nicholas bloom raffaella sadun john van...

THE ORGANIZATION OF FIRMS ACROSS COUNTRIES*

Nicholas Bloom

Raffaella Sadun

John Van Reenen

We argue that social capital as proxied by trust increases aggregate prod-uctivity by affecting the organization of firms. To do this we collect new data onthe decentralization of investment, hiring, production, and sales decisions fromcorporate headquarters to local plant managers in almost 4,000 firms in theUnited States, Europe, and Asia. We find that firms headquartered inhigh-trust regions are significantly more likely to decentralize. To help identifycausal effects, we look within multinational firms and show that higher levelsof bilateral trust between the multinational’s country of origin and subsidiary’scountry of location increases decentralization, even after instrumenting trustusing religious similarities between the countries. Finally, we show evidencesuggesting that trust raises aggregate productivity by facilitating reallocationbetween firms and allowing more efficient firms to grow, as CEOs can decen-tralize more decisions. JEL Codes: L2, M2, O32, O33.

I. INTRODUCTION

Economists have become increasingly aware of the import-ance of culture on international performance (e.g., Guiso, Sapi-enza, and Zingales 2006). One influential line of research arguesthat social capital, usually proxied by measures of social trust,fosters faster growth (e.g., Knack and Keefer 1997; La Porta et al.1997). The mechanisms through which this might happen are notfully understood, however. In this article, we present evidence

*We would like to thank Larry Katz, Elhanan Helpman, three anonymousreferees and Abhijit Banerjee, Luis Garicano, Bob Gibbons, Avner Greif, OliverHart, Bengt Holmstrom, Roberto Rigobon, Andrei Shleifer, Julie Wulf, LuigiZingales, and seminar participants for helpful comments. We are very gratefulfor the endorsement letters from the Banque de France, Bank of Greece, Bank ofJapan, Bank of Portugal, Bundesbank, Confederation of Indian Industry,European Central Bank, European Union, Greek Employers Federation, IUISweden, Ministero delle Finanze, National Bank of Poland, Peking University,Peoples Bank of China, Polish Treasury, Reserve Bank of India, ShenzhenDevelopment Bank, Sveriges Riksbank, U.K. Treasury, and the Warsaw StockExchange. We would like to thank the Economic and Social Research Council andthe International Growth Centre for their financial support. We received no fund-ing from the global management consultancy firm we worked with in developingthe survey tool.Corresponding author: John Van Reenen, [email protected], CEP, LondonSchool of Economics, London, WC2A 2AE, UK.

! The Author(s) 2012. Published by Oxford University Press, on behalf of President andFellows of Harvard College. All rights reserved. For Permissions, please email: [email protected] Quarterly Journal of Economics (2012), 1663–1705. doi:10.1093/qje/qje029.Advance Access publication on September 7, 2012.

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that high social capital in an area increases decentralized deci-sion making within firms, and this decentralization may improveproductivity by supporting larger equilibrium firm size.

We develop a model building on Garicano (2000) to analyzehow trust affects the organization of firms. The CEO can eithersolve production problems directly or delegate these decisions toplant managers. When trust is high, plant managers tend to solveproblems ‘‘correctly’’ (rather than, for example, stealing from thefirm) so that CEOs are more likely to delegate. Furthermore, bydelegating, the CEO can leverage his or her ability over a largerteam, which leads to larger firm size. We take these predictions tothe data and find support from the hypotheses that trust increasesdecentralization and raises firm size. Although other mechan-isms, such as high-powered incentives or stricter monitoring,could also make it more likely for a plant manager to performcorrect actions, trust may have an effect over and above these.This aspect of corporate culture is certainly emphasized bymany social scientists as critical in fostering autonomy andproductivity.

Our article subjects the ‘‘organizational’’ view of social cap-ital to rigorous econometric investigation and concludes thattrust is critical to the ability of a firm to decentralize. We showthat trust in a region (even after controlling for country dummiesand many other factors) is associated with much more decentra-lized decision making. To probe whether this effect is causal, weexploit the fact that some of our data are drawn from multina-tional subsidiaries. We find that the level of trust prevalent in thecountry where the multinational is headquartered has a strongpositive correlation with decentralization in the affiliate’s foreignlocation: for example, in California, a multinational affiliate fromSweden (a high-trust country) would typically be more decentra-lized than a multinational affiliate from France (a relativelylow-trust country). We further show that this is driven by thelevel of bilateral trust between countries, which seems to affectnot only flows of trade and investment between countries (as inGuiso, Sapienza, and Zingales 2009) but also the internal organ-ization of multinationals. Moreover, the effect of trust on decen-tralization is present even when we instrument bilateral trustwith measures of religious similarity between countries, whichare arguably exogenous to the firm.

Countries that find decentralization more costly may sufferlower welfare because it will be difficult for more efficient firms to

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grow large. Penrose (1959) and Chandler (1962) argued thatdecentralization was essential for the creation of large firms be-cause CEOs are time constrained over the number of decisionsthey can make. As firms grow large and more complex, CEOsneed to increasingly decentralize decision making power totheir senior management. In our data, we find that larger firmsare indeed significantly more decentralized and that high-trustregions are able to sustain firms of large equilibrium size. This isimportant because for capital and labor to be effectively reallo-cated across firms, productive firms need to grow large and takemarket share from unproductive firms. This reallocation is amajor factor driving growth in developed countries like theUnited States.1 But in emerging economies like India, wherefirms are typically quite centralized, average firm size is smaller,so that the more productive firms have a relatively smallermarket share than in developed economies (see, for example,Hsieh and Klenow 2009).

Our analysis is focused on a novel international data set pro-viding detailed information on the internal organization of firms.The economic theory of organization has made great strides in thepast two decades in furthering our understanding of activitieswithin the boundary of the firm (see Gibbons and Roberts 2012),but empirical research on this has lagged far behind because of alack of organizational data. The few data sets that exist are eitherfrom a single industry or (at best) across many firms in a singlecountry.2 We address this lacuna by analyzing data on the organ-ization of almost 4,000 firms across 12 countries in Europe, NorthAmerica, and Asia. We designed and collected these data using anew survey tool, and we measure the decentralization of invest-ment, hiring, production, and marketing decisions from the cen-tral headquarters (CHQ) to plant managers. These data revealstartling differences in the cross-country decentralization of

1. See, for example, Foster, Haltiwanger, and Krizan (2006) and Foster,Haltiwanger and Syverson (2008), who show that about 50% of productivitygrowth in the manufacturing sector and about 90% in the retail sector comesfrom reallocation.

2. On single industry studies, see Baker and Hubbard (2003, 2004) on trucks,Garicano and Heaton (2010) on policing or Garicano and Hubbard (2007) on legalservices. For cross-industry studies of firms see, for example, Acemoglu et al. (2007)on France and the United Kingdom; Colombo and Delmastro (2004) and Kastl,Martimort, and Piccolo (2008) on Italy; Marin and Verdier (2008) on Germanyand Austria; and Rajan and Wulf (2006) for the United States.

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firms: those in the United States and northern Europe appear tobe the most decentralized, and those in southern Europe and Asiaare the most centralized. The survey also includes detailed ques-tions on management practices modeled as in Bloom and VanReenen (2007), which enables us to control for managerial ability,a possible omitted variable that could be correlated with bothgreater decentralization and higher trust.

Our article links to several literatures. First, there arepapers examining the impact of social capital. La Porta et al.(1997) found in cross-country regressions that the combinedsize of the largest 25 public quoted firms was positively correlatedto trust. Guiso, Sapienza, and Zingales (2009) examine the role oftrust in explaining patterns of economic exchange (includingforeign direct investment [FDI] flows) between countries. In asimilar spirit, Bottazzi, Da Rin, and Hellman (2010) study theimportance of cultural factors in explaining flows of venture cap-ital investments across countries. Although our work builds onthis literature, a key distinction is the disaggregation of our ana-lysis. To the best of our knowledge, this is the first article lookingat the role of trust on the organizational structure of firms acrossmultiple countries, as opposed to country-level relationships.

Second, our article links to an emerging literature in trade onmultinationals and comparative advantage. Helpman, Melitz,and Yeaple (2004), Burstein and Monge-Naranjo (2009), andAntras, Garicano, and Rossi-Hansberg (2008) emphasize theimportance of firm-level comparative advantage in multina-tionals. In these models, firms have some productivity advantage,typically deriving from a different managerial or organizationaltechnology, which their multinationals transplant to their over-seas affiliates. Our evidence on the transplanting of a multina-tional’s domestic organizational practices abroad providesempirical support for this assumption.

