Tiago Silveira Gontijo
Izabela Hendrix University, Brazil
E-mail: tsgontijo@hotmail.com
Alexandre de Cássio Rodrigues
FUMEC University, Brazil
Izabela Hendrix University, Brazil
E-mail: alexandrerodrigues.engprod@gmail.com
Cristiana Fernandes De Muylder
FUMEC University, Brazil
E-mail: cristiana.muylder@fumec.br
Submission: 18/07/2017
Revision: 04/04/2018
Accept: 16/04/2018
ABSTRACT
The
classical Data Envelopment Analysis (DEA) allows us to evaluate public
organizations’ effectiveness, however, such models can classify as efficient
organizations that, in fact, are not. This paper aims to evaluate, through a
DEA model that incorporates managerial preferences, the efficiency of the
twenty five National Department of Mineral Production (DNPM) superintendence's. In
this paper we considered as output the amount of servers in the middle and end
areas. The number of mining titles granted in 2016 was analyzed as input. In
order to upgrade the classical DEA mode, we utilized manager preferences
regarding outputs by the assurance region method. The results exhibited that,
when incorporating management preferences into the classic DEA models, the
superintendence number that showed maximum operational efficiency reduced from
eight to five. For superintendence classified as inefficient, we identified the
benchmarks and a performance target, since they can support the action planning
aimed at reducing the high liabilities pending processes for analysis by the
municipality. The findings of this study shall reduce the DNPM's slowness in
granting mining bonds, contributing to the Brazilian economy.
Keywords: efficiency;
managerial preferences; data envelopment analysis
1. INTRODUCTION
The
New Public Management movement, which emerged in the 1970s, presented itself
with the primary objective of "transform the government" into a
private company, thereby acquiring efficiency, reducing costs, and achieving
greater productivity in service delivery (MOTTA, 2013). As a consequence, it
has become common for public organizations to use increasingly sophisticated
performance evaluation systems (NOGUEIRA et al., 2012).
In
the Brazilian context, the inclusion in 1998 of the efficiency principles in
the Public Administration stimulated the applied academic research, with the
purpose of measuring the government efficiency (DINIZ; LIMA, 2014) using
classical Data Envelopment Analysis (DEA) models (MACEDO; NOVA; ALMEIDA, 2010).
The DEA is a benchmarking technique, since it compares similar operating units,
which spent inputs to produce outputs, (LIU et al, 2013; EMROUZNEJAD; YANG, 2018).
A
major limitation of classical DEA models is the flexibility in the selection of
weights to assign to the choiced output / input in determining the efficiency
of each Decision Making Unity (DMUs) (Charnes, Cooper 1978; Banker, Charnes,
& Cooper, 1984), that is, the classical models do not incorporate
information on the managers' preferences (LIU; SHARP; WU, 2006; THANASSOULIS;
PORTELA; ALLEN, 2004). This flexibility makes the method benevolent, (ROLF; SHAWNA; DIMITRIS, 2015; ZHU, 2015),
enabling a bias in the analysis (FERREIRA; GOMES, 2009). This limitation
strongly affects the public organizations efficiency evaluation. After all, a
public school with high approval level but low learning target can be
classified as efficient, although these "two concepts are equally
important for the education quality" (INEP, 2017).
When
stakeholders have managerial preferences, it is possible to incorporate the
preferences into DEA models by new constraints that deal with the appropriated
weights to inputs and outputs. Joro and Korhonen (2015) presented a global
review of the DEA models evolution, including the weight restriction
methodology. Recent applications of these models include the efficiency
evaluation of public hospitals (GONÇALVES, 2010), participating countries of
Olympic Games (SHIROUYEZZAD; YAZDANI, 2014; LI et al., 2015), electric power
companies (SARTORI, 2016), and graduate programs (SILVA; CORRÊA; GOMES, 2017).
This
paper aims to apply DEA models with preferences to evaluate the efficiency of
the National Department of Mineral Production (DNPM) superintendencies, a
federal authority responsible for promoting the granting of mining titles. The
DNPM's "slowness" in granting mining titles, a possible consequence
of the low labor force and the high liabilities of pending review processes
(TCU, 2011; DNPM, 2017c) has resulted in a reduction in the Investment in the
mineral sector (TOMAZ, 2014), which is strategic for the Brazilian economy
(IBRAM, 2015). The choice of DEA models with preferences is justified because
DNPM managers, depending on the goals they have to fulfill to be entitled to a
performance bonus (BRASIL, 2004), have different preferences on the titles
granted.
