Wherever You Go, No Matter the Weather, Always Bring Your Own Sunshine: Revisiting Thermal Climate Models of Trust with Cross-National Panel Data, 1981-2009

2012 ◽  
Author(s):  
Blaine G. Robbins
Keyword(s):  
Panel Data ◽  
Climate Models ◽  
Thermal Climate ◽  
Cross National

Sore Losers: Does Terrorism and Approval of Terrorism Increase in Democracies When Election Losers Refuse to Accept Election Results?

2021 ◽  
pp. 106591292110544
Author(s):  
James A. Piazza
Keyword(s):  
Time Series ◽  
Public Opinion ◽  
Panel Data ◽  
Political Order ◽  
Democratic Institutions ◽  
Terrorist Activity ◽  
Democratic Rule ◽  
Election Results ◽  
Democratic Elections ◽  
Cross National

A necessary component of peaceful democratic rule is the willingness of election losers to accept election defeats. When politicians and parties acknowledge defeat in democratic elections, they reinforce the peaceful transition of power that sustains political order. When election losers in democracies reject election results, the public’s confidence in democratic institutions is weakened, grievances and polarization abound, and the potential for violent mobilization grows. In this environment, terrorist activity is more likely. I test this proposition using cross-national time series panel data and within-country public opinion data for a wide set of democracies. I find that democracies experience significantly more domestic terrorist casualties when election losers reject election results. Moreover, I find that public willingness to tolerate and justify terrorism as a tactic increases in democratic countries where election losers reject election results.

scholarly journals HOW ENTREPRENEURIAL CHARACTERISTICS INFLUENCE COMPANY CREATION: A CROSS-NATIONAL STUDY OF 22 COUNTRIESTESTED WITH PANEL DATA METHODOLOGY / VERSLUMO CHARAKTERISTIKŲ ĮTAKA ĮMONĖS KŪRIMUI: STUDIJOS, ATLIKTOS 22 VALSTYBĖSE, REZULTATŲ PRISTATYMAS

2011 ◽  
Vol 12 (3) ◽  
pp. 529-545 ◽  
Author(s):  
Agustín Álvarez-Herranz ◽  
Pilar Valencia-De-Lara ◽  
María Pilar Martínez-Ruiz
Keyword(s):  
Panel Data ◽  
National Study ◽  
Research Approach ◽  
Cross Sectional ◽  
Income Levels ◽  
Development Policies ◽  
Regional Development Policies ◽  
Entrepreneurial Activities ◽  
Cross National Study ◽  
Cross National

This study analyzes, from a multicountry perspective, the influence of the sociodemographic profiles of nascent and new entrepreneurs on their behavior. The panel data-based research approach combines temporal series and cross-sectional data to assess entrepreneurial activities across 22 countries with varying income levels. The results show that entrepreneurs' characteristics influence entrepreneurial behavior significantly and positively, in the following order: previous experience of the founder, age, and education. These findings suggest valid recommendations for stimulating entrepreneurship, both for enterprising business founders and for the institutions responsible for designing economic and regional development policies. Santrauka Straipsnyje pristatomų tyrimų rezultatai apima sociodemografinių veiksnių įtakos naujai kuriamam verslui ir jo aplinkai analizę. Tyrimas buvo atliktas 22 valstybėse. Jo rezultatai rodo, kad tam tikros individualios asmens verslumo savybės turi teigiamą įtaką paties verslo organizavimui, pavyzdžiui, ankstesnė verslininko patirtis, amžius ir išsilavinimas. Straipsnio autoriai pateikia rekomendacijas, susijusias su verslumo skatinimu, jos turės įtakos ne tik ekonominei, bet ir regioninei vystymosi politikai.

scholarly journals Constraining Governments: New Indices of Vertical, Horizontal, and Diagonal Accountability

2020 ◽  
Vol 114 (3) ◽  
pp. 811-820
Author(s):  
ANNA LÜHRMANN ◽  
KYLE L. MARQUARDT ◽  
VALERIYA MECHKOVA
Keyword(s):  
Civil Society ◽  
Panel Data ◽  
Bayesian Methods ◽  
Good Governance ◽  
Index Construction ◽  
State Institutions ◽  
The Media ◽  
Research Gap ◽  
Empirical Framework ◽  
Cross National

Accountability—constraints on a government’s use of political power—is one of the cornerstones of good governance. However, conceptual stretching and a lack of reliable measures have limited cross-national research on this concept. To address this research gap, we use V-Dem data and innovative Bayesian methods to develop new indices of accountability and its subtypes: the extent to which governments are accountable to citizens (vertical accountability), other state institutions (horizontal accountability), and the media and civil society (diagonal accountability). In this article, we describe the conceptual and empirical framework underlying these indices and demonstrate their content, convergent, and construct validity. The resulting indices have unprecedented coverage (1900–present) and offer researchers and policymakers new opportunities to investigate the causes and consequences of accountability and its disaggregated subtypes. Furthermore, the methodology provides a framework for theoretically driven index construction to scholars working with cross-national panel data.

scholarly journals Climate change and U.S. agriculture: Accounting for multidimensional slope heterogeneity in panel data

2020 ◽  
Vol 11 (4) ◽  
pp. 1391-1429 ◽  
Author(s):  
Michael Keane ◽  
Timothy Neal
Keyword(s):  
Climate Change ◽  
Panel Data ◽  
Climate Models ◽  
Crop Yields ◽  
Greenhouse Gas Emission ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Spatial And Temporal Variation ◽  
Local Climate

We study potential impacts of future climate change on U.S. agricultural productivity using county‐level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO‐OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950–89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%.

scholarly journals Aging, Urbanization, and Energy Intensity based on Cross-national Panel Data

2017 ◽  
Vol 122 ◽  
pp. 214-220 ◽  
Author(s):  
Fangtao Liu ◽  
Miao Yu ◽  
Pu Gong
Keyword(s):  
Panel Data ◽  
Energy Intensity ◽  
Cross National

scholarly journals IRT Models for Expert-Coded Panel Data

2018 ◽  
Vol 26 (4) ◽  
pp. 431-456 ◽  
Author(s):  
Kyle L. Marquardt ◽  
Daniel Pemstein
Keyword(s):  
Panel Data ◽  
Simulated Data ◽  
Average Score ◽  
Data Sets ◽  
Data Set ◽  
Standard Practice ◽  
Irt Models ◽  
Social Scientific ◽  
Multiple Experts ◽  
Cross National

Data sets quantifying phenomena of social-scientific interest often use multiple experts to code latent concepts. While it remains standard practice to report the average score across experts, experts likely vary in both their expertise and their interpretation of question scales. As a result, the mean may be an inaccurate statistic. Item-response theory (IRT) models provide an intuitive method for taking these forms of expert disagreement into account when aggregating ordinal ratings produced by experts, but they have rarely been applied to cross-national expert-coded panel data. We investigate the utility of IRT models for aggregating expert-coded data by comparing the performance of various IRT models to the standard practice of reporting average expert codes, using both data from the V-Dem data set and ecologically motivated simulated data. We find that IRT approaches outperform simple averages when experts vary in reliability and exhibit differential item functioning (DIF). IRT models are also generally robust even in the absence of simulated DIF or varying expert reliability. Our findings suggest that producers of cross-national data sets should adopt IRT techniques to aggregate expert-coded data measuring latent concepts.

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