Fair Incentives Research Articles

Fair Incentives for Repeated Engagement

We study a decision-maker’s problem of finding optimal monetary incentive schemes for retention when faced with agents whose participation decisions (stochastically) depend on the incentive they receive. Our focus is on policies constrained to fulfill two fairness properties that preclude outcomes wherein different groups of agents experience different treatment on average. We formulate the problem as a high-dimensional stochastic optimization problem and study it through the use of a closely related deterministic variant. We show that the optimal static solution to this deterministic variant is asymptotically optimal for the dynamic problem under fairness constraints. Though solving for the optimal static solution gives rise to a nonconvex optimization problem, we uncover a structural property that allows us to design a tractable, fast-converging heuristic policy. Traditional schemes for retention ignore fairness constraints; indeed, the goal in these is to use differentiation to incentivize repeated engagement with the system. Our work (i) shows that even in the absence of explicit discrimination, dynamic policies may unintentionally discriminate between agents of different types by varying the type composition of the system, and (ii) presents an asymptotically optimal policy to avoid such discriminatory outcomes.

Incentive-compatible primary frequency response ancillary service market mechanism for incorporating diverse frequency support resources

Frequency vulnerability will be prominent in future low-inertia power systems with high renewable penetration, which calls for primary frequency response (PFR) services from a wide range of entities. The challenge, however, is to design effective and fair market mechanisms to incentivize diverse PFR resources. To this end, this paper proposes a PFR ancillary service market mechanism to ensure the security of frequency nadir and quasi-steady-state (QSS) frequency while providing equitable settlements to multiple PFR providers. First, bidding rules are designed for accurate depictions of dynamic performances from diverse PFR resources. Then, a PFR market-clearing model, incorporating frequency security constraints that explicitly express PFR providers' contributions, is developed to deploy sufficient PFR resources while maximizing social welfare. Finally, two unified marginal PFR prices are derived to reflect the scarcity of frequency support capabilities for maintaining frequency nadir and QSS frequency security, enabling fair settlements to successful bidders based on their contributions. Moreover, the pricing and settlement mechanism is theoretically proven incentive-compatible, ensuring the PFR market stability. Case studies verify the proposed PFR market's effectiveness in ensuring frequency security, providing fair incentives to diverse PFR providers, maintaining market stability, and supporting scalable implementations.

A Blockchain-based federated learning framework for secure aggregation and fair incentives

Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that integrates federated learning with blockchain technology to facilitate secure model aggregation and fair incentives in untrustworthy environments. The framework designs a reputation evaluation method using quality as an indicator, and a consensus method based on reputation feedback. The trustworthiness of nodes is dynamically assessed to achieve an efficient and trustworthy model aggregation process while avoiding reputation monopolisation. Furthermore, the paper defines a tailored contribution calculation process for nodes in different roles in an untrusted environment. A reward and punishment scheme based on the joint constraints of contribution and reputation is proposed to attract highly qualified workers to actively participate in federated learning tasks. Theoretical analysis and simulation experiments demonstrate the framework's ability to maintain efficient and secure aggregation under a certain degree of attack while achieving fair incentives for each role node with significantly reduced consensus consumption.

Analisis Pemeliharaan Sumber Daya Manusia Kesehatan (SDMK) di PT Tirta Medical Indonesia

Human resource maintenance is very important for every company, because it not only helps to retain human resources, but also can increase dedication, discipline, and employee loyalty. Human resource maintenance will protect workers from workplace accidents and maintain communication to remain harmonious among human resources. This research uses a qualitative method and deductive approach, where the informants explain the principles of learning content and its application in specific situations. The purpose of this study is to determine the procedures for implementing human resource management maintenance at PT Tirta Medical Indonesia. From the results of the study, it can be seen that PT Tirta Medical Indonesia has implemented good work maintenance methods in accordance with the theory of Drs. H. Melayu S. P Hasibuan, namely effective communication between the company and employees, fair incentives, attention to employee welfare, health, occupational safety, and Pancasila industrial relations. The author recommends that PT Tirta Medical Indonesia continue to maintain and improve human resource maintenance for better employee loyalty and performance.

