Suchergebnisse

When negotiating investment treaties, states balance two goals: providing strong protections for investors (investor protection), which is thought to attract foreign direct investment, and maintaining the ability to regulate their economies (regulatory autonomy). In this article we argue that treaty content can tell us about the latent preferences that states have over the level of investor protection enshrined in BITs. We use an item response theory (IRT) model and a dataset of 1,144 treaties to estimate latent preferences on this scale for signatory countries. Our measure is of use to scholars interested in studying bilateral investment treaties, international law, and foreign direct investment, and our model is of use to anyone aiming to estimate latent preferences from jointly produced manifestations.

Textual data are plagued by underreporting bias. For example, news sources often fail to report human rights violations. Cook et al. propose a multi-source estimator to gauge, and to account for, the underreporting of state repression events within human codings of news texts produced by the Agence France-Presse and Associated Press. We evaluate this estimator with Monte Carlo experiments, and then use it to compare the prevalence and seriousness of underreporting when comparable texts are machine coded and recorded in the World-Integrated Crisis Early Warning System dataset. We replicate Cook et al.'s investigation of human-coded state repression events with our machine-coded events, and validate both models against an external measure of human rights protections in Africa. We then use the Cook et al. estimator to gauge the seriousness and prevalence of underreporting in machine and human-coded event data on human rights violations in Colombia. We find in both applications that machine-coded data are as valid as human-coded data.