Cultural Biases in Olympic Figure Skating Judgments

Mary Yang Mathematical Methods in the Social Sciences

Northwestern University Senior Thesis

Advisor: Paola Sapienza

May 2006

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Cultural Biases in Olympic Figure Skating Judgments

Mary Yang1

May 2006

Abstract

This paper examines whether bias exists in the judging of Olympic figure skating competitions from 1976 to 2002. Bias is measured using characteristics between the skater’s country and the judge’s country, such as their geographical distance, trust, press coverage, history of war, and commonality of law, language, and religion. Regressions are run accounting for the fixed effects of skaters and judges and for changes in bias over time. It is determined that geography, common legal systems, press coverage, and nationalism play a part in influencing the judges’ scores, but the mean advantage gained from these biases is less than the smallest increment in figure skating marks.

1 I am extremely grateful for the invaluable insight and guidance provided by my advisor, Paola Sapienza. I would also like to thank Jon Huntley and Matt Reynolds for their assistance in gathering and assembling the data. Many thanks also go to my friends and family for their encouragement along the way.

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Table of Contents I. Introduction ..................................................................................................................... 3

II. Survey of Literature ....................................................................................................... 6

III. The Study ...................................................................................................................... 8

A. Data ............................................................................................................................ 8

B. Methodology ............................................................................................................ 13

IV. Results and Analysis................................................................................................... 16

A. OLS Regressions...................................................................................................... 16

B. Fixed Effects ............................................................................................................ 19

C. Regressions Over Time ............................................................................................ 20

V. Conclusion ................................................................................................................... 25

References......................................................................................................................... 27

Appendices........................................................................................................................ 28

Appendix A: Definition of Variables and Country Abbreviations ............................... 28

Appendix B: Summary Statistics and Table of Correlations ........................................ 29

Appendix C: Detailed Regression Results.................................................................... 30

C1: OLS Regressions................................................................................................ 30

C2: Fixed Effects Regressions .................................................................................. 33

C3: Regressions Over Time ...................................................................................... 34

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I. Introduction

Every four years, the world’s greatest athletes gather together to compete in a

myriad of sports at the Olympic Games. In most of these sporting events, winners are

determined by some undisputed measurable standard, such as distance, time, or number

of goals scored. Other sports, however, must rely on less objective criteria to determine

placements in athletic competition by using the judgments of a panel of experts. One such

sport is figure skating, which has historically used a panel of 9 judges to score the

performances of skaters in the Olympics. The quandary that arises when winners are

placed in the hands of the subjective opinions of human beings is the possibility of bias

on the part of the judges.

There have been instances in the past where the objectivity of judges has been

called into question in regards to favoritism towards skaters from their own countries.

Some examples include the 1924 Olympics at Chamonix Mont-Blanc in which a Czech

judge placed a Czech skater first, two Austrian judges placed the Austrian first, and all

other judges ranked a Swedish skater first. In the 1948 Olympics at St. Moritz, all judges

ranked an American skater who successfully completed the first double axel in first place

while a Swiss judge placed a Swiss skater first.2 More recently, there has been evidence

of judges trading votes or voting similarly within a bloc of countries. In the 2002

Olympics pairs competition, the French judge Marie-Reine Le Gougne admitted that she

was pressured by her national federation to vote for the Russian pair so that the Russian

2 Wallechinsky, 1991.

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judge would vote for the French ice dancers, indicating that the national federation was

pushing a judge to be more biased than she wanted to be.3

In judging skating competitions, judges are given specific guidelines established

by the International Skating Union for each phase of the competition. In general, a

competition consists of a two-minute short program worth a third of the total score and a

free skate of about four and a half minutes in duration worth two-thirds of the total

score.4 In each program, two component marks are given. The technical merit score

evaluates the difficulty, variety, and cleanness of jumps, spins, footwork, and other

elements in the program. The second mark for presentation judges a skater’s artistic

impression, interpretation of music, and composition and originality of program, which

are factors that are not as easily quantified.

Although the Judges’ Handbook outlines specific points to be subtracted from a

skater’s score for various errors that a skater may make during the competition in order to

standardize judging criteria and scoring, judges still often disagree on different aspects of

a performance, which would explain the disparity in scores observed in Olympic

competition. In particular, the subjective nature of the presentation score leaves

considerable room for disagreement among the judges. However, the process by which

judges are selected to be on an Olympic panel helps to accommodate these differences of

opinion. Olympic judges are assigned based on the number of competitors a country

enters in various events, and the countries with the largest number of participants in an

3 Zitzewitz, 2006. Subsequently, the scandal led to a reform of the figure skating judging and scoring system by the International Skating Union in which specific criteria were outlined for both skaters and judges in a format that breaks down the point values of each element in a routine. 4Up until the 1988 Olympics, a preliminary round of competition also took place in the form of compulsory figures, in which skaters trace shapes on the ice and are judged according to the alignment, size, and precision of the etchings.

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event earn a seat on the judges’ panel. Each of those countries’ national federations then

nominates a list of judges chosen on the basis of experience. In general, those judges

have over 20 years of experience, have judged international competitions, and have

attended schools sponsored by the ISU. These requirements ensure that there is some

uniformity to the training and experience among international judges that fosters a

consensus on skating standards.

Despite this general standard of experience, judges are still influenced by a host of

other factors that may affect their scoring. For example, judges have individual

preferences for music, choreography, and costume. They can also be influenced by

expectations of an individual skater based on past performance in previous competitions.

Other studies have found that judges have been affected by the voting behavior of other

judges and of political loyalties. The following study takes into account cultural,

geographical, and historical differences between the country of the judge and the country

of the skater to determine whether these outside factors play a role in judges’ decisions.

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II. Survey of Literature

In an effort to find whether nationalism bias exists in Olympic figure skating

judging, researchers in the past have used several different methodologies in determining

bias.

In a study by Seltzer and Glass (1991), a ranking system was created in which the

total score of each skater was ranked according to each judge’s score, 1 given to the

lowest total score. The mean of each judging country’s ranks was taken to determine

which countries historically gave out higher or lower scores. Then, the average rank of

judges scoring skaters from their own country was calculated, and its deviation from the

overall mean of that particular country was recorded. Using Z scores, it was determined

that self-ranking significantly differed from zero. Because the data used comprised of all

Olympic figure skating and ice dance events during a period (1968-1988) in which the

Cold War dominated the political environment, voting records for political blocs were

also examined. The evidence suggested that certain countries in the western bloc favored

western skaters while penalizing eastern skaters and vice versa.

Campbell and Galbraith (1996) developed a variable under a binomial distribution

to test the null hypothesis that there is no nationalism bias in judging. The variable was

structured such that it was equal to one if the judge gave a higher score to a skater from

his own country than the median score given for that skater by all judges and zero

otherwise. A simple linear model estimating the bias was also implemented using the

difference in the judge’s score with the median score, given that the judge and the skater

are from the same country. The study used data from the Olympics in 1976, 1988, 1992,

and 1994 with two component scores (technical merit and presentation) in the short and

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long programs for the men, ladies, and pairs events. Both tests revealed strong evidence

that favoritism existed when judges scored skaters from their own country in each scoring

component, Olympic year, event, and overall. However, while the tests showed a bias for

the top six competitors, but there was no significant bias for those in the bottom third.

The linear model also showed that the mean advantage gained from a judge of the same

nationality was quite small, averaging around 0.07, which is less than the smallest

increment in the scoring decision, 0.1.

More recently, Zitzewitz (2006) did a study on nationalism in the judging of

figure skating and ski jumping events surrounding (and including) the 2002 Olympic

Games and its impact on organizational decision making. Nationalistic biases were

measured using performance and judge fixed effects, and the bias from a judge to a skater

from the same country was found to translate to an average placement of 0.7 positions

higher. Zitzewitz also determined that biased judges were more likely to be chosen to

judge the Olympics because those judges are designated by their national federations.

Furthermore, his study suggested that judging countries were divided into two blocs, with

judges within the same bloc reinforcing each other’s biases or appearing to engage in

vote trading.

While these past studies have all examined the bias that a judge would have

towards a skater from his own country, this paper aims to explore whether specific

differences between two countries, such as their history, geographical distance, and

cultural characteristics, have an effect on judging in Olympic figure skating events.

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III. The Study A. Data The data set of scores span figure skating competitions from eight Winter

Olympic Games: 1976 (Sapporo, Japan), 1980 (Lake Placid, USA), 1984 (Sarajevo,

Yugoslavia), 1988 (Calgary, Canada), 1992 (Albertville, France), 1994 (Lillehammer,

Norway), 1998 (Nagano, Japan), and 2002 (Salt Lake City, USA). Data for each

Olympics comprise of scores from the men, ladies, and pairs events. For each skater,

separate scores are given by each of the nine judges in the short (also called the technical

or original) program and the free skate (also called the long program). In each of these

two performances, skaters are judged according to technical merit (or required elements)

and presentation (or artistic impression). From 1976 to 1988, singles skaters (men and

ladies) also completed a third compulsory round consisting of three figures, patterns that

skaters must trace on the ice. Scores from these compulsory figures were omitted from

the data set because figures are judged more objectively as skaters leave etchings on the

ice to show how precisely they have traced the patterns.

In each case, a skater receives a score between 0.0 and 6.0 with increments of 0.1

from each of the nine judges. The final rank is determined using a system of median

ranks.5 In testing for cultural biases in judging, this study uses the individual scores from

judges before they are aggregated to determine the final placements.

