coups, corporations, and classified information e k s neconweb.umd.edu/~kaplan/coups.pdf · 2012....

COUPS, CORPORATIONS, AND CLASSIFIEDINFORMATION∗

ARINDRAJIT DUBE

ETHAN KAPLAN

SURESH NAIDU

We estimate the impact of coups and top-secret coup authorizations on as-set prices of partially nationalized multinational companies that stood to ben-efit from U.S.-backed coups. Stock returns of highly exposed firms reacted tocoup authorizations classified as top-secret. The average cumulative abnormalreturn to a coup authorization was 9% over 4 days for a fully nationalized com-pany, rising to more than 13% over 16 days. Precoup authorizations accountedfor a larger share of stock price increases than the actual coup events them-selves. There is no effect in the case of the widely publicized, poorly executedCuban operations, consistent with abnormal returns to coup authorizations re-flectingcredibleprivate information. Wealsointroducetwonewintuitiveandeasyto implement nonparametric tests that do not rely on asymptotic justifications.JEL Codes: F50, G14.

I. INTRODUCTION

Covert operations conducted by intelligence agencies were a keycomponent of superpower foreign policy during the Cold War. Forthe United States, many of these operations had the expressedgoal of replacing “unfriendly” regimes—often ones that had ex-propriated multinational corporate property—and were plannedunder extreme secrecy. Since corporate property was always re-stored after a successful regime change, these operations werepotentially profitable tonationalizedcompanies. If foreknowledgeof these operations was truly secret, then precoup asset pricesshouldnot havereflectedtheexpectedfuturegains. However, thisarticle shows that not only were U.S.-supported coups valuable to

∗We thank Martin Berlin, Remeike Forbes, Nathan Lane, Zihe Liu, EttorePanetti, Andre Shepley, and Laurence Wilse-Samson for excellent research assis-tance. Frans Buelens helped us greatly in obtaining data. Noel Maurer sharedhis list of U.S. multinational expropriations with us. Oliver Boguth provided his-torical data on three Fama-French factors. Marcos Chamon, Stefano DellaVigna,Ray Fisman, Eric Freeman, David Gibbs, Lena Nekby, Torsten Persson, JohnPrados, Gerard Roland, and seminar participants at CEMFI, Hampshire College,LSE, IIES, NBERPolitical Economy Summer Institute, the NewSchool, NYU, theSanta Fe Institute, the Stockholm University Economics Department, the Stock-holm School of Economics, UC Berkeley, UC Riverside, the University of Michiganat Ann Arbor, the University of Oslo, and the University of Warwick all providedhelpful comments.c© The Author(s) 2011. Published by Oxford University Press, on behalf of President and

Fellows of Harvard College. All rights reserved. For Permissions, please email: [email protected] Quarterly Journal of Economics, (2011) 126, 1375–1409. doi:10.1093/qje/qjr030.Advance Access publication on August 11, 2011.

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1376 QUARTERLY JOURNAL OF ECONOMICS

partially nationalized multinationals, but in addition, assettraders arbitraged supposedly “top-secret” information concern-ing plans to overthrow foreign governments.

Specifically, we estimate the effect of secret U.S., as well asallied government decisions to overthrow foreign governments onthe stock prices of companies that stood to benefit from regimechange. We consider companies that had a large fraction of theirassets expropriated by a government that was subsequently atarget of a U.S.-sponsored covert operation aimed at overthrow-ing the regime. Using timelines reconstructed from official CIAdocuments, we find statistically and economically significant ef-fects on stock prices both from the regime change itself and from“top-secret” authorizations. Total stock price gains from coup au-thorizations were three times larger in magnitude than pricechanges fromthecoups themselves. Wethus showthat thereweresubstantial economic incentives for firms to lobby for these op-erations. Although we are unable to discern precisely who wastrading, or whether these economic incentives were decisive forU.S. policy makers (versus political ideology or geopolitics), we doshow that regime changes led to significant economic gains forcorporations that stood to benefit from U.S. interventions in de-veloping countries.

Ourfindings complement otherevidenceinempirical politicaleconomy that large, politically connectedfirms benefit from favor-able political regimes (Fisman 2001; Faccio 2006; Knight 2006;Snowberg, Wolfers, and Zeitzwitz 2007). However, we show thatfirms benefit not only from publicly announced events but alsofrom top-secret events, suggesting information flows from covertoperations into markets. Our results are consistent with recentpapers that have used asset price data to show that companiescanprofit fromconflict (GuidolinandLaFerrara2007; DellaVignaand La Ferrara 2010). We also provide evidence that private in-formation generally leaks into asset prices slowly over time. Thisis consistent with both private information theories of asset pricedetermination (Allen, Morris, and Shin 2006) and the empiricalliterature on insider trading (Meulbroek 1992). We differentiateour work from the prior work on insider trading in so far as theprivate information being traded on concerns government policy,and not company decisions or other information generated withinthe company.

The theoretical literature on coups in economics has empha-sized the role of domestic elites (Acemoglu and Robinson 2006).

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COUPS, CORPORATIONS, AND CLASSIFIED INFORMATION 1377

However, antidemocratic political transitions have often beeninstigated, planned, and even partially executed from abroad,most notably by the United States and the former Soviet Unionduring the Cold War. Operating under the threat of nuclearwar, direct conflict between the two superpowers was replaced bycovert and proxy operations to install supporting regimes (Chom-sky 1986; Kinzer 2006). According to Easterly et al. (2010), 24countryleaders wereinstalledbytheCIA and16 bytheKGBsincethe end of World War II.

Our article also makes a methodological contribution to hy-pothesis testing in event studies. The structure of our event studyallows us to improve on existing nonparametric tests. Nonpara-metric tests used in event studies do not use exact small sam-ple distributions but tests with faster asymptotic convergence toa normal distribution (Corrado and Zivney 1992; Campbell, Lo,and Mackinlay 1997). We introduce two new small sample testsmotivatedby Fisher’s exact test that are validwithout asymptoticjustifications.

Section II discusses the history of U.S. covert interventions,with background on each of the coups in our sample. Section IIIdescribes the data and our selection of companies and events.Section IV outlines our estimation strategies and Section Vreports ourmainresults alongwithanumberofrobustness checksand small sample tests. Section VI provides an interpretation ofour main results; we decompose coup gains to a multinationalinto public and private information components. We conclude inSection VII.

II. BACKGROUND AND HISTORY

The Central Intelligence Agency was created in 1947 underthe National Security Act. The act allowed for “functions and du-ties related to intelligence affecting the national security,” in ad-dition to intelligence gathering (Weiner 2007). Initially, the scopeof the CIA was relegated tointelligence, though a substantial andvocal group advocated for a more active role for the agency. Thisculminated in National Security Council Directive No. 4, whichordered the CIA toundertake covert actions against communism.In the UnitedStates, covert operations designedtooverthrowfor-eign governments were usually first approved by the director ofthe CIA and then subsequently by the president of the UnitedStates (Weiner 2007).

