Social capital formation across space: proximity and trust in European regions

An extensive economics and regional science literature has discussed the importance of social capital for economic growth and development. Yet, what social capital is and how it is formed are elusive issues, which require further investigation. Here, we refer to social capital in terms of civic capital and good culture , as rephrased by Guiso, Sapienza and Zingales (2010) and Tabellini (2010). The accumulation of this kind of capital allows the emerging of regional informal institutions, which may help explaining diff erences in regional development. In this paper, we take a regional perspective and use exploratory space and space-time methods to assess whether geography, via proximity, contributes to the formation of social capital across European regions. In particular, we ask whether generalized trust, a fundamental constituent of social capital and an ingredient of economic development, tends to be clustered across space and over time. From the policy standpoint, the spatial hysteresis of regional trust may contribute to the formation of spatial traps of social capital and act as a further barrier to regional economic development and convergence.

Moral Opinion Polarization and the Erosion of Trust

Since Puntam's seminal work on declining levels of social capital, the question of how social trust is formed has reached unprecedented heights of critical enquiry. While most of the current research concentrates on ethnic diversity and income inequality as the main influences driving down generalized trust, we focus on opinion polarization as another potential impact factor on trust. In more detail, we investigate the extent to which polarization over morally charged issues such as homsexuality, abortion and euthanasia affects individuals' likelihood to trust others. We hypothesize that moral issues have a natural tendency to divide societies' opinions into opposing poles and, thus, to challenge social cohesion in modern civil societies. Based on hierarchical analyses of the fifth wave of the World Values Survey (WVS) — comprising a sample of 39 countries — our results reveal that individuals living in countries characterized by more opinion polarization tend to have less trust in other people.

Beyond the income inequality hypothesis and human health: a worldwide exploration

OBJECTIVE: To analyze whether the relationship between income inequality and human health is mediated through social capital, and whether political regime determines differences in income inequality and social capital among countries. METHODS: Path analysis of cross sectional ecological data from 110 countries. Life expectancy at birth was the outcome variable, and income inequality (measured by the Gini coefficient), social capital (measured by the Corruption Perceptions Index or generalized trust), and political regime (measured by the Index of Freedom) were the predictor variables. Corruption Perceptions Index (an indirect indicator of social capital) was used to include more developing countries in the analysis. The correlation between Gini coefficient and predictor variables was calculated using Spearman's coefficients. The path analysis was designed to assess the effect of income inequality, social capital proxies and political regime on life expectancy. RESULTS: The path coefficients suggest that income inequality has a greater direct effect on life expectancy at birth than through social capital. Political regime acts on life expectancy at birth through income inequality. CONCLUSIONS: Income inequality and social capital have direct effects on life expectancy at birth. The "class/welfare regime model" can be useful for understanding social and health inequalities between countries, whereas the "income inequality hypothesis" which is only a partial approach is especially useful for analyzing differences within countries.

Seeds of distrust: conflict in Uganda

We study the effect of civil conflict on social capital, focusing on Uganda's experience during the last decade. Using individual and county-level data, we document large causal effects on trust and ethnic identity of an exogenous outburst of ethnic conflicts in 2002-2005. We exploit two waves of survey data from Afrobarometer (Round 4 Afrobarometer Survey in Uganda, 2000, 2008), including information on socioeconomic characteristics at the individual level, and geo-referenced measures of fighting events from ACLED. Our identification strategy exploits variations in the both the spatial and ethnic intensity of fighting. We find that more intense fighting decreases generalized trust and increases ethnic identity. The effects are quantitatively large and robust to a number of control variables, alternative measures of violence, and different statistical techniques involving ethnic and spatial fixed effects and instrumental variables. Controlling for the intensity of violence during the conflict, we also document that post-conflict economic recovery is slower in ethnically fractionalized counties. Our findings are consistent with the existence of a self-reinforcing process between conflicts and ethnic cleavages.

