Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constraining how this information can be incorporated and evaluated in claim-frequency models. This study examines how geographic information from alternative data sources can be incorporated into actuarial models for Motor Third Party Liability (MTPL) claim prediction under such constraints. Using the BeMTPL97 dataset, we adopt a zone-level modeling framework and evaluate predictive performance on unseen postcodes. Geographic information is introduced through two channels: environmental indicators from OpenStreetMap and CORINE Land Cover, and orthoimagery released by the Belgian National Geographic Institute for academic use. We evaluate the predictive contribution of coordinates, environmental features, and image embeddings across three baseline models: generalized linear models (GLMs), regularized GLMs, and gradient-boosted trees, while raw imagery is modeled using convolutional neural networks. Our results show that augmenting actuarial variables with constructed geographic information improves accuracy. Across experiments, both linear and tree-based models benefit most from combining coordinates with environmental features extracted at 5 km scale, while smaller neighborhoods also improve baseline specifications. Generally, image embeddings do not improve performance when environmental features are available; however, when such features are absent, pretrained vision-transformer embeddings enhance accuracy and stability for regularized GLMs. Our results show that the predictive value of geographic information in zone-level MTPL frequency models depends less on model complexity than on how geography is represented, and illustrate that geographic context can be incorporated despite limited individual-level spatial information.

As predictive algorithms grow in popularity, using the same dataset to both train and test a new model has become routine across research, policy, and industry. Sample-splitting attains valid inference on model properties by using separate subsamples to estimate the model and to evaluate it. However, this approach has two drawbacks, since each task uses only part of the data, and different splits can lead to widely different estimates. Averaging across multiple splits, I develop an inference approach that uses more data for training, uses the entire sample for testing, and improves reproducibility. I address the statistical dependence from reusing observations across splits by proving a new central limit theorem for a large class of split-sample estimators under arguably mild and general conditions. Importantly, I make no restrictions on model complexity or convergence rates. I show that confidence intervals based on the normal approximation are valid for many applications, but may undercover in important cases of interest, such as comparing the performance between two models. I develop a new inference approach for such cases, explicitly accounting for the dependence across splits. Moreover, I provide a measure of reproducibility for p-values obtained from split-sample estimators. Finally, I apply my results to two important problems in development and public economics: predicting poverty and learning heterogeneous treatment effects in randomized experiments. I show that my inference approach with repeated cross-fitting achieves better power than previous alternatives, often enough to find statistical significance that would otherwise be missed.

What is a fair performance metric?

Constructing high resolution air pollution maps at lower cost is crucial for sustainable city management and public health risk assessment. However, traditional fixed-site monitoring lacks spatial coverage, while mobile low-cost sensors exhibit significant data instability. This study integrates PM2.5 data from 320 taxi-mounted mobile low-cost sensors and 52 fixed monitoring stations to address these limitations. By employing the machine learning methods, an appropriate mapping relationship was established between fixed and mobile monitoring concentration. The resulting pollution maps achieved 500-meter spatial and 5-minute temporal resolutions, showing close alignment with fixed monitoring data (+4.35% bias) but significant deviation from raw mobile data (-31.77%). The fused map exhibits the fine-scale spatial variability also observed in the mobile pollution map, while showing the stable temporal variability closer to that of the fixed pollution map (fixed: 1.12 plus or minus 0.73%, mobile: 3.15 plus or minus 2.44%, mapped: 1.01 plus or minus 0.65%). These findings demonstrate the potential of large-scale mobile low-cost sensor networks for high-resolution air quality mapping, supporting targeted urban environmental governance and health risk mitigation.

The housing market in Ghana has been facing significant challenges, with the rental sector being particularly affected by issues such as the advance rent system, asymmetrical perceptions between landlords and tenants, and the lack of an institutional framework for regulating the market [2]. These challenges create a highly dynamic and often opaque rental environment, where both tenants and landlords face difficulties in determining fair rental prices. This issue is further exacerbated by the absence of comprehensive and up-to-date data on rental trends, making it challenging for stakeholders to make informed decisions. In recent years, the use of machine learning in real estate has gained traction globally as a means to address such challenges. Machine learning (ML) models can analyse large datasets, uncover hidden patterns, and make accurate predictions, thereby providing valuable insights for various stakeholders in the housing market.

Recent work has highlighted the culturally-contingent nature of commonsense knowledge. We introduce AMAMMER${\epsilon}$, a test set of 525 multiple-choice questions designed to evaluate the commonsense knowledge of English LLMs, relative to the cultural contexts of Ghana and the United States. To create AMAMMER${\epsilon}$, we select a set of multiple-choice questions (MCQs) from existing commonsense datasets and rewrite them in a multi-stage process involving surveys of Ghanaian and U.S. participants. In three rounds of surveys, participants from both pools are solicited to (1) write correct and incorrect answer choices, (2) rate individual answer choices on a 5-point Likert scale, and (3) select the best answer choice from the newly-constructed MCQ items, in a final validation step. By engaging participants at multiple stages, our procedure ensures that participant perspectives are incorporated both in the creation and validation of test items, resulting in high levels of agreement within each pool. We evaluate several off-the-shelf English LLMs on AMAMMER${\epsilon}$. Uniformly, models prefer answers choices that align with the preferences of U.S. annotators over Ghanaian annotators. Additionally, when test items specify a cultural context (Ghana or the U.S.), models exhibit some ability to adapt, but performance is consistently better in U.S. contexts than Ghanaian. As large resources are devoted to the advancement of English LLMs, our findings underscore the need for culturally adaptable models and evaluations to meet the needs of diverse English-speaking populations around the world.