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Network Structure and Efficiency Gains from Mergers

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View the page of Yanyou Chen

Yanyou Chen examines competition among oligopolistic network firms and the influence of market power on the resulting equilibrium outcomes.

Image courtesy of interviewee. June 18, 2024

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This video discusses the significance of the transportation sector in modern society, detailing how transportation networks can be established through centralized planning, decentralized individual actions, or by transportation companies investing in infrastructure. It highlights the market power of large transportation firms and poses research questions regarding mergers and their impact on transportation costs.

The existing literature typically focuses on the roles of social planners or individual players with little market power. The author argues for incorporating oligopolistic competition into the analysis of transportation networks to better understand how mergers might affect costs and market dynamics.

The paper's central findings are based on an examination of the U.S. freight railroad industry between 1985 and 2005, revealing that mergers led to significant efficiency gains, including a 13% decrease in shipment costs and a rise in markup of 77.2%. It points out that while mergers may increase market power and raise prices, they also create efficiencies that can lower shipment costs.

The study identifies key factors related to network characteristics influencing cost reductions and discusses how the overlap and complementarity of merging networks affect outcomes. The findings have implications for policymakers in evaluating future mergers, emphasizing the importance of network centrality and the balance of market power and efficiency.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.105 --> 00:02.565
The transportation sector essentially serves

00:02.665 --> 00:05.685
as like the life lifeblood of our modern society,

00:05.875 --> 00:09.685
playing a very important role in like facilitating commerce,

00:09.935 --> 00:11.365
trade, and social interactions.

00:11.715 --> 00:15.165
Okay? The networks underpin this,

00:15.185 --> 00:18.565
sector are typically in established in like three ways.

00:19.105 --> 00:23.085
So first, they can be established by a social planner,

00:23.085 --> 00:26.285
essentially a centralized, method you can think about,

00:26.355 --> 00:28.605
like planners decides to build highways,

00:29.505 --> 00:33.565
and second, they can emerge from like individual players in

00:33.605 --> 00:34.685
a decentralized way.

00:35.505 --> 00:38.485
So in this case, this includes like the container ships

00:38.625 --> 00:41.045
or taxi drivers who decide where

00:41.065 --> 00:43.245
to go from the origin to destination.

00:43.555 --> 00:46.525
Okay? And then the networks emerge from their

00:46.535 --> 00:47.805
cumulative decisions.

00:48.185 --> 00:49.805
That's a second, scenario.

00:50.385 --> 00:51.565
And for the third case,

00:51.705 --> 00:55.085
we will have those transportation companies who will decide,

00:55.545 --> 00:59.365
like in investment over the infrastructure, by them,

00:59.425 --> 01:02.645
by themselves, and they will construct the networks.

01:03.435 --> 01:06.285
This case includes like the railroad companies,

01:06.285 --> 01:07.285
which I'm going to study,

01:07.745 --> 01:10.365
the railroad companies they will invest into like the

01:10.605 --> 01:12.765
physical trucks and to build their own networks.

01:13.025 --> 01:16.405
And also this includes like the transmission pipelines will

01:16.425 --> 01:19.245
lay out their own, networks, okay?

01:20.355 --> 01:23.165
Typically, those large transportation companies,

01:23.235 --> 01:25.565
they will have a significant market power.

01:26.065 --> 01:28.925
And this will drive us to some very,

01:28.925 --> 01:32.245
some very important research questions, including how

01:32.245 --> 01:34.645
to analyze mergers within those firms

01:35.345 --> 01:39.325
and how to understand the re, like how to understand

01:40.015 --> 01:42.925
those mergers and how they affect the translation costs.

01:43.275 --> 01:46.165
Okay? So those will be the questions I'm going to study.

01:46.625 --> 01:48.325
Uh, like in my, research

01:49.165 --> 01:51.665
and I, explained, a little earlier,

01:51.755 --> 01:53.105
there were three ways

01:53.165 --> 01:55.625
to establish a transportation network, okay?

01:55.965 --> 01:59.185
And in general, the existing literature focused

01:59.245 --> 02:00.425
on transportation markets.

02:01.645 --> 02:04.425
And they typically take two, approaches.

