Turbulence, Firm Decentralization and Growth in Bad Times

Philippe Aghion∗, Nicholas Bloom†, Brian Lucking‡, Raffaella Sadun§, and John Van Reenen¶

October 11, 2019

Abstract

What is the optimal form of firm organization during “bad times”? The greater turbulence follow-

ing macro shocks may benefit decentralized firms because the value of local information increases (the

“localist” view). On the other hand, the need to make tough decisions may favor centralized firms

(the “centralist” view). Using two large micro datasets on decentralization in firms in ten OECD coun-

tries (WMS) and US establishments (MOPS administrative data), we find that firms that delegated more

power from the Central Headquarters to local plant managers prior to the Great Recession out-performed

their centralized counterparts in sectors that were hardest hit by the subsequent crisis (as measured by

the exogenous component of export growth and product durability). Results based on measures of tur-

bulence based on product churn and stock market volatility provide further support to the localist view.

This conclusion is robust to alternative explanations such as managerial fears of bankruptcy and changing

coordination costs. Although decentralization will be sub-optimal in many environments, it does appear

to be beneficial for the average firm during bad times.

JEL No. O31, O32, O33, F23Keywords: decentralization, growth, turbulence, Great RecessionAcknowledgments: We would like to thank the editors (Alex Mas and Neale Mahoney), two anony-

mous referees, Ufuk Akcigit, Laura Alfaro, Richard Blundell, Erik Brynjolfsson, Gabriel Chodorow-Reich,Bob Gibbons, Rebecca Henderson, Bengt Holmstrom, Caroline Hoxby, Guy Laroque, Eddie Lazear,Kristina McElheran, Antoinette Schoar, David Thesmar, Jean Tirole and participants in seminars in theAEA, UC Berkeley, Columbia, Harvard, MIT, Northwestern, Stanford and Toronto for helpful discus-sions. The Economic and Social Research Centre, European Research Council, Kauffman Foundation,National Science Foundation and Sloan Foundation have all provided generous funding.

Disclaimer: Any opinions and conclusions expressed herein are those of the authors and do notnecessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure thatno confidential information is disclosed.

∗College de France, LSE and Centre for Economic Performance,†Stanford University, Centre for Economic Performance, NBER and CEPR‡Stanford University.§Harvard University, Centre for Economic Performance, NBER and CEPR¶MIT, Centre for Economic Performance, NBER and CEPR

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1 Introduction

What makes firms more resilient to large negative macro shocks? A recent literature has focused on firms’

technological, financial and governance structures as possible factors affecting their ability to cope with

sudden negative changes in external conditions,1 but much less is known about the role of the internal

organization of the firm. This paper focuses on how a specific organizational aspect of a firm, namely the

extent to which decision-making is decentralized down from headquarters to plant managers, affects the

response to an economic crisis.

The optimal organizational response to a crisis is not a priori obvious. One common argument (the

“centralist” view) is that centralized firms are best equipped to survive a recession because of the importance

of decisive and coordinated action which, due to conflicting interests within the firm and the partial infor-

mation available to local units, may be best directed from corporate headquarters. An alternative “localist”

view is that recessions are periods of rapid change, and being decentralized provides firms with the necessary

flexibility and local perceptiveness needed to respond to turbulent business conditions.

Chandler (1962) vividly illustrates these conflicting views in his account of how the depression of 1920-21

affected Dupont — at the time one of the major US corporations. Dupont’s managers had quickly realized

that the company’s centralized organizational structure — which allocated a great deal of authority to

central functions at the expense of local product divisions — was a poor fit for the more volatile business

environment that had emerged in the early 1920s, especially in its recently established lines of consumer-

facing products, such as paints. However, it took several months, countless internal debates, and worsening

financial outcomes for Dupont’s executives to agree on what to do. Frederik W. Pickard, one of Dupont’s key

senior managers, called for the appointment of a “dictator,” [...] a single man with “absolute jurisdiction over

personnel and full authority to do what he could to meet the crisis.” What was needed, he insisted, was [...]

“decision and action and that you get from an individual and not from on organization [...].” Another senior

manager, H. Fletcher Brown, instead believed that decentralization would allow the company to better cope

with the crisis and allow the business to “adjust itself to present conditions.” Eventually, Brown’s views

prevailed and in September 1921 Dupont finally moved to a decentralized organizational structure, which

provided the [...] “head of each Industrial Department full authority and responsibility for the operation of

his industry, subject only to the authority of the Executive Committee as a whole.” This strategic choice

eventually allowed the firm to re-establish its prominence in both its core and peripheral businesses.2

1For example, see Aghion, Askenazy, Berman, Cette, and Eymard (2012) on technology; Chodorow-Reich (2014) on financialstructure and Alfaro and Chen (2012) or D’Aurizio, Oliviero and Romano (2015) on governance.

2The initial proposal to decentralize to product lines had been originally made by a committee of young managers early in1920, based on the rationale that this move would allow each division to best adapt to each individual condition. However,this first proposal had been strongly objected to by senior management and the President, who were concerned about losingthe efficiencies gained within each function over time. They instead proposed greater investments in better information andknowledge to be fed to central HQ. This internal debate continued – with some organizational compromises done in the meantime– until 1921 when the growing postwar recession of 1921 resulted in major losses on every product except explosives (their corebusiness at the time). Wide disagreements persisted until the decision to move to a multidivisional form was finally made in

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Similar contrasting views over how best to organize for “recovery and survival” have emerged more

recently following the Great Recession of 2009-08, with some proposing centralization as a way to respond

in a coordinated fashion to the crisis, and others instead emphasizing the benefits of decentralization as a

way to more swiftly adapt to changing conditions.3

To advance the study of these issues from selected case studies to larger and more representative samples

of firms, we create two new panel datasets with explicit measures of decentralization measured prior to the

Great Recession. One dataset, the World Management Survey (WMS) has firm level data across ten OECD

countries (France, Germany, Greece, Italy, Japan, Poland, Portugal Sweden, the UK and US). The other

dataset, the Management and Organizational Practices Survey (MOPS), is a plant4 level dataset which we

constructed in partnership with the US Census Bureau. We combine these datasets with firm and plant

performance data before and after the 2009-08 crisis.

From a theoretical perspective, there are at least two countervailing effects of decentralization on firm

performance during a crisis. On the one hand, a large negative shock is more likely to reduce the level

of congruence between the CEO (headquarters) and the plant manager – for example, tough decisions on

closing down projects and laying off staff may well be resisted by local managers. On the other hand, a

crisis can also increase turbulence/uncertainty, thus making local information more valuable. In this case,

decentralized firms suffer less than their more centralized counterparts in a crisis because local managers

can better understand and respond more quickly to the turbulent business environment than the central

headquarters. This result emerges from a wide class of models where higher turbulence and uncertainty

increase the value of local knowledge and the benefits of decentralization. The net effect of decentralization

on firm performance is thus theoretically ambiguous, a result which we discuss intuitively in more detail in

the next section (see Online Appendix A for technical details) building on Aghion and Tirole (1997).

In our empirical analysis we find compelling evidence that in sectors that were exogenously hit harder by

the global financial crisis, decentralized firms outperformed their centralized rivals in terms of their survival

chances as well as in their growth of sales, productivity, profits and market value. We use several measures

of the shock, including the actual changes in trade patterns (exports in an establishment’s industry by

country cell) and alternative designs to isolate exogenous shifters such as a pre-recession measure of product

1921. The crisis of 1920-21 also motivated the reorganization of General Motors under the guidance of Alfred Sloan, though inthat case the efforts were aimed at the creation of more efficient integration and coordination systems to guide the activity ofthe already largely centralized business units (Chandler, 1962).

3Gulati et al. (2010), for example, discuss how firms frequently – though not always successfully – resort to centralization toimplement faster and more extensive cost-cutting initiatives during recessions, but also emphasize the importance of “stayingconnected to customer need” during more turbulent times. The starkly conflicting advice that managers were getting in thedepths of the Great Recession is best exemplified by two 2009 articles, both published by the Economist Intelligence Unit. InJune 2009 they wrote in favor of decentralization during the crisis: “Companies have to deal with dramatically more uncertainty,complexity and ambiguity in the current recession. Success does not come from centralization. True flexibility arises when thosewho are closest to customers are empowered to respond to constant shifts in demand, preferences and attitudes.” Yet a fewmonths later in December 2009 the same publication supported centralization: “Firms should be centralizing their decision-making processes. [...] In a recession investments and other decisions are scrutinized more carefully by senior management anda greater emphasis is placed on projects that provide benefits across the enterprise rather than individual units.”

4We use the terms “establishment” and “plant” interchangeably throughout.

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durability (demand for durable goods falls more in recessions as consumers can postpone purchases) and a

Bartik-style instrument (following Mayer, Melitz and Ottaviano, 2008).

Importantly, we show that our empirical results are driven by the fact that the industries which had

the most severe downturns during the Great Recession also had the largest increase in turbulence.5 To

demonstrate this, we employ a novel industry level measure of turbulence, the rate of new product additions

and subtractions (product churn), which we built from the US Census of Manufactures ten digit product

data. As shown in Bernard and Okubo (2015), product churn rises sharply during recessions – in a crisis

establishments both destroy more existing products and also create more new products.6 Using this measure

on the US Census MOPS sample, we find that decentralization significantly protected establishments from

the downturn in industries which had a bad shock, and an increase in product churn. We validate these

results using an alternative measure of turbulence based on stock market volatility for both the MOPS and

the WMS. Alternative explanations of our results (e.g. reduced agency problems, financial conditions, lower

coordination costs and omitted variables) seem less consistent with the data. Finally, although organizational

change is slow (we show evidence of large adjustment costs), firms subject to big negative shocks appear

more likely to decentralize.

A drawback of this econometric approach is that it relies on weak exogeneity of lagged decentralization, an

identification condition that could be violated if there was a variable correlated with lagged decentralization

that had a differential effect on future firm growth in those industries hardest hit by the Great Recession.

We assess this issue in three ways. First, we use our rich micro data to include interactions of the negative

shock with a large number of firm and industry observables. We can draw on existing work examining the

determinants of decentralization in our data, as well as the wider empirical literature, when considering such

confounders. Second, because MOPS has multiple plants belonging to the same firm, we can exploit the

variation in growth across plants with different degrees of decentralization within the same firm. Third,

we run placebo analysis in the pre-Great Recession period. Our results are robust to all three types of

experiment. Of course, it would be ideal to have an instrumental variable for decentralization, but in the

absence of a randomized control trial, this is very challenging.

Overall, our paper suggests that the internal organization of firms may serve as an important mediating

factor through which macroeconomic shocks affect firm performance and, ultimately, growth. Importantly,

we are not claiming that decentralization is always the “best” form of firm organization. Our findings are

entirely consistent with rational, forward looking firms choosing their optimal degree of decentralization based5Bloom, Floetotto, Jaimovich, Saporta-Eksten and Terry (2018) shows a large variety of datasets that suggest that turbulence

and uncertainty rise in downturns.6Contrary to Bernard and Okubo (2015), Broda and Weinstein (2010) report a pro-cyclical product churn. However, they

have a a very different focus – looking at the net change in the product offering in retail stores (the number of new bar codeproducts sold less current products no-longer sold) – and a different time period (1994 and 1999-2003) spanning one mildrecession. In contrast, our measure is gross product churn (new products plus dropped products), is built on manufacturingestablishment production data, and spans 15 years from 1997-2012, exploiting aggregate and industry variation.

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on their expectations of the (stochastic) economic environment. Firms will choose different optimal degrees of

decentralization as they face different environments (e.g. they operate in different parts of the product space)

and will have different histories of past idiosyncratic shocks. When an unexpected large negative shock occurs,

such as the global financial crisis, adjustment costs over decentralization prevent firms from immediately

shifting to the new optimal organizational form.7 Over time, if the shock has a permanent component, firms

will organizationally adjust to the new optimal structure. Hence, such unexpected shocks combined with

non-trivial adjustment costs can help reveal if there is an empirical regularity that decentralization is an

advantage in bad times.

Related Literature. Our paper builds on an extensive prior literature. The benefits of exploiting local

knowledge harks back to a classic economic debate over economic systems between Lange (1936) and Von

Hayek (1945). Lange argued that a centralized socialist economy would outperform a decentralized market

economy, partly because the central planner could co-ordinate better, for example by setting prices to inter-

nalize externalities. By contrast, Hayek argued that it was impossible to aggregate all the local knowledge of

agents, and it was more efficient to allow individuals to make their decentralized choices based on the their

local information. Modern organizational economics builds upon these trade-offs within a firm rather than

across the economy as a whole. On the theory side, our paper relates to the literature on decentralization

within the firm (see Gibbons, Matouschek and Roberts, 2013, or Garicano and Rayo, 2016 for recent surveys)

and incomplete contracts (see Gibbons and Roberts, 2013). In particular, Hart and Moore (2005) analyze

the optimal allocation of authority in multi-layer hierarchies. Dessein (2002) analyzes how the allocation of

control can help incorporate the agent’s information into decision-making in a situation where the agent has

private information.

Our paper also relates to the existing empirical literature on the determinants and effects of decentraliza-

tion. Rajan and Wulf (2006) and Blundell et al. (2016) document a movement towards flatter organizations

and decentralized firms in the US and UK respectively. Caroli and Van Reenen (2001) and Bresnahan,

Brynjolfsson and Hitt (2002) point at positive correlations between decentralization and both human capital

and information technology. Closest to our analysis is Acemoglu et al. (2007), whose model assumes firms

can learn about the outcome of an investment decision from observing other firms. Hence, in sectors with

more heterogeneity/turbulence or where the firm is closer to the technological frontier (so that learning is

more limited) decision-making control should be more decentralized. In the contract literature, Prendergast

(1982) suggested that the “puzzle” of performance pay in uncertain and turbulent environments (where higher

risk should make the agent less willing to accept a high-powered contract) could be because of the need to

exploit local information more effectively. Similarly, in the firm boundaries literature, Lafontaine and Slade

(2007) also suggest that a similar puzzle over the lack of a negative impact of turbulence on franchising (vs.7For evidence of high organizational adjustment costs see Cyert and March (1963), Gibbons and Henderson (2012) or Bloom,

Sadun and Van Reenen (2016).

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direct control), could again be related to the need to exploit the franchiser’s superior local knowledge, which

is more important in such environments. None of these papers, however, look at the interplay between firm

decentralization, shocks and turbulence which is the center of our analysis.8

There is also a growing literature on the empirical factors influencing decentralization within firms (see

the survey by Aghion, Bloom and Van Reenen, 2014, for example) including contributions by Guadalupe and

Wulf (2009), Katayama, Meagher and Wait (2016), Mcelheran (2014) and others. As noted above, previous

work has examined some of the factors influencing decentralization in earlier vintages of the WMS data (e.g.

Bloom, Sadun and Van Reenen, 2012) such as size, skills, technology, trust and ownership. To our knowledge

this is the first paper to analyze how the impact of negative shocks affects the future performance of firms

and plants with differential degrees of decentralization.

The rest of the paper is organized as follows. Section 2 presents a simple model to motivate the analysis.

Section 3 presents the data and methodology and Section 4 establishes our main empirical finding that in

times of crisis decentralized firms outperform their centralized counterparts. Section 5 considers extensions,

showing that volatility seems to matter rather than other mechanisms such as changing levels of congruence,

and Section 6 concludes. Online Appendices present the theoretical model (A) and discuss data (B) and

magnitudes (C) in more detail.

2 A simple model

To guide our empirical analysis of the relationship between firm performance and decentralization in bad

times, here we develop a simple model based upon Aghion and Tirole (1997). The key idea is that there is

a trade off between incentives and local information. Misalignment of interests between the CEO and plant

manager makes centralization seem natural. But the plant manager is likely to have better local information

than the CEO which is a force for decentralization. A negative shock which makes the environment more

turbulent will affect the returns to decentralization]in two opposing ways. First, it may heighten the costs

of decentralization by increasing the misalignment of interests between the CEO and the plant manager.

Second, it may boost the benefits of decentralization by increasing the informational asymmetry between

the CEO and the plant manager, and thus the value of local information.

2.1 The setup

We consider a one-period model of a firm with a single principal (the CEO/central headquarters) and a single

agent (the plant manager). The CEO cares about the profitability of the business whereas the plant manager8Bradley et al. (2011) report a positive relationship between firm independence – which they interpret as a proxy for greater

autonomy in resource allocation decisions – and firm survival during downturns using Swedish data.

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wants to maximize private benefits and is not responsive to monetary incentives.9 Taking an uninformed

action involves potentially disastrous outcomes, thus an action will be taken only if at least one of the two

parties is informed. Also, the agent obtains private benefits only if the firm remains in business.

There are n ≥ 3 possible actions (or projects) and at any point in time only two of them – call them a1

and a2 – are "relevant", i.e. avoid negative payoffs to the parties. Among these two actions, one maximizes

monetary profitability and one maximizes the agent’s private utility. Other actions lead to very negative

payoffs to both parties. With ex ante probability α the agent’s preferred action (conditional upon the firm

remaining in business) will also be the action that maximizes profits (or monetary efficiency). The variable

α captures the degree of congruence between the principal’s preferences and the agent’s preferences. If

preferences coincide, then the action that maximizes the private utility of the agent also yields monetary

utility B to the principal. If preferences do not coincide, the action that maximizes the agent’s private utility

yields monetary payoff B − k to the principal.

Informational assumptions: We assume that only the agent is informed ex ante about the projects’ pay-

offs and therefore can choose a course of action. Yet, the principal can obtain an early signal of forthcoming

performance, e.g. a current realization of income, at some cost C and then correct the choice of action if she

believes that the signal is due to the agent choosing the non profit-maximizing action.

Turbulence: Suppose that in the absence of turbulence, the signal reveals the bad action choice perfectly.

But the higher the degree of turbulence, the more difficult it is for the principal to infer action choice from

performance. More formally, suppose that current performance is given by: y = a + ε where a ∈ a1, a2

denotes the agent’s action choice (e.g. a decision whether or not to introduce a new product),10 with a1 < a2

and ε is a noise term uniformly distributed on the interval [−u, u] .

2.2 Analysis

The CEO will infer the action choice from observing the signal realization: y = a + ε if and only if y ∈

[a1 − u, a2 − u) ∪ (a1 + u, a2 + u]. In this case, the principal can correct the action if she has control rights,

i.e. the firm is centralized. By Bayes’ rule the probability of the CEO guessing the action choice is:

P (u) = Pr(y ∈ [a1 − u, a2 − u) ∪ (a1 + u, a2 + u]) = min 2(a2 − a1)

a2 − a1 + 2u, 1 (1)

9This insensitivity assumption is to rule out implementation of a performance pay contract to overcome the principal-agentproblem. Obviously, we could allow some incentive contracts and as long as these only partially deal with the agency problem,the mechanisms we describe here would still be at play.

10Equivalently, this could be whether to drop an existing product from the portfolio or to make an investment in marketingor sales that enhances the product’s value to the consumer. The key thing is that the decision has to have some irreversibility.

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The probability of guessing the correct action is clearly declining in the amount of noise parameterized by

u. Hence the probability Ω(u) that the profit-maximizing action will be taken eventually under centralization,

is equal to Ω(u) = P (u) + (1−P (u))α, where p is the probability that the principal acquires the information

about projects payoffs. The ex ante CEO’s payoff under decentralization, is:

Πd = αB + (1− α)(B − k)

The ex ante CEO’s payoff under centralization is:

Πc = Ω(u)B + [1− Ω(u)](B − k)− C (2)

Therefore, the net gain from centralization is then given by:

∆Π = Πc −Πd = P (u)(1− α)k − C. (3)

2.3 Two counteracting effects of a bad shock

We think of a bad shock as reducing congruence between the principal and the agent, to the extent that the

principal has invested her wealth in the project whereas the agent is subject to limited liability. In other

words, a bad shock increases k. For example, Gulati et al. (2010) mention that the threat of cost cutting

initiatives which often emerge in a recession end up building a sense of mistrust between local units and

CHQ, i.e. an increase in k in the model. For given level of uncertainty u, this will make centralization more

attractive as:

∂∆Π

∂k= P (u)(1− α) > 0.

There is, however, also much evidence that negative macro shocks are usually associated with greater

uncertainty (i.e. with a higher u), see Bloom et al. (2018) and cites therein. For example, in times of crisis

customers may alter their relative preferences for quality vs. value in ways that are hard to fully decipher

from CHQ in the short-run. This makes centralization less attractive since:

∂∆Π

∂u= (1− α)kP ′(u) ≤ 0.

If the level of turbulence u does not change after the occurrence of a bad shock, then the overall effect of

a bad shock is to make centralization unambiguously more attractive. However, if uncertainty increases with

a bad shock and k does not change, the bad shock makes centralization less attractive. Hence, the impact

of a bad shock is theoretically ambiguous. In the next sections we will empirically investigate which of the

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two mechanisms dominates.

3 Data Description and Measurement

We start by describing in some detail our decentralization data since this involved an extensive new survey

process. We then describe the accounting and administrative data matched with the survey-based measures

of decentralization and the proxies measuring the severity of the Great Recession. We describe our measures

of turbulence in Section 5 when we discuss theoretical mechanisms. More details on the data are in online

Appendix A.

3.1 Decentralization

Cross-country data: World Management Survey (WMS) Our international decentralization data

was collected in the context of the World Management Survey (WMS), a large scale project aimed at

collecting high quality data on management and organizational design across firms around the world. The

survey is conducted through an interview with a plant manager in medium sized manufacturing firms.

We asked four questions on decentralization from the central headquarters to the local plant manager.

First, we asked how much capital investment a plant manager could undertake without prior authorization

from the corporate headquarters. This is a continuous variable enumerated in national currency that we

convert into dollars using PPPs.11 We also inquired on where decisions were effectively made in three other

dimensions: (a) the introduction of a new product, (b) sales and marketing decisions and (c) hiring a new

full-time permanent shop floor employee. These more qualitative variables were scaled from a score of 1,

defined as all decisions taken at the corporate headquarters, to a score of 5 defined as complete power (“real

authority”) of the plant manager. In Appendix Table A1 we detail the individual questions in the same

order as they appeared in the survey. Since the scaling may vary across all these questions, we standardized

the scores from the four decentralization questions to z-scores by normalizing each question to mean zero

and standard deviation one. We then average across all four z-scores and then z-score the average again to

have our primary measure of overall decentralization. In the same survey we collected a large amount of

additional data to use as controls, including management practice information following the methodology of

Bloom and Van Reenen (2007) and human resource information (e.g. the proportion of the workforce with

college degrees, average hours worked, the gender and age breakdown within the firm).

We attempt to achieve unbiased survey responses to our questions by taking a range of steps. First, the

survey was conducted by telephone without telling the managers they were being scored on organizational

or management practices. This enabled scoring to be based on the interviewer’s evaluation of the firm’s11One reason that the main regressions control for size is that the value of this question might be mechanically greater for

larger firms and plants.

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actual practices, rather than their aspirations, the manager’s perceptions or the interviewer’s impressions.

To run this “blind scoring” we used open questions (i.e. “To introduce a new product, what agreement would

your plant need from corporate headquarters?”), rather than closed questions (e.g. “Can you introduce new

products without authority from corporate headquarters?” [yes/no]) (see question in Table A1). Second,

the interviewers did not know anything about the firm’s financial information or performance in advance

of the interview.12 Consequently, the survey tool is “double blind” - managers do not know they are being

scored and interviewers do not know the performance of the firm. These manufacturing firms (the median

size was 250 employees) are mostly privately held and too small to attract coverage from the business media.

Third, each interviewer ran 85 interviews on average, allowing us to remove interviewer fixed effects from

all empirical specifications. This helps to address concerns over inconsistent interpretation of responses.

Fourth, we collected information on the interview process itself (duration, day-of-the-week), on the manager

(seniority, job tenure and location), and on the interviewer (for removing analyst fixed effects and subjective

reliability score). These survey metrics are used as “noise controls” to help reduce residual variation.

We decided to focus on the manufacturing sector where productivity is easier to measure than in the

non-manufacturing sector. We also focused on medium sized firms, selecting a sampling frame of firms with

between 50 and 5,000 workers. Very small firms have little publicly available data. Very large firms are likely

to be more heterogeneous across plants. We drew a sampling frame from each country to be representative

of medium sized manufacturing firms and then randomly chose the order of which firms to contact.

Each interview took an average of 48 minutes and the main wave was run in the summer of 2006. We

achieved a 45% response rate, which is very high for company surveys, because (i) the interview did not

discuss firm’s finances (we obtained these externally); (ii) we had the written endorsement of many official

institutions like the Bundesbank, Treasury and World Bank, and (iii) we hired high quality MBA-type

students. We also ran some follow up surveys in 2009 and 2010 following the same firms sampled in 2006 to

form a panel which we use to look at changes in decentralization.

U.S. Census data: Management and Organizational Practices Survey (MOPS) The 2010 Man-

agement and Organizational Practices Survey (MOPS) was jointly funded by the Census Bureau and the

National Science Foundation as a supplement to the Annual Survey of Manufactures (ASM). The design

was based on the World Management Survey and was mailed to the establishment plant manager (see Bryn-

jolfsson and McElheran 2016 and Bloom, Brynjolfsson, Foster, Jarmin, Patnaik, Saporta-Eksten and Van

Reenen, 2019). The survey contained six questions on decentralization with four of these covering the same

domain as WMS – plant manager autonomy over (a) capital investments, (b) hiring of full time employees,

(c) product introduction and (d) sales and marketing – with two additional question on e) pay increases12This was achieved by selecting medium sized manufacturing firms and by providing only firm names and contact details to

the interviewers (but no financial details).

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of at least 10%, and (f) product pricing decisions. For each question, respondents were asked to choose

among three options capturing where the specific decisions were made: “only at this establishment” (coded

as 3), “only at headquarters” (coded as 1), or “both at this establishment and at headquarters” (coded as 2).

There were five choices for the question on autonomy in capital investments, starting with “Under $1,000”

(coded as 1) up until “$1 million or more” (coded as 5). Each of these six questions was then z-scored, and

then averaged, and then z-scored again. The survey also included management practice questions and some

background questions on the establishment and respondent.13 The respondent was asked about conditions

in 2010 and 2005.

