vu pham capital and labor reallocation within firms holger m. mueller nyu, nber, cepr, & ecgi

Vu Pham

Capital and Labor Reallocationwithin Firms

Holger M. MuellerNYU, NBER, CEPR, & ECGI

Mueller Keynote St. Gallen Capital and Labor Reallocation within Firms 2

Roadmap

• Part I: Internal Capital Markets and the Boundaries of the Firm

• Part II: Empirical Evidence

• Part III: “Capital and Labor Reallocation within Firms”

• Part IV: What next?


Part I: Internal Capital Markets and the Boundaries of the Firm


• Coase (1937): “If production could be carried out without any organisation at all, well might we ask, why is there any organisation?”

• Why are there firms?• Why are certain transactions carried out within firms and others between firms?• What determines the boundaries of the firm?

• Coase (1937): transaction costs.• Difficult to write and enforce fully contingent contracts that specify what should happen

in all future situations (“incomplete contracts”).• Firms emerge in response to this inefficiency because a firm’s owner can simply direct

employees what do.• What exactly are the (transaction) costs of contractual incompleteness?

Internal Capital Markets and the Boundaries of the Firm


• Williamson (1975, 1985), Klein, Crawford, and Alchian (1978): hold-up problem.

• Incomplete contracts give rise to ex-post opportunism when there are relationship-specific investments.

• Fear of ex-post expropriation discourages contracting parties from making ex-ante relationship-specific investments.

• How exactly is hold-up behavior mitigated within a firm?

• Grossman and Hart (1986), Hart and Moore (1990) (GHM): property-rights approach.

• Bargaining power in ex-post renegotiation derives from asset ownership. Owner has residual control rights over asset and right to exclude others from its use.

• Asset ownership affects incentives to make ex-ante relationship-specific investments.• Party whose investment is relatively more important should own asset. Protection

against ex-post hold-up by other party.• Is GHM model a good way to think about real firms?



• Critique of GHM.• GHM model implies that individual managers should own productive assets.• In reality, however, firms—not individual managers—own assets!• “[T]his model, despite its express objective to explain the boundaries of the firm, fails

to do so, at least if the model is interpreted literally. The model offers a theory of individual ownership of assets, that is, how control over assets should be distributed among individuals, but it does not explain why firms own assets” (Holmström, 1999).

• “[I]t is not so clear how one would use this model to understand, for example, the acquisition by a large multidivisional firm of one of its suppliers. Managers don’t own their companies’ assets, though they may control their use. How then might we think about the boundaries of the firm when managers control assets but don’t own them?” (Bolton and Scharfstein, 1989).



• Reality check: control over assets resides with headquarters (HQ):• … even though HQ itself is not the owner of the assets; it is merely given authority

by the firm’s owners (i.e., shareholders),• … even though HQ makes no relationship-specific investments.• “The Grossman-Hart Moore framework … predicts that control should be allocated to

parties whose relationship-specific investments are most important to the relationship. Yet headquarters is given control, even though it does not really make such investments” (Bolton and Scharfstein, 1989).

• (Realistic) theory of firm boundaries:• Centralized decision making under HQ.• HQ, while not owner, has effective control over firm, including assets.• Can control divisions’ investment, giving more funds to some and less to others.• External lender, such as a bank, cannot prevent firm from going to another bank; nor

can it redistribute funds across borrowers.



• “Capital-allocation-centric” view on question of firm boundaries (Alchian, 1969; Williamson, 1975; Stein, 1997):

• “Loosely speaking, a collection of assets should optimally reside under the roof of a single firm to the extent that the firm’s internal capital market can do a more efficient job of allocating capital to these assets than would the external capital market, if the assets were located in distinct firms” (Stein, 2003).



• Why might HQ do a better (or worse) job of allocating capital to projects than external capital market?

• HQ authority can simply ⇒ dictate efficient reallocation, while bank cannot.

• HQ authority stronger monitoring incentives (Alchian, 1969; Williamson, 1975; ⇒Gertner, Stein, and Scharfstein, 1994; Stein, 1997).