Each sample point constitutes a score given by each judge for the performance of

a skater or pair in a particular event (short program or free skate) given for a particular

scoring component (technical merit or presentation). For each sample point, the following

5 For details on this procedure, see Bassett and Persky (1994).

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are recorded: the score, the skater’s mean score across the nine judges for this event and

scoring component, the name and nationality of the skater and judge, and the skater’s

final rank.

The other part of the data comes from a data set compiled and used by Guiso,

Sapienza, and Zingales (2005), which consists of characteristics between two countries to

measure differences in how one country’s people might view another country. The

characteristics used in this study to see whether they have an influence on Olympic

judging include trust, geographical distance, commonality of law, press coverage, history

of war, and shared language and religion.6

Measures of trust are obtained from a set of surveys conducted by the

Eurobarometer sponsored by the European Commission. Samples were conducted on

about 1,000 individuals per country in Europe from 1970 to 1995, with the number of

countries varying over time. Citizens were asked the following: “I would like to ask you a

question about how much trust you have in people from various countries. For each,

please tell me whether you have a lot of trust, some trust, not very much trust, or no trust

at all.” The answers are re-coded such that 1 = no trust at all, 2 = not very much trust, 3 =

some trust, and 4 = a lot of trust. For the purposes of this study, the median level of trust

is taken for each year from each country of origin to country of destination. Because the

time frame does not coincide exactly with all years in the skating data, the average of

these median levels of trust over all years are taken in order to avoid dropping a

substantial number of data points.

6 For all following variables, see Guiso, Sapienza, Zingales (2005) for more specific references and classifications of variables.

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Geographical distance between two countries is computed using the log of

distance in kilometers between the capitals of the respective countries. Distance between

countries could influence the amount of information and likelihood of interaction

between judge and skater and thus could affect a judge’s view of a skater from a

particular country. Also included is a dummy variable that indicates when two countries

share a common land border.

The commonality of law variable is also a dummy variable that is equal to one if

the two countries both have common or coded law or if they both have civil law,

indicating that the two countries’ legal systems have a common origin. Commonality in

law between two countries may have an effect on political differences or similarities

between countries and could give evidence to bloc judging.

To measure the level of information citizens of one country have on citizens of

another country, press coverage is measured using the number of times a country name i

or a citizen from country i appears in the headlines of a major newspaper in country j,

using the most diffused newspaper present. This number is then taken as a percentage of

total coverage in the newspaper.

Three measures of cultural perceptions are used. First, a war variable is used to

measure how many years a pair of countries have been at war from the end of the first

millennium to 1970. Presumably, countries that have a long history of conflict will tend

to hold an unfavorable view of each other. Cultural formation of views take place at

school and prolong the memory of events that have occurred through history, thus

shaping current citizen opinions and stereotypes of other countries.

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Secondly, a measure of language commonality is used as the products of the

percentage of people who speak the same language in each pair of countries, using all

primary languages spoken in those two countries. Finally, an indicator of religious

similarity is used to measure cultural bias. This variable represents the probability that

two randomly chosen individuals in two countries will have the same religion and is

obtained by taking the product of the fraction of individuals in country i and country j

that have religion k and then summing across k.7

After the skating data is merged with the characteristics data, 18 skater countries

and 10 judge countries are represented in a total of 2870 observations, as shown in Table

1. The distribution of observations across years and events/disciplines are shown in Table

2 and Table 3, respectively. However, because these observations are limited to those that

had available data for bilateral country characteristics, the results gathered should be used

with caution, as these observations only represent 28.08% of total figure skating scores

from 1976 to 2002.8

7 k = Catholic, Protestant, Jewish, Muslim, Hindu, Buddhist, Orthodox, no religion, or other affiliation 8 There are a total of 10,222 individual scores given for the men, ladies, and pairs short programs and free skates from 1976-2002.

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Table 1: Nationalities Represented in Data Skater's Nationality Frequency Percent Judge's Nationality Frequency Percent

AUT 60 2.09 AUT 140 4.88 BEL 28 0.98 BEL 56 1.95 CHN 180 6.27 DEN 202 7.04 DEN 56 1.95 FIN 202 7.04 ESP 44 1.53 FRA 490 17.07 FIN 88 3.07 FRG 476 16.59 FRA 296 10.31 GBR 494 17.21 FRG 212 7.39 GER 418 14.56 GBR 272 9.48 ITA 304 10.59 GER 116 4.04 SWE 88 3.07 HOL 8 0.28 Total 2,870 100 HUN 72 2.51 ITA 148 5.16 JPN 312 10.87 POL 88 3.07 SUI 124 4.32 SWE 70 2.44 USA 696 24.25 Total 2,870 100

Table 2: Years Represented in Data Year Frequency Percent

1976 232 8.08 1980 468 16.31 1984 392 13.66 1988 296 10.31 1992 546 19.02 1994 300 10.45 1998 288 10.03 2002 348 12.13 Total 2,870 100

Table 3: Distribution of Data in Discipline and Event Event, Scoring Component Ladies Men Pairs Total Short Program, Technical Merit 356 242 133 731 Short Program, Presentation 356 242 133 731 Free Skate, Technical Merit 340 233 131 704 Free Skate, Presentation 340 233 131 704 Total 1392 950 528 2870

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B. Methodology

In this study, three methods are used in determining whether there exist cultural

biases in the judging of Olympic figure skating competition. The first is an ordinary least

squares regression using the individual scores given by each judge as the dependent

variable, regressed on the independent bilateral variables of trust, log of distance between

countries, border, commonality of law, press coverage, number of years at war, similarity

of language and religion, and average score:

score = β0 + β1avgscore + β2avgtrust + β3ldist + β4border + β5comcivlaw +

β6coverage + β7twars + β8language + β9religion + ε (1)9

Of these, border and comcivlaw are binary variables equal to one if the judge’s country

and skater’s country share a border or have a common origin of law, respectively. The

average score of a particular skater is used to control for the differences in skill level and

quality of each skater or systematic differences in different countries’ skaters, and it is

taken across the nine judgments that every skater receives for each scoring component in

the short program and free skate. This regression is used over all the data and for each

event and scoring component in order to determine whether there are differences in bias

for each subset of scores.

In order to determine whether nationalism bias exists for judges evaluating the

performance of skaters from their own country, a dummy variable home is created for

those data points in which the two countries are the same. To see the effect of any

nationalism bias while using average score to control for skater quality, the following

regression is used:

9 See Appendix A for definitions of each variable.

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score = β0 + β1avgscore + β2home + ε (2)

This separate regression is used to test for nationalism bias because home cannot be

included in model (1) since it is highly correlated (or defined to be correlated) with many

of the other independent variables10, including coverage, ldist, language, and religion,

which would skew the results of the regression.

Secondly, fixed effects are used in order to offset bias for a particular skater or

bias from a particular judge. Dummy variables are created for each skater and each judge

in the following regressions:

score = β0 + β1avgtrust + β2ldist + β3border + β4comcivlaw + β5coverage +

β6twars + β7language + β8religion + β*d_skater* + ε (3)

This regression takes into account any fixed amount of bias that a particular skater may

incite from judges that may influence the overall result of the independent variables.

score = β0 + β1avgtrust + β2ldist + β3border + β4comcivlaw + β5coverage +

β6twars + β7language + β8religion + β*1d_skater* + β*2d_judge* + ε (4)

This regression takes into account the fixed effect of each skater as well as any fixed

amount of bias that any particular judge may have towards all skaters that would alter the

overall result of the independent variables. In equations (3) and (4), d_skater* and

d_judge* indicate all the dummy variables for all skaters and judges. Average score is not

included in these two regressions because the dummy variables for each skater already

control for each person’s fixed quality.

Thirdly, dummy variables for each Olympic year are used to examine whether

there have been changes in scoring over time:

10 See Appendix B for a table of correlations.

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score = β0 + β1avgscore + β2avgtrust + β3ldist + β4border + β5comcivlaw +

β6coverage + β7twars + β8language + β9religion + δ*yr* + ε (5)

In this equation, yr* represents the dummy variables for each year except for 2002, which

is omitted to avoid multicollinearity. After controlling for all the independent variables,

this equation would allow us to see whether there have been differences or trends in

overall scoring for each Olympics from 1976 to 2002. We can also interact the time

dummy variables with each of the characteristic variables in order to see if there exist any

changes in bias for each variable over time:

score = β0 + β1avgscore + β2avgtrust + β3ldist + β4border + β5comcivlaw +

β6coverage + β7twars + β8language + β9religion + δ1y1976·INTVAR + δ2y1980·

INTVAR + δ3y1984 · INTVAR + δ4y1988· INTVAR + δ5y1992· INTVAR +

δ6y1994· INTVAR + δ7y1998· INTVAR + δ*yr* + ε (6)

In this equation, INTVAR is the characteristic variable that is being interacted with the

each of the time dummy variables. If the interacting variable is twars, this model is

structured such that the return to twars for 2002 is β7, and the return to twars in 1976 is β7

+ δ1, β7 + δ2 in 1980, and so on.