Luottamuksen lähteet. Vertaileva tutkimus yleistynyttä luottamusta synnyttävistä mekanismeista

Tutkimuksen aiheena on yleistynyt luottamus. Väitöskirjassa tutkitaan mistä tuntemattomien kansalaisten toisiinsa kohdistama luottamus kumpuaa ja haetaan vastauksia tähän kysymykseen sekä maakohtaisen että vertailevan tutkimuksen avulla. Tutkimus koostuu yhteenvedon lisäksi viidestä tutkimusartikkelista, joissa luottamuksen syntyä tarkastellaan sekä yksilöiden mikrotason vuorovaikutuksen että maiden välisten eroavaisuuksien näkökulmasta. Yleistyneen luottamuksen synnystä on esitetty useita eri teorioita. Tässä tutkimuksessa tarkastellaan näistä kahta keskeisintä. Osa tutkijoista korostaa kansalaisyhteiskunnan ja ruohonjuuritason verkostojen roolia yleistyneen luottamuksen synnyn taustalla. Tämän hypoteesin mukaan kansalaiset, jotka viettävät aikaansa yhdistyksissä tai muissa sosiaalisissa verkostoissa, oppivat muita helpommin luottamaan paitsi täysin tuntemattomiin ihmisiin myös yhteiskunnallisiin instituutioihin (kansalaisyhteiskuntakeskeinen hypoteesi). Toiset taas painottavat yhteiskunnan julkisten instituutioiden merkitystä. Tämä hypoteesi korostaa instituutioiden reiluutta ja oikeudenmukaisuutta (instituutiokeskeinen hypoteesi). Ihmiset pystyvät luottamaan toisiinsa ja ratkaisemaan kollektiivisia ongelmiaan yhdessä silloin kun esimerkiksi poliittiset ja lainsäädännölliset instituutiot pystyvät luomaan tähän tarvittavan toimintaympäristön. Aineistoina käytetään kansallisia (Hyvinvointi- ja palvelut) sekä kansainvälisiä vertailevia kyselytutkimuksia (European Social Survey ja ISSP). Yksilö- ja makrotason analyyseja yhdistämällä selvitetään yleistynyttä luottamusta selittäviä tekijöitä sekä mekanismeja joiden kautta yleistynyt luottamus muodostuu. Väitöskirjan tulokset tukevat suurimmaksi osaksi instituutiokeskeiseen suuntaukseen sisältyviä hypoteeseja yleistyneen luottamuksen kasautumisesta. Kuitenkin myös esimerkiksi yhdistystoiminnalla havaittiin olevan joitakin yhdistysjäsenien ulkopuolelle ulottuvia myönteisiä vaikutuksia kansalaisten luottamukseen, mikä taas tukee kansalaisyhteiskuntakeskeistä hypoteesia. Tutkimuksen keskeinen tulos on, että kaiken kaikkiaan luottamus näyttäisi kukoistavan maissa, joissa kansalaiset kokevat julkiset instituutiot oikeudenmukaisina sekä reiluina, kansalaisyhteiskunnan roolin luottamuksen synnyttämisessä ollessa tälle alisteinen. Syyksi tähän on oletettu, että näissä maissa (erityisesti pohjoismaiset hyvinvointivaltiot) harjoitettu universaali hyvinvointipolitiikka ja palvelut ovat keskeisiä korkeaa yleistynyttä luottamusta selittäviä tekijöitä. Toisaalta maavertailuissa tätä yhteyttä on selitetty myös sillä, että näissä yhteiskunnassa ei ole paikannettavissa selkeää kulttuurisesti erottuvaa alaluokkaa. Tämän tutkimuksen tulokset tukevat enemmän universaalin hyvinvointivaltion oikeudenmukaisuuteen liittyviä ominaisuuksia alaluokkaistumishypoteesin sijaan. Toisaalta mikrotasolla tarkasteltuna yleistyneen luottamuksen ja hyvinvointipalvelujen välinen yhteys liittyy enemmän palveluiden riittävyyteen kuin niiden universaalisuuden asteeseen. Niin ikään maavertailuissa esimerkiksi verotuksen oikeudenmukaisena kokeminen näyttäisi olevan palvelujen saatavuutta tai niihin liittyviä oikeudenmukaisuuden kokemuksia tärkeämpi seikka yleistyneen luottamuksen kannalta.

