scholarly journals Political and legal antecedents of affirmative action: a comparative framework

2018 ◽  
Vol 39 (2) ◽  
pp. 359-391 ◽  
Author(s):  
Udi Sommer ◽  
Victor Asal
Keyword(s):  
Time Series ◽  
Affirmative Action ◽  
Social Transformation ◽  
Empirical Approach ◽  
Policy Feedback ◽  
Cross Sectional ◽  
Comparative Framework ◽  
Large N ◽  
Compensatory Justice

AbstractMuch of the literature on affirmative action is normative. Further, in scholarship that takes an empirical approach to examine this topic, the object of inquiry is typically the ramifications of such provisions – most notably the extent to which they foster social transformation. Yet, we know surprisingly little about the antecedents of affirmative action. This work examines what variables systematically predict affirmative action. We focus on the policy feedback literature and compensatory justice frameworks to examine the effects of democracy, modernisation and globalisation on affirmative action programmes. Time-series cross-sectional analyses of data for hundreds of groups from all over the globe for the period 1985–2003 confirm our hypotheses. This is the first work to examine affirmative action programmes in a large-N framework of such scale. We find that such programmes systematically correlate with democracy, modernisation and globalisation.

An easy way to create duration variables in binary cross-sectional time-series data

2020 ◽  
Vol 20 (4) ◽  
pp. 916-930
Author(s):  
Andrew Q. Philips
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Data ◽  
Series Data ◽  
Duration Dependence ◽  
Cross Sectional ◽  
Common Solution ◽  
The Common

In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into common parametric and nonparametric approximations.

scholarly journals Spectral Pitch Similarity is a Predictor of Perceived Change in Sound- as Well as Note-Based Music

2019 ◽  
Vol 2 ◽  
pp. 205920431984735
Author(s):  
Roger T. Dean ◽  
Andrew J. Milne ◽  
Freya Bailes
Keyword(s):  
Time Series ◽  
Piano Music ◽  
Individual Response ◽  
Discrete Events ◽  
Acoustic Intensity ◽  
Spectral Parameters ◽  
Cross Sectional ◽  
Perceived Change ◽  
Predictive Role ◽  
The Individual

Spectral pitch similarity (SPS) is a measure of the similarity between spectra of any pair of sounds. It has proved powerful in predicting perceived stability and fit of notes and chords in various tonal and microtonal instrumental contexts, that is, with discrete tones whose spectra are harmonic or close to harmonic. Here we assess the possible contribution of SPS to listeners’ continuous perceptions of change in music with fewer discrete events and with noisy or profoundly inharmonic sounds, such as electroacoustic music. Previous studies have shown that time series of perception of change in a range of music can be reasonably represented by time series models, whose predictors comprise autoregression together with series representing acoustic intensity and, usually, the timbral parameter spectral flatness. Here, we study possible roles for SPS in such models of continuous perceptions of change in a range of both instrumental (note-based) and sound-based music (generally containing more noise and fewer discrete events). In the first analysis, perceived change in three pieces of electroacoustic and one of piano music is modeled, to assess the possible contribution of (de-noised) SPS in cooperation with acoustic intensity and spectral flatness series. In the second analysis, a broad range of nine pieces is studied in relation to the wider range of distinctive spectral predictors useful in previous perceptual work, together with intensity and SPS. The second analysis uses cross-sectional (mixed-effects) time series analysis to take advantage of all the individual response series in the dataset, and to assess the possible generality of a predictive role for SPS. SPS proves to be a useful feature, making a predictive contribution distinct from other spectral parameters. Because SPS is a psychoacoustic “bottom up” feature, it may have wide applicability across both the familiar and the unfamiliar in the music to which we are exposed.

scholarly journals Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

2021 ◽  
Vol 13 (14) ◽  
pp. 2741
Author(s):  
John Gibson ◽  
Geua Boe-Gibson
Keyword(s):  
Time Series ◽  
Economic Activity ◽  
Validation Studies ◽  
National Level ◽  
Predictive Performance ◽  
The United States ◽  
Cross Sectional ◽  
Nation States ◽  
Nighttime Lights ◽  
Defense Meteorological Satellite Program

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation.

Free Traders and Drug Smugglers: The Effects of Trade Openness on States' Ability to Combat Drug Trafficking

2009 ◽  
Vol 51 (2) ◽  
pp. 117-145 ◽  
Author(s):  
Horace A. Bartilow ◽  
Kihong Eom
Keyword(s):  
Time Series ◽  
Statistical Model ◽  
Drug Trafficking ◽  
Trade Openness ◽  
Cross Sectional ◽  
Theoretical Literature ◽  
Openness To Trade ◽  
Pooled Time Series

AbstractThe theoretical literature presents conflicting expectations about the effects of trade openness on the ability of states to interdict drug trafficking. One view expects that trade openness will undermine drug interdiction; a second argues the opposite; a third argues that trade openness does not necessarily affect drug interdiction. This article assesses empirically the effects of trade openness on drug interdiction for countries in the Americas using a pooled time-series cross-sectional statistical model. It finds that trade openness decreases the interdiction capabilities of states in drug-consuming countries while increasing those of states in drug-producing countries. Greater openness to trade does not have a consistently significant effect on the interdiction capabilities of states in drug transit countries.

