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Analysis of the technological features of regional public transport companies: the Tunisian case

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Abstract

The purpose of this paper is to assess the cost-structure of the public transport industry in Tunisia. Thus, we use a translog variable cost function to identify the firms’ technological features. In this analysis, we work on a sample of 12 Tunisian regional transport companies (RTC) over the 2000/2010 period. The main results show the critical productive situation of the urban public transport system in Tunisia. This can be exemplified by the presence of a short and long term diseconomy of scale, given that the overall factor productivity is low and almost zero.

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Notes

  1. Equalization payments: they are cash payments made with the objective of reducing both wealth disparities and inequality, but at the expense of transparency in real costs, which can bias the decisions. Equalization is a rule of public service management. In fact, it is a system of resource redistribution among several administrative units. For example, the State sets up an equalization program in the operating subsidies awarded to counties so that the smallest of them can benefit from an acceptable budget.

  2. ES = 1: elasticity is equal to 1, therefore, the economies of scale are constant.

    ES < 1: elasticity is greater than 1, there are diseconomies. 

    ES > 1: elasticity is less than 1, there are economies of scale.

  3. The capital is owned by the organizing authority and represents the number of buses at the park, on 31/12 every year. Each company thus minimizes the costs of a subset of production factors, conditionally at the level of the capital provided by the authority. The capital may then be considered as a quasi-fixed production factor and the cost function is therefore no longer considered as a total cost function but rather a variable cost function. Such an approach makes us consider that the urban transport industry in all the periods is unbalanced compared to the available capacity provided by the organizing authority (Gagnepain 1998).

  4. For details on the duality conditions between the production sets and the cost functions, see Shephard (1953, 1970) and Uzawa (1962).

  5. According to Fraquelli et al. 2001, the parameter estimates are invariant to the choice of which equation is deleted as long as the Iterated SUR (or Maximum Likelihood) estimation method is employed on the M − 1 factor-share equations.

  6. Beja, Bizerte, Gabes, Gafsa, Jendouba, Kairouan, Kasserine, Kef, Mednine, Nabeul, Sfax and Sousse.

  7. Throughout this article the abbreviation Y SK is used, which denotes number of seats per kilometre.

  8. See Appendix.

  9. The model is estimated by the STATA 11.2 econometric software, with the command "SUREG".

  10. Breusch-Pagan test of independence: Chi2(3) = 126.672(P value = 0.000, i.e. <1 %).

  11. Since the variables costs and all the continuous explanatory variables are in logarithms, the estimated first order coefficients can be interpreted as cost elasticities.

  12. See (Uzawa 1962; Berndt and Wood 1975).

  13. See Blackorby and Russell (1989).

  14. A Cobb-Douglas would not be appropriate because the elasticity of substitution is imposed to be equal to unity for all factors of production.

  15. We are facing a natural monopoly, in which the price is higher than marginal cost (P > mc), which causes a loss of well-being (a Malthusian behavior). For this, the Ministry of Transportation (The organizing authority of the natural monopoly) prohibits investors to enter the public transport market by building strategic barriers in order to make the most loyal customers.

  16. Bus rapid transit: the system of rapid and inexpensive buses compared to the subway is very successful in both the developing and in the industrialized countries. Operating on dedicated traffic lanes, the BRT system greatly increases the average speed of the public transport network. Moreover, it fits into a broader mobility management and seeks an interaction with the urban planning. For detail see, e.g., Levinson et al. (2003); Hensher and Golob (2008); Widanapathiranage et al. (2015).

  17. A Cobb-Douglas function may not be appropriate because the substitution elasticity must be equal to the unity for all the production factors.

Abbreviations

P :

Price of inputs

VC :

The variable cost

Y :

Production vector

x :

Vector of the used inputs

β :

Coefficient

K :

Quasi-fixed capital

L :

Labor

E :

Energy

MS :

Materials and services

Z :

The length of network

Trend :

Technical progress (time indicator)

S i :

Share factor of input i

RMSE :

The root-mean-square error i.e. the square root of the variance, known as the standard deviation

R-sq :

R-squared

Chi 2 :

Chi-squared test

P value :

P value is automatically determined by STATA. The latter takes into account the number of degrees of freedom and tells us at what level our coefficient is significant

SE :

Standard error

ES SR--ES LR :

Short and long-run economies of scale

ED SR -ED LR :

Short and long-run economies of density

σ A ii :

Own-price elasticity of Allen

σ A ij :

Cross-price elasticity of Allen

M :

Morishima’s elasticity of substitution

PGY :

Overall productivity production (Y = YSK)

PGX :

Overall productivity of factors (X)

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Authors and Affiliations

  1. Laboratory URED, Faculty of Economics and Management of Sfax, University of Sfax, Street of airport, km 4.5, LP 1088, Sfax, 3018, Tunisia

    Ahmed Ayadi & Sami Hammami

Authors
  1. Ahmed Ayadi
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  2. Sami Hammami
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Correspondence to Ahmed Ayadi.

Appendix

Appendix

Choosing the most efficient output for our study is made by carrying out a correlation test between three types of output measures and the variable cost.

As shown in Table 14, most of the correlation coefficients are below 0.8, which corresponds to the limit set by Kennedy (1985) from which we usually start to have serious problems of multi-collinearity. Nevertheless, this does not prevent us from finding a strong correlation between outputs “Traveled kilometres”, “Passenger per kilometres” and the dependent variable “VC” therefore exceeding the threshold of 0.8. This raises a problem of multi-collinearity between the output and the dependent variable. For this reason, we opt for the supply measure represented by the variable SK to identify our output.

Table 14 Multi-collinearity test
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Ayadi, A., Hammami, S. Analysis of the technological features of regional public transport companies: the Tunisian case. Public Transp 7, 429–455 (2015). https://doi.org/10.1007/s12469-015-0109-4

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