J Prod Anal (2011) 36:113–123 DOI 10.1007/s11123-011-0208-4
Analysing the lobby-effect of port competitiveness’ determinants: a stochastic frontier approach Elvira Haezendonck • Julien van den Broeck Tim Jans
•
Published online: 15 February 2011 Ó Springer Science+Business Media, LLC 2011
Abstract Building upon a formerly performed study on port competitiveness, this article discusses the use of a stochastic frontier model as an interesting novel use to test, identify and correct respondents’ bias by applying it to competitiveness analysis based on perceptions of senior executives. Measuring the importance of competition determinants of seaports, conventionally analyzed using a SWOT-analysis based on (transport) infrastructure as a prime requirement for port activity growth, is an important issue to port management. However, it seems that the ‘‘institutional’’ environment of a seaport is also critical in obtaining a competitive advantage. In Haezendonck et al. (2000 and 2001) those port specific advantages and disadvantages were identified using factor analysis and L1-regression on the perceptions of 75 respondents, all senior executives and experts, through a survey. As regards the results of this study, critiques were formulated on the use of perceptions, often biased due to the political lobbying potential of the results. Since respondents often see independent studies as an opportunity to obtain more or early government subsidies, attract new investment projects or at least highlight the attention on their specific
problems and demands, they were prone to underestimating the positive impact of the key success factors of the studied seaport compared to its main rivals, in this case major seaports in the so-called Hamburg–Le Havre competitive range. The purpose of this article is to test the assumption that respondents significantly underestimate the positive impact of port specific advantages and to see which of the respondent subgroups within the 75 respondents sample are more responsible than others for this underestimation. In addition, we argue and demonstrate that the use of a stochastic frontier method is appropriate for this matter. Each of 25 considered competition determinants of the original study is decomposed into a noise and ‘‘efficiency’’ term, based on the Bayesian stochastic frontier model (BSFM). In this article, we find evidence that BSFM could be used to test the ‘‘lobby-effect’’ or underestimation of the real effect of determinants, that terminal operators as a subgroup of respondents, are more likely to underestimate the key success factors than the subgroup of port experts and that those determinants that are directly related to government action show more underestimation than competitiveness determinants that result from private investments. Keywords Port competitiveness Biased determinants Stochastic frontier
E. Haezendonck (&) T. Jans Faculty of Economic, Social and Political Sciences and Solvay Business School, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium e-mail:
[email protected] T. Jans e-mail:
[email protected] J. van den Broeck Faculty of Applied Economics, University of Antwerp (UA), Prinsstraat 13, 2000 Antwerp, Belgium
JEL Classification
C11 C81 H54 C83
1 Introduction Both port competition and the critical determinants of a port’s competitive position have been researched extensively in the academic literature (e.g. Hayuth 1993; Goss 1990, Slack 1985). Yet, these issues always merit further
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investigation, as port authorities and port operators, responsible for port strategy, are faced with a rapidly changing environment. Ports are affected by various new forces driving global competition, including the far reaching unitization of general cargo, the rise of megacarriers, the market entry of logistics integrators, the creation of network linkages among port operators functioning in different ports, the development of inland transport networks etc. (UNCTAD 1995; Notteboom and Winkelmans 2001). In order to outperform rivals in the long run, the actors involved in a port cluster should not only pursue conventional micro-economic goals such as profit maximization, cost reductions, productivity improvements, etc., whether at the level of a single organization or at the level of the entire port cluster, but they should also engage in global benchmarking efforts in order to assess the quality of the port cluster’s resource pool, including resources such as labor skills and client relationships. In this context, Haezendonck et al. (2000, 2001) empirically determined, based on an extended version of Porter’s (1990) diamond framework and on the resource-based approach,1 the determinants of competitive advantage of the Antwerp seaport as compared to its rivals. More specifically, the most important location and port specific advantages determining the port of Antwerp’s competitive position for containers and conventional cargo were identified using factor analysis and L1-regression (Haezendonck et al. 2001). In order to perform this analysis, 75 respondents were selected, i.e. port operators, experts, forwarders, shipping companies and agents, and were interviewed. Based on their answers, a large data set was constructed and analyzed. Although the collection and analysis of the data was carefully and transparently performed, we might assume that the used data are biased by various effects, such as order bias, central tendency effect, interviewer bias or cheating and a halo-effect. The researcher (Haezendonck et al. (2001)) tried to anticipate most of these effects by carrying out personally in-depth face-to-face interviews. Besides the more general interview and survey effects, several academic and policy experts pointed at the weakness of using perceptions for identifying key port success factors. Rather than identifying current competitive advantages, port managers may have tried through their answers in this survey to influence policy makers and investors to their advantage (referred to as the ‘‘lobbyeffect’’). In fact, they may have hoped to be able to create new competitive advantages thanks to the impact of independent academic studies.
1
For more information on Porter’s diamond approach as well as on the resource-based view (RBV) as conceptual basis for the empirical study used in this paper, we refer to Haezendonck et al. (2000, 2001).
