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We examine the impact of an anticipated income shock on Singaporean taxi labor supply. This income shock is due to a government provision allowing employees to withdraw part of their pension savings at the age of 55. The results show that salary income, number of trips, total hours worked, and active hours worked decrease after drivers reach age 55 by 3.5%, 3.2%, 1.6%, and 3.1%, respectively. The impact on labor supply is more marked right after drivers reach age 55, and the response is stronger among those with higher education or with lower salary income. Moreover, we find that the anticipated income shock decreases the extensive margin by 5.79%.

We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced form estimation with structural analyses using machine-learning-based demand predictions. We estimate an upper-bound of the cross-price elasticity of taxi bookings to surge factors of only 0.26, but incorporating surge factors into a demand-prediction model improves the out-of-sample accuracy by 12-15%. Our structural analyses based on a driver guidance system finds the improved accuracy reduces drivers' vacant roaming times by 9.4% and increases average trips per taxi by 2.3%, suggesting the price information is valuable across platforms, even if elasticities are low.

We use a unique dataset from a Singaporean taxi fleet consisting of 3,250 drivers with 23 months of daily work routines to test if taxi drivers exhibit learning by doing (LBD). We find strong evidence of LBD. Next, we document the channels of learning: drivers learn through temporal (when) and locational (where) and technology (booking) optimization, and we further show that drivers with consistent wage growth utilize the locational and technological channels more than others. We also discuss the implication of our findings for taxi markets experiencing a rise in use of technology (i.e. booking systems based on applications like Uber), which has recently drawn the interest of policymakers.

On May 6, 2010, the US equity markets experienced a brief but highly unusual drop in prices across a number of stocks and indices. The Dow Jones Industrial Average (DJIA) fell by approximately 9% in a matter of minutes, and several stocks were traded down sharply before recovering a short time later. Earlier research by Lee, Cheng and Koh (2010) identified the conditions under which a “flash crash” can be triggered by systematic traders running highly similar trading strategies, especially when they are “crowding out” other liquidity providers in the market. The authors contend that the events of May 6, 2010 exhibit patterns consistent with the type of “flash crash” observed in their earlier study (2010). While some commentators assigned blame to high-frequency trading, our analysis was unable to identify a direct link to high-frequency trading per se, but rather the domination of market activities by trading strategies that are responding to the same set of market variables in similar ways, as well as various pre-existing market micro-structural safety mechanisms with unintended consequences when triggered simultaneously. The consequent lack of market participants interested in the “other side” of their trades may result in a significant liquidity withdrawal during extreme market movements. This paper describes the results of 9 different simulations created by using a large-scale computer model to reconstruct the critical elements of the market events of May 6, 2010. The resulting price distribution provides a reasonable resemblance to the descriptive statistics of the second-by-second prices of S&P500 e-Mini futures from 14:30 to 15:00 on May 6, 2010. There are no a priori assumptions made on asset price distributions, and our description of market dynamics is purely based on the structure of the market and the key types of market participants involved. This type of simulation avoids “over-fitting” historical data, and can therefore provide regulators with deeper insights on the possible drivers of the “flash crash”, as well as what type of policy responses may work or may not work under comparable market circumstances in the future. Our results also lead to a natural question for policy markers: If certain prescriptive measures such as position limits have a low probability of meeting its policy objectives on a day like May 6, will there be any other more effective counter measures without unintended consequences?

We construct an agent-based model to study the interplay between extreme price shocks and illiquidity in the presence of systematic traders known as trend followers. The agent-based approach is particularly attractive in modeling commodity markets because the approach allows for the explicit modeling of production, capacities, and storage constraints. Our study begins by using the price stream from a market simulation involving human participants and studies the behavior of various trend-following strategies, assuming initially that their participation will not impact the market. We notice an incremental deterioration in strategy performance as and when strategies deviate further and further from the theoretical strategy of lookback straddles (Fung and Hsieh 2001), due to the negative impacts of transaction cost and imperfect execution. Next, the trend followers are allowed to participate in the market, trading against “uninformed” computer traders making randomized bids and offers. We notice that market prices begin to break down as the percentage of trend followers in the market reaches 80%. In addition, in a market dominated by “smart traders”, it becomes increasingly difficult for any of them to generate profits using what is supposed to be a “long gamma” strategy. After all, trading is a zero-sum game: It is not feasible for any “long gamma” trader to generate a consistent profit unless someone else is willing to be on the other side of his/her trades. In any such market dominated by “smart traders” with low liquidity and extreme price instability, one proposed solution (as proposed earlier by the U.S. Commodity Futures Trading Commission) is to control position size limits, by either decreasing them (in the original proposal) or increasing them (for completeness in our analysis). Based on our simulation results, we have found no evidence supporting that such a solution will be effective; in fact, doing so will only lead to erratic price behavior as well as a variety of practical issues when imposing such changes to position size limits. An alternative proposal is to intervene in the market direct/indirectly, such as by using a market maker to inject/reduce liquidity. Our simulation results show evidence that injecting and reducing liquidity by the market maker can both be effective. However, a market maker can accumulate a large negative P&L by buying in a one-sided, falling market in which it is the only bidder, or vice versa. Therefore, in practice, no market maker may volunteer to participate in any such “market rescue” efforts unless governments are willing to underwrite some of its large potential losses. In short, direct/indirect intervention by controlling liquidity is not a panacea, and there are practical limits to its effectiveness.