Performance analysis of publicly owned urban bus companies in India.
Singh, Sanjay K. ; Raghav, Shalini
1. INTRODUCTION
Cities play a vital role in promoting economic growth and
prosperity. But, economic growth momentum can be sustained if and only
if cities function efficiently. City efficiency largely depends on the
effectiveness of its transport system, that is, efficacy with which
people and goods are moved throughout the city. Although Indian cities
have lower vehicle ownership rate, number of vehicles per capita, than
their counterparts in developed countries, they suffer from worse
congestion, delay, pollution, and accidents than cities in the
industrialized world. The main reason for all these is the prevailing
imbalance in modal split besides inadequate transport infrastructure and
its sub-optimal use. Public transport systems in cities have not been
able to keep pace with the rapid and substantial increases in demand
over the past few years. As a result, people have turned towards
personalized modes such as mopeds, scooters, motorcycles, and cars and
intermediate public transport modes such as auto-rickshaws, tempos, and
taxis. Cities cannot afford to cater only to the private and
intermediate public transport vehicles and there has to be a general
recognition that policy should be designed in such a way that reduces
the need to travel by personalized and para-transit modes and boosts
public transport system (Singh, 2012).
Public transport system in India heavily relies on its bus
transport system since urban rail services are extremely limited. Only
four cities--Mumbai, Delhi, Kolkata and Chennai are served by suburban
rail systems. A few other cities also have limited suburban rail systems
but they hardly meet the large transport demand existing in these
cities. Although, few years back, bus transport services were available
mainly in the cities located in southern and western regions of India,
but they are now available in most of the metropolitan cities, thanks to
the Government of India's Jawaharlal Nehru National Urban Renewal
Mission (JNNURM). Services are mostly run by publicly owned Urban Bus
Companies (UBCs), which are usually called State Transport Undertakings
(STUs).
Presently, fifty-two STUs are operating in India with more than a
hundred thousand of buses and seven hundred thousand of workers. Out of
these, thirteen are urban bus companies which are operating with around
24,000 buses and employing 149,000 people. During the year 2010-11, the
total bus-kilometers operated by them were more than 1,600 million, the
number of passengers carried was more than 7,300 million, and the volume
of operations had crossed the mark of 77,000 million
passenger-kilometers. From the very beginning, UBCs in India faced huge
financial losses from their operation. UBCs' total revenue during
the year 2010-11 was just Rs. 51,505 million in comparison to total cost
of Rs. 84,200 million. Due to this, they faced a net loss of more than
Rs. 32,000 million during the year 2010-11. On an average, every bus-km
operated by these companies resulted in a loss of around Rs. 20 during
the same year.
Why do UBCs in India face such a huge financial loss? Answer of
this question requires answer of the following one. What determines
financial performance of a firm in general and of a public enterprise in
particular? It is easy to show that productivity and prices are the key
determinants of profitability of a firm. Productivity of a firm is
defined as the ratio of outputs to inputs. Productivity growth,
therefore, can arise from an increase in the output that can be produced
for a given level of input, or a decrease in input necessary to produce
a given amount of output. Therefore, productivity growth has potential
to improve the financial performance of a firm through increase in the
efficiency of production. However, productivity and profitability would
move together in the same direction if and only if input factor prices
in comparison to output prices are unchanged. Financial performance of a
firm may improve even if its productivity remains the same. This can
happen when increase in output price is higher than the increase in
input factor prices.
The main of aim of this study is to analyze the productivity and
profitability performance of publicly owned urban bus companies in India
during 2000s. A well-known multilateral index procedure proposed by
Caves, Christensen, and Diewert (1982) is used to compute the growth and
relative levels of total factor productivity (TFP) of the UBCs. The
estimates of TFP are then examined for their association with operating
characteristics of the individual UBC. Estimates of TFP are also
compared with partial factor productivity indicators such as labor
productivity (passenger-kilometers per employee), fuel productivity
(bus-kilometers per liter of diesel), and bus productivity
(bus-kilometers per bus held by the UBC). We also analyzed the link
between productivity, prices, and profitability. For this, we use a
model to decompose changes in profitability into two components
capturing changes in TFP and price recovery ability (i.e., tracking the
growth of output price related to the input factor prices).
2. PRODUCTIVITY PERFORMANCE OF UBCs
TFP has been recognized as a robust measure of productivity in the
economics and management literature. TFP measurement and modeling is
based on the assumption that inputs are combined so as to minimize the
cost of a given output. The more restrictive assumption of profit
maximization is not required, nor is the assumption that output is sold
in a competitive market. However, it is assumed that inputs are
purchased at prices perceived to be constant. For the UBCs in India,
these assumptions seem to be reasonable.
Where multiple outputs exist, TFP is described as a ratio of an
index number describing aggregate output levels to an index number
describing aggregate input levels. To compute TFP indices, Caves,
Christensen, and Diewert (1982) proposed the following formula for
cross-sectional or panel data set:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
where TFP is total factor productivity index, k and l are adjacent
observations, the [Y.sub.im] are output indices, the [X.sub.jm] are
input indices, the [R.sub.im] are output revenue shares, and the
[S.sub.jm] are input cost shares. There are j number of input factors
used to [produce.sub.i] number of outputs. A bar over a variable
indicates the arithmetic mean and a tilde over a variable indicates the
geometric mean.
To compute the TFP indices for UBCs in India, we have to specify
output and input factors used in their production process. Since UBCs
are involved only in passenger transport business, it is felt that the
useful measure of output would be passenger-kilometers. Labor, diesel,
and bus can be considered as the three most important inputs to be used
in the production process. In relation to the cost of inputs, labor cost
is total expenditure on employees; diesel cost is total spending on
diesel; and bus cost is sum of maintenance cost (which includes costs on
auto spare parts, springs, lubricants, tyres & tubes, batteries,
general items, and reconditioned items), interest payment and
deprecation. Therefore, total operating cost comprises of labor cost,
diesel cost, and bus cost. So, accordingly (1) is modified as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)
where specific definitions of the variables are given below:
TFP = total factor productivity; Y = output, defined as
passenger-kilometers; [S.sub.1] = factor share of labor, defined as
total labor cost divided by total operating cost; [S.sub.2] = factor
share of diesel, defined as total expenditure on diesel divided by total
operating cost; [S.sub.3] = factor share of bus, defined as total bus
cost (i.e., sum of maintenance cost, interest payment, and depreciation)
divided by total operating cost; [X.sub.1] = total number of employees;
[X.sub.2] = total diesel consumed; [X.sub.3] = total number of buses
(held); and
Total operating cost is sum of labor cost, diesel cost, and bus
cost.