2. DEA
2.1.
Classic
DEA models
Efficiency
is a relative concept that compares what has been produced, given the available
resources, with what could be produced with the same resources (ZHU, 2015).
Efficiency makes use of two approaches which vary according to the production
process, as follows: (i) orientation to inputs (minimize the use of inputs
given level of output); (ii) orientation to outputs (maximize the level of
output given levels of the inputs) (COOPER; SEIFORD; ZHU, 2011).
The
DEA is a non-parametric efficiency measurement technique, which was
disseminated especially from the seminal works of Charnes, Cooper, and Rhodes
(1978) and Banker, Charnes, and Cooper (1984). The main difference between
these two works is that, while the first presupposes constant returns to scale
(RCE), that is, any variation in the inputs implies proportional variation in
the outputs, the second involves the assumption of variable returns to scale
(RVE).
In the DEA models’ mathematic formulation,
it is assumed that N Decision Making
Units – DMUs utilize the same production technology to transform m inputs in s outputs. Therefore, the efficiency score of the DMU0 (object),, is given by:
|
|
(1) |
where (i = 1,..., m) and (j= 1, ..., s)
detonate the weights that the DMUo give to the inputs and outputs,
respectively. The
conditions can be formalized as follows.
|
|
(2) |
Although it allows us to easily interpret the efficiency
of a DMU, the problem expressed in (2) admits infinite solutions (ZHU, 2014).
To circumvent this situation in the output-oriented models, which will be used
in this work, one must make the numerator of the objective function equal to a
constant, usually one, and transform the constraint into a difference between
the numerator and the denominator, which makes the efficiency scores between
zero and one. The result is the multiplier models, which are shown in Table 1:
DEA/RCE
Model |
|
DEA/RVE
Model |
|
|
(3) |
|
(4) |
Source: Adapted from ZHU
(2014, p. 50).
As
expressed in the problem (4), it is an unrestricted
variable in signal that indicates whether scale return is constant (= 0) or variable (≠ 0). Denoting the optimal solution of (3) and (4) by, the DMUo will be efficient, if and only if, =1 and all of the e values are positive.
Otherwise, the DMUo will
be classified as inefficient one, and its benchmarks will be the DMUs
associated with the active inequality constraints in the optimal solution:
|
|
(5) |
The
efficiency score of DMUo calculated under the constant returns to
scale assumption,, measures the
overall technical efficiency, while that obtained under the variable returns to
scale assumption, , Measures the
pure technical efficiency, which is related to the operational aspect. The
ratio between these measures provides the efficiency to scale, which indicates that the DMU operates at an optimal
scale (ZHU, 2014).
2.2.
DEA
MODELS WITH PREFERENCES
The
classical DEA models attribute weights to the inputs and the outputs, so they maximize the DMUs efficiency scores.
This allows to identify inefficient DMUs, which perform poorly even after
choosing the weights that are most favorable to them. In addition, such
flexibility ignores any preferences of managers in relation to inputs and
outputs, since larger weights can be attributed to minor variables, which makes
an a priori inefficient DMU Classified as efficient.
Managerial
preferences may include judgments about prior views regarding inputs and
outputs, the relationship between some inputs and outputs, efficient and
inefficient DMUs, and input/output substitutions (Allen et al., 1997). These
preferences can be incorporated into the classic DEA models by restricting the
weights assigned to inputs and outputs. In this sense, the methods of direct
restriction to weights, safety regions, cone ratio and restriction to virtual
inputs and outputs are highlighted.
