Finanza Imprenditoriale: Teoria del Portafoglio e Costo del Capitale

This thesis focuses on the limits that may prevent an entrepreneur from maximizing her value, and the benefits of diversification in reducing her cost of capital. After reviewing all relevant literature dealing with the differences between traditional corporate finance and entrepreneurial finance, we focus on the biases occurring when traditional finance techniques are applied to the entrepreneurial context. In particular, using the portfolio theory framework, we determine the degree of under-diversification of entrepreneurs. Borrowing the methodology developed by Kerins et al. (2004), we test a model for the cost of capital according to the firms' industry and the entrepreneur's wealth commitment to the firm. This model takes three market inputs (standard deviation of market returns, expected return of the market, and risk-free rate), and two firm-specific inputs (standard deviation of the firm returns and correlation between firm and market returns) as parameters, and returns an appropriate cost of capital as an output. We determine the expected market return and the risk-free rate according to the huge literature on the market risk premium. As for the market return volatility, it is estimated considering a GARCH specification for the market index returns. Furthermore, we assume that the firm-specific inputs can be obtained considering new-listed firms similar in risk to the firm we are evaluating. After we form a database including all the data needed for our analysis, we perform an empirical investigation to understand how much of the firm's total risk depends on market risk, and which explanatory variables can explain it. Our results show that cost of capital declines as the level of entrepreneur's commitment decreases. Therefore, maximizing the value for the entrepreneur depends on the fraction of entrepreneur's wealth invested in the firm and the fraction she sells to outside investors. These results are interesting both for entrepreneurs and policy makers: the former can benefit from an unbiased model for their valuation; the latter can obtain some guidelines to overcome the recent financial market crisis.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

The Integrated European Project "GEHA - GEnetics of Healthy Aging": recruitment, health status assessment and survival of the Italian 90+ sibpairs

The present study is part of the EU Integrated Project “GEHA – Genetics of Healthy Aging” (Franceschi C et al., Ann N Y Acad Sci. 1100: 21-45, 2007), whose aim is to identify genes involved in healthy aging and longevity, which allow individuals to survive to advanced age in good cognitive and physical function and in the absence of major age-related diseases. Aims The major aims of this thesis were the following: 1. to outline the recruitment procedure of 90+ Italian siblings performed by the recruiting units of the University of Bologna (UNIBO) and Rome (ISS). The procedures related to the following items necessary to perform the study were described and commented: identification of the eligible area for recruitment, demographic aspects related to the need of getting census lists of 90+siblings, mail and phone contact with 90+ subjects and their families, bioethics aspects of the whole procedure, standardization of the recruitment methodology and set-up of a detailed flow chart to be followed by the European recruitment centres (obtainment of the informed consent form, anonimization of data by using a special code, how to perform the interview, how to collect the blood, how to enter data in the GEHA Phenotypic Data Base hosted at Odense). 2. to provide an overview of the phenotypic characteristics of 90+ Italian siblings recruited by the recruiting units of the University of Bologna (UNIBO) and Rome (ISS). The following items were addressed: socio-demographic characteristics, health status, cognitive assessment, physical conditions (handgrip strength test, chair-stand test, physical ability including ADL, vision and hearing ability, movement ability and doing light housework), life-style information (smoking and drinking habits) and subjective well-being (attitude towards life). Moreover, haematological parameters collected in the 90+ sibpairs as optional parameters by the Bologna and Rome recruiting units were used for a more comprehensive evaluation of the results obtained using the above mentioned phenotypic characteristics reported in the GEHA questionnaire. 3. to assess 90+ Italian siblings as far as their health/functional status is concerned on the basis of three classification methods proposed in previous studies on centenarians, which are based on: • actual functional capabilities (ADL, SMMSE, visual and hearing abilities) (Gondo et al., J Gerontol. 61A (3): 305-310, 2006); • actual functional capabilities and morbidity (ADL, ability to walk, SMMSE, presence of cancer, ictus, renal failure, anaemia, and liver diseases) (Franceschi et al., Aging Clin Exp Res, 12:77-84, 2000); • retrospectively collected data about past history of morbidity and age of disease onset (hypertension, heart disease, diabetes, stroke, cancer, osteopororis, neurological diseases, chronic obstructive pulmonary disease and ocular diseases) (Evert et al., J Gerontol A Biol Sci Med Sci. 58A (3): 232-237, 2003). Firstly these available models to define the health status of long-living subjects were applied to the sample and, since the classifications by Gondo and Franceschi are both based on the present functional status, they were compared in order to better recognize the healthy aging phenotype and to identify the best group of 90+ subjects out of the entire studied population. 4. to investigate the concordance of health and functional status among 90+ siblings in order to divide sibpairs in three categories: the best (both sibs are in good shape), the worst (both sibs are in bad shape) and an intermediate group (one sib is in good shape and the other is in bad shape). Moreover, the evaluation wanted to discover which variables are concordant among siblings; thus, concordant variables could be considered as familiar variables (determined by the environment or by genetics). 5. to perform a survival analysis by using mortality data at 1st January 2009 from the follow-up as the main outcome and selected functional and clinical parameters as explanatory variables. Methods A total of 765 90+ Italian subjects recruited by UNIBO (549 90+ siblings, belonging to 258 families) and ISS (216 90+ siblings, belonging to 106 families) recruiting units are included in the analysis. Each subject was interviewed according to a standardized questionnaire, comprising extensively utilized questions that have been validated in previous European studies on elderly subjects and covering demographic information, life style, living conditions, cognitive status (SMMSE), mood, health status and anthropometric measurements. Moreover, subjects were asked to perform some physical tests (Hand Grip Strength test and Chair Standing test) and a sample of about 24 mL of blood was collected and then processed according to a common protocol for the preparation and storage of DNA aliquots. Results From the analysis the main findings are the following: - a standardized protocol to assess cognitive status, physical performances and health status of European nonagenarian subjects was set up, in respect to ethical requirements, and it is available as a reference for other studies in this field; - GEHA families are enriched in long-living members and extreme survival, and represent an appropriate model for the identification of genes involved in healthy aging and longevity; - two simplified sets of criteria to classify 90+ sibling according to their health status were proposed, as operational tools for distinguishing healthy from non healthy subjects; - cognitive and functional parameters have a major role in categorizing 90+ siblings for the health status; - parameters such as education and good physical abilities (500 metres walking ability, going up and down the stairs ability, high scores at hand grip and chair stand tests) are associated with a good health status (defined as “cognitive unimpairment and absence of disability”); - male nonagenarians show a more homogeneous phenotype than females, and, though far fewer in number, tend to be healthier than females; - in males the good health status is not protective for survival, confirming the male-female health survival paradox; - survival after age 90 was dependent mainly on intact cognitive status and absence of functional disabilities; - haemoglobin and creatinine levels are both associated with longevity; - the most concordant items among 90+ siblings are related to the functional status, indicating that they contain a familiar component. It is still to be investigated at what level this familiar component is determined by genetics or by environment or by the interaction between genetics, environment and chance (and at what level). Conclusions In conclusion, we could state that this study, in accordance with the main objectives of the whole GEHA project, represents one of the first attempt to identify the biological and non biological determinants of successful/unsuccessful aging and longevity. Here, the analysis was performed on 90+ siblings recruited in Northern and Central Italy and it could be used as a reference for others studies in this field on Italian population. Moreover, it contributed to the definition of “successful” and “unsuccessful” aging and categorising a very large cohort of our most elderly subjects into “successful” and “unsuccessful” groups provided an unrivalled opportunity to detect some of the basic genetic/molecular mechanisms which underpin good health as opposed to chronic disability. Discoveries in the topic of the biological determinants of healthy aging represent a real possibility to identify new markers to be utilized for the identification of subgroups of old European citizens having a higher risk to develop age-related diseases and disabilities and to direct major preventive medicine strategies for the new epidemic of chronic disease in the 21st century.

