Capital formation, capital rate of return and economic inequality in Middle East and North Africa

Economic inequality is turning into one of the most pressing issues in the 21st century. The inequality gap between the rich and the poor raises red flags to the world\u27s economic, social and political well-being. A small minority is controlling the world\u27s economy and has the most influential power, which will result in more political instability and lower social inclusion within the world. This was witnessed by the reasons studied behind the rise of the Arab Spring and the turmoil in the MENA region. The literature exhibits a wealth of studies and ongoing debate for the reasons behind inequality among and within the countries in the world. However, there is a dearth in literature on MENA Region when it comes to analyzing the causes leading to inequality and the factors that determine its level in the region, with an emphasis on the effect of the capital and the rate of returns. This study analyzed panel data from 1963 to 2012 for MENA countries to empirically investigate the effect of the capital formation, rate of returns on capital, controlling for the economic growth and natural resources rent on economic inequality in MENA. The empirical results showed that that the factors used to measure the capital formation such as Gross Domestic Savings and Gross Fixed Capital Formation are positively related to economic inequality and hence heighten the inequality gap. Conversely, the factors used to measure the capital rate of returns such as the real interest and the deposit interest rate are negatively related to economic inequality. When the deposit interest rate increases, the economic inequality decreases. Policy recommendations are made to develop comprehensive strategies for inclusive development and better wealth distribution. Revised fiscal and monetary policies to reform tax and benefits are needed to increase fair and re-distributive effects and avoid the effect of capital and power accumulation

A Novel Privacy Disclosure Risk Measure and Optimizing Privacy Preserving Data Publishing Techniques

A tremendous amount of individual-level data is generated each day, with a wide variety of uses. This data often contains sensitive information about individuals, which can be disclosed by “adversaries”. Even when direct identifiers such as social security numbers are masked, an adversary may be able to recognize an individual\u27s identity for a data record by looking at the values of quasi-identifiers (QID), known as identity disclosure, or can uncover sensitive attributes (SA) about an individual through attribute disclosure. In data privacy field, multiple disclosure risk measures have been proposed. These share two drawbacks: they do not consider identity and attribute disclosure concurrently, and they make restrictive assumptions on an adversary\u27s knowledge and disclosure target by assuming certain attributes are QIDs and SAs with clear boundary in between. In this study, we present a Flexible Adversary Disclosure Risk (FADR) measure that addresses these limitations, by presenting a single combined metric of identity and attribute disclosure, and considering all scenarios for an adversary’s knowledge and disclosure targets while providing the flexibility to model a specific disclosure preference. In addition, we employ FADR measure to develop our novel “RU Generalization” algorithm that anonymizes a sensitive dataset to be able to publish the data for public access while preserving the privacy of individuals in the dataset. The challenge is to preserve privacy without incurring excessive information loss. Our RU Generalization algorithm is a greedy heuristic algorithm, which aims at minimizing the combination of both disclosure risk and information loss, to obtain an optimized anonymized dataset. We have conducted a set of experiments on a benchmark dataset from 1994 Census database, to evaluate both our FADR measure and RU Generalization algorithm. We have shown the robustness of our FADR measure and the effectiveness of our RU Generalization algorithm by comparing with the benchmark anonymization algorithm