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Hence, one important bias could not be avoided, i.e. the tendency that respondents have to underestimate the positive impact of port specific advantages on the competitiveness of the Antwerp port, compared to its main rivals in the Hamburg–Le Havre range. In fact, businesses in the port sector could see competitiveness studies as an opportunity to attract government subsidies for new investment or infrastructure projects. Therefore, the results do not fully reflect the reality of port specific advantages of the considered seaport but are more related to a certain degree of ‘‘wishful thinking’’. This assumption is further investigated in the present article. Moreover, a model application is proposed that could correct the original data in order to avoid biased results in future competitiveness analyses.
2 Determinants of port competitiveness A number of academic papers and consultants’ reports have emphasized the importance of financial parameters in order for ports to thrive in a competitive international environment. These studies view cost related elements benefiting the various actors in a port cluster as the critical determinants of this cluster’s overall performance in interport competition (see Heaver 1995; Slack 1985; UNCTAD 1992, 1995). However, Frankel (1989) and Hayuth (1995) have suggested that the extent to which a seaport is subject to competition also depends upon a number of non-cost driven factors, such as the quality of its services, labor regulations etc. Moreover, Slack (1985) has argued that quantitative elements can only provide a first approximation of a port’s sources of competitive advantage. In addition, Sletmo and Holste (1993) have observed that ports in advanced industrialized nations, such as the Northwestern European seaports, cannot expect to compete on the basis of low-cost inputs. Instead, they must attempt to develop higher order sources of competitive advantage. Haezendonck et al. (2001) provided empirical evidence that non-cost related parameters and qualitative elements indeed play an important role in port competition in the Hamburg–Le Havre range. In this study, an alternative framework was proposed to explain the competitive position of a seaport. This framework proposed a resourcebased approach that allowed to identify the basic elements determining a seaport’s competitiveness. These 25 determinants are listed in Table 1. In Haezendonck et al. (2001) a field survey was used to identify the perceptions of senior business executives and port experts on the key sources of competitive advantage of the Antwerp seaport, in the Hamburg–Le Havre range context. 75 in-depth face-to-face interviews were conducted, based on a questionnaire containing a
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Table 1 Determinants of competitive advantage of the Antwerp port 1. port infrastructure
9. client-relationships in the port
17. transshipment
2. port superstructure
10. client-relationships outside the port
18. warehousing
3. port labor (human capital)
11. local government intervention
19. value added logistics
4. logistical techniques and communication systems
12. regional government intervention
20. manufacturing
5. intra-port competition
13. (supra)national government intervention
21. activities by agents, forwarders etc.
6. inter-port competition
14. port supporting services
22. distribution activities within port
7. intra-port co-operation
15. activities maritime accessibility
23. road transport
8. extra-port co-operation
16. shipping
24. rail transport 25. inland navigation
Source Haezendonck et al. (2001)
‘‘competitiveness-matrix’’.2 Respondents were asked to provide a discrete score between 1 and 5 on a Likert-scale, for all variables in the matrix, in function of the perceived impact of this particular variable on the competitiveness of the Antwerp port as compared to its main rivals in the Hamburg–Le Havre range. The data obtained through the survey conducted among port operators and port experts in Antwerp, were analyzed by using factor analysis to group the respondents’ perceptions of variables influencing the competitiveness of the Antwerp seaport. Hence, the factors resulting from the data analysis represented the critical bundles of resources or competences underlying the port of Antwerp’s success. With regard to the results of Haezendonck’s study (Haezendonck et al. 2001), criticism was often expressed on the use of perceptions in the data set. In fact, the use of perceptions or observations to build datasets for research analyses is more widely criticized because they would not objectively or correctly reflect the analyzed parameters or variables. In this study, several academic and field experts indeed doubted the reliability and objectivity of the data 2
This matrix was created based upon a combination of the functional activities performed within a port from a logistics chain perspective (Cooper, 1994) and the resources required to perform those activities, as suggested by the extended ‘‘diamond’’ framework of competitive advantage (Porter, 1990). The functional activities performed in a port (horizontal axis of the matrix) can be subdivided into activities related to its foreland (maritime transport and maritime access), activities within the port sector itself (such as transshipment, warehousing, value added logistics, manufacturing, forwarding and distribution) and activities in relation to its hinterland (road transport, rail transport and inland navigation). The companies performing those activities represent the key actors engaged in port competition. The distinction made among those port activities enables to locate strengths and weaknesses in specific areas of the port logistics chain. The vertical axis of the competitiveness matrix represents the resources necessary to perform the port related activities. These resources consist of the extended ‘‘diamond’’ framework components (Porter 1990). Each component can be further subdivided into a number of resources relevant to the port sector. Each combination of an individual activity with a selected resource represented a variable (cell) in the competitiveness matrix.
