Roie 12125

The Impact of Visibility on Trade: Evidencefrom the World Cup

Omer Bayar and Georg Schaur*

AbstractSuccess in the FIFA World Cup provides countries with substantial international visibility. This paper usesthis information shock associated with the World Cup to show that visibility has a significant impact ontrade flows. In isolating the visibility effect, two identification problems are solved. Match outcomes in theWorld Cup are subject to significant uncertainty. This uncertainty, when combined with controls for eco-nomic development, makes World Cup success exogenous to exports. By contrast, hosting the World Cup ispotentially endogenous owing to self-selection issues. The paper exploits FIFA’s host selection policy toconstruct exogenous instruments for hosting. The results show that success in the World Cup raises exportstemporarily by around 5%.

1. Introduction

Countries pay considerable sums of money to host mega events such as the OlympicGames and the FIFA World Cup, which led researchers to estimating the economicbenefit of hosting these events. Rose and Spiegel (2011) examine whether countrieshost the Olympic Games to signal their desire for trade openness. Identifying theimpact of an Olympic signal on trade flows is problematic because countries self-select into bidding to become hosts. The identification of the Olympic signal’s tradeeffect is further complicated by the fact that hosts devote substantial resources toimproving the nation’s roads, airports and utility networks. These infrastructureimprovements may impact trade flows through reduced transport costs, independentof any signaling effect suggested by Rose and Spiegel.

In this paper, we isolate a low-cost information channel through which a megaevent may affect trade flows. Discussing the impact of hosting the Olympics on theBritish economy, The Economist reckons that “the real effect of the [Olympic] gameswill come from having billions of eyes on Britain for two weeks.”1 If the media report-ing of the event attracts the attention of importers from other countries, the resultingboost to a country’s visibility may impact its trade with such importers. Based on thisnotion, we test the hypothesis that an increase in a country’s international visibilityassociated with a mega event increases the country’s exports.

We focus on the FIFA World Cup as the mega event, rather than the Olympics. Amajor advantage of using the World Cup is that success (i.e. advancing to later stagesof the tournament) brings visibility to a country in much the same way as hostingdoes, but without the associated internal investment. Then, any positive trade effect

* Bayar: Schroeder School of Business, University of Evansville, 1800 Lincoln Avenue, Evansville, IN47722, USA. Tel: +1-812-488-2867; Fax: +1-812-488-2872. E-mail: [email protected]. Schaur: Departmentof Economics, The University of Tennessee, 505A Stokely Management Center, 916 Volunteer Blvd., Knox-ville, TN 37996, USA. The authors thank Bill Neilson, Scott Holladay, Luiz Lima, Mike Price, ChristianVossler, and seminar participants at Western Kentucky University for comments. They also thank the editorand referees for helpful suggestions. Ahiteme Houndonougbo provided excellent research support. Anyremaining errors are their own.

Review of International Economics, 22(4), 759–782, 2014DOI:10.1111/roie.12125

© 2014 John Wiley & Sons Ltd

mailto:[email protected]

associated with success can only result from increased visibility, and not fromimproved infrastructure or any other unobserved hosting-related mechanism.

Our most conservative estimates show that reaching the quarterfinal stage in theWorld Cup raises a country’s exports temporarily by 4–7%. The advantage of such abroad definition of success is that it maximizes identifying variation, as a relativelylarge number of countries advanced to World Cup quarterfinals in our sample. Thecaveat is that quarterfinalists may receive less visibility than, say, winners. Becauselimited visibility would make it more difficult to find evidence in support of ourhypothesis, reaching the quarterfinals is a useful definition as long as we reject the nullhypothesis that success in the World Cup has no impact on trade.2

We also provide evidence that the visibility shock associated with the World Cupdiverts a country’s exports from nearby to remote markets. This finding is consistentwith the view that an information shock raises the exporter’s visibility disproportion-ately more to distant and unfamiliar destinations compared with close and familiarones.3

In isolating an export effect associated with the World Cup, we face several identifi-cation issues. Countries submit a prior bid to host the World Cup, thereby self-selecting into the bidding pool. Those countries that are already highly visible andopen to trade may be more likely to bid and be chosen to host. To account for theresulting endogeneity, we develop exogenous instruments based on FIFA’s officialhost selection policy. Our study is the first effort to develop exogenous instruments toaccount for self-selection into bidding to host mega events.

Alternatively, Rose and Spiegel examine the export effect of hosting the Olympicsusing a matching estimator that accounts for endogenous host selection. Matchingmethodology faces two challenges. First, the estimator is sensitive to balancing condi-tions, which may substantially impact results. Maennig and Richter (2012) show thatRose and Spiegel’s estimates suffer from this particular problem. Second, the estima-tor accounts for selection by comparing countries that hosted the Olympics withsimilar countries that did not. Because the comparison is made in terms of observablecountry characteristics, matching estimator fails to account for selection on unobserv-able factors. By contrast, our instrumental variable approach accounts for selection onunobservable as well as observable information.

Addressing the potential endogeneity of success in the World Cup proves to bemuch more elaborate and we take a series of steps. First, Bernard and Busse (2004)show that population and economic resources predict success in international sportingevents. These two factors also impact trade flows. To eliminate the potential omittedvariable bias, we specify a country’s observed level of economic development, as sug-gested by Bernard and Busse, by including population and gross domestic product(GDP) information in the estimated model. Then, we augment this model withvarious fixed and time-varying effects to account for unobserved country-specificfactors that may be correlated with both exports and World Cup success, essentiallyaccounting for a country’s unobserved level of economic development. Our mainidentification assumption is that, because countries cannot plan in advance to succeedin the World Cup, once we control for a country’s observed and unobserved levels ofdevelopment, success in the World Cup is exogenous to exports.

Second, we review data from past World Cups and obtain evidence that individualmatch scores in the tournament are subject to considerable uncertainty. This findingimplies that winning outcomes in the World Cup are highly unpredictable ex ante.

Our third step goes further to cover the possibility that, even after accounting forobserved and unobserved country characteristics, the empirical model may still omit

760 Omer Bayar and Georg Schaur


unobserved country-specific ability to win matches in the World Cup. In that instance,we expect that those countries that successfully go through pre-tournament qualifica-tion rounds to participate in the World Cup are much more homogenous in theirunobserved football ability. To exploit this variation across low-quality and high-quality teams, we control for those countries that participate in the World Cup. Conse-quently, this third strategy identifies the impact of success in the World Cup in a groupof countries that are similar in terms of their unobserved football ability.

We apply two different models to identify the visibility effect that differ in how theyaccount for country-by-time specific information, such as multilateral resistance terms(Anderson and Van Wincoop, 2003). With bilateral trade data, a common way toaccount for these unobserved country-by-time specific factors is to absorb them withexporter-by-time and importer-by-time fixed effects. This approach would wipe outour main variable of interest, as success in the World Cup is also exporter-by-time spe-cific. Instead, we first estimate a bilateral trade model that specifies export flows as afunction of country pair-specific variables, such as distance, and absorbs exporter-time-specific and importer-time-specific information with indicator variables. Fromthis first stage, we collect coefficient estimates on exporter-by-time specific indicators.In the second stage, we regress these coefficient estimates on our World Cup variables,measures of the exporter’s level of development such as per capita GDP and popula-tion, exporter-specific fixed effects, and exporter-specific time trends. The advantage ofthe two-stage approach is that we start with a bilateral trade model consistent withtrade theory in that it accounts for country-by-time specific information.

For comparison, we also directly estimate the impact of World Cup success in abilateral trade model with a less rigorous treatment of country-by-time specific infor-mation that includes exporter-specific fixed effects and exporter-specific time trends.The advantage of this approach is that it allows us to examine heterogeneity in theimpact of visibility across importers, such as trade diversion. Both approaches showthat success in the World Cup, as measured by reaching quarterfinals, has a positiveimpact on exports.

The World Cup provides an ideal opportunity to measure a visibility effect on trade.The audience for the World Cup is similar in size, if not greater, than that of the Olym-pics.4 Unlike the Olympics, the World Cup focuses on national teams rather than indi-vidual athletes, and it has a single winner. Except for final matches of the group stage,no two games are scheduled at the same time such that football fans can follow allgames to the end.

