This paper undertakes a regression discontinuity (RD) framework with multiple cutoffs unlike typical RD setting where researchers normalize the score variable and pool all the observations. This paper explores this heterogeneity in the effect of Islamic mayor on female secular high schooling in Turkey using the multiple cutoff RD framework developed in Cattaneo et al. (2016). The presence of many parties in the 1994 municipality election in Turkey means that vote share of the strongest opponent party can vary substantially leading to different cutoffs. Meyersson (2014) finds that Islamic mayors of 1994 promoted female high schooling using a normalized and pooled RD framework, which averages the effect across all the different cutoffs. We extend his work by segregating the effect of Islamic mayor across different opponent party's vote shares. Our results suggest that the positive effect on female secular high school attainment was more pronounced in municipalities where the strongest opponent party was secular than where the opponent was conservative. This heterogeneity can be attributed to a policy change in 1999, which restricted religious high school graduates from entering universities.

Risk and uncertainty play a vital role in almost every significant economic decision, and an individual’s propensity to make riskier decisions also depends on various circumstances. This article aims to investigate the effects of social and economic covariates on an individual’s willingness to take general risks and extends the scope of existing works by using quantitative measures of risk-taking from the GPS and Gallup datasets (in addition to the qualitative measures used in the literature). Based on the available observed risk-taking data for one year, this article proposes a semi-supervised machine learning-based approach that can efficiently predict the observed risk index for those countries/individuals for years when the observed risk-taking index was not collected. We find that linear models are insufficient to capture certain patterns among risk-taking factors, and non-linear models, such as random forest regression, can obtain better root mean squared values than those reported in past literature. In addition to finding factors that agree with past studies, we also find that subjective well-being influences risk-taking behavior.

Risk and uncertainty play a vital role in almost every significant economic decision, and an individual's propensity to make riskier decisions also depends on various circumstances. Studies on general and financial risk preference mostly kept their investigation restricted to analyzing the correlation between the sociodemographic and economic aspects of risk-taking, using ordinary least-square and supervised machine learning paradigms. This article aims to investigate the effects of social and economic covariates on an individual's willingness to take general risks and extends the literature by using quantitative measures of risk-taking from the GPS and Gallup datasets in addition to the qualitative measures used in the literature. Based on the available observed risk-taking data for one year, this article proposes a semi-supervised machine learning-based approach that can efficiently predict the observed risk index for those countries/individuals for years when the observed risk-taking index was not collected. We find that linear models are insufficient to capture certain patterns among risk-taking factors, and non-linear models, such as random forest regression, can obtain better root mean squared values than those reported in past literature. In addition to finding factors that agree with past studies, we also find that subjective well-being influences risk-taking behavior.