.

    Sloper, P. Facilitators and barriers for co-ordinated multi-agency services. Child Care Health Dev. 30, 571–580 (2004).

  43. 43.

    United Nations General Assembly. 73rd Session New Zealand Statement (2018).

  44. 44.

    Poulton, R., Moffitt, T. E. & Silva, P. A. The Dunedin Multidisciplinary Health and Development Study: overview of the first 40 years, with an eye to the future. Soc. Psych. Psych. Epid. 50, 679–693 (2015).

  45. 45.

    Costello, E. J., Edelbrock, C., Kalas, R., Kessler, M. & Klaric. S. A. Diagnostic Interview Schedule for Children (DISC) (National Institute of Mental Health, 1982).

  46. 46.

    Diagnostic and Statistical Manual of Mental Disorders (DSM-III) (American Psychiatric Association, 1980).

  47. 47.

    Caspi, A. et al. The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clin. Psychol. Sci. 2, 119–137 (2014).

  48. 48.

    Jaffee, S. R., Harrington, H., Cohen, P. & Moffitt, T. E. Cumulative prevalence of psychiatric disorder in youths. J. Am. Acad. Child. Psy. 44, 406–407 (2005).

  49. 49.

    Belsky, D. W. et al. Cardiorespiratory fitness and cognitive function in midlife: neuroprotection or neuroselection? Ann. Neurol. 77, 607–617 (2015).

  50. 50.

    Pavot, W. & Diener, E. Review of the Satisfaction with Life scale. Psychol. Assess. 5, 164–172 (1993).

  51. 51.

    Gini, C. W. Variability and mutability, contribution to the study of statistical distribution and relations. Studi Economico-Giuricici della R (1912).

  52. 52.

    Cohen, P. N. Gini Code (no date).

Download references


Supported by grants from the National Institute on Aging (Nos. AG032282, AG049789 and P30AG034424), the National Institute of Child Health and Human Development (NICHD; No. HD077482), the UK Medical Research Council (Nos. P005918 and G1002190), the Jacobs Foundation and the Avielle Foundation. The Dunedin Multidisciplinary Health and Development Research Unit is supported by the New Zealand Health Research Council and the New Zealand Ministry of Business, Innovation and Employment (MBIE). L.S.R.-R. was supported by a postdoctoral fellowship from the NICHD (T32-HD007376) through the Frank Porter Graham Child Development Institute at the University of North Carolina at Chapel Hill. S.H.A. was supported by the Rockwool Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank A. O’Rand. We thank the Statistics New Zealand Methods Team for their assistance and the Public Policy Institute at the University of Auckland for access to their Statistics New Zealand Data Lab. The results in this paper are not official statistics. They have been created for research purposes from the Integrated Data Infrastructure (IDI) managed by Statistics New Zealand. The opinions, findings, recommendations and conclusions expressed in this paper are those of the authors, not Statistics NZ. Access to the anonymized data used in this study was provided by Statistics NZ under the security and confidentiality provisions of the Statistics Act 1975. Only people authorized by the Statistics Act 1975 are allowed to see data about a particular person, household, business or organization and the results in this paper have been confidentialized to protect these groups from identification and to keep their data safe. Careful consideration has been given to the privacy, security and confidentiality issues associated with using administrative and survey data in the IDI. Further detail can be found in the privacy impact assessment for the IDI available from

Author information

L.S.R.-R., A.C., B.J.M. and T.E.M. designed the research. L.S.R.-R., S.H.A., S.H., R.P., S.R., A.C., B.J.M. and T.E.M. performed research, L.S.R.-R., S.D.S., S.H.A., R.M.H. and B.J.M. analysed data and L.S.R.-R., A.C. and T.E.M. wrote the paper. All authors reviewed drafts, provided critical feedback and approved the final manuscript.

Correspondence to Leah S. Richmond-Rakerd.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary handling editor: Aisha Bradshaw.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Results, Supplementary Tables 1–11 and Supplementary References.

Reporting Summary

Supplementary Software

Statistical code used for analyses of the New Zealand nationwide administrative data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Richmond-Rakerd, L.S., D’Souza, S., Andersen, S.H. et al. Clustering of health, crime and social-welfare inequality in 4 million citizens from two nations. Nat Hum Behav (2020).

Download citation