Inflation and income inequality: A link through the job-search process

In this paper I devise a new channel by means of which the (empirically documented) positive correlation between ináation and income inequality can be understood. Available empirical evidence reveals that ináation increases wage dispersion. For this reason, the higher the ináation rate, the higher turns out to be the beneÖt, for a worker, of making additional draws from the distribution of wages, before deciding whether to accept or reject a job o§er. Assuming that some workers have less access to information (wage o§ers) than others, I show that the Gini coe¢ cient of income distribution turns out to be an increasing function of the wage dispersion and, consequently, of the rate of ináation. Two examples are provided to illustrate the mechanism.

Income inequality: the role of impatience in a job-search process

This paper investigates the income inequality generated by a jobsearch process when di§erent cohorts of homogeneous workers are allowed to have di§erent degrees of impatience. Using the fact the average wage under the invariant Markovian distribution is a decreasing function of the time preference (Cysne (2004)), I show that the Lorenz curve and the between-cohort Gini coe¢ cient of income inequality can be easily derived in this case. An example with arbitrary measures regarding the wage o§ers and the distribution of time preferences among cohorts provides some quantitative insights into how much income inequality can be generated, and into how it varies as a function of the probability of unemployment and of the probability that the worker does not Önd a job o§er each period.

Measurements of income inequality: a critique. Inflation and income inequality: a link through the job- search process

In this paper I claim that, in a long-run perspective, measurements of income inequality, under any of the usual inequality measures used in the literature, are upward biased. The reason is that such measurements are cross-sectional by nature and, therefore, do not take into consideration the turnover in the job market which, in the long run, equalizes within-group (e.g., same-education groups) inequalities. Using a job-search model, I show how to derive the within-group invariant-distribution Gini coefficient of income inequality, how to calculate the size of the bias and how to organize the data in arder to solve the problem. Two examples are provided to illustrate the argument.

Standardization efforts: The relationship between knowledge dimensions, search processes and innovation outcomes

We explore how a standardization effort (i.e., when a firm pursues standards to further innovation) involves different search processes for knowledge and innovation outcomes. Using an inductive case study of Vanke, a leading Chinese property developer, we show how varying degrees of knowledge complexity and codification combine to produce a typology of four types of search process: active, integrative, decentralized and passive, resulting in four types of innovation outcome: modular, radical, incremental and architectural. We argue that when the standardization effort in a firm involves highly codified knowledge, incremental and architectural innovation outcomes are fostered, while modular and radical innovations are hindered. We discuss how standardization efforts can result in a second-order innovation capability, and conclude by calling for comparative research in other settings to understand how standardization efforts can be suited to different types of search process in different industry contexts.

Job Search Methods and Outcomes

Using Portuguese data, this paper investigates the effects of job search methods on escape rates from unemployment and of job-finding methods on earnings. The effectiveness of the job search process is also evaluated in terms of the periodicity of the resulting job match. Emphasis is accorded the role of the public employment service. Despite its frequency as a search vehicle, the state employment agency is shown to have a low hit rate, and to lead to lower-paying, shorter-lasting jobs.

Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series

Based on an algorithm for pattern matching in character strings, we implement a pattern matching machine that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series are encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, we develop a genetic algorithm to breed patterns that maximize a user-defined fitness function. In an application to financial data, we show that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.

Metaheuristics for Search Problems in Genomics - New Algorithms and Applications

In this thesis we made the first steps towards the systematic application of a methodology for automatically building formal models of complex biological systems. Such a methodology could be useful also to design artificial systems possessing desirable properties such as robustness and evolvability. The approach we follow in this thesis is to manipulate formal models by means of adaptive search methods called metaheuristics. In the first part of the thesis we develop state-of-the-art hybrid metaheuristic algorithms to tackle two important problems in genomics, namely, the Haplotype Inference by parsimony and the Founder Sequence Reconstruction Problem. We compare our algorithms with other effective techniques in the literature, we show strength and limitations of our approaches to various problem formulations and, finally, we propose further enhancements that could possibly improve the performance of our algorithms and widen their applicability. In the second part, we concentrate on Boolean network (BN) models of gene regulatory networks (GRNs). We detail our automatic design methodology and apply it to four use cases which correspond to different design criteria and address some limitations of GRN modeling by BNs. Finally, we tackle the Density Classification Problem with the aim of showing the learning capabilities of BNs. Experimental evaluation of this methodology shows its efficacy in producing network that meet our design criteria. Our results, coherently to what has been found in other works, also suggest that networks manipulated by a search process exhibit a mixture of characteristics typical of different dynamical regimes.

Search, matching, and self-organization of a marketplace

In many developing countries, clusters of small shops are the typical market-place. We investigate an economic model in which, between buyers and sellers in a marketplace, a circular causality including the search process produces agglomeration forces, given the initial location of the marketplace location exogenously in a linear city. We conclude that initial number of buyers and sellers is important in forming a large marketplace.

The role of networking and social media tools during job search: an information behaviour perspective.

The paper presents a critical analysis of the extant literature pertaining to the networking behaviours of young jobseekers in both offline and online environments. A framework derived from information behaviour theory is proposed as a basis for conducting further research in this area. Method. Relevant material for the review was sourced from key research domains such as library and information science, job search research, and organisational research. Analysis. Three key research themes emerged from the analysis of the literature: (1) social networks, and the use of informal channels of information during job search, (2) the role of networking behaviours in job search, and (3) the adoption of social media tools. Tom Wilson’s general model of information behaviour was also identified as a suitable framework to conduct further research. Results. Social networks have a crucial informational utility during the job search process. However, the processes whereby young jobseekers engage in networking behaviours, both offline and online, remain largely unexplored. Conclusion. Identification and analysis of the key research themes reveal opportunities to acquire further knowledge regarding the networking behaviours of young jobseekers. Wilson’s model can be used as a framework to provide a holistic understanding of the networking process, from an information behaviour perspective.