• HQ authority threat of resources being taken away weakens managerial ex-ante ⇒incentives (Aghion and Tirole, 1997; Brusco and Panunzi, 2000).

• HQ authority vulnerable to lobbying & rent-seeking may distort efficient ⇒ ⇒allocation (Milgrom, 1988; Meyer, Milgrom, and Roberts, 1992; Scharfstein and Stein, 2000; Rajan, Servaes, and Zingales, 2000).

• Additional benefit of integration:• Coinsurance across imperfectly correlated projects improves debt capacity and

relaxes financing constraints (Lewellen, 1971; Stein, 1997; Inderst and Mueller, 2003).



• Benefits of Integration:

• “Smarter-money” effect: i) HQ has incentives to become well informed about prospects of individual projects; ii) HQ uses this high-quality information to make value-enhancing reallocations across projects (“winner-picking”).

• “More-money” effect: more external financing than could be raised by projects operating as stand-alone entities. Beneficial if there is underinvestment.

• Costs of integration:

• Less efficient capital allocation: i) powerful but ill-informed HQ; ii) authority to reallocate resources makes HQ vulnerable to rent-seeking may favor weaker ⇒projects (“socialism”).

• Weaker managerial incentives.

Summary Part I


Part II: Empirical Evidence


• Diversification (or conglomerate) discount.

• Conglomerate firms trade at discount relative to portfolio of comparable stand-alone firms (Lang and Stulz, 1994; Berger and Ofek, 1995; Servaes, 1996; Lins and Servaes, 1999).

• Explanation: poor capital allocation due to agency problems within firms?

• Methodology.

• Diversification discount: difference between conglomerate firm’s Tobin’s q and weighted q of portfolio of “comparable” firms.

• Conglomerate’s Tobin’s q: market value of firm divided by replacement value of firm’s assets. Alternatively: divide by book value of debt and equity (“market-to-book”).

• “Comparable” Tobin’s q: for each conglomerate segment, compute mean Tobin’s q of single-segment firms operating in same 3-digit SIC code. Then take weighted average of these (imputed) segment qs.

Empirical Evidence


• Data issues (Compustat segment data).

• Firms self-report segment data and changes in number of segments may reflect changes in reporting practice, not changes in diversification. Hyland (1997): 25% of cases.

• Segments may span several industries. In 80% of cases, segment SIC code assigned by Compustat is not SIC code of segment’s largest industry (Villalonga, 2004).

• Using Census (BITS) data—which provides correct SIC code of establishments—Villalonga (2004) finds that conglomerate discount turns into a premium!

• Endogeneity of decision to become a conglomerate.

• Do conglomerates destroy value or are those firms that choose to form a conglomerate simply worse firms?

• Campa and Kedia (2002), Graham, Lemmon, and Wolf (2002), Villalonga (2004): “correcting” for endogeneity of decision to form a conglomerate makes discount disappear or even turn into premium!

Empirical Evidence


• Investment within conglomerate firms.

• Scharfstein (1998), Shin and Stulz (1998), Rajan, Servaes, and Zingales (2000): conglomerate firms overinvest (relative to stand-alone firms) in segments with low investment opportunities and underinvest in segments with good investment opportunities inefficient cross-subsidization (“corporate socialism”).⇒

• Chevalier (2000), Whited (2001): endogeneity of diversification decision. Conglomerate segments may not have same investment opportunities as stand-alone firms.

– Chevalier (2000): conglomerate divisions exhibit same investment behavior already in the years before they merge!

– Whited (2001): no difference between investment behavior of conglomerates and stand-alone firms after correcting for measurement error.

Empirical Evidence


• Sharper tests? Response to investment opportunity shocks.

• “Efficient ICM hypothesis:”

• “Thus, for example, if a company owns two unrelated divisions A and B, and the appeal of investing in B suddenly increases, the argument would seem to imply that investment in A would decline—even if it is positive NPV at the margin—as corporate headquarters channels relatively more of its scarce resources toward B” (Stein, 1997).

• Similarly, Shin and Stulz (1998) define an internal capital market to be efficient if “its allocation of funds to a segment falls when other segments have better investment opportunities.”

