Thesis advisor: S.J. Richard A. McGowan / This thesis will examine the free rider problem in a unique setting: how states compete over gambling revenues. As the economy tightens, states continue to look for revenue streams to supplement their already strained budgets. Gambling is an attractive option for many states, as it is a steady and reliable source of income each month. With appealing funds available, different states have intensified their competition, authorizing new casinos on neighboring state borders to entice out-of-state visitors. States receive money from these visitors but are not responsible for their social problems, which they bring back to their home states. This phenomenon is a modern incarnation of the free rider problem. This paper explores three main questions. To begin, does the gambling market expand with the introduction of a new state's gambling facilities? Next, are states able to successfully reclaim revenues? Lastly, what are the effects of changes in tax rates on state revenues? These questions are examined in two regions. First, the newly authorized slots in Pennsylvania are having a direct impact on the casinos in Atlantic City, NJ. Also, a variety of tax changes in the Midwest states of Illinois, Indiana, and Missouri is shifting revenues among these three states. The free rider problem relating to states and gambling will continue to be an important and relevant issue for years to come. / Thesis (BS) — Boston College, 2008. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Economics. / Discipline: College Honors Program.

Thesis advisor: Michael Barry / This paper will study the effects of airline alliances on the economic welfare of passengers and airlines by studying how membership in an airline alliance affects ticket price, revenue passenger miles, and available seat utilization. This paper will analyze three sets of data from the US Department of Transportation, including the DB1BTicket Report, the T-100 International Segment Report, and the T1: US Air Carrier Traffic and Capacity Summary by Service Class. The purpose of this paper is to determine how airline alliances effect consumer welfare. The results show that airline alliances lead to higher fares on domestic routes as well as greater passenger revenue miles and available seat utilization. This paper shows that more anti-trust investigation should be taking place regarding airline alliances. / Thesis (BS) — Boston College, 2011. / Submitted to: Boston College. Carroll School of Management. / Discipline: College Honors Program. / Discipline: Finance.

