Measuring the Efficiency of Public Transport Sector in India: An Application of Data Envelopment Analysis by Shivi Agarwal Department of Mathematics, Birla Institute of Technology and Science, Pilani, India.
Measuring the Efficiency of Public Transport Sector in India: An Application of Data Envelopment Analysis Introduction DEA Approach and Methodology Data and Variables Model Used Results and Discussions Sensitivity Analysis Conclusions Policy Implications
Passenger Road Transport Private Public State Road Transport Corporation (STUs) Imperative mode of passenger mobility State Government Well regulated and organized
Why DEA? State Road Transport Corporations (STUs): Service Business; Have multiple inputs and outputs; Difficult to determine the efficient amount of resources required to produce various service outputs; Unknown functional relation between inputs and outputs;
Literature Review Hensher (1992), Hensher and Daniels (1995): evaluate total factor productivity y( (TFP) growth of public bus firms, Tretheway et al. (1997): examine the productivity performance of Canadian Railways in transport sector by using translog production function method, Singh (2000): uses index number approach to estimate the growth and relative level of productivity of 21 STUs in India for the period 1983-84 to 1996-97, Hjalmarsson and Odeck (1996): assess the performance of trucks in road construction and maintenance using DEA,
Literature Review Contd Patankar (1985): assesses the productivity growth of the four largest metropolitan road transport services in India through DEA for the year 1982-83 83 Ramanathan (1999): applies DEA for the assessment of the productivity of 29 State Transport Undertakings (STUs) in India for the year 1993-94 Odeck (2000): analyses the relative efficiency and productivity growth of 67 units in the Norwegian Motor Vehicle Inspection Agencies for the period 1989-1991 Odeck and Alkadi (2001): evaluate the performance of 47 Norwegian bus companies for the year 1994, within the framework of DEA
Literature Review Contd Odeck (2005): investigates the target achievements of the 19 operational units of the Norwegian Public Roads Administration (NPRA) charged with traffic safety services. The DEA and MPI framework is applied with a unique constant input, or equivalently, with no inputs, Odeck (2006): determines the impact of inputs on operator s efficiency and investigates operations characteristics associated with inefficient use of inputs in the Norwegian bus industry for the year 1994 by applying DEA, Agarwal et al.(2006): estimate the relative efficiencies of Uttar Pradesh State Road Transport Corporation for the year 2002-20032003 by applying DEA-AR AR model,
First Step Selection of the Homogeneous DMUs State Road Transport Corporations (STUs) 29 STUs Data from CIRT (2005-2008) 2008) Period from year 2004-05 to year 2007-08.
Second Step Selection of Input and Output Variables Inputs: Fleet Size (FS) ; Total Staff (TS); Fuel Consumption (FC), Output: Passenger kilometers (Pass-Km).
Second Step Selection of Input and Output Variables Descriptive Statistics of Inputs and Output INPUTS OUTPUT INPUTS OUTPUT 2004-2005 2005-2006 FS TS FC Pass Km FS TS FC Pass Km Min 28 720 4.474 185 31 680 4.736 187 Max 19105 117400 4395.085 762554 19357 115946 4517.647 820459 Mean 3189.414 21555 800.653 140899.45 3311.345 21650.9 826.7622 151028.86 S.D. 4312.449 27511.81 1012.972 163600.53 4289.937 27019.72 1006.507 171757.04 2006-2007 2007-2008 FS TS FC Pass Km FS TS FC Pass Km Min 28 680 4.006 189 28 650 3.897 301 Max 19232 115529 4652.662 878557 19558 114699 4846.463 925866 Mean 3356.483 21669.93 846.6709 157022.36 3465.069 22380.66 893.3138 169512.31 S.D. 4247.105 26778.37 1030.318 185367.41 4312.45 26288.83 1071.709 194630.8
Third Step Selection of the models Input oriented New Slack Model (NSM) with categorical DMUs CRS assumption; VRS assumption;
Fourth Step Selection of the category of the STUs Category 1: operated in the Rural areas: the most advantageous situation, Category 2: operated in the Hill areas: the severe situation, Category 3: operated in the Urban areas.