provided by the respondents. Although in the study a lot of possible biasing effects, including order bias, central tendency effect, interviewer bias or cheating and a halo-effect, were anticipated (e.g. by including respondents from outside the considered port of Antwerp and by performing in-depth interviews), we recognize the collected data may be biased by the potentiality of the results of the study to be used for political lobbying. In fact, independent research and academic studies often serve as expert information and reliable evidence for policy makers to decide whether or not to invest some of their limited funds in a certain geographical area or in a specific project. In the case of port investment projects, timing is crucial and anticipating maritime transport developments and building capacity is necessary for a port in order to cope with the competition. Therefore, it is not unlikely that respondents have taken the opportunity of being surveyed to ‘‘exaggerate their needs’’ and try to influence or accelerate government action or funding by doing so. As an example, we could expect that respondents underestimate the positive impact of the Antwerp seaport infrastructure, such as terminal capacity and rail connections, compared to their main rivals, in order to prioritize government funding to those areas. We assume the lobby-effect only biased the results in one direction, as all respondents have no credible and rational reason to overestimate the positive impact of competitiveness determinants. In this unlikely case they would actually renounce new or more investments or even suggest divestment. Given the dramatic increase in cargo volumes to the West-European seaports at the moment of the analysis, it is impossible that the respondents of the study would not have encouraged investments and fostered growth. In the present paper, we therefore assume that respondents underestimated the positive impact of certain determinants (i.e. gave a lower score to determinants), in order to possibly attract more government attention and/or investments. Thus, the expected deviation from the objective impact is one-sided and the objective (‘‘true’’) impact of each determinant can be considered as a frontier.
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The expected deviation, distance and the level of inefficiency are the conventional terms used in BSFM applications. In the context of this paper, inefficiency as a term does not make sense, but is rather the bias or distance from the rational, objective or true answer, therefore the level of reality (of the perceptions of respondents in a survey). This article can, as a result, be considered as an important contribution to the legitimacy of the results obtained in Haezendonck’s study (Haezendonck et al. 2001) and offers a new tool for minimizing biased statistical results. We are aware that the use of BSFM in this context is unusual. However, we believe that this interesting model can serve other domains and issues and has the potential of being additionally used outside the scope of the traditional research area of productivity analysis.
3 A Bayesian stochastic frontier model: a new approach for reducing bias This article presents a new approach for analyzing the onesided bias of perceptions or scores. The hypothesis that the data provided by the respondents are underestimated is tested by using the Bayesian stochastic frontier model, as developed by van den Broeck et al. (1994). In fact, a frontier approach allows us to analyze the difference of the score with regard to the objective or ‘‘true’’ positive or negative impact of a determinant (this objective impact is considered as frontier). In this section, we argue how this approach could be used in this context. Where in various BSFM empirical studies (e.g. Notteboom et al. 2000) efficiency was measured, using the maximum efficiency as frontier, here this technique is used to measure the bias of data from the objective impact the considered determinants have (objective impact equals frontier). Therefore, this article is not only an empirical study on analysing the objectivity of expert respondents or the lobby-effect of port competitiveness analyses. It moreover—and probably even more interestingly at a higher level—argues that the BSFM is a tool to analyse situations where distance with respect to a frontier could be more broadly defined, for example here as a maximum rational level of expert information or answers to specific survey questions, and are tested, without imposing one ‘‘true’’ model. The Bayesian stochastic frontier model is a parametric method of analysis. The stochastic method as opposed to the deterministic method, explicitly takes into account the stochastic nature of the data. A stochastic approach enables us to eliminate statistical noise, taking into account classic errors such as errors of measurement and outliers. The stochastic frontier model was originally introduced to measure productive efficiency (Meeusen and van den
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Broeck 1977 and Aigner et al. 1977), taking into account inefficient behaviour and statistical noise separately (composed error). The proposed models have been substantially elaborated in a Bayesian way by van den Broeck et al. (1994). They consider a stochastic production function of the form: yi ¼ hðxi ; bÞ þ vi zi with: yi h xi b vi zi
the output variable of observation i the frontier function the column vector of exogenous input variables vector of unknown parameters symmetric disturbance capturing measurement error of the stochastic frontier a non-negative disturbance modelling the level of ‘‘inefficiency’’ (or in this paper’s context called ‘‘bias’’)
Any parametric Bayesian approach requires full specification of the likelihood function. In order to meet this requirement, van den Broeck et al. (1994) assume that vi is normally distributed with mean zero and variance r2, zi is a gamma distribution with shape parameter j (j = 1, 2, 3; Erlang models) and unknown scale parameter k, leading to different statistical models. By attaching prior probabilities to the parameters of the Erlang models, which do not affect the salient features of the models, the Bayesian approach leads to their posterior probabilities. In the case of this article we use the following prior densities: pðb; r2 ; kÞ ¼ cr2 k1 These are diffuse priors because we only know the range of the parameters (i.e. Jeffreys type prior) (Jeffreys 1967). Because no additional information is available, it is true for each Erlang model. This means that the diffusiveness of all the parameters, which are common to all Erlang models, remains. The model enables to pool posterior moments based on different distributional assumptions about zi. By doing so the uncertainty in the model is averaged out without imposing a specific form for the density function of zi. The rationale behind the pooling procedure is inspired by philosophical considerations. The tradition of modelling is largely focused on searching a ‘‘true’’ model. In case two or more models are considered, this tradition commonly leads to the rejection of one model in favour of another, based on hypothesis testing. Winkler (1989) correctly states that there is no such thing as a ‘‘true’’ model. As such, the results obtained from the different Erlang models are pieces of information and are not necessarily in competition for the ‘‘true’’ model. The pooling of results leads to the
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aggregation of valuable information and enables to achieve a reduction of uncertainty and an increase in accuracy. The baseline model, M0, is the simple case without composed error, i.e. all zi are zero and thus all entities (in this case 25 determinants) are positioned on the frontier function. This model does not describe reality, because it would mean that all respondents answered 100% ‘‘true’’ or objectively on the questions, which we assume is not the case. M0 is solely used as a benchmark against which the improvements of the various composed error models are evaluated. Three types of Erlang distributions, Mj, are distinguished (j = 1, 2, 3).
