Predicting the Number of Parties: A Quantitative Model of Duverger's Mechanical Effect
In: American political science review, Band 87, Heft 2, S. 455-464
ISSN: 0003-0554
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In: American political science review, Band 87, Heft 2, S. 455-464
ISSN: 0003-0554
In: Journal of elections, public opinion and parties, Band 21, Heft 1, S. 1-27
ISSN: 1745-7297
In: American political science review, Band 92, Heft 2, S. 329-342
ISSN: 1537-5943
We rely on data from India and the United States to show that political and economic centralization can influence the number of national parties in single-member simple-plurality electoral systems. Historically, in both countries the number of parties in local electoral districts has been near two, but the number of national parties has fluctuated. Periods of a small number of national parties in both countries correspond to periods of centralization. We argue that, as national governments centralize power and make policies that affect local areas, candidates have greater incentives to associate with national organizations, and voters have greater incentives to abandon locally competitive but nationally noncompetitive parties.
In: Social science quarterly, Band 98, Heft 5, S. 1391-1405
ISSN: 1540-6237
ObjectivesThe objective of this study is to explore how party systems can affect turnout by exploring the conditional effect of number of parties and party polarization on democracies.MethodsUsing Comparative Manifesto Project data from 26 democracies, this study develops a measure of party systems that interacts party polarization and number of parties to explain turnout.ResultsFindings show that the composition of the party system as a whole is a key determinate of a voter's propensity to vote. Highly polarized systems with few parties spur individuals to vote, while low levels of polarization and many parties reduce incentives to vote.ConclusionsResults have important implications for theories of turnout, resolving the confusion surrounding how party systems affect political participation.
In: American political science review, Band 92, Heft 2, S. 329-342
ISSN: 0003-0554
We rely on data from India and the United States to show that political and economic centralization can influence the number of national parties in single-member simple-plurality electoral systems. Historically, in both countries the number of parties in local electoral districts has been near two, but the number of national parties has fluctuated. Periods of a small number of national parties in both countries correspond to periods of centralization. We argue that, as national governments centralize power and make policies that affect local areas, candidates have greater incentives to associate with national organizations, and voters have greater incentives to abandon locally competitive but nationally noncompetitive parties. (American Political Science Review / FUB)
World Affairs Online
In: Party politics: an international journal for the study of political parties and political organizations, Band 21, Heft 3, S. 404
ISSN: 1354-0688
Due to the particularities of SARS-CoV-2, public health policies have played a crucial role in the control of the COVID-19 pandemic. Epidemiological parameters for assessing the stage of the outbreak, such as the Effective Reproduction Number (R-t), are not always straightforward to calculate, raising barriers between the scientific community and non-scientific decision-making actors. The combination of estimators ofR(t)with elaborated Machine Learning-based forecasting techniques provides a way to support decision-making when assessing governmental plans of action. In this work, we develop forecast models applying logistic growth strategies and auto-regression techniques based on Auto-Regressive Integrated Moving Average (ARIMA) models for each country that records information about the COVID-19 outbreak. Using the forecast for the main variables of the outbreak, namely the number of infected (I), recovered (R), and dead (D) individuals, we provide a real-time estimation ofR(t)and its temporal evolution within a timeframe. With such models, we evaluateR(t)trends at the continental and country levels, providing a clear picture of the effect governmental actions have had on the spread. We expect this methodology of combining forecast models for raw data to calculateR(t)to serve as valuable input to support decision-making related to controlling the spread of SARS-CoV-2. ; Centre for Biotechnology and Bioengineering-CeBiB (PIA project, Conicyt, Chile) FB0001 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) 21181435
BASE
In: National civic review: publ. by the National Municipal League, Band 79, Heft 6, S. 529
ISSN: 0027-9013
In: Party politics: an international journal for the study of political parties and political organizations, Band 18, Heft 4, S. 523-545
ISSN: 1354-0688
In: Acta politica: AP ; international journal of political science ; official journal of the Dutch Political Science Association (Nederlandse Kring voor Wetenschap der Politiek), Band 41, Heft 2, S. 133-145
ISSN: 0001-6810
Due to the particularities of SARS-CoV-2, public health policies have played a crucial role in the control of the COVID-19 pandemic. Epidemiological parameters for assessing the stage of the outbreak, such as the Effective Reproduction Number (R_t), are not always straightforward to calculate, raising barriers between the scientific community and non-scientific decision-making actors. The combination of estimators of R_t with elaborated Machine Learning-based forecasting techniques provides a way to support decision-making when assessing governmental plans of action. In this work, we develop forecast models applying logistic growth strategies and auto-regression techniques based on Auto-Regressive Integrated Moving Average (ARIMA) models for each country that records information about the COVID-19 outbreak. Using the forecast for the main variables of the outbreak, namely the number of infected (I), recovered (R), and dead (D) individuals, we provide a real-time estimation of R_t and its temporal evolution within a timeframe. With such models, we evaluate R_t trends at the continental and country levels, providing a clear picture of the effect governmental actions have had on the spread. We expect this methodology of combining forecast models for raw data to calculate R_t to serve as valuable input to support decision-making related to controlling the spread of SARS-CoV-2.
BASE
In: Electoral studies: an international journal on voting and electoral systems and strategy, Band 72, S. 102349
ISSN: 1873-6890
In: Party politics: an international journal for the study of political parties and political organizations, Band 14, Heft 2, S. 211-222
ISSN: 1354-0688
In: Politics in Central Europe, Band 6, Heft 1, S. 110-123
This article focuses on the analysis of the institutionalization of party systems. The objects of the analysis are four party systems of post-communist countries – Bulgaria, Croatia, Romania and Slovenia. To assess the degree of institutionalization, three quantitative criteria are used: electoral volatility, the effective number of parties and the parliamentary age of parties. The main aims of the analysis are to compare aforementioned party systems' degree of institutionalization and simultaneously confirm the assumption that post-communist party systems are in a far more heterogeneous category than is often suggested. At first, the article defines and explains the institutionalization of party systems and uncovers the possibilities of its quantitative assessment. Then, the level of institutionalization of Bulgarian, Croatian, Romanian and Slovenian party systems is evaluated. There are two main conclusions. First, the institutionalization of a party system in the case of Slovenia and Croatia is on a considerably higher level than in the cases of Bulgaria and Romania, although there is some positive progress in the case of Romania in the last five years. Second, common trends, connected with institutionalization and often mentioned as overall, don't have a strong reliance on empirical measures.