МАГИСТЕРСКАЯ ДИССЕРТАЦИЯ

MASTER THESIS

Тема: Договорные матчи в советском футболе: миф или реальность?

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Title: Cheating in Soviet Football: Myth or Reality?

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Студент/ Student: Sergey Vorontsov (Ф.И.О. студента, выполнившего работу)

Научный руководитель/ Аdvisor: Ruben Enikolopov, Irina Khovanskaya, Maria Petrova and Konstantin Sonin (ученая степень, звание, место работы, Ф.И.О.)

Оценка/ Grade:

Подпись/ Signature:

Москва 2011 Sergey Vorontsov1

Cheating in Soviet Football: Myth or Reality?

Abstract Establishing unfair behavior in sports through econometric analysis of results is a quite challenging task of extracting hidden information from publicly available data only, as it can be seen from fascinating work on cheating in sumo championships by Mark Duggan and Steven Levitt (2002). In this paper, the USSR football premier league is investigated with basic hypothesis being that a football club from the capital of the Ukraine, FC Dynamo Kiev, enjoyed support from other Ukrainian teams in form of fixed matches. An ordered probit model for the outcomes (win, draw or defeat) of matches of Dynamo Kiev is estimated controlling for the strength of the teams, home/away games, and year and Ukrainian team fixed effects. The results suggest that its overperformance in successful years against Ukrainian teams is higher than against other rivals, which favors the original hypothesis. The effect becomes statistically significant when the sample is limited to the most recent part of the sample. Also, it is found that the introduction of new Politburo members could actually increase the performance of teams from their native cities.

