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PUBLIC AGENCIES’ PERFORMANCE
BENCHMARKING IN THE CASE OF DEMAND
UNCERTAINTY WITH AN APPLICATION TO
ESTONIAN, FINNISH AND S...
Topic and novelties of the study
Nowadays, one could talk about a practical public sector
management and administration fie...
Topic and novelties of the study continued
In addition, although quite vast trade press covers the topics
of fire and rescu...
The aim of the doctoral thesis is...
...to develop the theoretical concept and application to measure
the performance of p...
Research tasks
Application:
Illustrate with a real
situation in fire and
rescue services
Compare different
methods
Empiric...
Performance, reasons of its variation and intended
purposes of measuring it in the public sector
Performance measurement i...
The dimensions of performance
objectives inputs activities outputs
final
outcomes
intermediate
outcomes
Public agency
need...
The case of fire and rescue services
Figure: The main levers to reach the goal of a rescue service (based on
Jaldell, 2002).
Production theory as a basis of performance measurement
Let’s consider a service industry in which it is not possible to
p...
Demand uncertainty
...has not been addressed on most occasions when assessing
the (cost-)efficiency of public agencies.
And ...
MSL
Minimum service level (MSL) - a target level, which would insure
the decision-maker against the upsurges of demand. Su...
Figure: The conceptional model of demand uncertainty (Source: Author’s
compilation)
Formalisation I
Under weak regularity assumptions, the production
possibilities set can be represented using distance func...
Formalisation II
Subunit i seeks to use the allocated inputs to provide the
services demanded. Subunits’ period-t optimisa...
Possible methods for performance measurement
Figure: Different measurement methods used to measure performance in
a public ...
Frontier analysis methods and the current case
The frontier analysis methods, which are mainly used in the
framework of pr...
Critique on the frontier analysis methods
Although popular amongst scholars, they are rarely used as
direct policy tools. ...
ESTONIAN, FINNISH AND SWEDISH FIRE AND
RESCUE SERVICES
Estonian FRS is centralized and managed by Estonian Rescue
Board (s...
Notation
I = 65 brigades / 22 fire departments / 126 − 275
municipalities
T = 5 periods / 12 periods / 11 periods (-2015)
q...
Figure: Number of emergency departures per 100,000 population in
Estonia, Finland and Sweden (Source: Estonian Rescue Boar...
Figure: Costs (’000 of 2011 e) of FRS per 100,000 population in Estonia,
Finland and Sweden (Source: Estonian Rescue Board...
Industry-level cost-efficiency of FRS
The cost-efficiency has been calculated for each country using
a na¨ıve model and a mode...
The cost-efficiency and potential savings (thousands of
2011 euros) of ERB using the DEA
Real Na¨ıve Na¨ıve
Year costs CE CE...
The cost-efficiency and potential savings (thousands of
2011 euros) of ERB using the FDH
Real Na¨ıve Na¨ıve
Year costs CE CE...
The cost-efficiency and potential savings (thousands of
2011 euros) of ERB using the DFA
Real Na¨ıve Na¨ıve
Year costs CE CE...
Figure: The Pearson correlations, densities and scatterplots of
cost-efficiency estimates using different methods (Source: Es...
Insights
Estonia
CE has decreased in time - the number of emergencies decreased,
the costs increased.
CE estimates correla...
Under-resourcing of FRS subunits
After the central agency (or government/municipality) has
allocated the resources between...
Table: The percentage of under-resourced FRS subunits in Estonia,
Finland and Sweden, estimated by DEA, FDH and DFA
DEA FD...
Insights
Although the indications of under-resourced subunits of FRS
are not very robust and alternate between different mo...
Output efficiency of subunits
The central agency is interested in how well the resources are utilized by
the local subunits ...
Figure: The boxplots of estimated OTMEs in Estonia using DEA (Source:
Estonian Rescue Board, authors’ calculations).
Figure: The boxplots of estimated OTMEs in Estonia using FDH (Source:
Estonian Rescue Board, authors’ calculations).
Figure: The boxplots of estimated OTEs in Estonia using DFA (Source:
Estonian Rescue Board, authors’ calculations).
Insights
Estonia
OT(M)Es are consistent in terms of fluctuations between years
in different models, the lowest estimates are...
Discussion
The concept of demand uncertainty is new to efficiency
studies. Such framework of demand uncertainty as a
compone...
Discussion continued
These models do not account for the whole spectrum of
activities (e.g. prevention), so promoting tunn...
