Using Natural Experiments to Study the Impact of Media on the Family

The randomized trial is the gold standard in scientific research and is used by several fields to study the effects of media. Although useful for studying the immediate response to media exposure, the experimental approach is not well suited to studying long-term effects or behavior outside the laboratory. The "natural experiment" approach, a quasi-experimental method commonly used by economists, exploits settings in which there is naturally occurring variation that "randomly" influences the amount and type of media that individuals are exposed to. We discuss the methodological issues of natural experiments and the ways in which findings based on this approach can complement the findings from laboratory or other experimental settings. The examples we discuss highlight the value of interdisciplinary work in better understanding media's impact on family issues such as fertility, divorce, domestic violence, and child well-being.Key Words: family, media, natural experimentsThe question of how media affects families is important from both a scientific and practical perspective, as it can help inform the decisions of both practitioners and parents. Family members are increasingly exposed to a variety of media, including television, movies, print and digital advertisements, and the Internet (Pergrams & Zaradic, 2006). Some of these media may have negative effects, whereas other types of media may have positive effects. Social scientists from a variety of disciplines have used different techniques to model and estimate these impacts. In this paper, we discuss what economists have learned about these questions through the use of "natural experiments." We view the economist's approach as complementary to those in other disciplines (and also recognize its occasional use by other disciplines). A primary aim of this article is to encourage a more interdisciplinary approach to evaluating the impact of the media on the family.The gold standard in scientific research is the randomized controlled trial. In a randomized controlled trial, subjects are randomly assigned to a treatment or control group. By construction, a correctly implemented randomized experiment yields a causal estimate of the effect of treatment on the outcome of interest because it balances all of the potential confounding factors between the two groups. There are, however, limitations to real randomized trials. First, many experiments would be unethical or immoral. Second, many important outcome measures are impractical to measure in a laboratory. Third, laboratory and especially field experiments can be very expensive to administer, especially for research questions that involve large interventions or require large samples. Attrition is also a major problem in long-term experimental studies.When laboratory or field experiments are not possible, economists have tried to take advantage of ' 'natural experiments. " At the broadest level, a natural experiment is a situation that is thought to approximate a randomized trial even though there was no actual randomization involved (DiNardo, 2008). Economists have searched for settings in which there is naturally occurring variation in treatment, for example, a sudden change in public policy (e.g., law changes), random environmental shocks (e.g., weather), or other unexpected events (such as the birth of twins). The setting must be one where treatment is arguably independent of all the confounding factors that might otherwise bias an estimate.Natural experiments are a quasi-experimental methodology, and as such the key question for internal validity is whether the identifying variation (the "treatment") is actually random. Except in rare cases, there are no direct statistical tests to establish internal validity. This is because it is impossible to test whether treatment is correlated with an unobserved variable. Instead, the researcher must provide arguments and indirect tests in an attempt to convince the reader that the natural experiment can be used to estimate a causal effect. …