A New Goodness of Fit Measure Based on Income Inequality Curves

This paper uses inequality-measurement techniques to assess goodness of fit in income distribution models. It exposes the shortcomings of the use of conventional goodness of fit criteria in face of the big income data and proposes a new set of metrics, based on income inequality curves. In this note, we mentioned that the distance between theoretical and empirical inequality curves can be considered as a goodness of fit criterion. We demonstrate certain advantages of this measure over the other general goodness of fit criteria. Unlike other goodness of fit measures, this criterion is bounded. It is 0 in minimum difference and 1 in maximum distance. Furthermore, there is a consistency between this new goodness of fit measure and the other conventional criteria. A simulation study based on fitted distribution to real income data is performed in order to investigate some statistical properties of the new goodness of fit measure. An empirical study and comparisons are also provided.

The impact of different data sources on the level and structure of income inequality

This paper aims to analyze the effect on measured inequality and its structure of using administrative data instead of survey data. Different analyses are carried out based on the Spanish Survey on Income and Living Conditions (ECV) that continued to ask households for their income despite assigning their income data as provided by the Tax Agency and the Social Security Administration. Our main finding is that the largest discrepancies between administrative and survey data are in the tails of the distribution. In addition to that, there are clear differences in the level and structure of inequality across data sources. These differences matter, and our results should be a wake-up call to interpret the results based on only one source of income data with caution.

In-Firm Planning and Business Processes Management Using Deep Neural Networks

Objective - The objective of this paper is to consider using machine learning approaches for in-firm processes prediction and to give an estimation of such values as effective production quantities. Methodology - The research methodology used is a synthesis of a deep-learning model, which is used to predict half of real business data for comparison with the remaining half. The structure of the convolutional neural network (CNN) model is provided, as well as the results of experiments with real orders, procurements, and income data. The key findings in this paper are that convolutional with a long-short-memory approach is better than a single convolutional method of prediction. Findings - This research also considers useof such technologies on business digital platforms. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Novelty - This paper describes the practical usage of 1-dimensional(1D) convolutional neural networks and a mixed approach with convolutional and long-short memory networks for in-firm planning tasks such as income prediction, procurements, and order demand analysis. Type of Paper - Empirical. Keywords: Business; Neural, Networks; CNN; Platform JEL Classification: C45

Child Poverty and Policy - A Comparison of New Zealand and Scandinavia

<p>This thesis provides a comparative analysis of poverty among children in New Zealand, Norway and Sweden using the most recent available income data from year 2000. By comparing different countries with different levels of poverty, and differing policies it attempts to say something about the causes of child poverty and evaluate the importance of policy as a remedy. This is done within one theoretical framework and understanding of what poverty is and why it is important. More specifically, the purpose is to explore to what extent policy in the form of benefits (and taxes) explain the differences in child poverty, in this case why children in New Zealand are so much more likely to be poor than those in Norway and Sweden. This is in part done by a process of elimination, where poverty levels before and after taxes and transfers are compared in various sectors of the population divided by demographic, ethnic, educational, employment status and other factors. Even though the picture formed by the findings is complex and far from straight forward, and policy differences cannot explain all the differences, policy variables are fundamental in explaining the differences in child poverty levels. In order to summarize the findings in a more accessible way the last part of the thesis puts together findings from previous chapters by asking a counterfactual 'what if?' question, based on the statistics in chapter 4 and 5. It estimates what the impact would have been on child poverty levels in various groups in the New Zealand community if its policy had achieved the same rate of poverty reduction as the equivalent groups experience in Scandinavia. In the counterfactual chapter the importance of differences across the countries in demographic composition and market income (i.e. the income before government intervention through taxes and benefits) are also tested for. This way of presenting the findings further reinforces the image of complexity with few straightforward causal mechanisms. However, while the thesis shows that many variables play a role in explaining the variation in outcome across the countries, it leaves little room for doubting that much of this variation must be explained by government intervention. There is, in other words, scope for governments to reduce poverty more than the New Zealand policies did in year 2000.</p>

Child Poverty and Policy - A Comparison of New Zealand and Scandinavia

<p>This thesis provides a comparative analysis of poverty among children in New Zealand, Norway and Sweden using the most recent available income data from year 2000. By comparing different countries with different levels of poverty, and differing policies it attempts to say something about the causes of child poverty and evaluate the importance of policy as a remedy. This is done within one theoretical framework and understanding of what poverty is and why it is important. More specifically, the purpose is to explore to what extent policy in the form of benefits (and taxes) explain the differences in child poverty, in this case why children in New Zealand are so much more likely to be poor than those in Norway and Sweden. This is in part done by a process of elimination, where poverty levels before and after taxes and transfers are compared in various sectors of the population divided by demographic, ethnic, educational, employment status and other factors. Even though the picture formed by the findings is complex and far from straight forward, and policy differences cannot explain all the differences, policy variables are fundamental in explaining the differences in child poverty levels. In order to summarize the findings in a more accessible way the last part of the thesis puts together findings from previous chapters by asking a counterfactual 'what if?' question, based on the statistics in chapter 4 and 5. It estimates what the impact would have been on child poverty levels in various groups in the New Zealand community if its policy had achieved the same rate of poverty reduction as the equivalent groups experience in Scandinavia. In the counterfactual chapter the importance of differences across the countries in demographic composition and market income (i.e. the income before government intervention through taxes and benefits) are also tested for. This way of presenting the findings further reinforces the image of complexity with few straightforward causal mechanisms. However, while the thesis shows that many variables play a role in explaining the variation in outcome across the countries, it leaves little room for doubting that much of this variation must be explained by government intervention. There is, in other words, scope for governments to reduce poverty more than the New Zealand policies did in year 2000.</p>