SUMMARY
The thesis developed a concept for analysing the performance
of public agencies under demand uncertainty.
The fram...
Extensions
The demand uncertainty and the alteration of decision-making
process, effect on performance
MSL can be of intere...
Thank you for your attention!
Questions, comments, recommendations.
Tarmo Puolokainen
tarmo.puolokainen@eas.ee
Battese, G. E. and Coelli, T. J. (1993). A stochastic frontier
production function incorporating a model for technical
ine...
Greene, W. (2005). Fixed and random effects in stochastic frontier
models. Journal of productivity analysis, 23(1):7–32.
ID...
Pollitt, C. and Bouckaert, G. (2011). Public Management Reform:
A comparative analysis-new public management, governance,
...
Tarmo Puolokainen: Public Agencies’ Performance Benchmarking in the Case of Demand Uncertainty with an Application to Esto...
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Tarmo Puolokainen: Public Agencies’ Performance Benchmarking in the Case of Demand Uncertainty with an Application to Estonian, Finnish and Swedish Fire and Rescue Services

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Eesti Panga teaduspreemia 2018. aasta laureaatide võidutööde tutvustus

Publicada em: Economia e finanças
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Tarmo Puolokainen: Public Agencies’ Performance Benchmarking in the Case of Demand Uncertainty with an Application to Estonian, Finnish and Swedish Fire and Rescue Services

  1. 1. PUBLIC AGENCIES’ PERFORMANCE BENCHMARKING IN THE CASE OF DEMAND UNCERTAINTY WITH AN APPLICATION TO ESTONIAN, FINNISH AND SWEDISH FIRE AND RESCUE SERVICES Presentation of the PhD thesis at the Bank of Estonia Tarmo Puolokainen University of Tartu November 28, 2018
  2. 2. Topic and novelties of the study Nowadays, one could talk about a practical public sector management and administration field, which focuses on improving the management decisions in the public sector. And a theoretical microeconomic production theory, aiming to framework the choices and decisions a producer faces. More often than not, these two fields of study live separate lives. This thesis tries to merge these fields on the approach to demand uncertainty in a public agencies’ performance. The thesis introduces the concept of demand uncertainty into the public agencies’ performance measurement and management through frontier analysis methods.
  3. 3. Topic and novelties of the study continued In addition, although quite vast trade press covers the topics of fire and rescue services, it is a novel and understudied application in the efficiency studies. It is the first attempt to analyse systematically the performance of fire and rescue services in three countries, Estonia, Finland and Sweden. This opens opportunities for a discussion on how to reach the levels of Scandinavian fire and rescue services quality, which is the goal of Estonian Rescue Board, stated in its strategy.
  4. 4. The aim of the doctoral thesis is... ...to develop the theoretical concept and application to measure the performance of public agencies in the case of demand uncertainty. The suggested models would be the basis for planning resource allocation improvement in public agencies. The models are implemented using the example of the Estonian, Finnish and Swedish fire and rescue services.
  5. 5. Research tasks Application: Illustrate with a real situation in fire and rescue services Compare different methods Empirically measure the performance of FRS Assess possibilities of using such framework in decision-making Methods: Methods implemented on the measurement of the public agencies performance Measurement methodology that would be able to incorporate demand uncertainty Theoretical framework: Defining and measuring the performance in public agencies Potential uses of performance measurements in public agencies Impact of demand uncertainty on the public agencies in the provision of services Figure: Research tasks (Source: Author’s compilation)
  6. 6. Performance, reasons of its variation and intended purposes of measuring it in the public sector Performance measurement is the process of quantifying action, where measurement is the process of quantification and action leads to performance (Neely et al., 1995, pg 1228). The performance might differ because agencies’ have different objectives, needs, ways of service provision, interactions with other organisations, efficiency, accounting, reporting and measuring methodology as well as random fluctuations. So, it might be useful to conduct a performance measurement in order to plan and improve the work of public agencies or to evaluate and benchmark against other public agencies. Planning and improving is of most value for internal purposes; evaluation and benchmarking, on the other hand, is mostly targeted for external audiences, who, in turn can influence the operations of subunits.
  7. 7. The dimensions of performance objectives inputs activities outputs final outcomes intermediate outcomes Public agency needs environment socio-econom ic situation utility and sustainability relevance econom y effectiveness efficiency cost effectiveness other stakeholders Figure: The conceptional model of performance (based on Pollitt and Bouckaert, 2011; Van Dooren et al., 2015).
