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This paper, the first one to use individual-level Turkish panel data, examines the labor market transitions in Turkey along the formal/informal employment divide. The purpose of this paper is to contribute to the limited body of empirical evidence available on mobility and informality in the Turkish labor market.

Toward this end, the authors use Turkish income and Living Conditions Survey panel data for 2006, 2007, 2008 and 2009 to compute the Markov transition probabilities of individuals moving across six different labor market states: formal-salaried (FS), informal-salaried, formal self-employed, informal self-employed, unemployed and inactive. In order to examine the nature of mobility patterns in more detail, the authors then estimate six multinomial logit models individually for each transition adopting a number of individual and employment characteristics as explanatory variables.

The authors find evidence that mobility patterns are fairly similar across different time spans, the probability of remaining in initial state is higher than the probability of transition into another state for all the labor market states, except for unemployment, there is only very limited mobility into the FS state. Gender, education and sector of economic activity are observed to display significant effects on mobility patterns. The results reveal several relationships between the covariates and likelihood of variant transitions.

This study provides a comprehensive and detailed diagnosis of the Turkish labor market. The market is observed to display a rather static structure throughout the period considered. The results indicate that a well recognition of underlying dynamics may help policy makers to produce various effective tools for addressing informality.

First study to analyze labor market mobility across formal/informal sectors using newly available panel data.

The purpose of this paper is to identify drought conditions in Jordan in the period 1960‐2006 for two major basins – the Yarmouk and the Zarqa. This study aims to look at the long‐term drought conditions (hydrological droughts) and to investigate the possibility of changes in the monthly precipitation pattern for the period of 1997‐2006, as this period has suffered from the average slightly dry conditions for the two basins.

The Standard Precipitation Index (SPI) is used to assess drought conditions in the Yarmouk and Zarqa basins. The SPI is applied on an annual basis to assess the hydrological long‐term droughts. Average monthly precipitation is used as the input to the SPI procedure.

The SPI is applied on an annual basis (12 months) SPI‐12. Results of the SPI‐12 assessment show that the years 1978, 1995, and 1999 represented extremely dry conditions in both basins. The years 1989 and 1993 represented severely dry conditions in the Zarqa basin and extremely dry conditions in the Yarmouk basin. The statistical procedure for precipitation anomalies identifies precipitation anomalies for the years 2000, 2003, and 2006. However, due to the limited number of anomaly years this study does not conclude that there is a change in the pattern of monthly precipitation of the tested period 1997‐ 2006.

Using the SPI method this work can be extended to compare drought conditions in the remaining major basins in the country.

The paper provides important information about drought conditions in Jordan and ways to assess precipitation anomalies and pattern changes. This can be beneficial to the planning of water resources in Jordan and the associated developments (agricultural, industrial, tourism, and residential).

The purpose of this paper is to propose a novel hybrid approach called as Markov System Dynamics (MSD) approach which combines the Markov approach with system dynamics (SD) simulation approach for availability modeling and to study the dynamic behavior of repairable systems.

In the proposed approach the identification of the single unit repairable system all possible states has been performed by using the Markov approach. The remaining stages of traditional Markov analysis are highly mathematically intensive. The present work proposes a hybrid approach called as MSD approach which combines the Markov approach with SD simulation approach to overcome some of the limitations of Markov process in a simple and efficient way for availability modeling and to study the dynamic behavior of this system.

The proposed framework is illustrated for a single unit repairable system. The worked out example shows the steady state point and also it gives the point, interval and steady state availabilities and also the dynamic behavior of the system. However this methodology can be extended easily for more complex multi-state maintainable systems. The results of the simulation when compared with that obtained by traditional Markov analysis clearly validate the proposed approach as an alternative approach for availability modeling of repairable systems.

In many practical situations we require to find the time at which our system reaches steady state conditions for planning maintenance activities. The proposed MSD method in this paper is capable of finding this steady state point very easily.

The proposed approach clearly indicates the time at which the system reaches its steady state and calculates the point, interval availabilities for planning maintenance activities. The different parties, i.e., engineers and machine operators, can jointly work with this model in order to understand the dynamic behavior of repairable systems.

Degraded failures and sudden critical failures are quite prevalent in industries. Degradation processes commonly belong to Weibull family and critical failures are found to follow exponential distribution. Therefore, it becomes important to carry out reliability and availability analysis of such systems. From the reported literature, it is learnt that models are available for the situations where the degraded failures as well as critical failures follow exponential distribution. The purpose of this paper is to present models suitable for reliability and availability analysis of systems where the degradation process follows Weibull distribution and critical failures follow exponential distribution.

