Weighted convergence and regional clusters across China
2010; Elsevier BV; Volume: 90; Issue: 4 Linguagem: Inglês
10.1111/j.1435-5957.2010.00339.x
ISSN1435-5957
AutoresM.J. Herrerías, Vicente Orts, Emili Tortosa‐Ausina,
Tópico(s)Fiscal Policy and Economic Growth
ResumoPapers in Regional ScienceVolume 90, Issue 4 p. 703-734 Weighted convergence and regional clusters across China M.J. Herrerías, M.J. Herrerías Universitat Jaume I, Departament d'Economia, Campus del Riu Sec, 12071 Castelló, Spain (e-mail: [email protected], [email protected], [email protected]) M.J. Herrerías and Vicente Orts gratefully acknowledge the financial support of the Ministerio de Ciencia e Innovación (ECO2008-06057/ECON) and Generalitat Valenciana (BFPI06/442 and PROMETEO/2009/068). Emili Tortosa-Ausina acknowledges the financial support of Fundació Caixa Castelló-Bancaixa (P1.1B2008-46), Ministerio de Ciencia e Innovación (ECO2008-03813/ECON and ECO2008-05908-C02-01/ECON) and Generalitat Valenciana (PROMETEO/2009/066). We also thank Qiao Yongyuan for his helpful assistance on data issues, as well as to three anonymous referees for helpful comments. The usual disclaimer applies.Search for more papers by this authorVicente Orts, Vicente Orts Universitat Jaume I, Departament d'Economia, Campus del Riu Sec, 12071 Castelló, Spain (e-mail: [email protected], [email protected], [email protected]) Universitat Jaume I, Institut d'Economia Internacional, Campus del Riu Sec, 12071 Castelló, SpainSearch for more papers by this authorEmili Tortosa-Ausina, Emili Tortosa-Ausina Universitat Jaume I, Departament d'Economia, Campus del Riu Sec, 12071 Castelló, Spain (e-mail: [email protected], [email protected], [email protected]) Ivie, C/Guàrdia Civil, 22, València, València 46020, SpainSearch for more papers by this author M.J. Herrerías, M.J. Herrerías Universitat Jaume I, Departament d'Economia, Campus del Riu Sec, 12071 Castelló, Spain (e-mail: [email protected], [email protected], [email protected]) M.J. Herrerías and Vicente Orts gratefully acknowledge the financial support of the Ministerio de Ciencia e Innovación (ECO2008-06057/ECON) and Generalitat Valenciana (BFPI06/442 and PROMETEO/2009/068). Emili Tortosa-Ausina acknowledges the financial support of Fundació Caixa Castelló-Bancaixa (P1.1B2008-46), Ministerio de Ciencia e Innovación (ECO2008-03813/ECON and ECO2008-05908-C02-01/ECON) and Generalitat Valenciana (PROMETEO/2009/066). We also thank Qiao Yongyuan for his helpful assistance on data issues, as well as to three anonymous referees for helpful comments. The usual disclaimer applies.Search for more papers by this authorVicente Orts, Vicente Orts Universitat Jaume I, Departament d'Economia, Campus del Riu Sec, 12071 Castelló, Spain (e-mail: [email protected], [email protected], [email protected]) Universitat Jaume I, Institut d'Economia Internacional, Campus del Riu Sec, 12071 Castelló, SpainSearch for more papers by this authorEmili Tortosa-Ausina, Emili Tortosa-Ausina Universitat Jaume I, Departament d'Economia, Campus del Riu Sec, 12071 Castelló, Spain (e-mail: [email protected], [email protected], [email protected]) Ivie, C/Guàrdia Civil, 22, València, València 46020, SpainSearch for more papers by this author First published: 27 December 2010 https://doi.org/10.1111/j.1435-5957.2010.00339.xCitations: 16 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract We analyse per capita GDP convergence among 28 Chinese provinces between 1952 and 2005 using the distribution dynamics approach. Compared with previous studies, we provide a more complete view by including some additional information such as the asymptotic half-life of convergence, mobility indices and the continuous version of the ergodic distributions. In addition, we also extend the analysis to evaluate whether patterns could differ if weighted by either the population living in each province or their economic sizes, together with the existence and magnitude of spatial spillovers. The unweighted, unconditional analysis corroborates and supplements previous findings, especially those indicating that convergence patterns differ strongly under either pre- or post-reform trends. Both the weighted and space-conditioned analyses indicate that convergence could be much faster when these factors are introduced in the analysis. Implications are especially relevant when weighing by population, since results indicate that the number of people escaping from relative poverty would be much higher than the figure predicted by the unweighted analysis. Resumen Analizamos aquí la convergencia del PIB per cápita entre 28 provincias de China entre 1952 y 2005, utilizando el enfoque de evolución dinámica de distribuciones. En comparación con estudios previos, proporcionamos una visión más completa mediante la incorporación de información adicional como la semivida (half-life) asintótica de la convergencia, índices de movilidad y la versión continua de las distribuciones ergódicas. Además, ampliamos nuestro análisis a evaluar si pudiera haber diferencias en los patrones si se hiciera una ponderación basada en la población que vive en cada provincia o en sus tamaños económicos, junto con la existencia y magnitud de spillovers espaciales. El análisis incondicional, no ponderado, corrobora y complementa resultados de estudios anteriores, especialmente aquellos que indican que los patrones de convergencia difieren en gran medida bajo tendencias pre o post reforma económica. Tanto los análisis de naturaleza espacial como los ponderados indican que la convergencia podría suceder mucho más rápidamente si en el análisis se introdujesen estos factores . Las implicaciones son especialmente relevantes para la ponderación por población, ya que los resultados indican que el número de personas que podrían lograr salir de la una pobreza relativa podría ser mucho mayor que la cifra pronosticada por el análisis no ponderado. 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