Economies of Scale and Efficiency Measurement in Switzerland’s Nursing Homes

Farsi, Mehdi; Filippini, Massimo; Lunati, Diego

doi:10.1007/bf03399259

Cited by 15 publications

(19 citation statements)

References 30 publications

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“…According to Farsi et al (2008), Chattopadhyay and Ray (1996) and Worthington (2004), the literature on measuring efficiency in the nursing home industry dates back to the 1980s, and in the earlier econometric studies, the estimated relation reflected an average (based on best-fit) rather than an efficient or frontier cost function. Thus, inefficiencies were confounded with pure random shocks.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Hoffler and Rungeling, 1994;Vitaliano and Toren, 1994;Anderson et al, 1999;Crivelli et al 2002;Knox et al, 2007;and Farsi et al, 2008). This frontier is usually estimated using a maximum likelihood method which assumes that any deviation from the technology (frontier) is composed of two parts, one representing randomness (or statistical noise) and the other inefficiency.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Borge and Haraldsvik (2009) also applied DEA and found that high fiscal capacity, a low degree of user-charge financing, and a fragmented local council are associated with low efficiency in the elderly care sector in Norway. Other studies which used similar methods and data in the measurement of efficiency in long-term care provision are: Laine et al (2005), Wang and Chou (2005), Knox et al (2007), Farsi et al (2008), Garavaglia et al (2011), Chang and Cheng (2013), and DeLellis and Ozcan (2013). 1 The aim of this paper is to provide robust estimates of both technical and scale efficiency scores for the long-stay nursing home sector in Ireland.…”

Section: Introductionmentioning

confidence: 99%

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Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

Luasa

Dineen

Zieba

2016

Health Care Manag Sci

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This article provides methodological and empirical insights into the estimation of technical efficiency in the nursing home sector. Focusing on long-stay care and using primary data, we examine technical and scale efficiency in 39 public and 73 private Irish nursing homes by applying an input-oriented data envelopment analysis (DEA). We employ robust bootstrap methods to validate our nonparametric DEA scores and to integrate the effects of potential determinants in estimating the efficiencies. Both the homogenous and two-stage double bootstrap procedures are used to obtain confidence intervals for the bias-corrected DEA scores. Importantly, the application of the double bootstrap approach affords true DEA technical efficiency scores after adjusting for the effects of ownership, size, case-mix, and other determinants such as location, and quality. Based on our DEA results for variable returns to scale technology, the average technical efficiency score is 62%, and the mean scale efficiency is 88%, with nearly all units operating on the increasing returns to scale part of the production frontier. Moreover, based on the double bootstrap results, Irish nursing homes are less technically efficient, and more scale efficient than the conventional DEA estimates suggest. Regarding the efficiency determinants, in terms of ownership, we find that private facilities are less efficient than the public units. Furthermore, the size of the nursing home has a positive effect, and this reinforces our finding that Irish homes produce at increasing returns to scale. Also, notably, we find that a tendency towards quality improvements can lead to poorer technical efficiency performance.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

Luasa

Dineen

Zieba

2016

Health Care Manag Sci

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“…Bradley and Colvin (1999) focused on mail sorting and estimated a multiproduct total cost function for 250 US sorting centers between 1988 and 1996. They found evidence for substantial 5 For example, see for example Caves et al (1981), Caves et al (1984), Cabini and Filippini (2003) or (transport sector), Koshal and Koshal (1999) or Sav (2004) (higher education), Fu and Heffernan (2008) (banking sector), Preyra and Pink (2006), Filippini (2001) or Farsi et al (2008b) (health sector), Nerlove (1963), Filippini (1996), Fraquelli et al (2004), Farsi et al (2007) or Farsi et al (2008a) (electricity distribution), Filippini and Luchsinger (2007) (hydropower). 6 See also NERA (2004) for an overview on the relevant empirical literature.…”

Section: Previous Studies On Economies Of Scale and Scopementioning

confidence: 99%

Economies of scale and scope and opening hours in post offices and agencies

Filippini¹,

Koller²,

Trinkner³

2010

Heightening Competition in the Postal and Delivery Sector

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The purpose of this study is to analyze the cost structure of Swiss Post's postal outlets. In particular, the idea is to assess economies of scale and scope in post offices and franchised postal agencies.Information on their optimal size and production structure is of importance from the policy-makers' point of view because this hypothetical situation may be a basis for calculation of reimbursements when providing the universal service. Two important novelties are introduced in this study. First, the latent class model accounts for postal outlets with different underlying production technologies, caused by unobserved factors. Second, the cost model includes standby time as an indicator of public service because regulated accessibility and negotiated opening hours that enhance public service frequently lead to opening hours that exceed the time necessary to operate the demand. Overall, this analysis confirms the existence of increasing unexploited economies of scale and scope with falling outputs in the Swiss Post office network. Furthermore, the results for the latent class model point to the existence of unobserved heterogeneity in the industry.

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“…To cope with a situation caused by a change in the demographic structure in the near future followed by an increased resource scarcity is also a political issue in other European countries (Lemke and Moos, 1989;Perry, 2000;Crivelli et al, 2002;Hougaard et al, 2004;Hillmer et al, 2005;Moynihan and Pandey, 2005;Farsi et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Provider category and quality of care in the Norwegian nursing home industry

Hole

2016

Nordic J Health Eco

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This paper examines empirically if there is a link between quality of care in the Norwegian nursing home industry and exposure of the industry to competition. Exposing public care to competition implies that the responsibility for providing care services is shared between public authorities and private actors. In Norway, exposure to competition means tender competition. Suppliers bid for a contract issued by the Norwegian authorities for a limited number of years. Quality of care in an institution is the major competitive factor. The provider categories of elderly care are: 1) care provided by institutions run by municipalities, 2) care provided by institutions run by private companies, which have won a tender competition, 3) care provided by institutions run by private companies owned by private families, voluntary religious or idealistic organizations. Nurse-to-patient ratio is used as a proxy for quality of care. The regression analysis indicates a relationship between quality of care and exposure to competition. The quality of care in provider category 2 is significantly lower than in provider category 1, but there are more variations in the quality of care in provider category 1 than in provider category 2. We find the lowest quality of care in provider category 1. There is also a relationship between the quality of care in an institution and the educational level of the staff, the location, the workforce, and the size of an institution. Finally, there is a relationship between the quality of care in an institution and the real and the required capacity, and the financial status in a region.JEL classification: H42, H43, H53, H75, I11, I18, J14

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Economies of Scale and Efficiency Measurement in Switzerland’s Nursing Homes

Cited by 15 publications

References 30 publications

Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

Economies of scale and scope and opening hours in post offices and agencies

Provider category and quality of care in the Norwegian nursing home industry

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