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In this chapter, we applied Data Envelopment Analysis (DEA) to a group of property and casualty insurance companies' data from 2018 to 2020. The calculated relative efficiencies were compared with selected traditionally used financial measures. We conclude that DEA and its relative efficiency calculation provide a consistent measure with selected IRIS ratios. The result and method can be used for situations when multiple ratios and change-based financial metrics provide inconsistent conclusions.

Using a large sample of diversified firms from 38 countries we investigate the influence of several national-level institutional factors or “institutional voids” on the value of corporate diversification. Specifically, we explore whether the presence of frictions in a country’s capital markets, labor markets, and product markets, affects the excess value of diversified firms. We find that the value of diversified firms relative to their single-segment peers is higher in countries with less-efficient capital and labor markets, but find no evidence that product market efficiency affects the relative value of diversification. These results provide support for the theory of internal capital markets that argues that internal capital allocation would be relatively more beneficial in the presence of frictions in the external capital markets. In addition, the results show that diversification can be beneficial in the presence of frictions in the labor market.

Skincare, hair care, make-up, perfumes, toiletries and deodorants, and oral cosmetics are the main product categories of the cosmetic market. Since the early twentieth century, the production of cosmetics and beauty products has been controlled by a handful of multi-national corporations. COVID-19 impacted the cosmetics industry in several different and sometimes conflicting ways. This study benchmarks the performance of 20 largest cosmetics companies against their competition as well as against their previous years to analyze the impact of COVID-19. We find that only one company has consistently performed than its peers over the period of 2015–2020. We also find that average efficiency score of cosmetics companies declines in 2020 relative to 2019.

In this chapter, the concepts of technical efficiency, efficiency, effectiveness, and productivity are illustrated. It is discussed that when firms are not homogeneous, the situation is the same as when each factor has a different unit of measurement from one firm to another, and therefore, no meaningful discrimination can be expressed, unless a set of known weights are introduced to standardize data. A linear programming data envelopment analysis model is used when a set of known weights are given to calculate the technical efficiency and efficiency of a set of homogeneous DMUs with multiple input factors and output factors. A numerical example is also provided.

Real estate investment trusts (REITs) provide a mechanism through which investors can participate in the real estate market with liquidity and transparency. In this study, we benchmark the performance of 11 residential REITs for the period 2009–2013. The study tracks the performance of residential REITs through the economic crisis period. The data envelopment analysis (DEA) model uses well-performing units (efficiency of 1% or 100%) that are closest to the underperforming unit on the efficiency frontier as a “role model” (peer units) for the underperforming unit. In addition, the DEA model also calculates by how much a nonperforming unit should increase the output level or decrease the inputs level to be on the efficiency frontier (100%) (slack values). Thus, the DEA model identifies the underperforming units and the most feasible path to move to efficiency frontier. The DEA model identifies the peer units that are closely related to these units and calculates the value of the slack variables required to achieve the same efficiency level as their peers.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

The purpose of this study is twofold. First, the author posits and finds a significant positive relation between environmental performance (i.e., environmental efficiency) and firm performance (i.e., firm efficiency) by using a large panel sample from 1987 to 2015. The results are consistent with the notion in prior research (e.g., Porter, 1991; Porter & van der Linde, 1995) that pollution indicates a form of resource inefficiency and reducing pollution can increase firm performance. Second, managerial ability has recently received tremendous research attention. The author investigates the impact of managerial ability on the relation between environmental efficiency and firm efficiency and finds that the results are mainly driven by firms with low managerial ability.

This chapter studies the technical efficiencies of Chinese airports by using a meta-frontier production function model which accounts for airports in different regions accessing different technologies. Our empirical results show that the technical efficiency scores of airports and provincial output in the coastal region are higher than their counterparts in the inland region. However, the technical efficiency scores of airports and provincial output in inland region are steadily increasing while the counterparts of airports and provincial output in coastal region are slowly declining. In addition, our analysis of provincial efficiency changes shows that airport productivity has a positive and statistically significant effect on the technical changes of provincial output. Our results partially confirm the success of the government policy of promoting airport construction and development in the western inland region.

This chapter analyses the efficiency of African airlines using a two-stage network DEA (Data Envelopment Analysis) model. Network DEA models usually take into account the production process with intermediate inputs derived from the first stage and a second stage that departs from it. This fundamental feature enables one to view the airline production process as a carry-over activity. The analysis covers the 2010–2013 period. The relative efficiency ranks are presented and policy implications are derived.