Finally, we link to the literature on the ‘‘transportation’’ ofculture by individuals across countries. For example, Fisman andMiguel (2007) show that the parking fine behavior of diplomats inNew York is strongly predicted by indices of corruption in theirhome countries.3 Our evidence suggests that firms also take partof their home country’s ‘‘culture’’ abroad. Interestingly, this holds

3. In the social domain, Fernandez and Fogli (2009) show that fertility ratesamong second-generation Americans are correlated with fertility in the countries oftheir parents.



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even in multinationals when all the managers come from thecountry of location, suggesting that firms offer a mechanism fortransporting culture across countries in addition to individualmigration.

The article is organized as follows. Section II sketches asimple model of trust and organizational structure and its empir-ical implications. Section III details the data, and Section IV hassome descriptive statistics. The empirical results on the effect oftrust on decentralization (and firm size) are contained in SectionV, and Section VI concludes.

II. THEORY

II.A. A Model of Trust and Decentralization

Our starting point is the models of Garicano (2000) andGaricano and Rossi-Hansberg (2007) on the hierarchical organiza-tion of expertise. Firms have to solve production decisions to gen-erate output. Decisions are made at the lowest hierarchical level atwhich an agent is able to make them. In determining their hier-archical organization firms face a trade-off between informationacquisition costs (a) and communication (‘‘helping’’) costs (h).Making decisions at lower levels implies increasing the cognitiveburden of agents at those levels. For example, decentralizing fromthe CEO to plant managers over the decision whether to invest innew equipment requires training plant managers to discount cashflows using the appropriate cost of capital to compare these to thecost of investment. To the extent that the plant manager is unableto make this decision, it will be passed up to the corporate head-quarters. But this increases communication costs in the hierarchybecause the plant manager will have to explain some of the detailsbehind the potential investment project, and after solving the prob-lem the CEO will have to explain what the manager must do. Thus,the extent of decentralization depends on the optimal trade-offbetween knowing versus asking for directions.

We extend the Garicano (2000) model by adding the idea oftrust. The CEO may not trust the manager’s decision because ofmisaligned incentives—for example, she may worry about theplant manager taking bribes from equipment sellers.4 If the

4. Alternatively, it may be more a question of ability—the plant manager maynot be trusted to take the correct decision because even if he has acquired the formalknowledge to do the task (e.g., through training) he might still make a mistake.



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CEO does not trust the plant manager to take the right action,there will be less decentralization. This allows us to analyze theeffect of trust on firm size. We show that firm size is increasing inthe CEO’s trust in the plant manager because when she is able todelegate decision making, the CEO needs to spend less time help-ing any individual plant manager make decisions.5

Production. Firms are composed of a CEO and an endogenousset of production plants, each with a single plant manager. Theseproduction plants draw management problems z from the inter-val [0,1] each period. Production at each plant only takes place ifall of these problems are solved, otherwise nothing is produced.We normalize to 1 the unit of output per plant per time period ifproduction problems are solved. The frequency of these manage-ment problems is denoted by f(z) with a corresponding cumulativedistribution of F(z). Optimality requires that the plant managerslearn the common problems and asks about the exceptions; wethus reverse sort the problems in frequency order, so f0(z)< 0.

Managers. All managers have a priori the same cost of acquir-ing information, a, which we label ‘‘management skill.’’ So, forexample, if the firm trains plant managers to solve zM (where0< zM< 1) management problems then this costs azM. If a plantmanager draws a problem he cannot solve, he passes it up to theCEO at a communication cost h per problem denoted in terms ofmanagement time. Total costs are reduced if employees aretrained to deal with the common problems but pass up the rareproblems. This is the ‘‘management by exception’’ model.

Trust. We also assume that even after acquiring formal know-ledge plant managers only behave in the ‘‘correct way’’ to perform� tasks (0<�� 1) and fail to correctly perform (1 – �) tasks. Here� reflects the fact that the plant manager may have private bene-fits from doing the ‘‘wrong’’ action. Empirically we use measuresof trust to proxy shifts in the � parameter. We view variations in �across countries as reflecting CEO perceptions of differences inthe preferences for taking appropriate actions. For example, we

5. Garicano (2000) shows under general conditions a larger span between theCEO and plant manager will be replicated down the hierarchy, so firm size will bemonotonically increasing in the number of plant managers per CEO.



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assume that CEOs believe that Swedish plant managers would beless likely to accept a bribe (Sweden is a high-trust country) tobuy an overpriced piece of equipment than would Greek plantmanagers (Greece is a low-trust country). As such, the variationsin � reflect variations in beliefs over individual plant manager’sutility functions arising from different levels of social capital.

Firm Organization. The principal hires some agents whomust be trained to deal with tasks up to point zM and pass theremaining (less frequently occurring) management problems upto the principal, which in this two-layer model is assumed to bethe CEO. In each particular case, production per problem is asfollows:

Production ¼ F zMð Þ�þ 1� F zMð Þð Þ ¼ 1� F zMð Þð1� �Þ,ð1Þ

where the term F zMð Þ� reflects the share of problems solved bythe plant manager times the probability that he correctly solvesthem, and the term 1� F zMð Þ reflects the share of problemspassed up to the CEO (who we assume without loss of generalitycan correctly solve all problems). Thus if �= 1, the plant managercan be trusted and production proceeds correctly with probability1.

The CEO takes h units of time to communicate and solveeach referred problem. The problem of the principal is to maxi-mize the firm’s profits, V, by choosing decentralization (zM) andthe number of plant managers (n):

V ¼ maxzM, n

½ð1� F zMð Þ 1� �ð ÞÞn� �zMn� !n�ð2Þ

s:t: 1� F zMð Þð Þnh ¼ 1,ð3Þ

where the CEO is the residual claimant and receives the profitsobtained after paying wage ! to the plant managers—their out-side utility. Equation (3) follows from the time constraint of theCEO, who has 1 unit of time in total to solve all the (1�F(zM))referred problems at a time cost of h per problem. The cost ofdelegating more problems is twofold: lower level managers needto be trusted, as they may not perform adequately, and they needto be trained to deal with more problems.



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Decentralization. Solving the constrained maximization prob-lem gives an equation implicitly defining the optimal degree ofdecentralization (with * denoting an optimized value):

ð�� !Þ

�¼ z�M þ

ð1� F z�M� �Þ

f ðz�MÞ:ð4Þ

From first-order condition (4), we derive the main predictionfrom our model.

PROPOSITION 1: Higher trust leads to more decentralization.

An increase in trust (l rises) is associated with a higherdegree of decentralization ðz�MÞ,

@z�M@�

> 0,

where the positive sign is because f 0 z�M� �

< 0 due to tasks beingsorted in reverse frequency order. The intuition for Proposition 1is straightforward—if the CEO trusts plant managers, she be-lieves that the marginal returns from letting them handle tasksis greater as more problems are solved correctly.

An interesting corollary of equation (4) is that higher plantmanager skill (indexed by a lower value of �, the cost of acquiringknowledge) leads to greater decentralization:

@z�M@�

< 0:ð5Þ

The intuition here is that the more skilled the plant manageris at solving problems, the more decisions the CEO will delegateto him.6 Although we have no formal test of equation (5) becausewe do not have an instrument for skill supply, this correlation ispresent in the data and we generally control for human capital inthe estimation of the decentralization equation.

Size. The second key result relates to size. We derive the re-lationship between the number of plant managers that work withthe CEO in equilibrium, which is from equation (3):

n� ¼1

½ 1� F z�M� ��

h�:

6. The complementarity between skills and decentralization is broadly con-sistent with the findings of Caroli and Van Reenen (2001) and Bresnahan,Brynjolfsson, and Hitt (2002).



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By combining this with proposition 1 we can establish oursecond proposition.

PROPOSITION 2: Higher trust increases firm size.