Equation (2) is used to compute the TFP indices for nine UBCs for
which consistent data are available during 2000s. Sample UBCs include
Bangalore Metropolitan Transport Corporation (BMTC), Calcutta State
Transport Corporation (CSTC), Delhi Transport Corporation (DTC),
Ahmedabad Municipal Transport Service (AMTS), Brihanmumbai Electricity
Supply and Transport Undertaking (BEST), Kolhapur Municipal Transport
Undertaking (KMTU), Thane Municipal Transport Undertaking (TMTU),
Chandigarh Transport Undertaking (CHNTU), and Metropolitan Transport
Corporation Limited (Chennai) (MTCL). Table 1 presents some recent
descriptive statistics of these UBCs. The primary source of data is
Performance Statistics of STUs, 2000-01 to 2010-11 published for the
Association of Road Transport Undertakings (ASRTU), New Delhi by the
Central Institute of Road Transport (CIRT), Pune, India.
Sample UBCs are publicly owned, operate throughout their respective
jurisdiction (often throughout the city), mainly provide intra-urban bus
transport services, and do business in the field of passenger
transportation only, but differ in size and the level of out produced.
The size of the UBCs, as measured by passenger-kilometers (PKm) in
2010-11, ranges from 391 million PKm for KMTU to 21,796 million PKm for
MTCL. Fleet strength of UBCs varies drastically, from 135 buses for KMTU
to 6,110 buses for BMTC. Number of workers employed by UBCs also varies
from 1,184 for KMTU to 35,557 for DTC. In almost all respect, KMTU is
the smallest UBC whereas in terms of number of buses held and
bus-kilometer operated, BMTC is the largest UBC. However, in terms of
passenger-kilometers produced and number of passengers carried, MTCL is
the largest one. Moreover, DTC is the largest UBC in terms of number of
workers employed.
Table 2 presents output indices for sample UBCs for the period
2000-01 to 2010-11. Output indices are based on passenger-kilometers
where all values can be shown relative to any one UBC for chosen year.
We have chosen BMTC in 2000-01 as reference. Among sample UBCs, KMTU
(12.60%) and BMTC (11.92%) achieved very high growth in their output
during the sample period. MTCL (4.97%) and BEST (2.01%) also experienced
significant increase in their output. However, CSTC, DTC, and TMTU faced
decline whereas AMTS and CHNTU experienced negligible increase in their
output during the sample period.
Table 3 presents aggregate input indices (X 's), which are
based on the following equation: ln [MATHEMATICAL EXPRESSION NOT
REPRODUCIBLE IN ASCII], where variables and notations have their
previous meanings. Among sample UBCs, BMTC, KMTU, TMTU, and MTCL used
more inputs in 2010-11 than what they used in 2000-01. All other UBCs
experienced decline in their input use; decline in input use is fairly
high in CSTC (4.19% per year), DTC (3.42% per year), AMTS (3.32% per
year), and BEST (2.61% per year) whereas decline in input use is
negligible in CHNTU.
Using equation (2), TFP indices, which are widely used measure of
productivity, are computed for sample UBCs. The level of TFP for UBCs is
presented in Table 4 along with their compound annual growth rate (CAGR)
from the year 2000-01 to 2010-11. In Table 5, we rank the UBCs by their
level of TFP in 2000-01 and 201011. In the same table we also include
UBCs' rank according a measure of their size and growth in their
TFP and output. The wide disparity in growth rates over the period
2000-01 to 2010-11 resulted in considerable changes in the ranking of
the firms. For example, from 2000-01 to 2010-11 period MTCL rose from
3rd to 1st rank, KMTU rose from 9th to 7th rank, BEST rose from 6th to
5th rank, and AMTS rose from 7th to 6th rank while TMTU fell from 5th to
8th rank, DTC fell from 1st to 3rd rank, and CSTC fell from 8th to 9th
rank. During the sample period, CHNTU and BMTC seem to be consistent
performers. CHNTU was either the most productive or the 2nd most
productive UBC whereas BMTC was the 4th most productive UBC in most of
the years during 2000s.
It seems that there is a positive relationship between TFP and size
of UBCs (see, Figure 1). Correlation coefficient between TFP and size of
UBCs (measured in terms of passenger-kilometers) is 0.599 with
t-statistic of 7.17, which shows that the coefficient is statistically
significant at 1% level of significance. (2) Due to this, there is a
similarity in UBCs' rank according to their size and TFP; in
2010-11, the most productive firm was the largest one (MTCL) and five
most productive firms include four largest firms of the urban bus
transport industry. In general, UBCs which experienced higher increase
in their productivity are the ones which experienced greater increase in
their output during 2000s (see, Table 5). This may be because UBCs in
India experience economies of scale in their production process and
consequently increase in output will increase their productivity. (3)
In terms of TFP growth, the UBCs fall in four distinct categories.
One UBC, KMTU (9.17%) achieved very high growth rate of productivity,
though its level of productivity is not very high. Three UBCs achieved
productivity growth that could be considered high ranging from 3.22% per
year (MTCL) to 4.74% per year (BEST) during 2000s. Two UBCs, BMTC
(1.46%) and CHNTU (0.55%), achieved low productivity growth whereas
three UBCs, DTC (-0.54%), TMTU (-0.66%), and CSTC (-1.75%) faced decline
in their productivity during the sample period.
Estimates of TFP can be compared with more traditional indicators
of transport productivity e.g., labor productivity through
passenger-kilometers per employee (PKm/E), fuel productivity through
bus-kilometers per liter of diesel (BKm/D), and bus productivity through
bus-kilometers per bus held (BKm/B). We present these partial factor
productivity indices from the period 2000-01 to 2010-11 in Table 6
(labor productivity), Table 7 (fuel productivity), and Table 8 (bus
productivity), along with their compound annual growth rate over the
sample period. In Table 9, we present the 2000-01 and 2010-11 rankings
of the UBCs based on their labor productivity, fuel productivity, bus
productivity, and total factor productivity.