The Influence of Work Environment and Incentives on Employee Performance through Motivation

Optimal service for health service users is a necessity for the continuity of hospital operations. The determinant of service quality lies in employee performance. There are several factors that influence employee performance, including work environment, incentives and work motivation. This research aims to: examine the influence of the work environment and incentives on work motivation; examine the influence of the work environment, incentives and work motivation on employee performance; and examine the influence of the work environment and incentives on employee performance through motivation. The research population was 445 civil servant employees at Dr. Wahidin Sudiro Husodo Hospital. Determining the number of samples used the Hair formula, namely the number of indicators multiplied by 6 (23x6), so that a sample of 138 people was obtained. The sampling technique uses proportional random sampling. Meanwhile, data collection uses a questionnaire distributed to respondents. The data analysis technique uses SEM PLS. The research results show that: (1) work environment and incentives have a significant effect on motivation; (2) work environment, incentives and motivation have a significant effect on employee performance; and (3) work environment and incentives have a significant effect on employee performance through motivation. For this reason, if hospital management attempts to improve employee performance, this can be done by creating a conducive work environment and providing fair incentives accompanied by strengthening work motivation.

Testing Open Science Tools

Data management plans (DMP) are an intrinsic part of planning for and implementing openness in scientific research. DMPs contain integral information on research projects: IT requirements, legal and ethical considerations and strategies for opening data. However, all too often, the DMP is seen as a chore drawn up at the request of external funders rather than a tool adopted for the benefit of project members. Similarly, the information gathered into DMPs is rarely utilised by other stakeholders such as institutional research services, nor linked to external parties such as data repositories. Machine-actionable DMPs aim to change this landscape by offering better guidance and support for researchers preparing their plans, and facilitating a research data management system that allows data and information to be shared across institutions and repositories. In fact, one of the recommendations of the EOSC Nordic project, which studied FAIR incentives and expected impact in the Nordics, Baltics and EOSC, was the implementation of machine-actionable DMPs for seamless information workflow and supporting FAIR adoption in everyday research work. Tools such as Data Stewardship Wizard and Argos do this by taking researchers through a set of simple structural questions on research data management from which answers are exported into funder templates resulting in downloadable DMP documents. These machine-actionable online tools transform tick-the-box answers that are easy to fill in into full-text DMPs that comply with funder requirements. This poster introduces a pilot project testing the Data Stewardship Wizard and localising its Knowledge Template to the needs of Aalto University. It presents the project’s findings and stages, including the process of creating a template for the Research Council of Finland DMPs and testing the tool with researchers. In doing so, it examines machine-actionable DMPs from a practice-based perspective and comments on the process of institutional implementation.

Analisis Pemeliharaan Sumber Daya Manusia Kesehatan (SDMK) di PT Tirta Medical Indonesia


 Maintenance of human resources is very important for every company, because besides aiming to maintain human resources it can also increase employee dedication, discipline and loyalty. Maintenance of human resources will protect workers from work accidents and maintain harmonious communication between human resources. The type of research used in this research is qualitative type research and the approach used in this research is a deductive approach, where the informants provide explanations about the principles of learning content, then explain them in the form of their application or examples in certain situations. The purpose of this study was to determine the procedures for carrying out the maintenance of human resource management at PT. Tirta Medical Indonesia. From the results of this study it can be seen that PT. Tirta Medical Indonesia has implemented good work maintenance methods and in accordance with the theory of Drs. H. Melayu S. P Hasibuan which includes effective communication between companies and employees, providing fair incentives, paying attention to employee welfare, occupational health and safety and conducting Pancasila industrial relations. The author suggests that PT. Tirta Medical Indonesia must continue to be able to maintain and improve the maintenance of better Human Resources in order to increase employee loyalty and performance.
 Keywords: Maintenance, Employees, Methods, Human Resources

