Can a Paradigm Shift from Risk Management to Critical Reflection Improve Child-Inclusive Practice?

Child protection systems within Anglophone countries have been increasingly dominated by neoliberal managerial, risk-dominant paradigms over the past three decades. Assumed to deliver a cost-effective strategy to increase the safety of children, there are many ways this paradigmatic combination systematically undermines child welfare, participation, and well-being. This paper specifically focuses on the ways that risk assessment, neoliberal, and managerial discourses have infiltrated practice and operate to silence and exclude children’s voices. It draws on two case studies to showcase key findings of a comprehensive, state-wide research project called Empowering Children’s Voices, which was initiated by UnitingCare, a non-government organisation within Queensland, Australia, and conducted in partnership with researchers from Queensland University of Technology. It will be argued that a paradigm shift towards a critically reflective reinterpretation of risk can be far more effective at promoting child-inclusive practice and establishing children’s empowered voices as a protective factor against harm.

Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models

This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a nuisance function, such as the propensity score or the conditional choice probability, which we estimate by modern machine learning tools. We first adjust the moment function so that the gradient of the future loss function is insensitive (formally, Neyman-orthogonal) with respect to the first-stage regularization bias, preserving the single index property. We then take the loss function to be an indefinite integral of the adjusted moment function with respect to the single index. The proposed Lasso estimator converges at the oracle rate, where the oracle knows the nuisance function and solves only the parametric problem. We demonstrate our method by estimating the short-term heterogeneous impact of Connecticut’s Jobs First welfare reform experiment on women’s welfare participation decision.

Unemployment Insurance and Means-Tested Program Interactions: Evidence from Administrative Data

We study the ways in which unemployment insurance (UI) benefits interact with other elements of the social safety net around job losses. We exploit a cutoff for UI eligibility, based on a workers’ highest quarterly earnings in the past year, to generate quasi-experimental variation in UI receipt. We find that UI receipt cuts welfare (TANF) receipt by half among low-earning UI applicants but has no impact on SNAP or Medicaid usage. However, because welfare participation is low in this population, overall crowdout is small. In the quarter following layoff, UI increases total income by 55 percent (including labor earnings and transfers) (JEL E24, H53, I18, I38, J64, J65).