On Recurrent and Deep Neural Networks

L'apprentissage profond est un domaine de recherche en forte croissance en apprentissage automatique qui est parvenu à des résultats impressionnants dans différentes tâches allant de la classification d'images à la parole, en passant par la modélisation du langage. Les réseaux de neurones récurrents, une sous-classe d'architecture profonde, s'avèrent particulièrement prometteurs. Les réseaux récurrents peuvent capter la structure temporelle dans les données. Ils ont potentiellement la capacité d'apprendre des corrélations entre des événements éloignés dans le temps et d'emmagasiner indéfiniment des informations dans leur mémoire interne. Dans ce travail, nous tentons d'abord de comprendre pourquoi la profondeur est utile. Similairement à d'autres travaux de la littérature, nos résultats démontrent que les modèles profonds peuvent être plus efficaces pour représenter certaines familles de fonctions comparativement aux modèles peu profonds. Contrairement à ces travaux, nous effectuons notre analyse théorique sur des réseaux profonds acycliques munis de fonctions d'activation linéaires par parties, puisque ce type de modèle est actuellement l'état de l'art dans différentes tâches de classification. La deuxième partie de cette thèse porte sur le processus d'apprentissage. Nous analysons quelques techniques d'optimisation proposées récemment, telles l'optimisation Hessian free, la descente de gradient naturel et la descente des sous-espaces de Krylov. Nous proposons le cadre théorique des méthodes à région de confiance généralisées et nous montrons que plusieurs de ces algorithmes développés récemment peuvent être vus dans cette perspective. Nous argumentons que certains membres de cette famille d'approches peuvent être mieux adaptés que d'autres à l'optimisation non convexe. La dernière partie de ce document se concentre sur les réseaux de neurones récurrents. Nous étudions d'abord le concept de mémoire et tentons de répondre aux questions suivantes: Les réseaux récurrents peuvent-ils démontrer une mémoire sans limite? Ce comportement peut-il être appris? Nous montrons que cela est possible si des indices sont fournis durant l'apprentissage. Ensuite, nous explorons deux problèmes spécifiques à l'entraînement des réseaux récurrents, à savoir la dissipation et l'explosion du gradient. Notre analyse se termine par une solution au problème d'explosion du gradient qui implique de borner la norme du gradient. Nous proposons également un terme de régularisation conçu spécifiquement pour réduire le problème de dissipation du gradient. Sur un ensemble de données synthétique, nous montrons empiriquement que ces mécanismes peuvent permettre aux réseaux récurrents d'apprendre de façon autonome à mémoriser des informations pour une période de temps indéfinie. Finalement, nous explorons la notion de profondeur dans les réseaux de neurones récurrents. Comparativement aux réseaux acycliques, la définition de profondeur dans les réseaux récurrents est souvent ambiguë. Nous proposons différentes façons d'ajouter de la profondeur dans les réseaux récurrents et nous évaluons empiriquement ces propositions.

Essays on risk-taking in corporate finance

This thesis consists of three independent essays on risk-taking in corporate finance. The first essay explores how community-level social capital (CSC), framed as a cultural characteristic of individuals born in different provinces of Italy, affects investment behavior in equity crowdfunding. Results show that investors born in high-CSC provinces invest more money in ventures characterized by an enhanced risk profile. Observed risk-taking is theoretically linked to higher generalized trust endowed to people born in high-CSC areas. The second essay focuses on how convexity of Chief Financial Officers’ stock options affects their hedging decisions in the oil and gas industry. Highly convex CFOs hedge less commodity price risk, even if the Chief Executive Officer’s incentives are consistent with a more conservative hedging strategy. Finally, the third essay is a systematic literature review on how different sources of compensation-based risk-taking incentives of Chief Executive Officers affect decision-making in corporate finance.

Testing for Measurement Equivalence in Surveys: Dimensions of Social Trust across Cultural Contexts

Our study evaluates the dimensionality and equivalence of social trust across cultural contexts, using new data from Switzerland and the World Values Survey 2005–2008. Whereas some scholars assert that trust should be regarded as a coherent concept, others claim that trust is better conceived of as a multidimensional concept. In contrast to the conventional dichotomy of the forms of social trust, we identify three distinct forms of trust, namely, particularized, generalized, and identity-based trust. Moreover, we dispute the view that respondents understand the wording of survey questions regarding social trust differently between different cultural contexts, which would imply that comparative research on trust is a pointless endeavor. Applying multiple-group confirmatory factor analysis to the various constructs of social trust, we conclude that one may study relationships among the three forms of trust and other theoretical constructs as well as compare latent means across cultural contexts. Our analyses therefore provide an optimistic outlook for future comparative analyses that investigate forms of social trust across cultural contexts.