  8. 8. The case of fire and rescue services Figure: The main levers to reach the goal of a rescue service (based on Jaldell, 2002).
  9. 9. Production theory as a basis of performance measurement Let’s consider a service industry in which it is not possible to provide more services than are demanded (cannot store the outputs, e.g., fire and rescue services). A ‘central agency’ allocates resources to ‘subunits’ in different jurisdictions in the face of uncertainty about the services that will be demanded in each jurisdiction. The central agency tries to minimise the cost of providing enough resources to meet a minimum service level (MSL) in each jurisdiction. Each subunit tries to use his/her allocated inputs to provide the services demanded in his/her jurisdiction. One would be interested in (a) the cost efficiency of the central agency (b) any under-resourcing of subunits, and (c) the technical and mix efficiency of each subunit.
  10. 10. Demand uncertainty ...has not been addressed on most occasions when assessing the (cost-)efficiency of public agencies. And probably have resulted in underestimating their efficiency in many cases. Risk averse behaviour of the decision-maker (public agencies managers face social pressure to satisfy a large percentage of demand). The excess capacity is an insurance or more generally a service to the public, should someone unpredictably require the services. The agencies will allocate resources which are ex ante optimal, given expected demand, but are not ex post efficient, given realized levels of demand. The challenge is to distinguish the necessary standby capacity from excessive mismanagement.
  11. 11. MSL Minimum service level (MSL) - a target level, which would insure the decision-maker against the upsurges of demand. Such standby capacity should be incorporated to the analysis, as it alternates the decision-makers behaviour. The MSL in a given jurisdiction is the P-th percentile of the (estimated) probability distribution of services demanded in that jurisdiction. Can be chosen freely. Can be used for contracting purposes when outsourcing the services.
  12. 12. Figure: The conceptional model of demand uncertainty (Source: Author’s compilation)
  13. 13. Formalisation I Under weak regularity assumptions, the production possibilities set can be represented using distance functions. The central agency chooses inputs to minimise the cost of meeting the MSL in each jurisdiction. The period-t optimisation problem of the central agency is min x≥0 wtx : Dt I (xi , mit, zit) ≥ 1 for i = 1, . . . , I} (1) where wt = (w1t, . . . , wIt) and x = (x1, . . . , xI ). The input vector that solves this problem is x∗ t = (x∗ 1t , . . . , x∗ It ). From there, cost efficiency CEt(xt, wt, mt, zt) = wtx∗ t /wtxt
  14. 14. Formalisation II Subunit i seeks to use the allocated inputs to provide the services demanded. Subunits’ period-t optimisation problem is max q {Q(q) : q ≤ dit, Dt O(xit, q, zit) ≤ 1} (2) where Q(.) is a nonnegative, nondecreasing, linearly- -homogeneous, scalar-valued aggregator function with weights that represent the values the subunit places on outputs. The output vector that solves this problem is ˆqit ≡ ˆqt(xit, dit, zit). The associated aggregate output is Q(ˆqit). The output-oriented technical and mix efficiency (OTME) of subunit i in period t is OTMEt(xit, qit, dit, zit) = Q(qit)/Q(ˆqit).
  15. 15. Possible methods for performance measurement Figure: Different measurement methods used to measure performance in a public agency (Source: Author’s compilation).
  16. 16. Frontier analysis methods and the current case The frontier analysis methods, which are mainly used in the framework of productivity analysis, have a common purpose of modelling the frontier of feasible performance. The frontier can be estimated under various underlying assumptions and estimation methods. As the next step, observed organisation’s performance indicator is then compared to such frontier and relative efficiency is found. Mainly two schools of thought are distinguished: some prefer econometric methods which use stochastic and parametric models (e.g Battese and Coelli, 1993; Kumbhakar and Lovell, 2003; Greene, 2005), and others who prefer linear programming methods which use mainly deterministic and non-parametric models (e.g Simar and Wilson, 1998; Thanassoulis, 2001; Cooper et al., 2011).
  17. 17. Critique on the frontier analysis methods Although popular amongst scholars, they are rarely used as direct policy tools. Such scarce use can be attributed to the limits of these techniques (Daraio and Simar, 2007). The impossibility to extend the efficiency analysis beyond the current regression sample - making it useless for subunits not included in the sample. Similarly, comparison of two different efficiency studies is of little use. Several assumptions made about the production function and inefficiencies cannot be successfully verified, and ensuring the robustness of the results is complicated. Similarly, the lack of knowledge about the ‘true’ production process restricts the development of convincing theoretical model (Martin and Smith, 2005). Initial result of efficiency analysis is a single composite measure, which might not be helpful from managerial point of view. There is no consensus on how to take the environmental influences and dynamic effects into account.
