Decentralization, social capital, and regional growth: The case of the Italian North-South divide

This paper aims to show how a region’s constant level of social capital may have a very different impact on its economic growth depending on whether the central or the local level of government is responsible for regional policy. Our case study is the economic performance of Northern and Southern Italy in the post-World War II period, when a long phase of regional convergence came to a sudden halt in the early 1970s. We focus on the economic effects of the 1970s institutional reforms on government decentralization and wage bargaining. Our main hypothesis is that decentralization allocates the provision of public capital to institutions, the local ones, more exposed to a territory’s social capital. Since social capital is lower in the Southern regions, decentralization made their developmental policies less effective from 1970 onwards, and regional inequality increased. We build an endogenous growth model augmented to include the interaction between social capital and public investment as well as the reform of the Italian labour market. We calibrate our model using data of the Italian regions for 1951–71. Our quantitative results indicate that decentralization triggered the influence of local social capital on growth and played a central role in halting the convergence path of the low-social-capital regions

Growth maximizing government size, social capital, and corruption

Our paper intersects two topics in growth theory: the growth maximizing government size and the role of Social Capital in development. We modify a simple overlapping generations framework by introducing two key features: a production function \ue0 la Barro\ua0together with the possibility that public officials steal a fraction of public resources under their own control. As underlined by the literature on corruption, Social Capital affects public officials' accountability through many channels which also affect the probability of being caught for embezzlement and misappropriation of public resources. Therefore, in our endogenous growth model such probability is taken as a proxy of Social Capital. We find that maximum growth rates are compatible with Big Government size, measured both in terms of expenditures and public officials, when associated with high levels of Social Capital

Malignancy risk analysis in patients with inadequate fine needle aspiration cytology (FNAC) of the thyroid

Background Thyroid fine needle aspiration cytology (FNAC) is the standard diagnostic modality for thyroid nodules. However, it has limitations among which is the incidence of non-diagnostic results (Thy1). Management of cases with repeatedly non-diagnostic FNAC ranges from simple observation to surgical intervention. We aim to evaluate the incidence of malignancy in non-diagnostic FNAC, and the success rate of repeated FNAC. We also aim to evaluate risk factors for malignancy in patients with non-diagnostic FNAC. Materials and Methods Retrospective analyses of consecutive cases with thyroid non diagnostic FNAC results were included. Results Out of total 1657 thyroid FNAC done during the study period, there were 264 (15.9%) non-diagnostic FNAC on the first attempt. On repeating those, the rate of a non-diagnostic result on second FNAC was 61.8% and on third FNAC was 47.2%. The overall malignancy rate in Thy1 FNAC was 4.5% (42% papillary, 42% follicular and 8% anaplastic), and the yield of malignancy decreased considerably with successive non-diagnostic FNAC. Ultrasound guidance by an experienced head neck radiologist produced the lowest non-diagnostic rate (38%) on repetition compared to US guidance by a generalist radiologist (65%) and by non US guidance (90%). Conclusions There is a low risk of malignancy in patients with a non-diagnostic FNAC result, commensurate to the risk of any nodule. The yield of malignancy decreased considerably with successive non-diagnostic FNAC

Extra-Nuclear Signalling of Estrogen Receptor to Breast Cancer Cytoskeletal Remodelling, Migration and Invasion

BACKGROUND: Estrogen is an established enhancer of breast cancer development, but less is known on its effect on local progression or metastasis. We studied the effect of estrogen receptor recruitment on actin cytoskeleton remodeling and breast cancer cell movement and invasion. Moreover, we characterized the signaling steps through which these actions are enacted. METHODOLOGY/PRINCIPAL FINDINGS: In estrogen receptor (ER) positive T47-D breast cancer cells ER activation with 17beta-estradiol induces rapid and dynamic actin cytoskeleton remodeling with the formation of specialized cell membrane structures like ruffles and pseudopodia. These effects depend on the rapid recruitment of the actin-binding protein moesin. Moesin activation by estradiol depends on the interaction of ER alpha with the G protein G alpha(13), which results in the recruitment of the small GTPase RhoA and in the subsequent activation of its downstream effector Rho-associated kinase-2 (ROCK-2). ROCK-2 is responsible for moesin phosphorylation. The G alpha(13)/RhoA/ROCK/moesin cascade is necessary for the cytoskeletal remodeling and for the enhancement of breast cancer cell horizontal migration and invasion of three-dimensional matrices induced by estrogen. In addition, human samples of normal breast tissue, fibroadenomas and invasive ductal carcinomas show that the expression of wild-type moesin as well as of its active form is deranged in cancers, with increased protein amounts and a loss of association with the cell membrane. CONCLUSIONS/SIGNIFICANCE: These results provide an original mechanism through which estrogen can facilitate breast cancer local and distant progression, identifying the extra-nuclear G alpha(13)/RhoA/ROCK/moesin signaling cascade as a target of ER alpha in breast cancer cells. This information helps to understand the effects of estrogen on breast cancer metastasis and may provide new targets for therapeutic interventions

A Bayesian State Space Approach to Cointegration in Panel Data Models

DiSES Working Papers, University of Trieste, Ital

A Metropolis-Hastings algorithm for reduced rank covariance matrices with application to Bayesian factor models

Most of the proposed Markov chain Monte Carlo (MCMC) algorithms for estimating static and dynamic Bayesian factor models are parametrized in terms of the loading matrix and the latent common factors which are sampled into two separate blocks. In this paper, we propose a novel implementation of the MCMC algorithm which is designed for the model parametrized in terms of the reduced rank covariance matrix underlying the factor model. Hence, the strategy proposed makes it possible to sample directly from the reduced rank covariance matrix. The alternative parameterization of the model is undoubtedly more natural for the linear dynamic factor model. Furthermore, it allows us to rewrite the static factor model as a hierarchical (multilevel) linear model. In this way, a better mixing of the Markov chain is obtained. We adopt, as prior for the singular covariance matrix, the noninformative prior distribution first considered by Diaz-Garcia and Gutierrez (2006). We implement an efficient MCMC algorithm characterized by the sampling of the singular covariance matrix and the associated (unobserved) systematic component in one block. Furthermore, we propose the sampling of the singular covariance matrix marginalized over the systematic component. For this purpose, we develop a Metropolis-Hastings (M-H) step that takes explicitly into account the curved geometry of the support of the target distribution. The proposal distribution is based on a mixture of Wishart Singular distributions; see Diaz-Garcia et al. (1997). It is worth noting that, as a result of working with singular distributions, the prior and posterior densities, as well as the density of the proposal distribution in the M-H step, are specified with respect to Hausdorff measure and integral. The curved geometry of the support has implications for the Bayesian inference on the reduced rank covariance matrix too. That is, a Bayesian point estimator which preserves the original structure of the singular covariance matrix is required. We propose a Bayesian point estimator which is obtained by the generalized Choleski decomposition for reduced rank covariance matrices. We apply our approach to static factor models and present an empirical illustration on exchange rates. Moreover, we consider a simple but important example of linear dynamic factor model in time series analysis: the multivariate local level model with common trends; see Harvey and Koopman (1997). A Bayesian analysis of three monthly US short-term interest rates is presented

Life insurance in Italy: A sub-regional panel data analysis

Lavoro presentato ad "International Thematic workshop on Geographic Information Analysis for economic and spatial development and planning. Case studies, methods and models\u201d (Geog An Mod 2010 GO Local), 3-4 Maggio 2010, Gorizia, Italia