Trustee Related Determinants of Scheme Design in Occupational Defined Contribution Schemes in Kenya

This paper aims to provide an understanding of trustee related determinants of scheme design in occupational defined contribution schemes (ODCS) in Kenya. ODCS involve no promises about the size of the benefits and no risk to the employer. The risk of ending up with low or no benefits falls entirely on the scheme members. It is necessary therefore, that determinants of scheme design are carefully considered in establishment and review of defined contribution schemes to deliver adequate benefits to members. Based on modern portfolio and the life cycle theories, the study investigated the key trustee related determinants of scheme design in ODCS in Kenya. Primary data were collected using a questionnaire administered to scheme administrators in the sample. Descriptive statistics were used to profile respondents, describe sample characteristics and a logistic econometric model was applied to evaluate the trustee related determinants of scheme design. The study showed that the key trustee related determinant of scheme design was investment strategy. From the findings, it was recommended that trustees should in addition consider investment returns, target pension, charges by service providers and annuity rates in designing schemes. This would guarantee members a reasonable standard of living after retirement. Keywords: Scheme Design, Occupational Defined Contribution Schemes, Trustees

Maize Output Supply Response to Climate Change in Kenya: An Econometric Analysis

Sufficient production of maize in Kenya is synonymous to food security and a source of income. Majority of the households in the country grow maize as the main staple food and forms the diet of over 85 percent of the population. Climate change potentially compromises maize production as 98 percent of agriculture is rainfed, threatening food security and rural livelihoods. This study sought to understand the effects of the changing temperature and rainfall patterns in Kenya on maize output. The study adopted Autoregressive distributed lag econometric modeling approach using data for the period between 1970 and 2014. The findings shows mixed response of maize output to rainfall and temperature changes depending on the period, with temperature variability having negative effects. In absence of climate change adaptation and mitigation, Kenya will become more food insecure. There is need to formulate all-inclusive policies paramount in building adaptation and mitigation mechanisms

Employer Related Determinants of Scheme Design in Occupational Defined Contribution Schemes in Kenya

This paper aims to provide an understanding of employer related determinants of scheme design in occupational defined contribution schemes (ODCS) in Kenya. ODCS involve no promises about the size of the benefits and no risk to the employer. The risk of ending up with low or no benefits falls entirely on the scheme members. It is necessary therefore, that determinants of scheme design are carefully considered in establishment and review of defined contribution schemes to deliver adequate benefits to members. Based on modern portfolio and the life cycle theories, the study investigated the key employer related determinants of scheme design in ODCS in Kenya. Primary data were collected using a questionnaire administered to scheme administrators in the sample. Descriptive statistics were used to profile respondents, describe sample characteristics and a logistic econometric model was applied to evaluate the employer related determinants of scheme design. The study showed that the key employer related determinants of scheme design were the employer’s budgetary constraint and recognition of the length of service of scheme members. From the findings, it was recommended that employers should in addition consider pensionable salary, retirement age and occupation in designing schemes. This would guarantee members a reasonable standard of living after retirement. Keywords: Scheme Design, Occupational Defined Contribution Schemes

Influence of Working Capital Management Practices on Financial Performance of Small and Medium Enterprises in Machakos Sub-County,Kenya

Working capital can be considered as source of existence for all types of organizations, whether profit or non-profit organizations, therefore, it is a vital component for any profit making organizations for it influences operational level and sales volume. The purpose of this study was to assess the influence of Working Capital Management Practices on Financial Performance of SMEs in Machakos Sub-County, Kenya. This study was based on these objectives: assessment of the influence of cash management practices on financial performance, determination of the influence of receivables management practices on financial performance and the analysis of the extent to which inventory management practices influences financial performance of SMEs. The study adopted a cross-sectional survey research design which allowed the collection of primary quantitative data through structured questionnaires and interview methods. The target population was 159 Owners / Managers of SMEs trading in Machakos Sub-County. Random sampling technique was used to obtain a sample of 22 SMEs trading in Machakos Sub-County. The data was analyzed using both descriptive and inferential statistics. The findings of the study revealed that; working capital management practices were low amongst the SMEs, since majority had not adopted formal Working Capital Management Practices and there Financial Performance was on a low average. The study further revealed that SMEs financial performance was positively related to efficient cash management, efficient receivable management and efficient inventory management at 0.01 significance level