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After Eisenhower’s election in 1952, Allen Dulles was ap-pointeddirectorof theagency. UnderDulles, theCIA expandeditsrole to include planning and executing overthrows of foreign gov-ernments using military force. All but eight of the CIA operationslisted in Table I, including four of the five studied in this articlebeganduringDulles’s reignas CIA directorundertheEisenhoweradministration. Allen Dulles was supported by his brother, JohnFoster Dulles, who was the contemporaneous Secretary of State.The Dulles brothers together wielded substantial influence overAmerican foreign policy from 1952 to 1960.

In1974, partlyduetopublicoutcryovertheU.S. involvementin the military coupin Chile, the Hughes-Ryan Act increasedcon-gressional oversight of CIA covert operations. In 1975, the U.S.legislature formed subcommittees to investigate American covert

TABLE I

COUP SELECTION

Planning DocsProject Country Year Declassified Description Coup Exprop.

Ajax Iran 1953 Yes Coup against Mossadeq Yes YesFU/Belt Chile 1970–73 Yes Coup against Allende Yes YesBloodstone Germany 1946 No Recruitment of Nazis No NoBrushfire US 1955 Yes Propaganda at Universities No NoCamelot Chile 1960s No Funded Anthro. Research No NAST/Circus Tibet 1955 No Trained Tibetan Rebels Yes NoDemocracy Nicaragua 1985 No Anti-Sandinista Operations No YesIA/Feature Angola 1975 No Supported Savimbi No YesFiend Albania 1949 No Insurgency Yes NoPM/Forget All over 1950s No Pro-U.S. Media Distortion No NAHaik Indonesia 1956/57 No Military Support for Rebels Yes YesHardNose Vietnam 1965 No Disrupt Viet Cong Supplies No NoMomentum Laos 1959 No Trained Hmong in Laos No NoMongoose Cuba 1961 Yes Post-Bay of Pigs Operations No YesOpera France 1951 No Electoral Manipulations No NoPaper China 1951 No Invasion from Burma No NoPB/Fortune, Guatemala 1952–54 Yes Coup Against Arbenz Yes Yes

PB/SuccessStole N. Korea 1950/51 No Sabotage No NoTiger Syria 1956 Yes Assassination Attempts No NoWashtub Guatemala 1954 Yes Anti-Arbenz Propaganda No YesWizard Congo 1960 Yes Lumumba Assassination Yes YesZapata Cuba 1960–61 Yes Bay of Pigs Yes Yes

Notes. Project is the name of the operation. Country is the target country of the operation. Year isthe year when the operation was carried out. Planning documents records yes if the planning documentsare publicly available. Description is a description of the operation. Coup is recorded as yes if a coup wasplanned as part of the operation and no otherwise, and Exprop. refers to whether the regime nationalized (orexpropriated) property from multinational firms operating within the country.

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action. Afterward, the intensity and scope of U.S. covert actionsfell substantially (Johnson 1989). The height of covert CIA activ-ity lasted slightly more than 20 years, encompassing the periodbetween 1952 and 1974.

Our sample of coups includes five such covert attempts. Thefirst one occurred in Iran in August 1953, when the CIA, jointlywith the United Kingdom’s MI6, engineered a toppling of PrimeMinister Mossadegh. Mossadegh had nationalized the oil fieldsand refinery at Abadan, which were the property of the Anglo-Iranian Oil Company, itself a partially publicly owned companyof the U.K. government. In Guatemala, the CIA overthrow ofJacobo Arbenz Guzman in June 1954 occurred after the Arbenzgovernment had nationalized most of United Fruit’s assets inGuatemala. Next, in 1960 and 1961, both the United States andBelgium engagedin independent operations topolitically neutral-ize the government of Patrice Lumumba in the Congo. Lumumbahad refused to allow Katanga, a copper-rich enclave controlledby the Belgian multinational Union Miniere, to secede and avoidtaxation and potential nationalization. In Cuba, the Castro gov-ernment nationalized all U.S. property in 1960, one year beforethe failed Bay of Pigs coup attempt in April 1961. Finally, theChilean nationalization of copper and other foreign-owned assetsbegan under the Frei government, but proposed compensationwas substantially lower and nationalizations more frequent afterthe Allende government came to power in late 1970. Allende wasinofficeless thanthreeyears beforehewas killedduringa couponSeptember 11, 1973. In Online Appendix A, we provide a more de-tailedsynopsis of each coup, focusing on the nature of the precoupregime, the motivations behind the expropriations, the foreignresponses, and the resolution of the coup.

The qualitative evidence on links between business and coupplanners is substantial. First, much of the early CIA leadershipwas recruited from Wall Street. A 1945 report on the CIA’s pre-cursor by Colonel Richard Park claimed that the “hiring andpromotion of senior officers rested not on merit but on an old boynetwork from Wall Street” (Weiner 2007). Second, there was di-rect contact between the companies that had been nationalizedandtheCIA. Forexample, at thetimeofthecoupplanningagainstArbenz, three high-ranking members of the executive branch ofgovernment had strong connections with the United Fruit Com-pany. AlanDulles, a formermemberof theboardofdirectors of theUnited Fruit Company, was director of the CIA. Thomas Dudley

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Cabot held at different times the positions of director of Interna-tional Security Affairs in the State Department and CEO of theUnited Fruit Company. His younger brother, John Moore Cabot,was secretary of Inter-American Affairs during much of the coupplanning in 1953 and 1954. Besides the fact that Anglo-Iranianwas a majority state-owned company, the company met with CIAagent (and later historian) Kermit Roosevelt, who alleged in his1954 history that the initial plan for the coupwas proposedby theAnglo-Iranian Oil Company. In Belgium, the royal court and thepowerful bank Societe Generale tied together a social and finan-cial network of colonial officials and businesses. De Witte writesthat “the incontrovertible political conclusion is that the politicalclass, including the [Belgian] court, had a direct material interestin the outcome of the Congocrisis” (De Witte 2001). Most directly,theministerof AfricanAffairs, a keyinstigatorandplannerof Op-erationBarracuda, Haroldd’Aspremont-Lydenwas thenephewofGobert d’Aspremont-Lyden who was an administrator for UnionMiniere. The Senate Church Committee reported that the CIAheld meetings with U.S. multinationals involved in Chile on aregular basis, even tothe point of ITT (whose boardincludedJohnMcCone, a formerdirectorof theCIA) notoriouslyofferingtheCIA$1 million to overthrow Allende’s government (Weiner 2007). Inshort, social links between the government officials responsiblefor the coups and financial interests are well documented. Secretplans for regime change could have easily made it into the ears offinancial actors who, even if not directly connected to the affectedcompanies, could arbitrage this information on the market.

