Report for Gundagai Shire on Efficiency and Scale Projections Arising From Proposed
Amalgamations.
This report provides detailed consideration of scale, efficiency, liability and harmonisation
matters raised in my earlier report: ‘Response to IPART Assessments: Coolamon, Gundagai,
Junee and Temora Councils’. In particular I consider the proposed merger of ‘Gundagai and
Tumut’ and the tripartite proposal of ‘Gundagai, Tumut and Tumbarumba’ against the
Gundagai stand-alone case.
IPART deemed Gundagai Shire to be ‘Not Fit’ according to (i) scale and capacity, (ii)
sustainability and (iii) efficiency. In particular IPART (2015, p. 219) state that ‘the proposed
Rural Council Proposal (sic) is not as good as the ILGRP’s preferred option to merge with
Tumut’. Moreover, the IPART (2015, p. 219) report states that ‘Gundagai’s small and
declining population of 3,450 in 2031 means it is unlikely to cost-effectively provide services
to the local community’. Therefore IPART makes three claims which can be subject to
empirical test: (i) that the scale of Gundagai is presently insufficient and that it can be
improved through the proposed amalgamation, (ii) that the proposed amalgamation will be
more cost-effective (technically efficient) than the stand-alone case and (iii) that the council
is relatively inefficient. Assessing these claims is the principle aim of the report, although I
also provide information on other important matters relating to amalgamation which should
be considered in the absence of a comprehensive merger business case. I stress that this report
should not be considered as a substitute for due diligence.
1
Data Envelopment Analysis of Existing Scale and Efficiency
Data Envelopment Analysis (DEA) allows for a nuanced estimation of relative municipal
scale and technical efficiency. Unlike other empirical techniques DEA does not require a
priori specification of functional form, is able to accommodate multiple outputs, and provides
specific point estimates for each council or proposed amalgamated entity. Technical
efficiency (TE) is evaluated in terms of the ability of a council to convert inputs (staff and
capital) into a set of outputs (number of residential assessments, number of farm assessments,
number of business assessments and total length of municipal roads) (see Drew, Kortt and
Dollery (2015a) for the justification of the DEA specification employed). It is important to
note that the Independent Local Government Review Panel (ILGRP), Office of Local
Government (OLG) and Independent Pricing and Regulatory Tribunal (IPART) have
provided various estimates of an assumed optimal scale for rural councils based on 2031
population projections. However, none of the agencies have provided a publicly documented
empirical analysis to support the assumptions. Moreover, it is clear that 2031 population
projections are the incorrect functional unit for evaluating municipal scale. Put simply,
councils do not produce people.
Instead, local government produces a heterogeneous mix of goods and services orientated
towards ‘service to property’ (for example, rubbish, water and sewerage). Higher tiers of
government tend to have a ‘services to individual’ orientation (for instance, education, health
and welfare) but Australian local government does not. Moreover, over a quarter of
municipal expenditure is associated with the provision of road infrastructure and this is, in
fact, negatively correlated with population (ie. as population increases the length of municipal
road responsibilities tend to decrease). Therefore, the approach taken by the various
independent and Government agencies implicitly and erroneously assumes that:
2
1. Councils do not spend resources in servicing businesses
2. Councils do not spend resources on building and maintaining transport infrastructure
3. The cost of servicing individuals living on farms is the same as the cost structure for
servicing individuals living in ‘town’
4. That the cost of servicing a property increases in proportion to the number of
individuals living in a property (ie. that the cost of servicing a house with four people
is four times the cost of servicing a house with a single person).
Clearly, the implied assumptions are void ab initio. Moreover, this fundamental
misconception regarding the nature of NSW local government has resulted in incorrect
conclusions regarding appropriate municipal scale and concomitant effects on efficiency.
Thus, the following DEA scenario testing fills a critical gap in the ‘independent expert’s’
analysis.
DEA employs linear programming to create an efficient frontier (comprised of the councils
which most efficiently convert inputs into outputs) and then estimates relative efficiency of
councils lying in the interior of the efficiency frontier according to their distance from the
frontier.
The data envelopment algorithm is detailed below:
min θ,λ θ,
s.t.
