On the Political Economy of Sequential Reforms

This paper proposes an explanation for why efficient reforms are not carried out when losers have the power to block their implementation, even though compensating them is feasible. We construct a signaling model with two-sided incomplete information in which a government faces the task of sequentially implementing two reforms by bargaining with interest groups. The organization of interest groups is endogenous. Compensations are distortionary and government types differ in the concern about distortions. We show that, when compensations are allowed to be informative about the government’s type, there is a bias against the payment of compensations and the implementation of reforms. This is because paying high compensations today provides incentives for some interest groups to organize and oppose subsequent reforms with the only purpose of receiving a transfer. By paying lower compensations, governments attempt to prevent such interest groups from organizing. However, this comes at the cost of reforms being blocked by interest groups with relatively high losses.

Political Influence, Economic Interests and Endogenous Tax Structure in a Computable Equilibrium Framework: with Applicatioon to the United States, 1973 and 1983

A full understanding of public affairs requires the ability to distinguish between the policies that voters would like the government to adopt, and the influence that different voters or group of voters actually exert in the democratic process. We consider the properties of a computable equilibrium model of a competitive political economy in which the economic interests of groups of voters and their effective influence on equilibrium policy outcomes can be explicitly distinguished and computed. The model incorporates an amended version of the GEMTAP tax model, and is calibrated to data for the United States for 1973 and 1983. Emphasis is placed on how the aggregation of GEMTAP households into groups within which economic and political behaviour is assumed homogeneous affects the numerical representation of interests and influence for representative members of each group. Experiments with the model suggest that the changes in both interests and influence are important parts of the story behind the evolution of U.S. tax policy in the decade after 1973.

Fast-Track Authority and International Trade Negotiations

Fast Track Authority (FTA) is the institutional procedure in the Unites States whereby Congress grants to the President the power to negotiate international trade agreements. Under FTA, Congress can only approve or reject negotiated trade deals, with no possibility of amending them. In this paper, we examine the determinants of FTA voting decisions and the implications of this institutional procedure for trade negotiations. We describe a simple two-country trade model, in which industries are unevenly distributed across con- stituencies. In the foreign country, trade negotiating authority is delegated to the executive, while in the home country Congress can retain the power to amend trade agreements. We show that legislators’ FTA voting behavior depends on the trade policy interests of their own constituencies as well as those of the majority of Congress. Empirical analysis of the determinants of all FTA votes between 1974 (when fast track was first introduced) and 2002 (when it was last granted) provides strong support for the predictions of our model.

Democratic Peace and Electoral Accountability

One of the few stylized facts in international relations is that democracies, unlike autoc- racies, very rarely fight each other. We examine the sustainability of international peace between democracies and autocracies, where the crucial difference between these two po- litical regimes is whether or not policymakers are subject to periodic elections. We show that the fear of losing office can deter democratic leaders from engaging in military con- flicts. Crucially, this discipline effect can only be at work if incumbent leaders can be re-elected, implying that democracies in which the executives are subject to term limits should be more conflict prone. To assess the validity of our predictions, we construct a large dataset on countries with executive term limits. Our analysis of inter-state conflicts for the 1816-2001 period suggests that electoral incentives are indeed behind the democratic peace phenomenon: while democratic dyads are in general less likely to be involved in conflicts than any other dyads, this result does not hold for democracies in which the executive faces binding term limits; moreover, the dispute patterns of democracies with term limits depend on whether the executive is in the last or penultimate mandate.

One Person, Many Votes: Divided Majority and Information Aggregation

In elections, majority divisions pave the way to focal manipulations and coordination failures, which can lead to the victory of the wrong candidate. This paper shows how this flaw can be addressed if voter preferences over candidates are sensitive to information. We consider two potential sources of divisions: majority voters may have similar preferences but opposite information about the candidates, or opposite preferences. We show that when information is the source of majority divisions, Approval Voting features a unique equilibrium with full information and coordination equivalence. That is, it produces the same outcome as if both information and coordination problems could be resolved. Other electoral systems, such as Plurality and Two-Round elections, do not satisfy this equivalence. The second source of division is opposite preferences. Whenever the fraction of voters with such preferences is not too large, Approval Voting still satisfies full information and coordination equivalence.