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IV. Results and Analysis

A. OLS Regressions

When modeling specification (1) with respect to all data, each event, and each

component score, the following results are obtained, as seen in Table 4.11

Table 4: OLS Regression Results for Each Event/Scoring Component

Variable Coefficient Short Program Free Skate Technical Merit Presentation All

avgscore 0.99*** 1.04*** 1.00*** 1.01*** 1.00*** [0.01] [0.01] [0.01] [0.01] [0.01] avgtrust 0.01 0.00 0.00 0.01 0.01 [0.01] [0.01] [0.01] [0.01] [0.01] ldist 0.00 0.00 0.00 0.00 0.00 [0.01] [0.00] [0.01] [0.00] [0.00] border 0.03** 0.02* 0.03* 0.03* 0.03*** [0.02] [0.01] [0.02] [0.01] [0.01] comcivlaw 0.02** 0.01 0.02* 0.02 0.02** [0.01] [0.01] [0.01] [0.01] [0.01] coverage 0.10** 0.05 0.08** 0.08** 0.08*** [0.04] [0.03] [0.04] [0.04] [0.03] twars 0.00 0.00** 0.00* 0.00 0.00** [0.00] [0.00] [0.00] [0.00] [0.00] language 0.00 0.00** 0.00* 0.00 0.00* [0.00] [0.00] [0.00] [0.00] [0.00] religion -0.06* 0.01 -0.04 -0.01 -0.02 [0.03] [0.03] [0.03] [0.03] [0.02] R-squared 0.9184 0.9320 0.9308 0.9103 0.9240 Number of obs. 1462 1408 1435 1435 2870 Notes: Standard errors for each coefficient are in brackets. *significant at 10%; **significant at 5%; ***significant at 1%

For all data points, average score, border, commonality of law, press coverage,

number of years at war, and language are statistically significant. Average score is the

fixed quality of the skater and, as one would expect, it has a very high t-value (171) and

an almost one-to-one ratio with the score. The dummy variable border indicating whether

two countries are adjacent to each other by land is statistically significant at the 1% level;

11 See Appendix C for detailed regression results.

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its coefficient of 0.03 indicates that a judge would be likely to score a skater 0.03 points

higher if the skater is from a country which shares a border with the judge’s country, or if

the judge and the skater are both from the same country. Commonality of law is

significant at the 5% level with a coefficient of 0.02, which shows that judges are likely

to favor a skater from a country with a similar legal system as theirs by 0.02 of a point.

Press coverage is also significant at the 1% level; because this variable is calculated as a

percentage of newspaper coverage of skater’s country in the judge’s country, the

coefficient of 0.08 means that 10% more press coverage is likely to make the judge score

a skater higher by 0.008 of a point. Also significant at the 5% and 1% level, respectively,

are the variables twars and language. Twars has a negative coefficient, which intuitively

makes sense because it is likely that a judge would hold a less favorable view towards a

skater from a country in which the judge’s country has had a history of war and conflict.

However, the coefficient is so small (-0.0001781) that its impact on the judge’s score is

minute. Similarly, the coefficient on language is also very small that, despite its slightly

significant t-value, it too does not have very much impact on the dependent variable. The

R-squared value of the regression indicates that 92.40% of the score can be explained by

the independent variables in the equation, though a majority of that likely comes from

avgscore.

When running the regression on subsets of the data set by event and scoring

component, the results are very similar. One may assume that because the free skate

holds more weight than the short program in calculating the total score of each skater, it

might induce more bias on the judge’s part. With respect to the scoring component, one

might also think it likely that more bias would exist in the presentation mark than the

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technical merit mark simply because of the subjective nature of the criteria for evaluating

that score. However, contrary to these beliefs, the free skate and the presentation mark

have the same or fewer numbers of statistically significant variables as their counterparts.

For all subsets of the data, border is the only variable that is statistically significant

across all four subsets, with an average coefficient of 0.03, while coverage is significant

in all subsets except the free skate, with an average coefficient of 0.09. Comcivlaw is

statistically significant for judges marking the short program and in the technical score,

with coefficients of 0.02. While language is slightly significant for the free skate and

technical merit, the coefficient is again very small that it would likely not have a

noticeable effect on the score.

The variable religion is slightly significant for the short program with a negative

coefficient of -0.06. This would seem to indicate that the greater likelihood that the

judge’s country and skater’s country share a religion or religions, the lower the score the

judge would give to that skater. This goes against what one would think makes sense

intuitively. One possible reason for the slight significance for this variable is that religion

has some correlation with ldist and coverage, which may have caused some

multicollinearity.12

When model (2) is used to test for nationalism bias, these results are obtained:

Table 5: Regressing for Nationalism Bias

Variable Coefficient Standard Error avgscore 1.00*** [0.01] home 0.07*** [0.01] R-squared 0.9234 Number of obs. 2870 *significant at 10%; **significant at 5%; ***significant at 1%

12 See Appendix B for correlations between variables.

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With a t-value of 7.83, the home binary variable is extremely significant. The coefficient

on the variable shows that when the judge and the skater are from the same country, the

skater gains an advantage of 0.07 in his or her score, after the quality of the skater has

been controlled for by including avgscore in the equation. This is consistent with past

findings, such as the study conducted by Campbell and Galbraith (1996).

B. Fixed Effects

When modeling specification (3), in which fixed effects are used for each skater,

and specification (4), in which fixed effects are used for each skater and judge, the

following regression results are obtained, as shown in Table 5:

Table 6: Fixed Effect Results Variable Coefficient

(3) f.e. skater (4) f.e. skater & judge avgtrust 0.02 0.01 [0.03] [0.03] ldist -0.03 -0.01 [0.02] [0.02] border 0.00 -0.02 [0.03] [0.03] comcivlaw 0.05** 0.01 [0.02] [0.02] coverage -0.03 0.07 [0.13] [0.15] twars 0.00** 0.00** [0.00] [0.00] language 0.00* 0.00 [0.00] [0.00] religion -0.12** -0.08 [0.05] [0.05] R-squared 0.7563 0.7741 Number of obs. 2870 2870 Notes: Standard errors for each coefficient are in brackets. *significant at 10%; **significant at 5%; ***significant at 1%

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When taking into account the fixed effect of each particular skater, the variables

comcivlaw, twars, language, and religion are significant, while only twars is significant

when taking into account the fixed effect of each skater and each judge. Of these

variables, only comcivlaw and religion have non-zero coefficients. The coefficient for

comcivlaw suggests that after each skater’s unobserved fixed effect has been accounted

for, a common legal system between the skater and the judge’s country may allow the

judge to score the skater 0.05 points higher. As before, the negative relationship of

religion to score may be skewed by multicollinearity.

These results imply that much of the score is explained by the fixed effect of each

person. In fact, the dummy variables are jointly significant in both regressions, with F-

critical values of 32.39 and 29.18, respectively. However, the R-squared values show that

only 75.63% and 77.41% of score is explained by the independent variables, and even

this may be an inflated number. In fixed effect dummy variable regressions, the R-

squared is usually rather high because a dummy variable is included for each cross-

sectional unit, which explains much of the variation in the data. Despite the fact that each

person’s unobserved fixed effect is accounted for here, the R-squared value for the OLS

regression (0.9240) show that that equation is a better fit for the data.

C. Regressions Over Time

After regressing equation (5) in which dummy variables are included for each

Olympic year, the following results are obtained:

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Table 7: Regression with Year Dummies Variable Coefficient Standard Error avgscore 1.00*** [0.01] avgtrust 0.01 [0.01] ldistwei 0.00 [0.00] border 0.03*** [0.01] comcivlaw 0.02*** [0.01] coverage 0.08*** [0.03] twars 0.00** [0.00] language 0.00** [0.00] religion 0.02 [0.02] yr1976 0.03*** [0.01] yr1980 0.02 [0.01] yr1984 0.02 [0.01] yr1988 0.01 [0.01] yr1992 0.01 [0.01] yr1994 0.01 [0.01] yr1998 0.01 [0.01] R-squared 0.9244 0.9244 Number of obs. 2870 2870 Notes: Standard errors for each coefficient are in brackets. *significant at 10%; **significant at 5%; ***significant at 1%

After controlling for the average score and the various characteristics between two

countries, we find that the dummy variable for 1976 is significant, with a positive

coefficient of 0.03. This implies that in 1976, judges tended to score skaters higher by

0.03 of a point. Although the dummy variables for 1980 to 1998 are not significant13,

their coefficients show a general trend that decreases over time, which suggests that there

may have been a slight overall decrease in judges’ scores due to reasons not captured by

the explanatory variables.

When modeling specification (6), we interact each explanatory variable with the

yearly dummy variables to see whether the bias has changed over time. Table 8 outlines

the regression results, and Table 9 shows the return for each variable in each year.