The
direct weight restriction method, generalized by Roll et al. (1991) imposes
limits on multipliers for the purpose of not ignoring or overestimating inputs
and/or outputs in the analysis. These constraints are given by (6), where V and
U are constants that respectively represent the limits imposed on the inputs
and outputs weights. A drawback of this method is the possibility of generating
an infeasible linear programming problem.
|
|
(6) |
The
assurance region method, proposed by Thompson et al. (1990), adds limits to the
multipliers, thus restricting weights to a given region. For this, the
following restrictions on input and output weights are added to the classical
DEA models, respectively:
|
|
(7) |
From
each of the presented constraints in (7), we derive two more conditions, which
are given by (8) and (9), respectively:
|
|
(8) |
|
|
(9) |
The
cone ratio method is a generalization of the safety region method by Charnes et
al. (1989). In this approach, the weights assigned to the inputs are
constrained by a convex cone defined by k vectors (i = 1, ....,
k):
|
|
(10) |
Similarly,
the weights assigned to the outputs are constrained by a connected cone defined
by l vectors (j = 1, ....,
l):
|
|
(11) |
The
method proposed by Wong and Beasley (1990) imposes a limitation on the proportion
of the total virtual input of the DMUo used by input i (output j) to
the interval , stipulated by the
decision maker, which reflects the importance given to input i (output j) by
DMUo. However, this limitation, expressed in (12), can lead to
problems of infeasibility of difficult solution (ALCÂNTRA; SANT'ANA; LINS,
2003).
|
|
(12)
|
3. MATERIAL AND METHODS
The
methodological instrument used was an ex post evaluation, classified as a
cross-sectional work based on quantitative methods (DEA, in particular).
According to the most recent information available the data, related to the
2016 year, were obtained on the site and upon request to the Citizen
Information Service of the Municipality. Considering
that the DEA technique aims to compare the efficiencies of productive units
that perform similar activities, such as DMUs, DNPM’s superintendence:
I - carry outs activities related to
levy, charging, granting, surveys, citizen-user assistance, fiscal action,
legality analysis of , obtaining data and information on mineral economy and
the use of geotechnologies;
II -
promotes budgetary and financial execution within its constituency; and
III - make materials management,
assets, documents, personnel, infrastructure, information technology and
general services (MME, 2011, Art. 85).
The
sample consisted of all the DNPM superintendence in the federation states
(DMUs), with the exception of Acre. However, the circumscription of the
supervisory authority of DNPM (Goiás) covers the Federal District and the
oversight of the DNPM (Rondônia) covers Acre. Thus, the research included 25
superintendence. Once defined the DMUs, inputs/outputs were selected. This is a
fundamental step in DEA, since the efficiency scores are directly influenced by
these variables (COOK; TONE; ZHU, 2014). There are no previous studies on the
DNPM efficiency, so the inputs/outputs used in recent works that evaluated the
efficiency of Judiciary were used as reference (Table 2). This is consistent
because the DNPM, like the Judiciary, deals with process analysis.
Work |
Inputs |
Outputs |
Nogueira et al. (2012) |
Total expenditure, total staff, IT
expenses, new cases, total magistrates and internal resources. |
Costs, recollections and several sentences. |
Diniz and Lima (2014) |
Total expenditure. |
Number of cases dropped in 1st and 2nd grades, in the special court
and in the recursal class. |
Araújo, Dias and Gomes (2015) |
Number of pending and new cases and number of servers awarded and
assigned. |
Number of sentenced and resolved cases. |
Oliveira et al. (2016) |
Number of non-criminal and criminal cases and misdemeanors, total
employees number and number of computers. |
Number of dispatches, returns,
judgments, hearings, interlocutory decisions, actions, conciliations and
cumulative activities. |
Source: Prepared by the authors based on Nogueira et al. (2012), Diniz e Lima (2012), Aráujo, Dias e Gomes (2015) e
Oliveira et al. (2016).
