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Public sector revenue : principles, policies and management
In this time of acute financial pressure on public budgets, there is an increasing interest worldwide in alternative ways for governments to raise money, and how public authorities can develop the capacity to administer revenues efficiently and effectively. 'Public Sector Revenue' sets itself apart from other textbooks through its exclusive focus on the revenue side of public financial management.
BOOK
A Statistical Analysis of Impact of COVID19 on the Global Economy and Stock Index Returns
The outbreak of pandemic COVID-19 across the world has completely disrupted the political, social, economic, religious, and financial structures of the world. According to the data of April 22nd, 2020, more than 4.6 million people have been screened, in which the infection has made more than 2.7 million people positive, in which 182,740 people have died due to infection. More than 80 countries have closed their borders from transitioning countries, ordered businesses to close, instructed their populations to self-quarantine, and closed schools to an estimated 1.5 billion children. The world’s top ten economies such as the United States, China, Japan, Germany, United Kingdom, France, India, Italy, Brazil, and Canada stand on the verge of complete collapse. In addition, stock markets around the world have been pounded, and tax revenue sources have fallen off a cliff. The epidemic due to infection is having a noticeable impact on global economic development. It is estimated that by now the virus could exceed global economic growth by more than 2.0% per month if the current situation persists. Global trade may also fall from 13 to 32% depending on the depth and extent of the global economic slowdown. The full impact will not be known until the effects of the epidemic occurred. This research analyses the impact of COVID-19 on the economic growth and stock market as well. The aim of this research is to present how well COVID-19 correlated with economic growth through gross domestic products (GDP). In addition, the research considers the top five other tax revenue sources like S&P500 (GPSC), Crude oil (CL = F), Gold (GC = F), Silver (SI = F), Natural Gas (NG = F), iShares 20 + Year Treasury Bond (TLT), and correlate with the COVID-19. To fulfill the statistical analysis purpose this research uses publically available data from yahoo finance, IMF, and John Hopkins COVID-19 map with regression models that revealed a moderated positive correlation between them. The model was used to track the impact of COVID 19 on economic variation and the stock market to see how well and how far in advance the prediction holds true, if at all. The hope is that the model will be able to correctly make predictions a couple of quarters in advance, and describe why the changes are occurring. This research can support how policymakers, business strategy makers, and investors can understand the situation and use the model for prediction.
Journal Article
The Risks of Innovation
While innovation matters for competitiveness, it may expose firms to survival risks. Using plant-product data for Chile and discrete-time hazard models, we show that innovating plants have a lower hazard of exit. However, risk has a strong impact on the innovation-exit relationship: only innovators that retain diversified sources of revenue or face lower market risk are less likely to die. Single-product innovators are at greater risk of exiting. Exposure to technical risk does not affect exit probabilities differentially. We provide tentative evidence that singleproduct innovators have higher profits, which helps to rationalize their innovation decision despite the increased risk of exit.
Journal Article
Fiscal management in resource-rich countries : essentials for economists, public finance professionals, and policy makers
The extractive industries sector (EI) occupies an outsize space in the economies of many developing countries. Policy makers, economists, and public finance professionals working in such countries are frequently confronted with issues that require an in-depth understanding of the sector, its economics, governance, and policy challenges, as well as the implications of natural resource wealth for fiscal and public financial management. The objective of the two-volume Essentials for Economists, Public Finance Professionals, and Policy Makers, published in the World Bank Studies series, is to provide a concise overview of the EI-related topics these professionals are likely to encounter. This second volume, Fiscal Management in Resource-Rich Countries, addresses critical fiscal challenges typically associated with large revenue flows from the EI sector. The volume discusses fiscal policy across four related dimensions: short-run stabilization, the management of fiscal risks and vulnerabilities, the promotion of long-term sustainability, and the importance of good public financial management and public investment management systems. The volume subsequently examines several institutional mechanisms used to aid fiscal management, including medium-term expenditure frameworks, resource funds, fiscal rules, and fiscal councils. The volume also discusses the earmarking of revenue, resource revenue projections as applied to the government budget, and fiscal transparency, and outlines several fiscal indicators used to assess the fiscal stance of resource-rich countries. The authors hope that economists, public finance professionals, and policy makers working in resource-rich countries-- including decision makers in ministries of finance, international organizations, and other relevant entities-- will find the volume useful to their understanding and analysis of fiscal management in resource-rich countries.
Book
A Nonparametric Approach to Modeling Choice with Limited Data
Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the \"right\" model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and overfitting/underfitting. Thus motivated, we visit the following problem: For a \"generic\" model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal information about these distributions), how may one predict revenues from offering a particular assortment of choices? An outcome of our investigation is a
nonparametric
approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models; this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward \"automating\" the crucial task of choice model selection.
This paper was accepted by Yossi Aviv, operations management.
Journal Article
Revenue management in manufacturing : state of the art, application and profit impact in the process industry
This book focuses on the application of revenue management in the manufacturing industry. Though previous books have extensively studied the application of revenue management in the service industry, little attention has been paid to its application in manufacturing, despite the fact that applying it in this context can be highly profitable and instrumental to corporate success. With this work, the author demonstrates that the manufacturing industry also fulfills the prerequisites for the application of revenue management. The book includes a summary of empirical studies that effectively illustrate how revenue management is currently being applied across Europe and North America, and what the profit potential is -- Backcover.
Book
Robust Assortment Optimization in Revenue Management Under the Multinomial Logit Choice Model
We study robust formulations of assortment optimization problems under the multinomial logit choice model. The novel aspect of our formulations is that the true parameters of the logit model are assumed to be unknown, and we represent the set of likely parameter values by a compact uncertainty set. The objective is to find an assortment that maximizes the worst-case expected revenue over all parameter values in the uncertainty set. We consider both static and dynamic settings. The static setting ignores inventory consideration, whereas in the dynamic setting, there is a limited initial inventory that must be allocated over time. We give a complete characterization of the optimal policy in both settings, show that it can be computed efficiently, and derive operational insights. We also propose a family of uncertainty sets that enables the decision maker to control the trade-off between increasing the average revenue and protecting against the worst-case scenario. Numerical experiments show that our robust approach, combined with our proposed family of uncertainty sets, is especially beneficial when there is significant uncertainty in the parameter values. When compared to other methods, our robust approach yields over 10% improvement in the worst-case performance, but it can also maintain comparable average revenue if average revenue is the performance measure of interest.
Journal Article
Revenue Management Under the Markov Chain Choice Model
We consider revenue management problems when customers choose among the offered products according to the Markov chain choice model. In this choice model, a customer arrives into the system to purchase a particular product. If this product is available for purchase, then the customer purchases it. Otherwise, the customer transitions to another product or to the no purchase option, until she reaches an available product or the no purchase option. We consider three classes of problems. First, we study assortment problems, where the goal is to find a set of products to offer to maximize the expected revenue obtained from each customer. We give a linear program to obtain the optimal solution. Second, we study single resource revenue management problems, where the goal is to adjust the set of offered products over a selling horizon when the sale of each product consumes the resource. We show how the optimal set of products to offer changes with the remaining resource inventory. Third, we study network revenue management problems, where the goal is to adjust the set of offered products over a selling horizon when the sale of each product consumes a combination of resources. A standard linear programming approximation of this problem includes one decision variable for each subset of products. We show that this linear program can be reduced to an equivalent one with a substantially smaller size. We give an algorithm to recover the optimal solution to the original linear program from the reduced linear program. The reduced linear program can dramatically improve the solution times for the original linear program.
The online appendix, data files, and source code are available at
https://doi.org/10.1287/opre.2017.1628
.
Journal Article