1 The author is grateful to his advisors – Ruben Enikolopov, Irina Khovanskaya, Maria Petrova and Konstantin Sonin – for their unlimited patience, inspiring advice and valuable comments. Also, discussions and suggestions by Kirill Borusyak, Andrei Bremzen, Oleg Groshev, Sergei Guriev, Sergei Izmalkov as well as thoughtful questions by seminar and conference participants at NES are thankfully acknowledged. 1 Introduction While sports are not usually of direct economic interest, they draw researchers‟ attention as they allow investigating strategic behavior of individuals and organized groups of people. Among advantages of sports as object of research are strict and clear rules, readily available data and wide variety of games existing. It has become quite common in literature to address behavioral questions through analyzing sport results. Vincenzo Scoppa (2008) finds that football referee decisions may be biased towards the home team, mostly due to the social pressure by supporter crowds. Pierre-André Chiappori et al. (2002) find an appropriate phenomenon – football penalty kicks – to analyze mixed strategy equilibria in real life. Another interesting problem to look into is cheating in sports. This work investigates a case of USSR football championship. It was a common opinion among Soviet supporters that trading matches, especially involving teams with no tournament motivation left of, conversely, teams struggling to stay in the premier league. However, such cheating patterns can hardly be distinguished from the case when simply higher motivation induces higher effort. A prominent paper by Mark Duggan and Steven D. Levitt (2002) seems to have succeeded in this challenging task. They looked into corruption among Japanese elite sumo wrestlers. There incentives for trading matches arise due to non-linearity in prize structure (eighth out of fifteen possible wins in a tournament brings more marginal value in terms of rating and prize money than others do). The authors find that wrestles on the margin for their eighth victory perform significantly better than on average. To provide evidence consistent with the hypothesis that cheating, but not effort is responsible for that, they use several quite creative techniques, some of them are listed below. First, after winning on the margin the player has lower than average probability of victory in the next match against the same opponent. This suggests that a mechanism of match exchange exists. Second, signs of match fixing disappear in the years of media scandals on sumo corruption. Finally, two former wrestlers declared some of sportsmen not corrupt and those really did not show the common cheating pattern. Unfortunately, these all tricks are almost impossible to implement when analyzing football, primarily because of small number of matches – only two games per year between each pair of teams. However, match fixing of political nature can still be found. This paper makes an attempt to confirm the allegations of throwing matches to the club from the capital of Ukraine, Dynamo Kiev, made against other Ukrainian teams. Such behavior should maximize the probability of Dynamo Kiev and could possibly be promoted and protected by highly influential Ukrainian politicians (members of Politburo). Though the rumors on Dynamo Kiev‟s unfair play are quite common among supporters, even anecdotal evidence of politically conditioned match fixing is hard to find. In an interview to a Ukrainian paper, Dynamo Kiev‟s player Viktor Serebryanikov admitted that they were occasionally involved in match fixing, but did not mention any motives other that just financial. Hence, this work can not only contribute to methodology of cheating detection but also shed light on an interesting historical case. An ideologically relevant paper on politically motivated cheating in sports by Charles C. Moul and John V. C. Nye (2009) has investigated possible collusions of Soviet chess players. The hypothesis tested there is that players from the USSR deliberately drew matches (exerted less effort and spent less time) against each other to focus attention on other games thus maximizing the probability of a Soviet player winning a tournament. However, more complicated strategic patterns, such as throwing matches to a Soviet player who showed himself most successful against favorites from other countries, were not looked into. Again, part of the success of the paper is due to the quality of data: the same grand masters play against each other quite often, and a widely accepted rating system exists. Both of these features are not inherent in football championships making the problem of this work quite challenging with respect to that about chess. This paper suggests the following methodology: compare the increase in performance of Dynamo Kiev in its successful years against Ukrainian and all other teams. Assuming that with more chances of Dynamo Kiev winning a championship there should be more fixed matches against Ukrainian clubs, the positive sign of the interaction between rival being from the Ukraine and the season being successful would support the hypothesis of Dynamo Kiev‟s cheating. The positive sign is indeed observed, controlling for the strength of the teams, home/away games, and Ukrainian team and year fixed effects. In placebo tests, the specification is faked by substituting Dynamo Kiev and Ukrainian teams for Dynamo Moscow and other relevant groups of teams, either one at time or both. The results of those tests turn to be ambiguous, and hence, while data is consistent with Dynamo Kiev‟s fixing matches, the team cannot be decisively accused of unfair behavior basing on statistical investigation only. Also, the paper tries to detect the direct influence of politicians on teams‟ performance. Places of birth of Politburo members are used to predict the teams they could favor. A small (but statistically significant) positive effect on the performance of “native” teams is actually found in the seasons after the introduction of new members. This finding does not explicitly help understanding how Dynamo Kiev could organize fixed matches with other Ukrainian teams, but at least provides some empirical justification for the discussions of possible political reasons behind the statistically revealed cheating, such as of Moul and Nye (1999) or of this thesis. The rest of the paper is organized as follows: Section 2 formulates the hypotheses to test, Section 3 describes initial data as well as construction of specific variables, Section 4 presents and discusses the results of empirical analysis, and Section 5 concludes. 2 Hypotheses Two hypotheses are analyzed in the paper. One is that FC Dynamo Kiev enjoyed support of other Ukrainian teams in form of fixed matches. Assuming that there were more incentives for cheating in seasons when Dynamo Kiev was a real contender for the USSR championship title, the hypothesis implies that in those seasons an increase in Dynamo Kiev‟s performance relative to other seasons is larger against Ukrainian teams than against others. This particular implication is tested in the paper. The other hypothesis that is investigated can be generally formulated as follows: politicians could affect the results of football matches and teams‟ performance directly. More specifically, it is tested whether the introduction of new Politburo2 members increased performance of the teams that could be labeled as “native” to these members, if any.

3 Data Methodology for both hypotheses includes estimating probabilities of outcomes of matches, either played by Dynamo Kiev (for the first hypothesis), or by any teams (for the second one). Match results and calendars of 1963 – 1968, 1970 – 1975, 1977 – 1991 USSR football championships are taken from online database WildStat, in total 27 seasons with 866 observations – matches played by Dynamo Kiev. Seasons of 1969 and 1976 are dropped because of their unusual tournament scheme. Also, the season to begin the sample period with is not random – in previous years, tournaments schemes were different as well. Hence the largest possible homogenous sample (among those containing the latest years) is taken. Several explanatory variables are constructed. Their meaning and summary statistics are presented in Table 1. Most of the covariates are binary ones. There are indicators of Dynamo Kiev playing at home, against Ukrainian team and in a successful year as well as their pairwise interactions. Apart from binary variables, continuous measure of performance of Dynamo Kiev and its rival in a certain season in a certain type of match (home/away) is constructed. Dynamo Kiev has won 13 Soviet championship titles. One of those is out of the sample (1963) while every other winning season is taken as successful: 1966, 1967, 1968, 1971, 1974, 1975, 1977, 1980, 1981, 1985, 1986 and 1990. The choice is consistent with Dynamo Kiev‟s performance in the USSR cup and European leagues. One could argue that the championships where Dynamo Kiev finished second or third should also be treated as successful. This concern is addressed in the robustness check section. Also, despite finishing 7th in 1983 championship, Dynamo Kiev demonstrated abnormally high performance against Ukrainian teams. Another

