The Importance of Industry Links in Merger Waves

Discussed ByGerard Hoberg

University of Maryland

Authored byKen Ahern and Jarad Harford

University of Michigan and University of Washington

For presentation at the UBC Winter Conference

March, 2011

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Summary

Vertical Merger Waves are Pervasive– Use Input/Output tables to quantify vertical

relatedness network.– Use graph theory to show that merger waves travel

through this network.– Effects are economically large.

Propagation and Centrality– Propagation across more distant network links also

occurs, but takes more time.– Waves in central industries related to aggregate

activity.

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Big PictureERGM is a mainstay in other fields, and is well suited for financial applications.We all know that vertical mergers exist, the new contribution here is that they cluster.If shocks drive vertical mergers, clustering should result (shocks can affect whole industries).Dynamics and propagation: little is currently known.

Overall: Interesting and ambitious. But needs more development.

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Need more links to Theory

Finance and Industrial Organization– Asset Complementarity among vertical pairs. Technology

shocks can affect a whole industry. [Rerun tests for shocked versus unshocked industries or x-terms]

– Liquidity, as shown in Harford (2005), might have common component along vertically linked industries? [Similar]

– Diversification, or a role for systematic risk? [Covariance]– Incomplete Contracts. Legal changes or changes in the

informational environment can affect whole industries.– Monopoly Power.– Internalize Product Diversity.– Informational Issues or Misvaluation Motives.

Perry (1989) is a nice review. Which of the above explains tendency to cluster in waves?

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Static Tests (1): Sequential?Authors suggest that they isolated merger activity sequentially in the supply chain?

Consider a study of industry C’s mergers.Predict: vertical links to B and D matter, but A is irrelevant.A powerful falsification test. Industry A can matter under some alternatives including asset complementarity. Method: The vertical network is described by a MxM matrix “G”. Squaring (“G2”) indicates firms that are 2-steps away in the supply chain. The ERGM model should include G and G2 terms, and only the former should matter.Authors consider a distance metric, but it is broad and does not isolate sequential ordering.

A B C DSupply

Chain

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Static Tests (2): Industry SizeThe authors do not control for the number of

firms in each industry.– If mergers have a component related to random

opportunity arrival, then industries with more firms will have more mergers.

– Direct Solution: Use firm-level network instead of industry network. Add own-industry as a control network. ERGM uses whole network, so issue is gone.

– Alternative: Add a control for the number of firms in each industry. It should be positive.

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Static Tests (3): Conglomerates

How are conglomerates handled empirically?– 1) How are conglomerates assigned to the

industry network? Many of the observed transactions currently labeled as cross industry might simply be a conglomerate adding to existing segments.

– Examining robustness to dropping conglomerates would be informative. Accounting for their complexity better.

– 2) Confirming test: if authors are right, a large fraction of conglomerates should be vertically integrated. Testable!

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Static Tests (4): Mismeasurement

and other industry linksPoorly Measured Industries.– Hoberg and Phillips (2010), and Ruah and Sufi

(2010), suggest SIC codes are highly mismeasured.

– HP show that cross-industry mergers are actually in highly related product markets. Many of these links are non-vertical and related to asset complementarities or scope economies.

– Problem: many mergers classified as vertical might in fact be (A) same industry mergers or (B) economies of scope/asset complementarity driven mergers, and these are the real economic drivers.

– Solution is controls: Industry classifications are networks in their own right.

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Dynamic Tests 1Even more ambitious. Authors now try to predict future activity.The paper does NOT use ERGM here.– Why not?– Goal is still to explain merger activity as a function of network properties

(this time lagged properties).– Need ERGM at the firm level to examine own-industry mergers.

[computer should handle this]– Many benefits: can also assess dynamics of cross industry

The Metropolis Hastings Algorithm noted in Handcock, Hunter et al (2008) can be used to model “ a dynamically changing network over time”.

Dynamic Tests 2: Propagation

Authors predict that – Nearby industries positively spill over next year. BUT THEN

NEGATIVELY spill over two years later. – Distant industries negatively spill over next year. BUT

THEN POSITIVELY spill over two years later.

This requires that– Merger activity follows sinusoids “wave-like pattern”, and

not impulse response.

Impulse/Response has high activity

Followed by decay but not reversal

Sinusoids have below average periods following above average

ones

Is the aftermath of an economic shock more like impulse/response?

Dynamic Tests 2b: PropagationSuppose real world is that shocks have an impact of 2 units, which fades to 1, zero. Also

suppose full transmission. Distant Close Focal

t-1 2 0 0t 1 2 0 Authors are trying to predict focalt+1 0 1 2 at time t+1

Conclude: All you need to know is what is going on in the close industry at year t. So we expect the following variables to be significant.

Predict Authors Observe Distant Close Distant Close

t-1 0 0 + -t 0 + - +

Why: industry mismeasurement can cause leakage through other channels other than vertical network…or next page

Dynamic Tests 2c: PropagationSuppose same real world, but now some industries have 3 year

decay rate and some still have two year decay.– Examining transmission speed a fascinating research topic all its own. Does

labor intensiveness, asset tangibility drive speeds?

Predict Authors Observe Distant Close Distant Close

t-1 + 0 + -t 0 + - +

Why the negatives?

Dynamic Tests 2d: PropagationAuthors consider ongoing propogation, but how about a shock originating in the close

industry.

Year tDistant Close Focal

Suppose shock requires shrinkage of close industry. Focal and distant industries can benefit from buying assets due to complementarities.

Distant Close Focal

Predict Authors Observe Distant Close Distant Close

t-1 + 0 + -t - + - +

Main point: dynamics deeper than sinusoidal patterns. Separate paper?

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Dynamic Tests 3: CentralityWhich industries are central?– Are big industries most central? Industry size can relate to aggregate

merger waves too, especially if other industries are not so far in the product space.

– To know which industries are central will identify alternative explanations (other than vertical motives).

What about terminal nodes (industries that only serve consumers or government)– Although they are dead ends for merger flow, terminal nodes in the

network are not really dead ends economically. These firms interact more with consumers, and are thus closer to aggregate consumption, a factor with strong theoretical aggregate market links.

– If central nodes using one direction on the supply chain are terminal nodes using the other direction, activity in these nodes has a potentially different link to aggregate merger waves through a real channel possibly related to liquidity as in Harford (2005).

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Conclusions1 Technology used by paper is excellent, but is under-used.2 Potential payoffs from this research are right-skew. But more development needed, and more links to theory.3 Need to control for other industry linkages and examine robustness to industry mismeasurement (network leakage).4 Dynamics, propagation, and speed of propagation a very deep question.5 Some items here could be another paper, or alternatively the paper can be split into static and dynamic parts with deeper focus on each.