Generalized linear models extend classical linear models in two ways. They allow the fitting of a linear model to a dependent variable whose expected values have been transformed using a "link" function. They allow for a range of error families other than the normal. They are widely used to fit models to count data and to binomial-type data, including models with errors that may exhibit extra-binomial or extra-Poisson variation. The discussion extends to models in the generalized additive model framework, and to ordinal regression models. Survival analysis, also referred to as time-to-event analysis, is principally concerned with the time duration of a given condition, often but not necessarily sickness or death. In nonmedical contexts, it may be referred to as failure time or reliability analysis. Applications include the failure times of industrial machine components, electronic equipment, kitchen toasters, light bulbs, businesses, loan defaults, and more. There is an elegant methodology for dealing with "censoring" – where all that can be said is that the event of interest occured before or after a certain time, or in a specified interval.

Chapter 8 demonstrates that such effects can be augmented by stimulating solidarity among women with a focus on gender consciousness-raising, but that such actions generate backlash. It tests the importance of social solidarity in stimulating women’s collective action by exploiting arbitrary variation in the delivery of a gender consciousness-raising program to SHGs. It shows that women are more likely to undertake collective action after identifying shared experiences of deprivation and forming a bond based on their gender identity. This collective action is also more likely to be aimed at women’s strategic interests – their interests rooted in their patriarchal suppression – and therefore garner more resistance from men in the community, including through increased experiences of (public) violence and harassment. It shows that women navigate this resistance through their collective strength and solidarity.

Chapter 7 provides cause for optimism: women’s participation in apolitical women’s groups enhances their political agency and doubles their political participation. Leveraging a natural experiment to identify the impact of access to SHGs, or small women-only credit collectives, it shows that access spaces outside of the household with other women generates solidaristic collective action oriented toward women’s political participation that succeeds in changing women’s political behavior: SHG members were significantly and substantially more likely to participate in politics than nonmembers. Further, this impact is evident in the larger village political network; women are more densely politically connected, and gender emerges as a more salient political cleavage. This positive impact of SHGs occurs despite no change in women’s economic resources.

Whereas rents in conquest petrostates have been a source of relatively peaceful authoritarianism, this has not been the case in many Muslim countries that do not produce oil. Muslim non-oil producers – many of whom were also exposed to the institutional legacy of Muslim conquest – have tended to experience more frequent and violent political upheaval since the 1960s. This chapter leverages a quasi-natural experiment of oil price driven foreign aid disbursements to show that buoyant levels of foreign aid receipts strengthened dictatorship in Muslim non-oil producers, but their subsequent decline led to heightened political instability in the form of civil war. However, despite this greater political upheaval (during periods of lower aid inflows), Muslim recipients remained staunchly non-democratic

The purpose of this chapter is to give readers a sense of the breadth of experimental applications in the social sciences. The chapter reviews lab, field, and survey experiments, as well as naturally-occurring experiments such as lotteries. Each type of experiment is illustrated by reviewing in detail an exemplary study, drawing from experimental literature in psychology, development economics, health, and political science. Special attention is paid to the design choices that researchers made when recruiting subjects, measuring outcomes, and allocating subjects to experimental conditions. Discussion of each study includes the analysis of its main statistical findings. By showing how experiments are designed and analyzed, this chapter lays the groundwork for the practice experiment that readers will undertake in Chapter 6.

Having reviewed examples of social science experiments in Chapter 4 and ethical considerations in Chapter 5, this chapter walks readers through the design, implementation, and analysis of an experiment involving human participants. After laying out the ground rules for this practice experiment – most importantly, that the study poses no appreciable risks to subjects – the chapter offers examples of inexpensive and brief experiments that can be approved by an institutional review committee and completed in the context of a semester-long course. The chapter provides a checklist of items that should be described in the write-up of the experimental design and results.

Study design is fundamental to good science. A poorly designed study will waste time and resources and could produce misleading or uninterpretable results. A good study design aims to minimise random variation and eliminate confounding variables. Correlational studies make use of natural variation in the variables of interest, whereas experimental studies manipulate variables to understand their causal effects on behaviour. There are advantages and disadvantages to both types of study, but experiments uniquely allow inferences about causation. A good experimental design requires subjects to be randomly allocated to experimental groups. Randomisation ensures the generalisability of results and eliminates confounds in experimental studies. Measurements should ideally be made blind to group membership. Blinding minimises biases caused by the conscious or unconscious expectations of the experimenter or subjects. Careful consideration should be given to when behaviour is measured, as time can affect behaviour. Power calculations can be used to determine the appropriate sample size.

The term natural experiment is used inconsistently. In one interpretation, it refers to an experiment where a treatment is randomly assigned by someone other than the researcher. In another interpretation, it refers to a study in which there is no controlled random assignment, but treatment is assigned by some external factor in a way that loosely resembles a randomized experiment–often described as an "as if random" assignment. In yet another interpretation, it refers to any non-randomized study that compares a treatment to a control group, without any specic requirements on how the treatment is assigned. I introduce an alternative definition that seeks to clarify the integral features of natural experiments and at the same time distinguish them from randomized controlled experiments. I define a natural experiment as a research study where the treatment assignment mechanism (i) is neither designed nor implemented by the researcher, (ii) is unknown to the researcher, and (iii) is probabilistic by virtue of depending on an external factor. The main message of this definition is that the difference between a randomized controlled experiment and a natural experiment is not a matter of degree, but of essence, and thus conceptualizing a natural experiment as a research design akin to a randomized experiment is neither rigorous nor a useful guide to empirical analysis. Using my alternative definition, I discuss how a natural experiment differs from a traditional observational study, and other practical recommendations for researchers who wish to use natural experiments to study causal effects.

Saddam Hussein’s unexpected 1990 invasion of Kuwait forced 300, 000 Kuwaitis of Palestinian descent to flee into Jordan. By 1991, this large exogenous population shock increased Jordan’s population by about 10 percent. Jordanian law allowed these refugees to work, live, and vote in Jordan immediately upon entry. The refugees did not bring social capital that eroded Jordan’s institutions. On the contrary, we find that Jordan’s economic institutions substantially improved in the decade after the refugees arrived. Our empirical methodology employs difference‐in‐ differences and the synthetic control method, both of which indicate that the significant improvement in Jordanian economic institutions would not have happened to the same extent without the influx of refugees. Our case study indicates that the refugee surge was the main mechanism by which Jordan’s economic institutions improved over this time.