The research uses Semi-Markov modeling using the approach of method of stages which is suitable when the failure processes follow Weibull distribution. The paper considers various states of the system and uses state transition diagram to present the transition of the system among good state, degraded state and failed state. Method of stages is used to convert the semi-Markov model to Markov model. The number of stages calculated in Method of stages is usually not an integer value which needs to be round off. Method of stages thus suffers from the rounding off error. A unique approach is proposed to arrive at failure rates to reduce the error in method of stages. Periodic inspection and repairs of systems are commonly followed in industries to take care of system degradation. This paper presents models to carry out reliability and availability analysis of the systems including the case where degraded failures can be arrested by appropriate inspection and repair.

The proposed method for estimating the degraded failure rate can be used to reduce the error in method of stages. The models and the methodology are suitable for reliability and availability analysis of systems involving degradation which is very common in systems involving moving parts. These models are very suitable in accurately estimating the system reliability and availability which is very important in industry. The models conveniently cover the cases of degraded systems for which the model proposed by Hokstad and Frovig is not suitable.

The models developed consider the systems where the repair phenomenon follows exponential and the failure mechanism follows Weibull with shape parameter greater than 1.

These models can be suitably used to deal with reliability and availability analysis of systems where the degradation process is non-exponential. Thus, the models can be practically used to meet the industrial requirement of accurately estimating the reliability and availability of degradable systems.

A unique approach is presented in this paper for estimating degraded failure rate in the method of stages which reduces the rounding error. The models presented for reliability and availability analyses can deal with degradable systems where the degradation process follows Weibull distribution, which is not possible with the model presented by Hokstad and Frovig.

Purpose of this study is to classify the states of Markov Chain for the implementation of Markov Password for effective security. Password confirmation is more often required in all authentication process, as the usage of computing facilities and electronic devices have developed hugely to access networks. Over the years with the increase in numerous Web developments and internet applications, each platform needs ID and password validation for individual users.

In the technological development of cloud computing, in recent times, it is facing security issues. Data theft, data security, denial of service, patch management, encryption management, key management, storage security and authentication are some of the issues and challenges in cloud computing. Validation in user login authentications is generally processed and executed by password. To authenticate universally, alphanumeric passwords are used. One of the promising proposed methodologies in this type of password authentication is Markov password. Markov passwords – a rule-based password formation are created or generated by using Markov chain. Representation of Markov password formation can be done by state space diagram or transition probability matrix. State space classification of Markov chain is one of the basic and significant properties. The objective of this paper is to classify the states of Markov chain to support the practice of this type of password in the direction of effective authentication for secure communication in cloud computing. Conversion of some sample obvious password into Markov password and comparative analysis on their strength is also presented in this paper. Analysis on strength of obvious password of length eight has shown range of 7%–9% although the converted Markov password has shown more than 82%. As an effective methodology, this password authentication can be implemented in cloud portal and password login validation process.

The objective of this paper is to classify the states of Markov chain to support the practice of this type of password in the direction of effective authentication for secure communication in cloud computing. Conversion of some sample obvious password into Markov password and comparative analysis on their strength is also presented in this paper.

Validation in user login authentications is generally processed and executed by password. To authenticate universally, alphanumeric passwords are used. One of the promising proposed methodologies in this type of password authentication is Markov password.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

The purpose of this paper is to analyze the historical gas allocation pattern for seeking appropriate arrangement and utilization of potentially insufficient natural gas supply available in Pakistan up to 2030.

This study presents Markov chain-based modeling of historical gas allocation data followed by its validation through error evaluation. Structural prediction using classical Chapman–Kolmogorov method and varying-order polynomial regression in the historical transition matrices are presented.

Markov chain model reproduces the terminal state vector with 99.8 per cent accuracy, thus demonstrating its validity for capturing the history. Lower order polynomial regression results in better structural prediction compared with higher order ones in terms of closeness with Markov approach-based prediction.

The data belongs to a certain geographic region with specific gas demand and supply profile. The proposition may be tested further by researchers to check the validity for other comparable structural predictions/analyses.

This study can facilitate policy-making in the field of natural gas allocation and management in Pakistan specifically and other comparable countries generally.

Two major literature gaps filled through this study are: first, Markov chain model becomes stationary when projected using Chapman–Kolmogorov relation in terms of a fixed, average transition matrix resulting in an equilibrium state after a finite number of future steps. Second, most of the previous studies analyze various gas consumption sectors individually, thus lacking integrated gas allocation policy.