02:04.765 --> 02:07.625
So they either look at the decisions about social planner

02:07.965 --> 02:12.145
or they assume there is a numerous of atomistic players

02:12.485 --> 02:14.465
who doesn't have any market power, okay?

02:14.895 --> 02:18.465
Such as individual taxi drivers or cont container ships.

02:18.635 --> 02:22.625
While these approaches are effective in their own like,

02:22.905 --> 02:25.265
respective sectors, but it's necessary

02:25.265 --> 02:28.665
to further con consider the like, market power

02:28.805 --> 02:30.985
of transportation companies, like I mentioned

02:31.085 --> 02:33.985
for railroad companies and the pipeline companies.

02:34.885 --> 02:37.185
So building on the groundwork established

02:37.205 --> 02:38.825
by the existing literature,

02:39.365 --> 02:43.505
my paper incorporates oligopolistic competition into those,

02:43.765 --> 02:46.625
like network competitions, okay?

02:47.165 --> 02:48.625
It allows for each firm

02:48.765 --> 02:52.225
to make like optimized decisions across the network,

02:52.475 --> 02:56.025
while also taking into account the strategic decisions

02:56.045 --> 02:57.225
of their competitors.

02:58.135 --> 03:02.245
This actually expands the scope of the literature to,

03:02.315 --> 03:03.965
like a relevant way, okay?

03:04.035 --> 03:08.725
Because this will facilitate the analysis of mergers that's

03:08.725 --> 03:11.285
regarding to the literature for like a network

03:11.465 --> 03:12.485
and transportation markets.

03:13.145 --> 03:16.845
And regarding the literature for like merger analysis,

03:18.145 --> 03:19.205
my paper contributes

03:19.265 --> 03:21.965
to the literature examining the potential efficiency

03:22.015 --> 03:23.085
gains from mergers.

03:23.905 --> 03:26.325
But merger on one hand will give the, like,

03:26.375 --> 03:27.965
merge entity some market power.

03:28.225 --> 03:31.285
So the merge firm could potentially like raise prices,

03:31.985 --> 03:35.125
but on the other hand, there may be sinners between firms,

03:35.425 --> 03:38.405
so which will reduce the like, cost of production, okay?

03:40.465 --> 03:41.605
And the price effects

03:41.625 --> 03:44.645
of mergers have actually been extensively studied.

03:45.025 --> 03:47.245
And they, however, in contrast,

03:47.805 --> 03:49.925
a few empirical studies have looked at,

03:49.925 --> 03:52.525
looked at like the efficiency gains of mergers.

03:52.865 --> 03:56.405
And my paper contributes to providing some direct evidence

03:56.785 --> 03:58.125
for this efficiency gains.

03:58.635 --> 04:02.605
Okay? And economists actually have been, like aware

04:02.605 --> 04:05.085
of this trade off, between market power

04:05.145 --> 04:06.405
and efficiency gains,

04:06.665 --> 04:10.245
and least since Williams in 19, 1968.

04:10.905 --> 04:14.005
And this trade off has actually been central

04:14.225 --> 04:16.765
to the antitrust analysis and

04:17.205 --> 04:19.565
provided guide guidelines for horizontal mergers.

04:19.785 --> 04:23.325
So my PA paper contributes to showing that actually

04:23.345 --> 04:25.245
for some network based industries,

04:25.255 --> 04:27.405
there is significant efficiency gains.

04:28.045 --> 04:32.285
I actually applied my model into the freight US railroads.

04:32.875 --> 04:36.765
This is because the industry has undergone like significant

04:36.765 --> 04:40.245
changes in the competitive landscape over the,

04:40.245 --> 04:42.405
like past 30, like three decades.

04:43.705 --> 04:46.925
In addition to its historical, like significance

04:47.065 --> 04:48.485
for economic development,

04:49.145 --> 04:52.445
the freight railroad is still super important

04:52.625 --> 04:54.205
for economic activities.