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‘‘terminal operators’’ (7 of 75 respondents) are hypothezised to be responsible for the most pronounced underestimations, because of their close involvement in the port’s core activities and competitive performance. Therefore, these three subgroups are selected for the analysis. In this article, a linear function is used, with the output (Yi) being the mean score on each of the 25 determinants of port competitiveness for all 75 respondents and the independent variables (Xih) being the individual scores of selected respondents of the subgroups on the same variable, plus a constant: Yi ¼ b0 þ b1 xi1 þ b2 xi2 þ b3 xi3 þ . . . þ bh xih þ vi zi with:
4 Application of the Bayesian stochastic frontier model and results The empirical application of the Bayesian stochastic frontier technique carried out in this paper uses a MonteCarlo approximation. Different types of the so-called Erlang models are computed. The individual results are pooled and a final efficiency score indicating how close the scores (i.e. perceptions of the respondents) are as regards the reality of port specific advantages. Moreover, it analyses what the bias is of a specific subgroup of respondents out of the total respondent group on the general output (the mean score on each of the 25 variables for all 75 respondents). In this paper, we hypothesize that each respondent group distribution (columnar distribution, see also Table 9) varies significantly beneath unity. This is our main hypothesis. Besides, we also hypothesize that the results for terminal operators (representing the port’s core business) do more significantly vary beneath unity than for port experts. Moreover, we hypothesize that for some determinants, within the considered respondent groups, the variation is more significant, namely for determinants more directly related to public funding, such as maritime and hinterland accessibility. In order to implement the Bayesian stochastic frontier technique, the empirical application focuses on two respondent groups, namely port experts and terminal operators. These two subgroups can be considered as the best providers of information on the port specific advantages, because they are closely involved in the port’s core activities (i.e. attracting cargo and transshipment) and competitive performance. The two groups are analysed separately. In addition, the most relevant port experts (five crucial port experts), which are supposed to provide the most accurate information thanks to their vast expertise and reputation at stake, are also analyzed separately. The respondents in these three subgroups, i.e. ‘‘port experts’’ (12 of 75 respondents), ‘‘5 most important port experts’’ (5 out of 12 port experts of the 75 respondents) and
Yi i b0 bh xih vi zi
the output variable, i.e. the mean score on each variable for all 75 respondents; determinant (variable) number (1 to 25) a constant term; unknown coefficients of independent variables; individual scores of selected respondents (h) on the same variable (i); error of measurement; level of inefficiency, in our application of BSFM to interpret as the level of underestimation, i.e. the distance of the reported response (perception) to the theoretical true or objective response (the frontier).
Thus, the dataset consists of 25 observations (one for each determinant of port competitiveness). The number of independent variables is equal to the number of selected respondents, being 12 port experts, 5 significant port experts and 7 terminal operators respectively for the three selected respondent subgroups. The descriptive statistics for variables (12 port experts, 5 significant port experts and 7 terminal operators) represent the mean scores of the respondents on a particular determinant.3 The coefficients that will be presented in the following sections are the results of three Erlang models with diffuse prior (i.e. Jeffreys’ prior). Table 2 provides ordinary least square estimated coefficients for M0 for the case of 12 port experts, 5 significant port experts and 7 terminal operators. These coefficients are used as the starting values in further analyses, described in the remainder of Sect. 4. 4.1 Posterior regression coefficients for 12 port experts In this section, the results are reported for the Erlang models (j = 1, 2, 3) with independent diffuse prior densities on all parameters (determinants) and with common k to all three models. In order to calculate the estimates, a 3
The descriptive statistics can be obtained from the authors upon request.
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Table 2 Estimated parameters of the M0 model (SD in parenthesis) Coefficient
12 Port experts
5 Sign. port exp.
7 Term. oper.
b0
-3.4250**
-2.0352**
0.9960
Table 3 Estimated parameters of reality frontier
b0 (0.9156)
(0.5872)
(0.6034)
0.1832?