An increase in trust (�) is associated with a larger firmsize n�:

@n�

@�> 0

The intuition is that higher trust allows the CEO to delegatemore decisions, so she is able to spend less time helping any in-dividual plant manager. Thus the CEO is able to employ moreplant managers and expand the size of the firm. Trust essentiallyallows talented CEOs to leverage their managerial ability over agreater number of employees, and is similar to increasing themanagerial leverage parameter in Lucas (1978).7

This result links with the early literature on firm size, whichalso focused on the issue of decentralization as the key determin-ant of firm growth. For example, Penrose (1959) developed the‘‘resource-based’’ view of the firm, claiming that managerialcapacity was a key resource in determining firm size. If seniormanagement time could be leveraged across a larger group ofplant managers, then firm size could be increased. Chandler(1962) examined the growth of large U.S. multidivisional firmsafter the 1850s. He argued that these larger firms were createdthrough setting up ‘‘local field units,’’ regional factories orsales outlets, with decentralized power from the headquarters.Again, decentralization was necessary to allow distant unitsto operate, because limits on communication prevented theCEO from directing managers operating hundreds of milesaway. Without decentralization, these firms would have notbeen able to grow.

We take these two propositions to the data and find empiricalsupport for both of them: all else equal, exogenously high-trustareas will have more decentralized and larger firms.

7. LaPorta et al. (1997) also noted that repeated interactions are a substitutefor trust and make large organizations harder to sustain in low-trust environments.Hart and Holmstrom (2010) present a model where plant managers may ‘‘shade’’ ifthey feel aggrieved by the CHQ, which will also tend to reduce delegation inlow-trust environments.



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II.B. General Equilibrium Considerations

The model described above applied to the optimization deci-sions of an individual firm and does not incorporate general equi-librium effects. There are at least three general equilibriumeffects that could be a concern: wages, house prices/amenityvalues, and selection effects.

Equilibrium Wages. We take the wage as fixed, whereas inthe context of a regional labor market equilibrium wages willchange as trust changes. An increase in trust will increase firmsize and mean that more workers are drawn into the labor force.There is an ability cutoff (aMIN) for the marginal individual who isindifferent between being a worker or entrepreneur. To drawmore workers in to the (larger) firms, wages must rise, shiftingaMIN to the right. Compared to the fixed-wage case, higher wageswill make firms smaller and less decentralized as CEOs do morethemselves to avoid higher labor costs. Although higher equilib-rium wages offset the delegation and size effects in our propos-itions, since it is a second-order effect it will never completelyreverse them.

House Prices. If labor and capital are mobile between regions,there will be equilibrium effects on amenity values, such as hous-ing. If trust increases in one region, what stops all firms migrat-ing there? The standard model has an inelastically supplied localamenity like housing (e.g., Roback 1982). As more workers moveinto the high-trust region, house prices rise, which offsets thehigher nominal wages. Eventually real wages (nominal wagesless house prices) equilibrate so marginal workers are indifferentto moving because they receive a higher nominal wage, but sufferhigher house prices. Higher housing costs indirectly affect firmsize by limiting the number of workers who are prepared to live inthe high-trust area. Again, however, this will not completely re-verse the positive effect of trust on size and decentralization.

Selection between High- and Low-Productivity Firms. Wecould allow heterogeneity among firms following Garicano andRossi-Hansberg (2007) and consider individuals of different abil-ities. More able workers can more easily solve problems and willsort themselves into the largest firms. This generates a



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continuum of firms with firms nearest the cutoff being the lowestproductivity and the most centralized, employing workers oflower ability. When trust increases, more productive firmsexpand and the least productive firms exit (i.e., their managersbecome workers). This selection effect will mean that the averagedecentralization in the remaining firms is higher because theleast decentralized firms have exited. Thus, the selection effectbetween firms reinforces the within-firm effect of trust increasingdecentralization.

Summary. Although the two general equilibrium effects ofrises in wages and house prices will offset the main positiveeffect of trust on decentralization and size, they will not reversethe effect. Furthermore, the reallocation effect should reinforceour main positive within-firm effect of trust.

II.C. Other Models of Trust and Decentralization

Our model focuses on decentralization in a cognitive model ofthe firm, but other papers have seen this through the prism ofincentive mechanisms (e.g., Aghion and Tirole 1997; Prendergast2002). Acemoglu et al. (2007) consider delegation in an incentivemodel with learning where a firm is choosing a new technologywith uncertain and heterogeneous returns. The CEO-ownerwants to maximize value, but the agent-manager has greaterlocal private knowledge, and this trade-off between informationand incentives determines the optimal degree of decentralization.If trust reflects a greater congruence of preferences between prin-cipal and agent, this should lead to great delegation. Even wheredecentralization is efficient, Baker, Gibbons, and Murphy (1999)emphasize that delegation is generally informal because the CEOmust usually make a formal sign-off on decisions. The issue iswhether the CEO can credibly commit to delegating to theplant manager and to avoid the temptation to override his deci-sions. Thus, the level of decentralization is the outcome of arepeated game between the CEO and manager,8 and preferencesand beliefs will influence delegation. Trust is emphasized in thesocial capital and experimental game theory literatures as one

8. Other models, like Rajan and Zingales (2001), focus on the intangible capitalview of the firm, with ownership being structured so that employees cannot easilysplit off to create rival firms.



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factor that leads to cooperation (Fukuyama 1995; Glaeser et al.2000; Putnam 2002). If there are heterogeneous types in thepopulation with some types who are ex ante more likely to cooper-ate than others, then the cooperative outcome (decentralization)is more likely with higher trust.

In principle, an alternative to trust in sustaining cooperationis rule of law. When the employer (or employee) can successfullysue for breach of contract, this will make contracts easier to en-force and sustainable delegation more likely. This will be particu-larly important in larger firms (Greif 1993). In the empiricalspecifications where we do not control for country dummies, wealso consider the independent influence of rule of law alongsidetrust.

Since there are models other than our extension of Garicano(2000) that would predict a positive relationship between trustand decentralization we do not regard our empirical examinationthe final word on the correct theoretical model but as a usefulframework for organizing our thinking.

III. DATA

To investigate the role of trust on decentralization, we firsthave to construct a robust measure of organizational practicesovercoming four hurdles: measuring decentralization, collectingaccurate responses, ensuring international comparability, andobtaining interviews with managers. We discuss these in turn.We have also posted online the full anonymized data set anddo-files to replicate all results (see http://www.worldmanage-mentsurvey.com).

III.A. Measuring Decentralization

Our measure of decentralization is obtained through anin-depth interview with a representative plant manager from amedium-sized manufacturing firm, excluding those where theCEO and the plant manager is the same person (this occurredin only 4.9% of our interviews). We asked four questions on plantmanager decentralization. First, we asked how much capital in-vestment a plant manager could undertake without prior author-ization from the corporate headquarters. This is a continuousvariable enumerated in national currency that we convert intodollars using purchasing power parity (PPP). We also inquired on



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where decisions were effectively made in three other dimensions:(a) hiring a new full-time permanent shop floor employee, (b) theintroduction of a new product, and (c) sales and marketing deci-sions. These more qualitative variables were scaled from a scoreof 1, defined as all decisions taken at the corporate headquarters,to a score of 5 defined as complete power (‘‘real authority’’) of theplant manager. In Online Appendix Table A1 we detail the indi-vidual questions in the same order they appeared in the survey.9

Because the scaling may vary across all these questions, weconverted the scores from the four decentralization questions toz-scores by normalizing each one to mean 0 and standard devi-ation 1. In our main econometric specifications, we take the un-weighted average across all four z-scores as our primary measureof overall decentralization, but we also experiment with otherweighting schemes and present regressions using the individualquestions as dependent variables.

One issue is over the measurement of decentralization acrossdifferent types of firms. Figure I provides four examples to helpexplain how we did this. Example A shows the classic case, wherethe firm has one CHQ in New York and one production site inPhoenix. The plant manager is defined as the most senior man-ager at the Phoenix site, with our decentralization measure eval-uating how much autonomy he has from his manager in NewYork. In Example B we depict a firm with multiple plants, inwhich we would usually survey one plant and assume this repre-sented the degree of decentralization for the firm as a whole(Section III.F discusses how we test this assumption). InExample C we have a firm with the production facilities andCHQ on the same site. In this case, if the plant manager wasthe CEO we could not define decentralization (so these observa-tions were dropped).10 If the plant manager and CEO were dif-ferent people on the same site, we would define decentralizationas usual, but we show how our results are weaker in these

9. Some of these four questions are similar to others used in the past to meas-ure decentralization. Acemoglu et al. (2007) use a similar question on hiring in theBritish WERS data, and Colombo and Delmastro (2004) have a question similar toours on investment for Italian establishments.