During the year 2010-11, CHNTU achieved the highest level of labor
productivity, 950,000 passenger-km per employee, whereas CSTC faced the
lowest level, 198,000 passenger-km per employee. It is interesting to
note that CHNTU achieved the highest level of labor productivity during
every year of the sample period, though it could achieve only around 1%
per year growth in its labor productivity. Besides CHNTU, MTCL and BMTC
also achieved a high level of labor productivity with their index of
labor productivity consistently being above 1 for all the years during
2000s. CSTC, which provides intra-urban bus transport services in the
city of Kolkata, requires immediate attention since not only the level
of labor productivity is very low but also its employee cost share is
extremely high; during the year 2010-11, CSTC's labor cost share
was more than 70% of its total operating cost, highest among the UBCs.
This is one of the main reasons why its financial performance is worst
among UBCs; during the year 2010-11, CSTC's total revenue was just
26% of its total cost. CSTC has to lower the employee cost particularly
through reduction in staff to bus ratio to improve its financial
performance; CSTC's staff to operational bus ratio in 2010-11 was
12.2, highest among the UBCs and twice than that of BMTC which is the
most profitable UBC in India. Reduction in number of employees, ceteris
paribus, will not only reduce the employee cost but also increase the
labor productivity. Increase in labor productivity is the key for CSTC
since it is paying relatively high wages (in relation to the market
rate) to its employees which are also increasing significantly every
year. Other UBCs particularly those having high staff to operational bus
ratio such as TMTU (12.1), KMTU (9.5), DTC (8.2), and AMTS (7.8) may
have to follow similar policy measure to improve their financial
performance.
As expected, there is a high degree of positive correlation between
TFP and labor productivity indices. Correlation coefficient between TFP
and PKm/E is 0.914 with t-statistic of 21.57. As a consequence, there is
a major similarity in rankings drawn from TFP and PKm/E indices. For
example, in 2010-11, five most labor productive firms include four most
productive firms according to their level of TFP. Moreover, three least
productive firms (CSTC, TMTU, and KMTU) have the same rank according to
both measures of productivity during the same year.
Fuel productivity, defined as bus-km per litre of diesel, is
another measure of partial factor productivity in urban bus companies.
This not only depends on exogenous factors such as road quality,
congestion on roads, etc. but also on driving habits and maintenance
practices adopted by the UBCs. Fuel productivity of an UBC can be
improved by introducing fuel efficient buses, optimal vehicle scheduling
to minimize the dead-kilometerage, driver training to adopt good driving
practices to reduce fuel wastage, proper vehicle maintenance, scrapping
over-aged fleet, etc. Table 7 presents fuel productivity indices of UBCs
during the sample period. In the year 2010-11, MTCL achieved the highest
level of fuel productivity, 4.39 bus-km per litre of diesel, whereas
TMTU faced the lowest level, 2.86 bus-km per litre of diesel. There are
only four UBCs which experienced fuel productivity above 4.00 bus-km per
litre of diesel whereas two UBCs, TMTU and BEST, have fuel productivity
below 3.00 bus-km per litre of diesel. As far as changes in fuel
productivity from 2000-01 to 2010-11 is concerned, four UBCs (MTCL, DTC,
TMTU, and BMTC) experienced significant variation. In a span of 10
years, MTCL and DTC improved their fuel productivity by 24% and 12%,
respectively whereas TMTU and BMTC faced decline in their fuel
productivity by 14% and 7%, respectively.
The relationship between TFP and fuel productivity is not as strong
as it was between TFP and labor productivity. Correlation coefficient
between TFP and fuel productivity indices is 0.496 with t-statistic of
5.48. This is expected since changes in fuel productivity depend largely
on exogenous factors. As a result, similarities in rankings drawn from
TFP and fuel productivity are relatively low.
Table 8 presents bus productivity indices of sample UBCs during the
sample period. This Table reveals that bus productivity varies
drastically across UBCs as well as over the years. During the year
2010-11, CSTC faced the lowest level of bus productivity, around 100
bus-km per bus held per day, whereas MTCL experienced the highest level
of bus productivity among the sample UBCs, around 280 bus-km per bus
held per day. Besides MTCL, only three UBCs--BMTC (205), KMTU (220), and
CHNTU (256) could operate their buses more than 200 km per day whereas
other UBCs operate in the range between 100 and 154 km per day. As far
as change in bus productivity is concerned, only KMTU (4.74% per year)
and MTCL (3.07% per year) could increase their bus productivity from
2000-01 to 2010-11. All other UBCs faced decline in their bus
productivity ranging from 0.16% per year for BMTC to 5.35% per year for
TMTU.
The variation in bus productivity across UBCs has been
significantly lower when we measure bus productivity in terms of bus-km
per bus on road (refer Table 10). This may not be the robust measure of
bus productivity since the UBCs which achieve lower level of fleet
utilization (i.e., the ratio of number of buses on road to the fleet
held by the UBC) are rewarded whereas UBCs experiencing high level of
fleet utilization are not recognized. For example, during the year
2010-11, CSTC's bus productivity in terms of bus-km per bus held
was 35% lower than that of BEST whereas its bus productivity measured in
terms of bus-km per bus on road was 9% higher than that of BEST. This is
because CSTC's fleet utilization is far lower than that of BEST.
Fleet utilization of UBCs varies from 52% for CSTC to 94% for CHNTU
during the year 2010-11. From 2000-01 to 2010-11, six out of nine UBCs
faced decline in their fleet utilization; TMTU and CSTC faced steep
decline, AMTS recorded moderate decline, and decline in CHNTU, BMTC, and
BEST was negligible. Only three UBCs--DTC, KMTU, and MTCL could improve
their fleet utilization over the sample period. It is noted that as far
as growth in bus productivity and fleet utilization is concerned, KMTU
shows the highest growth whereas TMTU shows the greatest decline from
2000-01 to 2010-11.
We also found that bus productivity, measured in terms of bus-km
per bus held, and total factor productivity is positively correlated.
Correlation coefficient between these two indices is 0.619 with
t-statistic of 7.56. As a consequence, similarities in rankings drawn
from BKm/B and TFP indices are relatively high. For example, in 2010-11,
except DTC and KMTU, all other UBCs have same rank according to both
measures of productivity (refer Table 9).
