Voting Transitions in the 2019 Valencian Autonomous Community’s Elections

Paraules clau: vote transitions, ecological inference, Spanish elections

Resum

The political fragmentation following the 2008 Financial Crisis and its economic, social, political and institutional fall-out have led to a growing left-right polarisation of politics and a weakening of the middle ground. The effective number of parliamentary parties is at an all-time high both in
the Spanish Parliament (Congreso) and in the Valencian Autonomous Parliament (Corts). Voters are spoilt for choice and switch party more often. This paper uses transfer matrices to analyse the shifting voting patterns in the European, General, Regional, and Local elections held during 2019 in The Valencian Country. The most salient result is the ever-shifting pattern at each end of the political spectrum. On the right wing, there is the steady advance of Vox. On the left wing, UP and Compromís draw from virtually the same pool of fickle voters, with UP picking up most votes in national elections and Compromís winning hands-down in regional and local elections.

##plugins.generic.usageStats.downloads##

##plugins.generic.usageStats.noStats##

Autor Biografies

Jose M Pavia, Universitat de Valencia

He has a Degree in Mathematics and a PhD in Economics. Pavía is the Director of the Electoral Processes and Public Opinion Research Group, and Full Professor of Quantitative Methods at Valencia University (UV). His research interests include electoral processes, forecasting, statistical machine) learning, ecological inference, experiments, surveys, crime detection, public opinion, regional economy, and inequality. 

Cristina Aybar, Universitat de València

She has a PhD in Economics and Business Science and is a tenured Professor at Universitat de València (UV). Aybar has published various studies on: over-population models; the estimation of proportions; panel data; funding Small and Medium-sized Enterprises (SMEs); survey methodology; ‘barometers’ for Spain’s Sociological Research Centre (CIS). She has also taken part in research groups covering: Electoral Processes and Public Opinion Research Group; Adaptation to the Eurostat Standard for Local Economic Indicators.

Referències

Antentas, J. M. (2017). Spain: From the Indignados Rebellion to Regime Crisis (2011-2016). Labor History, 58(1), 106-131. DOI: http://doi.org/10.1080/0023656X.2016.1239875.

Bacharach, M. (1970). Biproportional Matrices and Input-Output Change. Cambridge: Cambridge University Press. DOI: http://doi.org/10.1080/0953531042000219259

Becker, S., Fetzer, T., Novy, D. (2017). Who Voted for Brexit? A Comprehensive District-level Analysis. Economic Policy, 32(92), 601-650. DOI: http://doi.org/10.1093/epolic/eix017

Benedicto, J. and Ramos, M. (2018). Young People’s Critical Politicization in Spain in the Great Recession: A

Generational Reconfiguration? Societies, 8(89), 1-30. DOI: http://doi.org/10.3390/soc8030089

Biemer, P. P. (2010). Total Survey Error. Design, Implementation and Evaluation. Public Opinion Quarterly, 74, 817-848. DOI: http://doi.org/10.1093/poq/nfq058

Brown, P. J. and Payne, C. D. (1986). Aggregate Data, Ecological Regression and Voting Transitions. Journal of the American Statistical Association, 81, 453-460. DOI: http://doi.org/10.1007/978-3-642-11363-5_54

Cho, W. K. T. (1998). If the Assumption Fits: A Comment on the King Ecological Inference Solution. Political Analysis, 7, 143-163. DOI: http://doi.org/10.1093/pan/7.1.143

CIS (2019a). Estudio n. 3.242. Macrobarómetro de marzo 2019. Preelectoral Elecciones Generales 2019. Nota metodológica. Modelos CIS V108.

CIS (2019b). Estudio n. 3.244. Preelectoral Elecciones Autonómicas 2019. Comunidad Valenciana. Nota metodológica. Modelos CIS V41.

CIS (2019c) Estudio n. 3.245. Macrobarómetro de abril 2019. Preelectoral Elecciones al Parlamento Europeo, Autonómicas y Municipales 2019.