Although the World Cup provides extensive visibility, the information viewersreceive from the World Cup is not particularly informative about characteristics of acountry’s products. Accordingly, the proposed visibility mechanism works throughuninformative advertising rather than informative advertising.5

Our results contribute to a literature that examines why countries host mega eventsdespite their considerable cost and the impact of these events on domestic economicactivity. As mentioned above, Rose and Spiegel investigate the effect of bidding forand hosting the Olympics on a country’s exports and find positive effects. Maennigand Richter examine the robustness of this result with respect to technical issuesregarding the matching estimator and come out less optimistic about the positiveOlympic effect. Baade and Matheson (2004) estimate the impact of the 1994 WorldCup held by the USA on income growth, and conclude that the event likely had anegative impact on the average host city. Similarly, Hagn and Maennig (2008) find thatthe 1974 World Cup held in Germany did not have any medium- or long-termemployment effects.

THE IMPACT OF VISIBILITY ON TRADE 761


The present study is structured as follows. Section 2 explains the empirical modeland the identification strategy. Section 3 summarizes the data. We report and interpretour results in section 4 and conclude in section 5.

2. Impact of Visibility on International Trade

This section explains the empirical model and estimators we apply to identify theimpact of the World Cup on a country’s exports. The empirical model follows fromRose and Spiegel (2011). We apply least squares and treatment effect estimators toaccommodate several assumptions regarding the regression error with respect toendogeneity.

Specification

We define EXijt as free on board exports from country i to country j in year t. In exam-ining the permanent impact of visibility shocks on trade, let the World Cup host indi-cator be hostit = 1 for all years in the sample after a country hosts the tournament, andzero otherwise.6 Similarly, let the success indicator be successit = 1 for all years in thesample after country i is successful in the tournament, and zero otherwise. We definesuccess in two ways: first, getting through the initial pool play to reach the quarterfinalstage in the tournament, and second, winning the tournament. The tradeoff betweenquarterfinalists and winners is that quarterfinalists provide more variation to explorethan winners, but winners receive a stronger visibility shock to isolate an export effectthan quarterfinalists.

In examining the temporary impact of visibility shocks on trade, we let host,quarterfinalist and winner indicators equal 1 only for 4 years between consecutivetournaments, and 0 otherwise. Hence, we account for the arrival of new informationevery 4 years from a new World Cup, which is consistent with the notion that visibilityshocks associated with each tournament have a significant but dissipating impact onexport flows.

For control variables, we specify country i’s exports as a function of the distance toits bilateral trade partners, Dij, and additional variables collected in Xijt accounting foreconomic size, bilateral trade resistance, and various fixed and time-varying effects toabsorb unobserved exporter and importer characteristics.7 The resulting export speci-fication is:

ln lnEX D X host success uijt ij ijt it it ijt( ) = + ( ) + + + +α α β γ γ0 1 1 2 (1)

In this specification, γ1 identifies the combined export effect of hosting the World Cupthat works through signaling (via infrastructure investments) and visibility channels. γ2

isolates the visibility effect because World Cup quarterfinalists and winners do notundertake any of the costly investments required to send a signal, nor do they benefitfrom resulting improvements in infrastructure.

Suppose that γ1 > 0. Then, hosting the World Cup raises exports, but we are unableto separate the individual impact of the two channels, signaling and visibility.

If γ2 > 0, we conclude that an increase in visibility lowers information barriers andraises the demand for a country’s exports, but why would visibility raise exports? Anincrease in trade owing to greater visibility is consistent with existing work on coordi-nation economies and uninformative advertising. Bagwell and Ramey (1994) showthat in a coordination economy an increase in expected market share results in better



deals for consumers in the form of greater product variety and lower prices. Theresulting boost in the demand for the firm’s products leads to greater market share.Their model shows how uninformative advertising can help form coordination econo-mies by raising the advertiser’s expected market share. If World Cup success is unin-formative advertising for firms located in the successful country and raises theirexpected share of global markets, then better deals and the subsequent increase indemand work to raise their exports.

Lastly, if γ1 < 0 and γ2 < 0, we conclude that visibility alone raises exports but capitalinvestment costs of hosting outweigh the combined benefits of signaling and greatervisibility that results from hosting. How could the hosting effect be negative when vis-ibility alone raises exports? Hosting mega events requires substantial spending thatmay have to be financed through local taxation, which may in turn adversely impactprivate business activity. Furthermore, hosting the World Cup may distract localauthorities from domestic policy considerations and divert limited resources awayfrom critical public projects (i.e. education, healthcare, infrastructure, etc.) therebypreventing the formation of a sound long-term economic strategy. Also, the WorldCup may cause widespread distraction across the working population of the hostnation before, during and after the tournament, thereby reducing labor productivityacross the board.

The advantage of model (1) is that it explains bilateral variation in exports, whichallows us to examine heterogeneity in the impact of visibility across importers. Forexample, we expect the visibility channel to have a greater impact on trade to remotedestinations, because the exporter is already highly visible to nearby trading partners.To test this notion, we extend specification (1) by including the interaction betweenthe success indicator and the bilateral distance between trade partners. Our predictionis that the coefficient on the interaction term is positive such that the visibility effecton exports increases with distance to the importer.

The disadvantage of model (1) is that it is not consistent with trade theory. Forexample, Anderson and Van Wincoop (2003) demonstrate that multilateral resistanceterms, variables that vary for each exporter and importer across time, are importantpredictors of trade flows. A common way to account for these terms in bilateral trademodels is to treat them as unobservable parameters that can be estimated withexporter-by-time and importer-by-time specific indicators.8 In particular, let μjt beunobservable importer-by-time specific information for importer j in year t and χit beunobservable exporter-by-time specific information for exporter i in year t that impactlog exports according to the specification:

ln lnEX D X uijt ij ijt jt it ijt( ) = + ( ) + + + +α α β μ χ0 1 (2)

To estimate (2), let IMjt = 1 for importer j in year t, and zero otherwise. Similarly, letIXit = 1 for exporter i in year t, and zero otherwise. Then, regress log exports on countrypair-specific information, such as distance, importer-by-time indicator variables IMand exporter-by-time indicator variables IX. The estimated coefficient χ̂it on the indi-cator IXit then captures the unobserved information χit, including the export effect ofthe World Cup. To test how hosting and success in the World Cup affects exports, asecond stage estimates the model:

χ̂ α β γ γit it it it itX host success u= + + + +0 1 2 (3)

where Xit captures variables that vary across exporters and time such as exporter percapita GDP, exporter population and various fixed and time-varying exporter-specific



effects. Coefficients γ1 and γ2 reveal the impact of hosting and success and are inter-preted the same way as in model (1).

Identification Strategy

To obtain informative estimates regarding the export effect of the World Cup, wetackle two main identification issues. The first is whether success in internationalsporting events is exogenous to export flows. We rely on existing literature and useevidence from past World Cups to deal with this problem. The second identificationissue is that countries may self-select into hosting the World Cup based on observedand unobserved information, which makes hosting potentially endogenous. To tacklethis second problem, we use a treatment model and make use of exogenous instru-ments we create to proxy the host indicator.

Exogeneity of success in the World Cup Previous literature (Bernard and Busse,2004) suggests that rich, populous and economically developed countries tend to bemore successful in international sporting events. To account for the potentialendogeneity of World Cup success, we control for a country’s observed and unob-served levels of development in our export specification. In line with the existing lit-erature, GDP per capita and population are added to account for the observed levelof development. Several combinations of fixed and time-varying country-specificeffects, which are explained below, are used to account for any remaining unob-served information that predicts both export flows and match outcomes in theWorld Cup.