0.2588**
0.2386?
(0.1005)
(0.0520)
(0.1290)
-0.1571?
-0.1364*
0.0358
(0.0761)
(0.0581)
(0.1756)
b3
-0.0812 (0.1862)
1.0920** (0.1373)
-0.0667 (0.0715)
b4
0.0138
0.1678**
-0.0785
(0.0760)
(0.0323)
(0.1596)
1.4005**
0.3149**
-0.0745
(0.3293)
(0.0793)
b1 b2
b5 b6 b7 b8
(0.0933)
0.1647**
0.2133?
(0.0405)
(0.1196)
0.0120
0.4062?
(0.0789)
(0.1960)
b1 b2 b3 b4
(0.0940)
0.2046
-0.1754
(0,1085)
(0.1168)
(0.0717)
-0.1715
-0.1731
-0.0678
(0,0932)
(0.0874)
(0.1296)
-0.1418
-0.1245
-0.2340
(0,1977)
(0.2175)
(0.1520)
0.0364
0.0211
0.1320
0.1918 -0.1721 -0.1354 0.0308
b6
0.1755
0.1754
0.1556
0.1755
(0.0469)
(0.0464)
(0.0362)
b7 b8 b9 b10
b12
0.0790 Lambda
Number of observations: 25
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0.1843
1.4629
(0.1455)
computer program BSFM has been used (Arickx et al. 1997). In the first stage, the BSFM software was applied to the data set for 12 port experts. The starting values for the coefficients for the Monte Carlo calculations were taken from the results of the M0 model. The Monte-Carlo method was then applied to each of the Erlang models. In addition, the results of the three models were pooled. The adoption of the Bayesian approach enables to pool the results, averaging out model uncertainty. Moreover, the pooling procedure results in more complex distributions, which could not be obtained ex ante. Table 3 provides the posterior results of bj along with its standard deviation of the application of the Bayesian stochastic frontier models on a data set of 12 port experts. The estimated frontier appears to be a minimal non-neutral shift of the base model M0, because the pooled coefficients differ slightly from those of the M0 model.
-3.5163
(0.6261)
2.0308 (0.2712)
b11
p \ 0.10, * p \ 0.05, ** p \ 0.01
-3.8076
(0.9934)
(0.0723)
b11
?
-3.5269
(0.9759)
1.4530 (0.3614)
(0.0824) 0.3890*
12 Port experts, 5 significant port experts, 7 terminal operators as independent variables
-3.5101
(0.0830)
-0.0482
(0.1050)
Pooled
1.4683 (0.3433)
b10
b12
Erlang 3
(0.0912)
0.0652 (0.0807)
Erlang 2
b5
0.1413
b9
Erlang 1
Sigma
0.0190
0.0130
-0.0962
(0.0860)
(0.0830)
(0.0365)
0.1589
0.1579
0.0994
(0.1027)
(0.0992)
(0.0874)
0.0733
0.0667
0.0168
0.1586 0.0709
(0.0898)
(0.0864)
(0.0621)
-0.0687
-0.0694
0.2270
(0.0962)
(0.0995)
(0.0897)
0.4231
0.4245
0.1245
(0.1667)
(0.1607)
(0.1757)
0.0484
0.0677
0.1077
(0.1245)
(0.1121)
(0.0622)
0.0460 (0.0370)
0.0304 (0.0201)
0.0936 (0.0006)
0.0403 0.0620
0.0599
0.0656
0.1301
(0.0260)
(0.0191)
(0.0002)
VRf
0.5505
0.6374
0.3918
TVf
0.0077
0.0073
0.0432
Log lj
36.0669
35.3180
11.8785
Log pj
14.8354
14.3992
54.7085
Posterior average efficiencies
0.9572
0.9428
0.7646
(0.0597)
(0.0569)
(0.1142)
0.9541
0.9406
0.8021
(0.0630)
(0.0569)
(0.0802)
Posterior Z-average efficiency
0.0168
-0.0689 0.4236 0.0555
0.9519
0.9491
Mean of the total of 75 respondents as dependent variable Number of observations: 25 (SD in parenthesis) 12 Port experts as independent variables
In Table 3, k is a parameter for the ‘‘underestimation’’ measure and r is the standard error of the normally distributed statistical noise. VRf is the part of the variance that is caused by statistical noise. The relative importance of the statistical error (VRf) in the posterior out-of-sample error
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Table 4 Pooling values
Table 5 Estimated parameters of reality frontier
Model no.