The MOPS survey was sent to all ASM establishments in the ASM mail-out sample. Overall, 49,782

MOPS surveys were successfully delivered, and 37,177 responses were received, yielding a response rate of

78%. The Organization Module of MOPS is only for plants where headquarters is off site - plants with

headquarters on site are told to skip this section - which takes the sample to about 20,000 plants. We further

require the sample to match to the 2006 ASM and 2009 ASM to calculate the main dependent variable

(growth in sales) which brings the sample down to 8,800 plants.14 Table A2 shows how our various samples

are derived from the universe of establishments.

3.2 Accounting data

Cross-country WMS data We build firm level measures of sales, employment, capital, profits, market

value and materials using accounting data extracted from Bureau Van Dijk’s ORBIS. These are digitized

versions of company accounts covering very large samples (close to the population in most of our countries)

of private and publicly listed firms. In our baseline specifications we estimate in three-year (annualized)

growth rates. We are able to build firm level measure of sales growth for at least one year for 1,330 out of

the 2,351 firms with decentralization data in 2006.

U.S. MOPS data In addition to our decentralization data, we also use data from other Census and non-

Census data sets to create our measures of performance (growth in sales, productivity, and profitability).

We use establishment level data on sales, value-added and labor inputs from the ASM to create measures of

growth and labor productivity. As described in more detail in Appendix A, we also combined the plant-level

capital stock data from the Census of Manufactures with investment data from the ASM and applied the

perpetual inventory method to construct annual capital stocks. Finally, we measure plant profitability using

profits as a percent of capital stock, with plant-level profits defined as sales less total salaries and wages,

material costs, and rental expenses.13The full questionnaire is available on http://www.census.gov/mcd/mops/how_the_data_are_collected/MP-

10002_16NOV10.pdf.14The ASM is a stratified randomly sampled rotating 4 year panel, so many plants are not included across panels, which

accounts for over 90% of this drop in sample size

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3.3 Measuring the Great Recession

Our baseline measure of the intensity of impact of the Great Recession (“SHOCK”) at an industry by country

cell level comes from the UN COMTRADE database of world trade. This is an international database of

six-digit product level information on all bilateral imports and exports between any given pairs of countries.

We aggregate COMTRADE data from its original six-digit product level to three-digit US SIC-1987 level

using the Pierce and Schott (2010) concordance. We deflate the industry and country specific export value

series by a country and year specific CPI from the OECD to measure “real exports.”15One concern is that

declining export performance could be due to an industry-country specific supply (rather than demand)

shock. We are able to check this by considering a Bartik style IV where we predict the change in exports for

an industry-country pair using pre-recession data on the HS six digit commodity by country export patterns

interacted with the subsequent country-level growth in demand (following Mayer, Melitz and Ottaviano,

2016).

For the U.S. MOPS data we are able to construct a more detailed “SHOCK” variable which varies at

the establishment level. Specifically, we use pre-recession product level revenue data from the 2006 ASM

to measure each establishment’s distribution of sales across 7 digit NAICS products before the onset of the

Great Recession. We then aggregate the Longitudinal Firm Trade Transactions Database (LFTTD), which

contains the universe of import and export transactions for U.S. firms, to the product-year level. By matching

each establishment’s pre-recession distribution of sales across products to product level export growth, we

are able to obtain a more precise measure of the intensity of the Great Recession which measures export

growth in the products which the establishment produces. All results from the U.S. MOPS data use this

establishment specific formulation of the “SHOCK” measure.16 The plant-specific shock is advantageous in

that it addresses an important potential bias arising from mismeasurement of the relevant economic shock

for diversified plants. To the extent that diversification of product mix is correlated with decentralization,

using an industry level shock introduces non-random measurement error and may bias the results. Our

plant-specific shock built from plant-product data addresses this concern.

Figure A1 shows the evolution of annualized export growth in the years preceding and during Great

Recession using industry level data for all countries (for a total of 5,641 manufacturing sector by country

cells). Exports were growing by about 13% in 2007 and 9% in 2008, and experienced a dramatic fall (-20%)

in 2009 compared to 2008. Industry sales fell even faster than exports in 2008 and 2009. In the empirical

analysis, we build empirical proxies for the Great Recession by averaging 2007 and 2006 (pre-recession)

and 2009 and 2008 (in-recession) levels and calculate log differences between the two sub-periods for each15We find similar results using other measures of the shock (such as industry sales derived from aggregating firm level data in

ORBIS), but trade data is attractive as it has a large external component driven by demand in world markets and is availableat a detailed level for every country and industry in our sample.

16All of the MOPS results are robust to using the same three-digit SIC “SHOCK” variable which is used in the cross-countryWMS analysis.

12

three-digit industry by country cell.17

Since recessions typically have a greater impact on reducing the expenditure on durable versus non-

durable goods (e.g. King and Rebelo, 1999), we use as an alternative variable to capture the intensity of

the Great Recession shock the average durability of the goods produced in the industry, drawn from Ramey

and Nekarda (2013). This combines data gathered by Bils and Klenow (1998) with information from the Los

Angeles HOA Management “Estimating Useful Life for Capital Assets” to assign a service life to the product

of each four-digit industry. As a cross-sectional measure this is simply used at the 4-digit industry level, and

is a continuous measure.18

3.4 Descriptive Statistics

Panel A of Table 1 contains some descriptive statistics from the WMS. The median (average) firm has 250

(574) employees and $67m ($184m) in sales. Firm sales declined by about 6% per year over this time period

(2011-2006). Panel B has the equivalent information from MOPS. Despite being a quite different sample, the

values look broadly comparable - MOPS firms are a little larger in terms of jobs (423 vs 250 at the median).

MOPS plants shrank by 7% a year, similar to the WMS average. Exports fell in 51% of the industries in

the sample. While the median growth rate of real exports across the whole sample is about -0.4% and -0.8%

in the WMS and MOPS samples, respectively, the data shows considerable variation both within and across

countries.

In Bloom et al. (2012) we show that the WMS decentralization measure is correlated with other decen-

tralization indicators from different datasets at the country level. MOPS allows another sense check as it

contains information across multiple plants of the same firms. If our decentralization measure is meaningful

we would expect managers to be making different decisions in different plants and therefore there would be

greater across-plant/within firm variation of inputs (and outcomes). In Table A15 we confirm that more

decentralized firms do display a greater dispersion in input decisions (jobs and products) and outputs across

their establishments. For example, regressing the standard deviation of plant-level jobs growth within a firm

on the firm’s average decentralization reveals a positive and significant correlation.17We also run robustness checks using discrete measure of SHOCK, in which we code an industry-country cell to be unity

if exports fell over this period and zero otherwise.18We also consider a discrete version using a dummy equal to 1 if the durability in the industry is greater than the median

(and zero otherwise).

13

4 Main results

4.1 Descriptive analysis of the main result

Our main empirical finding is illustrated in Figure 1, in which Panel A refers to the results using the cross

country WMS data, and Panel B uses the US MOPS data. Panel A shows the annualized average three-year

growth rate in sales for all firms included in the WMS decentralization sample computed using data ending

in the years 2011, 2010 and 2009 (hence, averaging across three different growth periods: 2011-08, 2010-07

and 2009-06).19 These are all years involving the Great Recession.20 Panel B shows sales growth for all

plants in the MOPS decentralization sample (2009-06 growth rate). We exclude the 2011-08 and 2010-07

periods from the MOPS sample because the recession was over in the US in 2010.21

The sample in Figure 1 Panel A is subdivided in four categories of firms. First, we split firms according to

whether they experienced a drop in exports in an industry by country cell in the main Great Recession years

(the 2008 and 2009 average) compared to the latest pre-recession years (2006 and 2007 average).22 Second,

we split firms by above/below the mean level of decentralization measured before the advent of the Great

Recession. Not surprisingly, all our groupings of firms experienced a drop in average sales and furthermore,

the drop in sales is clearly (and significantly) larger for firms classified in industries experiencing a negative

export shock (compare the two bars on the right with the two on the left). However, within the group of firms

experiencing a negative shock (those on the right of the figure), the decline in sales was significantly larger for

firms that were more centralized prior to the recession. In the WMS sample, for firms in an industry-country

pair hit by a greater negative shock, decentralized firms had a 8.2% fall in sales compared to about 11.8% in

the centralized firms, for a difference of 3.6 percentage points which is significant at the 5% level (compared

to an insignificant difference of -0.1% in industries that did not experienced a shock). Panel B of Figure 1

performs the analogous exercise on the MOPS sample of US establishments. The difference in differences is

very similar at 3.5 percentage points, also significant at the 5% level.

The performance differential between decentralized and centralized firms appears confined to the crisis

period. Using the same four categories as in Figure 1, Figure 2 plots the difference in sales growth between

decentralized and centralized firms (or plants), again distinguishing between those which experienced a drop

in exports in an industry by country cell during the Great Recession years, including the years before and

after the Great Recession. As before, the y-axis is the annualized three year growth rate in sales, with the

year 2010, for example, corresponding to the 2010-07 growth rate. In both the WMS sample in Panel A and19We use long differences to smooth over some of the transitory measurement error. The results are robust to choosing

alternative methods of long differencing.20We also test the robustness of the results to dropping the 2008-2011 period, in which the Recession was starting to taper

off in Europe.21In Europe (where most of our WMS data is from) the crisis persisted due to the Eurozone currency crisis and fiscal austerity

policies.22To be precise we first divide the value of nominal exports by a country and time specific CPI. We then construct average

real exports in (i) 2009 and 2008 and (ii) 2007 and 2007. We then take the log difference between these two periods.

14

the MOPS sample in Panel B, decentralized firms (plants) and centralized firms (plants) have similar sales

growth rates in the pre-recession periods (before 2008), regardless of whether they subsequently experienced

a decline in exports during the Great Recession (to see this, note that the two lines in each panel do not

diverge until 2007). The performance differentials between decentralized and centralized firms (plants) in

industries hit by the Great Recession start to emerge in 2008, and converge in both datasets after roughly

five years.23

The basic finding emerging from the raw data is that decentralization was associated with relatively better

performance for firms or establishments facing the toughest environment during the crisis. Moreover, the

improved performance associated with decentralization is unique to the crisis period, as these firms (plants)

did not outperform their peers before the crisis, and temporary, as these firms (plants) do not appear to be

systematically outperforming their centralized counterparts after the crisis.24

We now turn to more formal tests of this basic result using alternative measurement strategies and

controls for many other possible confounders.

4.2 Baseline regression equation

Our baseline specification is:

∆ lnYijct = αDECi0 + β(DECi0 ∗ SHOCKjc) + γSHOCKjc + δxi0 + θc + φj + τt + εijct (4)

where ∆ lnYijct is the sales growth rate: the three year annualized change in ln(real sales) for firm (or plant)

i in industry j in country c in end-year t.25 DECi0 is firm (or plant) i’s level of decentralization (measured

in the initial year of 2006 for WMS and 2005 for MOPS); SHOCKjk is our measure of the severity of the

shock of recession in the industry-country cell; xi0 is a set of controls also measured pre-recession (firm and

plant size, survey noise and the proportion of college-educated employees); θc are country dummies, φj are

industry dummies, τt are year dummies and εicjt and is an error term. Standard errors are clustered at

the industry by country level, or just industry level depending on the variables used to proxy for the Great

Recession and the specific sample used. When we use export growth as a measure of the shock the key

hypothesis we examine is whether β < 0, i.e. whether decentralized firms and plants do relatively better23In the US MOPS data, although not in the cross-country WMS, centralized plants in 2012 experience a more rapid recovery

in the industries most affected by the Great Recession.24One might ask why should centralized firms not systematically outperform their decentralized counterparts in “good times”?

One reason related to the model in Appendix A is that although turbulence/uncertainty spikes in deep recessions (albeit todifferent degrees in different industries) it does not do so in other times (see Bloom et al, 2016, especially Table 2). A secondreason is that, although the Great Recession is a plausibly unexpected shock to which a firm’s optimal decentralization did notreflect pre-recession, industry growth trends were less unusual in the pre-crisis period so firm decentralization had already beenchosen endogenously to reflect these trends.

25As discussed above, for the long differences we are using the three overlapping time periods for WMS, but for MOPS wecan only use one of these long differences, 2009-2006. Hence for MOPS the time dummy is absorbed by the constant in theregression.

15

in bad times. When we use product durability as a measure of the magnitude of the shock the equivalent

hypothesis is that β > 0, as the more durable goods industries are expected to have (and do have) the largest

fall in demand.

Our underlying identification assumption in equation (4) is that in the pre-Great Recession period firms

were in an initial equilibrium where they had adopted their optimal degree of decentralization ( DECi0 )

based on their current and expected environment.26 The SHOCKjk associated with the Great Recession was

largely unexpected and, since organizational form is likely subject to large adjustment costs, firms could not

immediately respond by changing to the optimal form of organization (i.e. becoming more decentralized) in

the new environment. Thus, DECi0 can be considered weakly exogenous in equation (4). We investigate the

adjustment costs assumption by using repeat observations on decentralization for the same firms or plants

over time. We find decentralization to be highly persistent over the time in both the WMS and MOPS

samples.27 Note that our identification assumption does not require decentralized firms to have the same

observable and unobservable characteristics as centralized firms (they do not), but it does require that such

characteristics correlated with DECi0 are not solely responsible for generating better performance in those

industry-country pairs worst hit by the Great Recession. We present a battery of tests consistent with this

assumption including (i) running placebo analysis in pre-Great Recession period; (ii) using our rich micro

data to include interactions of the negative shock with a large number of firm and industry observables and

(iii) exploiting only the variation in decentralization and growth across plants within the same firm.

4.3 Baseline results

Sales Growth as an outcome Column (1) of Table 2 shows the results from estimating a simple specifi-

cation including export growth as our recession shock indicator and a full set of country, year and three-digit

SIC industry dummies. A one percent increase in industry exports is associated with a significant 0.07

percentage point increase in sales growth. We also find a positive and weakly significant association between

sales growth and lagged initial decentralization (in 2006). A one standard deviation increase in our decentral-

ization index is associated with a 0.58 percentage point increase in sales growth (e.g. growth increases from

say 2.0% a year to 2.6% a year).28 In column (2) we introduce an interaction term between decentralization

and the export shock variable. The interaction term is negative and significant (0.042 with a standard error

of 0.013), which indicates that decentralized firms shrank much less than their centralized counterparts when

they were hit by a negative export shock. Note that the coefficient on the linear decentralization term is26Formally, we do not need to assume fully optimizing behavior in the pre-period, only that DECi0 is weakly exogenous.27We estimate that the annual AR(1) coefficient on decentralization as 0.965 in MOPS and 0.707 in WMS. The true persistence

parameter is likely to lie between these as MOPS estimate is likely to be an over-estimate because of recall bias and the WMSis likely to be an underestimate because of classical measurement error. See Bloom, Sadun and Van Reenen (2016) for morestructural estimation of adjustment costs in WMS also showing high degrees of persistence of organizational form.

28Note that the growth rates of both firm sales and industry exports used throughout all regressions are multiplied by 100(i.e 1% is 1 not 0.01)

16

insignificant when the interaction term is added to the specification, which indicates that decentralized firms

did not grow significantly faster or slower in those sectors that had zero export growth.

The magnitudes of the coefficients are non-trivial. Consider a macro shock causing a 1% fall in exports.

The coefficients in column (2) of Table 2 suggests that the sales of an average firm (with mean decentralization

score of zero) will shrink three times as much as those of a decentralized firm (with a score one standard

deviation above the mean).29 Panel A of Figure 3 shows the implied marginal effect of decentralization on

sales growth as a function of export growth. These plots are obtained using the coefficients reported in

column (2) of Table 2. According to these estimates, decentralization has a positive association with sales

growth in all industries experiencing country-industry export growth below 8%. This corresponds to two-

thirds of the WMS sample in the post recession period, but only 12% of firms in the pre-recession periods

(this is shown in Panel B of Figure 3). In other words, the positive association between decentralization and

firm growth appear to be contingent on the wider demand conditions in the aggregate environment facing

the firm, which in turn may be one of the possible reasons for the heterogeneous levels of decentralization

observed in 2006. It is important to emphasize that we are not claiming that decentralization is always the

optimal form of firm organization – it is very much contingent on the different conditions that firms face.30

The recession shock measure is industry and country specific. Therefore, in column (3) of Table 2 we

include a full set of industry dummies interacted with country dummies, as well as a set of other firm

controls (measured in 2006). The linear export shock is absorbed by the industry by country dummies, but

we can still identify the interaction of the shock with initial firm decentralization. Even in this demanding

specification, the interaction between decentralization and the shock remains negative and significant.31

A possible concern with the estimates is that the SHOCK variable uses information dated over the same

period as the dependent variable, which may give raise to an endogeneity bias. Consequently, we test for

the robustness of the main results using as a proxy for the intensity of the Great Recession a measure of

the durability of the products in the four-digit industry calculated prior to the recession. We include a full

set of four-digit industry dummies to absorb the linear effects in column (4). Consistent with the earlier

results, the interaction between decentralization and the SHOCK is positive (since more durable industries

experienced greater drops in demand during the recession) and significant.32

29Assuming the effects were causal for illustrative purposes, the average firm will see a drop in sales of 0.062% (the coefficienton export growth) whereas the decentralized firm will see a fall in sales of just 0.020% (0.062 minus 0.042, the coefficient onthe interaction).

30In other work done using the WMS decentralization data (Bloom, Sadun and Van Reenen, 2012) we discuss other influenceson firm decentralization such as scale, human capital, complexity and culture. We exploit one source of this variation (culture,as proxied by trust) in an instrumental variable approach discussed below (footnote 38). We show robustness to the inclusionof proxies for scale, human capital and firm complexity in Tables A5 to A8.

31Other measures of the demand shock give similar qualitative results to using exports. For example, using industry outputbuilt from aggregating the ORBIS population data in the same way as exports (across the three digit industry by country cellbetween the 2009-08 and 2007-06 periods) generates a coefficient (standard error) on the interaction term of 0.060 (0.015).

32The specification in column (4) can be regarded as the reduced form of an IV regression where we use durability as aninstrumental variable for the shock. When we use decentralization*durability to instrument for decentralization*SHOCK inan IV specification on the sample in column (3), we obtain a coefficient (standard error)of -0.165 (0.052) on the decentraliza-tion*SHOCK interaction.

17

Columns (5) and (6) of Table 2 repeat the specifications of columns (3) and (4) using the MOPS sample.33

Remarkably, although drawn from a distinct dataset, a single country (US) and different survey methodology,

the results in this larger sample of plants are extremely similar to the ones reported using the cross country

WMS data. The coefficients on the interaction terms are of the same sign, statistically significant and of a

broadly comparable magnitude.

Other performance measures as outcomes The results discussed so far suggest the presence of a

positive relationship between firm and establishment sales growth and decentralization in the industries

most affected by the Great Recession. In Table 3 we explore whether this relationship persists even when

we examine Total Factor Productivity (TFP), i.e. we estimate the most general econometric model of Table

2, column (3) but also control for increases in other inputs such as employment, capital and materials on

the right hand side of the equation. As discussed in the introduction, some have argued that firms need

to centralize during crises, so tough cost controls and efficiency-enhancing measures can be driven down

throughout the company. This would imply that, although decentralized firms (or plants) may fare better

on protecting sales revenue during downturns, they will do worse in terms of productivity.

Column (1) of Table 3 reports the baseline results for sales growth on the subsample of firms with data on

factor inputs, while column (2) reports the productivity results.34 Decentralization is also significantly and

positively associated with an increase in TFP during a crisis.35 Column (3) uses the growth of profitability

(Earnings Before Interest and Tax divided by the capital stock) as the dependent variable and column (4)

uses the growth in Tobin’s Q (the ratio of the firm’s stock market value to the capital stock) as a more forward

looking, market-based indicator of firm performance. In both columns there is a negative coefficient on the

interaction although it is not significant at conventional levels. In column (5) the dependent variable is a

dummy for survival taking the value of zero if the firm exited to bankruptcy between 2007 and 2011 and one

otherwise (the regression is a Linear Probability Model, and the reported coefficients are multiplied by 100 for

readability). This shows that more decentralized firms also had a significantly higher probability of survival

in industries that were worse hit by the crisis. Columns (6) though (9) repeat the analysis using the MOPS

data and show even stronger results. The key coefficient on the interaction term between decentralization

and the shock is negative and significant for sales, productivity, profits and Tobin’s Q growth.36

33Note that the linear export shock in column (5) is not absorbed by the industry fixed effects as the MOPS export shockvaries at the plant level.

34The sample for the TFP regression is smaller due to missing data on some of the additional inputs needed for the productionfunctions specification (in many countries revenues are a mandatory item on company accounts, but other inputs such as capitalare not).

35The sum of the unreported coefficients on employment, capital and materials growth is about 0.9 suggesting decreasingreturns to scale (and/or market power). Measurement error may also be responsible for attenuating the coefficients on factorinputs towards zero. Note that if we calculate TFP as a residual using cost shares as weights on the factor inputs and usethis as the dependent variable (dropping the factor shares from the right hand side) are results are similar to those from theestimated production function.

36We have no exit data for MOPS as the survey was run in 2011 after the Great Recession, with our main results using therecall question on decentralization in 2005.

18

It is reassuring that Table 3, which uses more refined measures of firm performance that take inputs into

account, is consistent with Table 2, which uses sales growth. We continue to focus on sales growth on as

our baseline outcome as it is the simplest measure and is non-missing for most firms (TFP, for example,

also requires data on capital and employment), but note that our results are robust to these alternative firm

performance outcomes.

4.4 Turbulence: Product churn and stock market volatility

Our empirical findings strongly suggest that decentralization becomes more valuable in bad times. The

simple model in Appendix A suggests that one reason for this was that negative shocks may be associated

with greater turbulence (a higher u), which increase the benefits of local information. We now study whether

there is any direct evidence to support this idea.

Product Churn Our main measure of turbulence is changes in product churn in recession versus non

recession years as a proxy. Product churn is measured using data from the US Census of Manufactures

(CM). The CM, which is conducted in years ending in 2 and 7, asks manufacturing plants to list the value of

annual shipments by 10-digit product code. Plants receive a list of all the product codes typically produced

in their industry, along with corresponding descriptions of each code. Plants which produce products not

listed on the form are instructed to write in the appropriate product code.37We then measure the amount of

product churn at the plant level as the number of products added or dropped between the previous Census

and the current Census, divided by the average number of products produced in both Censuses. That is,

product churn for establishment i in year t is defined as:

Product Churn i,t =#Products Added i,t + #Products Dropped i,t

0.5 (# Products i,t + #Products i,t−5)∈ [0, 2] (5)

Our measure of industry product churn is the average plant level product churn among all plants within an

industry (three digit US SIC-1987) which produce at least 3 products. We restrict attention to plants with

at least three products in order to reduce measurement error from product code misreporting.38 Finally,

in order to measure the change in product churn by industry during the Great Recession, we calculate the

change in product churn from 2007 to 2012 as industry-level product churn in 2012 minus industry-level

product churn in 2007 (constructed from the 2007 and 2002 Censuses). 39

37The ASM also has a 10-digit product trailer, but the question is formulated in a way that results in less detailed responsesthan the 5-yearly CMF question, so we use the CMF to measure churn.

38Establishments which produce the same portfolio of products in consecutive Censuses but misreport a product code inone year will be incorrectly measured as having switched products. Product code misreporting is particularly problematic forestablishments with 1 or 2 products, for whom a single reporting mistake would result in very high measured product churn.Our results are robust to using industries with plants with a lower cut-off of 2 or more products or a higher cut-off of 5 or moreproducts.

39Note that the measure is based on plants who survived between Census years. We also constructed an alternative measurethat included plants which died and entered between Census years in the construction of equation (5). This broader measure

19

Before examining the relationship between sales growth, decentralization and turbulence (as measured by

product churn), we first examined whether decentralization really was greater in industries where turbulence

was higher. Figure A2 shows that this is indeed the case: plants in the top quintile of product churn

industries had a decentralization index about 0.2 of a standard deviation higher than those in the bottom

quintile. More formally, Table A11 finds a positive and significant relationship between decentralization

(the dependent variable) and product churn, particularly for decentralization of decisions regarding product

introduction and sales and marketing, as the theory would suggest. Furthermore, we checked whether

product churn had indeed increased more in (i) industries that experienced a larger drop in exports during

the Great Recession or (ii) operated in industries that produced in more durable goods industries. This is

also the case in the data, as shown in Panel A of Figure A3.

To investigate the empirical validity of the turbulence-based theoretical mechanism, we extend our basic

equation (4) to include both the change in CHURN and also its interaction with decentralization

∆ lnYij = αDECi0 + β(DECi0 ∗ SHOCKj) + γSHOCKj (6)

+η∆CHURNj + µ (DECi0 ∗∆CHURNj) + δxi0 + φj + τt + εij

where ∆CHURNj is the change in churn in industry j (since we estimate this regression model only in

the US MOPS sample we omit the country sub-script). According to the model µ > 0 , since churn increases

the value of decentralization. Moreover, to the extent that our export shock variable is proxying for rising

turbulence during recessions, we would also expect β to drop in magnitude in equation (6) compared to

equation (4).

Table 4 shows the results of this exercise.40 In column (1) we estimate the specification in column (4)

of Table 2 for the subset of establishments for which an industry level measure of product churn could

be built. This has similar results to the overall sample, i.e. the coefficient on the interaction DECi0 ∗

SHOCKj is negative and statistically significant. Column (2) includes the DECio ∗∆CHURNj interaction

instead of the DECi0 ∗ SHOCKj interaction. In line with the model’s prediction, the coefficient on the

interaction with changes in product churn is positive and significant, i.e. sales growth appears to have a

positive association with decentralization in industries that experienced a greater increase in turbulence, as

proxied by product churn. Column (3) includes both interactions. The coefficient on the interaction between

decentralization and product churn remains positive and significant, while the coefficient on the interaction

between decentralization and growth in industry exports drops by a quarter in magnitude compared to

column (1) and is statistically insignificant.

led to similar results.40Since we are measuring churn 2012-2007 (our Census of Manufactures years) we use as our dependent variable the change

in ln(sales) between 2007 and 2012 which is why the sample is slightly smaller.