04:54.295 --> 04:58.045
Today, according to the Association

04:58.045 --> 05:01.765
of American railroads actually in like, say 2016,

05:02.985 --> 05:05.445
the railroads carries about 40%

05:05.545 --> 05:07.885
of the total like inter city transportation,

05:08.215 --> 05:11.405
which is like higher than any other trans transportation

05:11.515 --> 05:12.645
mode, okay?

05:13.295 --> 05:16.765
Given the like importance of railroad, like industry,

05:17.445 --> 05:20.045
actually, there also have been like a lot of changes

05:20.585 --> 05:22.445
on the like competitive landscape.

05:23.325 --> 05:27.245
A series of mergers has happened from year like 90, 85 all

05:27.245 --> 05:28.445
the way to 2005.

05:29.105 --> 05:32.245
The number of class one railroads has decreased from

05:32.305 --> 05:33.565
39 to seven.

05:34.385 --> 05:37.085
And if we look at the market share of the top four firms,

05:37.345 --> 05:40.885
it has increased from 66 to like 92%.

05:41.235 --> 05:44.005
Okay? So huge increase in market concentration,

05:44.745 --> 05:48.045
but at the same time, the shipment price has reduced

05:48.265 --> 05:49.605
by about 20%.

05:50.385 --> 05:52.725
So given we have this large increase in market

05:53.085 --> 05:54.925
concentration, but we also see this

05:55.205 --> 05:56.565
reduction of shipment price,

05:56.895 --> 05:58.565
Price, this may indicate there is some

05:58.565 --> 05:59.925
efficiency gain of mergers.

06:00.475 --> 06:04.405
This is why I first, like propo applied my model

06:04.585 --> 06:07.605
to study the efficiency gains in this particular industry.

06:08.295 --> 06:11.725
There are actually three main findings of my, paper.

06:12.065 --> 06:16.525
So first I do find significant, like gains of mergers

06:16.795 --> 06:18.525
between the railroad companies

06:19.535 --> 06:21.645
among the Class one railroad companies

06:21.645 --> 06:24.005
between 1985 and 2005.

06:24.685 --> 06:27.645
I find that the mergers have led to an average

06:27.905 --> 06:31.445
of like 13% decrease in ship shipment cost

06:31.945 --> 06:35.885
and about eight 8.8% reduction in shipment prices.

06:37.025 --> 06:41.525
And I also find an increase in the markup by 77.2%.

06:42.075 --> 06:46.765
Okay? My analysis also find that this increased markup

06:46.865 --> 06:50.525
for railroad firm is not driven by the decrease of number

06:50.545 --> 06:53.805
of firms in local market is actually driven

06:53.985 --> 06:57.205
by the strategic reactions of non merging parties.

06:57.905 --> 06:59.285
So those non merging parties,

06:59.475 --> 07:02.365
they move resources away from the regions

07:02.375 --> 07:06.005
where the merged company has a large like efficiency gain.

07:06.425 --> 07:09.525
So this further results in, increased market share

07:09.745 --> 07:11.245
for the merged, firm.

07:11.555 --> 07:13.725
Okay? So this is the main reason why we have

07:13.725 --> 07:14.885
this increased markup.

07:15.745 --> 07:17.485
And then I also investigate

07:17.555 --> 07:21.125
what are some key network characteristics that are related

07:21.425 --> 07:23.085
to this, cost reduction.

07:23.925 --> 07:25.285
I find that markets

07:25.475 --> 07:28.405
with low centrality in the pre-merger network

07:28.745 --> 07:32.605
but become central in the post merger network are more

07:32.605 --> 07:35.845
likely to have a higher, like cost reduction.

07:36.955 --> 07:38.325
Intuitively. This means

07:38.435 --> 07:43.325
that if a market is located on the like, age of the,

07:43.325 --> 07:44.805
merger network before the merger,

07:45.265 --> 07:48.685
but becomes more central in the post merger network,

07:49.275 --> 07:52.645
it's more likely to have a larger like cost

07:52.925 --> 07:53.965
reduction following the merger.

07:54.945 --> 07:59.125
And my final, result is to show that what kind

07:59.125 --> 08:02.085
of mergers are more likely to improve welfare.

08:02.505 --> 08:05.805
So this could shape light on like future merger policies.

