We identify three separate stages in the post-World War II history of applied microeconomic research: A generally non-mathematical period; a period of consensus (from the 1960s through the early 1990s) characterized by the use of mathematical models, optimization and equilibrium to generate and...

New data-gathering techniques, often referred to as "Big Data" have the potential to improve statistics and empirical research in economics. In this paper we describe our work with online data at the Billion Prices Project at MIT and discuss key lessons for both inflation measurement and some...

I review a subset of the empirical tools available for competition analysis. The tools discussed are those needed for the empirical analysis of; demand, production efficiency, product repositioning, and the evolution of market structure. Where relevant I start with a brief review of tools...

Large language models (LLMs) such as ChatGPT have the potential to revolutionize research in economics and other disciplines. I describe 25 use cases along six domains in which LLMs are starting to become useful as both research assistants and tutors: ideation, writing, background research, data...

Relational contracts - informal self-enforcing agreements sustained by repeated interactions - are ubiquitous both within and across organizational boundaries. This review highlights recent empirical contributions in selected areas. We begin by reviewing some recent work that explicitly takes...

We survey the recent literature in economics measuring what is on top of people's minds using open-ended questions. We first provide an overview of studies in political economy, macroeconomics, finance, labor economics, and behavioral economics that have employed such measurement. We next...

Using dissertation research topics found in the EconLit database and large-scale algorithmic methods that identify author demographics based on names, we explore the link between race and ethnicity and fields of economic research. We find that underrepresented racial and ethnic minority (URM)...

Machine learning algorithms can find predictive signals that researchers fail to notice; yet they are notoriously hard-to-interpret. How can we extract theoretical insights from these black boxes? History provides a clue. Facing a similar problem - how to extract theoretical insights from their...

Statisticians have proposed meta-analysis to combine the findings of multiple studies of health risks or treatment response. The standard practice is to compute a weighted-average of the estimates. Yet it is not clear how to interpret a weighted average of estimates reported in disparate...

Tutoring--defined here as one-on-one or small-group instructional programming by teachers, paraprofessionals, volunteers, or parents--is one of the most versatile and potentially transformative educational tools in use today. Within the past decade, dozens of preK-12 tutoring experiments have...