20

One concern with Table 4 is that we are assuming that an increase in product churn causes all establish-

ments to experience an increase in turbulence. It may be that churn matters much less for some firms than

others, as churn is a weaker signal of the true increase in uncertainty (which our theory suggests increases

the benefits of delegation) for some firms than others. In Appendix Table A3 we investigate this by showing

that product churn matters more for decentralized firms when they operate in younger (as measured by the

entry rate) and more product differentiated (as measured by Rauch, 1999) industries41 and (to some extent)

when they are smaller. This seems consistent with theoretical intuition.

An alternative measure of the shock is product durability. Panel B of A3 shows that durable goods

industries experienced a larger increase in product churn, consistent with the idea that downward demand

shocks are accompanied by increased turbulence. Columns (4) to (6) of Table 4 repeat the same specifications

as the first three columns, only this time using durability as an alternative industry level proxy for the

Great Recession. The coefficient on the interaction between decentralization and product churn is positive

and significant, and its inclusion again reduces the magnitude of the coefficient on the interaction between

decentralization and durability to insignificance.

Stock Market volatility Stock-returns volatility is a useful alternative measure of turbulence in that it

captures all changes in outcomes (or expectations) that impact the firm weighted by their impact of total

discounted profits. As such, for firms hit by a huge variety of shocks, stock-returns volatility is a useful

average measure of overall turbulence across a wide variety of sources. We measure the standard deviation

in monthly firm-level stock market returns in an industry by year cell over the population of publicly listed

firms in each country. This stock returns measure of volatility is similar to those used by Leahy and Whited

(1996), for example, as a measure of uncertainty. Indeed, in a stochastic volatility model based on Dixit

and Pindyck (1994) the variance of stock returns is monotonically (indeed almost linearly) related to the

volatility of the underlying driving process. These measures are then used in changes as an alternative proxy

for the increase in turbulence. In the US we pool at the three digit SIC level as there are about 2,000 publicly

listed firms. In the other OECD countries there are fewer publicly listed firms so we construct the measure

at the SIC 2 digit level. An advantage of this measure is that it is available for the WMS as well as the

MOPS, but a disadvantage is that it is constructed only from firms listed on the stock market (in the same

industry).

Table 5 shows the results. In column (1) we reproduce the specification in column (2) of Table 2.42 In

column (2) we use the interaction between decentralization and the change in the standard deviation of stock

market returns instead of our usual interaction. As expected from the theory, the coefficient is positive and41We use the concordance in Salas (2015) to map Rauch’s measures to the US manufacturing codes we use in the Census

data.42The only difference is that we are using two-digit dummies instead of three-digit dummies to match the level of aggregation

for the stock market volatility measures.

21

significant suggesting that decentralized firms outperform their centralized counterparts in industries where

stock market volatility has increased by most. In column (3) we include both interactions. The stock market

volatility interaction remains positive and significant whereas the coefficient on the export growth interaction

falls by a third in magnitude and is now only significant at the 10% level. The next three columns reproduce

the same specifications using the MOPS data showing a qualitatively similar pattern.

Summary on Turbulence Taking Tables 4 and 5 together, it appears that decentralized firms did rela-

tively better in industries where turbulence increased. At least part of the reason why decentralized firms do

better in bad times appears to be because the industries worse hit by the Great Recession were also those

where turbulence also increased.

4.5 Magnitudes

In Table A16 we consider some simple calculations of cross-country magnitudes. Our thought experiment is

to consider the Great Recession as a global shock as reflected by a fall in trade. We use the US value of the

shock from COMTRADE of a fall in exports of 7.7 percent. This is the empirical difference between 2009-08

vs. 2007-06 that we use as our industry-country specific shock measure elsewhere in the paper.

We take the 2006 average levels of cross-country decentralization by country (column (1) of Table A16)

and the empirical estimates in column (2) of Table 2 to estimate the average annual implied effect of GDP

of the shock (column (2) of Table A16). We express this relative to the US in column (3). For all countries

except Sweden there is a negative relative implied effect because decentralization in the US is greater than

every other country except Sweden. Column (4) displays the actual annual change in GDP growth since the

start of the global financial crisis (from World Bank data) for each of our countries and then again expresses

these relative to the US base in column (5). Every country except Poland (which is still in a strong catch-up

phase of development) experienced a slower growth performance than the US over this period, averaging

just over a third of a percentage point (base of column). Column (6) divides the column (3) into column (5)

which is the fraction of relative economic performance accounted for by decentralization (note that since we

are assuming a common shock, none of this difference is due to the magnitude of the crisis being worse in

some countries than others).

Overall, column (6) of Table A16 suggests that an average of 15% of the post-crisis growth experience

between countries is accounted for by decentralization. This is non-trivial as mentioned in the text, but

it is worth noting that there is a large degree of heterogeneity between countries underneath this average.

Almost all of the differential growth experience of France and Japan compared to the US can be accounted

for by decentralization (96% and 95% respectively), whereas decentralization accounts for virtually none of

the UK’s performance. In particular, as noted above, because Sweden is more decentralized than the US we

22

should expect it to have outperformed the US, whereas it grew about half a percentage point more slowly. If

we drop Sweden, the importance of decentralization doubles to accounting for almost a third of the difference

(32%). Note that the contribution is also negative for Poland, because although Poland is more centralized

than the US, it grew more quickly over this time period.

5 Alternative Mechanisms: Identification and Robustness

We have emphasized that decentralized organizations appear more resilient to negative shocks and our

interpretation that this is because they are able to respond more flexibly to turbulent environments. We

turn next to various challenges to our conclusions from a theoretical perspective.

5.1 Do bad times reduce the costs of decentralization? Evidence from financial

shocks

Our theory suggested that congruence could fall in recessions (the “centralist” view) leading to an increase

in the value of centralization. Our main result rejected this as decentralized firms performed better in bad

times. There may, however, be alternative rationalizations of these results. Imagine, for example, that bad

times reduce the costs of decentralization because the plant manager fears that performing the non-profit

maximizing action might cause the firm to go bankrupt, and this will be more costly to the manager than

CEO, as he will take a larger hit to their income (e.g. through longer unemployment). To test this idea

we examine environments where the firm-specific risk of bankruptcy rose rapidly in the Great Recession.

We constructed several indicators of increased bankruptcy risk. In particular, we used the measures of

exogenous increases in exposure to financial crisis exploited by Chodorow-Reich (2014) such as exposure

to mortgage-backed securities (affected by the sub-prime crisis) and a firm’s pre-existing relationship with

Lehman Brothers or similar “at-risk” banks. These are pre-Great Recession conditions relating to the supply

of finance rather than product demand. We also used more conventional measures such as leverage ratios.

We found that these measures do predict negative performance in sales and other outcomes (see Ap-

pendix Table A4), as in Chodorow-Reich (2014). However, in no case did including these bankruptcy risk

variables (and their interactions with SHOCK or other covariates) materially alter the coefficient on the

key interaction of Decentralization ∗ SHOCK when included in equation (4).43 This led us to conclude

that the crisis was not leading to greater decentralization by fostering greater alignment between the central

headquarters and plant manager.43The coefficients on the Lehman Brothers variable cannot be reported due to Census disclosure rules. Note, because of the

need to match our data with the Chodorow-Reich (2014) data our sample size falls to 2,000 observations, so many of our resultsare not statistically significant, but point estimates are similar and unaffected by the controls for financial conditions.

23

5.2 Co-ordination costs

When there are large externalities between different plants belonging to the same firm, decentralization is

likely to be more costly (e.g. Alonso et al, 2008). For example, coordinating prices and product decisions

from the central headquarters is important if the sales of one establishment’s products cannibalize those of

another establishment belonging to the same firm. If co-ordination became less important in a downturn

this could be an alternative rationalization of our results. However, Bolton and Farrell (1990) have argued

that co-ordination is more likely to be important when urgency increases which is more likely in crisis

situations. Nonetheless, to examine whether our results may reflect the changing importance of coordination

in bad times, in Tables A7 and A8 we included interactions with many measurable characteristics reflecting

environments where coordination costs should be more important such as firm and/or plant size and whether

a firm was multi-plant (so more need for coordination) and, if so, whether these plants are located in different

countries or different states. Similarly, we looked at whether a firm was producing goods across multiple

sectors (“diversification” dummy) or whether it was part of a foreign multinational enterprise. We also

considered the degree of outsourcing (a direct question in WMS) and alternatively as measured by the ratio

of intermediate goods inputs to total sales.

In all cases the main interaction between decentralization and export growth remained significant, and

in only one of the 17 cases was one of the other interactions significant at the 5% level.44 Although co-

ordination costs matter in general for centralization, they do not seem to account for the better performance

of decentralized firms during downturns.

5.3 Does decentralization reflect other establishment characteristics?

We investigated whether the Decentralization ∗SHOCK interaction actually reflects other firm level char-

acteristics correlated with decentralization exploiting the very rich data we have compiled.45 Specifically in

Tables A5 and A6 we augment the baseline specification of column (3) in Table 2 with interactions terms

between the Great Recession indicator and a series of additional firm and plant controls.

First, it may be that decentralized firms are more resilient to negative shocks because they have better

management quality. To test this we include interactions with the overall management quality of the firm

(in the WMS measured as in Bloom and Van Reenen, 2007) or the plant (in the MOPS). We also have rich44This is the materials share in column (9) in the WMS regressions of Table 7. Two other interactions with decentraliza-

tion–firm size and the number of manufacturing industries in columns (4) and (8) of the MOPS regressions in Table 8–aresignificant at the 10% level. This could be taken as (weak) evidence that firms with more co-ordination issues with supplychains, scale or industry diversification do worse during downturns when presumably lack of co-ordination becomes more costly.

45Ideally, we we would have an instrumental variable for decentralization, but there is no obvious candidate. We did trylooking at the regional variation in generalized trust in the population around the firm’s headquarters is strongly correlatedwith decentralization (see Bloom, Sadun and Van Reenen 2012). We found that firms in high trust areas outperform others indownturns – an interaction between regional trust and our export shock variable is significantly negative in the performanceregressions of Table 2. This reduced form is consistent with a mechanism whereby trust causes greater firm decentralizationand therefore fosters resilience in bad times. However, there may also be other mechanisms through which higher trust helpsfirms outperform others during downturns, so trust cannot be reliably excluded from the second stage.

24

information on plant manager characteristics (age, immigrant status and gender). We also have measures of

human capital in general (the proportion of employees with college degrees).

Second, we know that smaller, less profitable firms may be more vulnerable to downturns. For example,

firms in low profit margin industries with relatively homogeneous products may be more likely to exit.

The concern is that these more marginal firms may also be more centralized, so that more decentralization

simply reflects more efficient plants. To address this we include interactions between the SHOCK and (a)

pre-recession profit margins; (b) firm and plant size; (c) technology adoption (data-driven decision making)

and (d) union strength.

In Tables A7 and A8 we also tested the robustness of the results to the inclusion of measures of scale

(size of the plant and/or the firm), decentralization from the plant manager to production workers, tech-

nology adoption (data-driven decision making), and union strength. Throughout these experiments the

coefficient on our key Decentralization ∗ SHOCK interaction remained significant, even when all variables

were simultaneously included in the final column.46

5.4 Types of decentralization

As a related experiment to shed light on the model we looked at the different sub-questions which form the

overall decentralization index, as shown in Appendix Table A9. Since the Great Recession was associated

with a decrease in output demand, we would expect that decentralization capturing managerial discretion

over outputs (sales and new products) would be more important than delegation over inputs (like labor and

capital). We start in column (1) by showing the baseline result of Table 2, column (3). In columns (2)

and (3) we repeat the estimation using as the decentralization index a z-scored average of the two questions

capturing plant manager decentralization for hiring and investment decisions in column (2), and for sales

and marketing and product introduction in column (3). In columns (4) to (6) we repeat the same exercise

for the U.S. MOPS sample.47 In both cases, the positive effect of decentralization in a crisis is primarily

driven by the output related questions. This finding provides additional insight on the possible mechanism

through which decentralization may positively affect performance during a downturn, namely the ability to

better adapt to more turbulent demand conditions.48

One concern with these findings is the belief that in practice plant managers do not have meaningful

autonomy in decisions regarding sales and marketing and product introduction, and that these decisions46Although the additional variables were usually insignificant, there are exceptions. In Table A5, decentralization from plant

manager to workers exhibits a similar pattern to our main decentralization measure of power between the central headquartersand plant manager. This suggests that decentralizing decision-making throughout the hierarchy is beneficial during times ofcrisis. The management interaction is also weakly significant, although in this case the coefficient is positive. In other words,well managed firms perform relatively better in good times than in bad times.

47In the U.S. sample we have 3 questions capturing plant manager decentralization for hiring and investment decisions incolumn (5) and 3 capturing plant manager decentralization for sales and marketing and product introduction in column (6).

48Consistent with the previous sub-section Appendix Table A12 shows that the positive interaction between decentralizationand product churn is driven primarily by the sales and marketing and product introduction questions.

25

are typically undertaken in the marketing department of firm headquarters. It is worth recalling that while

this may be the case in business-to-consumer firms which sell their goods to households directly or through

retail establishments, it is less obvious in business-to-business firms which sell their manufacturing output

to other firms. The latter scenario encompasses a significant share of US and EU manufacturing activity.49

Moreover, our firms are not so large – a median of 250 employees in WMS and 423 in MOPS so few of them

are likely to have standalone marketing divisions.

5.5 Changes in decentralization over time

Recall that our identification assumption is that pre-recession decentralization is weakly exogenous and that

there are some adjustment costs which mean that after the Great Recession shock firms do not immediately

adopt the new optimal (more decentralized) organizational form. A corollary of our theory, however, is that

firms will start moving to a more decentralized form (to the extent that they believe the shock is likely to be

long-lasting). Hence, we should expect to see some increase in decentralization for firms and establishments

more exposed to the shock. Table A14 examines this by using the change in decentralization as a dependent

variable. This is a demanding specification, especially for WMS where the panel element of decentralization

is limited (we have data in 2009 and 2010 for a sub-sample of the 2006 wave). Nevertheless, in both WMS

and MOPS we do see that firms facing a larger negative shocks are more likely to decentralize.

5.6 Robustness

A concern with the results is that our decentralization interaction is simply picking up longer term trends

or proxying for some unobserved variable. To address these issues we took several steps.

Placebo test in a pre-crisis period First, we address the concern that the Decentralization∗SHOCK

interaction may simply be picking up some other time-invariant industry characteristic associated with the

magnitude of the recession and firm decentralization. As shown in Figure 2, the raw data suggest that the

differentials in performance between decentralized and centralized firms are confined to the Great Recession.

To further probe this result, we examine the relationship between sales growth and the Decentralization ∗

SHOCK interaction in a sample including years preceding the Great Recession in Table 6. Finding the

same results in this period would raise the concern that the SHOCK dummy captures unobserved industry

heterogeneity unrelated to the Great Recession such that decentralized firms always did better in certain

sectors. Thus, we regard this as a placebo test. We look again at three year differences in growth but instead49According the Bureau of Economic Analysis, over 90 percent of US manufacturing output goes to the man-

ufacturing sector, which will be primarily business-to-business transactions: https://www.bea.gov/industry/xls/io-annual/IOMake_Before_Redefinitions_1997-2015_Sector.xlsx. This will be similar in Europe, which like the US has a higher-end manufacturing sector focused more at business consumers (Chinese manufacturing output, in contrast, is more consumerfocused).

26

pool across the 3-year differences 2008-05, 2007-04, 2006-03 and 2005-02 to define the pre-recession growth

rates (in column (1) labeled (“year<=2005”), and 2011-08, 2010-07 and 2009-06 (as in the earlier tables) to

define the post-recession years (column (2)). Column (1) shows that the coefficient on Decentralization ∗

SHOCK is actually positive, although insignificant, in the years preceding the Great Recession. Column

(2) repeats the results of the specification of Table 2, column (3). Column (3) repeats the regression on

the pooled pre-crisis and post-crisis samples of the first two columns, and includes a full set of interactions

with a dummy indicator taking a value of one for all crisis years (the three year differences from 2009-06

and later) to estimate a “differences in differences in differences” specification. The coefficient on the triple

interaction POST2006 ∗Decentralization ∗ SHOCK interaction is negative and significant, which implies

that the effect of decentralization in industries hit by the Great Recession is arising entirely from the Great

Recession years. We repeated the same analysis on productivity with very similar results in the last three

columns.50

An Alternative Instrumental Variable An alternative exogenous shifter of the shock measure is to

construct a Bartik style IV where we predict the change in exports from an industry-country pair. We

constructed this for every HS six digit commodity in a country by interacting the lagged (i.e., built using

2006/2007 data) export share of the commodity from country r to a partner country p with the partner

country’s growth in imports (of that commodity) between 2006/07 and 2008/09 from all countries except

country r. Summing this across all partner countries and then aggregating to the three digit industry level

gives an IV for the export shock. The results from using this Bartik IV are very similar to those shown in

Table 2.51

Validity of exports as a shock measure We have argued that trade changes are an attractive indicator

of the Great Recession shock, as they are more likely to reflect what is happening to demand in world markets

than being a reflection of country and industry specific supply factors. As a further check we estimated our

models separately for exporting establishments vs. non-exporting establishments using the MOPS data

(export data is not an item required in the company accounts data). As expected, the results are driven by

the exporting plants who are most directly exposed to trade shocks.52

50We also checked that the results presented in Tables A5 and A7 using the WMS sample are robust to using the placebospecification presented in Table 6. Furthermore, the results in the DDD specification are robust to the inclusion of firm fixedeffects (results available upon request).

51For example, the IV coefficient (standard error) on the interaction of export growth and decentralization is -0.065(0.029)using the Bartik IV. This is similar with the OLS estimate of -0.047(0.018) in column (3) of 2. The first stage is strong with anF-statistic of 29.5.

52For example, using the baseline MOPS specification in 2 column (5) we estimate a coefficient(standard error) of -0.036(0.012)on the Decentralization*SHOCK variable for the exporters (4,200 observations) and -0.011(0.012) for the non-exporters. Theseresults are shown in Appendix Table A13.

27

Asymmetries We investigated whether a negative shock differed from a positive shock by allowing different

coefficients on positive than negative shocks (defined either as positive export growth or export growth

above/below the median value). In all cases we found we could not reject symmetry. This is unsurprising

since in the Great Recession period most firms were experiencing various degrees of a negative shock.

Including firm fixed effects In the MOPS data we can implement a particularly tough test. Since

we measure decentralization in multiple plants within the same firm, for multi-plant firms we are able to

include an interaction between the Great Recession indicator and average firm decentralization.53 This

means that the coefficient on the Decentralization ∗ SHOCK interaction is identified solely off differences

in decentralization across plants within the same firm. Remarkably, the results remain significant even in

the presence of the firm level of decentralization and its interaction with export growth (coefficient of -0.023

and standard error of 0.010).

Other Industry characteristics A further concern is that the SHOCK measure could be reflecting other

industry characteristics rather than the demand fall. In Appendix Table A10 we show that our key interaction

is robust to including interactions of decentralization with a number of other industry characteristics such

as asset tangibility, inventories, dependency on external finance and labor costs. The key interaction is also

robust to including other interactions such as firm age, plant age and the financial health.

6 Conclusion

Are decentralized firms more resilient to large negative shocks? On the one hand, a shock like the Great

Recession may reduce the congruence between the CEO and the plant manager, thus making centralized firms

more resilient (the “centralist” view). On the other hand, recessions are associated with greater turbulence,

making the plant manager’s local information more valuable, which would imply that decentralized firms

will perform relatively better in unexpected downturns (the “localist” view).

To empirically investigate these issues we collected new data on a panel of firms in 10 OECD countries

(WMS), and plants in the US (MOPS) and exploited the negative shock of the Great Recession which

reduced demand across industries and countries in heterogeneous ways. Using our pre-recession data on

decentralization we find that negative shocks hurt growth in centralized firms and establishments significantly

more than in their decentralized counterparts. This is true whether we use export shocks which vary at

the industry by country (WMS) or establishment (MOPS) level, or exogenous predictors of these negative

shocks like product durability. Further, as the localist view suggests, this effect is driven by the industries

which experienced a greater increase in the turbulence (as measured by product churn and stock market53Bloom, Brynjolfsson, Foster, Jarmin, Patnaik, Saporta-Eksten and Van Reenen (2019) show there is considerable variation

in organization within firms across plants at a point in time.

28

volatility) that accompanied the crisis. Potentially, the fact that the US has relatively more decentralized

(and therefore flexible) firms, meant that it could weather the global economic storm better than many

more centralized countries (e.g. in Southern Europe). Online Appendix C suggests these effects could be

nontrivial in magnitude.

We see our paper as a first attempt to unravel the relationship between growth and the internal orga-

nization of firms using micro data with observable measures of decentralization. There are many directions

to take the research. First, we need to look at the ways in which, in the longer-run, firms change their

organizational forms. For example, as the effects of the Great Recession recede, how will the growth effects

and degree of decentralization change? Second, we would like to go deeper into the relation between the debt

structure of companies (and so their bankruptcy risk) and the incentives for firms to change. Finally, it would

be valuable to examine the macro-economic implications of our modeling framework in more detail. Do the

effects we identify matter in terms of thinking about business cycles and how economies and companies can

be resilient to these adverse events?

References

Acemoglu, D., P. Aghion, C. Lelarge, J. Van Reenen and F. Zilibotti (2007), “Technology, Information, andthe Decentralization of the Firm”, Quarterly Journal of Economics, 122 (4), 1759-1799.

Aghion, P. and J. Tirole (1997), “Formal and Real Authority in Organizations”, Journal of Political Econ-omy, 105(1) 1-29.

Aghion, P., N. Bloom and J. Van Reenen (2014) “Incomplete contracts and the internal organization offirms”, Journal of Law, Economics and Organization 30(1), 37-64.

Aghion, P., P. Askenazy, N. Berman, G. Cette and L. Eymard (2012) “Credit Constraints and the Cyclicalityof R&D Investment: Evidence from France”, Journal of the European Economic Association, 10, 1001-1024.

Alfaro, L. and M. Chen (2012) “Surviving the Global Financial Crisis: Foreign Ownership and EstablishmentPerformance.” American Economic Journal: Economic Policy 4(3), 30–55.

Alonso, R., W. Dessein and N. Matouschek (2008) “When Does Coordination Require Centralization?”American Economic Review, 98(1): 145-79.

Bernard, A. and T. Okubo (2015) “Product switching and the business cycle”, CEP Discussion Paper 1432.

Bils, M. and P. Klenow (1998) “Using Consumer Theory to Test Competing Business Cycle Models.” Journalof Political Economy, 106(2): 233–61.

Bloom, N. and J. Van Reenen (2007), “Measuring and Explaining Management Practices Across Firms andCountries”, Quarterly Journal of Economics, 122(4), 1341-1408.

Bloom, N., R. Sadun and J. Van Reenen (2012) “The Organization of Firms Across Countries”, QuarterlyJournal of Economics, 127(4), 1663-1705.

29

Bloom, N., R. Sadun and J. Van Reenen (2016) “Management as a Technology”, NBER Working Paper22327.

Bloom, N., M. Floetotto, N. Jaimovich, I. Saporta-Eksten and S. Terry (2018) “Really Uncertain BusinessCycles”, Econometrica. 86(3) 1031-1065

Bloom, N., E. Brynjolfsson, L. Foster, R. Jarmin, M. Patnaik, I. Saporta-Eksten and J. Van Reenen (2019)“What Drives Differences in Management Practices?” American Economic Review 109(5) 1648–1683

Bolton, P. and J. Farrell (1990) “Decentralization, Duplication, and Delay” Journal of Political Economy,98(4) 803-826.

Blundell, R., D. A. Green and W. Jin (2016), “The UK Wage Premium Puzzle: How did a large increase inuniversity graduates leave the education premium unchanged?”, Institute for Fiscal Studies Working Paper16/01.

Bradley W., H. Aldrich, D. A. Shepherd and J. Wiklund (2011), “Resources, Environmental Change, andSurvival: Asymmetric Paths of Young Independent and Subsidiary Organizations”, Strategic ManagementJournal, 32: 486-509.

Bresnahan, T., E. Brynjolfsson and L. Hitt, (2002) “Information Technology, Workplace Organization, andthe Demand for Skilled Labor: Firm-Level Evidence,” Quarterly Journal of Economics, 117 (1), 339-376.

Brynjolfsson, E. and K. McElheran (2016) “The rapid rise of data-driven decision making” American Eco-nomic Review: Papers and Proceedings, 106 (5), 133-139.

Broda, C. and D. Weinstein (2010) “Product creation and destruction: Evidence and price implications”American Economic Review, 100, 691-723.

Caroli, E., and J. Van Reenen (2001) “Skill Biased Organizational Change”, Quarterly Journal of Economics,116(4), 1449-1492.

Chandler D., Alfred (1962) Chapters in the History of the American Industrial Enterprise. Beard Books,Washington, D.C.

Chodorow-Reich, G. (2014) “The employment effects of credit market disruptions: Firm-level evidence fromthe 2008-09 financial crisis” Quarterly Journal of Economics, 129(1), 1-59.

Cyert, R. and J. March (1963) A Behavioral Theory of the Firm. Englewood Clis, NJ: Prentice-Hall.

D’Aurizio, L., T. Oliviero and L. Romano (2015) “Family firms, soft information and bank lending in afinancial crisis” Journal of Corporate Finance 33(3) 279-292.

Dessein, W. (2002) “Authority and Communication in Organizations”, Review of Economic Studies, 69 (4),811.

Dixit, A. and R. Pindyck (1994) Investment under uncertainty, Princeton: Princeton University Press.

Garicano, L. and L. Rayo (2016) “Why Organizations Fail: Models and Cases” Journal of Economic Liter-ature, 54 (1), 137-192.