08:07.125 --> 08:09.765
I essentially focus on the degree of overlap

08:09.905 --> 08:11.645
and the degree of complementarity

08:11.715 --> 08:13.565
between the two merging networks.

08:14.325 --> 08:17.725
I find that both a high degree of overlap

08:17.825 --> 08:21.005
and high degree of between the, a high degree

08:21.005 --> 08:22.885
of complementarity is going

08:22.885 --> 08:24.685
to result in more cost reductions.

08:25.305 --> 08:27.205
But a high degree of overlap

08:27.205 --> 08:30.325
between the two merchant networks will also result in a

08:30.325 --> 08:31.565
high increase of markup.

08:31.865 --> 08:34.565
So which will make firms more profitable,

08:34.625 --> 08:38.045
but may harm the consumers in conclusion.

08:38.225 --> 08:40.725
So my paper essentially examines this

08:40.745 --> 08:43.325
how transportation networks are established under

08:43.375 --> 08:45.205
early istic competition.

08:45.705 --> 08:47.805
And by applying my proposed model

08:47.945 --> 08:50.245
to the US freight railroad industry,

08:50.725 --> 08:54.885
I do find significant efficiency gain from mergers and

08:54.885 --> 08:58.345
My findings should light on like future merger policies.

08:58.525 --> 09:01.985
Uh, so for policy makers how to evaluate future mergers,

09:02.745 --> 09:06.065
I show that within a network for the knots

09:06.065 --> 09:09.265
that were located on the outer age of the network,

09:09.565 --> 09:12.065
but become central, they are more likely to benefit.

09:12.965 --> 09:15.945
And if we compare between different types of mergers,

09:16.285 --> 09:19.785
we should look at whether those types of mergers have,

09:19.855 --> 09:21.505
like high degree of overlap

09:21.685 --> 09:23.625
and high degree of complementarity.

09:24.005 --> 09:25.105
And as mentioned earlier,

09:25.365 --> 09:29.105
so if the two merging networks have high degree of overlap,

09:29.535 --> 09:32.745
then it is likely to have high cost reduction,

09:32.965 --> 09:35.425
but also may have a high increase of markup.

09:35.765 --> 09:38.825
So the policy makers may want to pay spatial attention

09:39.205 --> 09:42.585
to mergers when, where they have this high degree of overlap

09:42.585 --> 09:43.305
between each other.

00:00 - The transportation sector essentially serves as like the life lifeblood of our modern society, playing a very important role in like facilitating commerce, trade, and social interactions. Okay? The networks underpin this, sector are typically in established in like three ways.

00:19 - So first, they can be established by a social planner, essentially a centralized, method you can think about, like planners decides to build highways, and second, they can emerge from like individual players in a decentralized way. So in this case, this includes like the container ships or taxi drivers who decide where to go from the origin to destination. Okay? And then the networks emerge from their cumulative decisions.

00:48 - That's a second, scenario. And for the third case, we will have those transportation companies who will decide, like in investment over the infrastructure, by them, by themselves, and they will construct the networks. This case includes like the railroad companies, which I'm going to study, the railroad companies they will invest into like the physical trucks and to build their own networks. And also this includes like the transmission pipelines will

01:16 - lay out their own, networks, okay? Typically, those large transportation companies, they will have a significant market power. And this will drive us to some very, some very important research questions, including how to analyze mergers within those firms and how to understand the re, like how to understand those mergers and how they affect the translation costs. Okay? So those will be the questions I'm going to study. like in my, research

01:49 - and I, explained, a little earlier, there were three ways to establish a transportation network, okay? And in general, the existing literature focused on transportation markets. And they typically take two, approaches. So they either look at the decisions about social planner or they assume there is a numerous of atomistic players who doesn't have any market power, okay? Such as individual taxi drivers or cont container ships.

