David K. Park

Rich State, Poor State, Red State, Blue State: What's the Matter with Connecticut?

For decades, the Democrats have been viewed as the party of the poor, with the Republicans representing the rich. Recent presidential elections, however, have shown a reverse pattern, with Democrats performing well in the richer blue states in the northeast and coasts, and Republicans dominating in the red states in the middle of the country and the south. Through multilevel modeling of individuallevel survey data and county- and state-level demographic and electoral data, we reconcile these patterns.

A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data

The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.

Splitting a Predictor at the Upper Quarter or Third and the Lower Quarter or Third

A linear regression of y on x can be approximated by a simple difference: the average values of y corresponding to the highest quarter or third of x, minus the average values of y corresponding to the lowest quarter or third of x. A simple theoretical analysis, similar to analyses that have been done in psychometrics, shows this comparison to perform reasonably well, with 80%–90% efficiency compared to the regression if the predictor is uniformly or normally distributed. By discretizing x into three categories, we claw back about half the efficiency lost by the commonly used strategy of dichotomizing the predictor.We illustrate with the example that motivated our research: an analysis of income and voting which we had originally performed for a scholarly journal but then wanted to communicate to a general audience.

Models, assumptions and model checking in ecological regressions

Ecological regression is based on assumptions that are untestable from aggregate data. However, these assumptions seem more questionable in some applications than in others. There has been some research on implicit models of individual data underlying aggregate ecological regression modelling. We discuss ways in which these implicit models can be checked from aggregate data. We also explore the differences in applications of ecological regressions in two examples: estimating the effect of radon on lung cancer in the United States and estimating voting patterns for different ethnic groups in New York City.

Echocardiographic analysis of ventricular geometry and function during repair of congenital septal defects

BACKGROUND This study investigated changes in left ventricular (LV) geometry and systolic function after corrective surgery for atrial (ASD) and ventricular septal defects (VSD). METHODS Transesophageal LV short-axis echocardiograms were recorded before and after operative repair of ASD (n = 11) and VSD (n = 7). Preload was measured using LV end-diastolic area indexed for body surface area. Measurements of septal-freewall (D1) and anterior-posterior (D2) endocardial diameters were used to assess LV symmetry from D1/D2. Systolic indices included stroke area, area ejection fraction, and fractional shortening. RESULTS Preload, stroke area, area ejection fraction, and fractional shortening of D1 increased after ASD repair but decreased after VSD repair (p < 0.05). End-diastolic symmetry increased after ASD closure and decreased after VSD closure (p < 0.05). Increases in stroke area and ejection fraction after ASD correction primarily reflected increased shortening of D1. A positive correlation was found overall between percent change in end-diastolic area (EDA) and percent change in area ejection fraction (r(2) = 0.80, p < 0.0001, n = 18). CONCLUSIONS Preload was the primary determinant of changes in LV function in this series of ASD and VSD repairs. Intraoperative changes in position of the interventricular septum affected systolic and diastolic LV symmetry and septal free wall shortening. Additional studies are needed to define changes in afterload and contractility as well as diastolic compliance and systolic mechanics.

What does "Do campaigns matter?" mean?

Scholars disagree over the extent to which presidential campaigns activate predispositions in voters or create vote preferences that could not be predicted. When campaign related information flows activate predispositions, election results are largely predetermined given balanced resources. They can be accurately forecast well before a campaign has run its course. Alternatively, campaigns may change vote outcomes beyond forcing predispositions to some equilibrium level. We find most evidence for the former: opinion poll data are consistent with Presidential campaigns activating predispositions, with fundamental variables increasing in importance as a presidential election draws near.

Computational Data Sciences and the Regulation of Banking and Financial Services

The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate policy processes at a scale unimaginable even a few years ago. We apply these scalable NLP and ML techniques to analyze the United States Government’s regulation of the banking and financial services sector. First, we employ NLP techniques to convert the text of financial regulation laws into feature vectors and infer representative “topics” across all the laws. Second, we apply ML algorithms to the feature vectors to predict various attributes of each law, focusing on the amount of authority delegated to regulators. Lastly, we compare the power of alternative models in predicting regulators’ discretion to oversee financial markets. These methods allow us to efficiently process large amounts of documents and represent the text of the laws in feature vectors, taking into account words, phrases, syntax, and semantics. The vectors can be paired with predefined policy features, thereby enabling us to build better predictive measures of financial sector regulation. The analysis offers policymakers and the business community alike a tool to automatically score policy features of financial regulation laws to and measure their impact on market performance.

Big Data and the Regulation of Financial Markets

The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate processes, such as government regulation of financial markets, at a scale unimaginable even a few years ago. This paper develops scalable NLP and ML algorithms (classification, clustering and ranking methods) that automatically classify laws into various codes/labels, rank feature sets based on use case, and induce best structured representation of sentences for various types of computational analysis. The results provide standardized coding labels of policies to assist regulators to better understand how key policy features impact financial markets.