Mathematical models of cognitive processes

The research activity carried out during the PhD course was focused on the development of mathematical models of some cognitive processes and their validation by means of data present in literature, with a double aim: i) to achieve a better interpretation and explanation of the great amount of data obtained on these processes from different methodologies (electrophysiological recordings on animals, neuropsychological, psychophysical and neuroimaging studies in humans), ii) to exploit model predictions and results to guide future research and experiments. In particular, the research activity has been focused on two different projects: 1) the first one concerns the development of neural oscillators networks, in order to investigate the mechanisms of synchronization of the neural oscillatory activity during cognitive processes, such as object recognition, memory, language, attention; 2) the second one concerns the mathematical modelling of multisensory integration processes (e.g. visual-acoustic), which occur in several cortical and subcortical regions (in particular in a subcortical structure named Superior Colliculus (SC)), and which are fundamental for orienting motor and attentive responses to external world stimuli. This activity has been realized in collaboration with the Center for Studies and Researches in Cognitive Neuroscience of the University of Bologna (in Cesena) and the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA). PART 1. Objects representation in a number of cognitive functions, like perception and recognition, foresees distribute processes in different cortical areas. One of the main neurophysiological question concerns how the correlation between these disparate areas is realized, in order to succeed in grouping together the characteristics of the same object (binding problem) and in maintaining segregated the properties belonging to different objects simultaneously present (segmentation problem). Different theories have been proposed to address these questions (Barlow, 1972). One of the most influential theory is the so called “assembly coding”, postulated by Singer (2003), according to which 1) an object is well described by a few fundamental properties, processing in different and distributed cortical areas; 2) the recognition of the object would be realized by means of the simultaneously activation of the cortical areas representing its different features; 3) groups of properties belonging to different objects would be kept separated in the time domain. In Chapter 1.1 and in Chapter 1.2 we present two neural network models for object recognition, based on the “assembly coding” hypothesis. These models are networks of Wilson-Cowan oscillators which exploit: i) two high-level “Gestalt Rules” (the similarity and previous knowledge rules), to realize the functional link between elements of different cortical areas representing properties of the same object (binding problem); 2) the synchronization of the neural oscillatory activity in the γ-band (30-100Hz), to segregate in time the representations of different objects simultaneously present (segmentation problem). These models are able to recognize and reconstruct multiple simultaneous external objects, even in difficult case (some wrong or lacking features, shared features, superimposed noise). In Chapter 1.3 the previous models are extended to realize a semantic memory, in which sensory-motor representations of objects are linked with words. To this aim, the network, previously developed, devoted to the representation of objects as a collection of sensory-motor features, is reciprocally linked with a second network devoted to the representation of words (lexical network) Synapses linking the two networks are trained via a time-dependent Hebbian rule, during a training period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from linguistic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with some shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits). PART 2. The ability of the brain to integrate information from different sensory channels is fundamental to perception of the external world (Stein et al, 1993). It is well documented that a number of extraprimary areas have neurons capable of such a task; one of the best known of these is the superior colliculus (SC). This midbrain structure receives auditory, visual and somatosensory inputs from different subcortical and cortical areas, and is involved in the control of orientation to external events (Wallace et al, 1993). SC neurons respond to each of these sensory inputs separately, but is also capable of integrating them (Stein et al, 1993) so that the response to the combined multisensory stimuli is greater than that to the individual component stimuli (enhancement). This enhancement is proportionately greater if the modality-specific paired stimuli are weaker (the principle of inverse effectiveness). Several studies have shown that the capability of SC neurons to engage in multisensory integration requires inputs from cortex; primarily the anterior ectosylvian sulcus (AES), but also the rostral lateral suprasylvian sulcus (rLS). If these cortical inputs are deactivated the response of SC neurons to cross-modal stimulation is no different from that evoked by the most effective of its individual component stimuli (Jiang et al 2001). This phenomenon can be better understood through mathematical models. The use of mathematical models and neural networks can place the mass of data that has been accumulated about this phenomenon and its underlying circuitry into a coherent theoretical structure. In Chapter 2.1 a simple neural network model of this structure is presented; this model is able to reproduce a large number of SC behaviours like multisensory enhancement, multisensory and unisensory depression, inverse effectiveness. In Chapter 2.2 this model was improved by incorporating more neurophysiological knowledge about the neural circuitry underlying SC multisensory integration, in order to suggest possible physiological mechanisms through which it is effected. This endeavour was realized in collaboration with Professor B.E. Stein and Doctor B. Rowland during the 6 months-period spent at the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA), within the Marco Polo Project. The model includes four distinct unisensory areas that are devoted to a topological representation of external stimuli. Two of them represent subregions of the AES (i.e., FAES, an auditory area, and AEV, a visual area) and send descending inputs to the ipsilateral SC; the other two represent subcortical areas (one auditory and one visual) projecting ascending inputs to the same SC. Different competitive mechanisms, realized by means of population of interneurons, are used in the model to reproduce the different behaviour of SC neurons in conditions of cortical activation and deactivation. The model, with a single set of parameters, is able to mimic the behaviour of SC multisensory neurons in response to very different stimulus conditions (multisensory enhancement, inverse effectiveness, within- and cross-modal suppression of spatially disparate stimuli), with cortex functional and cortex deactivated, and with a particular type of membrane receptors (NMDA receptors) active or inhibited. All these results agree with the data reported in Jiang et al. (2001) and in Binns and Salt (1996). The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain the fundamental aspects of multisensory integration, and provides a biologically plausible hypothesis about the underlying circuitry.