Finding the optimal solution to address problems in sewer management systems has always challenged asset managers. An understanding of deterioration mechanisms in sewers can help asset managers in developing prediction models for estimating whether or not sewer collapse is likely. The effective use of deterioration prediction models along with the development and use of life cycle cost analysis (LCCA) can contribute to the goals of reducing construction, operation and maintenance costs in sewer systems. When sewer system maintenance/rehabilitation options are viewed as investment alternatives, it is important, and in some cases, imperative, to make decisions based on life cycle costs instead of relying totally on initial construction costs. The objective of this paper is to discuss the application of deterioration modelling and life cycle cost principles in sewer system management, and to explore the role of the Markov chain model in decision making regarding sewer rehabilitation. A test case is used to demonstrate the application of the Markov chain decision model for sewer system management. The analysis includes evaluation of this concept using dynamic programming and the policy improvement algorithm.

This study aims to determine the number of segments of green consumer behavior on toiletries products, and the tendency of transition between clusters is estimated. This study also provides recommendations based on the results.

This study used primary data collected through an online and offline questionnaire. The questionnaire was intended to identify the socio-demographic characteristics, green consumer behavior state according to the environment as well as the willingness of the respondents to purchase various toiletries products (current, less green, and greener). Prior to segmenting green consumer behavior, scale purification using confirmatory factor analysis was performed to ensure the indicators used were valid. The k-means clustering algorithm was used for the segmentation, while discriminant analysis was used to validate the segmentation result. The Markov chain approach was performed to estimate the tendency of the transition between constructed segments, where the logistic regression model was applied to predict the individual transition probability.

The clustering algorithm resulted in three segments: light green, green and dark green. The light green segment has the lowest attitude toward the environmental criteria while the members of the dark green segment have the highest attitude among the other segments. The logistic regression indicated that the tendency of individuals to stay in the current segment or move to the adjacent segment was influenced by socio-demographic factors. The one-step transition probability matrix revealed that the tendency of a particular segment to move to the greener segment was greater than to stay or even move to the less green segment. The Markov chain approach then showed that the steady-state condition will emerge after 18 steps.

This study was limited geographically and by the criteria used for segmenting the green consumer behavior; therefore, it is recommended that this study be replicated on a greater scale with more criteria. A wider geographic area could be considered, including a national study, and more criteria, such as social influences, could be considered. This study does not focus on specific toiletries products. Selecting more specific toiletries products could be considered to provide a more reliable response from the respondents. Moreover, factors around the willingness to pay for green products were not investigated in greater detail although these factors might become indicators that can distinguish between two or more segments.

This study empirically supports the theory that consumer environmentally friendly behavior can be used to appropriately categorize consumers into several segments, and thereby guide the development of a more differentiated policy approach for business and government.

Green consumer behavior may help save the environment and it will be beneficial in reducing environmental damage.

The study extends the existing literature related to green consumer behavior by segmenting the green consumer behavior based on the environmental criteria and applying the Markov chain approach to estimate the tendency of transition between segments.

The purpose of this paper is to investigate the best frequency description of a chain dependent Markov process for the daily simulation of precipitation. The influence of the order of the Markov chain model to simulate daily precipitation occurrence is evaluated. A mixed‐order model is constructed and compared to a simple first‐order model to evaluate the importance of the model order for the pricing of a rainfall index put option.

For the first time a mixed‐order Markov chain model is presented where the monthly varying order was chosen based on a Bayesian information criteria analysis of rainfall data for one weather station in the US. The outcome of this model is compared to simpler Markov models and to burn analysis results.

The comparison indicate that there is only a slightly better representation of the rain statistics in the theoretically best mixed‐order Markov chain model compared to a more simple first‐order model. Clear differences between the daily simulation and the burn method are found when pricing a put option on a rainfall index. All daily simulation models underestimate the volatility of the monthly rainfall amount especially in the summer months.

To assess the robustness and any geographical dependence of the bias in the volatility a systematic analysis could be applied to more weather stations across the US in further studies.

The bias in the volatility has significant influence on the price of the put option considered here and limits the use of such a model for risk analyses, e.g. for an extreme event cover.

For the first time a multi‐order Markov chain model is applied to price a precipitation derivative. While the focus of previous studies was the appropriate choice for the intensity process, the importance of the frequency process is investigated in this paper.