13 The dummy variable for 2002 is omitted to avoid multicollinearity.

22

Table 8: Regressions with Explanatory Variables (INTVAR) Interacted with Year

Variable Coefficient for INTVAR interacted with year14 avgscore avgtrust ldist border comcivlaw coverage twars language religion

avgscore 0.95*** 1.00*** 1.00*** 1.00*** 1.00*** 1.00*** 1.00*** 1.00*** 1.00*** [0.02] [0.01] [0.01] [0.01] [0.01] [0.01] [0.01] [0.01] [0.01] avgtrust 0.01 -0.04* 0.01 0.01 0.01 0.01 0.01 0.01 0.01 [0.01] [0.02] [0.01] [0.01] [0.01] [0.01] [0.01] [0.01] [0.01] ldist 0.00 0.00 0.01* 0.00 0.00 0.00 0.00 0.00 0.00 [0.00] [0.00] [0.01] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] border 0.03*** 0.03*** 0.03*** 0.02 0.03*** 0.03*** 0.03*** 0.03*** 0.03*** [0.01] [0.01] [0.01] [0.02] [0.01] [0.01] [0.01] [0.01] [0.01] comcivlaw 0.02*** 0.02** 0.02*** 0.02** 0.03 0.02** 0.02*** 0.02** 0.02** [0.01] [0.01] [0.01] [0.01] [0.02] [0.01] [0.01] [0.01] [0.01] coverage 0.08*** 0.08*** 0.08*** 0.08*** 0.08*** -0.40 0.08*** 0.08*** 0.09*** [0.03] [0.03] [0.03] [0.03] [0.03] [0.80] [0.03] [0.03] [0.03] twars 0.00** 0.00** 0.00** 000** 0.00** 0.00* 0.00 0.00* 0.00** [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] language 0.00** 0.00** 0.00** 0.00* 0.00** 0.00** 0.00** 0.00* 0.00** [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] religion -0.01 -0.02 -0.02 -0.02 -0.02 -0.03 -0.02 -0.02 -0.04 [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.04] y1976*INTVAR 0.02 -0.03 -0.01 -0.01 0.02 0.04 0.00 0.00 0.00 [0.04] [0.04] [0.01] [0.03] [0.03] [0.10] [0.00] [0.00] [0.05] y1980*INTVAR 0.06*** 0.06** -0.01* 0.01 -0.01 -0.01 0.00 0.00 -0.01 [0.02] [0.03] [0.01] [0.03] [0.02] [0.08] [0.00] [0.00] [0.04] y1984*INTVAR 0.06** 0.04* -0.01 0.01 -0.04* 0.16* 0.00 0.00 0.01 [0.02] [0.03] [0.01] [0.03] [0.02] [0.08] [0.00] [0.00] [0.05] y1988*INTVAR 0.06** 0.04 0.00 0.03 0.05** 0.08 0.00** 0.00 0.02 [0.02] [0.03] [0.01] [0.03] [0.03] [0.10] [0.00] [0.00] [0.05] y1992*INTVAR 0.03 0.09*** -0.01*** 0.02 -0.03 -0.06 0.00* 0.00* 0.05 [0.02] [0.03] [0.01] [0.03] [0.02] [0.08] [0.00] [0.00] [0.04] y1994*INTVAR 0.05* 0.03 -0.01 -0.01 -0.01 0.08 0.00 0.00 -0.01 [0.03] [0.03] [0.01] [0.03] [0.03] [0.10] [0.00] [0.00] [0.05] y1998*INTVAR 0.09*** 0.08*** -0.01** 0.00 -0.05* 0.07 0.00 0.00** 0.06 [0.02] [0.03] [0.01] [0.03] [0.03] [0.12] [0.00] [0.00] [0.05] R-squared 0.9249 0.9249 0.9246 0.9244 0.9250 0.9251 0.9247 0.9246 0.9245 Number of obs. 2870 2870 2870 2870 2870 2870 2870 2870 2870 Notes: Standard errors for each coefficient are in brackets. *significant at 10%; **significant at 5%; ***significant at 1%

14 For coefficients on each of the year dummy variables, see Appendix C3.

23

Table 9: Return to Explanatory Variable Interacted with Year

INTVAR Return to INTVAR

1976 (β*+δ1)

1980 (β*+δ2)

1984 (β*+δ3)

1988 (β*+δ4)

1992 (β*+δ5)

1994 (β*+δ6)

1998 (β*+δ7)

2002 (β*)

avgscore 0.97 1.01 1.01 1.01 0.98 1.00 1.04 0.95 avgtrust -0.07 0.02 0.00 0.00 0.05 -0.01 0.04 -0.04 ldist 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 border 0.01 0.03 0.03 0.05 0.04 0.01 0.02 0.02 comcivlaw 0.05 0.02 -0.01 0.08 0.00 0.02 -0.02 0.03 coverage -0.36 -0.41 -0.24 -0.32 -0.46 -0.32 -0.33 -0.40 twars 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 language 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 religion -0.04 -0.05 -0.03 -0.02 0.01 -0.05 0.02 -0.04

The results show that ldist, border, twars, and religion tend to remain constant during

each Olympics. It is unlikely that geography and composition of religion would have

changed during this time period, and since twars is calculated as the total number of years

up until 1970, before the beginning of the skating data, it would have remained constant

throughout these 8 Olympics as well. These results indicate that people’s views of

geography/distance, religion, and history of war between countries have not increased or

diminished with time in such a fashion as to affect the judging of a skater in that country.

The bias for comcivlaw and coverage during 1976 to 2002 fluctuates without

having a general trend. The fluctuations in comcivlaw could be due to the changing levels

of importance that having a common legal system holds in people’s minds as political

climate changes and different world events take place, such as the Cold War. Because

media coverage is susceptible to the different world events that occur and changing

public opinions of various countries, coverage is likely to affect people’s views in

different ways depending upon what’s going on in the world at the time.

Avgtrust can be seen to have a tendency to increase by a small amount over time,

suggesting that the bias coming from the amount of trust between two countries has given

24

off a more positive effect in scoring, though it is not statistically significant. The bias for

the variables that have been consistently significant, such as border, comcivlaw, coverage,

twars, and language do not show a general trend for increasing or decreasing bias over

time.

25

V. Conclusion

Based on the regressions used in this study, the variables that have consistently

been significant with non-zero coefficients have been border, comcivlaw, and coverage.

The implications drawn from this are that judges tend to score skaters more favorably if

the skaters are from a neighboring country of the judge, if those two countries share a

similar legal system, and if the judge is likely to have been exposed to more news about

the skater’s country. These three variables are either equal to one or close to one when

the judge and the skater are from the same country, suggesting that nationalism biases do

play a part in influencing judging. Furthermore, the linear model using only average

score and the dummy variable home shows that a judge is likely to score a skater from his

or her own country higher by 0.07 of a point. With regards to the effect of comcivlaw,

common legal systems can often give rise to similar forms of government. Because those

governments may be inclined to unite into political blocs, this result could be interpreted

to give support to the evidence put forth by Seltzer and Glass (1991).

Despite the significance of these variables, it should be noted that the mean

advantage gained from any of these factors is, in fact, quite small. It is usually between

0.02 and 0.09 of a point, which is less than the smallest increment used in figure skating

judging, 0.1. However, suppose a situation arises such that a judge cannot make up his or

her mind between two scores that are 0.1 points apart. If the judge decides to go with the

higher possibility for a skater who is from the same country or a neighboring country and

chooses randomly for skaters who are not, then he is exhibiting a bias of 0.05. When

viewed in this light, these factors do hold some weight in affecting judges’ decision.

26

In the interpretation of these results, some factors concerning the structure of the

competition should be noted. First, most competitors in the Winter Olympics come from

a fairly small set of countries, and typically, most skaters have one judge from his or her

own country on the panel of nine. However, since different judges have different biases,

it could be that any advantage gained could be cancelled overall. Secondly, Bassett and

Persky (1994), in rating the judging system in skating, have shown that the final ranking

system based on median ranks is fairly robust to these types of biases. Because the effect

of each of these biases is quite small, it is possible that overall, the final rankings are not

affected to a great extent.

27

References Bassett, Gilbert W. and Joseph Persky, September 1994, "Rating Skating." Journal of the American Statistical Association, Vol. 89, Issue 427, p. 1075-1079. Campbell, Bryan and John W. Galbraith, 1996, "Non-Parametric Tests of the Unbiasedness of Olympic Figure-Skating Judgments." The Statistician, Vol. 45, No. 4, p. 521-526. International Skating Union, 1982. Judges’ Handbook: Free Skating and the Short Program. International Skating Union. Seltzer, Richard and Wayne Glass, September 1991, "International Politics and Judging in Olympic Skating Events: 1968-88." Journal of Sport Behavior, Vol. 14, Issue 3, p. 189-200. Guiso, Luigi, Paola Sapienza, and Luigi Zingales, May 2005, "Cultural Biases in Economic Exchange." Working Paper. Wanderer, J.J., 1987, “Social Factors in Judges’ Rankings of Competitors in Figure Skating Championships.” Journal of Sport Behavior, Vol. 10, p. 93-102. Wallechinsky, D, 1991, The Complete Book of the Olympics, 3rd edition. New York: Little, Brown. Zitzewitz, Eric, 2006. "Nationalism in Winter Sports Judging and Its Lessons for Organizational Decision Making." Journal of Economics and Management Strategy.