Gibbons, R., N. Matouschek and J. Roberts (2013) “Decisions in Organizations” Chapter 10 373- 431 in R.Gibbons and J. Roberts (eds) Handbook of Organizational Economics.

30

Gibbons, R. and J. Roberts (2013) Handbook of Organizational Economics. Princeton: Princeton UniversityPress.

Gibbons, R. and R. Henderson (2012) “Relational Contracts and Organizational Capabilities” OrganizationalScience, 23(5): 1350-1364.

Grossman, S. and O. Hart (1986) “The Costs and Benefits of Ownership: A Theory of Vertical and LateralIntegration,” Journal of Political Economy, 691-719.

Guadalupe, M. and J. Wulf (2010) “The Flattening Firm and Product Market Competition: The Effect ofTrade Liberalization on Corporate Hierarchies,” American Economic Journal: Applied Economics, 2 (4),105–127.

Gulati, R., N. Nohria and F. Wohlgezogen (2010) “Roaring Out of Recession,” Harvard Business Review,March.

Hart, O. and J. Moore (2005) “On the Design of Hierarchies: Coordination versus Specialization,” Journalof Political Economy, 113(4) 675-702.

Katayama, H., K. Meagher and A. Wait (2016), “Authority and communication in firms: Estimatingarchetypal decision-making structures” University of Sydney mimeo,

King, R.G. and S.T. Rebelo (1999) “Resuscitating Real Business Cycles”, in Handbook of Macroeconomics,John B. Taylor and Michael Woodford (eds.), Elsevier.

Lafontaine, F. and M. Slade (2007) “Vertical Integration and Firm boundaries”, Journal of Economic Lit-erature, 45(3) 629-685 .

Lange, O. (1936) “On the Economic Theory of Socialism” Review of Economic Studies, 4(1) 53-71.

Leahy, J. and T. Whited (1996), “The Effects of Uncertainty on Investment: Some Stylized Facts", Journalof Money Credit and Banking, 28, 64-83.

Mayer, T., M. Melitz and G. Ottaviano (2016) “Product Mix and Firm Productivity Responses to TradeCompetition," NBER Working Papers 22433.

McCelheran, K. (2014) “Delegation in multi-establishment firms: Evidence from IT Purchasing” Journal ofEconomics and Management Strategy 23(2) 225-281.

Pierce, J. and P. Schott (2009) “A Concordance between Ten-Digit U.S. Harmonized System Codes andSIC/NAICS Product Classes and Industries”, Mimeo, Yale University.

Pierce, J. and P. Schott (2010) “Concording US Harmonized System Codes Over Time”, Mimeo, YaleUniversity.

Prendergast, C. (2002) “The Tenuous Trade-Off between Risk and Incentives” Journal of Political Economy,110(5) 1071-1102.

Ramey, V. and C. Nekarda (2013) “The cyclical behavior of the price-cost markup" UC San Diego mimeo.

Rauch, J.E. (1999) “Networks versus markets in international trade.” Journal of International Economics48, 7–35.

31

Rajan, R.G. and J. Wulf (2006) “The flattening firm: Evidence from panel data on the changing nature ofcorporate hierarchies,” Review of Economics and Statistics, 88 (4), 759–773.

Salas, Jorge (2015) “A Tale of Two Sectors: Product Differentiation and Heterogeneity in Price Stickinessin a General Equilibrium Model,” Mimeo, University of Maryland.

Von Hayek, F. (1945) “The Use of Knowledge in Society” American Economic Review, 35(4) 519-53.

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Figure 1: Changes in Sales by Shock and DecentralizationPanel A - WMS Data

0.933251 0.933252

BelowMean AboveMean BelowMean AboveMean neg_comtrade_x_w_r_lbel means sds n his lows zorgshock ci(95%)

beta -2.831201 -3.451616 -11.03695 -8.2037 0 0 -2.831201 12.53098 695 -1.89795 -3.764453 1 0.933251

1.96*sigma(95%CI) 0.933251 0.900187 0.92306 0.851839 0 1 -3.451616 13.47348 863 -2.551429 -4.351803 2 0.900187

1 0 -11.03695 12.75569 736 -10.11389 -11.96001 4 0.92306

1 1 -8.2037 12.70531 857 -7.351861 -9.055539 5 0.851839

Panel B - MOPS Data

ExportShock=0 Exportshock=1

BelowMean AboveMean BelowMean AboveMean

beta -5.81 -6.29 -11.05 -8

1.96*sigma(95%CI) 0.58 0.72 0.98 0.91

WMSCHARTDATA

ExportShock=0 Exportshock=1

MOPSCHARTDATA

-14

-12

-10

-8

-6

-4

-2

0

Ann

ualiz

ed a

vera

ge s

ales

gro

wth

(201

1-06

)

Decentralization Score

ExportShock=0 ExportShock=1

Below Mean Above Mean Below Mean Above Mean

-14

-12

-10

-8

-6

-4

-2

0

Ann

ualiz

ed a

vera

ge s

ales

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(200

9-06

)

Decentralization Score

ExportShock=0 ExportShock=1

Below Mean Above Mean Below Mean Above Mean

Notes: Panel A uses WMS firm data from 10 OECD countries. In Panel A the bars plot annualized average of three-yearfirm-level change in ln(sales) over 2011-08, 2010-07 and 2009-06. 95% confidence interval bands reported. “Export Shock” iswhether firms were in a country-industry cell that experienced a drop in the average level of exports in 2008 and 2009 (themain Great Recession years) compared to the average level in 2006 and 2007 (the latest pre-Recession years). Right hand sidebars are industry-country cells were the shock was worst. Firms are split into whether they are decentralized (above the overallmean of decentralization in 2006) or centralized. Sample size in each bar in Panel A (from left to right) is (1) 695 observationover 296 firms; (2) 863 obs, 352 firms; (3) 736 obs, 316 firms; (4) 857 obs, 367 firms. Panel B uses MOPS data on US plantsand is the same as Panel A except we just use one 2009-06 long difference for plant sales growth and decentralization dated in2005. The sample in Panel B includes 8,800 US plants in 3,150 firms.

33

Figure 2: Changes in Sales by Shock, Difference between Decentralized vs. Centralized FirmsFigure 1 - Change in Sales by Shock, Decentralized minus CentralizedPanel A - WMS data

Panel B - MOPS data

Notes: Panel A used WMS firm data from 10 OECD countries. In Panel A the lines plotannualized average of three-year firm-level change in ln(sales) for decentralized firms minusannualized average three-year change in ln(sales) for centralized firms. Growth rates areshown for each year starting with the 2005-02 growth rate through the 2014-11 growth rate."Shock" is whether firms were in a country-industry cell that experienced a drop in exports in2008 and 2009 (the main Great Recession years) compared to 2006 and 2007 (the latest pre-Recession years). Panel B used MOPS plant data from the U.S. In Panel B the lines plotannualized average of three-year plant-level change in ln(sales) for decentralized plants minusannualized average three-year change in ln(sales) for centralized plants, and growth rates areshown for each year starting with the 2004-01 growth rate through the 2015-12 growth rate."Shock" is whether plants produced products (measured before the crisis) which on averageexperienced a drop in exports in 2008 and 2009 (the main Great Recession years) compared to2006 and 2007 (the latest pre-Recession years).

-4-2

02

4D

ecen

traliz

ed -

Cen

traliz

ed fi

rms

2004 2006 2008 2010 2012 2014Year of accounts data

No Shock Shock

-20

24

Dec

entra

lized

- C

entra

lized

firm

s

2004 2006 2008 2010 2012 2014Year of accounts data

No Shock Shock

Notes: Panel A uses WMS firm data from 10 OECD countries. In Panel A, the lines plot annualized average three-year firm-level change in ln(sales) for decentralized firms minus annualized average three-year change in ln(sales) for centralized firms,distinguishing between firms that experienced an export shock during the Great Recessions, versus those that did not. Growthrates are shown for each year starting with the 2005-02 growth rate through the 2014-11 growth rate. "Shock" is whether firmswere in a country-industry cell that experienced a drop in exports in 2008 and 2009 (the main Great Recession years) comparedto 2006 and 2007 (the latest pre-Recession years). Panel B uses MOPS plant data from the U.S. In Panel B, the lines plotannualized average three-year plant-level change in ln(sales) for decentralized plants minus annualized average three-year changein ln(sales) for centralized plants, distinguishing between plants that experienced an export shock during the Great Recessions,versus those that did not. Growth rates are shown for each year starting with the 2004-01 growth rate through the 2015-12growth rate. "Shock" is whether plants produced products (measured before the crisis) which on average experienced a dropin exports in 2008 and 2009 (the main Great Recession years) compared to 2006 and 2007 (the latest pre-Recession years).

34

Figure 3: Effect of Increase in Decentralization on Sales GrowthPanel A

Panel B

Figure 2 - Effect of increase in decentralization on sales growth

Notes: Panel A plots the implied marginal effect of decentralization on firmsales growth using the coefficients in Table 2 column (2) as a function of theshock (export growth in cell). Panel B shows the distribution of firms inindustry-country cells with different levels in cell). Panel B shows thedistribution of firms in industry-country cells with different levels of exportgrowth before and after the Recession.

-4-2

02

4Ef

fect

of i

ncre

ase

in d

ecen

traliz

atio

n on

sal

es g

row

th

-35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50 55 60Export growth

0.0

1.0

2.0

3

-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50 55 60

Export growth pre Recession Export growth post Recession

Notes: WMS Data. Panel A plots the implied marginal effect of decentralization on firm sales growth using the coefficients inTable 2 column (2) as a function of the shock (export growth in cell). Panel B shows the distribution of firms in industry-countrycells with different levels in cell). Panel B shows the distribution of firms in industry-country cells with different levels of exportgrowth before and after the Great Recession.

35

Tab

le1:

SummaryStatistics

Pane

l A W

orld

Man

agem

ent S

urve

y

Vari

able

Mea

nM

edia

nSt

anda

rd

Dev

iatio

nSa

les

Leve

ls ($

Mill

ions

)18

4.14

67.0

751

3.41

Sale

s G

row

th (3

yea

rs a

nnua

lized

log

chan

ge)

-6.3

8-5

.81

13.3

1Em

ploy

men

t (fir

m)

574.

3925

0.00

2,14

4.77

Empl

oym

ent (

plan

t)23

2.93

150.

0025

4.36

% E

mpl

oyee

s w

ith a

Col

lege

Deg

ree

16.3

210

.00

17.5

1D

ecen

traliz

atio

n Sc

ore

0.00

-0.0

41.

00Ex

ports

(con

tinuo

us, %

cha

nge

in s

ecto

r/cou

ntry

exp

ort i

n 08

/09

rela

tive

to 0

6/07

)-1

.96

-0.4

320

.96

Dur

abili

ty (c

ontin

uous

, med

ian

year

s of

ser

vice

of g

oods

pro

duce

d in

the

indu

stry

)13

.03

10.0

019

.50

Pane

l B U

.S. C

ensu

s Dat

a - M

OPS

Vari

able

Mea

nM

edia

nSt

anda

rd

Dev

iatio

nSa

les

Leve

ls (2

009)

($M

illio

ns)

137.

4050

.50

403.

60Sa

les

Gro

wth

(3 y

ears

ann

ualiz

ed lo

g ch

ange

)-7

.09

-6.0

618

.44

Empl

oym

ent (

firm

)1,

354

423.

32,

812

Empl

oym

ent (

plan

t)24

9.81

135.

0048

1.91

% E

mpl

oyee

s w

ith a

Col

lege

Deg

ree

11.8

47.

2811

.69

Dec

entra

lizat

ion

Scor

e0.

00-0

.17

1.00

Expo

rts (c

ontin

uous

, % c

hang

e in

pro

duct

exp

orts

in 0

8/09

rela

tive

to 0

6/07

)-1

.51

2.83

29.9

4D

urab

ility

(con

tinuo

us, m

edia

n ye

ars

of s

ervi

ce o

f goo

ds p

rodu

ced

in th

e in

dust

ry)

12.9

812

.20

13.1

7

Notes:These

aretheregression

samples

used

inTab

le2.

Pan

elA

contains

descriptivestatistics

from

theW

MSan

dPan

elB

from

theMOPS.

36

Tab

le2:

Decentralizationan

dSa

lesGrowth

-Mainresults

(1)

(2)

(3)

(4)

(5)

(6)

Dep

ende

nt V

aria

ble

= Sa

les

Gro

wth

Dec

entr

aliz

atio

n0.

579*

0.36

30.

041

-0.4

600.

583*

*-0

.182

(0.3

02)

(0.3

02)

(0.4

17)

(0.5

72)

(0.2

30)

(0.2

34)

EX

POR

T G

row

th0.

069*

*0.

062*

*0.

027

(0.0

29)

(0.0

29)

(0.0

21)

Dec

ent.*

EX

POR

T G

row

th-0

.042

***

-0.0

47**

-0.0

23**

(0.0

13)

(0.0

18)

(0.0

09)

Dec

ent.*

DU

RA

BIL

ITY

0.50

2**

0.38

1***

(0.1

94)

(0.0

91)

Firm

s1,

330

1,33

01,

330

1,33

03,

150

3,15

0O

bser

vatio

ns3,

151

3,15

13,

151

3,15

18,

800

8,80

0B

asel

ine

cont

rols

Yes

Yes

Yes

Yes

Yes

Yes

Indu

stry

dum

mie

sY

esY

esY

esY

esY

esIn

dust

ry b

y co

untr

y du

mm

ies

Yes

Firm

& p

lant

em

ploy

men

t, sk

ills

Yes

Yes

Yes

Yes

Clu

ster

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C4

SIC

3SI

C3

Wor

ld M

anag

emen

t Sur

vey

(WM

S)U

.S. C

ensu

s D

ata

(MO

PS)

Not

es: *

sign

ifica

nt a

t 10%

; **

5%; *

** 1

%. E

stim

ated

by

OLS

with

sta

ndar

d er

rors

clu

ster

ed a

t thr

ee-d

igit

indu

stry

by

coun

try le

vel i

n co

lum

ns (1

)-(3

)and

just

indu

stry

inco

lum

ns(4

)-(6)

.The

depe

nden

tvar

iabl

eis

the

annu

aliz

edth

ree-

year

chan

geof

firm

ln(s

ales

).20

11-0

8,20

10-0

7an

d20

09-0

6ar

epo

oled

inW

MS

(col

umns

(1)-(

4))a

ndju

st20

09-0

6in

MO

PS(c

olum

ns(5

)and

(6)).

Dec

entra

lizat

ion

mea

sure

din

2006

for

WM

San

d20

05fo

rM

OPS

."E

XPO

RT

Gro

wth

"is

chan

gein

ln(e

xpor

ts)

inco

untry

byth

ree

digi

tin

dust

ryce

llbe

twee

nth

e20

08an

d20

09av

erag

e(th

em

ain

Gre

atR

eces

sion

year

s)co

mpa

red

toth

e20

06an

d20

07av

erag

e(th

ela

test

pre-

Rec

essi

onye

ars)

inco

lum

ns(1

)-(4)

,an

dis

the

aver

age

chan

ge(2

008/

2009

aver

age

com

pare

dto

2006

/200

7)in

ln(e

xpor

ts)

atth

epr

oduc

tle

vel

(HS7

)for

the

prod

ucts

the

plan

tpr

oduc

edju

stpr

iort

oth

eG

reat

Rec

essi

onin

2006

for

colu

mn

(5).

All

colu

mns

incl

ude

thre

edi

git

indu

stry

(four

digi

tin

colu

mn

(4)),

coun

tryan

dye

ardu

mm

ies

and

"noi

seco

ntro

ls"

(pla

ntm

anag

er's

tenu

re a

nd h

iera

rchi

cal s

enio

rity

and

the

inte

rvie

w's

relia

bilit

y sc

ore,

day

of t

he w

eek

and

dura

tion,

WM

S al

so in

clud

es a

naly

st d

umm

ies

Notes:*significan

tat

10%;**

5%;***1%

level.

Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

bycoun

trylevelin

columns

(1)-(3)an

djust

indu

stry

incolumns

(4)-(6).

The

depe

ndentvariab

leis

thean

nualized

three-year

chan

geof

firm

ln(sales).

2011-08,

2010-07an

d2009-06arepo

oled

inW

MS

(colum

ns(1)-(4))

andjust

2009-06in

MOPS(colum

ns(5)an

d(6)).Decentralizationmeasuredin

2006

forW

MSan

d2005

forMOPS.

"EXPORT

Growth"is

chan

gein

ln(exp

orts)in

coun

tryby

threedigitindu

stry

cellbe

tweenthe2008

and2009

average(the

mainGreat

Recession

years)

compa

redto

the2006

and2007

average

(the

latest

pre-Recession

years)

incolumns

(1)-(4),

andis

theaveragechan

ge(2008/2009

averagecompa

redto

2006/2

007)

inln(exp

orts)at

theprod

uctlevel(H

S7)

fortheprod

ucts

theplan

tprod

uced

just

priorto

theGreat

Recession

in2006

forcolumn(5).

“DURABILIT

Y”is

theaveragedu

rabilityof

thego

odsprod

uced

inthe

four-digit

indu

stry

(inyears),draw

nfrom

Ram

eyan

dNekarda

(2013).Baselinecontrols

arecoun

tryan

dyear

dummiesan

d"n

oise

controls"(plant

man

ager’s

tenu

rean

dhierarchical

seniorityan

dtheinterview’s

relia

bilityscore,

dayof

theweekan

ddu

ration

,W

MSalso

includ

esan

alystdu

mmiesan

dMOPSwhether

thesurvey

was

answ

ered

onlin

eor

bymail).Indu

stry

dummiesareat

thethreedigitSIC

level(four

digits

incolumn4).Firm

andplan

tem

ploymentaremeasuredas

log(em

ployment),

andskillsaremeasuredas

ln(%

ofem

ployeeswithacolle

gedegree).

37

Tab

le3:

Alterna

tive

Firm

LevelO

utcomes

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Dep

ende

nt V

aria

ble

Sale

s gr

owth

TFP

gro

wth

Prof

it gr

owth

Tob

in's

Q

grow

thSu

rviv

alSa

les

grow

thT

FP

grow

thPr

ofit

grow

thT

obin

's Q

gr

owth

Dec

entr

aliz

atio

n -0

.017

-0.2

63-0

.396

-0.6

731.

330.

583*

*-0

.358

-0.0

27-0

.062

(0.4

00)

(0.3

57)

(1.5

97)

(1.3

09)

(0.9

13)

(0.2

30)

(0.5

08)

(0.7

61)

(0.6

67)

Dec

ent.*

EX

POR

T G

row

th-0

.048

***

-0.0

33**

-0.0

68-0

.071

5-0

.086

*-0

.023

**-0

.054

**-0

.064

**-0

.053

***

(0.0

17)

(0.0

13)

(0.0

65)

(0.0

56)

(0.0

47)

(0.0

09)

(0.0

22)

(0.0

31)

(0.0

19)

Firm

s1,

211

1,21

11,

192

1,02

42,

663

3,15

03,

150

3,15

015

0O

bser

vatio

ns2,

839

2,83

92,

712

866

2,66

38,

800

8,80

08,

800

1,80

0B

asel

ine

cont

rols

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Firm

& p

lant

em

ploy

men

t, sk

ills

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Indu

stry

dum

mie

sY

esY

esY

esY

esIn

dust

ry b

y co

untr

y du

mm

ies

Yes

Yes

Yes

Yes

Yes

Clu

ster

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C3

SIC

3SI

C3

SIC

3

Not

es: *

sign

ifica

nt a

t 10%

; **

5%; *

** 1

%.

Estim

ated

by

OLS

with

sta

ndar

d er

rors

clu

ster

ed a

t thr

ee-d

igit

indu

stry

by

coun

try le

vel i

n co

lum

ns (1

)-(5

) and

just

indu

stry

in

colu

mns

(6)-

(9).

Sale

sgr

owth

isth

ean

nual

ized

thre

e-ye

arch

ange

offir

mln

(sal

es).

TFP

grow

this

the

sam

eas

sale

sgr

owth

exce

ptw

ein

clud

eth

egr

owth

ofem

ploy

men

t,ca

pita

land

mat

eria

lson

the

right

hand

side

ofth

ere

gres

sion

.Pro

fitgr

owth

isEB

IT/c

apita

lfor

WM

San

dgr

oss

prof

its/c

apita

lfor

MO

PS(p

rofit

sm

easu

red

aspl

ants

ales

-w

age

bill

-m

ater

ials

-re

ntal

expe

nses

).To

bin'

sQ

ism

arke

tval

uedi

vide

dby

the

capi

tals

tock

.For

allt

hese

depe

nden

tvar

iabl

esw

epo

olth

elo

ngdi

ffer

ence

2011

-08,

2010

-07

and

2009

-06

inW

MS

and

just

2009

-200

6in

MO

PS).

Surv

ival

iseq

ualt

o1

ifth

efir

msu

rviv

edaf

ter2

008

and

0if

itex

ited

toba

nkru

ptcy

.Dec

entra

lizat

ion

mea

sure

din

2006

for W

MS

and

2005

for M

OPS

."EX

POR

T G

row

th"

is c

hang

e in

ln(e

xpor

ts) i

n co

untry

by

thre

e di

git i

ndus

try c

ell b

etw

een

the

2008

and

200

9 av

erag

e (th

e m

ain

Gre

at

Rec

essi

onye

ars)

com

pare

dto

the

2006

and

2007

aver

age

(the

late

stpr

e-R

eces

sion

year

s)in

colu

mns

(1)-

(5),

and

isth

eav

erag

ech

ange

(200

8/20

09av

erag

eco

mpa

red

to20

06/2

007)

inln

(exp

orts

)att

hepr

oduc

tlev

el(H

S7)f

orth

epr

oduc

tsth

epl

antp

rodu

ced

just

prio

rto

the

Gre

atR

eces

sion

in20

06in

colu

mns

(6)-

(9).

All

colu

mns

incl

ude

thre

e di

git i

ndus

try b

y co

untry

and

yea

r dum

mie

s an

d co

ntro

ls fo

r firm

and

pla

nt s

ize,

ski

lls a

nd "

nois

e" c

ontro

ls.

Wor

ld M

anag

emen

t Sur

vey

(WM

S)U

.S. C

ensu

s D

ata

(MO

PS)

Notes:*significan

tat

10%;**

5%;**

*1%

level.

Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

bycoun

trylevelin

columns

(1)-(3)an

djust

indu

stry

incolumns

(5)-(7).

Salesgrow

this

thean

nualized

three-year

chan

geof

firm

ln(sales).

TFP

grow

this

thesameas

salesgrow

thexcept

weinclud

ethegrow

thof

employ

ment,

capitalan

dmaterials

ontherigh

tha

ndside

oftheregression

.Profit

grow

this

EBIT

/cap

ital

forW

MSan

dgrossprofi

ts/cap

ital

forMOPS(profits

measuredas

plan

tsales-wagebill-materials

-rental

expe

nses).

Tob

in’s

Qis

marketvaluedividedby

thecapitalstock.

Forallthesedepe

ndentvariab

lewepo

olthelong

diffe

rence2011-08,

2010-07an

d2009-06in

WMSan

djust

2009-2006in

MOPS).Su

rvival

isequa

lto

1ifthefirm

survived

after2008

and0ifit

exited

toba

nkruptcy.The

coeffi

cienton

theexit

regression

aremultipliedby

100forread

ability.Decentralizationmeasuredin

2006

forW

MSan

d2005

forMOPS.

"EXPORT

Growth"ischan

gein

ln(exp

orts)in

coun

tryby

threedigitindu

stry

cellbe

tweenthe2008

and2009

average(the

mainGreat

Recession

years)

compa

redto

the2006

and

2007

averag

e(the

latest

pre-Recession

years)

incolumns

(1)-(4),

andis

theaveragechan

ge(2008/2009

averag

ecompa

redto

2006/2007)

inln(exp

orts)at

theprod

uct

level(H

S7)fortheprod

ucts

theplan

tprod

uced

just

priorto

theGreat

Recession

in2006

incolumns

(5)-(7).

Baselinecontrols

areyear

dummiesan

d"n

oise

controls"

(plant

man

ager’s

tenu

rean

dhierarchical

seniorityan

dtheinterview’s

relia

bilityscore,

dayof

theweekan

ddu

ration

,W

MSalso

includ

esan

alystdu

mmiesan

dMOPS

whether

thesurvey

was

answ

ered

onlin

eor

bymail).Firm

andplan

tem

ploy

mentaremeasuredas

log(em

ploy

ment),an

dskillsaremeasuredas

ln(%

ofem

ployeeswith

acolle

gedegree).

38

Tab

le4:

Decentralizationan

dTurbu

lence(asmeasuredby

Produ

ctChu

rn)

(1)

(2)

(3)

(4)

(5)

(6)

Dep

ende

nt V

aria

ble:

Sal

es g

row

th ('

07-'1

2)

Dec

entra

lizat

ion

1.02

61.

524*

*1.

854*

**-0

.019

1.52

4**

1.61

1(0

.713

)(0

.681

)(0

.686

)(1

.043

)(0

.681

)(1

.004

)D

ecen

traliz

atio

n*C

hang

e in

Pro

duct

Chu

rn4.

722*

**4.

330*

**4.

722*

**4.

649*

**(1

.43)

(1.5

24)

(1.4

3)(1

.492

)D

ecen

traliz

atio

n*Ex

port

Gro

wth

('07

-'12)

-0.0

40**

-0.0

31(0

.019

)(0

.019

)D

ecen

traliz

atio

n*D

urab

ility

0.73

4*0.

409

(0.4

30)

(0.4

05)

Obs

erva

tions

8,20

08,

200

8,20

08,

200

8,20

08,

200

Bas

elin

e co

ntro

lsY

esY

esY

esY

esY

esY

esY

esFi

rm &

pla

nt e

mpl

oym

ent,

skill

sY

esY

esY

esY

esY

esY

esY

esIn

dust

ry d

umm

ies

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Clu

ster

SIC

3SI

C3

SIC

3SI

C3

SIC

3SI

C3

SIC

3

Exp

orts

Dur

abili

ty

Not

es:M

OPS

data

.*si

gnifi

cant

at10

%;*

*5%

;***

1%.E

stim

ated

byO

LSw

ithst

anda

rder

rors

clus

tere

dat

thre

e-di

giti

ndus

tryle

vel.