Using the web to look for work: Implications for online job seeking and recruiting

Purpose - The web is now a significant component of the recruitment and job search process. However, very little is known about how companies and job seekers use the web, and the ultimate effectiveness of this process. The specific research questions guiding this study are: how do people search for job-related information on the web? How effective are these searches? And how likely are job seekers to find an appropriate job posting or application? Design/methodology/approach - The data used to examine these questions come from job seekers submitting job-related queries to a major web search engine at three points in time over a five-year period. Findings - Results indicate that individuals seeking job information generally submit only one query with several terms and over 45 percent of job-seeking queries contain a specific location reference. Of the documents retrieved, findings suggest that only 52 percent are relevant and only 40 percent of job-specific searches retrieve job postings. Research limitations/implications - This study provides an important contribution to web research and online recruiting literature. The data come from actual web searches, providing a realistic glimpse into how job seekers are actually using the web. Practical implications - The results of this research can assist organizations in seeking to use the web as part of their recruiting efforts, in designing corporate recruiting web sites, and in developing web systems to support job seeking and recruiting. Originality/value - This research is one of the first studies to investigate job searching on the web using longitudinal real world data. © Emerald Group Publishing Limited.

A human-simulated immune evolutionary computation approach

Premature convergence to local optimal solutions is one of the main difficulties when using evolutionary algorithms in real-world optimization problems. To prevent premature convergence and degeneration phenomenon, this paper proposes a new optimization computation approach, human-simulated immune evolutionary algorithm (HSIEA). Considering that the premature convergence problem is due to the lack of diversity in the population, the HSIEA employs the clonal selection principle of artificial immune system theory to preserve the diversity of solutions for the search process. Mathematical descriptions and procedures of the HSIEA are given, and four new evolutionary operators are formulated which are clone, variation, recombination, and selection. Two benchmark optimization functions are investigated to demonstrate the effectiveness of the proposed HSIEA.

The experience of being an advanced practice nurse within Australian acute care settings : a systematic review of qualitative evidence

Background There is a vast amount of international literature which, although agreeing on the need for advanced practice nurse roles, simultaneously debates and discusses the difficulties with nomenclature, definition and subsequent implementation of such roles. Due to this ambiguity it is difficult to equally compare evidence in this field across different countries. A context-specific systematic review on the qualitative evidence of the experience of being an advanced practice nurse in Australia has not been undertaken previously, however it is imperative for nursing managers and leaders to understand the complexities of advanced nursing roles in order to effectively utilise and retain these experienced and valuable nurses. With the creation of a national nursing regulating authority in 2010, it is timely to explore in-depth the experience of being an advanced practice nurse from a national perspective. Objective To identify the experience of being an advanced practice nurse working in Australian acute care settings. Inclusion criteria -Types of participants Registered nurses working in advanced practice roles in acute care settings throughout Australia. -Phenomena of interest The experience of being an advanced practice registered nurse working in an Australian acute care setting, as reported by the nurses themselves. -Types of studies Interpretive qualitative studies including designs such as phenomenology, grounded theory and ethnography. -Search strategy A three step search strategy was used to identify published and unpublished studies. The search process was conducted from August to October 2011 and considered published and unpublished studies from 1990 to October 2011. -Methodological quality Studies were appraised for methodological quality by two independent reviewers using the Joanna Briggs Qualitative Assessment and Review Instrument. -Data extraction Data was extracted from the papers included in the review using the standardised Joanna Briggs Institute Qualitative Assessment and Review Instrument data extraction tool. -Data synthesis Research findings were pooled using the Joanna Briggs Institute Qualitative Data and Review Instrument. Results Three published studies and one unpublished dissertation were included in the review. From these four studies, 216 findings were extracted, forming 18 categories which were then analysed to create six synthesised findings. Six meta-syntheses under the headings of expert knowledge, confidence, education, relationships, negative experiences and patient centred experience were formed from the findings. Conclusions The synthesised findings confirm that the experience of advanced practice nurses in Australian acute care settings is complex and greatly influenced personally and professionally by the organisation as well as the unpredictable nature of working with people.

Selection procedures for module discovery : exploring evolutionary algorithms for cognitive science

Evolutionary algorithms are playing an increasingly important role as search methods in cognitive science domains. In this study, methodological issues in the use of evolutionary algorithms were investigated via simulations in which procedures were systematically varied to modify the selection pressures on populations of evolving agents. Traditional roulette wheel, tournament, and variations of these selection algorithms were compared on the “needle-in-a-haystack” problem developed by Hinton and Nowlan in their 1987 study of the Baldwin effect. The task is an important one for cognitive science, as it demonstrates the power of learning as a local search technique in smoothing a fitness landscape that lacks gradient information. One aspect that has continued to foster interest in the problem is the observation of residual learning ability in simulated populations even after long periods of time. Effective evolutionary algorithms balance their search effort between broad exploration of the search space and in-depth exploitation of promising solutions already found. Issues discussed include the differential effects of rank and proportional selection, the tradeoff between migration of populations towards good solutions and maintenance of diversity, and the development of measures that illustrate how each selection algorithm affects the search process over generations. We show that both roulette wheel and tournament algorithms can be modified to appropriately balance search between exploration and exploitation, and effectively eliminate residual learning in this problem.