Three essays on the concept of trust and its foundations

The present dissertation focuses on trust and comprises three empirical essays on the concept itself and its foundations. All three essays investigate trust as an expectation and rely on selfreport measures of trust. Whereas the first two chapters investigate social trust, the third chapter investigates political trust. Essentially, there are three related important debates to which the following chapters contribute. A first debate discusses problems with current selfreport measures. Scholars recently started to question whether standard trust questions really measure the same across countries and languages. Chapter 1 engages in this debate. Using data from Switzerland it studies whether different trust questions measure the same latent trust constructs across individuals belonging to three different culturallinguistic regions. The second debate concerns the socalled forms or dimensions of trust. Recently, scholars started investigating whether trust is a onedimensional construct, i.e. whether an individual's trust judgment differs for categories of trustees such as strangers, neighbors, family members and friends or not. Relying on confirmatory factor analysis Chapter 2 investigates whether individuals really do make a difference between different trustee categories and to what extent these judgments can be summarized into higherorder latent trust constructs. The third debate is concerned with causes of differences in trust across humans. Chapter 3 focuses on the role of laterlife experiences, more precisely victimization experiences and investigates their causal relationship with generalized social trust. Chapter 4 focuses on the impact of direct democratic institutions on the trust relationship between citizens and political authorities.

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

Dependence of the crossover exponent with the diffusion rate in the generalized contact process model

We study how the crossover exponent, phi, between the directed percolation (DP) and compact directed percolation (CDP) behaves as a function of the diffusion rate in a model that generalizes the contact process. Our conclusions are based in results pointed by perturbative series expansions and numerical simulations, and are consistent with a value phi = 2 for finite diffusion rates and phi = 1 in the limit of infinite diffusion rate.

Data collapse, scaling functions, and analytical solutions of generalized growth models

We consider a nontrivial one-species population dynamics model with finite and infinite carrying capacities. Time-dependent intrinsic and extrinsic growth rates are considered in these models. Through the model per capita growth rate we obtain a heuristic general procedure to generate scaling functions to collapse data into a simple linear behavior even if an extrinsic growth rate is included. With this data collapse, all the models studied become independent from the parameters and initial condition. Analytical solutions are found when time-dependent coefficients are considered. These solutions allow us to perceive nontrivial transitions between species extinction and survival and to calculate the transition's critical exponents. Considering an extrinsic growth rate as a cancer treatment, we show that the relevant quantity depends not only on the intensity of the treatment, but also on when the cancerous cell growth is maximum.

Generalized connectivity between any two nodes in a complex network

This article focuses on the identification of the number of paths with different lengths between pairs of nodes in complex networks and how these paths can be used for characterization of topological properties of theoretical and real-world complex networks. This analysis revealed that the number of paths can provide a better discrimination of network models than traditional network measurements. In addition, the analysis of real-world networks suggests that the long-range connectivity tends to be limited in these networks and may be strongly related to network growth and organization.

Generalized Sznajd model for opinion propagation

In the last decade the Sznajd model has been successfully employed in modeling some properties and scale features of both proportional and majority elections. We propose a version of the Sznajd model with a generalized bounded confidence rule-a rule that limits the convincing capability of agents and that is essential to allow coexistence of opinions in the stationary state. With an appropriate choice of parameters it can be reduced to previous models. We solved this model both in a mean-field approach (for an arbitrary number of opinions) and numerically in a Barabaacutesi-Albert network (for three and four opinions), studying the transient and the possible stationary states. We built the phase portrait for the special cases of three and four opinions, defining the attractors and their basins of attraction. Through this analysis, we were able to understand and explain discrepancies between mean-field and simulation results obtained in previous works for the usual Sznajd model with bounded confidence and three opinions. Both the dynamical system approach and our generalized bounded confidence rule are quite general and we think it can be useful to the understanding of other similar models.