10. As noted, this occurred in less than 5% of our observations. The CEO–plantmanagers were typically in smaller firms (a mean firm employment of 159 for theCEO–plant manager firms versus 843 for the rest of the sample). There was nosignificant correlation between the share of firms dropped in each country and itsaverage decentralization measure.



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Central HQ(New York Site)

Example A: US Domestic Firm2 Sites, Single Plant

Plant(Phoenix Site)

A classic case

D, Decentralization

Central HQ(New York Site)

Example B: US Domestic FirmMulti-Site, Multi-Plants

Plant 1(Detroit Site)

Plant 3(Scranton Site)

Plant 2(Phoenix Site)

D1 D2 D3

We typically observe just one plant per firm & assume this is representative,but sometimes we sample more than 1 plant

FIGURE I

Examples of Firm Organizational Structures



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Example C:US Domestic FirmSingle site, Single plant

Central HQ(Phoenix Site)

Plant 1(Phoenix Site)

D

Some firms have a site with multiple “buildings,” such as a CHQ and production plants. We only keep these if the plant manager is not the CEO, as decentralization is still possible even if the CEO is on-site (think of Universities, which typically have one-site but Departmental Heads have some autonomy from the Dean). We also test robustness to this assumption in Appendix A.

Example DJapanese MNE

We have affiliates of multinationals if they are under 5000 workers. We Measure D between the domestic CHQ and the plant manager.

Global HQ(Tokyo Site)

French Plant(Paris Site)

Sweden Plant(Stockholm Site)

noitazilartneceD ,DnoitazilartneceD ,D

FIGURE I

Continued



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‘‘same-site’’ observations (where trust matters less because directmonitoring is easier).11 Finally, in Example D we show a multi-national subsidiary, which we treat the same as domestic firms,defining decentralization as the autonomy of the plant from theglobal CHQ. We use the multinationals to get closer to the causaleffects of trust on decentralization by using bilateral trust infor-mation as explained in Section V.

In the same survey we collected a large amount of additionaldata to use as controls, including management practice informa-tion following the methodology of Bloom and Van Reenen (2007)and human resource information (e.g., the proportion of the work-force with college degrees, average hours worked, and the genderand age breakdown within the firm). During the interview, wealso collected ownership information from the managers, whichwe cross-checked against external databases (see Section III.E fordetails). From the sampling frame database we also have infor-mation for most firms on their basic accounting variables, likesales and capital. This is collected directly from the reportedand audited company accounts from private sector data suppliers(Bureau Van Dijk’s Amadeus, Icarus and Oriana products, andCMIE FirstSource for India). These are an entirely independentdatabase to the organizational survey (data details are in OnlineAppendix A).

III.B. Collecting Accurate Responses

To achieve unbiased responses to our questions, we took sev-eral steps. First, the survey was conducted by telephone withouttelling the managers they were being scored on organizational ormanagement practices. This enabled scoring to be based on theinterviewer’s evaluation of the firm’s actual practices, rather thantheir aspirations, the manager’s perceptions, or the interviewer’simpressions. To run this ‘‘blind’’ scoring we used open questions(i.e. ‘‘To hire a full-time permanent shop floor worker, what agree-ment would your plant need from corporate headquarters?’’),rather than closed questions (e.g., ‘‘Can you hire workers without

11. While plant managers with CEOs on site typically have less autonomy(something we control for empirically), it is not the case they have no autonomy.The CEO will typically be involved in a number of other tasks, such as finance,strategy, and sales (which could involve other nonproduction sites), whereas theplant manager runs the daily production process. An example in a university con-text would be the university dean and the head of the Economics Department—theyare usually on the same campus, but the department head still has some autonomy.



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authority from corporate headquarters?’’ [yes/no]). Following theinitial question, the discussion continued until the interviewercould make an accurate assessment of the firm’s typical practices.For example, if the plant manager responded, ‘‘It is my decision,but I need sign-off from corporate HQ,’’ the interviewer would ask‘‘How often would sign-off typically be given?’’ with the response‘‘So far it has never been refused’’ scoring a 4 and the response‘‘Typically agreed in about 80% of the case’’ scoring a 3.

Second, the interviewers did not know anything about thefirm’s financial information or performance in advance of theinterview. This was achieved by selecting medium-sized manu-facturing firms and providing only firm names and contact detailsto the interviewers (no financial details). Consequently, thesurvey tool is ‘‘double blind’’—managers do not know they arebeing scored, and interviewers do not know the performance ofthe firm. These manufacturing firms (the median size was 270employees) are too small to attract much coverage from the busi-ness media. All interviews were conducted in the manager’snative language.

Third, each interviewer ran 85 interviews on average, allow-ing us to remove interviewer fixed effects from all empirical spe-cifications. This helps address concerns over inconsistentinterpretation of categorical responses, standardizing the scoringsystem.

Fourth, the survey instrument was targeted at plant man-agers, who are typically senior enough to have an overview oforganizational practices but not so senior as to be detachedfrom day-to-day operations.

Fifth, we collected a detailed set of information on the inter-view process itself (number and type of prior contacts before ob-taining the interviews, duration, local time of day, date, and dayof the week), on the manager (gender, seniority, nationality, com-pany and job tenure, internal and external employment experi-ence, and location), and on the interviewer (individualinterviewer fixed effects, time of day, and subjective reliabilityscore). These survey metrics are used as ‘‘noise controls’’ to helpreduce residual variation.

III.C. Ensuring International Comparability

In analyzing organizational and management surveys acrosscountries, we have to be extremely careful to ensure



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comparability of responses. To maximize comparability, weundertook three steps. First, every interviewer had the same ini-tial three days of interview training, provided jointly by theCentre for Economic Performance (CEP) at the London Schoolof Economics (LSE) and the international consultancy firm wepartnerered with. This training included three ‘‘calibration’’ ex-ercises, in which the group all scored a role-played interview andthen discussed scoring of each question together. This was aimedat ensuring that every interviewer had a common interpretationof the scoring grid. In addition, every Friday afternoon through-out the survey period the group met for 90 minutes for trainingand to discuss any problems with interpretation of the survey.

Second, the team operated from one location, the LSE. Thedifferent national survey teams were thus organized and mana-ged in the same way; ran the surveys using exactly the sametelephone, computer, and software technology; and were able todirectly discuss any interpretation issues.12 Third, the individualinterviewers interviewed firms in multiple countries. The teamall spoke their native language plus English, so interviewers wereable to interview firms from their own country (as managers wereinterviewed in their native language) plus the United Kingdomand the United States. As a result the median number of coun-tries that each interviewer scored firms in was three, enabling usto remove interviewer fixed effects even in the cross-countryanalysis.

III.D. Obtaining Interviews with Managers

Each interview took on average 48 minutes and was run inthe summer of 2006. Overall, we obtained a relatively high re-sponse rate of 45%, which was achieved through several steps.First, the interview was introduced as ‘‘a piece of work,’’ withoutdiscussion of the firm’s financial position or its company accounts.Interviewers did not discuss financials in the interviews, both tomaximize the participation of firms and to ensure our interviewerswere truly ‘‘blind’’ on the firm’s financial position. Second, thesurvey began with the least controversial questions (on shopfloor operations management), leading on to monitoring, incen-tives, and organizational structure. Third, interviewers’ perform-ance was monitored, as was the proportion of interviews achieved,

12. See http://www.youtube.com/watch?v=HgJXt8KwhA8 for video footage ofthe survey team.



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so they were persistent in chasing firms.13 Fourth, the writtenendorsement of many official institutions14 helped demonstrateto managers that this was an important academic exercise withofficial support. Fifth, we hired high-quality (mainly M.B.A. stu-dent) interviewers,15 mostly with prior manufacturing experience,which helped signal to managers the high-quality nature of theinterview.