28

Appendices Appendix A: Definition of Variables and Country Abbreviations

Variable Description score Individual score given by each judge avgscore Average of scores by 9 judges for that component and event avgtrust Average trust between two countries from Eurobarometer surveys, 1970-1995 ldist Log of distance in kilometers from capital to capital border 1 if two countries share a common land border and if home=1 comcivlaw 1 if two countries both have common law or civil law coverage Press coverage over total coverage twars Number of years at war, 1000-1970 language Products of the percentage of people who speak the same language in both countries religion Probability that two randomly chosen individuals in two countries will have the same religion home 1 if skater's country and judge's country are the same d_skater* 1 for each particular skater d_judge* 1 for each particular judge yr1976 1 if year=1976 yr1980 1 if year=1980 yr1984 1 if year=1984 yr1988 1 if year=1988 yr1992 1 if year=1992 yr1994 1 if year=1994 yr1998 1 if year=1998 yr2002 1 if year=2002 AUT Austria BEL Belgium CHN China DEN Denmark ESP Spain FIN Finland FRA France FRG Federal Republic of Germany (West), used 1968-1988 GBR Great Britain GER Germany, usage resumed 1992 HOL Netherlands HUN Hungary ITA Italy JPN Japan POL Poland SUI Switzerland SWE Sweden USA United States of America

29

Appendix B: Summary Statistics and Table of Correlations Summary Statistics

Variable Obs Mean Std. Dev. Min Max score 2870 5.092021 0.5309807 2.4 6 avgscore 2870 5.094998 0.5094825 2.833333 5.9 avgtrust 2870 2.916437 0.3849047 1.8 4 ldist 2870 6.968825 2.450831 1 9.196924 border 2870 0.2439024 0.4295094 0 1 comcivlaw 2870 0.625784 0.4840041 0 1 coverage 2870 0.1165459 0.3073462 0.0000344 0.9829106 twars 2870 20.48362 41.59477 0 198 language 2870 1844.066 3718.28 0 10000 religion 2870 0.3665881 0.2742732 0.0144185 1 home 2870 0.1170732 0.3215635 0 1

Table of Correlations P-value for significance is shown below the correlation between the variables.

score avgscore avgtrust ldist border comcivlaw coverage twars language religion home score 1 avgscore 0.9601 1 0 avgtrust 0.1894 0.1753 1 0 0 ldist 0.0846 0.1238 -0.4703 1 0 0 0 border -0.032 -0.0707 0.3813 -0.7487 1 0.0869 0.0002 0 0 comcivlaw -0.1982 -0.2352 -0.0101 -0.3202 0.4392 1 0 0 0.5884 0 0 coverage 0.0263 -0.014 0.4904 -0.8838 0.6396 0.2762 1 0.1595 0.4533 0 0 0 0 twars -0.0997 -0.0844 -0.1323 -0.092 0.0815 -0.0919 -0.179 1 0 0 0 0 0 0 0 language 0.0878 0.0589 0.4684 -0.7044 0.6178 0.375 0.7997 -0.1742 1 0 0.0016 0 0 0 0 0 0 religion 0.0179 -0.0143 0.4631 -0.8201 0.6728 0.2173 0.8404 -0.1502 0.7207 1 0.3375 0.4425 0 0 0 0 0 0 0 home 0.022 -0.0193 0.4838 -0.887 0.6411 0.2816 0.9993 -0.1794 0.7989 0.8411 1 0.2385 0.3023 0 0 0 0 0 0 0 0

30

Appendix C: Detailed Regression Results C1: OLS Regressions All Data . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 9, 2860) = 3863.65 Model | 747.413879 9 83.0459866 Prob > F = 0.0000 Residual | 61.4733962 2860 .021494194 R-squared = 0.9240 -------------+------------------------------ Adj R-squared = 0.9238 Total | 808.887275 2869 .281940493 Root MSE = .14661 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.004604 .0058703 171.13 0.000 .9930941 1.016115 avgtrust | .0055983 .0088339 0.63 0.526 -.0117231 .0229197 ldist | .0017134 .0034839 0.49 0.623 -.0051178 .0085445 border | .0290934 .0107599 2.70 0.007 .0079954 .0501913 comcivlaw | .0169771 .0071243 2.38 0.017 .0030077 .0309464 coverage | .0791709 .0270205 2.93 0.003 .0261892 .1321526 twars | -.0001781 .0000845 -2.11 0.035 -.0003438 -.0000124 language | -2.33e-06 1.35e-06 -1.72 0.085 -4.98e-06 3.22e-07 religion | -.0226707 .0208706 -1.09 0.277 -.0635937 .0182523 _cons | -.0653951 .0474313 -1.38 0.168 -.1583982 .0276079 ------------------------------------------------------------------------------ Short Program . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion Source | SS df MS Number of obs = 1462 -------------+------------------------------ F( 9, 1452) = 1816.78 Model | 421.719091 9 46.8576768 Prob > F = 0.0000 Residual | 37.4494438 1452 .025791628 R-squared = 0.9184 -------------+------------------------------ Adj R-squared = 0.9179 Total | 459.168535 1461 .314283734 Root MSE = .1606 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | .9884786 .0083855 117.88 0.000 .9720296 1.004928 avgtrust | .0080587 .0134451 0.60 0.549 -.0183152 .0344326 ldist | .002793 .0053176 0.53 0.600 -.0076381 .0132241 border | .0344345 .0164885 2.09 0.037 .0020906 .0667784 comcivlaw | .0233177 .0109462 2.13 0.033 .0018457 .0447897 coverage | .1036671 .0415593 2.49 0.013 .0221444 .1851898 twars | -.0001448 .0001298 -1.12 0.265 -.0003994 .0001098 language | -1.41e-06 2.08e-06 -0.68 0.498 -5.50e-06 2.67e-06 religion | -.0557366 .0318455 -1.75 0.080 -.1182047 .0067315 _cons | .0121068 .0717027 0.17 0.866 -.128545 .1527587 ------------------------------------------------------------------------------

31

Free Skate . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion Source | SS df MS Number of obs = 1408 -------------+------------------------------ F( 9, 1398) = 2128.42 Model | 318.221046 9 35.357894 Prob > F = 0.0000 Residual | 23.2239296 1398 .016612253 R-squared = 0.9320 -------------+------------------------------ Adj R-squared = 0.9315 Total | 341.444976 1407 .242675889 Root MSE = .12889 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.035915 .0082253 125.94 0.000 1.01978 1.05205 avgtrust | .0004737 .011195 0.04 0.966 -.021487 .0224344 ldist | -.0014264 .0044128 -0.32 0.747 -.0100829 .0072302 border | .0222076 .0135362 1.64 0.101 -.0043458 .048761 comcivlaw | .0122044 .0089376 1.37 0.172 -.0053282 .029737 coverage | .0467173 .0338634 1.38 0.168 -.0197114 .1131459 twars | -.0002158 .0001061 -2.03 0.042 -.0004238 -7.70e-06 language | -3.49e-06 1.69e-06 -2.06 0.040 -6.81e-06 -1.64e-07 religion | .0090578 .0263753 0.34 0.731 -.0426815 .0607972 _cons | -.2001088 .0611222 -3.27 0.001 -.3200099 -.0802076 ------------------------------------------------------------------------------ Technical Merit . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion Source | SS df MS Number of obs = 1435 -------------+------------------------------ F( 9, 1425) = 2131.13 Model | 441.708225 9 49.0786917 Prob > F = 0.0000 Residual | 32.8169206 1425 .023029418 R-squared = 0.9308 -------------+------------------------------ Adj R-squared = 0.9304 Total | 474.525146 1434 .330910143 Root MSE = .15175 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.00086 .0078437 127.60 0.000 .9854735 1.016246 avgtrust | .0006019 .0129019 0.05 0.963 -.024707 .0259107 ldist | .0012196 .0051013 0.24 0.811 -.0087872 .0112264 border | .0294583 .0157507 1.87 0.062 -.0014387 .0603553 comcivlaw | .0195799 .0104233 1.88 0.061 -.0008667 .0400266 coverage | .0821342 .0395917 2.07 0.038 .00447 .1597984 twars | -.0002233 .0001237 -1.81 0.071 -.000466 .0000193 language | -3.35e-06 1.98e-06 -1.69 0.091 -7.24e-06 5.32e-07 religion | -.0361679 .0305493 -1.18 0.237 -.0960943 .0237585 _cons | -.0208717 .0678852 -0.31 0.759 -.1540373 .1122939 ------------------------------------------------------------------------------

32

Presentation . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion Source | SS df MS Number of obs = 1435 -------------+------------------------------ F( 9, 1425) = 1607.36 Model | 289.429358 9 32.1588175 Prob > F = 0.0000 Residual | 28.5103287 1425 .020007248 R-squared = 0.9103 -------------+------------------------------ Adj R-squared = 0.9098 Total | 317.939687 1434 .221715263 Root MSE = .14145 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.012669 .0093042 108.84 0.000 .9944173 1.03092 avgtrust | .0094996 .0121116 0.78 0.433 -.0142589 .0332582 ldist | .0018075 .0047632 0.38 0.704 -.0075361 .0111512 border | .028326 .0146848 1.93 0.054 -.0004801 .0571322 comcivlaw | .0151972 .0097482 1.56 0.119 -.0039251 .0343195 coverage | .075502 .0368411 2.05 0.041 .0032334 .1477706 twars | -.0001351 .0001153 -1.17 0.242 -.0003613 .0000912 language | -1.40e-06 1.85e-06 -0.76 0.449 -5.02e-06 2.22e-06 religion | -.0100136 .028489 -0.35 0.725 -.0658984 .0458713 _cons | -.1266779 .0674662 -1.88 0.061 -.2590216 .0056658 ------------------------------------------------------------------------------ Testing for Nationalism Bias . reg score avgscore home Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 2, 2867) =17270.07 Model | 746.891616 2 373.445808 Prob > F = 0.0000 Residual | 61.9956592 2867 .021623878 R-squared = 0.9234 -------------+------------------------------ Adj R-squared = 0.9233 Total | 808.887275 2869 .281940493 Root MSE = .14705 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.001384 .0053896 185.80 0.000 .9908167 1.011952 home | .066898 .0085392 7.83 0.000 .0501545 .0836415 _cons | -.0178629 .0276339 -0.65 0.518 -.0720471 .0363214 ------------------------------------------------------------------------------

