Fairness, effectiveness, and needs satisfaction: new options for designing climate policies

Financial compensations are often proposed to address regressive distributional impacts of carbon taxes. While financial compensations have shown to benefit vulnerable groups distributionally, little is known about their impacts on emission reduction or needs satisfaction. A potential problem with cash compensations is that if households spend this money back into the economy while no additional decarbonisation policies are implemented, emission reductions that arose from the tax may at least partly be reversed. In this letter, we compare the emission savings and impacts on fuel and transport poverty of two compensation options for carbon taxes in 27 European countries. The first option consists of equal per capita rebates for home energy and motor fuel taxes. The second option is the provision of universal green vouchers for renewable electricity and public transport, supported by additional investments in green infrastructures to meet increased demand for such green consumption. Results show that the first option of tax rebates only supports small emission reductions. In contrast, universal green vouchers with expanded green infrastructures would reduce home energy emissions by 92.3 MtCO2e or 13.4%, and motor fuel emissions by 177.5 MtCO2e or 23.8%. If green vouchers and infrastructure were provided without a prior tax, emission savings would be slightly lower compared to the ‘tax and voucher’ scheme, but fuel and transport poverty would drop by 4.1 and 2.2 percentage points, respectively. In contrast, taxes with rebates would increase fuel and transport poverty by 4.1 and 1.8 percentage points. These findings demonstrate that it is important to take environmental and energy poverty impacts of compensations for unfair distributional impacts of climate policies into account at the design stage. Such compensation measures can achieve higher emission reductions and reduce energy poverty if they involve an expansion of the provision of green goods and services, and if everyone is given fair access to these goods and services.

Medicaid, Earnings, and Heterogeneous Treatment Effects: Evidence from the Oregon Health Insurance Experiment

The Medicaid and labor supply empirical literature offers competing conclusions of zero effects and significant reductions in earnings. However, zero effects are only theoretically consistent with the earnings distribution’s extremes. Medicaid participants with positive pre-treatment labor supply should unequivocally decrease earnings. This paper clarifies the literature’s ambiguity by combining quantile regression with data from the Oregon Health Insurance Experiment. The distributional impacts imply that zero effects are not universally representative of Medicaid households. The annual earnings impact of Medicaid participation ranges between increases of $1400 to deceases of $3120 for single adults. Pre-existing mental illness or health constraints on work account for counterintuitive positive earnings impacts. By demonstrating that sample compositional differences determine whether Medicaid’s labor supply impact is zero or negative, this paper offers a reconciliation to the range of existing estimates in the empirical literature.

Algorithmic fairness in credit scoring

The use of machine learning as an input into decision-making is on the rise, owing to its ability to uncover hidden patterns in large data and improve prediction accuracy. Questions have been raised, however, about the potential distributional impacts of these technologies, with one concern being that they may perpetuate or even amplify human biases from the past. Exploiting detailed credit file data for 800,000 UK borrowers, we simulate a switch from a traditional (logit) credit scoring model to ensemble machine-learning methods. We confirm that machine-learning models are more accurate overall. We also find that they do as well as the simpler traditional model on relevant fairness criteria, where these criteria pertain to overall accuracy and error rates for population subgroups defined along protected or sensitive lines (gender, race, health status, and deprivation). We do observe some differences in the way credit-scoring models perform for different subgroups, but these manifest under a traditional modelling approach and switching to machine learning neither exacerbates nor eliminates these issues. The paper discusses some of the mechanical and data factors that may contribute to statistical fairness issues in the context of credit scoring.