We also consider the possible relationship between total factor
productivity and load factor. Load factor, defined as ratio of revenue
passenger-kilometers to available passenger-kilometers, can be taken as
an indicator of capacity utilization. The a priori expectation is that
higher load factor is associated with higher TFP. In Table 11, we
present the UBCs' load factor in 2000-01 and 2010-11 and their
average annual increase over this period. Load factor of UBCs varies
from 60% for CSTC to 92% for CHNTU during the year 2010-11. Besides
CHNTU, only three UBCs had 2010-11 load factor in excess of 70% whereas
rest UBCs had load factor in the range of 60% to 70%. From 2000-01 to
2010-11, six UBCs experienced improvement in their load factor whereas
two UBCs, CSTC and DTC, faced decline in the same. One UBC, MTCL,
experienced virtually no change in its load factor.
As expected, there is a high degree of positive correlation between
TFP and load factor. Correlation coefficient between TFP and load factor
is 0.764 with t-statistic of 11.35. As a consequence, there is a major
similarity in rankings drawn from TFP and load factor. For example, in
2010-11, five UBCs having highest load factor include four most
productive firms according to their level of TFP. It is also found that
the UBCs which experienced higher improvement in their TFP are the ones
which recorded the higher improvement in their load factor. For example,
KMTU, BEST, and AMTS ranked 1st, 2nd, and 3rd respectively according to
growth in TFP as well as increase in load factor from 2000-01 to
2010-11. This shows that the improvement in load factor might be an
important contributor to UBCs' productivity improvement.
[FIGURE 1 OMITTED]
3. PROFITABILITY PERFORMANCE AND ITS LINKAGES WITH PRODUCTIVITY OF
UBCS
Profitability performance of any organization depends on revenue in
comparison to costs. Here in this case, revenue has two
components--traffic revenue and non-traffic revenue. Traffic revenue is
nothing but total earnings from passengers whereas non-traffic revenue
includes earnings from advertisement, shops in depot, subsidy from local
government for providing concessional travel facility to students,
freedom fighters, elected representatives, senior citizens, journalists,
etc., and any other revenue from non-core business. Non-traffic revenue
component of total revenue is not homogenous across UBCs. For example,
during the year 2010-11, non-traffic revenue for CSTC and KMTU was just
over 5% of their total revenue whereas it was more than 10% of the total
revenue for DTC, TMTU CHNTU, and MTCL. As far as cost in UBCs is
concerned, it depends on personnel cost, fuel (diesel) cost, capital
(bus) cost including maintenance cost and interest payment, and taxes
paid to the government. Taxes, which include passenger tax, motor
vehicle tax, and other miscellaneous taxes, are also not homogenous
across UBCs. For example, during the year 2010-11, it varied from 0% of
total cost for CSTC to 7% of total cost for CHNTU. Since non-traffic
revenue component of total revenue and tax component of total cost is
not homogenous across UBCs, total revenue minus total cost (or a ratio
of total revenue to total cost) would measure financial profitability
rather than economic profitability. To make a proper inter-firm
comparison of profitability, heterogeneous component of both revenues
and costs should be excluded. Since traffic revenue and operating cost
is homogenous across UBCs, to make a proper inter-firm comparison we can
define economic profitability as traffic revenue minus operating cost
(or a ratio of traffic revenue to operating cost).
Table 12 presents financial as well as economic profitability of
UBCs for the year 2000-01 and 2010-11. This Table reveals that
profitability performance varies greatly among the sample UBCs. During
the year 2010-11, BMTC was the most profitable UBC whereas CSTC was the
least profitable one. In fact, BMTC is the only UBC which experienced
financial as well as economic profit during every year of the sample
period, though the level of economic profit is not as high as financial
profit. Among the sample UBCs, only KMTU's profitability
performance is closer to that of BMTC whereas losses incurred by other
UBCs are huge. For example, in 2010-11, traffic revenue was hardly 25%
of operating cost for CSTC and DTC, 44% of operating cost for AMTS, and
around 70% of operating cost for MTCL, BEST, CHNTU, and TMTU whereas
total revenue was less than 30% of total cost for CSTC and DTC, less
than 50% of total cost for AMTS, 75% of total cost for BEST and CHNTU,
and around 80% of total cost for MTCL and TMTU. As a consequence, all
UBCs except BMTC and KMTU incurred huge financial losses varying from
Rs. 133 million for TMTU to Rs. 23,351 million for DTC during 2010-11.
Table 12 also shows that none of the UBCs except KMTU could improve
their economic or financial profitability from 2000-01 to 2010-11
despite the fact that five of them experienced improvement in their
productivity during the same period.
However, there is a direct link between productivity, prices, and
profitability. To illustrate this link, we use simple algebra for two
time periods, T-l and T. Let [P.sub.T-1] and [P.sub.T] denote output
prices (indices); [Y.sub.T-1] and [Y.sub.T] the corresponding output
quantities (indices); [W.sub.T-1] and [W.sub.T] denote input factor
prices (indices); and [X.sub.T-1] and [X.sub.T] the corresponding input
quantities (indices). The p is a measure of profit, which is, for
analytical convenience, defined as the ratio of revenues R to costs C
rather than their difference. Therefore, change in profit can be written
as:
[[pi].sub.T]/[[pi].sub.T-1] = [[R.sub.T]/[C.sub.T]] /
[[R.sub.T-1]/[C.sub.T-1]] = ([Y.sub.T][Y.sub.T]/([W.sub.T][X.sub.T]) /
([Y.sub.T-1][Y.sub.T-1]/([W.sub.T-1][X.sub.T-1]) (3)
Equation (3) can be rewritten as:
[[pi].sub.T]/[[pi].sub.T-1] = ([[Y.sub.T]/[X.sub.T]] /
[[Y.sub.T-1]/[X.sub.T-1]]) ([[P.sub.T]/[W.sub.T]] /
[[P.sub.T-1]/[W.sub.T-1]]) (4)
Since TFP (4) is defined as a ratio of an aggregate output quantity
index to aggregate input quantity index and price recovery (PR)5 is
defined as a ratio of an aggregate output price index to aggregate input
price index, any change in profit of the firm reflects the change in
total factor productivity and change in price recovery. The first half
of the right hand side of equation (4) is TFP growth whereas second half
is PR growth. Therefore, equation (4) can be rewritten as:
[[pi].sub.T]/[[pi].sub.T-1] =
([TFP.sub.T]/[TFP.sub.T-1])([PR.sub.T]/[PR.sub.T-1]) (5)
Equation (5) reveals that improvement in total factor productivity
and/or price recovery will lead to improvement in profitability. In
other words, productivity and profitability would move together in the
same direction if and only if input factor prices in comparison to
output prices are unchanged. Moreover, this decomposition is useful for
identifying to what extent the change in profitability is influenced by
changes in output and input prices and by changes in total factor
productivity. We tried to find out the same for UBCs in India.