Federated Learning With Fair Incentives and Robust Aggregation for UAV-Aided Crowdsensing

Unmanned aerial vehicles (UAVs) combined with artificial intelligence (AI) have recently gathered significant interest to enable intelligent and on-demand crowdsensing applications. In conventional AI approaches, a wealth of UAVs’ sensory data (which may be privacy-sensitive) needs to be mitigated to the central storage for model training, which poses severe privacy and data misuse risks. The promising federated learning (FL) allows UAVs to cooperatively train a shared model while keeping the private raw data locally. However, FL imposes heavy communication loads on battery-limited UAVs owing to frequent local training and global synchronization. Due to the heterogeneity of UAVs and presence of free-riders and Byzantine UAVs, the quality of UAVs’ model updates can vary dramatically, raising great challenges for fair incentives and robust model aggregation. In this paper, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</u> air and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> obust <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</u> ederated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u> earning (FRFL) scheme in UAV-aided crowdsensing. Specifically, we first exploit edge computing-enabled 5G heterogeneous networks in the FL framework to offer proximal FL services with high data rate and low latency for UAVs. Then, based on contract theory, we design an optimal incentive mechanism to precisely and fairly encourage UAVs’ participation in FL under information asymmetry. The method is proved to be truthful, contractual feasible, and computationally efficient. Furthermore, we develop Byzantine-robust aggregation rules and fair model profit allocation rules via contribution index measurement. We also propose a reputation mechanism for credible UAVs recruitment and free-rider prevention by leveraging historical learning records and exploring record freshness for weight assignment. Simulation results validate the efficiency of FRFL in terms of user utility, communication efficiency, and robustness.

An Incentivized and Optimized Dynamic Mechanism for Demand Response for Managing Voltage in Distribution Networks

The voltage regulation in distribution networks is one of the major obstacles when increasing the penetration of distributed generators (DGs) such as solar photovoltaics (PV), specially during cloud transients, causing potential stress on network voltage regulations. Residential demand response (DR) is one of the cost-effective solutions for voltage management in distribution networks. However, the main barriers of DR implementation are the complexities of controlling a large number and different types of residential loads, satisfying customers’ preferences and providing them fair incentives while identifying the optimum DR implementation locations and sizing as well as cooperating with the existing network equipment for the effective voltage management in the networks. A holistic and practical approach of DR implementation is missing in the literature. This study proposes a dynamic fair incentive mechanism using a multi-scheme load control algorithm for a large number of DR participants coordinating with the existing network equipment for managing voltage at medium voltage (MV) networks. The multi-scheme load control is comprised of short-interval (10-minute) and long-interval (2-hour) DR schemes. The dynamic incentive rates are optimised based on the energy contribution of DR participating consumers, their influence on the network voltage and total power loss improvement. The proposed method minimises the DR implementation cost and size, fairly incentivises the consumers participating in the DR and priorities their consumption preferences while reduces the network power losses and DGs’ reactive power contributions to effectively manage the voltage in the MV networks. An improved hybrid particle swarm optimisation algorithm (IHPSO) is proposed for the load controller to provide fast convergence and robust optimisation results. The proposed approach is comprehensively tested using the IEEE 33-bus and IEEE 69-bus networks with several scenarios considering a large number of DR participants coordinated with the DGs and on-load tap changer (OLTC) in the networks.

