Patterns of regional differentiation in post-Soviet Russia

This doctoral thesis aims at contributing to the literature on transition economies focusing on the Russian Federations and in particular on regional income convergence and fertility patterns. The first two chapter deal with the issue of income convergence across regions. Chapter 1 provides an historical-institutional analysis of the period between the late years of the Soviet Union and the last decade of economic growth and a presentation of the sample with a description of gross regional product composition, agrarian or industrial vocation, labor. Chapter 2 contributes to the literature on exploratory spatial data analysis with a application to a panel of 77 regions in the period 1994-2008. It provides an analysis of spatial patterns and it extends the theoretical framework of growth regressions controlling for spatial correlation and heterogeneity. Chapter 3 analyses the national demographic patterns since 1960 and provides a review of the policies on maternity leave and family benefits. Data sources are the Statistical Yearbooks of USSR, the Statistical Yearbooks of the Russian Soviet Federative Socialist Republic and the Demographic Yearbooks of Russia. Chapter 4 analyses the demographic patterns in light of the theoretical framework of the Becker model, the Second Demographic Transition and an economic-crisis argument. With national data from 1960, the theoretically issue of the pro or countercyclical relation between income and fertility is graphically analyzed and discussed, together with female employment and education. With regional data after 1994 different panel data models are tested. Individual level data from the Russian Longitudinal Monitoring Survey are employed using the logit model. Chapter 5 employs data from the Generations and Gender Survey by UNECE to focus on postponement and second births intentions. Postponement is studied through cohort analysis of mean maternal age at first birth, while the methodology used for second birth intentions is the ordered logit model.

Analisi dei predittori e della traiettoria dello sviluppo neuromotorio a 24 mesi nei bambini very low birth weight

Neurodevelopment of preterm children has become an outcome of major interest since the improvement in survival due to advances in neonatal care. Many studies focused on the relationships among prenatal characteristics and neurodevelopmental outcome in order to identify the higher risk preterms’ subgroups. The aim of this study is to analyze and put in relation growth and development trajectories to investigate their association. 346 children born at the S.Orsola Hospital in Bologna from 01/01/2005 to 30/06/2011 with a birth weight of <1500 grams were followed up in a longitudinal study at different intervals from 3 to 24 months of corrected age. During follow-up visits, preterms’ main biometrical characteristics were measured and the Griffiths Mental Development Scale was administered to assess neurodevelopment. Latent Curve Models were developed to estimate the trajectories of length and of neurodevelopment, both separately and combined in a single model, and to assess the influence of clinical and socio-economic variables. Neurodevelopment trajectory was stepwise declining over time and length trajectory showed a steep increase until 12 months and was flat afterwards. Higher initial values of length were correlated with higher initial values of neurodevelopment and predicted a more declining neurodevelopment. SGA preterms and those from families with higher status had a less declining neurodevelopment slope, while being born from a migrant mother proved negative on neurodevelopment through the mediating effect of a being taller at 3 months. A longer stay in NICU used as a proxy of preterms’ morbidity) was predictive of lower initial neurodevelopment levels. At 24 months, neurodevelopment is more similar among preterms and is more accurately evaluated. The association among preterms’ neurodevelopment and physiological growth may provide further insights on the determinants of preterms’ outcomes. Sound statistical methods, exploiting all the information collected in a longitudinal study, may be more appropriate to the analysis.