Fifth Step Calculate the OTE of a STU s s Min θ = + k θ k m s x y subject n j = 1 n j = 1 λ λ to m s + 1 ik rk + i= 1 ik r = 1 rk + y s = y r = 1,..., s jk rj rk rk x + s = θ x i = 1,..., m jk ij ik k ik λ jk 0 j = 1,..., n + s, s 0 ; r = 1,..., s, i = 1,..., m rk ik
Sixth Step Calculate the OTE of a STU The solutions of the NSM-DEA models are carried out by using MATLAB
Results and Discussions Descriptive Statistics of Overall Technical Efficiency Years 2004-05 2005-06 2006-07 2007-08 Min 0321 0.321 0314 0.314 0270 0.270 0326 0.326 Max 1.00 1.00 1.00 1.00 Mean 0.672 0.662 0.587 0.631 S.D. 0194 0.194 0210 0.210 0205 0.205 0192 0.192 No. of efficient STUs 3 (10.3%) 3 (10.3%) 3 (10.3%) 3 (10.3%) No. of STUs 29 29 29 29
Results and Discussions Contd Descriptive Statistics of Scale Efficiency Years 2004-05 2005-06 2006-07 2007-08 Min 0.502 0.544 0.494 0.555 Max 1.00 1.00 1.00 1.00 Mean 0860 0.860 0883 0.883 0866 0.866 0882 0.882 S.D. 0.145 0.148 0.168 0.140 No. of efficient STUs 3 (10.3%) 3(10.3%) 4 (13.8%) 3 (10.3%) No. of STUs 29 29 29 29
Results and Discussions Contd Distribution of OTE scores across years 14 12 Nu umber of STU Us 10 8 6 4 2 0 1 0.80-0.99 0.99 0.60-0.79 0.79 0.40-0.59 0.59 belo w 0.40 2004-05 2005-06 2006-07 2007-08 OTE Scores
Results and Discussions Contd Distribution of SE scores across years 20 18 16 14 r of STUs Numbe 12 10 8 6 4 2 0 1 080099 0.80-0.99 060079 0.60-0.79 below 0.60 2004-05 2005-06 2006-07 2007-08 SE Scores
Sensitivity Analysis Model s s Min η a, b = θ k + m s x y subject j J { b} j J { b} λ λ to m s + 1 ik rk + i= 1 ik r = 1 rk + y s = y r = 1,..., s jk rj rk rk x + s = θ x i = 1,..., m jk ij ik k ik λ jk 0 j J { b} + s, s 0 ; r = 1,..., s, = 1,..., m rk ik i
Sensitivity Analysis Contd Results: Descriptive Statistics of OTE 2004-05 2005-06 Efficient STUs Efficient STUs S11 S20 S24 S11 S20 S24 Min 0.321 0.322 0.379 0.322 0.314 0.314 0.354 0.314 Max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Mean 0.672 0.706 0.684 0.695 0.662 0.677 0.675 0.674 S.D. 0.194 0.200 0.185 0.188 0.210 0.210 0.203 0.203 2006-07 2007-08 Efficient STUs Efficient STUs S11 S20 S24 S11 S20 S24 Min 0.270 0.338 0.270 0.270 0.326 0.366 0.326 0.326 Max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Mean 0.587 0.658 0594 0.594 0.621 0.631 0669 0.669 0630 0.630 0671 0.671 S.D. 0.205 0.208 0.204 0.217 0.192 0.190 0.195 0.202
Performance Conclusions Not improved over the earlier three years Improved in the last year Still very far from the optimal level. Sensitivity Analysis NSM-DEA efficiency scores are robust and stable. All the three efficient STUs are not the outliers.
Conclusions Average OTE scores: gradually decreased over the earlier three years Improved in the last year of the sample period, Average SE scores: no any clear trend, No change in the number of STUs at MPSS for the entire period, Overall technical and scale efficient for the entire e period: only 10.3% STUs, Best-practice STUs,
Policy Implications Recruiting motivated and trained workers Capacity building of the existing staff training and orientation programs Some motivational policies should be introduced Some motivational policies should be introduced Systematic assessment of their periodical work Promotions Performance based reward systems Offer incentives to consistently better performing employees
Policy Implications Reduce input slacks in inefficient STUs Downsizing of employee strength, Reduce aggregate expenditures by alter the practice patterns making it similar to the best practice STUs, Increase the fleet utilisation, Regular training ii for the di drivers in newtechnological l buses, Replacement of old buses and id inductioni of modern buses,
Policy Implications Attain the better fuel efficiency Rationalise the number of stops, Maintenance of the fleet, Maintenance of proper tyre pressures Traveling at a steady speed Create consciousness among the operating staff so that they may appreciate the value of fuel conservation. reward and promotion schemes
Policy Implications Requiring attention on the quality of the outputs lack of awareness of available facilities i and services offered by STUs, lack of consumer awareness, the absence of advocacy groups, lack of adequate equipment, the lack of training i institutes, i poor transport facilities
Rf References 1. Agarwal S., (2008), Assessment of Efficiency i and Productivity i of Public Transport Sector using DEA Techniques Ph.D. Thesis, Indian Institute of Roorkee, Roorkee, India. 2. Agarwal S., Yadav S.P. and Singh S.P. (2006), A Data Envelopment Analysis Based Efficiency Assessment of Public Transport Sector of Uttar Pradesh in India, Indian J. Transport Management, 30(1), 5-30. 3. Banker, R.D., A. Charnes and W.W. Cooper, Some Models for the Estimation of Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science, 30, (1984), 1078-1092. 4. Banker R.D. and Morey R.C., The use of Categorical Variables in Data Envelopment Analysis, Management Sciences, 32(12), (1986), 1613-1627. 5. Charnes A., Cooper W.W. and Rhodes E., Measuring the Efficiency of Decision Making Units, European Journal of Operational Research, 2, (1978), 429-441.
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