Self-adaptive particle swarm optimization : a review and analysis of convergence

Particle swarm optimization (PSO) is a population-based, stochastic search algorithm inspired by the flocking behaviour of birds. The PSO algorithm has been shown to be rather sensitive to its control parameters, and thus, performance may be greatly improved by employing appropriately tuned parameters. However, parameter tuning is typically a time-intensive empirical process. Furthermore, a priori parameter tuning makes the implicit assumption that the optimal parameters of the PSO algorithm are not time-dependent. To address these issues, self-adaptive particle swarm optimization (SAPSO) algorithms adapt their control parameters throughout execution. While there is a wide variety of such SAPSO algorithms in the literature, their behaviours are not well understood. Specifically, it is unknown whether these SAPSO algorithms will even exhibit convergent behaviour. This paper addresses this lack of understanding by investigating the convergence behaviours of 18 SAPSO algorithms both analytically and empirically. This paper also empirically examines whether the adapted parameters reach a stable point and whether the final parameter values adhere to a well-known convergence criterion. The results depict a grim state for SAPSO algorithms; over half of the SAPSO algorithms exhibit divergent behaviour while many others prematurely converge.The National Research Foundation (NRF) of South Africa (Grant Number 46712) and the Natural Sciences and Engineering Research Council of Canada (NSERC).http://link.springer.com/journal/117212019-09-01hj2018Computer Scienc

Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm

The particle swarm optimization (PSO) algorithm is a stochastic search technique based on the social dynamics of a flock of birds. It has been established that the performance of the PSO algorithm is sensitive to the values assigned to its control parameters. Many studies have examined the long-term behaviours of various PSO parameter configurations, but have failed to provide a quantitative analysis across a variety of benchmark problems. Furthermore, two important questions have remained unanswered. Specifically, the effects of the balance between the values of the acceleration coefficients on the optimal parameter regions, and whether the optimal parameters to employ are time-dependent, warrant further investigation. This study addresses both questions by examining the performance of a global-best PSO using 3036 different parameter configurations on a set of 22 benchmark problems. Results indicate that the balance between the acceleration coefficients does impact the regions of parameter space that lead to optimal performance. Additionally, this study provides concrete evidence that, for the examined problem dimensions, larger acceleration coefficients are preferred as the search progresses, thereby indicating that the optimal parameters are, in fact, time-dependent. Finally, this study provides a general recommendation for the selection of PSO control parameter values.The National Research Foundation (NRF) of South Africa (Grant Number 46712) and the Natural Sciences and Engineering Research Council of Canada (NSERC).http://www.elsevier.com/locate/swevo2019-08-01hj2018Computer Scienc

A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows

In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container transportation problem. In a long planning horizon of multiple shifts, transport tasks are completed satisfying the time constraints. Due to the different travel distances and time of tasks, there are two types of shifts (long shift and short shift) in this problem. The unit driver cost for long shifts is higher than that of short shifts. A mathematical model of this Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW) is established in this paper, with two objectives of minimizing the total driver payment and the total travel distance. Due to the large scale and nonlinear constraints, the exact search showed is not suitable to MS-VRPTW. An initial solution construction heuristic (EBIH) and a selective perturbation Hyper-Heuristic (GIHH) are thus developed. In GIHH, five heuristics with different extents of perturbation at the low level are adaptively selected by a high level selection scheme with the Hill Climbing acceptance criterion. Two guidance indicators are devised at the high level to adaptively adjust the selection of the low level heuristics for this bi-objective problem. The two indicators estimate the objective value improvement and the improvement direction over the Pareto Front, respectively. To evaluate the generality of the proposed algorithms, a set of benchmark instances with various features is extracted from real-life historical datasets. The experiment results show that GIHH significantly improves the quality of the final Pareto Solution Set, outperforming the state-of-the-art algorithms for similar problems. Its application on VRPTW also obtains promising results