Esta pesquisa tem como objetivo principal desenvolver um modelo capaz de captar como mudanças nas alíquotas dos tributos afetam as diferentes classes de renda e quais são seus impactos na arrecadação do governo. A justificativa para desenvolver o estudo é que as autoridades públicas, no Brasil, não dispõem de mecanismos que permitam fazer tal análise e, provavelmente, esse é um dos motivos pelos quais não foi dada a devida atenção à forma como os impostos indiretos recaem sobre a população. O modelo desenvolvido no presente estudo requer três informações fundamentais: a carga tributária por faixa de renda, a estimação da matriz de elasticidades Marshallianas e o padrão de consumo da população. As simulações são feitas com base nos microdados da POF de 2002-2003. Sendo assim, para alcançar os objetivos propostos pela tese, é feito um estudo detalhado da atual incidência do sistema tributário brasileiro, que vai da averiguação da carga tributária direta, indireta e total por faixa de renda até a estimação de um modelo de sacrifício eqüitativo. Buscou-se detalhar ao máximo as alíquotas dos impostos indiretos, tomando como base as normas tributárias da Federação, das Unidades da Federação e respectivas capitais. Cruzando essas informações com os microdados da POF de 2002-2003, foi possível verificar que o sistema tributário brasileiro é regressivo quando tomada como base a renda. Isso se deve em grande parte aos impostos indiretos, mais especificamente ao ICMS, ao PIS e à COFINS. Contudo, é importante ressaltar que a baixa participação dos impostos diretos não permite equilibrar a carga por faixa de renda. O IR tem uma taxa efetiva bem abaixo da alíquota que está prevista em lei e o IPTU chega a ser regressivo quando analisada a renda familiar total. O estudo também revelou que há diferenças regionais no comportamento dos impostos indiretos. No caso específico dos alimentos, que é um grupo de despesa relevante para as famílias de baixa renda, as maiores cargas foram verificadas nas regiões Norte e Nordeste. Na estimação da matriz de elasticidades Marshallianas a partir dos microdados da POF de 2002-2003 é usada a versão não-linear do sistema quase ideal de demanda (NL-AIDS) para 27 grupos de produtos. Como não é possível obter todos os preços necessários para os produtos nãoalimentícios na POF, foi necessário buscar informações de preços em outras fontes. Para estimar uma função de tributação pressupondo obediência ao princípio de sacrifício eqüitativo, é utilizada a forma proposta por Hoffmann, Silveira e Pintos-Payeras (2006). Os resultados sugerem que o coeficiente de aversão à desigualdade está aumentando no Brasil e isto pode ser interpretado como o desejo da sociedade por uma tributação mais justa. As simulações feitas mostram que o modelo desenvolvido no último capítulo apresenta um razoável desempenho para avaliar os efeitos das mudanças no sistema tributário. Certamente é um recurso adicional que pode ser usado pelas autoridades públicas antes de fazer alterações nas alíquotas tributárias. O modelo permitiu observar que quando é buscada maior progressividade do sistema tributário, é necessário combinar alterações nos impostos diretos e indiretos. / The main objective of this research is to develop a model that is able to evaluate how the effects of changes in tax rates are distributed among the different income classes as well as their impact in the government revenues. This study is relevant, because Brazilian public authorities do not have mechanisms for such analysis, and this is possibly one the reasons why the way indirect taxes affect the population has not been carefully considered so far. The model developed in this study requires three fundamental types of information: the tax burden per income class, the estimate of Marshalls elasticities matrix, and the populations pattern of consumption. Simulations are based on Household Budgeting Survey (POF-2002-2003) microdata. Thus, to reach the objectives proposed by this thesis, a detailed study of the current incidence of the Brazilian tax system was carried out, including the evaluation of direct, indirect and per-class tax burden and the estimation of a tax function assuming equitable sacrifice. Indirect tax rates were detailed as much as possible, considering the tax regulations of the country, states and their respective capital cities. Combining this information with the 2002-2003 POF database, it could be verified that the Brazilian tax system is regressive when based on income. This is mainly due to indirect taxes, and more specifically to Value added tax (ICMS), Social Security (PIS), and Social Security Financing Tax (COFINS). However, we must point out that the low participation of indirect taxes does not allow a balance of the tax burden per income class. Income tax has an effective rate well below the rate established by the law, and municipal tax on properties (IPTU) is regressive, when the total family income is analyzed. The study also showed that there are regional differences as to indirect taxes. Particularly in the case of food, which is a relevant expense group for low income families, higher tax burdens were found for the North and Northeast regions. Marshalls elasticities matrix was estimated using a non-linear version of the almost ideal demand system (NL-AIDS), considering 27 product groups out of the 2002-2003 POF database. As not all the necessary prices for non-food products could be obtained from the POF database, other information sources were used. Tax functions assuming equitable sacrifice were estimated using the procedure proposed by Hoffmann, Silveira and Pintos-Payeras (2006). The results suggest that the coefficient of aversion to inequality is increasing in Brazil and this can be interpreted as the populations desire for a fairer tax system. The simulations show that the model developed in the last chapter has a reasonable performance in the evaluation of the effect of changes in the tax system. It is certainly an additional instrument which can be used by public authorities before establishing changes in the tax rates. The model allowed us to observe that when a higher progressivity of the tax system is attempted, it is necessary to combine changes in direct and indirect taxes.

This thesis consists of two parts. In the first part, we concentrate on the computation of Market Equilibria and settle the long-standing open problem regarding the computation of an approximate Arrow-Debreu market equilibrium in markets with CES utilities. We prove that the problem is PPAD-complete when the Constant Elasticity of Substitution parameter $\rho$ is any constant less than -1. Building on this result, we introduce the notion of non-monotone utilities, which covers a wide variety of utility functions in economic theory, and prove that it is PPAD-hard to compute an approximate Arrow-Debreu market equilibrium in markets with linear and non-monotone utilities. In the second part, we study Revenue Maximization. We begin by resolving the complexity of the revenue-optimal Bayesian Unit-demand Item Pricing problem when the buyer's values for the items are independent. We show that the problem can be solved in polynomial time for distributions of support size 2; but its decision version is NP-complete for distributions of support size 3. Next, we study the optimal mechanism design problem for a single unit-demand buyer with item values drawn from independent distributions. We show that, for distributions of support-size 2 and the same high value, Item Pricing can achieve the same revenue as any menu of lotteries. On the other hand, we provide simple examples where randomization improves revenue. Finally, we show that unless the polynomial-time hierarchy collapses, namely P^{NP}=P^{#P}, there is no universal efficient randomized algorithm that implements an optimal mechanism even when distributions have support size 3.