Corominas A., Lusa, A., Valvet M. D. (2015). Computing Voter Transitions: The Elections for the Catalan Parliament,from 2010 to 2012. Journal of Industrial Engineering and Management, 8(1), 122-136. DOI: http://doi.org/10.1080/00207543.2018.1530477

Couperus, S. and Tortola. P. D. (2019). Right-wing Populism’s (Ab)use of the Past in Italy and The Netherlands. Debats. Journal on Culture, Power and Society, 4, 105-118. https://doi.org/10.28939/iam.debats-en.2019-9

Duncan, O. and Davis, B. (1953). An Alternative to Ecological Correlation. American Sociological Review, 18, 665-666. DOI: http://doi.org/10.1177/0193841X9101500602

Forcina, A. and Marchetti, G. M. (2011). The Brown and Payne Model of Voter Transition Revisited. In S. Ingrassia, R. Rocci, and M. Vichi (ed.), New Perspectives in Statistical Modeling and Data Analysis: Studies in Classification, Data Analysis, and Knowledge Organization. Berlín: Springer. DOI: http://doi.org/10.1007/978-3-642-11363-5_1

Füle, E. (1994). Estimating Voter Transitions by Ecological Regression. Electoral Studies, 13, 313-330. DOI: http://doi.org/10.1016/0261-3794(94)90043-4.

Goodman, L. A. (1953). Ecological Regressions and the Behaviour of Individuals. American Sociological Review, 18, 663-666. DOI: http://doi.org/10.2307/2088122

Goodman, L. A. (1959). Some Alternatives to Ecological Correlation. American Journal of Sociology, 64(6), 610-625.

Greiner, D. and Quinn, K. M. (2010). Exit Polling and Racial Bloc Voting: Combining Individual-level and RxC Ecological Data. The Annals of Applied Statistics, 4, 1.774-1.796. DOI: http://doi.org/10.1214/10-AOAS353

Haunberger, S. (2010). The Effects of Interviewer, Respondent and Area Characteristics on Cooperation in Panel Surveys: a Multilevel Approach. Quality & Quantity, 44, 957-969. DOI: http://doi.org/10.1007/s11135-009-9248-5

Hawkes, A. G. (1969). An Approach to the Analysis of Electoral Swing. Journal of the Royal Statistical Society, Series A, 132, 68-79. DOI: http://doi.org/10.2307/2343756

Hunter, W. and Power, T. J. (2019). Bolsonaro and Brazil’s Illiberal Backlash. Journal of Democracy, 30(1), 68-82.

King, G. (1997). A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data. Princeton, Nueva Jersey: Princeton University Press. DOI: http://doi.org/10.2307/2585686

King, G., Rosen, O., Tanner, M. A. (1999). Binomial-beta Hierarchical Models for Ecological Inference. SociologicalMethods & Research, 28, 61-90. DOI: http://doi.org/10.1177/0049124199028001004

King, G., Rosen, O., Tanner, M. A. (ed.) (2004). Ecological Inference: New Methodological Strategies. New York: Cambridge University Press.

Klima, A., Thurner, P. W., Molnar, C., Schlesinger, T., Küchenhoff, H. (2016). Estimation of Voter Transitions Based on Ecological Inference: An Empirical Assessment of Different Approaches. AStA — Advances in Statistical Analysis, 100, 133-159. DOI: http://doi.org/10.1007/s10182-015-0254-8

Klima, A., Schlesinger, T., Thurner, P. W., Küchenhoff, H. (2019). Combining Aggregate Data and Exit Polls

for the Estimation of Voter Transitions. Sociological Methods & Research, 48(2), 296-325. DOI: http://doi.org/10.1177/0049124117701477

Krumpal, I. (2013). Determinants of Social Desirability Bias in Sensitive Surveys: A Literature Review. Quality & Quantity, 47, 2.025-2.047. DOI: http://doi.org/10.1007/s11135-011-9640-9