02:18 - While these approaches are effective in their own like, respective sectors, but it's necessary to further con consider the like, market power of transportation companies, like I mentioned for railroad companies and the pipeline companies. So building on the groundwork established by the existing literature, my paper incorporates oligopolistic competition into those, like network competitions, okay? It allows for each firm

02:48 - to make like optimized decisions across the network, while also taking into account the strategic decisions of their competitors. This actually expands the scope of the literature to, like a relevant way, okay? Because this will facilitate the analysis of mergers that's regarding to the literature for like a network and transportation markets. And regarding the literature for like merger analysis, my paper contributes

03:19 - to the literature examining the potential efficiency gains from mergers. But merger on one hand will give the, like, merge entity some market power. So the merge firm could potentially like raise prices, but on the other hand, there may be sinners between firms, so which will reduce the like, cost of production, okay? And the price effects of mergers have actually been extensively studied. And they, however, in contrast,

03:47 - a few empirical studies have looked at, looked at like the efficiency gains of mergers. And my paper contributes to providing some direct evidence for this efficiency gains. Okay? And economists actually have been, like aware of this trade off, between market power and efficiency gains, and least since Williams in 19, 1968. And this trade off has actually been central to the antitrust analysis and

04:17 - provided guide guidelines for horizontal mergers. So my PA paper contributes to showing that actually for some network based industries, there is significant efficiency gains. I actually applied my model into the freight US railroads. This is because the industry has undergone like significant changes in the competitive landscape over the, like past 30, like three decades. In addition to its historical, like significance for economic development,

04:49 - the freight railroad is still super important for economic activities. Today, according to the Association of American railroads actually in like, say 2016, the railroads carries about 40% of the total like inter city transportation, which is like higher than any other trans transportation mode, okay? Given the like importance of railroad, like industry, actually, there also have been like a lot of changes

05:20 - on the like competitive landscape. A series of mergers has happened from year like 90, 85 all the way to 2005. The number of class one railroads has decreased from 39 to seven. And if we look at the market share of the top four firms, it has increased from 66 to like 92%. Okay? So huge increase in market concentration, but at the same time, the shipment price has reduced by about 20%.

05:50 - So given we have this large increase in market concentration, but we also see this reduction of shipment price, Price, this may indicate there is some efficiency gain of mergers. This is why I first, like propo applied my model to study the efficiency gains in this particular industry. There are actually three main findings of my, paper. So first I do find significant, like gains of mergers between the railroad companies

06:19 - among the Class one railroad companies between 1985 and 2005. I find that the mergers have led to an average of like 13% decrease in ship shipment cost and about eight 8.8% reduction in shipment prices. And I also find an increase in the markup by 77.2%. Okay? My analysis also find that this increased markup for railroad firm is not driven by the decrease of number of firms in local market is actually driven by the strategic reactions of non merging parties.

06:57 - So those non merging parties, they move resources away from the regions where the merged company has a large like efficiency gain. So this further results in, increased market share for the merged, firm. Okay? So this is the main reason why we have this increased markup. And then I also investigate what are some key network characteristics that are related to this, cost reduction.

07:23 - I find that markets with low centrality in the pre-merger network but become central in the post merger network are more likely to have a higher, like cost reduction. Intuitively. This means that if a market is located on the like, age of the, merger network before the merger, but becomes more central in the post merger network, it's more likely to have a larger like cost reduction following the merger.

07:54 - And my final, result is to show that what kind of mergers are more likely to improve welfare. So this could shape light on like future merger policies. I essentially focus on the degree of overlap and the degree of complementarity between the two merging networks. I find that both a high degree of overlap and high degree of between the, a high degree of complementarity is going to result in more cost reductions.

08:25 - But a high degree of overlap between the two merchant networks will also result in a high increase of markup. So which will make firms more profitable, but may harm the consumers in conclusion. So my paper essentially examines this how transportation networks are established under early istic competition. And by applying my proposed model to the US freight railroad industry,

08:50 - I do find significant efficiency gain from mergers and My findings should light on like future merger policies. so for policy makers how to evaluate future mergers, I show that within a network for the knots that were located on the outer age of the network, but become central, they are more likely to benefit. And if we compare between different types of mergers, we should look at whether those types of mergers have, like high degree of overlap and high degree of complementarity.

09:24 - And as mentioned earlier, so if the two merging networks have high degree of overlap, then it is likely to have high cost reduction, but also may have a high increase of markup. So the policy makers may want to pay spatial attention to mergers when, where they have this high degree of overlap between each other.

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