III.E. Sampling Frame and Additional Data

Because our aim is to compare across countries, we decided tofocus on the manufacturing sector, where productivity is easier tomeasure than in the nonmanufacturing sector. We also focusedon medium-sized firms, selecting a sample of firms that had be-tween 100 and 5,000 workers. Very small firms have little pub-licly available data. Very large firms are likely to be moreheterogeneous across plants. We drew a sampling frame fromeach country to be representative of medium-sized manufactur-ing firms and then randomly chose the order of which firms tocontact (see Online Appendix B for details). Because we use twodifferent databases to generate the sampling frame (BVD’s Orbisfor Europe, the United States, China, and Japan; and CMIE’sFirstsource for India) we had concerns regarding the cross-country comparisons. Therefore, we include country dummiesin most of the specifications. Comparing responding firms withthose in the sampling frame, we found no evidence that the re-sponders were systematically different on the observable meas-ures to the nonresponders. The only exception was on size andmultinational status, where our firms were slightly larger andmore likely to be multinational than those in the samplingframe (details in Online Appendix A).

13. We found no significant correlation between the number, type, and timespan of contacts before an interview was conducted and the management score.

14. The Banque de France, Bank of Greece, Bank of Japan, Bank of Portugal,Beijing University, Bundesbank, Confederation of Indian Industry, EuropeanCentral Bank, European Commission, Greek Employers Federation, IUISweden, Ministero delle Finanze, National Bank of Poland, Peoples Bank ofChina, Polish Treasury, Reserve Bank of India, Shenzhen Development Bank,Sveriges Riksbank, U.K. Treasury, and the Warsaw Stock Exchange.

15. Interviewers were postgraduate students drawn from the following univer-sities: Berkeley, City of London, Columbia, Harvard, HEC, IESE, Imperial, Insead,Kellogg, LBS, LSE, Lund, MIT, Nova de Lisbon, Oxford, Stanford, and Yale.



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III.F. Evaluating and Controlling for Measurement Error

Our survey data potentially suffer from several types ofmeasurement error. To quantify this, we performed repeat inter-views with 72 firms, contacting different managers in differentplants at the same firm, using different interviewers. To theextent that our organizational measure is truly picking upcompany-wide practices, these two scores should be correlated,whereas to the extent the measure is driven by noise the meas-ures should be independent. The correlation of the first interviewagainst the second interviews was .513 (p-value of .000).Furthermore, there is no obvious (or statistically significant)relationship between the degree of measurement error and thedecentralization score. That is, firms that reported very low orhigh decentralization scores in one plant appeared to be genu-inely very centralized or decentralized in their other plants,rather than extreme draws of sampling measurement error.

III.G. Measuring Trust

We build trust measures using the World Values Survey(WVS), a collection of surveys administered to representativesamples of individuals in 66 countries between 1981 and 2004.These questionnaires contain information on several social, reli-gious, and political attitudes. The WVS aims at measuring gen-eralized trust, namely, the expectation of the respondentregarding the trustworthiness of other individuals. The wordingof this question is: ‘‘Generally speaking, would you say that mostpeople can be trusted, or that you can’t be too careful in dealingwith people?’’ The trust variable that we use in the regressions isthe percentage of people choosing the first option in the trustquestion (‘‘Most people can be trusted,’’ with the alternativebeing ‘‘Can’t be too careful’’) within the geographical areawhere CHQ of the plant are located.16 We thought this wasmost appropriate, because the decision to decentralize is madeat the CHQ level, but we also check for the independent import-ance of trust in the plant’s location when the firms’ CHQ is locatedin a different region or country.

This is the most common measure of trust used in the litera-ture and appears to be correlated with trusting behavior. Fehr

16. For domestic firms and domestic multinationals, this is the region of loca-tion of the CHQ. For foreign multinationals this is the country where the parent’sCHQ is based (region of CHQ is unavailable for most of the foreign multinationals).



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et al. (2004) ran a series of experiments suggesting that the WVSquestion does indeed measure trust, and Johnson and Mislin(2011) present cross-country experimental evidence also suggest-ing that the WVS question capture trust. Glaeser et al. (2000), bycontrast, ran experiments on undergraduates and argued thatthe WVS trust question better measures the trustworthiness ofsubjects. Sapienza, Toldra, and Zingales (2007) reconcile thesefindings: they provide evidence that the WVS question is drivenby what they call the ‘‘belief-based component of trust.’’ In otherwords, when you are not extrapolating the trustworthiness ofothers based on your own trustworthiness (as Fehr et al. 2004),the large-sample WVS really does measure trust rather thantrustworthiness. In our context, we want to measure trust ofthe headquarters (toward the plant manager), so the WVS ques-tion seemed appropriate for the task.

Several authors have emphasized the fact that generalizedtrust may vary quite substantially even within countries (Guiso,Sapienza, and Zingales 2008; Tabellini 2008). To exploit thiswithin-country variation for identification purposes, we identifythe specific region where the corporate headquarters of each ofthe plants included in our survey are located, pool together indi-vidual responses from four different WVS waves (1981–1984,1989–1993, 1994–1999, and 1999–2004), and compute the aver-age level of trust in this area. We take simple averages for eachregion-country cell over all available years, so that every individ-ual observation has equal weight. The precise level of aggregationof the trust measure at the subnational level varies according togeographical detail included in our own decentralization surveyand in the WVS.17 Through our survey data, we are able to allo-cate plants belonging to purely domestic firms and domesticmultinationals (2,744 observations in total, or about two thirdsof our entire sample) to narrowly defined regions within countries

17. Regional classifications are fairly stable for most countries in our sampleover time, but they vary somewhat over time for WVS interviews conducted inChina, France, Portugal, and Sweden. We show in Table B2 in the OnlineAppendix that the main results of the article are robust to the use alternative ag-gregation methods based on using just the latest or the largest (in terms of individ-ual observations) WVS wave for each region, and to introducing controls for thespecific wave used to build the aggregated trust measures. More details on the WVScoverage by country can be found in the Online Appendix (Section A.4 and TableA8).



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(e.g., NUTS3 levels in Europe or individual states in India).18

However, because the level of geographical detail provided bythe WVS varies within countries, we are sometimes forced towork at a higher level of aggregation.19 In the case of the 881plants that belong to foreign multinationals, we match theplant with information on the level of trust in the countrywhere the global ultimate owner of the plant is headquartered,since the country (but not region) of the global ultimate ownerwas also collected in the decentralization survey.

Figure II plots the median level of trust by country and itsregional dispersion. We view the geographical variation in cur-rent levels of trust as driven by some very long-term historicalfactors. The current level of trust in different Italian regions, forexample, seems to depend on crucial events in city-states duringthe medieval period and earlier (Guiso, Sapienza, and Zingales2008). Durante (2010) has shown the positive impact of theannual variability of weather conditions (especially precipitationand temperature in the growing season months) over the 1500–1750 period in stimulating trust across regions of Europe today,and Tabellini (2008) argues for the importance of past literacyrates and nondespotic political institutions. It is thus likely thatthe level of trust in a firm’s location is largely exogenous, which iswhy we use this source of variation (rather than the trust withinthe firm, which may more sensitively depend on the firm’s ownendogenous policies). Nevertheless, to examine the plausibility ofthis exogeneity assumption, we also use long-run cultural andhistorical instrumental variables, such as religious similarity,to instrument the bilateral trust between the multinational’sparent HQ and affiliate plant’s country of location.

IV. DESCRIPTIVE STATISTICS

IV.A. Decentralization

Our preferred measure of decentralization is an averageacross four z-scored measures of plant manager autonomy on

18. In the vast majority of cases the plant is actually located within the sameregion of the headquarters (93% of the plants belonging to purely domestic firms ordomestic multinationals).

19. For example, in the United States and China the WVS only provides broadergeographical markers, which correspond to group of states. See Online Appendix Bfor details.



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hiring, capital expenditure, marketing, and product innovations.The resulting variable is what we define as decentralization (orequivalently, autonomy of the plant manager). The cross-countryaverages of decentralization and the within country dispersionshown in Figure III reveal some interesting patterns. Firmslocated in Asia (China, Japan, and India) tend to be much morecentralized than firms located in Anglo-Saxon and northernEuropean countries (Germany, the United Kingdom, UnitedStates, and Sweden). The rest of Europe tends to be in the

FIGURE II

Trust by Country (across Regions)

The graph shows levels of regional trust by country at the 25th percentile(bottom line of the box), median (middle line of the box), and 75th percentile(top line of the box). Upper and lower adjacent values are also shown. Regionaltrust measures are computed by pooling four successive waves of the WorldValues Survey (1981–1984, 1989–1993, 1994–1999, and 1999–2004), with theexception of Greece, for which the trust measure is built from the EuropeanSocial Survey (all waves between 2000 and 2005). Trust is the percentage ofpeople in the region answering ‘‘Most people can be trusted’’ to the question‘‘Generally speaking, would you say that most people can be trusted or that youneed to be very careful in dealing with people?,’’ with the alternative being‘‘Can’t be too careful.’’ The number of responses used to generate the trustmeasure for each country is as follows: Portugal = 2,124; France = 2,499;Greece = 4,972; Poland = 2,768; Italy = 3,877; Germany = 7,870; UnitedKingdom = 3,434; India = 6,032; Japan = 4,254; United States = 4,410;China = 2,047; Sweden = 1,918.