Blockchain Empowered Federated Learning for Data Sharing Incentive Mechanism

In the machine learning, data sharing between different participants can increase the amount of data, improve the quality of the dataset, and thereby improve the quality of the model. Under the condition of data supervision, federated learning, as a distributed machine learning, aims to protect data while training models through collaboration among all parties to achieve data sharing and improve model quality. However, there are still some issues. For instance, the lack of trust between the participants makes it impossible to establish a secure and reliable sharing mechanism. In addition, how to fairly share the benefits generated by the model, identify honest participants and punish malicious participants is still a challenge. In this paper, we propose a new federated learning scheme based on blockchain architecture for federated learning data sharing. Moreover, an incentive mechanism based on reputation points and Shaply values is proposed to improve the sustainability of the federated learning system, which provides a credible participation mechanism for data sharing based on federated learning and fair incentives. The experimental results and analysis show that the loss of federated learning is more smooth than that of centralized machine learning.

Towards a Smart Privacy-Preserving Incentive Mechanism for Vehicular Crowd Sensing

Vehicular crowd sensing is a promising approach to address the problem of traffic data collection by leveraging the power of vehicles. In various applications of vehicular crowd sensing, there exist two burning issues. First, privacy can be easily compromised when a vehicle is performing a crowd sensing task. Second, vehicles have no incentive to submit high-quality data due to the lack of fairness, which means that everyone gets the same paid, regardless of the quality of the submitted data. To address these issues, we propose a smart privacy-preserving incentive mechanism (SPPIM) for vehicular crowd sensing. Specifically, we first propose a new SPPIM model for the scenario of vehicular crowd sensing via smart contract on the blockchain. Then, we design a privacy-preserving incentive mechanism based on budget-limited reverse auction. Anonymous authentication based on zero-knowledge proof is utilized to ensure the privacy preservation of vehicles. To ensure fairness, the reward payments of winning vehicles are determined by not only the bids of vehicles but also their reputation and the data quality. Then, any rewarded vehicle can get the fair payment; on the contrary, malicious vehicles or task initiators will be punished. Finally, SPPIM is implemented by using smart contracts written via Solidity on a local Ethereum blockchain network. Both security analysis and experimental results show that the proposed SPPIM achieves privacy preservation and fair incentives at acceptable execution costs.

Comments: Carbon Tariffs - Fair Incentives for Change?

Carbon credits, carbon taxes, and emissions trading systems are familiar terms in discussions about limiting global warming, the Paris Agreement, and net-zero emissions goals. A more recent addition to the glossary of climate policy is “carbon tariff.” While the concept is not new, it recently surfaced in nascent policymaking in the EU. In 2019, European Commission President Ursula von der Leyen proposed a “carbon border adjustment mechanism (CBAM)” as part of a proposed green deal. In March, the European Parliament adopted a resolution on a World Trade Organization (WTO)-compatible CBAM. A carbon tariff, or the EU’s CBAM, is a tax applied to carbon-intensive imports. Countries that have pledged to be more ambitious in reducing emissions—and in some cases have implemented binding targets—may impose carbon costs on their own businesses. Being eyed now are cross-border or overseas businesses that make products in countries in which no costs are imposed for emissions, resulting in cheaper carbon-intensive goods. Those products are exported to the countries aiming for reduced emissions. The concern lies in the risk of locally made goods becoming unfairly disadvantaged against competitors that are not taking similar steps to deal with climate change. A carbon tariff is being considered to level the playing field: local businesses in countries applying a tariff can better compete as climate policies evolve and are adopted around the world. Complying with WTO rules to ensure fair treatment, the CBAM will be imposed only on high-emitting industries that compete directly with local industries paying a carbon price. In the short term, these are likely to be steel, chemicals, fertilizers, and cement. The Parliament’s statement introduced another term to the glossary of climate policy: carbon leakage. “To raise global climate ambition and prevent ‘carbon leakage,’ the EU must place a carbon price on imports from less climate-ambitious countries.” It refers to the situation that may occur if businesses were to transfer production to other countries with laxer emission constraints to avoid costs related to climate policies. This could lead to an increase in total emissions in the higher-emitting countries. “The resolution underlines that the EU’s increased ambition on climate change must not lead to carbon leakage as global climate efforts will not benefit if EU production is just moved to non-EU countries that have less ambitious emissions rules,” the Parliament said. It also emphasized the tariff “must not be misused to further protectionism.” A member of the environment committee, Yannick Jadot, said, “It is a major political and democratic test for the EU, which must stop being naïve and impose the same carbon price on products, whether they are produced in or outside the EU, to ensure the most polluting sectors also take part in fighting climate change and innovate towards zero carbon. This will give us the best chance of remaining below the 1.5°C warming limit, whilst also pushing our trading partners to be equally ambitious in order to enter the EU market.” The Commission is expected to present a legislative proposal on a CBAM in the second quarter of 2021 as part of the European Green Deal.

