Trade barriers can lead to the disappearance of products and impose huge costs. Allowing for the realistic possibility that imported products are substituted by domestic varieties this paper finds that the cost of protection that allows for disappearance of products, the ‘Romer cost,’ is higher below a tariff threshold. This threshold depends on the substitutability of domestic for foreign products. This is important for developing countries where inferior technology leads to poor substitutability and traditional calculations underestimate the cost. Analysis of new varieties trade after the Indian liberalization supports the findings in the context of a developing country.

In this work we simulate algorithmic trading (AT) in asset markets to clarify its impact. Our markets consist of human and algorithmic counterparts of traders that trade based on technical and fundamental analysis, and statistical arbitrage strategies. Our specific contributions are: (1) directly analyze AT behavior to connect AT trading strategies to specific outcomes in the market; (2) measure the impact of AT on market quality; and (3) test the sensitivity of our findings to variations in market conditions and possible future events of interest. Examples of such variations and future events are the level of market uncertainty and the degree of algorithmic versus human trading. Our results show that liquidity increases initially as AT rises to about 10% share of the market; beyond this point, liquidity increases only marginally. Statistical arbitrage appears to lead to significant deviation from fundamentals. Our results can facilitate market oversight and provide hypotheses for future empirical work charting the path for developing countries where AT is still at a nascent stage.

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The purpose of this paper is two-fold. First, in the context of a spatial model we generalize two indices. One is a dynamic generalization of the emanating effect that was introduced by Kelejian and colleagues. This index describes how events in one unit spill over time to other units due to spatial interactions. As an analogy, it corresponds to the effect that the smoking habits of a given teenager might have on the smoking habits of each of his/her friends. In a sense, our second index, termed the vulnerability index, is the reverse of the first one in that it describes the response of a given unit over time to events in neighbouring units. The analogy here would be how the smoking habits of a given teenager is affected by the smoking habits of all of his/her friends. Second, we empirically implement our indices in the context of a model explaining GDP per capita growth in various countries. In this context the vulnerability index describes the sensitivity of GDP growth in one country with respect to events in other countries; the emanating effect describes how events in one country effect the GDP growth in other countries. JEL classification: C2, C3Key words: Spatial models, dynamic emanating and vulnerability indices * We would like to thank James LeSage for very helpful comments on an earlier version of this paper. Of course, he is not responsible for any shortcomings that remain! 1 Among others, other applications of the emanating effect are given in