Price Recovery index for sample urban bus companies is computed by
dividing output price index by input price index. In this case, output
price index is constructed by dividing traffic revenue index by the
output quantity index. Similarly, the input price index is calculated by
dividing operating cost index by the aggregate input quantity index. All
indices are set equal to unity for BMTC in 2000-01 for this analysis.
Table 13 presents output price index compared to input price index for
the year 2000-01 and 2010-11 for the sample UBCs.
Between 2000-01 and 2010-11, all UBCs faced higher increase in
their input factor prices in comparison to their output prices. As a
consequence, price recovery index declined for all the firms from
2000-01 to 2010-11; decline is very steep for AMTS (51%), BEST (43%),
and KMTU (43%) whereas decline is fairly moderate for BMTC (14%). Since
UBCs could not increase their fare in line with increase in their input
factor prices, they require significant productivity growth to improve
their productivity performance.
Figure 2 presents annual growth rate in economic profit,
productivity, and price recovery for the sample UBCs during 2000s.
Between 2000-01 and 2010-11, none of the UBCs except KMTU could improve
their economic profitability. During 2000s, CSTC, DTC, TMTU, AMTS,
CHNTU, MTCL, and BEST faced decline in their economic profitability by
4.77%, 4.09%, 3.05%, 3.03%, 2.01%, 1.20%, and 1.04% per year,
respectively; however, BMTC's economic profitability did not
decline considerably.
Decline in economic profitability of CSTC, DTC, and TMTU is because
they not only faced decline in their price recovery but also in their
total factor productivity. BEST, AMTS, and MTCL faced decline in their
profitability despite the fact that they achieved tremendous growth in
their productivity. This is because increase in their input factor
prices was far higher than increase in their output prices. CHNTU could
not improve its profitability mainly because it could not achieve
significant improvement in its productivity. BMTC's profitability
was virtually unchanged because increase in its productivity (1.46% per
year) was offset by decrease in its price recovery (1.49% per year).
KMTU is the only UBC that experienced improvement in profitability
(3.21% per year) despite the fact that it faced decline in its price
recovery (5.46% per year), thanks to tremendous improvement in its
productivity (9.17% per year). The analysis shows that the sample
UBCs' profitability improved at lesser rate than the improvement in
their productivity which reveals that the productivity gains achieved by
most of them are being passed through to their customers.
4. CONCLUDING REMARKS
Nine UBCs are compared on the basis of their productivity and
profitability performance during 2000s. We found that there is a wide
disparity among UBCs according to both the measures of performance.
Analysis shows that there is a positive relationship between
productivity and size of UBCs; due to this, there is a similarity in
UBCs' rank according to their size and productivity. In general,
UBCs which experienced higher increase in their productivity are the
ones which experienced greater increase in their output during 2000s.
This may be because UBCs in India exhibit increasing returns to scale
and therefore, ceteris paribus, increase in output will improve their
productivity. We also consider the possible relationship between
productivity and load factor. As expected, there is a high degree of
positive correlation between productivity and load factor; correlation
coefficient between these two variables is 0.764 with t-statistic of
11.35. As a consequence, there is a major similarity in rankings drawn
from productivity and load factor. This shows that the improvement in
load factor might be an important contributor to UBCs' productivity
improvement.
As far as profitability of UBCs is concerned, during the year
2010-11, BMTC was the most profitable UBC whereas CSTC was the least
profitable one. In fact, BMTC is the only UBC which experienced
financial as well as economic profit during every year of the sample
period. Among the sample UBCs, only KMTU's profitability
performance is closer to that of BMTC whereas losses incurred by other
UBCs are huge varying from Rs. 133 million for TMTU to Rs. 23,351
million for DTC in 201011. We found that KMTU is the only UBC that could
improve its profitability from 2000-01 to 2010-11. During the same
period, none of the other UBCs could improve their profitability despite
the fact that five of them experienced improvement in their
productivity. Furthermore, all UBCs faced higher increase in their input
factor prices in comparison to their output prices. As a result, price
recovery index declined for all of them from 2000-01 to 2010-11.
CSTC, DTC, and TMTU faced decline not only in their price recovery
but also in their productivity which is the main reason for
deterioration in their economic profitability. BEST, AMTS, and MTCL
faced decline in their profitability despite the fact that they achieved
tremendous growth in their productivity. This is because increase in
their input factor prices was far higher than increase in their output
prices. CHNTU could not improve its profitability mainly because it
could not achieve significant improvement in its productivity.
BMTC's profitability was virtually unchanged because increase in
its productivity was offset by decrease in its price recovery. KMTU is
the only UBC that improved its profitability despite the fact that it
faced decline in its price recovery. This is because KMTU achieved
tremendous growth in its productivity, highest among the sample UBCs.
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Notes
(1.) CAGR signifies compound annual growth rate during the sample
period. Throughout this paper, we follow the convention that growth is
continuously compounded. Compounded percentage growth rates are
rounded-off after two decimal points.
(2.) A simple method to test the null hypothesis that the
correlation coefficient is zero can be obtained using Student's
t-test on the t-statistic = r [square root of (N - 2/1 - [r.sup.2])] df
= N-2; where r is correlation coefficient and N is the number of
observations. So, in this case when r = 0.599 and N-2 = 92, t-statistic
= 7.17. Hence, we can reject the null hypothesis that the correlation
coefficient is zero at 1% level of significance since critical t-value
is 2.62. Therefore, we can infer at 1% level of significance that the
two series are correlated and the non-zero correlation did not happen by
chance.
(3.) For detail discussion on economies of scale experienced by
UBCs in India, see Singh (2005).
(4.) TFP is the broadest measure of productivity; it can either be
measured using a nonparametric index number approach or through the
econometric approach where productivity is estimated as the time shift
in a cost (or production) function. The TFP measurement and its
interpretation by these two approaches are not necessarily identical,
but these differences can be ignored for this study. The index number
approach is a gross measure of TFP; it does not distinguish between
sources of productivity growth. Econometric estimates of TFP are
technological shift measure; they exclude productivity gains from
economies of scale or similar endogenous production characteristics
(see, for the example, Diewert, 1992).