Our results are consistent with the presence of both direct in-formation leakage between political decision makers andthe com-panies that stood to benefit, as well as indirect information flowstothemarket. Weareunabletoproducedefinitiveevidenceontheidentity of the traders or pinpoint the exact source of the informa-tion leakage.

III. DATA

We focused on the set of all CIA coups where (a) the CIAattempted to effect regime change, (b) the relevant planningdocuments have been declassified, and (c) the government hadexpropriated property from a publicly listed multinational. Thedetails of how we obtained a comprehensive list of coups, de-classified documents, and expropriations are described in Online

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Appendix B. We are left with five coups where all three of ourcriteria are satisfied: Iran, Guatemala, Congo, Cuba, and Chile.Online Appendix A provides detailed historical background foreach of these coups.

We first extract all of the coup authorization events from thetimelines based on the declassified planning documents. Theseevents are restricted to those where either a coup was explicitlyapproved by the head of a government or ministry (the presidentof the United States, prime minister of the United Kingdom, orthe Ministry for African Affairs in Belgium), the head of an intel-ligence agency (the CIA or MI6), or where US$1 million or morewas allocatedtotheoverthrowofaforeigngovernment. Inthecaseof Congo, we include the date of the assassination of Lumumba,which happened in secrecy and was not known publicly for closeto one month. Authorization events are coded as “good”(+1) or“bad”(−1) depending on whether they increase or decrease thelikelihood of a coup. Our selection and coding of authorizationevents is presented in Table II.

We also extract public events from the official timelines foruse as controls in some specifications. Publicevents are restricted

TABLE II

AUTHORIZATION EVENT SELECTION

Date Country Description Good Canceled

September 15, 1970 Chile Nixon Authorizes Anti-Allende Plan (Incl.Poss.Coup) Y NJanuary 28, 1971 Chile 40 Committee Appropriates $1.2 Million Y NOctober 26, 1972 Chile 40 Committee Appropriates $1.4 Million Y NAugust 20, 1973 Chile 40 Committee Appropriates $1 Million Y NAugust 18, 1960 Congo Eisenhower Endorses Lumumba’s Elimination Y YSeptember 12, 1960 Congo Belgian Operation Barracuda Begins Y YOctober 11, 1960 Congo Operation Barracuda Canceled N YDecember 5, 1960 Congo CIA Stops Operation N YJanuary 18, 1961 Congo Lumumba Secretly Killed Y NMarch 17, 1960 Cuba Eisenhower Approves Plan to Overthrow Castro Y NAugust 18, 1960 Cuba Eisenhower Approves $13 Million to Overthrow Castro Y NJanuary 30, 1961 Cuba Kennedy Authorizes Continuing Bay of Pigs Op Y NNovember 4, 1961 Cuba Operation Mongoose Planning Authorized Y YFebruary 26, 1962 Cuba Operation Mongoose Scaled Back N YOctober 30, 1962 Cuba Operation Mongoose Cancelled N YAugust 18, 1952 Guatemala DCIA Approves PBFortune (Coup to Overthrow Arbenz) Y YOctober 8, 1952 Guatemala PBFortune Halted N YDecember 9, 1953 Guatemala DCIA Approves PBSuccess (Coup to Overthrow Arbenz) Y NApril 19, 1954 Guatemala Full Approval Given to PBSuccess Y NJune 19, 1953 Iran CIA/MI6 Both Approve Coup Y NJuly 1, 1953 Iran Churchill Approves Coup Y NJuly 11, 1953 Iran Eisenhower Appoves Coup Y N

Notes. Date is the date of the event. Country is the target country of the coup attempt. Description givesa brief description of the event. Good is coded as Y if the event should raise the share value of the companyand N if the event should lower the share value of the company. Canceled is coded as Y if the operation wascancelled and N if it was executed; the 40 Committee was the subgroup of the executive branch NationalSecurity Council responsible for authorizing covert actions after 1964.

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to dates where company assets are nationalized or regime tran-sitions and consolidations occur. The public events are coded as“good”(+1) or “bad”(−1), where “good” events are those that arelikely to increase the stock price and “bad” events are ones thatare likely to cause a decline in the stock price. The public eventsandtheircodingare listedinOnlineAppendixTableAI. Table VIIlists the dates of the regime changes themselves.

In addition to the data on the events, we also construct adata set of daily stock returns for publicly traded companies thatwere expropriated by the regimes that were then overthrown bythe CIA. Using a variety of sources, also documented in OnlineAppendix A, we obtain the lists of companies expropriatedin eachcountry. For each of these companies, we obtain the amounts ex-propriated from various sources; we obtain daily stock marketdata either from CRSP or from archival sources. We define theexposure of a company to be the value of the assets expropriateddivided by the average market capitalization in the year prior tothe nationalizing regime coming into power. We also use market-level daily Fama-French four factors: excess return of the NYSE,high minus low(book toprice ratio), small minus big (market cap-italization), and momentum. For years prior to1962, we obtainedthe daily HML and SMB factor data series from Oliver Boguth,and we constructed the daily momentum factor ourselves. Post-1962 data on the factors come from Ken French’s website. Addi-tionally, we used a Perl script to generate a daily count of thenumber of New York Times articles mentioning both the name ofthe country and the country’s leader. Summary statistics of themain variables are presented in Table III.

IV. METHODOLOGY

Our main hypothesis is that authorization events result inan increase in the stock price of the affected company over thedays following the event. We consider cumulative abnormal re-turns after the authorization events. In contrast to public events,we expect stock price reactions to top-secret events to potentiallydiffuse slowly. Our benchmark specification estimates a 4-day re-turn starting at the event date, though we consider alternativespecifications ranging from 1 to 21 days. We employ two differentestimation strategies: a regression using the augmented Fama-French four-factor model, and a newset of distribution-free smallsampletests.

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IV.A. Regression Method

Fortheregressionmethod, weregress acompany’s stockpricereturnonanindicatorforauthorizationevents interactedwiththecompany’s exposure. Wealsocontrol for fourFama-Frenchfactors(excess return of the NYSE, SMB, HML, and momentum):

(1) Rft = Xtβf + γcEft(k) + εft ;

here Rft is the one-day raw stock return for firm f between theclosing price at date t− 1 and the closing price at date t, and Xt isthe vector of factors. Eft(k) is a variable that takes on the value ofa company’s exposure for a k-day period beginning with an autho-rization day, and 0 otherwise. The average daily abnormal returnover the k-day period in country c is γc. The cumulative abnormalreturn is kγc.1 We consider values of k ranging from 1 to21. In ourmultiple country regressions, we report the mean of the country-

specificcoefficients∑

c γc

C , whereC is thetotal numberof countries.Our sample is the time period starting exactly one year before the

nationalizing regime comes to power until the day before the be-ginning of the coup. The standard error for the cumulative abnor-mal return is given by the maximum of robust standard errors,standard errors clustered on date, and standard errors clusteredon company.

