The State of Applied Econometrics: Causality and Policy Evaluation

Abstract

The gold standard for drawing inferences about the effect of a policy is a randomized controlled experiment. However, in many cases, experiments remain difficult or impossible to implement, for financial, political, or ethical reasons, or because the population of interest is too small. For example, it would be unethical to prevent potential students from attending college in order to study the causal effect of college attendance on labor market experiences, and politically infeasible to study the effect of the minimum wage by randomly assigning minimum wage policies to states. Thus, a large share of the empirical work in economics about policy questions relies on observational data-that is, data where policies were determined in a way other than through random assignment. Drawing inferences about the causal effect of a policy from observational data is quite challenging. To understand the challenges, consider the example of the minimum wage. A naive analysis of the observational data might compare the average employment level of states with a high minimum wage to that of states with a low minimum wage. This difference is surely not a credible estimate of the causal effect of a higher minimum wage, defined as the change in employment that would occur if the low-wage states raised their minimum wage. For example, it might be the case that states with higher costs of living, as well as more price-insensitive consumers, choose higher levels of the minimum wage

OM Forum—Causal Inference Models in Operations Management

“…Second, when applying causal inference models to analyzing big data, there are high-dimensional econometric and machine learning techniques, such as LASSO (least absolute shrinkage and selection operator), the post-double-selection method, random forest, and bagging (bootstrap aggregating), that researchers can use to handle large data sets. Interested readers can refer to Tibshirani (1996), Belloni et al (2013Belloni et al ( , 2014, Varian (2014), Athey and Imbens (2017), and Wager and Athey (2017) for discussions on such methods. These methods have yet to see widespread applications.…”

The signalling effect of eco-labels in modern coastal tourism

“…In this article we employ the SCM. This method allows choosing the best counterfactual scenario for each treated TA through a weighted average of TAs that did not receive or lose a Blue Flag award in the relevant years, with weights chosen so that the weighted average is similar to the treated TA in terms of lagged outcomes and covariates (Athey & Imbens, 2016). Besides, unlike most of the estimators used in the literature, the SCM accounts for the presence of time-varying unobservable confounders.…”

“…The most widely used test for this is what is referred to as a placebo test (Athey & Imbens, 2016). For each treated TA, this approach consists in virtually reassigning the treatment to nontreated TAs included in the final donor pool.…”

Natural disasters and university enrolment: evidence from L’Aquila earthquake

“…This method builds on difference-indifferences estimation, but uses arguably more attractive comparisons to get causal effects (Athey and Imbens 2016). It has three main advantages.…”