As
was reported in Table 2, it is possible to observe that, in general, the inputs
are associated with the number of servers and the outputs, with the number of
analyzed processes. In the DNPM case, these variables are quite appropriate,
since the low labor force of the municipality and the high liabilities of
pending analysis processes (TCU, 2011; DNPM, 2017) make problems in mining
titles granting, which, consequently, has hindered the institution mission
fulfillment. Therefore, we consider the inputs and outputs as follows (Table
3):
Inputs |
||
Indicator |
Relevance |
|
1. Number of effective servers in the
middle area (serv_middle) |
The DNPM middle area is made up of servers with assignments geared to
the administrative exercise and logistic activities, which make use of all
the equipment and resources available for the accomplishment of these
activities. |
|
2. Number of effective servers in the
end area (serv_end) |
The DNPM final area consists of servers with attributions dedicated to
the activities inherent to the promotion and control of the exploration and
mineral resources exploitation, to the inspection and fossiliferous deposits
protection, to the monitoring and analysis of geological, mineral and mineral
technology research, The performance monitoring of the Brazilian and
international mineral economy, the mineral policy implementation, the
promotion of the rational and efficient mineral resources use, supervision of
the collection of financial compensation for the mineral resources
exploration, and fostering the development of scientific and technological
research, aimed at knowledge, sustainable use, conservation and mineral
resources management. |
|
Outputs |
||
Indicator |
Relevance |
|
1. Number of published research
permits (perm_pub) |
The Research Permit authorizes the execution of works focused on the
deposit definition, its evaluation and the determination of the feasibility
of its economic use. |
|
2. Number of licenses granted
(lic_granted) |
Licensing accredits its possessor the mineral exploitation of
substances destined for immediate employment in construction. |
|
3. Number of mining permits granted
(perm_granted) |
The mining permit allows the use of mineral extractable materials,
which by their nature, especially its small volume and irregular
distribution, do not often justify investment in research work. |
|
4. Number of extraction records
granted (reg_granted) |
The extraction register allows to the organs of the direct or
autarchic administration of the Union, the States, the Federal District and
the Municipalities, exclusively, to extract substances of immediate use in
the civil construction, so that they are used only in public works, being
prohibited its sale, Third party or transfer to private companies. |
|
Source: Elaborated by the authors based on avaliable information in
Brazil (2004) and DNPM (2017b).
The
indicators related to the inputs were obtained by request to the Citizen
Information Service of the municipality (DNPM, 2017d) in June 2017, as these
were not detailed in the Management Report of 2016 (DNPM, 2017c). The outputs
were collected on the entity's website (DNPM, 2017a). The descriptive
statistics of the selected inputs and outputs are shown in Table 4:
Variable |
Type |
Minimum |
Maximum |
Mean |
Stan. deviation |
serv_middle |
Input |
2 |
31 |
13,32 |
7,34 |
serv_end |
2 |
58 |
17,12 |
13,40 |
|
perm_pub |
Output |
15 |
2926 |
539,36 |
734,74 |
lic_granted |
7 |
251 |
66,52 |
62,09 |
|
perm_granted |
0 |
150 |
7,84 |
29,82 |
|
reg_granted |
0 |
45 |
5,96 |
11,68 |
Source: Prepared by the authors based on data from DNPM (2017d) and DNPM
(2017a)
Table
4 highlights that at least one of the outputs related to the number of mining
permits granted (perm_granted) and the number of extraction records granted
(reg_granted) was null. Since the DEA models only admit non-zero positive
variables (CHARNES; COOPER; RHODES, 1978; BANKER; CHARNES; COOPER, 1984), for
all DMUs a unit was added to those outputs. This procedure, consisted on
variables translation, which does not change the efficiency scores by DEA,
since it moves the efficiency frontier for all DMUs (ZHU, 2015).
It is
worthwhile to note that the large inputs and outputs amount compared to the
number of DMUs decreases the discriminating power of the DEA (COOK; TONE; ZHU,
2014). In this sense, Ferreira and Gomes (2009, p.149) recommend that "for
each pair of input variables and for each pair of product variables [one should
be excluded] when they have high correlation (for example, above 0.8) ".
Thus, to verify that condition, we calculated the correlations between the
variables, which are shown in Table 5.
Variable
|
serv_middle
|
serv_end
|
perm_pub
|
lic_granted
|
perm_granted
|
reg_granted
|
serv_middle |
1,00
|
|
|
|
|
|
serv_end |
0,62
|
1,00
|
|
|
|
|
perm_pub |
0,47*
|
0,80
|
1,00
|
|
|
|
lic_granted |
0,47*
|
0,83
|
0,86*
|
1,00
|
|
|
perm_granted |
0,19
|
0,32
|
0,32
|
0,36
|
1,00
|
|
reg_granted |
0,29
|
0,58
|
0,76
|
0,60
|
0,35
|
1,00
|
Source: Prepared by the authors based on data from DNPM (2017d) and DNPM
(2017a) Note: The asterisk indicates significant correlation at the 5% level (2
tailed Pearson correlation test).
