Three Essays on Income and Wealth Inequality

This dissertation consists of three essays on income and wealth inequality. The essays examine various aspects of this complex feature of the economic system. The first essay shows that the distribution of firm sizes in an economy is an important determinant of wage distribution. I use data from the U.S. Current Population Survey and the ExecuComp between 1992 and 2012 to construct a new dataset and estimate wage distribution and various measures of wage inequality. I decompose differences in wage inequality across firm sizes and over time by using semi-parametric methods. In 1992, wages were distributed more unequally in small than in large firms. A decomposition shows that this was solely due to inequality among workers with the same observed characteristics, i.e. residual inequality. Inequality due to the distribution of observed characteristics and returns to those characteristics was higher in large firms at that time. By 2012, inequality in small firms grew further, but not as fast as in large firms. Over the same period, employment share of large firms increased but this had little effect on changes in overall wage distribution. The second essay proposes a general equilibrium model suitable for studying wealth distribution. One of the challenges in modeling wealth distribution is reproducing the high concentration of wealth observed in the U.S. I build upon the benchmark model developed by Krusell and Smith (1998) by introducing firm heterogeneity and managerial class. I develop a model, propose a numerical method for solving it, simulate it, and compare the results with the data and the benchmark model. The simulated distribution fits the data well in the upper tail of the distribution. The third essay explores the role that changes in income inequality may play in households balance sheets. This is particularly important considering the high levels of household debt that was a key contributing factor in the financial crisis of 2008. I use a family of error-correction models to estimate long-run relationship between income inequality and household debt. Tests based on Westerlund (2007), Pedroni (1999, 2004) and Johansen (1988) are used on a panel of 14 developed countries. No evidence of cointegration between the time series for income inequality and household debt is found

Development of new computational amino acid parameters for protein structure/function analysis within the resonant recognition model

The Resonant Recognition Model (RRM) is a physico-mathematical model developed for analysis of protein and DNA sequences. Biological function of proteins and their 3D structures are determined by the linear sequences of amino acids. Previously, the electron-ion interaction potentials (EIIP) of amino acids have been used to determine the characteristic patterns of different proteins independent of their biological activity. In this study, the effect of various other amino acid parameters on periodicity, obtained using the RRR, were assessed. Here, we are proposing new computational amino acid parameters that could be used successfully for protein analysis instead of EIIP within the RRM

Impact of quality and innovation strategies on business performance of Bosnian B2B and B2C companies

The aim of this paper was to compare two business models, namely business to business and business to customer. The first model is oriented towards the delivery of intermediate goods ordered by an immense foreign market leader that is outsourcing a part of its business. The second model is consumers oriented and is more about innovation and the creation of goods ready to be immediately sold on the market. Bosnia is a developing country, and often a place for establishing B2B businesses. However, we are lacking in innovation, our own know-how, and the creation of challenging business opportunities. It also faces the problem of brain drain; therefore, it is in a need of a plan for retaining the youth within the country. The analysis aimed to show how the boosting jobs and living standards in Bosnia, are affected by the investments, exports, and salaries within the B2B and B2C companies. The results showed that Bosnia has a significant potential for raising living standards, employment and salary, if investments are directed towards innovations, knowledge-intensive and B2C businesses instead of labor-intensive investments

Sleep onset estimator: evaluation of parameters

The electroencephalographic (EEG) alterations during the human sleep onset (falling asleep period) has been evaluated by several studies in the past. However, the analysis part has been limited due to standard signal processing methods. This paper has attempted to evaluate a number of advanced parameters for improved sleep onset estimation, such as EEG non-parametric coherence, power frequency and spectral band power. These parameters can be utilised in an on-line algorithm design for neurofeedback applications

Bioactive peptide design using the Resonant Recognition Model

With a large number of DNA and protein sequences already known, the crucial question is to find out how the biological function of these macromolecules is "written" in the sequence of nucleotides or amino acids. Biological processes in any living organism are based on selective interactions between particular bio-molecules, mostly proteins. The rules governing the coding of a protein's biological function, i.e. its ability to selectively interact with other molecules, are still not elucidated. In addition, with the rapid accumulation of databases of protein primary structures, there is an urgent need for theoretical approaches that are capable of analysing protein structure-function relationships. The Resonant Recognition Model (RRM) [1,2] is one attempt to identify the selectivity of protein interactions within the amino acid sequence. The RRM [1,2] is a physico-mathematical approach that interprets protein sequence linear information using digital signal processing methods. In the RRM the protein primary structure is represented as a numerical series by assigning to each amino acid in the sequence a physical parameter value relevant to the protein's biological activity. The RRM concept is based on the finding that there is a significant correlation between spectra of the numerical presentation of amino acids and their biological activity. Once the characteristic frequency for a particular protein function/interaction is identified, it is possible then to utilize the RRM approach to predict the amino acids in the protein sequence, which predominantly contribute to this frequency and thus, to the observed function, as well as to design de novo peptides having the desired periodicities. As was shown in our previous studies of fibroblast growth factor (FGF) peptidic antagonists [2,3] and human immunodeficiency virus (HIV) envelope agonists [2,4], such de novo designed peptides express desired biological function. This study utilises the RRM computational approach to the analysis of oncogene and proto-oncogene proteins. The results obtained have shown that the RRM is capable of identifying the differences between the oncogenic and proto-oncogenic proteins with the possibility of identifying the "cancer-causing" features within their protein primary structure. In addition, the rational design of bioactive peptide analogues displaying oncogenic or proto-oncogenic-like activity is presented here

Non-thermal effects of 500 MHz-900MHz microwave radiation on enzyme kinetics

Enzymes are essential for the catalysis of biochemical reactions and in the regulation of metabolic pathways. They function by greatly accelerating the rate of specific chemical reactions that would otherwise be slow. It has been shown that extremely low-power microwaves can influence enzyme activity [1¿5]. This study is focused at investigating the effects of low level microwave exposures ranging from 500MHz to 900MHz on L-Lactate Dehydrogenase (LDH) enzyme activity. The results obtained revealed the increased bioactivity of the LDH upon microwave radiation at two particular frequencies 500MHz and 900MHz

Investigation of the applicability of dielectric relaxation properties of amino acid solutions within the resonant recognition model

The resonant recognition model (RRM) is a physicomathematical approach used to analyze the interactions of a protein and its target, using digital signal processing methods. The RRM is based on the finding that there is a significant correlation between the spectra of numerical presentation of protein sequences and their biological activities. Initially, the electron-ion interaction potential was used to represent each amino acid in the protein sequences. In this paper, the dielectric constant (ε') and dielectric loss tangent (tan δ) parameters have been determined for their possible use in the RRM. These parameters are based on the values of capacitance and conductance obtained experimentally for 20 amino acid solutions using dielectric spectroscopy for the case of the real component of dielectric permittivity; the parameter used is the dielectric increment (Δε'), the difference between dielectric constant of the amino acid solution and that of the solvent alone. The results of multiple cross-spectral analyses have shown that parameters analyzed generate in the consensus spectrum one dominant peak corresponding to the common biological activity of proteins studied, allowing the conclusion that these new parameters are suitable for use in the RRM approach