Provided that countries cannot plan to succeed in the World Cup ex ante, once weaccount for all observed and unobserved country characteristics, our main identifica-tion assumption is that success in the World Cup is exogenous to export flows; thereis no omitted information in the regression error that systematically predictsquarterfinalists and winners. This assumption is reasonable, as predicting if and when acountry is successful in the World Cup is challenging because match outcomes aresubject to considerable uncertainty. For instance, countries go through arduous pre-tournament qualification rounds to participate in the World Cup. According to FIFA,193 countries played 777 games to qualify for the 2002 World Cup. Qualificationrounds result in variation in participation across countries and time. For example, inaddition to regulars like England, Brazil, Italy and Germany, Ukraine, Czech Repub-lic, Serbia & Montenegro, Trinidad & Tobago, Angola, Ghana, Cote D’Ivoire and Togoall participated in their first World Cup in 2006.9 Sometimes the surprise is in whodoes not participate. A strong team from the Netherlands missed the World Cup in2002, while a very good team from Turkey failed to qualify for 2006. Also, qualificationrounds eliminate low-skill teams resulting in a group of participants that is relativelyhomogenous in football ability, indicating that there is room for idiosyncratic eventsto determine match winners in the World Cup.10

Small goal differences across all stages of the World Cup provide evidence that thegroup of participants is fairly homogenous and that chance events can make a big dif-ference. For example, during the group play stage, many games end in a goal differ-ence of less than or equal to one (61%). Most of the single elimination matches thatcome after the group stage also end in a goal difference of less than or equal to 1(62%). Ties after regular time are not rare and the winner must prevail in extra-timeor penalty shoot-outs (14% of single elimination matches).11 In all, games are decided



on a thin margin across all stages of the World Cup and events such as a team’s per-formance on a given day or a goalie error may have a significant impact in determin-ing which team advances to the next stage or eventually wins the tournament.

Peculiar events that impact match outcomes are abound in football owing toinjury, controversial refereeing decisions and player idiosyncratic shocks. To give afew examples, Ronaldo of Brazil, the most prolific scorer in World Cup history (15goals, one more than the German strikers Gerd Mueller and Miroslav Klose) andthree-time best footballer of the year, spent the night before the 1998 World Cupfinal at the hospital because of a convulsive fit. Brazil lost the final to France 3–0.12

Another example is the famous “Wembley Goal” of the 1966 World Cup. In theextra time at a score of 2–2, Geoff Hurst of England hit the cross bar from whichthe ball bounced down “around” the line in a way that sparked a discussion fordecades to come about whether it was a goal or not.13 The referee counted the goaland England went on to win its first World Cup. Germany got partial vindication inthe 2010 World Cup in South Africa, when England’s Frank Lampard was deniedthe equalizer against Germany at a score of 2–1 for Germany.14 Germany went onto win the match 4–1. In the final match of the 2006 World Cup, the French footballicon Zinedine Zidane was sent off with a red card in the 110th minute after head-butting Marco Materazzi of Italy. Before the game, Zidane was named the mostvaluable player of the tournament and was one of his team’s most reliable penaltyshooters. France lost the penalty shoot-out 3–5 and Italy won the tournament for thefirst time since 1982.

While accepting that outcomes of the individual matches are highly uncertainmay come natural to a football fan, more traditional economic evidence forunpredictability can be obtained from actual trading behavior where traders bid forshares of the team they believe will win the tournament.15 To quote The Economist:“The pricing is based on the perceived probability of a given team’s winning the finalround, or at least one of the earlier rounds . . . These online exchanges leave plenty ofscope for trader noise. It is unlikely that anyone studying the pricing, even withweightings to offset the skewed incentives, would get reliably closer to predicting aWorld Cup winner.”

Overall, it is difficult to predict in advance individual match scores in the World Cupand there is substantial uncertainty in who wins the tournament. Consequently, oncewe control for observed and unobserved country-level heterogeneity via GDP, popu-lation, and various fixed and time-varying effects, it is reasonable to assume thatWorld Cup success is exogenous to a country’s export flows. The issue of exogeneity ofsuccess in the World Cup is explored further in a later subsection in section 4.

Endogeneity of hosting the World Cup We now discuss the estimators used toaccount for the potential endogeneity in hosting the World Cup. The first is standardordinary least squares (OLS). As stated above, we control for observed country char-acteristics, which predict exports and are likely correlated with hosting or success, byadding GDP per capita and population to least squares regressions. To account forunobserved country characteristics, we first augment the estimated model withexporter-specific fixed effects. Exporter-specific fixed effects account for unobservableexporter characteristics that are constant over time, such as unobserved advantages inhealth and organisation or a successful football tradition. We also add exporter-specific time trends to address two concerns: first, export-related unobserved advan-tages in health and organisation may change over time, and second, FIFA may usefavourable trends to identify countries with exceptional growth potential and select



those countries as hosts to maximise its own marketing and advertising revenues. Allour specifications include a year effect to control for changes in overall export flowsover time. We also experiment with importer-specific fixed effects in model (1) toexamine the robustness of our results.

Second, we use a two-step treatment framework (Maddala, 1983). In the treatmentmodel, the first step estimates the probability of hosting the World Cup, via a standardbinary probit, based on a set of instruments. From this first-step probit regression, weobtain the probability of hosting the World Cup for each country and time period. Thesecond step employs these probabilities in an OLS estimation to identify the coeffi-cients in the export specification.

For use in the treatment estimation, we exploit FIFA’s continental rotation policyand develop instruments for the host indicator. Continental rotation requires that notwo consecutive World Cups are hosted on the same continent; FIFA rotated theWorld Cup between Europe and Americas (north, central, and south combined) start-ing with the 1958 event held in Sweden until the 2002 event held jointly by Japan andSouth Korea.

Consistent with the rotation policy, we use as an instrument the exporter’s distanceto the last World Cup host. The rationale for using the distance to the last host is asfollows. It is important to trade between the USA and Canada that these two coun-tries are relatively close to each other. Even though respective distances of the USAand Canada to South Africa (host of the last World Cup in 2010) matter to FIFA inselecting the USA or Canada as the 2014 host, these distances to the last host do notcontribute in a meaningful way to the empirical model that explains exports from theUSA to Canada. Because FIFA’s actual host selection procedures are not publicinformation, we also experiment with an alternative definition of the distance instru-ment by replacing the distance to the last World Cup host with the distance to the lastWorld Cup host in the exporter’s own region.

Next, we include in the first-step probit model two regional dummies associatedwith FIFA’s official policy of rotating the host nation between Europe and Americasfrom 1958 to 2002. The first regional dummy takes the value 1 if the exporter islocated in Europe and 0 otherwise. Similarly, the second regional dummy takes thevalue 1 if the exporter is located in Americas and 0 otherwise.

It is plausible that a country’s observed level of development influences its chancesof hosting the World Cup; all else equal, FIFA may be more likely to select a devel-oped country to ensure robust spending on tournament-related activities, or a devel-oping country to help stimulate its growth. Therefore, we add exporter’s GDP andpopulation information to the first-step model.

Finally, we include in the selection model the interaction between the distanceinstrument (distance to the last host or the distance to the last host in the exporter’sown region) and GDP. The objective here is to test the idea that the exporter’s geo-graphic location may be a more important determinant when FIFA selects a develop-ing country to host the tournament than a developed country.

A quick look at data confirms strict continental rotation. Between 1958 and 2002,there are no consecutive hosts from Europe or Americas, which indicates that if theexporter is on the same continent as the last host, then its chance of being the nexthost equals essentially zero. To implement this idea, we experiment with an alterna-tive first-step selection model. Here, we estimate the probability of hosting for onlya subsample of eligible countries, those that are not located on the same continentas the last host. Then, to return to the full sample, we manually assign a zero prob-ability of hosting to those countries that are on the same continent as the last host.



Details of this procedure are in the Appendix. In the second step, we use the result-ing probabilities of hosting for the full sample to determine the export effect of theWorld Cup.

As with least squares regressions, we ensure the exogeneity of success in the treat-ment model by controlling for observed and unobserved country characteristics. Tothat end, we include in treatment models GDP per capita, population, and country-specific fixed and time-varying factors.