Table
5 shows that the correlation between the inputs (0.62) was less than 0.8 and
not significant at the 5% level. Therefore, through the correlation criterion,
it was not possible to exclude any input. In terms of the outputs, it was noted
that lic_granted and perm_pub had high correlation (0.86), which was
significant at the 5% level. However, it was decided to maintain the outputs,
since those variables being tied to the evaluation of the institutional
performance of the DNPM, as will be shown below, the discrimination power of
the DEA is little affected when the number of DMUs is equal to a minimum of
three times the amount of inputs and outputs (ZHU, 2015). Table 5 shows that,
except for service with perm_pub and lic_granted, no significant correlation
was observed between inputs and outputs, which is not an impediment because the
DEA technique does not require a functional relational between inputs and
outputs (FERREIRA; GOMES, 2009).
In
the data analysis, output-oriented DEA models were adopted. The DNPM
superintendencies were classified as efficient, taking into account if the
number of servers in the middle and end areas can maximize the number of
research permits publications and licensing concessions, mining permit mining
and extraction logs. The orientation to outputs was appropriate since the
orientation to inputs was adopted, and the objective would be to reduce them,
maintaining the current levels of outputs.
It is
important to note that it is desirable to increase the outputs, since,
according to an audit carried out by the Federal Audit Court (TCU), the
liabilities of DNPM's pending cases are high (TCU, 2011). It is also understood
that the work force cannot be easily reduced, since the servers are, in
general, effective and therefore have stability. In addition, “human capital”,
the main input of DNPMi is insufficient to meet the demands (TCU,
2011, DNPM, 2017c) and
it was further reduced due to the retirement of the civil servants and the lack
of public tenders to recompose these vacancies, which increases the need to
optimize existing human resources.
It is
important to note that the DNPM performance is assessed annually as to the
achievement of the organizational objectives, which are set by the entity's
Director General (BRASIL, 2004). Regarding the management of mining titles, in
2016, 18,700 requirements were analyzed as detailed in Table 6:
Mining title |
Goal |
% |
Search permit |
16.600 |
88,8 |
Licensing |
1.700 |
9,1 |
Perforation of mining prospector |
200 |
1,1 |
Extraction log |
200 |
1,1 |
Total |
18.700 |
100,0 |
Source: Prepared by the authors based on DNPM (2017c).
The
institutional evaluation results have a significant impact on the remuneration
of the employees, since 80% of the compensation due to them, which corresponds
to approximately 60% of the total remuneration (BRAZIL, 2017. As a consequence
of the stipulated goals for the requirements analysis, it is reasonable to
assume that DNPM managers have a preference to analyze requirements for a permit, for exploration,
for licensing and for permitting mining, and for the latter, indifferent with
respect to extraction logs. Therefore, the security regions method was used to
incorporate these preferences into classic DEA models:
DEA/RCE Model |
|
DEA/RVE Model |
|
|
(13) |
|
(14) |
Source: Prepared by the authors.
In
the implementation of these models, the software Integrated Decision Support
System (SIAD), proposed by Meza et al. (2005) was adopted. The results are shown below.
4. ANALYSIS AND RESULTS DEMONSTRATION
In
this section, the research results are presented and discussed. Initially,
Table 8 compares the efficiency scores of the DNPM superintendencies,
calculated through the classic and preferred DEA models.