IV.B. Small Sample Tests

One problem with the regression method as well as tradi-tional event studies is that the distribution of abnormal returnsis often non-normal, and the number of events is often small. As aresult, use of conventional standard errors may produce an incor-rect test size. We provide two nonparametric small sample testsbased on the sign and rank tests used in the literature. Unlikethe conventional rank and sign tests, however, we use “exact”distributions that do not rely on asymptotic justifications.

The standard rank and sign tests are motivated by the obser-vation that these test statistics converge much faster to a normaldistribution than the mean. Others have noted that the sign testhas an analogue to Fisher’s exact test, which uses the binomialdistribution to calculate a distribution-free test for significance,

1. Note that this is a standard approximation to (1 + γk)k − 1.

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which we also implement. We also note that the percentile, basedupon the rank, has a uniform distribution. This permits an anal-ogous distribution-free test for the average rank.

We begin by estimating a market model with the four factorsinan“estimationwindow”that is priortoanycoup-relatedevents.Ourestimationwindowis twocalendaryears in lengthandbeginsthree years before the nationalizing regime comes to power. Weestimate firm-specific cumulative abnormal returns (CARS) forfour-day windows starting with authorization dates. We weightthese CARs by relative company exposure within country andformcountry-portfoliospecificCARs. Theoverall CARtakes asim-ple average of returns over country-portfolios.

We first generalize the sign test by considering the number ofevents that have a k-day CAR greater than a given percentile p,where p is computed using k-day CARs in the estimation samplefrom country c. When cumulative abnormal returns are indepen-dentlydistributedacross countries andevents, theone-sidedprob-ability of getting mp or more abnormal returns above the pth is:

(2) 1−M∑

i=mp

(Mi

)

pi (1− p)M−i ,

where M is the total number of events. This is the p-value ofthe one-sided binomial sign test. Since the pth percentile returnis estimated based on a finite estimation sample, and multipleevents within the same country use the same estimated pth per-centile cutoff, this will induce a cross-event correlation in themeasured percentiles within countries. Therefore, besides calcu-lating the p-value analytically using Equation 2, we also followthe literature on randomization inference (Andrews 2003; Conleyand Taber 2011) and simulate our test statistic. First we drawTc percentiles from a uniform distribution, where Tc is the sizeof country c’s estimation window. We then draw Mc additionalreturns, where Mc is the number of events, from a uniform dis-tribution.2 We then estimate the pth percentile return from theTc draws. Next, we count the number of Mc draws above the pthpercentile of the Tc draws. We do this for all five countries andthen compute the average number of event returns above the pth

2. Both the binomial and the uniform tests can be shown to be independentof the distribution of the underlying returns for a wide class of continuous distri-bution functions.

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percentile, andrepeat this procedure 10,000 times toestimate thesimulated counterpart to Equation 2.

Finally, parallel to the binomial test, we construct an ana-logueoftheranktest (Corrado1989; Campbell, Lo, andMackinlay1997) exploiting the independence of events in our country port-foliosample toobtain exact inference. We rank each of our eventsrelative to the distribution of abnormal returns in the estimationwindow. We then convert the rank into a percentile. Noting that,fori.i.d. variables, percentile is uniformlydistributed, wecomputethe CDF for the sum of the percentiles of M independently anduniformly distributed random variables over the interval [0, 1].3

Without loss of generality, we assume that the mean percentilem ≥ 0.5. Given the symmetry of the cumulative distribution func-tion, the one-sided p-value of getting a percentile rank greaterthan m is then:

(3) 1−M∑

j=0

((−1)j (m− j)M 1(m ≥ j)

j!(M − j) !

)

We derive test statistics using the analytical equation fromEquation 3. However, similar to the binomial test, we also sim-ulate the distribution of average ranks. We report the modifiedsign and rank test results by country as well for the successfulcoups and the full sample. Finally, for the purpose of comparison,we also report asymptotic standard errors using the standard de-viations of returns in the estimation window (Campbell, Lo, andMackinlay 1997).

V. RESULTS

V.A. Baseline Results

In Table IV, we report the cumulative abnormal returns forauthorization events interacted with exposure over periods rang-ing from 1 to 16 days in length. We use (0, k − 1) to denote thek-day period beginning with the day of the event. We find clearevidencethat stockprices react positivelytoauthorizationevents.Row 1 of Table IV shows that in the pooled sample of all compa-nies, the average four-day stock price return for an authorization

3. This test was suggested, but not pursued, by Corrado (1989).

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TA

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0938

0.09

900.

1055

0.12

040.

1342

(0.0

162)∗∗∗

(0.0

270)∗∗∗

(0.0

345)∗∗∗

(0.0

390)∗∗∗

(0.0

424)∗∗∗

(0.0

522)∗∗

2 215

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157

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s0.

0551

0.12

080.

1274

0.13

090.

1459

0.16

40(0

.020

1)∗∗∗

(0.0

336)∗∗∗

(0.0

425)∗∗∗

(0.0

481)∗∗∗

(0.0

523)∗∗∗

(0.0

647)∗∗

8555

8555

8555

8555

8555

8555

Can

cele

dC

oup

s0.

0729

0.13

410.

1414

0.13

590.

1564

0.19

71(0

.033

7)∗∗

(0.0

546)∗∗

(0.0

681)∗∗

(0.0

730)∗

(0.0

777)∗∗

(0.1

018)∗

1525

715

257

1525

715

257

1525

715

257

Ch

ile

−0.

0095

0.01

720.

0003

0.02

140.

0183

0.01

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.006

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9160

9160

9160

9160

9160

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ongo

0.16

670.

2270

0.20

140.

2429

0.22

830.

2581

(0.0

771)∗∗

(0.1

196)∗

(0.1

335)

(0.1

426)∗

(0.1

546)

(0.1

719)

421

421

421

421

421

421

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TA

BL

EIV

( CO

NT

INU

ED

)

(0,0

)(0

,3)

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,9)

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2)(0

,15)

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go-B

elgi

um

even

ts0.

2730

0.26

320.

3179

0.42

600.

3914

0.46

22(0

.079

4)∗∗∗

(0.1

895)

(0.1

972)

(0.2

029)∗∗

(0.2

182)∗

(0.2

260)∗∗

421

421

421

421

421

421

Cu

ba−

0.00

30−

0.01

41−

0.01

470.