-yi + Yλ ≥ 0,
θxi – Xλ ≥ 0,
I1′λ = 1
λ≥0
3
where yi is a vector of outputs and xi is a vector of inputs, θ is a scalar (the efficiency score
for each council) and λ a vector of constants. The subscript i refers to the ith council and the
inequalities ensure non-negative weights. The CRS (Constant Returns to Scale) specification
evaluates inefficient councils against any peer on the frontier – irrespective of size. The
variable returns to scale (VRS) algorithm is achieved by adding the convexity constraint I1′λ
= 1 so that inefficient councils are only evaluated against municipalities of a similar size.
Under both estimates efficient councils are given a score of 1 and inefficient councils
assigned a score between 0 and 11. Scale estimates are simply the quotient of CRS and VRS
efficiency scores and a third estimate (non-increasing returns to scale (NIRS)) is made by
imposing the restriction I1′λ ≤ 1 so that the nature of the scale inefficiency can be determined.
An input orientation was adopted for the DEA which ‘minimises inputs while satisfying at
least the given output levels’ (Ji and Lee, 2010, p. 268). This orientation is to be preferred
given that councils take outputs as exogenous (that is, councils have little control over the
number of rateable properties nor the length of municipal roads in the short-run).
Tables 1 and 2 present measures of relative scale and technical efficiency for the existing
municipal structures in NSW calculated according to 2014 financial data. This provides
valuable contextual information for interpreting the individual scale and efficiency estimates
of existing Gundagai, Tumut and Tumbarumba councils (see, Table 3 for details). The
bootstrapped DEA estimates suggest that Gundagai and Tumbarumba are slightly under
optimal scale, whilst Tumut is considerably over-scaled2.Thus, any merger predicated on a
correct specification of scale would appear to be ill-advised.
1
Thus a council with an efficiency score of 0.95 is far more technically efficient than a council with a score of
0.45.
2
It might be noted that the scale estimates are slightly different to the previous report. This is because the
current estimates have been bias corrected which is an empirical method for providing greater certainty in
estimates for important decision-making purposes. Essentially, bias correction repeats the DEA routine
hundreds of times using re-sampling procedures and then employs a supplementary algorithm to adjust ‘raw’
4
We now turn to scenario testing in order that we might fully appreciate the scale and
technical efficiency implications of the proposed amalgamation structures.
Table 1. Scale Results for New South Wales Councils – Pre Amalgamation 2014
Amalgamation Scale Number Mean scale Stand. Dev. Min.
Max.
status
Entire State
OS
12
1
0
1
1
IRS
72
0.93758
0.080932
0.664797 0.999984
DRS
68
0.924319
0.090352
0.580372 0.999587
Notes: OS = optimal scale; IRS = increasing returns to scale; DRS = decreasing returns to
scale.
Table 2. Efficiency Results (CRS) for New South Wales Councils – Pre Amalgamation
2014
Amalgamation Efficiency Number
Mean CRS Stand.
Min.
Max.
status
Measure
Efficiency Dev.
Entire State
OS
12
1
0
1
1
IRS
72
0.676465
0.149675
0.328472 0.99997
DRS
68
0.701715
0.156546
0.3776
0.999002
Notes: OS = optimal scale; IRS = increasing returns to scale; DRS = decreasing returns to
scale.
Table3. Bootstrapped Existing Scale and Efficiency of Gundagai, Tumut and
Tumbarumba Municipalities, 2014
Amalgamation
CRS
Scale
Returns to
status
Scale
Gundagai
0.636079
0.960236
IRS
Tumut
0.56616
0.822201
DRS
Tumbarumba
0.387183
0.971039
IRS
Notes: OS = optimal scale; IRS = increasing returns to scale; DRS = decreasing returns to
scale.
scale and efficiency scores (see, Simar and Wilson, 2000). It is a time consuming procedure which is only
possible in a dedicated report of this type.
5
Data Envelopment Analysis Scenario Testing
Cooper, Seiford and Tone (2007) outline an empirical method for estimating the efficiency
and scale effects arising from proposed mergers in the Japanese banking industry. I have
adapted this method in order that I might estimate the efficiency and scale effects of putative
municipal merger options.