3. Data

Export data and trade resistance variables are from Rose and Spiegel (2011).16 Thisdataset includes annual bilateral observations for 196 countries for the period from1950 to 2006. We choose to work with this dataset for two reasons. First, we want tokeep our results and discussion comparable with the literature. Second, we want touse variation from a large number of World Cup tournaments, which requires that weemploy data going back in time as far as possible. The tradeoff for the long time-seriesdimension is that we do not have variation across different product categories, which,to our knowledge, is available only for much shorter panels of trade data. In con-structing the World Cup related indicators, we use information from the FIFA websiteat http://www.fifa.com/worldcup/archive/index.html.

Table A2 in the Appendix lists hosts, winners and quarterfinalists of the FIFA WorldCup for the sample period. During the period under consideration, 14 different coun-tries hosted the World Cup: Brazil, Switzerland, Sweden, Chile, England, Mexico,Germany, Argentina, Spain, Italy, USA, France, Japan and South Korea. These formerWorld Cup hosts are the origin of 1.5% of all observations in the temporary specifica-tion and 11.3% of all observations in the permanent specification; they account for6.4% of the total export volume in the temporary specification and 41.1% of the totalexport volume in the permanent specification.

Our narrow definition of success is winning the tournament. In our sample, WorldCup winners include Uruguay, Germany, Brazil, England, Argentina, Italy andFrance. These countries are the origin of 1.3% of all observations in the temporaryspecification and 6.6% of all observations in the permanent specification; theyaccount for 3.3% of the total export volume in the temporary specification and23.8% of the total export volume in the permanent specification. Consistent withprior expectations, the amount of variation we can extract from existing data aboutwinning is limited, which is why we choose to broaden the definition of success toinclude quarterfinalists.

In four of the 15 events in our sample, the winner also hosted the World Cup. Theseevents are the 1966 tournament hosted and won by England, 1974 by Germany, 1978by Argentina and 1998 by France. These four cases are the origin of 0.5% of all obser-vations in the temporary specification and 5.4% of all observations in the permanentspecification; they account for 1.4% of the total export volume in the temporary speci-fication and 23.1% of the total export volume in the permanent specification.

As for quarterfinalists, although the number of quarterfinalists is eight in mostcases, it also varies owing to the evolving structure of the FIFA World Cup. Here, wedefine the quarterfinal stage as the round between the initial pool play and the semi-final stage. Accordingly, quarterfinalists are the origin of 8.8% of all observations inthe temporary specification and 23.3% of all observations in the permanent specifica-tion; they account for 24.6% of the total export volume in the temporary specificationand 50.3% of the total export volume in the permanent specification. As expected,



http://www.fifa.com/worldcup/archive/index.html

more variation is obtained with this broader definition of success; the standard devia-tion of the temporary quarterfinal indicator at 0.28 is more than double the standarddeviation of the temporary winning indicator at 0.11.

4. Results

This section reports estimation results based on estimators discussed in section 2 andthe data discussed in section 3.

Baseline

To establish a benchmark, we first apply the OLS estimator to specification (1).Column (1) of Table 1 reports these results. Here, we use the temporary definitionsof hosting and success (i.e. indicators equal 1 for 4 years between successive WorldCups, and 0 otherwise), while success is measured by reaching the quarterfinal stagein the World Cup. Starting with control variables, trade decreases with distance tothe destination market and increases with economic size. Currency unions, commonlanguage, regional trade agreements, common borders and colonial impacts increasetrade. Countries that are large in terms of geographic size export less, likely owingto high inland transportation costs. These estimates are in line with the gravityliterature.17

As for variables of interest, hosting the World Cup lowers exports temporarily byaround 11%, whereas reaching quarterfinals raises exports temporarily by 23%. Thesuccess effect is statistically significant and large in magnitude, with the caveat that inthis specification we do not account for any of the unobserved effects discussed above,except year effects. Consequently, any omitted variable that is correlated with bothsuccess and exports would add an upward bias to the impact of visibility on exportflows.

Specifications in columns (2)–(5) add to the baseline model fixed and time-varyingcountry-specific effects to account for different sources of endogeneity. Estimates incolumn (2) account for exporter-specific effects that are constant over time. In thiscase, the impact of hosting remains fairly stable, while the impact of success falls inmagnitude, but remains significant. A similar pattern emerges in remaining columns;we obtain comparable estimates in these more rigorous specifications that alsoaccount for importer-specific fixed effects and exporter-specific time trends.

Column (5) is our most robust specification, accounting for an exporter-specifictime trend in addition to year, importer-specific and exporter-specific fixed effects. Byincluding exporter-specific time trends in the estimated model, we control for thenotion that FIFA may select as hosts those countries with considerable developmentpotential to expand its own market size. That is, the selection committee may preferthose countries with positive trends in exports to facilitate their openness and reapfuture benefits of their development.18 In this column, the hosting effect remains nega-tive and significant at 15%. By contrast, success, as measured by reaching the quarter-final stage, raises exports by a moderate and temporary 5%.

Across the table, we see that accounting for country-specific unobserved factors viafixed effects and time trends does not make much difference to the magnitude of thehosting effect on export flows. By contrast, the inclusion of a rigorous set of unob-served effects reduces the impact on exports of success in the World Cup from animplausible 23% to a more modest and reasonable 5%.



Two-stage Model

Table 2 reports estimates from a pooled OLS implementation of the two-stagemodel.19 Similar to Table 1, accounting for unobserved exporter-specific information isimportant to the magnitude of the export impact of World Cup success. In column (1),

Table 1. Temporary OLS for Quarterfinal

World Cup Host −0.11*** −0.13*** −0.20*** −0.18*** −0.15***(0.02) (0.02) (0.02) (0.02) (0.02)

World Cup Quarterfinalist 0.23*** 0.16*** 0.08*** 0.09*** 0.05***(0.01) (0.01) (0.01) (0.01) (0.01)

Log Distance −1.11*** −1.27*** −1.26*** −1.31*** −1.33***(0.01) (0.01) (0.01) (0.01) (0.01)

Log Exporter Population 1.07*** −0.29*** 0.81*** 0.80*** 0.36***(0.01) (0.03) (0.01) (0.01) (0.12)

Log Importer Population 0.88*** 0.91*** 0.91*** 0.29*** 0.37***(0.01) (0.01) (0.01) (0.02) (0.02)

Log Exporter p/c GDP 1.57*** 1.27*** 1.19*** 1.22*** 0.73***(0.01) (0.02) (0.01) (0.01) (0.03)

Log Importer p/c GDP 1.18*** 1.22*** 1.22*** 0.85*** 0.86***(0.01) (0.01) (0.01) (0.01) (0.01)

Currency Union 1.05*** 0.84*** 0.80*** 0.61*** 0.59***(0.03) (0.03) (0.03) (0.03) (0.03)

Language 0.46*** 0.43*** 0.43*** 0.35*** 0.34***(0.01) (0.01) (0.01) (0.01) (0.01)

Regional Trade Agreement 0.27*** 0.31*** 0.33*** 0.46*** 0.43***(0.01) (0.01) (0.01) (0.01) (0.01)

Border 0.69*** 0.45*** 0.46*** 0.47*** 0.45***(0.02) (0.02) (0.02) (0.02) (0.02)

Island 0.21*** 0.37*** 0.39*** 0.55*** −4.41***(0.01) (0.01) (0.01) (0.03) (0.86)

Log Area −0.07*** −0.06*** −0.06*** 0.01** 0.11(0.01) (0.01) (0.01) (0.01) (0.09)

Common Colony 0.57*** 0.56*** 0.59*** 0.77*** 0.76***(0.02) (0.02) (0.02) (0.02) (0.02)

Current Colony 0.61** 0.83*** 0.75*** 0.94*** 0.75***(0.08) (0.07) (0.07) (0.06) (0.07)

Ever Colony 1.39*** 1.52*** 1.49*** 1.40*** 1.44***(0.02) (0.02) (0.02) (0.02) (0.02)

Same Country 0.07 −0.51*** −0.41*** −0.93*** −0.83***(0.16) (0.11) (0.11) (0.12) (0.12)

Unobserved EffectsYear fixed effects Yes Yes Yes Yes YesImporter fixed effects Yes YesExporter fixed effects Yes YesExporter time trend Yes Yes Yes

R2 0.61 0.66 0.66 0.69 0.70RMSE 2.17 2.03 2.03 1.93 1.90

Notes: 447,334 bilateral annual observations for 196 countries, 1950–2006. Robust standard errors are inparentheses. RMSE = Root mean square error. *,**,***Denote significantly different at the 10%, 5% and1% level, respectively.



the impact is positive and large. As we add exporter-specific fixed effects and exporter-specific time trends, the impact of success on the exporter-time-specific effect falls to0.07; all else equal, success in the World Cup raises the exporter-time-specific effect inmodel (3) by 0.07. In model (2), an increase in χit of 0.07 increases exports by around7%, which is only slightly larger than the 5% reported in the last column of Table 1.

