Essentially, there are two competing propositions on tax base choices. The optimal tax theory on taxation asserts that the broader the tax base the better the tax. On the other hand, some public choice proponents have argued that, at the constitutional level, we should choose to restrict the power to tax and thus limit the available base. These theories assert fundamentally different views on the state and its citizens. Within the traditional optimal tax framework, governments maximize residents utility and tax base broadening lowers the tax rate, thus there is a revenue neutral response. When, however, governments do not choose to maximize residents utility, then increases in the tax base can have an impact on governments revenues and spending. In order to determine if tax bases influence government spending data on forty-eight states were compiled for the years 1977 through 1992. A state finance system of equations was developed. Using three-stage least squares estimation in a fixed effects econometric model, the relationship between the broadness of a tax base and state government spending was estimated. The state sales tax base was the tax base used to study this relationship. The results of this estimation found that states with broader sales tax bases had higher spending, all else equal. This result suggest that governments do not act as if they maximize resident utility when making tax base and rate decisions, otherwise base broadness would have no impact on spending. An additional result from this empirical analysis, is that tax base and rates are inversely related, but the relationship does not lead to revenue-neutral adjustments.

This thesis makes a contribution to the algorithmic pricing literature by proposing and analyzing techniques for automatically pricing digital and information goods in order to maximize profit in different settings. We also consider the effect on social welfare when agents use these pricing algorithms. The digital goods considered in this thesis are electronic commodities that have zero marginal cost and unlimited supply e.g., iTunes apps. On the other hand, an information good is an entity that bridges the knowledge gap about a product between the consumer and the seller when the consumer cannot assess the utility of owning that product accurately e.g., Carfax provides vehicle history and can be used by a potential buyer of a vehicle to get information about the vehicle. With the emergence of e-commerce, the customers are increasingly price sensitive and search for the best opportunies anywhere. It is almost impossible to manually adjust the prices with rapidly changing demand and competition. Moreover, online shopping platforms also enable sellers to change prices easily and quickly as opposed to updating price labels in brick and mortar stores so they can also experiment with different prices to maximize their revenue. Therefore, e-marketplaces have created a need for designing sophisticated practical algorithms for pricing. This need has evoked interest in algorithmic pricing in the computer science, economics, and operations research communities. In this thesis, we seek solutions to the following two algorithmic pricing problems: (1) In the first problem, a seller launches a new digital good (this good has unlimited supply and zero marginal cost) but is unaware of its demand in a posted-price setting (i.e., the seller quotes a price to a buyer, and the buyer makes a decision depending on her willingness to pay); we look at the question --- how should the seller set the prices in order to maximize her infinite horizon discounted revenue? This is a classic problem of learning while earning. We propose a few algorithms for this problem and demonstrate their effectiveness using rigorous empirical tests on both synthetic datasets and real-world datasets from auctions at eBay and Yahoo!, and ratings on jokes from Jester, an online joke recommender system. We also show that under certain conditions the myopic Bayesian strategy is also Bayes-optimal. Moreover, this strategy has finite regret (independent of time) which means that it also learns very fast. (2) The second problem is based on search markets: a consumer is searching for a product sequentially (i.e., she examines possible options one by one and on observing them decides whether to buy or not). However, merely observing a good, although partially informative, does not typically provide the potential purchaser with the complete information set necessary to execute her buying decision. This lack of perfect information about the good creates a market for intermediaries (we refer to them as experts) who can conduct research on behalf of the buyer and sell her this information about the good. The consumer can pay these intermediaries to learn more about the good which can help her in making a better decision about whether to buy the good or not. In this case, we study various pricing schemes for these information intermediaries in a search-based environment (e.g., selling a package of $k$ reports instead of selling a single report or offering a subscription based service). We show how subsidies can be an effective tool for a market designer to increase the social welfare. We also model quality level in the experts and study competition dynamics by computing equilibrium strategies for the searcher and two experts with different qualities. Surprisingly, sometimes an improvement in the quality of the higher-quality expert (holding everything constant) can be pareto-improving: not only that expert's profit increase, so does the other expert's profit and the searcher's utility. / Ph. D.

This dissertation contains three papers from two projects. The first two papers (Chapter Two and Chapter Three) are from a project entitled “Managing Flounder Openings for Maximum Revenue.” The objective of this project is to (1) estimate the monthly dockside price of summer flounder and identify seasonality in this price; and (2) set up a mathematical programming model to maximize the landing revenue by allocating the federal government quota on summer flounder across twelve months. In the first paper (Chapter Two), various forms of inverse demand equations are used to estimate the dockside price of summer flounder. These models are evaluated based on their out-of-sample forecasting performance. A structural functional form is selected. In the second paper (Chapter Three), the selected price equation for summer flounder is applied into a revenue maximization model with both the federal government quota constraint and biological constraints from twelve months. The model is solved using CONPOT Solver of GAMS 21.5. The results of the scenarios indicate that the industry should move the landing effort from the period of October – February to the period of March – August. Comparing with historical data, this method can increase $44.73 million for the industry of landing summer flounder from 1991 to 2005. The third paper (Chapter Four) investigates how China's soybean import prices and domestic prices of soybeans and soybean products affect China's soybean imports. Since 2000, soybeans have been the U.S. leading agricultural exports for bulk commodities. China is the largest importer of U.S. soybean exports. For China's soybean crushing industry, imported soybeans are inputs rather than final products and used to produce soybean meal and oil. A differential production model, which is derived from a two-stage profit maximization model in producer theory, is adopted in this research. Estimates are used to calculate conditional and unconditional price elasticities for China's soybean imports from its major source countries – the United States, Argentina, and Brazil. In addition, the Divisia index and unconditional output price elasticities are obtained for China's soybean imports. Estimation results support the hypothesis that China's soybean imports are determined by its domestic demand for soybean meal, rather than soybean oil. This implies that U.S. agribusinesses should pay attention to the dominant role of China's demand for soybean meal and animal feed. U.S. agribusinesses can also use results in this research to evaluate how China's soybean imports from different source countries will change when either international market prices or China's domestic market prices change. / Ph. D.

In this work we present a general duality-theory framework for revenue maximization in additive Bayesian auctions involving multiple items and many bidders whose values for the goods follow arbitrary continuous joint distributions over some multi-dimensional real interval. Although the single-item case has been resolved in a very elegant way by the seminal work of Myerson [1981], optimal solutions involving more items still remain elusive. The framework extends linear programming duality and complementarity to constraints with partial derivatives. The dual system reveals the natural geometric nature of the problem and highlights its connection with the theory of bipartite graph matchings. We demonstrate the power of the framework by applying it to various special monopoly settings where a seller of multiple heterogeneous goods faces a buyer with independent item values drawn from various distributions of interest, to design both exact and approximately optimal selling mechanisms. Previous optimal solutions were only known for up to two and three goods, and a very limited range of distributional priors. The duality framework is used not only for proving optimality, but perhaps more importantly, for deriving the optimal mechanisms themselves. Some of our main results include: the proposal of a simple deterministic mechanism, which we call Straight-Jacket Auction (SJA) and is defined in a greedy, recursive way through natural geometric constraints, for many uniformly distributed goods, where exact optimality is proven for up to six items and general optimality is conjectured; a scheme of sufficient conditions for exact optimality for two-good settings and general independent distributions; a technique for upper-bounding the optimal revenue for arbitrarily many goods, with an application to uniform and exponential priors; and the proof that offering deterministically all items in a single full bundle is the optimal way of selling multiple exponentially i.i.d. items.

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