The

depe

nden

tvar

iabl

eis

the

annu

aliz

edth

ree-

year

chan

geof

firm

ln(s

ales

)20

12-0

7D

ecen

traliz

atio

nm

easu

red

in20

05."

EXPO

RT

Gro

wth

"is

aver

age

chan

ge(2

008/

2009

aver

age

com

pare

dto

2006

/200

7av

erag

e)in

ln(e

xpor

ts)

atth

epr

oduc

tle

vel

(HS7

)fo

rth

epr

oduc

tsth

epl

ant

prod

uced

just

prio

rto

the

Gre

atR

eces

sion

in20

06.

All

colu

mns

incl

ude

thre

edi

giti

ndus

trydu

mm

ies,

firm

and

plan

tsiz

e,sk

ills

and

"noi

seco

ntro

ls"

(pla

ntm

anag

er's

tenu

rean

dhi

erar

chic

alse

nior

ityan

dth

ein

terv

iew

'sre

liabi

lity

scor

e,da

yof

the

wee

kan

ddu

ratio

n,an

dw

heth

erth

esu

rvey

was

answ

ered

onlin

eor

bym

ail).

"PR

OD

UC

TC

HU

RN

"is

the

thre

edi

giti

ndus

tryof

valu

eof

the

aver

age

chan

gein

the

(num

bero

fpr

oduc

ts a

dded

bet

wee

n t a

nd t-

5 p

lus

the

num

ber p

rodu

cts

drop

ped

betw

een

t and

t-5)

/(ave

rage

num

ber o

f pro

duct

s be

twee

n t a

nd t-

5).

Notes:*significan

tat

10%;*

*5%

;***

1%level.Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

level.The

depe

ndentvariab

leisthean

nualized

five-year

chan

geof

firm

ln(sales)2012-07Decentralizationmeasuredin

2005."E

XPORTGrowth"isaverag

echan

ge(2008/2009

averagecompa

redto

2006/2007average)

inln(exp

orts)at

theprod

uctlevel(HS7

)fortheprod

ucts

theplan

tprod

uced

just

priorto

theGreat

Recession

in2006.Allcolumns

includ

ethreedigitindu

stry

dummies,

firm

andplan

tsize,skills

and"n

oise

controls"(plant

man

ager’stenu

rean

dhierarchical

seniorityan

dtheinterview’srelia

bilityscore,

dayof

theweekan

ddu

ration

,and

whether

thesurvey

was

answ

ered

onlin

eor

bymail)."P

RODUCT

CHURN"is

thethreedigitindu

stry-level

valueof

theaveragechan

gein

the(num

berof

prod

ucts

addedbe

tweentan

dt-5plus

thenu

mbe

rprod

ucts

drop

pedbe

tweentan

dt-5)/(averagenu

mbe

rof

prod

ucts

betw

eentan

dt-5).Baselinecontrolsareyear

dummiesan

d"n

oise

controls"(plant

man

ager’stenu

rean

dhierarchical

seniorityan

dtheinterview’srelia

bilityscore,

dayof

theweekan

ddu

ration

,whether

thesurvey

was

answ

ered

onlin

eor

bymail).Firm

andplan

tem

ploymentaremeasuredas

log(em

ploy

ment),an

dskillsaremeasuredas

ln(%

ofem

ployeeswithacolle

gedegree).

39

Tab

le5:

Decentralizationan

dTurbu

lence(asmeasuredby

StockMarketVolatility)

Tab

le 8

- D

ecen

tral

izat

ion

and

Unc

erta

inty

(1)

(2)

(3)

(4)

(5)

(6)

Dep

ende

nt V

aria

ble

= Sa

les

Gro

wth

Dec

entr

aliz

atio

n0.

208

0.42

10.

289

0.58

3**

0.31

790.

283

(0.3

31)

(0.3

29)

(0.3

21)

(0.2

30)

(0.2

16)

(0.2

06)

EX

POR

T G

row

th0.

088*

**0.

090*

**0.

027

1.19

0(0

.032

)(0

.027

)(0

.021

)(1

.633

)D

ecen

t*E

XPO

RT

Gro

wth

-0.0

34**

-0.0

24*

-0.0

23**

-0.0

19**

*(0

.017

)(0

.014

)(0

.009

)(0

.007

)D

ecen

t.*C

hang

e in

SD

(sto

ck r

etur

ns)

7.14

2***

6.30

4***

1.20

8***

1.28

5***

(1.3

41)

(2.3

54)

(0.4

02)

(0.3

74)

Firm

s1,

330

1,33

01,

330

3,15

03,

150

3,15

0O

bser

vatio

ns3,

151

3,15

13,

151

8,80

08,

800

8,80

0B

asel

ine

cont

rols

Yes

Yes

Yes

Yes

Yes

Yes

Indu

stry

dum

mie

sY

es (S

IC2)

Yes

(SIC

2)Y

es (S

IC2)

Yes

Yes

Yes

Firm

& p

lant

em

ploy

men

t, sk

ills

Yes

Yes

Yes

Yes

Yes

Yes

Clu

ster

SIC

2*C

tySI

C2*

Cty

SIC

2*C

tySI

C3

SIC

3SI

C3

Wor

ld M

anag

emen

t Sur

vey

U.S

. Cen

sus

Dat

a (M

OPS

)

Not

es: *

sign

ifica

nt a

t 10%

; **

5%; *

** 1

%. E

stim

ated

by

OLS

with

sta

ndar

d er

rors

clu

ster

ed a

t thr

ee-d

igit

indu

stry

leve

l. Th

e de

pend

ent v

aria

ble

is th

e an

nual

ized

thre

e-ye

ar

chan

ge o

f firm

ln(s

ales

) in

2009

-06.

Dec

entra

lizat

ion

mea

sure

d in

200

5. "

EXPO

RT

Gro

wth

" is

cha

nge

in ln

(exp

orts

) in

coun

try b

y th

ree

digi

t ind

ustry

cel

l bet

wee

n th

e 20

08 a

nd

2009

ave

rage

(the

mai

n G

reat

Rec

essi

on y

ears

) com

pare

d to

the

2006

and

200

7 av

erag

e (th

e la

test

pre

-Rec

essi

on y

ears

) in

colu

mns

(1)-(

3), a

nd is

the

aver

age

chan

ge (2

008/

2009

av

erag

e co

mpa

red

to 2

006/

2007

) in

ln(e

xpor

ts) a

t the

pro

duct

leve

l (H

S7) f

or th

e pr

oduc

ts th

e pl

ant p

rodu

ced

just

prio

r to

the

Gre

at R

eces

sion

in 2

006

in c

olum

ns (4

)-(6)

. "C

hang

e in

SD

(sto

ck re

turn

s)"

is th

e ch

ange

in s

tand

ard

devi

atio

n of

sto

ck re

turn

s in

thre

e di

git i

ndus

try c

ell b

etw

een

2008

and

200

9 av

erag

e co

mpa

red

to 2

006.

"C

hang

e in

Log

(SD

(sto

ck

retu

rns)

)" is

def

ined

ana

lgou

sly

usin

g th

e lo

g of

the

stan

dard

dev

iatio

n. A

ll co

lum

ns in

clud

e th

ree

digi

t ind

ustry

dum

mie

s an

d "n

oise

con

trols

" (p

lant

man

ager

's te

nure

and

hi

erar

chic

al s

enio

rity

and

the

inte

rvie

w's

relia

bilit

y sc

ore,

day

of t

he w

eek

and

dura

tion,

and

whe

ther

the

surv

ey w

as a

nsw

ered

onl

ine

or b

y m

ail).

Firm

and

pla

nt s

ize

are

ln(e

mpl

oym

ent)

are

skill

s is

the

ln(%

of e

mpl

oyee

s w

ith a

col

lege

deg

ree)

.

Notes:*significan

tat

10%;**

5%;***1%

level.The

depe

ndentvariab

leisthean

nualized

three-year

chan

geof

firm

ln(sales)in

2009

-06.

"EXPORT

Growth"ischan

gein

ln(exp

orts)in

coun

tryby

threedigitindu

stry

cellbe

tweenthe2008

and2009

average(the

mainGreat

Recession

years)

compa

redto

the2006

and2007

average

(the

latest

pre-Recession

years)

incolumns

(1)-(3),an

distheaveragechan

ge(2008/2009

averag

ecompa

redto

2006/2007)

inln(exp

orts)at

theprod

uctlevel(H

S7)for

theprod

ucts

theplan

tprod

uced

just

priorto

theGreat

Recession

in2006

incolumns

(4)-(6).

Colum

ns(1)-(3):

estimated

byOLSwithstan

dard

errors

clusteredat

two-digitindu

stry

bycoun

trylevel.

"Cha

ngein

SD(stock

returns)"is

thechan

gein

stan

dard

deviationof

stockreturnsin

twodigitindu

stry

bycoun

trycellbe

tween

2008

and2009

averagecompa

redto

2006.These

columns

includ

etw

odigitindu

stry

bycoun

trydu

mmies.

Colum

ns(4)-(6)estimated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

level.

"Cha

ngein

SD(stock

returns)"is

thechan

gein

stan

dard

deviationof

stockreturnsin

threedigitindu

stry

cellbe

tween2008

and2009

averagecompa

redto

2006.These

columns

includ

ethreedigitindu

stry

dummies.

Baselinecontrolsarecoun

tryan

dyear

dummiesan

d"n

oise

controls"(plant

man

ager’stenu

rean

dhierarchical

seniorityan

dtheinterview’srelia

bilityscore,

dayof

theweekan

ddu

ration

,WMSalso

includ

esan

alystdu

mmiesan

dMOPSwhether

thesurvey

was

answ

ered

onlin

eor

bymail).Indu

stry

dummiesareat

thethreedigitSIC

level(fou

rdigits

incolumn4).Firm

andplan

tem

ploymentaremeasuredas

log(em

ploy

ment),an

dskillsaremeasuredas

ln(%

ofem

ployeeswithacolle

gedegree).

40

Tab

le6:

Placebo

Test

(1)

(2)

(3)

(4)

(5)

(6)

Dep

ende

nt V

aria

ble:

Sam

ple

Yea

r<=2

005

Yea

r>=2

006

All

Yea

r<=2

005

Yea

r>=2

006

All

Dec

entr

aliz

atio

n 0.

221

0.04

10.

365

-0.1

17-0

.263

0.03

8(0

.334

)(0

.417

)(0

.310

)(0

.306

)(0

.357

)(0

.262

)D

ecen

tral

izat

ion*

EX

POR

T G

row

th0.

005

-0.0

47**

0.00

40.

004

-0.0

33**

0.00

4(0

.017

)(0

.018

)(0

.015

)(0

.015

)(0

.013

)(0

.012

)PO

ST*E

XPO

RT

Gro

wth

0.08

9***

0.11

5***

(0.0

24)

(0.0

21)

POST

*Dec

entr

aliz

atio

n-0

.389

-0.3

87(0

.427

)(0

.350

)PO

ST*D

ecen

tral

izat

ion*

EX

POR

T G

row

th-0

.052

***

-0.0

36**

(0.0

19)

(0.0

16)

Firm

s1,

080

1,33

01,

330

991

1,21

11,

211

Obs

erva

tions

3,66

43,

151

6,81

53,

265

2,83

96,

104

Bas

elin

e co

ntro

lsY

esY

esY

esY

esY

esY

esFi

rm &

pla

nt e

mpl

oym

ent,

skill

sY

esY

esY

esY

esY

esY

esIn

dust

ry b

y co

untr

y du

mm

ies

Yes

Yes

Yes

Yes

Yes

Yes

Clu

ster

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C3*

Cty

Sale

s G

row

thT

FP G

row

th

Not

es:

*sig

nific

anta

t10%

;**

5%;*

**1%

.Es

timat

edby

OLS

with

stan

dard

erro

rscl

uste

red

atth

ree-

digi

tin

dust

ryby

coun

tryle

vel.

Sale

sgr

owth

isth

ean

nual

ized

thre

e-ye

arch

ange

offir

mln

(sal

es).

TFP

grow

this

the

sam

eas

sale

sgr

owth

exce

ptw

ein

clud

eth

egr

owth

ofem

ploy

men

t,ca

pita

land

mat

eria

lson

the

right

hand

side

ofth

ere

gres

sion

.For

colu

mns

(2)a

nd(5

)we

use

long

diff

eren

ces

2011

-08,

2010

-07

and

2009

-06

and

inco

lum

ns(1

)and

(4)w

eus

elo

ngdi

ffer

ence

s20

08-0

5,20

07-0

4,20

06-0

3an

d20

05-0

2.C

olum

ns(3

)an

d(6

)po

olal

lthe

selo

ngdi

ffer

ence

sto

geth

er.

"PO

ST"

isa

dum

my

taki

ngva

lue

1in

ally

ears

afte

r20

06in

clud

ed.F

irman

dpl

ante

mpl

oym

ent

are

mea

sure

din

2006

."EX

POR

TG

row

th"

isch

ange

inln

(exp

orts

)in

coun

tryby

thre

edi

giti

ndus

tryce

llbe

twee

nth

e20

08an

d20

09av

erag

e(th

em

ain

Gre

atR

eces

sion

year

s)co

mpa

red

toth

e20

06an

d20

07av

erag

e(th

ela

test

pre-

Rec

essi

onye

ars)

.A

llco

lum

nsin

clud

edu

mm

ies

for

year

and

for

thre

edi

giti

ndus

tryby

coun

trypa

irs,a

ndco

ntro

lsfo

rfir

man

dpl

ants

ize,

skill

san

d"n

oise

"(p

lant

man

ager

'ste

nure

and

hie

rarc

hica

l sen

iorit

y an

d th

e in

terv

iew

's re

liabi

lity

scor

e, d

ay o

f the

wee

k an

d du

ratio

n an

d an

alys

t dum

mie

s).

Notes:W

MSsample.

*significan

tat

10%;**

5%;***1%

level.Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

bycoun

trylevel.Sa

lesgrow

this

thean

nualized

three-year

chan

geof

firm

ln(sales).

TFP

grow

this

thesameas

salesgrow

thexcept

weinclud

ethegrow

thof

employ

ment,

capitalan

dmaterials

ontherigh

tha

ndside

oftheregression

.Fo

rcolumns

(2)an

d(5)weuselong

diffe

rences

2011-08,

2010-07an

d2009-06an

din

columns

(1)an

d(4)weuselong

diffe

rences

2008-05,

2007-04,

2006-03an

d2005-02.

Colum

ns(3)an

d(6)po

olallthese

long

diffe

rences

together."P

OST

"isadu

mmytaking

value1in

ally

ears

after2006

includ

ed.

Firm

andplan

tem

ploymentaremeasuredin

2006."EXPORTGrowth"ischan

gein

ln(exp

orts)in

coun

tryby

threedigitindu

stry

cellbe

tweenthe2008

and2009

average

(the

mainGreat

Recession

years)

compa

redto

the2006

and2007

averag

e(the

latest

pre-Recession

years).Baselinecontrols

areyear

dummiesan

d"n

oise

controls"

(plant

man

ager’stenu

rean

dhierarchical

seniorityan

dtheinterview’srelia

bilityscore,

dayof

theweekan

ddu

ration

,and

analystdu

mmies.

Firm

andplan

tem

ploy

ment

aremeasuredas

log(em

ploy

ment),an

dskillsaremeasuredas

ln(%

ofem

ployeeswithacolle

gedegree).

41

Web Appendices - Not Intended for Publication

A Data Appendix

A.1 Industry-level variables

Exports

We measure changes in exports in an industry by country cell using the UN COMTRADE database of world trade. This is

an international database of six-digit product level information on all bilateral imports and exports between any given pairs

of countries. We first aggregate the COMTRADE value of export data (in US dollars)from its original six-digit product level

to three-digit US SIC-1987 level using the Pierce and Schott (2010) concordance. We deflate the industry and country specific

export value series by a country and year specific CPI from the World Bank (2010 base year) to measure “real exports.” The

Export growth variable is defined as the logarithmic change in exports in 2008-09 (the average in a cell across these two Great

Recession years) relative to 2006-07 (the average across the two years immediately prior to the Great Recession). The real

export growth variable is winsorized at the 5th and the 95th percentile.

Durability

Data on the average durability of the goods produced in the industry are drawn from Ramey and Nekarda (2013). This combines

data gathered by Bils and Klenow (1998) with information from the Los Angeles HOA Management “Estimating Useful Life for

Capital Assets” to assign a service life to the product of each four-digit industry. This is a continuous cross-sectional measure

at the 4-digit industry level.

Bartik Instrument

The Bartik IV for export growth in a country-industry cell is constructed as the change in world import demand (WID) for

commodity m in country r between time and t (2008 and 2009) and t− 1 (2006 and 2007), is defined following Mayer, Melitz

and Ottaviano (2008) as:

4zmr,t =∑p

smpr,t−1 ∗ 4WIDmpr′,t

where smpr,t−1 denotes the share of exports of commodity m from country r to partner country p at time t− 1; WIDmpr′,t is

the log change in total imports of commodity c in partner country p between t and t− 1 from all countries excluding country

r (hence the r’ sub-script). Consider, for example, the Bartik IV for changes in US exports of anti-ulcer drugs. For a given

partner, like the UK, we calculate the share of all American anti-ulcer drugs exported that were exported to the UK in t− 1 ,

sdrugs,UK,US,07−06, and then multiply this by the change in the imports of anti-ulcer drugs into the UK from every country

(except the US), 4WIDdrugs,UK,US′,09−08. This is a prediction of what the demand for US exports to the UK will be

coming from exogenous world demand (rather than US specific factors). We repeat this for every country in the world (not just

the UK) and then sum over all the US partner countries in the world.

Commodity m is measured at the 6-digit level of the Harmonized Commodity Description and Coding System (HS).

Commodity level measures are then mapped into Industry j three-digit Standard Industry Classification (SIC) codes using the

Pierce and Schott (2010) concordance.

42

A.2 World Management Survey (WMS) International Data

Firm-level Accounting Databases

Our sampling frame was based on the Bureau van Dijk (BVD) ORBIS which is composed of the BVD Amadeus dataset for

Europe (France, Germany, Greece, Italy, Poland, Portugal, and the United Kingdom); BVD Icarus for the United States, BVD

Oriana for Japan. These databases all provide sufficient information on companies to conduct a stratified telephone survey

(company name, address, and a size indicator). These databases also typically have accounting information on employment,

sales and assets. Apart from size, we did not insist on having accounting information to form the sampling population, however.

Amadeus are constructed from a range of sources, primarily the National registries of companies (such as Companies House in

the United Kingdom). Icarus is constructed from the Dun & Bradstreet database, which is a private database of over 5 million

U.S. trading locations built up from credit records, business telephone directories, and direct research. Oriana is constructed

from the Teikoku Database in Japan.The full WMS consists of 34 countries but because we need decentralization data in 2006

we are restricted to the 12 countries surveyed in the 2006 wave. Because we wanted to focus on mature economies we dropped

China and India which left us with 10 OECD countries (France, Great Britain, Germany, Greece, Italy, Japan, Poland, Portugal,

Sweden and the US).

The Organizational Survey

In every country the sampling frame for the organization survey was all firms with a manufacturing primary industry code

with between 50 and 5,000 employees on average over the most recent three years of data. Interviewers were each given a

randomly selected list of firms from the sampling frame. More details are available in Bloom, Sadun and Van Reenen (2012)

where we compare the sampling frame with Census demographic data from each country and show that the sample is broadly

representative of medium sized manufacturing firms. We also analyzed sample selection - the response rate was 45% and

respondents appear random with respect to company performance, although larger firms where slightly more likely to respond.

We collected a detailed set of information on the interview process itself (number and type of prior contacts before obtaining

the interviews, duration, local time-of-day, date and day-of-the-week), on the manager (gender, seniority, nationality, company

and job tenure, internal and external employment experience, and location), and on the interviewer (we can include individual

“analyst” fixed effects, time-of-day, and subjective reliability score). We used a subset of these “noise controls” (see text) to help

reduce residual variation.

In analyzing organizational surveys across countries we also have to be extremely careful to ensure comparability of responses.

One step was the team all operated from two large survey rooms in the London School of Economics. Every interviewer also

had the same initial three days of interview training, which provided three “calibration” exercises, where the group would all

score a role-played interview and then discuss scoring together of each question. This continued throughout the survey, with

one calibration exercise every Friday afternoon as part of the weekly group training sessions. Finally, the analysts interviewed

firms in multiple countries since they all spoke their native language plus English, so interviewers were able to interview firms

from their own country plus the UK and US, enabling us to remove interviewer fixed effects.

The construction of the degree of decentralization measures (from Central Headquarters to Plant Manager) is discussed in

some detail in the text. The questions are addressed to the plant manager. We only keep observations where at least two of

the four decentralization questions were answered (and we include a control for the number of non-missing questions in the set

of noise controls). We drop observations where the plant manager is also the CEO (5% of firms). In cases were the Central

Headquarters is on the same site as the plant we interviewed we add a dummy variable to indicate this (one of the noise controls)

to reflect potentially greater monitoring. We use the data from the 2006 wave in all cases except when we analyze changes in

decentralization as an outcome where we exploit the fact that we ran another wave in 2009 and 2010 for a sub-sample of firms.

43

As a check of potential survey bias and measurement error we performed repeat interviews on 72 firms in 2006, contacting

different managers in different plants at the same firm, using different interviewers. To the extent that our organizational

measure is truly picking up company-wide practices these two scores should be correlated, while to the extent the measure is

driven by noise the measures should be independent. The correlation of the first interview against the second interviews was

0.513 (p-value of 0.000), with no obvious (or statistically significant) relationship between the degree of measurement error and

the decentralization score. That is to say, firms that reported very low or high decentralization scores in one plant appeared to

be genuinely very centralized or decentralized in their other plants, rather than extreme draws of sampling measurement error.

Firm-level variables

Our firm accounting data on sales, employment, capital (fixed assets), profits and intermediate inputs came from BVD ORBIS.

Whether the variable is reported depends on the accounting standards in different countries. Sales are deflated by a three

digit industry producer price index. BVD has extensive information on ownership structure, so we can use this to identify

whether the firm was part of a multinational enterprise. We also asked specific questions on the multinational status of the

firm (whether it owned plants aboard and the country where the parent company is headquartered) to be able to distinguish

domestic multinationals from foreign multinationals.

We collected many other variables through our survey including information on plant size, skills, organization, etc. as

described in the main text. We also collected management practices data in the survey. These were scored following the

methodology of Bloom and Van Reenen (2007), with practices grouped into four areas: operations (three practices), monitor-

ing (five practices), targets (five practices), and incentives (five practices). The shop-floor operations section focuses on the

introduction of lean manufacturing techniques, the documentation of processes improvements, and the rationale behind intro-

ductions of improvements. The monitoring section focuses on the tracking of performance of individuals, reviewing performance,

and consequence management. The targets section examines the type of targets, the realism of the targets, the transparency

of targets, and the range and interconnection of targets. Finally, the incentives section includes promotion criteria, pay and

bonuses, and fixing or firing bad performers, where best practice is deemed the approach that gives strong rewards for those

with both ability and effort. Our management measure uses the unweighted average of the z-scores of all 18 dimensions.

Our basic industry code is the U.S. SIC (1997) three digit level—which is our common industry definition in all countries.

We allocate each firm to its main three digit sector (based on sales).

A.3 U.S. Census Bureau Data: MOPS

Sample

Table A2 shows how our sample is derived from the universe of U.S. business establishments. The U.S. Census Bureau data on

decentralization comes from the 2010 Management and Organizational Practices Survey (MOPS), which was a supplement to

the 2010 Annual Survey of Manufactures (ASM). The MOPS survey was sent to all ASM establishments in the ASM mail-out

sample. Overall, 49,782 MOPS surveys were successfully delivered, and 37,177 responses were received, yielding a response rate

of 78%.

The MOPS contains 36 multiple choice questions, split into 3 modules: management practices (16 questions), organization

(13 questions), and background characteristics (7 questions). Decentralization measures come from the “Organization” module

of the MOPS. Only establishments with headquarters located off-site are instructed to answer questions in the organization

module. This reduces the sample to about 20,000 establishments. We also require matches to the 2006 and 2009 ASM in

order to calculate the growth rates used in the analysis. This reduces the sample size substantially for two reasons. First, all

of the establishments in our sample must have been operating in both 2006 and 2009. The second reason is related to the

44

ASM sample design. The ASM is a rotating 5-year panel which samples large establishments with certainty but also includes

a random sample of smaller establishments. The ASM sample is refreshed every five years, most recently in 2009, thus we lose

establishments which were in the 2009 and 2010 ASM samples and responded to the MOPS, but were not in the 2006 ASM

sample. Finally, we require that respondents answer all 6 of the questions about decentralization (described below) and have

positive value added and imputed capital in 2010. The final sample contains 8,800 establishments and 3,150 firms.

Decentralization

Our measure of decentralization is constructed from 6 questions on the MOPS (questions 18 through 23), which measure the

allocation of real decision making rights between manufacturing plant managers and their central headquarters. Respondents

are asked whether decisions about hiring, pay increases, product introductions, pricing, and advertising are conducted at the

establishment, headquarters or both, and about the largest capital expenditure plant managers can make without authorization

from headquarters. The survey asks about organizational practices in 2005 and 2010. We use information on decentralization in

2005 in the main analysis because firms may endogenously respond to the crisis in 2010 by changing organizational structures.

Each of the 6 decentralization questions is normalized on a scale from zero to one, with one being most decentralized and

zero being least decentralized. For example, question 18 reads “In 2005 and 2010, where were decisions on hiring permanent

full-time employees made?” There are three possible responses: “Only at this establishment” which is assigned the value one;

“Both at this establishment and at headquarters” which is assigned a value of one-half; “Only at headquarters” which is assigned

a value of zero. We then standardize each question to have a mean equal to zero and standard deviation equal to one, take

the mean over all six standardized questions, and then standardize this mean so that it has a mean equal to zero and standard

deviation equal to one.