In common with all empirical techniques some assumptions must be made. In particular, I
assume that the inputs and outputs of merged entities will be the sum of the inputs and
outputs of the existing councils. In the case of outputs this seems to be an indisputable
assumption – it is completely reasonable to expect that the merger of councils will not alter
the sum of the length of municipal roads nor the sum of residential farm and business
assessments in the short-run. Moreover, the prohibition on forced redundancies for nonsenior staff found in s354F of the Local Government Act (1993) along with the stipulation
that staff numbers should be maintained in rural centres (s228CA(2)) would seem to suggest
that staff expenditure following amalgamation is likely to be materially consistent with the
sum of existing staff expenditures. Indeed, staff expenditure increased in the short-run
following the 2007/08 Queensland amalgamations (Drew, Kortt and Dollery, 2013). It is
possible however, that a council may alter the quantum of capital employed to provide
municipal services subsequent to amalgamation and this could introduce a small error into the
scale and efficiency projections (Drew, Kortt and Dollery, 2015a). However, it should be
borne in mind that (i) evidence from the 2007/08 Queensland amalgamations suggests that
additional capital will be deployed in the short-run following merger (Drew, Kortt and
Dollery, 2013) and (ii) that the same assumption has been made for all four merger cases
(thus any error introduced is likely to be the same for all cases and will therefore have little
effect on the decision making utility of the empirical strategy). Once again, we have
employed an iterative algorithm to provide added confidence in the DEA estimates.
6
Table 4 provides details of the CRS efficiency and scale estimates arising from the various
merger scenarios. VRS efficiency is not relevant because it adjusts for the effect of scale –
the entire purpose of this exercise is to gauge the effect of merger on scale and hence on CRS
technical efficiency. Unsurprisingly (given the results detailed in Table3), both of the
proposed amalgamation scenarios would result in far worse scale outcomes than the standalone case. Moreover, the CRS relative technical efficiency estimates are lower for each of
the proposed amalgamations when compared with the stand-alone case. Thus, we have
empirically evaluated the first two assumptions made in the IPART (2015) report. In both
cases IPART’s (2015) assumptions, based on it’s fundamental misconception of the nature of
NSW municipal services, have been empirically shown to be incorrect: the proposed mergers
will in all probability result in decreasing returns to scale and less cost-effective service
provision. With respect to the third claim made by IPART (2015) – that Gundagai Shire is
relatively inefficient – it is important to note that Gundagai’s existing relative technical
efficiency is just marginally lower than the average for NSW councils with increasing returns
to scale (IRS).
Table 4. Bootstrapped Scale and Efficiency of Merged Entities Gundagai, Tumut and
Tumbarumba Municipalities, 2014
Amalgamation status
CRS
Scale
Returns to
Scale
Gundagai stand alone
0.636079 0.960236
IRS
Gundagai-Tumut
0.592894 0.64096
DRS
Gundagai-Tumut0.516644 0.488395
DRS
Tumbarumba
It is interesting to note that the empirical evidence seems to have close parallels with the
survey results cited in the IPART (2015, p. 220) report which had just ‘24% of respondents
agreeing to a merger, with 50% of these agreeing it should be with Tumut’. Therefore, it
7
appears that Gundagai residents intuitively understand that merger, in and of itself, will not
improve scale and technical efficiency.
This conclusion by the residents of Gundagai is also supported by IPART’s (2015) own data
relating to the ‘fitness’ outcomes of previously merged councils. Just 7 out of the 26 merged
entities arising from the 2000 to 2004 NSW amalgamations were deemed by IPART to be
‘fit’ for the future. It is therefore somewhat surprising that the very same document would
urge councils to merge in order to become ‘fit’3!
3
One council, Canada Bay, chose to pursue a voluntary merger in their quest for a ‘fit’ assessment.
8
Implicit and Explicit Liabilities
Table 5 provides details of the gross implicit and explicit liabilities which might be assumed
by Gundagai residents in the event of a merger. I have elected not to present this data nett of
assets given my observation that asset values may not be sufficiently robust as to allow for
accurate calculations. Gundagai’s total explicit and implicit liabilities per assessment is
comparable with Tumut but around half of Tumbarumba’s figure. A merger with Tumut
would result in a slightly lower total liability per assessment for Gundagai residents
(approximately $290 per assessment) whilst the tripartite merger proposal would result in a
slight increase for Gundagai residents (around $231 per assessment). However, great care
must be taken with this data – in particular, with respect to the estimated cost to bring assets
to a satisfactory condition. The OLG definition for this estimate in the special schedule of the
municipal financial statements is vague and, in fact, a tautology. As a result of this definition
deficit, there has been a significant unexplained reduction in this estimate across the state
(around 25% reduction in the ‘estimated cost to bring assets to a satisfactory standard’ over
the last two financial years). This emphasises the need for careful due diligence prior to an
unconditional offer to merge.