N |
DNPM Superintendence |
Classic DEA |
|
DEA with preferences |
||||
Technical Efficiency |
Efficiency to Scale |
|
Technical Efficiency |
Efficiency to Scale |
||||
Overall |
Pure |
|
Overall |
Pure |
||||
1 |
BA |
1,00 |
1,00 |
1,00 |
|
1,00 |
1,00 |
1,00 |
2 |
MG |
1,00 |
1,00 |
1,00 |
|
1,00 |
1,00 |
1,00 |
3 |
TO |
1,00 |
1,00 |
1,00 |
|
0,86 |
1,00 |
0,86 |
4 |
RO/AC |
1,00 |
1,00 |
1,00 |
|
0,68 |
1,00 |
0,68 |
5 |
PI |
1,00 |
1,00 |
1,00 |
|
1,00 |
1,00 |
1,00 |
6 |
PR |
0,90 |
0,97 |
0,93 |
|
0,84 |
0,91 |
0,91 |
7 |
MA |
0,65 |
0,69 |
0,94 |
|
0,58 |
0,60 |
0,97 |
8 |
GO/DF |
0,67 |
0,82 |
0,82 |
|
0,43 |
0,46 |
0,94 |
9 |
RS |
1,00 |
1,00 |
1,00 |
|
0,43 |
0,44 |
0,98 |
10 |
MT |
1,00 |
1,00 |
1,00 |
|
0,40 |
0,41 |
0,98 |
11 |
SP |
0,39 |
0,40 |
0,99 |
|
0,37 |
0,37 |
0,99 |
12 |
AL |
0,53 |
0,63 |
0,84 |
|
0,21 |
0,37 |
0,55 |
13 |
CE |
0,53 |
0,59 |
0,90 |
|
0,32 |
0,33 |
0,96 |
14 |
SC |
0,39 |
0,39 |
1,00 |
|
0,33 |
0,33 |
0,99 |
15 |
RJ |
0,46 |
0,53 |
0,87 |
|
0,27 |
0,28 |
0,95 |
16 |
PB |
0,43 |
0,43 |
0,99 |
|
0,24 |
0,28 |
0,84 |
17 |
RN |
0,45 |
0,55 |
0,81 |
|
0,27 |
0,28 |
0,96 |
18 |
MS |
0,48 |
0,49 |
0,99 |
|
0,22 |
0,27 |
0,83 |
19 |
ES |
0,37 |
0,40 |
0,94 |
|
0,26 |
0,27 |
0,97 |
20 |
SE |
0,37 |
0,37 |
1,00 |
|
0,25 |
0,26 |
0,96 |
21 |
PA |
1,00 |
1,00 |
1,00 |
|
0,19 |
0,19 |
0,98 |
22 |
PE |
0,20 |
0,23 |
0,88 |
|
0,12 |
0,12 |
0,96 |
23 |
AM |
0,12 |
0,16 |
0,74 |
|
0,09 |
0,11 |
0,86 |
24 |
RR |
0,27 |
0,31 |
0,87 |
|
0,08 |
0,08 |
1,00 |
25 |
AP |
0,30 |
0,32 |
0,93 |
|
0,05 |
0,06 |
0,96 |
Mean |
0,59 |
0,65 |
0,94 |
|
0,38 |
0,46 |
0,92 |
Source: Elaborated by the authors according to the research results.
In
Table 8, the score analysis calculated by the classical DEA models shows that
eight DNPM superintendents (BA, MG, TO, RO / AC, PI, RS, MT and PA) obtained
the maximum technical efficiency. According to these models, the average level
of overall inefficiency was 41% (1-0.59), which means that the
superintendencies evaluated could, on average, increase the number of research
permit publications by up to 41% and Licensing grants, mining permits and
extraction logs, without increasing the number of servers in the middle and end
areas. It is also noted that overall technical inefficiency is due to pure
technical (operational) inefficiency, quoted at 35% (1 - 0.65), rather than to
inefficiency of scale, whose average was 6% (1 - 0.94).
Still
in relation to the results shown in Table 8, it is verified that the
incorporation of management preferences reduced the number of efficient
superintendencies to five. In addition, it generated technical efficiency
scores less than or equal to those calculated by classical DEA models, which,
at the 5% level of significance, was confirmed by mean differences tests. These
results are due to the fact that the incorporation of management preferences
prevented low weights from being attributed to the outputs considered important
by the organization.
Table
9 shows that DNPM superintendencies located in BA, MG, TO, RO/AC and PI,
presented the highest pure technical efficiency, and therefore are benchmarks
for the others. In addition, the current numbers and the targets of
publications of research permits and licensing licenses, mining permits and
extraction records are highlighted. It should be noted that the targets expose the
consequences of inefficiency, since they indicate the outputs that should have
been obtained if the superintendencies were efficient. It is noted that if all
DNPM superintendencies were efficient, the institutional goals would be fully
met, which is not the case in the current situation.