A novel Dual-Blockchained structure for contract-theoretic LoRa-based information systems

LoRa serves as one of the most deployed technologies in Internet-of-Things-based information systems (IoT-IS), and self-motivated deployment is the key to the rollout of LoRa. Proper incentive can play an important role in encouraging the private deployment of LoRa, increasing coverage and promoting effective management of IoT-IS. However, existing incentive mechanisms have the vulnerabilities of insecure centralized architecture and excessive utility loss of LoRa Controllers and Gateways, due to asymmetric information between private owners of gateways and centralized controller (or service providers). Blockchain-based LoRa networks, as a promising solution, have not been comprehensively studied to address the vulnerabilities, let alone the other issues of security, scalability, and flexibility. In this paper, we propose a novel Dual-Chained LoRa-based information system (LoRa-IS) to provide globally cross-validated security. Behaviors, including state-of-the-art contract-theoretic incentive mechanism and new flow control protocol, can be secured with the tamper-resistance of Blockchains. Being part of the proposed incentive mechanism, the new self-driven flow control allows both the Dual-Chain system and the LoRa network to scale. To the best of our knowledge, the proposed system is the first comprehensive Blockchain-based LoRa-IS combined with contract theory. We also provide analysis and simulations, showing that our system can pay fair incentives under information asymmetry. With the new flow control, the system can optimize network coverage while improving the Blockchain scalability and flexibility.

Effect of Teachers’ Job Satisfaction on Their Commitment: In Case of Selected Governmental Preparatory Schools of Bale Zone in Focus

The purpose of this study was to investigate the effects of teachers’ job satisfactions on their commitment in selected governmental Preparatory Schools of Bale Zone. Hence, it examined the levels of teachers’ job satisfaction, the relation with their commitments as well as the effect of teachers’ job satisfaction on their commitment in the selected governmental Preparatory Schools of Bale Zone. To accomplish this purpose, the study was employed a concurrent nested research design. A total of 86 individuals have participated in the study. Among them 70 teachers were included through simple random sampling technique. Additionally, 7 directors and supervisors and 9 woreda educational expertises’ were included through census technique. Questionnaire and interview were the main instruments of data collection. The analysis of the quantitative data was carried out by using percent, mean, standard deviation and Pearson product correlation coefficient. Pearson product correlation coefficient results revealed that there was a significant and positive relationship between teachers’ job satisfaction and commitment (r = .48, N = 70), teachers’, and directors, supervisors and woreda educational expertise’s of teachers are respondents. Higher levels of teachers’ job satisfaction were associated with higher levels commitment in the study area. Furthermore, teachers’ high job satisfaction had positive effect on teachers’ commitment, as opposite low job satisfaction had negative effect on teachers’ commitment, preparation and students’ learning outcomes, teacher retention and absenteeism. As the mean and standard deviation teachers’, and directors, supervisors’ and wereda Education office coordinators revealed that the level of teachers’ job was dissatisfied and affected by salary and benefits, management system, work characteristics and load, advancement and promotion and interrelationship (mean= 2.48, std deviation= 1.09). Based on the these findings, it can be recommended that, Woreda and Zone education Departments should give an opportunity of Educational training in regular program, provide necessary equipment and material on time, give fair incentives and different benefits for preparatory schools, making the School environment secular and provide the working conditions with refreshment facilities. DOI: 10.7176/JRDM/72-05 Publication date: January 31 st 2021