All specifications in Table 2 account for exporter’s level of development by addingGDP per capita and population. We estimate a positive impact on both variables, inline with Table 1.

We report estimates from the first stage of the two-stage model in Table A3 in theAppendix. Country pair-specific information included in the first-stage model is thesame as Rose and Spiegel (2011) and our Table 1 with one exception: we drop Areaand Island variables owing to collinearity, as we include importer-by-time andexporter-by-time fixed effects.20 First-stage estimates are consistent with the gravityliterature and those reported in Table 1.

Treatment

Table 3 reports estimation results for model (3) when we apply the two-step treatmentestimator. Across five columns, we experiment with different specifications of the first-step selection model. All second-step regressions include the same set of control vari-ables and unobserved effects as column (4) of Table 2. Treatment estimates show thatthe positive export effect associated with World Cup success is not sensitive to selec-tion; reaching the quarterfinal stage in the World Cup raises exports temporarily, witha coefficient of 4–7%.

Table 2. Temporary OLS for Quarterfinal using Two-stage Method

(1) (2) (3) (4)

World Cup Host −0.02 −0.10 −0.14* −0.18***(0.12) (0.07) (0.07) (0.06)

World Cup Quarterfinalist 0.33*** 0.23*** 0.13*** 0.07***(0.05) (0.04) (0.04) (0.02)

Log Exporter Population 0.97*** −0.27*** 0.72*** 0.50*(0.01) (0.07) (0.02) (0.26)

Log Exporter p/c GDP 1.49*** 1.29*** 1.07*** 0.69***(0.01) (0.05) (0.03) (0.06)

Unobserved effectsYear fixed effects Yes Yes Yes YesExporter fixed effects Yes YesExporter time trend Yes Yes

R2 0.86 0.95 0.95 0.97RMSE 1.07 0.68 0.68 0.52

Notes: 447,334 bilateral observations for 196 countries for the period from 1950 to 2006 in the first stage,where we use OLS to regress exports on bilateral trade costs, exporter-by-year fixed effects and importer-by-year fixed effects. 6700 exporter–year annual observations in the second stage, where we use OLS toregress the estimated coefficients for exporter-by-year fixed effects from the first stage on World Cup indi-cators, exporter population, exporter per capita GDP and a set of unobserved effects. The table reportsOLS results from the second stage. Robust standard errors are in parentheses. RMSE = Root mean squareerror. *,**,***Denote significantly different at the 10%, 5% and 1% level, respectively.



Table 3. Temporary Treatment for Quarterfinal using Two-stage Method

(1) (2) (3) (4) (5)

Second-step resultsWorld Cup Host −0.93*** 0.47*** 0.47*** 0.17 −0.02

(0.07) (0.16) (0.15) (0.13) (0.14)World Cup Quarterfinalist 0.04* 0.07*** 0.07*** 0.07*** 0.07***

(0.02) (0.02) (0.02) (0.02) (0.02)Log Exporter Population 0.52** 0.50** 0.51** 0.50** 0.50**

(0.25) (0.25) (0.25) (0.26) (0.26)Log Exporter p/c GDP 0.70*** 0.69*** 0.69*** 0.69*** 0.69***

(0.05) (0.05) (0.06) (0.06) (0.06)

First-step resultsLog Distance to Last Host 0.01 0.01 −0.51*** −0.26

(0.02) (0.02) (0.15) (0.18)Log Distance to Last Host

in the Same Region−0.10(0.19)

Log Exporter Population 0.35*** 0.40*** 0.41*** 0.33*** 0.34***(0.03) (0.04) (0.04) (0.04) (0.05)

Log Exporter p/c GDP 0.54*** 0.27*** −0.06 0.26* 0.41***(0.06) (0.07) (0.13) (0.15) (0.11)

Region 1 1.05*** 1.07*** 0.83*** 0.81***(0.34) (0.31) (0.19) (0.19)

Region 2 0.99*** 1.00*** 0.92*** 0.88***(0.30) (0.29) (0.18) (0.18)

Log Distance to LastHost ∗ Log Exporterp/c GDP

0.06*** 0.03(0.02) (0.02)

Log Distance to LastHost in the SameRegion ∗ Log Exporterp/c GDP

0.01(0.02)

Correction for zero hostingprobability

Yes Yes

Unobserved effectsYear fixed effects Yes Yes Yes Yes YesExporter fixed effects Yes Yes Yes Yes YesExporter time trend Yes Yes Yes Yes Yes

Notes: 447,334 bilateral observations for 196 countries for the period from 1950 to 2006 in the first stage,where we use OLS to regress exports on bilateral trade costs, exporter-by-year fixed effects and importer-by-year fixed effects. Second stage is a two–step treatment model with 6700 exporter–year annual observa-tions. In the first step of the treatment model, we use a binary probit to regress the hosting dummy on thedistance to the last host, exporter population, exporter per capita GDP, regional indicators, and the interac-tion between the distance to the last host and the exporter per capita GDP. Column (5) is a replication ofcolumn (4), except we replace the distance to the last host with the distance to the last host in the sameregion. In the second step of the treatment model, we use OLS to regress the estimated coefficients forexporter-by-year fixed effects from the first stage on World Cup indicators, exporter population, exporterper capita GDP, and a set of unobserved effects. The table reports results for the treatment model from thesecond stage. Robust standard errors are in parentheses. *,**,***Denote significantly different at the 10%,5% and 1% level, respectively.



The same consistency cannot be found in the hosting effect, which varies markedlyin alternative selection structures. In column (1), we apply a minimal selection modelthat includes the distance to the last host, exporter population and exporter GDP, andfind that hosting has a significant negative impact on exports. In column (2), we addthe two regional dummies to the first-step model only to observe that the hostingeffect is positive. In column (3), we augment the selection model with the interactionbetween the distance to the last host and the exporter’s GDP. First-step estimatessuggest that even though the impact of distance to the last host is negative, this effectis reversed for developed countries with a high GDP per capita, as illustrated by thepositive estimate on the interaction term. The hosting effect on exports is large andpositive. In columns (4) and (5), we manually assign, in the first step, a zero probabilityof hosting to those countries that are on the same continent as the last host, consistentwith FIFA’s rotation policy. Moreover, in column (5), we combine the correction forzero hosting probability with the alternative definition of the distance instrument, thedistance to the last World Cup host in the exporter’s own region. In these specifica-tions, hosting does not significantly impact trade, whereas success has a temporary andpositive impact of 7%.21

In all, parameter estimates reported in Table 3 confirm that, once we account forthe endogenous selection of World Cup hosts, the export effect of success, defined asreaching quarterfinals in the World Cup, remains positive, significant and reasonableat 4–7%. This finding is in line with estimates reported in Tables 1 and 2. By contrast,the export effect of hosting the World Cup is inconclusive.

Controlling for a Participation Effect

In the empirical applications above, we assumed that success in the World Cup isexogenous to trade once we control for observed and unobserved country-specificfactors that are correlated with both success and exports. In this section, we proposean alternative specification that compares the impact of success in the World Cup withparticipating in the World Cup to provide additional support for our main identifica-tion assumption.

Suppose that, even after accounting for country-specific factors via observable andvarious fixed and time-varying effects, we are still omitting country-level heterogene-ity that is correlated with World Cup success and exports. We can then augment model(3) to obtain:

χ̂ α β γ γ ρ= + + + + +0 1 2X host success q u (4)

where X collects exporter GDP per capita, exporter population, and fixed and time-varying exporter-specific effects. New variable q is the unobserved, and thereforeomitted, country-specific factor that explains trade flows, which is also correlated withadvancing through various stages of the World Cup. Variable q may be the unobservedcountry-level football ability.

