Pareto’s Law

Hardy, Michael

doi:10.1007/s00283-010-9159-2

Cited by 71 publications

(36 citation statements)

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“…This assumption is based on the characteristics of the power-law distribution implied by the Pareto distribution (Reed 2001;Newman 2005), which implies that most families in this topmost interval will be skewed to the left-hand side of the distribution with a long tail to the right representing super-rich families. Specifically, assuming the Pareto distribution implies that the proportion of families whose income (y) exceeds the minimum (m) value of our topmost open-ended income intervals (y > y m , with y m equals $3000 for 2001 and $4000 for 2011) can be estimated with the formula y m /y ð Þ a , where α is the so-called Pareto index or Pareto exponent (Hardy 2010). Given this formula, if it is assumed that income within this interval is distributed according to the 70/30 rule (i.e.…”

Section: Notesmentioning

confidence: 99%

Income Inequality across Australian Regions during the Mining Boom: 2001–11

Fleming

Measham

2015

Australian Geographer

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As mining expands throughout the world, a growing body of literature is focusing on the relationship between mining and well-being in locations where resource extraction occurs. Although many topics such as employment and migration have been researched, the impacts of mining on income inequality have received less attention from scholars. Income inequality is a highly debated topic and the Gini coefficient (GC) one of the most popular indicators used to measure and discuss it. In this paper we estimate GCs for all sub-State regions of Australia and analyse their changes during the 'mining boom decade' (2001-11) across mining and non-mining regions. Our results show that, on average, income inequality increased by around 4.8 per cent in mining regions, compared to 8.7 per cent in the average non-mining region. However, the results also show important variation in changes of GC across mining regions, suggesting that the industry is likely to affect the distribution of local incomes in different ways. The method we propose to estimate GCs for regional areas and the results obtained across mining and non-mining regions provide important insights for future research and for regional policy makers, especially those concerned with the socio-economic impacts of industries such as mining across regions.

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Section: Notesmentioning

confidence: 99%

Income Inequality across Australian Regions during the Mining Boom: 2001–11

Fleming

Measham

2015

Australian Geographer

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“…Most companies account for 80% of the income coming from 20% of customers, which indicates that a group of 20% of clients generates 80% of a company's business. This 20% of clients bring four times more traffic than the other 80% [18][19][20]. This principle is known as the Pareto principle.…”

Section: Literature Overviewmentioning

confidence: 99%

Development of a Business Model by Introducing Sustainable and Tailor-Made Value Proposition for SME Clients

et al. 2019

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Due to the combination of vast technology trends brought by Industry 4.0, present-day consumers need and stiff global competition and require businesses from across many industries to constantly innovate. Clients request multifaceted support from manufacturers and service providers while the transition towards new symbiotic ecosystems disrupts traditionally established processes in some sectors. This is largely happening in the global automotive industry where in-car and around-the-car services with asset-sharing concepts are taking a dominant role rather than plain vehicles sales and car ownership. In our paper, we investigate the possibilities of new and improved business collaborations between a car retailer operating in several European countries and its SME (small and medium-sized enterprises) customers across various sectors currently owning car fleets of at least five vehicles. We test several hypotheses and conduct a systematic statistical analysis of the proposed business model’s aspects on the sample of almost 200 SMEs from five countries in Southeast Europe. By using the χ2 test and a one-tailed t-test we tested both frequency data and the continuous variables. Research indicates that SMEs look for new ways to organize their fleet management strategy more efficiently and thus more precisely plan associated costs. Regardless of the country of origin, SMEs want to explore a business model with more introduced to their fleet management and are ready to accept new technologies for fleet management operations.

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“…80-20 rule is originally proposed by the Italian economist Pareto, so it is also called Pareto's law. Commonly it is stated that 20% of all causes bring about 80% of all effects [24][25][26]. In this paper, we first define an arc set that records the distances between any two nodes, which contain the suppliers and the manufacturer.…”

Section: Neighborhood Solution Selectionmentioning

confidence: 99%

A Two-Phase Heuristic Algorithm for the Common Frequency Routing Problem with Vehicle Type Choice in the Milk Run

Lin

Bian

2015

Mathematical Problems in Engineering

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High frequency and small lot size are characteristics of milk runs and are often used to implement the just-in-time (JIT) strategy in logistical systems. The common frequency problem, which simultaneously involves planning of the route and frequency, has been extensively researched in milk run systems. In addition, vehicle type choice in the milk run system also has a significant influence on the operating cost. Therefore, in this paper, we simultaneously consider vehicle routing planning, frequency planning, and vehicle type choice in order to optimize the sum of the cost of transportation, inventory, and dispatch. To this end, we develop a mathematical model to describe the common frequency problem with vehicle type choice. Since the problem is NP hard, we develop a two-phase heuristic algorithm to solve the model. More specifically, an initial satisfactory solution is first generated through a greedy heuristic algorithm to maximize the ratio of the superior arc frequency to the inferior arc frequency. Following this, a tabu search (TS) with limited search scope is used to improve the initial satisfactory solution. Numerical examples with different sizes establish the efficacy of our model and our proposed algorithm.

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Pareto’s Law

Cited by 71 publications

References 1 publication

Income Inequality across Australian Regions during the Mining Boom: 2001–11

Income Inequality across Australian Regions during the Mining Boom: 2001–11

Development of a Business Model by Introducing Sustainable and Tailor-Made Value Proposition for SME Clients

A Two-Phase Heuristic Algorithm for the Common Frequency Routing Problem with Vehicle Type Choice in the Milk Run

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