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middle of the decentralization ranking—with the exception offirms located in Greece, which appear to be very centralized.The differences between the three groups of countries are statis-tically significant at the 1% level, even when we include a full setof firm characteristics and survey noise controls. Table A2 in theOnline Appendix provides more details behind these cross-country comparisons and reveals that although Sweden, theUnited Kingdom, and the United States are at the top of the de-centralization distribution across all four dimensions, the rank-ing varies for the rest of the countries. For example, Germanytends to be closer to the other continental European countriesincluded in our sample (i.e., less decentralized) with regard toautonomy of the plant manager in the hiring and firing decisions.

FIGURE III

Decentralization by Country (across Firms)

The graph shows levels of the z-scored decentralization index by country,measured as the average plant manager’s degree of autonomy over hiring, in-vestment, products, and prices, at the 25th percentile (bottom line of the box),median (middle line of the box), and 75th percentile (top line of the box), asmeasured in the CEP organizational survey. Upper and lower adjacent valuesare also shown. The number of firms surveyed in each country is as follows:Greece = 183; Japan = 120; India = 397; China = 537; Poland = 222; Portugal =145; France = 217; Italy = 96; Germany = 327; United Kingdom = 557; UnitedStates = 638; Sweden = 216.



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Japanese plant managers have limited autonomy because hiringis very centralized due to lifetime tenure, but they do have moreautonomy over capital investment decisions.

From Figure III, it is also clear that there is much firm-levelheterogeneity, even within countries. About 15% of the overallvariance in our decentralization measure is across countries,8% is across three-digit industry class, and 81% of the variationis orthogonal to both country and three-digit industry.

IV.B. External Validation

A possible concern is that the cross-country differences indecentralization may reflect the specific characteristics of thefirms that participated in the survey (i.e., medium-sized manu-facturing firms), rather than more general organizational fea-tures. Therefore, to validate our decentralization measure, wecompared it to two other cross-country decentralization indicesthat exist in the literature.

The first is the Power Distance rankings created by Hofstede(2001).20 The Power Distance Index (PDI) is a measure of inter-personal power or influence between a boss and their subordin-ate, built out of successive attitudinal surveys conducted on morethan 80,000 IBM employees across approximately 50 countries inthe 1960s and 1970s, and then supplemented with additionalinterviews on individuals from other firms and countries overtime (see Hofstede 2001 for more details). Our decentralizationvariable provides a factual description of the average autonomyallocated to the plant managers, whereas the PDI measures theperceptions of and the preferences for hierarchical relationshipsamong nonmanagerial IBM employees. The PDI measure isbased on aggregating questions relating to (1) nonmanagerialemployees’ perception that employees are afraid to disagreewith their managers; (2) subordinates’ perception that theirboss tends to make decisions in an autocratic or paternalisticway; and (3) subordinates’ preference for anything but a consulta-tive style of decision making. High PDI values reflect perceptionsof and preferences for self-determination. Figure IV shows thatthe country-level averages of the PDI and our decentralizationmeasure are extremely similar (correlation .80, significant at the1% level). This is reassuring because it suggests that our

20. These measures have been used by several economists too, for example,Gorodnichenko and Roland (2011).



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decentralization variable captures long-lived organizationaltraits across countries, rather than specific characteristics ofour sample of firms.

The second cross-country decentralization indices that weuse to validate our data are those created by Arzaghi andHenderson (2005) to evaluate fiscal decentralization across coun-tries. They generated an index on a 0 to 4 scale that was summedover scores for decentralization of government structure (unitaryversus federal) and the degree of autonomy and democratizationof state, province, and municipal governments over taxation, edu-cation, infrastructure, and policing. A value of 0 denotes the coun-try is fully centralized across every dimension, and a value of 4denotes a highly decentralized fiscal structure. This measure was

FIGURE IV

Decentralization and Power Distance Index by Country

The y-axis denotes levels of the z-scored decentralization index by country,measured as the average plant manager’s degree of autonomy over hiring, in-vestment, products, and prices by country, as measured in the CEP organiza-tional survey. The x-axis is Hofstede’s (2001) Power Distance Index downloadedfrom www.geerthofstede.nl/dimension-data-matrix. The number of firms used tobuild the decentralization measure in each country is as follows: Greece = 183;Japan = 120; India = 397; China = 537; Poland = 222; Portugal = 145; France =217; Italy = 96; Germany = 327; United Kingdom = 557; United States = 638;Sweden = 216.



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calculated for every country with 10 million or more employees in1995, which includes 10 of our 12 countries. Figure V shows thatthis fiscal decentralization index is also extremely close to ourdecentralization index (correlation of .827, significant at the 1%level). Thus, countries in our sample with decentralized firms alsotend to have decentralized governments, suggesting this is amore general phenomenon.

V. TRUST AND FIRM ORGANIZATIONAL STRUCTURE

V.A. Trust and Decentralization

Our theory predicts that greater trust of the CEO in the plantmanager should lead to increased managerial delegation

FIGURE V

Firm and Fiscal Decentralization by Country

The y-axis denotes levels of the z-scored decentralization index by country,measured as the average plant manager’s degree of autonomy over hiring,investment, products, and prices by country as measured in the CEP organiza-tional survey. The x-axis is Arzaghi and Henderson’s (2005) Fiscal Decentrali-zation Index. The number of firms used to build the decentralization measurein each country is as follows: Greece = 183; Japan = 120; India = 397; China =537; Poland = 222; Portugal = 145; France = 217; Italy = 96; Germany = 327;United Kingdom = 557; United States = 638; Sweden = 216.



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(Proposition 1). Column (1) of Table I presents the results of re-gressing our decentralization measure against average trust inthe area where the plant’s headquarters are located, with noother controls. The relationship between decentralization andtrust is positive and highly significant—a 1 standard deviationin trust (12 percentage points) is associated with about 0.15 astandard deviation increase in decentralization. A concern isthat high levels of trust could simply proxy for better law enforce-ment or higher levels of economic development. Column (2) in-cludes an indicator for country-wide ‘‘rule of law,’’21 grossdomestic product (GDP) per capita, and population. Rule of lawenters with a positive and significant coefficient,22 but trust alsoplays an independent role.

Trust may be associated with decentralization because itsustains larger equilibrium firm size or because skill levels arehigher (see Section II). Consistent with this, column (3) of Table Ishows that larger firms and plants tend to be more decentralized,as do those with more skilled workers.23 Conditioning on size andskills halves the trust coefficient compared to column (1), but itremains significant. In terms of our other covariates, foreignmultinationals are more decentralized relative to both homecountry multinationals and purely domestic firms. This could re-flect the greater complexity of managing across national bound-aries and larger global size.

In column (4) of Table I we include a full set of country dum-mies to address the concern that there might still be manyomitted unobserved country-level factors like regulation(Aghion et al. 2010) generating a spurious positive correlationbetween trust and decentralization. We also include three-digitindustry dummies, measures of local development (GDP percapita and population at the regional level), and ‘‘noise controls’’(for measurement error in the decentralization variable) such asinterviewer fixed effects. The coefficient on trust remains

21. This indicator was developed by the World Bank and measures ‘‘the extentto which agents have confidence in and abide by the rules of society, and in particu-lar the quality of contract enforcement, the police, and the courts, as well as thelikelihood of crime and violence’’ (Kaufmann, Kraay, and Mastruzzi 2006).

22. GDP per capita and population are insignificant at conventional levels. Thecoefficient (standard error) on ln(GDP per capita) is –0.082 (0.061), and for ln(popu-lation) is 0.042 (0.028).