Laakso, M. and Taagepera, R. (1979). Effective Number of Parties: A Measure with Application to West Europe. Comparative Political Studies, 12, 3-27. DOI: http://doi.org/10.1177/001041407901200101

Martín-Cubas, J., Bodoque, A., Pavía, J.M., Tasa, V., Veres-Ferrer, E. (2019). The ‘Big Bang’ of the Populist Parties in the European Union. The 2014 European Parliament Election. Innovation —The European Journal of Social Science Research, 32(2), 168-190. DOI: http://doi.org/10.1080/13511610.2018.1523711

McCarthy, C., Ryan, T. M. (1977). Estimates of Voter Transition Probabilities from the British General Elections of 1974. Journal of the Royal Statistical Society. Series A, 140, 78-85. DOI: http://doi.org/10.2307/2344516

Miller, W. L. (1972). Measures of Electoral Change Using Aggregate Data. Journal of the Royal Statistical Society, Series A, 135, 122-142. DOI: http://doi.org/10.1111/rssb.12318

Orriols, L. and Cordero, G. (2016). The Breakdown of the Spanish Two-Party System: The Upsurge of Podemos and Ciudadanos in the 2015 General Election. South European Society and Politics, 21(4), 469-492. DOI: http://doi.org/10.1080/13608746.20 16.1151127

Park, W. (2008). Ecological Inference and Aggregate Analysis of Elections. (PhD thesis, University of Michigan, The United States). Pavía-Miralles, J. M. (2005). Forecasts from Non-Random Samples: The Election Night Case. Journal of the American Statistical Association, 100, 1.113-1.122. DOI: http://doi.org/10.1198/016214504000001835

Pavía, J. M. (2010). Improving Predictive Accuracy of Exit Polls. International Journal of Forecasting, 26, 68-81. DOI: http://doi.org/10.1016/j.ijforecast.2009.05.001

Pavía, J. M. (2016). Transferencia*Electoral, software registrado en la Universitat de València, número 9382. Date: 01/02/2016.

Pavía, J. M. and Aybar, C. (2018). Field Rules and Bias in Random Surveys with Quota Samples: An Assessment of CIS Surveys. SORT (Statistics and Operations Research Transactions), 42(2), 183-206. DOI: http://doi.org/10.2436/20.8080.02.74

Pavía, J. M. and Cantarino, I. (2017). Dasymetric Distribution of Votes in a Dense City. Applied Geography, 86, 22- 31. DOI: http://doi.org/10.1016/j.apgeog.2017.06.021

Pavía, J. M. and López-Quilez, A. (2013). Spatial Vote Redistribution in Redrawn Polling Units. Journal of the Royal Statistical Society, Series A – Statistics in Society 176(3), 655-678. DOI: http://doi.org/10.1111/j.1467-985X.2012.01055.x

Pavía, J. M. and Veres Ferrer, E. J. (2016a). Un nuevo estimador para disgregar totales poblacionales: El caso de los nuevos electores. Anales de Economía Aplicada, XXX, 817-826.

Pavía Miralles, J. M. and Veres Ferrer, E. J. (2016b). Desagregando estadísticas de población. In J. M. Herrerías and J. Callejón (ed.), Investigaciones en métodos cuantitativos para la economía y la empresa (p. 543-555). Granada: Editorial Universidad de Granada.

Pavía, J. M., Badal, E., García-Cárceles, B. (2016). Spanish Exit Polls: Sampling Error or Nonresponse Bias? Revista Internacional de Sociología, 74(3), e043. DOI: http://doi.org/10.3989/ris.2016.74.3.043

Pavía, J. M., Bodoque, A., Martín, J. (2016). The Birth of a New Party: Podemos, a Hurricane in the Spanish Crisis of Trust. Open Journal of Social Sciences, 4, 67-86. DOI: http://doi.org/10.4236/jss.2016.49008

Pavía, J. M., Cabrer, B., Sala, R. (2009). Updating Input-Output Matrices: Assessing Alternatives through Simulation. Journal of Statistical Computation and Simulation, 79, 1.467-1.498. DOI: http://doi.org/10.1080/00949650802415154