Entropy and Wealth

While entropy was introduced in the second half of the 19th century in the international vocabulary as a scientific term, in the 20th century it became common in colloquial use. Popular imagination has loaded “entropy” with almost every negative quality in the universe, in life and in society, with a dominant meaning of disorder and disorganization. Exploring the history of the term and many different approaches to it, we show that entropy has a universal stochastic definition, which is not disorder. Hence, we contend that entropy should be used as a mathematical (stochastic) concept as rigorously as possible, free of metaphoric meanings. The accompanying principle of maximum entropy, which lies behind the Second Law, gives explanatory and inferential power to the concept, and promotes entropy as the mother of creativity and evolution. As the social sciences are often contaminated by subjectivity and ideological influences, we try to explore whether maximum entropy, applied to the distribution of a wealth-related variable, namely annual income, can give an objective description. Using publicly available income data, we show that income distribution is consistent with the principle of maximum entropy. The increase in entropy is associated to increases in society’s wealth, yet a standardized form of entropy can be used to quantify inequality. Historically, technology has played a major role in the development of and increase in the entropy of income. Such findings are contrary to the theory of ecological economics and other theories that use the term entropy in a Malthusian perspective.

Usage of Machine Learning In Strategy Planning On A Web-Based Business Platforms

This paper describes the practical usage of 1D convolutional neural networks in business platforms for such tasks as income prediction, procurements and order demand analysis. The structure of the CNN model is provided, as well as the results of experiments with real orders, procurements and income data. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Currently web-based platforms featuring advanced business functions are rapidly growing. Their new functions can use classic and modern concepts. The comparison between several approaches, including machine learning and regression are provided. Technologies used in such platforms are provided and analyzed. The structures of a such specific web-platforms frontend and backend systems are observed. Other prospective ideas of usage are formulated. Keywords: Business, Neural, Networks, CNN, Platform

Analisis Pengaruh Dana Pihak Ketiga, Pembiayaan, Bancassurance Terhadap Laba Dan Dampaknya Pada Kesejahteraan Masyarakat (Studi Kasus Bank Syariah Di Indonesia)

<p>This study aims to determine how the direct influence of deposits, financing and bancassurance on the profits of Islamic banks and the welfare of society and indirect influence or through the intervening variables of profits on the welfare of society and total influence. This type of research is quantitative research. Data collection methods used are documentation methods based on data in the form of monthly financial statements for the period 2014-2018 and per capita income data that has been published. The method of data analysis in this study is by using the path analysis method. The results of the study show that financing significantly affects earnings while third party funds and bancassurance do not directly affect earnings. Deposits and profits of Islamic banks significantly influence directly on public welfare and bancassurance negatively influences people's welfare while financing does not. Financing indirectly significantly influences welfare through profit variables, whereas DPK and bancassurance do not. Business activities of Islamic banks have a role in addition to achieving company profits, also have a positive impact on the welfare of the Indonesian people. Funding and third party funds that generate profits are a determining factor for improving community welfare rather than bancassurance sales.</p>

ANALISIS PENDAPATAN DAN PROFITABILITAS PADA PETERNAKAN SAPI PERAH SUMBER MULYA DI KABUPATEN KEPAHIANG

ABSTRACT The study was purposed to find out income and profitability was achieved by a Sumber Mulya dairy farm. Research was held in July – Agust 2018 in Sumber Mulya dairy farm Kepahiang. The methods used in this research is case study method. Primary Data obtained through interviews with the respondents and recording. Data obtained for time 12 months on technical aspects and financial data covering production costs, data revenue, income data, the amount of data the data the amount of milk production and livestock lactation. Secondary data retrieved from the data contained in these farms. Data analysis used analysis formulas use income and profitability ratios. The result showed that Sumber Mulya Dairy cattle farm was net incomeaverages per month is Rp. 5,427,176,91 with the number of 7.00 averagecows lactation flat tail. Total cost per month of Rp.10,032,823.29 Profitability achieved by the company said 10,78% profitable because the value of profitability is greater than the interest rate a bank of 4.35%. Key word : income, dairy farm, profitability, dairy farm, milk production

On Bayesian approach to composite Pareto models

In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to the composite Pareto models involving specification of the prior distribution on the proportion of data coming from the Pareto distribution, instead of assuming the prior distribution on the threshold, as often done in the literature. Based on a simulation study, it is found that the parameter estimates determined when using uniform prior on the proportion is less biased as compared to the point estimates determined when using uniform prior on the threshold. Applications on income data and finance are included for illustrative examples.