To participate in the World Cup, countries go through lengthy pre-tournamentqualification rounds, which eliminate low-quality teams. Suppose that after we controlfor all the information included in X there is still variation in the unobserved factor qacross teams that play in these qualification rounds. If the unobserved factor q pre-dicts match outcomes, then we expect that as qualification rounds eliminate low-quality teams, the remaining teams (those that qualify for the World Cup) become



much more homogenous in terms of their unobserved characteristics and the effect ofthe unobserved variable q on match outcomes disappears. Consequently, when wecondition on those countries that participate in the World Cup, the unobserved vari-able q should not bias the effect of reaching quarterfinals in the World Cup among themore homogenous (in q) set of participants.

To examine the impact of selection based on the unobserved factor q, we augmentthe trade specification with a temporary participant indicator that equals 1 for 4 yearsif a country successfully goes through pre-tournament matches and advances to theWorld Cup, and 0 otherwise. The extended model is:

χ̂ α β γ γ γ ρ= + + + + + +0 1 2 3X host success participant q u (5)

Now, suppose that the linear relationship between unobserved heterogeneity q andother variables in the model can be written as follows:

q X host success participant v= + + + + +δ δ δ δ δ0 1 2 3 4 (6)

If any δi ≠ 0 then the covariance between the composite error ρq + u and regressors isnon-zero causing specification (5) to yield inconsistent estimates. To see how, followWooldridge (2002, p. 62) by substituting (6) into (5) to obtain:

χ̂ α ρδ β ρδ γ ρδγ ρδ γ ρδ

= +( ) + +( ) + +( )+ +( ) + +( )

0 0 1 1 2

2 3 3 4

X host

success pparticipant v u+ +ρ (7)

Because v is not correlated with any of the explanatory variables by construction, theconsistent effect of success in the World Cup is γ2 + ρδ3. Here, ρδ3 is the asymptoticbias on the export effect of success caused by the unobserved variable q.

If World Cup participants are homogenous in terms of their unobserved footballability, there should be no systematic relationship between the unobserved variable qand success in the tournament once the participant indicator is added to the model(i.e. δ3 = 0). Subsequently, we obtain a consistent estimate of success: γ2 + ρδ3 = γ2. Withparticipants in the model, we are essentially measuring the impact of reaching quar-terfinals in the World Cup among the more homogenous group of participants withsuccessful football traditions.

We report OLS and treatment estimates for the model that includes the World Cupparticipation indicator in Table 4. Magnitude and precision of estimates are similar tothose presented in Tables 1–3; success in the World Cup generates a moderate but sig-nificant increase in exports.

Trade Diversion

We report evidence for trade diversion in Table 5. Here, we add to model (1) aninteraction between the quarterfinal indicator and the distance to the country’strade partner.22 Under two alternative specifications, we obtain positive and signifi-cant parameter estimates for the interaction term, which suggests that World Cupquarterfinalists experience a change in the composition of their export flows: adecrease in exports to nearby importers and an increase in exports to distantimporters.

Column (1) evaluates the combined impact of success in the World Cup on exportsat the 10th, 50th and 90th percentile of distance. The combined effect is −0.38, 0.12 and



Table 4. Temporary OLS/Treatment for Quarterfinal with Participation using Two-stage Method

(1) (2) (3) (4) (5)

OLS Treatment

Second-step resultsWorld Cup Host −0.14* −0.18*** 0.47*** 0.17 −0.02

(0.07) (0.06) (0.15) (0.13) (0.14)World Cup Quarterfinalist 0.12*** 0.06** 0.07*** 0.07** 0.06**

(0.04) (0.03) (0.03) (0.03) (0.03)World Cup Participant 0.01 0.01 0.01 0.01 0.01

(0.03) (0.02) (0.02) (0.02) (0.02)Log Exporter Population 0.72*** 0.50** 0.50** 0.50** 0.50**


(0.03) (0.06) (0.06) (0.06) (0.06)

First-step resultsLog Distance to Last Host −0.51*** −0.26

(0.15) (0.18)Log Distance to Last Host in the

Same Region−0.10(0.19)

Log Exporter Population 0.41*** 0.33*** 0.34***(0.04) (0.04) (0.05)

Log Exporter p/c GDP −0.06 0.26* 0.41***(0.13) (0.15) (0.11)

Region 1 1.07*** 0.83*** 0.81***(0.31) (0.19) (0.19)

Region 2 1.00*** 0.92*** 0.88***(0.29) (0.18) (0.18)

Log Distance to LastHost ∗ Log Exporterp/c GDP

0.06*** 0.03(0.02) (0.02)

Log Distance to LastHost in the SameRegion ∗ LogExporter p/c GDP

0.01(0.02)


Yes Yes

Unobserved effectsYear fixed effects Yes Yes Yes Yes YesExporter fixed effects Yes Yes Yes YesExporter time trend Yes Yes Yes Yes Yes

Notes: 447,334 bilateral observations for 196 countries for the period from 1950 to 2006 in the first stage,where we use OLS to regress exports on bilateral trade costs, exporter-by-year fixed effects, and importer-by-year fixed effects. The table reports results from the second stage; columns (1) and (2) follow the proce-dure from Table 2, whereas columns (3), (4) and (5) follow the procedure from Table 3. Robust standarderrors are in parentheses. *,**,***Denote significantly different at the 10%, 5% and 1% level, respectively.



0.37 respectively, which are all significant at 1% level. These findings are consistentwith the notion that the visibility shock experienced by World Cup quarterfinalistsdiverts trade from closer to more remote destinations.

In column (2), we further examine trade diversion by adding to the baseline modelexporter-by-year fixed effects and importer-specific fixed effects. The introduction ofexporter-by-year fixed effects alleviates endogeneity concerns by absorbing all unob-served factors that vary by exporter and time, including World Cup related informa-tion. Consequently, we cannot directly estimate parameters on World Cup hosts andquarterfinalists. We still obtain a positive and significant estimate for the interactionterm, which is consistent with the notion that information shocks are especially impor-tant with distant trade partners.

The result that visibility shocks have a larger impact on trade with remote locationsis interesting with respect to the literature that examines the impact of distance ontrade. A puzzling result in this literature is the large estimated magnitude of the dis-tance coefficient. Helpman et al. (2008) provide a selection and aggregation argumentto explain the size of this coefficient. Our finding of a larger visibility effect withremote destinations suggests that the distance coefficient potentially captures, amongother things, information issues; longer distances imply higher information barriersbetween trading nations, which can be overcome via large visibility shocks.

Table 5. Temporary OLS for Quarterfinal with Trade Diversion(Including all Controls from Table 1)

(1) (2)OLS OLS

World Cup Host −0.17***(0.02)

World Cup Quarterfinalist −2.84***(0.23)

Quarterfinalist ∗ Log Distance 0.36*** 0.40***(0.03) (0.03)

Quarterfinal impact10% of distance −0.38***

(0.04)50% of distance 0.12***

(0.02)90% of distance 0.37***

(0.03)

Unobserved effectsYear fixed effects YesImporter fixed effects Yes YesExporter fixed effects YesExporter time trend YesExporter–year fixed effects Yes

Notes: 447,334 bilateral annual observations for 196 countries, 1950–2006. The whole set of control variables from Table 1 is included but notreported. Robust standard errors are in parentheses for column (1).*,**,***Denote significantly different at the 10%, 5% and 1% level,respectively.



Permanent Export Effect of the World Cup

We have so far examined the temporary export effect of visibility associated withWorld Cup hosting and success, focusing on the first 4 years after the tournament. Inthis subsection, we turn to permanent definitions of World Cup indicators fromsection 2 to investigate whether the export promoting effect of the tournament lastsover time. This approach follows directly from Rose and Spiegel’s definition of a per-manent effect.