  18. 18. ESTONIAN, FINNISH AND SWEDISH FIRE AND RESCUE SERVICES Estonian FRS is centralized and managed by Estonian Rescue Board (since 2012). Altogether 72 national FRS brigades and 115 voluntary FRS brigades (not included into the analysis due to data limitations). Analysis of Estonian FRS is done on FRS brigade level. Finnish FRS is more decentralized and organised independently by 22 fire departments, under which are 370 national, 523 contracted (voluntary), and 105 industrial FRS brigades. The analysis is done on fire department level, which is the smallest possible unit with available data. Swedish FRS is even more decentralized - the FRS are offered by municipalities (290), which co-operate (165 FRS authorities). The analysis is done on municipality-level. The data is unbalanced.
  19. 19. Notation I = 65 brigades / 22 fire departments / 126 − 275 municipalities T = 5 periods / 12 periods / 11 periods (-2015) q1it = fires in buildings q2it = other fires q3it = traffic accidents q4it = other emergencies x1it = labour (no. of employees) x2it = other inputs (assumed proportional to no. of vehicles/no. of FRS brigades) zit = 1/area (because harder to service large areas) dnit = qnit (i.e., all demands for service were met) mnit = the value such that Pr(dnit ≥ mnit) = 0.05
  20. 20. Figure: Number of emergency departures per 100,000 population in Estonia, Finland and Sweden (Source: Estonian Rescue Board 2016; PRONTO 2016; IDA 2016; Author’s calculations)
  21. 21. Figure: Costs (’000 of 2011 e) of FRS per 100,000 population in Estonia, Finland and Sweden (Source: Estonian Rescue Board 2016; PRONTO 2016; IDA 2016; Author’s calculations)
  22. 22. Industry-level cost-efficiency of FRS The cost-efficiency has been calculated for each country using a na¨ıve model and a model, that accounts for the uncertain demand using minimum service level - MSL. Different estimation methods were used - data envelopment analysis (DEA), free disposal hull (FDH) and deterministic frontier analysis (DFA).
  23. 23. The cost-efficiency and potential savings (thousands of 2011 euros) of ERB using the DEA Real Na¨ıve Na¨ıve Year costs CE CE savings Savings (’000 e) (0,1] (0,...) (’000 e) (’000 e) 2011 22, 776 0.838 1.058 3, 698 −1, 011 2012 28, 111 0.843 0.902 4, 411 2, 324 2013 25, 857 0.753 0.930 6, 389 1, 518 2014 23, 820 0.814 0.919 4, 438 1, 650 2015 36, 304 0.772 0.906 8, 291 2, 723 Na¨ıve CE obtained by replacing mit with qit. Potential savings are in thousands of 2011 Euros.
  24. 24. The cost-efficiency and potential savings (thousands of 2011 euros) of ERB using the FDH Real Na¨ıve Na¨ıve Year costs CE CE savings Savings (’000 e) (0,1] (0,...) (’000 e) (’000 e) 2011 22, 776 0.985 1.781 334 −13, 200 2012 28, 111 0.976 1.626 670 −13, 163 2013 25, 857 0.962 1.634 980 −12, 037 2014 23, 820 0.975 1.411 587 −7, 397 2015 36, 304 0.956 1.470 1, 606 −11, 529 Na¨ıve CE obtained by replacing mit with qit. Potential savings are in thousands of 2011 Euros.
  25. 25. The cost-efficiency and potential savings (thousands of 2011 euros) of ERB using the DFA Real Na¨ıve Na¨ıve Year costs CE CE savings Savings (’000 e) (0,1] (0,...) (’000 e) (’000 e) 2011 22, 776 0.491 0.529 11, 601 10, 737 2012 28, 111 0.476 0.530 14, 743 13, 203 2013 25, 857 0.418 0.511 15, 045 12, 638 2014 23, 820 0.489 0.549 12, 168 10, 737 2015 36, 304 0.360 0.411 23, 243 21, 372 Na¨ıve CE obtained by replacing mit with qit. Potential savings are in thousands of 2011 Euros.
  26. 26. Figure: The Pearson correlations, densities and scatterplots of cost-efficiency estimates using different methods (Source: Estonian Rescue Board, Authors’ calculations).