2 Politburo (or Political Bureau of the Central Committee of the Communist Party of the Soviet Union) was a de facto central governing body of the USSR, consisted of 8 to 25 members in different periods [6]. reason to consider the season of 1983 irregular is that it is the only one from 1973 to 1990 when Dynamo Kiev was not led by a famous manager Valeriy Lobanovskyi. These two reasons seem convincing enough to drop 1983 from the sample leaving it with 26 seasons and 832 observations. The measure of strength is constructed as following. First, points per match earned in each season (in home and away matches separately) against non-Ukrainian teams is calculated for each club. For the first hypothesis only, Ukrainian teams are not taken into account in strength calculations to avoid possible distortions in strength estimates due to their alleged cheating behavior. Taking home and away points separately seems reasonable as such measure reflects the fact that relative home/away strength varies across teams. Second, the indicator is normalized to points per match to account for different number of teams in different seasons. Finally, two variables are built to be included into regressions: “DK‟s strength” and “Rival‟s strength”, where for each observation proper indicators are chosen, depending on whether Dynamo Kiev plays at home or away. To be more specific, when Dynamo plays at home “DK‟s strength” equals its points per match at home while “Rival‟s strength” equals rival‟s strength indicator calculated for away matches, and vice versa when Dynamo plays away. A list of all Politburo members that had their tenure intersecting with the main sample is taken from the respective Wikipedia article. The list contains members‟ tenure start and end dates as well as their place of birth. Luckily, most of them were born in small villages from all over the country, providing sufficient geographical variation. Then, the closest large city to the place of birth is taken and a football team from that city, if any, is assigned to the member as his native one. Finally, two binary variables, “Political support of home team” and “Political support of away team”, are constructed. They equal one if and only if the current season is the first one fully contained in some Politburo member‟s tenure, and the respective (home/away) team is assigned to that member. The first full season after a member‟s introduction into Politburo is taken hence as a proxy to that person‟s political career peak. 4 Results 4.1 Dynamo Kiev and Ukrainian teams An ordered probit model is estimated for outcomes of matches played by Dynamo Kiev with natural ordering: win – draw – defeat. The model is formulated as