Pj
Wj
1
0.3333
0.6323
2
0.3333
0.3677
3
0.3333
0.0000
b0 b1
Mean of the total of 75 respondents as dependent variable Number of observations: 25
b2
12 Port experts as independent variables b3
variance (TVf) suggests that the statistical noise component is mostly dominant in the result (55.05%, 63.74% and 39.18%). This indicates the importance of a stochastic approach and that the use of a deterministic approach would be wrong in this case. lj is the likelihood of model j and pj is the posterior density. The pooled posterior regression coefficients for the three Erlang models, presented in the last columns of Table 3, Table 5 and Table 7, are discussed below. When pooling the results, the same prior weight (33,33%) was given to each Erlang model, see Table 4. After an iteration process, excluding model uncertainty, the posterior probability for Erlang 1, 2 and 3 are respectively 63.23%, 36.77% and 0.00%. This clearly indicates that Erlang 3 is of no importance and that Erlang 1 (which in fact is an exponential function) is dominating. We expected the bias to be limited4 and that each perception was close to the frontier, but we ‘‘forced’’ the data to remove from the frontier. In case of Erlang 3, we suggest that the bias with respect to the frontier is rather important, but this assumption is not supported by the data. Therefore, Erlang 3 has no meaning and has thus no weight in the analysis. 4.2 Posterior regression coefficients for 5 significant port experts The first analysis on 12 port experts suggested that the perceptions on the determinants of 7 experts were not statistically significant (table 2), i.e. they had no impact on the explanation. Given the fact that the five remaining significant respondents are the crucial port experts of the Antwerp seaport (from the Antwerp port authority and influencing institutions and associations of the Antwerp seaport), this result was mostly expected. They are more likely to know what goes on in their port than ‘‘outsiders’’, i.e. from the regional, federal and European port institutional environment.
4
Given the careful selection of the respondents, it is normally assumed that their perceptions mirror or come as close as possible to the objective impact of the determinants on the Antwerp seaport’s competitiveness.
b4 b5 Lambda Sigma
Erlang 1
Erlang 2
Erlang 3
Pooled
-1.9599
-1.8131
-0.3246
-1.9180
(0.6641)
(0.6645)
(0.5540)
0.2438
0.2156
0.2185
(0.0605)
(0.0646)
(0.0890)
-0.1224
-0.1018
-0.1328
(0.0708)
(0.0776)
(0.1032)
1.1086
1.1327
0.8393
(0.1599)
(0.1730)
(0.1678)
0.1663
0.1632
0.1077
(0.0337)
(0.0317)
(0.0399)
0.2890 (0.0955)
0.2545 (0.1047)
0.1603 (0.1411)
0.2792 0.0467
0.0406
0.0622
0.0702
(0.0410)
(0.0152)
(0.0023)
0.0734
0.0416
0.1016
(0.0234)
(0.0212)
(0.0046)
VRf
0.6406
0.2102
0.4118
TVf
0.0093
0.0104
0.0251
Log lj
29.4457
28.5290
20.2225
Log pj
3.5376
2.8352
8.1110
Posterior average efficiencies
0.9624
0.8868
0.8160
(0.0597)
(0.0785)
(0.0932)
Posterior Z-average efficiency
0.9629
0.8908
0.8208
(0.0572)
(0.784)
(0.0669)
0.2358 -0.1165 1.1154 0.1654
0.0643
0.9409 0.9424
Mean of the total of 75 respondents as dependent variable Number of observations: 25 (SD in parenthesis) 5 Significant port experts as independent variables
Table 6 Pooling values Model no.
Pj
Wj
1
0.3333
0.7149
2
0.3333
0.2851
3
0.3333
0.0000
Mean of the total of 75 respondents as dependent variable Number of observations: 25 5 Significant port experts as independent variables
Table 5 and Table 6 confirm more or less the results obtained in the previous analysis. In contrast to Erlang 3, the constant term and the posterior regression coefficients bi of Erlang 1 and 2, are almost all statistically significant. We see that, compared to 12 port experts, the result is approximately the same, indicating that the 7 insignificant port experts have no impact on the result and can thus be excluded. Especially with regard to a repetition of the survey in time, for
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example to analyze dynamic capabilities of seaports, this result may lead to a considerable reduction of the respondent group. It is important to notice that in this analysis the statistical noise component is still important (64.06%, 21.02% and 41.18%). The pooled regression coefficients are presented in the last column of Table 6. The posterior probabilities for Erlang 1, 2 and 3 are respectively 71.49%, 28.51% and 0.00%. Here again, Erlang 1 is dominating and the relative importance of Erlang 3 in the pooling configuration appears to be zero.