(5.) The ratio of an aggregate output price index to aggregate
input price index is usually labeled as price recovery or price cost
recovery in the management literature (see, for example, Brayton, 1985;
Miller and Mohan, 1989; Aboganda, 1994; and Singh, 2002) or sometimes as
price performance (Sink et al., 1984). For this study, we termed it as
price recovery.
SANJAY K. SINGH * AND SHALINI RAGHAV **
* Associate Professor of Economics, Indian Institute of Management
Lucknow, Prabandh Nagar, Off Sitapur Road, Lucknow-226013, India,
E-mail: sanjay@iiml.ac.in
** Research Scholar, Department of Agricultural Economics,
Institute of Agricultural Sciences, Banaras Hindu University,
Varanasi-221005, India, E-mail: raghav.shalini9@gmail.com
Table 1
Descriptive Statistics of the Sample UBCs during 2010-11
UBCs Pass.-Km Bus-Km Pass. Carried No. of No. of buses
(million) (million) (million) employees held
BMTC 20103 458 1643 32953 6110
CSTC 1211 35 169 6102 956
DTC 13041 292 1107 35557 5765
AMTS 2102 53 290 5274 942
BEST 12307 262 1535 30183 4652
KMTU 391 11 25 1184 135
TMTU 685 14 86 2368 350
CHNTU 2022 44 79 2136 471
MTCL 21796 347 2015 23500 3414
Table 2
Output of UBCs, 2000-01 to 2010-11, Indices; BMTC (2000-01) = 1.000
UBCs 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06
BMTC 1.000 1.185 1.316 1.695 2.018 1.936
CSTC 0.340 0.348 0.335 0.311 0.299 0.279
DTC 2.991 2.486 1.543 1.391 1.703 1.895
AMTS 0.302 0.254 0.171 0.143 0.158 0.237
BEST 1.548 1.471 1.563 1.607 1.630 1.553
KMTU 0.018 0.033 0.046 0.047 0.070 0.064
TMTU 0.108 0.127 0.140 0.120 0.123 0.122
CHNTU 0.304 0.319 N.A. N.A. N.A. 0.345
MTCL 2.058 1.887 1.832 1.755 1.887 1.930
UBCs 2006-07 2007-08 2008-09 2009-10 2010-11 CAGR (1)
(%)
BMTC 2.150 2.305 2.463 2.739 3.085 11.92
CSTC 0.276 0.275 0.212 0.294 0.186 -5.87
DTC 1.606 1.555 1.317 1.303 2.001 -3.94
AMTS 0.325 0.350 0.330 N.A. 0.323 0.66
BEST 1.567 1.635 1.881 1.957 1.888 2.01
KMTU 0.070 0.035 0.021 0.023 0.060 12.60
TMTU 0.118 0.111 0.098 0.086 0.105 -0.31
CHNTU 0.338 0.344 0.335 0.289 0.310 0.19
MTCL 1.983 2.184 2.558 2.987 3.344 4.97
Note: N.A. denotes that the data is not available.
Table 3
Input of UBCs, 2000-01 to 2010-11, Indices; BMTC (2000-01) = 1.000
UBCs 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06
CAGR (%)
BMTC 1.000 1.032 1.075 1.176 1.379 1.522
10.31
CSTC 0.566 0.562 0.526 0.509 0.484 0.475
-4.19
DTC 1.816 1.599 1.186 1.374 1.353 1.284
-3.42
AMTS 0.432 0.408 0.311 0.264 0.250 0.262
-3.32
BEST 2.207 2.165 2.105 1.989 2.044 1.968
-2.61
KMTU 0.053 0.060 0.068 0.073 0.064 0.068
3.14
TMTU 0.144 0.151 0.152 0.161 0.160 0.156
0.36
CHNTU 0.2 0.202 N.A. N.A. N.A. 0.206
-0.35
MTCL 1.431 1.387 1.374 1.351 1.321 1.293
1.70
UBCs 2006-07 2007-08 2008-09 2009-10 2010-11
BMTC 1.690 2.021 2.208 2.501 2.669
CSTC 0.464 0.443 0.415 0.410 0.369
DTC 1.203 1.218 1.219 1.145 1.282
AMTS 0.326 0.381 0.343 N.A. 0.308
BEST 1.929 1.854 1.825 1.775 1.694
KMTU 0.079 0.075 0.073 0.073 0.072
TMTU 0.146 0.142 0.140 0.135 0.150
CHNTU 0.203 0.202 0.196 0.175 0.193
MTCL 1.277 1.455 1.606 1.635 1.694
Note: N.A. denotes that the data is not available.
Table 4
TFP of UBCs, 2000-01 to 2010-11, Indices; BMTC (2000-01) = 1.000
UBCs 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06
BMTC 1.000 1.148 1.225 1.441 1.463 1.272
CSTC 0.601 0.619 0.637 0.612 0.618 0.588
DTC 1.647 1.554 1.301 1.012 1.259 1.477
AMTS 0.700 0.622 0.550 0.540 0.634 0.904
BEST 0.701 0.679 0.742 0.808 0.798 0.789
KMTU 0.345 0.548 0.674 0.647 1.100 0.943
TMTU 0.751 0.841 0.921 0.744 0.765 0.783
CHNTU 1.522 1.581 N.A. N.A. N.A. 1.676
MTCL 1.439 1.360 1.333 1.300 1.429 1.492
UBCs 2006-07 2007-08 2008-09 2009-10 2010-11 CAGR (%)
BMTC 1.272 1.141 1.116 1.095 1.156 1.46
CSTC 0.594 0.621 0.511 0.716 0.504 -1.75
DTC 1.335 1.277 1.080 1.138 1.561 -0.54
AMTS 0.998 0.919 0.963 N.A. 1.048 4.11
BEST 0.812 0.882 1.031 1.103 1.115 4.74
KMTU 0.889 0.460 0.294 0.314 0.830 9.17
TMTU 0.810 0.776 0.701 0.638 0.702 -0.66
CHNTU 1.665 1.703 1.706 1.646 1.608 0.55
MTCL 1.552 1.501 1.593 1.827 1.974 3.22
Note: N.A. denotes that the data is not available.