Empirical Evidence


• Lamont (1997): oil price shock of 1986. When oil prices decline (by 50%), integrated oil companies cut investment across the board, including investment in non-oil segments.

• 26 oil companies.• No shock to investment opportunities. Rather shock to liquidity.

• Khanna and Tice (2001): Wal-Mart’s entry into local markets between 1975 and 1996. Conditional on staying in market, investment by discount divisions of diversified firms becomes more sensitive to division profitability than investment by stand-alone discount retailers. Also, diversified firms transfer funds away from failing discount divisions.

• 25 discount divisions of diversified firms & 24 stand-alone discount retailers.

Empirical Evidence


• Shin and Stulz (1998): segment investment does not depend on investment opportunities (qs) of other segments inefficient internal ⇒capital markets.

• Industry qs used to measure segments’ investment opportunities.• Compustat segment data.• No “shock” to investment opportunities. Simple cross-sectional variation in segments’

industry qs.

• Maksimovic and Phillips (2002): segment’s growth varies positively (negatively) with other segments’ productivity if segment’s change in sales at industry level is lower (higher) than that of firm’s median segment efficient internal capital markets.⇒

• Census data, albeit aggregated at firm-industry (“segment”) level.• No “shock” to investment opportunities. Simple cross-sectional variation in segments’

industry sales.

Empirical Evidence


• Inconclusive Evidence.

• Diversification discount or premium?• Efficient or inefficient internal capital markets?

• Data/sample issues:

• Compustat segment data.• Small-sample industry studies.

• “Shock” to investment opportunities?• Only Khanna and Tice (2001) (small-sample industry study).

Summary Part II


Part III: “Capital and Labor

Reallocation within Firms”(Giroud and Mueller, JF, 2015)


• U.S. Census Bureau plant-level data.

• Large sample (almost 300,000 plant-year observations).

• Plausibly exogenous shock to plant-level investment opportunities.

• Relatively tight identification (e.g., plant FE, MSA x year FE).

• How does (positive) shock to investment opportunities at one plant of a firm affect investment and employment at other plants within same firm?

Capital and Labor Reallocation within Firms


• “Efficient ICM hypothesis:”• “Thus, for example, if a company owns two unrelated divisions A and B, and the appeal

of investing in B suddenly increases, the argument would seem to imply that investment in A would decline—even if it is positive NPV at the margin—as corporate headquarters channels relatively more of its scarce resources toward B” (Stein, 1997).

• This paper: “natural experiment” that is close in spirit to thought experiment in Stein’s quote.

• Suppose appeal of investing in a plant suddenly increases. Does investment (and employment) in other plants within same firm decline?

• “Sudden increase in appeal of investing in plant:” introduction of new airline route that reduces travel time between HQ and plant.

• Reduction in travel time makes it easier for HQ to monitor plant, acquire information, give advice, etc. raises marginal productivity of capital and labor makes investing in ⇒ ⇒plant more appealing.



• Giroud (2013, QJE): new airline route between HQ and plant leads to increase in plant-level productivity and investment, consistent with notion that investment in plant has suddenly become more appealing.

• This paper: use increase in plant-level investment as starting point and ask whether it leads to a reallocation of resources within the firm.

• Does investment at other plants within same firm decline?• Which other plants are primarily affected?• Does reallocation depend on whether firm is financially constrained?• Does firm as a whole benefit from reallocation (as predicted by efficient ICM

hypothesis)?

• Endogeneity of new airline routes.• Omitted local shocks could lead to both increase in plant-level investment and

introduction of new airline routes. Treatment defined by two locations: location of plant’s and HQ’s home airports can control for local shocks.⇒



Financially Unconstrained Firms


Financially Constrained Firms

Δ Investment = 0Δ Employment = 0

Firm level: Δ Σ Investment > 0Δ Σ Employment > 0Δ Σ Productivity > 0

OP1 OP2

HQ

TP

Δ Investment > 0Δ Employment > 0

TP

Δ Investment < 0Δ Employment < 0

Firm level: Δ Σ Investment = 0Δ Σ Employment = 0

OP1 OP2

HQ

TP

Δ Investment > 0Δ Employment > 0

TP

Δ Σ Productivity > 0


• Treatment: • Introduction of new airline route that reduces travel time between HQ and plant ⇒

increases appeal of investing in treated plant.