Exports

Our proxy for the Great Recession is a plant-specific export shock constructed by matching the product files of the 2006 ASM

which disaggregate establishment revenues by product class to the Longitudinal Firm Trade Transactions (LFTTD) data which

contain the universe of export shipments at the firm-product level. To construct our measure, we first match the product

categories from LFTTD (ten-digit Harmonized System categories, or HS10) to the 7-digit NAICS product classes contained in

the ASM using the Pierce and Schott (2009) concordance. Next, we aggregate exports to the 7-digit NAICS level and calculate

the change in exports in each product over the Recession, defined as the logarithmic change in exports in 2008-09 (the average

in a cell across these two Great Recession years) relative to 2006-07 (the average across the two years immediately prior to the

Great Recession). Finally, we construct our plant-specific export shock as the weighted average of product export growth in the

crisis, where fore each plant, the weights assigned to each product category is that plant’s share of sales revenue in the product

as measured before the crisis in the 2006 ASM.

Product Churn

Product churn is constructed using data come from the US Census Bureau’s Census of Manufactures (CM). The CM asks

establishments to list the dollar value of annual shipments by 10-digit product code. Establishments receive a list of all the

product codes typically produced by establishments in their industry, along with corresponding descriptions of each code.

We start by calculating the total number of 10-digit products by each establishment in a given year, as well as the number

of added products and the number of dropped products for each establishment compared to the previous CM 5 years earlier.

This of course restricts the sample to manufacturing establishments which were alive five years earlier. We further restrict the

sample by dropping establishments producing fewer than 3 products in both Censuses. Product churn at the establishment

45

level is measured as the number of products added or dropped between the previous Census and the current Census, divided by

the average number of products produced in both Censuses. That is, product churn for establishment i in year t is defined as:

Product Churn i,t =Products Added i,t + Products Dropped i,t

0.5 (# Products i,t +# Products i,t−5)

Industry product churn in year t is the average establishment-level product churn among establishments within an industry

(three digit US SIC-1987). To calculate industry-level change in product churn, we simply subtract product churn in 2007

(constructed from the product data in the 2002 and 2007 Censuses) from product churn in 2012.

ASM variables

Directly from the ASM we obtain material inputs, shipments (deflated by a three digit price deflator) as our sales measure and

the headcount of employees for labor. Real capital stocks are constructed using the perpetual inventory method, following the

methodology in Bloom, Floetotto, Jaimovich, Saporta-Eksten and Terry (2018). In particular, we combine detailed data on the

book value of assets every 5 years from the CM with annual investment data from the ASM. We first convert CM capital stocks

from book to market value using BEA fixed asset tables. We then deflate capital stocks and investment using industry-year

price indices from the NBER-CES Manufacturing Industry Database. Finally, we apply the perpetual inventory method, using

the formula Kt = (1 − δt)Kt−1 + It . This procedure is done separately for structures and for equipment. However, since

the ASM contains investment broken down into investment in equipment and investment in structures, but the CM does not

break down capital stocks into these two components, we must apportion plant capital stocks into each component. We do this

by assigning the share of capital stock to equipment and structures which matches the share of investment in equipment and

structures.

46

Figure A1: Change in Industry/Country Exports and Sales before and after the Great Recession

Figure A1 - Changes in Industry/Country Exports and Sales before and after the Great Recession

CHART DATA

Notes: Each bar plots the yearly percentage change in real manufacturingexports. The countries included in the sample are France, Germany, Greece,Italy, Japan, Poland, Portugal, Sweden, UK and US.

-15

-10

-5

0

5

10

2007 2008 2009

YO

Y L

og C

hang

e -%

Exports SalesNotes: Each bar plots the yearly percentage change in real manufacturing exports. The countries included in the sample areFrance, Germany, Greece, Italy, Japan, Poland, Portugal, Sweden, UK and US.

47

Figure A2: Average Decentralization Z-score by Quintile of Product Churn

Figure A2 - Average Decentralization Z-score by Quintile of Product Churn

Notes: MOPS data. Industry product churn is the average of plant product churn.Plant product churn = (# products added from '02 to '07 + # products dropped from '02 and '07)/(0.5*# products produced in '02 + 0.5*# products produced in '07).

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5

Avg

. dec

entr

aliz

atio

n z-

scor

e

Quintile of product churn

Notes: MOPS data. Industry product churn is the average of plant product churn. Plant product churn = (# products addedfrom ’02 to ’07 + # products dropped from ’02 and ’07)/(0.5*# products produced in ’02 + 0.5*# products produced in ’07).

48

Figure A3: Change in Industry Product Churn and Economic ShocksFigure A3 - Economic Shocks and Change in Industry Product ChurnPanel A - Export Growth

Panel B - Product Durability

Figure A3 - Export Growth and Change in Industry Product Churn

Notes: Change in product churn is industry product churn in 2012 minus industry product churn in 2007.Exports growth is the change in ln(exports) from 2007 to 2012. Both variables are winzorized at the 5th and95th percentiles and measured at the level of the three-digit industry. Vingtiles plotted.

-.6-.4

-.20

.2Ch

ange

in Pr

oduc

t Chu

rn

-20 0 20 40 60Export Growth

Notes: MOPS data. Change in product churn is industry product churn in 2012 minus industry product churn in 2007. "ExportGrowth" is the change in ln(exports) from 2007 to 2012. “Durability” is the average durability of the goods produced in theindustry (in years), drawn from Ramey and Nekarda (2013). All variables are winzorized at the 5th and 95th percentiles andmeasured at the level of the three-digit industry. Ventiles plotted.

49

Tab

leA1:

Decentralizationqu

estion

sTa

ble

A1

- Dec

entr

aliz

atio

n qu

estio

ns

Scor

e 1

Scor

e 3

Scor

e 5

Scor

e 1

Scor

e 3

Scor

e 5

Scor

e 1

Scor

e 3

Scor

e 5

Que

stio

n D

5: “

Is th

e CH

Q o

n th

e sit

e be

ing

inte

rvie

wed”

?

Not

es: T

he e

lect

roni

c su

rvey

, tra

inin

g m

ater

ials

and

surv

ey v

ideo

foot

age

are

avai

labl

e on

ww

w.w

orld

man

agem

ents

urve

y.co

m

Que

stio

n D

4: “

How

muc

h of

sale

s and

mar

ketin

g is

carr

ied

out a

t the

pla

nt le

vel (

rath

er th

an a

t the

CH

Q)”

?

Prob

e un

til y

ou c

an a

ccur

atel

y sc

ore

the

ques

tion.

Als

o ta

ke a

n av

erag

e sc

ore

for s

ales

and

mar

ketin

g if

they

are

take

n at

diff

eren

t lev

els.

Scor

ing

grid

:N

one—

sale

s and

mar

ketin

g is

all

run

by C

HQ

Sale

s and

mar

ketin

g de

cisi

ons a

re sp

lit b

etw

een

the

plan

t and

CH

QTh

e pl

ant r

uns a

ll sa

les a

nd m

arke

ting

Prob

e un

til y

ou c

an a

ccur

atel

y sc

ore

the

ques

tion—

for e

xam

ple

if th

ey sa

y “I

t is c

ompl

ex, w

e bo

th p

lay

a ro

le,”

ask

“C

ould

you

talk

me

thro

ugh

the

proc

ess f

or a

rece

nt p

rodu

ct in

nova

tion?

”

Scor

ing

grid

:A

ll ne

w p

rodu

ct in

trodu

ctio

n de

cisi

ons a

re ta

ken

at

the

CH

QN

ew p

rodu

ct in

trodu

ctio

ns a

re jo

intly

det

erm

ined

by

the

plan

t and

CH

QA

ll ne

w p

rodu

ct in

trodu

ctio

n de

cisi

ons t

aken

at t

he

plan

t lev

el

Que

stio

n D

3: “

Whe

re a

re d

ecisi

ons t

aken

on

new

prod

uct i

ntro

duct

ions

—at

the

plan

t, at

the

CHQ

or b

oth”

?

For Q

uest

ions

D1,

D3,

and

D4

any

scor

e ca

n be

giv

en, b

ut th

e sc

orin

g gu

ide

is o

nly

prov

ided

for s

core

s of 1

, 3, a

nd 5

.

Que

stio

n D

1: “

To h

ire a

FU

LL-T

IME

PERM

ANEN

T SH

OPF

LOO

R wo

rker

wha

t agr

eem

ent w

ould

you

r pla

nt n

eed

from

CH

Q (C

entra

l Hea

d Q

uarte

rs)?

”

Prob

e un

til y

ou c

an a

ccur

atel

y sc

ore

the

ques

tion—

for e

xam

ple

if th

ey sa

y “I

t is m

y de

cisi

on, b

ut I

need

sign

-off

from

cor

pora

te H

Q.”

ask

“H

ow o

ften

wou

ld si

gn-o

ff be

giv

en?”

Scor

ing

grid

:N

o au

thor

ity—

even

for r

epla

cem

ent h

ires

Req

uire

s sig

n-of

f fro

m C

HQ

bas

ed o

n th

e bu

sine

ss

case

. Typ

ical

ly a

gree

d (i.

e. a

bout

80%

or 9

0% o

f th

e tim

e).

Com

plet

e au

thor

ity—

it is

my

deci

sion

ent

irely

Que

stio

n D

2: “

Wha

t is t

he la

rges

t CAP

ITAL

INVE

STM

ENT

your

pla

nt c

ould

mak

e wi

thou

t prio

r aut

horiz

atio

n fr

om C

HQ

?”

Not

es: (

a) Ig

nore

form

-fill

ing

(b) P

leas

e cr

oss c

heck

any

zer

o re

spon

se b

y as

king

“W

hat a

bout

buy

ing

a ne

w c

ompu

ter—

wou

ld th

at b

e po

ssib

le?”

and

then

pro

be…

.

(c) C

halle

nge

any

very

larg

e nu

mbe

rs (e

.g. >

$¼m

in U

S) b

y as

king

“To

con

firm

you

r pla

nt c

ould

spen

d $X

on

a ne

w p

iece

of e

quip

men

t with

out p

rior

cle

aran

ce fr

om C

HQ

?”

(d) U

se th

e na

tiona

l cur

renc

y an

d do

not

om

it ze

ros (

i.e. f

or a

U.S

. firm

twen

ty th

ousa

nd d

olla

rs w

ould

be

2000

0).

50

Tab

leA2:

MOPSSa

mpling

Tabl

e A2

-MO

PS S

ampl

ing

Sam

ple

Sour

ceSa

mpl

e C

rite

ria

Num

ber

of

esta

blis

hmen

ts

(in th

ousa

nds)

Tota

l em

ploy

men

t (in

thou

sand

s)

Aver

age

empl

oym

ent

(1) U

nive

rse

of e

stab

lishm

ents

LBD

Non

e7,

041

134,

637

19.1

(2) M

anuf

actu

ring

LBD

NA

ICS

31-3

329

812

,027

40.4

(3) A

nnua

l Sur

vey

of M

anuf

actu

res

ASM

NA

ICS

31-3

3, a

nd e

ither

ove

r 500

em

ploy

ees,

or in

A

SM ra

ndom

sam

ple.

Pos

itive

em

ploy

men

t and

sale

s, an

d ta

bbed

517,

387

143.

5

(4) M

OPS

resp

onde

nts

MO

PSA

s in

(3),

also

resp

onde

d to

MO

PS36

5,62

915

5.8

(5) O

RG

mod

ule

resp

onde

nts

MO

PSA

s in

(4),

and

resp

onde

d to

any

of M

OPS

que

stio

ns 1

8-23

203,

580

178.

4

(6) R

egre

ssio

n sa

mpl

eM

OPS

As i

n (5

), re

spon

ded

to a

ll O

RG

"re

call"

que

stio

ns,

mat

ch to

ASM

200

6 an

d A

SM 2

009,

pos

itive

val

ue

adde

d an

d im

pute

d ca

pita

l in

ASM

201

09

2,13

524

3.3

51

Tab

leA3:

Decentralization,

salesgrow

th,a

ndprod

uctchurnin

variou

ssubsam

ples

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Dep

ende

nt V

aria

ble:

Sal

es g

row

th ('

07-'1

2)

Sam

ple

Bas

elin

eD

iffer

entia

ted

Prod

ucts

Und

iffer

entia

ted

Prod

ucts

Imm

atur

e In

dust

ryM

atur

e In

dust

rySm

all

Est

ablis

hmen

tsL

arge

E

stab

lishm

ents

Dec

entr

aliz

atio

n 1.524**

2.464***

0.1530

1.6670

1.3880

2.355**

0.7670

(0.681)

(0.711)

(1.24)

(1.003)

(1.175)

(1.009)

(0.724)

Dec

ent.*

Prod

uct C

hurn

4.722***

5.582***

2.4280

6.176***

2.1520

6.150***

4.092**

(1.43)

(1.755)

(2.477)

(1.874)

(2.786)

(1.81)

(1.692)

Obs

erva

tions

8,200

4,500

3,700

4,300

4,000

4,100

4,100

Clu

ster

SIC3

SIC3

SIC3

SIC3

SIC3

SIC3

SIC3

Notes:MOPSda

ta.*significan

tat

10%;**

5%;***1%

.Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

level.The

depe

ndentvariab

leis

the

annu

alized

five-year

chan

geof

firm

ln(sales)2012-07.

Decentralizationmeasuredin

2005."P

rodu

ctChu

rn"isthethreedigitindu

stry

ofvalueof

theaveragechan

gein

the(num

berof

prod

ucts

addedbe

tweentan

dt-5plus

thenu

mbe

rprod

ucts

drop

pedbe

tweentan

dt-5)/(averagenu

mbe

rof

prod

ucts

betw

eentan

dt-5).Allcolumns

includ

ethreedigitindu

stry

dummies,

firm

andplan

tsize,skillsan

d"n

oise

controls"(plant

man

ager’s

tenu

rean

dhierarchical

seniorityan

dtheinterview’s

relia

bility

score,

dayof

theweekan

ddu

ration

,and

whether

thesurvey

was

answ

ered

onlin

eor

bymail).Colum

n(1)includ

estheba

selin

eprod

uctchurnsample.

Colum

ns(2)an

d(3)split

thissampleaccordingto

whether

anestablishm

entisin

anindu

stry

which

sells

diffe

rentiatedgo

odsor

not,accordingto

Rau

ch’sclassificationof

diffe

rentiated

andho

mogeneous

good

s(R

auch

1999).

Colum

n(4)includ

esestablishm

ents

in“immature”

indu

stries,defin

edas

indu

stries

which

therearean

aboveaverageshareof

firmswhich

werebo

rnin

thepa

st5years,i.e

.2000

throug

h2005.Colum

n(5)includ

esestablishm

ents

in“m

ature”

indu

stries

which

hadabe

low

averageshareof

firms

born

inthepa

st5years.

Colum

n(6)includ

esthesm

allest

50%

ofestablishm

ents

inthesamplean

dColum

n(7)includ

esthelargest50%

ofestablishm

ents

inthe

sample.

52

Tab

leA4:

Decentralization,

AgencyCosts

andFinan

cial

Con

straints

Tab

le A

11 -

Dec

entr

aliz

atio

n an

d Fi

nanc

ial C

onst

rain

ts

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Dep

ende

nt V

aria

ble

= Sa

les

Gro

wth

Dec

entr

aliz

atio

n0.

2651

0.35

061.

3797

0.17

520.

0764

0.34

9**

0.25

70(0

.477

0)(0

.76)

(2.3

903)

(0.4

01)

(1.2

689)

(0.1

585)

(0.4

831)

EX

POR

T G

row

th-3

.187

7-3

.155

0-2

.877

4-3

.210

4(3

.189

5)(3

.229

8)(3

.200

5)(3

.193

2)D

ecen

t*E

XPO

RT

Gro

wth

-0.0

392

-0.0

407*

-0.0

394

-0.0

392

(0.0

241)

(0.0

244)

(0.0

241)

(0.0

240)

AB

X e

xpos

ure

-0.4

003*

-0.9

120

(0.2

374)

(0.8

330)

Dec

ent.*

AB

X e

xpos

ure

-0.0

031

-0.2

883

(0.2

042)

(0.6

164)

Leh

man

exp

osur

ex

xx

xD

ecen

t.*L

ehm

an e

xpos

ure

xx

xx

Len

der

heal

th-0

.001

9-0

.273

0(0

.459

7)(1

.294

2)D

ecen

t*L

ende

r he

alth

0.00

01-0

.021

2(0

.340

7)(1

.011

1)

Obs

erva

tions

2,00

02,

000

2,00

02,

000

2,00

02,

000

2,00

0C

ontr

ols

Firm

& p

lant

em

ploy

men

t, sk

ills

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Clu

ster

Len

der

Len

der

Len

der

Len

der

Len

der

Len

der

Len

der

U.S

. Cen

sus

Dat

a (M

OPS

)

Not

es: *

sign

ifica

nt a

t 10%

; **

5%; *

** 1

%. E

stim

ated

by

OLS

with

sta

ndar

d er

rors

clu

ster

ed b

y th

e fir

m's

prim

ary

lend

er. T

he d

epen

dent

var

iabl

e is

the

annu

aliz

ed

thre

e-ye

ar c

hang

e of

firm

ln(s

ales

) fro

m 2

009-

06. D

ecen

traliz

atio

n is

mea

sure

d in

200

5. "

EXPO

RT

Gro

wth

" is

cha

nge

in ln

(exp

orts

) by

thre

e di

git i

ndus

try c

ell b

etw

een

the

2008

and

200

9 av

erag

e (th

e m

ain

Gre

at R

eces

sion

yea

rs) c

ompa

red

to th

e 20

06 a

nd 2

007

aver

age

(the

late

st p

re-R

eces

sion

yea

rs).

"Len

der e

xpos

ure

to h

ousi

ng

bubb

le"

is th

e . "

AB

X e

xpos

ure"

is th

e co

rrela

tion

of th

e fir

m's

lend

er's

daily

sto

ck re

turn

s w

ith th

e re

turn

on

the

AB

X A

AA

200

6-H

1 in

dex,

whi

ch fo

llow

s th

e pr

ice

mor

tgag

e-ba

cked

sec

uriti

es is

sued

with

a A

AA

ratin

g. "

Lend

er h

ealth

" is

an

aggr

egat

ion

of le

nder

bal

ance

she

et v

aria

bles

incl

udin

g tra

ding

acc

ount

loss

es, r

eal e

stat

e ch

arge

-offs

, and

the

depo

sits

to li

abili

ties

ratio

. W

e co

mbi

ne th

ese

varia

bles

into

one

lend

er h

ealth

mea

sure

by

norm

aliz

ing

each

to h

ave

mea

n 0

and

stan

dard

dev

iatio

n 1,

taki

ng a

n av

erag

e, a

nd th

en n

orm

aliz

ing

this

ave

rage

to h

ave

mea

n 0

and

stan

dard

dev

iatio

n 1.

All

colu

mns

incl

ude

"noi

se c

ontro

ls"

(pla

nt m

anag

er's

tenu

re a

nd

hier

arch

ical

sen

iorit

y an

d th

e in

terv

iew

's re

liabi

lity

scor

e, d

ay o

f the

wee

k an

d du

ratio

n, w

heth

er th

e su

rvey

was

ans

wer

ed o

nlin

e or

by

mai

l). F

irm a

nd p

lant

siz

e ar

e ln

(em

ploy

men

t) ar

e sk

ills

is th

e ln

(% o

f em

ploy

ees

with

a c

olle

ge d

egre

e).

Notes:*significan

tat

10%;**

5%;***1%

level.

Estim

ated

byOLSwithstan

dard

errors

clusteredby

thefirm’s

prim

arylend

er.

The

depe

ndentvariab

leis

the

annu

alized

three-year

chan

geof

firm

ln(sales)from

2009-06.

Decentralizationismeasuredin

2005."E

XPORT

Growth"isaveragechan

ge(2008/2009

averagecompa

red

to2006/2007average)

inln(exp

orts)at

theprod

uctlevel(H

S7)fortheprod

ucts

theplan

tprod

uced

just

priorto

theGreat

Recession

in2006."L

ehman

expo

sure"is

thefraction

ofthefirm’s

lend

er’s

synd

icated

loan

portfolio

where

Lehman

Brothersha

dalead

role

intheloan

deal."A

BX

expo

sure"is

thecorrelationof

thefirm’s

lend

er’s

daily

stockreturnswiththereturn

ontheABX

AAA

2006-H

1index,

which

follo

wsthepricemortgage-ba

cked

securities

issued

withaAAA

rating

."L

ender

health"is

anaggregationof

lend

erba

lanc

esheetvariab

lesinclud

ingtrad

ingaccoun

tlosses,real

estate

charge-offs,an

dthedepo

sits

tolia

bilitiesratio.

Wecombine

thesevariab

lesinto

onelend

erhealth

measure

byno

rmalizingeach

toha

vemean0an

dstan

dard

deviation1,

taking

anaverag

e,an

dthen

norm

alizingthis

averageto

have

mean0an

dstan

dard

deviation1.

Allcolumns

includ

e"n

oise

controls"(plant

man

ager’stenu

rean

dhierarchical

seniorityan

dtheinterview’srelia

bilityscore,

day

oftheweekan

ddu

ration

,whether

thesurvey

was

answ

ered

onlin

eor

bymail).Firm

andplan

tsize

areln(employ

ment)

areskillsistheln(percentageof

employeeswith

acolle

gedegree).

53

Tab

leA5:

Rob

ustnessof

resultsto

interactions

ofexpo

rtgrow

thwithotherfirm-le

velv

ariables

inW

MSda

taT

able

A4

- R

obus

tnes

s W

MS

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Dep

ende

nt V

aria

ble:

Sal

es G

row

th

Dec

entr

aliz

atio

n 0.

041

0.02

6-0

.098

0.05

00.

046

-0.2

410.

044

-0.0

788.

306

(0.4

17)

(0.4

16)

(0.4

23)

(0.4

40)

(0.4

31)

(0.4

51)

(0.4

17)

(0.4

24)

(10.

153)

Dec

entr

aliz

atio

n*E

XPO

RT

Gro

wth

-0.0

47**

-0.0

46**

-0.0

54**

*-0

.043

**-0

.043

**-0

.049

**-0

.047

**-0

.049

**-0

.044

*(0

.018

)(0

.018

)(0

.018

)(0

.019

)(0

.018

)(0

.020

)(0

.018

)(0

.019

)(0

.022

)L

og(%

em

ploy

ees

with

a c

olle

ge d

egre

e)0.

470

0.50

60.

439

0.31

20.

419

0.48

60.

483

0.42

30.

127

(0.3

30)

(0.3

33)

(0.3

29)

(0.3

46)

(0.3

36)

(0.3

56)

(0.3

34)

(0.3

40)

(0.4

01)

Log

(% e

mpl

oyee

s w

ith a

col

lege

deg

ree)

*EX

POR

T G

row

th0.

023

-0.0

84(0

.038

)(0

.435

)M

anag

emen

t 0.

977

1.02

5(0

.664

)(0

.695

)M

anag

emen

t*E

XPO

RT

Gro

wth

0.04

2*0.

528

(0.0

25)

(0.6

17)

Prof

it M

argi

n (p

re r

eces

sion

)7.

643

6.68

4(4

.833

)(5

.414

)Pr

ofit

Mar

gin

(pre

rec

essi

on)*

EX

POR

T G

row

th0.

117

-0.0

20(0

.208

)(4

.903

)W

orke

rs' d

ecen

tral

izat

ion

-0.0

381.

022

(1.0

15)

(1.1

35)

Wor

kers

' dec

entr

aliz

atio

n*E

XPO

RT

Gro

wth

-0.0

74*

-0.0

47(0

.040

)(0

.890

)Fo

reig

n Pl

ant M

anag

er

0.47

83.

215

(2.2

93)

(2.0

49)

Fore

ign

Plan

t Man

ager

*E

XPO

RT

Gro

wth

0.18

2***

-4.6

68*

(0.0

69)

(2.7

86)

Mal

e Pl

ant M

anag

er-0

.392

1.56

9(1

.662

)(1

.599

)M

ale

Plan

t Man

ager

*EX

POR

T G

row

th0.

046

0.05

1(0

.052

)(1

.557

)Pl

ant M

anag

er A

ge-3

.687

-2.2

78(2

.966

)(3

.417

)Pl

ant M

anag

er A

ge*E

xpor

t Gro

wth

-0.1

04-2

.601

(0.0

93)

(2.5

77)

Obs

erva

tions

3151

3151

3151

2905

3097

2784

3151

3125

2523

Bas

elin

e co

ntro

lsY

esY

esY

esY

esY

esY

esY

esY

esY

esFi

rm &

pla

nt e

mpl

oym

ent,

skill

sY

esY

esY

esY

esY

esY

esY

esY

esY

esIn

dust

ry b

y co

untr

y du

mm

ies

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Clu

ster

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C3*

Cty

SIC

3*C

ty

Not

es:

*sig

nific

anta

t10%

;**

5%;*

**1%

.Est

imat

edby

OLS

with

stan

dard

erro

rscl

uste

red

atth

ree-

digi

tind

ustry

byco

untry

leve

lin

allc

olum

ns.S

peci

ficat

ions

are

the

sam

eas

Tabl

e2

colu

mn

(3)e

xcep

taug

men

ted

with

addi

tiona

lvar

iabl

esfr

omth

eW

MS

(line

aran

din

tera

cted

with

expo

rtgr

owth

).M

anag

emen

tis

the

z-sc

ored

aver

age

of18

z-sc

ored

man

agem

ent

ques

tions

(see

Blo

oman

dV

anR

eene

n20

07fo

rde

tails

)."L

og(%

empl

oyee

sw

itha

colle

gede

gree

)"is

the

natu

ral

loga

rithm

ofth

epe

rcen

tof

empl

oyee

sw

itha

bach

elor

sde

gree

.Wor

ker

dece

ntra

lizat

ion

isth

ez-

scor

edav

erag

eof

2qu

estio

nson

wor

ker

auto

nom

y.Fo

reig

n/M

ale

plan

tman

ager

=1if

plan

tman

ager

isfr

om a

fore

ign

coun

try o

r mal

e, re

spec

tivel

y.