Problems of political transition in Ukraine : leadership failure and democratic consolidation

Democratisation and consolidation of a political system encompass a range of complex challenges, for which effective leadership is pivotal. However, the skills that a leader requires to break through and introduce change are not necessarily the same as those needed to maintain stability. This article examines the case of Viktor Yushchenko as president of Ukraine following the Orange Revolution. The negotiated transfer of power from the previous semi-authoritarian regime rendered consolidation difficult by limiting opportunities for a complete break. Within the residual 'grey area', a number of actors continued to participate and create tension. The regime that emerged was characterised by political infighting and instability, leading to the defeat of candidates associated with the Orange Revolution in the 2010 presidential elections. This article argues that the inability to move towards a consolidated democratic political system was due to the failure of the transitional leader, rather than the political and institutional configuration.

Landscapes of Education and the New Role of Social Work International Models of How to Combine Formal, Non-formal and Informal Education

Landscapes of education are a new topic within the debate about adequate and just education and human development for everybody. In particular, children and youths from social classes affected by poverty, a lack of prospects or minimal schooling are a focal group that should be offered new approaches and opportunities of cognitive and social development by way of these landscapes of education. It has become apparent that the traditional school alone does not suffice to meet this need. There is no doubt that competency-based orientation and employability are core areas with the help of which the generation now growing up will manage the start of its professional career. In addition and by no means less important, the development involves individual, social, cultural and societal perspectives that can be combined under the term of human development. In this context, the Capability Approach elaborated by Amartya Sen and Martha Nussbaum has developed a more extensive concept of human development and related it to empirical instruments. Using the analytic concept of individual capabilities and societal opportunities they shaped a socio-political formula that should be adapted in particular to modern social work. Moreover, the Capability Approach offers a critical foil with regard to further development and revision of institutionalised approaches in education and human development.

The difficulty of measuring culture and diversity. Comparison of three international models of cultural measurement: FCS-2009, ESSnet-2012 and CAB-2015

The growing interest in quantifying the cultural and creative industries, visualize the economic contribution of activities related to culture demands first of all the construction of internationally comparable analysis frameworks. Currently there are three major bodies which address this issue and whose comparative study is the focus of this article: the UNESCO Framework for Cultural Statistics (FCS-2009), the European Framework for Cultural Statistics (ESSnet-Culture 2012) and the methodological resource of the “Convenio Andrés Bello” group for working with the Satellite Accounts on Culture in Ibero-America (CAB-2015). Cultural sector measurements provide the information necessary for correct planning of cultural policies which in turn leads to sustaining industries and promoting cultural diversity. The text identifies the existing differences in the three models and three levels of analysis, the sectors, the cultural activities and the criteria that each one uses in order to determine the distribution of the activities by sector. The end result leaves the impossibility of comparing cultural statistics of countries that implement different frameworks.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

Long-range automaton models of earthquakes: Power-law accelerations, correlation evolution, and mode-switching

We introduce a conceptual model for the in-plane physics of an earthquake fault. The model employs cellular automaton techniques to simulate tectonic loading, earthquake rupture, and strain redistribution. The impact of a hypothetical crustal elastodynamic Green's function is approximated by a long-range strain redistribution law with a r(-p) dependance. We investigate the influence of the effective elastodynamic interaction range upon the dynamical behaviour of the model by conducting experiments with different values of the exponent (p). The results indicate that this model has two distinct, stable modes of behaviour. The first mode produces a characteristic earthquake distribution with moderate to large events preceeded by an interval of time in which the rate of energy release accelerates. A correlation function analysis reveals that accelerating sequences are associated with a systematic, global evolution of strain energy correlations within the system. The second stable mode produces Gutenberg-Richter statistics, with near-linear energy release and no significant global correlation evolution. A model with effectively short-range interactions preferentially displays Gutenberg-Richter behaviour. However, models with long-range interactions appear to switch between the characteristic and GR modes. As the range of elastodynamic interactions is increased, characteristic behaviour begins to dominate GR behaviour. These models demonstrate that evolution of strain energy correlations may occur within systems with a fixed elastodynamic interaction range. Supposing that similar mode-switching dynamical behaviour occurs within earthquake faults then intermediate-term forecasting of large earthquakes may be feasible for some earthquakes but not for others, in alignment with certain empirical seismological observations. Further numerical investigation of dynamical models of this type may lead to advances in earthquake forecasting research and theoretical seismology.