N |
DNPM Superintendence |
Benchmark |
Outputs |
||||||||||
perm_pub |
|
lic_granted |
|
perm_granted |
|
reg_granted |
|||||||
Actual(A) |
Target(T) |
|
A |
T |
|
A |
T |
|
A |
T |
|||
1 |
BA |
BA |
2746 |
2746 |
|
125 |
125 |
|
5 |
5 |
|
6 |
6 |
2 |
MG |
MG |
2926 |
2926 |
|
203 |
203 |
|
34 |
34 |
|
6 |
6 |
3 |
TO |
TO |
267 |
267 |
|
46 |
46 |
|
5 |
5 |
|
0 |
0 |
4 |
RO/AC |
RO/AC |
125 |
125 |
|
33 |
46 |
|
6 |
6 |
|
1 |
1 |
5 |
PI |
PI |
331 |
331 |
|
36 |
36 |
|
0 |
0 |
|
0 |
0 |
6 |
PR |
TO |
568 |
622 |
|
47 |
52 |
|
0 |
25 |
|
3 |
0 |
7 |
MA |
PI |
294 |
489 |
|
34 |
56 |
|
0 |
1 |
|
0 |
1 |
8 |
GO/DF |
BA |
1057 |
2305 |
|
177 |
386 |
|
2 |
6 |
|
2 |
6 |
9 |
RS |
BA |
581 |
1327 |
|
251 |
573 |
|
15 |
36 |
|
150 |
343 |
10 |
MT |
PI |
446 |
1087 |
|
40 |
97 |
|
25 |
62 |
|
7 |
18 |
11 |
SP |
BA |
708 |
1897 |
|
47 |
126 |
|
0 |
2 |
|
4 |
12 |
12 |
AL |
RO/AC |
58 |
156 |
|
24 |
64 |
|
0 |
2 |
|
0 |
5 |
13 |
CE |
BA |
732 |
1810 |
|
76 |
337 |
|
0 |
11 |
|
1 |
5 |
14 |
SC |
TO |
610 |
1851 |
|
62 |
188 |
|
0 |
2 |
|
15 |
48 |
15 |
RJ |
BA |
402 |
1413 |
|
84 |
295 |
|
0 |
3 |
|
0 |
3 |
16 |
PB |
TO |
175 |
616 |
|
21 |
74 |
|
4 |
17 |
|
0 |
3 |
17 |
RN |
TO |
249 |
903 |
|
62 |
225 |
|
1 |
6 |
|
0 |
6 |
18 |
MS |
TO |
164 |
607 |
|
44 |
163 |
|
0 |
3 |
|
0 |
3 |
19 |
ES |
TO |
303 |
1141 |
|
51 |
192 |
|
0 |
3 |
|
1 |
7 |
20 |
SE |
PI |
95 |
359 |
|
18 |
68 |
|
0 |
3 |
|
0 |
3 |
21 |
PA |
BA |
438 |
2314 |
|
69 |
365 |
|
45 |
242 |
|
0 |
4 |
22 |
PE |
BA |
222 |
1787 |
|
45 |
362 |
|
0 |
7 |
|
0 |
7 |
23 |
AM |
PI |
75 |
689 |
|
12 |
110 |
|
0 |
8 |
|
0 |
8 |
24 |
RR |
PI |
15 |
185 |
|
13 |
160 |
|
0 |
11 |
|
0 |
11 |
25 |
AP |
PI |
28 |
497 |
|
7 |
124 |
|
4 |
88 |
|
0 |
17 |
Total |
|
13615 |
28450 |
|
1627 |
4473 |
|
146 |
588 |
|
196 |
523 |
|
Goal |
|
16600 |
|
1700 |
|
200 |
|
200 |
Source: Elaborated by the authors from the research results.
It is
important to note that although the DEA makes it possible to calculate the
efficiency scores of the DMUs, this technique alone does not identify the
factors that affect them (ZHU, 2015). In order to complete the analysis, we
verified the effect of the number of protocols for search permits (alv_prot),
licensing (lic_prot), mining permits (perm_prot) and extraction logs (peg_prot)
in the superintendencies of the DNPM (the descriptive statistics of these variables
in the appendix), which cannot be controlled by the managers, on the pure
technical efficiency scores calculated by the DEA model with preferences. The
option to consider pure technical efficiency scores as a dependent variable is
justified because, as already pointed out, in the case of DNPM
superintendencies, the overall technical inefficiency is due more to pure
technical inefficiency than to inefficiency of scale.
