Matthew P. Hitt

Quality Over Quantity: Amici Influence and Judicial Decision Making

Interest groups often make their preferences known on cases before the U.S. Supreme Court via amicus curiae briefs. In evaluating the case and related arguments, we posit that judges take into account more than just the number of supporters for the liberal and conservative positions. Specifically, judges’ decisions may also reflect the relative power of the groups. We use network position to measure interest group power in U.S. Supreme Court cases from 1946 to 2001. We find that the effect of interest group power is minimal in times of heavily advantaged cases. However, when the two sides of a case are approximately equal in the number of briefs, such power is a valuable signal to judges. We also show that justice ideology moderates the effect of liberal interest group power. The results corroborate previous findings on the influence of amicus curiae briefs and add a nuanced understanding of the conditions under which the quality and reputation of interest groups matter, not just the quantity.

Advising, Consenting, Delaying, and Expediting: Senator Influences on Presidential Appointments

When, how, and under what conditions can individual legislators affect presidential appointments? Since the early 1900s, the senatorial norm of the blue slip has played a key role in the confirmation process of federal district and appeals court judges, and it is an important aspect of the individual prerogative that characterizes senatorial behavior more broadly. We analyze newly available blue slips, covering the historical period 1933–1960. We show that the blue slip functioned in this era most often to support and expedite nominations, indicating that senators used this device to shape the nominations agenda in this period. Additionally, we analyze the factors that contributed to an individual senators decision to support or oppose a nominee, or return a blue slip at all, finding that senators were more likely to return positive blue slips when the Judiciary Committee chair was not a coalition ally. We argue that while blue slips did at times provide an early warning for poor nominees, they more often offered a means by which senators ensured that their desired nominees were confirmed swiftly. The positive role of the blue slip demonstrates that this device protected the individual prerogatives of senators, allowing them a degree of agenda-setting authority with regard to nominees in the weak parties era.

Concluding Thoughts for the Time Series Analyst

We began this book by suggesting that scholars in the social sciences are often interested in how processes – whether political, economic, or social – changeover time. Throughout, we have emphasized that although many of our theories discuss that change, often our empirical models do not give the concept of change the same pride of place. Time series elements in data are often treated as a nuisance – something to cleanse from otherwise meaningful information – rather than part and parcel of the data-generating process that we attempt to describe with our theories. We hope this book is an antidote to this thinking. Social dynamics are crucial to all of the social sciences. We have tried to provide some tools to model and therefore understand some of these social dynamics. Rather than treat temporal dynamics as a nuisance or a problem to be ameliorated, we have emphasized that the diagnosis, modeling, and analysis of those dynamics are key to the substance of the social sciences. Knowing a unit root exists in a series tell us something about the data-generating process: shocks to the series permanently shift the series, integrating into it. Graphing the autocorrelation functions of a series can tell us whether there are significant dynamics at one lag (i.e., AR(1))or for more lags (e.g., an AR(3)). Again, this tells us something about the underlying nature of the data: how long does an event hold influence? The substance of these temporal dynamics is even more important when thinking about the relationships between variables.

Time Series Models as Difference Equations

INTRODUCTION The material in this appendix is aimed at readers interested in the mathematical underpinnings of time series models. As with any statistical method, one can estimate time series models without such foundational knowledge. But the material here is critical for any reader who is interested in going beyond applying existing “off the shelf” models and conducting research in time series methodology. Many social theories are formulated in terms of changes in time. We conceptualize social processes as mixes of time functions. In so doing, we use terms such as trend and cycle. A trend usually is a function of the form α × t where α is a constant and t is a time counter, a series of natural numbers that represents successive time points. When α is positive (negative), the trend is steadily increasing (decreasing). The time function sin α t could be used to represent asocial cycle, as could a positive constant times a negative integer raised to the time counter: α(−1) t . In addition, we argue that social processes experience sequences of random shocks and make assumptions about the distributions from which these shocks are drawn. For instance, we often assume that processes repeatedly experience a shock, ∈ t , drawn independently across time from a normal distribution with mean zero and unit variance. Social processes presumably are a combination of these trends, cycles, and shocks.