Notes:W

MSData.

*significan

tat

10%;**

5%;***1%

level.Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

bycoun

trylevelin

allcolumns.

Specification

sarethesameas

Tab

le2column(3)except

augm

entedwithad

dition

alvariab

lesfrom

theW

MS(linearan

dinteracted

withexpo

rtgrow

th).

Man

agem

ent

isthez-scored

averageof

18z-scored

man

agem

entqu

estion

s(see

Bloom

andVan

Reenen2007

fordetails).

“Log(percentageof

employeeswithacolle

gedegree)”

isthena

turallogarithm

ofthepe

rcentof

employeeswithaba

chelorsdegree.Profit

marginis

thepre-recessionlevelof

profi

tover

sales.

Workerdecentraliz

ationis

the

z-scored

averageof

2qu

estion

son

workerau

tono

my.

Foreign/

Maleplan

tman

ager=1ifplan

tman

ager

isfrom

aforeigncoun

tryor

male,

respectively.Baselinecontrols

areyear

dummiesan

d"n

oise

controls"(plant

man

ager’stenu

rean

dhierarchical

seniorityan

dtheinterview’srelia

bilityscore,

dayof

theweekan

ddu

ration

,and

analyst

dummies.

Firm

andplan

tem

ploymentaremeasuredas

log(em

ploy

ment),an

dskillsaremeasuredas

ln(%

ofem

ployeeswithacolle

gedegree).

54

Tab

leA6:

Rob

ustnessof

resultsto

interactions

ofexpo

rtgrow

thwithotherfirm-le

velv

ariables

inMOPSda

ta

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Dep

ende

nt V

aria

ble:

Sal

es G

row

th

Dec

entr

aliz

atio

n 0.

583*

*0.

573*

*0.

524*

*0.

566*

**0.

587*

**0.

565*

**0.

597*

0.53

1*(0

.23)

(0.2

28)

(0.2

27)

(0.2

29)

(0.2

3)(0

.23)

(0.3

09)

(0.2

98)

Dec

entr

aliz

atio

n*E

XPO

RT

Gro

wth

-0.0

23**

-0.0

23**

-0.0

24**

-0.0

23**

-0.0

23**

-0.0

23**

-0.0

23**

-0.0

23**

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

1)(0

.01)

Man

agem

ent

-0.2

42-0

.128

(0.2

29)

(0.2

52)

Man

agem

ent*

EX

POR

T G

row

th0.

007

0.00

6(0

.006

)(0

.008

)Pr

ofit

mar

gin

(pre

-rec

essi

on)

-7.4

58**

*-7

.47*

**(1

.153

)(1

.15)

Prof

it m

argi

n (p

re-r

eces

sion

)*E

XPO

RT

Gro

wth

-0.0

51-0

.05

(0.0

48)

(0.0

47)

Dat

a-D

rive

n D

ecis

ion-

Mak

ing

-0.3

45-0

.31

(0.2

06)

(0.2

21)

Dat

a-D

rive

n D

ecis

ion-

Mak

ing*

EX

POR

T G

row

th0.

332

0.03

9(0

.64)

(0.8

94)

Log

(% e

mpl

oyee

s w

ith a

col

lege

deg

ree)

*EX

POR

T G

row

th0.

101

0.09

7(0

.117

)(0

.119

)U

nion

-1.2

37**

-1.3

58**

(0.6

67)

(0.6

85)

Uni

on*E

XPO

RT

Gro

wth

0.00

10.

006

(0.0

26)

(0.0

25)

Firm

Dec

entr

aliz

atio

n-0

.019

-0.0

67(0

.461

)(0

.444

)Fi

rm D

ecen

tral

izat

ion*

EX

POR

T G

row

th0.

001

0.00

3(0

.015

)(0

.015

)Fi

rms

3,15

03,

150

3,15

03,

150

3,15

03,

150

3,15

03,

150

Obs

erva

tions

8,80

08,

800

8,80

08,

800

8,80

08,

800

8,80

08,

800

Bas

elin

e co

ntro

lsY

esY

esY

esY

esY

esY

esY

esY

esFi

rm &

pla

nt e

mpl

oym

ent,

skill

sY

esY

esY

esY

esY

esY

esY

esY

esIn

dust

ry d

umm

ies

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Clu

ster

SIC

3SI

C3

SIC

3SI

C3

SIC

3SI

C3

SIC

3SI

C3

Not

es: *

sign

ifica

nt a

t 10%

; **

5%; *

** 1

%. E

stim

ated

by

OLS

with

sta

ndar

d er

rors

clu

ster

ed a

t thr

ee-d

igit

indu

stry

lev

el in

all

colu

mns

. Th

e sp

ecifi

catio

n is

the

sam

e as

Tab

le 2

co

lum

n (5

) exc

ept a

ugm

ente

d w

ith a

dditi

onal

var

iabl

es fr

om th

e M

OPS

(lin

ear a

nd in

tera

cted

with

exp

ort g

row

th).

Man

agem

ent i

s th

e z-

scor

ed a

vera

ge o

f 18

z-sc

ored

man

agem

ent

ques

tions

(see

Blo

om e

t al.

2013

for d

etai

ls).

"Dat

a-D

riven

Dec

isio

n-M

akin

g" is

the

z-sc

ored

ave

rage

of 2

que

stio

ns o

n th

e us

e an

d av

aila

bilit

y of

dat

a in

dec

isio

n-m

akin

g. "

Log(

%

empl

oyee

s w

ith a

col

lege

deg

ree)

" is

the

natu

ral l

ogar

ithm

of t

he p

erce

nt o

f em

ploy

ees

with

a b

ache

lors

deg

ree.

"U

nion

" is

the

perc

ent o

f em

ploy

ees

that

are

mem

bers

of a

labo

r un

ion.

"Lo

g(%

em

ploy

ees

with

a c

olle

ge d

egre

e)*E

XPO

RT

Gro

wth

" is

equ

al to

100

tim

es "

Log(

% e

mpl

oyee

s w

ith a

col

lege

deg

ree)

" tim

es e

xpor

t gro

wth

. "D

ata-

Driv

en D

ecis

ion-

Mak

ing*

Expo

rt sh

ock"

is e

qual

to 1

00 ti

mes

"D

ata-

Driv

en D

ecis

ion-

Mak

ing"

tim

es e

xpor

t gro

wth

.

Notes:MOPSData.

*significan

tat

10%;*

*5%

;***

1%level.Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

levelinallcolum

ns.Sp

ecification

sarethesameas

Tab

le2column(5)except

augm

entedwithad

dition

alvariab

lesfrom

theMOPS(linearan

dinteracted

withexpo

rtgrow

th).

Man

agem

entis

thez-

scored

averag

eof

18z-scored

man

agem

entqu

estion

s(see

Bloom

etal.2013

fordetails).

Profit

marginis

thepre-recessionlevelof

profi

tover

sales.

“Data-Driven

Decision-Mak

ing”

isthez-scored

averageof

2qu

estion

son

theusean

davailabilityof

data

indecision

-mak

ing.

“Log(percentageof

employeeswithacolle

gedegree)”

isthena

turallogarithm

ofthepe

rcentof

employeeswithaba

chelorsdegree.“U

nion

”is

thepe

rcentage

ofem

ployeesthat

aremem

bers

ofalabo

run

ion.

"Data-Driven

Decision-Mak

ing*EXPORTGrowth"isequa

lto100times

"Data-DrivenDecision-Mak

ing"

times

expo

rtgrow

th.Baselinecontrolsareyear

dummiesan

d"n

oise

controls"

(plant

man

ager’s

tenu

rean

dhierarchical

seniorityan

dtheinterview’s

relia

bilityscore,

dayof

theweekan

ddu

ration

,whether

thesurvey

was

answ

ered

onlin

eor

bymail).Firm

andplan

tem

ploymentaremeasuredas

log(em

ploy

ment),an

dskillsaremeasuredas

ln(%

ofem

ployeeswithacolle

gedegree).

55

Tab

leA7:

IsDecentralizationProxy

ingforCoo

rdination?

WMSData

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Dep

ende

nt V

aria

ble:

Sal

es G

row

th

Dec

entr

aliz

atio

n 0.041

0.050

0.062

0.115

0.046

0.067

0.127

-0.013

0.361

-1.646

(0.417)

(0.418)

(0.417)

(0.422)

(0.413)

(0.419)

(0.413)

(0.448)

(0.983)

(3.515)

Dec

entr

aliz

atio

n*E

XPO

RT

Gro

wth

-0.047**

-0.047**

-0.045**

-0.046**

-0.047***

-0.047**

-0.050***

-0.046**

-0.094**

-0.049**

(0.018)

(0.018)

(0.018)

(0.018)

(0.018)

(0.018)

(0.018)

(0.019)

(0.047)

(0.024)

Ln(

empl

oyee

s)*E

XPO

RT

Gro

wth

-0.940

0.055

(0.816)

(0.532)

Ln(

plan

t em

ploy

ees)

-0.228

-0.326

(0.513)

(0.562)

Ln(

plan

t em

ploy

ees)

*EX

POR

T G

row

th0.008

0.641

(0.021)

(0.594)

No.

of p

rodu

ctio

n si

tes

-0.003

-0.023

(0.027)

(0.031)

No.

of p

rodu

ctio

n si

tes*

EX

POR

T G

row

th0.003

-0.024

(0.002)

(0.040)

Div

ersi

ficat

ion

1.302

2.196**

(0.898)

(0.973)

Div

ersi

ficat

ion*

EX

POR

T G

row

th0.027

-0.698

(0.055)

(0.754)

Mul

tinat

iona

l-2.478*

-0.710

(1.384)

(1.078)

Mul

tinat

iona

l*E

XPO

RT

Gro

wth

1.691

0.165

(1.730)

(1.025)

Fore

ign

Mul

tinat

iona

l dum

my

-1.820**

-1.929*

(0.833)

(1.061)

Fore

ign

Mul

tinat

iona

l*E

XPO

RT

Gro

wth

0.016

0.024

(0.039)

(0.900)

Ln(

shar

e ou

tsou

rced

pro

duct

ion)

-0.090

0.034

(0.281)

(0.292)

Ln(

shar

e ou

tsou

rced

pro

duct

ion)

*EX

POR

T G

row

th0.001

-0.260

(0.012)

(0.275)

Mat

eria

ls S

hare

-6.991

1.544

(7.065)

(5.192)

Mat

eria

ls S

hare

*EX

POR

T G

row

th0.817**

-2.681

(0.357)

(4.938)

Obs

erva

tions

3,151

3,151

3,105

3,127

3,151

3,151

3,151

3,029

1,201

2,968

Bas

elin

e co

ntro

lsYes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Firm

& p

lant

em

ploy

men

t, sk

ills

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Indu

stry

by

coun

try

dum

mie

sYes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Clu

ster

SIC3*Cty

SIC3*Cty

SIC3*Cty

SIC3*Cty

SIC3*Cty

SIC3*Cty

SIC3*Cty

SIC3*Cty

SIC3*Cty

SIC3*Cty

Notes:*significan

tat

10%;**

5%;***1%

level.

Specification

sarethesameas

Tab

le2column(3)except

augm

entedwithad

dition

alvariab

les(linearan

dinteracted

withexpo

rtgrow

th).

Multina

tion

al=1iftheplan

tbe

long

sto

aforeignor

domesticmultina

tion

al.Diversified=1ifthefirm

hasmultipleprim

arySIC4codes.

Shareof

outsou

rced

prod

uction

isaqu

estion

intheW

MSsurvey.Materialsshareisthefraction

ofsalesthat

areinterm

ediate

good

sinpu

ts(from

ORBIS).Baselinecontrolsare

year

dummiesan

d"n

oise

controls"(plant

man

ager’s

tenu

rean

dhierarchical

seniorityan

dtheinterview’s

relia

bilityscore,

dayof

theweekan

ddu

ration

,an

dan

alyst

dummies.

Firm

andplan

tem

ploymentaremeasuredas

log(em

ploy

ment),an

dskillsaremeasuredas

ln(%

ofem

ployeeswithacolle

gedegree).

56

Tab

leA8:

IsDecentralizationProxy

ingforCoo

rdination?

MOPSData

Tab

le 9

B -

Is d

ecen

tral

izat

ion

prox

ying

for

co-o

rdin

atio

n? M

OPS

dat

a(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)D

epen

dent

Var

iabl

e: S

ales

Gro

wth

Dec

entr

aliz

atio

n 0.

583*

*0.

611*

**0.

583*

*-0

.885

0.11

50.

376

0.63

6**

0.34

50.

856*

**-1

.539

(0.2

3)(0

.228

)(0

.23)

(0.7

98)

(0.3

29)

(0.2

67)

(0.2

81)

(0.2

67)

(0.2

66)

(1.5

19)

Dec

entr

aliz

atio

n*E

XPO

RT

Gro

wth

-0.0

23**

-0.0

232*

*-0

.023

**-0

.022

**-0

.024

**-0

.023

**-0

.023

**-0

.022

**-0

.023

**-0

.022

***

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

08)

Mul

tipro

duct

-1.0

60**

-1.0

47**

(0.4

95)

(0.4

89)

Mul

tipro

duct

*EX

POR

T G

row

th0.

0164

0.01

6(0

.018

5)(0

.019

)L

n(pl

ant e

mpl

oym

ent)

*EX

POR

T G

row

th0.

003

0.00

2(0

.006

)(0

.006

)L

n(fir

m e

mpl

oym

ent)

*EX

POR

T G

row

th0.

075

-0.0

86(0

.22)

(0.2

43)

Ln(

firm

em

ploy

men

t)*D

ecen

tral

izat

ion

0.19

8*0.

315

(0.1

18)

(0.2

52)

Ln(

No.

of p

lant

s)-0

.557

*-2

.069

**(0

.327

)(0

.818

)L

n(N

o. o

f pla

nts)

*Dec

entr

aliz

atio

n0.

196

0.06

48(0

.139

)(0

.415

)L

n(N

o. o

f sta

tes

w/ p

lant

s)-0

.022

0.11

4(0

.030

)(0

.073

)L

n(N

o. o

f sta

tes

w/ p

lant

s)*D

ecen

tral

izat

ion

0.02

0-0

.048

(0.0

16)

(0.0

50)

Plan

t is

in s

ame

stat

e as

larg

est p

lant

1.01

3*1.

339*

*(0

.554

)(0

.568

)Sa

me

stat

e as

larg

est p

lant

*Dec

entr

aliz

atio

n-0

.148

0.37

3(0

.349

)(0

.423

)L

n(N

o. o

f man

ufac

turi

ng in

dust

ries

)-0

.047

-0.0

63(0

.029

)(0

.043

)L

n(N

o. o

f man

ufac

turi

ng in

dust

ries

)*D

ecen

tral

izat

ion

0.36

2*0.

033

(0.0

20)

(0.0

35)

Plan

t is

in s

ame

indu

stry

as

larg

est p

lant

0.76

20.

533

(0.5

12)

(0.5

75)

Sam

e in

dust

ry a

s la

rges

t pla

nt*D

ecen

tral

izat

ion

-0.4

98-0

.38

(0.3

74)

(0.5

12)

Obs

erva

tions

8,80

08,

800

8,80

08,

800

8,80

08,

800

8,80

08,

800

8,80

08,

800

Bas

elin

e co

ntro

lsY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esFi

rm &

pla

nt e

mpl

oym

ent,

skill

sY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esIn

dust

ry d

umm

ies

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Clu

ster

SIC

3SI

C3

SIC

3SI

C3

SIC

3SI

C3

SIC

3SI

C3

SIC

3SI

C3

Not

es: S

peci

ficat

ion

are

the

sam

e as

Tab

le 2

col

umn

(5).

"Mul

tipro

duct

" eq

uals

1 if

a p

lant

pro

duce

d at

leas

t tw

o pr

oduc

ts (7

-dig

it N

AIC

S) in

200

9 an

d 0

othe

rwis

e. "

Ln(N

o. o

f man

ufac

turin

g in

dust

ries)

" is

the

log

of th

e nu

mbe

r of u

niqu

e pr

imar

y in

dust

ry c

odes

(6-D

igit

NA

ICS)

ass

igne

d to

the

firm

's m

anuf

actu

ring

esta

blis

hmen

ts in

200

9. "

Plan

t is

in s

ame

stat

e as

larg

est p

lant

" eq

uals

1 if

pla

nt is

in th

e sa

me

U.S

. sta

te a

s th

e fir

m's

larg

est p

lant

by

empl

oym

ent i

n 20

09, a

nd 0

oth

erw

ise.

"Pl

ant i

s in

sam

e in

dust

ry a

s la

rges

t pla

nt"

is d

efin

ed s

imila

rly w

ith a

n in

dust

ry d

efin

ed a

s 6-

digi

t NA

ICS

code

. "Ln

(firm

em

ploy

men

t)*EX

POR

T G

row

th"

is e

qual

to 1

00 ti

mes

the

natu

ral l

og o

f firm

em

ploy

men

t tim

es e

xpor

t gro

wth

.

Notes:**sign

ificant

at10%;**

5%;***1%

level.Sp

ecification

arethesameas

Tab

le2column(5).

"Multiprod

uct"

equa

ls1ifaplan

tprod

uced

atleasttw

oprod

ucts

(7-digit

NAIC

S)in

2009

and0otherw

ise.

"Ln(No.

ofman

ufacturing

indu

stries)"

isthelogof

thenu

mbe

rof

unique

prim

aryindu

stry

codes(6-D

igit

NAIC

S)assign

edto

thefirm’s

man

ufacturing

establishm

ents

in2009."P

lant

isin

samestateas

largestplan

t"equa

ls1ifplan

tis

inthesameU.S.stateas

thefirm’s

largestplan

tby

employmentin

2009,an

d0otherw

ise.

"Plant

isin

sameindu

stry

aslargestplan

t"is

defin

edsimila

rlywithan

indu

stry

defin

edas

6-digitNAIC

Scode."L

n(firm

employ

ment)*E

XPORTGrowth"isequa

lto100times

thena

turallog

offirm

employ

menttimes

expo

rtgrow

th.Baselinecontrolsareyear

dummiesan

d"n

oise

controls"

(plant

man

ager’s

tenu

rean

dhierarchical

seniorityan

dtheinterview’s

relia

bilityscore,

dayof

theweekan

ddu

ration

,whether

thesurvey

was

answ

ered

onlin

eor

bymail).Firm

andplan

tem

ploymentaremeasuredas

log(em

ploy

ment),an

dskillsaremeasuredas

ln(%

ofem

ployeeswithacolle

gedegree).

57

Tab

leA9:

DifferencesAcrossDecentralizationQuestions

Tab

le 6

- D

iffer

ence

s ac

ross

dec

entr

aliz

atio

n qu

estio

ns

(1)

(2)

(3)

(4)

(5)

(6)

Dep

ende

nt V

aria

ble:

Sal

es G

row

th

Dec

entr

aliz

atio

n 0.

041

0.58

3**

(0.4

17)

(0.2

30)

Dec

entr

aliz

atio

n*E

XPO

RT

Gro

wth

-0.0

47**

-0.0

23**

(0.0

18)

(0.0

09)

Dec

entr

aliz

atio

n - H

irin

g &

Inv

estm

ent

0.06

30.

808*

**(0

.396

)(0

.236

)D

ecen

tral

izat

ion

- Hir

ing

& I

nves

tmen

t*E

XPO

RT

Gro

wth

-0.0

02-0

.013

(0.0

19)

(0.0

08)

Dec

entr

aliz

atio

n -

Sale

s &

New

Pro

duct

s-0

.135

0.17

1(0

.379

)(0

.218

)D

ecen

tral

izat

ion

- Sa

les

& N

ew P

rodu

cts

-0.0

60**

*-0

.025

***

*EX

POR

T G

row

th(0

.017

)(0

.010

)Fi

rms

1,33

01,

330

1,33

03,

150

3,15

03,

150

Obs

erva

tions

3,15

13,

151

3,15

18,

800

8,80

08,

800

Clu

ster

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C3

SIC

3SI

C3

Wor

ld M

anag

emen

t Sur

vey

(WM

S)U

.S. C

ensu

s D

ata

(MO

PS)

Not

es:

*sig

nific

anta

t10%

;**

5%;*

**1%

.Est

imat

edby

OLS

with

stan

dard

erro

rscl

uste

red

atth

ree-

digi

tind

ustry

byco

untry

leve

lin

colu

mns

(1)-

(3)

and

just

indu

stry

inco

lum

ns(4

)-(6

).Th

ede

pend

ent

varia

ble

isth

ean

nual

ized

thre

e-ye

arch

ange

offir

mln

(sal

es).

2011

-08,

2010

-07

and

2009

-06

are

pool

edin

WM

S(c

olum

ns(1

)-(4

))an

dju

st20

09-2

006

inM

OPS

(col

umns

(5)

and

(6))

.Dec

entra

lizat

ion

mea

sure

din

2006

for

WM

San

d20

05fo

rM

OPS

."EX

POR

TG

row

th"

isch

ange

inln

(exp

orts

)in

coun

tryby

thre

edi

giti

ndus

tryce

llbe

twee

nth

e20

08an

d20

09av

erag

e(th

em

ain

Gre

atR

eces

sion

year

s)co

mpa

red

toth

e20

06an

d20

07av

erag

e(th

ela

test

pre-

Rec

essi

onye

ars)

inco

lum

ns(1

)-(3

),an

dis

the

aver

age

chan

ge(2

008/

2009

aver

age

com

pare

dto

2006

/200

7)in

ln(e

xpor

ts)a

tthe

prod

uctl

evel

(HS7

)for

the

prod

ucts

the

plan

tpro

duce

dju

stpr

ior

toth

eG

reat

Rec

essi

onin

2006

inco

lum

ns(4

)-(6

).A

llco

lum

nsin

clud

eth

ree

digi

tind

ustry

,cou

ntry

and

year

dum

mie

san

d"n

oise

cont

rols

"(p

lant

man

ager

'ste

nure

and

hier

arch

ical

seni

ority

and

the

inte

rvie

w's

relia

bilit

ysc

ore,

day

ofth

ew

eek

and

dura

tion,

WM

Sal

soin

clud

esan

alys

tdum

mie

san

dM

OPS

whe

ther

the

surv

eyw

asan

swer

edon

line

orby

mai

l).Fi

rman

dpl

ants

ize

are

ln(e

mpl

oym

ent)

are

skill

sis

the

ln(%

ofem

ploy

ees

with

aco

llege

degr

ee).

"Dec

entra

lizat

ion

-H

iring

&In

vest

men

t"is

the

z-sc

ored

aver

age

ofth

ez-

scor

edm

easu

res

ofpl

antm

anag

erau

tono

my

inhi

ring

and

capi

tali

nves

tmen

ts(a

ndal

sopa

yin

crea

ses

inth

eM

OPS

data

)."D

ecen

traliz

atio

n - S

ales

& N

ew P

rodu

cts"

is a

vera

ge fo

r pro

duct

intro

duct

ion

and

mar

ketin

g.

Notes:*significan

tat

10%;**

5%;***1%

level.

Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

bycoun

trylevel.

The

depe

ndentvariab

leis

thean

nualized

three-year

chan

geof

firm

ln(sales).

2011-08,

2010-07an

d2009-06arepo

oled

inW

MS(colum

ns(1)-(3))

andjust

2009-2006in

MOPS(colum

ns(4)to

(6)).Decentralizationmeasuredin

2006

forW

MSan

d2005

forMOPS.

"EXPORT

Growth"ischan

gein

ln(exp

orts)in

coun

tryby

threedigitindu

stry

cellbe

tweenthe

2008

and2009

average(the

mainGreat

Recession

years)

compa

redto

the2006

and2007

average(the

latest

pre-Recession

years)

incolumns

(1)-(3),an

distheaverage

chan

ge(2008/2009

averagecompa

redto

2006/200

7)in

ln(exp

orts)at

theprod

uctlevel(H

S7)fortheprod

ucts

theplan

tprod

uced

just

priorto

theGreat

Recession

in2006

incolumns

(4)-(6).

Allcolumns

includ

ethreedigitindu

stry,coun

tryan

dyear

dummiesan

d"n

oise

controls"(plant

man

ager’s

tenu

rean

dhierarchical

seniority

andtheinterview’srelia

bilityscore,

dayof

theweekan

ddu

ration

,WMSalso

includ

esan

alystdu

mmiesan

dMOPSwhether

thesurvey

was

answ

ered

onlin

eor

bymail).

Firm

andplan

tsize

areln(employ

ment)

areskillsis

theln(%

ofem

ployeeswithacolle

gedegree).

"Decentralization-Hiring&

Investment"

isthez-scored

average

ofthez-scored

measuresof

plan

tman

ager

autono

myin

hiring

andcapitalinvestments

(and

also

payincreasesin

theMOPSda

ta).

"Decentralization-Sa

les&

New

Produ

cts"

isaverageforprod

uctintrod

uction

andmarketing

.

58

Tab

leA10:Rob

ustnessof

resultsto

interactions

ofDecentralizationwithotherindu

stry-le

velv

ariables

Tab

le A

6 -

Dec

entr

aliz

atio

n an

d G

row

th -

Rob

ustn

ess t

o co

ntro

lling

for

othe

r in

dust

ry le

vel i

nter

actio

ns(1

)(2

)(3

)(4

)D

epen

dent

Var

iabl

e

Dec

entr

aliz

atio

n-0

.920

0.22

20.

