Marketing Department's Lecture

Theme：Identification Using Border Approaches and IVs

Speaker：Xing Li (Peking University)

Time：2019-09-25 10:00

Address：Room 1008, Mingde Business Building

Language：English/Chinese

Abstract：

We document that several recently used quasi-experimental strategies for identify- ing advertising effects can be derived from one simple model in which ad decisions are made at a more aggregate level than conversion is measured. Next, we show that the identifying variation in one of these strategies, the Border Approach, is conceptually similar to what are commonly known as Waldfogel IVs. We compare these, as well as supply-side instruments and fixed effects, in a data set on advertising in US presidential elections. Both border approaches and IVs are known to sacrifice statistical power and they do, but not by enough to affect statistical significance, in this application. The Waldfogel IVs are much more powerful than the supply-side IVs, and when combined, the standard errors are substantially reduced. On the other hand, each IV estimator has the potential to produce a local average treatment effect that weights local markets differently in aggregation. Estimates suggest differences may exist, but they are not significant. When both IVs are combined, the point estimate is identical to a fixed effect estimate that is likely to be unbiased. The Border Approach can also produce local effects at the disaggregate level when border and non-border regions differ. We find evidence of a statistically signficant difference when analysis is restricted to those counties where identifying assumptions are more plausible. The point estimate drops to nearly zero and becomes insignificant despite a standard error that is as small as the lowest IV standard error. We suspect local estimate concerns are greater for the Bor- der Approach because it identifies advertising effects that exclude the high population counties in all markets, whereas IVs may weight each market differently but include counties of all types within each market.

Short Bio：

Xing Li is currently an Assistant Professor of Marketing at Guanghua School of Management, Peking University. Before joining Peking University, he receives his Ph.D. in economics from Stanford University.

Xing Li's research applies empirical methods to analyze marketing and economic questions in different industries.  He has studied pricing decisions under copyright protections, product line decisions for CPG managers, advertising decision for mobile app developers, as well as incentive and targeting process in an organization. His current work focuses on the consumers’ purchase responses to air pollution, music-streaming app industry, and identification of advertising effects.