Analysis of the Influence of Incentives, Infrastructure, and Work Environment on Teacher Performance at Dhira Bhakti Foundation Malang, Indonesia

This study aims to describe incentives, work environment infrastructure, and teacher performance, analyze incentives, infrastructure, and work environment simultaneously affect teacher performance, analyze incentives, infrastructure, and work environment partially affect teacher performance, and analyze among variables. incentives, infrastructure, and work environment that have a dominant effect on teacher performance at the Dhira Bhakti Foundation Malang. The number of samples in this study was 88 respondents. The data analysis technique used multiple regression analysis. The results of the analysis show that incentives, infrastructure, and work environment simultaneously have a significant effect on teacher performance, which means that to improve teacher performance it is necessary to provide fair incentives, adequate and functioning infrastructure when needed, and a good work environment. Incentives, infrastructure, and work environment partially have a significant effect on teacher performance, which means that the better incentives for teachers can improve teacher performance. The more complete the infrastructure needed in the learning process can improve teacher performance. Better working environment conditions can improve teacher performance. Infrastructure has a dominant effect on teacher performance, this shows that the main thing that can improve teacher performance is infrastructure. The more complete the infrastructure needed in the learning process can improve teacher performance. Better working environment conditions can improve teacher performance. Infrastructure has a dominant effect on teacher performance, this shows that the main thing that can improve teacher performance is infrastructure. The more complete the infrastructure needed in the learning process can improve teacher performance. Better working environment conditions can improve teacher performance. Infrastructure has a dominant effect on teacher performance, this shows that the main thing that can improve teacher performance is infrastructure.

An Intelligent Incentive Model Based on Environmental Ergonomics for Food SMEs

In this study, an intelligent incentive model based on environmental ergonomics in food small and medium-sized enterprises (SMEs) was developed. Environmental ergonomics was defined as the impact of temperature and relative humidity within a certain range on a worker's heart rate during work. Optimum environmental ergonomics are highly required as a basic standard for food SMEs to provide fair incentives. Recommendable parameters from a genetic algorithm and fuzzy inference modeling were used to model customized incentives based on optimum heart rate, workplace temperature and relative humidity before and after working. The research hypothesis stated that industries should optimize their workload and workstation environment prior to customizing incentives. The research objectives were: 1) to recommend optimum environmental ergonomics parameters for customized incentives; 2) to determine the incentives at workstations of SMEs based on optimum environmental ergonomics parameters and fuzzy inference modeling. The optimum values for heart rate, workstation temperature and relative humidity used were based on recommendable values from the genetic algorithm. An inference model was developed to generate decisions whether a worker should receive an incentive based on a calculated index. The results indicated that 84.4% of workers should receive an incentive. The results of this research could be used to promote the concept of ergonomics-based customized incentives.

A rare disease patient/caregiver perspective on fair pricing and access to gene-based therapies.

Gene-based therapies are changing the landscape of medicine for patients with rare diseases. These one-time, potentially curative treatments pose a challenge in the US healthcare model, where high prices and insurance coverage variation may prevent patients from receiving life-altering therapies. Questioning of high prices occurs when patients, payers, and policy makers hold divergent views on the value and uncertainty of therapies. The key for patients is that high prices need to be justified, and companies need to partner authentically with patients. Companies should not automatically assume that the combination of the "gene therapy" label and a small patient population justifies high prices. To speed up the development process and potentially reduce costs, patients want the industry to improve clinical trial efficiency by sharing data, including natural history studies and failed trial results. From the patient perspective, current value assessment frameworks disadvantage people with disabilities, may not accurately reflect patients' and societal views, and omit necessary factors such as impact on caregivers, lost productivity, and the future value of innovation. Value determination methods need to provide fair incentives and outcomes to industry, payers, regulators, and especially patients-the courageous pioneers who need equitable and sustainable access to life-changing gene-based therapies.

