Abstract: Notwithstanding that regulatory mandates require that all security market design changes pass the dual test of fairness and efficiency, most regulators have not even defined efficiency let alone fairness. It should therefore come as little surprise why design changes such as the introduction of algorithmic and high frequency trading or dark pools are causing considerable controversy in the marketplace. There is no evidence-based policy framework within which such changes can be meaningfully evaluated. In this work we seek to develop a Market Quality Framework in which as a start both fairness and efficiency are defined. From these definitions we establish a series of empirical proxies. Thereafter, we develop a systems estimation model and demonstrate its use by analyzing the 2004-2011 explosive growth in algorithmic trading (AT) on the London Stock Exchange and NYSE Euronext Paris. Our results show that greater AT increases transaction cost efficiency and reduces information leakage in top quintile stocks. In less liquid stocks, manipulation at the close declines. We also document the tradeoff between reduced spreads and increased manipulation or information leakage following the introduction of MiFID1.