La stima e l'interpretazione dei divari regionali nel lungo periodo: i risultati principali e alcune tracce di ricerca

Felice, Emanuele

doi:10.3280/scre2015-003006

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…However, this first wave of industrialization is worth our attention for several reasons. According to Felice (2015a), in spite of the limited growth experienced by the country between its Unification and Second World War (with only a doubling of its GDP per capita), the dynamics taking place in this period created the premises for the economic boom started in the 1950s. From a more pragmatic perspective, studying the period immediately after Unification and before the Fascist rule presents several advantages: all internal borders and tariffs were removed and the administrative and legal framework became the same for all regions, making them more comparable across periods.…”

Section: Historical Background and Literaturementioning

confidence: 99%

Market versus endowment: explaining early industrial location in Italy (1871–1911)

Missiaia

2018

Cliometrica

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This article aims to explain the location of the manufacturing industries in Italy in the period 1871-1911. The analytical framework takes into account of two competing theories on the determinants of the location of economic activity: the Heckscher-Ohlin (H-O) theory on factor endowments and the new economic geography (NEG) theory on access to markets. The methodology used here is based on Midelfart-Knarvik et al. (The location of European industry, European Economy Economic Papers 142. European Commission, 2000) and has seen several historical applications. The location of industries is explained through interactions between characteristics of the regions and characteristics of the sectors, of both H-O type and NEG type. The main finding is that endowments, and in particular energy and human capital, were the determinants of the geography of the first Italian industrialization. Market access, at this point of industrialization, mattered only in its domestic formulation and only through economies of scale.

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Section: Historical Background and Literaturementioning

confidence: 99%

Market versus endowment: explaining early industrial location in Italy (1871–1911)

Missiaia

2018

Cliometrica

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“…Interestingly, a positive relationship between height has been noticed across Spanish regions over the period 1934-1973, and across Italian regions from the years after 1871 (Cámara and Garcia-Roman 2015; A'Hearn and Vecchi 2017a). For Italy, regional GDP per capita in 1871, estimated by Felice (2015), is significantly related to the average height of conscripts born in the same year ( r = 0.62). However, due to the lack of data on production per sector, regional GDP estimates for the first post-unitarian decades present wide margins of uncertainty.…”

Section: Regional Differences In Heightmentioning

confidence: 99%

“…Despite the margin of uncertainty, the estimates of GDP per capita suggest that, in the first post-unification decades, the gap between the centre-north and the south was modest (Daniele and Malanima 2011). For 1871, the difference in GDP per capita between the two areas was estimated at about 10-15 per cent (Felice 2015; Felice and Vasta 2015). Data on infant mortality and life expectancy at birth, as well as estimates on nutritional conditions, do not indicate significant north-south differences in the first decade after unification, except for education levels (Vecchi and Coppola 2006; Atella et al .…”

Section: Introductionmentioning

confidence: 99%

Health, Height and Regional Disparities in Italy: Evidence From Conscripts’ Data, 1843-1871

Daniele,

Samà

2023

RHE / JILAEH

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This article contributes to the debate on regional disparities in living standards in Italy at the time of national unification (1861) by examining the health standards of army conscripts born between 1843 and 1871. Data regarding the conscripts born in 1843-1856 show that 35.4 per cent of youths examined were unfit for military service. Overall, the rejection rate in the peninsular south was similar to that of the northern regions. In the south, however, the share of conscripts rejected for insufficient height was notably higher. It is very likely that the persistent north-south gradient in average height in Italy is related to genetic factors.

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“…Italy has significant territorial differences [26] attributable, in addition to the dual pattern of the North South divide [27,28], to the imbalanced development between cities and rural areas [29,30], between plains and mountains [31], between coastal and inner areas [32], and between centers and inner areas [1,33]. These territorial differences are then reflected in inequality [34][35][36], which may depend on the presence and differentiated access to resources-economic, social, and environmental-and to the services offered in a certain territory.…”

Section: Introductionmentioning

confidence: 99%

The Spatial Dimension of Income Inequality: An Analysis at Municipal Level

Mastronardi

Cavallo

2020

Sustainability

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This paper focuses on the analysis on income inequality in Italy at the municipal level of the areas defined by the National Strategy for Inner Areas. We discuss an analysis of the economic and spatial dynamics of the phenomenon through the construction of the Gini's coefficient and the estimation of the regression model for the evaluation of the determinants of inequality. We highlight the influence of the spatial dimension on income inequality in Italy. Inequality appears to be greater in densely populated urban centers with a strong incidence of tertiary activities and young population. Conversely, in the inner areas, the distribution of income is more balanced due probably to the weakness of the social and economic structure that determines low levels of income and job opportunities mainly in the agricultural sector. Sustainability 2020, 12, 1622 2 of 18 Sustainability 2020, 12, x FOR PEER REVIEW 11 of 18 355 Figure 2. Map of significance of the LM index for the values of the Gini coefficient. Source: own 356 elaboration. 357Consequently, the analyses are based on a classic linear regression model. Table 4 shows its main 358 results. The histogram of the residuals is showed Figure 3. 359A first element of reflection is that the model explains 60% of the observed variability, 360 confirming the strong territorial characterization of inequality in Italy. A second consideration is that, 361 except L, all the selected variables are quite significant. In detail, the t ratio between coefficients and 362 standard errors reports the relative variation of Gini coefficent, associated with a unitary variation of 363 the explanatory variables. The last columns on the right show the p values and the relative 364 significance under the hypothesis of robust standard errors with respect to heteroskedasticity (i.e., in 365 the presence of a nonconstant variance between the residuals or discrepancies between observed and 366 estimated values of the dependent variable, a possible cause of distorted estimations of the model 367 parameters). 369Lisa significance map High-High (928) High-Low (39) Low-High (27) Low-Low (809)

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La stima e l'interpretazione dei divari regionali nel lungo periodo: i risultati principali e alcune tracce di ricerca

Cited by 10 publications

References 46 publications

Market versus endowment: explaining early industrial location in Italy (1871–1911)

Market versus endowment: explaining early industrial location in Italy (1871–1911)

Health, Height and Regional Disparities in Italy: Evidence From Conscripts’ Data, 1843-1871

The Spatial Dimension of Income Inequality: An Analysis at Municipal Level

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