  27. 27. Insights Estonia CE has decreased in time - the number of emergencies decreased, the costs increased. CE estimates correlate weakly negatively with the population reached within 15 minutes. So, the FRS brigades with less population in close vicinity would be estimated higher CE. CE estimates correlate weakly positively with the average arrival time to the scene. One can argue, that being faster is costlier.
  28. 28. Under-resourcing of FRS subunits After the central agency (or government/municipality) has allocated the resources between different subunits, the subunits have to respond to emergencies with the given input bundles (a fixed number of rescuers and vehicles). The standard case - the input-oriented technical efficiency (ITE) would be calculated in order to analyse, whether the subunits would have been able to respond to the observed number of emergencies with fewer amounts of inputs. Introducing the MSL to the ITE framework, one is able to distinguish the FRS subunits that would have not been able to meet the expected MSL, in case the demand would have been higher as it were observed.
  29. 29. Table: The percentage of under-resourced FRS subunits in Estonia, Finland and Sweden, estimated by DEA, FDH and DFA DEA FDH DFA EST FIN SWE EST FIN SWE EST FIN SWE 2004 40.9 40.9 4.5 2005 27.3 14.2 59.1 63.0 0 3.4 2006 22.7 12.7 59.1 61.1 0 4.1 2007 27.3 13.0 59.1 62.2 0 4.3 2008 22.7 12.4 54.5 56.9 0 2.4 2009 27.3 10.2 59.1 55.8 4.5 3.7 2010 18.2 8.4 59.1 54.4 4.5 5.9 2011 61.5 18.2 7.6 87.7 63.6 52.7 16.9 4.5 4.7 2012 36.9 18.2 8.9 87.7 59.1 55.7 16.9 4.5 7.3 2013 33.8 18.2 8.8 87.7 54.5 54.9 16.9 4.5 6.0 2014 32.3 18.2 8.8 80 59.1 54.6 13.8 4.5 6.3 2015 29.2 18.2 7.7 78.4 54.5 55.3 13.8 4.5 10.1 Source: Estonian Rescue Board 2016; PRONTO 2016; IDA 2016; Author’s calculations.
  30. 30. Insights Although the indications of under-resourced subunits of FRS are not very robust and alternate between different models, in most cases the trend has been to better resource allocation (with an exception of Swedish municipalities estimated by DFA). This means, that with time, the share of under-resourced subunits has decreased. That might be due to fewer emergencies, as the number of inputs has stayed quite steady across years (outputs decrease as inputs stay constant). This complies with the assessment to the cost-efficiency of FRS.
  31. 31. Output efficiency of subunits The central agency is interested in how well the resources are utilized by the local subunits in different jurisdictions in comparison to their most efficient counterparts. For that, the output-oriented technical (and mix) efficiencies (OTME) should be estimated. This indicates, how many more emergencies the subunits could have responded to, in case there would have been demand for. When taking the demand uncertainty into account one can argue, that the OTME should be one (the FRS subunits are efficient) if they are able to respond to every emergency in their service area - which is the current case (demand does not exceed the supply). While taking this into account, one cannot expect that a FRS subunit would increase its outputs (as services cannot be stored). In other words, even if a FRS subunits would have been able to respond to more emergencies, there was no demand for that (and one should not label this as inefficiency). So, only the na¨ıve OTMEs will be estimated, which would demonstrate the potential of FRS subunits.
  32. 32. Figure: The boxplots of estimated OTMEs in Estonia using DEA (Source: Estonian Rescue Board, authors’ calculations).
  33. 33. Figure: The boxplots of estimated OTMEs in Estonia using FDH (Source: Estonian Rescue Board, authors’ calculations).
  34. 34. Figure: The boxplots of estimated OTEs in Estonia using DFA (Source: Estonian Rescue Board, authors’ calculations).
  35. 35. Insights Estonia OT(M)Es are consistent in terms of fluctuations between years in different models, the lowest estimates are in year 2013 (when also least emergencies occurred). The correlations between the estimates of different methods are positive (0.48-0.73), with population positive and with the average time to the scene negative.