Table 7 Estimated parameters of reality frontier
4.3 Posterior regression coefficients for 7 terminal operators
b5 b6
In this section, the results of the three Erlang models are discussed using another respondent group, i.e. 7 terminal operators. Table 7 provides the posterior regression and statistical indicators. The pooled constant term and most of the pooled coefficients are statistically significant. The estimated frontier appears to be a quasi neutral shift of the base model M0, because the pooled coefficients are nearly equal to those of the M0 model. In Table 7, VRf indicates that the statistical noise component is dominant in the result (85.51%, 68.21% and 56.42%). This indicates clearly the importance of a stochastic approach. The pooled regression coefficients are presented in the last column of Table 7. The posterior probabilities for Erlang 1, 2 and 3 are respectively 88.79%, 11.21% and 0.00% (Table 8). Here again, Erlang 1 is dominating and the relative importance of Erlang 3 in the pooling configuration is zero. In order to show graphically the out-of-sample ‘‘bias’’ of the Erlang models, the posterior densities for j = 1, 2 and 3 and overall (pooled) are plotted separately. The pooled plot is the result of the use of the posterior probabilities for each of the Erlang models. Figures 1 and 2 represent the plot of densities for both total (12) and significant (5) group of port experts. Figure 3 represents the plot of densities for the 7 terminal operators. These figures illustrate that the pooling plot is closely related to the Erlang 1 plot.
b0 b1 b2 b3 b4
b7 Lambda
Erlang 1
Erlang 2
Erlang 3
1.0566
1.2390
0.7139
(0.6639)
(0.6828)
(0.7166)
0.2425
0.2454
0.0037
(0.1403)
(0.1387)
(0.1107)
0.0215
-0.0001
0.0378
(0.1934)
(0.1945)
(0.1584)
-0.0655
-0.0692
-0.1688
(0.0764)
(0.0808)
(0.0612)
-0.0822
-0.0914
0.1145
(0.1725)
(0.1636)
(0.1663)
-0.0761 (0.1030)
-0.0696 (0.0977)
-0.0362 (0.1085)
0.2140
0.2181
0.3376
(0.1279)
(0.1323)
(0.1493)
0.4178
0.4173
0.5420
(0.2150)
(0.2117)
(0.2358)
0.0518
0.0817
0.1001
(0.0559)
(0.0124)
(0.0039)
Sigma
0.1816
0.1679
0.1962
(0.0380)
(0.0338)
(0.0210)
VRf
0.8551
0.6821
0.5642
TVf
0.0401
0.0430
0.0690
Log lj
10.7459
9.9001
2.6864
Log pj
4.0996
5.6276
11.1293
Posterior average efficiencies
0.9532
0.8549
0.7512
(0.0741)
(0.0940)
(0.1196)
Posterior Z-average efficiency
0.9531 (0.0743)
0.8543 (0.0852)
0.7634 (0.1129)
Pooled 1.0771 0.2428 0.0190 -0.0659 -0.0832 -0.0754 0.2144 0.4177 0.0551 0.1797
0.9422 0.9420
Mean of the total of 75 respondents as dependent variable Number of observations: 25 (SD in parenthesis) 7 Terminal operators as independent variables
Table 8 Pooling values Model no.
Pj
Wj
1
0.3333
0.8879
2
0.3333
0.1121
3
0.3333
0.0000
Mean of the total of 75 respondents as dependent variable Number of observations: 25
4.4 Posterior reality efficiencies
7 Terminal operators as independent variables
The results in Table 9 are a computation of the distance of the reported answer (in the interviews) to the theoretical true, objective or rational answer (i.e. the frontier). These results are generated in the output of the BSFM software program of Arickx et al. (1997). This software computes the distances to the theoretical frontiers (i.e. the ‘‘true’’
answer) based on a method described by Koop et al. (1994). As such, Table 9 shows the posterior pooled underestimations of all 25 determinants for the three analyses carried out. As a result, we are able to explain those underestimations for each group of respondents
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Fig. 1 Posterior densities for Erlang model 1, 2 and 3 and pooling; 12 port experts
Fig. 2 Posterior densities for Erlang model 1, 2 and 3 and pooling; 5 significant port experts
analyzed. The underestimation or bias is significant (p \ 0.001) for all three respondent groups. We observe that the results in Table 9 are close to unity. However, the fact that they do deviate from 1 means that there is respondent bias. This deviation from 1 is highly significant for all three respondent groups (p \ 0.001). In other words, respondent bias is significant for all three respondent groups, which confirms our main hypothesis. As regards the port expert group, it appears that rail transport was underestimated for its positive impact on the Antwerp seaport’s competitiveness. The significant port experts underestimated largely the positive impact of inland navigation, maritime access (depth of the river Scheldt) and communication systems in the port (e.g. Seagha, which is at the time of the analysis a common initiative supported by government), with underestimation
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Fig. 3 Posterior densities for Erlang model 1, 2 and 3 and pooling; 7 terminal operators
values of 0.9052, 0.8688 and 0.8997 respectively. The seven terminal operators analyzed, underestimated not less than six determinants, being maritime accessibility (0.8922), port supporting services (0.8610), regional government intervention (0.8930), national and supranational government intervention (0.8852), client relationships in the port (0.8599) and communication systems (0.9084). Most of the underestimations have a direct link to government action (e.g. rail and inland navigation, being exploited by the State in the late nineties, when the analysis was carried out, and maritime accessibility, i.e. the deepening of the river Scheldt, being a responsibility and investment project of the regional government). Moreover, most of the underestimated determinants refer to investments or efforts that usually need a long period before they become operational or reach their full positive competitiveness effect. It is clear that by deliberately underestimating the impact of these determinants on port competitiveness, the respondents tried to increase the sense of urgency of government or investors’ attention and hope to have the investments operational by the time it is actually necessary for maintaining a port’s competitive position.5 These results support our hypothesis claiming that data are biased caused by the potentiality of the results of the study to be used for political lobbying. Therefore, it is 5
This proactive behavior of key port operators and experts may result from their experience that institutional factors and/or late stakeholder involvement can seriously delay port projects and lead to competitive disadvantages. For example, if new terminal capacity for large and rapidly growing container operators is not available in time, these operators may opt to move their operations to a competing port, even if certain competitiveness determinants are less positive in that competing port, such as port labor, client relationships or port supporting services.