0039

0.01

830.

0147

(0.0

079)

(0.0

125)

(0.0

178)

(0.0

202)

(0.0

222)

(0.0

263)

1360

213

602

1360

213

602

1360

213

602

Gu

atem

ala

0.04

910.

1650

0.20

490.

1365

0.20

110.

1859

(0.0

203)∗∗

(0.0

530)∗∗∗

(0.0

896)∗∗

(0.1

136)

(0.1

274)

(0.1

662)

1234

1234

1234

1234

1234

1234

Iran

0.01

440.

0739

0.10

300.

1229

0.13

590.

2017

(0.0

110)

(0.0

184)∗∗∗

(0.0

428)∗∗

(0.0

385)∗∗∗

(0.0

349)∗∗∗

(0.0

792)∗∗

809

809

809

809

809

809

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es.F

orsi

ngl

eco

un

try

regr

essi

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the

rep

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edby

the

len

gth

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lti-

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por

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ficie

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ud

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ance

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event is 9.4% with a standard error of 2.7%. This implies that ahypothetical company that had all its assets expropriated couldbe expected, on average, to experience roughly a 9.4% increase inits stock price within the four days following the secret authoriza-tion of a CIA coup. The cumulative abnormal returns are gener-ally significant at the 1% level for the all-country sample from 4through 13 days after the event. The abnormal returns continuetoincrease between days 4 and16 after the event, consistent withthe hypothesis that private information is incorporated intoassetprices with a delay.

In row 2, we restrict attention to the set of four successfulcoups (i.e., excluding Cuba), and the corresponding estimates areconsistently larger by around 25%–30%. The sample size dropssubstantially due to the large number of expropriated firms inCuba. In row 3, we restrict attention to the events that were au-thorizations (and deauthorizations) of coups that were later can-celed. The mean effect increases somewhat in magnitude (13.4%after 4 days), reaching a maximum of 19.7% at 10% significanceafter 16 days. We interpret the results on the canceled coups toprovideadditional evidencethat thestockpricereactions reflectedchanges in beliefs due to the authorizations themselves, and notthe expected coup or trends leading up to the coup.4

Rows 4–9 show the results separately for each country. ForChile, the effect is positive by the fourth day after the authoriza-tion event, but small andinsignificant. It alsostays small throughthelongerhorizons considered. Inrow5, weconsiderCongo, whichexhibits a large 16.7% effect on the day of an authorization event.The cumulative abnormal return increases to 22.7% after 4 daysand then stabilizes, becoming statistically insignificant after 10days. In row 6, we restrict attention to the events in the Congosample that were decisions made by Belgian officials, as the af-fected company was Belgian and the operation was independentof the United States. Effects in this sample are even larger, withan immediate 27.3% effect after the event, rising to a 5% signifi-cant 46.2% after 16 days.

Row 7 shows the results for Cuba. There are two operationsand thus two sets of events in Cuba. The first is the failed Bay

4. Although not reported in the table, if we further restrict attention to thedeauthorizationevents themselves, thestockpriceof a fullyexposedcompanyfallsby 11.7% within four days of a deauthorization, which further confirms this inter-pretation.

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of Pigs invasion. The second is Operation Mongoose, which wasstartedaftertheBayofPigs but was ultimatelycanceled. Morede-tails about theCubanoperations areavailableinOnlineAppendixA. There is virtually no effect in the Cuba subsample even after16 days and, though not reported in the tables, for both opera-tions considered individually. The qualitative evidence suggeststwo possible reasons for the absence of an effect in Cuba: (1) Dueto the high degree of public aggression from the United States to-ward Cuba, including numerous bombing missions, the coup wasalready commonly believed to be in planning and thus informa-tion about top-secret authorizations were not considered “news”by financial market actors.5 (2) Traders were pessimistic aboutsuccess, partiallyowingtoacombinationof incompetenceandlackof political commitment towardthe coupby the Kennedy adminis-tration. Thoughwearenot abletoconvincinglyreject eitherexpla-nation, we do provide additional evidence later that some tradersdid believe in the possibility of a successful Bay of Pigs operation.

Rows 8 and 9 show the results for Guatemala and Iran, re-spectively. Guatemala shows an immediate and significant 4.9%increase, which continues to grow to 16.5% after four days and20.5% after seven days, also significant at 5% confidence. Afterthis, the coefficient in the Guatemala subsample is not statisti-cally significant, although the point estimate generally remainslarge. In the Iran subsample, we do not see an immediate reac-tion to the event, but we do see a significant 7.4% effect after 4days, increasing to 10.3% after 7 days and continuing to increaseto 20.2% at 16 days, all significant at the 1% or 5% level. Overall,our country results shows that in the three out of the five coun-tries with statistically significant effects, the results were visibleand clear within four days. However, in all these cases, the ef-fects tended to grow over the following days, consistent with slowdiffusion of private information into asset prices.

The effects reported in Table IV are for a hypothetical com-pany that was fully nationalized. To obtain the average effect forthe sample of companies in a given country, we would need tomultiply the coefficient by the mean exposure for companies inthat country. The average exposure in the sample was 17.9%, sothe second column of Table IV implies that the cumulative returnin the sample companies was 1.6% after four days. As a specific

5. “WhenKennedyreads the[NYT]storyheexclaims that Castrodoesn’t needspies in the United States; all he has to do is read the newspaper” (Wyden 1979).

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example, Union Miniere had 33.8% of its overall assets exposed,which implies that the cumulative abnormal return in the Congosubsample was 7.6% after four days. Similarly, United Fruit had14.8% of its assets exposed, which implies a 2.4% return over fourdays. Finally, Anglo-Iranian had 31.0% of its assets nationalizedin Iran, andsothe impliedcumulative four-day increase followingan authorization event for that company was 2.3%.

Figure I provides graphical evidence, parallel with Table IV,on abnormal returns around an authorization event, with 95%confidence intervals shown. We compute cumulative abnormalreturns CAR(k) using the regression method aggregated acrossevents foreachofthe20 days priortoas well as followinganevent.Forthe20 days priortotheevent, weaggregatebackwardstarting

FIGURE I

Cumulative Abnormal Returns: All Countries

The thicker line represents the average of country-specific coefficients on anindicator for authorization events interacted with company exposure, mulipliedby the length of the window. The horizontal axis labels denote the number of daysbeforeoraftertheauthorizations whichareincludedas part ofthedummyvariablefor the authorization event, e.g., 4 refers tothe return between the event date andfourdays aftertheevent datewhile−4 refers tothereturnbetweenfourdays priorto the event date and the event date. All regressions control for an interaction ofa country-specific company dummy with the four Fama-French factors. All dateswhere a company changed its name or changed its outstanding shares by morethan5% weredropped. Onedaypricechanges greaterthan50% inmagnitudeweredropped. The thinner lines (and square symbols) represent the 95% confidenceintervals using standard errors that are the maximum of clustered by company,clustered by date, and robust.