• “Other” plants:• Belong to same firm as treated plant.

• Difference-in-differences methodology:• Before versus after introduction of new airline route.• Treated/“Other” plants versus control plants.

• Control group:• Plants that have not (yet) been treated or have not (yet) been “other” plants. Staggered

introduction of new airline routes plants remain in control group until they become either ⇒treated or “other” plant.

• Financing Constraints:• FC dummy equals one if plant belongs to firm that is financially constrained in year prior to

treatment.




1985 (Before)

MEMATL

BOS

ORDJFK

1986 (After)

MEMATL

BOS

JFKORD



1985 (Before)

MEMATL

BOS

1986 (After)

ORDJFK

MEM MEMMEM

ATL

BOS

ORDJFKORD

JFK JFKORD


• Plant-level data:– Census of Manufactures (CMF), Annual Survey of Manufactures (ASM), Longitudinal

Business Database (LBD): all plants with at least 250 employees; smaller plants randomly sampled.

• Firm-level data:• Compustat. Link to Census plant-level data via Compustat-SSEL bridge.

• Airline data:• T-100 Domestic Segment Database, ER-586 Service Segment Data. All flights that have

taken place between any two airports in U.S.

• Sample period:• 1977 – 2005.

• Selection criteria:• Firms covered in Compustat. “Pure” manufacturing firms: plants in ASM/CMF account

for at least 90% of firm’s employees.• Final sample of 291,358 plant-year observations.



Dependent Variable: Investment Employment

KZ-Index WW-Index KZ-Index WW-Index

[1] [2] [3] [4] [5] [6]

Treated 0.010*** 0.025***(0.001) (0.004)

Other -0.001 -0.002(0.001) (0.003)

Treated × FC 0.008*** 0.008*** 0.019*** 0.019**(0.002) (0.003) (0.006) (0.008)

Treated × Non-FC 0.012*** 0.011*** 0.028*** 0.026***(0.002) (0.001) (0.005) (0.004)

Other × FC -0.002* -0.003 -0.006* -0.007(0.001) (0.002) (0.004) (0.006)

Other × Non-FC 0.000 -0.000 0.001 -0.000(0.001) (0.001) (0.004) (0.003)

Control Variables Yes Yes Yes Yes Yes YesPlant Fixed Effects Yes Yes Yes Yes Yes YesYear Fixed Effects Yes Yes Yes Yes Yes YesMSA × Year Fixed Effects Yes Yes Yes Yes Yes Yes

Observations 291,358 291,358 291,358 291,358 291,358 291,358R-squared 0.32 0.32 0.32 0.92 0.92 0.92



• Coefficient on “Treated x FC” implies an increase in capital expenditures at treated plant equal to 0.8% of capital stock.

• For FC firms, increase at treated plant is roughly of same magnitude as decline at other plants:• Investment increases by $186,000 at treated plant, while it declines by $179,000 at all

other plants combined.• Employment increases by 5 employees at treated plant, while it declines by 6 employees

at all other plants combined.

• Investment and employment move in same direction.• Capital and labor are complements in firm’s production function.• Can examine issue more directly by using capital-to-labor ratio as dependent variable.

Remains unchanged throughout (at firm level, at treated plant, at other plants).



• Average spillover effect is weak and (at best) marginally significant:

• Amount of resources needed to “feed” treated plant—and thus amount that must be taken away from other plants—is relatively modest.

• Amount is divided among many other plants, meaning average amount taken away from any individual plant is small.

• Spillover effect should become stronger if we focus on firms with relatively few other plants.