Modelli non lineari statici e dinamici su dati microeconomici: analisi delle condizioni finanziarie delle famiglie italiane

The thesis studies the economic and financial conditions of Italian households, by using microeconomic data of the Survey on Household Income and Wealth (SHIW) over the period 1998-2006. It develops along two lines of enquiry. First it studies the determinants of households holdings of assets and liabilities and estimates their correlation degree. After a review of the literature, it estimates two non-linear multivariate models on the interactions between assets and liabilities with repeated cross-sections. Second, it analyses households financial difficulties. It defines a quantitative measure of financial distress and tests, by means of non-linear dynamic probit models, whether the probability of experiencing financial difficulties is persistent over time. Chapter 1 provides a critical review of the theoretical and empirical literature on the estimation of assets and liabilities holdings, on their interactions and on households net wealth. The review stresses the fact that a large part of the literature explain households debt holdings as a function, among others, of net wealth, an assumption that runs into possible endogeneity problems. Chapter 2 defines two non-linear multivariate models to study the interactions between assets and liabilities held by Italian households. Estimation refers to a pooling of cross-sections of SHIW. The first model is a bivariate tobit that estimates factors affecting assets and liabilities and their degree of correlation with results coherent with theoretical expectations. To tackle the presence of non normality and heteroskedasticity in the error term, generating non consistent tobit estimators, semi-parametric estimates are provided that confirm the results of the tobit model. The second model is a quadrivariate probit on three different assets (safe, risky and real) and total liabilities; the results show the expected patterns of interdependence suggested by theoretical considerations. Chapter 3 reviews the methodologies for estimating non-linear dynamic panel data models, drawing attention to the problems to be dealt with to obtain consistent estimators. Specific attention is given to the initial condition problem raised by the inclusion of the lagged dependent variable in the set of explanatory variables. The advantage of using dynamic panel data models lies in the fact that they allow to simultaneously account for true state dependence, via the lagged variable, and unobserved heterogeneity via individual effects specification. Chapter 4 applies the models reviewed in Chapter 3 to analyse financial difficulties of Italian households, by using information on net wealth as provided in the panel component of the SHIW. The aim is to test whether households persistently experience financial difficulties over time. A thorough discussion is provided of the alternative approaches proposed by the literature (subjective/qualitative indicators versus quantitative indexes) to identify households in financial distress. Households in financial difficulties are identified as those holding amounts of net wealth lower than the value corresponding to the first quartile of net wealth distribution. Estimation is conducted via four different methods: the pooled probit model, the random effects probit model with exogenous initial conditions, the Heckman model and the recently developed Wooldridge model. Results obtained from all estimators accept the null hypothesis of true state dependence and show that, according with the literature, less sophisticated models, namely the pooled and exogenous models, over-estimate such persistence.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.