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at the event date (date 0), so CAR(−k) is the cumulative abnor-mal return between dates −k and 0. For returns starting priorto date 0, we also include as a control an indicator for a 10-dayperiod after an authorization date, in the case when the eventsare sufficiently close together that cumulative returns prior toone authorization event includes returns that follow another au-thorization event. The only country where the windows overlapis Iran, and none of the other figures look different if we do notaccount for the overlap in CAR(k) and CAR(−k) windows whencumulating over days prior to the event. For our full sample, cu-mulative abnormal returns become significant at a 5% level onthe day of an event and remain significant. The rise over this pe-riod is generally monotonic until day 16 and seems to be perma-nent. Considering returns prior to the event date, however, theCAR(−k)’s shownotrends andare never significant. We concludethat there was no pre-existing trend in the stock price prior toan event, suggesting that the CIA did not authorize coups in re-sponse to proximate drops in the value of connected companiesor pre-existing political trends that would also be priced into thestock return. Figure II shows the CAR graphs separately by coun-try. As expected, individual country time paths are more impre-cise due to sample size limitations, with consistently significantresults only in Congo, Guatemala, and Iran. There is no evidenceof a persistent and significant pretrend in any of the individualcountries. Overall, the evidence on timing shows that authoriza-tion events led to positive asset price movements—usually withsome lag.

V.B. Robustness

Our benchmark specification (second column of Table IV)shows that abnormal returns were positive and significant in thefourdays followinganauthorizationevent. However, this couldbedue to downturns in the broad market, contemporaneous infor-mation about public events, or positive industry-specific shocks.To show that the positive abnormal returns reflect changes incompany-specific returns, we consider a number of robustnesschecks in Table V. All are estimated for the pooled sample, theset of successful coups, and separately by country. We computecumulative abnormal returns over a four-day period followingan authorization event. Except for the first and fifth columns,all specifications include the four Fama-French factors interacted

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FIGURE II

Cumulative Abnormal Returns by Country

The thicker line represents the coefficients on an indicator for authorizationevents interacted with company exposure, muliplied by the length of the window.The horizontal axis labels denote the number of days before or after the autho-rizations which are included as part of the dummy variable for the authorizationevent, e.g., 4 refers to the return between the event date and four days after theevent date while −4 refers to the return between four days prior to the eventdate and the event date. All regressions control for an interaction of a companydummy with the four Fama-French factors. All dates where a company changedits name or changed its outstanding shares by more than 5% were dropped. Oneday price changes greater than 50% in magnitude were dropped. The thinner lines(andsquaresymbols)represent the95% confidenceintervals usingstandarderrorsthat are the maximum of clustered by company, clustered by date, and robust.

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TA

BL

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trol

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me

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ps

0.09

470.

0723

0.12

490.

0989

0.00

020.

0068

19.0

429

(0.0

282)∗∗∗

(0.0

222)∗∗∗

(0.0

137)∗∗∗

(0.0

034)∗∗∗

(0.0

011)

(0.0

216)

(2.2

102)∗∗∗

2215

722

157

7123

2215

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157

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920

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1210

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390.

1153

0.12

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0.00

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0111

26.4

944

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ps

(0.0

350)∗∗∗

(0.0

275)∗∗∗

(0.0

332)∗∗∗

(0.0

372)∗∗∗

(0.0

082)

(0.0

268)

(3.3

202)∗∗∗

8555

8555

5224

8555

8555

6670

7324

Ch

ile

0.03

650.

0191

0.10

060.

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0.01

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0.01

4920

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0(0

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1)(0

.027

9)(0

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5)(0

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9)(0

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6)(0

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7)(0

.753

4)∗∗∗

6091

6091

3530

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4764

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( 0.0

133)

(0.0

216)

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142

1–

421

421

322

–

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TA

BL

EV

(CO

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)

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1386

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1360

213

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5532

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(0.0

603)∗∗

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621)∗∗∗

(0.0

224)

(0.0

916)

(12.

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)∗∗

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0.08

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610.

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710.

0528

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9)∗∗∗

(0.0

189)∗∗∗

(0.0

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400)

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with a company dummy (or country-specific company dummiesfor multi-country regressions) as controls. As in Table IV, we re-port the coefficient on the authorization dummy interacted withthe company’s exposure, multiplied by the number of days in thewindow(four in this case); multicountry estimates average the co-efficients across the countries.

First, we regress raw returns, unadjusted by any of the mar-ket factors, on our authorization events. We confirm that thecumulative abnormal return effects were due to increases in theaffectedcompany’s stockprices, andnot duetochanges inmarket-level movements. The first column of Table V shows a four-daycumulative abnormal return of 9.5%, virtually identical to ourbenchmark specification.

Top-secret decisions to overthrow foreign governments mayhave coincided with public events in the targeted countries.This could bias our estimates, reflecting the effect of publicnews rather than private information. In the second, we con-trol for the number of articles in the New York Times men-tioning both the country and the country leader by name,as well as other public events; these other events are na-tionalizations of foreign-owned property as well as electoraltransitions and consolidations which are also mentioned inthe timelines. Public events are listed in Online AppendixTable AI. We multiply these measures with company exposureand the country dummies, and include them as controls in ourmain specification. The coefficient in the pooled sample is onlyslightly smaller than the one in the main specification, and stillshows a 7.2% four-day return which is significant at the 1% level.In the third column we drop all dates where the New York Timeshad at least one article mentioning both the country and theleader by name (Meulbroek 1992). Since most days have at leastone political article about the coup countries, we lose over two-thirds of our sample in this specification, making this a strongtest. However, our effect actually becomes stronger despite thecountrywiththelargest baselineeffect, Congo, droppingout of thesample. The mean effect in the pooled country sample is a 12.5%return over four days and still significant at the 1% level. Congois very prominently covered in the news, and hence does not haveany events that are not contemporaneous with some New YorkTimes coverage. Though all the countries lose observations fromthe sample restrictions, the estimates for Chile andIran are actu-ally larger than in the baseline specification, and the coefficients

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for Guatemala and Iran are still significant at least at the 5%confidencelevel. Cuba onlyhas oneauthorizationdatethat has nocontemporaneous New York Times articles about Cuba and Cas-tro, reflecting the extensive leakage of the Bay of Pigs operationas well as general news interest in Cuba over the sample period.The scaling back of the second operation, Mongoose, on Febru-ary 2, 1962, does indeed fall on a news-free day. Although notsignificant, the positive and relatively larger coefficient on thissubsample is consistent with our interpretation that secret deau-thorizations docause decreases in stock prices when they actuallyconstitute “news.”