There has been some conjecture that NSW government compensation will address inequities
arising from assumption of liabilities. This is completely incorrect. Under the current Fit for
the Future package it is quite likely that the compensation provided by Government will not
even cover the one-off costs of amalgamation (let alone ongoing disruption costs and
diseconomies of scale). Moreover, it has been claimed, in the media, that the estimated cost
to bring assets up to a satisfactory condition is not an implicit liability. This statement is also
demonstrably false. If the combined entity is ever going to address infrastructure backlogs –
which is one of the principal motivations for Fit for the Future – then clearly at some stage
residents of the combined shire will be required to provide the funding.
9
Table 5. Implicit and Explicit Liabilities upon Merger (per Assessment Share in
Parentheses) $’000
Council
No.
Assessments
Borrowings
(Current and
Non-Current)
Other
Liabilities
(excluding
borrowings)
Gundagai
2,539
Tumut
6,377
Tumbarumba
2,513
GundagaiTumut
GundagaiTumutTumbarumba
8,916
3,002
(1.182)
6,878
(1.079)
3,641
(1.449)
9,880
(1.108)
13,521
(1.183)
2,728
(1.074)
5,075
(0.796)
4,560
(1.815)
7,803
(0.875)
12,363
(1.082)
11,429
Estimated
Cost to Bring
Assets to a
Satisfactory
Standard
60
(0.024)
0
(0.000)
2,753
(1.096)
60
(0.007)
2,813
(0.246)
Total
Explicit
and
Implicit
Liabilities
5,790
(2.280)
11,953
(1.874)
10,954
(4.359)
17,743
(1.990)
28,697
(2.511)
Source: Number of assessments from OLG (2015); Financial data from audited financial
statements 2014-15.
Community of Interest
There have now been a total of five municipal de-amalgamations following forced mergers:
four in Queensland and one in Victoria. Thus, the spectre of subsequent de-amalgamation
must be considered to be a very real proposition. When amalgamation is followed by deamalgamation the community is generally required to pay for both events – the effect on
municipal financial sustainability is therefore devastating. One reason why amalgamations
fail is because ‘experts’ in capital cities have neglected to consider whether the existing
communities are sufficiently similar as to avoid a loss in economic welfare (Drew and
Dollery, 2015g). The best people to make this judgement are the residents of the respective
jurisdictions. However, a large difference in the OLG socio-economic rating along with
differences in the proportion of individuals identifying in certain demographic categories
suggests that there may be significant differences in the populations.
10
The whole raison d’etre for decentralised government is that ‘by tailoring outputs of such
goods and services to the particular preferences and circumstances of their constituencies,
decentralised provision increases economic welfare above that which results from the more
uniform levels of such services that are likely under national provision’ (Oates, 1999, p.
1121). Therefore, it follows that if communities with different preferences for municipal
goods are forced to amalgamate then a loss of economic welfare will be the likely result. This
in turn may promote de-amalgamation activism (Drew and Dollery, 2015g).
Table 6. Community Indicators, 2014.
Council
Population
No.
Councillors
ATSI
NESB
Average
wage
Gundagai
Tumut
Tumbarumba
3,747
11,316
3,521
8
7
8
2.4
4.5
2.5
1.5
3.2
3
43,134.90
45,408.50
41,838.80
Socio
Economic
Rating
64
41
59
Over
65
Under
15
19.9
19.6
20.1
20.7
20.2
18.7
Source: ABS (2015).
It is also important for careful consideration to be given to the political structure of any
proposed merged entity in order to ensure that a merger, rather than a take-over results
(Spicer, 2012). There is no correct solution; however, I have proposed a possible political
structure just to illustrate the complexity and importance of this decision. My proposal is for
two wards to be drawn according to the previous municipal boundaries (with four
representatives apiece). This would increase the likelihood that both communities might have
an equal voice in the early years following amalgamation. However, this structure could also
result in a ‘hung’ chamber (as was the case leading up to the Delatite de-amalgamation).