33

C2: Fixed Effects Regressions Fixed effects for skaters . reg score avgtrust ldist border comcivlaw coverage twars language religion d_skater* Source | SS df MS Number of obs = 2870 -------------+------------------------------ F(214, 2655) = 38.51 Model | 611.792627 214 2.85884405 Prob > F = 0.0000 Residual | 197.094648 2655 .074235272 R-squared = 0.7563 -------------+------------------------------ Adj R-squared = 0.7367 Total | 808.887275 2869 .281940493 Root MSE = .27246 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgtrust | .021986 .0263178 0.84 0.404 -.0296195 .0735915 ldist | -.033993 .0221649 -1.53 0.125 -.0774552 .0094693 border | .003346 .0263516 0.13 0.899 -.0483256 .0550177 comcivlaw | .0502499 .0205051 2.45 0.014 .0100422 .0904575 coverage | -.0289732 .1349967 -0.21 0.830 -.2936825 .235736 twars | -.000448 .000204 -2.20 0.028 -.000848 -.0000479 language | -5.62e-06 3.26e-06 -1.73 0.085 -.000012 7.66e-07 religion | -.1173353 .0493614 -2.38 0.018 -.2141261 -.0205445

... d_skater258 | -.9401949 .204155 -4.61 0.000 -1.340514 -.539876 _cons | 5.704159 .2069852 27.56 0.000 5.29829 6.110027 ------------------------------------------------------------------------------ Fixed effects for skaters and judges . reg score avgtrust ldist border comcivlaw coverage twars language religion d_skater* d_judge* Source | SS df MS Number of obs = 2870 -------------+------------------------------ F(257, 2612) = 34.83 Model | 626.150591 257 2.43638362 Prob > F = 0.0000 Residual | 182.736684 2612 .069960446 R-squared = 0.7741 -------------+------------------------------ Adj R-squared = 0.7519 Total | 808.887275 2869 .281940493 Root MSE = .2645 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgtrust | .01474 .0290729 0.51 0.612 -.0422682 .0717482 ldist | -.0062228 .0243376 -0.26 0.798 -.0539457 .0415 border | -.0244051 .0276864 -0.88 0.378 -.0786946 .0298845 comcivlaw | .0100352 .0246867 0.41 0.684 -.0383722 .0584426 coverage | .0665309 .1469325 0.45 0.651 -.221585 .3546468 twars | -.0004153 .0002107 -1.97 0.049 -.0008285 -2.14e-06 language | 2.77e-06 4.19e-06 0.66 0.508 -5.45e-06 .000011 religion | -.0768759 .0502315 -1.53 0.126 -.1753734 .0216216

... d_skater258 | -.9170718 .2216036 -4.14 0.000 -1.351608 -.4825354 d_judge1 | .0598418 .099373 0.60 0.547 -.1350159 .2546996

... d_judge98 | .007103 .1154269 0.06 0.951 -.2192344 .2334404 d_judge99 | (dropped) _cons | 5.318252 .2423196 21.95 0.000 4.843094 5.793409 ------------------------------------------------------------------------------

34

C3: Regressions Over Time Regression with yearly dummy variables . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 16, 2853) = 2178.80 Model | 747.695964 16 46.7309978 Prob > F = 0.0000 Residual | 61.1913109 2853 .021448059 R-squared = 0.9244 -------------+------------------------------ Adj R-squared = 0.9239 Total | 808.887275 2869 .281940493 Root MSE = .14645 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.002614 .0059573 168.30 0.000 .9909328 1.014295 avgtrust | .0059187 .0088867 0.67 0.505 -.0115062 .0233436 ldist | .0021468 .0035033 0.61 0.540 -.0047225 .009016 border | .0294094 .0107737 2.73 0.006 .0082843 .0505345 comcivlaw | .0188269 .0072682 2.59 0.010 .0045755 .0330783 coverage | .0822721 .0271766 3.03 0.002 .0289844 .1355598 twars | -.0001869 .000085 -2.20 0.028 -.0003537 -.0000202 language | -2.85e-06 1.38e-06 -2.06 0.039 -5.57e-06 -1.39e-07 religion | -.0206506 .0209592 -0.99 0.325 -.0617473 .0204461 yr1976 | .0348896 .012819 2.72 0.007 .0097542 .0600251 yr1980 | .0158702 .0105372 1.51 0.132 -.0047912 .0365315 yr1984 | .0160092 .0109264 1.47 0.143 -.0054154 .0374337 yr1988 | -.0058652 .0118672 -0.49 0.621 -.0291344 .017404 yr1992 | .0087061 .0102524 0.85 0.396 -.0113968 .028809 yr1994 | .0143335 .0119212 1.20 0.229 -.0090415 .0377085 yr1998 | .0062626 .0118063 0.53 0.596 -.0168871 .0294123 _cons | -.0711727 .0491147 -1.45 0.147 -.1674766 .0251312 ------------------------------------------------------------------------------ Interacting avgscore with time . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 y1976avgscore y1980avgscore y1984avgscore y1988avgscore y1992avgscore y1994avgscore y1998avgscore Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 23, 2846) = 1523.21 Model | 748.113585 23 32.5266776 Prob > F = 0.0000 Residual | 60.7736907 2846 .021354073 R-squared = 0.9249 -------------+------------------------------ Adj R-squared = 0.9243 Total | 808.887275 2869 .281940493 Root MSE = .14613 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | .9518306 .0177998 53.47 0.000 .9169288 .9867325 avgtrust | .0062712 .0089499 0.70 0.484 -.0112777 .0238202 ldist | .0030915 .0035254 0.88 0.381 -.0038212 .0100041 border | .0297171 .0107936 2.75 0.006 .0085531 .0508811 comcivlaw | .0187021 .0073202 2.55 0.011 .0043486 .0330555 coverage | .0839933 .0272681 3.08 0.002 .030526 .1374606 twars | -.0001883 .0000851 -2.21 0.027 -.0003552 -.0000215 language | -3.12e-06 1.39e-06 -2.25 0.025 -5.83e-06 -3.99e-07 religion | -.0143037 .0210528 -0.68 0.497 -.055584 .0269767 yr1976 | -.0673907 .1888378 -0.36 0.721 -.4376635 .3028821

35

yr1980 | -.3222266 .1086143 -2.97 0.003 -.5351973 -.109256 yr1984 | -.2596682 .1141812 -2.27 0.023 -.4835544 -.035782 yr1988 | -.3057094 .1235709 -2.47 0.013 -.548007 -.0634117 yr1992 | -.1632069 .1110522 -1.47 0.142 -.3809578 .0545441 yr1994 | -.2171291 .1310437 -1.66 0.098 -.4740794 .0398212 yr1998 | -.4671241 .124212 -3.76 0.000 -.7106788 -.2235694 y1976avgsc~e | .0211414 .0355269 0.60 0.552 -.0485197 .0908024 y1980avgsc~e | .0657598 .0210112 3.13 0.002 .0245611 .1069584 y1984avgsc~e | .0536807 .0222515 2.41 0.016 .0100501 .0973113 y1988avgsc~e | .0585375 .0241666 2.42 0.015 .0111516 .1059233 y1992avgsc~e | .0329946 .0216635 1.52 0.128 -.0094831 .0754724 y1994avgsc~e | .0450346 .0254932 1.77 0.077 -.0049524 .0950216 y1998avgsc~e | .0926923 .0242155 3.83 0.000 .0452107 .1401739 _cons | .1802628 .0996188 1.81 0.070 -.0150694 .3755951 ------------------------------------------------------------------------------ Interacting avgtrust with time . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 y1976avgtrust y1980avgtrust y1984avgtrust y1988avgtrust y1992avgtrust y1994avgtrust y1998avgtrust Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 23, 2846) = 1524.53 Model | 748.162427 23 32.5288012 Prob > F = 0.0000 Residual | 60.7248488 2846 .021336911 R-squared = 0.9249 -------------+------------------------------ Adj R-squared = 0.9243 Total | 808.887275 2869 .281940493 Root MSE = .14607 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.002197 .0060121 166.70 0.000 .9904084 1.013985 avgtrust | -.0384244 .0197051 -1.95 0.051 -.0770622 .0002133 ldist | .0024899 .0035092 0.71 0.478 -.004391 .0093708 border | .0322756 .0107853 2.99 0.003 .0111278 .0534234 comcivlaw | .0182514 .0072624 2.51 0.012 .0040113 .0324916 coverage | .0822199 .0272311 3.02 0.003 .0288253 .1356146 twars | -.0001925 .0000852 -2.26 0.024 -.0003597 -.0000254 language | -2.89e-06 1.38e-06 -2.09 0.036 -5.60e-06 -1.82e-07 religion | -.0211284 .0209304 -1.01 0.313 -.0621687 .019912 yr1976 | .138247 .1060773 1.30 0.193 -.0697491 .346243 yr1980 | -.1520332 .0760129 -2.00 0.046 -.3010791 -.0029872 yr1984 | -.1087906 .076807 -1.42 0.157 -.2593937 .0418125 yr1988 | -.1154082 .0882624 -1.31 0.191 -.288473 .0576565 yr1992 | -.2509306 .082298 -3.05 0.002 -.4123004 -.0895608 yr1994 | -.0693525 .0834254 -0.83 0.406 -.2329328 .0942277 yr1998 | -.2320443 .0813603 -2.85 0.004 -.3915755 -.0725132 y1976avgtr~t | -.0327438 .0359443 -0.91 0.362 -.1032233 .0377357 y1980avgtr~t | .0584775 .0259415 2.25 0.024 .0076115 .1093435 y1984avgtr~t | .0439939 .0263586 1.67 0.095 -.0076899 .0956778 y1988avgtr~t | .0388512 .0301308 1.29 0.197 -.0202293 .0979316 y1992avgtr~t | .0902771 .0283534 3.18 0.001 .0346818 .1458723 y1994avgtr~t | .0298144 .0288306 1.03 0.301 -.0267165 .0863453 y1998avgtr~t | .0840044 .0283823 2.96 0.003 .0283524 .1396564 _cons | .05431 .0718804 0.76 0.450 -.0866328 .1952529 ------------------------------------------------------------------------------