Estimation results for the same specifications from Tables 3 and 4 that include indi-cators capturing the permanent, rather than temporary, effect of the World Cup arereported in Table 6. With exporter-specific fixed effects and exporter-specific timetrends in the model, we see that World Cup success does not have a permanent impacton exports. We conclude that while success in the World Cup provides a temporaryboost to visibility and exports, it does not have a permanent structural impact ontrade.

5. Conclusions

This paper uses data from the FIFA Football World Cup to show that visibility is animportant predictor of export flows. The World Cup provides an ideal opportunity toisolate a visibility effect on trade because the tournament is watched by hundreds ofmillions of football fans around the world and success in the tournament is exogenousto trade flows after tackling some important identification issues.

Our estimates show that success in the World Cup raises exports temporarily by5–7%. This finding is robust to controlling for participation and including a rigorousset of unobserved effects. We also provide evidence for trade diversion. World Cupsuccess diverts a country’s exports from proximate to distant locations. This result is inline with the notion that information barriers are especially important betweenremote trade partners and suggests that the distance variable in conventional tradespecifications proxy, among other things, these information barriers.

Our results identify an information channel in trade that is otherwise difficult toobserve. An increase in a country’s international visibility, even if it is uninformativeabout the country’s product characteristics, has a significant positive effect onexports. Therefore, those countries that are less open can use certain visibility-enhancing tools as a trade policy instrument to increase their level of openness. Anexample is Mexico’s launch of a multi-million dollar advertising campaign topromote tourism.23



Table 6. Permanent OLS/Treatment for Quarterfinal with Participation using Two-stage Method

(1) (2) (3) (4) (5)

OLS Treatment

Second-step resultsWorld Cup host 0.02 −0.20*** 0.48*** −0.18*** −0.21***

(0.06) (0.05) (0.06) (0.05) (0.05)World Cup Quarterfinalist 0.23*** −0.01 0.01 −0.01 −0.01

(0.06) (0.05) (0.04) (0.05) (0.05)World Cup Participant −0.26*** −0.06 0.07* −0.06 −0.06

(0.04) (0.04) (0.04) (0.04) (0.04)Log Exporter Population 0.74*** 0.51** 0.51** 0.51** 0.51**


(0.03) (0.06) (0.06) (0.06) (0.06)

First-step resultsLog Distance to Last Host −0.18 −0.23

(0.12) (0.16)Log Distance to Last Host

in the Same Region0.02

(0.17)Log Exporter Population 0.53*** 0.48*** 0.48***

(0.03) (0.03) (0.03)Log Exporter p/c GDP 0.55*** 0.57*** 0.86***

(0.10) (0.13) (0.10)Region 1 2.11*** 1.75*** 1.69***

(0.21) (0.17) (0.18)Region 2 2.25*** 2.05*** 1.98***

(0.21) (0.17) (0.18)Log Distance to Last Host ∗ Log

Exporter p/c GDP0.02 0.03*

(0.01) (0.02)Log Distance to Last Host

in the Same Region ∗ LogExporter p/c GDP

−0.01(0.02)


Yes Yes

Unobserved effectsYear fixed effects Yes Yes Yes Yes YesExporter fixed effects Yes Yes Yes YesExporter time trend Yes Yes Yes Yes Yes

Notes: 447,334 bilateral observations for 196 countries for the period from 1950 to 2006 in the first stage,where we use OLS to regress exports on bilateral trade costs, exporter-by-year fixed effects and importer-by-year fixed effects. The table reports results from the second stage; columns (1) and (2) follow the proce-dure from Table 2, whereas columns (3), (4) and (5) follow the procedure from Table 3. Robust standarderrors are in parentheses. *,**,***Denote significantly different at the 10%, 5% and 1% level, respectively.



Appendix

In this section, we explain the first-step selection model implemented to estimatecolumns (4) and (5) in Table 3. The endogeneity issue arises because FIFA may selecthost countries based on unobserved information, which may also predict the exportpotential of the country. Consequently, the host indicator in the main export specifica-tion may be correlated with the disturbance term that accounts for all unobservedvariables impacting exports.

FIFA’s continental rotation policy splits the sample into two types of countries:those countries that are eligible to host the next tournament and those countries thatare exogenously excluded by FIFA policy because they are located on the same conti-nent as the last host. For the group of ineligible countries, there is no endogeneityconcern because we observe the necessary selection information: whether or not thecountry is on the same continent as the last host. For the group of eligible countries,we correct for potential endogeneity by using the standard two-step treatment estima-tor. Our equation of interest is:

ln .EX X z eijt it ijtijt( ) = + +β δ (A1)

In this expression, Xijt collects all independent variables from the trade specificationexcept the potentially endogenous binary host indicator denoted by zit. Hosting out-comes result from an unobserved process as function of instruments wit:

z w uit it it∗ = +γ

where zit = 1 if zit∗ > 0, and zit = 0 otherwise.

Consistent estimation of parameters in the trade specification (β) requires thathazard values from the first-step model are added to the second-step regression (A1).To that end, we obtain the probability of hosting from a binary probit on the sampleof those countries eligible to host, as determined by:

Pr z w wit it it=( ) = ( )1 Φ ˆ .γ

From these probabilities, hazard values for eligible countries are defined ash w wit it it= ( ) ( )φ γ γˆ ˆΦ if zit = 1, and h w wit it it= − ( ) − ( ){ }φ γ γˆ ˆ1 Φ if if zit = 0, where ϕ(·) isthe standard normal probability density and Φ(·) is the cumulative density.

For those countries that are ineligible to host, the hazard value equals zero.To see why, note that the appropriate hazard for ineligible countries ish w wit it it= − ( ) − ( ){ }φ γ γˆ ˆ1 Φ . Because the probability of hosting for these countries iszero, based on FIFA’s rotation policy, the denominator of the hazard equals 1. Ahosting probability of zero indicates that the cumulative density Φ(·) is evaluated atnegative infinity. For consistency, we evaluate the numerator of the hazard at the samevalue as the denominator. Because ϕ(·) approaches zero at negative infinity, thehazard for those countries that are ineligible to host equals zero.

In summary, to account for potential endogeneity, we first estimate a selectionmodel for those countries that are eligible to host, compute hazard rates and includethem in the export specification. For those countries that are ineligible to host, thehazard rate is zero because these countries are exogenously excluded from considera-tion by FIFA policy.



Table A1. Control Variables in Estimated Models

Description MeanStandarddeviation Minimum Maximum

Distance Log distance between thetwo countries in the pair

8.10 0.85 3.68 9.42

Exporter Population Log exporter population 9.26 1.80 3.66 14.09Importer Population Log importer population 9.09 1.88 2.81 14.09Exporter p/c GDP Log exporter per capita

GDP8.73 1.11 5.14 11.28

Importer p/c GDP Log importer per capitaGDP

8.63 1.13 5.14 11.35

Currency Union Dummy for currency unionbetween the twocountries in the pair attime t

0.02 0.12 0 1

Language Dummy for sharedlanguage between thetwo countries in the pair

0.21 0.41 0 1

Regional TradeAgreement

Dummy for regional tradeagreement between thetwo countries in the pairat time t

0.22 0.41 0 1

Border Dummy for shared landborder between the twocountries in the pair

0.03 0.18 0 1

Island Number of island countriesin the pair

0.31 0.52 0 2

Area Log product of the landareas of the twocountries in the pair

24.08 3.36 7.12 32.77

Common Colony Dummy for whether thetwo countries in the pairwere colonized by thesame country

0.09 0.28 0 1

Current Colony Dummy for whether oneof the two countries inthe pair colonizes theother at time t

0.01 0.04 0 1

Ever Colony Dummy for whether oneof the two countries inthe pair ever colonizedthe other

0.02 0.15 0 1

Same Country Dummy for whether thetwo countries in the pairare part of the samecountry at time t

0.01 0.02 0 1



Table A2. Hosts, Winners, and Quarterfinalists in the FIFA World Cup

Year Host Winner Quarterfinalists

1950 Brazil Uruguay Uruguay, Brazil, Sweden, Spain1954 Switzerland Germany Germany, Hungary, Austria, Uruguay, Brazil, England, Yugoslavia,