This dissertation consists of three papers in revenue management: on-line assortment optimization with reusable resources, spatial distribution of surge price under incentive compatible assignment for drivers and optimal price rebates for demand response under power flow constraints. In Chapter 2, we study an on-line assortment optimization problem of substitutable products with fixed reusable capacities. At any time, a potential user with her preference model (possibly adversarially chosen) arrives to the selling platform and the platform offers a subset of products from the available set of products to the user. The user selects a product with probability given by her preference model, uses it for a random duration, which is distributed according to a distribution that only depends on the product selected, and generates revenue to the seller. The revenue contribution depends on the product selected and the actual usage time of this user. The goal of the seller is to find a policy for determining the assortment offered to each arrival to maximize the expected cumulative revenue over a time horizon. We find that a simple myopic policy offering the available assortment that maximizes the expected revenue from a single user at her arrival time provides a good approximation for the problem. In particular, we show that the myopic policy is $1/2$-competitive, i.e., the expected cumulative revenue of the myopic policy is at least $1/2$ times the expected cumulative revenue of an optimal clairvoyant policy that has full information about the adversarially chosen user sequence, including their preference models and arrival epochs. The proof is based on partitioning the expected revenue of optimal clairvoyant policy into two parts and a coupling argument that allows us to bound the two parts in terms of the expected revenue of the myopic policy. In Chapter 3, we study the surge pricing problem on a ride sharing platform when there is a demand shock to the traffic network. The goal of the platform is to maximize the revenue by setting the prices over the network and the assignments between drivers and riders. In particular, we model the city as a continuous two dimensional network with exogenous arrivals of baseline riders, available drivers and demand shocks. We consider the demand shock only exists in a short time scale, so the rider chooses to request the ride or not depending on their willingness to pay and the price quoted to them, and the driver accepts any price to provide service. Since drivers can see the price distribution on driver app, they only accept the assignment from the locations that are incentive compatible for them. Thus, the price change at one location may affect the operations over the network and the platform must consider the incentive of drivers when assigning them. We develop a model for this surge pricing problem and show the structural properties of an optimal solution. Once the prices at the location with demand shock is determined, we can determine the optimal prices on other part of the network. Then, the optimal assignments between riders and drivers can be determined analytically. The surge pricing problem reduces to one that only depends on the price at the location with demand shock. We then extend our model by including strategic behavior of riders, using throughput as objective, dealing with multiple demand shocks, un-constraining the price and considering movement time. We also conduct numerical experiments to study the properties of the model which can not be explored analytically. In Chapter 4, we study the demand response problem of computing price rebates to offer to the customers to reduce the consumption in the presence of power flow constraints and transmission losses on the distribution grid. In particular, we employ alternating current power flow model for the power flow constraints with transmission loss. However, the demand response problem with alternating current power flow constraints is known as a non-convex problem, which is in-tractable to solve. To overcome this, we apply a semi-definite relaxation of alternating current power flow model to obtain a convex approximation for the problem. At the same time, to handle the uncertainty in the power reduction of customers, we use sample average to approach the expected cost and linear injection approximation to estimate the impact of uncertainty in the power reduction. Based on these relaxations and approximations, we propose an efficient iterative heuristic to solve the near-optimal offer price under alternating current power flow constraints and transmission losses. We conduct a substantial amount of numerical tests on our heuristic and compare its performance with other popular models. The result shows that our iterative heuristic leads to a significant reduction in the rebates that one needs to offer to shed a certain demand than the solution which does not consider full transmission loss in its model.

