Life-Cycle Bias Adjustment and Intergenerational Association in Crime
研究提出广义误差变量模型修正犯罪代际关联估计中的生命周期偏差，发现传统方法低估代际关联30-50%，调整后新西兰父子犯罪弹性约为0.7（是否犯罪）和0.5（犯罪次数）。
Using age-specific measures as proxies for lifetime outcomes can introduce bias into estimates of intergenerational associations. We extend the generalized errors-in-variables (GEiV) model to the estimation of intergenerational crime associations and implement a data-driven method to correct for life-cycle bias. The GEiV adjustment accounts for both the extensive and intensive margins and introduces an elasticity estimator that accommodates zeros. Our results show that intergenerational associations are underestimated by 30–50%, even when criminal behaviors are measured during the teenage years, a period least affected by life-cycle bias. The GEiV-adjusted intergenerational elasticity between fathers and sons in New Zealand is approximately 0.7 for the likelihood of committing any crime and 0.5 for the number of crimes committed. Mother–child elasticities are smaller than father–child elasticities. The GEiV-adjusted estimates remain stable across ages, birth cohorts, number of aggregated years, and crime types and severity.