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122 Table 9 Posterior pooled ‘‘reality efficiencies’’ of determinants
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Determinants
12 Port experts
5 Sign. port exp.
7 Term. oper.
Port infrastructure
0.9212
0.9678
0.9357
Port superstructure
0.9696
0.9771
0.9533
Port labor (human capital)
0.9477
0.9445
0.9607
Logistical techn. and comm. systems
0.9319
0.8997
0.9084
Intra-port competition
0.9542
0.9163
0.9270
Inter-port competition
0.9727
0.9777
0.9396
Intra-port co-operation
0.9480
0.9382
0.9288
Extra-port co-operation
0.9657
0.9642
0.9258
Client-relationships in the port
0.9648
0.9543
0.8599
Client-relationships outside the port
0.9708
0.9614
0.9528
Local government intervention
0.9504
0.9573
0.9367
Regional government intervention
0.9561
0.9229
0.8930
(Supra)national government intervention
0.9578
0.9399
0.8852
Port supporting services
0.9575
0.9401
0.8610
Activities maritime accessibility Shipping
0.9297 0.9449
0.8688 0.9188
0.8922 0.9355
Transshipment
0.9532
0.9616
0.9571
Warehousing
0.9714
0.9710
0.9592
Value added logistics
0.9344
0.9510
0.9456
Manufacturing
0.9651
0.9377
0.9499
Activities by agents, forwarders etc.
0.9540
0.9463
0.9244
Distribution activities within port
0.9778
0.9761
0.9556
Road transport
0.9676
0.9389
0.9306
Rail transport
0.8961
0.9187
0.9472
Inland navigation
0.9411
0.9052
0.9313
Average
0.9521
0.9422
0.9279
One-sided t test (test value = 1) t
-12.727
-10.675
-12.410
p
\0.001
\0.001
\0.001
likely that respondents have taken the opportunity of being surveyed to try to influence governments’ and investors’ actions by doing so. Related to the number of underestimations, we could suggest that terminal operators are more likely to underestimate the key success factors than port experts, because their gain in attracting more government attention is more directly related to their business profits. We may also conclude that those determinants that are directly related to government action (infrastructure in the port, hinterland connections, regional and supranational government intervention), show more underestimation than determinants that result from private investments such as transshipment, intra-port competition and superstructure. The obtained results in this article also provide information on how to adjust the original data in order to avoid biased or in this case underestimated results. By calculating the arithmetic average of the posterior pooled underestimations or ‘‘reality efficiencies’’ of the determinants
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(Table 9), excluding those of the determinants where underestimation was statistically indicated, and by dividing this arithmetic average with the efficiency of each biased determinant score (Table 9), we obtain an unbiased score: [biased score * (average efficiency/biased efficiency) = unbiased score]. This adjustment would minimize underestimation and provide a better approximation of the real impact of port competitiveness determinants.
5 Conclusion In this article, we find evidence that terminal operators are more likely to underestimate the key success factors than port experts, because their gain in attracting more government attention is more directly related to their business profits. We may also conclude that those determinants that are directly related to government action (infrastructure in the port, hinterland connections, regional and supranational
J Prod Anal (2011) 36:113–123
government intervention), show more underestimation than determinants that result from private investments such as client relationships and superstructure. In addition to validating the assumption that at least some key respondents underestimated the positive impact of competitiveness determinants of seaports, we find that the Bayesian stochastic frontier model offers an appropriate tool to analyze as well as minimize the biased results of a port competitiveness analysis. Besides the fact that underestimation of the impact of competitiveness determinants has clearly occurred and that the stochastic frontier model is an interesting new approach in this context, the obtained results from this BSFM application also provide information on how to adjust the original data in order to avoid biased results on competitiveness determinants. Hence, it offers an answer to the criticism voiced with regard to the original competitiveness study and feedback for the study itself, but it also provides a tool for preventing this shortcoming in future port competitiveness studies based on perceptions and shows a new application of the stochastic frontier model. We do not claim that BSFM is a useful tool for all kinds of perception based analyses, but at least this contribution shows that also outside the narrow scope of productivity efficiency, BSFM may have important merits and a potentially wider range of applicability.
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