Dependent Variable: Investment Employment


[1] [2] [3] [4] [5] [6]

Treated 0.010*** 0.025***(0.001) (0.004)

Other -0.001 -0.002(0.001) (0.003)

Treated × FC 0.008*** 0.008*** 0.019*** 0.019**(0.002) (0.003) (0.006) (0.008)

Treated × Non-FC 0.012*** 0.011*** 0.028*** 0.026***(0.002) (0.001) (0.005) (0.004)

Other × FC -0.002* -0.003 -0.006* -0.007(0.001) (0.002) (0.004) (0.006)

Other × Non-FC 0.000 -0.000 0.001 -0.000(0.001) (0.001) (0.004) (0.003)



Dependent Variable:


[1] [2] [3] [4]

Treated × FC 0.008*** 0.008*** 0.019*** 0.019**(0.002) (0.003) (0.006) (0.008)

Treated × Non-FC 0.012*** 0.011*** 0.028*** 0.026***(0.002) (0.001) (0.005) (0.004)

Other × FC × (# Other Plants < Median) -0.004** -0.005** -0.011** -0.014**(0.002) (0.003) (0.006) (0.007)

Other × FC × (# Other Plants ≥ Median) -0.001 -0.002 -0.003 -0.002(0.002) (0.004) (0.006) (0.010)

Other × Non-FC × (# Other Plants < Median) 0.000 -0.001 0.001 0.001(0.002) (0.002) (0.006) (0.005)

Other × Non-FC × (# Other Plants ≥ Median) 0.001 0.000 -0.000 -0.001(0.002) (0.001) (0.004) (0.004)

Control Variables Yes Yes Yes YesPlant Fixed Effects Yes Yes Yes YesYear Fixed Effects Yes Yes Yes YesMSA × Year Fixed Effects Yes Yes Yes Yes

Observations 291,358 291,358 291,358 291,358R-squared 0.32 0.32 0.92 0.92

Investment Employment



• Another reason for weak significance is that average spillover effect is estimated with much noise.

• HQ may not uniformly “tax” all of firm’s other plants in same way: while some plants may experience large drop in resources, others may experience none.

• Examine which other plants are primarily affected by resource reallocation. Spillover effects should become stronger if we focus on these plants.

• Plant productivity (yes).• Peripheral vs. main industries (yes).• Same vs. different industries (no).• Acquired vs. own plants (no).• Proximity to HQ (yes).



• Aggregate effect at firm level?

• For FC firms, plant-level results suggest that increase in investment and employment at treated plant is of same magnitude as decrease at other plants aggregate (or net) effect at firm level should be roughly ⇒ zero.

• For Non-FC firms, no offsetting effect at other plants aggregate (or net) ⇒effect at firm level should be positive.




Dependent Variable:


[1] [2] [3] [4] [5] [6]

Treatment 0.002*** 0.004**(0.001) (0.002)

Treatment × FC 0.000 0.000 -0.000 0.001(0.001) (0.001) (0.002) (0.003)

Treatment × Non-FC 0.004*** 0.003*** 0.009*** 0.006***(0.001) (0.001) (0.002) (0.002)

Control Variables Yes Yes Yes Yes Yes YesFirm Fixed Effects Yes Yes Yes Yes Yes YesYear Fixed Effects Yes Yes Yes Yes Yes Yes

Observations 33,695 33,695 33,695 33,695 33,695 33,695R-squared 0.41 0.41 0.41 0.88 0.88 0.88

Investment Employment

Firm-Level Regressions: Investment & Employment


• “Efficient ICM hypothesis:”• While resources may be taken away from projects that are positive NPV at the margin,

they are channeled toward other projects whose investment prospects are even better.

• Alternative hypothesis: resource reallocation is inefficient, e.g., outcome of lobbying by managers of treated plant who suddenly find it easier to lobby for larger budget given that travel time to HQ is reduced.• Could explain why treated plant gains at expense of other plants (if firm is financially

constrained).• Examine effect on aggregate firm-level productivity (TFP) and profitability (ROC, OM).