Switzerland1958 Sweden Brazil Brazil , Sweden, France, Germany, Yugoslavia, Russia, Wales, Northern

Ireland1962 Chile Brazil Brazil , Czechoslovakia, Chile, Yugoslavia, Hungary, Russia, Germany,

England1966 England England England , Germany, Portugal, Russia, Argentina, Hungary, Uruguay,

North Korea1970 Mexico Brazil Brazil, Italy, Germany, Uruguay, Russia, Mexico, Peru, England1974 Germany Germany Germany, Netherlands, Poland, Brazil, Sweden, Yugoslavia, Argentina1978 Argentina Argentina Argentina, Netherlands, Brazil, Italy, Poland, Germany, Austria, Peru1982 Spain Italy Italy, Germany, Poland, France, Brazil, England, Russia, Belgium,

Argentina, Spain, Austria, Northern Ireland1986 Mexico Argentina Argentina, Germany, France, Belgium, Brazil, Mexico, Spain, England1990 Italy Germany Germany, Argentina, Italy, England, Yugoslavia, Czechoslovakia,

Cameroon, Ireland1994 USA Brazil Brazil, Italy, Sweden, Bulgaria, Germany, Romania, Netherlands, Spain1998 France France France, Brazil, Croatia, Netherlands, Italy, Argentina, Germany,

Denmark2002 Japan &

SouthKorea

Brazil Brazil, Germany, Turkey, South Korea, Spain, England, Senegal, USA

2006 Germany Italy Italy, France, Germany, Portugal, Brazil, Argentina, England, Ukraine

Table A3. OLS from First Stage of Two-stage Model

Distance −1.33***(0.01)

Currency union 0.51***(0.03)

Language 0.33***(0.01)

Regional trade agreement 0.54***(0.01)

Border 0.42***(0.02)

Common colony 0.78***(0.01)

Current colony 0.70***(0.09)

Ever colony 1.44***(0.02)

Same country −0.90***(0.16)

Unobserved effectsImporter–year fixed effects YesExporter–year fixed effects Yes

R2 0.73RMSE 1.85

Notes: 447,334 bilateral annual observations for 196 countries, 1950–2006. Robuststandard errors are in parentheses. RMSE = Root mean square error.*,**,***Denote significantly different at the 10%, 5% and 1% level, respectively.



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Notes

1. “Going for Gold, The Economic Impact of the games,” The Economist, 21st July 2012.2. We also consider narrower definitions of success. For winning, estimates are unstable andmostly insignificant. For finalists and semifinalists, we find evidence of a slightly larger visibilityeffect compared with quarterfinalists, consistent with the intuition that reaching the later stagesof the tournament results in a greater visibility shock. We focus our attention on quarterfinaliststo maximize the amount of identifying variation.3. Disdier and Head (2008) examine the persistence of the negative impact of distance ontrade. If visibility is one of the factors that drive trade, then trade naturally falls with distancebecause visibility decreases with the distance between trade partners.4. FIFA reports that the home television coverage for the final match of the 2010 FIFA WorldCup held in South Africa reached over 3.2 billion people around the world, almost half of theworld population. http://www.fifa.com/worldcup/archive/southafrica2010/organisation/media/newsid=1473143/index.html5. Previous literature shows that uninformative advertising is prevalent in the marketplace. Forinstance, Abernethy and Butler (1992) report that 37.5% of US television advertising and15.6% of US magazine advertising do not have information content regarding product cuessuch as price, quality, performance and components. Bertrand et al. (2010) use field experiments



http://www.fifa.com/worldcup/archive/southafrica2010/organisation/media/newsid=1473143/index.html

http://www.fifa.com/worldcup/archive/southafrica2010/organisation/media/newsid=1473143/index.html

to show that even the picture of an attractive female in an advertisement increases the demandfor the product.6. Mexico and Germany hosted two World Cups in our sample. In specifying the hosting indica-tor, we assume that the largest trade impact derives from the first time a country hosts.7. Table A1 in the Appendix defines control variables in the model and provides summarystatistics.8. Wooldridge (2002, p. 273) discusses the treatment of fixed effects as unobserved parametersthat can be estimated.9. http://www.fifa.com/worldcup/archive/germany2006/preliminaries/index.html10. One can eliminate from the sample countries with “low-quality” teams and estimate theimpact of hosting and success over the more homogenous sample of countries that feature“high-quality” teams. This task is difficult to achieve in practice because a high-quality team inone World Cup may be a low-quality team in future World Cups. For example, Czechoslovakiaand Hungary, traditional powerhouses of the 1950s and 1960s, are now past their glory days, sug-gesting that over time it is difficult to determine who the low- and high-quality participants are.11. The structure of the World Cup has changed over the years. In three of the 15 tournamentsin our sample (1974, 1978 and 1982) teams had to play a second round of pool play to advanceto the single elimination stage. This might affect team efforts in the early stages of the tourna-ment and add further uncertainty to match outcomes. For a literature on end of game incen-tives, see Abrevaya (2003).12. http://sportsillustrated.cnn.com/soccer/world/events/1998/worldcup/news/1998/07/14/ronaldoconvulsions/13. http://www.robots.ox.ac.uk/~vgg/publications/papers/reid96.pdf14. http://www.telegraph.co.uk/sport/football/teams/england/7857382/England-v-Germany-Frank-Lampards-disallowed-goal-highlights-stupidity-of-Fifas-ruling.html15. “Trading World Cup Volatility,” The Economist, 6 June 2006. http://www.economist.com/node/702564116. We thank Andrew Rose for making the data available on his website. For details on thisdataset, please see Rose and Spiegel (2011).17. Specifications reported in this table are estimated with robust standard errors. We alsocluster standard errors by exporter-year and country pairs and obtain similar results.18. Evidence that the selection of the World Cup hosts is endogenous to development potentialcan be gained from Joseph Blatter, the reigning president of FIFA, who said that “The WorldCup is going to new lands and I am a happy President as we speak of the development of foot-ball” in his announcement that Qatar will be the host of the 2022 World Cup. http://www.fifa.com/newscentre/news/newsid=1344500.html19. All specifications in this table are estimated with robust standard errors, as the dependentvariable is an estimated parameter (Saxonhouse, 1977).20. Area is the log product of the trade partners’ land areas and Island is the number of islandnations in the country pair.21. In explaining inconclusive results for the hosting coefficient, we note that there are only ahandful of World Cup hosts in the sample period, which limits the amount of variation neededto identify a consistent impact. Given the lack of sufficient data, it is not surprising that smallchanges in the first-step selection model lead to large changes in the sign, significance and themagnitude of the hosting effect.22. We cannot examine trade diversion in model (3), as it does not include variation acrossimporters.23. http://www.youtube.com/watch?v=hMjtsfcBrPk, http://www.orange-blog.com/2009/06/15/visit-mexico-campaign/



http://www.fifa.com/worldcup/archive/germany2006/preliminaries/index.html

http://sportsillustrated.cnn.com/soccer/world/events/1998/worldcup/news/1998/07/14/ronaldoconvulsions/

http://sportsillustrated.cnn.com/soccer/world/events/1998/worldcup/news/1998/07/14/ronaldoconvulsions/

http://www.robots.ox.ac.uk/~vgg/publications/papers/reid96.pdf

http://www.telegraph.co.uk/sport/football/teams/england/7857382/England-v-Germany-Frank-Lampards-disallowed-goal-highlights-stupidity-of-Fifas-ruling.html

http://www.telegraph.co.uk/sport/football/teams/england/7857382/England-v-Germany-Frank-Lampards-disallowed-goal-highlights-stupidity-of-Fifas-ruling.html

http://www.economist.com/node/7025641

http://www.economist.com/node/7025641

http://www.fifa.com/newscentre/news/newsid=1344500.html

http://www.fifa.com/newscentre/news/newsid=1344500.html

http://www.youtube.com/watch?v=hMjtsfcBrPk

http://www.orange-blog.com/2009/06/15/visit-mexico-campaign/

http://www.orange-blog.com/2009/06/15/visit-mexico-campaign/

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