36

Interacting ldist with time . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 y1976ldist y1980ldist y1984ldist y1988ldist y1992ldist y1994ldist y1998ldist Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 23, 2846) = 1518.13 Model | 747.9255 23 32.5185 Prob > F = 0.0000 Residual | 60.9617753 2846 .02142016 R-squared = 0.9246 -------------+------------------------------ Adj R-squared = 0.9240 Total | 808.887275 2869 .281940493 Root MSE = .14636 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.002428 .0060129 166.71 0.000 .9906378 1.014218 avgtrust | .0074594 .0089297 0.84 0.404 -.0100499 .0249687 ldist | .0101744 .0053713 1.89 0.058 -.0003576 .0207065 border | .0302196 .0107736 2.80 0.005 .0090948 .0513444 comcivlaw | .0192567 .0072711 2.65 0.008 .0049994 .0335139 coverage | .0805578 .0273671 2.94 0.003 .0268964 .1342192 twars | -.0001947 .0000851 -2.29 0.022 -.0003615 -.0000279 language | -2.99e-06 1.39e-06 -2.15 0.032 -5.71e-06 -2.64e-07 religion | -.0218188 .0209634 -1.04 0.298 -.0629238 .0192862 yr1976 | .0955417 .0426302 2.24 0.025 .0119526 .1791309 yr1980 | .0883775 .0400994 2.20 0.028 .0097506 .1670043 yr1984 | .0782677 .0405928 1.93 0.054 -.0013266 .1578619 yr1988 | .0259371 .042031 0.62 0.537 -.0564771 .1083513 yr1992 | .1063373 .0394193 2.70 0.007 .0290441 .1836305 yr1994 | .0608843 .0419195 1.45 0.146 -.0213113 .14308 yr1998 | .091586 .0435095 2.10 0.035 .0062727 .1768992 y1976ldist | -.0077891 .0056137 -1.39 0.165 -.0187964 .0032183 y1980ldist | -.0095567 .0051752 -1.85 0.065 -.0197042 .0005908 y1984ldist | -.0080922 .0052785 -1.53 0.125 -.0184422 .0022578 y1988ldist | -.0035317 .0055184 -0.64 0.522 -.0143521 .0072887 y1992ldist | -.0131421 .005075 -2.59 0.010 -.0230932 -.0031911 y1994ldist | -.0057451 .0054386 -1.06 0.291 -.016409 .0049188 y1998ldist | -.0113705 .0056655 -2.01 0.045 -.0224794 -.0002617 _cons | -.1360722 .0576194 -2.36 0.018 -.2490522 -.0230923 ------------------------------------------------------------------------------ Interacting border with time . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 y1976border y1980border y1984border y1988border y1992border y1994border y1998border Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 23, 2846) = 1513.65 Model | 747.758675 23 32.5112467 Prob > F = 0.0000 Residual | 61.1286009 2846 .021478778 R-squared = 0.9244 -------------+------------------------------ Adj R-squared = 0.9238 Total | 808.887275 2869 .281940493 Root MSE = .14656 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.002667 .0059889 167.42 0.000 .9909237 1.01441 avgtrust | .0055368 .0089388 0.62 0.536 -.0119905 .023064 ldist | .0017526 .0035289 0.50 0.619 -.0051668 .008672 border | .020246 .0216974 0.93 0.351 -.0222982 .0627903 comcivlaw | .0183435 .0073254 2.50 0.012 .0039799 .0327071

37

coverage | .0797492 .0275128 2.90 0.004 .0258021 .1336964 twars | -.0001971 .0000857 -2.30 0.022 -.0003651 -.000029 language | -2.66e-06 1.41e-06 -1.89 0.060 -5.43e-06 1.07e-07 religion | -.0219458 .0210873 -1.04 0.298 -.0632938 .0194022 yr1976 | .037569 .0147225 2.55 0.011 .0087011 .0664369 yr1980 | .0139551 .0118793 1.17 0.240 -.0093378 .037248 yr1984 | .0133207 .0125152 1.06 0.287 -.011219 .0378603 yr1988 | -.0129524 .0135228 -0.96 0.338 -.0394679 .0135631 yr1992 | .0051215 .0115282 0.44 0.657 -.0174831 .0277261 yr1994 | .0160237 .0132759 1.21 0.228 -.0100076 .042055 yr1998 | .005681 .0134488 0.42 0.673 -.0206894 .0320514 y1976border | -.0071105 .0297638 -0.24 0.811 -.0654713 .0512503 y1980border | .0093234 .0258338 0.36 0.718 -.0413315 .0599783 y1984border | .0120068 .0263188 0.46 0.648 -.039599 .0636127 y1988border | .0289652 .0284689 1.02 0.309 -.0268566 .084787 y1992border | .0169174 .0254724 0.66 0.507 -.0330289 .0668636 y1994border | -.009815 .0301981 -0.33 0.745 -.0690273 .0493973 y1998border | .0047786 .0283223 0.17 0.866 -.0507556 .0603128 _cons | -.0647907 .0497344 -1.30 0.193 -.1623099 .0327284 ------------------------------------------------------------------------------ Interacting comcivlaw with time . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 y1976comcivlaw y1980comcivlaw y1984comcivlaw y1988comcivlaw y1992comcivlaw y1994comcivlaw y1998comcivlaw Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 23, 2846) = 1525.89 Model | 748.212572 23 32.5309814 Prob > F = 0.0000 Residual | 60.6747032 2846 .021319291 R-squared = 0.9250 -------------+------------------------------ Adj R-squared = 0.9244 Total | 808.887275 2869 .281940493 Root MSE = .14601 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.001466 .0059621 167.97 0.000 .9897758 1.013157 avgtrust | .0054709 .0088874 0.62 0.538 -.0119555 .0228974 ldist | .0013149 .00355 0.37 0.711 -.0056459 .0082757 border | .0283268 .0107595 2.63 0.009 .0072296 .049424 comcivlaw | .0303921 .0190749 1.59 0.111 -.0070098 .0677941 coverage | .0767013 .0273007 2.81 0.005 .0231702 .1302323 twars | -.0001948 .0000852 -2.29 0.022 -.0003618 -.0000277 language | -3.29e-06 1.39e-06 -2.37 0.018 -6.01e-06 -5.64e-07 religion | -.0164302 .0211735 -0.78 0.438 -.0579472 .0250867 yr1976 | .0292197 .0215557 1.36 0.175 -.0130467 .071486 yr1980 | .0223337 .0193401 1.15 0.248 -.0155883 .0602558 yr1984 | .044687 .0201836 2.21 0.027 .0051112 .0842629 yr1988 | -.0329534 .0208764 -1.58 0.115 -.0738879 .0079811 yr1992 | .0270507 .0188787 1.43 0.152 -.0099667 .0640681 yr1994 | .0218266 .0203646 1.07 0.284 -.0181043 .0617576 yr1998 | .0400919 .0222187 1.80 0.071 -.0034744 .0836583 y1976comci~w | .0158385 .0271199 0.58 0.559 -.0373382 .0690152 y1980comci~w | -.0077207 .0231125 -0.33 0.738 -.0530396 .0375982 y1984comci~w | -.0423027 .0239686 -1.76 0.078 -.0893002 .0046949 y1988comci~w | .0498481 .0254558 1.96 0.050 -.0000656 .0997617 y1992comci~w | -.027769 .0225119 -1.23 0.217 -.0719102 .0163722 y1994comci~w | -.008605 .0253674 -0.34 0.734 -.0583453 .0411354 y1998comci~w | -.0479827 .0262931 -1.82 0.068 -.0995381 .0035728 _cons | -.0666563 .0534916 -1.25 0.213 -.1715425 .0382298 ------------------------------------------------------------------------------