Dependent Variable:

KZ-Index WW-Index KZ-Index WW-Index KZ-Index WW-Index

[1] [2] [3] [4] [5] [6] [7] [8] [9]

Treatment 0.003*** 0.004*** 0.003***(0.001) (0.001) (0.001)

Treatment × FC 0.002** 0.002** 0.003** 0.003** 0.002** 0.002*(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Treatment × Non-FC 0.005*** 0.004*** 0.006*** 0.005*** 0.004*** 0.004***(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Control Variables Yes Yes Yes Yes Yes Yes Yes Yes YesFirm Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes YesYear Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 33,695 33,695 33,695 33,695 33,695 33,695 33,695 33,695 33,695R-squared 0.51 0.51 0.51 0.61 0.61 0.61 0.65 0.65 0.65

TFP ROC OM

Firm-Level Regressions: Productivity & Profitability



• Other sources of funding?

• Plant-level results suggest that FC firms fund expansion at treated plant entirely by reallocating internal resources would not expect to see ⇒increases in other sources of funding.

• Non-FC firms do not reallocate internal resources would expect to see ⇒increases in other sources of funding.



Dependent Variable:

FC Index: KZ-Index WW-Index KZ-Index WW-Index KZ-Index WW-Index KZ-Index WW-Index

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

Treated -0.001 -0.000 0.001 0.000(0.001) (0.003) (0.001) (0.002)

Treated × FC 0.000 -0.000 0.000 -0.000 0.000 -0.000 -0.000 -0.000(0.001) (0.002) (0.004) (0.004) (0.002) (0.002) (0.003) (0.003)

Treated × Non-FC -0.002* -0.002 -0.001 -0.000 0.002 0.002* 0.001 0.000(0.001) (0.001) (0.003) (0.003) (0.001) (0.001) (0.003) (0.002)

Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesFirm Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesYear Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 33,695 33,695 33,695 33,695 33,695 33,695 33,695 33,695 33,695 33,695 33,695 33,695R-squared 0.30 0.30 0.30 0.28 0.28 0.28 0.10 0.10 0.10 0.14 0.14 0.14

Cash Equity Short-Term Debt Long-Term Debt

Firm-Level Regressions: Other Sources of Funding



• Resource reallocation within financially constrained firms:• Positive shock to investment opportunities increase in investment and employment ⇒

at treated plant and decline in investment and employment (of same magnitude) at other plants.

• Firm-wide productivity and profitability increase consistent with “efficient ICM ⇒hypothesis.”

• Potential “dark side” of internal labor markets.• Unless workers are transferred across plants—which is less likely if plants are located

far away from one another—results imply that treated plant hires new workers, while “other” plants are forced to lay off workers.

• Some workers are laid off not because their plant is doing poorly but merely because some other plant within same firm is doing relatively better.

• Layoff risk due to HQ engaging in “winner-picking” may explain why conglomerates pay higher wages on average (e.g., Schoar, 2002).

Summary Part III


Part IV: What Next?


• Do ICM create value?

• Giroud & Mueller (2015): ICM actively reallocate scarce resources, beneficial for firm as a whole (see also Khanna and Tice, 2001; Maksimovic and Phillips, 2002).

• How does this compare to external resource reallocation? “Capital-allocation-centric” view of firm boundaries: do ICM do a better job than external capital (and labor) markets? And if so, under what circumstances and why?

• Main challenge: endogeneity of decision to join or form conglomerate/multi-unit firm.

What Next?


• How should we think of “capital and labor reallocation” within a firm?

• Presumably, what gets reallocated are primarily budgets. These, in turn, determine the level of investment and employment at the division (or establishment or project) level.

• However, casual and empirical evidence suggests that, at least to some degree, employees are also physically reallocated across firm units.

• Employer-employee matched data (e.g., LEHD). See, e.g., Tate and Yang (2015).

What Next?


• ICM and Macroeconomics.

• If “shocks” are transmitted through ICM across industries or regions, what are the aggregate implications?

• Do ICM dampen or amplify business cycle shocks?• If geographic regions (e.g., counties, states) are connected through firms’ ICM, does this

result in stronger co-movements of regional business cycles?

What Next?


Thank You!

vu pham capital and labor reallocation within firms holger m. mueller nyu, nber, cepr, & ecgi

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