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Reducing poverty among children: Evidence from state policy simulations.

ABSTRACT: State approaches to reducing child poverty vary considerably. We exploit this state-level variation to estimate what could be achieved in terms of child poverty if all states adopted the most generous or inclusive states' policies. Specifically, we simulate the child poverty reductions that would occur if every state were as generous or inclusive as the most generous or inclusive state in four key policies: Supplemental Nutrition Assistance Program (SNAP), state Earned Income Tax Credits (EITC), Temporary Assistance for Needy Families (TANF), and state Child Tax Credits (CTC). We find that adopting the most generous or inclusive state EITC policy would have the largest impact on child poverty, reducing it by 1.2 percentage points, followed by SNAP, TANF, and lastly state CTC. If all states were as generous or inclusive as the most generous or inclusive state in all four policies, the child poverty rate would decrease by 2.5 percentage points, and five and a half million children would be lifted out of poverty.

SUBMITTER: Pac J

PROVIDER: S-EPMC7194072 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Reducing poverty among children: Evidence from state policy simulations.

Pac Jessica J Garfinkel Irwin I Kaushal Neeraj N Nam Jaehyun J Nolan Laura L Waldfogel Jane J Wimer Christopher C

Children and youth services review 20200501

State approaches to reducing child poverty vary considerably. We exploit this state-level variation to estimate what could be achieved in terms of child poverty if all states adopted the most generous or inclusive states' policies. Specifically, we simulate the child poverty reductions that would occur if every state were as generous or inclusive as the most generous or inclusive state in four key policies: Supplemental Nutrition Assistance Program (SNAP), state Earned Income Tax Credits (EITC), ...[more]

PMID: 32362701

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Project description:BackgroundOpportunities exist to disseminate evidence-based cancer control strategies to state-level policy makers in both the legislative and executive branches. We explored factors that influence the likelihood that state-level policy makers will find a policy brief understandable, credible, and useful.MethodsA systematic approach was used to develop four types of policy briefs on the topic of mammography screening to reduce breast cancer mortality: data-focused brief with state-level data, data-focused brief with local-level data, story-focused brief with state-level data, and story-focused brief with local-level data. Participants were recruited from three groups of state-level policy makers-legislative staff, legislators, and executive branch administrators- in six states that were randomly chosen after stratifying all 50 states by population size and dominant political party in state legislature. Participants from each of the three policy groups were randomly assigned to receive one of the four types of policy briefs and completed a questionnaire that included a series of Likert scale items. Primary outcomes-whether the brief was understandable, credible, likely to be used, and likely to be shared-were measured by a 5-point Likert scale according to the degree of agreement (1 = strongly disagree, 5 = strongly agree). Data were analyzed with analysis of variance and with classification trees. All statistical tests were two-sided.ResultsData on response to the policy briefs (n = 291) were collected from February through December 2009 (overall response rate = 35%). All three policy groups found the briefs to be understandable and credible, with mean ratings that ranged from 4.3 to 4.5. The likelihood of using the brief (the dependent variable) differed statistically significantly by study condition for staffers (P = .041) and for legislators (P = .018). Staffers found the story-focused brief containing state-level data most useful, whereas legislators found the data-focused brief containing state-level data most useful. Exploratory classification trees showed distinctive patterns for brief usefulness across the three policy groups.ConclusionOur results suggest that taking a "one-size-fits-all" approach when delivering information to policy makers may be less effective than communicating information based on the type of policy maker.

Project description:PurposeMetamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes.MethodsWe combine 2 algorithm adaptation methods-multitarget stacking and regression chain with maximum correlation-with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR), random forests (RFs), and neural networks. We optimize integrated models using variable selection and hyperparameter tuning. We compare the accuracy, efficiency, and interpretability of different approaches. As an example application, we develop metamodels to emulate a microsimulation model of testing and treatment strategies for hepatitis C in correctional settings.ResultsOutput variables from the simulation model were correlated (average ρ = 0.58). Without multioutput algorithm adaptation methods, in-sample fit (measured by R2) ranged from 0.881 for LR to 0.987 for GPR. The multioutput algorithm adaptation method increased R2 by an average 0.002 across base learners. Variable selection and hyperparameter tuning increased R2 by 0.009. Simpler models such as LR, EE, and RF required minimal training and prediction time. LR and EE had advantages in model interpretability, and we considered methods for improving the interpretability of other models.ConclusionsIn our example application, the choice of base learner had the largest impact on R2; multioutput algorithm adaptation and variable selection and hyperparameter tuning had a modest impact. Although advantages and disadvantages of specific learning algorithms may vary across different modeling applications, our framework for metamodeling in policy analyses with multivariate outcomes has broad applicability to decision analysis in health and medicine.

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Reducing poverty among children: Evidence from state policy simulations.

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Reducing poverty among children: Evidence from state policy simulations.

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