One potential explanation for our findings is price momen-tum around the authorization dates. This may either reflect pre-existing information flows or trading activities unrelated to coupplanning. We include a control that interacts the exposure mea-sure with a dummy that is equal to 1 in a 20-day window aroundeach authorization event. This specification tests whether the ab-normal returns are higher in the 4 days right after an authoriza-tion than in the average of the 20-day period surrounding eachauthorization event. The fourth column of Table V shows that thefour-day abnormal return is 9.9%, actually slightly higher thanour benchmark, and statistically significant at the 1% level. Pre-existing price trends do not explain our results.

We also consider two placebos. In the fifth column of Table Vwe regress NYSE index returns on our private information vari-able, omittingtheotherthreefactors. Ourpooledestimateis equalto 0.02% and is insignificant. None of the country-specific regres-sions are significant at the 10% level either. In the sixth columnof Table V, we use daily stock returns from a matched company,where the match is constructed by taking the company closestin the Mahalanobis metric (constructed from market capitaliza-tion and market beta) within each three-digit industry code, sub-ject to having data available for all of the authorization dates.The matched companies are listed in Online Appendix Table AII.This placebo is also insignificant in the pooled sample as well asall the subsamples, suggesting that our effects are not driven byindustry-specific shocks.

Finally, we consider the effect of authorizations on the log oftrading volumes for the set of countries for which data is avail-able. In both the pooled samples as well as the individual coun-try regressions, our event variable is positive and significant.This is true even in Chile and Cuba, where the effect on returns

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was insignificant. The finding of increased trading in the fourdays including and just after authorization days is consistentwith theoretical predictions of heterogeneous belief models (Wang1994) of stock trading as well as prior empirical work on the vol-ume impacts of insider trading (Cornell and Sirri 1992).

V.C. Time-Shifted Placebos

As additional evidence that our effects are not an artifact ofthe data, we reestimate our main specification on a set of placebodates. We take our 4-day cumulative abnormal returns and shiftour authorization events forwards as well as backward by 5, 10,15, 20, 25, 30, 35, and 40 days. For an s day shift, we estimate:

(4) Rft = Xtβf + γc,sEft+s(4)+ εft

As in our baseline specification, we report the mean cumulativefour-day return across countries:

∑c γc,s

C . We exclude all days withother authorizations, public events, or that occur during the coupitself. We graph our estimates against the number of days shiftedin Figure III.

Out ofthe19 time-shiftedregressions, γs is onlysignificant fors = 0, our benchmark specification with cumulative abnormal re-turnof approximately9.38% fora fullyexposedcompany, whichissignificant at the 1% level. None of the 18 other dates have a mag-nitude above 4% and none of them are significant at even the 10%level. The placeboestimates reinforce that our baseline estimatesare not due to local serial correlation in returns. The pattern ofno abnormal returns before a decision, sizable abnormal returnsjust after a decision, and smaller possible abnormal returns inthe medium run after a decision is consistent with our hypothe-sis of secret authorization events causing an increase in the stockprice.

V.D. Small Sample Tests

In Table VI, we present the results from our small sam-ple tests. First, we present the four day CARs of countryportfolios, based on out-of-sample estimates. The CARs here rep-resent the actual (exposure-weighted) change in stock prices foraffected companies in a given sample, while the regression coeffi-cients represent the effects for a hypothetical company that wasfullyexposed. Forcomparability, theregressioncoefficients would

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FIGURE III

Time-Shifted Placebos

The thick line plots the average of country-specific coefficients for a regres-sion of daily stock returns on an indicator for authorization events interactedwithcompany exposure and multiplied by the four day window including and after anauthorizationevent. Thehorizontal axis labels denotethenumberofdays bywhichwe shift the authorization date, e.g., 20 represents the four day return if we shiftthe authorization day forward by 20 days, while−20 represents a four day returnif we shift the authorization date backwards by 20 days. All regressions control foran interaction of a country-specific company dummy with the four Fama-Frenchfactors. All dates where a company changed its name or changed its outstandingshares by more than 5% were dropped. One day price changes greater than 50%in magnitude were dropped. The dashed lines represent the 95% confidence inter-vals using standard errors that are the maximum of standard errors clustered bycompany, clustered by date, and robust.

need to be multiplied by mean exposure levels, although the com-parability is inexact due to how exposures are treated in the twocases. The results are listedin row1 of Table VI. For the full sam-ple, theaveragefour-dayweightedCAR was 2.6%. Theestimateisstatistically significant at the 1% level using asymptoticstandarderrors.

Turning to our small sample tests, we find that 18 out ofthe 22 events in the full country sample have returns greaterthan the median return in the “estimation window” (i.e., the yearprior to any nationalization event), producing a one-sided proba-bility value under the null hypothesis of 0.35%.6 Thirteen of thoseevents have returns above the 80th percentile, which would oc-cur by chance alone with probability less than 0.02%. Eight of theevents have returns greater than the 90th percentile, which has

6. Inthetext, wereport thehigherof theanalytical andsimulatedprobabilityvalues. Both are reported in the table. All reported probability values are one-sided.

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an associated probability value of 0.11% under the null. Finally,the average rank of all 22 events is 0.74, which would be obtainedby chance with a probability less than 0.06%. When we con-sider the set of four successful coups, the conclusion is strength-ened. The probability values associated with the uniform ranktest, as well as the binomial sign tests (for 50th, 80th, and 90thpercentiles) are all under 0.1%.

Due to small sample size, the individual country tests havelow power and thus p-values are larger. Congo and Guatemalaconsistently produce probability values under 10% for all thetests, andsmallerformost. Iranproduces probabilityvalues rang-ing between 3% and 14% except for the 90th percentile, whereasChile ranges from 5% to33%. Finally, consistent with our results,Cuba shows no systematic increase in returns following autho-rization events. For example, only three out of the six events showpositive returns, and the rest are negative.

Our results also show heterogeneity across events. Thoughthere does not seem to be a substantial reaction to a few events,most showpositive reactions. Andmany showreactions that werevery strong, as exemplified by the fact that 8 out of 22 events areabove the 90th percentile in returns.

Overall, our modified sign and rank tests provide strong evi-dence that the four-day returns after authorization events are, onaverage, highly statistically significant, and our conclusions arenot driven by the size of our sample and non-normal distributionof returns. Also, they show us that there are reactions to someevents and not to others. However, when there is a reaction, theeffect is strong and unmistakable.

VI. ASSESSING THE GAINS FROM COUPS

We also estimate abnormal returns for the coup attemptsthemselves using our main specification. We do this for two rea-sons. First, we want to test if these companies were affected bythe actual coup attempts, confirming that companies were ben-efiting from the anticipated regime change. Second, we want tocompare the direct effect of the coup itself tothe total net rise dueto precoup authorizations.

