TY - DATA T1 - Regional labour force participation across the European Union: a time–space recursive modelling approach with endogenous regressors PY - 2016/10/10 AU - Solmaria Halleck Vega AU - J. Paul Elhorst UR - https://tandf.figshare.com/articles/journal_contribution/Regional_labour_force_participation_across_the_European_Union_a_time_space_recursive_modelling_approach_with_endogenous_regressors/4004346 DO - 10.6084/m9.figshare.4004346.v1 L4 - https://ndownloader.figshare.com/files/6432933 KW - Labour force participation KW - European Union regions KW - dynamic spatial panels KW - endogenous regressors KW - 劳动力参与 KW - 欧盟区域 KW - 动态空间面板 KW - 内生迴归因数 KW - participation de la population active régions de l’Union européenne KW - panneaux spatiaux dynamiques KW - régresseurs endogènes KW - participación de la población activa KW - regiones de la Unión Europea KW - paneles espaciales y dinámicos KW - regresores endógenos KW - C23 KW - C26 KW - R23 N2 - Regional labour force participation across the European Union: a time–space recursive modelling approach with endogenous regressors. Spatial Economic Analysis. Although there is an abundant regional labour market literature taking a spatial perspective, only a few studies have explored extending the analysis of labour force participation with spatial effects. This paper revisits this important issue, proposing a time–space recursive modelling approach that builds on and appraises Fogli and Veldkamp’s methodology from 2011 and finding for the United States that participation rates vary with past values in nearby regions. Major shortcomings in their study are corrected for, including stationarity and the control for endogenous regressors other than the time and space–time-lagged dependent variable using system generalized method of moments (GMM). The paper also highlights interaction effects among explanatory variables for the first time in this context. Using a panel of 108 regions across the European Union over 1986–2010, the results for total, male and female participation rates throw a new light on the socio-economic relevance of different determinants. Importantly, characteristics in neighbouring regions play a significant role, and neglecting endogeneity is found to have serious consequences, underlining increased attention on the specification and estimation of spatial econometric models with endogenous regressors. ER -