Pavía, J. M., Gil-Carceller, I., Rubio-Mataix, A., Coll, V., Alvarez-Jareño, J. A., Aybar, C., Carrasco-Arroyo, S. (2019). The Formation of Aggregate Expectations: Wisdom of the Crowds or Media Influence? Contemporary Social Science, 14(1), 132-143. DOI: http://doi.org/10.1080/21582041.2017.1367831

Payne, C., Brown, P., Hanna, V. (1986). By-election Exit Polls. Electoral Studies, 5, 277-287. DOI: http://doi.org/10.1016/0261- 3794(86)90015-6

Plescia, C. and De Sio, L. (2018). An Evaluation of the Performance and Suitability of R×C Methods for Ecological Inference with Known True Values. Quality & Quantity, 52(2), 669-683. DOI: http://doi.org/10.1007/s11135-017-0481-z

Puig, X. and Ginebra, J. (2015). Ecological Inference and Spatial Variation of Individual Behavior: National Divide and Elections in Catalonia. Geographical Analysis, 47(3), 262-283. DOI: http://doi.org/10.1111/gean.12056

Rama Caamaño, J. (2016). Ciclos electorales y sistema de partidos en España. Revista Jurídica Universidad Autónoma de Madrid, 34(II), 241-266. DOI: http://doi.org/10.1177/1354068815601347

Robinson, W. S. (1950). Ecological Correlations and the Behavior of Individuals. American Sociological Review, 15(3), 351-357. DOI: http://doi.org/10.2307/2087175

Romero, R. (2014). Un modelo matemático para estimar el trasvase de votos entre partidos. Revista Digital de la Real Academia de Cultura Valenciana, 3-23.

Romero, R. (2015). Trasvase de votos entre partidos en las elecciones autonómicas catalanas del 27 de septiembre de 2015. Revista Digital de la Real Academia de Cultura Valenciana, 3-15.

Romero, R. (2016). Movilidad electoral entre las elecciones del 20D y del 26J en las comunidades autónomas valenciana, madrileña y andaluza. Revista Digital de la Real Academia de Cultura Valenciana. Segunda época, 1,1-25.

Romero, R., Pavía, J. M., Martín, J., Romero, G. (2019). Assessing Uncertainty of Voter Transitions Estimated from Aggregated Data: Application to 2017 French Presidential Elections, en revisión.

Rosen, O., Jiang, W., King, G., Tanner, M. A. (2001). Bayesian and Frequentist Inference for Ecological Inference: The RxC Case. Statistica Neerlandica, 55, 134-56. DOI: http://doi.org/10.1111/1467-9574.00162

Royo, S. (2014). Institutional Degeneration and the Economic Crisis in Spain. American Behavioral Scientist, 58(12), 1.568-1.591. DOI: http://doi.org/10.1177/0002764214534664

Skonieczny, A. (2018). Emotions and Political Narratives: Populism, Trump and Trade. Politics and Governance, 6(4), 62-72. DOI: http://doi.org/10.17645/pag.v6i4.1574

Torcal, M. (2014). The Decline of Political Trust in Spain and Portugal: Economic Performance or Political Responsiveness? American Behavioral Scientist, 58(12), 1.542-1.567. DOI: http://doi.org/10.1177/0002764214534662

Wakefield, J. (2004). Ecological Inference for 2x2 Tables (with discussion). Journal of Royal Statistical Society, Series A, 167, 385-445. DOI: http://doi.org/10.1111/j.1467-985x.2004.02046.x

Publicades
2020-12-31
Com citar
Pavia, J. M. and Aybar, C. (2020) “Voting Transitions in the 2019 Valencian Autonomous Community’s Elections”, Debats. Revista de cultura, poder i societat, 50, pp. 27-49. doi: 10.28939/iam.debats-en.2020-2.
Secció
SPECIAL ISSUE 1