  36. 36. Discussion The concept of demand uncertainty is new to efficiency studies. Such framework of demand uncertainty as a component in efficiency studies produced plausible results (when taking into account, the potential savings are lower, which is an expected result). MSL as a concept can be appealing for planning activities (outsourcing, contracting, etc.), also, it can be of importance for popularising the efficiency studies. The results are quite robust across methods. CET-type production function produces plausible results (theoretically-plausible functional form; coefficients have signs that are consistent with prior expectation; most are statistically significant; surprisingly (?) high elasticity of scale). Identifying the under-resourced FRS subunits would allow to improve the resource allocation process. OT(M)E would indicate the potential of FRS subunits in case of upsurges in demand.
  37. 37. Discussion continued These models do not account for the whole spectrum of activities (e.g. prevention), so promoting tunnel-vision. Limitation of the comparison between countries. Structure of the management. Differences in the data sets. DEA and FDH would get into trouble with the biggest subunits in the sample, as the MSL would lie outside the estimated frontier.
  38. 38. SUMMARY The thesis developed a concept for analysing the performance of public agencies under demand uncertainty. The framework considers a two-tier decision-making structure involving a ‘central agency’ and a group of ‘subunits’. DEA, FDH and DFA can be used to evaluate (a) the cost efficiency of the central agency, (b) any under-resourcing of subunits, and (c) the technical and mix efficiency of each subunit. In an empirical illustration, one can find evidence for Estonian, Finnish and Swedish FRS subunits that (a) the cost-efficiency would be estimated higher when taking the demand uncertainty into account, and (b) in most estimates, there are some FRS subunits, who would have not met the targeted MSL. (c) The potential of FRS subunits to respond to more emergencies has been identified by all the models.
  39. 39. Extensions The demand uncertainty and the alteration of decision-making process, effect on performance MSL can be of interest to analyse the negotiation issues (different stakeholders have different goals) Stochastic Frontier Analysis (SFA) framework Different application Using the arrival times to the scene as indicators for MSL Effects of voluntary FRS brigades (Estonian case) Other outputs (e.g., prevention activities) Factors affecting MSLs (e.g., prevention activities, choice of α) Spillovers (i.e., providing services in another jurisdiction) Environmental uncertainty (e.g., weather, population) Productivity analysis
  40. 40. Thank you for your attention! Questions, comments, recommendations. Tarmo Puolokainen tarmo.puolokainen@eas.ee
  41. 41. Battese, G. E. and Coelli, T. J. (1993). A stochastic frontier production function incorporating a model for technical inefficiency effects. Working Papers in Econometrics and Applied Statistics, 69. Department of Econometrics, University of New England Armidale. Cooper, W. W., Seiford, L. M., and Zhu, J. (2011). Data envelopment analysis: history, models, and interpretations. In Cooper, W. W., Seiford, L. M., and Zhu, J., editors, Handbook on data envelopment analysis, pages 1–39. New York, NY: Springer. Daraio, C. and Simar, L. (2007). Advanced Robust and nonparametric methods in efficiency analysis: methodology and applications. New York, NY: Springer Science & Business Media. Estonian Rescue Board (2016). A combined database from internal sources. A statistics database gathered especially for the thesis in cooperation with the Estonian Rescue Board.
  42. 42. Greene, W. (2005). Fixed and random effects in stochastic frontier models. Journal of productivity analysis, 23(1):7–32. IDA (2016). Statistics system of Swedish rescue services (IDA). A statistics system developed and maintained by the Swedish Civil Contingencies Agency [https://ida.msb.se/]. Jaldell, H. (2002). Essays on the performance of fire and rescue services. PhD thesis, G¨oteborg University,. Kumbhakar, S. C. and Lovell, C. K. (2003). Stochastic frontier analysis. Cambridge: Cambridge University Press. Martin, S. and Smith, P. C. (2005). Multiple public service performance indicators: Toward an integrated statistical approach. Journal of Public Administration Research and Theory, 15(4):599–613. Neely, A., Gregory, M., and Platts, K. (1995). Performance measurement system design: a literature review and research agenda. International Journal of Operations & Production Management, 15(4):80–116.
  43. 43. Pollitt, C. and Bouckaert, G. (2011). Public Management Reform: A comparative analysis-new public management, governance, and the Neo-Weberian state. Oxford: Oxford University Press. PRONTO (2016). Statistics system of Finnish rescue services (PRONTO). A statistics system developed and maintained by the Emergency Services College in Kuopio, Finland [https://prontonet.fi/]. Simar, L. and Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1):49–61. Thanassoulis, E. (2001). Introduction to the theory and application of data envelopment analysis. Boston, MA: Springer. Van Dooren, W., Bouckaert, G., and Halligan, J. (2015). Performance management in the public sector. London: Routledge.

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