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, including healthcare use data, and state-of-the-art econometric methods to generate causal evidence on these issues. Your work will produce insights directly relevant to labour-market policy, family services
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, including healthcare use data, and state-of-the-art econometric methods to generate causal evidence on these issues. Your work will produce insights directly relevant to labour-market policy, family services
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methods, including causal econometric analysis and predictive economic modelling, to assess the economic and distributional impacts of policy interventions across healthcare and disability service systems
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aims to understand the supply-side drivers of patient fees for mental health services and their impact on socioeconomic inequities in access to care. It will use econometric methods and population-wide
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significant health and disability policy reforms. The successful candidate will undertake data-driven, policy-relevant research using advanced quantitative methods, including causal econometric analysis and
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disciplines include health economics, labour economics, economics and econometrics. We will also consider other quantitative disciplines such as data science, mathematical statistics, actuarial science or
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health economics, labour economics, economics and econometrics. We will also consider other quantitative disciplines such as data science, mathematical statistics, actuarial science or public health
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econometrics. We will also consider other quantitative disciplines such as data science, mathematical statistics, actuarial science or public health or psychology with strong statistical training. You can check
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eligibility Applicants should have a 4-year bachelor degree or master's degree (any length) in economics, econometrics or a related quantitative discipline, demonstrated research experience, and strong academic
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-style interventions, and preventative medication. Analysis will utilise best practice in health inequalities measurement, modern econometric techniques, behavioural experiments, and modelling