Secure Data Aggregation of Lightweight E-Healthcare IoT Devices With Fair Incentives

With rapid development of e-healthcare systems, patients that are equipped with resource-limited e-healthcare devices (Internet of Things) generate huge amount of health data for health management. These health data possess significant medical value when aggregated from these distributed devices. However, efficient health data aggregation poses several security and privacy issues such as confidentiality disclosure and differential attacks, as well as patients may be reluctant to contribute their health data for aggregation. In this paper, we propose a privacy-preserving heath data aggregation scheme that securely collects health data from multiple sources and guarantee fair incentives for contributing patients. Specifically, we employ signature techniques to keep fair incentives for patients. Meanwhile, we add noises into the health data for differential privacy. Furthermore, we combine Boneh–Goh–Nissim cryptosystem and Shamir’s secret sharing to keep data obliviousness security and fault tolerance. Security and privacy discussions show that our scheme can resist differential attacks, tolerate healthcare centers failures, and keep fair incentives for patients. Performance evaluations demonstrate cost-efficient computation, communication and storage overhead.

OPTIMALISASI BAGI HASIL PADA PRODUK PEMBIAYAAN MUDHARABAH DALAM MENINGKATKAN LABA DAN MARKET SHARE

&lt;p&gt;&lt;em&gt;Sharia in accounting are required to apply Sharia principles one is not allowed the existence of interest because it belongs in the category of usury, so that an Islamic banking system for the results, one of them on mudharabah financing, which is the core product of Sharia banking. The financial report on the statistics on Islamic banking income and market share of financing has the lowest proportion of mudharabah, optimization of products for mudharabah financing is important to increase profit and market share. This research is qualitative research by taking the location of research in PT Bank BRISyariah Malang Soekarno-Hatta. Data taken researchers use interview techniques and other secondary data, the researchers then perform data analysis and interpret the data obtained. The results of this research show that at PT Bank BRISyariah Malang branch Soekarno Hatta in applying the principle for the outcome has been in accordance with the rules of Fatwa DSN MUI No. 07/DSN-MUI/IV/2000, but Mudharabah financing is not a core product Because there are more uses for Mutanaqisah and Murabahah financing. The optimization efforts that have been done are not fully optimal, another way that can be done is to give fair incentives to customers who report their business honestly. If it can be applied then profit will increase so that the market share financing Mudarabah will also increase&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;/p&gt;

Neighborhoods and National Housing Policy: Toward Circumscribed, Neighborhood-Sensitive Reforms

This article provides a holistic analysis of why and how federal assisted housing policy (specifically, public housing, Low-Income Housing Tax Credit [LIHTC], and voucher programs) should be reformed in ways that would be more conducive to socially desirable outcomes at the neighborhood level. First, I argue that past research has documented mutually causal interrelationships between assisted housing policy and neighborhoods that have been couched as having negative connotations for both. Second, I argue that there is there a rationale on grounds of both efficiency and equity for altering assisted housing policy so it would encourage the creation and preservation of neighborhoods that are physically of good quality and economically diverse. Third, I advocate a circumspect menu of programmatic reforms that would be gradualist, option enhancing, and relatively budget neutral, yet would garner these positive impacts. As overarching reforms, I propose regional housing institution-building, fair housing law revisions, impaction standards, and diversity incentives built into Affirmatively Furthering Fair Housing. As reforms to site-based assistance programs, I propose: a new formula for disbursing LIHTC, to repeal and replace the Qualified Census Tract bonus, diversification/preservation incentives for existing assisted private developments, and preserving assisted housing in revitalizing neighborhoods. As reforms to tenant-based assistance programs, I propose: Small Area Fair Market Rents, premove and postmove mobility counseling, ancillary family supports postmove, reducing barriers to lease-up, and diversification incentives in U.S. Department of Housing and Urban Development regulations.