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This chapter examines the effects that airports have had on economic development in cities from 1950 to 2010. It uses a novel dataset consisting of previously unexploited data on the origins and history of the aviation system in the United States. Applying the method of synthetic controls to a set of medium and small airports, I examine both the overall impacts and the heterogeneity within the outcomes of various airports. Then, I use regression analysis to determine key factors differentiating successful airports from less successful ones, as it pertains particularly to population and employment growth. I find that, first, on average, cities have benefited from airports over this period. Airports, overall, provided a causal contribution of 0.2– 0.6% per year on population and employment growth over the time period. Second, I show that city-level factors contributing to airport success include: (1) closer proximity to a major research university, (2) a capital city location, and (3) climate factors, particularly higher January mean temperatures and/or hours of sunshine. City size is a consideration as well; cities in larger metropolitan areas, with larger shares of employment in nontradables in the 1950s, were also better positioned to reap the benefits that airports provided on city growth. Significant differences were not found across regions, airport governance structures, or other factors.

What would have been the hypothetical effect of monetary policy shocks had a country never joined the euro area, in cases where we know that the country in question actually did join the euro area? It is one thing to investigate the impact of joining a monetary union, but quite another to examine two things at once: joining the union and experiencing actual monetary policy shocks. The authors propose a methodology that combines synthetic control ideas with the impulse response functions to uncover dynamic response paths for treated and untreated units, controlling for common unobserved factors. Focusing on the largest euro area countries, Germany, France, and Italy, the authors find that an unexpected rise in interest rates depresses inflation and significantly appreciates exchange rate, whereas gross domestic product (GDP) fluctuations are less successfully controlled when a country belongs to the monetary union than would have been the case under the independent monetary policy. Importantly, Italy turns out to be the overall beneficiary, since all three channels – price, GDP, and exchange rate – deliver the desired results. The authors also find that stabilizing an economy within a union requires somewhat smaller policy changes than attempting to stabilize it individually, and therefore provides more policy space.

In the typical study comparing the evolution of economic inequality among different territorial units, an inequality indicator is chosen, and its value is calculated from sample data. Thus, the problem turns out to be the selection of the inequality indicator.

This paper shows that there is no need for a selection of a single inequality indicator. A whole set of inequality indicators are considered and calculated for the European Countries, using income data from European Community Household Panel (ECHP). The information they provide is then collapsed into a composite inequality indicator, through an adaptation of Principal Component Analysis (PCA). We analyze the conditions needed to make longitudinal comparisons possible. Results obtained with this composite indicator are used to compare and analyze the trends in economic inequality in the EU Countries.

Companies spend millions on training their sales representatives. Thousands of textbooks have been published; thousands of training videos have been recorded. Hundreds of good pieces of advice and tips for sales representatives have been presented along with hundreds of sales methods and techniques. Probably the largest number of indicators and measures are applied in sales and distribution. On the one hand, this is a result of the fact that sales provide revenue and profit to a company; on the other hand, the concept of management by objectives turns out to be most effective in regional sales teams with reference to sales representatives and methods of performance evaluation. As a result, a whole array of indices has been created which enable the evaluation of sales representatives’ work and make it possible to manage goods distribution in a better way.

The indices presented in this chapter are rooted in the consumer market and are applied most often to this type of market (particularly in relation to fast-moving consumer goods at the level of retail trade). Nevertheless, many of them can be used on other markets (services, means of production) and at other trade levels (wholesale).

Although the values of many indices presented herein are usually calculated by market research agencies and delivered to companies in the form of synthetic results, we have placed the emphasis on the ability to determine them independently, both in descriptive and exemplifying terms. We consider it important to understand the genesis of indices and build the ability to interpret them on that basis. What is significant is that the indices can be interpreted differently; the same index may provide a different assessment of a product’s, brand or company’s position in the market depending on the parameters taken into account. Therefore, we strive to show a certain way of thinking rather than give ready-made recipes and cite ‘proven’ principles. Sales and distribution are dynamic phenomena, and limiting them within the framework of ‘one proper’ interpretation would be an intellectual abuse.

This chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.

The authors evaluate financial stability and capital flows management objectives of capital controls in the context of four capital control events: removing or imposing controls on capital inflows and removing or imposing controls on capital outflows. The authors use synthetic control method to solve the endogeneity problem stemmed from the timing of capital control implementation. The authors find new evidence that capital controls are not consistently effective in reaching financial stability outcomes but are consistent in reaching capital flows management outcomes. The authors compare our results to estimates using difference-in-difference (DID) and carry out placebo analysis. Finally, we use synthetic DID to correct for the parallel trend bias and show that the results still hold.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.