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Left Leaning Models: AI Assumptions on Economic Policy
Maxim Chupilkin
https://arxiv.org/abs/2507.15771 https://arxiv.org/pdf/2507.15…

Left Leaning Models: AI Assumptions on Economic Policy
How does AI think about economic policy? While the use of large language models (LLMs) in economics is growing exponentially, their assumptions on economic issues remain a black box. This paper uses a conjoint experiment to tease out the main factors influencing LLMs' evaluation of economic policy. It finds that LLMs are most sensitive to unemployment, inequality, financial stability, and environmental harm and less sensitive to traditional macroeconomic concerns such as economic growth, inflat…

A generalised discrete mixture model to better capture preference heterogeneity in discrete choice data
Thomas O. Hancock, John Buckell
https://arxiv.org/abs/2506.14094

A generalised discrete mixture model to better capture preference heterogeneity in discrete choice data
Arguably the key issue in modelling discrete choice data is capturing preference heterogeneity. This can be through observed characteristics, and/or using techniques for capturing random heterogeneity across respondents. On the latter, in health economics, the two main approaches are the mixed multinomial logit (MMNL) and the latent class (LC) model. In this paper, we revisit the discrete mixture (DM) model as a third alternative to these. The DM model is similar to LC but allows for any combin…

Leveraging Covariates in Regression Discontinuity Designs
Matias D. Cattaneo, Filippo Palomba
https://arxiv.org/abs/2507.14311 https://

Leveraging Covariates in Regression Discontinuity Designs
It is common practice to incorporate additional covariates in empirical economics. In the context of Regression Discontinuity (RD) designs, covariate adjustment plays multiple roles, making it essential to understand its impact on analysis and conclusions. Typically implemented via local least squares regressions, covariate adjustment can serve three main distinct purposes: (i) improving the efficiency of RD average causal effect estimators, (ii) learning about heterogeneous RD policy effects, …