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Policy Experimentation in China: The Political Economy of Policy Learning

Journal of Political Economy 2025 133(7), 2180-2228
Governments use policy experiments to facilitate learning, but the nature and effects of these experiments remain unclear. We analyze China's policy experimentation since 1980—among the most systematic in history—and document three facts. First, most experiments exhibit positive sample selection. Second, local politicians exert excessive efforts during experiments that are not replicable during policies’ national rollout. Third, the central government is not fully sophisticated when interpreting experimentation outcomes. These facts suggest that policy learning may be biased and national policies may be distorted. Thus, while China’s institutions enable experimentation at an unparalleled scale, the complex political environments can also limit effective policy learning.

Skewed Bidding in Pay-per-Action Auctions for Online Advertising

American Economic Review 2009 99(2), 441-447
Online search as well as keyword-based contextual advertising on third-party publishers is primarily priced using pay-per-click (PPC): advertisers pay only when a consumer clicks on the advertisement. Slots for advertisements are auctioned, and per-click bids are weighted by the probability of a click given that the advertisement is displayed (the “click-through rate”) in addition to other factors. The PPC method allows the advertising platform (e.g. Google) to bundle together otherwise heterogeneous items (impressions on different positions on a search page, on different search phrases sharing common “keywords,” and on different publishers) into more homogeneous units, simplifying the advertiser's bidding problem. However, PPC pricing has some drawbacks. First, all clicks are not created equal: clicks on a Paris, France hotel website that is displayed on a search for Paris Hilton may result in lower profit conditional on the click. Second, for infrequently searched phrases on search engines or small content providers, it is difficult for the advertiser to accurately estimate conversion rates, increasing the risk and monitoring costs for the advertiser and diminishing their incentives to advertise broadly (indeed, on contextual networks, the advertising platform may not even provide the advertiser with sufficient accounting data about where the advertisements were displayed to allow the advertiser to distinguish sources of clicks, and the publisher mix may change on an ongoing basis.) Third, the problem of click fraud is fairly pervasive: when publishers receive a share of advertising revenue, advertisers place a single bid applying to many publishers, and revenue