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•Price match (PMGs) and low price (LPG) guarantees are perceived differently.•LPGs (vs. PMGs) are more strongly associated with lower prices.•For those promotion focused, purchase ...intentions higher for LPGs (vs. PMGs).•But, for those promotion focused, repurchase intentions lower for LPGs (vs. PMGs).
Some retailers offer price match guarantees, whereas others offer guarantees making the same promise but labeling these low-price guarantees. Do consumers respond differently to these different price guarantee labels? To address this research question, this paper leverages insights from pricing, signaling, and regulatory focus theories to demonstrate – across multiple studies – that consumers perceive price match versus low price guarantees differently. In turn, contingent on consumers’ regulatory focus, this difference in perceptions feeds forward and influences consumers’ purchase intentions and post purchase (e.g., repurchase) intentions. This paper contributes to theory, not only by showing differences across price match versus low price guarantees, but also by being the first to jointly examine purchase and post purchase intentions relating to price guarantees. This paper also speaks to practice, noting contingencies that determine whether the price match or the low-price guarantee label should be used.
Government guarantees of bank liabilities have a long-standing history and are now ubiquitous. We study a model where financial sophistication enhances banks’ ability to exploit government guarantees ...and fuels inefficient economic booms. Driven by financial engineering, bank rent extraction creates a disconnect between lending decisions and borrower repayment prospects: In equilibrium, banks over-lend and only break-even courtesy of trading book profit. Exploitability is affected not only by financial sophistication but also by regulation. Given the pattern for regulatory changes in the last few decades, we posit that the Great Recession, partly, reversed a Great Distortion.
•Government guarantees of bank debt are widespread around the world.•These guarantees can be exploited but, in a simple world, the effect is relatively mild.•We show that financial sophistication enhances banks’ ability to exploit guarantees.•Such exploitation fuels inefficient economic booms and distorts economic activity.
This paper estimates the impact of public guarantees on crisis predictive indicators among small and mid-size enterprises (SMEs). We use a confidential database provided by the Italian Ministry of ...Economic Development on the universe of guarantees granted by the Central Guarantee Fund. We apply difference-in-difference regressions and propensity-score matching estimators to a sample of approximately 40,000 SMEs over the 2010–2018 period. We find that obtaining a public guarantee improves profitability both in the short- and medium-term. On the other hand, SMEs’ financial health worsens in the short run, but financial burdens are alleviated 2 years after the issuance of a guarantee. The economic and financial effects of government-backed loans are amplified for micro-sized firms, companies operating in the service sector and direct guarantees. Our results can thus support public authorities in designing credit guarantee schemes capable of preventing SMEs’ zombification and protecting them from the risk of debt overhang.
Plain English Summary
Access to public credit guarantee schemes negatively impact SMEs’ financial equilibrium, but their recovery occurs 2 years after guarantee issue. How does one select eligible firms to prevent zombification? Using a confidential dataset provided by the Italian Ministry of Economic Development on guarantees issued by the Central Guarantee Fund, we investigate this topic with an unprecedented level of salience. Our findings reveal the need for cautious interventions on firms in financial distress and for the introduction of stress tests to select beneficiaries. Our results show that specific guarantee lines could be applied for direct guarantees granted to micro-sized enterprises and companies operating in the service sector to maximize the additionality of public resources. This study has practical, policy and societal implications, guiding SMEs in their assessment of the overall medium-term effects of guarantees and policy-makers in their rethinking of guarantee schemes to resolve trade-offs between effectiveness and sustainability.
The UK has had a commitment to loan guarantee schemes since 1981 when it introduced the Small Firms Loan Guarantee (SFLG) scheme to address access to debt finance issues for smaller firms. Over the ...last 40 years, its support has been unwavering, and in the Covid-19 crisis, it once again turned to loan guarantees as a means of supporting smaller firms through the crisis-induced slump in trading activities. Of its three core Covid-19 guarantee schemes, the Bounce Back Loan (BBL) scheme was the most numerous with 1,531,095 loans issued amounting to a total of £46.5bn in lending. The BBL scheme provided a 100% capital guarantee on loans between £2,000 and £50,000, and firms were allowed to borrow up to 25% of their trading income, with a fixed interest rate of 2.5% of which the first years interest was paid by the government to the lending bank. Our findings suggest that the government losses may range between £7bn and £12bn depending on the underlying assumptions; however, we estimate Covid-19 guarantee schemes may have protected 118,639 businesses and 1,117,849 jobs. Looking to the future, we suggest that a new loan guarantee is justified that more resembles the former SFLG than the restrictive Enterprise Finance Guarantee (EFG) as more than one million small businesses will be heavily indebted and unable to borrow to invest in future growth opportunities. This would support the ‘levelling-up’ agenda and help prevent a post-Covid-19 low investment–low growth scenario.
The celebrated sparse representation model has led to remarkable results in various signal processing tasks in the last decade. However, despite its initial purpose of serving as a global prior for ...entire signals, it has been commonly used for modeling low dimensional patches due to the computational constraints it entails when deployed with learned dictionaries. A way around this problem has been recently proposed, adopting a convolutional sparse representation model. This approach assumes that the global dictionary is a concatenation of banded circulant matrices. While several works have presented algorithmic solutions to the global pursuit problem under this new model, very few truly effective guarantees are known for the success of such methods. In this paper, we address the theoretical aspects of the convolutional sparse model providing the first meaningful answers to questions of uniqueness of solutions and success of pursuit algorithms, both greedy and convex relaxations, in ideal and noisy regimes. To this end, we generalize mathematical quantities, such as the l 0 norm, mutual coherence, Spark and restricted isometry property to their counterparts in the convolutional setting, intrinsically capturing local measures of the global model. On the algorithmic side, we demonstrate how to solve the global pursuit problem by using simple local processing, thus offering a first of its kind bridge between global modeling of signals and their patch-based local treatment.
