Prediction markets are starting to look less like crowd polls and more like electronic markets. The central question is therefore no longer only whether these markets forecast well, but what happens when institutional liquidity enters: do spreads tighten, does price discovery improve, and do those gains actually reach the traders who are slowest to react when information arrives? This paper offers a research design for answering that question. It defines a broad market-quality lens, separates the main channels through which institutional liquidity enters, and maps the identification problems that arise in live venue data. It also uses a synthetic microstructure laboratory as a proof of concept for the measurement pipeline. The main lesson of the synthetic exercise is deliberately narrow. Market-maker coverage, liquidity incentives, and automation do not have to work through the same channel; average liquidity gains do not have to translate into equal gains for all traders; and the sharpest welfare losses are most likely to appear in shock states, when slower takers receive the least pass-through of tighter quoted markets. The synthetic results are useful because they stress-test the design, not because they settle the live empirical question.

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