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Do adjustment costs constrain public healthcare providers’ technical efficiency? Evidence from the New Zealand Public Healthcare System

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Abstract

Efficiency analysis is crucial in healthcare to optimise resource allocation and enhance patient outcomes. However, the prompt adaptation of inputs can be hindered by adjustment costs, which impact Long-Run Technical Efficiency (LRTE). To bridge this gap in healthcare literature, this research employs a Bayesian Dynamic Stochastic Frontier Model to estimate parameters and explore healthcare efficiency dynamics over time. The study reveals the LRTE for New Zealand District Health Boards (DHBs) as 0.76, indicating around 32% more input utilisation due to adjustment costs. Most DHBs exhibit consistent short-run operational efficiency, with the national Short-Run Technical Efficiency (SRTE) very close to the LRTE. Among the tertiary providers, Auckland and Capital & Coast DHBs operate below the LRTE level, setting them apart from other tertiary providers. Similarly, Tairawhiti and West Coast DHBs also fall below the LRTE level, as indicated by their SRTE scores, potentially influenced by their unique healthcare settings and resource challenges. This research brings a new perspective to policy discussions by incorporating the temporal dynamics of decision-making and considering adjustment costs. It underscores the need to balance short-term and long-term technical efficiency, underlining their collective significance in fostering a sustainable and efficient healthcare system in New Zealand.

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Notes

  1. The Treasury [1] uses the Consumer Price Index to inflate the 1996 nominal dollar value to the 2016 real dollar value.

  2. It is acknowledged that other factors may contribute to high adjustment costs; however, in this paper, policy and infrastructural issues have been used to motivate the estimation of efficiency under a dynamic system.

  3. One FTE refers to 40 h of labour per week and is calculated based on accrual. However, working more than 40 h is only counted as one FTE. For more information on the calculation of FTEs, please refer to the Nationwide Service Framework [45].

  4. A substantial correlation (close to 0.97) is observed between the FTEs of various labour groups.

  5. It is recognised that employees may only sometimes be paid at their marginal product rate when a unionised workforce is prevalent. Nonetheless, given the limited availability of detailed compensation data, it is reasonable to make this assumption in the context of this research.

  6. The price of any labour group can be used as a weighting factor.

  7. The price index for various goods and services can be accessed at Statistics New Zealand [53].

  8. For more information, refer to The Australian Institute of Health and Welfare [54].

  9. The purchase unit is a classification system used to measure and quantify a healthcare service. For more information refer to Ministry of Health [56].

  10. The relative inefficiency factor of a parameter is computed by using the formula \(1+2{\sum }_{\varphi =1}^{\infty }g\left(\varphi \right),\) where \(g\left(\varphi \right)\) is the sample autocorrelation of the draws on the parameter at lag \(\varphi\).

  11. The LRTE corresponds to the expected value of LRTE calculated as \({\left[1+exp\left\{{\omega }_{j}/(1-{\rho }_{j})\right\}\right]}^{-1}\), where \(j\) indicates the \(j\) th draw from the posterior density.

  12. DHBs with the same average efficiency scores and CVs are assigned the same ranking. On the other hand, a DHB with a low CV but the same average efficiency compared to its counterpart is given a high ranking. A low CV indicates low variability in efficiency signifying consistent performance.

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Authors and Affiliations

  1. College of Business Administration, Ajman University, Al Jurf 1, Ajman, UAE

    Antony Andrews

  2. Department of Economics, University of Crete, Rethymno, Greece

    Grigorios Emvalomatis

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Appendices

Appendicex

1.1 Appendix 1

Table 5

Table 5 Variable definitions
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Appendix 2

Table 6

Table 6 Mean efficiency performance by year
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Appendix 3

Figure 1

Fig. 1
figure 1

Kernel density plot of technical efficiency scores

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Appendix 4

Figure 2

Fig. 2
figure 2

Technical efficiency scores overtime by DHB

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Andrews, A., Emvalomatis, G. Do adjustment costs constrain public healthcare providers’ technical efficiency? Evidence from the New Zealand Public Healthcare System. Health Care Manag Sci (2024). https://doi.org/10.1007/s10729-024-09668-5

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