One fundamental problem in revenue management that arises in many settings including retail and display-based advertising is assortment planning. Here, the focus is on understanding how consumers select from a large number of substitutable items and identifying the optimal offer set to maximize revenues. Typically, for tractability, we assume a model that captures consumer preferences and focus on computing the optimal offer set. A significant challenge here is the lack of knowledge on consumer preferences. In this thesis, we consider the multinomial logit choice model, the most popular model for this application domain and develop tractable robust algorithms for assortment planning under uncertainty. We also quantify the fundamental performance limits from both computational and information theoretic perspectives for such problems. The existing methods for the dynamic problem follow ``estimate, then optimize'' paradigm, which require knowledge of certain parameters that are not readily available, thereby limiting their applicability in practice. We address this gap between theory and practice by developing new theoretical tools which will aid in designing algorithms that judiciously combine exploration and exploitation to maximize revenues. We first present an algorithm based on the principle of ``optimism under uncertainty'' that is simultaneously robust and adaptive to instance complexity. We then leverage this theory to develop a Thompson Sampling (TS) based framework with theoretical guarantees for the dynamic problem. This is primarily motivated by the growing popularity of TS approaches in practice due to their attractive empirical properties. We also indicate how to generalize the TS framework to design scalable dynamic learning algorithms for high-dimensional data and discuss empirical gains of such approaches from preliminary implementations on Flipkart, a large e-commerce firm in India.

Revenue management (RM) is the science of selling the right product, to the right person, at the right price. A key to the success of RM, which now spans a broad array of industries, is its grounding in mathematical modeling and analytics. This dissertation contributes to the development of new RM tools by: (1) exploring some fundamental tradeoffs underlying any RM problems, and (2) designing efficient algorithms for some RM applications. Another underlying theme of this dissertation is the modeling of customer preferences, a key component of any RM problem. The first chapters of this dissertation focus on the model selection problem: many demand models are available but picking the right model is a challenging task. In particular, we explore the tension between the richness of a model and its tractability. To quantify this tradeoff, we focus on the assortment optimization problem, a very general and core RM problem. To capture customer preferences in this context, we use choice models, a particular type of demand model. In Chapters 1, 2, 3 and 4 we design efficient algorithms for the assortment optimization problem under different choice models. By assessing the strengths and weaknesses of different choice models, we can quantify the cost in tractability one has to pay for better predictive power. This in turn leads to a better understanding of the tradeoffs underlying the model selection problem. In Chapter 5, we focus on a different question underlying any RM problem: choos- ing how to sell a given product. We illustrate this tradeoff by focusing on the problem of selling ad impressions via Internet display advertising platforms. In particular, we study how the presence of risk-averse buyers affects the desire for reservation con- tracts over real time buy via a second-price auction. In order to capture the risk aversion of buyers, we study different utility models.

The thesis is focused on a comparison of IFRS and US GAAP rules for revenue recognition and measurement. Some topics are complemented by Czech accounting regulations appropriately. The practical part includes an analysis of recognition of expenses and revenues associated with customer loyalty programs by the airlines and futher an application of revenue recognition rules in SKANSKA Group. In the final chapter is outlined the expected development in the area of revenue recognition.

The master thesis is divided into two parts. The first part is theoretical and deals with the development of tourism, business strategy in hotel trade and marketing mix. The second part is practical and is focused on the analysis of business strategy of Smaragd Hotel. Within the business strategy strengths and weaknesses of the hotel, competition, income and expenses, and marketing mix are analyzed. Based on the analysis, possible solutions of the future development of the hotel are proposed.

This paper tests a revenue catering theory under which investors have time-varying demand for revenue growth and managers will cater to this demand by delivering higher revenue when investors place a higher premium on revenue. I document the time-series variation in the "revenue surprise premium" - a proxy for investor demand for revenue growth, where the "revenue surprise premium" is measured as the earnings announcement period stock return response to good news in revenue after controlling for news in earnings. I investigate whether managers cater to the time-varying "revenue surprise premium" by meeting or beating market expectations of revenue. I find evidence consistent with revenue catering behavior. Firms are more likely to meet or beat analyst forecasts of revenue when the previous quarter's revenue surprise premium is high. I also find evidence that firms use aggressive revenue recognition practices when catering to investors. The results are most pronounced among firms in high-tech and health sectors whose revenue surprise premiums are higher relative to other sectors.