23. The results are unchanged when we include measures of regional skills,which is positive but insignificant.



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significant and is similar in magnitude to the simpler specifica-tion in column (3).24

An implication of our model is that trust should matter morewhen the CEO is located at a different site from the plant becausecommunication costs will be higher and monitoring is more diffi-cult, so centralization becomes more costly. Column (5) of Table Iestimates the regressions on the subsample where the CEO is offsite (such as Example B in Figure I), and column (6) on the sub-sample where the CEO is on site (i.e., the headquarter building islocated at the same site as the plant manager we interviewed,such as Example C in Figure I). Although the coefficient on trustis positive in both cases, it is much larger and only significantwhen the CEO is farther away from the plant manager, as wewould expect. When the CEO is on site, presumably monitoring iseasier so that trust becomes less important for the decentraliza-tion decision.25

The magnitude of the association between decentralizationand trust is large. As noted, column (1) implies a 1 standard de-viation in trust and is associated with 0.15 standard deviationincrease in decentralization. Including the full set of covariatesin column (4) halves this to 0.07 of a standard deviation. The sizeof these differences are substantial, for example, moving from thelowest trust region (Assam in India) to the highest trust region(Norrland in Sweden) would be associated with an increase of thedecentralization index of 0.37 of a standard deviation.26 Finally,running instrumental variable regressions, as we do in next,leads to even larger magnitudes.

V.B. Exploiting Differences in the Location of the Plant and ItsHeadquarters

About a third of our sample (1,094 observations) has head-quarters located in a different geographical area (region or

24. Although the coefficient on trust declines monotonically when we add morecontrols in Table I, this is notalways the case. For example, the coefficient (standarderror) on trust rises to 0.838 (0.234) when we just add the survey ‘‘noise’’ controls tocolumn (3).

25. This difference is not simply a reflection of size. When we split the sampleinto firms with more than and less than 250 employees, the trust coefficient wassignificant in both subsamples and only slightly larger in the smaller firms (0.883vs. 0.824). See Online Appendix Table B2.

26. This calculation uses the 0.596 coefficient on trust in Table I, column (4), andthe trust values in Assam and Norrland of 0.13 and 0.76, respectively.



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country) from the plant itself, including 881 affiliates of foreignmultinationals. This subsample is interesting for two reasons.First, we can include fixed effects for the regional location ofthe plant, removing any bias associated with other geographicalcharacteristics spuriously correlated with local trust and decen-tralization.27 Second, by focusing on the sample of foreign multi-nationals, we can study whether country of origincharacteristics—such as trust—have an effect on the multina-tional’s structure. This has long been a preoccupation of businesscase studies and the more recent trade literature on the organ-ization of multinationals.28 In particular, for 422 of these foreignaffiliates we have information on bilateral trust between coun-tries, derived from a series of surveys conducted for the EuropeanCommission. These surveys asked around 1,000 individuals ineach country the following question: ‘‘I would like to ask you aquestion about how much trust you have in people from variouscountries. For each, please tell me whether you have a lot of trust,some trust, not very much trust, or no trust at all.’’ This questionwas asked about all other EU countries and a number of non-EUcountries like the United States, Japan, and Canada. For ourpurposes, the bilateral trust variable is ideal because it allowsus to analyze the role of trust for decentralization controllingfor a full set of region of location and country-of-origin dummies.

The results of this analysis are shown in Table II. Theseregressions are based on the specification of column (4) inTable I, where we test the relationship between decentralizationand trust.29 Column (1) simply shows that the coefficient on trust

27. This also includes any potential language or national bias in the interviewprocess, since multinationals are always interviewed in the local language, with thequestion on the ownership of the firm only asked at the end of the interview. Thismeans our results for multinationals imply, for example, that even if we inter-viewed a Japanese subsidiary in Sweden in Swedish, it would still display organ-izational characteristics of its Japanese parent firm.

28. See, for example, Helpman, Melitz, and Yeaple (2004), Antras, Garicano,and Rossi-Hansberg (2008), or Burstein and Monge-Naranjo (2009).

29. The only difference is that we use two-digit rather than three-digit industrydummies because of the smaller sample size. In the subsample of 422 subsidiaries offoreign multinationals that we analyze in Table II columns (4) to (7), for example,there are 83 distinct three-digit industries, but 20% of them are populated only by asingle firm (the median number of observation per three-digit industry is 3). Whenwe move to a specification with two-digit dummies, we can identify only 18 distinctindustries, but of these, only one is populated by a single firm (the median number ofobservations per two-digit industry is 21).



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remains positive and significant (0.606 with a standard error of0.270, compared to 0.596 with a standard error of 0.219) in thissubsample where region of plant and CHQ are different. Incolumn (2) we repeat the specification adding fixed effects forthe plant’s region of location. Both the magnitude and the stand-ard error of the trust variable remain similar with the inclusion ofthe regional dummies. From column (3) onward, we focus exclu-sively on the subsample of subsidiaries of foreign multinationals.Columns (3) and (4) show that the association between decentral-ization and trust in the country of origin is still positive and sig-nificant in the subsample of 881 subsidiaries of foreignmultinationals, and the even smaller sample of 422 foreign multi-nationals with data on bilateral trust. In column (5) we look at therelationship between trust and decentralization using the bilat-eral trust measure for our foreign multinational sample. We findthat multinational subsidiaries located in a country that theirparent country tends to trust (like the subsidiary of a Frenchmultinational in Belgium) are typically more decentralized thansubsidiaries located in a country that the multinational’s parentcountry does not trust (like a French subsidiary located inBritain). This bilateral trust variable drives the coefficient ongeneral trust at the CHQ level to 0. In column (6) we includeboth a full set of country location and origin dummies, so thatwe are only identifying the trust effect of the pairwise variation intrust. Even in this demanding specification higher bilateral trustis associated with significantly more decentralization.

One concern is that there could still be an endogeneity biasaffecting the coefficient on trust. For example, greater decentral-ization in multinationals might engender home country trust, orthere might be an omitted bilateral variable increasing trust anddecentralization. As already discussed, our view is that regionaltrust is in large part exogenous determined by historical events inthe distant past. Nevertheless, to investigate more carefully thecausal effect of trust on decentralization columns (7) and (8) welook at the relationship between decentralization and trust usingthe measure of religious similarity developed by Guiso, Sapienza,and Zingales (2009) as an instrumental variable for bilateraltrust.30 This measure arguably captures long-standing cultural

30. Guiso, Sapienza, and Zingales (2009) measured religious similarities as theproduct of the fraction of individuals in each country belonging to each religion.They also employed as an additional instrument a measure of genetic distance,



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differences determined many centuries ago and is plausibly ex-ogenous to other characteristics. It is very significant in explainingvariations in bilateral trust between countries (an F-test of 28.56).

When trust is instrumented with religious similarity the co-efficient on trust is larger than the ordinary least squares (OLS)coefficient. Column (7) shows that the coefficient on bilateraltrust in the two-stage least squares (2SLS) regression, andcolumn (8) shows the presence of a positive and significant rela-tionship between decentralization and religious similarity in thereduced form. This result is suggestive of a causal effect of truston decentralization in firms and also provides one potentialmechanism for the Guiso, Sapienza, and Zingales (2009) FDIresults. Multinational firms have a greater need to decentralizeto foreign subsidiaries due to the local managers’ better privateinformation, but they will be reluctant to do so when they do nottrust the local management. Being able to decentralize will in-crease the attractiveness of these locations for FDI as in Guiso,Sapienza, and Zingales (2009). These results also suggest a cross-country selection mechanism for industrial location. Industriesrequiring greater levels of decentralization should operate inhigher trust countries. In Online Appendix Table B1, we showthese patterns of comparative advantage in action. High-trustareas tend to attract industries that are likely to be decentralized(as measured by the degree of decentralization in the UnitedKingdom or the United States).31

Finally, there could still be some concern that religious simi-larity may proxy for broader measures of cultural interaction

calculated as the somatic gap between countries in terms of differences in hair color,facial shape, and height (see Online Appendix for details). The idea is that countrieswith different religions and different visual appearances are less likely to bilat-erally trust each other. Guiso, Sapienza, and Zingales (2009) showed that these twomeasures are an important predictor of bilateral trust and are robust to controls forsimilarities in law, language, and informational overlap. When we include the gen-etic distance measure in the instrument set, we find very similar results for the firstand the second stage shown in column (7). The F-test on the first stage is 18.27, andthe coefficient (standard error) on bilateral trust in the second stage is 2.695 (1.078).However, the reduced form in column (6) is weaker: the genetic distance measureappears with a coefficient (standard error) of –0.031 (0.046), and the religious dis-tance has a coefficient (standard error) of 0.413 (0.264).