Table 5
UBCs ranked by 2000-01 & 2010-11 level of TFP and Size
UBCs TFP TFP TFP TFP TFP
rank level rank level CAGR
2000-01 2000-01 2010-11 2010-11 rank level (%)
BMTC 4 1.000 4 1.156 5 1.46
CSTC 8 0.601 9 0.504 9 -1.75
DTC 1 1.647 3 1.561 7 -0.54
AMTS 7 0.700 6 1.048 3 4.11
BEST 6 0.701 5 1.115 2 4.74
KMTU 9 0.345 7 0.830 1 9.17
TMTU 5 0.751 8 0.702 8 -0.66
CHNTU 2 1.522 2 1.608 6 0.55
MTCL 3 1.439 1 1.974 4 3.22
UBCs Out- Out- Out- Out- Output
put put put put CAGR
rank level rank level
2000-01 2000-01 2010-11 2010-11 rank level (%)
BMTC 4 1.000 2 3.085 2 11.92
CSTC 5 0.340 7 0.186 9 -5.87
DTC 1 2.991 3 2.001 8 -3.94
AMTS 7 0.302 5 0.323 5 0.66
BEST 3 1.548 4 1.888 4 2.01
KMTU 9 0.018 9 0.060 1 12.60
TMTU 8 0.108 8 0.105 7 -0.31
CHNTU 6 0.304 6 0.310 6 0.19
MTCL 2 2.058 1 3.344 3 4.97
Table 6
Labor Productivity (PKm per Employee) of UBCs, 2000-01 to 2010-11,
Indices; BMTC (2000-01) = 1.000
UBCs 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06
CAGR ($)
BMTC 1.000 1.161 1.247 1.475 1.544 1.384
2.44
CSTC 0.491 0.531 0.544 0.525 0.525 0.499
-1.70
DTC 1.665 1.363 0.863 0.633 0.793 0.905
-7.48
AMTS 0.589 0.497 0.442 0.456 0.540 0.820
3.50
BEST 0.556 0.540 0.602 0.627 0.617 0.613
4.35
KMTU 0.267 0.451 0.542 0.467 1.221 1.062
9.95
TMTU 0.575 0.691 0.746 0.610 0.631 0.635
0.47
CHNTU 1.786 1.855 N.A. N.A. N.A. 2.050
1.01
MTCL 1.303 1.239 1.249 1.243 1.407 1.479
4.03
UBCs 2006-07 2007-08 2008-09 2009-10 2010-11
BMTC 1.421 1.227 1.216 1.201 1.272
CSTC 0.515 0.537 0.437 0.594 0.414
DTC 0.792 0.743 0.614 0.601 0.765
AMTS 0.810 0.813 0.777 N.A. 0.831
BEST 0.642 0.698 0.847 0.894 0.851
KMTU 0.685 0.369 0.239 0.263 0.688
TMTU 0.622 0.590 0.528 0.468 0.603
CHNTU 2.069 2.103 2.096 1.873 1.974
MTCL 1.538 1.410 1.539 1.766 1.935
Note: N.A. denotes that the data is not available.
Table 7
Fuel Productivity (Bus-Km per Litre of Diesel) of UBCs, 2000-01 to
2010-11, Indices; BMTC (2000-01) = 1.000
UBCs 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06
BMTC 1.000 1.037 1.067 1.104 1.100 1.081
CSTC 0.803 0.826 0.858 0.868 0.858 0.858
DTC 0.882 0.893 0.875 0.882 0.893 0.914
AMTS 0.858 0.852 0.849 0.000 0.805 0.833
BEST 0.705 0.703 0.705 0.735 0.742 0.768
KMTU 0.838 0.821 0.840 0.863 0.856 0.893
TMTU 0.768 0.761 0.761 0.738 0.733 0.729
CHNTU 0.928 0.930 N.A. N.A. N.A. 0.961
MTCL 0.819 0.819 0.821 0.831 0.847 0.875
UBCs 2006-07 2007-08 2008-09 2009-10 2010-11 CAGR (%)
BMTC 1.056 1.032 1.014 0.954 0.930 -0.72
CSTC 0.858 0.826 0.828 0.805 0.812 0.12
DTC 0.912 0.905 0.937 0.984 0.986 1.13
AMTS 0.812 0.807 0.810 N.A. 0.810 -0.58
BEST 0.742 0.719 0.698 0.682 0.675 -0.44
KMTU 0.900 0.893 0.893 0.831 0.861 0.27
TMTU 0.745 0.752 0.754 0.733 0.664 -1.45
CHNTU 0.942 0.949 0.951 0.000 0.949 0.22
MTCL 0.889 0.914 0.984 1.035 1.019 2.20
Note: N.A. denotes that the data is not available.
Table 8
Bus Productivity (Bus-Km per Bus Held) of UBCs, 2000-01 to 2010-11,
Indices; BMTC (2000-01) = 1.000
UBCs 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06
BMTC 1.000 1.060 1.048 1.078 1.050 1.044
CSTC 0.615 0.674 0.758 0.735 0.660 0.595
DTC 0.801 0.837 0.819 0.918 0.924 0.983
AMTS 0.801 0.686 0.528 0.589 0.673 0.822
BEST 0.934 0.909 0.923 0.936 0.927 0.930
KMTU 0.663 1.083 1.094 1.060 1.006 1.058
TMTU 0.914 0.968 0.977 0.893 0.856 0.790
CHNTU 1.468 1.474 N.A. N.A. N.A. 1.538
MTCL 0.987 0.934 0.979 0.985 0.987 1.000
UBCs 2006-07 2007-08 2008-09 2009-10 2010-11 CAGR (%)
BMTC 1.042 1.020 1.035 1.015 0.984 -0.16
CSTC 0.576 0.584 0.559 0.535 0.479 -2.47
DTC 0.780 0.703 0.634 0.714 0.666 -1.84
AMTS 0.867 0.888 0.910 N.A. 0.732 -0.90
BEST 0.915 0.862 0.806 0.813 0.738 -2.33
KMTU 1.085 1.148 1.119 1.072 1.054 4.74
TMTU 0.672 0.679 0.600 0.550 0.527 -5.35
CHNTU 1.509 1.533 1.534 1.313 1.225 -1.79
MTCL 0.976 1.098 1.257 1.362 1.335 3.07
Note: N.A. denotes that the data is not available.
