2017
DOI: 10.1111/pirs.12217
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Regional growth with spatial dependence: A case study on early Italian industrialization

Abstract: Abstract. This paper estimates a conditional β-convergence model of labour productivity growth in Italy's manufacturing industry during 1871-1911, accounting for spatial dependence. The empirical evidence is based on a recent set of data at provincial (NUTS 3) level on manufacturing value added at 1911 prices, and a new set of data on human and social capital, political participation, and infrastructures. By focusing on a country and a time when the agglomeration forces and spillover effects advocated by the n… Show more

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Cited by 26 publications
(8 citation statements)
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“…Among the controls, and in line with the results in Ciccarelli and Fachin (2017) and Bozzano and Cappelli (2019), both the Pupil − teacher ratio and the SchoolDens variables are positively correlated with our measure of territorial diffusion of literacy at 5% level of significance, while the coefficient of Exp. − pupil is only significant at 10% significance level.…”
Section: Zero-stage Estimation Resultssupporting
confidence: 85%
“…Among the controls, and in line with the results in Ciccarelli and Fachin (2017) and Bozzano and Cappelli (2019), both the Pupil − teacher ratio and the SchoolDens variables are positively correlated with our measure of territorial diffusion of literacy at 5% level of significance, while the coefficient of Exp. − pupil is only significant at 10% significance level.…”
Section: Zero-stage Estimation Resultssupporting
confidence: 85%
“…Among the controls, and in line with the results in Ciccarelli and Fachin (2017) and Cappelli (2018, 2019), both the Pupil − teacher ratio and the SchoolDens variable are positively correlated with our measure of territorial diffusion of literacy at 5% level of significance, while the coefficient of Exp. − pupil is only significant at 10% significance level.…”
Section: Zero-stage Estimation Resultssupporting
confidence: 84%
“…In contrast, the pre-treatment values of the synthetic control group (column 2) resemble more closely the predictors' pre-treatment values for the "treated" province (column 1). This confirms that the synthetic control group acts as a better counterfactual for the treated 24 The number of postal offices and number of schools in Italian provinces are also used as control variables in Ciccarelli and Fachin (2017), who investigate the main determinants of the productivity growth in the Italian manufacturing industry during 1871-1911. 25 As mentioned in the main text, the treated provinces need to be collapsed at province and year level as they belonged to one single "treated" province.…”
Section: Evidence From the Scm Approachmentioning
confidence: 75%