Moreover, it effectively halves the democratic representation for Gundagai residents. My
point in floating this suggestion is to illustrate that the political structure of an amalgamated
entity will be critical to the success of the venture. I do not believe sufficient attention has
been given to this matter by councils contemplating amalgamation.
11
Decision makers might also consider the strategic implications of a voluntary amalgamation.
It is true that a voluntary merger should secure Government funding. However, it is also true
that the Fit for the Future funding model is unlikely to cover the cost of merger. Moreover, it
is by no means certain that the Government will take the political risk of denying the same
level of funding to communities forced to amalgamate. In addition, to my mind, voluntary
merger effectively shuts the door on any prospect of a de-amalgamation should the merged
entity fail to satisfy residents. Given the increasing occurrence of de-amalgamations, and the
empirical evidence in this report which casts doubt on the success of the proposed mergers, I
believe it is important for residents to consider whether it might be prudent to keep future
options open.
12
Harmonisation
Table 7 details the existing fees and rates applicable to various categories of properties in the
three municipalities. After an amalgamation, fees and charges will need to be harmonised.
The final rates set for an amalgamated entity would be decided by the new Administration.
However, it is reasonable to expect that residents currently paying relatively low fees (for
instance Gundagai’s average domestic waste charge of $283.92) might be required to pay
more should the council merge with an entity with higher charges (for example, Tumut which
has a domestic waste charge of $344.75). The probability of this outcome is quite high when
one considers that the international scholarly evidence has demonstrated that service levels
are invariably increased following amalgamation (see, for instance, Dur and Staal, 2008;
Steiner, 2003).
Table 7. Existing Rates and Charges Requiring Harmonisation ($)
Council
Average
Residential
Rate
434.93
Gundagai
697.47
Tumut
Tumbarumba 384.18
Source: OLG (2015).
Average
Farm Rate
Average
Business
Rate
Typical
Water and
Sewer
1,967.81
1,043.91
1,640.69
505.88
1,861.43
709.68
1,123
1,107
1,268
Average
Domestic
Waste
Charge
283.92
344.75
312.90
13
Conclusion
IPART (2015) made three empirically testable claims in its assessment of Gundagai. The first
two claims – based on what appears to have been flawed assumptions – have failed to stand
up to robust empirical scrutiny. Specifically, I find both merger scenarios will result in overscaled entities with a concomitant reduction in efficiency. With respect to the last claim – that
the council is relatively inefficient – the empirical evidence suggests that Gundagai shire
exhibits technical efficiency which is only marginally below the level of it’s peers.
I also examined the implicit and explicit liabilities which might be assumed by Gundagai
residents should the merger proceed. I found that – on a per assessment basis – a merger
with Tumut would result in a marginal decrease in total gross liabilities, but that a merger
with Tumut and Tumbarumba might be expected to result in a slight increase in total gross
liabilities. I also explored some of the complex decision making which will need to be
addressed around political structure and rate and fee harmonisation. I believe that the
success of any merged venture will depend heavily on the detail relating to these decisions.
In short, neither of the proposed merger scenarios will produce superior results to the standalone case in terms of technical efficiency or scale. Continued participation in REROC
should adequately address capacity elements. Thus, there is no empirical basis for the
assumptions put forward by IPART(2015) in its report4.
This is an independent critique of the IPART assessments based on a desk top analysis of
IPART reports, audited financial statements, and Fit For the Future proposals. I have no
conflict of interest to declare and had no part in the IPART assessment process, nor in
compiling the various Fit For the Future proposals for Gundagai Shire.
It is my understanding that Teresa Boyd, CA is investigating the effect of a merger on the ‘sustainability’
claim made by IPART (2015).
4
14
The advice provided is based on my professional judgement after reviewing various
documents associated with the IPART assessment and conducting a number of empirical
estimations. I have taken source documents at face value and have made no efforts to verify
the accuracy of financial statements or other documentation as this was outside of my brief. It
must therefore be stressed that this report is in no way to be considered as a substitute for due
diligence. Moreover, any council contemplating merger should seriously consider conducting
thorough due diligence, particularly in relation to the written down value of assets, estimates
of required maintenance and accuracy of non-current liabilities.
15
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