where is a vector of covariates, and are cutoff values, and is a c.d.f. of normal distribution. The basic specification is presented in column 1 of Table 2. The coefficient of interest (bold in table) is that of interaction between successful season indicator strong and Ukrainian rival indicator. It is positive but not significant at 10% level. In magnitude, a switch from 0 to 1 in “DK vs. Ukrainian team in a successful year” yields around 40% of the effect of switching from away to home match. Hence the interaction effect can be evaluated as being quite large, which is consistent with the idea that in lucky years Dynamo Kiev overperformed Ukrainian teams more than in overperformed other clubs, as compared to “usual” seasons. Columns 2 to 4 drop some of the controls: team fixed effects, strength indicators or both, respectively. The main result proves to be robust to such changes in specification: the coefficient of interest does not change much and does not become significant as well. Coefficients at strength measures are always highly significant and of correct signs. When they are dropped (column 3) part of their explanatory power is transferred to home match dummy. With strength measures present, the coefficient at “DK at home” is even negative compensating for the explanation of the effect by the difference in points earned at home and away. Another result worth noting is that excluding year fixed effects from specification does not bring significance to successful season dummy, and its coefficient is very small in magnitude. It is consistent with the main hypothesis as matches against Ukrainian teams seem to yield the only difference between successful and non-successful years. Thus it might be the case that match rigging was a major factor determining championship outcomes, and ex-post selected seasons are successful mostly because of cheating. Still, these are speculations and not much weight should be put on the implications of this paragraph. 4.1.1 Dynamics Among possible ways of looking into the dynamics of the effect are the following two. First, to control for possible within year variation several types of trend were included into the specification: linear, quadratic and month fixed effects (not reported). However, no regular pattern was discovered while the main result did not respond. Note that month fixed effects not only can account for accumulated through the season fatigue or changes in incentives along one season but also may serve as proxies for the temperature. In order to investigate possible effects of weather, dummies for the coldest months (March and November) were also interacted with performance measures; unfortunately, no results to interpret were found suggesting that the weather is a bad predictor of match outcomes. Second, the intensity of the alleged cheating can vary over seasons: the sample spans over almost 30 years – a long period, probably longer than most illegal collusive deals can live. In columns (1) and (2) of Table 3, the data is split into two parts containing equal number of seasons: those from 1963 to 1977 and from 1978 to 1991. Interestingly, the interaction coefficient is even negative though not significant in the former half of the sample, while it is very high in magnitude and significant at 5% level in the latter half. Hence it might be suggested that most of the alleged match rigging occurred in the last decade of Soviet championship existence. However, no refined overall trend in the interaction could be found in the data, other than this difference between the effects in the first and the last parts of the sample. Perhaps, a more logical way to split the sample is to draw the line between the seasons of 1975 and 1977 instead of dividing it just in halves. The season of 1976 may be suitable as a kind of a turning point, after Dynamo Kiev‟s winning the prestigious European Cup Winners‟ Cup in 1975, and Dynamo Kiev‟s (not successfully) representing the USSR in 1986 World Cup. Columns (3) and (4) demonstrate that this change in the point of sample splitting is not crucial and the effect exhibits similar dynamic pattern in this case. 4.1.2 Placebo tests To verify the whole match-fixing story four placebo tests are conducted. First, a team is chosen for which a similar specification can be run: Dynamo Moscow; its successful seasons are 1963, 1967, 1973, 1975, 1981, 1986 and 1990. Second, a relevant group of teams is formed: clubs from Moscow (not all Russian teams as there are too many of them in comparison to Ukrainian teams). These teams are all principal rivals and it can hardly be believed that they would help either Dynamo Kiev or Dynamo Moscow. Also, a group of five teams from Caucasus is formed; for them, any cooperation is impossible to imagine. Finally, a basic specification is estimated for pairs Dynamo Moscow – Ukrainian teams (1), Dynamo Moscow – Moscow teams (2), Dynamo Kiev – Moscow teams (3), and Dynamo Kiev –teams from Caucasus (4). The results are shown in specified columns of Table 4. To make the table easier to read, common names of variables are used for all three specifications: „Team‟ and „Group‟ should be understood properly in different columns. The two tests, where the existence of the effect is least expected, indeed show almost no positive interaction (Dynamo Moscow and teams from Moscow) or even negative but not significant interaction (Dynamo Kiev and teams from Caucasus). These two results strengthen the main findings; however, two other placebo tests do not yield satisfactory results – the interaction term in both „Dynamo Kiev vs. teams from Moscow‟ and „Dynamo Moscow vs. teams from the Ukraine‟ regressions is positive and statistically significant. This fact can be interpreted in two ways. Probably, this test undermines the value of the main result suggesting that the coefficient at the interaction term can be significant for some (unknown) reason other than cheating. The other explanation is that, on the contrary, political influence of Dynamo Kiev (or its protectors) was so high that not only Ukrainian teams provided „help‟ but also those from Moscow. The approach of the paper cannot fully distinguish between these two conjectures, the following section though might shed some light on the issue by looking into which teams are “responsible” for the effect. 4.1.3 Decomposition by teams The idea of this subsection is to look into the horizontal structure of the effect. Technically it implies including all the interactions between team fixed effects and successful year dummy, which is done not only for the main specification, but also for three placebo tests (see Table 5 for the results). The successfully passed “Dynamo Moscow vs. teams from Moscow” test does not show any significant team-specific interaction coefficient as well. On the other hand, both main specification and the two failed test reveal quite a lot of variation of the effect between the teams, including at least some significant and large enough positive or even negative interaction coefficients. The results leave the room open for various speculations, such as allegations of cheating against specific teams, but thorough investigation of these issues appears to lie beyond the scope and methodology of the paper. 4.1.4 Robustness tests As mentioned above, one could argue that taking as successful the title-winning seasons only is too restrictive. Indeed, it could be the case that Ukrainian teams did help Dynamo Kiev during a season, but the help turned to be just not enough to win the championship. Table 6 addresses this issue along with the problem of the “weird” 1983 season. There the coefficient of interest is presented from running the basic specification with different sets of successful years (those when Dynamo Kiev finished in the 1st place, in the 1st or the 2nd place, and in the 1st, 2nd or 3rd place) and with different treatment of the season of 1983 (dropped from the sample, included as successful or just included). The year 1983 proves not to be pivotal to the result as the coefficient of interest does not change much when this year is included or both included and treated as successful. On the other hand, the interaction effect vanishes when places other than the first are claimed successful. 4.2 Politburo members and their “native” teams In the previous subsection, no specific mechanism has been proposed to explain the alleged cheating behavior of some of the teams. Supposedly, such activities were politically motivated and could be a result of pressure from the ruling communist party (especially its Ukrainian republican branch). This subsection does not provide respective analysis either, though an attempt is made to reveal at least some direct effect of politicians on Soviet football match results. An ordered probit model is estimated for outcomes of all matches with the natural ordering from the point of view of the home team: that team‟s win, draw or defeat. The results of the regression are shown in Table 7. The coefficients at political dummies are of proper signs and even statistically significant at 10% level, which is consistent with the hypothesis of possible direct influence of Politburo on football outcomes. The magnitude of the coefficients at political variables seems to be quite small, but one could not expect finding major effect, probably due to the low number of teams3 that were successfully assigned their “patrons”, which hinders identification. 5 Conclusion The hypothesis of Dynamo Kiev playing too well against Ukrainian teams in the years of successful overall performance in the USSR football championship seems not to contradict the data. Splitting the sample into two parts suggests that it should be the 1980s when the alleged match rigging happened, if any. Fake specifications involving Dynamo Moscow and Dynamo Kiev from the one side and teams from Moscow, the Ukraine or Caucasus from the other side are run in attempt to verify the story. However, only two of the four placebo tests are passed. Some horizontal (team-specific) structure of the effect, both for the main specification and for placebo tests), is provided, though it is left open for interpretation. While no attempt is made to determine factors under revealed unfair behavior, and there is still room for further research on the question, the hypothesis of direct political influence on teams‟ performance is not rejected. 6 References 1. Chiappori, P.-A., S. D. Levitt and T. Groseclose (2002): “Testing Mixed-Strategy Equilibria When Players Are Heterogeneous: The Case of Penalty Kicks in Soccer,” The American Economic Review, 92, 1138-1151; 2. Duggan, M. and S. D. Levitt (2002): “Winning Isn't Everything: Corruption in Sumo Wrestling,” The American Economic Review, 92, 1594-1605; 3. “Levyi Bereg” (04/21/2010): interview with Viktor Serebryanikov (in Russian), http://lb.ua/news/2010/04/21/39073_Viktor_Serebryanikov_Maslov_snyal.html 4. Moul, C. C. and J. V. C. Nye (2009): “Did the Soviets collude? A statistical analysis of championship chess 1940–1978,” Journal of Economic Behavior & Organization, 70, 10-21;