Table 9
UBCs Ranked by Labor Productivity (PKm/E), Fuel Productivity (BKm/D),
Bus Productivity (BKm/B), and Total Factor Productivity
UBCs PKm / E PKm / E BKm / D BKm / D
2000-01 2010-11 2000-01 2010-11
rank level rank level rank level rank level
BMTC 4 1.000 3 1.272 1 1.000 4 0.930
CSTC 8 0.491 9 0.414 7 0.803 6 0.812
DTC 2 1.665 6 0.765 3 0.882 2 0.986
AMTS 5 0.589 5 0.831 4 0.858 7 0.810
BEST 7 0.556 4 0.851 9 0.705 8 0.675
KMTU 9 0.267 7 0.688 5 0.838 5 0.861
TMTU 6 0.575 8 0.603 8 0.768 9 0.664
CHNTU 1 1.786 1 1.974 2 0.928 3 0.949
MTCL 3 1.303 2 1.935 6 0.819 1 1.019
UBCs BKm / B BKm / B TFP TFP
2000-01 2010-11 2000-01 2010-11
rank level rank level rank level rank level
BMTC 2 1.000 4 0.984 4 1.000 4 1.156
CSTC 9 0.615 9 0.479 8 0.601 9 0.504
DTC 6 0.801 7 0.666 1 1.647 3 1.561
AMTS 7 0.801 6 0.732 7 0.700 6 1.048
BEST 4 0.934 5 0.738 6 0.701 5 1.115
KMTU 8 0.663 3 1.054 9 0.345 7 0.830
TMTU 5 0.914 8 0.527 5 0.751 8 0.702
CHNTU 1 1.468 2 1.225 2 1.522 2 1.608
MTCL 3 0.987 1 1.335 3 1.439 1 1.974
Table 10
Bus Utilization by UBCs
UBCs Bus-km per bus Bus-km per bus on Fleet utilization
held per day road per day (in percentage)
2000-01 2010-11 2000-01 2010-11 2000-01 2010-11
BMTC 209 205 220 222 95 92
CSTC 128 100 193 191 66 52
DTC 167 139 233 185 72 75
AMTS 167 153 207 213 81 72
BEST 195 154 212 176 92 88
KMTU 138 220 227 238 61 93
TMTU 191 110 209 197 91 56
CHNTU 306 256 323 271 95 94
MTCL 206 279 251 316 82 88
Table 11
UBCs ranked by Load Factor (LF) and Total Factor Productivity
UBCs LF LF LF
2000-01 2010-11 CAGR
rank level (%) rank level (%) rank level (%)
BMTC 6 58 7 67 4 1.45
CSTC 4 74 9 60 9 -2.08
DTC 2 83 4 74 8 -1.14
AMTS 8 53 8 66 3 2.22
BEST 7 55 5 70 2 2.44
KMTU 9 39 6 68 1 5.72
TMTU 5 71 3 80 5 1.20
CHNTU 3 83 1 92 6 1.03
MTCL 1 87 2 87 7 0.00
UBCs TFP TFP TFP
2000-01 2010-11 CAGR
rank level (%) rank level (%) rank level (%)
BMTC 4 1.000 4 1.156 5 1.46
CSTC 8 0.601 9 0.504 9 -1.75
DTC 1 1.647 3 1.561 7 -0.54
AMTS 7 0.700 6 1.048 3 4.11
BEST 6 0.701 5 1.115 2 4.74
KMTU 9 0.345 7 0.830 1 9.17
TMTU 5 0.751 8 0.702 8 -0.66
CHNTU 2 1.522 2 1.608 6 0.55
MTCL 3 1.439 1 1.974 4 3.22
Table 12
Financial and Economic Profitability of UBCs during 2000-01 and
2010-11 (Monetary units in Rs. million)
UBCs Traffic Total Operating
Revenue Revenue Cost
(TrR) (TR) (OC)
2000-01 2010-11 2000-01 2010-11 2000-01 2010-11
BMTC 2568 12112 2761 13275 2552 12101
CSTC 531 619 552 654 1325 2514
WC 2769 8228 5752 9870 7323 32993
AMTS 822 1014 854 1089 1362 2286
BEST 6465 10021 6961 11128 8340 14359
KMTU 75 295 112 312 108 309
TMTU 358 547 363 674 362 753
CHNTU 488 996 549 1115 554 1386
MTCL 3109 7834 3601 9202 4015 11419
UBCs Total Economic Financial
Cost Profit Profit
(TC) (TrR) / OC) (TR / TC)
2000-01 2010-11 2000-01 2010-11 2000-01 2010-11
BMTC 2630 12772 1.006 1.001 1.050 1.039
CSTC 1325 2514 0.401 0.246 0.417 0.260
WC 7564 33221 0.378 0.249 0.760 0.297
AMTS 1375 2301 0.604 0.444 0.621 0.473
BEST 8698 14942 0.775 0.698 0.800 0.745
KMTU 115 312 0.695 0.953 0.977 1.000
TMTU 397 807 0.990 0.726 0.914 0.835
CHNTU 563 1491 0.880 0.718 0.974 0.748
MTCL 4065 11516 0.774 0.686 0.886 0.799
Table 13
Output Price, Input Price, and Price Recovery Indices of UBCs during
2000-01
and 2010-11 (BMTC (2000-01) = 1.000)
UB Cs Output Price Input Price Price Recovery
(Output Price / Input
Price)
2000-01 2010-11 2000-01 2010-11 2000-01 2010-11
BMTC 1.000 1.529 1.000 1.777 1.000 0.861
CSTC 0.608 1.297 0.918 2.672 0.662 0.485
DTC 0.360 1.601 1.580 10.085 0.228 0.159
AMTS 1.060 1.224 1.237 2.910 0.857 0.421
BEST 1.626 2.066 1.481 3.321 1.098 0.622
KMTU 1.604 1.914 0.801 1.678 2.001 1.141
TMTU 1.287 2.025 0.982 1.972 1.311 1.027
CHNTU 0.624 1.250 1.086 2.815 0.575 0.444
MTCL 0.588 0.912 1.100 2.641 0.535 0.345
Figure 2: Growth in Economic Profit, Productivity, and Price Recovery
TMTU MTCL KMTU DTC CSTC CHNTU
a Economic Profit -3.05 -1.20 3.21 -4.09 -4.77 -2.01
b Productivity -0.66 3.22 9.17 -0.54 -1.75 0.55
c Price Recovery -2.41 -4.29 -5.46 -3.54 -3.06 -2.55
BMTC BEST AMTS
a Economic Profit -0.05 -1.04 -3.03
b Productivity 1.46 4.74 4.11
c Price Recovery -1.49 -5.52 -6.86
