If You Don't Have Solid Borders,You Get Walls

Anti‐immigrant populists seek to create a familiar space for their own kind in a world of tumult fomented by strangers.     

Political Choice, Public Policy, and Distributional Outcomes

I address the functioning of the U.S. governing system by analyzing distributional outcomes from 1947 to 2000. The key question is whether public policy influences distributional outcomes. The macropolitics model and power resource theory suggest that left policies should equalize the distribution of income. I utilize single equation error correction models to assess the impact of policy on income inequality through two mechanisms—market conditioning and redistribution. Since nearly every government action influences markets in some way, I examine policy in the aggregate rather than focusing only on policies explicitly designed to redistribute income. The analysis indicates that policy influences inequality through both mechanisms, with left policy producing more equality. The results are consistent with power resource theory and strongly support the macropolitics model. Furthermore, I find that market conditioning is as important as, and works in tandem with, explicit redistribution.     

Governmental failures in evaluating programs

Consider a government that adopts a program, sees a noisy signal about its success, and decides whether to continue the program. Suppose further that the success of a program is greater if people think it will be continued. This paper considers outcomes when government cannot commit. We find that welfare can be higher when information is poor, that government should at times commit to continuing a program it believes had failed, and that a government which fears losing power may acquire either too much or too little information.     

What is an algorithm? Financial regulation in the era of high-frequency trading

Abstract

In response to the flash crashes and market manipulations blamed on high-frequency trading (HFT), algorithms have been brought inside the regulatory perimeter. This paper focuses on the most ambitious regulation directed at the practice: the algorithm-tagging rule in the German High-Frequency Trading Act. Fifteen interviews with stakeholders in the Act’s implementation serve to reconstruct how regulators defined an algorithm and help pose the question of to what extent regulatory definitions and data need accurately to represent financial practices to be useful. Although tentative in its findings, the research suggests that the algorithm-tagging rule may be providing valuable signals in the noise to trade surveillance officers and having virtuous effects on the cultures of trading firms. The conclusion argues that sociologists of finance should adopt a more balanced approach when evaluating regulatory technologies and heed MacKenzie’s 2005 call to open up their black boxes.