The Initial Return and Its Conditional Return Volatility: Evidence from the Chinese IPO Market

2014 ◽  
Vol 17 (04) ◽  
pp. 1450022 ◽  
Author(s):  
M. Monica Hussein ◽  
Zhong-Guo Zhou
Keyword(s):  
Time Series ◽  
Linear Functions ◽  
Return Volatility ◽  
Cross Sectional ◽  
Variance Model ◽  
Initial Return ◽  
Traditional Market ◽  
Mean And Variance ◽  
The Mean ◽  
Sectional Analysis

This paper investigates the monthly initial return and its conditional return volatility for Chinese IPOs. We find that the mean initial return (IR) and cross-sectional return volatility are highly auto- and cross-correlated, and time-varying. We propose a system of two simultaneous equations: a GARCH-in-mean (GARCH-M) process with an ARMA(1,1) adjustment in the residuals for the IR and an EGARCH process for the conditional return volatility, assuming that the IR and its conditional return volatility are linear functions of the same market, firm- and offer-specific characteristics. We find that the model captures both time-series and cross-sectional correlations at the mean and variance levels. Our findings suggest that the conditional return volatility affects the IR positively and significantly, in addition to the traditional market, firm- and offer-specific characteristics. IPOs with higher conditional return volatility, as a proxy for information asymmetry, tend to be underpriced more. The paper demonstrates the merit of using a conditional variance model, along with time series and cross-sectional analysis to price Chinese IPOs.

Measuring Value Change in Western Industrialized Societies: The Impact of Unemployment

1991 ◽  
Vol 85 (3) ◽  
pp. 905-920 ◽  
Author(s):  
Harold D. Clarke ◽  
Nitish Dutt
Keyword(s):  
Time Series ◽  
Economic Conditions ◽  
Aggregate Level ◽  
Value Change ◽  
Short Term ◽  
Cross Sectional ◽  
The Past ◽  
Value Priorities ◽  
Time Series Analyses ◽  
The Impact

During the past two decades a four-item battery administered in biannual Euro-Barometer surveys has been used to measure changing value priorities in Western European countries. We provide evidence that the measure is seriously flawed. Pooled cross-sectional time series analyses for the 1976–86 period reveal that the Euro-Barometer postmaterialist-materialist value index and two of its components are very sensitive to short-term changes in economic conditions, and that the failure to include a statement about unemployment in the four-item values battery accounts for much of the apparent growth of postmaterialist values in several countries after 1980. The aggregate-level findings are buttressed by analyses of panel data from three countries.

What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large-N, Large-T Data

2021 ◽  
pp. 008117502110463
Author(s):  
Ryan P. Thombs ◽  
Xiaorui Huang ◽  
Jared Berry Fitzgerald
Keyword(s):  
Fixed Effects ◽  
Directional Asymmetry ◽  
Cross Sectional ◽  
Monte Carlo Experiments ◽  
Long Run ◽  
Large N ◽  
Fixed Effects Estimator ◽  
Cross Sectional Dependence ◽  
Using Data ◽  
Heterogeneous Panel

Modeling asymmetric relationships is an emerging subject of interest among sociologists. York and Light advanced a method to estimate asymmetric models with panel data, which was further developed by Allison. However, little attention has been given to the large- N, large- T case, wherein autoregression, slope heterogeneity, and cross-sectional dependence are important issues to consider. The authors fill this gap by conducting Monte Carlo experiments comparing the bias and power of the fixed-effects estimator to a set of heterogeneous panel estimators. The authors find that dynamic misspecification can produce substantial biases in the coefficients. Furthermore, even when the dynamics are correctly specified, the fixed-effects estimator will produce inconsistent and unstable estimates of the long-run effects in the presence of slope heterogeneity. The authors demonstrate these findings by testing for directional asymmetry in the economic development–CO2 emissions relationship, a key question in macro sociology, using data for 66 countries from 1971 to 2015. The authors conclude with a set of methodological recommendations on modeling directional asymmetry.

Cross-Sectional and Time-Series Momentum in the US Sovereign Bond Market

2021 ◽  
pp. jfi.2021.1.127
Author(s):  
Lionel Martellini ◽  
Riccardo Rebonato ◽  
Jean-Michel Maeso
Keyword(s):  
Time Series ◽  
Bond Market ◽  
Cross Sectional ◽  
Sovereign Bond ◽  
The Us
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