In this study, we test for asymmetries in the tourism‐led growth hypothesis. Asymmetric causality tests allow us to examine whether positive or negative changes in tourism cause growth. Our specification is based on an extended Solow growth model and draws from recent articles on tourism and growth. Using Tonga as a case study over the period 1981–2018, we find that tourism asymmetrically Granger causes economic growth. The magnitude of effects from the nonlinear ARDL model shows that economic growth is more sensitive to a downward movement compared to an upward movement in visitor arrivals.

Using yearly panel data from 2011 to 2017 on New Zealand District Health Boards (DHBs), this study combines principal component analysis and data envelopment intertemporal analysis with the double-bootstrap approach to estimate the technical efficiency of health care providers along with the trend of efficiency performances. The results show that although most large DHBs have improved their level of technical efficiency between 2011 and 2017, the majority of medium- and small-sized DHBs have not seen any noticeable improvement in their level of technical efficiency. The results also show that large and tertiary DHBs operate at a high level of technical efficiency. In contrast, most of the medium- and small-sized DHBs posted some of the lowest technical efficiency scores. Furthermore, the results show that medium- and small-sized DHBs have a higher average length of hospital stays which is found to be associated with decreasing levels of technical efficiency scores.

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Across developed countries, rising healthcare costs are a significant challenge for government budgets and a motivation for persuading policymakers to consider ways of reducing inefficiency and increasing productivity. Government expenditures in the healthcare system face severe consequences due to inefficiency, including lower health outcomes and larger unmet population needs [1]. Since the early application of the efficiency measurement technique by Nunamaker [2], which measured the technical efficiency of hospitals in Wisconsin, the analysis of hospital efficiencies has gained considerable momentum. Currently, a vast array of studies uses varying sophistication of econometric methodologies to determine levels of healthcare efficiency. Comprehensive reviews of health care efficiency studies by Hussey et al.[3], Hollingsworth and Peacock [1], and Jacobs et al. [4] identify data envelopment analysis (DEA) and stochastic frontier analysis (SFA) as the dominant methodologies in the literature.DEA and SFA belong to a class of methodologies using the economic theory of production to measure the performance of a healthcare provider against the so-called 'efficient frontier', determined by the most efficient healthcare providers. In other words, healthcare services are products generated in a production process that uses healthcare inputs in the form of labour, capital, and other intermediaries. The most efficient healthcare provider will use the least amount of inputs to provide a given number of healthcare services. The efficient frontiers are constructed based on the fundamental assumption that healthcare providers, like other manufacturing agents, can reduce or increase their inputs depending on their particular demand for healthcare services. However, this assumption in healthcare production function may not hold, for two reasons. First, the demand for healthcare services cannot always be predicted with enough accuracy, and, as a result, healthcare providers are bound to operate with excess capacity. Second, the presence of excess capacity suggests that healthcare providers cannot readily alter their level of inputs in the pursuit of greater efficiency, leading to rigidities in the adjustment of healthcare inputs. Several studies [5][6][7][8] show that there is a substantial excess inpatient capacity among hospitals, with one study by the American Hospital Association [9] approximating the occupancy rates to be as low as 63% in American hospitals. Moreover, given the highly regulated operating environment and the prevalence of public finances as a key source of healthcare services in most developed countries [4], healthcare providers are often required to maintain a reserve or standby capacity to meet unforeseen health needs [5, 10]; for example, the recent COVID-19 pandemic.The existence of rigidity in healthcare inputs suggests that inefficiency among healthcare providers will persist through time, and, as a result, healthcare providers will always have a level of inefficiency with them. Therefore, each healthca...

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