Identifying Cause and Effect to Understand Population Behavior
DPRC affiliates have long conducted innovative research in the United States and developing countries to isolate the causal mechanisms of population and health processes. Many DPRC projects use research designs that rely on specific disruptions—whether natural disasters, changes in public policy that affect available financial or social support, or research interventions—to trace their effects and understand out how these unanticipated shifts change people’s lives, their health and the population’s structure.
Such unanticipated events have been used to study whether the private or public nature of family planning education affects women’s ability to control their fertility, and whether capital transfers can reduce mental health issues in children, for example. Modeling causes and effects in this manner is not the only way to answer numerous demographic questions, but it is necessary to uncover the mechanisms that allow models to be translated into policies that will improve demographic and health processes.
This approach takes advantage of DPRC scholars’ leadership both in collecting data on individuals within large groups and in analyzing the events that can change their lives. Studying natural disasters, or historical events as well as devising experimental interventions all require a degree of creativity in design and measurement that fosters innovation. For instance, while an experimental intervention may be based in theory, it requires the translation of complex concepts from social science such as altruism, risk aversion, and capacity for planning and execution into a game or questionnaire. It also requires a plan to implement the program in a way that “treats” some subjects but leaves others as controls, as well as attention to methods and modes of data collection to measure responses.
Designing studies and collecting data surrounding unanticipated events that constitute “natural experiments” requires integrating knowledge of the prior and remaining institutional settings, and of local history, with appropriate questionnaires and modes of constructing or eliciting measurements from exposed and non-exposed groups. For example, studying the health effects of the migration of African-Americans to Northern industrial cities in the last century requires knowledge of the history of railroad construction in the era, and details of the lives of men and women who were, or were not, born in a town with railroad access. Most often, the original data was not intended to capture the variation that drives such quasi-experiments, creativity in reconstructing the missing elements is essential. In other cases, a major event that has the potential to change people’s lives, such as the 2004 tsunami, can only be studied through rapidly fielding original data collection in a changing and uncertain environment.
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