In
the estimation of the model coefficients, given by (15), the tobit regression
was adopted, the most indicated when the dependent variable is censored
(WOOLDRIGE, 2006), as is the case with efficiency scores, which are unit
limited.
|
|
(15)
|
The
estimates results are shown in Table 10. It is observed that the overall
regression significance is guaranteed, according to the statistic X². Other
tests showed that there were no problems of collinearity, endogeneity,
heteroscedasticity or abnormality of the residues.
Variable |
Coefficient |
Standard-Error |
z |
P-value |
Constant |
0,2223 |
0,0745 |
2,9860 |
0,0028 |
alv_prot |
0,0003 |
0,0001 |
2,2920 |
0,0219 |
lic_prot |
0,0010 |
0,0013 |
0,7879 |
0,4308 |
perm_prot |
-0,0037 |
0,0003 |
-1,4530 |
0,1463 |
peg_prot |
-0,0037 |
0,0298 |
-1,2490 |
0,2116 |
X²
statistics |
|
|
25,89 |
0,000 |
Source: Elaboration of the authors from the research results.
After
analyzing the econometric indicators, we discuss the significance and the sign
of the estimated coefficients highlighted in Table 10. Thus, at the 5% level of
significance, only the independent variable alv_prot had a significant effect
on the scores of pure technical efficiency of DNPM superintendencies. The
positive sign of the variable coefficient indicates that the more research
requirements are filed, the greater the efficiency of superintendencies tends
to be. This is an indication that the superintendencies in which more research
requirements are filed may be under pressure to analyze them more quickly,
which is consistent with the fact that the institutional goals of analysis of
those processesare preferred by managers.
5. FINAL CONSIDERATIONS
In
this paper, we utilized DEA models to evaluate the efficiency of DNPM's
superintendence in 2016. For this purpose the servers in the end-and-a-half
areas and the number of published and granted mining titles were the inputs.
This paper deals with the managers preferences by means of weight restrictions,
using the safety regions method.
The
DEA models scores revealed that, from the 25 DNPM superintendents, eight (BA,
MG, TO, RO / AC, PI, RS, MT and PA) obtained maximum technical efficiency
(global and pure). By incorporating the preferences of managers in relation to
the outputs, it was verified that only three superintendence (BA, MG and PI)
were globally efficient and that, like these, the superintendence of TO and
RO/AC also had maximum efficiency pure technique.
It
was also found that in both models, the overall technical inefficiency of DNPM
superintendence is due more to the pure technical inefficiency than to the
inefficiency to scale. In this sense, considering the model that incorporates
managers' preferences, it was verified that the number of mining titles
published and granted could increase by 62% without changing the workforce. It
was also found that DNPM superintendence tend to be more efficient. This is not
surprise, since the institutional goals contributes to manager's preference.
After all, the compensation of DNPM's employees is directly related to the
goals fulfillment.
This
work has highlighted importance on planning at the efficiency level,
demonstrating benchmarks and realistic performance targets to the DNPM's
superintendence. The present findings might help to solve the pending processes
analysis by the autarky, thus promoting investments to the mineral sector,
which is of the utmost importance for the country. We hope that our research will serve as a basis for future
studies on the governmental efficiency.
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APPENDIX
Variables |
Minimum |
Maximum |
Mean |
Stand. Deviation |
perm_pub |
42 |
2761 |
560,40 |
655,23 |
lic_granted |
8 |
392 |
108,72 |
100,49 |
perm_granted |
0 |
615 |
62,16 |
139,18 |
reg_granted |
0 |
119 |
8,52 |
23,69 |
Source:
Prepared by the authors based on data from DNPM (2017a)
i In 2016, the
expenses committed by DNPM were R $ 311,189,292.24. Of this amount, R $
253,006,130.64, equivalent to 81.3% of the total, were allocated to the payment
of salaries and social charges of the serve (DNPM, 2017c).