38

Interacting coverage with time . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 y1976coverage y1980coverage y1984coverage y1988coverage y1992coverage y1994coverage y1998coverage Source | SS df MS Number of obs = 2534 -------------+------------------------------ F( 23, 2510) = 1347.04 Model | 675.513963 23 29.3701723 Prob > F = 0.0000 Residual | 54.7267939 2510 .021803504 R-squared = 0.9251 -------------+------------------------------ Adj R-squared = 0.9244 Total | 730.240757 2533 .288290863 Root MSE = .14766 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | .9961945 .0067126 148.41 0.000 .9830318 1.009357 avgtrust | .0064346 .0103909 0.62 0.536 -.013941 .0268103 ldist | .0032156 .004222 0.76 0.446 -.0050633 .0114945 border | .0332813 .011136 2.99 0.003 .0114447 .0551179 comcivlaw | .0180142 .0079376 2.27 0.023 .0024493 .0335791 coverage | -.4007197 .797251 -0.50 0.615 -1.964057 1.162617 twars | -.0001711 .0000924 -1.85 0.064 -.0003523 .0000101 language | -3.78e-06 1.74e-06 -2.18 0.030 -7.20e-06 -3.73e-07 religion | -.0251547 .0219475 -1.15 0.252 -.0681917 .0178824 yr1976 | .0303483 .0183806 1.65 0.099 -.0056944 .066391 yr1980 | .0154483 .0136732 1.13 0.259 -.0113636 .0422601 yr1984 | -.0057067 .0153489 -0.37 0.710 -.0358044 .024391 yr1988 | -.0148933 .0168005 -0.89 0.375 -.0478377 .018051 yr1992 | .0083669 .0136581 0.61 0.540 -.0184154 .0351492 yr1994 | .0061791 .0166139 0.37 0.710 -.0263991 .0387574 yr1998 | -.0109292 .0163438 -0.67 0.504 -.0429778 .0211195 y1976cover~e | .0416694 .1015511 0.41 0.682 -.1574631 .240802 y1980cover~e | -.0101356 .0777109 -0.13 0.896 -.1625197 .1422484 y1984cover~e | .1630911 .0847413 1.92 0.054 -.0030789 .3292611 y1988cover~e | .075743 .1031918 0.73 0.463 -.1266068 .2780928 y1992cover~e | -.0592023 .0783196 -0.76 0.450 -.2127799 .0943754 y1994cover~e | .079602 .099762 0.80 0.425 -.1160223 .2752263 y1998cover~e | .074462 .12026 0.62 0.536 -.1613569 .3102809 _cons | -.0417251 .0612623 -0.68 0.496 -.161855 .0784047 ------------------------------------------------------------------------------ Interacting twars with time . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 y1976twars y1980twars y1984twars y1988twars y1992twars y1994twars y1998twars Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 23, 2846) = 1519.87 Model | 747.990044 23 32.5213062 Prob > F = 0.0000 Residual | 60.8972317 2846 .021397481 R-squared = 0.9247 -------------+------------------------------ Adj R-squared = 0.9241 Total | 808.887275 2869 .281940493 Root MSE = .14628 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.002427 .0059913 167.31 0.000 .9906794 1.014175 avgtrust | .0054313 .0089515 0.61 0.544 -.0121207 .0229834 ldist | .0024075 .0035302 0.68 0.495 -.0045146 .0093295 border | .0291162 .0110473 2.64 0.008 .0074546 .0507778

39

comcivlaw | .0189987 .0073114 2.60 0.009 .0046626 .0333348 coverage | .0833955 .0271754 3.07 0.002 .03011 .136681 twars | .0002883 .0003556 0.81 0.418 -.000409 .0009856 language | -3.00e-06 1.39e-06 -2.15 0.032 -5.73e-06 -2.65e-07 religion | -.0174288 .0211854 -0.82 0.411 -.058969 .0241114 yr1976 | .0318569 .0140693 2.26 0.024 .00427 .0594439 yr1980 | .0201426 .0116973 1.72 0.085 -.0027934 .0430786 yr1984 | .0191262 .0122448 1.56 0.118 -.0048833 .0431358 yr1988 | .0094268 .013339 0.71 0.480 -.0167282 .0355818 yr1992 | .0196753 .0114119 1.72 0.085 -.0027011 .0420517 yr1994 | .020869 .0131173 1.59 0.112 -.0048513 .0465894 yr1998 | .0097857 .0134537 0.73 0.467 -.0165943 .0361657 y1976twars | -.00009 .0004048 -0.22 0.824 -.0008838 .0007037 y1980twars | -.0003971 .0003937 -1.01 0.313 -.001169 .0003749 y1984twars | -.0003297 .0004094 -0.81 0.421 -.0011324 .000473 y1988twars | -.0010477 .0004412 -2.37 0.018 -.0019127 -.0001826 y1992twars | -.0006906 .0003726 -1.85 0.064 -.0014212 .00004 y1994twars | -.0005051 .0003893 -1.30 0.195 -.0012684 .0002582 y1998twars | -.000402 .0003957 -1.02 0.310 -.0011778 .0003738 _cons | -.0773578 .0495827 -1.56 0.119 -.1745795 .0198639 ------------------------------------------------------------------------------ Interacting language with time . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 y1976language y1980language y1984language y1988language y1992language y1994language y1998language Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 23, 2846) = 1516.37 Model | 747.860287 23 32.5156646 Prob > F = 0.0000 Residual | 61.0269885 2846 .021443074 R-squared = 0.9246 -------------+------------------------------ Adj R-squared = 0.9239 Total | 808.887275 2869 .281940493 Root MSE = .14643 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.001503 .0059793 167.50 0.000 .989779 1.013227 avgtrust | .0064686 .0089171 0.73 0.468 -.011016 .0239532 ldist | .0023822 .0035184 0.68 0.498 -.0045168 .0092811 border | .0314789 .0108595 2.90 0.004 .0101856 .0527723 comcivlaw | .0186476 .0073346 2.54 0.011 .0042659 .0330293 coverage | .0806409 .0272687 2.96 0.003 .0271726 .1341093 twars | -.0001851 .0000852 -2.17 0.030 -.0003523 -.000018 language | -8.21e-06 3.67e-06 -2.24 0.025 -.0000154 -1.01e-06 religion | -.0208116 .0210395 -0.99 0.323 -.0620659 .0204427 yr1976 | .0383215 .0146018 2.62 0.009 .0096903 .0669526 yr1980 | .0132507 .0113202 1.17 0.242 -.0089461 .0354474 yr1984 | .0110324 .0117491 0.94 0.348 -.0120051 .0340699 yr1988 | -.008063 .0130248 -0.62 0.536 -.033602 .0174759 yr1992 | .0024491 .0108776 0.23 0.822 -.0188797 .023778 yr1994 | .0120276 .0131303 0.92 0.360 -.0137183 .0377735 yr1998 | -.00423 .0126503 -0.33 0.738 -.0290347 .0205748 y1976langu~e | 3.24e-06 4.08e-06 0.79 0.428 -4.76e-06 .0000112 y1980langu~e | 4.91e-06 3.87e-06 1.27 0.204 -2.67e-06 .0000125 y1984langu~e | 6.12e-06 4.03e-06 1.52 0.129 -1.78e-06 .000014 y1988langu~e | 4.75e-06 4.08e-06 1.16 0.245 -3.25e-06 .0000127 y1992langu~e | 7.04e-06 3.89e-06 1.81 0.070 -5.82e-07 .0000147 y1994langu~e | 4.91e-06 4.05e-06 1.21 0.226 -3.03e-06 .0000128 y1998langu~e | 9.70e-06 4.24e-06 2.29 0.022 1.39e-06 .000018 _cons | -.065403 .049723 -1.32 0.188 -.1628997 .0320937

40

Interacting religion with time . reg score avgscore avgtrust ldist border comcivlaw coverage twars language religion yr1976 yr1980 yr1984 yr1988 yr1992 yr1994 yr1998 y1976religion y1980religion y1984religion y1988religion y1992religion y1994religion y1998religion Source | SS df MS Number of obs = 2870 -------------+------------------------------ F( 23, 2846) = 1515.79 Model | 747.838796 23 32.5147303 Prob > F = 0.0000 Residual | 61.0484793 2846 .021450625 R-squared = 0.9245 -------------+------------------------------ Adj R-squared = 0.9239 Total | 808.887275 2869 .281940493 Root MSE = .14646 ------------------------------------------------------------------------------ score | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgscore | 1.002765 .0059824 167.62 0.000 .9910345 1.014495 avgtrust | .0072993 .0089338 0.82 0.414 -.010218 .0248167 ldist | .0021383 .0035149 0.61 0.543 -.0047538 .0090303 border | .0297908 .0107943 2.76 0.006 .0086253 .0509562 comcivlaw | .0186202 .007315 2.55 0.011 .0042769 .0329635 coverage | .085006 .0274149 3.10 0.002 .0312508 .1387611 twars | -.0001927 .0000855 -2.25 0.024 -.0003604 -.000025 language | -2.88e-06 1.39e-06 -2.08 0.038 -5.59e-06 -1.63e-07 religion | -.0419023 .0384187 -1.09 0.276 -.1172336 .0334291 yr1976 | .0375346 .0216498 1.73 0.083 -.0049163 .0799855 yr1980 | .0191597 .0177899 1.08 0.282 -.0157227 .0540421 yr1984 | .0151743 .0185776 0.82 0.414 -.0212527 .0516012 yr1988 | -.0109472 .0202954 -0.54 0.590 -.0507423 .0288479 yr1992 | -.0096625 .0167811 -0.58 0.565 -.042567 .0232419 yr1994 | .0182506 .019296 0.95 0.344 -.0195851 .0560862 yr1998 | -.0139898 .0201916 -0.69 0.488 -.0535814 .0256018 y1976relig~n | -.0017449 .0487858 -0.04 0.971 -.0974039 .0939142 y1980relig~n | -.0050442 .0437108 -0.12 0.908 -.0907523 .0806639 y1984relig~n | .005757 .0454608 0.13 0.899 -.0833826 .0948965 y1988relig~n | .017203 .0475703 0.36 0.718 -.0760728 .1104789 y1992relig~n | .0546108 .0419267 1.30 0.193 -.027599 .1368206 y1994relig~n | -.0082145 .0466743 -0.18 0.860 -.0997333 .0833042 y1998relig~n | .0572749 .0480909 1.19 0.234 -.0370217 .1515715 _cons | -.0694458 .0505385 -1.37 0.170 -.1685415 .0296499 ------------------------------------------------------------------------------