A Delicate Balancing Act

Historically Arab regimes have played critical roles in securing women’s rights in their societies. Yet regimes remain concerned about domestic, especially Islamist and traditionalist, reactions to women’s rights. When regimes feel they can overcome this resistance they honor commitments to women’s rights. When they fear more domestic opposition they renege. This article argues that Arab regimes are less likely to resist domestic opposition to women’s rights when US military presence increases in the region. The authors test the argument using cross-national data including an original expert-coder scale of Islamist power, and estimate an instrumental variable model to allay concerns of endogeneity. A case study of Jordan explicates their causal argument. The results are robust to different measures of Islamist strength and to different estimation techniques. Understanding this unintended consequence of US military deployments to the Arab world is important for future analysis of female empowerment in the Arab world.

Foreign aid and judicial autonomy

Foreign aid donors increasingly embrace judicial autonomy as an important component of advancing democracy and promoting investment abroad. Recipient governments also recognize the importance of judicial reform for improving the investment climate at home. However, developing countries often lack the necessary state capacity that would enable them to implement these reforms. We argue that recipient countries that lack the state capacity to undertake reforms on their own turn to donors, who readily assist in judicial reforms via targeted democracy and governance interventions. At the same time, we suggest that the external assistance matters less for recipients that are able to implement judicial reforms by themselves. We employ an instrumental variable model to test this argument in a global sample of aid-eligible countries.

Estimation of local treatment effects under the binary instrumental variable model

Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper, we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges for causal estimation with a binary outcome, and show that surprisingly, it can be more difficult than the case with a continuous outcome. We propose novel modelling and estimating procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness and efficiency. Our approach is illustrated via simulation studies and a real data analysis.

Measurement errors in the binary instrumental variable model

Summary Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment and outcome. We first consider nondifferential measurement errors, that is, the mismeasured variable does not depend on other variables given its true value. We show that the measurement error of the instrumental variable does not bias the estimate, that the measurement error of the treatment biases the estimate away from zero, and that the measurement error of the outcome biases the estimate toward zero. Moreover, we derive sharp bounds on the causal effects without additional assumptions. These bounds are informative because they exclude zero. We then consider differential measurement errors, and focus on sensitivity analyses in those settings.