31. These decentralized industries have higher levels of research and develop-ment, investment, and education per employee. This may generate a wider distri-bution of problems as production is more complex, so that greater decentralizationis optimal.



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between countries beyond trust, as Guiso, Sapienza, and Zingales(2009) discuss. To investigate this we run a battery of tests inOnline Appendix Table B4, including gravity measures like geo-graphical distance, colonial links, a common legal origin, and acommon language, and we find the results to be robust.

V.C. Robustness and Extensions

We have extensively tested the robustness of the decentral-ization and trust relationship, and we report the main experi-ments in Table III. Column (1) represents the baselinespecification of column (4) in Table I. We were concerned thatthe relationship could represent unobserved management qual-ity, so we used the management practices measure from the CEPsurvey as detailed in Bloom and Van Reenen (2007, 2010) incolumn (2). Firms with better management practices appearedto be significantly more decentralized, but the coefficient ontrust was essentially unaltered. Could the effect of trust be proxy-ing for some other mechanism, such as incentive pay? Firmsadopting high-powered incentives (as measured by the percent-age of remuneration linked to individual performance) also ap-peared to be more decentralized (in line with Prendergast 2002),but this does not affect the coefficient on trust (column (3)). Someauthors have stressed the prevalence of family firms (who areusually more centralized) as a result of low trust levels (e.g.,Mueller and Philippon, 2011). We do find a negative coefficienton family-run firms, but the coefficient was insignificant whenthe trust variable is included (see column (4)). Column (5)includes the prevalence of ‘‘hierarchical religions,’’ defined follow-ing La Porta et al. (1997) as the percentage of the populationbelonging to the Catholic, Islamic, or Eastern Orthodox faiths,with the idea that hierarchical religion reduces (or reflects) thelower taste for autonomy in the local population and reduces theprobability of decentralization. Hierarchical religion does seemnegatively associated with decentralization.32 Column (6) in-cludes a measure capturing the intensity of product market com-petition (the number of self-reported competitors). Consistent

32. Hierarchical religion could also reduce trust, which would further depressdecentralization. Interestingly, religion in the plant’s region of location matters,rather than religion in the CHQ: when CHQ religion is used in column (5) it isinsignificant. This suggests that what matters is plant managers (and perhapsworker) tastes, rather than CHQ preferences.



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TA

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with other papers, competition is associated with decentraliza-tion.33 Finally, in column (7) we include all the extra variablessimultaneously. In all these experiments, trust remains positiveand significant with only small changes to its coefficient.

We report a more extensive range of robustness checks inOnline Appendix B (see Tables B2–B4). These analyze measure-ment error in the trust variable and alternative functional formsfor decentralization. For example, we show that the trust meas-ure is robust to constructing it from the largest wave of thesurvey, the latest wave, and dropping the ESS survey completely(see columns (6)–(8) in Table B2). We also include a host of otherpotentially confounding variables, such as indicators for civicresponsibility, personal autonomy, and gravity type variables.In additional results (available on request) we also show therobustness of our results to including collectivist versus individu-alistic attitudes, population density, and alternative measures ofinherited trust (following Algan and Cahuc 2010).

Finally, we examined the interaction between decentraliza-tion and information technology and found some suggestive evi-dence that these are complements at the firm level (see Bloom,Sadun, and Van Reenen 2009). In other words, increases in in-formation technology are more strongly associated with totalfactor productivity when firms are decentralized (i.e., whentrust is higher). The model of Section II could be extended togenerate these effects (see Caliendo and Rossi-Hansberg 2012for the mapping between average variable cost and productivityin the type of Garicano 2000 model we use here).

V.D. Trust and Firm Size

Proposition 2 of our model is that trust should also increaseaverage firm size, since a CEO could manage more plants throughincreased decentralization. We investigate this idea using infor-mation on the population of all public and private firms appearingin the accounting databases which were used to constructthe sampling frame of the organizational survey. OnlineAppendix A provides detailed information on these sources,which are external to our organizational survey.

We begin by using employment data on all domestic firms(i.e., we drop subsidiaries of foreign multinationals) appearing

33. For example, Alonso, Dessein, and Matouschek (2008), Bloom, Sadun, andVan Reenen (2010), and Guadalupe and Wulf (2010).



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in the accounting databases to build a measure of average domes-tic firm size (in the region of the plant’s location) and analyze thecorrelation between this variable and regional trust. In column(1) of Table IV we show that domestic firms in a given region aremuch larger when trust in the region is higher. This is consistentwith the earlier cross-country trust results in La Porta et al.(1997) and Kummar, Raghuram, and Zingales (2005). Incolumn (2) we go beyond the prior literature by including a fullset of country dummies and exploiting within-country variationsin trust. The coefficient on trust remains positive and significant.In columns (3)–(5) we focus instead on the subsidiaries of foreignmultinationals, again finding a strong positive relationship be-tween firm size and trust. To do this, we aggregate by countryof location, country of origin pair, and investigate the relationshipbetween the average size of the subsidiaries of foreign multina-tionals and bilateral trust from the parent firm’s country of originto the subsidiary firm’s country of location. Similar to our findingson decentralization in Table II, the association between bilateraltrust and average subsidiary size appears to be positive and sig-nificant, even after including a full set of dummy variables for themultinational’s country of origin and the subsidiary’s country oflocation. In columns (4) and (5) we show that bilateral trust is alsopositively correlated with total employment and total number ofsubsidiaries originating from a specific country. This is similar tothe result in Guiso, Sapienza, and Zingales (2009) showing thatFDI is larger when bilateral trust is higher.

The magnitude of the trust coefficient in column (2) is large—a 1 standard deviation increase in trust (12 percentage points)would be associated with about a 30% increase (exp(2.27 * 0.12) –1) in firm size. In terms of regions, moving from the lowest trustregion (Assam in India) to the highest trust region (Norrland inSweden) would be associated with a tripling of firm size (exp(2.27* (0.76 – 0.13)) – 1). Given the importance of large firms for re-allocation and aggregate productivity growth, this highlights apotentially important role for social capital and culture in ex-plaining aggregate productivity (e.g., Hsieh and Klenow 2009).34

34. This is consistent with recent field experiments on Indian firms showingthat improvements in management led to more decentralized decision making,which facilitated growth by allowing firm owners to manage more plants giventheir fixed supply of time (Bloom et al. forthcoming).



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TA

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VI. CONCLUSIONS

We have argued that social capital as proxied by trust en-hances aggregate productivity through affecting the internal or-ganization of firms. Higher trust regions are able to sustain moredecentralized and larger firms, which aids productivity throughreallocation. Trust is even important when we look at subsidi-aries of multinational firms—delegation is much more likely forpairs of countries with high bilateral trust. These findings areconsistent with a simple model of trust and delegation based onGaricano (2000), which predicts that higher trust leads toincreased decentralization, larger firm size, and a higher mar-ginal impact of information technologies on firm performance.

A second contribution of our article is to start to provide datainfrastructure for the analysis of firm organization across coun-tries. Despite many theoretical advances, the empirical literatureon organizational economics lacks comparable measures of firms’internal organization. By collecting original data on decentraliza-tion across many thousands of firms in 12 countries, we start toaddress this gap.

There are many future directions for this work. One is run-ning field experiments on organizational changes within largefirms to obtain further micro organizational evidence. Anotheris to investigate the role of changes in information and technol-ogy. Garicano (2000) and Garicano and Rossi-Hansberg (2007)have stressed that information and communication technologieswill increase decentralization. This can be tested using the kindof data developed here (see Bloom et al. 2010). Third, we haveconsidered trust as being exogenously endowed on firms andcountries due to long-run effects of history and culture (such asreligion). But corporate cultures do change over time, and model-ing the endogenous evolution of trust and incentives to invest in itwould be a fascinating avenue for future research.

Stanford, Centre for Economic Performance, NBER,

and CEPR

Harvard University, Centre for Economic Performance,

and NBER

London School of Economics, Centre for Economic

Performance, NBER, and CEPR



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Supplementary Material

An Online Appendix for this article can be found at QJEonline (qje.oxfordjournals.org).

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