3 They are Dnepr Dnepropetrovsk (1972 Scherbitsky and 1988 Chebrikov), Kairat Alma-Ata (1972 Kunaev), Krylia Sovetov Samara (1977 Ustinov), Rotor Volgograd (1990 Kryuchkov) and Shahter Donetsk (1986 Ryzhkov). 5. Scoppa, V. (2008): “Are subjective evaluations biased by social factors or connections? An econometric analysis of soccer referee decisions,” Empirical Economics, 35, 123-140; 6. Wikipedia article on Politburo, http://en.wikipedia.org/wiki/Politburo_of_the_Central_Committee_of_the_Communi st_Party_of_the_Soviet_Union 7. WildStat football results and statistics, http://wildstat.com

Table 1 Summary statistics variable mean sd binary comments DK at home 0.500 Yes Dynamo Kiev plays at home

DK vs. Ukrainian team 0.224 Yes Dynamo Kiev plays against Ukrainian team

Successful year 0.464 Yes Successful year for Dynamo Kiev

DK vs. Ukrainian team at home 0.112 Yes Dynamo Kiev plays against Ukrainian team at home

DK vs. Ukrainian team in a successful year 0.111 Yes Dynamo Kiev plays against Ukrainian in a successful year

DK at home in a successful year 0.232 Yes Dynamo Kiev plays at home in a successful year