386

-0.0

30(1

.788

)(2

.482

)(0

.603

)(1

.485

)D

ecen

tral

izat

ion*

EX

POR

T G

row

th-0

.050

***

-0.0

47**

-0.0

52**

*-0

.046

**(0

.019

)(0

.020

)(0

.019

)(0

.019

)D

ecen

tral

izat

ion*

Ass

et ta

ngib

ility

3.40

4(6

.019

)D

ecen

tral

izat

ion*

Inve

ntor

y/Sa

les

-1.1

07(1

5.51

0)D

ecen

tral

izat

ion*

Ext

erna

l fin

ance

dep

ende

ncy

-1.1

85(1

.604

)D

ecen

tral

izat

ion*

Lab

or c

osts

0.38

1(7

.916

)Fi

rms

1,33

01,

330

1,33

01,

330

Obs

erva

tions

3,15

13,

151

3,15

13,

151

Clu

ster

SIC

3*C

tySI

C3*

Cty

SIC

3*C

tySI

C3*

Cty

Sale

s Gro

wth

Not

es:

*sig

nific

anta

t10%

;**

5%;*

**1%

.Est

imat

edby

OLS

with

stan

dard

erro

rscl

uste

red

atth

ree-

digi

tind

ustry

byco

untry

leve

lin

all

colu

mns

.Spe

cific

atio

nsar

eth

esa

me

asTa

ble

2co

lum

n(3

)exc

ept

augm

ente

dw

ithad

ditio

nalv

aria

bles

."A

sset

Tang

ibili

ty"

isth

era

tioof

tang

ible

asse

ts,i

.e.n

etpr

oper

ty,p

lant

and

equi

pmen

t,to

tota

las

sets

for

the

corr

espo

ndin

gin

dust

ryin

the

US

over

the

perio

d19

80-1

989,

com

pute

dat

theI

SIC

3re

v1

leve

l(in

vers

emea

sure

ofcr

edit

cons

train

ts).

"Inv

ento

ry/S

ales

"ism

easu

red

asth

ein

vent

orie

sto

tota

lsal

esfo

rthe

corr

espo

ndin

gin

dust

ryin

theU

Sov

erth

eper

iod

1980

-198

9(m

easu

reof

liqui

dity

depe

nden

ce).

"Ext

erna

lfin

ance

depe

nden

cy"i

sm

easu

red

asca

pita

lexp

endi

ture

sm

inus

cash

flow

divi

ded

byca

shflo

wfo

rthe

corr

espo

ndin

gin

dust

ryin

the

US

over

the

perio

d19

80-1

989

(mea

sure

ofcr

edit

cons

train

t)."L

abor

cost

s"is

mea

sure

das

the

tota

llab

ourc

osts

toto

tals

ales

fort

heco

rres

pond

ing

indu

stry

inth

eU

Sov

erth

epe

riod

1980

-198

9 (a

noth

er m

easu

re o

f liq

uidi

ty d

epen

denc

e).

Notes:W

MSData.

*significan

tat

10%;**

5%;***1%

level.Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

bycoun

trylevelin

allcolumns.

Specification

sarethesameas

Tab

le2column(3)except

augm

entedwithad

dition

alvariab

les.

"Asset

Tan

gibility"

istheratioof

tang

ible

assets,i.e

.netprop

erty,

plan

tan

dequipm

ent,to

totalassets

forthecorrespo

ndingindu

stry

intheUSover

thepe

riod

1980-1989,

compu

tedat

theISIC

3rev1level(inv

erse

measure

ofcredit

constraints)."Inv

entory/S

ales"is

measuredas

theinventoriesto

totalsalesforthecorrespo

ndingindu

stry

intheUSover

thepe

riod

1980-1989(m

easure

ofliq

uidity

depe

ndence).

"Externa

lfin

ance

depe

ndency"is

measuredas

capitalexpe

nditures

minus

cash

flow

dividedby

cash

flow

forthecorrespo

ndingindu

stry

intheUSover

thepe

riod

1980-1989(m

easure

ofcreditconstraint).

"Lab

orcosts"

ismeasuredas

thetotallabo

rcoststo

totalsalesforthecorrespo

ndingindu

stry

intheUSover

the

period

1980-1989(ano

ther

measure

ofliq

uidity

depe

ndence).

59

Tab

leA11:Decentralizationan

dProdu

ctChu

rnT

able

A9

- Dec

entr

aliz

atio

n an

d Pr

oduc

t Chu

rn

(1)

(2)

(3)

(4)

(5)

(6)

Dep

ende

nt V

aria

ble:

Dec

entr

aliz

atio

n z-

scor

e

Dec

entr

aliz

atio

n Q

uest

ions

Prod

uct C

hurn

0.01

6***

0.01

6***

0.00

40.

008*

*0.

020*

**0.

017*

**(0

.004

)(0

.004

)(0

.004

)(0

.004

)(0

.004

)(0

.004

)M

anag

emen

t-0

.010

***

0.00

5-0

.019

***

(0.0

04)

(0.0

04)

(0.0

04)

Log

(% e

mpl

oyee

s w

ith a

col

lege

deg

ree)

0.05

7***

0.04

4***

0.04

9***

(0.0

04)

(0.0

04)

(0.0

04)

Log

(pla

nt e

mpl

oym

ent)

0.03

5***

0.05

6***

0.00

6*(0

.004

)(0

.004

)(0

.004

)lo

g(fir

m e

mpl

oym

ent)

-0.0

12**

*-0

.002

-0.0

16**

*(0

.002

)(0

.002

)(0

.002

)

Obs

erva

tions

8,80

08,

800

8,80

08,

800

8,80

08,

800

Con

trol

sIn

dust

ry (S

IC3)

Yes

Yes

Yes

Noi

seY

esY

esY

esC

lust

erSI

C3

SIC

3SI

C3

SIC

3SI

C3

SIC

3

U.S

. Cen

sus

Dat

a - M

OPS

Not

es:

*sig

nific

anta

t10%

;**

5%;*

**1%

.Est

imat

edby

OLS

with

stan

dard

erro

rscl

uste

red

atth

ree-

digi

tin

dust

ryle

vel.

The

depe

nden

tva

riabl

ein

colu

mns

(1)a

nd(2

)is

over

alld

ecen

traliz

atio

nz-

scor

e.Th

ede

pend

entv

aria

ble

inco

lum

ns(3

)and

(4)

isth

ez-

scor

edav

erag

eof

the

z-sc

ored

mea

sure

sof

plan

tman

ager

auto

nom

yin

hirin

g,ca

pita

linv

estm

ents

,and

pay

rais

es.T

hede

pend

entv

aria

ble

inco

lum

ns(5

)and

(6)i

sth

ez-

scor

edav

erag

efo

rpr

oduc

tint

rodu

ctio

nan

dm

arke

ting

ques

tions

."Pr

oduc

tChu

rn"

isth

eth

ree

digi

tind

ustry

leve

lval

ueof

the

aver

age

chan

ge in

the

(num

ber o

f pro

duct

s ad

ded

betw

een

t and

t-5

plu

s th

e nu

mbe

r pro

duct

s dr

oppe

d be

twee

n t a

nd t-

5)/(a

vera

ge n

umbe

r of p

rodu

cts

betw

een

t and

t-5)

.

All

Cap

ital E

xpen

ditu

re,

Hir

ing,

and

Rai

ses

Prod

uct I

ntro

duct

ions

an

d S

ales

and

M

arke

ting

Notes:MOPSData.

*significan

tat

10%;*

*5%

;***

1%level.Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

level.The

depe

ndentvariab

lein

columns

(1)an

d(2)isoveralld

ecentralizationz-score.

The

depe

ndentvariab

lein

columns

(3)an

d(4)isthez-scored

averageof

thez-scored

measuresof

plan

tman

ager

autono

myin

hiring

,capitalinvestments,an

dpa

yraises.The

depe

ndentvariab

lein

columns

(5)an

d(6)isthez-scored

averageforprod

uctintrod

uction

andmarketing

question

s."P

rodu

ctChu

rn"isthethreedigitindu

stry

levelvalueof

theaveragechan

gein

the(num

berof

prod

ucts

addedbe

tweentan

dt-5plus

thenu

mbe

rprod

ucts

drop

pedbe

tweentan

dt-5)/(averagenu

mbe

rof

prod

ucts

betw

eentan

dt-5).

60

Tab

leA12:Decentralizationan

dProdu

ctChu

rn,b

ytype

ofDecentralization

Tabl

e A10

- D

ecen

tral

izat

ion

and

Prod

uct C

hurn

, By

Type

of D

ecen

tral

izat

ion

(1)

(2)

(3)

Pane

l A: D

ecen

tral

izat

ion

of S

ales

, Mar

ketin

g, a

nd N

ew P

rodu

cts

Dep

ende

nt V

aria

ble:

Sal

es g

row

th ('

12-'0

7)

Dec

entra

lizat

ion

0.17

10.

191

0.30

4*(0

.218

)(0

.151

8)(0

.162

2)D

ecen

t*C

hang

e in

Pro

duct

Chu

rn1.

859*

**1.

587*

*(0

.370

)(0

.396

)D

ecen

t*Ex

port

Gro

wth

('12

-'07)

-0.0

25**

-0.0

11(0

.010

)(0

.008

)D

ecen

t*D

urab

ility

Firm

s3,

004

3,00

43,

004

Obs

erva

tions

8,24

38,

243

8,24

3

Pane

l A: D

ecen

tral

izat

ion

of H

irin

g &

Inve

stm

ent

Dep

ende

nt V

aria

ble:

Sal

es g

row

th ('

12-'0

7)

Dec

entra

lizat

ion

0.80

8***

0.69

2***

0.74

3***

(0.2

36)

(0.1

57)

(0.1

66)

Dec

ent*

Cha

nge

in P

rodu

ct C

hurn

0.60

4*0.

541

(0.3

30)

(0.3

51)

Dec

ent*

Expo

rt G

row

th ('

12-'0

7)-0

.013

-0.0

04(0

.008

)(0

.008

)D

ecen

t*D

urab

ility

Firm

s3,

004

3,00

43,

004

Obs

erva

tions

8,24

38,

243

8,24

3C

lust

erSI

C3

SIC

3SI

C3

Exp

orts

Not

es:*

sign

ifica

ntat

10%

;**

5%;*

**1%

.Es

timat

edby

OLS

with

stan

dard

erro

rscl

uste

red

atth

ree-

digi

tind

ustry

leve

l.Th

ede

pend

ent

varia

ble

isth

ean

nual

ized

five-

year

chan

geof

firm

ln(s

ales

),20

12-2

007.

The

depe

nden

tvar

iabl

ein

Pane

lAis

the

z-sc

ored

aver

age

ofth

ez-

scor

edm

easu

res

ofpl

antm

anag

erau

tono

my

inhi

ring,

capi

tali

nves

tmen

ts,a

ndpa

yra

ises

.The

depe

nden

tvar

iabl

ein

Pane

lBis

the

z-sc

ored

aver

age

for

prod

ucti

ntro

duct

ion

and

mar

ketin

gqu

estio

ns.T

heva

riabl

eC

hang

ein

Prod

uctC

hurn

"is

mea

sure

dby

subt

ract

ing

2007

-200

2in

dust

rypr

oduc

tchu

rnfr

om20

12-2

007

indu

stry

prod

uctc

hurn

."EX

PORT

Gro

wth

"is

the

2001

2-20

07ch

ange

inln

(exp

orts

)by

thre

edi

giti

ndus

tryce

ll.A

llco

lum

nsin

clud

eth

ree

digi

tind

ustry

dum

mie

sand

cont

rols

forf

irman

dpl

ants

ize,

skill

sand

"noi

se"

(pla

ntm

anag

er's

tenu

rean

dhi

erar

chic

alse

nior

ityan

dth

ein

terv

iew

'sre

liabi

lity

scor

e,da

yof

the

wee

kan

ddu

ratio

n,w

heth

erth

esu

rvey

was

answ

ered

onl

ine

or b

y m

ail).

Notes:*significan

tat

10%;*

*5%

;***

1%level.Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

level.The

depe

ndentvariab

leisthean

nualized

five-year

chan

geof

firm

ln(sales),2012-2007.

The

depe

ndentvariab

lein

Pan

elA

isthez-scored

averageof

thez-scored

measuresof

plan

tman

ager

autono

myin

hiring

,capitalinv

estm

ents,a

ndpa

yraises.The

depe

ndentvariab

lein

Pan

elB

isthez-scored

averageforprod

uctintrod

uction

andmarketing

question

s.The

variab

le“C

hang

ein

Produ

ctChu

rn"is

measuredby

subtracting2007-2002indu

stry

prod

uctchurnfrom

2012-2007indu

stry

prod

uctchurn.

"EXPORT

Growth"is

2012-2007chan

gein

ln(exp

orts)at

theprod

uctlevel(H

S7)fortheprod

ucts

theplan

tprod

uced

just

priorto

theGreat

Recession

in2006.Allcolumns

includ

ethreedigitindu

stry

dummies

andcontrols

forfirm

andplan

tsize,skillsan

d"n

oise"(plant

man

ager’s

tenu

rean

dhierarchical

seniorityan

dtheinterview’s

relia

bilityscore,

dayof

theweekan

ddu

ration

,whether

thesurvey

was

answ

ered

onlin

eor

bymail).

61

Tab

leA13:

Decentralizationan

dSa

lesGrowth,b

yExp

ortStatus

Tabl

e A7

- Dec

entr

aliz

atio

n an

d sa

les g

row

th, b

y ex

port

stat

us

(1)

(2)

Dep

ende

nt V

aria

ble

= Sa

les G

row

th

Sam

ple

Exp

orte

rsN

on-e

xpor

ters

Dec

entr

aliz

atio

n 0.

4452

0.55

24*

0.27

690.

3245

Dec

ent.*

EX

POR

T G

row

th-0

.035

8***

-0.0

105

0.01

150.

0117

Obs

erva

tions

4,20

04,

600

Clu

ster

SIC

3*C

tySI

C3*

Cty

U.S

. Cen

sus D

ata

(MO

PS)

Not

es: *

sign

ifica

nt a

t 10%

; **

5%; *

** 1

%.

Estim

ated

by

OLS

with

stan

dard

err

ors c

lust

ered

at t

hree

-dig

it in

dust

ry.

Sale

s gro

wth

is th

e an

nual

ized

thre

e-ye

ar c

hang

e of

firm

ln(s

ales

). "E

XPO

RT G

row

th"

is c

hang

e in

ln(e

xpor

ts) i

n co

untry

by

thre

e di

git i

ndus

try c

ell b

etw

een

the

2008

and

200

9 av

erag

e (th

e m

ain

Gre

at R

eces

sion

yea

rs) c

ompa

red

to th

e 20

06 a

nd 2

007

aver

age

(the

late

st p

re-R

eces

sion

yea

rs).

All

colu

mns

incl

ude

thre

e di

git i

ndus

try d

umm

ies

and

cont

rols

for f

irm a

nd p

lant

size

, ski

lls a

nd "

nois

e" c

ontro

ls.

Notes:*signific

antat

10%;**

5%;***1%

level.

Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry.Sa

lesgrow

this

thean

nualized

three-year

chan

geof

firm

ln(sales).

"EXPORT

Growth"is

theaveragechan

ge(2008/2009

averagecompa

redto

2006/2007)

inln(exp

orts)at

theprod

uctlevel(H

S7)forthe

prod

ucts

theplan

tprod

uced

just

priorto

theGreat

Recession

in2006.Allcolumns

includ

ethreedigitindu

stry

dummiesan

dcontrolsforfirm

andplan

tsize,skills

and

"noise"controls.

62

Tab

leA14:

Cha

nges

inDecentralization

Tab

le A

8 - C

hang

es in

Dec

entr

aliz

atio

n (1

)(2

)W

orld

Man

agem

ent S

urve

yU

.S. C

ensu

s D

ata

Dep

ende

nt V

aria

ble

Cha

nge

in D

ecen

tral

izat

ion

(201

0/20

09 -

2006

)

Cha

nge

in

Dec

entr

aliz

atio

n

(2

010-

2005

)

EX

POR

T G

row

th-0

.012

**-0

.023

2(0

.006

)(0

.020

4)O

bser

vatio

ns22

28,

800

Con

trol

sC

ount

ryY

esY

ear

Yes

Indu

stry

(SIC

2)Y

esY

esL

og fi

rm a

nd p

lant

em

ploy

men

tY

esY

esSk

ills

Yes

Yes

Noi

seY

esY

esC

lust

erSI

C3*

Cty

SIC

3

Not

es:*

sign

ifica

ntat

10%

;**

5%;*

**1%

.Es

timat

edby

OLS

with

stan

dard

erro

rscl

uste

red

atth

ree-

digi

tin

dust

ryby

coun

tryle

vel

inco

lum

n(1

)an

dju

stin

dust

ryin

colu

mn

(2).

The

depe

nden

tva

riabl

eis

the

2010

/200

9-20

06ch

ange

inz-

scor

edde

cent

raliz

atio

nin

colu

mn

(1)a

ndth

e20

10-2

005

chan

gein

colu

mn

(2).

"EX

POR

TG

row

th"

isch

ange

inln

(exp

orts

)in

coun

tryby

thre

edi

git

indu

stry

cell

betw

een

the

2008

and

2009

aver

age

(the

mai

nG

reat

Rec

essi

onye

ars)

com

pare

dto

the

2006

and

2007

aver

age

(the

late

stpr

e-R

eces

sion

year

s).

All

colu

mns

incl

ude

two

digi

tind

ustry

,cou

ntry

and

year

dum

mie

san

d"n

oise

cont

rols

"(p

lant

man

ager

'ste

nure

and

hier

arch

ical

seni

ority

and

the

inte

rvie

w's

relia

bilit

ysc

ore,

day

ofth

ew

eek

and

dura

tion,

WM

Sal

soin

clud

esan

alys

tdum

mie

san

dM

OPS

whe

ther

the

surv

eyw

asan

swer

edon

line

orby

mai

l). F

irm a

nd p

lant

siz

e ar

e ln

(em

ploy

men

t) ar

e sk

ills

is th

e ln

(% o

f em

ploy

ees

with

a c

olle

ge d

egre

e).

Notes:*significan

tat

10%;**

5%;***1%

level.

Estim

ated

byOLSwithstan

dard

errors

clusteredat

three-digitindu

stry

bycoun

trylevelin

column(1)an

djust

indu

stry

incolumn(2).

The

depe

ndentvariab

leis

the2010/2009-2006

chan

gein

z-scored

decentraliz

ationin

column(1)an

dthe2010-2005chan

gein

column(2).

"EXPORT

Growth"is

chan

gein

ln(exp

orts)in

coun

tryby

threedigitindu

stry

cellbe

tweenthe2008

and2009

average(the

mainGreat

Recession

years)

compa

red

tothe2006

and2007

average(the

latest

pre-Recession

years)

incolumn(1),

andis

theaveragechan

ge(2008/2009

averagecompa

redto

2006/2007)

inln(exp

orts)at

theprod

uctlevel(H

S7)fortheprod

ucts

theplan

tprod

uced

just

priorto

theGreat

Recession

in2006

incolumn(2).

Allcolumns

includ

etw

odigitindu

stry,coun

try

andyear

dummiesan

d"n

oise

controls"(plant

man

ager’stenu

rean

dhierarchical

seniorityan

dtheinterview’srelia

bilityscore,

dayof

theweekan

ddu

ration

,WMSalso

includ

esan

alystdu

mmiesan

dMOPSwhether

thesurvey

was

answ

ered

onlin

eor

bymail).Firm

andplan

tsize

areln(employment)

areskillsis

theln(percentageof

employeeswithacolle

gedegree).

63

Tab

leA15:

IsDecentralizationcorrelated

withwithin-firm

dispersion

ofplan

tinpu

tdecision

san

dplan

tou

tput?MOPSda

ta

(1)

(2)

(3)

Dep

ende

nt V

aria

ble

is st

anda

rd d

evia

tion

of:

Em

ploy

men

t Gro

wth

('12

-'07)

Prod

uct A

dditi

ons (

'12-

'07)

Sale

s Gro

wth

('12

-'07)

Dec

entra

lizat

ion

0.01

2**

0.02

4***

0.01

64**

(0.0

05)

(0.0

07)

(0.0

07)

Con

stan

t0.

452*

**0.

333*

**0.

560*

**(0

.007

)(0

.011

)(0

.009

)

Obs

erva

tions

8,80

08,

200

8,80

0N

otes

: *si

gnifi

cant

at 1

0%; *

* 5%

; ***

1%

. Es

timat

ed b

y O

LS w

ith st

anda

rd e

rror

s clu

ster

ed a

t the

firm

leve

l. In

col

umn

(1) t

he d

epen

dent

var

iabl

e is

the

pare

nt

firm

's st

anda

rd d

evia

tion

of e

stab

lishm

ent s

ales

gro

wth

- th

e an

nual

ized

five

-yea

r cha

nge

of e

stab

lishm

ent l

n(sa

les)

- ac

ross

all

the

firm

's es

tabl

ishm

ents

. In

colu

mn

(2) t

he d

epen

dent

var

iabl

e is

the

pare

nt fi

rm's

stan

dard

dev

iatio

n of

est

ablis

hmen

t pro

duct

add

ition

s, an

d in

col

umn

(3) t

he d

epen

dent

var

iabl

e is

the

pare

nt fi

rm's

stan

dard

dev

iatio

n of

est

ablis

hmen

t sal

es g

row

th. T

he in

depe

nden

t var

iabl

e in

all

colu

mns

is e

stab

lishm

ent d

ecen

traliz

atio

n.

Tab

le A

13 -

Dec

entr

aliz

atio

n co

rrel

ated

with

with

in-f

irm

dis

pers

ion

in p

lant

inpu

t dec

isio

ns a

nd p

lant

out

put,

MO

PS d

ata

Notes:*significan

tat

10%;**

5%;***1%

.Estim

ated

byOLSwithstan

dard

errors

clusteredat

thefirm

level.

Incolumn(1)thede

pend

entvariab

leis

thepa

rent

firm’s

coeffi

cientof

variationof

establishm

entsalesgrow

th-thean

nualized

five-year

chan

geof

establishm

entln(sales)-across

allthefirm’s

establishm

ents.In

column

(2)thede

pend

entvariab

leisthepa

rent

firm’scoeffi

cientof

variationof

establishm

entprod

uctad

dition

s,an

din

column(3)thedepe

ndentvariab

leisthepa

rent

firm’s

coeffi

cientof

variationof

establishm

entsalesgrow

th.The

indepe

ndentvariab

lein

allcolumns

isestablishm

entdecentraliz

ation.

64

Tab

leA16:Decentralizationan

dCross-C

ountry

Growth

Tab

le A

12 -

Dec

entr

aliz

atio

n an

d C

ross

Cou

ntry

Gro

wth

12

34

56

Dec

entr

ali

zatio

n In

dex

Impl

ied

G

DP

Gro

wth

Fran

ce-0

.357

-0.7

2-0

.453

0.24

-0.4

7396

%G

B0.

292

-0.2

8-0

.007

0.07

4-0

.64

1%G

erm

any

0.13

4-0

.39

-0.1

160.

443

-0.2

7143

%G

reec

e-0

.801

-1.0

3-0

.758

-5.4

38-6

.152

12%

Ital

y-0

.242

-0.6

4-0

.374

-1.2

43-1

.957

19%

Japa

n-0

.642

-0.9

2-0

.648

0.02

9-0

.685

95%

Pola

nd-0

.344

-0.7

1-0

.444

2.53

41.

82-2

4%Po

rtug

al-0

.264

-0.6

6-0

.389

-1.4

2-2

.134

18%

Swed

en0.

544

-0.1

0.16

60.

567

-0.1

47-1

13%

US

0.30

3-0

.27

0.71

4A

vera

ge1

-0.5

72-0

.336

-0.3

5-1

.182

15%

Not

es:A

llG

DP

grow

thnu

mbe

rsin

perc

enta

gepo

ints

.Im

plie

dG

DP

grow

thin

colu

mn

(2)u

ses

thec

oeff

icie

ntso

nth

em

odel

ofco

lum

n(3

)Tab

le2

com

bine

dw

ithth

eval

ueof

dece

ntra

lizat

ion

from

(1)a

ndan

assu

med

shoc

kof

7.7%

(the

empi

rical

fall

inag

greg

ate

US

expo

rtsin

the

Gre

atR

eces

sion

asin

ourm

odel

).A

ctua

lGD

Pgr

owth

inco

lum

n(4

)is

take

nfr

omth

eWor

ldB

ank

mar

kets

ecto

rGD

Pse

ries.

Rel

ativ

eval

uesi

nco

lum

n(3

)and

(5)a

reth

esi

mpl

ediff

eren

ces

from

the

US

base

.Sw

eden

hasa

nega

tive

valu

ein

colu

mn

(6)b

ecau

seit

isth

eonl

yco

untry

mor

ede

cent

raliz

edth

anU

S,bu

thad

aw

eake

rGD

Ppe

rfor

man

ce.P

olan

dha

sane

gativ

eva

lueb

ecau

seit

had

fast

ergr

owth

than

the

US

desp

itebe

ing

mor

e ce

ntra

lized

(it i

s stil

l in

a ca

tch

up p

hase

of g

row

th).

Diff

eren

ce in

im

plie

d G

DP

grow

th r

elat

ive

to U

S

Act

ual a

nnua

l av

erag

e G

DP

grow

th (2

012-

2008

)

Diff

eren

ce in

ac

tual

GD

P gr

owth

rel

ativ

e to

U

S

% o

f gro

wth

diff

eren

ce

acco

unte

d fo

r by

D

ecen

tral

izat

ion

Notes:AllGDP

grow

thnu

mbe

rsin

percentage

points.Im

pliedGDP

grow

thin

column(2)uses

thecoeffi

cients

onthemod

elof

column(2)Tab

le2combinedwiththe

valueof

decentraliz

ationfrom

(1)an

dan

assumed

shockof

7.7pe

rcent(the

empiricalfallin

aggregateUSexpo

rtsin

theGreat

Recession

asin

ourmod

el).

Actua

lGDPgrow

thin

column(4)istakenfrom

theWorld

Ban

kmarketsector

GDPseries.Relativevalues

incolumn(3)an

d(5)arethesimplediffe

rences

from

theUSba

se.

Sweden

hasanegative

valuein

column(6)be

causeitistheon

lycoun

trymoredecentraliz

edthan

US,

butha

daweakerGDPpe

rforman

ce.Polan

dha

sanegative

value

becauseitha

dfaster

grow

ththan

theUSdespitebe

ingmorecentraliz

ed(itis

still

inacatchup

phaseof

grow

th).

65