We look at two estimates of the effect of the coup: abnormalreturns duringthecoupwindowandabnormal returns onthefirstday of the new regime. We define the coup window as the periodfrom and including the first day of the coup to and including the

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first day of the newregime (the last day of the coupattempt in thecase of Cuba). These dates are listed in Table VII.

Over the duration of the coup, the average cumulative returnacross countries was 12.1%. The result is slightly higher at 13.4%when we restrict attention tothe successful coups. The first day ofthe new government measure is slightly lower for both the full aswell as successful coups samples at 10.0% and11.8%, respectively.

The individual country estimates are also relatively simi-lar across the two measures for most of our sample. Chile’s andCongo’s coups are both one-day events, and so the effect is identi-cal across measures: 6.1% andsignificant at the 5% level for Chileand 8.7% and insignificant at conventional levels for Congo. Theeffects for Cuba are near −5% for both measures. The first dayof the new government effect is significant at the 1% level, rein-forcing that there is belief in the possibility of a successful coup inCuba.7 The coupwindoweffect is larger for Iran than the first dayof the new government. The coup window effect, 18.8%, is signif-icant at the 10% level; the first day of the new government effectis substantially smaller at 7.0%. For Guatemala, the sign actuallyflips. The coupwindoweffect for Guatemala is negative andsome-what sizable. The first day of the newgovernment effect, however,is quite a bit larger and positive in sign. The two numbers are−10.3% and 22.7%, the latter number being statistically signifi-cant at the1% level. Weattributethestockprice fall overthecoupwindowtothe fact that the junta which initially took power whenArbenz resigned did not support the return of assets to UnitedFruit. Further exacerbating the uncertainty around United Fruitassets, the 11-days following Arbenz’s resignation saw four in-terim governments come to power. Finally, the candidate backedby the CIA, Castillo Armas, took power (Gleijeses 1991). De-spite the uncertainty, Armas eventually returned United Fruitassets.

We now compare the magnitudes of the net authorizationevents to the coup event effects. We use the country-specific 13-day CARs to compute the value per authorization for each coun-try. The longer horizon return is used to capture the full assetprice change due to a leaked authorization. The total rise in thestockpriceduetoauthorizations is thenjust oneplus thereturnto

7. In a prior version of the article, we also included an estimate of the returnon the first day of the coup. For Cuba, the estimate was positive and significant,reinforcingtheviewthat sometraders thought that a successful coupwas possible.

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an authorization raised tothe power of the net number of events8

plus the return over the coup window:

(5) (1 + RC, Auth)N (1 + RCoup

),

where RC, Auth is the 13-day cumulative abnormal return in coun-try C, N is the net number of authorization events, and RCoup isthe cumulative abnormal return in country C on the first day ofthe new regime. We use the return on the first day of the newgovernment because, due to the length of the coup in Guatemalaand the ensuing political instability after the end of the Arbenzregime, there is a net negative change in the stock price over theexact coup window.

The results are listed in Table VII. Though we can combinethe effects of the authorization events and the coup itself in mostof the countries, the failure of the operation in Cuba makes inter-pretation of the resulting comparison difficult. Thus we interpretthe Cuba numbers as the relative magnitude of stock price move-ments from the coup event and the authorization events. The in-clusion of Cuba in our cross-country sample also makes the fullsample decomposition difficult to interpret. Although we reportboth the Cuba decomposition and full sample decomposition, wefocus on the successful coup sample and the other four countries.

If we assume the only source of coup-related asset pricemovements are our events, together with the coup itself, we canestimate the total gains from the coup. The average gain per au-thorization in the all country sample is 12.0%, and the mean re-turn on the first day of the postcoup regime is 10.0%. For the setof successful coups, the gain from authorization events is roughlythree times that from the coup events; 75.5% of the relative gaincomes from authorization events. By country, the total gains fromthe couprangedgreatly. For a fully exposedcompany, the returnsrange from 14.1% in Chile to 77.1% in Guatemala. We also com-pute that the relative percentage benefit of the coup attributableto ex ante authorization events, which amounted to 55.0% inChile, 66.1% in Guatemala, 72.4% in Congo, and 86.9% in Iran.

8. In the case of Guatemala, the number of net events is two out of a totalof four events since one event was an aborted coup and thus counted as negative;in the case of Congo, the number of net events is one, because out of five events,two are negative; in the case of Cuba, the net events is two because two of the sixevents are negative. For the pooled country samples, we use the mean number ofevents across countries as the net events. Thus gives us 2.6 for the full countrysample and 2.4 for the successful coups sample.

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Overall, much of the gains from the coup occurred before the coupitself due to speculation from top-secret information. This sug-gests that estimates of the value of the coup to a company thatonly considered the stock price reaction to the coup itself wouldbe dramatically understated.

VII. CONCLUSION

Covert operations organized and abetted by foreign govern-ments have playeda substantial role in the political andeconomicdevelopment of poorer countries around the world. We look atCIA-backed coups against governments that had nationalized aconsiderable amount of foreign investment. Using an event-studymethodology, we find that private information regarding coup au-thorizations and planning increased the stock prices of expropri-ated multinationals that stood to benefit from the regime change.The presence of these abnormal returns suggests that there wereleaks of classified information to asset traders. Consistent withtheories of asset price determination under private information,this information often took some time to be fully reflected in thestock price.

We find that coup authorizations, on net, contributed sub-stantially more to stock price rises of highly exposed companiesthan the coup events themselves. This suggests that most of thevalueofthecouptotheaffectedcompanies hadalreadybeenantic-ipated and incorporated into the asset prices before the operationwas undertaken.

Our results are robust to a variety of controls for alternatesources of information, including publicevents andnewspaper ar-ticles. They are also robust across countries with the exceptionof Cuba. The anomalous results for Cuba are potentially due topublic information leaks and inadequate organization that sur-rounded that particular coup attempt. Our results are consistentwithevidenceinpolitical sciencethat business interests exert dis-proportionate influence on foreign policy (Jacobs and Page 2005),as well as historical accounts that suggest that protecting for-eigninvestments was amotivationforundertakingregimechange(Kinzer 2006). However, further empirical research is neededto uncover whether economic factors were decisive determi-nants of U.S. government decisions to covertly overthrow foreigngovernments.

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UNIVERSITY MASSACHUSETTS AMHERST AND IZAUNIVERSITY OF MARYLAND AT COLLEGE PARK AND INSTITUTE FOR

INTERNATIONAL ECONOMIC STUDIES, STOCKHOLM UNIVERSITY

COLUMBIA UNIVERSITY AND NBER

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