DK’s strength 1.335 0.352 DK’ strength at home 1.617 0.210 DK’s strength away 1.005 0.209 Points earned per match by Dynamo Kiev and its rival respectively playing against non-Ukrainian teams in the Rival’s strength 0.995 0.414 same type of match (home/away) Rival’s strength at home 1.283 0.280 Rival’s strength away 0.706 0.313 Notes: summary statistics are calculated for the sample including years 1963 – 1968, 1970 – 1975, 1977 – 1982 and 1984 – 1991, in total 832 observations. Successful years are those when Dynamo Kiev won the championship: 1966, 1967, 1968, 1971, 1974, 1975, 1977, 1980, 1981, 1985, 1986 and 1990. Statistics for DK’s strength account for the variable being identical within a season. Table 2 Ordered probit estimation (1) (2) (3) (4)

DK vs Ukrainian team 0.257 0.172 0.187 0.160 in a successful year (0.194) (0.178) (0.175) (0.181) DK’s strength 1.553*** 1.549*** 1.336*** (0.215) (0.218) (0.112)

Rival’s strength -0.749*** -0.745*** -0.742*** (0.160) (0.161) (0.157)

DK at home -0.302* -0.298* 0.867*** -0.193* (0.155) (0.158) (0.103) (0.112)

DK vs. Ukrainian team -0.0762 -0.141 -0.0706 (0.173) (0.165) (0.172)

DK vs. Ukrainian team at home 0.0114 -0.00614 0.147 -0.00909 (0.235) (0.234) (0.227) (0.231)

DK at home 0.0557 0.0528 0.173 0.0648 in a successful year (0.129) (0.127) (0.165) (0.122) Successful year 0.0620

(0.0491)

Season fixed effects Yes Yes Yes

Ukrainian team fixed effects Yes

Observations 832 832 832 832 Pseudo R-squared 0.133 0.129 0.104 0.126 Notes: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by season in parentheses. Seasons included are 1963 – 1968, 1970 – 1975, 1977 – 1982 and 1984 – 1991, in total 26 years. Dependent variable: outcomes of Dynamo Kiev’s matches with natural ordering (win – draw – defeat). Successful years are those when Dynamo Kiev won the championship: 1966, 1967, 1968, 1971, 1974, 1975, 1977, 1980, 1981, 1985, 1986 and 1990. Strength measures are points earned per match by Dynamo Kiev and its rival respectively against non-Ukrainian teams in the same type of match (home/away).

Table 3 Split sample (1) (2) (3) (4)

63 – 77 78 – 91 63 – 75 77 – 91

DK vs. Ukrainian team -0.294 0.731** -0.167 0.522* in a successful year (0.253) (0.304) (0.216) (0.270) DK’s strength 1.522*** 1.749*** 1.508*** 1.677*** (0.237) (0.644) (0.263) (0.618)

Rival’s strength -0.516** -0.960*** -0.493* -0.982*** (0.253) (0.197) (0.265) (0.190)

DK at home -0.0708 -0.611* -0.0676 -0.570* (0.227) (0.332) (0.231) (0.312)

DK vs. Ukrainian team 0.0520 0.114 0.0309 0.0610 (0.362) (0.361) (0.417) (0.305)

DK vs. Ukrainian team at home -0.0169 0.111 -0.0395 0.141 (0.139) (0.256) (0.150) (0.222)

DK at home -0.294 0.731** -0.167 0.522* in a successful year (0.253) (0.304) (0.216) (0.270) Season fixed effects Yes Yes Yes Yes Ukrainian team fixed effects Yes Yes Yes Yes Observations 424 408 394 438 Pseudo R-squared 0.143 0.140 0.139 0.136 Notes: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by season in parentheses. Seasons included in column (1) are 1963 – 1968, 1970 – 1975 and 1977, in total 13 years. Seasons included in column (2) are 1978 – 1982 and 1984 – 1991, in total 13 years. Seasons included in column (3) are 1963 – 1968 and 1970 – 1975, in total 12 years. Seasons included in column (4) are 1977 – 1982 and 1984 – 1991, in total 14 years. Dependent variable: outcomes of Dynamo Kiev’s matches with natural ordering (win – draw – defeat).