In this paper, we consider a retailer adopting a “money‐back‐guaranteed” (MBG) sales policy, which allows customers to return products that do not meet their expectations to the retailer for a full ...or partial refund. The retailer either salvages returned products or resells them as open‐box items at a discount. We develop a model in which the retailer decides on the quantity to procure, the price for new products, the refund amount, as well as the price of returned products when they are sold as open‐box. Our model captures important features of MBG sales including demand uncertainty, consumer valuation uncertainty, consumer returns, the sale of returned products as open‐box items, and consumer choice between new and returned products and possibility of exchanges when restocking is considered. We show that selling with MBGs increases retail sales and profit. Furthermore, the second‐sale opportunity created by restocking returned products enables the retailer to generate additional revenues. Our analysis identifies the ideal conditions under which this practice is most beneficial to the retailer. Offering an MBG without restocking increases the new product price. We show that if the retailer decides to resell the returned items as open‐box, the price of the new product further increases, while open‐box items are sold at a discount. On the other hand, customers enjoy more generous refunds along with lower restocking fees. The opportunity to resell returned products also generally decreases the initial stocking levels of the retailer. Our extensive numerical study substantiates the analytical results and sharpens our insights into the drivers of performance of MBG policies and their impact on retail decisions.
Analiza modernă a drepturilor și libertăților omului subliniază importanța garanțiilor drepturilor fundamentale, deoarece o declarație solemnă a libertăților nu este suficientă, altfel avem nevoie de ...eficiența acestora. Studiul garanțiilor drepturilor de proprietate privată sub aspectul științei dreptului constituțional permite să determinăm, la cel mai înalt nivel legislativ, funcțiile, rolul și locul lor în sistemul de relații, în care este pus în aplicare dreptul constituțional la proprietate privată, în modernizarea economiei, dezvoltarea societății civile, precum și a unui stat democratic. PRIVATE PROPERTY AND THE SYSTEM OF PROTECTION GUARANTEES OF THE RIGHT TO PROPERTYThe modern analysis of human rights and freedoms emphasizes the importance of guarantees of fundamental rights, a formal declaration of freedoms not being sufficient, we need to proof their effectiveness. The study of the guarantees of the private property rights from the aspect of the science of constitutional law allows us to determine their functions, role and place in the system of relations, in which the constitutional law of private property is being implemented, in the process of modernizing the economy, developing a civil society and a democratic state.
State-backed credit guarantee schemes aim to close the gap in the financing of small enterprises or startups caused by lacking collateral and high information asymmetry. The present study discusses ...the effectiveness of German guarantee banks compared to credit guarantee schemes in other countries and quantifies their economic and fiscal net benefits in the new federal states of Germany, where economic development is still lacking behind. Using data of five guarantee banks and from enterprise and bank surveys, we measure finance and project additionality of loan and equity guarantees provided over the period 1991–2015. Cost-benefit analyses show that the economic benefits of the guarantee banks are considerable because of increased production and employment, while the economic costs are negligible. The real GDP increases by about 1.2 euro per euro guarantee each year. For the years 2008–2014, we find net fiscal gains of several hundred million euros in each federal state.
Crowdsourcing contests are contests by which organizations tap into the wisdom of crowds by outsourcing tasks to large groups of people on the Internet. In an online environment often characterized ...by anonymity and lack of trust, there are inherent uncertainties for participants of such contests. This study focuses on crowdsourcing contests with winner-take-all prizes. During these contests, submissions are made sequentially and contest hosts can provide public in-process feedback to the submissions as soon as they are received. Drawing on the uncertainty literature, we examine how the use of prize guarantees (guaranteeing that a winner will be picked and paid) and in-process feedback (numeric ratings to individual designs and public textual comments during the contest) can help attract more submissions by influencing the various uncertainties faced by the contestants. We find that guaranteeing the prize increases submissions. The volume of in-process feedback (both numeric reviews and textual comments) has a positive effect on the number of submissions, and such an effect is bigger in contests without prize guarantees. In addition, providing highly positive or extremely negative feedback discourages overall future submissions, and the negative effect of highly positive feedback is mitigated in guaranteed contests.
Introduction/Main Objectives: This study explores the application of machine-learning techniques to risk-based premium calculations for insurance guarantee schemes within the Indonesian insurance ...market. This study aims to develop a risk-based premium calculation model using machine-learning techniques in the Indonesian context. Background Problems: A gap exists in determining risk-based premiums for both the life and non-life insurance sectors within the Indonesian insurance market. Identifying and understanding the key variables that significantly influence risk-based capital (RBC) is important, and this research addresses this need. Novelty: This paper is the first to apply machine learning to calculate risk-based premiums in the context of the Indonesian insurance market. The distinction between the life and non-life insurance sectors in terms of the importance of its variables and itsselection of an optimal model further enrich its unique approach. Research Methods: We employed gradient-boosted and decision-tree models to identify key factors impacting risk-based capital. Furthermore, we leveraged clustering techniques to categorize companies into distinct risk tiers, aiming to enable more precise risk-based premium rate calculations. Finding/Results: The findings reveal significant differences between the life and non-life insurance sectors in terms of key variables that impact their risk-based capital. These insights lead to the categorization of insurance companies into distinct risk tiers whichhelps to more accurately calculate risk-based premiums. Conclusion: Machine learning can serve as a powerful tool in refining insurance risk management practices, ultimately benefiting insurers, policyholders, and regulators alike.
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