Table 4 Placebo tests (1) (2) (3) (4)

DM – Ukr DM – Msk DK – Msk DK – Cau

Team vs. Group 0.374* 0.0940 0.455** -0.311 in a successful year (0.214) (0.261) (0.208) (0.228)

Team’s strength 0.922*** 0.617*** 1.009*** 0.940*** (0.201) (0.231) (0.276) (0.247)

Rival’s strength -1.758*** -1.857*** -1.725*** -2.161*** (0.158) (0.210) (0.146) (0.261)

Team at home -0.795*** -0.445** -0.805*** -0.455** (0.131) (0.176) (0.197) (0.225)

Team at home vs. Group 0.216 0.0822 0.179 -0.128 (0.214) (0.262) (0.235) (0.227)

Team at home 0.0777 -0.374** 0.212 -0.0480 in a successful year (0.119) (0.146) (0.145) (0.199) Season fixed effects Yes Yes Yes Yes Group team fixed effects Yes Yes Yes Yes Observations 866 866 832 832 Pseudo R-squared 0.145 0.114 0.192 0.176 Notes: *** p<0.01, ** p<0.05, * p<0.1. The model estimated is ordered probit, all the variables included are analogous to that of the basic specification – column (1) of Table 2. Standard errors clustered by season in parentheses. Seasons included in columns (1) and (2) are 1963 – 1968, 1970 – 1975, 1977 – 1991, in total 27 years. In columns (3) and (4) the season of 1983 is dropped. Dependent variable: outcomes of Team's matches with natural ordering (win – draw – defeat). “Team” stands for Dynamo Moscow in columns (1) and (2) and for Dynamo Kiev in columns (3) and (4). “Group” stands for Ukrainian teams in column (1), for teams from Moscow in columns (2) and (3) and for teams from Caucasian republics in column (4)

Table 5 Decomposition of interaction effect by teams Ukrainian teams vs. Moscow teams vs. DK DM DK DM

Karpaty -1.638** -0.475 Dynamo 0.115

(L’vov) (0.729) (0.822) (0.360)

Metallist -0.0602 1.094** Lokomotiv -0.00517 0.333 (Kharkov) (0.462) (0.555) (0.479) (0.470) Chernomoret 0.678* 0.474 Spartak 1.657*** -0.0298 s (Odessa) (0.369) (0.381) (0.440) (0.336) SKA -4.517*** 0.200** Torpedo 0.351 -0.151 (Odessa) (0.290) (0.0903) (0.310) (0.430) Shahter 0.763** 0.143 CSKA 0.0872 0.432 (Donetsk) (0.387) (0.356) (0.374) (0.349) Zarya -0.0222 0.309

(Lugansk) (0.457) (0.652)

Observations 832 866 Observations 832 866 Pseudo R-sq 0.139 0.147 Pseudo R-sq 0.200 0.115 Notes: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by season in parentheses. The model estimated is ordered probit, all the specifications are basically the extensions of the those presented in Table 2 (column 1) and Table 4 (columns 1-3). The only change made is the replacement of “Team vs. Group in successful year” variable with interactions between team fixed effects and successful years, coefficients at which are presented in this table.

Table 6 Coefficient of interest with different successful year specifications Count of seasons Coefficient at “DK vs Ukrainian Successful years (successful/all) team in a successful year” 12/27 0.186 1st places (0.197) 13/27 0.295 1st places & 1983 (0.190) 1st places (season 1983 12/26 0.257 dropped) – basic specification (0.194) 17/27 -0.0115 1st and 2nd places (0.228) 18/27 0.125 1st and 2nd places & 1983 (0.225) 1st and 2nd places (season 1983 17/26 0.0882 dropped) (0.230) 18/27 -0.0915 1st, 2nd and 3rd places (0.239) 19/27 0.0503 1st, 2nd and 3rd places & 1983 (0.238) 1st, 2nd and 3rd places (season 18/26 0.0152 1983 dropped) (0.245) Notes: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by season in parentheses. The controls included are those from basic specification – column (1) of Table 2.

Table 7 Ordered probit estimation of political influence on teams' performance (1)

Political support of home team 0.0422* (0.0251)

Political support of away team -0.0274** (0.0128)

Strength of home team 1.733*** (0.0166)

Strength of away team -1.696*** (0.0201)

Season fixed effects Yes

Observations 7,442 Pseudo R-squared 0.138 Notes: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by season in parentheses. The binary political support variables equal one if and only if the current season is the first one fully contained in some Politburo member’s tenure, and the respective (home/away) team is assigned to that member.